././@PaxHeader0000000000000000000000000000003200000000000010210 xustar0026 mtime=1773309590.57596 neo-0.14.4/0000755000175100017510000000000015154507227012037 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/AUTHORS0000644000175100017510000000003315154507213013076 0ustar00runnerrunnerSee doc/source/authors.rst ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/LICENSE.txt0000644000175100017510000000273515154507213013664 0ustar00runnerrunnerCopyright (c) 2010-2025, Neo authors and contributors All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of the copyright holders nor the names of the contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/MANIFEST.in0000644000175100017510000000030715154507213013570 0ustar00runnerrunnerinclude README.rst include LICENSE.txt include CITATION.rst prune drafts include examples/*.py recursive-include doc * prune doc/build exclude doc/source/images/*.svg exclude doc/source/images/*.dia ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5743675 neo-0.14.4/PKG-INFO0000644000175100017510000002164015154507227013137 0ustar00runnerrunnerMetadata-Version: 2.4 Name: neo Version: 0.14.4 Summary: Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats Author: Neo authors and contributors License-Expression: BSD-3-Clause Project-URL: homepage, https://neuralensemble.org/neo Project-URL: documentation, http://neo.readthedocs.io/ Project-URL: repository, https://github.com/NeuralEnsemble/python-neo Project-URL: download, http://pypi.python.org/pypi/neo Classifier: Development Status :: 4 - Beta Classifier: Programming Language :: Python :: 3 Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Science/Research Classifier: Natural Language :: English Classifier: Topic :: Scientific/Engineering Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 Classifier: Programming Language :: Python :: 3 :: Only Requires-Python: >=3.10 Description-Content-Type: text/x-rst License-File: LICENSE.txt License-File: AUTHORS Requires-Dist: packaging Requires-Dist: numpy>=1.25.2 Requires-Dist: quantities>=0.16.4 Provides-Extra: iocache Requires-Dist: joblib>=1.0.0; extra == "iocache" Provides-Extra: test Requires-Dist: pytest; extra == "test" Requires-Dist: pytest-cov; extra == "test" Requires-Dist: datalad; extra == "test" Requires-Dist: scipy>=1.0.0; extra == "test" Requires-Dist: pyedflib; extra == "test" Requires-Dist: h5py; extra == "test" Requires-Dist: igor2; extra == "test" Requires-Dist: klusta; extra == "test" Requires-Dist: tqdm; extra == "test" Requires-Dist: nixio; extra == "test" Requires-Dist: matplotlib; extra == "test" Requires-Dist: ipython; extra == "test" Requires-Dist: joblib>=1.0.0; extra == "test" Requires-Dist: coverage; extra == "test" Requires-Dist: coveralls; extra == "test" Requires-Dist: pillow; extra == "test" Requires-Dist: sonpy; python_version < "3.10" and extra == "test" Requires-Dist: pynwb; extra == "test" Requires-Dist: probeinterface; extra == "test" Requires-Dist: zugbruecke>=0.2; extra == "test" Requires-Dist: wenv; extra == "test" Provides-Extra: docs Requires-Dist: docutils; extra == "docs" Requires-Dist: sphinx; 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extra == "ced" Provides-Extra: nwb Requires-Dist: pynwb; extra == "nwb" Provides-Extra: maxwell Requires-Dist: h5py; extra == "maxwell" Provides-Extra: biocam Requires-Dist: h5py; extra == "biocam" Provides-Extra: med Requires-Dist: dhn_med_py<2.0; extra == "med" Provides-Extra: plexon2 Requires-Dist: zugbruecke>=0.2; sys_platform != "win32" and extra == "plexon2" Requires-Dist: wenv; sys_platform != "win32" and extra == "plexon2" Provides-Extra: neuralynx Requires-Dist: python-dateutil; extra == "neuralynx" Provides-Extra: all Requires-Dist: coverage; extra == "all" Requires-Dist: coveralls; extra == "all" Requires-Dist: h5py; extra == "all" Requires-Dist: igor2; extra == "all" Requires-Dist: ipython; extra == "all" Requires-Dist: joblib>=1.0.0; extra == "all" Requires-Dist: klusta; extra == "all" Requires-Dist: matplotlib; extra == "all" Requires-Dist: nixio>=1.5.0; extra == "all" Requires-Dist: pillow; extra == "all" Requires-Dist: probeinterface; extra == "all" Requires-Dist: pyedflib; extra == "all" Requires-Dist: pynwb; extra == "all" Requires-Dist: pytest; extra == "all" Requires-Dist: pytest-cov; extra == "all" Requires-Dist: scipy>=1.0.0; extra == "all" Requires-Dist: sonpy; extra == "all" Requires-Dist: tqdm; extra == "all" Requires-Dist: wenv; sys_platform != "win32" and extra == "all" Requires-Dist: zugbruecke>=0.2; sys_platform != "win32" and extra == "all" Requires-Dist: python-dateutil; extra == "all" Dynamic: license-file === Neo === Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer_ (data analysis and visualization), Elephant_ (data analysis), the G-node_ suite (databasing), PyNN_ (simulations), tridesclous_ (spike sorting) and ephyviewer_ (data visualization). OpenElectrophy_ (data analysis and visualization) uses an older version of neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. A project with similar aims but for neuroimaging file formats is `NiBabel`_. Neo is a `NumFocus Affiliated Project`_. Code status ----------- .. image:: https://img.shields.io/pypi/v/neo.svg :target: https://pypi.python.org/pypi/neo :alt: PyPI Release Version .. image:: https://github.com/NeuralEnsemble/python-neo/actions/workflows/core-test.yml/badge.svg?event=push&branch=master :target: https://github.com/NeuralEnsemble/python-neo/actions?query=event%3Apush+branch%3Amaster :alt: Core Test Status (Github Actions) .. image:: https://github.com/NeuralEnsemble/python-neo/actions/workflows/io-test_trigger.yml/badge.svg?event=push&branch=master :target: https://github.com/NeuralEnsemble/python-neo/actions?query=event%3Apush+branch%3Amaster :alt: IO Test Status (Github Actions) .. image:: https://coveralls.io/repos/NeuralEnsemble/python-neo/badge.png :target: https://coveralls.io/r/NeuralEnsemble/python-neo :alt: Unit Test Coverage More information ---------------- - Home page: http://neuralensemble.org/neo - Mailing list: http://neuralensemble.org/community - Documentation: http://neo.readthedocs.io/ - Bug reports: https://github.com/NeuralEnsemble/python-neo/issues For installation instructions, see doc/source/install.rst To cite Neo in publications, see CITATION.txt :copyright: Copyright 2010-2026 by the Neo team, see doc/source/authors.rst. :license: 3-Clause Revised BSD License, see LICENSE.txt for details. Funding ------- Development of Neo has been partially funded by the European Union Sixth Framework Program (FP6) under grant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007­-2013) under grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP), and by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1), No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3), and by the European Union's Research and Innovation Program Horizon Europe Grant Agreement No. 101147319 (EBRAINS 2.0). .. _OpenElectrophy: https://github.com/OpenElectrophy/OpenElectrophy .. _Elephant: http://neuralensemble.org/elephant .. _G-node: http://www.g-node.org/ .. _Neuroshare: http://neuroshare.org/ .. _SpykeViewer: https://spyke-viewer.readthedocs.org/en/latest/ .. _NiBabel: http://nipy.sourceforge.net/nibabel/ .. _PyNN: http://neuralensemble.org/PyNN .. _quantities: http://pypi.python.org/pypi/quantities .. _`NeuralEnsemble mailing list`: http://neuralensemble.org/community .. _`issue tracker`: https://github.c .. _tridesclous: https://github.com/tridesclous/tridesclous .. _ephyviewer: https://github.com/NeuralEnsemble/ephyviewer .. _`NumFocus Affiliated Project`: https://numfocus.org/sponsored-projects/affiliated-projects ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/README.rst0000644000175100017510000001034715154507213013526 0ustar00runnerrunner=== Neo === Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer_ (data analysis and visualization), Elephant_ (data analysis), the G-node_ suite (databasing), PyNN_ (simulations), tridesclous_ (spike sorting) and ephyviewer_ (data visualization). OpenElectrophy_ (data analysis and visualization) uses an older version of neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. A project with similar aims but for neuroimaging file formats is `NiBabel`_. Neo is a `NumFocus Affiliated Project`_. Code status ----------- .. image:: https://img.shields.io/pypi/v/neo.svg :target: https://pypi.python.org/pypi/neo :alt: PyPI Release Version .. image:: https://github.com/NeuralEnsemble/python-neo/actions/workflows/core-test.yml/badge.svg?event=push&branch=master :target: https://github.com/NeuralEnsemble/python-neo/actions?query=event%3Apush+branch%3Amaster :alt: Core Test Status (Github Actions) .. image:: https://github.com/NeuralEnsemble/python-neo/actions/workflows/io-test_trigger.yml/badge.svg?event=push&branch=master :target: https://github.com/NeuralEnsemble/python-neo/actions?query=event%3Apush+branch%3Amaster :alt: IO Test Status (Github Actions) .. image:: https://coveralls.io/repos/NeuralEnsemble/python-neo/badge.png :target: https://coveralls.io/r/NeuralEnsemble/python-neo :alt: Unit Test Coverage More information ---------------- - Home page: http://neuralensemble.org/neo - Mailing list: http://neuralensemble.org/community - Documentation: http://neo.readthedocs.io/ - Bug reports: https://github.com/NeuralEnsemble/python-neo/issues For installation instructions, see doc/source/install.rst To cite Neo in publications, see CITATION.txt :copyright: Copyright 2010-2026 by the Neo team, see doc/source/authors.rst. :license: 3-Clause Revised BSD License, see LICENSE.txt for details. Funding ------- Development of Neo has been partially funded by the European Union Sixth Framework Program (FP6) under grant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007­-2013) under grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP), and by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1), No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3), and by the European Union's Research and Innovation Program Horizon Europe Grant Agreement No. 101147319 (EBRAINS 2.0). .. _OpenElectrophy: https://github.com/OpenElectrophy/OpenElectrophy .. _Elephant: http://neuralensemble.org/elephant .. _G-node: http://www.g-node.org/ .. _Neuroshare: http://neuroshare.org/ .. _SpykeViewer: https://spyke-viewer.readthedocs.org/en/latest/ .. _NiBabel: http://nipy.sourceforge.net/nibabel/ .. _PyNN: http://neuralensemble.org/PyNN .. _quantities: http://pypi.python.org/pypi/quantities .. _`NeuralEnsemble mailing list`: http://neuralensemble.org/community .. _`issue tracker`: https://github.c .. _tridesclous: https://github.com/tridesclous/tridesclous .. _ephyviewer: https://github.com/NeuralEnsemble/ephyviewer .. _`NumFocus Affiliated Project`: https://numfocus.org/sponsored-projects/affiliated-projects ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.4979136 neo-0.14.4/doc/0000755000175100017510000000000015154507226012603 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/Makefile0000644000175100017510000000611515154507213014242 0ustar00runnerrunner# Makefile for Sphinx documentation # # You can set these variables from the command line. 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"filename": "example_data.txt", "delimiter": " ", "timecolumn": null, "units": "mV", "time_units": "ms", "sampling_rate": { "value": 1.0, "units": "kHz" }, "method": "genfromtxt", "signal_group_mode": "all-in-one" }././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/make.bat0000644000175100017510000000577515154507213014222 0ustar00runnerrunner@ECHO OFF REM Command file for Sphinx documentation set SPHINXBUILD=sphinx-build set BUILDDIR=build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\neo.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\neo.ghc goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5004013 neo-0.14.4/doc/old_stuffs/0000755000175100017510000000000015154507227014754 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/core.rst0000644000175100017510000002653615154507213016445 0ustar00runnerrunner******** Neo core ******** .. currentmodule:: neo.core This figure shows the main data types in Neo, with the exception of the newly added ImageSequence and RegionOfInterest classes: .. image:: images/base_schematic.png :height: 500 px :alt: Illustration of the main Neo data types :align: center Neo objects fall into three categories: data objects, container objects and grouping objects. Data objects ------------ These objects directly represent data as arrays of numerical values with associated metadata (units, sampling frequency, etc.). * :py:class:`AnalogSignal`: A regular sampling of a single- or multi-channel continuous analog signal. * :py:class:`IrregularlySampledSignal`: A non-regular sampling of a single- or multi-channel continuous analog signal. * :py:class:`SpikeTrain`: A set of action potentials (spikes) emitted by the same unit in a period of time (with optional waveforms). * :py:class:`Event`: An array of time points representing one or more events in the data. * :py:class:`Epoch`: An array of time intervals representing one or more periods of time in the data. * :py:class:`ImageSequence`: A three dimensional array representing a sequence of images. Container objects ----------------- There is a simple hierarchy of containers: * :py:class:`Segment`: A container for heterogeneous discrete or continuous data sharing a common clock (time basis) but not necessarily the same sampling rate, start time or end time. A :py:class:`Segment` can be considered as equivalent to a "trial", "episode", "run", "recording", etc., depending on the experimental context. May contain any of the data objects. * :py:class:`Block`: The top-level container gathering all of the data, discrete and continuous, for a given recording session. Contains :class:`Segment` and :class:`Group` objects. Grouping/linking objects ------------------------ These objects express the relationships between data items, such as which signals were recorded on which electrodes, which spike trains were obtained from which membrane potential signals, etc. They contain references to data objects that cut across the simple container hierarchy. * :py:class:`ChannelView`: A set of indices into :py:class:`AnalogSignal` objects, representing logical and/or physical recording channels. For spike sorting of extracellular signals, where spikes may be recorded on more than one recording channel, the :py:class:`ChannelView` can be used to reference the group of recording channels from which the spikes were obtained. * :py:class:`Group`: Can contain any of the data objects, views, or other groups, outside the hierarchy of the segment and block containers. A common use is to link the :class:`SpikeTrain` objects within a :class:`Block`, possibly across multiple Segments, that were emitted by the same neuron. * :py:class:`CircularRegionOfInterest`, :py:class:`RectangularRegionOfInterest` and :py:class:`PolygonRegionOfInterest` are three subclasses that link :class:`ImageSequence` objects to signals (:class:`AnalogSignal` objects) extracted from them. For more details, see :doc:`grouping`. NumPy compatibility =================== Neo data objects inherit from :py:class:`Quantity`, which in turn inherits from NumPy :py:class:`ndarray`. This means that a Neo :py:class:`AnalogSignal` is also a :py:class:`Quantity` and an array, giving you access to all of the methods available for those objects. For example, you can pass a :py:class:`SpikeTrain` directly to the :py:func:`numpy.histogram` function, or an :py:class:`AnalogSignal` directly to the :py:func:`numpy.std` function. If you want to get a numpy.ndarray you use magnitude and rescale from quantities:: >>> np_sig = neo_analogsignal.rescale('mV').magnitude >>> np_times = neo_analogsignal.times.rescale('s').magnitude Relationships between objects ============================= Container objects like :py:class:`Block` or :py:class:`Segment` are gateways to access other objects. For example, a :class:`Block` can access a :class:`Segment` with:: >>> bl = Block() >>> bl.segments # gives a list of segments A :class:`Segment` can access the :class:`AnalogSignal` objects that it contains with:: >>> seg = Segment() >>> seg.analogsignals # gives a list of AnalogSignals In the :ref:`neo_diagram` below, these *one to many* relationships are represented by cyan arrows. In general, an object can access its children with an attribute *childname+s* in lower case, e.g. * :attr:`Block.segments` * :attr:`Segments.analogsignals` * :attr:`Segments.spiketrains` * :attr:`Block.groups` These relationships are bi-directional, i.e. a child object can access its parent: * :attr:`Segment.block` * :attr:`AnalogSignal.segment` * :attr:`SpikeTrain.segment` * :attr:`Group.block` Here is an example showing these relationships in use:: from neo.io import AxonIO import urllib.request url = "https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/axon/File_axon_3.abf" filename = './test.abf' urllib.request.urlretrieve(url, filename) r = AxonIO(filename=filename) blocks = r.read() # read the entire file > a list of Blocks bl = blocks[0] print(bl) print(bl.segments) # child access for seg in bl.segments: print(seg) print(seg.block) # parent access In some cases, a one-to-many relationship is sufficient. Here is a simple example with tetrodes, in which each tetrode has its own group.:: from neo import Block, Group bl = Block() # the four tetrodes for i in range(4): group = Group(name='Tetrode %d' % i) bl.groups.append(group) # now we load the data and associate it with the created channels # ... Now consider a more complex example: a 1x4 silicon probe, with a neuron on channels 0,1,2 and another neuron on channels 1,2,3. We create a group for each neuron to hold the spiketrains for each spike sorting group together with the channels on which that neuron spiked:: bl = Block(name='probe data') # one group for each neuron view0 = ChannelView(recorded_signals, index=[0, 1, 2]) unit0 = Group(view0, name='Group 0') bl.groups.append(unit0) view1 = ChannelView(recorded_signals, index=[1, 2, 3]) unit1 = Group(view1, name='Group 1') bl.groups.append(unit1) # now we add the spiketrains from Unit 0 to unit0 # and add the spiketrains from Unit 1 to unit1 # ... Now each putative neuron is represented by a :class:`Group` containing the spiketrains of that neuron and a view of the signal selecting only those channels from which the spikes were obtained. See :doc:`usecases` for more examples of how the different objects may be used. .. _neo_diagram: Neo diagram =========== Object: * With a star = inherits from :class:`Quantity` Attributes: * In red = required * In white = recommended Relationship: * In cyan = one to many * In yellow = properties (deduced from other relationships) .. image:: images/simple_generated_diagram.png :width: 750 px :download:`Click here for a better quality SVG diagram <./images/simple_generated_diagram.svg>` .. note:: This figure does not include :class:`ChannelView` and :class:`RegionOfInterest`. For more details, see the :doc:`api_reference`. Initialization ============== Neo objects are initialized with "required", "recommended", and "additional" arguments. - Required arguments MUST be provided at the time of initialization. They are used in the construction of the object. - Recommended arguments may be provided at the time of initialization. They are accessible as Python attributes. They can also be set or modified after initialization. - Additional arguments are defined by the user and are not part of the Neo object model. A primary goal of the Neo project is extensibility. These additional arguments are entries in an attribute of the object: a Python dict called :py:attr:`annotations`. Note : Neo annotations are not the same as the *__annotations__* attribute introduced in Python 3.6. Example: SpikeTrain ------------------- :py:class:`SpikeTrain` is a :py:class:`Quantity`, which is a NumPy array containing values with physical dimensions. The spike times are a required attribute, because the dimensionality of the spike times determines the way in which the :py:class:`Quantity` is constructed. Here is how you initialize a :py:class:`SpikeTrain` with required arguments:: >>> import neo >>> st = neo.SpikeTrain([3, 4, 5], units='sec', t_stop=10.0) >>> print(st) [ 3. 4. 5.] s You will see the spike times printed in a nice format including the units. Because `st` "is a" :py:class:`Quantity` array with units of seconds, it absolutely must have this information at the time of initialization. You can specify the spike times with a keyword argument too:: >>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_stop=10.0) The spike times could also be in a NumPy array. If it is not specified, :attr:`t_start` is assumed to be zero, but another value can easily be specified:: >>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_start=1.0, t_stop=10.0) >>> st.t_start array(1.0) * s Recommended attributes must be specified as keyword arguments, not positional arguments. Finally, let's consider "additional arguments". These are the ones you define for your experiment:: >>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_stop=10.0, rat_name='Fred') >>> print(st.annotations) {'rat_name': 'Fred'} Because ``rat_name`` is not part of the Neo object model, it is placed in the dict :py:attr:`annotations`. This dict can be modified as necessary by your code. Annotations ----------- As well as adding annotations as "additional" arguments when an object is constructed, objects may be annotated using the :meth:`annotate` method possessed by all Neo core objects, e.g.:: >>> seg = Segment() >>> seg.annotate(stimulus="step pulse", amplitude=10*nA) >>> print(seg.annotations) {'amplitude': array(10.0) * nA, 'stimulus': 'step pulse'} Since annotations may be written to a file or database, there are some limitations on the data types of annotations: they must be "simple" types or containers (lists, dicts, tuples, NumPy arrays) of simple types, where the simple types are ``integer``, ``float``, ``complex``, ``Quantity``, ``string``, ``date``, ``time`` and ``datetime``. Array Annotations ----------------- Next to "regular" annotations there is also a way to annotate arrays of values in order to create annotations with one value per data point. Using this feature, called Array Annotations, the consistency of those annotations with the actual data is ensured. Apart from adding those on object construction, Array Annotations can also be added using the :meth:`array_annotate` method provided by all Neo data objects, e.g.:: >>> sptr = SpikeTrain(times=[1, 2, 3]*pq.s, t_stop=3*pq.s) >>> sptr.array_annotate(index=[0, 1, 2], relevant=[True, False, True]) >>> print(sptr.array_annotations) {'index': array([0, 1, 2]), 'relevant': array([ True, False, True])} Since Array Annotations may be written to a file or database, there are some limitations on the data types of arrays: they must be 1-dimensional (i.e. not nested) and contain the same types as annotations: ``integer``, ``float``, ``complex``, ``Quantity``, ``string``, ``date``, ``time`` and ``datetime``. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/developers_guide.rst0000644000175100017510000002154315154507213021033 0ustar00runnerrunner================= Developers' guide ================= These instructions are for developing on a Unix-like platform, e.g. Linux or macOS, with the bash shell. If you develop on Windows, please get in touch. Mailing lists ------------- General discussion of Neo development takes place in the `NeuralEnsemble Google group`_. Discussion of issues specific to a particular ticket in the issue tracker should take place on the tracker. Using the issue tracker ----------------------- If you find a bug in Neo, please create a new ticket on the `issue tracker`_, setting the type to "defect". Choose a name that is as specific as possible to the problem you've found, and in the description give as much information as you think is necessary to recreate the problem. The best way to do this is to create the shortest possible Python script that demonstrates the problem, and attach the file to the ticket. If you have an idea for an improvement to Neo, create a ticket with type "enhancement". If you already have an implementation of the idea, create a patch (see below) and attach it to the ticket. To keep track of changes to the code and to tickets, you can register for a GitHub account and then set to watch the repository at `GitHub Repository`_ (see https://help.github.com/en/articles/watching-and-unwatching-repositories). Requirements ------------ * Python_ 3.8 or later * numpy_ >= 1.19.5 * quantities_ >= 0.14.1 * joblib >= 1.0.0 * pytest (for running tests) * Sphinx_ (for building documentation) There are many optional dependencies for different IO modules (see below). If you don't install these, tests for those modules will be skipped. We strongly recommend you develop within a virtual environment (from virtualenv, venv or conda). Getting the source code ----------------------- We use the Git version control system. The best way to contribute is through GitHub_. You will first need a GitHub account, and you should then fork the repository at `GitHub Repository`_ (see http://help.github.com/en/articles/fork-a-repo). To get a local copy of the repository:: $ cd /some/directory $ git clone git@github.com:/python-neo.git Now you need to make sure that the ``neo`` package is on your PYTHONPATH. You can do this by installing Neo with the *editable* option, which avoids reinstalling when there are changes in the code:: $ cd python-neo $ pip install -e . To install all dependencies needed for testing, including optional dependencies for IO modules, run:: $ pip install -e .[test] or if using the zsh shell:: $ pip install -e ".[test]" To update to the latest version from the repository:: $ git pull Running the test suite ---------------------- Before you make any changes, run the test suite to make sure all the tests pass on your system:: $ pytest At the end, if you see "OK", then all the tests passed (or were skipped because certain dependencies are not installed), otherwise it will report on tests that failed or produced errors. To run tests from an individual file:: $ pytest test_analogsignal.py Writing tests ------------- You should try to write automated tests for any new code that you add. If you have found a bug and want to fix it, first write a test that isolates the bug (and that therefore fails with the existing codebase). Then apply your fix and check that the test now passes. To see how well the tests cover the code base, run:: $ pytest --cov=neo Working on the documentation ---------------------------- All modules, classes, functions, and methods (including private and subclassed builtin methods) should have docstrings. Please see `PEP257`_ for a description of docstring conventions. Module docstrings should explain briefly what functions or classes are present. Detailed descriptions can be left for the docstrings of the respective functions or classes. Private functions do not need to be explained here. Class docstrings should include an explanation of the purpose of the class and, when applicable, how it relates to standard neuroscientific data. They should also include at least one example, which should be written so it can be run as-is from a clean newly-started Python interactive session (that means all imports should be included). Finally, they should include a list of all arguments, attributes, and properties, with explanations. Properties that return data calculated from other data should explain what calculation is done. A list of methods is not needed, since documentation will be generated from the method docstrings. Method and function docstrings should include an explanation for what the method or function does. If this may not be clear, one or more examples may be included. Examples that are only a few lines do not need to include imports or setup, but more complicated examples should have them. Examples can be tested easily using the iPython `%doctest_mode` magic. This will strip >>> and ... from the beginning of each line of the example, so the example can be copied and pasted as-is. The documentation is written in `reStructuredText`_, using the `Sphinx`_ documentation system. Any mention of another Neo module, class, attribute, method, or function should be properly marked up so automatic links can be generated. The same goes for quantities or NumPy. To build the documentation:: $ cd python-neo/doc $ make html Then open `some/directory/python-neo/doc/build/html/index.html` in your browser. Committing your changes ----------------------- Once you are happy with your changes, **run the test suite again to check that you have not introduced any new bugs**. It is also recommended to check your code with a code checking program, such as `pyflakes`_ or `flake8`_. Then you can commit them to your local repository:: $ git commit -m 'informative commit message' If this is your first commit to the project, please add your name and affiliation/employer to :file:`doc/source/authors.rst` You can then push your changes to your online repository on GitHub:: $ git push Once you think your changes are ready to be included in the main Neo repository, open a pull request on GitHub (see https://help.github.com/en/articles/about-pull-requests). Python version -------------- Neo should work with Python 3.8 or newer. If you need support for Python 2.7, use Neo v0.8.0 or earlier. Coding standards and style -------------------------- All code should conform as much as possible to `PEP 8`_, and should run with Python 3.8 or newer. You can use the `pep8`_ program to check the code for PEP 8 conformity. You can also use `flake8`_, which combines pep8 and pyflakes. However, the pep8 and flake8 programs do not check for all PEP 8 issues. In particular, they do not check that the import statements are in the correct order. Also, please do not use ``from xyz import *``. This is slow, can lead to conflicts, and makes it difficult for code analysis software. Making a release ---------------- .. TODO: discuss branching/tagging policy. Add a section in :file:`/doc/source/whatisnew.rst` for the release. First check that the version string (in :file:`pyproject.toml`) is correct. To build source and wheel packages:: $ python -m build Tag the release in the Git repository and push it:: $ git tag $ git push --tags origin $ git push --tags upstream To upload the package to `PyPI`_ (the members of the `maintainers team`_ have the necessary permissions to do this):: $ twine upload dist/neo-0.X.Y.tar.gz dist/neo-0.X.Y-py3-none-any.whl .. talk about readthedocs .. make a release branch If you want to develop your own IO module ----------------------------------------- See :ref:`io_dev_guide` for implementation of a new IO. Project governance ------------------ The :doc:`governance` document describes how decisions about the project are taken. .. _Python: https://www.python.org .. _Setuptools: https://pypi.python.org/pypi/setuptools/ .. _tox: http://codespeak.net/tox/ .. _coverage: https://coverage.readthedocs.io/ .. _`PEP 8`: https://www.python.org/dev/peps/pep-0008/ .. _`issue tracker`: https://github.com/NeuralEnsemble/python-neo/issues .. _`Porting to Python 3`: http://python3porting.com/ .. _`NeuralEnsemble Google group`: https://groups.google.com/forum/#!forum/neuralensemble .. _reStructuredText: http://docutils.sourceforge.net/rst.html .. _Sphinx: http://www.sphinx-doc.org/ .. _numpy: https://numpy.org/ .. _quantities: https://pypi.org/project/quantities/ .. _PEP257: https://www.python.org/dev/peps/pep-0257/ .. _PEP394: https://www.python.org/dev/peps/pep-0394/ .. _PyPI: https://pypi.org .. _GitHub: https://github.com .. _`GitHub Repository`: https://github.com/NeuralEnsemble/python-neo/ .. _pep8: https://pypi.org/project/pep8/ .. _flake8: https://pypi.org/project/flake8/ .. _pyflakes: https://pypi.org/project/pyflakes/ .. _`maintainers team`: https://github.com/orgs/NeuralEnsemble/teams/neo-maintainers ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/examples.rst0000644000175100017510000000054215154507213017320 0ustar00runnerrunner**************** Examples **************** .. currentmodule:: neo Introduction ============= A set of examples in :file:`neo/examples/` illustrates the use of Neo classes. .. literalinclude:: ../../examples/read_files_neo_io.py .. literalinclude:: ../../examples/read_files_neo_rawio.py .. literalinclude:: ../../examples/plot_with_matplotlib.py ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/gif2011workshop.rst0000644000175100017510000000766215154507213020362 0ustar00runnerrunner************************************ Gif 2011 workshop decisions ************************************ This have been writtent before neo 2 implementation just after the wokshop. Not every hting is up to date. After a workshop in GIF we are happy to present the following improvements: =========================================================================== 1. We made a few renames of objects - "Neuron" into "Unit" - "RecordingPoint" into "RecordingChannel" to remove electrophysiological (or other) dependencies and keep generality. 2. For every object we specified mandatory attributes and recommended attributes. For every attribute we define a python-based data type. The changes are reflected in the diagram #FIXME with red (mandatory) and blue (recommended) attributes indicated. 3. New objects are required for operational performance (memory allocation) and logical consistency (neo eeg, etc): - AnalogSignalArray - IrregularlySampledAnalogSignal - EventArray - EpochArray - RecordingChannelGroup Attributes and parent objects are available on the diagram #FIXME 4. Due to some logical considerations we remove the link between "RecordingChannel" and "Spiketrain". "SpikeTrain" now depends on "Unit", which in its turn connects to "RecordingChannel". For inconsistency reasons we removed link between "SpikeTrain" and a "Spike" ("SpikeTrain" is an object containing numpy array of spikes, but not a container of "Spike" objects - which is performance-unefficient). The same idea is applied to AnalogSignal / AnalogSignalArray, Event / EventArray etc. All changes are relected in # FIXME 5. In order to implement flexibility and embed user-defined metadata into the NEO objects we decided to assign "annotations" dictionnary to very NEO object. This attribute is optional; user may add key-value pairs to it according to its scientific needs. 6. The decision is made to use "quantities" package for objects, representing data arrays with units. "Quantities" is a stable (at least for python2.6) package, presented in pypi, easy-embeddable into NEO object model. Points of implementation are presented in the diagram # FIXME 7. We postpone the solution of object ID management inside NEO. 8. In AnalogSignal - t_stop become a property (for consistency reasons). 9. In order to provie a support for "advanced" object load we decided to include parameters - lazy (True/False) - cascade (True/False) in the BaseIO class. These parameters are valid for every method, provided by the IO (.read_segment() etc.). If "lazy" is True, the IO does not load data array, and makes array load otherwise. "Cascade" parameter regulates load of object relations. 10. We postpone the question of data analysis storage till the next NEO congress. Analysis objects are free for the moment. 11. We stay with Python 2.6 / 2.7 support. Python 3 to be considered in a later discussions. New object diagram discussed =============================================== .. image:: images/neo_UML_French_workshop.png :height: 500 px :align: center Actions to be performed: =============================================================== promotion: at g-node: philipp, andrey in neuralesemble: andrew within incf network: andrew thomas at posters: all logo: samuel paper: next year in the web: pypi object struture: common: samuel draft: yann andrey tree diagram: philipp florant io: ExampleIO : samuel HDF5 IO: andrey doc: first page: andrew thomas object disription: samuel draft+ andrew io user/ io dev: samuel example/cookbook: andrey script, samuel NeuroConvert, doctest unitest: andrew packaging: samuel account for more licence: BSD-3-Clause copyright: CNRS, GNode, University of Provence hosting test data: Philipp Other questions discussed: =========================== - consistency in names of object attributes and get/set functions ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/grouping.rst0000644000175100017510000001307115154507213017335 0ustar00runnerrunner************************* Grouping and linking data ************************* Migrating from ChannelIndex/Unit to ChannelView/Group ===================================================== While the basic hierarchical :class:`Block` - :class:`Segment` structure of Neo has remained unchanged since the inception of Neo, the structures used to cross-link objects (for example to link a signal to the spike trains derived from it) have undergone changes, in an effort to find an easily understandable and usable approach. Below we give some examples of how to migrate from :class:`ChannelIndex` and :class:`Unit`, as used in Neo 0.8, to the new classes :class:`Group` and :class:`ChannelView` introduced in Neo 0.9. Note that Neo 0.9 supports the new and old API in parallel, to facilitate migration. IO classes in Neo 0.9 can read :class:`ChannelIndex` and :class:`Unit` objects, but do not write them. :class:`ChannelIndex` and :class:`Unit` will be removed in Neo 0.10.0. Examples -------- A simple example with two tetrodes. Here the :class:`ChannelIndex` was not being used for grouping, simply to associate a name with each channel. Using :class:`ChannelIndex`:: import numpy as np from quantities import kHz, mV from neo import Block, Segment, ChannelIndex, AnalogSignal block = Block() segment = Segment() block.segments.append(segment) for i in (0, 1): signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=1 * kHz,) segment.analogsignals.append(signal) chx = ChannelIndex(name=f"Tetrode #{i + 1}", index=[0, 1, 2, 3], channel_names=["A", "B", "C", "D"]) chx.analogsignals.append(signal) block.channel_indexes.append(chx) Using array annotations, we annotate the channels of the :class:`AnalogSignal` directly:: import numpy as np from quantities import kHz, mV from neo import Block, Segment, AnalogSignal block = Block() segment = Segment() block.segments.append(segment) for i in (0, 1): signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=1 * kHz, channel_names=["A", "B", "C", "D"]) segment.analogsignals.append(signal) Now a more complex example: a 1x4 silicon probe, with a neuron on channels 0,1,2 and another neuron on channels 1,2,3. We create a :class:`ChannelIndex` for each neuron to hold the :class:`Unit` object associated with this spike sorting group. Each :class:`ChannelIndex` also contains the list of channels on which that neuron spiked. :: import numpy as np from quantities import ms, mV, kHz from neo import Block, Segment, ChannelIndex, Unit, SpikeTrain, AnalogSignal block = Block(name="probe data") segment = Segment() block.segments.append(segment) # create 4-channel AnalogSignal with dummy data signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz) # create spike trains with dummy data # we will pretend the spikes have been extracted from the dummy signal spiketrains = [ SpikeTrain(np.arange(5, 100) * ms, t_stop=100 * ms), SpikeTrain(np.arange(7, 100) * ms, t_stop=100 * ms) ] segment.analogsignals.append(signal) segment.spiketrains.extend(spiketrains) # assign each spiketrain to a neuron (Unit) units = [] for i, spiketrain in enumerate(spiketrains): unit = Unit(name=f"Neuron #{i + 1}") unit.spiketrains.append(spiketrain) units.append(unit) # create a ChannelIndex for each unit, to show which channels the spikes come from chx0 = ChannelIndex(name="Channel Group 1", index=[0, 1, 2]) chx0.units.append(units[0]) chx0.analogsignals.append(signal) units[0].channel_index = chx0 chx1 = ChannelIndex(name="Channel Group 2", index=[1, 2, 3]) chx1.units.append(units[1]) chx1.analogsignals.append(signal) units[1].channel_index = chx1 block.channel_indexes.extend((chx0, chx1)) Using :class:`ChannelView` and :class:`Group`:: import numpy as np from quantities import ms, mV, kHz from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal block = Block(name="probe data") segment = Segment() block.segments.append(segment) # create 4-channel AnalogSignal with dummy data signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz) # create spike trains with dummy data # we will pretend the spikes have been extracted from the dummy signal spiketrains = [ SpikeTrain(np.arange(5, 100) * ms, t_stop=100 * ms), SpikeTrain(np.arange(7, 100) * ms, t_stop=100 * ms) ] segment.analogsignals.append(signal) segment.spiketrains.extend(spiketrains) # assign each spiketrain to a neuron (now using Group) units = [] for i, spiketrain in enumerate(spiketrains): unit = Group([spiketrain], name=f"Neuron #{i + 1}") units.append(unit) # create a ChannelView of the signal for each unit, to show which channels the spikes come from # and add it to the relevant Group view0 = ChannelView(signal, index=[0, 1, 2], name="Channel Group 1") units[0].add(view0) view1 = ChannelView(signal, index=[1, 2, 3], name="Channel Group 2") units[1].add(view1) block.groups.extend(units) Now each putative neuron is represented by a :class:`Group` containing the spiketrains of that neuron and a view of the signal selecting only those channels from which the spikes were obtained. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/index.rst0000644000175100017510000000620115154507213016607 0ustar00runnerrunner.. module:: neo .. image:: images/neologo.png :width: 600 px Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Kwik and HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to representation of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer_ (data analysis and visualization), Elephant_ (data analysis), the G-node_ suite (databasing), PyNN_ (simulations), tridesclous_ (spike sorting) and ephyviewer_ (data visualization). OpenElectrophy_ (data analysis and visualization) used an older version of Neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities_ package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. A project with similar aims but for neuroimaging file formats is `NiBabel`_. Documentation ------------- .. toctree:: :maxdepth: 1 install core usecases io rawio examples api_reference whatisnew developers_guide io_developers_guide authors License ------- Neo is free software, distributed under a 3-clause Revised BSD license (BSD-3-Clause). Support ------- If you have problems installing the software or questions about usage, documentation or anything else related to Neo, you can post to the `NeuralEnsemble mailing list`_. If you find a bug, please create a ticket in our `issue tracker`_. Contributing ------------ Any feedback is gladly received and highly appreciated! Neo is a community project, and all contributions are welcomed - see the :doc:`developers_guide` for more information. `Source code `_ is on GitHub. Citation -------- .. include:: ../../CITATION.txt .. _OpenElectrophy: https://github.com/OpenElectrophy/OpenElectrophy .. _Elephant: http://neuralensemble.org/elephant .. _G-node: http://www.g-node.org/ .. _Neuroshare: http://neuroshare.org/ .. _SpykeViewer: https://spyke-viewer.readthedocs.io/en/latest/ .. _NiBabel: https://nipy.org/nibabel/ .. _PyNN: http://neuralensemble.org/PyNN .. _quantities: https://pypi.org/project/quantities/ .. _`NeuralEnsemble mailing list`: https://groups.google.com/forum/#!forum/neuralensemble .. _`issue tracker`: https://github.com/NeuralEnsemble/python-neo/issues .. _tridesclous: https://github.com/tridesclous/tridesclous .. _ephyviewer: https://github.com/NeuralEnsemble/ephyviewer ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/install.rst0000644000175100017510000000345215154507213017153 0ustar00runnerrunner************ Installation ************ Neo is a pure Python package, so it should be easy to get it running on any system. Installing from the Python Package Index ======================================== Dependencies ------------ * Python_ >= 3.7 * numpy_ >= 1.18.5 * quantities_ >= 0.12.1 You can install the latest published version of Neo and its dependencies using:: $ pip install neo Certain IO modules have additional dependencies. If these are not satisfied, Neo will still install but the IO module that uses them will fail on loading: * scipy >= 1.0.0 for NeoMatlabIO * h5py >= 2.5 for KwikIO * klusta for KwikIO * igor >= 0.2 for IgorIO * nixio >= 1.5 for NixIO * stfio for StimfitIO * pillow for TiffIO These dependencies can be installed by specifying a comma-separated list with the ``pip install`` command:: $ pip install neo[nixio,tiffio] Or when installing a specific version of neo:: $ pip install neo[nixio,tiffio]==0.9.0 These additional dependencies for IO modules are available:: * igorproio * kwikio * neomatlabio * nixio * stimfitio * tiffio To download and install the package manually, download: |neo_github_url| Then: .. parsed-literal:: $ unzip neo-|release|.zip $ cd neo-|release| $ python setup.py install Installing from source ====================== To install the latest version of Neo from the Git repository:: $ git clone git://github.com/NeuralEnsemble/python-neo.git $ cd python-neo $ python setup.py install .. _`Python`: https://www.python.org/ .. _`numpy`: https://numpy.org/ .. _`quantities`: https://pypi.org/project/quantities/ .. _`pip`: https://pypi.org/project/pip/ .. _`setuptools`: http://pypi.python.org/pypi/setuptools .. _Anaconda: https://www.anaconda.com/distribution/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/io.rst0000644000175100017510000003111715154507213016113 0ustar00runnerrunner****** Neo IO ****** .. currentmodule:: neo Preamble ======== The Neo :mod:`io` module aims to provide an exhaustive way of loading and saving several widely used data formats in electrophysiology. The more these heterogeneous formats are supported, the easier it will be to manipulate them as Neo objects in a similar way. Therefore the IO set of classes propose a simple and flexible IO API that fits many format specifications. It is not only file-oriented, it can also read/write objects from a database. At the moment, there are 3 families of IO modules: 1. for reading closed manufacturers' formats (Spike2, Plexon, AlphaOmega, BlackRock, Axon, ...) 2. for reading(/writing) formats from open source tools (KlustaKwik, Elan, WinEdr, WinWcp, ...) 3. for reading/writing Neo structure in neutral formats (HDF5, .mat, ...) but with Neo structure inside (NeoHDF5, NeoMatlab, ...) Combining **1** for reading and **3** for writing is a good example of use: converting your datasets to a more standard format when you want to share/collaborate. Introduction ============ There is an intrinsic structure in the different Neo objects, that could be seen as a hierarchy with cross-links. See :doc:`core`. The highest level object is the :class:`Block` object, which is the high level container able to encapsulate all the others. A :class:`Block` has therefore a list of :class:`Segment` objects, that can, in some file formats, be accessed individually. Depending on the file format, i.e. if it is streamable or not, the whole :class:`Block` may need to be loaded, but sometimes particular :class:`Segment` objects can be accessed individually. Within a :class:`Segment`, the same hierarchical organization applies. A :class:`Segment` embeds several objects, such as :class:`SpikeTrain`, :class:`AnalogSignal`, :class:`IrregularlySampledSignal`, :class:`Epoch`, :class:`Event` (basically, all the different Neo objects). Depending on the file format, these objects can sometimes be loaded separately, without the need to load the whole file. If possible, a file IO therefore provides distinct methods allowing to load only particular objects that may be present in the file. The basic idea of each IO file format is to have, as much as possible, read/write methods for the individual encapsulated objects, and otherwise to provide a read/write method that will return the object at the highest level of hierarchy (by default, a :class:`Block` or a :class:`Segment`). The :mod:`neo.io` API is a balance between full flexibility for the user (all :meth:`read_XXX` methods are enabled) and simple, clean and understandable code for the developer (few :meth:`read_XXX` methods are enabled). This means that not all IOs offer the full flexibility for partial reading of data files. One format = one class ====================== The basic syntax is as follows. If you want to load a file format that is implemented in a generic :class:`MyFormatIO` class:: >>> from neo.io import MyFormatIO >>> reader = MyFormatIO(filename="myfile.dat") you can replace :class:`MyFormatIO` by any implemented class, see :ref:`list_of_io` Modes ====== An IO module can be based on a single file, a directory containing files, or a database. This is described in the :attr:`mode` attribute of the IO class. >>> from neo.io import MyFormatIO >>> print(MyFormatIO.mode) 'file' For *file* mode the *filename* keyword argument is necessary. For *directory* mode the *dirname* keyword argument is necessary. Ex: >>> reader = io.PlexonIO(filename='File_plexon_1.plx') >>> reader = io.TdtIO(dirname='aep_05') Supported objects/readable objects ================================== To know what types of object are supported by a given IO interface:: >>> MyFormatIO.supported_objects [Segment , AnalogSignal , SpikeTrain, Event, Spike] Supported objects does not mean objects that you can read directly. For instance, many formats support :class:`AnalogSignal` but don't allow them to be loaded directly, rather to access the :class:`AnalogSignal` objects, you must read a :class:`Segment`:: >>> seg = reader.read_segment() >>> print(seg.analogsignals) >>> print(seg.analogsignals[0]) To get a list of directly readable objects :: >>> MyFormatIO.readable_objects [Segment] The first element of the previous list is the highest level for reading the file. This mean that the IO has a :meth:`read_segment` method:: >>> seg = reader.read_segment() >>> type(seg) neo.core.Segment All IOs have a read() method that returns a list of :class:`Block` objects (representing the whole content of the file):: >>> bl = reader.read() >>> print(bl[0].segments[0]) neo.core.Segment Read a time slice of Segment ============================ Some objects support the ``time_slice`` argument in ``read_segment()``. This is useful to read only a subset of a dataset clipped in time. By default ``time_slice=None`` will load everything. This reads everything:: seg = reader.read_segment(time_slice=None) This reads only the first 5 seconds:: seg = reader.read_segment(time_slice=(0*pq.s, 5.*pq.s)) .. _section-lazy: Lazy option and proxy objects ============================= In some cases you may not want to load everything in memory (RAM) because it could be too big. For this scenario, some IOs implement ``lazy=True/False``. Since neo 0.7, a new lazy system has been added for some IO modules (all IO classes that inherit from rawio). To know if a class supports lazy mode use ``ClassIO.support_lazy``. With ``lazy=True`` all data objects (AnalogSignal/SpikeTrain/Event/Epoch) are replaced by proxy objects (AnalogSignalProxy/SpikeTrainProxy/EventProxy/EpochProxy). By default (if not specified), ``lazy=False``, i.e. all data is loaded. These proxy objects contain metadata (name, sampling_rate, id, ...) so they can be inspected but they do not contain any array-like data. All proxy objects contain a ``load()`` method to postpone the real load of array like data. Furthermore the ``load()`` method has a ``time_slice`` argument to load only a slice from the file. In this way the consumption of memory can be finely controlled. Here are two examples that read a dataset, extract sections of the signal based on recorded events, and average the sections. The first example is without lazy mode, so it consumes more memory:: lim0, lim1 = -500 * pq.ms, +1500 * pq.ms seg = reader.read_segment(lazy=False) triggers = seg.events[0] sig = seg.analogsignals[0] # here sig contain the whole recording in memory all_sig_chunks = [] for t in triggers.times: t0, t1 = (t + lim0), (t + lim1) sig_chunk = sig.time_slice(t0, t1) all_sig_chunks.append(sig_chunk) apply_my_fancy_average(all_sig_chunks) The second example uses lazy mode, so it consumes less memory:: lim0, lim1 = -500*pq.ms, +1500*pq.ms seg = reader.read_segment(lazy=True) triggers = seg.events[0].load(time_slice=None) # this loads all triggers in memory sigproxy = seg.analogsignals[0] # this is a proxy all_sig_chunks = [] for t in triggers.times: t0, t1 = (t + lim0), (t + lim1) sig_chunk = sigproxy.load(time_slice=(t0, t1)) # here real data are loaded all_sig_chunks.append(sig_chunk) apply_my_fancy_average(all_sig_chunks) In addition to ``time_slice``, AnalogSignalProxy supports the ``channel_indexes`` argument. This allows loading only a subset of channels. This is useful when the channel count is very high. .. TODO: add something about magnitude mode when implemented for all objects. In this example, we read only three selected channels:: seg = reader.read_segment(lazy=True) anasig = seg.analogsignals[0].load(time_slice=None, channel_indexes=[0, 2, 18]) .. _neo_io_API: Details of API ============== The :mod:`neo.io` API is designed to be simple and intuitive: - each file format has an IO class (for example for Spike2 files you have a :class:`Spike2IO` class). - each IO class inherits from the :class:`BaseIO` class. - each IO class can read or write directly one or several Neo objects (for example :class:`Segment`, :class:`Block`, ...): see the :attr:`readable_objects` and :attr:`writable_objects` attributes of the IO class. - each IO class supports part of the :mod:`neo.core` hierarchy, though not necessarily all of it (see :attr:`supported_objects`). - each IO class has a :meth:`read()` method that returns a list of :class:`Block` objects. If the IO only supports :class:`Segment` reading, the list will contain one block with all segments from the file. - each IO class that supports writing has a :meth:`write()` method that takes as a parameter a list of blocks, a single block or a single segment, depending on the IO's :attr:`writable_objects`. - some IO are able to do a *lazy* load: all metadata (e.g. :attr:`sampling_rate`) are read, but not the actual numerical data. - each IO is able to save and load all required attributes (metadata) of the objects it supports. - each IO can freely add user-defined or manufacturer-defined metadata to the :attr:`annotations` attribute of an object. If you want to develop your own IO ================================== See :doc:`io_developers_guide` for information on how to implement a new IO. .. _list_of_io: List of implemented formats =========================== .. automodule:: neo.io Logging ======= :mod:`neo` uses the standard Python :mod:`logging` module for logging. All :mod:`neo.io` classes have logging set up by default, although not all classes produce log messages. The logger name is the same as the full qualified class name, e.g. :class:`neo.io.nixio.NixIO`. By default, only log messages that are critically important for users are displayed, so users should not disable log messages unless they are sure they know what they are doing. However, if you wish to disable the messages, you can do so:: >>> import logging >>> >>> logger = logging.getLogger('neo') >>> logger.setLevel(100) Some io classes provide additional information that might be interesting to advanced users. To enable these messages, do the following:: >>> import logging >>> >>> logger = logging.getLogger('neo') >>> logger.setLevel(logging.INFO) It is also possible to log to a file in addition to the terminal:: >>> import logging >>> >>> logger = logging.getLogger('neo') >>> handler = logging.FileHandler('filename.log') >>> logger.addHandler(handler) To only log to the terminal:: >>> import logging >>> from neo import logging_handler >>> >>> logger = logging.getLogger('neo') >>> handler = logging.FileHandler('filename.log') >>> logger.addHandler(handler) >>> >>> logging_handler.setLevel(100) This can also be done for individual IO classes:: >>> import logging >>> >>> logger = logging.getLogger('neo.io.nixio.NixIO') >>> handler = logging.FileHandler('filename.log') >>> logger.addHandler(handler) Individual IO classes can have their loggers disabled as well:: >>> import logging >>> >>> logger = logging.getLogger('neo.io.nixio.NixIO') >>> logger.setLevel(100) And more detailed logging messages can be enabled for individual IO classes:: >>> import logging >>> >>> logger = logging.getLogger('neo.io.nixio.NixIO') >>> logger.setLevel(logging.INFO) The default handler, which is used to print logs to the command line, is stored in :attr:`neo.logging_handler`. This example changes how the log text is displayed:: >>> import logging >>> from neo import logging_handler >>> >>> formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') >>> logging_handler.setFormatter(formatter) For more complex logging, please see the documentation for the logging_ module. .. note:: If you wish to implement more advanced logging as describe in the documentation for the logging_ module or elsewhere on the internet, please do so before calling any :mod:`neo` functions or initializing any :mod:`neo` classes. This is because the default handler is created when :mod:`neo` is imported, but it is not attached to the :mod:`neo` logger until a class that uses logging is initialized or a function that uses logging is called. Furthermore, the handler is only attached if there are no handlers already attached to the root logger or the :mod:`neo` logger, so adding your own logger will override the default one. Additional functions and/or classes may get logging during bugfix releases, so code relying on particular modules not having logging may break at any time without warning. .. _`logging`: https://docs.python.org/3/library/logging.html ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/io_developers_guide.rst0000644000175100017510000001340415154507213021517 0ustar00runnerrunner.. _io_dev_guide: ******************** IO developers' guide ******************** .. _io_guiline: Guidelines for IO implementation ================================ There are two ways to add a new IO module: * By directly adding a new IO class in a module within :mod:`neo.io`: the reader/writer will deal directly with Neo objects * By adding a RawIO class in a module within :mod:`neo.rawio`: the reader should work with raw buffers from the file and provide some internal headers for the scale/units/name/... You can then generate an IO module simply by inheriting from your RawIO class and from :class:`neo.io.BaseFromRaw` For read only classes, we encourage you to write a :class:`RawIO` class because it allows slice reading, and is generally much quicker and easier (although only for reading) than implementing a full IO class. For read/write classes you can mix the two levels neo.rawio for reading and neo.io for writing. Recipe to develop an IO module for a new data format: 1. Fully understand the object model. See :doc:`core`. If in doubt ask the `mailing list`_. 2. Fully understand :mod:`neo.rawio.examplerawio`. It is a fake IO to explain the API. If in doubt ask the list. 3. Copy/paste ``examplerawio.py`` and choose clear file and class names for your IO. 4. Implement all methods that **raise(NotImplementedError)** in :mod:`neo.rawio.baserawio`. Return None when the object is not supported (spike/waveform) 5. Write good docstrings. List dependencies, including minimum version numbers. 6. Add your class to :mod:`neo.rawio.__init__`. Keep imports inside ``try/except`` for dependency reasons. 7. Create a class in :file:`neo/io/` 8. Add your class to :mod:`neo.io.__init__`. Keep imports inside ``try/except`` for dependency reasons. 9. Create an account at https://gin.g-node.org and deposit files in :file:`NeuralEnsemble/ephy_testing_data`. 10. Write tests in :file:`neo/rawio/test_xxxxxrawio.py`. You must at least pass the standard tests (inherited from :class:`BaseTestRawIO`). See :file:`test_examplerawio.py` 11. Write a similar test in :file:`neo.tests/iotests/test_xxxxxio.py`. See :file:`test_exampleio.py` 12. Make a pull request when all tests pass. Miscellaneous ============= * If your IO supports several versions of a format (like ABF1, ABF2), upload to the gin.g-node.org test file repository all file versions possible. (for test coverage). * In the docstring, explain where you obtained the file format specification if it is a closed one. * If your IO is based on a database mapper, keep in mind that the returned object MUST be detached, because this object can be written to another url for copying. Tests ===== :py:class:`neo.rawio.tests.common_rawio_test.BaseTestRawIO` and :py:class:`neo.test.io.commun_io_test.BaseTestIO` provide standard tests. To use these you need to upload some sample data files at `gin-gnode`_. They will be publicly accessible for testing Neo. These tests: * check for compliance with the schema: hierarchy, attribute types, ... * For IO modules confirm they able to both write and read data; they compare a generated dataset with the same data after a write/read cycle. The test scripts download all files from `gin-gnode`_ and store them locally in ``/ 'ephy_testing_data'``. Subsequent test runs use the previously downloaded files, rather than trying to download them each time. Each test must have at least one class that inherits ``BaseTestRawIO`` and that has 3 attributes: * ``rawioclass``: the class * ``entities_to_test``: a list of files (or directories) to be tested one by one * ``files_to_download``: a list of files to download (sometimes bigger than ``entities_to_test``) Here is an example test script taken from the distribution: :file:`test_axonrawio.py`: .. literalinclude:: ../../neo/test/rawiotest/test_axonrawio.py Logging ======= All IO classes by default have logging using the standard :mod:`logging` module already set up. The logger name is the same as the fully qualified class name, e.g. :class:`neo.io.nixio.NixIO`. The :attr:`class.logger` attribute holds the logger for easy access. There are generally 3 types of situations in which an IO class should use a logger * Recoverable errors with the file that the users need to be notified about. In this case, please use :meth:`logger.warning` or :meth:`logger.error`. If there is an exception associated with the issue, you can use :meth:`logger.exception` in the exception handler to automatically include a backtrace with the log. By default, all users will see messages at this level, so please restrict it only to problems the user absolutely needs to know about. * Informational messages that advanced users might want to see in order to get some insight into the file. In this case, please use :meth:`logger.info`. * Messages useful to developers to fix problems with the io class. In this case, please use :meth:`logger.debug`. A log handler is automatically added to :mod:`neo`, so please do not user your own handler. Please use the :attr:`class.logger` attribute for accessing the logger inside the class rather than :meth:`logging.getLogger`. Please do not log directly to the root logger (e.g. :meth:`logging.warning`), use the class's logger instead (:meth:`class.logger.warning`). In the tests for the io class, if you intentionally test broken files, please disable logs by setting the logging level to `100`. ExampleIO ========= .. autoclass:: neo.rawio.ExampleRawIO .. autoclass:: neo.io.ExampleIO Here are the entire files: .. literalinclude:: ../../neo/rawio/examplerawio.py .. literalinclude:: ../../neo/io/exampleio.py .. _`mailing list`: https://groups.google.com/forum/#!forum/neuralensemble .. _gin-gnode: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/rawio.rst0000644000175100017510000002072115154507213016624 0ustar00runnerrunner********* Neo RawIO ********* .. currentmodule:: neo.rawio .. _neo_rawio_API: Neo RawIO API ============= For performance and memory consumption reasons a new layer has been added to Neo. In brief: * **neo.io** is the user-oriented read/write layer. Reading consists of getting a tree of Neo objects from a data source (file, url, or directory). When reading, all Neo objects are correctly scaled to the correct units. Writing consists of making a set of Neo objects persistent in a file format. * **neo.rawio** is a low-level layer for reading data only. Reading consists of getting NumPy buffers (often int16/int64) of signals/spikes/events. Scaling to real values (microV, times, ...) is done in a second step. Here the underlying objects must be consistent across Blocks and Segments for a given data source. The neo.rawio API has been added for developers. The neo.rawio is close to what could be a C API for reading data but in Python/NumPy. Not all IOs are implemented in :mod:`neo.rawio` but all classes implemented in :mod:`neo.rawio` are also available in :mod:`neo.io`. Possible uses of the :mod:`neo.rawio` API are: * fast reading chunks of signals in int16 and do the scaling of units (uV) on a GPU while scaling the zoom. This should improve bandwidth HD to RAM and RAM to GPU memory. * load only some small chunk of data for heavy computations. For instance the spike sorting module tridesclous_ does this. The :mod:`neo.rawio` API is less flexible than :mod:`neo.io` and has some limitations: * read-only * AnalogSignals must have the same characteristics across all Blocks and Segments: ``sampling_rate``, ``shape[1]``, ``dtype`` * AnalogSignals should all have the same value of ``sampling_rate``, otherwise they won't be read at the same time. * Units must have SpikeTrain event if empty across all Block and Segment * Epoch and Event are processed the same way (with ``durations=None`` for Event). For an intuitive comparison of :mod:`neo.io` and :mod:`neo.rawio` see: * :file:`example/read_file_neo_io.py` * :file:`example/read_file_neo_rawio.py` One speculative benefit of the :mod:`neo.rawio` API should be that a developer should be able to code a new RawIO class with little knowledge of the Neo tree of objects or of the :mod:`quantities` package. Basic usage =========== First create a reader from a class:: >>> from neo.rawio import PlexonRawIO >>> reader = PlexonRawIO(filename='File_plexon_3.plx') Then browse the internal header and display information:: >>> reader.parse_header() >>> print(reader) PlexonRawIO: File_plexon_3.plx nb_block: 1 nb_segment: [1] signal_channels: [V1] spike_channels: [Wspk1u, Wspk2u, Wspk4u, Wspk5u ... Wspk29u Wspk30u Wspk31u Wspk32u] event_channels: [] You get the number of blocks and segments per block. You have information about channels: **signal_channels**, **spike_channels**, **event_channels**. All this information is internally available in the *header* dict:: >>> for k, v in reader.header.items(): ... print(k, v) signal_channels [('V1', 0, 1000., 'int16', '', 2.44140625, 0., 0)] event_channels [] nb_segment [1] nb_block 1 spike_channels [('Wspk1u', 'ch1#0', '', 0.00146484, 0., 0, 30000.) ('Wspk2u', 'ch2#0', '', 0.00146484, 0., 0, 30000.) ... Read signal chunks of data and scale them:: >>> channel_indexes = None  #could be channel_indexes = [0] >>> raw_sigs = reader.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=1024, i_stop=2048, channel_indexes=channel_indexes) >>> float_sigs = reader.rescale_signal_raw_to_float(raw_sigs, dtype='float64') >>> sampling_rate = reader.get_signal_sampling_rate() >>> t_start = reader.get_signal_t_start(block_index=0, seg_index=0) >>> units =reader.header['signal_channels'][0]['units'] >>> print(raw_sigs.shape, raw_sigs.dtype) >>> print(float_sigs.shape, float_sigs.dtype) >>> print(sampling_rate, t_start, units) (1024, 1) int16 (1024, 1) float64 1000.0 0.0 V There are 3 ways to select a subset of channels: by index (0 based), by id or by name. By index is unambiguous 0 to n-1 (included), whereas for some IOs channel_names (and sometimes channel_ids) have no guarantees to be unique. In such cases, using names or ids may raise an error. A selected subset of channels which is passed to get_analog_signal_chunk, get_analog_signal_size, or get_analog_signal_t_start has the additional restriction that all such channels must have the same t_start and signal_size. Such subsets of channels may be available in specific RawIOs by using the get_group_signal_channel_indexes method, if the RawIO has defined separate group_ids for each group with those common characteristics. Example with BlackrockRawIO for the file FileSpec2.3001:: >>> raw_sigs = reader.get_analogsignal_chunk(channel_indexes=None) #Take all channels >>> raw_sigs1 = reader.get_analogsignal_chunk(channel_indexes=[0, 2, 4])) #Take 0 2 and 4 >>> raw_sigs2 = reader.get_analogsignal_chunk(channel_ids=[1, 3, 5]) # Same but with there id (1 based) >>> raw_sigs3 = reader.get_analogsignal_chunk(channel_names=['chan1', 'chan3', 'chan5'])) # Same but with there name print(raw_sigs1.shape[1], raw_sigs2.shape[1], raw_sigs3.shape[1]) 3, 3, 3 Inspect units channel. Each channel gives a SpikeTrain for each Segment. Note that for many formats a physical channel can have several units after spike sorting. So the nb_unit could be more than physical channel or signal channels. >>> nb_unit = reader.spike_channels_count() >>> print('nb_unit', nb_unit) nb_unit 30 >>> for unit_index in range(nb_unit): ... nb_spike = reader.spike_count(block_index=0, seg_index=0, unit_index=unit_index) ... print('unit_index', unit_index, 'nb_spike', nb_spike) unit_index 0 nb_spike 701 unit_index 1 nb_spike 716 unit_index 2 nb_spike 69 unit_index 3 nb_spike 12 unit_index 4 nb_spike 95 unit_index 5 nb_spike 37 unit_index 6 nb_spike 25 unit_index 7 nb_spike 15 unit_index 8 nb_spike 33 ... Get spike timestamps only between 0 and 10 seconds and convert them to spike times:: >>> spike_timestamps = reader.spike_timestamps(block_index=0, seg_index=0, unit_index=0, t_start=0., t_stop=10.) >>> print(spike_timestamps.shape, spike_timestamps.dtype, spike_timestamps[:5]) (424,) int64 [ 90 420 708 1020 1310] >>> spike_times = reader.rescale_spike_timestamp( spike_timestamps, dtype='float64') >>> print(spike_times.shape, spike_times.dtype, spike_times[:5]) (424,) float64 [ 0.003 0.014 0.0236 0.034 0.04366667] Get spike waveforms between 0 and 10 s:: >>> raw_waveforms = reader.spike_raw_waveforms( block_index=0, seg_index=0, unit_index=0, t_start=0., t_stop=10.) >>> print(raw_waveforms.shape, raw_waveforms.dtype, raw_waveforms[0,0,:4]) (424, 1, 64) int16 [-449 -206 34 40] >>> float_waveforms = reader.rescale_waveforms_to_float(raw_waveforms, dtype='float32', unit_index=0) >>> print(float_waveforms.shape, float_waveforms.dtype, float_waveforms[0,0,:4]) (424, 1, 64) float32 [-0.65771484 -0.30175781 0.04980469 0.05859375] Count events per channel:: >>> reader = PlexonRawIO(filename='File_plexon_2.plx') >>> reader.parse_header() >>> nb_event_channel = reader.event_channels_count() nb_event_channel 28 >>> print('nb_event_channel', nb_event_channel) >>> for chan_index in range(nb_event_channel): ... nb_event = reader.event_count(block_index=0, seg_index=0, event_channel_index=chan_index) ... print('chan_index',chan_index, 'nb_event', nb_event) chan_index 0 nb_event 1 chan_index 1 nb_event 0 chan_index 2 nb_event 0 chan_index 3 nb_event 0 ... Read event timestamps and times for chanindex=0 and with time limits (t_start=None, t_stop=None):: >>> ev_timestamps, ev_durations, ev_labels = reader.event_timestamps(block_index=0, seg_index=0, event_channel_index=0, t_start=None, t_stop=None) >>> print(ev_timestamps, ev_durations, ev_labels) [1268] None ['0'] >>> ev_times = reader.rescale_event_timestamp(ev_timestamps, dtype='float64') >>> print(ev_times) [ 0.0317] List of implemented formats =========================== .. automodule:: neo.rawio .. _tridesclous: https://github.com/tridesclous/tridesclous ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/specific_annotations.rst0000644000175100017510000000161615154507213021707 0ustar00runnerrunner.. _specific_annotations: ******************** Specific annotations ******************** Introduction ------------ Neo imposes and recommends some attributes for all objects, and also provides the *annotations* dict for all objects to deal with any kind of extensions. This flexible feature allow Neo objects to be customized for many use cases. While any names can be used for annotations, interoperability will be improved if there is some consistency in naming. Here we suggest some conventions for annotation names. Patch clamp ----------- .. todo: TODO Network simultaion ------------------ Spike sorting ------------- **SpikeTrain.annotations['waveform_features']** : when spike sorting the waveform is reduced to a smaller dimensional space with PCA or wavelets. This attribute is the projected matrice. NxM (N spike number, M features number. KlustakwikIO supports this feature. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/old_stuffs/usecases.rst0000644000175100017510000001723415154507213017323 0ustar00runnerrunner***************** Typical use cases ***************** Recording multiple trials from multiple channels ================================================ In this example we suppose that we have recorded from an 8-channel probe, and that we have recorded three trials/episodes. We therefore have a total of 8 x 3 = 24 signals, grouped into three :class:`AnalogSignal` objects, one per trial. Our entire dataset is contained in a :class:`Block`, which in turn contains: * 3 :class:`Segment` objects, each representing data from a single trial, * 1 :class:`Group`. .. image:: images/multi_segment_diagram.png :width: 75% :align: center :class:`Segment` and :class:`Group` objects provide two different ways to access the data, corresponding respectively, in this scenario, to access by **time** and by **space**. .. note:: Segments do not always represent trials, they can be used for many purposes: segments could represent parallel recordings for different subjects, or different steps in a current clamp protocol. **Temporal (by segment)** In this case you want to go through your data in order, perhaps because you want to correlate the neural response with the stimulus that was delivered in each segment. In this example, we're averaging over the channels. .. doctest:: import numpy as np from matplotlib import pyplot as plt for seg in block.segments: print("Analyzing segment %d" % seg.index) avg = np.mean(seg.analogsignals[0], axis=1) plt.figure() plt.plot(avg) plt.title("Peak response in segment %d: %f" % (seg.index, avg.max())) **Spatial (by channel)** In this case you want to go through your data by channel location and average over time. Perhaps you want to see which physical location produces the strongest response, and every stimulus was the same: .. doctest:: # We assume that our block has only 1 Group group = block.groups[0] avg = np.mean(group.analogsignals, axis=0) plt.figure() for index, name in enumerate(group.annotations["channel_names"]): plt.plot(avg[:, index]) plt.title("Average response on channels %s: %s' % (index, name) **Mixed example** Combining simultaneously the two approaches of descending the hierarchy temporally and spatially can be tricky. Here's an example. Let's say you saw something interesting on the 6th channel (index 5) on even numbered trials during the experiment and you want to follow up. What was the average response? .. doctest:: index = 5 avg = np.mean([seg.analogsignals[0][:, index] for seg in block.segments[::2]], axis=1) plt.plot(avg) Recording spikes from multiple tetrodes ======================================= Here is a similar example in which we have recorded with two tetrodes and extracted spikes from the extra-cellular signals. The spike times are contained in :class:`SpikeTrain` objects. * 3 :class:`Segments` (one per trial). * 7 :class:`Groups` (one per neuron), which each contain: * 3 :class:`SpikeTrain` objects * an annotation showing which tetrode the spiketrains were recorded from In total we have 3 x 7 = 21 :class:`SpikeTrains` in this :class:`Block`. .. image:: images/multi_segment_diagram_spiketrain.png :width: 75% :align: center .. note:: In this scenario we have discarded the original signals, perhaps to save space, therefore we use annotations to link the spiketrains to the tetrode they were recorded from. If we wished to include the original extracellular signals, we would add a reference to the three :class:`AnalogSignal` objects for the appropriate tetrode to the :class:`Group` for each neuron. There are three ways to access the :class:`SpikeTrain` data: * by trial (:class:`Segment`) * by neuron (:class:`Group`) * by tetrode **By trial** In this example, each :class:`Segment` represents data from one trial, and we want a PSTH for each trial from all units combined: .. doctest:: plt.figure() for seg in block.segments: print(f"Analyzing segment {seg.index}") stlist = [st - st.t_start for st in seg.spiketrains] plt.subplot(len(block.segments), 1, seg.index + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH in segment {seg.index}") plt.show() **By neuron** Now we can calculate the PSTH averaged over trials for each unit, using the :attr:`block.groups` property: .. doctest:: plt.figure() for i, group in enumerate(block.groups): stlist = [st - st.t_start for st in group.spiketrains] plt.subplot(len(block.groups), 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH of unit {group.name}") plt.show() **By tetrode** Here we calculate a PSTH averaged over trials by channel location, blending all units: .. doctest:: plt.figure() for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]): stlist = [] for unit in block.filter(objects=Group, tetrode_id=tetrode_id): stlist.extend([st - st.t_start for st in unit.spiketrains]) plt.subplot(2, 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH blend of tetrode {tetrode_id}") plt.show() Spike sorting ============= Spike sorting is the process of detecting and classifying high-frequency deflections ("spikes") on a group of physically nearby recording channels. For example, let's say you have recordings from a tetrode containing 4 separate channels. Here is an example showing (with fake data) how you could iterate over the contained signals and extract spike times. (Of course in reality you would use a more sophisticated algorithm.) .. doctest:: # generate some fake data seg = Segment() seg.analogsignals.append( AnalogSignal([[0.1, 0.1, 0.1, 0.1], [-2.0, -2.0, -2.0, -2.0], [0.1, 0.1, 0.1, 0.1], [-0.1, -0.1, -0.1, -0.1], [-0.1, -0.1, -0.1, -0.1], [-3.0, -3.0, -3.0, -3.0], [0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1]], sampling_rate=1000*Hz, units='V')) # extract spike trains from all channels st_list = [] for signal in seg.analogsignals: # use a simple threshold detector spike_mask = np.where(np.min(signal.magnitude, axis=1) < -1.0)[0] # create a spike train spike_times = signal.times[spike_mask] st = SpikeTrain(spike_times, t_start=signal.t_start, t_stop=signal.t_stop) # remember the spike waveforms wf_list = [] for spike_idx in np.nonzero(spike_mask)[0]: wf_list.append(signal[spike_idx-1:spike_idx+2, :]) st.waveforms = np.array(wf_list) st_list.append(st) At this point, we have a list of spiketrain objects. We could simply create a single :class:`Group` object, assign all spiketrains to it, and then also assign the :class:`AnalogSignal` on which we detected them. .. doctest:: unit = Group() unit.spiketrains = st_list unit.analogsignals.extend(seg.analogsignals) Further processing could assign each of the detected spikes to an independent source, a putative single neuron. (This processing is outside the scope of Neo. There are many open-source toolboxes to do it, for instance our sister project OpenElectrophy.) In that case we would create a separate :class:`Group` for each cluster, assign its spiketrains to it, and still store in each group a reference to the original recording. .. EEG .. Network simulations ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/requirements_docs.txt0000644000175100017510000000016015154507213017070 0ustar00runnerrunnerdocutils<0.18 numpy>=1.16.1 quantities>=0.12.1 sphinx-inline-tabs sphinx-gallery ipython matplotlib nixio pynwb ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5050538 neo-0.14.4/doc/source/0000755000175100017510000000000015154507227014104 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1773309590.491367 neo-0.14.4/doc/source/_static/0000755000175100017510000000000015154507226015531 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5052874 neo-0.14.4/doc/source/_static/css/0000755000175100017510000000000015154507227016322 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/_static/css/custom.css0000644000175100017510000000032615154507213020342 0ustar00runnerrunner .navbar-header-items li.nav-item { text-align: center; } html[data-theme="light"] { --pst-color-inline-code: rgb(11, 96, 38); } html[data-theme="dark"] { --pst-color-inline-code: rgb(15, 127, 51); } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/add_file_format.rst0000644000175100017510000001465415154507213017742 0ustar00runnerrunner.. _io_dev_guide: ==================================== Adding support for a new file format ==================================== If you would like to use Neo with a file format that is not yet supported, you have two options: 1. Make an enhancement request (see :doc:`bug_reports`). There is no guarantee that a Neo developer will have the time to work on this, but the more information you can give about the format, and the more people upvote your request, the more likely it will be implemented. 2. Implement it yourself. If you'd like to try this approach, first read :doc:`contributing`, then return and read the rest of this page. There are two ways to implement a new IO module: 1. by directly adding a new IO class in a module within :mod:`neo.io`: the reader/writer will deal directly with Neo objects; 2. by adding a RawIO class in a module within :mod:`neo.rawio`: the reader should work with raw buffers from the file and provide some internal headers for the scale/units/name/... You can then generate an IO module simply by inheriting from your RawIO class and from :class:`neo.io.BaseFromRaw`. For read-only classes, we encourage you to write a :class:`RawIO` class because it allows slice reading, and is generally much quicker and easier than implementing a full IO class. For read/write classes you can mix the two levels: ``neo.rawio`` for reading and ``neo.io`` for writing. .. _section-raw-io-recipe: Recipe to develop an IO module for a new data format ==================================================== 1. Fully understand the object model. If in doubt ask the `mailing list`_. 2. Fully understand :mod:`neo.rawio.examplerawio`, which is a fake IO module to explain the API. If in doubt ask the list. 3. Copy/paste ``examplerawio.py`` and choose clear file and class names for your IO. 4. implement all methods that raise :class:`NotImplementedError` in :mod:`neo.rawio.baserawio`. Return None when the object is not supported (e.g., spikes, waveforms) 5. Write good docstrings. List dependencies, including minimum version numbers. 6. Add your class to :mod:`neo.rawio.__init__`. Keep imports inside ``try/except`` for dependency reasons. 7. Create a class in :file:`neo/io/` 8. Add your class to :mod:`neo.io.__init__`. Keep imports inside ``try/except`` for dependency reasons. 9. Create an account at https://gin.g-node.org and deposit files in `NeuralEnsemble/ephy_testing_data`_. 10. Write tests in :file:`neo/rawio/test_xxxxxrawio.py`. You must at least pass the standard tests (inherited from :class:`BaseTestRawIO`). See :file:`test_examplerawio.py` 11. Write a similar test in :file:`neo.tests/iotests/test_xxxxxio.py`. See :file:`test_exampleio.py` 12. Make a pull request when all tests pass. Additional suggestions ---------------------- - If your IO supports several versions of a format (like ABF1, ABF2), upload to the gin.g-node.org test file repository all file versions possible (for test coverage). - :py:func:`neo.core.Block.create_many_to_one_relationship` offers a utility to complete the hierarchy when all one-to-many relationships have been created. - In the docstring, explain where you obtained the file format specification, especially if it is a closed one. - If your IO is based on a database mapper, keep in mind that the returned object MUST be detached, because this object can be written to another url for copying. Tests ===== :py:class:`neo.rawio.tests.common_rawio_test.BaseTestRawIO` and :py:class:`neo.test.io.commun_io_test.BaseTestIO` provide standard tests. To use these you need to upload some sample data files at `gin-gnode`_. They will be publicly accessible for testing Neo. These tests: - check compliance with the schema: hierarchy, attribute types, ... - for IO modules able to both write and read data, compare a generated dataset with the same data after a write/read cycle. The test scripts download all files from `gin-gnode`_ and store them locally in a temporary directory. Subsequent test runs use the previously downloaded files, rather than trying to download them each time. Each test must have at least one class that inherits ``BaseTestRawIO`` and that has 3 attributes: - ``rawioclass``: the class - ``entities_to_download``: a list of files or directories to download - ``entities_to_test``: a list of files (or directories) to be tested one by one Here is an example test script taken from the distribution: :file:`test_axonrawio.py`: .. literalinclude:: ../../neo/test/rawiotest/test_axonrawio.py Logging ======= All IO classes by default have logging using the standard :mod:`logging` module: already set up. The logger name is the same as the fully qualified class name, e.g. :class:`neo.io.nixio.NixIO`. The :attr:`class.logger` attribute holds the logger for easy access. There are generally 3 types of situations in which an IO class should use a logger: - Recoverable errors with the file that the users need to be notified about. In this case, please use :meth:`logger.warning` or :meth:`logger.error`. If there is an exception associated with the issue, you can use :meth:`logger.exception` in the exception handler to automatically include a backtrace with the log. By default, all users will see messages at this level, so please restrict it only to problems the user absolutely needs to know about. - Informational messages that advanced users might want to see in order to get some insight into the file. In this case, please use :meth:`logger.info`. - Messages useful to developers to fix problems with the io class. In this case, please use :meth:`logger.debug`. A log handler is automatically added to :mod:`neo`, so please do not use your own handler. Please use the :attr:`class.logger` attribute for accessing the logger inside the class rather than :meth:`logging.getLogger`. Please do not log directly to the root logger (e.g. :meth:`logging.warning`), use the class's logger instead (:meth:`class.logger.warning`). In the tests for the io class, if you intentionally test broken files, please disable logs by setting the logging level to `100`. ExampleIO ========= .. autoclass:: neo.rawio.ExampleRawIO .. autoclass:: neo.io.ExampleIO Here are the entire files: .. literalinclude:: ../../neo/rawio/examplerawio.py .. literalinclude:: ../../neo/io/exampleio.py .. _`mailing list`: http://neuralensemble.org/community .. _gin-gnode: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data .. _`NeuralEnsemble/ephy_testing_data`: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/api_reference.rst0000644000175100017510000000122615154507213017421 0ustar00runnerrunner====================== Neo core API Reference ====================== Relationships between Neo objects ================================= Object: * With a star = inherits from :class:`Quantity` Attributes: * In red = required * In white = recommended Relationships: * In cyan = one to many .. image:: images/simple_generated_diagram.png :width: 750 px :download:`Click here for a better quality SVG diagram <./images/simple_generated_diagram.svg>` .. note:: This figure does not include :class:`ChannelView` and :class:`RegionOfInterest`. .. automodule:: neo.core .. testsetup:: * from neo import SpikeTrain import quantities as pq ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/authors.rst0000644000175100017510000001356515154507213016330 0ustar00runnerrunner======================== Authors and contributors ======================== The following people have contributed code and/or ideas to the current version of Neo. The institutional affiliations are those at the time of the contribution, and may not be the current affiliation of a contributor. * Samuel Garcia [1] * Andrew Davison [2, 21] * Chris Rodgers [3] * Pierre Yger [2] * Yann Mahnoun [4] * Luc Estabanez [2] * Andrey Sobolev [5] * Thierry Brizzi [2] * Florent Jaillet [6] * Philipp Rautenberg [5] * Thomas Wachtler [5] * Cyril Dejean [7] * Robert Pröpper [8] * Domenico Guarino [2] * Achilleas Koutsou [5] * Erik Li [9] * Georg Raiser [10] * Joffrey Gonin [2] * Kyler Brown * Mikkel Elle Lepperød [11] * C Daniel Meliza [12] * Michael Denker [13, 29] * Julia Sprenger [13, 6] * Maximilian Schmidt [13] * Johanna Senk [13] * Carlos Canova [13] * Hélissande Fragnaud [2] * Mark Hollenbeck [14] * Mieszko Grodzicki * Rick Gerkin [15] * Matthieu Sénoville [2] * Chadwick Boulay [16] * Björn Müller [13] * William Hart [17] * erikli(github) * Jeffrey Gill [18] * Lucas (lkoelman@github) * Mark Histed * Mike Sintsov [19] * Scott W Harden [20] * Chek Yin Choi (hkchekc@github) * Corentin Fragnaud [21] * Alexander Kleinjohann * Christian Kothe * rishidhingra@github * Hugo van Kemenade * Aitor Morales-Gregorio [13] * Peter N Steinmetz [22] * Shashwat Sridhar * Alessio Buccino [23, 24, 38] * Regimantas Jurkus [13] * Steffen Buergers [24] * Etienne Combrisson [6] * Ben Dichter [24] * Elodie Legouée [21] * Oliver Kloss [13] * Heberto Mayorquin [24] * Thomas Perret [25] * Kyle Johnsen [26, 27] * Zach McKenzie [28,39] * Moritz Alexander Kern [29] * Joscha Schmiedt * Daniel P. Crepeau [30] * Divyansh Gupta [31] * Nate Dolensek [3] * Philipp Hornauer [32, 45] * Robert Wolff [42] * Jules Lebert [33] * Benjamin Heasly * Baptiste Grimaud [34] * Cody Baker [24] * Fernando J. Chaure [35] * Filipe*github * Matthias Klumpp * Greg Knoll [36] * Chris Halcrow [37] * Szonja Weigl [24] * Chris Heydrick [40] * Kyu Hun Lee * Téo Lohrer * Anthony Pinto [41] * Xin Niu * Nikhil Chandra [40] * Luigi Petrucco [42] * Tommaso Lambresa [43] * Nina Kudryashova [37] * Rémi Proville [44] * Paul Adkisson [24] * Luiz Tauffer [24] * Akshaj Verma [46] * Letizia Signorelli [47] * Daniel Parthier [48] * Kuntal Kokate [49] 1. Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292 - INSERM U1028 - Université Claude Bernard Lyon 1 2. Unité de Neuroscience, Information et Complexité, CNRS UPR 3293, Gif-sur-Yvette, France 3. University of California, Berkeley 4. Laboratoire de Neurosciences Intégratives et Adaptatives, CNRS UMR 6149 - Université de Provence, Marseille, France 5. G-Node, Ludwig-Maximilians-Universität, Munich, Germany 6. Institut de Neurosciences de la Timone, CNRS UMR 7289 - Université d'Aix-Marseille, Marseille, France 7. Centre de Neurosciences Integratives et Cognitives, UMR 5228 - CNRS - Université Bordeaux I - Université Bordeaux II 8. Neural Information Processing Group, TU Berlin, Germany 9. Department of Neurobiology & Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA 10. University of Konstanz, Konstanz, Germany 11. Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Norway 12. University of Virginia 13. INM-6, Forschungszentrum Jülich, Germany 14. University of Texas at Austin 15. Arizona State University 16. Ottawa Hospital Research Institute, Canada 17. Swinburne University of Technology, Australia 18. Case Western Reserve University (CWRU) · Department of Biology 19. IAL Developmental Neurobiology, Kazan Federal University, Kazan, Russia 20. Harden Technologies, LLC 21. Institut des Neurosciences Paris-Saclay, CNRS UMR 9197 - Université Paris-Sud, Gif-sur-Yvette, France 22. Neurtex Brain Research Institute, Dallas, TX, USAs 23. Bio Engineering Laboratory, DBSSE, ETH, Basel, Switzerland 24. CatalystNeuro 25. Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Lyon, France 26. Georgia Institute of Technology 27. Emory University 28. Harvard Medical School 29. Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Germany 30. Dark Horse Neuro, Inc. 31. IST Austria 32. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland 33. University College London 34. École Normale Supérieure Paris-Saclay 35. Institute of Biomedical Engineering, University of Buenos Aires, Argentina 36. Bernstein Center for Computational Neuroscience, Berlin, Germany 37. University of Edinburgh 38. Allen Institute for Neural Dynamics, Seattle, WA, United States 39. Massachusetts General Hospital, Department of Molecular Biology 40. Plexon Inc. 41. Paris Brain Institute 42. Istituto Italiano di Tecnologia (IIT), Genoa, Italy 43. University of Genoa, Italy 44. AquiNeuro, SAS 45. Maxwell Biosystems AG 46. Brain Center, University Medical Center Utrecht, Utrecht University, The Netherlands 47. Centre for Molecular Medicine Norway (NCMM), University of Oslo, Norway 48. Charité - Universitätmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin 49. Swartz Center for Computational Neuroscience, University of California, San Diego If we've somehow missed you off the list we're very sorry - please let us know. Acknowledgements ---------------- .. image:: https://www.braincouncil.eu/wp-content/uploads/2018/11/wsi-imageoptim-EU-Logo.jpg :alt: "EU Logo" :height: 104px :width: 156px :align: right Development of Neo has been partially funded by the European Union Sixth Framework Program (FP6) under grant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007­-2013) under grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP), and by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1), No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/bug_reports.rst0000644000175100017510000000232415154507213017165 0ustar00runnerrunner======================================= Reporting bugs, requesting new features ======================================= If you find a bug, unclear documentation, or would like to add a new feature to Neo, please go to https://github.com/NeuralEnsemble/python-neo/issues/. Searching the issue tracker =========================== Before creating a new issue, please read through the existing list of issues to check whether someone else has already created a similar one. You can also use Github's search capabilities for this. If you do find an existing issue, then please add a comment to say that you have also encountered this bug, or would like this feature to be added. If your issue is similar, but not identical, to one or more existing issues, then create a new issue making reference to the existing one, and explaining how your issue differs. Creating a new issue ==================== To create a new issue, click on "New issue", then click "Get started" next to the appropriate template: - "Bug report" - "Confusing documentation" - "Feature request" Please fill in all the information requested in the template. If you'd like to open an issue that isn't covered by these three options, click "Open a blank issue". ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/conf.py0000644000175100017510000002005015154507213015373 0ustar00runnerrunner# # neo documentation build configuration file, created by # sphinx-quickstart on Fri Feb 25 14:18:12 2011. # # This file is execfile()d with the current directory set to its containing # dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import os import sys import re from packaging.version import Version with open("../../pyproject.toml", mode="r") as f: txt = f.read() neo_release = re.findall('version = "(\S+)"', txt)[0] neo_version = ".".join((str(e) for e in Version(neo_release).release[:2])) AUTHORS = "Neo authors and contributors " # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.append(os.path.abspath('.')) # -- General configuration ---------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.doctest", "sphinx.ext.todo", "IPython.sphinxext.ipython_console_highlighting", "IPython.sphinxext.ipython_directive", "matplotlib.sphinxext.plot_directive", "sphinx_gallery.gen_gallery", "sphinx_inline_tabs", "sphinx_design", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix of source filenames. source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8' # The master toctree document. master_doc = "index" # General information about the project. project = "Neo" copyright = "2010-2026, " + AUTHORS # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = neo_version # The full version, including alpha/beta/rc tags. release = neo_release # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. # unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = [] # The reST default role (used for this markup: `text`) # to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output -------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. # html_theme = 'default' # html_theme = 'sphinxdoc' # html_theme = 'haiku' # html_theme = 'scrolls' # html_theme = 'agogo' # html_theme = 'alabaster' html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "logo": { "image_light": "images/neologo_small.png", "image_dark": "images/neologo_small_darkmode.png", }, "icon_links": [ {"name": "GitHub", "url": "https://github.com/NeuralEnsemble/python-neo", "icon": "fa-brands fa-square-github"}, { "name": "PyPI", "url": "https://pypi.org/project/neo", "icon": "fa-solid fa-box", }, ], "icon_links_label": "External Links", # for screen reader "use_edit_page_button": False, "navbar_end": ["theme-switcher", "navbar-icon-links"], } # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "images/neologo_small.png" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "images/neologo_favicon.png" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["images", "_static"] html_css_files = [ "css/custom.css", ] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {'index': 'index.html'} # If false, no module index is generated. # html_use_modindex = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = "neodoc" # -- Options for LaTeX output ------------------------------------------------- # The paper size ('letter' or 'a4'). # latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). # latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, # documentclass [howto/manual]). latex_documents = [("index", "neo.tex", "Neo Documentation", AUTHORS, "manual")] # The name of an image file (relative to this directory) to place at the # top of the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # Additional stuff for the LaTeX preamble. # latex_preamble = '' # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_use_modindex = True todo_include_todos = True # set to False before releasing documentation rst_epilog = """ .. |neo_github_url| replace:: https://github.com/NeuralEnsemble/python-neo/archive/neo-{}.zip """.format(neo_release) sphinx_gallery_conf = { # 'only_warn_on_example_error': True, # helps with debugging broken examples "examples_dirs": "../../examples", # path to your example scripts "gallery_dirs": "examples", # path to where to save gallery generated output } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/contributing.rst0000644000175100017510000005026315154507213017346 0ustar00runnerrunner=============================== Contributing to Neo development =============================== Contributions to Neo, whether it be fixing bugs, adding support for new file formats, improving the documentation, or adding new features, are welcome. Many :doc:`contributors to Neo` are PhD students or postdocs, often with little or no previous experience of software development or open-source projects, so as a community we try to be particularly welcoming to new contributors. If you're new to open source software, `How to Contribute to Open Source`_ is a good introductory guide. Discussing with other Neo developers ==================================== If you have an idea for improving Neo, the first step is to discuss it with other Neo developers on the NeuralEnsemble `mailing list`_. Discussion of issues specific to a particular ticket in the `issue tracker`_ should take place on the tracker. .. note:: Note that the mailing list is shared with other projects in the general domain of computational and systems neuroscience, so please don't be confused if you see posts that don't relate to Neo! .. goodforbeginners label Setting up a development environment ==================================== These instructions are written for beginning developers. If you are an experienced developer, you can skim this section. If any steps are unclear or confusing, please let us know. Fork the project on Github -------------------------- To work on Neo you will need a Github account. Once you have an account, go to the Neo project at https://github.com/NeuralEnsemble/python-neo and click on the "Fork button" near the top-right corner (`more information on forking`_). Now you can go to your own copy of the project at ``https://github.com//python-neo/`` Get the source code ------------------- We use the Git version control software to work on Neo. Git makes it easier to keep track of changes to the Neo code. If you don't have git installed on your computer, and/or haven't used git before, see the `GitHub Docs on git`_. On the homepage of your own copy of Neo, click on the green "Code" button, and copy either the HTTPS or SSH link, which will look like ``https://github.com//python-neo.git`` or ``git@github.com:/python-neo.git``. If you choose the HTTPS method you will be asked for your Github username and password regularly. To avoid this, you can `add an SSH key to your Github account`_. Now choose a location on your computer where you'd like to put the Neo source code, change to that directory, and run *one* of the following commands (depending on whether you chose HTTPS or SSH): :: git clone https://github.com//python-neo.git :: git clone git@github.com:/python-neo.git This copies the "master" branch of the Neo code to a directory named "python-neo". Now we need to make sure you can easily get the latest changes to Neo. Run:: cd python-neo git remote -v You should see something like:: origin git@github.com:/python-neo.git (fetch) origin git@github.com:/python-neo.git (push) which means that Git is using the alias "origin" to refer to your fork of the code. So that you can easily download any ongoing changes to Neo after the initial "git clone", we are going to add an alias "upstream" to refer to the main Neo repository:: git remote add upstream https://github.com/NeuralEnsemble/python-neo.git Create a virtual environment ---------------------------- It is strongly recommended to always work in a Python "virtual environment", to avoid conflicts between different versions of packages you might need for different projects, and to avoid causing problems with your operating system. With a virtual environment, you are working in an isolated space, which contains only the packages you specifically install into that environment. You can, for example, create different environments with different versions of Python, of NumPy, etc., and easily switch between them. We give instructions here for :mod:`conda` and for Python's built-in :mod:`venv` tool. Other virtual environment tools, such as virtualenv, are also available. :mod:`venv` is a built-in Python module, so if you already have Python installed on your computer, you can immediately get started without needing to install conda. The disadvantage is that you can only use the same version(s) of Python as already installed on your system, whereas conda allows you to easily create environments with different Python versions. .. tab:: conda We suggest using Miniconda_, which provides a minimal Python environment, but still allows you to easily install packages from the Anaconda repository. Install Miniconda using the instructions on its website, then create a conda environment using:: conda create --name my_neo_env where "my_neo_env" is a name for your environment: you can choose any name you wish. You then need to activate the environment:: conda activate my_neo_env If you wish to leave the environment:: conda deactivate .. tab:: venv Unlike with :mod:`conda`, which stores your environment in a standard location, with :mod:`venv` you have to choose where to store the environment files. One option is to create a folder :file:`venv` in your home directory, and then create your virtual envs in subdirectories like :file:`venv/neo`. Another option is to create :file:`env` within the Neo source code folder, e.g.: .. tab:: Unix/macOS .. code-block:: bash python3 -m venv env .. tab:: Windows .. code-block:: bat py -m venv env You then need to activate the environment: .. tab:: Unix/macOS .. code-block:: bash source env/bin/activate .. tab:: Windows .. code-block:: bat .\env\Scripts\activate If you want to switch projects or otherwise leave your virtual environment, simply run:: deactivate Install dependencies -------------------- The following commands will install all the packages needed to use Neo, run tests, and build the documentation. .. tab:: conda .. code-block:: bash conda install --file requirements_dev.txt pip install -e . .. tab:: venv The Neo testsuite uses Datalad_ to download test files. Datalad in turn depends on git-annex, which is not a Python package, and so cannot be installed with pip. See `installing Datalad`_ for instructions about installing git-annex on your system, then continue from here: .. code-block:: bash pip install -e .[test] This does not install all the optional dependencies for different file formats. If you're planning to work on a specific IO module, e.g. :class:`NixIO`, you can install the dependencies for that module:: pip install -e .[nixio] Or you can install all optional dependencies with:: pip install -e .[all] .. note:: the "-e" flag is for "editable". It means that any changes you make to the Neo code will be immediately available in your virtual environment. Without this flag, you would need to re-run ``pip install`` every time you change the code. Running the test suite ====================== To run the full test-suite, run:: pytest The first time this is run, all of the Neo test files will be downloaded to your machine, so the run time can be an hour or more. For subsequent runs, the files are already there, so the tests will run much faster. Because Neo downloads datasets this can lead to issues in the course of offline development or for packaging Neo (e.g. for a Linux distribution). In order to not download datasets and to skip tests which require downloaded datasets the environment variable :code:`NEO_TESTS_NO_NETWORK` can be set to any truthy value (e.g. :code:`'True'``). For macOS/Linux this can be done by doing: .. code-block:: bash NEO_TESTS_NO_NETWORK='True' pytest . For Windows this can be done by doing: .. code-block:: bat set NEO_TESTS_NO_NETWORK=true pytest . This can also be done with a conda environment variable if developing in a conda env. To configure these see the docs at `conda env vars documentation`_. It is often helpful to run only parts of the test suite. To test only the :mod:`neo.core` module, which is much quicker than testing :mod:`neo.io`, run:: pytest neo/test/coretest You can also run only tests in a specific file, e.g.:: pytest neo/test/coretest/test_analogsignal.py and you can even run individual tests, e.g.:: pytest neo/test/coretest/test_analogsignal.py::TestAnalogSignalConstructor::test__create_from_1d_quantities_array Test coverage ------------- A good way to start contributing to Neo is to improve the test coverage. If you run pytest with the "--cov" option: :: pytest --cov=neo --cov-report html --cov-report term Then pytest will track which lines of Neo code are executed while running the tests, and will generate a set of local web pages showing which lines have not been executed, and hence not tested. Our goal is 100% coverage, so adding tests to increase coverage is always welcome. Writing tests ============= You should try to write automated tests for any new code that you add. If you have found a bug and want to fix it, first write a test that isolates the bug (and that therefore fails with the existing codebase). Then apply your fix and check that the test now passes. We use Python's built-in :mod:`unittest` module to structure our tests, which are placed in the :file:`neo/test` directory. Related tests are grouped into classes, and each individual test is a method of that class, so take a look at the existing tests before deciding whether to add a new method, a new class or a new file. Some guidelines on writing tests: - Each test must be independent, the results shouldn't depend on the order in which tests are run. - Fast tests are preferred: we have a lot of tests, so the total time adds up quickly. - Where there are branches in the code (e.g. ``if`` statements), try to test all possible branches. Coding standards and style ========================== All code should conform as much as possible to `PEP 8`_, with a maximum line length of 99 characters, and should run with Python 3.8 or newer. You can use the `pep8`_ program to check the code for PEP 8 conformity. You can also use `flake8`_, which combines pep8 and pyflakes. However, the pep8 and flake8 programs do not check for all PEP 8 issues. In particular, they do not check that the import statements are in the correct order (standard library imports first, then other dependencies, then Neo's own code). Working on the documentation ============================ All modules, classes, functions, and methods (including private and subclassed builtin methods) should have docstrings. Please see `PEP 257`_ for a description of docstring conventions. Module docstrings should explain briefly what functions or classes are present. Detailed descriptions can be left for the docstrings of the respective functions or classes. Class docstrings should include an explanation of the purpose of the class and, when applicable, how it relates to standard neuroscience data. They should also include at least one example, which should be written so it can be run as-is from a clean, newly-started Python interactive session (this means all imports should be included). Finally, they should include a list of all arguments, attributes, and properties, with explanations. Properties that return data calculated from other data should explain what calculation is done. A list of methods is not needed, since documentation will be generated from the method docstrings. Method and function docstrings should include an explanation of what the method or function does. If this may not be clear, one or more examples may be included. Examples that are only a few lines do not need to include imports or setup, but more complicated examples should have them. The documentation is written in `reStructuredText`_, using the `Sphinx`_ documentation system. Any mention of another Neo module, class, attribute, method, or function should be properly marked up so automatic links can be generated. To build the documentation:: pip install -e .[docs] cd doc make html Then open :file:`build/html/index.html` in your browser. Writing an IO module ==================== The topic of writing a new IO module is addressed in :doc:`add_file_format`. Working with Git ================ If you're new to Git, there are many good learning resources on the web, such as `Blischak, Davenport and Wilson (2016)`_. We recommend the following best practices, based on `this document by Luis Matos`_: **Commit related changes** Try to ensure that each commit contains only changes related to a single topic. For example, fixing two different bugs should result in two separate commits. Small commits make it easier for other developers to understand the changes. You don't have to commit all the changes in your working copy: use the `staging area`_ and the ability to stage only parts of a file to only include relevant changes. Graphical Git tools such as Sourcetree_ and GitKraken_ can make this very easy. **Commit and push often** Committing often helps to (i) keep your commits small, (ii) commit only related changes, (iii) reduce the risk of losing work, and (iv) share your code more frequently with others, which is important for a fairly busy project like Neo because it makes it easier to integrate different people's changes. **Test your code before you commit** Since the Neo IO module tests take a long time to run, you may not wish to run them before every commit. However, the core module tests run quickly, so you can run them every time, and of course if you're working on a particular IO module you should run the tests for that module. **Write informative commit messages** The first line of your commit message should be a short summary of your changes. Then add a blank line. Then give additional information, for example explaining the motivation for the change and adding links to related Github issue(s). **Use branches** **Always** work in a branch specific to the bug you're trying to fix or the feature you're trying to add, never in the master/main branch. Branches should ideally be short lived, to minimise the risk of conflicts when merging into the master branch. You should aim to synchronize the master/main branch in your own fork with the upstream NeuralEnsemble master branch frequently, and always create a new branch off this master branch. Making a pull request ===================== When you think your bug fix, new feature, or cleanup is ready to be merged into Neo, `open a pull request on GitHub`_. If this is your first pull request to the project, please include a commit in which you add your name and affiliation/employer (if any) to :file:`doc/source/authors.rst`. Reviewing pull requests ======================= In addition to writing code and documentation, constructive reviewing of other people's code is also a great way to contribute to Neo development. Please review the `Code of Conduct`_ before reviewing. Code reviews are a big part of what determines whether contributing to Neo is a positive experience or not. Like reviewing scientific papers, reviewing pull requests (PRs) requires both attention to detail and seeing the big picture. **Attention to detail** - For every line of code added or removed, ensure you understand the purpose of the change. If this is unclear, add a comment in the PR. - Ensure that unit tests have been added or modified appropriately to test the modified code. - Check that all of the automated checks have passed. Occasionally a test failure is not related to the specific pull request, but to some change in the CI system, or some other unrelated change. If you're *absolutely sure* this is the case, then it's ok to approve the PR, but please add a comment explaining why you think the failure is unrelated. - If you see a more efficient way to implement something, feel free to add a comment about this, but please ensure all comments are polite and constructive. - Check that new code is adequately documented, with docstrings for new functions or classes, comments to explain complex pieces of code, and changes to the user guide if adding or removing features. - Ensure there is no temporary, commented out code. Since Neo is a library, there should be no, or very few ``print()`` functions. **The big picture** - Is this code maintainable? If the algorithm is highly complex so that few people can understand it, or it's a very niche feature, then it may not be possible to maintain this code in the long term. - Are you aware of other people working on the same issue, or a related one? If that's the case, please make the submitter aware of this so they can coordinate with the others. - Pull requests don't have to be perfect, especially if they're from first-time or inexperienced contributors. Sometimes it's ok to accept a partial or sub-optimal solution that can be improved later, as long as it moves the project in a good direction. Making a release ================ - Create a new file with the release notes in the folder :file:`doc/source/releases` then add a link to it in :file:`/doc/source/releases.rst`. - Ensure you are in the master branch. - Check that all tests pass, and that the documentation builds correctly (see above). - Tag the release in the Git repository and push it:: git tag git push --tags origin git push --tags upstream - Wait for the `continuous integration server`_ to run all the tests, ensure there are no failures. If there are failures, fix them, and move the tag to the new commit. - Build source and wheel packages:: python -m build - Upload the package to `PyPI`_ (the members of the :ref:`section-maintainers` team have the necessary permissions to do this):: twine upload dist/neo-0.X.Y.tar.gz - Check the `Read the Docs`_ documentation has built correctly. If not, you may need to manually activate the new version in the `docs configuration page`_. .. _`mailing list`: http://neuralensemble.org/community .. _`issue tracker`: https://github.com/NeuralEnsemble/python-neo/issues/ .. _`add an SSH key to your Github account`: https://docs.github.com/en/authentication/connecting-to-github-with-ssh .. _`more information on forking`: http://help.github.com/en/articles/fork-a-repo .. _`GitHub Docs on git`: https://docs.github.com/en/get-started/quickstart/set-up-git .. _`How to Contribute to Open Source`: https://opensource.guide/how-to-contribute/ .. _Miniconda: https://docs.conda.io/en/latest/miniconda.html .. _Datalad: https://www.datalad.org .. _`installing Datalad`: https://handbook.datalad.org/en/latest/intro/installation.html#installation-and-configuration .. _pep8: https://pypi.org/project/pep8/ .. _flake8: https://pypi.org/project/flake8/ .. _pyflakes: https://pypi.org/project/pyflakes/ .. _reStructuredText: http://docutils.sourceforge.net/rst.html .. _Sphinx: http://www.sphinx-doc.org/ .. _`PEP 257`: https://www.python.org/dev/peps/pep-0257/ .. _`PEP 8`: https://www.python.org/dev/peps/pep-0008/ .. _pep8: https://pypi.org/project/pep8/ .. _`Blischak, Davenport and Wilson (2016)`: https://doi.org/10.1371/journal.pcbi.1004668 .. _`this document by Luis Matos`: https://gist.github.com/luismts/ .. _`staging area`: https://coderefinery.github.io/git-intro/staging-area/ .. _Sourcetree: https://www.sourcetreeapp.com .. _GitKraken: https://www.gitkraken.com .. _`open a pull request on GitHub`: https://help.github.com/en/articles/about-pull-requests .. _`Code of Conduct`: https://github.com/NeuralEnsemble/python-neo/blob/master/CODE_OF_CONDUCT.md .. _`maintainers team`: https://github.com/orgs/NeuralEnsemble/teams/neo-maintainers .. _PyPI: https://pypi.org/project/neo .. _`continuous integration server`: https://github.com/NeuralEnsemble/python-neo/actions .. _`Read the Docs`: https://neo.readthedocs.io/en/latest/ .. _`docs configuration page`: https://readthedocs.org/projects/neo/ .. _`conda env vars documentation`: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/example_data.txt0000644000175100017510000165146015154507213017301 0ustar00runnerrunner-5.8469e+01 -5.2855e+01 -5.8790e+01 -5.8815e+01 -5.9061e+01 -5.0518e+01 -5.2003e+01 -5.7240e+01 -5.9010e+01 -5.0510e+01 -5.1988e+01 -5.7199e+01 -5.8960e+01 -5.0503e+01 -5.1973e+01 -5.7158e+01 -5.8911e+01 -5.0495e+01 -5.1958e+01 -5.7117e+01 -5.8861e+01 -5.0488e+01 -5.1944e+01 -5.7076e+01 -5.8812e+01 -5.0480e+01 -5.1929e+01 -5.7036e+01 -5.8763e+01 -5.0473e+01 -5.1914e+01 -5.6996e+01 -5.8715e+01 -5.0466e+01 -5.1900e+01 -5.6956e+01 -5.8666e+01 -5.0457e+01 -5.1885e+01 -5.6917e+01 -5.8618e+01 -5.0442e+01 -5.1871e+01 -5.6877e+01 -5.8570e+01 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matplotlib import pyplot from matplotlib.patches import Rectangle, ArrowStyle, FancyArrowPatch from matplotlib.font_manager import FontProperties import neo line_height = 0.22 fontsize = 10.5 left_text_shift = 0.1 dpi = 100 def get_rect_height(name, obj): """ calculate rectangle height """ nlines = 1.5 nlines += len(getattr(obj, "_all_attrs", [])) nlines += len(getattr(obj, "_child_objects", [])) if name == "Group": # because we represent RegionOfInterest only once # not the individual subclasses nlines -= 2 return nlines * line_height def annotate(ax, coord1, coord2, connectionstyle, color, alpha): arrowprops = dict( arrowstyle="simple", shrinkA=0.3, shrinkB=0.3, fc=color, ec=color, connectionstyle=connectionstyle, alpha=alpha, ) bbox = dict(boxstyle="square", fc="w") a = ax.annotate( "", coord1, coord2, ha="right", va="center", size=fontsize, arrowprops=arrowprops, bbox=bbox, ) a.set_zorder(-4) def calc_coordinates(pos, height): x = pos[0] y = pos[1] + height - line_height * 0.5 return x, y def initialize_dummy_neo(name): if name in ["Block", "Segment", "Group", "Event", "Epoch"]: return getattr(neo, name)() elif name in ["ChannelView"]: sig = neo.AnalogSignal([] * pq.V, sampling_rate=1 * pq.Hz) return neo.ChannelView(sig, [0]) elif name in ["ImageSequence"]: return neo.ImageSequence( np.array([1]).reshape((1, 1, 1)) * pq.V, spatial_scale=1 * pq.m, sampling_rate=1 * pq.Hz ) elif name in ["AnalogSignal"]: return neo.AnalogSignal([] * pq.V, sampling_rate=1 * pq.Hz) elif name in ["SpikeTrain"]: return neo.SpikeTrain([] * pq.s, 0 * pq.s) elif name in ["IrregularlySampledSignal"]: return neo.IrregularlySampledSignal([] * pq.s, [] * pq.V) elif name in ["RegionOfInterest"]: imgseq = initialize_dummy_neo("ImageSequence") return neo.core.regionofinterest.CircularRegionOfInterest(imgseq, 0, 0, 0) else: raise ValueError(f"Unknown neo object: {name}") def generate_diagram(rect_pos, rect_width, figsize): rw = rect_width fig = pyplot.figure(figsize=figsize) ax = fig.add_axes([0, 0, 1, 1]) all_h = {} objs = {} for name in rect_pos: objs[name] = initialize_dummy_neo(name) all_h[name] = get_rect_height(name, objs[name]) # draw connections color = ["c", "m", "r"] alpha = [0.8, 0.8, 0.5] CHILD_OBJECTS = 0 CHILD_PROPERTIES = 1 CUSTOM_RELATIONS = 2 for name, pos in rect_pos.items(): obj = objs[name] relationships = [getattr(obj, "_child_objects", []), getattr(obj, "_child_properties", [])] # additional references custom_relations = [] for attr in obj._all_attrs: attr_name, attr_type = attr[0], attr[1] if isinstance(attr_type, str) and hasattr(neo, attr_type): custom_relations.append(attr_type) elif isinstance(attr_type, tuple): for attr_t in attr_type: if isinstance(attr_t, str) and hasattr(neo, attr_t): custom_relations.append(attr_t) relationships.append(custom_relations) for r in (CHILD_OBJECTS, CHILD_PROPERTIES, CUSTOM_RELATIONS): for child_name in relationships[r]: if ("RegionOfInterest") in child_name: child_name = "RegionOfInterest" if child_name not in rect_pos: continue x1, y1 = calc_coordinates(rect_pos[child_name], all_h[child_name]) x2, y2 = calc_coordinates(pos, all_h[name]) # autolink if child_name == name: rad = 1 y1 += 0.2 x1 += 0.1 else: rad = (y1 - y2) / 50 if r == CUSTOM_RELATIONS: rad = -0.1 if r in (CHILD_OBJECTS, CHILD_PROPERTIES, CUSTOM_RELATIONS): x2 += rect_width connectionstyle = f"arc3,rad={rad}" annotate( ax=ax, coord1=(x1 - 0.02, y1 - 0.05), coord2=(x2 - 0.05, y2), connectionstyle=connectionstyle, color=color[r], alpha=alpha[r], ) # draw boxes for name, pos in rect_pos.items(): htotal = all_h[name] obj = objs[name] allrelationship = [] for item in getattr(obj, "_child_containers", []): if item not in allrelationship: # this is to avoid having "regionsofinterest" appear three times in Group # for the three subclasses allrelationship.append(item) rect = Rectangle(pos, rect_width, htotal, facecolor="w", edgecolor="k", linewidth=2.0) ax.add_patch(rect) # title green pos2 = pos[0], pos[1] + htotal - line_height * 1.5 rect = Rectangle(pos2, rect_width, line_height * 1.5, facecolor="g", edgecolor="k", alpha=0.5, linewidth=2.0) ax.add_patch(rect) # single relationship relationship = list(getattr(obj, "_child_objects", [])) if name == "Group": relationship.remove("RectangularRegionOfInterest") relationship.remove("PolygonRegionOfInterest") pos2 = pos[1] + htotal - line_height * (1.5 + len(relationship)) rect_height = len(relationship) * line_height rect = Rectangle((pos[0], pos2), rect_width, rect_height, facecolor="c", edgecolor="k", alpha=0.5) ax.add_patch(rect) # necessary attr pos2 = pos[1] + htotal - line_height * (1.5 + len(allrelationship) + len(obj._necessary_attrs)) rect = Rectangle( (pos[0], pos2), rect_width, line_height * len(obj._necessary_attrs), facecolor="r", edgecolor="k", alpha=0.5 ) ax.add_patch(rect) # name if hasattr(obj, "_quantity_attr"): post = "* " else: post = "" ax.text( pos[0] + rect_width / 2.0, pos[1] + htotal - line_height * 1.5 / 2.0, name + post, horizontalalignment="center", verticalalignment="center", fontsize=fontsize + 2, fontproperties=FontProperties(weight="bold"), ) # relationship for i, relat in enumerate(allrelationship): ax.text( pos[0] + left_text_shift, pos[1] + htotal - line_height * (i + 2), relat + ": list", horizontalalignment="left", verticalalignment="center", fontsize=fontsize, ) # attributes for i, attr in enumerate(obj._all_attrs): attrname, attrtype = attr[0], attr[1] if hasattr(obj, "_quantity_attr") and obj._quantity_attr == attrname: t1 = attrname + " (object itself)" else: t1 = attrname if attrtype == pq.Quantity: if attr[2] == 0: t2 = "Quantity scalar" else: t2 = "Quantity %dD" % attr[2] elif attrtype == np.ndarray: t2 = "np.ndarray %dD dt='%s'" % (attr[2], attr[3].kind) elif attrtype == datetime: t2 = "datetime" elif type(attrtype) == tuple: t2 = str(attrtype).replace("(", "").replace(")", "") t2 = t2.replace("'", "").replace(", ", " or ") else: t2 = attrtype.__name__ t = t1 + " : " + t2 # abbreviating lines to match in rectangles char_limit = int(rect_width * 13.4) # 13.4 = arbitrary calibration if len(t) > char_limit: t = t[: char_limit - 3] + "..." ax.text( pos[0] + left_text_shift, pos[1] + htotal - line_height * (i + len(allrelationship) + 2), t, horizontalalignment="left", verticalalignment="center", fontsize=fontsize, ) xlim, ylim = figsize ax.set_xlim(0, xlim) ax.set_ylim(0, ylim) ax.set_xticks([]) ax.set_yticks([]) return fig def generate_diagram_simple(): figsize = (18, 12) rw = rect_width = 3.0 bf = 1.5 rect_pos = { # col 0 "Block": (0.5 + rw * bf * 0, 7), # col 1 "Segment": (0.5 + rw * bf * 0.9, 8.5), "Group": (0.5 + rw * bf * 0.9, 3.7), # col 2 "ChannelView": (0.5 + rw * bf * 1.8, 2.5), "RegionOfInterest": (0.5 + rw * bf * 1.8, 1.0), # col 3 "AnalogSignal": (0.5 + rw * bf * 3, 10), "IrregularlySampledSignal": (0.5 + rw * bf * 3, 8.4), "ImageSequence": (0.5 + rw * bf * 3, 6.35), "SpikeTrain": (0.5 + rw * bf * 3, 3.8), "Event": (0.5 + rw * bf * 3, 2.1), "Epoch": (0.5 + rw * bf * 3, 0.3), } fig = generate_diagram(rect_pos, rect_width, figsize) fig.savefig("simple_generated_diagram.png", dpi=dpi) fig.savefig("simple_generated_diagram.svg", dpi=dpi) if __name__ == "__main__": generate_diagram_simple() pyplot.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/images/generate_io_overview.py0000644000175100017510000000612615154507213022132 0ustar00runnerrunner# -*- coding: utf-8 -*- """ This generate diagram of the (raw)ios and formats Author: Julia Sprenger """ import pygraphviz import neo # from datetime import datetime # # import numpy as np # import quantities as pq # from matplotlib import pyplot # from matplotlib.patches import Rectangle, ArrowStyle, FancyArrowPatch # from matplotlib.font_manager import FontProperties # # from neo.test.generate_datasets import fake_neo # # line_heigth = .22 # fontsize = 10.5 # left_text_shift = .1 # dpi = 100 default_style = {"shape": "rectangle", "color": "black", "fontcolor": "black"} IO_style = default_style.copy() IO_style["fontsize"] = "30" IO_style["penwidth"] = 6 styles = { "IO": {"ro": IO_style.copy(), "rw": IO_style.copy(), "raw": IO_style.copy()}, "main": default_style.copy(), "ext": default_style.copy(), } styles["IO"]["ro"]["color"] = "#20B2AA " styles["IO"]["rw"]["color"] = "#4169E1 " styles["IO"]["raw"]["color"] = "#008080 " styles["ext"]["shape"] = "circle" styles["ext"]["fillcolor"] = "red" styles["ext"]["style"] = "filled" # styles['ext']['fixedsize'] = 'True' def generate_diagram(filename, plot_extensions=False): dia = pygraphviz.AGraph(strict=False, splines="true") G = dia G.node_attr["fontname"] = "Arial" # G.node_attr['shape'] = 'circle' # G.node_attr['fixedsize'] = 'true' # G.node_attr['sep'] = '-100' G.node_attr["fixedsize"] = "False" # G.graph_attr['overlap'] = 'False' G.graph_attr["packMode"] = "clust" # G.graph_attr['levelsgap'] = -500 G.node_attr["fontsize"] = "20" G.edge_attr["minlen"] = "0" # G.node_attr['style'] = 'filled' # G.graph_attr['outputorder'] = 'edgesfirst' # G.graph_attr['splines'] = "compound" G.graph_attr["label"] = "NEO {}".format(neo.__version__) G.graph_attr["ratio"] = "1.0" # G.edge_attr['color'] = '#1100FF' G.edge_attr["style"] = "setlinewidth(4)" dia.add_node("NEO", shape="circle", fontsize=50) for io in neo.io.iolist: io_name = io.name rawio = False if issubclass(io, neo.io.basefromrawio.BaseFromRaw): rawio = True if io_name == "BaseIO": io_name = io.__name__.rstrip("RawIO") if io_name is None: io_name = io.__name__.rstrip("IO") if "example" in io_name: continue if io.is_writable and io.is_readable: mode = "rw" elif io.is_readable: mode = "ro" if rawio: mode = "raw" dia.add_node(io_name, **styles["IO"][mode]) dia.add_edge("NEO", io_name) if plot_extensions: for ext in io.extensions: dia.add_node(ext, **styles["ext"]) dia.add_edge(io_name, ext, minlen=0) dia.layout(prog="fdp") # neato, dot, twopi, circo, fdp, nop, wc, acyclic, gvpr, gvcolor, # ccomps, sccmap, tred, sfdp. for ext in ["png", "svg", "eps"]: dia.draw("{}.{}".format(filename, ext)) if __name__ == "__main__": generate_diagram("IODiagram", plot_extensions=False) generate_diagram("IODiagram_ext", plot_extensions=True) # pyplot.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/images/incf_endorsed_with_spacing.png0000644000175100017510000024142715154507213023425 0ustar00runnerrunner‰PNG  IHDR&ÂÛóO IDATxœìÝw|ÝW}ÿñ×9ßq§¶5¼÷Jbg“ÁÈ$)Â,PFÙ3̖Ѱ -£…ÐRÚ”R(¡$@MÈ„ØqìØ±oË–­=ïú®óûã{¯tå ¢X¶%ùó|<”XÒÕÕ½ÒÕ÷ûþžó9ŸB!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„B!„'„šÈ�;rþ±zâ³5t ·wzDÀs[ZÃ�¡³‘±- Müêp‘­ƒ!/jM°£òà`@Â@ƒ«hMi†¼ˆK›]lÍïº=¶ç¥,έwX7ðX.`UÖbU�ÍÎ\ÈÝ48Ìr=¾!ÖÔØì/„lÏ…¼¸%A.4ü©ßg80tû†«Z\z¼ˆ®RDÖRÜÛçó’Ù &5¿éò°4ÔÚš¥‹?õû,I[<8à3'iÑY YScÓQ2œVk±qȧÁÑ,D\Öì²nÀ'e)ŠÍÃ6Ø †Å)ÍþbÄûÖÔÚd5dmÅÞBH�­8»Îáž¾€~?âÒY6�… †å›»û< Š>œSo±¿áaX•µè,Dôù”âêÖ=^ÄÎ\ÀÖá€kf'èö 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The page title must be in rST for it to show in next/prev page buttons. Therefore we add a special style rule to only this page that hides h1 tags (thank you MNE Python for this idea) .. raw:: html ================= Neo documentation ================= .. grid:: .. grid-item-card:: Neo is a Python package for working with **electrophysiology data**. It implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data. .. grid-item-card:: Goal: improve **interoperability** between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a **common, shared object model**. .. grid-item-card:: .. image:: images/incf_endorsed_with_spacing.png :width: 100% :alt: Endorsed as a community standard by INCF :target: https://www.incf.org/sbp/neo .. grid:: .. grid-item-card:: Support for reading a **wide range of neurophysiology file formats**, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt and Igor Pro, and for writing to **open formats** such as NWB and NIX. .. grid-item-card:: Neo objects behave just like **normal NumPy arrays**, but with additional **metadata**, checks for **dimensional consistency** and **automatic unit conversion**. .. grid-item-card:: In order to be as **lightweight** a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. .. image:: images/neologo.png :width: 100% :alt: Neo logo :class: only-light .. image:: images/neologo_darkmode.png :width: 100% :alt: Neo logo :class: only-dark .. toctree:: :hidden: :maxdepth: 1 Download & Install Read and analyze data Share data in an open format Use Neo in a library or app Add support for a new file format examples/index Report a bug, request a feature Contribute to Neo Who else is using Neo? API reference rawio Supported formats rawiolist releases authors governance .. grid:: .. grid-item-card:: Download & Install :link: install.html .. grid-item-card:: Read and analyze data :link: read_and_analyze.html .. grid-item-card:: Share data in an open format :link: share_data.html .. grid:: .. grid-item-card:: Use Neo in a library or app :link: use_neo_as_dependency.html .. grid-item-card:: Add support for a new file format :link: add_file_format.html .. grid-item-card:: Examples :link: examples/index.html .. grid:: .. grid-item-card:: Report a bug or request a new feature :link: bug_reports.html .. grid-item-card:: Contribute to Neo :link: contributing.html .. grid-item-card:: See who is using Neo :link: neo_users.html .. grid:: .. grid-item-card:: API reference :link: api_reference.html .. grid-item-card:: Optimisation of performance and memory consumption :link: rawio.html .. grid-item-card:: Supported file formats :link: iolist.html ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/install.rst0000644000175100017510000000466515154507213016312 0ustar00runnerrunner======================== Download and install Neo ======================== Neo is a pure Python package, so it should be easy to get it running on any system. Installing with pip =================== You can install the latest published version of Neo and its dependencies using:: $ pip install neo Dependencies ------------ * Python_ >= 3.8 * numpy_ >= 1.19.5 * quantities_ >= 0.12.1 Certain IO modules have additional dependencies. If these are not satisfied, Neo will still install but the IO module that uses them will fail on loading: * scipy >= 1.0.0 for NeoMatlabIO * h5py >= 2.5 for KwikIO * klusta for KwikIO * igor2 >= 0.5.2 for IgorIO * nixio >= 1.5 for NixIO * stfio for StimfitIO * pillow for TiffIO These dependencies can be installed by specifying a comma-separated list with the ``pip install`` command, e.g.:: $ pip install neo[nixio,tiffio] Or when installing a specific version of neo:: $ pip install neo[nixio,tiffio]==0.9.0 The following IO modules have additional dependencies: * igorproio * kwikio * neomatlabio * nixio * stimfitio * tiffio Installing from source ====================== To download and install the package manually, download: |neo_github_url| Then: .. parsed-literal:: $ unzip neo-|release|.zip $ cd neo-|release| $ pip install . Alternatively, to install the latest version of Neo from the Git repository:: $ git clone git://github.com/NeuralEnsemble/python-neo.git $ cd python-neo $ pip install . Installing with Conda ===================== :: $ conda config --add channels conda-forge $ conda config --set channel_priority strict $ conda install -c conda-forge python-neo Installing from a package repository ==================================== To install Neo if you're using Fedora_ Linux:: $ sudo dnf install python-neo .. NeuroDebian seems out of date - still has Trac as homepage - how to update? To install Neo if you're using the Spack_ package manager:: $ spack install py-neo .. _`Python`: https://www.python.org/ .. _`numpy`: https://numpy.org/ .. _`quantities`: https://pypi.org/project/quantities/ .. _`pip`: https://pypi.org/project/pip/ .. _`setuptools`: http://pypi.python.org/pypi/setuptools .. _Anaconda: https://www.anaconda.com/distribution/ .. _Fedora: https://src.fedoraproject.org/rpms/python-neo .. _Spack: https://spack.readthedocs.io/en/latest/package_list.html#py-neo ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/iolist.rst0000644000175100017510000000012615154507213016133 0ustar00runnerrunnerList of implemented IO modules ============================== .. automodule:: neo.io ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/neo_users.rst0000644000175100017510000000267015154507213016640 0ustar00runnerrunner================= Who is using Neo? ================= .. note:: This page has not yet been written. It will highlight some of the projects that are using Neo, such as Elephant_, ephyviewer_, PyNN_, Neurotic_, SpikeInterface_, MEArec_, tridesclous_, uncertainpy_ and eFEL_. .. note:: We also plan to maintain a list of publications that mention use of Neo and/or cite the Neo article. For now, you can view mentions on SciCrunch_ and on `Google Scholar`_, and read `comments from Neo users`_ from the INCF review of Neo as a community standard. .. _Elephant: https://github.com/NeuralEnsemble/elephant .. _ephyviewer: https://github.com/NeuralEnsemble/ephyviewer .. _PyNN: https://github.com/NeuralEnsemble/PyNN .. _Neurotic: https://github.com/jpgill86/neurotic .. _SpikeInterface: https://github.com/SpikeInterface .. _MEArec: https://github.com/alejoe91/MEArec .. _tridesclous: https://github.com/tridesclous/tridesclous .. _uncertainpy: https://github.com/simetenn/uncertainpy .. _eFEL: https://github.com/BlueBrain/eFEL .. _SciCrunch: https://scicrunch.org/resolver/SCR_000634/mentions?q=&i=rrid:scr_000634 .. _`Google Scholar`: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=%22RRID%3ASCR_000634%22+OR+%22SCR_000634%22+OR+%22neuralensemble.org%2Fneo%2F%22+OR+%22github.com%2FNeuralEnsemble%2Fpython-neo%22&btnG= .. _`comments from Neo users`: https://f1000research.com/documents/11-658 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/rawio.rst0000644000175100017510000002676115154507213015766 0ustar00runnerrunner.. _section-neo-rawio-API: ============= Neo RawIO API ============= .. currentmodule:: neo.rawio For performance and memory consumption reasons, Neo provides a low-level, developer-oriented read-only API for reading different file formats. Neo's full-featured IO modules are built on this, but it is also available for direct use. In brief: - **neo.io** is the user-oriented read/write layer. Reading consists of getting a tree of Neo objects from a data source (file, url, or directory). When reading, all Neo objects are correctly scaled to the correct units. Writing consists of making a set of Neo objects persistent in a file format. - **neo.rawio** is a low-level layer for reading data only. Reading consists of getting NumPy buffers (often int16/int64) of signals/spikes/events. Scaling to real values (microV, times, ...) is done in a second step. Here the underlying objects must be consistent across Blocks and Segments for a given data source. The neo.rawio API is close in spirit to a C API for reading data, but in Python/NumPy. Many, but not all of the file formats supported in :mod:`neo.io` also have a :mod:`neo.rawio` interface. Possible uses of the :mod:`neo.rawio` API are: - fast reading chunks of signals in int16 and do the scaling of units (uV) on a GPU while scaling the zoom. This should improve bandwidth from HD/SSD to RAM and from RAM to GPU memory. - load only a small chunk of data for heavy computations. For instance the spike sorting module tridesclous_ does this. The :mod:`neo.rawio` API is less flexible than :mod:`neo.io` and has some limitations: - read-only - AnalogSignals must have the same characteristics across all Blocks and Segments: ``sampling_rate``, ``shape[1]``, ``dtype`` - AnalogSignals should all have the same value of ``sampling_rate``, otherwise they won't be read at the same time. - Units must have SpikeTrains even if empty across all Block and Segment - Epoch and Event are processed the same way (with ``durations=None`` for Event). For an intuitive comparison of :mod:`neo.io` and :mod:`neo.rawio` see: - :doc:`examples/read_files_neo_io` - :doc:`examples/read_files_neo_rawio` One benefit of the :mod:`neo.rawio` API is that a developer should be able to code a new RawIO class with little knowledge of the Neo tree of objects or of the :mod:`quantities` package. Basic usage =========== .. Download example files .. ipython:: python :suppress: import os.path from urllib.request import urlretrieve url_repo = 'https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/' for distantfile in ('plexon/File_plexon_2.plx', 'plexon/File_plexon_3.plx', 'blackrock/FileSpec2.3001.nev', 'blackrock/FileSpec2.3001.ns5'): localfile = distantfile.split("/")[1] if not os.path.exists(localfile): urlretrieve(url_repo + distantfile, localfile) First create a reader from a class: .. ipython:: In [1]: from neo.rawio import PlexonRawIO In [2]: reader = PlexonRawIO(filename='File_plexon_3.plx') Then browse the internal header and display information: .. ipython:: In [3]: reader.parse_header() In [4]: reader Out[4]: PlexonRawIO: File_plexon_3.plx nb_block: 1 nb_segment: [1] signal_channels: [V1] spike_channels: [Wspk1u, Wspk2u, Wspk4u, Wspk5u ... Wspk29u Wspk30u Wspk31u Wspk32u] event_channels: [] You get the number of blocks and segments per block. You have information about channels: :attr:`signal_channels`, :attr:`spike_channels`, :attr:`event_channels`. All this information is available in the :attr:`header` dict: .. ipython:: In [5]: for k, v in reader.header.items(): ...: print(k, v) Out[5]: signal_channels [('V1', 0, 1000., 'int16', '', 2.44140625, 0., 0)] event_channels [] nb_segment [1] nb_block 1 spike_channels [('Wspk1u', 'ch1#0', '', 0.00146484, 0., 0, 30000.) ('Wspk2u', 'ch2#0', '', 0.00146484, 0., 0, 30000.) Read chunks of signal data and scale them ----------------------------------------- .. ipython:: In [6]: channel_indexes = None #could be channel_indexes = [0] In [7]: raw_sigs = reader.get_analogsignal_chunk(block_index=0, seg_index=0, ...: i_start=1024, i_stop=2048, ...: channel_indexes=channel_indexes) In [8]: float_sigs = reader.rescale_signal_raw_to_float(raw_sigs, dtype='float64') In [9]: sampling_rate = reader.get_signal_sampling_rate() In [10]: t_start = reader.get_signal_t_start(block_index=0, seg_index=0) In [11]: units = reader.header['signal_channels'][0]['units'] In [12]: raw_sigs.shape, raw_sigs.dtype Out[12]: ((1024, 1), dtype('int16')) In [13]: float_sigs.shape, float_sigs.dtype Out[13]: ((1024, 1), dtype('float64')) In [14]: sampling_rate, t_start, units Out[14]: (1000.0, 0.0, '') There are 3 ways to select a subset of channels: by index (0 based), by id or by name. By index is unambiguous 0 to n-1 (inclusive), whereas for some IOs channel_names (and sometimes channel_ids) are not guaranteed to be unique. In such cases, using names or ids may raise an error. A selected subset of channels which is passed to :func:`get_analog_signal_chunk()`, :func:`get_analog_signal_size()`, or :func:`get_analog_signal_t_start()` has the additional restriction that all such channels must have the same :attr:`t_start` and :attr:`signal_size`. Such subsets of channels may be available in specific RawIOs by using the :func:`get_group_signal_channel_indexes()` method, if the RawIO has defined separate :attr:`group_ids` for each group with those common characteristics. Example with BlackrockRawIO for the recording `FileSpec2.3001`_: .. ipython:: In [15]: from neo.rawio import BlackrockRawIO In [16]: reader = BlackrockRawIO(filename="FileSpec2.3001") In [17]: reader.parse_header() In [18]: raw_sigs = reader.get_analogsignal_chunk(channel_indexes=None) # Take all channels In [19]: raw_sigs1 = reader.get_analogsignal_chunk(channel_indexes=[0, 2, 4]) # Take 0 2 and 4 In [20]: raw_sigs2 = reader.get_analogsignal_chunk(channel_ids=['1', '3', '5']) # Same but with their id (1 based) In [21]: raw_sigs3 = reader.get_analogsignal_chunk(channel_names=['chan1', 'chan3', 'chan5']) # Same but with their name In [22]: raw_sigs1.shape[1], raw_sigs2.shape[1], raw_sigs3.shape[1] Out[22]: (3, 3, 3) Inspect spiking unit channels ----------------------------- Each channel gives a SpikeTrain for each Segment. Note that for many formats a physical channel can have several units after spike sorting. So the number of spike channels could be more than the number of physical channels or signal channels. .. ipython:: In [23]: nb_unit = reader.spike_channels_count() In [24]: print('nb_unit', nb_unit) nb_unit 4 In [25]: for spike_channel_index in range(nb_unit): ....: nb_spike = reader.spike_count(block_index=0, seg_index=0, spike_channel_index=spike_channel_index) ....: print('spike_channel_index', spike_channel_index, 'nb_spike', nb_spike) spike_channel_index 0 nb_spike 259 spike_channel_index 1 nb_spike 234 spike_channel_index 2 nb_spike 218 spike_channel_index 3 nb_spike 253 Get spike timestamps in a defined time range and convert them to spike times ---------------------------------------------------------------------------- .. ipython:: In [26]: spike_timestamps = reader.get_spike_timestamps(block_index=0, seg_index=0, spike_channel_index=0, ....: t_start=0, t_stop=10) In [27]: print(spike_timestamps.shape, spike_timestamps.dtype, spike_timestamps[:5]) (86,) uint32 [ 19312 49298 79301 139290 162170] In [28]: spike_times = reader.rescale_spike_timestamp( spike_timestamps, dtype='float64') In [29]: print(spike_times.shape, spike_times.dtype, spike_times[:5]) (86,) float64 [0.64373333 1.64326667 2.64336667 4.643 5.40566667] Get spike waveforms in a defined time range ------------------------------------------- .. ipython:: In [30]: raw_waveforms = reader.get_spike_raw_waveforms(block_index=0, seg_index=0, spike_channel_index=0, ....: t_start=0, t_stop=10) In [31]: print(raw_waveforms.shape, raw_waveforms.dtype, raw_waveforms[0, 0, :4]) (86, 1, 48) int16 [-209 -224 -74 205] In [32]: float_waveforms = reader.rescale_waveforms_to_float(raw_waveforms, dtype='float32', spike_channel_index=0) In [33]: print(float_waveforms.shape, float_waveforms.dtype, float_waveforms[0,0,:4]) (86, 1, 48) float32 [-52.25 -56. -18.5 51.25] Count events per channel ------------------------ .. ipython:: In [34]: reader = PlexonRawIO(filename='File_plexon_2.plx') In [35]: reader.parse_header() In [36]: nb_event_channel = reader.event_channels_count() In [37]: print('nb_event_channel', nb_event_channel) nb_event_channel 28 In [38]: for chan_index in range(nb_event_channel): ....: nb_event = reader.event_count(block_index=0, seg_index=0, event_channel_index=chan_index) ....: print('chan_index',chan_index, 'nb_event', nb_event) chan_index 0 nb_event 1 chan_index 1 nb_event 0 chan_index 2 nb_event 0 chan_index 3 nb_event 0 ... Read event timestamps and times ------------------------------- .. ipython:: In [39]: ev_timestamps, ev_durations, ev_labels = reader.get_event_timestamps( ....: block_index=0, seg_index=0, event_channel_index=0, ....: t_start=None, t_stop=None) In [40]: print(ev_timestamps, ev_durations, ev_labels) [1268] None ['0'] In [41]: ev_times = reader.rescale_event_timestamp(ev_timestamps, dtype='float64') In [42]: print(ev_times) [ 0.0317] Signal streams and signal buffers --------------------------------- For reading analog signals **neo.rawio** has 2 important concepts: 1. The **signal_stream** : it is a group of channels that can be read together using :func:`get_analog_signal_chunk()`. This group of channels is guaranteed to have the same sampling rate, and the same duration per segment. Most of the time, this group of channel is a "logical" group of channels. In short they are from the same headstage or from the same auxiliary board. Optionally, depending on the format, a **signal_stream** can be a slice of or an entire **signal_buffer**. 2. The **signal_buffer** : it is group of channels that share the same data layout in a file. The most simple example is channel that can be read by a simple :func:`signals = np.memmap(file, shape=..., dtype=... , offset=...)`. A **signal_buffer** can contain one or several **signal_stream**'s (very often it is only one). There are two kind of formats that handle this concept: * Formats which use :func:`np.memmap()` internally * Formats based on hdf5 There are many formats that do not handle this concept: * the ones that use an external python package for reading data (edf, ced, plexon2, ...) * the ones with a complicated data layout (e.g. those where the data blocks are split without structure) To check if a format makes use of the buffer api you can check the class attribute flag `has_buffer_description_api` of the rawio class. List of implemented formats =========================== See :doc:`rawiolist`. .. _tridesclous: https://github.com/tridesclous/tridesclous .. _FileSpec2.3001: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/src/master/blackrock././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/rawiolist.rst0000644000175100017510000000013715154507213016647 0ustar00runnerrunnerList of implemented RawIO modules ================================= .. automodule:: neo.rawio ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/read_and_analyze.rst0000644000175100017510000004516615154507213020125 0ustar00runnerrunner=================================== Reading and analyzing data with Neo =================================== .. currentmodule:: neo.core Getting started =============== Neo is a library for working with neurophysiology data in the Python programming language. One of the big advantages of Neo is that it works with many different file formats: it doesn't matter which format your data is stored in, Neo provides a standard way to read the data, and then represents it in a standardised way, as a set of Python objects. .. loading data with an IO module The first step in reading data is to import the appropriate Neo input-output (or IO) module for your data. For this example, we're going to work with membrane potential traces stored in a text file, so we use the :class:`~neo.io.AsciiSignalIO` module: .. ipython:: In [1]: from neo.io import AsciiSignalIO In [2]: data = AsciiSignalIO("example_data.txt", delimiter=" ").read() .. link to list of IO modules .. note:: For a full list of IO modules provided by Neo, see :doc:`iolist`. .. navigating through the tree .. ipython:: In [3]: data Out[3]: [Block with 1 segments file_origin: 'example_data.txt' # segments (N=1) 0: Segment with 1 analogsignals # analogsignals (N=1) 0: AnalogSignal with 4 channels of length 10001; units mV; datatype float32 name: 'multichannel' sampling rate: 1.0 kHz time: 0.0 s to 10.001 s] Different data files can contain different amounts of data, from single traces to multiple recording sessions. To provide consistent behaviour, for all IO modules, the :meth:`read()` method returns a list of data blocks. A :class:`Block` typically represents a recording session. Each block contains a list of segments, where each :class:`Segment` contains data recorded at the same time. In this example file, we see a single type of data, the "analog signal", which represents continuous time series sampled at a fixed interval. The other types of data that can be contained in a :class:`Segment` are discussed below under :ref:`section-data-types`. .. note:: :meth:`read()` reads the entire file into memory at once. If you only want to access part of the data, you can do so using Neo's "lazy" data loading - see the section on :ref:`section-performance-memory` below. .. plotting data Neo data objects are based on NumPy_ arrays, and behave very similarly. For example, they can be plotted just like arrays: .. ipython:: In [4]: import matplotlib.pyplot as plt In [5]: signal = data[0].segments[0].analogsignals[0] In [6]: plt.plot(signal.times, signal) Out[6]: [, , , ] In [7]: plt.xlabel(f"Time ({signal.times.units.dimensionality.string})") Out[7]: Text(0.5, 0, 'Time (s)') In [8]: plt.ylabel(f"Membrane potential ({signal.units.dimensionality.string})") Out[8]: Text(0, 0.5, 'Membrane potential (mV)') In [9]: plt.savefig("example_plot.png") .. image:: example_plot.png .. link to examples. You now know enough to start using Neo. For more examples, see :doc:`../examples/index`. If you want to know more, read on. NumPy ===== Neo is based on NumPy_. All Neo data classes behave like NumPy arrays, but have extra functionality. The first addition is support for units. In contrast to a plain NumPy array, an :class:`AnalogSignal` knows the units of the data it contains, e.g.: .. ipython:: In [10]: signal.units Out[10]: array(1.) * mV This helps avoid errors like adding signals with different units, lets you auto-generate figure axis labels, and makes it easy to change units, like here from millivolts to volts e.g.: .. ipython:: In [11]: signal.magnitude[:5] Out[11]: array([[-58.469, -52.855, -58.79 , -58.815], [-59.061, -50.518, -52.003, -57.24 ], [-59.01 , -50.51 , -51.988, -57.199], [-58.96 , -50.503, -51.973, -57.158], [-58.911, -50.495, -51.958, -57.117]], dtype=float32) In [12]: signal.rescale("V").magnitude[:5] Out[12]: array([[-0.058469 , -0.052855 , -0.05879 , -0.058815 ], [-0.059061 , -0.050518 , -0.052003 , -0.05724001], [-0.05901 , -0.05051 , -0.051988 , -0.05719901], [-0.05896 , -0.050503 , -0.051973 , -0.057158 ], [-0.058911 , -0.050495 , -0.051958 , -0.057117 ]], dtype=float32) The second addition is support for structured metadata. Some of these metadata are *required*. For example, an :class:`AnalogSignal` must always have a :attr:`sampling_rate` attribute, and Neo will produce an Exception if you try to add two signals with different sampling rates: .. ipython:: In [13]: signal.sampling_rate Out[13]: array(1.) * kHz Some of these metadata are recommended but optional, like a name for each signal. Such metadata appear as attributes of the data objects: .. ipython:: In [14]: signal.name Out[14]: 'multichannel' And finally, some metadata are fully optional. These are stored in the :attr:`annotations` and :attr:`array_annotations` attributes: .. ipython:: In [15]: signal.array_annotations Out[15]: {'channel_index': array([0, 1, 2, 3])} For more information about this, see :ref:`section-annotations`. .. treating Neo data objects like numpy arrays, quantity arrays, converting between them, rescaling Most NumPy array methods also work on Neo data objects, e.g.: .. ipython:: In [16]: signal.mean() Out[16]: array(-56.33598, dtype=float32) * mV Data objects can be sliced like arrays (array annotations are automatically sliced appropriately): .. ipython:: In [17]: signal[100:110, 1:3] Out[17]: AnalogSignal with 2 channels of length 10; units mV; datatype float32 name: 'multichannel' sampling rate: 1.0 kHz time: 0.1 s to 0.11 s In [18]: signal[100:110, 1:3].array_annotations Out[18]: {'channel_index': array([1, 2])} To convert a Neo data object to a plain NumPy array, use the :attr:`magnitude` attribute: .. ipython:: In [19]: signal[100:110, 1:3].magnitude Out[19]: array([[-60.654, -60. ], [-60.724, -60. ], [-60.792, -60. ], [-60.859, -60. ], [-60.924, -60. ], [-60.989, -60. ], [-61.052, -60. ], [-61.114, -60. ], [-61.175, -60. ], [-61.234, -60. ]], dtype=float32) .. _section-data-types: Data types ========== The following classes directly represent data as arrays of numerical values with associated metadata (units, sampling frequency, etc.). * :py:class:`AnalogSignal`: A regular sampling of a single- or multi-channel continuous analog signal. * :py:class:`IrregularlySampledSignal`: A non-regular sampling of a single- or multi-channel continuous analog signal. * :py:class:`SpikeTrain`: A set of action potentials (spikes) emitted by the same unit in a period of time (with optional waveforms). * :py:class:`Event`: An array of time points representing one or more events in the data. * :py:class:`Epoch`: An array of time intervals representing one or more periods of time in the data. * :py:class:`ImageSequence`: A three dimensional array representing a sequence of images. AnalogSignal ------------ We have already met the :class:`AnalogSignal`, which represents continuous time series sampled at a fixed interval. In addition to reading data from a file, as above, it is also possible to create new signal objects directly, e.g.: .. ipython:: In [20]: import numpy as np In [21]: from quantities import mV, kHz In [22]: from neo import AnalogSignal In [23]: signal = AnalogSignal(np.random.normal(-65.0, 5.0, size=(100, 5)), ....: units=mV, sampling_rate=1 * kHz) In [24]: signal Out[24]: AnalogSignal with 5 channels of length 100; units mV; datatype float64 sampling rate: 1.0 kHz time: 0.0 s to 0.1 s IrregularlySampledSignal ------------------------ :class:`IrregularlySampledSignal` represents continuous time series sampled at non-regular time points. This means that instead of specifying the sampling rate or sampling interval, you must specify the array of times at which the signal was sampled. .. ipython:: In [25]: from quantities import ms, nA In [26]: from neo import IrregularlySampledSignal In [27]: isignal = IrregularlySampledSignal( ....: times=[0.0, 1.11, 4.27, 16.38, 19.33] * ms, ....: signal=[0.5, 0.8, 0.5, 0.7, 0.2] * nA, ....: description="input current") In [28]: isignal Out[28]: IrregularlySampledSignal with 1 channels of length 5; units nA; datatype float64 description: 'input current' sample times: [ 0. 1.11 4.27 16.38 19.33] ms .. note:: in case of multi-channel data, samples are assumed to have been taken at the same time points in all channels. If you need to specify different time points for different channels, use one signal object per channel. SpikeTrain ---------- A :class:`SpikeTrain` represents the times of occurrence of action potentials (spikes). .. ipython:: In [29]: from neo import SpikeTrain In [30]: spike_train = SpikeTrain([3, 4, 5], units='sec', t_stop=10.0) In [31]: spike_train Out[31]: SpikeTrain containing 3 spikes; units s; datatype float64 time: 0.0 s to 10.0 s It may also contain the waveforms of the action potentials, stored as :class:`AnalogSignals` *within* the spike train object - see the reference documentation for more on this. Event ----- It is common in electrophysiology experiments to record the times of specific events, such as the times at which stimuli are presented. An :class:`Event` contains an array of times at which events occurred, together with an *optional* array of labels for the events, e.g.: .. ipython:: In [32]: from neo import Event In [33]: events = Event(np.array([5, 15, 25]), units="second", ....: labels=["apple", "rock", "elephant"], ....: name="stimulus onset") In [34]: events Out[34]: Event containing 3 events with labels; time units s; datatype int64 name: 'stimulus onset' Epoch ----- A variation of events is where something occurs over a certain period of time, in which case we need to know both the start time and the duration. An :class:`Epoch` contains an array of start or onset times together with an array of durations (or a single value if all epochs have the same duration), and an optional array of labels. .. ipython:: In [35]: from neo import Epoch In [36]: epochs = Epoch(times=np.array([5, 15, 25]), ....: durations=2.0, ....: units="second", ....: labels=["apple", "rock", "elephant"], ....: name="stimulus presentations") In [37]: epochs Out[37]: Epoch containing 3 epochs with labels; time units s; datatype int64 name: 'stimulus presentations' ImageSequence ------------- In addition to electrophysiology, neurophysiology signals may be obtained through functional microscopy. The :class:`ImageSequence` class represents a sequence of images, as a 3D array organized as [frame][row][column]. It behaves similarly to :class:`AnalogSignal`, but in 3D rather than 2D. .. ipython:: In [38]: from quantities import Hz, micrometer In [39]: from neo import ImageSequence In [40]: img_sequence_array = [[[column for column in range(20)]for row in range(20)] ....: for frame in range(10)] In [41]: image_sequence = ImageSequence(img_sequence_array, units='dimensionless', ....: sampling_rate=1 * Hz, ....: spatial_scale=1 * micrometer) In [42]: image_sequence Out[42]: ImageSequence 10 frames with width 20 px and height 20 px; units dimensionless; datatype int64 sampling rate: 1.0 Hz spatial_scale: 1.0 um .. _section-annotations: Annotations =========== .. and array annotations Neo objects have certain *required* metadata, such as the :attr:`sampling_rate` for :class:`AnalogSignals`. There are also certain *recommended* metadata, such as a name and description. For any metadata not covered by the required or recommended fields, additional annotations can be added, e.g.: .. ipython:: In [43]: from quantities import um as µm In [44]: signal.annotate(pipette_tip_diameter=1.5 * µm) In [45]: signal.annotations Out[45]: {'pipette_tip_diameter': array(1.5) * um} For those IO modules that support writing data to file, annotations will also be written, provided they can be serialized to JSON format. .. todo: we should provide a custom ``JSONEncoder`` that supports quantities Array annotations ----------------- Since certain Neo objects contain array data, it is sometimes necessary to annotate individual array elements, or individual columns. For 1D arrays, the array annotations should have the same length as the array, e.g. .. ipython:: In [46]: events.shape In [47]: events.array_annotate(secondary_labels=["red", "green", "blue"]) For 2D arrays, the array annotations should match the shape of the channel dimension, e.g. .. ipython:: In [48]: signal.shape In [49]: signal.array_annotate(quality=["good", "good", "noisy", "good", "noisy"]) Dataset structure ================= The overall structure of a Neo dataset is shown in this figure: .. image:: images/base_schematic.png :height: 500 px :alt: Illustration of the main Neo data types :align: center Beyond the core data classes, Neo has various classes for grouping and structuring different data objects. We have already met two of them, the :class:`Block` and :class:`Segment`. Tree structure -------------- :class:`Block` and :class:`Segment` provide a basic two-level hierarchical structure: :class:`Blocks` contain :class:`Segments`, which contain data objects. :class:`Segments` are used to group data that have a common time basis, i.e. that were recorded at the same time. A :class:`Segment` can be considered as equivalent to a "trial", "episode", "run", "recording", etc., depending on the experimental context. :class:`Segments` have the following attributes, used to access lists of data objects: - :attr:`analogsignals` - :attr:`epochs` - :attr:`events` - :attr:`imagesequences` - :attr:`irregularlysampledsignals` - :attr:`spiketrains` :class:`Block` is the top-level container gathering all of the data, discrete and continuous, for a given recording session. It contains :class:`Segment` and :class:`Group` (see next section) objects in the attributes :attr:`segments` and :attr:`groups`. Grouping and linking objects ---------------------------- Sometimes your data have a structure that goes beyond a simple two-level hierarchy. For example, suppose that you wish to group together signals that were recorded from the same tetrode in multi-tetrode recording setup. For this, Neo provides a :class:`Group` class: .. ipython:: In [50]: from neo import Group In [51]: signal1 = AnalogSignal(np.random.normal(-65.0, 5.0, size=(100, 5)), units=mV, sampling_rate=1 * kHz) In [52]: signal2 = AnalogSignal(np.random.normal(-65.0, 5.0, size=(1000, 5)), units=nA, sampling_rate=10 * kHz) In [53]: group = Group(objects=(signal1, signal2)) In [54]: group Out[54]: Group with 2 analogsignals Since :class:`AnalogSignals` can contain data from multiple channels, sometimes we wish to include only a subset of channels in a group. For this, Neo provides the :class:`ChannelView` class, e.g.: .. ipython:: In [55]: from neo import ChannelView In [56]: channel_of_interest = ChannelView(obj=signal1, index=[2]) In [57]: signal_with_spikes = Group(objects=(channel_of_interest, spike_train)) In [58]: signal_with_spikes Out[58]: Group with 1 spiketrains, 1 channelviews .. todo: give some examples of using ROI with ImageSequence .. _section-performance-memory: Performance and memory consumption ================================== .. lazy loading In some cases you may not wish to load everything in memory, because it could be too big, or you know you only need to access a subset of the data in a file. For this scenario, some IO modules provide an optional argument to their :attr:`read()` methods: ``lazy=True/False``. With ``lazy=True`` all data objects (:class:`AnalogSignal`/:class:`SpikeTrain`/:class:`Event`/:class:`Epoch`/:class:`ImageSequence`) are replaced by proxy objects (:class:`AnalogSignalProxy`/:class:`SpikeTrainProxy`/:class:`EventProxy`/:class:`EpochProxy`/:class:`ImageSequenceProxy`). .. todo: implement ImageSequenceProxy By default (if not specified), ``lazy=False``, i.e. all data are loaded. These proxy objects contain metadata (:attr:`name`, :attr:`sampling_rate`, ...) so they can be inspected, but they do not contain any array-like data. When you want to load the actual data from a proxy object, use the :func:`load()` method to return a real data object of the appropriate type. Furthermore :func:`load()` has a ``time_slice`` argument, which allows you to load only a slice of data from the file. In this way the consumption of memory can be finely controlled. .. ipython: : .. In [59]: lazy_data = AsciiSignalIO("example_data.txt", delimiter=" ").read(lazy=True) .. In [60]: lazy_data .. todo: implement AsciiSignalRawIO Examples ======== For more examples of using Neo, see :doc:`../examples/index`. Citing Neo ========== If you use Neo in your work, please mention the use of Neo in your Methods section, using our RRID_: ``RRID:SCR_000634``. If you wish to cite Neo in publications, please use: Garcia S., Guarino D., Jaillet F., Jennings T.R., Pröpper R., Rautenberg P.L., Rodgers C., Sobolev A.,Wachtler T., Yger P. and Davison A.P. (2014) Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8:10: doi:`10.3389/fninf.2014.00010`_ A BibTeX entry for LaTeX users is:: @article{neo14, author = {Garcia S. and Guarino D. and Jaillet F. and Jennings T.R. and Pröpper R. and Rautenberg P.L. and Rodgers C. and Sobolev A. and Wachtler T. and Yger P. and Davison A.P.}, doi = {10.3389/fninf.2014.00010}, full_text = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00010/full}, journal = {Frontiers in Neuroinformatics}, month = {February}, title = {Neo: an object model for handling electrophysiology data in multiple formats}, volume = {8:10}, year = {2014} } .. _NumPy: https://numpy.org/ .. _`10.3389/fninf.2014.00010`: https://doi.org/10.3389/fninf.2014.00010 .. _RRID: https://www.rrids.org ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5176184 neo-0.14.4/doc/source/releases/0000755000175100017510000000000015154507227015707 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.10.0.rst0000644000175100017510000000562215154507213017155 0ustar00runnerrunner======================== Neo 0.10.0 release notes ======================== 27th July 2021 New IO modules -------------- .. currentmodule:: neo.io * :class:`CedIO` - an alternative to :class:`Spike2IO` * :class:`AxonaIO` * :class:`OpenEphysIO` - handle the binary format * :class:`PhyIO` * :class:`SpikeGLXIO` * :class:`NWBIO` - support for a subset of the `NWB:N`_ format * :class:`MaxwellIO` Bug fixes and improvements in IO modules ---------------------------------------- * :class:`NeuralynxIO` was refactored and now supports new file versions (neuraview) and single file loading. * Legacy versions of old IOs were removed for NeuralynxIO (neuralynxio_v1), BlackrockIO, NeoHdf5IO. * :class:`NixIOfr` now supports array annotations of :class:`AnalogSignal` objects. * :class:`NSDFIO` was removed because we can no longer maintain it. * all IOs now accept :class:`pathlib.Path` objects. * The IO modules of this release have been tested with version 0.1.0 of the `ephy_testing_data`_. Removal of Unit and ChannelIndex -------------------------------- .. currentmodule:: neo.core In version 0.9.0 :class:`Group` and :class:`ChannelView` were introduced, replacing :class:`Unit` and :class:`ChannelIndex`, which were deprecated. In this version the deprecated :class:`Unit` and :class:`ChannelIndex` are removed and only the new :class:`Group` and :class:`ChannelView` objects are available. Supported Python and NumPy versions ----------------------------------- We no longer support Python 3.6, nor versions of NumPy older than 1.16. Other new or modified features ------------------------------ * Lists of :class:`SpikeTrain` objects can now also be created from two arrays: one containing spike times and the other unit identities of the times (:class:`SpikeTrainList`). * Object identity is now preserved when using utility :func:`time_slice()` methods. See all `pull requests`_ included in this release and the `list of closed issues`_. RawIO modules ------------- Internal refactoring of the neo.rawio module regarding channel grouping. Now the concept of a signal stream is used to handle channel groups for signals. This enhances the way the :attr:`annotation` and :attr:`array_annotation` attributes are rendered at neo.io level. Acknowledgements ---------------- Thanks to Samuel Garcia, Julia Sprenger, Peter N. Steinmetz, Andrew Davison, Steffen Bürgers, Regimantas Jurkus, Alessio Buccino, Shashwat Sridhar, Jeffrey Gill, Etienne Combrisson, Ben Dichter and Elodie Legouée for their contributions to this release. .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.10.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2020-11-10+milestone%3A0.10.0 .. _`ephy_testing_data`: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/src/v0.1.0 .. _`NWB:N`: https://www.nwb.org/nwb-neurophysiology/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.10.1.rst0000644000175100017510000000351615154507213017156 0ustar00runnerrunner======================== Neo 0.10.1 release notes ======================== 2nd March 2022 Bug fixes and improvements in IO modules ---------------------------------------- * :class:`NeuralynxIO` memory performace was improved during initialization [#998](https://github.com/NeuralEnsemble/python-neo/pull/990) and new arguments for file selection were added [#1023](https://github.com/NeuralEnsemble/python-neo/pull/1023) [#1043](https://github.com/NeuralEnsemble/python-neo/pull/1043) * :class:`TdtIO` can load single block tdt datasets [#1057](https://github.com/NeuralEnsemble/python-neo/pull/1057) * :class:`SpikeGLXIO` supports neuropixel 2.0 format [#1045](https://github.com/NeuralEnsemble/python-neo/pull/1045) and uses corrected gain values [#1069](https://github.com/NeuralEnsemble/python-neo/pull/1069) * :class:`NixIO` some bug fix related to nixio module * :class:`NwbIO` various improvement [#1052](https://github.com/NeuralEnsemble/python-neo/pull/1052) [#1054](https://github.com/NeuralEnsemble/python-neo/pull/1054) * :class:`OpenEphysIO` small bug fix [#1062](https://github.com/NeuralEnsemble/python-neo/pull/1062) * :class:`MaxwellIO` bug fix [#1074](https://github.com/NeuralEnsemble/python-neo/pull/1074) * :class:`NeuroscopeIO` bug fix [#1078](https://github.com/NeuralEnsemble/python-neo/pull/1078) * The IO modules of this release have been tested with version 0.1.1 of the `ephy_testing_data`_. Documentation ------------- A project governance guide has been added [#1048](https://github.com/NeuralEnsemble/python-neo/pull/1048) Acknowledgements ---------------- Thanks to Samuel Garcia, Julia Sprenger, Andrew Davison, Alessio Buccino, Ben Dichter, Elodie Legouée, Eric Larson and Heberto Mayorquin for their contributions to this release. .. _`ephy_testing_data`: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/src/v0.1.1 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.10.2.rst0000644000175100017510000000057215154507213017156 0ustar00runnerrunner======================== Neo 0.10.2 release notes ======================== 8th March 2022 This release fixes the following bugs introduced in 0.10.1: 1. Remove broken imports in `spike2rawio.py `_ 2. Fix the compilation of the documentation on `readthedoc `_ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.11.0.rst0000644000175100017510000000307215154507213017153 0ustar00runnerrunner======================== Neo 0.11.0 release notes ======================== 1st September 2022 .. currentmodule:: neo.io Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`NWBIO`, :class:`NeoMatlabIO`, :class:`NixIO`, :class:`AxonIO`, :class:`SpikeGLXIO`, :class:`NeuroshareIO`, :class:`NeuroScopeIO`, :class:`NeuralynxIO`, :class:`OpenEphysIO`, :class:`AsciiSignalIO`, :class:`AlphaOmegaIO`, and :class:`TdtIO`. New IO modules -------------- Modules :class:`BiocamIO` and :class:`EDFIO` have been added. Supported NumPy versions ------------------------ We no longer support versions of NumPy older than 1.18.5. Other changes ------------- .. currentmodule:: neo.io.spiketrainlist - documentation fixes and updates - improvements to :class:`SpikeTrainList` See all `pull requests`_ included in this release and the `list of closed issues`_. Acknowledgements ---------------- Thanks to Julia Sprenger, Samuel Garcia, Heberto Mayorquin, Andrew Davison, Tom Donoghue, Alessio Buccino, Thomas Perret, Saksham Sharda, Ben Dichter and Elodie Legouée for their contributions to this release. .. generated with git shortlog --since=2022-03-08 -sne then checking Github for PRs merged since the last release but with commits before then .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.11.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2022-03-08+milestone%3A0.11.0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.11.1.rst0000644000175100017510000000263215154507213017155 0ustar00runnerrunner======================== Neo 0.11.1 release notes ======================== 21st October 2022 .. currentmodule:: neo.io Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`OpenEphysBinaryIO`, :class:`BlackrockIO`, :class:`NWBIO`, :class:`PhyIO`, :class:`NeuralynxIO` Supported format versions ------------------------- :class:`BlackrockIO` now supports file version 3.0 Other changes ------------- New installation mode - Neo can now be installed including all optional dependencies via *pip install neo[all]* Faster import - Dependency imports have been optimized for faster import of Neo Epoch durations - Epoch durations of epochs can now be of type float See all `pull requests`_ included in this release and the `list of closed issues`_. Acknowledgements ---------------- Thanks to Andrew Davison, Julia Sprenger, Chadwick Boulay, Alessio Buccino, Elodie Legouée, Samuel Garcia for their contributions to this release. .. generated with git shortlog --since=2022-09-01 -sne then checking Github for PRs merged since the last release but with commits before then .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.11.1+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2022-09-01+milestone%3A0.11.1 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.12.0.rst0000644000175100017510000000414415154507213017155 0ustar00runnerrunner======================== Neo 0.12.0 release notes ======================== 17th February 2023 .. currentmodule:: neo.io Change of packaging system -------------------------- We have updated our tooling for building and installing Neo to follow the `latest recommendations`_ of the Python community (using pyproject.toml, etc.) For users, this means you should always use `pip` to install Neo, unless you're using another package manager such as `conda`, `spack`, or a Linux package manager. Improved file format detection ------------------------------ In addition to the existing :func:`get_io()` function, there is now an additional utility function for finding a suitable IO for a given dataset. :func:`list_candidate_ios()` provides a list of all IOs supporting the formats detected in a given path. This list is generated based on the file extension of the file or the extensions of the matching files in a given folder, potentially also scanning subfolders if required. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`OpenEphysBinaryIO` and :class:`NWBIO`. Other changes ------------- - The IO test suite has been greatly improved, with a substantial speed-up. See all `pull requests`_ included in this release and the `list of closed issues`_. Updated dependencies -------------------- We have dropped support for Python 3.7 and added support for 3.11. Neo now requires NumPy version >=1.19.5 and Quantities >=v0.14.1. Acknowledgements ---------------- Thanks to Julia Sprenger, Andrew Davison, and Samuel Garcia for their contributions to this release. .. generated with git shortlog --since=2022-10-21 -sne then checking Github for PRs merged since the last release but with commits before then .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.12.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2022-10-21+milestone%3A0.12.0 .. _`latest recommendations`: https://packaging.python.org/en/latest/././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.13.0.rst0000644000175100017510000001503615154507213017160 0ustar00runnerrunner======================== Neo 0.13.0 release notes ======================== 2nd February 2024 This release of Neo sees a lot of changes and improvements, with a documentation rewrite, final tweaks and refinements to the object model (in anticipation of a 1.0 release in the next few months), a new approach to filtering objects of interest from complex datasets, and a large number of bug fixes and performance improvements in IO modules. 23 people contributed to this release, which is a new record for Neo! See all `pull requests`_ included in this release and the `list of closed issues`_. Refinements to the Neo object model ----------------------------------- In order to simplify the structure of Neo datasets, and make a clearer distinction between the Block-Segment-data hierarchy and the more flexible data-grouping functionality, we no longer allow Groups to contain Segments. We have made the handling of RegionOfInterest subclasses consistent with :class:`ChannelView`, i.e., just as a :class:`ChannelView` gives a view of a subset of the channels in a multi-channel :class:`AnalogSignal`, so the :class:`RegionOfInterest`` subclasses give views of a subset of the pixels in an :class:`ImageSequence`. Neo now has automatic handling of relationships between objects. In previous versions, the child-parent relationships between objects had to be handled manually, for example when you added a :class:`SpikeTrain` to a :class:`Segment`, this created a parent-child relationship, but the reverse child-parent relationship was not automatically created, leading to potential inconsistencies. Now these relationships are created automatically, as the various child lists (``Segment.spiketrains``, ``Segment.analogsignals``, ``Block.segments``, etc.) are now represented by pseudo-list objects which take care of the book-keeping behind the scenes. As a convenience, all Neo containers (:class:`Block`, :class:`Segment`, :class:`Group`) now have an :meth:`add()` method that automatically puts the object in the correct list, for example:: segment.add(signal1, event2, spiketrain3) instead of:: segment.analogsignals.append(signal1) segment.events.append(event2) segment.spiketrains.append(spiketrain3) Documentation rewrite and new theme ----------------------------------- The Neo documentation has had a complete overhaul, with a rewrite aimed at providing different entry points to the documentation for different_ user_ needs_, using the Sphinx IPython extension to ensure code snippets are always up-to-date, and a switch to the `PyData Sphinx theme`_. Improved filtering ------------------ For complex datasets, it is often necessary to extract subsets of the data from a Neo object tree by filtering based on object types, names, and annotations. Neo's filtering capabilities have been improved, with the addition of a new :mod:`filters` module. For example, suppose we have a dataset from an animal performing a behavioural task. Our objective is to retain only the trials where the animal performed correctly, for one of the four trial types in the experiment, and where the recordings met certain quality criteria. Based on an annotated Neo dataset, we can extract the spike trains of interest in a single step, without for loops or complex list comprehensions, as follows: .. code-block:: python import neo.core.filters as nf # ... load data, `trial` is a Segment object ... sua_spiketrains = trial.filter( objects=neo.SpikeTrain, targdict=[ {"sua": True}, # only single-unit activity {"electrode_reject_HFC": False}, # } exclude different types {"electrode_reject_LFC": False}, # } of artefacts {"electrode_reject_IFC": False}, # } or markers of poor-quality {"noise": False}, # } recordings {"spike_count": nf.FilterGreaterThan(10000)} # only use neurons with a sufficiently high firing rate ] ) New IO modules -------------- Neo now supports MED_ format, with the new :class:`MedIO` and :class:`MedRawIO` classes. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`AsciiSignalIO`, :class:`BCI2000IO`, :class:`BiocamIO`, :class:`BlackrockIO`, :class:`IgorIO`, :class:`IntanIO`, :class:`KlustaKwikIO`, :class:`MaxwellIO`, :class:`MEArecIO`, :class:`NeoMatlabIO`, :class:`NeuralynxIO`, :class:`NixIO`, :class:`NWBIO`, :class:`OpenEphysIO`, :class:`PlexonIO`, :class:`Plexon2IO`, :class:`SpikeGLXIO`, and :class:`TdtIO`. Other changes ------------- - RawIO classes have a method :meth:`parse_header` that extracts all the information from the formats and therefore is usually a computationally heavy process. A flag ``is_header_parsed`` has now been added, to avoid re-extracting the header information if it is already available. This can produce significant speed ups. - Type hints have been added to the :mod:`baseio`, :mod:`baserawio`, and :mod:`exampleio` modules, with the aim of helping developers who wish to add a new IO module. - All of the code is now formatted with black_. - The continuous integration (CI) pipeline has been sped up. Updated dependencies -------------------- Neo now requires NumPy version >=1.20.3. Acknowledgements ---------------- Thanks to Julia Sprenger, Andrew Davison, Zach McKenzie, Alessio Buccino, Moritz Alexander Kern, Samuel Garcia, Heberto Mayorquin, Joscha Schmiedt, Daniel P. Crepeau, Divyansh Gupta, Nate Dolensek, Peter N. Steinmetz, Philipp Hornauer, Robert Wolff, Jules Lebert, Kyle Johnsen, Ben Dichter, Benjamin Heasly, Baptiste Grimaud, Cody Baker, Fernando J. Chaure, @Filipe, and Matthias Klumpp for their contributions to this release. .. generated with git shortlog --since=2023-02-17 -sne then checking Github for PRs merged since the last release but with commits before then .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.13.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2023-02-17+milestone%3A0.13.0 .. _`latest recommendations`: https://packaging.python.org/en/latest/ .. _black: https://black.readthedocs.io .. _`PyData Sphinx theme`: https://pydata-sphinx-theme.readthedocs.io .. _MED: https://medformat.org .. _different: https://neo.readthedocs.io/en/latest/read_and_analyze.html .. _user: https://neo.readthedocs.io/en/latest/share_data.html .. _needs: https://neo.readthedocs.io/en/latest/use_neo_as_dependency.html ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.13.1.rst0000644000175100017510000000370515154507213017161 0ustar00runnerrunner======================== Neo 0.13.1 release notes ======================== 7th May 2024 This release of Neo contains a lot of improvements to code quality and documentation, still with a focus on the planned 1.0 release, together with, as usual, bug fixes and performance improvements in IO modules. See all `pull requests`_ included in this release and the `list of closed issues`_. Code and documentation quality ------------------------------ Particular thanks go to Zach McKenzie, who has checked, and rewritten if necessary, every docstring in the project, to ensure they all have a consistent style. Zach has also updated and improved the :doc:`../examples/index` in the Sphinx documentation. The Neo code base contained a lot of ``assert``\s that should be exceptions. These have all been replaced with appropriate exceptions (again by Zach McKenzie). A new exception class, :class:`NeoReadWriteError` has been introduced. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`SpikeGLXIO`, :class:`OpenEphysIO`, :class:`SpikeGadgetsIO`, :class:`MaxwellIO`, :class:`AxonIO`, :class:`IntanIO`, :class:`TDTIO`, :class:`NeuralynxIO` and :class:`BlackrockIO`. Acknowledgements ---------------- Thanks to Zach McKenzie, Andrew Davison, Heberto Mayorquin, Alessio Buccino, Samuel Garcia, Peter N. Steinmetz, Aitor Morales-Gregorio, Greg Knoll, Chris Halcrow and Szonja Weigl for their contributions to this release. .. generated with git shortlog --since=2024-02-02 -sne then checking Github for PRs merged since the last release but with commits before then .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.13.1+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2024-02-02+milestone%3A0.13.1 .. _`examples gallery`: https://neo.readthedocs.io/en/latest/examples/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.13.2.rst0000644000175100017510000000276415154507213017166 0ustar00runnerrunner======================== Neo 0.13.2 release notes ======================== 1st August 2024 This release of Neo contains bug fixes, still with a focus on the planned 1.0 release, along with some performance improvements in IO modules and updates to the CI. See all `pull requests`_ included in this release and the `list of closed issues`_. Continuous Integration ---------------------- Core tests now run on python 3.12. IO tests now run on python 3.9 and python 3.11. Performance Improvements & Code quality --------------------------------------- The imports across the code base were standardized to standard library, 3rd party, and then 1st party for top level imports. Additionally, :code:`neo` import time was improved. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`IntanIO`, :class:`PlexonIO`, :class:`SpikeGadgetsIO`, :class:`NeuralynxIO`, :class:`SpikeGLXIO`, :class:`BlackrockIO`, and :class:`RawBinarySignalRawIO`. Acknowledgements ---------------- Thanks to Heberto Mayorquin, Zach McKenzie, Alessio Buccino, Andrew Davison, Chris Heydrick, Anthony Pinto, Kyu Hun Lee, Téo Lohrer, Tom Donoghue, Xin Niu, and Samuel Garcia for their contributions to this release. .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.13.2 .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+is%3Aclosed+milestone%3A0.13.2././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.13.3.rst0000644000175100017510000000177015154507213017163 0ustar00runnerrunner======================== Neo 0.13.3 release notes ======================== 28 August 2024 This release of Neo contains bug fixes, still with a focus on the planned 1.0 release, and will be the last release not to support NumPy 2.0. See all `pull requests`_ included in this release and the `list of closed issues`_. Updated dependencies -------------------- Neo has a limit of NumPy >= 1.19.5, < 2.0.0 and Quantities >= 14.0.1, < 0.16.0 Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`PlexonIO`, :class:`SpikeGLXIO`, :class:`BiocamIO`. Acknowledgements ---------------- Thanks to Zach McKenzie, Heberto Mayorquin, and Alessio Buccino for their contributions to this release. .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.13.3 .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+is%3Aclosed+milestone%3A0.13.3././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.13.4.rst0000644000175100017510000001336615154507213017170 0ustar00runnerrunner======================== Neo 0.13.4 release notes ======================== 21 October 2024 This release of Neo contains bug fixes across many IOs, a new IO for :code:`NeuroNexus`, drop of Python 3.8, NumPy 1.20 & 1.21, still with a focus on the planned 1.0 release. Additionally Neo now supports Quantities >=16.1 which brings us closer to support for NumPy 2.0 +. At the :code:`RawIO` level a new :code:`buffer api` has been introduced with the goal of better grouping streams of data together. This is an ongoing effort to provide better access to streams of data that are typically analyzed together without changes to the public API. This point release will be the last release to not support Python 3.13 and NumPy > 2.0. See all `pull requests`_ included in this release and the `list of closed issues`_. Updated dependencies -------------------- Neo has a limit of NumPy >= 1.22.4, < 2.0.0 Neo now supports Python >= 3.9, <3.13 Neo has a limit of Quantities >= 16.1 Neo has a limit of dhn_med_py < 2.0.0 (for reading MED format) Deprecations ------------ As Quantities has dropped support for the :code:`copy` argument when making Quantities arrays to be NumPy 2.0 compatible, the :code:`copy` argument has also been deprecated in all Neo core objects (e.g. :code:`SpikeTrain`, etc.). For this version and the next version the default is now :code:`copy=None`. If :code:`copy=True` or :code:`copy=False` are used an error will be raised. This also means that some functionality for rescaling and dtype conversion, which required :code:`copy=True` are no longer possible. Appropriate errors are raised if the user tries these now impossible behaviors at construction. Additional changes that occurred in Quantities can be viewed at their changelog: https://github.com/python-quantities/python-quantities/blob/master/CHANGES.txt Currently acceptable construction patterns can be found in the Neo test folder: https://github.com/NeuralEnsemble/python-neo/blob/master/neo/test/coretest Many previous behaviors can still be achieved by using additional lines of code, e.g.: .. code-block:: python >>> import quantities as pq >>> import neo # if we want to get a spiketrain in seconds, but we entered our times in ms # we used to be able to autoconvert by specifying units. But now this will # raise an error! >>> times = [1,2,3] * 1 * pq.ms >>> t_stop = 1 * pq.s >>> spiketrain = neo.SpikeTrain(times, t_stop=t_stop, units='s') ValueError: cannot rescale and return view # so instead we need to rescale in a second step >>> spiketrain = neo.SpikeTrain(times, t_stop=t_stop) >>> spiketrain >>> rescaled_spiketrain = spiketrain.rescale('s') >>> rescaled_spiketrain CI Additions/Changes -------------------- Neo has sped up the testing suite by ~15% and added additional testing for IOs: :class:`NeuralynxIO` and :class:`Plexon2IO`. Testing around :code:`copy` was removed from the core testing, since this argument is no longer possible. Addition of a New IO module --------------------------- Neo now has support for reading NeuroNexus :code:`.xdat` files with the new :class:`NeuroNexusIO`. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`MaxwellIO`, :class:`NeuroNexusIO`, :class:`IntanIO`, :class:`Plexon2IO`, :class:`IgorIO`, :class:`SpikeGadgetsIO`, :class:`PlexonIO`, and :class:`BrainVisionRawIO`, and :class:`EDFIO` Buffer API ---------- The motivation for this :code:`RawIO` was that many IOs have buffers of data (memmaps/hdf5) files, which allow for multiple unrelated streams of data to be packaged together. This has led to some inconsistencies in how IOs access streams of data. For example, the :code:`PlexonIO` stores WideBand and Filtered versions of the same data, but the end user likely wouldn't want to analyze them both at the same time as that would be duplication of information. :code:`SpikeGLX` also makes use of a sync channel which is stored with the electrophysiological channels, but should not be analyzed as an ephys channel. The Buffer API will be an ongoing set of PRs at the :code:`RawIO` level to better clarify how data enters and is mapped in Neo versus how the end-user might request streams of data. We hope that this process will allow the end-user better access to the data they want without having unrelated data mixed in. Importantly the public API is not being affected by this process at all. The end-user will still request their desired stream using :code:`stream_index` argument when interacting with a :code:`RawIO`. In this release, each IO was divided into whether it would fit with the buffer api requirements or not and the initial :code:`buffer_id` was applied to all IOs. This step has not changed any behavior in Neo. But the :code:`RawIO.header` information will now have an additional field that will be used in future releases of Neo under-the-hood. We want to emphasize this is not a public API change and over the next version we hope to fully implement this new schema to allow for better interaction with data at the :code:`RawIO` and :code:`IO` levels of Neo. This project has largely been spearheaded by Samuel Garcia and we thank him for his herculean efforts. Acknowledgements ---------------- Thanks to Zach McKenzie, Heberto Mayorquin, Samuel Garcia, Andrew Davison, Alessio Buccino, Nikhil Chandra, and Peter Steinmetz for their contributions to this release. .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.13.4 .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+is%3Aclosed+milestone%3A0.13.4././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.14.0.rst0000644000175100017510000000321315154507213017153 0ustar00runnerrunner======================== Neo 0.14.0 release notes ======================== 17 January 2025 This release of Neo is now compatible with NumPy 2.0 for core and IOs (with the exception of :class:`MedIO`) as well as Python 3.13, and includes IO bug fixes with an eye toward a 1.0 release. See all `pull requests`_ included in this release and the `list of closed issues`_. Updated dependencies -------------------- Neo now has a limit of NumPy >= 1.22.4 CI Improvements --------------- To ensure compatiblility between pre- and post- NumPy 2.0 the CI was changed to test on the lowest supported Python (3.9) and the highest supported Python (3.13) each with NumPy 1.26 as well as NumPy 2.0 for all :code:`RawIO` and :code:`IO` tests. We also no longer use a cached conda env for testing as we see that there is no speed benefit to caching and we had some issues with the caches getting corrupted. Testing of additional Python-NumPy combinations for core tests were added (NumPy 2.0 and 2.1 with their respective Python versions). Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :class:`NeuroNexusIO`, :class:`OpenEphysBinaryIO`, :class:`MicromedIO`, :class:`IntanIO` and :class:`SpikeGLX`. Acknowledgements ---------------- Thanks to Zach McKenzie, Heberto Mayorquin, Andrew Davison, Luigi Petrucco, Alessio Buccino, and Samuel Garcia. .. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.0 .. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.0././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.14.1.rst0000644000175100017510000000360315154507213017157 0ustar00runnerrunner======================== Neo 0.14.1 release notes ======================== 14 April 25 This release of Neo includes many bug fixes in various IOs, a new HTML based repr, and fixes to the CI all with an eye toward our 1.0 release. See all `pull requests`_ included in this release and the `list of closed issues`_. Updated dependencies -------------------- setuptools >= 78.0.2 numpy >= 1.24.4 Import improvements ------------------- As part of our ongoing effort to improve the speed of importing Neo we have moved the library over to using :code:`importlib.util.find_spec` rather than eagerly importing packages in :code:`try-except` blocks. CI Improvements --------------- The install for datalad/git-annex in the CI was fixed for our Python 3.12 tests allowing for all IOs to be tested on Python 3.9 and Python 3.12. The buidling of documentation was improved by fixing code style errors. HTML repr --------- Thanks to the work of Heberto Mayorquin Neo now supports an html based repr. To see an example check out `html repr`_. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :code:`BlackRockIO`, :code:`SpikeGLXIO`, :code:`MaxwellIO`, :code:`IntanIO`, :code:`TdtIO`, :code:`OpenEphysBinaryIO`, :code:`NeoMatlabIO`, :code:`EdfIO`, :code:`NeuralynxIO`, and :code:`SpikeGadgetsIO`. Acknowledgements ---------------- Thanks to Heberto Mayorquin, Zach McKenzie, Andrew Davison, Alessio Buccino, Rémi Proville, Paul Adkisson, Baptiste Grimaud, Robert Wolff, Tommaso Lambresa, and Nina Kudryashova. .. _`html repr` : https://github.com/NeuralEnsemble/python-neo/pull/1651 .. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.1 .. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.1././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.14.2.rst0000644000175100017510000000345715154507213017167 0ustar00runnerrunner======================== Neo 0.14.2 release notes ======================== 08 July 25 This release of Neo includes many bug fixes in various IOs, continued work on our CI, and official adoption of the NumFocus Code of Conduct with an eye to a 1.0 release. See all `pull requests`_ included in this release and the `list of closed issues`_. Code of Conduct --------------- As a NumFocus affiliated project, Neo has officially adopted the `NumFocus Code of Conduct`_. Please see `CODE_OF_CONDUCT.md`_ for more information. CI Changes ---------- We have transitioned IO testing to Python 3.9 and Python 3.13. We have also added NumPy 2.3.1 to our core testing matrix. We fixed our installation of datalad and git-annex. We changed our :code:`Plexon2IO` tests to be a cron job as conda-wine-plexon2 behavior was leading to flaky test results. We hope to track the root of these issues and eventually transition :code:`Plexon2IO` back to main testing. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :code:`BlackRockIO`, :code:`SpikeGLXIO`, :code:`MaxwellIO`, :code:`NeuralynxIO`, :code:`WinwcprIO`, and :code:`AxonaIO`. Acknowledgements ---------------- Thanks to Zach McKenzie, Andrew Davison, Luiz Tauffer, Heberto Mayorquin, Julia Sprenger, Daniel Parthier, Philipp Hornauer, Letizia Signorelli, and Akshaj Verma. .. _`NumFocus Code of Conduct`: https://numfocus.org/code-of-conduct .. _`CODE_OF_CONDUCT.md` : https://github.com/NeuralEnsemble/python-neo/blob/master/CODE_OF_CONDUCT.md .. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.2 .. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.2././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.14.3.rst0000644000175100017510000000233415154507213017161 0ustar00runnerrunner======================== Neo 0.14.3 release notes ======================== 13 October 25 This release of Neo includes many bug fixes in various IOs with an eye to a 1.0 release. See all `pull requests`_ included in this release and the `list of closed issues`_. Repository Clean-up ------------------- The :code:`setup.py` has been removed from the repo as installation using setup.py is deprecated by setuptools. Documentation Updates --------------------- We updated the documentation to make use of the new eBRAINS URLs. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :code:`BlackRockIO`, :code:`NeuroNexusIO`, :code:`OpenEphysBinaryIO`, :code:`BiocamIO`, :code:`SpikeGadgetsIO`, :code:`NeuralynxIO`, :code:`SpikeGLXIO`, and :code:`BrainvisionIO`. Acknowledgements ---------------- Thanks to Heberto Mayorquin, Zach McKenzie, Andrew Davison, Alessio Buccino, Christian Kothe, and Ariel Burman. .. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.3 .. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.3././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.14.4.rst0000644000175100017510000000313515154507213017162 0ustar00runnerrunner======================== Neo 0.14.4 release notes ======================== 12 March 2026 This release of Neo includes many bug fixes in various IOs with an eye to a 1.0 release. See all `pull requests`_ included in this release and the `list of closed issues`_. Dependency Changes ------------------ Per our policy of NEP29 + 1 year, we have dropped support for NumPy < 1.25. Testing ------- We are now testing against Python 3.14 for core tests with all possible combinations of NumPy and Python that we support. IO tests were updated to test against Python 3.9 (oldest supported) and Python 3.14 (newest supported) Python versions. :code:`BaseRawIO` Change ------------------------ The signal stream dtype name was lengthened from :code:`U64` to a :code:`U128` to deal with long file paths being incorporated into file names. See `file name issue`_ on SpikeInterface repo for an example of the truncation which required this fix. Bug fixes and improvements in IO modules ---------------------------------------- Bug fixes and/or improvements have been made to :code:`AxonIO`, :code:`BrainVisionIO`, :code:`SpikeGLXIO`. Acknowledgements ---------------- Thanks to Andrew Davison, Zach McKenzie, Kuntal Kokate, Heberto Mayorquin, Samuel Garcia, Alessio Buccino, and Chris Halcrow .. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.4 .. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.4 .. _`file name issue` : https://github.com/SpikeInterface/spikeinterface/issues/4185././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.5.0.rst0000644000175100017510000001360715154507213017103 0ustar00runnerrunner======================= Neo 0.5.0 release notes ======================= 22nd March 2017 For Neo 0.5, we have taken the opportunity to simplify the Neo object model. Although this will require an initial time investment for anyone who has written code with an earlier version of Neo, the benefits will be greater simplicity, both in your own code and within the Neo code base, which should allow us to move more quickly in fixing bugs, improving performance and adding new features. More detail on these changes follows: Merging of "single-value" and "array" versions of data classes ============================================================== In previous versions of Neo, we had :class:`AnalogSignal` for one-dimensional (single channel) signals, and :class:`AnalogSignalArray` for two-dimensional (multi-channel) signals. In Neo 0.5.0, these have been merged under the name :class:`AnalogSignal`. :class:`AnalogSignal` has the same behaviour as the old :class:`AnalogSignalArray`. It is still possible to create an :class:`AnalogSignal` from a one-dimensional array, but this will be converted to an array with shape `(n, 1)`, e.g.: .. code-block:: python >>> signal = neo.AnalogSignal([0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0], ... sampling_rate=10*kHz, ... units=nA) >>> signal.shape (9, 1) Multi-channel arrays are created as before, but using :class:`AnalogSignal` instead of :class:`AnalogSignalArray`: .. code-block:: python >>> signal = neo.AnalogSignal([[0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0], ... [0.0, 0.2, 0.4, 0.7, 0.9, 0.8, 0.7, 0.6, 0.3]], ... sampling_rate=10*kHz, ... units=nA) >>> signal.shape (9, 2) Similarly, the :class:`Epoch` and :class:`EpochArray` classes have been merged into an array-valued class :class:`Epoch`, ditto for :class:`Event` and :class:`EventArray`, and the :class:`Spike` class, whose main function was to contain the waveform data for an individual spike, has been suppressed; waveform data are now available as the :attr:`waveforms` attribute of the :class:`SpikeTrain` class. Recording channels ================== As a consequence of the removal of "single-value" data classes, information on recording channels and the relationship between analog signals and spike trains is also stored differently. In Neo 0.5, we have introduced a new class, :class:`ChannelIndex`, which replaces both :class:`RecordingChannel` and :class:`RecordingChannelGroup`. In older versions of Neo, a :class:`RecordingChannel` object held metadata about a logical recording channel (a name and/or integer index) together with references to one or more :class:`AnalogSignal`\s recorded on that channel at different points in time (different :class:`Segment`\s); redundantly, the :class:`AnalogSignal` also had a :attr:`channel_index` attribute, which could be used in addition to or instead of creating a :class:`RecordingChannel`. Metadata about :class:`AnalogSignalArray`\s could be contained in a :class:`RecordingChannelGroup` in a similar way, i.e. :class:`RecordingChannelGroup` functioned as an array-valued version of :class:`RecordingChannel`, but :class:`RecordingChannelGroup` could also be used to group together individual :class:`RecordingChannel` objects. With Neo 0.5, information about the channel names and ids of an :class:`AnalogSignal` is contained in a :class:`ChannelIndex`, e.g.: .. code-block:: python >>> signal = neo.AnalogSignal([[0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0], ... [0.0, 0.2, 0.4, 0.7, 0.9, 0.8, 0.7, 0.6, 0.3]], ... [0.0, 0.1, 0.3, 0.6, 0.8, 0.7, 0.6, 0.5, 0.3]], ... sampling_rate=10*kHz, ... units=nA) >>> channels = neo.ChannelIndex(index=[0, 1, 2], ... channel_names=["chan1", "chan2", "chan3"]) >>> signal.channel_index = channels In this use, it replaces :class:`RecordingChannel`. :class:`ChannelIndex` may also be used to group together a subset of the channels of a multi-channel signal, for example: .. code-block:: python >>> channel_group = neo.ChannelIndex(index=[0, 2]) >>> channel_group.analogsignals.append(signal) >>> unit = neo.Unit() # will contain the spike train recorded from channels 0 and 2. >>> unit.channel_index = channel_group Checklist for updating code from 0.3/0.4 to 0.5 =============================================== To update your code from Neo 0.3/0.4 to 0.5, run through the following checklist: 1. Change all usages of :class:`AnalogSignalArray` to :class:`AnalogSignal`. 2. Change all usages of :class:`EpochArray` to :class:`Epoch`. 3. Change all usages of :class:`EventArray` to :class:`Event`. 4. Where you have a list of (single channel) :class:`AnalogSignal`\s all of the same length, consider converting them to a single, multi-channel :class:`AnalogSignal`. 5. Replace :class:`RecordingChannel` and :class:`RecordingChannelGroup` with :class:`ChannelIndex`. .. note:: in points 1-3, the data structure is still an array, it just has a shorter name. Other changes ============= * added :class:`NixIO` (`about the NIX format`_) * added :class:`IgorIO` * added :class:`NestIO` (for data files produced by the `NEST simulator`_) * :class:`NeoHdf5IO` is now read-only. It will read data files produced by earlier versions of Neo, but another HDF5-based IO, e.g. :class:`NixIO`, should be used for writing data. * many fixes/improvements to existing IO modules. All IO modules should now work with Python 3. .. https://github.com/NeuralEnsemble/python-neo/issues?utf8=✓&q=is%3Aissue%20is%3Aclosed%20created%3A%3E2014-02-01%20 .. _`about the NIX format`: https://github.com/G-Node/nix/wiki .. _`NEST simulator`: https://www.nest-simulator.org/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.5.1.rst0000644000175100017510000000225515154507213017101 0ustar00runnerrunner======================= Neo 0.5.1 release notes ======================= 4th May 2017 * Fixes to :class:`AxonIO` (thanks to @erikli and @cjfraz) and :class:`NeuroExplorerIO` (thanks to Mark Hollenbeck) * Fixes to pickling of :class:`Epoch` and :class:`Event` objects (thanks to Hélissande Fragnaud) * Added methods :meth:`as_array()` and :meth:`as_quantity()` to Neo data objects to simplify the common tasks of turning a Neo data object back into a plain Numpy array * Added :class:`NeuralynxIO`, which reads standard Neuralynx output files in ncs, nev, nse and ntt format (thanks to Julia Sprenger and Carlos Canova). * Added the :attr:`extras_require` field to setup.py, to clearly document the requirements for different io modules. For example, this allows you to run :command:`pip install neo[neomatlabio]` and have the extra dependency needed for the :mod:`neomatlabio` module (scipy in this case) be automatically installed. * Fixed a bug where slicing an :class:`AnalogSignal` did not modify the linked :class:`ChannelIndex`. (Full `list of closed issues`_) .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.5.1+is%3Aclosed ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.5.2.rst0000644000175100017510000000151315154507213017076 0ustar00runnerrunner======================= Neo 0.5.2 release notes ======================= 27th September 2017 * Removed support for Python 2.6 * Pickling :class:`AnalogSignal` and :class:`SpikeTrain` now preserves parent objects * Added NSDFIO, which reads and writes NSDF files * Fixes and improvements to PlexonIO, NixIO, BlackrockIO, NeuralynxIO, IgorIO, ElanIO, MicromedIO, TdtIO and others. Thanks to Michael Denker, Achilleas Koutsou, Mieszko Grodzicki, Samuel Garcia, Julia Sprenger, Andrew Davison, Rohan Shah, Richard C Gerkin, Mieszko Grodzicki, Mikkel Elle Lepperød, Joffrey Gonin, Hélissande Fragnaud, Elodie Legouée and Matthieu Sénoville for their contributions to this release. (Full `list of closed issues`_) .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.5.2+is%3Aclosed ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.6.0.rst0000644000175100017510000000366515154507213017107 0ustar00runnerrunner======================= Neo 0.6.0 release notes ======================= 23rd March 2018 Major changes: * Introduced :mod:`neo.rawio`: a low-level reader for various data formats * Added continuous integration for all IOs using CircleCI (previously only :mod:`neo.core` was tested, using Travis CI) * Moved the test file repository to https://gin.g-node.org/NeuralEnsemble/ephy_testing_data - this makes it easier for people to contribute new files for testing. Other important changes: * Added :func:`time_index()` and :func:`splice()` methods to :class:`AnalogSignal` * IO fixes and improvements: Blackrock, TDT, Axon, Spike2, Brainvision, Neuralynx * Implemented `__deepcopy__` for all data classes * New IO: BCI2000 * Lots of PEP8 fixes! * Implemented `__getitem__` for :class:`Epoch` * Removed "cascade" support from all IOs * Removed lazy loading except for IOs based on rawio * Marked lazy option as deprecated * Added :func:`time_slice` in read_segment() for IOs based on rawio * Made :attr:`SpikeTrain.times` return a :class:`Quantity` instead of a :class:`SpikeTrain` * Raise a :class:`ValueError` if ``t_stop`` is earlier than ``t_start`` when creating an empty :class:`SpikeTrain` * Changed filter behaviour to return all objects if no filter parameters are specified * Fix pickling/unpickling of :class:`Events` Deprecated IO classes: * :class:`KlustaKwikIO` (use :class:`KwikIO` instead) * :class:`PyNNTextIO`, :class:`PyNNNumpyIO` (Full `list of closed issues`_) Thanks to Björn Müller, Andrew Davison, Achilleas Koutsou, Chadwick Boulay, Julia Sprenger, Matthieu Senoville, Michael Denker and especially Samuel Garcia for their contributions to this release. .. note:: version 0.6.1 was released immediately following 0.6.0 to fix a minor problem with the documentation. .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.6.0+is%3Aclosed ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.7.0.rst0000644000175100017510000000203215154507213017073 0ustar00runnerrunner======================= Neo 0.7.0 release notes ======================= 26th November 2018 Main added features: * array annotations Other features: * `Event.to_epoch()` * Change the behaviour of `SpikeTrain.__add__` and `SpikeTrain.__sub__` * bug fix for `Epoch.time_slice()` New IO classes: * RawMCSRawIO (raw multi channel system file format) * OpenEphys format * Intanrawio (both RHD and RHS) * AxographIO Many bug fixes and improvements in IO: * AxonIO * WinWCPIO * NixIO * ElphyIO * Spike2IO * NeoMatlab * NeuralynxIO * BlackrockIO (V2.3) * NixIO (rewritten) Removed: * PyNNIO (Full `list of closed issues`_) Thanks to Achilleas Koutsou, Andrew Davison, Björn Müller, Chadwick Boulay, erikli, Jeffrey Gill, Julia Sprenger, Lucas (lkoelman), Mark Histed, Michael Denker, Mike Sintsov, Samuel Garcia, Scott W Harden and William Hart for their contributions to this release. .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.7.0+is%3Aclosed ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.7.1.rst0000644000175100017510000000063315154507213017101 0ustar00runnerrunner======================= Neo 0.7.1 release notes ======================= 13th December 2019 * Add alias `duplicate_with_new_array` for `duplicate_with_new_data`, for backwards compatibility * Update `NeuroshareapiIO` and `NeurosharectypesIO` to Neo 0.6 * Create basic and compatibility test for `nixio_fr` Thanks to Chek Yin Choi, Andrew Davison and Julia Sprenger for their contributions to this release. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.7.2.rst0000644000175100017510000000042115154507213017075 0ustar00runnerrunner======================= Neo 0.7.2 release notes ======================= 10th July 2019 New RawIO class: * AxographRawIO Bug fixes: * Various CI fixes Thanks to Andrew Davison, Samuel Garcia, Jeffrey Gill and Julia Sprenger for their contributions to this release. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.8.0.rst0000644000175100017510000001101515154507213017075 0ustar00runnerrunner======================= Neo 0.8.0 release notes ======================= 30th September 2019 Lazy loading ------------ Neo 0.8 sees a major new feature, the ability to selectively load only parts of a data file (for supported file formats) into memory, for example only a subset of the signals in a segment, a subset of the channels in a signal, or even only a certain time slice of a given signal. This can lead to major savings in time and memory consumption, or can allow files that are too large to be loaded into memory in their entirety to be processed one section at a time. Here is an example, loading only certain sections of a signal:: lim0, lim1 = -500*pq.ms, +1500*pq.ms seg = reader.read_segment(lazy=True) # this loads only the segment structure and metadata # but all data objects are replaced by proxies triggers = seg.events[0].load() # this loads all triggers in memory sigproxy = seg.analogsignals[0] # this is a proxy object all_sig_chunks = [] for t in triggers.times: t0, t1 = (t + lim0), (t + lim1) sig_chunk = sigproxy.load(time_slice=(t0, t1)) # here the actual data are loaded all_sig_chunks.append(sig_chunk) Not all IO modules support lazy loading (but many do). To know whether a given IO class supports lazy mode, use ``SomeIO.support_lazy``. For more details, see :ref:`section-performance-memory`. Image sequence data ------------------- Another new feature, although one that is more experimental, is support for image sequence data, coming from calcium imaging of neuronal activity, voltage-sensitive dye imaging, etc. The new :class:`ImageSequence` object contains a sequence of image frames as a 3D array. As with other Neo data objects, the object also holds metadata such as the sampling rate/frame duration and the spatial scale (physical size represented by one pixel). Three new IO modules, :class:`TiffIO`, :class:`AsciiImageIO` and :class:`BlkIO`, allow reading such data from file, e.g.:: from quantities import Hz, mm, dimensionless from neo.io import TiffIO data = TiffIO(data_path).read(units=dimensionless, sampling_rate=25 * Hz, spatial_scale=0.05 * mm) images = data[0].segments[0].imagesequences[0] :class:`ImageSequence` is a subclass of the NumPy :class:`ndarray`, and so can be manipulated in the same ways, e.g.:: images /= images.max() background = np.mean(images, axis=0) preprocessed_images = images - background Since a common operation with image sequences is to extract time series from regions of interest, Neo also provides various region-of-interest classes which perform this operation, returning an :class:`AnalogSignal` object:: roi = CircularRegionOfInterest(x=50, y=50, radius=10) signal = preprocessed_images.signal_from_region(roi)[0] Other new features ------------------ * new neo.utils module * Numpy 1.16+ compatibility * :meth:`time_shift()` method for :class:`Epoch`/:class:`Event`/:class:`AnalogSignal` * :meth:`time_slice()` method is now more robust * dropped support for Python 3.4 See all `pull requests`_ included in this release and the `list of closed issues`_. Bug fixes and improvements in IO modules ---------------------------------------- * Blackrock * Neuroshare * NixIOFr * NixIO (array annotation + 1d coordinates) * AsciiSignal (fix + json metadata + IrregularlySampledSignals + write proxy) * Spike2 (group same sampling rate) * Brainvision * NeuralynxIO .. Warning:: Some IOs (based on rawio) when loading can choose to split each channel into its own 1-channel :class:`AnalogSignal` or to group them in a multi-channel :class:`AnalogSignal`. The default behavior (either ``signal_group_mode='split-all'`` or ``'group-same-units'``) is not the same for all IOs for backwards compatibility reasons. In the next release, all IOs will have the default ``signal_group_mode='group-same-units'`` Acknowledgements ---------------- Thanks to Achileas Koutsou, Chek Yin Choi, Richard C. Gerkin, Hugo van Kemenade, Alexander Kleinjohann, Björn Müller, Jeffrey Gill, Christian Kothe, Mike Sintsov, @rishidhingra, Michael Denker, Julia Sprenger, Corentin Fragnaud, Andrew Davison and Samuel Garcia for their contributions to this release. .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.8.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2018-11-27+milestone%3A0.8.0././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases/0.9.0.rst0000644000175100017510000002357115154507213017110 0ustar00runnerrunner======================= Neo 0.9.0 release notes ======================= 10th November 2020 Group and ChannelView replace Unit and ChannelIndex --------------------------------------------------- Experience with :class:`ChannelIndex` and :class:`Unit` has shown that these classes are often confusing and difficult to understand. In particular, :class:`ChannelIndex` was trying to provide three different functionalities in a single object: - providing information about individual traces within :class:`AnalogSignals` like the channel id and the channel name (labelling) - grouping a subset of traces within an :class:`AnalogSignal` via the ``index`` attribute (masking) - linking between / grouping :class:`AnalogSignals` (grouping) while grouping :class:`SpikeTrains` required a different class, :class:`Unit`. For more pointers to the difficulties this created, and some of the limitations of this approach, see `this Github issue`_. With the aim of making the three functionalities of labelling, masking and grouping both easier to use and more flexible, we have replaced :class:`ChannelIndex` and :class:`Unit` with: - array annotations (*labelling*) - already available since Neo 0.8 - :class:`~neo.core.ChannelView` (*masking*) - defines subsets of channels within an `AnalogSignal` using a mask - :class:`~neo.core.Group` (*grouping*) - allows any Neo object except :class`Segment` and :class:`Block` to be grouped For some guidance on migrating from :class:`ChannelIndex`/:class:`Unit` to :class:`Group` and :class:`ChannelView` see the appendix at the bottom of this page. Python 3 only ------------- We have now dropped support for Python 2.7 and Python 3.5, and for versions of NumPy older than 1.13. In future, we plan to follow NEP29_ + one year, i.e. we will support Python and NumPy versions for one year longer than recommended in NEP29. This was `discussed here`_. Change in default behaviour for grouping channels in IO modules --------------------------------------------------------------- Previously, when reading multiple related signals (same length, same units) from a file, some IO classes would by default create a separate, single-channel :class:`AnalogSignal` per signal, others would combine all related signals into one multi-channel :class:`AnalogSignal`. From Neo 0.9.0, the default for all IO classes is to create a one multi-channel :class:`AnalogSignal`. To get the "multiple single-channel signals" behaviour, use:: io.read(signal_group_mode="split-all") Other new or modified features ------------------------------ * added methods :func:`rectify()`, :func:`downsample` and :func:`resample` to :class:`AnalogSignal` * :func:`SpikeTrain.merge()` can now merge multiple spiketrains * the utility function :func:`cut_block_by_epochs()` gives a new :class:`Block` now rather than modifying the block in place * some missing properties such as ``t_start`` were added to :class:`ImageSequence`, and ``sampling_period`` was renamed to ``frame_duration`` * :func:`AnalogSignal.time_index()` now accepts arrays of times, not just a scalar. See all `pull requests`_ included in this release and the `list of closed issues`_. Bug fixes and improvements in IO modules ---------------------------------------- * NeoMatlabIO (support for signal annotations) * NeuralynxIO (fix handling of empty .nev files) * AxonIO (support EDR3 header, fix channel events bug) * Spike2IO (fix rounding problem, fix for v9 SON files) * MicromedIO (fix label encoding) Acknowledgements ---------------- Thanks to Julia Sprenger, Samuel Garcia, Andrew Davison, Alexander Kleinjohann, Hugo van Kemenade, Achilleas Koutsou, Jeffrey Gill, Corentin Fragnaud, Aitor Morales-Gregorio, Rémi Proville, Robin Gutzen, Marin Manuel, Simon Danner, Michael Denker, Peter N. Steinmetz, Diziet Asahi and Lucien Krapp for their contributions to this release. Appendix: Migrating from ChannelIndex/Unit to ChannelView/Group =============================================================== While the basic hierarchical :class:`Block` - :class:`Segment` structure of Neo has remained unchanged since the inception of Neo, the structures used to cross-link objects (for example to link a signal to the spike trains derived from it) have undergone changes, in an effort to find an easily understandable and usable approach. Below we give some examples of how to migrate from :class:`ChannelIndex` and :class:`Unit`, as used in Neo 0.8, to the new classes :class:`Group` and :class:`ChannelView` introduced in Neo 0.9. Note that Neo 0.9 supports the new and old API in parallel, to facilitate migration. IO classes in Neo 0.9 can read :class:`ChannelIndex` and :class:`Unit` objects, but do not write them. :class:`ChannelIndex` and :class:`Unit` will be removed in Neo 0.10.0. Examples -------- A simple example with two tetrodes. Here the :class:`ChannelIndex` was not being used for grouping, simply to associate a name with each channel. Using :class:`ChannelIndex`:: import numpy as np from quantities import kHz, mV from neo import Block, Segment, ChannelIndex, AnalogSignal block = Block() segment = Segment() segment.block = block block.segments.append(segment) for i in (0, 1): signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=1 * kHz,) segment.analogsignals.append(signal) chx = ChannelIndex(name=f"Tetrode #{i + 1}", index=[0, 1, 2, 3], channel_names=["A", "B", "C", "D"]) chx.analogsignals.append(signal) block.channel_indexes.append(chx) Using array annotations, we annotate the channels of the :class:`AnalogSignal` directly:: import numpy as np from quantities import kHz, mV from neo import Block, Segment, AnalogSignal block = Block() segment = Segment() segment.block = block block.segments.append(segment) for i in (0, 1): signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=1 * kHz, channel_names=["A", "B", "C", "D"]) segment.analogsignals.append(signal) Now a more complex example: a 1x4 silicon probe, with a neuron on channels 0,1,2 and another neuron on channels 1,2,3. We create a :class:`ChannelIndex` for each neuron to hold the :class:`Unit` object associated with this spike sorting group. Each :class:`ChannelIndex` also contains the list of channels on which that neuron spiked. :: import numpy as np from quantities import ms, mV, kHz from neo import Block, Segment, ChannelIndex, Unit, SpikeTrain, AnalogSignal block = Block(name="probe data") segment = Segment() segment.block = block block.segments.append(segment) # create 4-channel AnalogSignal with dummy data signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz) # create spike trains with dummy data # we will pretend the spikes have been extracted from the dummy signal spiketrains = [ SpikeTrain(np.arange(5, 100) * ms, t_stop=100 * ms), SpikeTrain(np.arange(7, 100) * ms, t_stop=100 * ms) ] segment.analogsignals.append(signal) segment.spiketrains.extend(spiketrains) # assign each spiketrain to a neuron (Unit) units = [] for i, spiketrain in enumerate(spiketrains): unit = Unit(name=f"Neuron #{i + 1}") unit.spiketrains.append(spiketrain) units.append(unit) # create a ChannelIndex for each unit, to show which channels the spikes come from chx0 = ChannelIndex(name="Channel Group 1", index=[0, 1, 2]) chx0.units.append(units[0]) chx0.analogsignals.append(signal) units[0].channel_index = chx0 chx1 = ChannelIndex(name="Channel Group 2", index=[1, 2, 3]) chx1.units.append(units[1]) chx1.analogsignals.append(signal) units[1].channel_index = chx1 block.channel_indexes.extend((chx0, chx1)) Using :class:`ChannelView` and :class:`Group`:: import numpy as np from quantities import ms, mV, kHz from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal block = Block(name="probe data") segment = Segment() segment.block = block block.segments.append(segment) # create 4-channel AnalogSignal with dummy data signal = AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz) # create spike trains with dummy data # we will pretend the spikes have been extracted from the dummy signal spiketrains = [ SpikeTrain(np.arange(5, 100) * ms, t_stop=100 * ms), SpikeTrain(np.arange(7, 100) * ms, t_stop=100 * ms) ] segment.analogsignals.append(signal) segment.spiketrains.extend(spiketrains) # assign each spiketrain to a neuron (now using Group) units = [] for i, spiketrain in enumerate(spiketrains): unit = Group([spiketrain], name=f"Neuron #{i + 1}") units.append(unit) # create a ChannelView of the signal for each unit, to show which channels the spikes come from # and add it to the relevant Group view0 = ChannelView(signal, index=[0, 1, 2], name="Channel Group 1") units[0].add(view0) view1 = ChannelView(signal, index=[1, 2, 3], name="Channel Group 2") units[1].add(view1) block.groups.extend(units) Now each putative neuron is represented by a :class:`Group` containing the spiketrains of that neuron and a view of the signal selecting only those channels from which the spikes were obtained. .. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.9.0+is%3Aclosed .. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2019-09-30+milestone%3A0.9.0 .. _NEP29: https://numpy.org/neps/nep-0029-deprecation_policy.html .. _`discussed here`: https://github.com/NeuralEnsemble/python-neo/issues/788 .. _`this Github issue`: https://github.com/NeuralEnsemble/python-neo/issues/456 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/releases.rst0000644000175100017510000000552415154507213016442 0ustar00runnerrunner============= Release notes ============= .. toctree:: :maxdepth: 1 releases/0.14.4.rst releases/0.14.3.rst releases/0.14.2.rst releases/0.14.1.rst releases/0.14.0.rst releases/0.13.4.rst releases/0.13.3.rst releases/0.13.2.rst releases/0.13.1.rst releases/0.13.0.rst releases/0.12.0.rst releases/0.11.1.rst releases/0.11.0.rst releases/0.10.2.rst releases/0.10.1.rst releases/0.10.0.rst releases/0.9.0.rst releases/0.8.0.rst releases/0.7.2.rst releases/0.7.1.rst releases/0.7.0.rst releases/0.6.0.rst releases/0.5.2.rst releases/0.5.1.rst releases/0.5.0.rst .. releases/0.2.0.rst .. releases/0.2.1.rst .. releases/0.3.0.rst .. releases/0.3.1.rst .. releases/0.3.2.rst .. releases/0.3.3.rst Version 0.4.0 ------------- * added StimfitIO * added KwikIO * significant improvements to AxonIO, BlackrockIO, BrainwareSrcIO, NeuroshareIO, PlexonIO, Spike2IO, TdtIO, * many test suite improvements * Container base class Version 0.3.3 ------------- * fix a bug in PlexonIO where some EventArrays only load 1 element. * fix a bug in BrainwareSrcIo for segments with no spikes. Version 0.3.2 ------------- * cleanup of io test code, with additional helper functions and methods * added BrainwareDamIo * added BrainwareF32Io * added BrainwareSrcIo Version 0.3.1 ------------- * lazy/cascading improvement * load_lazy_olbject() in neo.io added * added NeuroscopeIO Version 0.3.0 ------------- * various bug fixes in neo.io * added ElphyIO * SpikeTrain performance improved * An IO class now can return a list of Block (see read_all_blocks in IOs) * python3 compatibility improved Version 0.2.1 ------------- * assorted bug fixes * added :func:`time_slice()` method to the :class:`SpikeTrain` and :class:`AnalogSignalArray` classes. * improvements to annotation data type handling * added PickleIO, allowing saving Neo objects in the Python pickle format. * added ElphyIO (see http://neuro-psi.cnrs.fr/spip.php?article943) * added BrainVisionIO (see https://brainvision.com/) * improvements to PlexonIO * added :func:`merge()` method to the :class:`Block` and :class:`Segment` classes * development was mostly moved to GitHub, although the issue tracker is still at neuralensemble.org/neo Version 0.2.0 ------------- New features compared to Neo 0.1: * new schema more consistent. * new objects: RecordingChannelGroup, EventArray, AnalogSignalArray, EpochArray * Neuron is now Unit * use the quantities_ module for everything that can have units. * Some objects directly inherit from Quantity: SpikeTrain, AnalogSignal, AnalogSignalArray, instead of having an attribute for data. * Attributes are classified in 3 categories: necessary, recommended, free. * lazy and cascade keywords are added to all IOs * Python 3 support * better tests .. _quantities: https://pypi.org/project/quantities/ ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1773309590.518011 neo-0.14.4/doc/source/scripts/0000755000175100017510000000000015154507227015573 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/scripts/multi_tetrode_example.py0000644000175100017510000000645715154507213022547 0ustar00runnerrunner""" Example for usecases.rst """ from itertools import cycle import numpy as np from quantities import ms, mV, kHz import matplotlib.pyplot as plt from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal store_signals = False block = Block(name="probe data", tetrode_ids=["Tetrode #1", "Tetrode #2"]) block.segments = [ Segment(name="trial #1", index=0), Segment(name="trial #2", index=1), Segment(name="trial #3", index=2), ] n_units = {"Tetrode #1": 2, "Tetrode #2": 5} # Create a group for each neuron, annotate each group with the tetrode from which it was recorded groups = [] counter = 0 for tetrode_id, n in n_units.items(): groups.extend([Group(name=f"neuron #{counter + i + 1}", tetrode_id=tetrode_id) for i in range(n)]) counter += n block.groups.extend(groups) iter_group = cycle(groups) # Create dummy data, one segment at a time for segment in block.segments: # create two 4-channel AnalogSignals with dummy data signals = { "Tetrode #1": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #1"), "Tetrode #2": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #2"), } if store_signals: segment.analogsignals.extend(signals.values()) # create spike trains with dummy data # we will pretend the spikes have been extracted from the dummy signal for tetrode_id in ("Tetrode #1", "Tetrode #2"): for i in range(n_units[tetrode_id]): spiketrain = SpikeTrain(np.random.uniform(0, 100, size=30) * ms, t_stop=100 * ms) # assign each spiketrain to the appropriate segment segment.spiketrains.append(spiketrain) # assign each spiketrain to a given neuron current_group = next(iter_group) current_group.add(spiketrain) if store_signals: # add to the group a reference to the signal from which the spikes were obtained # this does not give a 1:1 correspondance between spike trains and signals, # for that we could use additional groups (and have groups of groups) current_group.add(signals[tetrode_id]) # Now plot the data # .. by trial plt.figure() for seg in block.segments: print(f"Analyzing segment {seg.index}") stlist = [st - st.t_start for st in seg.spiketrains] plt.subplot(len(block.segments), 1, seg.index + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH in segment {seg.index}") plt.show() # ..by neuron plt.figure() for i, group in enumerate(block.groups): stlist = [st - st.t_start for st in group.spiketrains] plt.subplot(len(block.groups), 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH of unit {group.name}") plt.show() # ..by tetrode plt.figure() for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]): stlist = [] for unit in block.filter(objects=Group, tetrode_id=tetrode_id): stlist.extend([st - st.t_start for st in unit.spiketrains]) plt.subplot(2, 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH blend of tetrode {tetrode_id}") plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/scripts/spike_sorting_example.py0000644000175100017510000000236315154507213022537 0ustar00runnerrunner""" Example for usecases.rst """ import numpy as np from neo import Segment, AnalogSignal, SpikeTrain, Group, ChannelView from quantities import Hz # generate some fake data seg = Segment() seg.analogsignals.append( AnalogSignal( [ [0.1, 0.1, 0.1, 0.1], [-2.0, -2.0, -2.0, -2.0], [0.1, 0.1, 0.1, 0.1], [-0.1, -0.1, -0.1, -0.1], [-0.1, -0.1, -0.1, -0.1], [-3.0, -3.0, -3.0, -3.0], [0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1], ], sampling_rate=1000 * Hz, units="V", ) ) # extract spike trains from all channels st_list = [] for signal in seg.analogsignals: # use a simple threshhold detector spike_mask = np.where(np.min(signal.magnitude, axis=1) < -1.0)[0] # create a spike train spike_times = signal.times[spike_mask] st = SpikeTrain(spike_times, t_start=signal.t_start, t_stop=signal.t_stop) # remember the spike waveforms wf_list = [] for spike_idx in np.nonzero(spike_mask)[0]: wf_list.append(signal[spike_idx - 1 : spike_idx + 2, :]) st.waveforms = np.array(wf_list) st_list.append(st) unit = Group() unit.spiketrains = st_list unit.analogsignals.extend(seg.analogsignals) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/share_data.rst0000644000175100017510000002132515154507213016727 0ustar00runnerrunner=========================================== Sharing neuroscience data in an open format =========================================== .. FAIR, advantages of open formats .. data from other formats, or from simulations When sharing data and metadata, it is important to consider the `FAIR guiding principles`_. Among these principles are *interoperability* and *reusability*, which include the idea that data and metadata should be stored in a format that can be read by many different tools, and should be thoroughly annotated with metadata. In neurophysiology, a large fraction of experimental data are obtained using commercial setups with dedicated software, which stores data in a proprietary format. While Neo can help greatly in being able to read many proprietary formats, (i) not everyone uses Python, and (ii) many metadata of interest cannot be contained in these formats, and metadata labels do not in general use standardised terminologies. Conversely, data from neuroscience simulations *are* often stored in open formats, such as plain text files or HDF5, but with considerable variability between users and little or no standardisation. It is therefore advantageous to convert to, or write data directly in, an open format that conforms to FAIR principles. Neo supports two such formats: - `NIX`_ - `Neurodata Without Borders (NWB)`_ Example ======= Before getting into the details of the formats, we present an example dataset that will be used to demonstrate the process of converting to open formats. Our example is a public dataset, "`Whole cell patch-clamp recordings of cerebellar granule cells`_", contributed to the EBRAINS_ repository by Marialuisa Tognolina from the laboratory of Egidio D'Angelo at the University of Pavia. As we can see from the dataset description, *This dataset provides a characterization of the intrinsic excitability and synaptic properties of the cerebellar granule cells. Whole-cell patch-clamp recordings were performed on acute parasagittal cerebellar slices obtained from juvenile Wistar rats (p18-p24). Passive granule cells parameters were extracted in voltage-clamp mode by analyzing current relaxation induced by step voltage changes (IV protocol). Granule cells intrinsic excitability was investigated in current-clamp mode by injecting 2 s current steps (CC step protocol). Synaptic transmission properties were investigated in current clamp mode by an electrical stimulation of the mossy fibers bundle (5 pulses at 50 Hz, EPSP protocol).* The dataset contains recordings from multiple subjects. For this example. let's download the data for Subject 15. You can download them by hand, by selecting each file then selecting "Download file", or run the following code: .. ipython:: In [1]: from urllib.request import urlretrieve In [2]: from urllib.parse import quote In [3]: dataset_url = "https://data-proxy.ebrains.eu/api/v1/buckets/p63ea6-hbp-d000017_PatchClamp-GranuleCells_pub" In [4]: folder = "GrC_Subject15_180116" In [5]: filenames = ["180116_0004 IV -70.abf", "180116_0005 CC step.abf", "180116_0006 EPSP.abf"] In [6]: for filename in filenames: ...: datafile_url = f"{dataset_url}/{folder}/{quote(filename)}" ...: local_file = urlretrieve(datafile_url, filename) Let's start with the current-clamp data. The data are in Axon format (suffix ".abf"), so we could import :class:`~neo.io.AxonIO` directly, but we can also ask Neo to guess the format using the :func:`get_io()` function: .. ipython:: In [7]: from neo.io import get_io In [8]: reader = get_io("180116_0005 CC step.abf") In [9]: data = reader.read() In [10]: data Out[10]: We can see that the file contains a single :class:`Block`, containing 15 :class:`Segments`, and each segment contains one :class:`AnalogSignal` with a single channel, and an :class:`Event`. .. note: the events are essentially empty To quickly take a look at the data, let's plot it: .. ipython:: In [11]: import matplotlib.pyplot as plt In [12]: fig = plt.figure(figsize=(10, 5)) In [13]: for segment in data[0].segments: ....: signal = segment.analogsignals[0] ....: plt.plot(signal.times, signal) In [14]: plt.xlabel(f"Time ({signal.times.units.dimensionality.string})") In [15]: plt.ylabel(f"Voltage ({signal.units.dimensionality.string})") In [16]: plt.savefig("open_format_example_cc_step.png") .. image:: open_format_example_cc_step.png Now we've read the data into Neo, we're ready to write them to an open format. NIX === The `NIX data model`_ allows storing a fully annotated scientific dataset, i.e. the data together with its metadata, within a single container. The current implementations use the HDF5 file format as a storage backend. For users of Neo, the advantage of NIX is that all Neo objects can be stored in an open format, HDF5, readable with many different tools, without needing to add extra annotations or structure the dataset in any specific way. Using Neo's :class:`~neo.io.NIXIO` requires some additional dependencies. To install Neo with NIXIO support, run:: $ pip install neo[nixio] Writing our example dataset to NIX format is straightforward: .. ipython:: In [17]: from neo.io import NixIO In [18]: writer = NixIO("GrC_Subject15_180116.nix", mode="ow") In [19]: writer.write(data) Neurodata Without Borders (NWB) =============================== `Neurodata Without Borders`_ (NWB:N) is an open standard file format for neurophysiology. Using Neo's :class:`~neo.io.NWBIO` requires some additional dependencies. To install Neo with NWB support, run:: $ pip install neo[nwb] :class:`NWBIO` can read NWB 2.0-format files, and maps their structure onto Neo objects and annotations. :class:`NWBIO` can also write to NWB 2.0 format. Since NWB has a more complex structure than Neo's basic :class:`Block` - :class:`Segment` hierarchy, and NWB requires fairly extensive metadata, it is recommended to annotate the Neo objects with special, NWB-specific annotations, to ensure data and metadata are correctly placed within the NWB file. The location of data stored in an NWB file depends on the source of the data, e.g. whether they are stimuli, intracellular electrophysiology recordings, extracellular electrophysiology recordings, behavioural measuremenets, etc. For this, we need to annotate all data objects with special metadata, identified by keys starting with "``nwb_``": .. ipython:: In [20]: signal_metadata = { ....: "nwb_group": "acquisition", ....: "nwb_neurodata_type": ("pynwb.icephys", "PatchClampSeries"), ....: "nwb_electrode": { ....: "name": "patch clamp electrode", ....: "description": "The patch-clamp pipettes were pulled from borosilicate glass capillaries " ....: "(Hilgenberg, Malsfeld, Germany) and filled with intracellular solution " ....: "(K-gluconate based solution)", ....: "device": { ....: "name": "patch clamp electrode" ....: } ....: }, ....: "nwb:gain": 1.0 ....: } In [21]: for segment in data[0].segments: ....: signal = segment.analogsignals[0] ....: signal.annotate(**signal_metadata) We can also provide global metadata, either attaching them to a Neo :class:`Block` or passing them to the :func:`write()` method. Here we take metadata from the dataset description on the EBRAINS search portal: .. ipython:: In [22]: global_metadata = { ....: "session_start_time": data[0].rec_datetime, ....: "identifier": data[0].file_origin, ....: "session_id": "180116_0005", ....: "institution": "University of Pavia", ....: "lab": "D'Angelo Lab", ....: "related_publications": "https://doi.org/10.1038/s42003-020-0953-x" ....: } Now that we have annotated our dataset, we can write it to an NWB file: .. ipython:: :okwarning: In [23]: from neo.io import NWBIO In [24]: writer = NWBIO("GrC_Subject15_180116.nwb", mode="w", **global_metadata) In [25]: writer.write(data) .. note:: Neo support for NWB is a work-in-progress, it does not currently support NWB extensions for example. If you encounter a problem reading an NWB file with Neo, please make a `bug report`_ (see :doc:`bug_reports`). .. _`Neurodata Without Borders`: https://www.nwb.org .. _`bug report`: https://github.com/NeuralEnsemble/python-neo/issues/new .. _`Whole cell patch-clamp recordings of cerebellar granule cells`: https://doi.org/10.25493/CHJG-7QC .. _EBRAINS: https://ebrains.eu/services/data-and-knowledge/ .. _`NIX data model`: https://nixpy.readthedocs.io/ .. _`FAIR guiding principles`: https://doi.org/10.1038/sdata.2016.18././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/doc/source/use_neo_as_dependency.rst0000644000175100017510000000701415154507213021151 0ustar00runnerrunner============================= Using Neo in a library or app ============================= .. Neo as a common layer for interoperability The main goal of Neo is to act as an interoperability layer for any software that deals with neurophysiology or optical imaging data, whether recorded experimentally or generated by simulations. Any software that uses the Neo object model as its internal representation, or which can import/export its internal data structures from/to Neo, will be automatically compatible with all other software that does the same. The following figure shows the Neo ecosystem: the Neo core classes, the IO modules, and some of the software tools built on top of Neo. .. image:: images/neo_ecosystem.svg To see more of the tools built on Neo, see the Github `dependency graph`_. At the time of writing, Github records 394 repositories that depend on Neo, of which 55 publish their own Python packages. Dependability ============= .. dependability - stable, community development, link to governance, continuous integration, testing library If you're using Neo as a key dependency for your project, you need to be confident that Neo is a stable base to build on. Here are some of the factors you might consider: **API stability** Neo was first released in 2010. Since then we have been gradually refining and updating the object model, based on experience in real-world projects, and feedback from users. Any changes that will break users' code are introduced gradually, with deprecation warnings. We are getting close to releasing version 1.0, at which point we expect to maintain backwards-compatibility. **Testing and continuous integration** Neo has an extensive test suite; we use Github Actions to run the tests whenever the code is changed. As part of this, we maintain an `open repository of data files`_ for all the formats Neo supports, which we use for our own tests and make available to others. **Community** Neo uses a community development model, with defined rules of :doc:`governance` under which frequent contributors can become maintainers, and a `code of conduct`_. This community model means that Neo does not rely on any one source of funding. **Licencing** Neo is open source software, available under a permissive BSD-3-clause `licence`_. Performance =========== .. low-level data reading - the RawIO layer (see rawio.rst) With the increasing quantities of data that can be generated by modern recording methods and by large-scale simulations, performance, in terms of both speed and memory consumption, is highly important. Neo has multiple optimizations to reduce import time, minimise the overhead due to using Neo objects rather than plain NumPy arrays, and minimize memory footprint. For using Neo as a dependency in another library of app, it is important to be aware of the "raw" IO layer, which exposes a low-level read-only API based only on dictionaries and NumPy buffers. Available for a large subset of the file formats Neo supports, the :mod:`neo.rawio` module provide much of the benefit of Neo's multiple file-format support without needing to use the Neo object model. For more details, see :doc:`rawio`. .. _`dependency graph`: https://github.com/NeuralEnsemble/python-neo/network/dependents .. _`open repository of data files`: https://gin.g-node.org/NeuralEnsemble/ephy_testing_data .. _`licence`: https://github.com/NeuralEnsemble/python-neo/blob/master/LICENSE.txt .. _`code of conduct`: https://github.com/NeuralEnsemble/python-neo/blob/master/CODE_OF_CONDUCT.md ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1773309590.519564 neo-0.14.4/examples/0000755000175100017510000000000015154507227013655 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/convert_to_nwb.py0000644000175100017510000000324215154507213017253 0ustar00runnerrunner""" Converting to NWB using Neo =========================== """ from urllib.request import urlretrieve from urllib.parse import quote from neo.io import get_io dataset_url = "https://object.cscs.ch/v1/AUTH_63ea6845b1d34ad7a43c8158d9572867/hbp-d000017_PatchClamp-GranuleCells_pub" folder = "GrC_Subject15_180116" # filenames = ["180116_0004 IV -70.abf", "180116_0005 CC step.abf", "180116_0006 EPSP.abf"] filenames = ["180116_0005 CC step.abf"] for filename in filenames: datafile_url = f"{dataset_url}/{folder}/{quote(filename)}" local_file = urlretrieve(datafile_url, filename) reader = get_io("180116_0005 CC step.abf") data = reader.read() global_metadata = { "session_start_time": data[0].rec_datetime, "identifier": data[0].file_origin, "session_id": "180116_0005", "institution": "University of Pavia", "lab": "D'Angelo Lab", "related_publications": "https://doi.org/10.1038/s42003-020-0953-x", } # data[0].annotate(**global_metadata) signal_metadata = { "nwb_group": "acquisition", "nwb_neurodata_type": ("pynwb.icephys", "PatchClampSeries"), "nwb_electrode": { "name": "patch clamp electrode", "description": "The patch-clamp pipettes were pulled from borosilicate glass capillaries " "(Hilgenberg, Malsfeld, Germany) and filled with intracellular solution " "(K-gluconate based solution)", "device": {"name": "patch clamp electrode"}, }, "nwb:gain": 1.0, } for segment in data[0].segments: signal = segment.analogsignals[0] signal.annotate(**signal_metadata) from neo.io import NWBIO writer = NWBIO("GrC_Subject15_180116.nwb", mode="w", **global_metadata) writer.write(data) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_igorio.py0000644000175100017510000000254115154507213016552 0ustar00runnerrunner""" IgorProIO Demo (BROKEN) ======================= """ ########################################################### # Import our packages import os from urllib.request import urlretrieve import zipfile import matplotlib.pyplot as plt from neo.io import get_io ############################################################# # Then download some data # Downloaded from Human Brain Project Collaboratory # Digital Reconstruction of Neocortical Microcircuitry (nmc-portal) # http://microcircuits.epfl.ch/#/animal/8ecde7d1-b2d2-11e4-b949-6003088da632 # NOTE: this dataset is not found as the link is broken. # datafile_url = "https://microcircuits.epfl.ch/data/released_data/B95.zip" # filename_zip = "B95.zip" # filename = "grouped_ephys/B95/B95_Ch0_IDRest_107.ibw" # urlretrieve(datafile_url, filename_zip) # zip_ref = zipfile.ZipFile(filename_zip) # create zipfile object # zip_ref.extract(path=".", member=filename) # extract file to dir # zip_ref.close() # ###################################################### # # Once we have our data we can use `get_io` to find an # # io (Igor in this case). Then we read the analogsignals # # Finally we will make some nice plots # reader = get_io(filename) # signal = reader.read_analogsignal() # plt.plot(signal.times, signal) # plt.xlabel(signal.sampling_period.dimensionality) # plt.ylabel(signal.dimensionality) # plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_imageseq.py0000644000175100017510000000276515154507213017065 0ustar00runnerrunner""" ImageSequences ============== """ ########################################################## # Let's import some packages from neo.core import ImageSequence from neo.core import RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest import matplotlib.pyplot as plt import quantities as pq import random ############################################################ # Now we need to generate some data # We will just make a nice box and then we can attach this # ImageSequence to a variety of ROIs # our ImageSequence will be 50 frames of 100x100 pixel images l = [] for frame in range(50): l.append([]) for y in range(100): l[frame].append([]) for x in range(100): l[frame][y].append(random.randint(0, 50)) ##################################################################### # we then make our image sequence and pull out our results from the # image_seq image_seq = ImageSequence(l, sampling_rate=500 * pq.Hz, spatial_scale="m", units="V") result = image_seq.signal_from_region( CircularRegionOfInterest(image_seq, 50, 50, 25), CircularRegionOfInterest(image_seq, 10, 10, 5), PolygonRegionOfInterest(image_seq, (50, 25), (50, 45), (14, 65), (90, 80)), ) ############################################################### # It is easy to plot our results using matplotlib for i in range(len(result)): plt.figure() plt.plot(result[i].times, result[i]) plt.xlabel("seconde") plt.ylabel("valeur") plt.tight_layout() plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_multi_tetrode_example.py0000644000175100017510000001251615154507213021660 0ustar00runnerrunner""" Analyzing and Plotting Data with Neo Structures =============================================== """ ###################################################### # First we import some packages. Since we are making simulated # data we will import quite a few neo features as well as use # quantities to provide units from itertools import cycle import numpy as np from quantities import ms, mV, kHz import matplotlib.pyplot as plt from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal ########################################################################## # For Neo we start with a block of data that will contain segments of data # so we will create a block of probe data that has a couple tetrodes # Then we will load in 3 segments (for examples trials of a stimulus) store_signals = False block = Block(name="probe data", tetrode_ids=["Tetrode #1", "Tetrode #2"]) block.segments = [ Segment(name="trial #1", index=0), Segment(name="trial #2", index=1), Segment(name="trial #3", index=2), ] # we will decide how many units each tetrode has found. If only science was this easy n_units = {"Tetrode #1": 2, "Tetrode #2": 5} ################################################################################## # Neo can also have groups. Groups are structures within a block that can cross segments # for example we could group a neuron across trials or across probes. # Create a group for each neuron, annotate each group with the tetrode from which it was recorded groups = [] counter = 0 for tetrode_id, n in n_units.items(): groups.extend([Group(name=f"neuron #{counter + i + 1}", tetrode_id=tetrode_id) for i in range(n)]) counter += n block.groups.extend(groups) iter_group = cycle(groups) ########################################################################################## # Segments are also containers of data. Segments can hold raw signal data like an AnalogSignal # Segments can also hold spiketrain data (in a SpikeTrain). It can also hold event data (which # we are not show in this example) # Create dummy data, one segment at a time for segment in block.segments: # create two 4-channel AnalogSignals with simulated data (because we have two tetrodes!) # note that the AnalogSignal with have numpy array-like data with units and sampling rates # Neo keeps track of these units while also giving you the flexibility of treating the raw data # like a numpy array signals = { "Tetrode #1": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #1"), "Tetrode #2": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #2"), } if store_signals: segment.analogsignals.extend(signals.values()) # create spike trains with simulated data # we will pretend the spikes have been extracted from the simulated signal for tetrode_id in ("Tetrode #1", "Tetrode #2"): for i in range(n_units[tetrode_id]): spiketrain = SpikeTrain(np.random.uniform(0, 100, size=30) * ms, t_stop=100 * ms) # assign each spiketrain to the appropriate segment segment.spiketrains.append(spiketrain) # assign each spiketrain to a given neuron current_group = next(iter_group) current_group.add(spiketrain) if store_signals: # add to the group a reference to the signal from which the spikes were obtained # this does not give a 1:1 correspondance between spike trains and signals, # for that we could use additional groups (and have groups of groups) current_group.add(signals[tetrode_id]) ################################################### # Now we will plot the data # Neo doesn't provide it's own plotting functions, but # since its data can be treated like numpy arrays # it is easy to use standard packages like matplotlib # for all your plotting needs # We do a classic in neuroscience and show various ways # to plot a PSTH (Peristimulus histogram) ################################################### # .. by trial plt.figure() for seg in block.segments: print(f"Analyzing segment {seg.index}") stlist = [st - st.t_start for st in seg.spiketrains] plt.subplot(len(block.segments), 1, seg.index + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH in segment {seg.index}") plt.tight_layout() plt.show() #################################################### # ..by neuron plt.figure() for i, group in enumerate(block.groups): stlist = [st - st.t_start for st in group.spiketrains] plt.subplot(len(block.groups), 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH of unit {group.name}") plt.tight_layout() plt.show() ########################################################### # ..by tetrode (or other electrode number) plt.figure() for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]): stlist = [] for unit in block.filter(objects=Group, tetrode_id=tetrode_id): stlist.extend([st - st.t_start for st in unit.spiketrains]) plt.subplot(2, 1, i + 1) count, bins = np.histogram(stlist) plt.bar(bins[:-1], count, width=bins[1] - bins[0]) plt.title(f"PSTH blend of tetrode {tetrode_id}") plt.tight_layout() plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_read_files_neo_io.py0000644000175100017510000000356015154507213020711 0ustar00runnerrunner""" Reading files with neo.io ========================= """ #################################################### # Start with package import and getting a datafile import urllib import neo url_repo = "https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/" # Plexon files distantfile = url_repo + "plexon/File_plexon_3.plx" localfile = "File_plexon_3.plx" urllib.request.urlretrieve(distantfile, localfile) ################################################### # Now we can create our reader and read some data # create a reader reader = neo.io.PlexonIO(filename="File_plexon_3.plx") # read the blocks blks = reader.read(lazy=False) print(blks) ###################################################### # Once we have our blocks we can iterate through each # block of data and see the contents of all parts of # that data # access to segments for blk in blks: for seg in blk.segments: print(seg) for asig in seg.analogsignals: print(asig) for st in seg.spiketrains: print(st) ####################################################### # Let's look at another file type # CED Spike2 files distantfile = url_repo + "spike2/File_spike2_1.smr" localfile = "./File_spike2_1.smr" urllib.request.urlretrieve(distantfile, localfile) # create a reader reader = neo.io.Spike2IO(filename="File_spike2_1.smr") ######################################################### # Despite being a different raw file format we can access # the data in the same way # read the block bl = reader.read(lazy=False)[0] print(bl) ########################################################## # Similarly we can view the different types of data within # the block (AnalogSignals and SpikeTrains) # access to segments for seg in bl.segments: print(seg) for asig in seg.analogsignals: print(asig) for st in seg.spiketrains: print(st) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_read_files_neo_rawio.py0000644000175100017510000001476515154507213021434 0ustar00runnerrunner""" Reading files with neo.rawio ============================ compare with read_files_neo_io.py """ ########################################################### # First we import a RawIO from neo.rawio # For this example we will use PlexonRawIO import urllib from neo.rawio import PlexonRawIO ############################################################## # Get Plexon files # We will be pulling these files down, but if you have a local file # then all you need to do is specify the file location on your # computer. NeuralEnsemble keeps a wide variety of freely accesible, small # test files that can be used. So for this example we will take advantage of that # fact. url_repo = "https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/" distantfile = url_repo + "plexon/File_plexon_3.plx" localfile = "File_plexon_3.plx" urllib.request.urlretrieve(distantfile, localfile) ############################################################### # Create a reader # All it takes to create a reader is giving the filename (or dirname) # Then we need to do the slow step of parsing the header with the # `parse_header` function. This collects metadata as well as # make all future steps much faster for us reader = PlexonRawIO(filename="File_plexon_3.plx") reader.parse_header() print(reader) # we can view metadata in the header print(reader.header) ############################################################### # Read signal chunks # This is how we read raw data. We choose indices that we want or # we can use None to mean look at all channels. We also need to # specify the block of data (block_index) as well as the segment # (seg_index). Then we give the index start and stop. Since we # often think in time: to go from time to index would just require # the sample rate (so index = time / sampling_rate) channel_indexes = None # could be channel_indexes = [0] raw_sigs = reader.get_analogsignal_chunk( block_index=0, seg_index=0, i_start=1024, i_stop=2048, channel_indexes=channel_indexes ) # raw_sigs are not voltages so to convert to voltages we do the follwing float_sigs = reader.rescale_signal_raw_to_float(raw_sigs, dtype="float64") # We can see that the shapes are the same, but that the datatypes # are different once we've rescaled our data print("Raw Data: ", raw_sigs.shape, raw_sigs.dtype) print("Scaled Data: ", float_sigs.shape, float_sigs.dtype) ############################################################### # Each rawio gives you access to lots of information about your data # some of this information comes from functions # other information is stored as metadata in the reader.header sampling_rate = reader.get_signal_sampling_rate() # Like above we need to indicate the block and segment t_start = reader.get_signal_t_start(block_index=0, seg_index=0) units = reader.header["signal_channels"][0]["units"] # and we can display all of this information print(f"{sampling_rate=}, {t_start=}, {units=}") #################################################################### # Some rawio's and file formats also provide information about spikes # If a rawio can't read this data it will raise an error, but lucky # for us PlexonRawIO does have spikes data!! # Count units and spikes per unit nb_unit = reader.spike_channels_count() print(f"nb_unit: {nb_unit}\n") # nb_unit stands for number of units print("spike_channel_index nb_spike") for spike_channel_index in range(nb_unit): nb_spike = reader.spike_count(block_index=0, seg_index=0, spike_channel_index=spike_channel_index) print(f"{spike_channel_index}: {nb_spike}\n") # Read spike times and rescale (just like analogsignal above) spike_timestamps = reader.get_spike_timestamps( block_index=0, seg_index=0, spike_channel_index=0, t_start=0.0, t_stop=10.0 ) print(f"{spike_timestamps.shape=}\n{spike_timestamps.dtype=}\n{spike_timestamps[:5]=}\n") spike_times = reader.rescale_spike_timestamp(spike_timestamps, dtype="float64") print(f"{spike_times.shape=}\n{spike_times.dtype=}\n{spike_times[:5]}\n") ####################################################################### # Some file formats can also give waveform information. We are lucky # again our file has waveform data!! We forms are a 3d dataset of # (nb_spike, nb_channel, nb_sample) # Read spike waveforms raw_waveforms = reader.get_spike_raw_waveforms( block_index=0, seg_index=0, spike_channel_index=0, t_start=0.0, t_stop=10.0 ) print(f"{raw_waveforms.shape=}\n{raw_waveforms.dtype=}\n{raw_waveforms[0, 0, :4]=}\n") float_waveforms = reader.rescale_waveforms_to_float(raw_waveforms, dtype="float32", spike_channel_index=0) print(f"{float_waveforms.shape=}\n{float_waveforms.dtype=}{float_waveforms[0, 0, :4]=}\n") ######################################################################### # RawIOs can also read event timestamps. But looks like our luck ran out # let's grab a new file to see this feature # Read event timestamps and times (take another file) distantfile = url_repo + "plexon/File_plexon_2.plx" localfile = "File_plexon_2.plx" urllib.request.urlretrieve(distantfile, localfile) ######################################################################### # Since this is a new file we need to read initialize our reader as well # as parse the header reader = PlexonRawIO(filename="File_plexon_2.plx") reader.parse_header() # if we look at this header we see it is different than the header above print(reader.header) ########################################################################### # Now let's look at some event data. This could be things like stimuli applied # during the course of an ephys recording nb_event_channel = reader.event_channels_count() print(f"nb_event_channel: {nb_event_channel}") # now iterate through the channels for chan_index in range(nb_event_channel): nb_event = reader.event_count(block_index=0, seg_index=0, event_channel_index=chan_index) print(f"chan_index: {chan_index} nb_event: {nb_event}\n") ############################################################################### # Finally we can get our actual event timestamps. Some file formats provide the # real timestamps (timestamps in s/ms) others have raw timestamps (in samples) # so we can do the same style of functions as above. Get the raw timestamps # and convert to real times with a rescale function. ev_timestamps, ev_durations, ev_labels = reader.get_event_timestamps( block_index=0, seg_index=0, event_channel_index=0, t_start=None, t_stop=None ) print(f"{ev_timestamps=}\n{ev_durations=}\n{ev_labels=}\n") ev_times = reader.rescale_event_timestamp(ev_timestamps, dtype="float64") print(f"{ev_times=}\n") ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_read_proxy_with_lazy_load.py0000644000175100017510000001010115154507213022516 0ustar00runnerrunner""" Demonstration of lazy load and proxy objects ============================================ """ ################################################ # Import our packages first # It is often nice to have units so we will also # import quantities import urllib import neo import quantities as pq import numpy as np ############################################### # Let's get a file # NeuralEnsemble maintains a wide variety of small test # datasets that are free to use. We can use urllib to pull # down one of these files for use url_repo = "https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/" # Get med file distantfile = url_repo + "micromed/File_micromed_1.TRC" localfile = "./File_micromed_1.TRC" urllib.request.urlretrieve(distantfile, localfile) ################################################## # create a reader # creating a reader for neo is easy it just requires using # the name of the desired reader and providing either a filename # or a directory name (reader dependent). Since we got a micromed # file we will use MicromedIO. reader = neo.MicromedIO(filename="File_micromed_1.TRC") reader.parse_header() ############################################################ # as always we can look view some interesting information about the # metadata and structure of a file just by printing the reader and # it's header print(reader) print(f"Header information: {reader.header}") ##################################################### # Now let's make a function that we want to apply to # look at lazy vs eager uses of the API def apply_my_fancy_average(sig_list): """basic average along triggers and then channels here we go back to numpy with magnitude to be able to use np.stack. Because neo uses quantities to keep track of units we can always get just the magnitude of an array with `.magnitude` """ sig_list = [s.magnitude for s in sig_list] sigs = np.stack(sig_list, axis=0) return np.mean(np.mean(sigs, axis=0), axis=1) ################################################# # Let's set our limits for both cases. We will # use quantities to include time dimensions. lim_start = -20 * pq.ms # 20 milliseconds before lim_end = +20 * pq.ms # 20 milliseconds after ################################################## # We start with eager (where `lazy=False`.) Everything # is loaded into memory. We will read a segment of data. # This includes analog signal data and events data # (final contents of a segment are dependent on the # underlying IO being used) seg = reader.read_segment(lazy=False) triggers = seg.events[0] anasig = seg.analogsignals[0] # here anasig contain the whole recording in memory all_sig_chunks = [] for t in triggers.times: t0, t1 = (t + lim_start), (t + lim_end) anasig_chunk = anasig.time_slice(t0, t1) all_sig_chunks.append(anasig_chunk) # After pulling all data into memory and then iterating through triggers # we end by doing our average m1 = apply_my_fancy_average(all_sig_chunks) ##################################################### # Here we do `lazy=True`, i.e. we do lazy loading. We # only load the data that we want into memory # and we use a proxy object for our analogsignal until we # load it chunk by chunk (no running out of memory!) seg = reader.read_segment(lazy=True) triggers = seg.events[0].load(time_slice=None) # this load all triggers in memory anasigproxy = seg.analogsignals[0] # this is a proxy all_sig_chunks = [] for t in triggers.times: t0, t1 = (t + lim_start), (t + lim_end) # at this step we load actual data into memory, but notice that we only load one # chunk of data at a time, so we reduce the memory strain anasig_chunk = anasigproxy.load(time_slice=(t0, t1)) # here real data are loaded all_sig_chunks.append(anasig_chunk) # Finally we apply the same average as we did above m2 = apply_my_fancy_average(all_sig_chunks) ########################################################## # We see that either way the result is the same, but # we do not exhaust our RAM/memory print(f"Eagerly loading data and averaging: {m1}") print(f"Lazy loading data and average {m2}") ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_roi_demo.py0000644000175100017510000000472115154507213017061 0ustar00runnerrunner""" Working with RegionOfInterest objects ===================================== """ ################################################################# # Import our packages # We can import a variety of neo objects from neo.core and since an ImageSquence # also uses units let's import quantities which is the "units" library # that neo uses under the hood import matplotlib.pyplot as plt import numpy as np from neo.core import CircularRegionOfInterest, RectangularRegionOfInterest, PolygonRegionOfInterest, ImageSequence from numpy.random import rand import random import quantities as pq ################################################################## # First we create our image_sequence. Let's generate some data # In this simulated dataset we will just make an image that is # 100x100 pixels and then we will make 50 frames of this image # finally we will fill with random values for the data l = [] for frame in range(50): l.append([]) for y in range(100): l[frame].append([]) for x in range(100): l[frame][y].append(random.randint(0, 50)) # make an ImageSquence in Neo image_seq = ImageSequence(l, sampling_rate=500 * pq.Hz, spatial_scale="m", units="V") ################################################################# # Now we will write a function for plotting an roi def plot_roi(roi, shape): img = rand(120, 100) pir = np.array(roi.pixels_in_region()).T img[pir[1], pir[0]] = 5 plt.imshow(img, cmap="gray_r") plt.clim(0, 5) ax = plt.gca() ax.add_artist(shape) ################################################################################ # Finally we will plot each roi to demonstrate how we could high regions of interest # First a nice circle roi = CircularRegionOfInterest(image_sequence=image_seq, x=50.3, y=50.8, radius=30.2) shape = plt.Circle(roi.centre, roi.radius, color="r", fill=False) plt.subplot(1, 3, 1) plot_roi(roi, shape) # Next a rectangle roi = RectangularRegionOfInterest(image_sequence=image_seq, x=50.3, y=40.2, width=40.1, height=50.3) shape = plt.Rectangle((roi.x - roi.width / 2.0, roi.y - roi.height / 2.0), roi.width, roi.height, color="r", fill=False) plt.subplot(1, 3, 2) plot_roi(roi, shape) # Finally we can make a polygon (in this case a triangle) roi = PolygonRegionOfInterest(image_seq, (20.3, 30.2), (80.7, 30.1), (55.2, 59.4)) shape = plt.Polygon(np.array(roi.vertices), closed=True, color="r", fill=False) plt.subplot(1, 3, 3) plot_roi(roi, shape) plt.tight_layout() plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/examples/plot_with_matplotlib.py0000644000175100017510000000222615154507213020464 0ustar00runnerrunner""" Plotting a Neo object with matplotlib ===================================== """ import urllib import numpy as np import quantities as pq from matplotlib import pyplot import neo distantfile = "https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/plexon/File_plexon_3.plx" localfile = "File_plexon_3.plx" urllib.request.urlretrieve(distantfile, localfile) # reader = neo.io.NeuroExplorerIO(filename='File_neuroexplorer_2.nex') reader = neo.io.PlexonIO(filename="File_plexon_3.plx") bl = reader.read(lazy=False)[0] for seg in bl.segments: print("SEG: " + str(seg.file_origin)) fig = pyplot.figure() ax1 = fig.add_subplot(2, 1, 1) ax2 = fig.add_subplot(2, 1, 2) ax1.set_title(seg.file_origin) ax1.set_ylabel("arbitrary units") mint = 0 * pq.s maxt = np.inf * pq.s for i, asig in enumerate(seg.analogsignals): times = asig.times.rescale("s").magnitude asig = asig.magnitude ax1.plot(times, asig) trains = [st.rescale("s").magnitude for st in seg.spiketrains] colors = pyplot.cm.jet(np.linspace(0, 1, len(seg.spiketrains))) ax2.eventplot(trains, colors=colors) pyplot.show() ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1773309590.519721 neo-0.14.4/neo/0000755000175100017510000000000015154507227012620 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/__init__.py0000644000175100017510000000063715154507213014732 0ustar00runnerrunner""" Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats """ import importlib.metadata # this need to be at the begining because some sub module will need the version __version__ = importlib.metadata.version("neo") import logging logging_handler = logging.StreamHandler() from neo.core import * from neo.io import * ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1773309590.523986 neo-0.14.4/neo/core/0000755000175100017510000000000015154507227013550 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/__init__.py0000644000175100017510000000355615154507213015665 0ustar00runnerrunner""" :mod:`neo.core` provides classes for storing common electrophysiological data types. Some of these classes contain raw data, such as spike trains or analog signals, while others are containers to organize other classes (including both data classes and other container classes). Classes from :mod:`neo.io` return nested data structures containing one or more classes from this module. Classes: .. autoclass:: Block .. automethod:: Block.filter .. autoclass:: Segment .. automethod:: Segment.filter .. autoclass:: Group .. autoclass:: AnalogSignal .. autoclass:: IrregularlySampledSignal .. autoclass:: ChannelView .. autoclass:: Event .. autoclass:: Epoch .. autoclass:: SpikeTrain .. autoclass:: ImageSequence .. autoclass:: RectangularRegionOfInterest .. autoclass:: CircularRegionOfInterest .. autoclass:: PolygonRegionOfInterest """ from neo.core.block import Block from neo.core.segment import Segment from neo.core.analogsignal import AnalogSignal from neo.core.irregularlysampledsignal import IrregularlySampledSignal # Import FilterClasses from neo.core import filters from neo.core.event import Event from neo.core.epoch import Epoch from neo.core.spiketrain import SpikeTrain from neo.core.imagesequence import ImageSequence from neo.core.regionofinterest import RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest from neo.core.view import ChannelView from neo.core.group import Group from neo.core.baseneo import NeoReadWriteError # Block should always be first in this list objectlist = [ Block, Segment, AnalogSignal, IrregularlySampledSignal, Event, Epoch, SpikeTrain, ImageSequence, RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest, ChannelView, Group, ] objectnames = [ob.__name__ for ob in objectlist] class_by_name = dict(zip(objectnames, objectlist)) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/analogsignal.py0000644000175100017510000007571515154507213016573 0ustar00runnerrunner""" This module implements :class:`AnalogSignal`, an array of analog signals. :class:`AnalogSignal` inherits from :class:`basesignal.BaseSignal` which derives from :class:`BaseNeo`, and from :class:`quantities.Quantity`which in turn inherits from :class:`numpy.array`. Inheritance from :class:`numpy.array` is explained here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html In brief: * Initialization of a new object from constructor happens in :meth:`__new__`. This is where user-specified attributes are set. * :meth:`__array_finalize__` is called for all new objects, including those created by slicing. This is where attributes are copied over from the old object. """ import logging from copy import deepcopy import numpy as np import quantities as pq from neo.core.baseneo import BaseNeo, MergeError, merge_annotations, intersect_annotations from neo.core.dataobject import DataObject from neo.core.basesignal import BaseSignal logger = logging.getLogger("Neo") def _get_sampling_rate(sampling_rate, sampling_period): """ Gets the sampling_rate from either the sampling_period or the sampling_rate, or makes sure they match if both are specified """ if sampling_period is None: if sampling_rate is None: raise ValueError("You must provide either the sampling rate or " + "sampling period") elif sampling_rate is None: sampling_rate = 1.0 / sampling_period elif sampling_period != 1.0 / sampling_rate: raise ValueError("The sampling_rate has to be 1/sampling_period") if not hasattr(sampling_rate, "units"): raise TypeError("Sampling rate/sampling period must have units") return sampling_rate def _new_AnalogSignalArray( cls, signal, units=None, dtype=None, copy=None, t_start=0 * pq.s, sampling_rate=None, sampling_period=None, name=None, file_origin=None, description=None, array_annotations=None, annotations=None, segment=None, ): """ A function to map AnalogSignal.__new__ to function that does not do the unit checking. This is needed for pickle to work. """ obj = cls( signal=signal, units=units, dtype=dtype, copy=copy, t_start=t_start, sampling_rate=sampling_rate, sampling_period=sampling_period, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) obj.segment = segment return obj class AnalogSignal(BaseSignal): """ Array of one or more continuous analog signals. A representation of several continuous, analog signals that have the same duration, sampling rate and start time. Basically, it is a 2D array: dim 0 is time, dim 1 is channel index Inherits from :class:`quantities.Quantity`, which in turn inherits from :class:`numpy.ndarray`. *Usage*:: >>> from neo.core import AnalogSignal >>> import quantities as pq >>> >>> sigarr = AnalogSignal([[1, 2, 3], [4, 5, 6]], units='V', ... sampling_rate=1*pq.Hz) >>> >>> sigarr >>> sigarr[:,1] >>> sigarr[1, 1] array(5) * V *Required attributes/properties*: :signal: (quantity array 2D, numpy array 2D, or list (data, channel)) The data itself. :units: (quantity units) Required if the signal is a list or NumPy array, not if it is a :class:`Quantity` :t_start: (quantity scalar) Time when signal begins :sampling_rate: *or* **sampling_period** (quantity scalar) Number of samples per unit time or interval between two samples. If both are specified, they are checked for consistency. *Recommended attributes/properties*: :name: (str) A label for the dataset. :description: (str) Text description. :file_origin: (str) Filesystem path or URL of the original data file. *Optional attributes/properties*: :dtype: (numpy dtype or str) Override the dtype of the signal array. :copy: (bool) True by default. :array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations \ for all data points Note: Any other additional arguments are assumed to be user-specific metadata and stored in :attr:`annotations`. *Properties available on this object*: :sampling_rate: (quantity scalar) Number of samples per unit time. (1/:attr:`sampling_period`) :sampling_period: (quantity scalar) Interval between two samples. (1/:attr:`quantity scalar`) :duration: (Quantity) Signal duration, read-only. (size * :attr:`sampling_period`) :t_stop: (quantity scalar) Time when signal ends, read-only. (:attr:`t_start` + :attr:`duration`) :times: (quantity 1D) The time points of each sample of the signal, read-only. (:attr:`t_start` + arange(:attr:`shape`[0])/:attr:`sampling_rate`) *Slicing*: :class:`AnalogSignal` objects can be sliced. When taking a single column (dimension 0, e.g. [0, :]) or a single element, a :class:`~quantities.Quantity` is returned. Otherwise an :class:`AnalogSignal` (actually a view) is returned, with the same metadata, except that :attr:`t_start` is changed if the start index along dimension 1 is greater than 1. Note that slicing an :class:`AnalogSignal` may give a different result to slicing the underlying NumPy array since signals are always two-dimensional. *Operations available on this object*: == != + * / """ _parent_objects = ("Segment",) _parent_attrs = ("segment",) _quantity_attr = "signal" _necessary_attrs = (("signal", pq.Quantity, 2), ("sampling_rate", pq.Quantity, 0), ("t_start", pq.Quantity, 0)) _recommended_attrs = BaseNeo._recommended_attrs def __new__( cls, signal, units=None, dtype=None, copy=None, t_start=0 * pq.s, sampling_rate=None, sampling_period=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Constructs new :class:`AnalogSignal` from data. This is called whenever a new class:`AnalogSignal` is created from the constructor, but not when slicing. __array_finalize__ is called on the new object. """ if copy is not None: raise ValueError( "`copy` is now deprecated in Neo due to removal in Quantites to support Numpy 2.0. " "In order to facilitate the deprecation copy can be set to None but will raise an " "error if set to True/False since this will silently do nothing. This argument will be completely " "removed in Neo 0.15.0. Please update your code base as necessary." ) signal = cls._rescale(signal, units=units) obj = pq.Quantity(signal, units=units, dtype=dtype).view(cls) if obj.ndim == 1: obj.shape = (-1, 1) if t_start is None: raise ValueError("`t_start` cannot be None") obj._t_start = t_start obj._sampling_rate = _get_sampling_rate(sampling_rate, sampling_period) obj.segment = None return obj def __init__( self, signal, units=None, dtype=None, copy=None, t_start=0 * pq.s, sampling_rate=None, sampling_period=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Initializes a newly constructed :class:`AnalogSignal` instance. """ # This method is only called when constructing a new AnalogSignal, # not when slicing or viewing. We use the same call signature # as __new__ for documentation purposes. Anything not in the call # signature is stored in annotations. # Calls parent __init__, which grabs universally recommended # attributes and sets up self.annotations DataObject.__init__( self, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) def __reduce__(self): """ Map the __new__ function onto _new_AnalogSignalArray, so that pickle works """ return _new_AnalogSignalArray, ( self.__class__, np.array(self), self.units, self.dtype, None, self.t_start, self.sampling_rate, self.sampling_period, self.name, self.file_origin, self.description, self.array_annotations, self.annotations, self.segment, ) def _array_finalize_spec(self, obj): """ Set default values for attributes specific to :class:`AnalogSignal`. Common attributes are defined in :meth:`__array_finalize__` in :class:`basesignal.BaseSignal`), which is called every time a new signal is created and calls this method. """ self._t_start = getattr(obj, "_t_start", 0 * pq.s) self._sampling_rate = getattr(obj, "_sampling_rate", None) return obj def __repr__(self): """ Returns a string representing the :class:`AnalogSignal`. """ return f"<{self.__class__.__name__}({super().__repr__()}, [{self.t_start}, {self.t_stop}], sampling rate: {self.sampling_rate})>" def __getitem__(self, i): """ Get the item or slice :attr:`i`. """ if isinstance(i, (int, np.integer)): # a single point in time across all channels obj = super().__getitem__(i) obj = pq.Quantity(obj.magnitude, units=obj.units) elif isinstance(i, tuple): obj = super().__getitem__(i) j, k = i if isinstance(j, (int, np.integer)): # extract a quantity array obj = pq.Quantity(obj.magnitude, units=obj.units) else: if isinstance(j, slice): if j.start: obj.t_start = self.t_start + j.start * self.sampling_period if j.step: obj.sampling_period *= j.step elif isinstance(j, np.ndarray): raise NotImplementedError("Arrays not yet supported") # in the general case, would need to return # IrregularlySampledSignal(Array) else: raise TypeError(f"{type(j)} not supported") if isinstance(k, (int, np.integer)): obj = obj.reshape(-1, 1) elif k is None: # matplotlib _check_1d() calls__getitem__ with (:, None) and # reacts appropriately if an IndexError or ValueError is raised raise IndexError("Cannot add dimensions to an AnalogSignal") obj.array_annotate(**deepcopy(self.array_annotations_at_index(k))) elif isinstance(i, slice): obj = super().__getitem__(i) if i.start: obj.t_start = self.t_start + i.start * self.sampling_period obj.array_annotations = deepcopy(self.array_annotations) elif isinstance(i, np.ndarray): # Indexing of an AnalogSignal is only consistent if the resulting number of # samples is the same for each trace. The time axis for these samples is not # guaranteed to be continuous, so returning a Quantity instead of an AnalogSignal here. new_time_dims = np.sum(i, axis=0) if len(new_time_dims) and all(new_time_dims == new_time_dims[0]): obj = np.asarray(self).T.__getitem__(i.T) obj = obj.T.reshape(self.shape[1], -1).T obj = pq.Quantity(obj, units=self.units) else: raise IndexError( "indexing of an AnalogSignals needs to keep the same number of " "sample for each trace contained" ) else: raise IndexError("index should be an integer, tuple, slice or boolean numpy array") return obj def __setitem__(self, i, value): """ Set an item or slice defined by :attr:`i` to `value`. """ # because AnalogSignals are always at least two-dimensional, # we need to handle the case where `i` is an integer if isinstance(i, int): i = slice(i, i + 1) elif isinstance(i, tuple): j, k = i if isinstance(k, int): i = (j, slice(k, k + 1)) return super().__setitem__(i, value) # sampling_rate attribute is handled as a property so type checking can # be done @property def sampling_rate(self): """ Number of samples per unit time. (1/:attr:`sampling_period`) """ return self._sampling_rate @sampling_rate.setter def sampling_rate(self, rate): """ Setter for :attr:`sampling_rate` """ if rate is None: raise ValueError("sampling_rate cannot be None") elif not hasattr(rate, "units"): raise ValueError("sampling_rate must have units") self._sampling_rate = rate # sampling_period attribute is handled as a property on underlying rate @property def sampling_period(self): """ Interval between two samples. (1/:attr:`sampling_rate`) """ return 1.0 / self.sampling_rate @sampling_period.setter def sampling_period(self, period): """ Setter for :attr:`sampling_period` """ if period is None: raise ValueError("sampling_period cannot be None") elif not hasattr(period, "units"): raise ValueError("sampling_period must have units") self.sampling_rate = 1.0 / period # t_start attribute is handled as a property so type checking can be done @property def t_start(self): """ Time when signal begins. """ return self._t_start @t_start.setter def t_start(self, start): """ Setter for :attr:`t_start` """ if start is None: raise ValueError("t_start cannot be None") self._t_start = start @property def duration(self): """ Signal duration (:attr:`size` * :attr:`sampling_period`) """ return self.shape[0] / self.sampling_rate @property def t_stop(self): """ Time when signal ends. (:attr:`t_start` + :attr:`duration`) """ return self.t_start + self.duration @property def times(self): """ The time points of each sample of the signal (:attr:`t_start` + arange(:attr:`shape`)/:attr:`sampling_rate`) """ return self.t_start + np.arange(self.shape[0]) / self.sampling_rate def __eq__(self, other): """ Equality test (==) """ if isinstance(other, AnalogSignal) and ( self.t_start != other.t_start or self.sampling_rate != other.sampling_rate ): return False return super().__eq__(other) def _check_consistency(self, other): """ Check if the attributes of another :class:`AnalogSignal` are compatible with this one. """ if isinstance(other, AnalogSignal): for attr in "t_start", "sampling_rate": if getattr(self, attr) != getattr(other, attr): raise ValueError(f"Inconsistent values of {attr}") # how to handle name and annotations? def _repr_pretty_(self, pp, cycle): """ Handle pretty-printing the :class:`AnalogSignal`. """ pp.text( f"{self.__class__.__name__} with {self.shape[1]} channels of length " f"{self.shape[0]}; units {self.units.dimensionality.string}; datatype " f"{self.dtype}" ) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) def _pp(line): pp.breakable() with pp.group(indent=1): pp.text(line) _pp(f"sampling rate: {self.sampling_rate}") _pp(f"time: {self.t_start} to {self.t_stop}") def time_index(self, t): """Return the array index (or indices) corresponding to the time (or times) `t`""" i = (t - self.t_start) * self.sampling_rate i = np.rint(i.simplified.magnitude).astype(np.int64) return i def time_slice(self, t_start, t_stop): """ Creates a new AnalogSignal corresponding to the time slice of the original AnalogSignal between times t_start, t_stop. Note, that for numerical stability reasons if t_start does not fall exactly on the time bins defined by the sampling_period it will be rounded to the nearest sampling bin. The time bin for t_stop will be chosen to make the duration of the resultant signal as close as possible to t_stop - t_start. This means that for a given duration, the size of the slice will always be the same. """ # checking start time and transforming to start index if t_start is None: i = 0 t_start = 0 * pq.s else: i = self.time_index(t_start) # checking stop time and transforming to stop index if t_stop is None: j = len(self) else: delta = (t_stop - t_start) * self.sampling_rate j = i + int(np.rint(delta.simplified.magnitude)) if (i < 0) or (j > len(self)): raise ValueError("t_start, t_stop have to be within the analog \ signal duration") # Time slicing should create a deep copy of the object obj = deepcopy(self[i:j]) obj.t_start = self.t_start + i * self.sampling_period return obj def time_shift(self, t_shift): """ Shifts a :class:`AnalogSignal` to start at a new time. Parameters ---------- t_shift: Quantity (time) Amount of time by which to shift the :class:`AnalogSignal`. Returns ------- new_sig: :class:`AnalogSignal` New instance of a :class:`AnalogSignal` object starting at t_shift later than the original :class:`AnalogSignal` (the original :class:`AnalogSignal` is not modified). """ new_sig = deepcopy(self) new_sig.t_start = new_sig.t_start + t_shift return new_sig # copy in splice is a deepcopy not a numpy copy so we can keep def splice(self, signal, copy=False): """ Replace part of the current signal by a new piece of signal. The new piece of signal will overwrite part of the current signal starting at the time given by the new piece's `t_start` attribute. The signal to be spliced in must have the same physical dimensions, sampling rate, and number of channels as the current signal and fit within it. If `copy` is False (the default), modify the current signal in place. If `copy` is True, return a new signal and leave the current one untouched. In this case, the new signal will not be linked to any parent objects. """ if signal.t_start < self.t_start: raise ValueError("Cannot splice earlier than the start of the signal") if signal.t_stop > self.t_stop: raise ValueError("Splice extends beyond signal") if signal.sampling_rate != self.sampling_rate: raise ValueError("Sampling rates do not match") i = self.time_index(signal.t_start) j = i + signal.shape[0] if copy: new_signal = deepcopy(self) new_signal.segment = None new_signal[i:j, :] = signal return new_signal else: self[i:j, :] = signal return self def downsample(self, downsampling_factor, **kwargs): """ Downsample the data of a signal. This method reduces the number of samples of the AnalogSignal to a fraction of the original number of samples, defined by `downsampling_factor`. This method is a wrapper of scipy.signal.decimate and accepts the same set of keyword arguments, except for specifying the axis of resampling, which is fixed to the first axis here. Parameters ---------- downsampling_factor: int Factor used for decimation of samples. Scipy recommends to call decimate multiple times for downsampling factors higher than 13 when using IIR downsampling (default). Returns ------- downsampled_signal: :class:`AnalogSignal` New instance of a :class:`AnalogSignal` object containing the resampled data points. The original :class:`AnalogSignal` is not modified. Notes ----- For resampling the signal with a fixed number of samples, see `resample` method. """ try: import scipy.signal except ImportError as err: raise ImportError("Decimating requires availability of scipy.signal") # Resampling is only permitted along the time axis (axis=0) if "axis" in kwargs: kwargs.pop("axis") downsampled_data = scipy.signal.decimate(self.magnitude, downsampling_factor, axis=0, **kwargs) downsampled_signal = self.duplicate_with_new_data(downsampled_data) # since the number of channels stays the same, we can also copy array annotations here downsampled_signal.array_annotations = self.array_annotations.copy() downsampled_signal.sampling_rate = self.sampling_rate / downsampling_factor return downsampled_signal def resample(self, sample_count, **kwargs): """ Resample the data points of the signal. This method interpolates the signal and returns a new signal with a fixed number of samples defined by `sample_count`. This method is a wrapper of scipy.signal.resample and accepts the same set of keyword arguments, except for specifying the axis of resampling which is fixed to the first axis here, and the sample positions. . Parameters ---------- sample_count: int Number of desired samples. The resulting signal starts at the same sample as the original and is sampled regularly. Returns ------- resampled_signal: :class:`AnalogSignal` New instance of a :class:`AnalogSignal` object containing the resampled data points. The original :class:`AnalogSignal` is not modified. Notes ----- For reducing the number of samples to a fraction of the original, see `downsample` method """ try: import scipy.signal except ImportError as err: raise ImportError("Decimating requires availability of scipy.signal") # Resampling is only permitted along the time axis (axis=0) if "axis" in kwargs: kwargs.pop("axis") if "t" in kwargs: kwargs.pop("t") resampled_data, resampled_times = scipy.signal.resample( self.magnitude, sample_count, t=self.times, axis=0, **kwargs ) resampled_signal = self.duplicate_with_new_data(resampled_data) resampled_signal.sampling_rate = (sample_count / self.shape[0]) * self.sampling_rate # since the number of channels stays the same, we can also copy array annotations here resampled_signal.array_annotations = self.array_annotations.copy() return resampled_signal def rectify(self, **kwargs): """ Rectify the signal. This method rectifies the signal by taking the absolute value. This method is a wrapper of numpy.absolute() and accepts the same set of keyword arguments. Returns ------- resampled_signal: :class:`AnalogSignal` New instance of a :class:`AnalogSignal` object containing the rectified data points. The original :class:`AnalogSignal` is not modified. """ # Use numpy to get the absolute value of the signal rectified_data = np.absolute(self.magnitude, **kwargs) rectified_signal = self.duplicate_with_new_data(rectified_data) # the sampling rate stays constant rectified_signal.sampling_rate = self.sampling_rate # since the number of channels stays the same, we can also copy array annotations here rectified_signal.array_annotations = self.array_annotations.copy() return rectified_signal def concatenate(self, *signals, overwrite=False, padding=False): """ Concatenate multiple neo.AnalogSignal objects across time. Units, sampling_rate and number of signal traces must be the same for all signals. Otherwise a ValueError is raised. Note that timestamps of concatenated signals might shift in oder to align the sampling times of all signals. Parameters ---------- signals: neo.AnalogSignal objects AnalogSignals that will be concatenated overwrite : bool If True, samples of the earlier (lower index in `signals`) signals are overwritten by that of later (higher index in `signals`) signals. If False, samples of the later are overwritten by earlier signal. Default: False padding : bool, scalar quantity Sampling values to use as padding in case signals do not overlap. If False, do not apply padding. Signals have to align or overlap. If True, signals will be padded using np.NaN as pad values. If a scalar quantity is provided, this will be used for padding. The other signal is moved forward in time by maximum one sampling period to align the sampling times of both signals. Default: False Returns ------- signal: neo.AnalogSignal concatenated output signal """ # Sanity of inputs if not hasattr(signals, "__iter__"): raise TypeError("signals must be iterable") if not all([isinstance(a, AnalogSignal) for a in signals]): raise TypeError("Entries of anasiglist have to be of type neo.AnalogSignal") if len(signals) == 0: return self signals = [self] + list(signals) # Check required common attributes: units, sampling_rate and shape[-1] shared_attributes = ["units", "sampling_rate"] attribute_values = [tuple((getattr(anasig, attr) for attr in shared_attributes)) for anasig in signals] # add shape dimensions that do not relate to time attribute_values = [(attribute_values[i] + (signals[i].shape[1:],)) for i in range(len(signals))] if not all([attrs == attribute_values[0] for attrs in attribute_values]): raise MergeError(f"AnalogSignals have to share {shared_attributes} attributes to be concatenated.") units, sr, shape = attribute_values[0] # find gaps between Analogsignals combined_time_ranges = self._concatenate_time_ranges([(s.t_start, s.t_stop) for s in signals]) missing_time_ranges = self._invert_time_ranges(combined_time_ranges) if len(missing_time_ranges): diffs = np.diff(np.asarray(missing_time_ranges), axis=1) else: diffs = [] if padding is False and any(diffs > signals[0].sampling_period): raise MergeError( f"Signals are not continuous. Can not concatenate signals with gaps. " f"Please provide a padding value." ) if padding is not False: logger.warning(f"Signals will be padded using {padding}.") if padding is True: padding = np.nan * units if isinstance(padding, pq.Quantity): padding = padding.rescale(units).magnitude else: raise MergeError( "Invalid type of padding value. Please provide a bool value " "or a quantities object." ) t_start = min([a.t_start for a in signals]) t_stop = max([a.t_stop for a in signals]) n_samples = int(np.rint(((t_stop - t_start) * sr).rescale("dimensionless").magnitude)) shape = (n_samples,) + shape # Collect attributes and annotations across all concatenated signals kwargs = {} common_annotations = signals[0].annotations common_array_annotations = signals[0].array_annotations for anasig in signals[1:]: common_annotations = intersect_annotations(common_annotations, anasig.annotations) common_array_annotations = intersect_annotations(common_array_annotations, anasig.array_annotations) kwargs["annotations"] = common_annotations kwargs["array_annotations"] = common_array_annotations for name in ("name", "description", "file_origin"): attr = [getattr(s, name) for s in signals] if all([a == attr[0] for a in attr]): kwargs[name] = attr[0] else: kwargs[name] = f"concatenation ({attr})" conc_signal = AnalogSignal( np.full(shape=shape, fill_value=padding, dtype=signals[0].dtype), sampling_rate=sr, t_start=t_start, units=units, **kwargs, ) if not overwrite: signals = signals[::-1] while len(signals) > 0: conc_signal.splice(signals.pop(0), copy=False) return conc_signal def _concatenate_time_ranges(self, time_ranges): time_ranges = sorted(time_ranges) new_ranges = time_ranges[:1] for t_start, t_stop in time_ranges[1:]: # time range are non continuous -> define new range if t_start > new_ranges[-1][1]: new_ranges.append((t_start, t_stop)) # time range is continuous -> extend time range elif t_stop > new_ranges[-1][1]: new_ranges[-1] = (new_ranges[-1][0], t_stop) return new_ranges def _invert_time_ranges(self, time_ranges): i = 0 new_ranges = [] while i < len(time_ranges) - 1: new_ranges.append((time_ranges[i][1], time_ranges[i + 1][0])) i += 1 return new_ranges ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/baseneo.py0000644000175100017510000003502115154507213015532 0ustar00runnerrunner""" This module defines :class:`BaseNeo`, the abstract base class used by all :module:`neo.core` classes. """ from copy import deepcopy from datetime import datetime, date, time, timedelta from decimal import Decimal import logging from numbers import Number import numpy as np ALLOWED_ANNOTATION_TYPES = ( int, float, complex, str, bytes, type(None), datetime, date, time, timedelta, Number, Decimal, np.number, np.bool_, ) logger = logging.getLogger("Neo") class MergeError(Exception): pass class NeoReadWriteError(IOError): """ This is the main neo-specific error that has to deal with ephys data that does not follow the requirements for the Neo object hierarchy. It should be used for: 1) When an IO does not support a specific read/write functionality 2) A file format does not support the structural requirements * Different sampling rates among streams * Different expectations for a file format (could also be a NotImplementedError depending on circumstances) It should NOT be used when other errors more accurately describe the problem: 1) ValueError: for incorrect values (like t_start, t_stop) 2) TypeError: use of an inappropriate type for an argument 3) FileNotFoundError: for use when a file is not found """ pass def _check_annotations(value): """ Recursively check that value is either of a "simple" type (number, string, date/time) or is a (possibly nested) dict, list or numpy array containing only simple types. """ if isinstance(value, np.ndarray): if not issubclass(value.dtype.type, ALLOWED_ANNOTATION_TYPES): raise ValueError(f"Invalid annotation. NumPy arrays with dtype {value.dtype.type}" f"are not allowed") elif isinstance(value, dict): for key, element in value.items(): if not isinstance(key, str): raise TypeError(f"Annotations keys must be strings not of type {type(key)}") _check_annotations(element) elif isinstance(value, (list, tuple)): for element in value: _check_annotations(element) elif not isinstance(value, ALLOWED_ANNOTATION_TYPES): raise ValueError(f"Invalid annotation. Annotations of type {type(value)} are not" f"allowed") def merge_annotation(a, b): """ First attempt at a policy for merging annotations (intended for use with parallel computations using MPI). This policy needs to be discussed further, or we could allow the user to specify a policy. Current policy: For arrays or lists: concatenate For dicts: merge recursively For strings: concatenate with ';' Otherwise: fail if the annotations are not equal """ if type(a) != type(b): raise TypeError(f"type({a}) {type(a)} != type({b}) {type(b)}") if isinstance(a, dict): return merge_annotations(a, b) elif isinstance(a, np.ndarray): # concatenate b to a return np.append(a, b) elif isinstance(a, list): # concatenate b to a return a + b elif isinstance(a, str): if a == b: return a else: return a + ";" + b else: if a != b: raise ValueError(f"{a} != {b}") return a def merge_annotations(A, *Bs): """ Merge two sets of annotations. Merging follows these rules: All keys that are in A or B, but not both, are kept. For keys that are present in both: For arrays or lists: concatenate For dicts: merge recursively For strings: concatenate with ';' Otherwise: warn if the annotations are not equal """ merged = A.copy() for B in Bs: for name in B: if name not in merged: merged[name] = B[name] else: try: merged[name] = merge_annotation(merged[name], B[name]) except BaseException as exc: # exc.args += ('key %s' % name,) # raise merged[name] = "MERGE CONFLICT" # temporary hack logger.debug(f"Merging annotations: A={A} Bs={Bs} merged={merged}") return merged def intersect_annotations(A, B): """ Identify common entries in dictionaries A and B and return these in a separate dictionary. Entries have to share key as well as value to be considered common. Parameters ---------- A, B : dict Dictionaries to merge. """ result = {} for key in set(A.keys()) & set(B.keys()): v1, v2 = A[key], B[key] if type(v1) != type(v2): raise TypeError(f"type({v1}) {type(v1)} != type({v2}) {type(v2)}") if isinstance(v1, dict) and v1 == v2: result[key] = deepcopy(v1) elif isinstance(v1, str) and v1 == v2: result[key] = A[key] elif isinstance(v1, list) and v1 == v2: result[key] = deepcopy(v1) elif isinstance(v1, np.ndarray) and all(v1 == v2): result[key] = deepcopy(v1) return result def _reference_name(class_name): """ Given the name of a class, return an attribute name to be used for references to instances of that class. For example, a Segment object has a parent Block object, referenced by `segment.block`. The attribute name `block` is obtained by calling `_container_name("Block")`. """ return class_name.lower() def _container_name(class_name): """ Given the name of a class, return an attribute name to be used for lists (or other containers) containing instances of that class. For example, a Block object contains a list of Segment objects, referenced by `block.segments`. The attribute name `segments` is obtained by calling `_container_name_plural("Segment")`. """ if "RegionOfInterest" in class_name: # this is a hack, pending a more principled way to handle this return "regionsofinterest" else: return _reference_name(class_name) + "s" class BaseNeo: """ This is the base class from which all Neo objects inherit. This class implements support for universally recommended arguments, and also sets up the :attr:`annotations` dict for additional arguments. Each class can define one or more of the following class attributes: :_parent_objects: Neo objects that can be parents of this object. Note that no Neo object can have more than one parent. An instance attribute named class.__name__.lower() will be automatically defined to hold this parent and will be initialized to None. :_necessary_attrs: A list of tuples containing the attributes that the class must have. The tuple can have 2-4 elements. The first element is the attribute name. The second element is the attribute type. The third element is the number of dimensions (only for numpy arrays and quantities). The fourth element is the dtype of array (only for numpy arrays and quantities). This does NOT include the attributes holding the parents or children of the object. :_recommended_attrs: A list of tuples containing the attributes that the class may optionally have. It uses the same structure as :_necessary_attrs: :_repr_pretty_attrs_keys_: The names of attributes printed when pretty-printing using iPython. The following helper properties are available: :_parent_containers: The names of the container attributes used to store :_parent_objects: :parents: All objects that are parents of the current object. :_all_attrs: All required and optional attributes. :_necessary_attrs: + :_recommended_attrs: The following "universal" methods are available: :__init__: Grabs the universally recommended arguments :attr:`name`, :attr:`file_origin`, and :attr:`description` and stores them as attributes. Also takes every additional argument (that is, every argument that is not handled by :class:`BaseNeo` or the child class), and puts in the dict :attr:`annotations`. :annotate(**args): Updates :attr:`annotations` with keyword/value pairs. :merge(**args): Merge the contents of another object into this one. The merge method implemented here only merges annotations (see :merge_annotations:). Subclasses should implementt their own merge rules. :merge_annotations(**args): Merge the :attr:`annotations` of another object into this one. Each child class should: 0) describe its parents (if any) and attributes in the relevant class attributes. :_recommended_attrs: should append BaseNeo._recommended_attrs to the end. 1) call BaseNeo.__init__(self, name=name, description=description, file_origin=file_origin, **annotations) with the universal recommended arguments, plus optional annotations 2) process its required arguments in its __new__ or __init__ method 3) process its non-universal recommended arguments (in its __new__ or __init__ method Non-keyword arguments should only be used for required arguments. The required and recommended arguments for each child class (Neo object) are specified in the _necessary_attrs and _recommended_attrs attributes and documentation for the child object. """ # these attributes control relationships, they need to be # specified in each child class # Parent objects whose children can have a single parent _parent_objects = () # Attribute names corresponding to _parent_objects _parent_attrs = () # Attributes that an instance is required to have defined _necessary_attrs = () # Attributes that an instance may or may have defined _recommended_attrs = (("name", str), ("description", str), ("file_origin", str)) # Attributes that are used for pretty-printing _repr_pretty_attrs_keys_ = ("name", "description", "annotations") is_view = False def __init__(self, name=None, description=None, file_origin=None, **annotations): """ This is the base constructor for all Neo objects. Stores universally recommended attributes and creates :attr:`annotations` from additional arguments not processed by :class:`BaseNeo` or the child class. """ # create `annotations` for additional arguments _check_annotations(annotations) self.annotations = annotations # these attributes are recommended for all objects. self.name = name self.description = description self.file_origin = file_origin # initialize parent containers for parent in self._parent_containers: setattr(self, parent, None) def annotate(self, **annotations): """ Add annotations (non-standardized metadata) to a Neo object. Example: >>> obj.annotate(key1=value0, key2=value1) >>> obj.key2 value2 """ _check_annotations(annotations) self.annotations.update(annotations) def _has_repr_pretty_attrs_(self): return any(getattr(self, k) for k in self._repr_pretty_attrs_keys_) def _repr_pretty_attrs_(self, pp, cycle): first = True for key in self._repr_pretty_attrs_keys_: value = getattr(self, key) if value: if first: first = False else: pp.breakable() with pp.group(indent=1): pp.text(f"{key}: ") pp.pretty(value) def _repr_pretty_(self, pp, cycle): """ Handle pretty-printing the :class:`BaseNeo`. """ pp.text(self.__class__.__name__) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) @property def _parent_containers(self): """ Containers for parent objects. """ return tuple([_reference_name(parent) for parent in self._parent_objects]) @property def parents(self): """ All parent objects storing the current object. """ return tuple([getattr(self, attr) for attr in self._parent_containers]) @property def _all_attrs(self): """ Returns a combination of all required and recommended attributes. """ return self._necessary_attrs + self._recommended_attrs def merge_annotations(self, *others): """ Merge annotations from the other object into this one. Merging follows these rules: All keys that are in the either object, but not both, are kept. For keys that are present in both objects: For arrays or lists: concatenate the two arrays For dicts: merge recursively For strings: concatenate with ';' Otherwise: fail if the annotations are not equal """ other_annotations = [other.annotations for other in others] merged_annotations = merge_annotations(self.annotations, *other_annotations) self.annotations.update(merged_annotations) def merge(self, *others): """ Merge the contents of another object into this one. See :meth:`merge_annotations` for details of the merge operation. """ self.merge_annotations(*others) def set_parent(self, obj): """ Set the appropriate "parent" attribute of this object according to the type of "obj" """ if obj.__class__.__name__ not in self._parent_objects: raise TypeError( ( f"{self.__class__.__name__} can only have parents of " f"type {self._parwents_objects}, not {obj.__class__.__name__}" ) ) loc = self._parent_objects.index(obj.__class__.__name__) parent_attr = self._parent_attrs[loc] setattr(self, parent_attr, obj) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/basesignal.py0000644000175100017510000002655015154507213016235 0ustar00runnerrunner""" This module implements :class:`BaseSignal`, an array of signals. This is a parent class from which all signal objects inherit: :class:`AnalogSignal` and :class:`IrregularlySampledSignal` :class:`BaseSignal` inherits from :class:`quantities.Quantity`, which inherits from :class:`numpy.array`. Inheritance from :class:`numpy.array` is explained here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html In brief: * Constructor :meth:`__new__` for :class:`BaseSignal` doesn't exist. Only child objects :class:`AnalogSignal` and :class:`IrregularlySampledSignal` can be created. """ import logging from copy import deepcopy import numpy as np import quantities as pq from neo.core.baseneo import MergeError, merge_annotations from neo.core.dataobject import DataObject, ArrayDict logger = logging.getLogger("Neo") class BaseSignal(DataObject): """ This is the base class from which all signal objects inherit: :class:`AnalogSignal` and :class:`IrregularlySampledSignal`. This class contains all common methods of both child classes. It uses the following child class attributes: :_necessary_attrs: a list of the attributes that the class must have. :_recommended_attrs: a list of the attributes that the class may optionally have. """ def _array_finalize_spec(self, obj): """ Called by :meth:`__array_finalize__`, used to customize behaviour of sub-classes. """ return obj def __array_finalize__(self, obj): """ This is called every time a new signal is created. It is the appropriate place to set default values for attributes for a signal constructed by slicing or viewing. User-specified values are only relevant for construction from constructor, and these are set in __new__ in the child object. Then they are just copied over here. Default values for the specific attributes for subclasses (:class:`AnalogSignal` and :class:`IrregularlySampledSignal`) are set in :meth:`_array_finalize_spec` """ super().__array_finalize__(obj) self._array_finalize_spec(obj) # The additional arguments self.annotations = getattr(obj, "annotations", {}) # Add empty array annotations, because they cannot always be copied, # but do not overwrite existing ones from slicing etc. # This ensures the attribute exists if not hasattr(self, "array_annotations"): self.array_annotations = ArrayDict(self._get_arr_ann_length()) # Globally recommended attributes self.name = getattr(obj, "name", None) self.file_origin = getattr(obj, "file_origin", None) self.description = getattr(obj, "description", None) # Parent objects self.segment = getattr(obj, "segment", None) @classmethod def _rescale(self, signal, units=None): """ Check that units are present, and rescale the signal if necessary. This is called whenever a new signal is created from the constructor. See :meth:`__new__' in :class:`AnalogSignal` and :class:`IrregularlySampledSignal` """ if units is None: if not hasattr(signal, "units"): raise ValueError("Units must be specified") elif isinstance(signal, pq.Quantity): # This test always returns True, i.e. rescaling is always executed if one of the units # is a pq.CompoundUnit. This is fine because rescaling is correct anyway. if pq.quantity.validate_dimensionality(units) != signal.dimensionality: signal = signal.rescale(units) return signal def __getslice__(self, i, j): """ Get a slice from :attr:`i` to :attr:`j`.attr[0] Doesn't get called in Python 3, :meth:`__getitem__` is called instead """ return self.__getitem__(slice(i, j)) def __ne__(self, other): """ Non-equality test (!=) """ return not self.__eq__(other) def _apply_operator(self, other, op, *args): """ Handle copying metadata to the new signal after a mathematical operation. """ self._check_consistency(other) f = getattr(super(), op) new_signal = f(other, *args) new_signal._copy_data_complement(self) # _copy_data_complement can't always copy array annotations, # so this needs to be done locally new_signal.array_annotations = deepcopy(self.array_annotations) return new_signal def _get_required_attributes(self, signal, units): """ Return a list of the required attributes for a signal as a dictionary """ required_attributes = {} for attr in self._necessary_attrs: if attr[0] == "signal": required_attributes["signal"] = signal elif attr[0] == "image_data": required_attributes["image_data"] = signal elif attr[0] == "t_start": required_attributes["t_start"] = getattr(self, "t_start", 0.0 * pq.ms) else: required_attributes[str(attr[0])] = getattr(self, attr[0], None) required_attributes["units"] = units return required_attributes def duplicate_with_new_data(self, signal, units=None): """ Create a new signal with the same metadata but different data. Required attributes of the signal are used. Note: Array annotations can not be copied here because length of data can change """ if units is None: units = self.units # else: # units = pq.quantity.validate_dimensionality(units) # signal is the new signal required_attributes = self._get_required_attributes(signal, units) new = self.__class__(**required_attributes) new._copy_data_complement(self) new.annotations.update(self.annotations) # Note: Array annotations are not copied here, because it is not ensured # that the same number of signals is used and they would possibly make no sense # when combined with another signal return new def _copy_data_complement(self, other): """ Copy the metadata from another signal. Required and recommended attributes of the signal are used. Note: Array annotations can not be copied here because length of data can change """ all_attr = {self._recommended_attrs, self._necessary_attrs} for sub_at in all_attr: for attr in sub_at: if attr[0] == "t_start": setattr(self, attr[0], deepcopy(getattr(other, attr[0], 0.0 * pq.ms))) elif attr[0] != "signal": setattr(self, attr[0], deepcopy(getattr(other, attr[0], None))) setattr(self, "annotations", deepcopy(getattr(other, "annotations", None))) # Note: Array annotations cannot be copied because length of data can be changed # here # which would cause inconsistencies def __rsub__(self, other, *args): """ Backwards subtraction (other-self) """ return self.__mul__(-1, *args) + other def __add__(self, other, *args): """ Addition (+) """ return self._apply_operator(other, "__add__", *args) def __sub__(self, other, *args): """ Subtraction (-) """ return self._apply_operator(other, "__sub__", *args) def __mul__(self, other, *args): """ Multiplication (*) """ return self._apply_operator(other, "__mul__", *args) def __truediv__(self, other, *args): """ Float division (/) """ return self._apply_operator(other, "__truediv__", *args) def __div__(self, other, *args): """ Integer division (//) """ return self._apply_operator(other, "__div__", *args) __radd__ = __add__ __rmul__ = __sub__ def merge(self, other): """ Merge another signal into this one. The signal objects are concatenated horizontally (column-wise, :func:`np.hstack`). If the attributes of the two signal are not compatible, an Exception is raised. Required attributes of the signal are used. """ for attr in self._necessary_attrs: if "signal" != attr[0]: if getattr(self, attr[0], None) != getattr(other, attr[0], None): raise MergeError(f"Cannot merge these two signals as the {attr[0]} differ.") if self.segment != other.segment: raise MergeError("Cannot merge these two signals as they belong to different segments.") if hasattr(self, "lazy_shape"): if hasattr(other, "lazy_shape"): if self.lazy_shape[0] != other.lazy_shape[0]: raise MergeError("Cannot merge signals of different length.") merged_lazy_shape = (self.lazy_shape[0], self.lazy_shape[1] + other.lazy_shape[1]) else: raise MergeError("Cannot merge a lazy object with a real object.") if other.units != self.units: other = other.rescale(self.units) stack = np.hstack((self.magnitude, other.magnitude)) kwargs = {} for name in ("name", "description", "file_origin"): attr_self = getattr(self, name) attr_other = getattr(other, name) if attr_self == attr_other: kwargs[name] = attr_self else: kwargs[name] = f"merge({attr_self}, {attr_other})" merged_annotations = merge_annotations(self.annotations, other.annotations) kwargs.update(merged_annotations) kwargs["array_annotations"] = self._merge_array_annotations(other) signal = self.__class__( stack, units=self.units, dtype=self.dtype, copy=None, t_start=self.t_start, sampling_rate=self.sampling_rate, **kwargs, ) signal.segment = self.segment if hasattr(self, "lazy_shape"): signal.lazy_shape = merged_lazy_shape return signal def time_slice(self, t_start, t_stop): """ Creates a new AnalogSignal corresponding to the time slice of the original Signal between times t_start, t_stop. """ NotImplementedError("Needs to be implemented for subclasses.") def concatenate(self, *signals): """ Concatenate multiple signals across time. The signal objects are concatenated vertically (row-wise, :func:`np.vstack`). Concatenation can be used to combine signals across segments. Note: Only (array) annotations common to both signals are attached to the concatenated signal. If the attributes of the signals are not compatible, an Exception is raised. Parameters ---------- signals : multiple neo.BaseSignal objects The objects that is concatenated with this one. Returns ------- signal : neo.BaseSignal Signal containing all non-overlapping samples of the source signals. Raises ------ MergeError If `other` object has incompatible attributes. """ NotImplementedError("Patching need to be implemented in subclasses") ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/block.py0000644000175100017510000001123115154507213015205 0ustar00runnerrunner""" This module defines :class:`Block`, the main container gathering all the data, whether discrete or continuous, for a given recording session. base class used by all :module:`neo.core` classes. :class:`Block` derives from :class:`Container`, from :module:`neo.core.container`. """ from datetime import datetime from neo.core.container import Container, unique_objs from neo.core.group import Group from neo.core.objectlist import ObjectList from neo.core.segment import Segment class Block(Container): """ Main container gathering all the data, whether discrete or continuous, for a given recording session. A block is not necessarily temporally homogeneous, in contrast to :class:`Segment`. *Usage*:: >>> from neo.core import Block, Segment, Group, AnalogSignal >>> from quantities import nA, kHz >>> import numpy as np >>> >>> # create a Block with 3 Segment and 2 Group objects ,,, blk = Block() >>> for ind in range(3): ... seg = Segment(name='segment %d' % ind, index=ind) ... blk.segments.append(seg) ... >>> for ind in range(2): ... group = Group(name='Array probe %d' % ind) ... blk.groups.append(group) ... >>> # Populate the Block with AnalogSignal objects ... for seg in blk.segments: ... for group in blk.groups: ... a = AnalogSignal(np.random.randn(10000, 64)*nA, ... sampling_rate=10*kHz) ... group.analogsignals.append(a) ... seg.analogsignals.append(a) *Required attributes/properties*: None *Recommended attributes/properties*: :name: (str) A label for the dataset. :description: (str) Text description. :file_origin: (str) Filesystem path or URL of the original data file. :file_datetime: (datetime) The creation date and time of the original data file. :rec_datetime: (datetime) The date and time of the original recording. Note: Any other additional arguments are assumed to be user-specific metadata and stored in :attr:`annotations`. *Container of*: :class:`Segment` :class:`Group` """ _container_child_objects = ("Segment", "Group") _recommended_attrs = ( ("file_datetime", datetime), ("rec_datetime", datetime), ("index", int), ) + Container._recommended_attrs _repr_pretty_attrs_keys_ = Container._repr_pretty_attrs_keys_ + ( "file_origin", "file_datetime", "rec_datetime", "index", ) _repr_pretty_containers = ("segments",) def __init__( self, name=None, description=None, file_origin=None, file_datetime=None, rec_datetime=None, index=None, **annotations, ): """ Initialize a new :class:`Block` instance. """ super().__init__(name=name, description=description, file_origin=file_origin, **annotations) self.file_datetime = file_datetime self.rec_datetime = rec_datetime self.index = index self._segments = ObjectList(Segment, parent=self) self._groups = ObjectList(Group, parent=self) segments = property( fget=lambda self: self._get_object_list("_segments"), fset=lambda self, value: self._set_object_list("_segments", value), doc="list of Segments contained in this block", ) groups = property( fget=lambda self: self._get_object_list("_groups"), fset=lambda self, value: self._set_object_list("_groups", value), doc="list of Groups contained in this block", ) @property def data_children_recur(self): """ All data child objects stored in the current object, obtained recursively. """ # subclassing this to remove duplicate objects such as SpikeTrain # objects in both Segment and Group # Only Block can have duplicate items right now, so implement # this here for performance reasons. return tuple(unique_objs(super().data_children_recur)) def list_children_by_class(self, cls): """ List all children of a particular class recursively. You can either provide a class object, a class name, or the name of the container storing the class. """ # subclassing this to remove duplicate objects such as SpikeTrain # objects in both Segment and Group # Only Block can have duplicate items right now, so implement # this here for performance reasons. return unique_objs(super().list_children_by_class(cls)) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/container.py0000644000175100017510000005625115154507213016110 0ustar00runnerrunner""" This module implements generic container base class that all neo container object inherit from. It provides shared methods for all container types. :class:`Container` is derived from :class:`BaseNeo` """ from copy import deepcopy from neo.core import filters from neo.core.baseneo import BaseNeo, _reference_name, _container_name from neo.core.objectlist import ObjectList from neo.core.spiketrain import SpikeTrain from neo.core.spiketrainlist import SpikeTrainList def unique_objs(objs): """ Return a list of objects in the list objs where all objects are unique using the "is" test. """ seen = set() return [obj for obj in objs if id(obj) not in seen and not seen.add(id(obj))] def filterdata(data, targdict=None, objects=None, **kwargs): """ Return a list of the objects in data matching *any* of the search terms in either their attributes or annotations. Search terms can be provided as keyword arguments or a dictionary, either as a positional argument after data or to the argument targdict. A key of a provided dictionary is the name of the requested annotation and the value is a FilterCondition object. E.g.: Equal(x), LessThan(x), InRange(x, y). targdict can also be a list of dictionaries, in which case the filters are applied sequentially. A list of dictionaries is handled as follows: [ { or } and { or } ] If targdict and kwargs are both supplied, the targdict filters are applied first, followed by the kwarg filters. A targdict of None or {} corresponds to no filters applied, therefore returning all child objects. Default targdict is None. """ # if objects are specified, get the classes if objects: if hasattr(objects, "lower") or isinstance(objects, type): objects = [objects] elif objects is not None: return [] # handle cases with targdict if targdict is None: targdict = kwargs elif not kwargs: pass elif hasattr(targdict, "keys"): targdict = [targdict, kwargs] else: targdict += [kwargs] if not targdict: results = data # if multiple dicts are provided, apply each filter sequentially elif not hasattr(targdict, "keys"): # for performance reasons, only do the object filtering on the first # iteration results = filterdata(data, targdict=targdict[0], objects=objects) for targ in targdict[1:]: results = filterdata(results, targdict=targ) return results else: # do the actual filtering results = [] for obj in data: for key, value in sorted(targdict.items()): if hasattr(obj, key) and getattr(obj, key) == value: results.append(obj) break if isinstance(value, filters.FilterCondition) and key in obj.annotations: if value.evaluate(obj.annotations[key]): results.append(obj) break if key in obj.annotations and obj.annotations[key] == value: results.append(obj) break # remove duplicates from results results = list({id(res): res for res in results}.values()) # keep only objects of the correct classes if objects: results = [result for result in results if result.__class__ in objects or result.__class__.__name__ in objects] if results and all(isinstance(obj, SpikeTrain) for obj in results): return SpikeTrainList(results) else: return results class Container(BaseNeo): """ This is the base class from which Neo container objects inherit. It derives from :class:`BaseNeo`. In addition to the setup :class:`BaseNeo` does, this class also automatically sets up the lists to hold the children of the object. Each class can define one or more of the following class attributes (in addition to those of BaseNeo): :_container_child_objects: Neo container objects that can be children of this object. An instance attribute named class.__name__.lower()+'s' will be automatically defined to hold this child and will be initialized to an empty list. :_data_child_objects: Neo data objects that can be children of this object. An instance attribute named class.__name__.lower()+'s' will be automatically defined to hold this child and will be initialized to an empty list. :_repr_pretty_containers: The names of containers attributes printed when pretty-printing using IPython. The following helper properties are available (in addition to those of BaseNeo): :_child_objects: All neo objects that can be children of this object. :_container_child_objects: + :_data_child_objects: :_container_child_containers: The names of the container attributes used to store :_container_child_objects: :_data_child_containers: The names of the container attributes used to store :_data_child_objects: :_single_child_containers: The names of the container attributes used to store :_single_child_objects: :_child_containers: All child container attributes. Same as :_single_child_containers: :_single_children: All objects that are children of the current object where the child can only have one parent of this type. :data_children: All data objects that are children of the current object. :container_children: All container objects that are children of the current object. :children: All Neo objects that are children of the current object. :data_children_recur: All data objects that are children of the current object or any of its children, any of its children's children, etc. :container_children_recur: All container objects that are children of the current object or any of its children, any of its children's children, etc. :children_recur: All Neo objects that are children of the current object or any of its children, any of its children's children, etc. The following "universal" methods are available (in addition to those of BaseNeo): :size: A dictionary where each key is an attribute storing child objects and the value is the number of objects stored in that attribute. :filter(**args): Retrieves children of the current object that have particular properties. :list_children_by_class(**args): Retrieves all children of the current object recursively that are of a particular class. :create_relationship(**args): For each child of the current object, set its parent to be the current object. :merge(**args): Annotations are merged based on the rules of :merge_annotations:. Child objects with the same name and a :merge: method are merged using that method. Other child objects are appended to the relevant container attribute. Parents attributes are NOT changed in this operation. Unlike :BaseNeo.merge:, this method implements all necessary merge rules for a container class. Each child class should: 0) call Container.__init__(self, name=name, description=description, file_origin=file_origin, **annotations) with the universal recommended arguments, plus optional annotations 1) process its required arguments in its __new__ or __init__ method 2) process its non-universal recommended arguments (in its __new__ or __init__ method """ # Child objects that are a container _container_child_objects = () # Child objects that have data _data_child_objects = () # Containers that are listed when pretty-printing _repr_pretty_containers = () def __init__(self, name=None, description=None, file_origin=None, **annotations): """ Initialize a new :class:`Container` instance. """ super().__init__(name=name, description=description, file_origin=file_origin, **annotations) def _get_object_list(self, name): """ Return the container's ObjectList with the given (private) attribute name Example: >>> segment._get_object_list("_analogsignals") """ return getattr(self, name) def _set_object_list(self, name, value): """ Set the contents of the container's ObjectList with the given (private) attribute name Example: >>> segment._set_object_list("_analogsignals", [sig1, sig2]) """ if isinstance(value, list): object_list = getattr(self, name) object_list.clear() object_list.extend(value) elif isinstance(value, ObjectList): # from __iadd__ setattr(self, name, value) else: TypeError("value must be a list or an ObjectList") @property def _child_objects(self): """ Return the names of the classes that can be children of this container. """ return self._container_child_objects + self._data_child_objects @property def _container_child_containers(self): """ Containers for child objects that are a container and have a single parent. """ return tuple([_container_name(child) for child in self._container_child_objects]) @property def _data_child_containers(self): """ Containers for child objects that have data and have a single parent. """ # the following construction removes the duplicate 'regionsofinterest' # while preserving the child order (which `set()` would not do) # I don't know if preserving the order is important, but I'm playing it safe return tuple({_container_name(child): None for child in self._data_child_objects}.keys()) @property def _child_containers(self): """ Containers for child objects with a single parent. """ return tuple({_container_name(child): None for child in self._child_objects}.keys()) @property def _single_children(self): """ All child objects that can only have single parents. """ childs = [list(getattr(self, attr)) for attr in self._child_containers] return tuple(sum(childs, [])) @property def data_children(self): """ All data child objects stored in the current object. Not recursive. """ childs = [list(getattr(self, attr)) for attr in self._data_child_containers] return tuple(sum(childs, [])) @property def container_children(self): """ All container child objects stored in the current object. Not recursive. """ childs = [list(getattr(self, attr)) for attr in self._container_child_containers] return tuple(sum(childs, [])) @property def children(self): """ All child objects stored in the current object. Not recursive. """ return self.data_children + self.container_children @property def data_children_recur(self): """ All data child objects stored in the current object, obtained recursively. """ childs = [list(child.data_children_recur) for child in self.container_children] return self.data_children + tuple(sum(childs, [])) @property def container_children_recur(self): """ All container child objects stored in the current object, obtained recursively. """ childs = [list(child.container_children_recur) for child in self.container_children] return self.container_children + tuple(sum(childs, [])) @property def children_recur(self): """ All child objects stored in the current object, obtained recursively. """ return self.data_children_recur + self.container_children_recur @property def size(self): """ Get dictionary containing the names of child containers in the current object as keys and the number of children of that type as values. """ return {name: len(getattr(self, name)) for name in self._child_containers} @property def _container_lookup(self): return { cls_name: getattr(self, container_name) for cls_name, container_name in zip(self._child_objects, self._child_containers) } def _get_container(self, cls): if hasattr(cls, "proxy_for"): cls = cls.proxy_for return self._container_lookup[cls.__name__] def add(self, *objects): """Add a new Neo object to the Container""" for obj in objects: if obj.__class__.__name__ in self._child_objects or ( hasattr(obj, "proxy_for") and obj.proxy_for.__name__ in self._child_objects ): container = self._get_container(obj.__class__) container.append(obj) else: raise TypeError( f"Cannot add object of type {obj.__class__.__name__} " f"to a {self.__class__.__name__}, can only add objects of the " f"following types: {self._child_objects}" ) def filter(self, targdict=None, data=True, container=False, recursive=True, objects=None, **kwargs): """ Return a list of child objects matching *any* of the search terms in either their attributes or annotations. Search terms can be provided as keyword arguments or a dictionary, either as a positional argument after data or to the argument targdict. A key of a provided dictionary is the name of the requested annotation and the value is a FilterCondition object. E.g.: equal(x), less_than(x), InRange(x, y). targdict can also be a list of dictionaries, in which case the filters are applied sequentially. A list of dictionaries is handled as follows: [ { or } and { or } ] If targdict and kwargs are both supplied, the targdict filters are applied first, followed by the kwarg filters. A targdict of None or {} corresponds to no filters applied, therefore returning all child objects. Default targdict is None. If data is True (default), include data objects. If container is True (default False), include container objects. If recursive is True (default), descend into child containers for objects. objects (optional) should be the name of a Neo object type, a neo object class, or a list of one or both of these. If specified, only these objects will be returned. If not specified any type of object is returned. Default is None. Note that if recursive is True, containers not in objects will still be descended into. This overrides data and container. Examples:: >>> obj.filter(name="Vm") >>> obj.filter(objects=neo.SpikeTrain) >>> obj.filter(targdict={'myannotation':3}) >>> obj.filter(name=neo.core.filters.Equal(5)) >>> obj.filter({'name': neo.core.filters.LessThan(5)}) """ if isinstance(targdict, str): raise TypeError("filtering is based on key-value pairs." " Only a single string was provided.") # if objects are specified, get the classes if objects: data = True container = True children = [] # get the objects we want if data: if recursive: children.extend(self.data_children_recur) else: children.extend(self.data_children) if container: if recursive: children.extend(self.container_children_recur) else: children.extend(self.container_children) filtered = filterdata(children, objects=objects, targdict=targdict, **kwargs) if objects == SpikeTrain: return SpikeTrainList(items=filtered) else: return filtered def list_children_by_class(self, cls): """ List all children of a particular class recursively. You can either provide a class object, a class name, or the name of the container storing the class. """ if not hasattr(cls, "lower"): cls = cls.__name__ container_name = _container_name(cls) objs = list(getattr(self, container_name, [])) for child in self.container_children_recur: objs.extend(getattr(child, container_name, [])) return objs def check_relationships(self, recursive=True): """ Check that the expected child-parent relationships exist. """ parent_name = _reference_name(self.__class__.__name__) for child in self._single_children: if hasattr(child, "proxy_for"): container = getattr(self, _container_name(child.proxy_for.__name__)) else: container = getattr(self, _container_name(child.__class__.__name__)) if container.parent is not None: if getattr(child, parent_name, None) is not self: raise AttributeError("Child should have its parent as an attribute") if recursive: for child in self.container_children: child.check_relationships(recursive=True) def create_relationship(self, force=False, recursive=True): """ For each child of the current object that can only have a single parent, set its parent to be the current object. For children of the current object, put the current object in the parent list. If force is True overwrite any existing relationships If recursive is True descend into child objects and create relationships there """ parent_name = _reference_name(self.__class__.__name__) for child in self._single_children: if hasattr(child, parent_name) and getattr(child, parent_name) is None or force: setattr(child, parent_name, self) if recursive: for child in self.container_children: child.create_relationship(force=force, recursive=True) def __deepcopy__(self, memo): """ Creates a deep copy of the container. All contained objects will also be deep copied and relationships between all objects will be identical to the original relationships. Attributes and annotations of the container are deep copied as well. :param memo: (dict) Objects that have been deep copied already :return: (Container) Deep copy of input Container """ cls = self.__class__ necessary_attrs = {} for k in self._necessary_attrs: necessary_attrs[k[0]] = getattr(self, k[0], None) new_container = cls(**necessary_attrs) new_container.__dict__.update(self.__dict__) memo[id(self)] = new_container for k, v in self.__dict__.items(): try: setattr(new_container, k, deepcopy(v, memo)) except TypeError: setattr(new_container, k, v) new_container.create_relationship() return new_container def merge(self, other): """ Merge the contents of another object into this one. Container children of the current object with the same name will be merged. All other objects will be appended to the list of objects in this one. Duplicate copies of the same object will be skipped. Annotations are merged such that only items not present in the current annotations are added. Note that the other object will be linked inconsistently to other Neo objects after the merge operation and should not be used further. """ # merge containers with the same name for container in self._container_child_containers: lookup = {obj.name: obj for obj in getattr(self, container)} ids = [id(obj) for obj in getattr(self, container)] for obj in getattr(other, container): if id(obj) in ids: continue if obj.name in lookup: lookup[obj.name].merge(obj) else: lookup[obj.name] = obj ids.append(id(obj)) getattr(self, container).append(obj) # for data objects, ignore the name and just add them for container in self._data_child_containers: objs = getattr(self, container) lookup = {obj.name: i for i, obj in enumerate(objs)} ids = [id(obj) for obj in objs] for obj in getattr(other, container): if id(obj) in ids: pass elif hasattr(obj, "merge") and obj.name is not None and obj.name in lookup: ind = lookup[obj.name] try: newobj = getattr(self, container)[ind].merge(obj) getattr(self, container)[ind] = newobj except NotImplementedError: getattr(self, container).append(obj) ids.append(id(obj)) else: lookup[obj.name] = obj ids.append(id(obj)) getattr(self, container).append(obj) obj.set_parent(self) # use the BaseNeo merge as well super().merge(other) def _repr_pretty_(self, pp, cycle): """ Handle pretty-printing. """ pp.text(self.__class__.__name__) pp.text(" with ") vals = [] for container in self._child_containers: objs = getattr(self, container) if objs: vals.append(f"{objs} {container}") pp.text(", ".join(vals)) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) for container in self._repr_pretty_containers: pp.breakable() objs = getattr(self, container) pp.text(f"# {container} (N={objs})") for i, obj in enumerate(objs): pp.breakable() pp.text(f"{i}: ") with pp.indent(3): pp.pretty(obj) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/dataobject.py0000644000175100017510000004342615154507213016226 0ustar00runnerrunner""" This module defines :class:`DataObject`, the abstract base class used by all :module:`neo.core` classes that can contain data (i.e. are not container classes). It contains basic functionality that is shared among all those data objects. """ from copy import deepcopy import warnings import quantities as pq import numpy as np from neo.core.baseneo import BaseNeo, _check_annotations def _normalize_array_annotations(value, length): """Check consistency of array annotations Recursively check that value is either an array or list containing only "simple" types (number, string, date/time) or is a dict of those. Parameters ---------- value : np.ndarray or list or tuple or dict Value to be checked for consistency. length : int Required length of the array annotation. Returns ------- np.ndarray The array_annotations from value in correct form Raises ------ ValueError In case value is not accepted as array_annotation(s) """ # First stage, resolve dict of annotations into single annotations if isinstance(value, dict): for key in value.keys(): if isinstance(value[key], dict): raise ValueError("Nested dicts are not allowed as array annotations") value[key] = _normalize_array_annotations(value[key], length) elif value is None: raise ValueError("Array annotations must not be None") # If not array annotation, pass on to regular check and make it a list, that is checked again # This covers array annotations with length 1 elif not isinstance(value, (list, np.ndarray, tuple)) or (isinstance(value, pq.Quantity) and value.shape == ()): _check_annotations(value) value = _normalize_array_annotations(np.array([value]), length) # If array annotation, check for correct length, only single dimension and allowed data else: # Get length that is required for array annotations, which is equal to the length # of the object's data own_length = length # Escape check if empty array or list and just annotate an empty array (length 0) # This enables the user to easily create dummy array annotations that will be filled # with data later on if len(value) == 0: if not isinstance(value, np.ndarray): value = np.ndarray((0,)) val_length = own_length else: # Note: len(o) also works for np.ndarray, it then uses the first dimension, # which is exactly the desired behaviour here val_length = len(value) if not own_length == val_length: raise ValueError(f"Incorrect length of array annotation: {val_length} != {own_length}") # Local function used to check single elements of a list or an array # They must not be lists or arrays and fit the usual annotation data types def _check_single_elem(element): # Nested array annotations not allowed currently # If element is a list or a np.ndarray, it's not conform except if it's a quantity of # length 1 if isinstance(element, list) or ( isinstance(element, np.ndarray) and not (isinstance(element, pq.Quantity) and (element.shape == () or element.shape == (1,))) ): raise ValueError("Array annotations should only be 1-dimensional") if isinstance(element, dict): raise ValueError("Dictionaries are not supported as array annotations") # Perform regular check for elements of array or list _check_annotations(element) # Arrays only need testing of single element to make sure the others are the same if isinstance(value, np.ndarray): # Type of first element is representative for all others # Thus just performing a check on the first element is enough # Even if it's a pq.Quantity, which can be scalar or array, this is still true # Because a np.ndarray cannot contain scalars and sequences simultaneously # If length of data is 0, then nothing needs to be checked if len(value): # Perform check on first element _check_single_elem(value[0]) return value # In case of list, it needs to be ensured that all data are of the same type else: # Conversion to numpy array makes all elements same type # Converts elements to most general type try: value = np.array(value) # Except when scalar and non-scalar values are mixed, this causes conversion to fail except ValueError as e: msg = str(e) if "setting an array element with a sequence." in msg: raise ValueError( "Scalar values and arrays/lists cannot be " "combined into a single array annotation" ) else: raise e # If most specialized data type that possibly fits all elements is object, # raise an Error with a telling error message, because this means the elements # are not compatible if value.dtype == object: raise ValueError("Cannot convert list of incompatible types into a single" " array annotation") # Check the first element for correctness # If its type is correct for annotations, all others are correct as well # Note: Empty lists cannot reach this point _check_single_elem(value[0]) return value class DataObject(BaseNeo, pq.Quantity): """ This is the base class from which all objects containing data inherit It contains common functionality for all those objects and handles array_annotations. Parameters ---------- name: str | None, default: None Name of the Neo object description: str | None, default: None Human readable string description of the Neo object array_annotations: dict | None, default: None Dictionary containing arrays / lists which annotate individual data points of the Neo object. **annotations: dict | None: Other keyword annotations to be included Notes ----- Common functionality that is not included in BaseNeo includes: - duplicating with new data - rescaling the object - copying the object - returning it as pq.Quantity or np.ndarray - handling of array_annotations Array_annotations are a kind of annotation that contains metadata for every data point, i.e. per timestamp (in SpikeTrain, Event and Epoch) or signal channel (in AnalogSignal and IrregularlySampledSignal). They can contain the same data types as regular annotations, but are always represented as numpy arrays of the same length as the number of data points of the annotated neo object. """ def __init__(self, name=None, description=None, file_origin=None, array_annotations=None, **annotations): """ This method is called by each data object and initializes the newly created object by adding array annotations and calling __init__ of the super class, where more annotations and attributes are processed. """ if not hasattr(self, "array_annotations") or not self.array_annotations: self.array_annotations = ArrayDict(self._get_arr_ann_length()) if array_annotations is not None: self.array_annotate(**array_annotations) BaseNeo.__init__(self, name=name, description=description, file_origin=file_origin, **annotations) def array_annotate(self, **array_annotations): """ Add array annotations (annotations for individual data points) as arrays to a Neo data object. Parameters ---------- **array_annotations: dict Series of keyword annotations to add to the object Examples -------- >>> obj.array_annotate(code=['a', 'b', 'a'], category=[2, 1, 1]) >>> obj.array_annotations['code'][1] 'b' """ self.array_annotations.update(array_annotations) def array_annotations_at_index(self, index): """ Return dictionary of array annotations at a given index or list of indices Parameters ---------- index: int | list | np.ndarray The index (indices) from which the annotations are extracted Returns ------- index_annotations: dict Dictionary of values or numpy arrays containing all array annotations for given index/indices Examples -------- >>> obj.array_annotate(code=['a', 'b', 'a'], category=[2, 1, 1]) >>> obj.array_annotations_at_index(1) {code='b', category=1} """ # Taking only a part of the array annotations # Thus not using ArrayDict here, because checks for length are not needed index_annotations = {} # Use what is given as an index to determine the corresponding annotations, # if not possible, numpy raises an Error for ann in self.array_annotations.keys(): # NO deepcopy, because someone might want to alter the actual object using this try: index_annotations[ann] = self.array_annotations[ann][index] except IndexError as e: # IndexError caused by 'dummy' array annotations should not result in failure # Taking a slice from nothing results in nothing if len(self.array_annotations[ann]) == 0 and not self._get_arr_ann_length() == 0: index_annotations[ann] = self.array_annotations[ann] else: raise e return index_annotations def _merge_array_annotations(self, other): """ Merges array annotations of 2 different objects. Parameters ---------- other: any The annotation to attemp to merge Returns ------- merged_array_annotations: dict The merged annotations Notes ----- The merge happens in such a way that the result fits the merged data In general this means concatenating the arrays from the 2 objects. If an annotation is only present in one of the objects, it will be omitted """ merged_array_annotations = {} omitted_keys_self = [] # Concatenating arrays for each key for key in self.array_annotations: try: value = deepcopy(self.array_annotations[key]) other_value = deepcopy(other.array_annotations[key]) # Quantities need to be rescaled to common unit if isinstance(value, pq.Quantity): try: other_value = other_value.rescale(value.units) except ValueError: raise ValueError("Could not merge array annotations " "due to different units") merged_array_annotations[key] = np.append(value, other_value) * value.units else: merged_array_annotations[key] = np.append(value, other_value) except KeyError: # Save the omitted keys to be able to print them omitted_keys_self.append(key) continue # Also save omitted keys from 'other' omitted_keys_other = [key for key in other.array_annotations if key not in self.array_annotations] # Warn if keys were omitted if omitted_keys_other or omitted_keys_self: warnings.warn( f"The following array annotations were omitted, because they were only " f"present in one of the merged objects: {omitted_keys_self} from the " f"one that was merged into and {omitted_keys_other} from the one that " f"was merged into the other", UserWarning, ) # Return the merged array_annotations return merged_array_annotations def rescale(self, units, dtype=None): """ Return a copy of the object converted to the specified units. Parameters ---------- units: quantity unit The units to convert the object to dtype: a numpy dtype Only exists for backward compatibility see [1] Returns ------- self.copy(): Any A copy of the object with the desired units Notes ----- [1] The `dtype` argument exists only for backward compatibility within quantities, see https://github.com/python-quantities/python-quantities/pull/204 """ # Use simpler functionality, if nothing will be changed dim = pq.quantity.validate_dimensionality(units) if self.dimensionality == dim: return self.copy() # Rescale the object into a new object obj = self.duplicate_with_new_data(signal=self.view(pq.Quantity).rescale(dim), units=units) # Expected behavior is deepcopy, so deepcopying array_annotations obj.array_annotations = deepcopy(self.array_annotations) obj.segment = self.segment return obj # Needed to implement this so array annotations are copied as well, ONLY WHEN copying 1:1 def copy(self, **kwargs): """ Returns a shallow copy of the object :return: Copy of self """ obj = super().copy(**kwargs) obj.array_annotations = self.array_annotations return obj def as_array(self, units=None): """ Return the object's data as a plain NumPy array. Parameters ---------- units: quantities units | None, default: None The data return as a np.ndarray with units requested Returns ------- data_array: np.ndarray The data as an array If `units` is specified, first rescale to those units. """ if units: return self.rescale(units).magnitude else: return self.magnitude def as_quantity(self): """ Return the object's data as a quantities array. """ return self.view(pq.Quantity) def _get_arr_ann_length(self): """ Return the length of the object's data as required for array annotations This is the last dimension of every object. :return Required length of array annotations for this object """ # Number of items is last dimension in of data object # This method should be overridden in case this changes try: length = self.shape[-1] # Note: This is because __getitem__[int] returns a scalar Epoch/Event/SpikeTrain # To be removed if __getitem__[int] is changed except IndexError: length = 1 return length def __deepcopy__(self, memo): """ Create a deep copy of the data object. All attributes and annotations are also deep copied. References to parent objects are not kept, they are set to None. :param memo: (dict) Objects that have been deep copied already :return: (DataObject) Deep copy of the input DataObject """ cls = self.__class__ necessary_attrs = {} # Units need to be specified explicitly for analogsignals/irregularlysampledsignals for k in self._necessary_attrs + (("units",),): necessary_attrs[k[0]] = getattr(self, k[0], self) # Create object using constructor with necessary attributes new_obj = cls(**necessary_attrs) # Add all attributes new_obj.__dict__.update(self.__dict__) memo[id(self)] = new_obj for k, v in self.__dict__.items(): # Single parent objects should not be deepcopied, because this is not expected behavior # and leads to a lot of stuff being copied (e.g. all other children of the parent as well), # thus creating a lot of overhead # But keeping the reference to the same parent is not desired either, because this would be unidirectional # When deepcopying top-down, e.g. a whole block, the links will be handled by the parent if k in self._parent_attrs: setattr(new_obj, k, None) continue try: setattr(new_obj, k, deepcopy(v, memo)) except TypeError: setattr(new_obj, k, v) return new_obj class ArrayDict(dict): """Dictionary subclass to handle array annotations When setting `obj.array_annotations[key]=value`, checks for consistency should not be bypassed. This class overrides __setitem__ from dict to perform these checks every time. The method used for these checks is given as an argument for __init__. """ def __init__(self, length, check_function=_normalize_array_annotations, *args, **kwargs): super().__init__(*args, **kwargs) self.check_function = check_function self.length = length def __setitem__(self, key, value): # Directly call the defined function # Need to wrap key and value in a dict in order to make sure # that nested dicts are detected value = self.check_function({key: value}, self.length)[key] super().__setitem__(key, value) # Updating the dict also needs to perform checks, so rerouting this to __setitem__ def update(self, *args, **kwargs): if args: if len(args) > 1: raise TypeError("update expected at most 1 arguments, " f"got {len(args)}") other = dict(args[0]) for key in other: self[key] = other[key] for key in kwargs: self[key] = kwargs[key] def __reduce__(self): return super().__reduce__() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/epoch.py0000644000175100017510000003537615154507213015231 0ustar00runnerrunner""" This module defines :class:`Epoch`, an array of epochs. :class:`Epoch` derives from :class:`BaseNeo`, from :module:`neo.core.baseneo`. """ from copy import deepcopy from numbers import Number import numpy as np import quantities as pq from neo.core.baseneo import BaseNeo, merge_annotations from neo.core.dataobject import DataObject, ArrayDict def _new_epoch( cls, times=None, durations=None, labels=None, units=None, name=None, description=None, file_origin=None, array_annotations=None, annotations=None, segment=None, ): """ A function to map epoch.__new__ to function that does not do the unit checking. This is needed for pickle to work. """ e = Epoch( times=times, durations=durations, labels=labels, units=units, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) e.segment = segment return e class Epoch(DataObject): """ Array of epochs. Parameters ---------- times: quantity array 1D | numpy array 1D | list | None, default: None The start times of each time period. If None, generates an empty array durations: quantity array 1D | numpy array 1D | list | quantity scalar | float | None, default: None The length(s) of each time period. If a scalar/float, the same value is used for all time periods. If None, generates an empty array labels: numpy.array 1D dtype='U' | list | None, default: None Names or labels for the time periods. If None, creates an empty array units: quantity units | str | None, default: None The units for the time Required if the times is a list or NumPy, not required if it is a :class:`Quantity` name: (str) A label for the dataset, description: (str) Text description, file_origin: (str) Filesystem path or URL of the original data file. *Usage*:: >>> from neo.core import Epoch >>> from quantities import s, ms >>> import numpy as np >>> >>> epc = Epoch(times=np.arange(0, 30, 10)*s, ... durations=[10, 5, 7]*ms, ... labels=np.array(['btn0', 'btn1', 'btn2'], dtype='U')) >>> >>> epc.times array([ 0., 10., 20.]) * s >>> epc.durations array([ 10., 5., 7.]) * ms >>> epc.labels array(['btn0', 'btn1', 'btn2'], dtype=' 1: raise ValueError("Times array has more than 1 dimension") if isinstance(durations, (list, tuple)): durations = np.array(durations) elif durations is None: durations = np.array([]) * pq.s elif isinstance(durations, Number): durations = durations * np.ones(times.shape) elif durations.size != times.size: if durations.size == 1: durations = durations * np.ones_like(times.magnitude) else: raise ValueError("Durations array has different length to times") if labels is None: labels = np.array([], dtype="U") else: labels = np.array(labels) if labels.size != times.size and labels.size: raise ValueError("Labels array has different length to times") if units is None: # No keyword units, so get from `times` try: units = times.units dim = units.dimensionality except AttributeError: raise ValueError("you must specify units") else: if hasattr(units, "dimensionality"): dim = units.dimensionality else: dim = pq.quantity.validate_dimensionality(units) if not hasattr(durations, "dimensionality"): durations = pq.Quantity(durations, dim) # check to make sure the units are time # this approach is much faster than comparing the # reference dimensionality if len(dim) != 1 or list(dim.values())[0] != 1 or not isinstance(list(dim.keys())[0], pq.UnitTime): ValueError(f"Unit {units} has dimensions {dim.simplified}, not [time]") obj = pq.Quantity.__new__(cls, times, units=dim) obj._labels = labels obj._durations = durations obj.segment = None return obj def __init__( self, times=None, durations=None, labels=None, units=None, name=None, description=None, file_origin=None, array_annotations=None, **annotations, ): """ Initialize a new :class:`Epoch` instance. """ DataObject.__init__( self, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) def __reduce__(self): """ Map the __new__ function onto _new_epoch, so that pickle works """ return _new_epoch, ( self.__class__, self.times, self.durations, self.labels, self.units, self.name, self.file_origin, self.description, self.array_annotations, self.annotations, self.segment, ) def __array_finalize__(self, obj): super().__array_finalize__(obj) self._durations = getattr(obj, "durations", None) self._labels = getattr(obj, "labels", None) self.annotations = getattr(obj, "annotations", None) self.name = getattr(obj, "name", None) self.file_origin = getattr(obj, "file_origin", None) self.description = getattr(obj, "description", None) self.segment = getattr(obj, "segment", None) # Add empty array annotations, because they cannot always be copied, # but do not overwrite existing ones from slicing etc. # This ensures the attribute exists if not hasattr(self, "array_annotations"): self.array_annotations = ArrayDict(self._get_arr_ann_length()) def __repr__(self): """ Returns a string representing the :class:`Epoch`. """ objs = [ f"{label}@{str(time)} for {str(dur)}" for label, time, dur in zip(self.labels, self.times, self.durations) ] return "" % ", ".join(objs) def _repr_pretty_(self, pp, cycle): labels = "" if self._labels is not None: labels = " with labels" pp.text( f"{self.__class__.__name__} containing {self.size} epochs{labels}; " f"time units {self.units.dimensionality.string}; datatype {self.dtype} " ) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) def rescale(self, units, dtype=None): """ Return a copy of the :class:`Epoch` converted to the specified units The `dtype` argument exists only for backward compatibility within quantities, see https://github.com/python-quantities/python-quantities/pull/204 :return: Copy of self with specified units """ # Use simpler functionality, if nothing will be changed dim = pq.quantity.validate_dimensionality(units) if self.dimensionality == dim: return self.copy() # Rescale the object into a new object obj = self.duplicate_with_new_data( times=self.view(pq.Quantity).rescale(dim), durations=self.durations.rescale(dim), labels=self.labels, units=units, ) # Expected behavior is deepcopy, so deepcopying array_annotations obj.array_annotations = deepcopy(self.array_annotations) obj.segment = self.segment return obj def __getitem__(self, i): """ Get the item or slice :attr:`i`. """ obj = super().__getitem__(i) obj._durations = self.durations[i] if self._labels is not None and self._labels.size > 0: obj._labels = self.labels[i] else: obj._labels = self.labels try: # Array annotations need to be sliced accordingly obj.array_annotate(**deepcopy(self.array_annotations_at_index(i))) obj._copy_data_complement(self) except AttributeError: # If Quantity was returned, not Epoch obj.times = obj obj.durations = obj._durations obj.labels = obj._labels return obj def __getslice__(self, i, j): """ Get a slice from :attr:`i` to :attr:`j`.attr[0] Doesn't get called in Python 3, :meth:`__getitem__` is called instead """ return self.__getitem__(slice(i, j)) @property def times(self): return pq.Quantity(self) def merge(self, other): """ Merge the another :class:`Epoch` into this one. The :class:`Epoch` objects are concatenated horizontally (column-wise), :func:`np.hstack`). If the attributes of the two :class:`Epoch` are not compatible, and Exception is raised. """ othertimes = other.times.rescale(self.times.units) otherdurations = other.durations.rescale(self.durations.units) times = np.hstack([self.times, othertimes]) * self.times.units durations = np.hstack([self.durations, otherdurations]) * self.durations.units labels = np.hstack([self.labels, other.labels]) kwargs = {} for name in ("name", "description", "file_origin"): attr_self = getattr(self, name) attr_other = getattr(other, name) if attr_self == attr_other: kwargs[name] = attr_self else: kwargs[name] = f"merge({attr_self}, {attr_other})" merged_annotations = merge_annotations(self.annotations, other.annotations) kwargs.update(merged_annotations) kwargs["array_annotations"] = self._merge_array_annotations(other) return Epoch(times=times, durations=durations, labels=labels, **kwargs) def _copy_data_complement(self, other): """ Copy the metadata from another :class:`Epoch`. Note: Array annotations can not be copied here because length of data can change """ # Note: Array annotations cannot be copied because length of data could be changed # here which would cause inconsistencies. This is instead done locally. for attr in ("name", "file_origin", "description"): setattr(self, attr, deepcopy(getattr(other, attr, None))) self._copy_annotations(other) def _copy_annotations(self, other): self.annotations = deepcopy(other.annotations) def duplicate_with_new_data(self, times, durations, labels, units=None): """ Create a new :class:`Epoch` with the same metadata but different data (times, durations) Note: Array annotations can not be copied here because length of data can change """ if units is None: units = self.units else: units = pq.quantity.validate_dimensionality(units) new = self.__class__(times=times, durations=durations, labels=labels, units=units) new._copy_data_complement(self) new._labels = labels new._durations = durations # Note: Array annotations can not be copied here because length of data can change return new def time_slice(self, t_start, t_stop): """ Creates a new :class:`Epoch` corresponding to the time slice of the original :class:`Epoch` between (and including) times :attr:`t_start` and :attr:`t_stop`. Either parameter can also be None to use infinite endpoints for the time interval. """ _t_start = t_start _t_stop = t_stop if t_start is None: _t_start = -np.inf if t_stop is None: _t_stop = np.inf indices = (self >= _t_start) & (self <= _t_stop) # Time slicing should create a deep copy of the object new_epc = deepcopy(self[indices]) return new_epc def time_shift(self, t_shift): """ Shifts an :class:`Epoch` by an amount of time. Parameters ---------- t_shift: Quantity (time) Amount of time by which to shift the :class:`Epoch`. Returns ------- epoch: :class:`Epoch` New instance of an :class:`Epoch` object starting at t_shift later than the original :class:`Epoch` (the original :class:`Epoch` is not modified). """ new_epc = self.duplicate_with_new_data(times=self.times + t_shift, durations=self.durations, labels=self.labels) # Here we can safely copy the array annotations since we know that # the length of the Epoch does not change. new_epc.array_annotate(**self.array_annotations) return new_epc def set_labels(self, labels): if self.labels is not None and self.labels.size > 0 and len(labels) != self.size: raise ValueError(f"Labels array has different length to times " f"({len(labels)} != {self.size})") self._labels = np.array(labels) def get_labels(self): return self._labels labels = property(get_labels, set_labels) def set_durations(self, durations): if self.durations is not None and self.durations.size > 0 and len(durations) != self.size: raise ValueError("Durations array has different length to times " f"({len(durations)} != {self.size})") self._durations = durations def get_durations(self): return self._durations durations = property(get_durations, set_durations) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/event.py0000644000175100017510000003604115154507213015242 0ustar00runnerrunner""" This module defines :class:`Event`, an array of events. :class:`Event` derives from :class:`BaseNeo`, from :module:`neo.core.baseneo`. """ from copy import deepcopy import numpy as np import quantities as pq from neo.core.baseneo import merge_annotations from neo.core.dataobject import DataObject, ArrayDict from neo.core.epoch import Epoch def _new_event( cls, times=None, labels=None, units=None, name=None, file_origin=None, description=None, array_annotations=None, annotations=None, segment=None, ): """ A function to map Event.__new__ to function that does not do the unit checking. This is needed for pickle to work. """ e = Event( times=times, labels=labels, units=units, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) e.segment = segment return e class Event(DataObject): """ Array of events which are the start times of events along with the labels of the events Parameters ---------- times: quantity array 1d | list The times of the events labels: numpy.ndarray 1d dtype='U' | list Names or labels for the events units: quantity units | None, default: None If times are list the units of the times If times is a quantity array this is ignored name: str | None, default: None An optional label for the dataset description: str | None, default: None An optional text descriptoin of the dataset file_orgin: str | None, default: None The filesystem path or url of the original data file array_annotations: dict | None, default: None Dict mapping strings to numpy arrays containing annotations for all data points **annotations: dict Additional user specified metadata stored in the annotations attribue Examples -------- >>> from neo.core import Event >>> from quantities import s >>> import numpy as np >>> >>> evt = Event(np.arange(0, 30, 10)*s, ... labels=np.array(['trig0', 'trig1', 'trig2'], ... dtype='U')) >>> >>> evt.times array([ 0., 10., 20.]) * s >>> evt.labels array(['trig0', 'trig1', 'trig2'], dtype=' 1: raise ValueError("Times array has more than 1 dimension") if labels is None: labels = np.array([], dtype="U") else: labels = np.array(labels) if labels.size != times.size and labels.size: raise ValueError("Labels array has different length to times") if units is None: # No keyword units, so get from `times` try: units = times.units dim = units.dimensionality except AttributeError: raise ValueError("you must specify units") else: if hasattr(units, "dimensionality"): dim = units.dimensionality else: dim = pq.quantity.validate_dimensionality(units) # check to make sure the units are time # this approach is much faster than comparing the # reference dimensionality if len(dim) != 1 or list(dim.values())[0] != 1 or not isinstance(list(dim.keys())[0], pq.UnitTime): ValueError(f"Unit {units} has dimensions {dim.simplified}, not [time].") obj = pq.Quantity(times, units=dim).view(cls) obj._labels = labels obj.segment = None return obj def __init__( self, times=None, labels=None, units=None, name=None, description=None, file_origin=None, array_annotations=None, **annotations, ): """ Initialize a new :class:`Event` instance. """ DataObject.__init__( self, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) def __reduce__(self): """ Map the __new__ function onto _new_event, so that pickle works """ return _new_event, ( self.__class__, np.array(self), self.labels, self.units, self.name, self.file_origin, self.description, self.array_annotations, self.annotations, self.segment, ) def __array_finalize__(self, obj): super().__array_finalize__(obj) self._labels = getattr(obj, "labels", None) self.annotations = getattr(obj, "annotations", None) self.name = getattr(obj, "name", None) self.file_origin = getattr(obj, "file_origin", None) self.description = getattr(obj, "description", None) self.segment = getattr(obj, "segment", None) # Add empty array annotations, because they cannot always be copied, # but do not overwrite existing ones from slicing etc. # This ensures the attribute exists if not hasattr(self, "array_annotations"): self.array_annotations = ArrayDict(self._get_arr_ann_length()) def __repr__(self): """ Returns a string representing the :class:`Event`. """ objs = [f"{label}@{str(time)}" for label, time in zip(self.labels, self.times)] return "" % ", ".join(objs) def _repr_pretty_(self, pp, cycle): labels = "" if self._labels is not None: labels = " with labels" pp.text( f"{self.__class__.__name__} containing {self.size} events{labels}; " f"time units {self.units.dimensionality.string}; datatype {self.dtype} " ) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) def rescale(self, units, dtype=None): """ Return a copy of the :class:`Event` converted to the specified units Parameters ---------- units: quantity units The units to convert the Event to dtype: None Exists for backward compatiblity within quantities see Notes for more info Returns ------- obj: neo.core.Event A copy of the event with the specified units Notes ----- The `dtype` argument exists only for backward compatibility within quantities, see https://github.com/python-quantities/python-quantities/pull/204 """ # Use simpler functionality, if nothing will be changed dim = pq.quantity.validate_dimensionality(units) if self.dimensionality == dim: return self.copy() # Rescale the object into a new object obj = self.duplicate_with_new_data(times=self.view(pq.Quantity).rescale(dim), labels=self.labels, units=units) # Expected behavior is deepcopy, so deepcopying array_annotations obj.array_annotations = deepcopy(self.array_annotations) obj.segment = self.segment return obj @property def times(self): return pq.Quantity(self) def merge(self, other): """ Merge another :class:`Event` into this one. Parameter --------- other: neo.core.Event The `Event` to merge into this one Notes ----- * The :class:`Event` objects are concatenated horizontally (column-wise), :func:`np.hstack`). * If the attributes of the two :class:`Event` are not compatible, and Exception is raised. """ othertimes = other.times.rescale(self.times.units) times = np.hstack([self.times, othertimes]) * self.times.units labels = np.hstack([self.labels, other.labels]) kwargs = {} for name in ("name", "description", "file_origin"): attr_self = getattr(self, name) attr_other = getattr(other, name) if attr_self == attr_other: kwargs[name] = attr_self else: kwargs[name] = f"merge({attr_self}, {attr_other})" print("Event: merge annotations") merged_annotations = merge_annotations(self.annotations, other.annotations) kwargs.update(merged_annotations) kwargs["array_annotations"] = self._merge_array_annotations(other) evt = Event(times=times, labels=labels, **kwargs) return evt def _copy_data_complement(self, other): """ Copy the metadata from another :class:`Event`. Note: Array annotations can not be copied here because length of data can change """ # Note: Array annotations, including labels, cannot be copied # because they are linked to their respective timestamps and length of data can be changed # here which would cause inconsistencies for attr in ("name", "file_origin", "description", "annotations"): setattr(self, attr, deepcopy(getattr(other, attr, None))) def __getitem__(self, i): obj = super().__getitem__(i) if self._labels is not None and self._labels.size > 0: obj.labels = self._labels[i] else: obj.labels = self._labels try: obj.array_annotate(**deepcopy(self.array_annotations_at_index(i))) obj._copy_data_complement(self) except AttributeError: # If Quantity was returned, not Event obj.times = obj return obj def set_labels(self, labels): if self.labels is not None and self.labels.size > 0 and len(labels) != self.size: raise ValueError(f"Labels array has different length to times " f"({len(labels)} != {self.size})") self._labels = np.array(labels) def get_labels(self): return self._labels labels = property(get_labels, set_labels) def duplicate_with_new_data(self, times, labels, units=None): """ Create a new :class:`Event` with the same metadata but different data Note: Array annotations can not be copied here because length of data can change """ if units is None: units = self.units else: units = pq.quantity.validate_dimensionality(units) new = self.__class__(times=times, units=units) new._copy_data_complement(self) new.labels = labels # Note: Array annotations cannot be copied here, because length of data can be changed return new def time_slice(self, t_start, t_stop): """ Creates a new `Event` corresponding to the time slice of the original `Event` between (and including) times `t_start` and `t_stop`. Parameters ---------- t_start: float | None The starting time of the time slice If None will use -np.inf for the starting time t_stop: float | None The stopping time of the time slice If None will use np.inf for the stopping time Returns ------- new_evt: neo.core.Event The new `Event` limited by the time points """ _t_start = t_start _t_stop = t_stop if t_start is None: _t_start = -np.inf if t_stop is None: _t_stop = np.inf indices = (self >= _t_start) & (self <= _t_stop) # Time slicing should create a deep copy of the object new_evt = deepcopy(self[indices]) return new_evt def time_shift(self, t_shift): """ Shifts an :class:`Event` by an amount of time. Parameters ---------- t_shift: Quantity (time) Amount of time by which to shift the :class:`Event`. Returns ------- epoch: Event New instance of an :class:`Event` object starting at t_shift later than the original :class:`Event` (the original :class:`Event` is not modified). """ new_evt = self.duplicate_with_new_data(times=self.times + t_shift, labels=self.labels) # Here we can safely copy the array annotations since we know that # the length of the Event does not change. new_evt.array_annotate(**self.array_annotations) return new_evt def to_epoch(self, pairwise=False, durations=None): """ Returns a new Epoch object based on the times and labels in the Event object. This method has three modes of action. 1. By default, an array of `n` event times will be transformed into `n-1` epochs, where the end of one epoch is the beginning of the next. This assumes that the events are ordered in time; it is the responsibility of the caller to check this is the case. 2. If `pairwise` is True, then the event times will be taken as pairs representing the start and end time of an epoch. The number of events must be even, otherwise a ValueError is raised. 3. If `durations` is given, it should be a scalar Quantity or a Quantity array of the same size as the Event. Each event time is then taken as the start of an epoch of duration given by `durations`. `pairwise=True` and `durations` are mutually exclusive. A ValueError will be raised if both are given. If `durations` is given, epoch labels are set to the corresponding labels of the events that indicate the epoch start If `durations` is not given, then the event labels A and B bounding the epoch are used to set the labels of the epochs in the form 'A-B'. """ if pairwise: # Mode 2 if durations is not None: raise ValueError("Inconsistent arguments. " "Cannot give both `pairwise` and `durations`") if self.size % 2 != 0: raise ValueError("Pairwise conversion of events to epochs" " requires an even number of events") times = self.times[::2] durations = self.times[1::2] - times labels = np.array([f"{a}-{b}" for a, b in zip(self.labels[::2], self.labels[1::2])]) elif durations is None: # Mode 1 times = self.times[:-1] durations = np.diff(self.times) labels = np.array([f"{a}-{b}" for a, b in zip(self.labels[:-1], self.labels[1:])]) else: # Mode 3 times = self.times labels = self.labels return Epoch(times=times, durations=durations, labels=labels) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/filters.py0000644000175100017510000001421415154507213015567 0ustar00runnerrunner""" This module implements :class:`FilterCondition`, which enables use of different filter conditions for neo.core.container.filter. Classes: - :class:`FilterCondition`: Abstract base class for defining filter conditions. - :class:`Equals`: Filter condition to check if a value is equal to the control value. - :class:`IsNot`: Filter condition to check if a value is not equal to the control value. - :class:`LessThanOrEquals`: Filter condition to check if a value is less than or equal to the control value. - :class:`GreaterThanOrEquals`: Filter condition to check if a value is greater than or equal to the control value. - :class:`LessThan`: Filter condition to check if a value is less than the control value. - :class:`GreaterThan`: Filter condition to check if a value is greater than the control value. - :class:`IsIn`: Filter condition to check if a value is in a list or equal to the control value. - :class:`InRange`: Filter condition to check if a value is in a specified range. The provided classes allow users to select filter conditions and use them with :func:`neo.core.container.filter()` to perform specific filtering operations on data. """ from abc import ABC, abstractmethod from numbers import Number from typing import Union, Any class FilterCondition(ABC): """ FilterCondition object is given as parameter to container.filter(): Usage: segment.filter(my_annotation=) or segment=filter({'my_annotation': }) """ @abstractmethod def __init__(self, control: Any) -> None: """ Initialize new FilterCondition object. Parameters ---------- control: Any The control value to be used for filtering. Notes ----- This is an abstract base class and should not be instantiated directly. """ @abstractmethod def evaluate(self, compare: Any) -> bool: """ Evaluate the filter condition for given value. Parameters: ----------- compare: Any The value to be compared with the control value. Returns ------- bool: True if the condition is satisfied, False otherwise. Notes ----- This method should be implemented in subclasses. """ class Equals(FilterCondition): """ Filter condition to check if target value is equal to the control value. """ def __init__(self, control: Any) -> None: self.control = control def evaluate(self, compare: Any) -> bool: return compare == self.control class IsNot(FilterCondition): """ Filter condition to check if target value is not equal to the control value. """ def __init__(self, control: Any) -> None: self.control = control def evaluate(self, compare: Any) -> bool: return compare != self.control class LessThanOrEquals(FilterCondition): """ Filter condition to check if target value is less than or equal to the control value. """ def __init__(self, control: Number) -> None: self.control = control def evaluate(self, compare: Number) -> bool: return compare <= self.control class GreaterThanOrEquals(FilterCondition): """ Filter condition to check if target value is greater than or equal to the control value. """ def __init__(self, control: Number) -> None: self.control = control def evaluate(self, compare: Number) -> bool: return compare >= self.control class LessThan(FilterCondition): """ Filter condition to check if target value is less than the control value. """ def __init__(self, control: Number) -> None: self.control = control def evaluate(self, compare: Number) -> bool: return compare < self.control class GreaterThan(FilterCondition): """ Filter condition to check if target value is greater than the control value. """ def __init__(self, control: Number) -> None: self.control = control def evaluate(self, compare: Number) -> bool: return compare > self.control class IsIn(FilterCondition): """ Filter condition to check if target is in control. """ def __init__(self, control: Union[list, tuple, set, int]) -> None: self.control = control def evaluate(self, compare: Any) -> bool: if isinstance(self.control, (list, tuple, set)): return compare in self.control if isinstance(self.control, int): return compare == self.control raise SyntaxError("parameter not of type list, tuple, set or int") class InRange(FilterCondition): """ Filter condition to check if a value is in a specified range. Parameters ----------- lower_bound: int The lower bound of the range. upper_bound: int The upper bound of the range. left_closed: bool If True, the range includes the lower bound (lower_bound <= compare). right_closed: bool If True, the range includes the upper bound (compare <= upper_bound). Returns ------- bool: whether the values are in range Examples -------- >>> InRange(lower_bound, upper_bound, left_closed=False, right_closed=False) """ def __init__( self, lower_bound: Number, upper_bound: Number, left_closed: bool = False, right_closed: bool = False ) -> None: if not isinstance(lower_bound, Number) or not isinstance(upper_bound, Number): raise ValueError("parameter is not a number") self.lower_bound = lower_bound self.upper_bound = upper_bound self.left_closed = left_closed self.right_closed = right_closed def evaluate(self, compare: Number) -> bool: if not self.left_closed and not self.right_closed: return self.lower_bound <= compare <= self.upper_bound if not self.left_closed and self.right_closed: return self.lower_bound <= compare < self.upper_bound if self.left_closed and not self.right_closed: return self.lower_bound < compare <= self.upper_bound return self.lower_bound < compare < self.upper_bound ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/group.py0000644000175100017510000001470115154507213015254 0ustar00runnerrunner""" This module implements :class:`Group`, which represents a subset of the channels in an :class:`AnalogSignal` or :class:`IrregularlySampledSignal`. It replaces and extends the grouping function of the former :class:`ChannelIndex` and :class:`Unit`. """ from neo.core.container import Container from neo.core.analogsignal import AnalogSignal from neo.core.container import Container from neo.core.objectlist import ObjectList from neo.core.epoch import Epoch from neo.core.event import Event from neo.core.imagesequence import ImageSequence from neo.core.irregularlysampledsignal import IrregularlySampledSignal from neo.core.segment import Segment from neo.core.spiketrainlist import SpikeTrainList from neo.core.view import ChannelView from neo.core.regionofinterest import RegionOfInterest class Group(Container): """ Can contain any of the data objects, views, or other groups, outside the hierarchy of the segment and block containers. A common use is to link the :class:`SpikeTrain` objects within a :class:`Block`, possibly across multiple Segments, that were emitted by the same neuron. *Required attributes/properties*: None *Recommended attributes/properties*: :objects: (Neo object) Objects with which to pre-populate the :class:`Group` :name: (str) A label for the group. :description: (str) Text description. :file_origin: (str) Filesystem path or URL of the original data file. *Optional arguments*: :allowed_types: (list or tuple) Types of Neo object that are allowed to be added to the Group. If not specified, any Neo object can be added. Note: Any other additional arguments are assumed to be user-specific metadata and stored in :attr:`annotations`. *Container of*: :class:`AnalogSignal`, :class:`IrregularlySampledSignal`, :class:`SpikeTrain`, :class:`Event`, :class:`Epoch`, :class:`ChannelView`, :class:`Group` """ _data_child_objects = ( "AnalogSignal", "IrregularlySampledSignal", "SpikeTrain", "Event", "Epoch", "ChannelView", "ImageSequence", "CircularRegionOfInterest", "RectangularRegionOfInterest", "PolygonRegionOfInterest", ) _container_child_objects = ("Group",) _parent_objects = ("Block",) def __init__(self, objects=None, name=None, description=None, file_origin=None, allowed_types=None, **annotations): super().__init__(name=name, description=description, file_origin=file_origin, **annotations) # note that we create the ObjectLists here _without_ a parent argument # since objects do not have a reference to the group(s) # they are contained in. self._analogsignals = ObjectList(AnalogSignal) self._irregularlysampledsignals = ObjectList(IrregularlySampledSignal) self._spiketrains = SpikeTrainList() self._events = ObjectList(Event) self._epochs = ObjectList(Epoch) self._channelviews = ObjectList(ChannelView) self._imagesequences = ObjectList(ImageSequence) self._regionsofinterest = ObjectList(RegionOfInterest) self._segments = ObjectList(Segment) # to remove? self._groups = ObjectList(Group) if allowed_types is None: self.allowed_types = None else: self.allowed_types = tuple(allowed_types) for type_ in self.allowed_types: if type_.__name__ not in self._child_objects: raise TypeError(f"Groups can not contain objects of type {type_.__name__}") if objects: self.add(*objects) analogsignals = property( fget=lambda self: self._get_object_list("_analogsignals"), fset=lambda self, value: self._set_object_list("_analogsignals", value), doc="list of AnalogSignals contained in this group", ) irregularlysampledsignals = property( fget=lambda self: self._get_object_list("_irregularlysampledsignals"), fset=lambda self, value: self._set_object_list("_irregularlysampledsignals", value), doc="list of IrregularlySignals contained in this group", ) events = property( fget=lambda self: self._get_object_list("_events"), fset=lambda self, value: self._set_object_list("_events", value), doc="list of Events contained in this group", ) epochs = property( fget=lambda self: self._get_object_list("_epochs"), fset=lambda self, value: self._set_object_list("_epochs", value), doc="list of Epochs contained in this group", ) channelviews = property( fget=lambda self: self._get_object_list("_channelviews"), fset=lambda self, value: self._set_object_list("_channelviews", value), doc="list of ChannelViews contained in this group", ) imagesequences = property( fget=lambda self: self._get_object_list("_imagesequences"), fset=lambda self, value: self._set_object_list("_imagesequences", value), doc="list of ImageSequences contained in this group", ) regionsofinterest = property( fget=lambda self: self._get_object_list("_regionsofinterest"), fset=lambda self, value: self._set_object_list("_regionsofinterest", value), doc="list of RegionOfInterest objects contained in this group", ) spiketrains = property( fget=lambda self: self._get_object_list("_spiketrains"), fset=lambda self, value: self._set_object_list("_spiketrains", value), doc="list of SpikeTrains contained in this group", ) segments = property( fget=lambda self: self._get_object_list("_segments"), fset=lambda self, value: self._set_object_list("_segments", value), doc="list of Segments contained in this group", ) groups = property( fget=lambda self: self._get_object_list("_groups"), fset=lambda self, value: self._set_object_list("_groups", value), doc="list of Groups contained in this group", ) def add(self, *objects): """Add a new Neo object to the Group""" for obj in objects: if self.allowed_types and not isinstance(obj, self.allowed_types): raise TypeError(f"This Group can only contain {self.allowed_types}, " f"but not {type(obj)}") super().add(*objects) def walk(self): """ Walk the tree of subgroups """ yield self for grp in self.groups: yield from grp.walk() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/imagesequence.py0000644000175100017510000002443415154507213016737 0ustar00runnerrunner""" This module implements :class:`ImageSequence`, a 3D array. :class:`ImageSequence` inherits from :class:`basesignal.BaseSignal` which derives from :class:`BaseNeo`, and from :class:`quantities.Quantity`which in turn inherits from :class:`numpy.array`. Inheritance from :class:`numpy.array` is explained here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html In brief: * Initialization of a new object from constructor happens in :meth:`__new__`. This is where user-specified attributes are set. * :meth:`__array_finalize__` is called for all new objects, including those created by slicing. This is where attributes are copied over from the old object. """ import quantities as pq import numpy as np from neo.core.analogsignal import AnalogSignal, _get_sampling_rate from neo.core.baseneo import BaseNeo from neo.core.basesignal import BaseSignal from neo.core.dataobject import DataObject class ImageSequence(BaseSignal): """ Representation of a sequence of images, as an array of three dimensions organized as [frame][row][column]. Inherits from :class:`quantities.Quantity`, which in turn inherits from :class:`numpy.ndarray`. *usage*:: >>> from neo.core import ImageSequence >>> import quantities as pq >>> >>> img_sequence_array = [[[column for column in range(20)]for row in range(20)] ... for frame in range(10)] >>> image_sequence = ImageSequence(img_sequence_array, units='V', ... sampling_rate=1 * pq.Hz, ... spatial_scale=1 * pq.micrometer) >>> image_sequence ImageSequence 10 frames with width 20 px and height 20 px; units V; datatype int64 sampling rate: 1.0 spatial_scale: 1.0 >>> image_sequence.spatial_scale array(1.) * um *Required attributes/properties*: :image_data: (3D NumPy array, or a list of 2D arrays) The data itself :units: (quantity units) :sampling_rate: *or* **frame_duration** (quantity scalar) Number of samples per unit time or duration of a single image frame. If both are specified, they are checked for consistency. :spatial_scale: (quantity scalar) size for a pixel. :t_start: (quantity scalar) Time when sequence begins. Default 0. *Recommended attributes/properties*: :name: (str) A label for the dataset. :description: (str) Text description. :file_origin: (str) Filesystem path or URL of the original data file. *Optional attributes/properties*: :dtype: (numpy dtype or str) Override the dtype of the signal array. :copy: deprecated Note: Any other additional arguments are assumed to be user-specific metadata and stored in :attr:`annotations`. *Properties available on this object*: :sampling_rate: (quantity scalar) Number of samples per unit time. (1/:attr:`frame_duration`) :frame_duration: (quantity scalar) Duration of each image frame. (1/:attr:`sampling_rate`) :spatial_scale: Size of a pixel :duration: (Quantity) Sequence duration, read-only. (size * :attr:`frame_duration`) :t_stop: (quantity scalar) Time when sequence ends, read-only. (:attr:`t_start` + :attr:`duration`) """ _parent_objects = ("Segment",) _parent_attrs = ("segment",) _quantity_attr = "image_data" _necessary_attrs = ( ("image_data", pq.Quantity, 3), ("sampling_rate", pq.Quantity, 0), ("spatial_scale", pq.Quantity, 0), ("t_start", pq.Quantity, 0), ) _recommended_attrs = BaseNeo._recommended_attrs def __new__( cls, image_data, units=pq.dimensionless, dtype=None, copy=None, t_start=0 * pq.s, spatial_scale=None, frame_duration=None, sampling_rate=None, name=None, description=None, file_origin=None, **annotations, ): """ Constructs new :class:`ImageSequence` from data. This is called whenever a new class:`ImageSequence` is created from the constructor, but not when slicing. __array_finalize__ is called on the new object. """ if copy is not None: raise ValueError( "`copy` is now deprecated in Neo due to removal in Quantites to support Numpy 2.0. " "In order to facilitate the deprecation copy can be set to None but will raise an " "error if set to True/False since this will silently do nothing. This argument will be completely " "removed in Neo 0.15.0. Please update your code base as necessary." ) if spatial_scale is None: raise ValueError("spatial_scale is required") image_data = np.stack(image_data) if len(image_data.shape) != 3: raise ValueError("list doesn't have the correct number of dimensions") obj = pq.Quantity(image_data, units=units, dtype=dtype).view(cls) obj.segment = None # function from analogsignal.py in neo/core directory obj.sampling_rate = _get_sampling_rate(sampling_rate, frame_duration) obj.spatial_scale = spatial_scale if t_start is None: raise ValueError("t_start cannot be None") obj._t_start = t_start return obj def __init__( self, image_data, units=pq.dimensionless, dtype=None, copy=None, t_start=0 * pq.s, spatial_scale=None, frame_duration=None, sampling_rate=None, name=None, description=None, file_origin=None, **annotations, ): """ Initializes a newly constructed :class:`ImageSequence` instance. """ DataObject.__init__(self, name=name, file_origin=file_origin, description=description, **annotations) def __array_finalize__spec(self, obj): self.sampling_rate = getattr(obj, "sampling_rate", None) self.spatial_scale = getattr(obj, "spatial_scale", None) self.units = getattr(obj, "units", pq.dimensionless) self._t_start = getattr(obj, "_t_start", 0 * pq.s) return obj def signal_from_region(self, *region): """ Method that takes 1 or multiple regionofinterest, uses the method of each region of interest to get the list of pixels to average. Return a list of :class:`AnalogSignal` for each regionofinterest """ if len(region) == 0: raise ValueError("no regions of interest have been given") region_pixel = [] for i, b in enumerate(region): r = region[i].pixels_in_region() if not r: raise ValueError("region " + str(i) + "is empty") else: region_pixel.append(r) analogsignal_list = [] for i in region_pixel: data = [] for frame in range(len(self)): picture_data = [] for v in i: picture_data.append(self.view(pq.Quantity)[frame][v[0]][v[1]]) average = picture_data[0] for b in range(1, len(picture_data)): average += picture_data[b] data.append((average * 1.0) / len(i)) analogsignal_list.append( AnalogSignal(data, units=self.units, t_start=self.t_start, sampling_rate=self.sampling_rate) ) return analogsignal_list def _repr_pretty_(self, pp, cycle): """ Handle pretty-printing the :class:`ImageSequence`. """ pp.text( f"{self.__class__.__name__} {self.shape[0]} frames with " f"width {self.shape[2]} px and height {self.shape[1]} px; " f"units {self.units.dimensionality.string}; datatype {self.dtype} " ) def _pp(line): pp.breakable() with pp.group(indent=1): pp.text(line) for line in [f"sampling rate: {self.sampling_rate}", f"spatial_scale: {self.spatial_scale}"]: _pp(line) def _check_consistency(self, other): """ Check if the attributes of another :class:`ImageSequence` are compatible with this one. """ if isinstance(other, ImageSequence): for attr in ("sampling_rate", "spatial_scale", "t_start"): if getattr(self, attr) != getattr(other, attr): raise ValueError(f"Inconsistent values of {attr}") # t_start attribute is handled as a property so type checking can be done @property def t_start(self): """ Time when sequence begins. """ return self._t_start @t_start.setter def t_start(self, start): """ Setter for :attr:`t_start` """ if start is None: raise ValueError("t_start cannot be None") self._t_start = start @property def duration(self): """ Sequence duration (:attr:`size` * :attr:`frame_duration`) """ return self.shape[0] / self.sampling_rate @property def t_stop(self): """ Time when Sequence ends. (:attr:`t_start` + :attr:`duration`) """ return self.t_start + self.duration @property def times(self): """ The time points of each frame in the sequence (:attr:`t_start` + arange(:attr:`shape`)/:attr:`sampling_rate`) """ return self.t_start + np.arange(self.shape[0]) / self.sampling_rate @property def frame_duration(self): """ Duration of a single image frame in the sequence. (1/:attr:`sampling_rate`) """ return 1.0 / self.sampling_rate @frame_duration.setter def frame_duration(self, duration): """ Setter for :attr:`frame_duration` """ if duration is None: raise ValueError("frame_duration cannot be None") elif not hasattr(duration, "units"): raise ValueError("frame_duration must have units") self.sampling_rate = 1.0 / duration ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/irregularlysampledsignal.py0000644000175100017510000006024215154507213021226 0ustar00runnerrunner""" This module implements :class:`IrregularlySampledSignal`, an array of analog signals with samples taken at arbitrary time points. :class:`IrregularlySampledSignal` inherits from :class:`basesignal.BaseSignal` which derives from :class:`BaseNeo`, from :module:`neo.core.baseneo`, and from :class:`quantities.Quantity`, which in turn inherits from :class:`numpy.ndarray`. Inheritance from :class:`numpy.array` is explained here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html In brief: * Initialization of a new object from constructor happens in :meth:`__new__`. This is where user-specified attributes are set. * :meth:`__array_finalize__` is called for all new objects, including those created by slicing. This is where attributes are copied over from the old object. """ from copy import deepcopy import numpy as np import quantities as pq from neo.core.baseneo import MergeError, merge_annotations, intersect_annotations from neo.core.basesignal import BaseSignal from neo.core.analogsignal import AnalogSignal from neo.core.dataobject import DataObject def _new_IrregularlySampledSignal( cls, times, signal, units=None, time_units=None, dtype=None, copy=None, name=None, file_origin=None, description=None, array_annotations=None, annotations=None, segment=None, ): """ A function to map IrregularlySampledSignal.__new__ to a function that does not do the unit checking. This is needed for pickle to work. """ iss = cls( times=times, signal=signal, units=units, time_units=time_units, dtype=dtype, copy=copy, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) iss.segment = segment return iss class IrregularlySampledSignal(BaseSignal): """ An array of one or more analog signals with samples taken at arbitrary time points. A representation of one or more continuous, analog signals acquired at time :attr:`t_start` with a varying sampling interval. Each channel is sampled at the same time points. Inherits from :class:`quantities.Quantity`, which in turn inherits from :class:`numpy.ndarray`. Parameters ---------- times: quantity array 1D |numpy array 1D | list The time of each data point. Must have the same size as `signal`. signal: quantity array 2D | numpy array 2D | list (data, channel) The data itself organized as (n_data x n_channel) units: quantity units | None, default: None The units for the signal if signal is numpy array or list Ignored if signal is a quantity array time_units: quantity units | None, default: None The units for times if times is a numpy array or list Ignored if times is a quantity array dtype: numpy dtype | string | None, default: None Overrides the signal array dtype Does not affect the dtype of the times which must be floats copy: bool | None, default: None deprecated and no longer used (for NumPy 2.0 support). Will be removed. name: str | None, default: None An optional label for the dataset description: str | None, default: None An optional text description of the dataset file_origin: str | None, default: None The filesystem path or url of the orginal data array_annotations: dict | None, default: None Dict mapping strings to numpy arrays containing annotations for all data points annotations: dict Optional additional metadata supplied by the user as a dict. Will be stored in the annotations attribute of the object Notes ----- Attributes that can accessed for this object: * sampling_intervals: quantity 1d array Interval between each adjacent pair of samples (times[1:] - times[:-1]) * duration: quantity scalar Signal duration, read-only (times[-1]-times[0]) * t_start: quantity scalar Time when signal begins, read-only (times[0]) * t_stop: quantity scalar Time when signal ends, read-only (times[-1]) Slicing * `IrregularlySampledSignal` objects can be sliced. When this occurs, a new `IrregularlySampledSignal` (actually a view) is returned, with the same metadata, except that `times` is also sliced in the same way. Operations * == * != * + * * * / Examples -------- >>> from neo.core import IrregularlySampledSignal >>> from quantities import s, nA >>> >>> irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], ... units='mV', time_units='ms') >>> irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*s, ... [[4, 5], [5, 4], [6, 3]]*nA) >>> irsig0 == irsig1 False """ _parent_objects = ("Segment",) _parent_attrs = ("segment",) _quantity_attr = "signal" _necessary_attrs = (("times", pq.Quantity, 1), ("signal", pq.Quantity, 2)) def __new__( cls, times, signal, units=None, time_units=None, dtype=None, copy=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Construct a new :class:`IrregularlySampledSignal` instance. This is called whenever a new :class:`IrregularlySampledSignal` is created from the constructor, but not when slicing. """ if copy is not None: raise ValueError( "`copy` is now deprecated in Neo due to removal in Quantites to support Numpy 2.0. " "In order to facilitate the deprecation copy can be set to None but will raise an " "error if set to True/False since this will silently do nothing. This argument will be completely " "removed in Neo 0.15.0. Please update your code base as necessary." ) signal = cls._rescale(signal, units=units) if time_units is None: if hasattr(times, "units"): time_units = times.units else: raise ValueError("Time units must be specified") elif isinstance(times, pq.Quantity): # could improve this test, what if units is a string? if time_units != times.units: times = times.rescale(time_units) # should check time units have correct dimensions obj = pq.Quantity.__new__(cls, signal, units=units, dtype=dtype, copy=copy) if obj.ndim == 1: obj = obj.reshape(-1, 1) if len(times) != obj.shape[0]: raise ValueError("times array and signal array must have same length") obj.times = pq.Quantity(times, units=time_units, dtype=float, copy=copy) obj.segment = None return obj def __init__( self, times, signal, units=None, time_units=None, dtype=None, copy=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Initializes a newly constructed :class:`IrregularlySampledSignal` instance. """ DataObject.__init__( self, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) def __reduce__(self): """ Map the __new__ function onto _new_IrregularlySampledSignal, so that pickle works """ return _new_IrregularlySampledSignal, ( self.__class__, self.times, np.array(self), self.units, self.times.units, self.dtype, None, self.name, self.file_origin, self.description, self.array_annotations, self.annotations, self.segment, ) def _array_finalize_spec(self, obj): """ Set default values for attributes specific to :class:`IrregularlySampledSignal`. Common attributes are defined in :meth:`__array_finalize__` in :class:`basesignal.BaseSignal`), which is called every time a new signal is created and calls this method. """ self.times = getattr(obj, "times", None) return obj def __repr__(self): """ Returns a string representing the :class:`IrregularlySampledSignal`. """ return f"<{self.__class__.__name__}({super().__repr__()} " f"at times {self.times})>" def __getitem__(self, i): """ Get the item or slice :attr:`i`. """ if isinstance(i, (int, np.integer)): # a single point in time across all channels obj = super().__getitem__(i) obj = pq.Quantity(obj.magnitude, units=obj.units) elif isinstance(i, tuple): obj = super().__getitem__(i) j, k = i if isinstance(j, (int, np.integer)): # a single point in time across some channels obj = pq.Quantity(obj.magnitude, units=obj.units) else: if isinstance(j, slice): obj.times = self.times.__getitem__(j) elif isinstance(j, np.ndarray): raise NotImplementedError("Arrays not yet supported") else: raise TypeError(f"{type(j)} not supported") if isinstance(k, (int, np.integer)): obj = obj.reshape(-1, 1) obj.array_annotations = deepcopy(self.array_annotations_at_index(k)) elif isinstance(i, slice): obj = super().__getitem__(i) obj.times = self.times.__getitem__(i) obj.array_annotations = deepcopy(self.array_annotations) elif isinstance(i, np.ndarray): # Indexing of an IrregularlySampledSignal is only consistent if the resulting # number of samples is the same for each trace. The time axis for these samples is not # guaranteed to be continuous, so returning a Quantity instead of an # IrregularlySampledSignal here. new_time_dims = np.sum(i, axis=0) if len(new_time_dims) and all(new_time_dims == new_time_dims[0]): obj = np.asarray(self).T.__getitem__(i.T) obj = obj.T.reshape(self.shape[1], -1).T obj = pq.Quantity(obj, units=self.units) else: raise IndexError( "indexing of an IrregularlySampledSignal needs to keep the same " "number of sample for each trace contained" ) else: raise IndexError("index should be an integer, tuple, slice or boolean numpy array") return obj @property def duration(self): """ Signal duration. (:attr:`times`[-1] - :attr:`times`[0]) """ return self.times[-1] - self.times[0] @property def t_start(self): """ Time when signal begins. (:attr:`times`[0]) """ return self.times[0] @property def t_stop(self): """ Time when signal ends. (:attr:`times`[-1]) """ return self.times[-1] def __eq__(self, other): """ Equality test (==) """ if isinstance(other, IrregularlySampledSignal) and not (self.times == other.times).all(): return False return super().__eq__(other) def _check_consistency(self, other) -> None: """ Check if the attributes of another :class:`IrregularlySampledSignal` are compatible with this one. Raises ------ ValueError * Dimensionality of objects don't match for signal * If times are different between the two objects Returns ------- None if check passes """ # if not an array, then allow the calculation if not hasattr(other, "ndim"): return # if a scalar array, then allow the calculation if not other.ndim: return # dimensionality should match if self.ndim != other.ndim: raise ValueError(f"Dimensionality does not match: {self.ndim} vs {other.ndim}") # if if the other array does not have a times property, # then it should be okay to add it directly if not hasattr(other, "times"): return # if there is a times property, the times need to be the same if not (self.times == other.times).all(): raise ValueError(f"Times do not match: {self.times} vs {other.times}") def __rsub__(self, other, *args): """ Backwards subtraction (other-self) """ return self.__mul__(-1) + other def _repr_pretty_(self, pp, cycle): """ Handle pretty-printing the :class:`IrregularlySampledSignal`. """ pp.text( f"{self.__class__.__name__} with {self.shape[1]} channels of length " f"{self.shape[0]}; units {self.units.dimensionality.string}; datatype " f"{self.dtype}" ) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) def _pp(line): pp.breakable() with pp.group(indent=1): pp.text(line) for line in [f"sample times: {self.times}"]: _pp(line) @property def sampling_intervals(self): """ Interval between each adjacent pair of samples. (:attr:`times[1:]` - :attr:`times`[:-1]) """ return self.times[1:] - self.times[:-1] def mean(self, interpolation=None): """ Calculates the mean, optionally using interpolation between sampling times. Parameters ---------- interpolation: function | None Optionally interpolate between samples. Not currently implemented If none uses the standard mean assuming that values change stepwise in sampling time. Returns ------- mean: float The mean of the IrregularlySampledSignal """ if interpolation is None: return (self[:-1] * self.sampling_intervals.reshape(-1, 1)).sum() / self.duration else: raise NotImplementedError def resample(self, sample_count, **kwargs): """ Resample the data points of the signal. This method interpolates the signal and returns a new signal with a fixed number of samples defined by `sample_count`. This function is a wrapper of scipy.signal.resample and accepts the same set of keyword arguments, except for specifying the axis of resampling which is fixed to the first axis here, and the sample positions. . Parameters ---------- sample_count: int Number of desired samples. The resulting signal starts at the same sample as the original and is sampled regularly. Returns ------- resampled_signal: :class:`AnalogSignal` New instance of a :class:`AnalogSignal` object containing the resampled data points. The original :class:`AnalogSignal` is not modified. """ try: import scipy.signal except ImportError: raise ImportError("Resampling requires availability of scipy.signal") # Resampling is only permitted along the time axis (axis=0) if "axis" in kwargs: kwargs.pop("axis") if "t" in kwargs: kwargs.pop("t") resampled_data, resampled_times = scipy.signal.resample( self.magnitude, sample_count, t=self.times.magnitude, axis=0, **kwargs ) new_sampling_rate = (sample_count - 1) / self.duration resampled_signal = AnalogSignal( resampled_data, units=self.units, dtype=self.dtype, t_start=self.t_start, sampling_rate=new_sampling_rate, array_annotations=self.array_annotations.copy(), **self.annotations.copy(), ) # since the number of channels stays the same, we can also copy array annotations here resampled_signal.array_annotations = self.array_annotations.copy() return resampled_signal def time_slice(self, t_start, t_stop): """ Creates a new :class:`IrregularlySampledSignal` corresponding to the time slice of the original :class:`IrregularlySampledSignal` Parameters ---------- t_start: float | None The starting time of the time slice If None it will use -np.inf to determine the start index t_stop: float | None The stopping time of the time slice If None it will use np,inf to determine the end index Returns ------- new_st: neo.core.IrregularlySampledSignal A new IrregularlySampledSignal with the demanded times """ _t_start = t_start _t_stop = t_stop if t_start is None: _t_start = -np.inf if t_stop is None: _t_stop = np.inf indices = (self.times >= _t_start) & (self.times <= _t_stop) count = 0 id_start = None id_stop = None for i in indices: if id_start is None: if i: id_start = count else: if not i: id_stop = count break count += 1 # Time slicing should create a deep copy of the object new_st = deepcopy(self[id_start:id_stop]) return new_st def time_shift(self, t_shift): """ Shifts a :class:`IrregularlySampledSignal` to start at a new time. Parameters ---------- t_shift: Quantity (time) Amount of time by which to shift the :class:`IrregularlySampledSignal`. Returns ------- new_sig: :class:`SpikeTrain` New instance of a :class:`IrregularlySampledSignal` object starting at t_shift later than the original :class:`IrregularlySampledSignal` (the original :class:`IrregularlySampledSignal` is not modified). """ new_sig = deepcopy(self) # As of numpy 2.0/quantities 0.16 we need to copy the array itself # in order to be able to time_shift new_sig.times = self.times.copy() new_sig.times += t_shift return new_sig def merge(self, other): """ Merge another signal into this one. The signal objects are concatenated horizontally (column-wise, :func:`np.hstack`). If the attributes of the two signals are not compatible, an Exception is raised. Required attributes of the signal are used. """ if not np.array_equal(self.times, other.times): raise MergeError("Cannot merge these two signals as the sample times differ.") if self.segment != other.segment: raise MergeError("Cannot merge these two signals as they belong to different segments.") if hasattr(self, "lazy_shape"): if hasattr(other, "lazy_shape"): if self.lazy_shape[0] != other.lazy_shape[0]: raise MergeError("Cannot merge signals of different length.") merged_lazy_shape = (self.lazy_shape[0], self.lazy_shape[1] + other.lazy_shape[1]) else: raise MergeError("Cannot merge a lazy object with a real object.") if other.units != self.units: other = other.rescale(self.units) stack = np.hstack((self.magnitude, other.magnitude)) kwargs = {} for name in ("name", "description", "file_origin"): attr_self = getattr(self, name) attr_other = getattr(other, name) if attr_self == attr_other: kwargs[name] = attr_self else: kwargs[name] = f"merge({attr_self}, {attr_other})" merged_annotations = merge_annotations(self.annotations, other.annotations) kwargs.update(merged_annotations) signal = self.__class__(self.times, stack, units=self.units, dtype=self.dtype, copy=None, **kwargs) signal.segment = self.segment signal.array_annotate(**self._merge_array_annotations(other)) if hasattr(self, "lazy_shape"): signal.lazy_shape = merged_lazy_shape return signal def concatenate(self, other, allow_overlap=False): """ Combine this and another signal along the time axis. The signal objects are concatenated vertically (row-wise, :func:`np.vstack`). Patching can be used to combine signals across segments. Note: Only array annotations common to both signals are attached to the concatenated signal. If the attributes of the two signal are not compatible, an Exception is raised. Required attributes of the signal are used. Parameters ---------- other : neo.BaseSignal The object that is merged into this one. allow_overlap : bool If false, overlapping samples between the two signals are not permitted and an ValueError is raised. If true, no check for overlapping samples is performed and all samples are combined. Returns ------- signal : neo.IrregularlySampledSignal Signal containing all non-overlapping samples of both source signals. Raises ------ MergeError If `other` object has incompatible attributes. """ for attr in self._necessary_attrs: if not (attr[0] in ["signal", "times", "t_start", "t_stop", "times"]): if getattr(self, attr[0], None) != getattr(other, attr[0], None): raise MergeError(f"Cannot concatenate these two signals as the {attr[0]} differ.") if hasattr(self, "lazy_shape"): if hasattr(other, "lazy_shape"): if self.lazy_shape[-1] != other.lazy_shape[-1]: raise MergeError("Cannot concatenate signals as they contain" " different numbers of traces.") merged_lazy_shape = (self.lazy_shape[0] + other.lazy_shape[0], self.lazy_shape[-1]) else: raise MergeError("Cannot concatenate a lazy object with a real object.") if other.units != self.units: other = other.rescale(self.units) new_times = np.hstack((self.times, other.times)) sorting = np.argsort(new_times) new_samples = np.vstack((self.magnitude, other.magnitude)) kwargs = {} for name in ("name", "description", "file_origin"): attr_self = getattr(self, name) attr_other = getattr(other, name) if attr_self == attr_other: kwargs[name] = attr_self else: kwargs[name] = f"merge({attr_self}, {attr_other})" merged_annotations = merge_annotations(self.annotations, other.annotations) kwargs.update(merged_annotations) kwargs["array_annotations"] = intersect_annotations(self.array_annotations, other.array_annotations) if not allow_overlap: if max(self.t_start, other.t_start) <= min(self.t_stop, other.t_stop): raise ValueError( "Can not combine signals that overlap in time. Allow for " 'overlapping samples using the "no_overlap" parameter.' ) t_start = min(self.t_start, other.t_start) t_stop = max(self.t_start, other.t_start) signal = IrregularlySampledSignal( signal=new_samples[sorting], times=new_times[sorting], units=self.units, dtype=self.dtype, copy=None, t_start=t_start, t_stop=t_stop, **kwargs, ) signal.segment = None if hasattr(self, "lazy_shape"): signal.lazy_shape = merged_lazy_shape return signal ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/objectlist.py0000644000175100017510000001002015154507213016250 0ustar00runnerrunner""" This module implements the ObjectList class, which is used to peform type checks and handle relationships within the Neo Block-Segment-Data hierarchy. """ import sys from neo.core.baseneo import BaseNeo class ObjectList: """ This class behaves like a list, but has additional functionality to handle relationships within Neo hierarchy, and perform type checks. """ def __init__(self, allowed_contents, parent=None): # validate allowed_contents and normalize it to a tuple if isinstance(allowed_contents, type) and issubclass(allowed_contents, BaseNeo): self.allowed_contents = (allowed_contents,) else: for item in allowed_contents: if not issubclass(item, BaseNeo): raise TypeError("Each item in allowed_contents must be a subclass of BaseNeo") self.allowed_contents = tuple(allowed_contents) self._items = [] self.parent = parent def _handle_append(self, obj): if not ( isinstance(obj, self.allowed_contents) or ( # also allow proxy objects of the correct type hasattr(obj, "proxy_for") and obj.proxy_for in self.allowed_contents ) ): raise TypeError(f"Object is a {type(obj)}. It should be one of {self.allowed_contents}.") if self._contains(obj): raise ValueError("Cannot add this object because it is already contained within the list") # set the child-parent relationship if self.parent: relationship_name = self.parent.__class__.__name__.lower() if relationship_name == "group": raise Exception("Objects in groups should not link to the group as their parent") current_parent = getattr(obj, relationship_name) if current_parent != self.parent: # use weakref here? - see https://github.com/NeuralEnsemble/python-neo/issues/684 setattr(obj, relationship_name, self.parent) def _contains(self, obj): if self._items is None: obj_ids = [] else: obj_ids = [id(item) for item in self._items] return id(obj) in obj_ids def __str__(self): return str(self._items) def __repr__(self): return repr(self._items) def __add__(self, objects): # todo: decision: return a list, or a new DataObjectList? if isinstance(objects, ObjectList): return self._items + objects._items else: return self._items + objects def __radd__(self, objects): if isinstance(objects, ObjectList): return objects._items + self._items else: return objects + self._items def __contains__(self, key): return key in self._items def __iadd__(self, objects): for obj in objects: self._handle_append(obj) self._items.extend(objects) return self def __iter__(self): return iter(self._items) def __getitem__(self, i): return self._items[i] def __len__(self): return len(self._items) def __setitem__(self, key, value): self._items[key] = value def append(self, obj): self._handle_append(obj) self._items.append(obj) def extend(self, objects): for obj in objects: self._handle_append(obj) self._items.extend(objects) def clear(self): self._items = [] def count(self, value): return self._items.count(value) def index(self, value, start=0, stop=sys.maxsize): return self._items.index(value, start, stop) def insert(self, index, obj): self._handle_append(obj) self._items.insert(index, obj) def pop(self, index=-1): return self._items.pop(index) def remove(self, value): return self._items.remove(value) def reverse(self): raise self._items.reverse() def sort(self, *args, key=None, reverse=False): self._items.sort(*args, key=key, reverse=reverse) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/regionofinterest.py0000644000175100017510000001710315154507213017505 0ustar00runnerrunnerfrom math import floor, ceil from neo.core.baseneo import BaseNeo from neo.core.imagesequence import ImageSequence class RegionOfInterest(BaseNeo): """Abstract base class""" _parent_objects = ("Group",) _parent_attrs = ("group",) _necessary_attrs = (("image_sequence", ("ImageSequence",), 1),) is_view = True def __init__(self, image_sequence, name=None, description=None, file_origin=None, **annotations): super().__init__(name=name, description=description, file_origin=file_origin, **annotations) if not ( isinstance(image_sequence, ImageSequence) or (hasattr(image_sequence, "proxy_for") and issubclass(image_sequence.proxy_for, ImageSequence)) ): raise ValueError("Can only take a RegionOfInterest of an ImageSequence") self.image_sequence = image_sequence def _get_obj(self): # for consistency with ChannelView return self.image_sequence def _set_obj(self, value): if not isinstance(value, ImageSequence): raise TypeError(f"Value must be ImageSequence, not of type: {type(value)}") self.image_sequence = value obj = property(fget=_get_obj, fset=_set_obj) def resolve(self): """ Return a signal from within this region of the underlying ImageSequence. """ return self.image_sequence.signal_from_region(self) class CircularRegionOfInterest(RegionOfInterest): """Representation of a circular ROI *Usage:* >>> roi = CircularRegionOfInterest(20.0, 20.0, radius=5.0) >>> signal = image_sequence.signal_from_region(roi) *Required attributes/properties*: :x, y: (integers or floats) Pixel coordinates of the centre of the ROI :radius: (integer or float) Radius of the ROI in pixels """ _necessary_attrs = (("image_sequence", ("ImageSequence",), 1), ("x", int), ("y", int), ("radius", int)) def __init__(self, image_sequence, x, y, radius, name=None, description=None, file_origin=None, **annotations): super().__init__(image_sequence, name, description, file_origin, **annotations) self.y = y self.x = x self.radius = radius @property def centre(self): return (self.x, self.y) @property def center(self): return self.centre def is_inside(self, x, y): if (x - self.x) * (x - self.x) + (y - self.y) * (y - self.y) <= self.radius * self.radius: return True else: return False def pixels_in_region(self): """Returns a list of pixels whose *centres* are within the circle""" pixel_in_list = [] for y in range(int(floor(self.y - self.radius)), int(ceil(self.y + self.radius))): for x in range(int(floor(self.x - self.radius)), int(ceil(self.x + self.radius))): if self.is_inside(x, y): pixel_in_list.append([x, y]) return pixel_in_list class RectangularRegionOfInterest(RegionOfInterest): """Representation of a rectangular ROI *Usage:* >>> roi = RectangularRegionOfInterest(20.0, 20.0, width=5.0, height=5.0) >>> signal = image_sequence.signal_from_region(roi) *Required attributes/properties*: :x, y: (integers or floats) Pixel coordinates of the centre of the ROI :width: (integer or float) Width (x-direction) of the ROI in pixels :height: (integer or float) Height (y-direction) of the ROI in pixels """ _necessary_attrs = ( ("image_sequence", ("ImageSequence",), 1), ("x", int), ("y", int), ("width", int), ("height", int), ) def __init__( self, image_sequence, x, y, width, height, name=None, description=None, file_origin=None, **annotations ): super().__init__(image_sequence, name, description, file_origin, **annotations) self.x = x self.y = y self.width = width self.height = height def is_inside(self, x, y): if ( self.x - self.width / 2.0 <= x < self.x + self.width / 2.0 and self.y - self.height / 2.0 <= y < self.y + self.height / 2.0 ): return True else: return False def pixels_in_region(self): """Returns a list of pixels whose *centres* are within the rectangle""" pixel_list = [] h = self.height w = self.width for y in range(int(floor(self.y - h / 2.0)), int(ceil(self.y + h / 2.0))): for x in range(int(floor(self.x - w / 2.0)), int(ceil(self.x + w / 2.0))): if self.is_inside(x, y): pixel_list.append([x, y]) return pixel_list class PolygonRegionOfInterest(RegionOfInterest): """Representation of a polygonal ROI *Usage:* >>> roi = PolygonRegionOfInterest( ... (20.0, 20.0), ... (30.0, 20.0), ... (25.0, 25.0) ... ) >>> signal = image_sequence.signal_from_region(roi) *Required attributes/properties*: :vertices: tuples containing the (x, y) coordinates, as integers or floats, of the vertices of the polygon """ _necessary_attrs = ( ("image_sequence", ("ImageSequence",), 1), ("vertices", list), ) def __init__(self, image_sequence, *vertices, name=None, description=None, file_origin=None, **annotations): super().__init__(image_sequence, name, description, file_origin, **annotations) self.vertices = vertices def polygon_ray_casting(self, bounding_points, bounding_box_positions): # from https://stackoverflow.com/questions/217578/how-can-i-determine-whether-a-2d-point-is-within-a-polygon # user Noresourses # Arrays containing the x- and y-coordinates of the polygon's vertices. vertx = [point[0] for point in bounding_points] verty = [point[1] for point in bounding_points] # Number of vertices in the polygon nvert = len(bounding_points) # Points that are inside points_inside = [] # For every candidate position within the bounding box for idx, pos in enumerate(bounding_box_positions): testx, testy = (pos[0], pos[1]) c = 0 for i in range(0, nvert): j = i - 1 if i != 0 else nvert - 1 if ((verty[i] * 1.0 > testy * 1.0) != (verty[j] * 1.0 > testy * 1.0)) and ( testx * 1.0 < (vertx[j] * 1.0 - vertx[i] * 1.0) * (testy * 1.0 - verty[i] * 1.0) / (verty[j] * 1.0 - verty[i] * 1.0) + vertx[i] * 1.0 ): c += 1 # If odd, that means that we are inside the polygon if c % 2 == 1: points_inside.append(pos) return points_inside def pixels_in_region(self): min_x, max_x, min_y, max_y = ( self.vertices[0][0], self.vertices[0][0], self.vertices[0][1], self.vertices[0][1], ) for i in self.vertices: if i[0] < min_x: min_x = i[0] if i[0] > max_x: max_x = i[0] if i[1] < min_y: min_y = i[1] if i[1] > max_y: max_y = i[1] list_coord = [] for y in range(int(floor(min_y)), int(ceil(max_y))): for x in range(int(floor(min_x)), int(ceil(max_x))): list_coord.append((x, y)) pixel_list = self.polygon_ray_casting(self.vertices, list_coord) return pixel_list ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/segment.py0000644000175100017510000002563215154507213015567 0ustar00runnerrunner""" This module defines :class:`Segment`, a container for data sharing a common time basis. :class:`Segment` derives from :class:`Container`, from :module:`neo.core.container`. """ from datetime import datetime from copy import deepcopy from neo.core.analogsignal import AnalogSignal from neo.core.container import Container from neo.core.objectlist import ObjectList from neo.core.epoch import Epoch from neo.core.event import Event from neo.core.imagesequence import ImageSequence from neo.core.irregularlysampledsignal import IrregularlySampledSignal from neo.core.spiketrainlist import SpikeTrainList from neo.core.view import ChannelView class Segment(Container): """ A container for data sharing a common time basis. A :class:`Segment` is a heterogeneous container for discrete or continuous data sharing a common clock (time basis) but not necessary the same sampling rate, start or end time. Parameters ---------- name: str | None, default: None A label for the dataset. description: str | None, default: None Text description. file_origin: str | None, default: None Filesystem path or URL of the original data file. rec_datetime: datetime.datetime| None, default: None The date and time of the original recording index: int | None, default: None You can use this to define a temporal ordering of your Segment. For instance you could use this for trial numbers. annotations: dict | None, Other keyword annotations for the dataset Examples -------- >>> from neo.core import Segment, SpikeTrain, AnalogSignal >>> from quantities import Hz, s >>> >>> seg = Segment(index=5) >>> >>> train0 = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) >>> seg.spiketrains.append(train0) >>> >>> train1 = SpikeTrain(times=[100.01, 103.3, 109.3], units='sec', ... t_stop=110) >>> seg.spiketrains.append(train1) >>> >>> sig0 = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', ... sampling_rate=1*Hz) >>> seg.analogsignals.append(sig0) >>> >>> sig1 = AnalogSignal(signal=[100.01, 103.3, 109.3], units='nA', ... sampling_period=.1*s) >>> seg.analogsignals.append(sig1) Notes ----- Container of: :class:`Epoch` :class:`Event` :class:`AnalogSignal` :class:`IrregularlySampledSignal` :class:`SpikeTrain` """ _data_child_objects = ("AnalogSignal", "Epoch", "Event", "IrregularlySampledSignal", "SpikeTrain", "ImageSequence") _parent_objects = ("Block",) _recommended_attrs = ( ("file_datetime", datetime), ("rec_datetime", datetime), ("index", int), ) + Container._recommended_attrs _repr_pretty_containers = ("analogsignals",) def __init__( self, name=None, description=None, file_origin=None, file_datetime=None, rec_datetime=None, index=None, **annotations, ): """ Initialize a new :class:`Segment` instance. """ super().__init__(name=name, description=description, file_origin=file_origin, **annotations) self._analogsignals = ObjectList(AnalogSignal, parent=self) self._irregularlysampledsignals = ObjectList(IrregularlySampledSignal, parent=self) self._spiketrains = SpikeTrainList(parent=self) self._events = ObjectList(Event, parent=self) self._epochs = ObjectList(Epoch, parent=self) self._channelviews = ObjectList(ChannelView, parent=self) self._imagesequences = ObjectList(ImageSequence, parent=self) self.block = None self.file_datetime = file_datetime self.rec_datetime = rec_datetime self.index = index analogsignals = property( fget=lambda self: self._get_object_list("_analogsignals"), fset=lambda self, value: self._set_object_list("_analogsignals", value), doc="list of AnalogSignals contained in this segment", ) irregularlysampledsignals = property( fget=lambda self: self._get_object_list("_irregularlysampledsignals"), fset=lambda self, value: self._set_object_list("_irregularlysampledsignals", value), doc="list of IrregularlySignals contained in this segment", ) events = property( fget=lambda self: self._get_object_list("_events"), fset=lambda self, value: self._set_object_list("_events", value), doc="list of Events contained in this segment", ) epochs = property( fget=lambda self: self._get_object_list("_epochs"), fset=lambda self, value: self._set_object_list("_epochs", value), doc="list of Epochs contained in this segment", ) channelviews = property( fget=lambda self: self._get_object_list("_channelviews"), fset=lambda self, value: self._set_object_list("_channelviews", value), doc="list of ChannelViews contained in this segment", ) imagesequences = property( fget=lambda self: self._get_object_list("_imagesequences"), fset=lambda self, value: self._set_object_list("_imagesequences", value), doc="list of ImageSequences contained in this segment", ) spiketrains = property( fget=lambda self: self._get_object_list("_spiketrains"), fset=lambda self, value: self._set_object_list("_spiketrains", value), doc="list of SpikeTrains contained in this segment", ) # t_start attribute is handled as a property so type checking can be done @property def t_start(self): """ Time when first signal begins. Returns ------- t_start: float | None Returns the starting time if exists otherwise None """ t_starts = [sig.t_start for sig in self.analogsignals + self.spiketrains + self.irregularlysampledsignals] for e in self.epochs + self.events: if hasattr(e, "t_start"): # in case of proxy objects t_starts += [e.t_start] elif len(e) > 0: t_starts += [e.times[0]] # t_start is not defined if no children are present if len(t_starts) == 0: return None t_start = min(t_starts) return t_start # t_stop attribute is handled as a property so type checking can be done @property def t_stop(self): """ Time when last signal ends. Returns ------- t_stop: float | None Returns the stopping time if exists otherwise None """ t_stops = [sig.t_stop for sig in self.analogsignals + self.spiketrains + self.irregularlysampledsignals] for e in self.epochs + self.events: if hasattr(e, "t_stop"): # in case of proxy objects t_stops += [e.t_stop] elif len(e) > 0: t_stops += [e.times[-1]] # t_stop is not defined if no children are present if len(t_stops) == 0: return None t_stop = max(t_stops) return t_stop def time_slice(self, t_start=None, t_stop=None, reset_time=False, **kwargs): """ Creates a time slice of a Segment containing slices of all child objects. Parameters ---------- t_start: Quantity Starting time of the sliced time window. t_stop: Quantity Stop time of the sliced time window. reset_time: bool, optional, default: False If True the time stamps of all sliced objects are set to fall in the range from t_start to t_stop. If False, original time stamps are retained. **kwargs Additional keyword arguments used for initialization of the sliced Segment object. Returns ------- subseg: Segment Temporal slice of the original Segment from t_start to t_stop. """ subseg = Segment(**kwargs) for attr in ["file_datetime", "rec_datetime", "index", "name", "description", "file_origin"]: setattr(subseg, attr, getattr(self, attr)) subseg.annotations = deepcopy(self.annotations) if t_start is None: t_start = self.t_start if t_stop is None: t_stop = self.t_stop t_shift = -t_start # cut analogsignals and analogsignalarrays for ana_id in range(len(self.analogsignals)): if hasattr(self.analogsignals[ana_id], "_rawio"): ana_time_slice = self.analogsignals[ana_id].load(time_slice=(t_start, t_stop)) else: ana_time_slice = self.analogsignals[ana_id].time_slice(t_start, t_stop) if reset_time: ana_time_slice = ana_time_slice.time_shift(t_shift) subseg.analogsignals.append(ana_time_slice) # cut irregularly sampled signals for irr_id in range(len(self.irregularlysampledsignals)): if hasattr(self.irregularlysampledsignals[irr_id], "_rawio"): ana_time_slice = self.irregularlysampledsignals[irr_id].load(time_slice=(t_start, t_stop)) else: ana_time_slice = self.irregularlysampledsignals[irr_id].time_slice(t_start, t_stop) if reset_time: ana_time_slice = ana_time_slice.time_shift(t_shift) subseg.irregularlysampledsignals.append(ana_time_slice) # cut spiketrains for st_id in range(len(self.spiketrains)): if hasattr(self.spiketrains[st_id], "_rawio"): st_time_slice = self.spiketrains[st_id].load(time_slice=(t_start, t_stop)) else: st_time_slice = self.spiketrains[st_id].time_slice(t_start, t_stop) if reset_time: st_time_slice = st_time_slice.time_shift(t_shift) subseg.spiketrains.append(st_time_slice) # cut events for ev_id in range(len(self.events)): if hasattr(self.events[ev_id], "_rawio"): ev_time_slice = self.events[ev_id].load(time_slice=(t_start, t_stop)) else: ev_time_slice = self.events[ev_id].time_slice(t_start, t_stop) if reset_time: ev_time_slice = ev_time_slice.time_shift(t_shift) # appending only non-empty events if len(ev_time_slice): subseg.events.append(ev_time_slice) # cut epochs for ep_id in range(len(self.epochs)): if hasattr(self.epochs[ep_id], "_rawio"): ep_time_slice = self.epochs[ep_id].load(time_slice=(t_start, t_stop)) else: ep_time_slice = self.epochs[ep_id].time_slice(t_start, t_stop) if reset_time: ep_time_slice = ep_time_slice.time_shift(t_shift) # appending only non-empty epochs if len(ep_time_slice): subseg.epochs.append(ep_time_slice) subseg.check_relationships() return subseg ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/spiketrain.py0000644000175100017510000011412715154507213016274 0ustar00runnerrunner""" This module implements :class:`SpikeTrain`, an array of spike times. :class:`SpikeTrain` derives from :class:`BaseNeo`, from :module:`neo.core.baseneo`, and from :class:`quantities.Quantity`, which inherits from :class:`numpy.array`. Inheritance from :class:`numpy.array` is explained here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html In brief: * Initialization of a new object from constructor happens in :meth:`__new__`. This is where user-specified attributes are set. * :meth:`__array_finalize__` is called for all new objects, including those created by slicing. This is where attributes are copied over from the old object. """ from copy import deepcopy import warnings import numpy as np import quantities as pq from neo.core.baseneo import BaseNeo, MergeError, merge_annotations from neo.core.dataobject import DataObject, ArrayDict # need this to avoid circular import issue import neo def check_has_dimensions_time(*values) -> None: """ Verify that all arguments have a dimensionality that is compatible with time. Raises ------ ValueError if any value is not compatible with time Returns ------- None: indicating that each value in values has the dimension time """ errmsgs = [] for value in values: dim = value.dimensionality.simplified if len(dim) != 1 or list(dim.values())[0] != 1 or not isinstance(list(dim.keys())[0], pq.UnitTime): errmsgs.append(f"value {value} has dimensions {dim}, not [time]") if errmsgs: raise ValueError("\n".join(errmsgs)) def _check_time_in_range(value, t_start, t_stop, view=False) -> None: """ Verify that all times in `value` are between `t_start` and `t_stop` (inclusive) Parameters ---------- value: array-like An array-like object with times t_start: float The starting time t_stop: float The stopping time view: bool, default: False If true views are used for the test. This increases speed but is only safe if certain that the dtype and units are the same. Raises ------ ValueError * If t_stop < t_start * value.min() < t_start * value.max() > t_start Returns ------- None: If check passes """ if t_start > t_stop: raise ValueError(f"t_stop ({t_stop}) is before t_start ({t_start})") if not value.size: return if view: value = value.view(np.ndarray) t_start = t_start.view(np.ndarray) t_stop = t_stop.view(np.ndarray) if value.min() < t_start: raise ValueError(f"The first spike ({value}) is before t_start ({t_start})") if value.max() > t_stop: raise ValueError(f"The last spike ({value}) is after t_stop ({t_stop})") def _check_waveform_dimensions(spiketrain) -> None: """ Verify that waveform is compliant with the waveform definition as quantity array 3D (spike, channel, time) Parameters ---------- spiketrain: neo.core.SpikeTrain The neo.core.SpikeTrain to check for waveforms Raises ------ ValueError * number of waveforms doesn't match the number of spikes Returns ------- None if test passes or no waveforms present """ if not spiketrain.size: return waveforms = spiketrain.waveforms if (waveforms is None) or (not waveforms.size): return if waveforms.shape[0] != len(spiketrain): raise ValueError( f"Spiketrain length ({len(spiketrain)}) does not match to number of " f"waveforms present ({waveforms.shape[0]})" ) def _new_spiketrain( cls, signal, t_stop, units=None, dtype=None, copy=None, sampling_rate=1.0 * pq.Hz, t_start=0.0 * pq.s, waveforms=None, left_sweep=None, name=None, file_origin=None, description=None, array_annotations=None, annotations=None, segment=None, unit=None, ): """ A function to map :meth:`BaseAnalogSignal.__new__` to function that does not do the unit checking. This is needed for :module:`pickle` to work. """ if annotations is None: annotations = {} obj = SpikeTrain( times=signal, t_stop=t_stop, units=units, dtype=dtype, copy=copy, sampling_rate=sampling_rate, t_start=t_start, waveforms=waveforms, left_sweep=left_sweep, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) obj.segment = segment obj.unit = unit return obj def normalize_times_array(times, units=None, dtype=None, copy=None): """ Return a quantity array with the correct units. There are four scenarios: A. times (NumPy array), units given as string or Quantities units B. times (Quantity array), units=None C. times (Quantity), units given as string or Quantities units D. times (NumPy array), units=None In scenarios A-C we return a tuple (times as a Quantity array, dimensionality) In scenario C, we rescale the original array to match `units` In scenario D, we raise a ValueError """ if copy is not None: raise ValueError( "`copy` is now deprecated in Neo due to removal in Quantites to support Numpy 2.0. " "In order to facilitate the deprecation copy can be set to None but will raise an " "error if set to True/False since this will silently do nothing. This argument will be completely " "removed in Neo 0.15.0. Please update your code base as necessary." ) if dtype is None: if not hasattr(times, "dtype"): dtype = float if units is None: # No keyword units, so get from `times` try: dim = times.units.dimensionality except AttributeError: raise ValueError("you must specify units") else: if hasattr(units, "dimensionality"): dim = units.dimensionality else: dim = pq.quantity.validate_dimensionality(units) if hasattr(times, "dimensionality"): if times.dimensionality.items() == dim.items(): units = None # units will be taken from times, avoids copying else: raise ValueError("cannot rescale and return view") # check to make sure the units are time # this approach is orders of magnitude faster than comparing the # reference dimensionality if len(dim) != 1 or list(dim.values())[0] != 1 or not isinstance(list(dim.keys())[0], pq.UnitTime): ValueError(f"Units have dimensions {dim.simplified}, not [time]") return ( pq.Quantity( times, units=units, dtype=dtype, ), dim, ) class SpikeTrain(DataObject): """ :class:`SpikeTrain` is a :class:`Quantity` array of spike times. It is an ensemble of action potentials (spikes) emitted by the same unit in a period of time. Parameters ---------- times: quantity array 1D | numpy array 1D | list The times of each spike. t_stop: quantity scalar | numpy scalar |float Time at which the SpikeTrain ended. This will be converted to the same units as `times`. This argument is required because it specifies the period of time over which spikes could have occurred. Note that :attr:`t_start` is highly recommended for the same reason. units: (quantity units) | None, default: None Required if `times` is a list or numpy.ndarray` Not required if times is a quantities.Quantity dtype: numpy dtype | str | None, default: None Due to change in `copy` behavior this argument is also deprecated during construction copy: bool, default: None Deprecated in order to support NumPy 2.0 and will be removed. sampling_rate: quantity scalar, default: 1.0 pq.Hz Number of samples per unit time for the waveforms. t_start: quantity scalar | numpy scalar | float Time at which the`SpikeTrain` began. This will be converted to the same units as `times`. waveforms: quantity array 3D (n_spikes, n_channels, n_time) | None, default: None The waveforms of each spike if given must be of the correct shape None indicates no waveforms left_sweep: (quantity array 1D) | None, default: None Time from the beginning of the waveform to the trigger time of the spike. name: str | None, default: None A label for the dataset. description: str | None, default: None A text description of this dataset file_origin: str | Filesystem path | URL | None, default: None The path or location of the original data file. array_annotations: dict A dictonary mapping of strings to numpy arrays containing annotations for all data points annotations: dict Other user defined metadata given as a dict Notes ----- Useful properties of a SpikeTrain * sampling_period: quantity scalar Interval between two samples (1/`sampling_rate`) * duration: quantity scalar Duration over which spikes can occur read-only (`t_stop` - `t_start`) * spike_duration: quantity scalar Duration of a waveform, read-only (`waveform`.shape[2] * `sampling_period`) * right_sweep: quantity scalar Time from the trigger times of the spikes to the end of the waveforms, read-only (`left_sweep` + `spike_duration`) * times: quantity array 1D Returns the :class:`SpikeTrain` as a quantity array. *Slicing*: :class:`SpikeTrain` objects can be sliced. When this occurs, a new :class:`SpikeTrain` (actually a view) is returned, with the same metadata, except that :attr:`waveforms` is also sliced in the same way (along dimension 0). Note that t_start and t_stop are not changed automatically, although you can still manually change them. Examples -------- >>> from neo.core import SpikeTrain >>> from quantities import s >>> >>> train = SpikeTrain([3, 4, 5]*s, t_stop=10.0) >>> train2 = train[1:3] >>> >>> train.t_start array(0.0) * s >>> train.t_stop array(10.0) * s >>> train >>> train2 """ _parent_objects = ("Segment",) _parent_attrs = ("segment",) _quantity_attr = "times" _necessary_attrs = (("times", pq.Quantity, 1), ("t_start", pq.Quantity, 0), ("t_stop", pq.Quantity, 0)) _recommended_attrs = ( ("waveforms", pq.Quantity, 3), ("left_sweep", pq.Quantity, 0), ("sampling_rate", pq.Quantity, 0), ) + BaseNeo._recommended_attrs def __new__( cls, times, t_stop, units=None, dtype=None, copy=None, sampling_rate=1.0 * pq.Hz, t_start=0.0 * pq.s, waveforms=None, left_sweep=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Constructs a new :class:`Spiketrain` instance from data. This is called whenever a new :class:`SpikeTrain` is created from the constructor, but not when slicing. """ if copy is not None: raise ValueError( "`copy` is now deprecated in Neo due to removal in Quantites to support Numpy 2.0. " "In order to facilitate the deprecation copy can be set to None but will raise an " "error if set to True/False since this will silently do nothing. This argument will be completely " "removed in Neo 0.15.0. Please update your code base as necessary." ) if len(times) != 0 and waveforms is not None and len(times) != waveforms.shape[0]: # len(times)!=0 has been used to workaround a bug occurring during neo import raise ValueError("the number of waveforms should be equal to the number of spikes") if dtype is not None and hasattr(times, "dtype") and times.dtype != dtype: raise ValueError("cannot change dtype during construction due to change in copy behavior") # if t_start.dtype or t_stop.dtype != times.dtype != dtype, # _check_time_in_range can have problems, so we set the t_start # and t_stop dtypes to be the same as times before converting them # to dtype below # see ticket #38 if hasattr(t_start, "dtype") and t_start.dtype != times.dtype: t_start = t_start.astype(times.dtype) if hasattr(t_stop, "dtype") and t_stop.dtype != times.dtype: t_stop = t_stop.astype(times.dtype) # Make sure units are consistent # also get the dimensionality now since it is much faster to feed # that to Quantity rather than a unit times, dim = normalize_times_array(times, units, dtype, copy) # Construct Quantity from data obj = times.view(cls) # spiketrain times always need to be 1-dimensional if len(obj.shape) > 1: raise ValueError("Spiketrain times array has more than 1 dimension") # if the dtype and units match, just copy the values here instead # of doing the much more expensive creation of a new Quantity # using items() is orders of magnitude faster if ( hasattr(t_start, "dtype") and t_start.dtype == obj.dtype and hasattr(t_start, "dimensionality") and t_start.dimensionality.items() == dim.items() ): obj.t_start = t_start.copy() else: obj.t_start = pq.Quantity(t_start, units=dim, dtype=obj.dtype) if ( hasattr(t_stop, "dtype") and t_stop.dtype == obj.dtype and hasattr(t_stop, "dimensionality") and t_stop.dimensionality.items() == dim.items() ): obj.t_stop = t_stop.copy() else: obj.t_stop = pq.Quantity(t_stop, units=dim, dtype=obj.dtype) # Store attributes obj.waveforms = waveforms obj.left_sweep = left_sweep obj.sampling_rate = sampling_rate # parents obj.segment = None obj.unit = None # Error checking (do earlier?) _check_time_in_range(obj, obj.t_start, obj.t_stop, view=True) return obj def __init__( self, times, t_stop, units=None, dtype=None, copy=None, sampling_rate=1.0 * pq.Hz, t_start=0.0 * pq.s, waveforms=None, left_sweep=None, name=None, file_origin=None, description=None, array_annotations=None, **annotations, ): """ Initializes a newly constructed :class:`SpikeTrain` instance. """ # This method is only called when constructing a new SpikeTrain, # not when slicing or viewing. We use the same call signature # as __new__ for documentation purposes. Anything not in the call # signature is stored in annotations. # Calls parent __init__, which grabs universally recommended # attributes and sets up self.annotations DataObject.__init__( self, name=name, file_origin=file_origin, description=description, array_annotations=array_annotations, **annotations, ) def _repr_pretty_(self, pp, cycle): waveforms = "" if self.waveforms is not None: waveforms = " with waveforms" pp.text( f"{self.__class__.__name__} containing {self.size} spikes{waveforms}; " f"units {self.units.dimensionality.string}; datatype {self.dtype} " ) if self._has_repr_pretty_attrs_(): pp.breakable() self._repr_pretty_attrs_(pp, cycle) def _pp(line): pp.breakable() with pp.group(indent=1): pp.text(line) _pp(f"time: {self.t_start} to {self.t_stop}") def rescale(self, units, dtype=None): """ Return a copy of the :class:`SpikeTrain` converted to the specified units The `dtype` argument exists only for backward compatibility within quantities, see https://github.com/python-quantities/python-quantities/pull/204 """ obj = super().rescale(units) obj.t_start = self.t_start.rescale(units) obj.t_stop = self.t_stop.rescale(units) obj.unit = self.unit return obj def __reduce__(self): """ Map the __new__ function onto _new_BaseAnalogSignal, so that pickle works """ import numpy return _new_spiketrain, ( self.__class__, numpy.array(self), self.t_stop, self.units, self.dtype, None, self.sampling_rate, self.t_start, self.waveforms, self.left_sweep, self.name, self.file_origin, self.description, self.array_annotations, self.annotations, self.segment, self.unit, ) def __array_finalize__(self, obj): """ This is called every time a new :class:`SpikeTrain` is created. It is the appropriate place to set default values for attributes for :class:`SpikeTrain` constructed by slicing or viewing. User-specified values are only relevant for construction from constructor, and these are set in __new__. Then they are just copied over here. Note that the :attr:`waveforms` attribute is not sliced here. Nor is :attr:`t_start` or :attr:`t_stop` modified. """ # This calls Quantity.__array_finalize__ which deals with # dimensionality super().__array_finalize__(obj) # Supposedly, during initialization from constructor, obj is supposed # to be None, but this never happens. It must be something to do # with inheritance from Quantity. if obj is None: return # Set all attributes of the new object `self` from the attributes # of `obj`. For instance, when slicing, we want to copy over the # attributes of the original object. self.t_start = getattr(obj, "t_start", None) self.t_stop = getattr(obj, "t_stop", None) self.waveforms = getattr(obj, "waveforms", None) self.left_sweep = getattr(obj, "left_sweep", None) self.sampling_rate = getattr(obj, "sampling_rate", None) self.segment = getattr(obj, "segment", None) self.unit = getattr(obj, "unit", None) # The additional arguments self.annotations = getattr(obj, "annotations", {}) # Add empty array annotations, because they cannot always be copied, # but do not overwrite existing ones from slicing etc. # This ensures the attribute exists if not hasattr(self, "array_annotations"): self.array_annotations = ArrayDict(self._get_arr_ann_length()) # Note: Array annotations have to be changed when slicing or initializing an object, # copying them over in spite of changed data would result in unexpected behaviour # Globally recommended attributes self.name = getattr(obj, "name", None) self.file_origin = getattr(obj, "file_origin", None) self.description = getattr(obj, "description", None) if hasattr(obj, "lazy_shape"): self.lazy_shape = obj.lazy_shape def __repr__(self): """ Returns a string representing the :class:`SpikeTrain`. """ return f"" def sort(self): """ Sorts the :class:`SpikeTrain` and its :attr:`waveforms`, if any, by time. """ # sort the waveforms by the times sort_indices = np.argsort(self) if self.waveforms is not None and self.waveforms.any(): self.waveforms = self.waveforms[sort_indices] self.array_annotate(**deepcopy(self.array_annotations_at_index(sort_indices))) # now sort the times # We have sorted twice, but `self = self[sort_indices]` introduces # a dependency on the slicing functionality of SpikeTrain. super().sort() def __getslice__(self, i, j): """ Get a slice from :attr:`i` to :attr:`j`. Doesn't get called in Python 3, :meth:`__getitem__` is called instead """ return self.__getitem__(slice(i, j)) def __add__(self, time): """ Shifts the time point of all spikes by adding the amount in :attr:`time` (:class:`Quantity`) If `time` is a scalar, this also shifts :attr:`t_start` and :attr:`t_stop`. If `time` is an array, :attr:`t_start` and :attr:`t_stop` are not changed unless some of the new spikes would be outside this range. In this case :attr:`t_start` and :attr:`t_stop` are modified if necessary to ensure they encompass all spikes. It is not possible to add two SpikeTrains (raises ValueError). """ spikes = self.view(pq.Quantity) check_has_dimensions_time(time) if isinstance(time, SpikeTrain): raise TypeError("Can't add two spike trains") new_times = spikes + time if time.size > 1: t_start = min(self.t_start, np.min(new_times)) t_stop = max(self.t_stop, np.max(new_times)) else: t_start = self.t_start + time t_stop = self.t_stop + time return SpikeTrain( times=new_times, t_stop=t_stop, units=self.units, sampling_rate=self.sampling_rate, t_start=t_start, waveforms=self.waveforms, left_sweep=self.left_sweep, name=self.name, file_origin=self.file_origin, description=self.description, array_annotations=deepcopy(self.array_annotations), **self.annotations, ) def __sub__(self, time): """ Shifts the time point of all spikes by subtracting the amount in :attr:`time` (:class:`Quantity`) If `time` is a scalar, this also shifts :attr:`t_start` and :attr:`t_stop`. If `time` is an array, :attr:`t_start` and :attr:`t_stop` are not changed unless some of the new spikes would be outside this range. In this case :attr:`t_start` and :attr:`t_stop` are modified if necessary to ensure they encompass all spikes. In general, it is not possible to subtract two SpikeTrain objects (raises ValueError). However, if `time` is itself a SpikeTrain of the same size as the SpikeTrain, returns a Quantities array (since this is often used in checking whether two spike trains are the same or in calculating the inter-spike interval. """ spikes = self.view(pq.Quantity) check_has_dimensions_time(time) if isinstance(time, SpikeTrain): if self.size == time.size: return spikes - time else: raise TypeError("Can't subtract spike trains with different sizes") else: new_times = spikes - time if time.size > 1: t_start = min(self.t_start, np.min(new_times)) t_stop = max(self.t_stop, np.max(new_times)) else: t_start = self.t_start - time t_stop = self.t_stop - time return SpikeTrain( times=spikes - time, t_stop=t_stop, units=self.units, sampling_rate=self.sampling_rate, t_start=t_start, waveforms=self.waveforms, left_sweep=self.left_sweep, name=self.name, file_origin=self.file_origin, description=self.description, array_annotations=deepcopy(self.array_annotations), **self.annotations, ) def __getitem__(self, i): """ Get the item or slice :attr:`i`. """ obj = super().__getitem__(i) if hasattr(obj, "waveforms") and obj.waveforms is not None: obj.waveforms = obj.waveforms.__getitem__(i) try: obj.array_annotate(**deepcopy(self.array_annotations_at_index(i))) except AttributeError: # If Quantity was returned, not SpikeTrain pass return obj def __setitem__(self, i, value): """ Set the value the item or slice :attr:`i`. """ if not hasattr(value, "units"): value = pq.Quantity(value, units=self.units) # or should we be strict: raise ValueError( # "Setting a value # requires a quantity")? # check for values outside t_start, t_stop _check_time_in_range(value, self.t_start, self.t_stop) super().__setitem__(i, value) def __setslice__(self, i, j, value): if not hasattr(value, "units"): value = pq.Quantity(value, units=self.units) _check_time_in_range(value, self.t_start, self.t_stop) super().__setslice__(i, j, value) def _copy_data_complement(self, other, deep_copy=False): """ Copy the metadata from another :class:`SpikeTrain`. Note: Array annotations can not be copied here because length of data can change """ # Note: Array annotations cannot be copied because length of data can be changed # here which would cause inconsistencies for attr in ("left_sweep", "sampling_rate", "name", "file_origin", "description", "annotations"): attr_value = getattr(other, attr, None) if deep_copy: attr_value = deepcopy(attr_value) setattr(self, attr, attr_value) def duplicate_with_new_data(self, signal, t_start=None, t_stop=None, waveforms=None, deep_copy=True, units=None): """ Create a new :class:`SpikeTrain` with the same metadata but different data (times, t_start, t_stop) Note: Array annotations can not be copied here because length of data can change """ # using previous t_start and t_stop if no values are provided if t_start is None: t_start = self.t_start if t_stop is None: t_stop = self.t_stop if waveforms is None: waveforms = self.waveforms if units is None: units = self.units else: units = pq.quantity.validate_dimensionality(units) signal = deepcopy(signal) new_st = SpikeTrain(signal, t_start=t_start, t_stop=t_stop, waveforms=waveforms, units=units) new_st._copy_data_complement(self, deep_copy=deep_copy) # Note: Array annotations are not copied here, because length of data could change # overwriting t_start and t_stop with new values new_st.t_start = t_start new_st.t_stop = t_stop # consistency check _check_time_in_range(new_st, new_st.t_start, new_st.t_stop, view=False) _check_waveform_dimensions(new_st) return new_st def time_slice(self, t_start, t_stop): """ Creates a new :class:`SpikeTrain` corresponding to the time slice of the original :class:`SpikeTrain` between (and including) times :attr:`t_start` and :attr:`t_stop`. Either parameter can also be None to use infinite endpoints for the time interval. """ _t_start = t_start _t_stop = t_stop if t_start is None: _t_start = -np.inf if t_stop is None: _t_stop = np.inf if _t_start > self.t_stop or _t_stop < self.t_start: # the alternative to raising an exception would be to return # a zero-duration spike train set at self.t_stop or self.t_start raise ValueError("A time slice completely outside the " "boundaries of the spike train is not defined.") indices = (self >= _t_start) & (self <= _t_stop) # Time slicing should create a deep copy of the object new_st = deepcopy(self[indices]) new_st.t_start = max(_t_start, self.t_start) new_st.t_stop = min(_t_stop, self.t_stop) if self.waveforms is not None: new_st.waveforms = self.waveforms[indices] return new_st def time_shift(self, t_shift): """ Shifts a :class:`SpikeTrain` to start at a new time. Parameters ---------- t_shift: Quantity (time) Amount of time by which to shift the :class:`SpikeTrain`. Returns ------- spiketrain: :class:`SpikeTrain` New instance of a :class:`SpikeTrain` object starting at t_shift later than the original :class:`SpikeTrain` (the original :class:`SpikeTrain` is not modified). """ # We need new to make a new SpikeTrain times = self.times.copy() + t_shift t_stop = self.t_stop + t_shift t_start = self.t_start + t_shift new_st = SpikeTrain( times=times, t_stop=t_stop, units=self.unit, sampling_rate=self.sampling_rate, t_start=t_start, waveforms=self.waveforms, left_sweep=self.left_sweep, name=self.name, file_origin=self.file_origin, description=self.description, array_annotations=deepcopy(self.array_annotations), **self.annotations, ) # Here we can safely copy the array annotations since we know that # the length of the SpikeTrain does not change. new_st.array_annotate(**self.array_annotations) return new_st def merge(self, *others): """ Merge other :class:`SpikeTrain` objects into this one. The times of the :class:`SpikeTrain` objects combined in one array and sorted. If the attributes of the :class:`SpikeTrain` objects are not compatible, an Exception is raised. """ for other in others: if isinstance(other, neo.io.proxyobjects.SpikeTrainProxy): raise MergeError( "Cannot merge, SpikeTrainProxy objects cannot be merged" "into regular SpikeTrain objects, please load them first." ) elif not isinstance(other, SpikeTrain): raise MergeError("Cannot merge, only SpikeTrain" "can be merged into a SpikeTrain.") if self.sampling_rate != other.sampling_rate: raise MergeError("Cannot merge, different sampling rates") if self.t_start != other.t_start: raise MergeError("Cannot merge, different t_start") if self.t_stop != other.t_stop: raise MergeError("Cannot merge, different t_stop") if self.left_sweep != other.left_sweep: raise MergeError("Cannot merge, different left_sweep") if self.segment != other.segment: raise MergeError("Cannot merge these signals as they belong to" " different segments.") all_spiketrains = [self] all_spiketrains.extend([st.rescale(self.units) for st in others]) wfs = [st.waveforms is not None for st in all_spiketrains] if any(wfs) and not all(wfs): raise MergeError("Cannot merge signal with waveform and signal " "without waveform.") stack = np.concatenate([np.asarray(st) for st in all_spiketrains]) sorting = np.argsort(stack) sorted_stack = stack[sorting] kwargs = {} kwargs["array_annotations"] = self._merge_array_annotations(others, sorting=sorting) for name in ("name", "description", "file_origin"): attr = getattr(self, name) # check if self is already a merged spiketrain # if it is, get rid of the bracket at the end to append more attributes if attr is not None: if attr.startswith("merge(") and attr.endswith(")"): attr = attr[:-1] for other in others: attr_other = getattr(other, name) # both attributes are None --> nothing to do if attr is None and attr_other is None: continue # one of the attributes is None --> convert to string in order to merge them elif attr is None or attr_other is None: attr = str(attr) attr_other = str(attr_other) # check if the other spiketrain is already a merged spiketrain # if it is, append all of its merged attributes that aren't already in attr if attr_other.startswith("merge(") and attr_other.endswith(")"): for subattr in attr_other[6:-1].split("; "): if subattr not in attr: attr += "; " + subattr if not attr.startswith("merge("): attr = "merge(" + attr # if the other attribute is not in the list --> append # if attr doesn't already start with merge add merge( in the beginning elif attr_other not in attr: attr += "; " + attr_other if not attr.startswith("merge("): attr = "merge(" + attr # close the bracket of merge(...) if necessary if attr is not None: if attr.startswith("merge("): attr += ")" # write attr into kwargs dict kwargs[name] = attr merged_annotations = merge_annotations(*(st.annotations for st in all_spiketrains)) kwargs.update(merged_annotations) train = SpikeTrain( sorted_stack, units=self.units, dtype=self.dtype, copy=None, t_start=self.t_start, t_stop=self.t_stop, sampling_rate=self.sampling_rate, left_sweep=self.left_sweep, **kwargs, ) if all(wfs): wfs_stack = np.vstack([st.waveforms.rescale(self.waveforms.units) for st in all_spiketrains]) wfs_stack = wfs_stack[sorting] * self.waveforms.units train.waveforms = wfs_stack train.segment = self.segment if train.segment is not None: self.segment.spiketrains.append(train) return train def _merge_array_annotations(self, others, sorting=None): """ Merges array annotations of multiple different objects. The merge happens in such a way that the result fits the merged data In general this means concatenating the arrays from the objects. If an annotation is not present in one of the objects, it will be omitted. Apart from that the array_annotations need to be sorted according to the sorting of the spikes. :return Merged array_annotations """ if sorting is None: raise ValueError("The order of the merged spikes must be known") merged_array_annotations = {} omitted_keys_self = [] keys = self.array_annotations.keys() for key in keys: try: self_ann = deepcopy(self.array_annotations[key]) other_ann = np.concatenate([deepcopy(other.array_annotations[key]) for other in others]) if isinstance(self_ann, pq.Quantity): other_ann.rescale(self_ann.units) arr_ann = np.concatenate([self_ann, other_ann]) * self_ann.units else: arr_ann = np.concatenate([self_ann, other_ann]) merged_array_annotations[key] = arr_ann[sorting] # Annotation only available in 'self', must be skipped # Ignore annotations present only in one of the SpikeTrains except KeyError: omitted_keys_self.append(key) continue omitted_keys_other = [ key for key in set([key for other in others for key in other.array_annotations]) if key not in self.array_annotations ] if omitted_keys_self or omitted_keys_other: warnings.warn( f"The following array annotations were omitted, because they were only " f"present in one of the merged objects: {omitted_keys_self} from the " f"one that was merged into and {omitted_keys_other} from the ones that " f"were merged into it.", UserWarning, ) return merged_array_annotations @property def times(self): """ Returns the :class:`SpikeTrain` as a quantity array. """ return pq.Quantity(self) @property def duration(self): """ Duration over which spikes can occur, (:attr:`t_stop` - :attr:`t_start`) """ if self.t_stop is None or self.t_start is None: return None return self.t_stop - self.t_start @property def spike_duration(self): """ Duration of a waveform. (:attr:`waveform`.shape[2] * :attr:`sampling_period`) """ if self.waveforms is None or self.sampling_rate is None: return None return self.waveforms.shape[2] / self.sampling_rate @property def sampling_period(self): """ Interval between two samples. (1/:attr:`sampling_rate`) """ if self.sampling_rate is None: return None return 1.0 / self.sampling_rate @sampling_period.setter def sampling_period(self, period): """ Setter for :attr:`sampling_period` """ if period is None: self.sampling_rate = None else: self.sampling_rate = 1.0 / period @property def right_sweep(self): """ Time from the trigger times of the spikes to the end of the waveforms. (:attr:`left_sweep` + :attr:`spike_duration`) """ dur = self.spike_duration if self.left_sweep is None or dur is None: return None return self.left_sweep + dur ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/spiketrainlist.py0000644000175100017510000004407715154507213017176 0ustar00runnerrunner# -*- coding: utf-8 -*- """ This module implements :class:`SpikeTrainList`, a pseudo-list which supports a multiplexed representation of spike trains (all times in a single array, with a second array indicating which neuron/channel the spike is from). """ import warnings import numpy as np import quantities as pq from .spiketrain import SpikeTrain, normalize_times_array from .objectlist import ObjectList def is_spiketrain_or_proxy(obj): return isinstance(obj, SpikeTrain) or getattr(obj, "proxy_for", None) == SpikeTrain def unique(quantities): """ Function to mimic np.unique for use with quantities Parameters ---------- quantities: array-like[pq.Quantity dtype] An array-like object containing quantities Returns ------- unique_quantities: array[pq.Quantity dtype] The unique entries within the array Notes ----- np.unique doesn't work with a list of quantities of different scale, this function can be used instead.""" # todo: add a tolerance to handle floating point discrepancies # due to scaling. if len(quantities) > 0: common_units = quantities[0].units scaled_quantities = pq.Quantity([q.rescale(common_units) for q in quantities], common_units) return np.unique(scaled_quantities) else: return quantities class SpikeTrainList(ObjectList): """ This class contains multiple spike trains, and can represent them either as a list of SpikeTrain objects or as a pair of arrays (all spike times in a single array, with a second array indicating which neuron/channel the spike is from). Parameters ---------- items: list[neo.SpikeTrain] | array[neo.SpikeTrain] | None, default: None The SpikeTrains to add to the list parent: neo.Segment | None, default: None The Segment to include the SpikeTrainList within or None for no parent Notes ----- A SpikeTrainList object should behave like a list of SpikeTrains for iteration and item access. It is not intended to be used directly by users, but is available as the attribute `spiketrains` of Segments. Examples -------- # Create from list of SpikeTrain objects >>> stl = SpikeTrainList(items=( ... SpikeTrain([0.5, 0.6, 23.6, 99.2], units="ms", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ... SpikeTrain([0.0007, 0.0112], units="s", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ... SpikeTrain([1100, 88500], units="us", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ... SpikeTrain([], units="ms", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ... ) ... ) >>> stl.multiplexed (array([0, 0, 0, 0, 1, 1, 2, 2]), array([ 0.5, 0.6, 23.6, 99.2, 0.7, 11.2, 1.1, 88.5]) * ms) # Create from a pair of arrays >>> stl = SpikeTrainList.from_spike_time_array( ... np.array([0.5, 0.6, 0.7, 1.1, 11.2, 23.6, 88.5, 99.2]), ... np.array([0, 0, 1, 2, 1, 0, 2, 0]), ... all_channel_ids=[0, 1, 2, 3], ... units='ms', ... t_start=0 * pq.ms, ... t_stop=100.0 * pq.ms) >>> list(stl) [, , , ] """ allowed_contents = (SpikeTrain,) def __init__(self, items=None, parent=None): """Initialize self""" if items is None: self._items = items else: for item in items: if not is_spiketrain_or_proxy(item): raise ValueError("`items` can only contain SpikeTrain objects or proxy objects") self._items = list(items) self._spike_time_array = None self._channel_id_array = None self._all_channel_ids = None self._spiketrain_metadata = {} from .segment import Segment # import here rather than at the top to avoid circular import if parent is not None and not isinstance(parent, Segment): raise AttributeError("If provided, the parent class must be a Segment") self.segment = parent @property def parent(self): return self.segment def __iter__(self): """Implement iter(self)""" if self._items is None: self._spiketrains_from_array() for item in self._items: yield item def __getitem__(self, i): """x.__getitem__(y) <==> x[y]""" if self._items is None: self._spiketrains_from_array() items = self._items[i] if is_spiketrain_or_proxy(items): return items else: return SpikeTrainList(items=items) def __setitem__(self, i, value): if self._items is None: self._spiketrains_from_array() self._items[i] = value def __str__(self): """Return str(self)""" if self._items is None: if self._spike_time_array is None: return str([]) else: return ( f"SpikeTrainList containing {self._spike_time_array.size} " f"spikes from {len(self._all_channel_ids)} neurons" ) else: return str(self._items) def __repr__(self): return "" def __len__(self): """Return len(self)""" if self._items is None: if self._all_channel_ids is not None: return len(self._all_channel_ids) elif self._channel_id_array is not None: return np.unique(self._channel_id_array).size else: return 0 else: return len(self._items) def _add_spiketrainlists(self, other, in_place=False): if self._spike_time_array is None or other._spike_time_array is None: # if either self or other is not storing multiplexed spike trains # we combine them using the list of SpikeTrains representation if self._items is None: self._spiketrains_from_array() if other._items is None: other._spiketrains_from_array() if in_place: self._items.extend(other._items) return self else: return self.__class__(items=self._items[:] + other._items) else: # both self and other are storing multiplexed spike trains # so we update the array representation if self._spiketrain_metadata["t_start"] != other._spiketrain_metadata["t_start"]: raise ValueError("Incompatible t_start") # todo: adjust times and t_start of other to be compatible with self if self._spiketrain_metadata["t_stop"] != other._spiketrain_metadata["t_stop"]: raise ValueError("Incompatible t_stop") # todo: adjust t_stop of self and other as necessary combined_spike_time_array = np.hstack((self._spike_time_array, other._spike_time_array)) combined_channel_id_array = np.hstack((self._channel_id_array, other._channel_id_array)) combined_channel_ids = set(list(self._all_channel_ids) + other._all_channel_ids) if len(combined_channel_ids) != (len(self._all_channel_ids) + len(other._all_channel_ids)): raise ValueError("Duplicate channel ids, please rename channels before adding") if in_place: self._spike_time_array = combined_spike_time_array self._channel_id_array = combined_channel_id_array self._all_channel_ids = combined_channel_ids self._items = None return self else: return self.__class__.from_spike_time_array( combined_spike_time_array, combined_channel_id_array, combined_channel_ids, t_start=self._spiketrain_metadata["t_start"], t_stop=self._spiketrain_metadata["t_stop"], ) def __add__(self, other): """Return self + other""" if isinstance(other, self.__class__): return self._add_spiketrainlists(other) elif other and is_spiketrain_or_proxy(other[0]): return self._add_spiketrainlists(self.__class__(items=other, parent=self.segment)) else: if self._items is None: self._spiketrains_from_array() return self._items + other def __iadd__(self, other): """Return self""" if isinstance(other, self.__class__): return self._add_spiketrainlists(other, in_place=True) elif other and is_spiketrain_or_proxy(other[0]): for obj in other: self._handle_append(obj) if self._items is None: self._spiketrains_from_array() self._items.extend(other) return self else: raise TypeError("Can only add a SpikeTrainList or a list of SpikeTrains in place") def __radd__(self, other): """Return other + self""" if isinstance(other, self.__class__): return other._add_spiketrainlists(self) elif other and is_spiketrain_or_proxy(other[0]): for obj in other: obj.segment = self.segment if self._items is None: self._spiketrains_from_array() self._items.extend(other) return self elif len(other) == 0: return self else: if self._items is None: self._spiketrains_from_array() return other + self._items def append(self, obj): """ Appends to the SpikeTrainList with a new neo.core.SpikeTrain Parameters ---------- obj: neo.core.SpikeTrain The new neo.core.SpikeTrain to append to the current SpikeTrainList Examples -------- # with SpikeTrainList stl with two SpikeTrains >>> len(stl) 2 >>> stl.append(SpikeTrain([0.5, 0.6, 23.6, 99.2], units="ms", t_start=0 * pq.ms, t_stop=100.0 * pq.ms)) >>> len(stl) 3 """ if not is_spiketrain_or_proxy(obj): raise ValueError("Can only append SpikeTrain objects") if self._items is None: self._spiketrains_from_array() self._handle_append(obj) self._items.append(obj) def extend(self, iterable): """Extends the SpikeTrainList with additional SpikeTrain's from an iterable Parameters ---------- iterable: iterable[neo.core.SpikeTrain] A list-like or array-like object containing neo.core.SpikeTrain to be added to the SpikeTrainList Examples -------- # with SpikeTrainList stl with two SpikeTrains and stl_other with three SpikeTrains >>> len(stl) 2 >>> len(stl_other) 3 >>> stl.extend(stl_other) >>> len(stl) 5 """ if self._items is None: self._spiketrains_from_array() for obj in iterable: self._handle_append(obj) self._items.extend(iterable) @classmethod def from_spike_time_array( cls, spike_time_array, channel_id_array, all_channel_ids, t_stop, units=None, t_start=0.0 * pq.s, **annotations ): """Create a SpikeTrainList object from an array of spike times and an array of channel ids. Parameters ---------- spike_time_array: quantity array 1D | numpy array 1D, | list The times of all spikes. channel_id_array: numpy array 1D of dtype int The id of the channel (e.g. the neuron) to which each spike belongs. This array should have the same length as the `spike_time_array` all_channel_ids: list | tuple, | numpy array 1D containing integers All channel ids. This is needed to represent channels in which there are no spikes. t_stop: quantity scalar | numpy scalar | float Time at which spike recording ended. This will be converted to the same units as `spike_time_array` or `units`. units: quantity units | None, default: None Required if `spike_time_array is not a :class:`~quantities.Quantity`. :t_start: quantity scalar | numpy scalar | float, default: 0.0 * pq.s Time at which spike recording began. This will be converted to the same units as `spike_time_array` or `units` **annotations: dict The neo.core.baseneo annotations (e.g. name, or user-defined) """ spike_time_array, dim = normalize_times_array(spike_time_array, units) obj = cls() obj._spike_time_array = spike_time_array obj._channel_id_array = channel_id_array obj._all_channel_ids = all_channel_ids obj._spiketrain_metadata = {"t_start": t_start, "t_stop": t_stop} for name, ann_value in annotations.items(): if ( not isinstance(ann_value, str) and hasattr(ann_value, "__len__") and len(ann_value) != len(all_channel_ids) ): raise ValueError(f"incorrect length for annotation '{name}'") obj._annotations = annotations return obj def _spiketrains_from_array(self): """Convert multiplexed spike time data into a list of SpikeTrain objects""" if self._spike_time_array is None: self._items = [] else: self._items = [] for i, channel_id in enumerate(self._all_channel_ids): mask = self._channel_id_array == channel_id times = self._spike_time_array[mask] spiketrain = SpikeTrain(times, **self._spiketrain_metadata) for name, value in self._annotations.items(): if ( not isinstance(value, str) and hasattr(value, "__len__") and len(value) == len(self._all_channel_ids) ): spiketrain.annotate(**{name: value[i]}) else: spiketrain.annotate(**{name: value}) spiketrain.annotate(channel_id=channel_id) spiketrain.segment = self.segment self._items.append(spiketrain) @property def multiplexed(self): """ Return spike trains as a pair of arrays. Returns ------- channel_id_array: np.ndarray The ids of the channels/neurons that produced each spike spike_time_array: np.ndarray of dtype Quantity The Quantity array containing the times of the spikes """ if self._spike_time_array is None: # need to convert list of SpikeTrains into multiplexed spike times array if self._items is None: return np.array([]), np.array([]) else: channel_ids = [] spike_times = [] dim = self._items[0].units.dimensionality for i, spiketrain in enumerate(self._items): if hasattr(spiketrain, "load"): # proxy object spiketrain = spiketrain.load() if spiketrain.times.dimensionality.items() == dim.items(): # no need to rescale spike_times.append(spiketrain.times) else: spike_times.append(spiketrain.times.rescale(dim)) if "channel_id" in spiketrain.annotations and isinstance(spiketrain.annotations["channel_id"], int): ch_id = spiketrain.annotations["channel_id"] else: ch_id = i channel_ids.append(ch_id * np.ones(spiketrain.shape, dtype=np.int64)) self._spike_time_array = np.hstack(spike_times) * self._items[0].units self._channel_id_array = np.hstack(channel_ids) return self._channel_id_array, self._spike_time_array @property def t_start(self): if "t_start" in self._spiketrain_metadata: return self._spiketrain_metadata["t_start"] else: t_start_values = unique( [item.t_start for item in self._items if isinstance(item, SpikeTrain)] ) # ignore proxy objects if len(t_start_values) == 0: raise ValueError("t_start not defined for an empty spike train list") elif len(t_start_values) > 1: warnings.warn("Found multiple values of t_start, returning the earliest") t_start = t_start_values.min() else: t_start = t_start_values[0] self._spiketrain_metadata["t_start"] = t_start return t_start @property def t_stop(self): if "t_stop" in self._spiketrain_metadata: return self._spiketrain_metadata["t_stop"] else: t_stop_values = unique( [item.t_stop for item in self._items if isinstance(item, SpikeTrain)] ) # ignore proxy objects if len(t_stop_values) == 0: raise ValueError("t_stop not defined for an empty spike train list") elif len(t_stop_values) > 1: warnings.warn("Found multiple values of t_stop, returning the latest") t_stop = t_stop_values.max() else: t_stop = t_stop_values[0] self._spiketrain_metadata["t_stop"] = t_stop return t_stop @property def all_channel_ids(self): if self._all_channel_ids is None: self._all_channel_ids = [] for i, spiketrain in enumerate(self._items): if "channel_id" in spiketrain.annotations and isinstance(spiketrain.annotations["channel_id"], int): ch_id = spiketrain.annotations["channel_id"] else: ch_id = i self._all_channel_ids.append(ch_id) return self._all_channel_ids ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/core/view.py0000644000175100017510000000667415154507213015104 0ustar00runnerrunner""" This module implements :class:`ChannelView`, which represents a subset of the channels in an :class:`AnalogSignal` or :class:`IrregularlySampledSignal`. It replaces the indexing function of the former :class:`ChannelIndex`. """ import numpy as np from .baseneo import BaseNeo from .basesignal import BaseSignal from .dataobject import ArrayDict class ChannelView(BaseNeo): """ A tool for indexing a subset of the channels within an :class:`AnalogSignal` or :class:`IrregularlySampledSignal`; Parameters ---------- obj: Neo.AnalogSignal | Neo.IrregularlySampledSignal The neo object to index index: list | np.ndarray Boolean or integer mask to select the channels of interest name: str | None, default: None A label for the dataset. description: str | None, default: None Text description. file_origin: str | None, default: None Filesystem path or URL of the original data file. array_annotations: dict | None, default: None Dict mapping strings to numpy arrays containing annotations for all data points annotations: dict Other use-specified metadata with keywords """ _parent_objects = ("Group",) _parent_attrs = ("group",) _necessary_attrs = ( ("obj", ("AnalogSignal", "IrregularlySampledSignal"), 1), ("index", np.ndarray, 1, np.dtype("i")), ) is_view = True # "mask" would be an alternative name, proposing "index" for # backwards-compatibility with ChannelIndex def __init__( self, obj, index, name=None, description=None, file_origin=None, array_annotations=None, **annotations ): super().__init__(name=name, description=description, file_origin=file_origin, **annotations) if not (isinstance(obj, BaseSignal) or (hasattr(obj, "proxy_for") and issubclass(obj.proxy_for, BaseSignal))): raise ValueError("Can only take a ChannelView of an AnalogSignal " "or an IrregularlySampledSignal") self.obj = obj # check type and dtype of index and convert index to a common form # (accept list or array of bool or int, convert to int array) self.index = np.array(index) if len(self.index.shape) != 1: raise ValueError("index must be a 1D array") if self.index.dtype == bool: # convert boolean mask to integer index if self.index.size != self.obj.shape[-1]: raise ValueError("index size does not match number of channels in signal") (self.index,) = np.nonzero(self.index) # allow any type of integer representation elif self.index.dtype.char not in np.typecodes["AllInteger"]: raise ValueError("index must be of a list or array of data type boolean or integer") if not hasattr(self, "array_annotations") or not self.array_annotations: self.array_annotations = ArrayDict(self._get_arr_ann_length()) if array_annotations is not None: self.array_annotate(**array_annotations) @property def shape(self): return (self.obj.shape[0], self.index.size) def _get_arr_ann_length(self): return self.index.size def array_annotate(self, **array_annotations): self.array_annotations.update(array_annotations) def resolve(self): """ Return a copy of the underlying object containing just the subset of channels defined by the index. """ return self.obj[:, self.index] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5348096 neo-0.14.4/neo/io/0000755000175100017510000000000015154507227013227 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/__init__.py0000644000175100017510000003026615154507213015342 0ustar00runnerrunner""" :mod:`neo.io` provides classes for reading and/or writing electrophysiological data files. Note that if the package dependency is not satisfied for one io, it does not raise an error but a warning. :attr:`neo.io.iolist` provides a list of successfully imported io classes. Functions: .. autofunction:: neo.io.get_io .. autofunction:: neo.io.list_candidate_ios Classes: * :attr:`AlphaOmegaIO` * :attr:`AsciiImageIO` * :attr:`AsciiSignalIO` * :attr:`AsciiSpikeTrainIO` * :attr:`AxographIO` * :attr:`AxonaIO` * :attr:`AxonIO` * :attr:`BCI2000IO` * :attr:`BiocamIO` * :attr:`BlackrockIO` * :attr:`BlkIO` * :attr:`BrainVisionIO` * :attr:`BrainwareDamIO` * :attr:`BrainwareF32IO` * :attr:`BrainwareSrcIO` * :attr:`CedIO` * :attr:`EDFIO` * :attr:`ElanIO` * :attr:`IgorIO` * :attr:`IntanIO` * :attr:`MEArecIO` * :attr:`KlustaKwikIO` * :attr:`KwikIO` * :attr:`MaxwellIO` * :attr:`MedIO` * :attr:`MicromedIO` * :attr:`NeoMatlabIO` * :attr:`NestIO` * :attr:`NeuralynxIO` * :attr:`NeuroExplorerIO` * :attr:`NeuroNexusIO` * :attr:`NeuroScopeIO` * :attr:`NeuroshareIO` * :attr:`NixIO` * :attr:`NWBIO` * :attr:`OpenEphysIO` * :attr:`OpenEphysBinaryIO` * :attr:`PhyIO` * :attr:`PickleIO` * :attr:`PlexonIO` * :attr:`Plexon2IO` * :attr:`RawBinarySignalIO` * :attr:`RawMCSIO` * :attr:`Spike2IO` * :attr:`SpikeGadgetsIO` * :attr:`SpikeGLXIO` * :attr:`StimfitIO` * :attr:`TdtIO` * :attr:`TiffIO` * :attr:`WinEdrIO` * :attr:`WinWcpIO` .. autoclass:: neo.io.AlphaOmegaIO .. autoattribute:: extensions .. autoclass:: neo.io.AsciiImageIO .. autoattribute:: extensions .. autoclass:: neo.io.AsciiSignalIO .. autoattribute:: extensions .. autoclass:: neo.io.AsciiSpikeTrainIO .. autoattribute:: extensions .. autoclass:: neo.io.AxographIO .. autoattribute:: extensions .. autoclass:: neo.io.AxonaIO .. autoattribute:: extensions .. autoclass:: neo.io.AxonIO .. autoattribute:: extensions .. autoclass:: neo.io.BCI2000IO .. autoattribute:: extensions .. autoclass:: neo.io.BiocamIO .. autoattribute:: extensions .. autoclass:: neo.io.BlackrockIO .. autoattribute:: extensions .. autoclass:: neo.io.BlkIO .. autoattribute:: extensions .. autoclass:: neo.io.BrainVisionIO .. autoattribute:: extensions .. autoclass:: neo.io.BrainwareDamIO .. autoattribute:: extensions .. autoclass:: neo.io.BrainwareF32IO .. autoattribute:: extensions .. autoclass:: neo.io.BrainwareSrcIO .. autoattribute:: extensions .. autoclass:: neo.io.CedIO .. autoattribute:: extensions .. autoclass:: neo.io.EDFIO .. autoattribute:: extensions .. autoclass:: neo.io.ElanIO .. autoattribute:: extensions .. .. autoclass:: neo.io.ElphyIO .. autoattribute:: extensions .. autoclass:: neo.io.IgorIO .. autoattribute:: extensions .. autoclass:: neo.io.IntanIO .. autoattribute:: extensions .. autoclass:: neo.io.KlustaKwikIO .. autoattribute:: extensions .. autoclass:: neo.io.KwikIO .. autoattribute:: extensions .. autoclass:: neo.io.MEArecIO .. autoattribute:: extensions .. autoclass:: neo.io.MaxwellIO .. autoattribute:: extensions .. autoclass:: neo.io.MedIO .. autoattribute:: extensions .. autoclass:: neo.io.MicromedIO .. autoattribute:: extensions .. autoclass:: neo.io.NeoMatlabIO .. autoattribute:: extensions .. autoclass:: neo.io.NestIO .. autoattribute:: extensions .. autoclass:: neo.io.NeuralynxIO .. autoattribute:: extensions .. autoclass:: neo.io.NeuroExplorerIO .. autoattribute:: extensions .. autoclass:: neo.io.NeuroNexusIO .. autoattribute:: extensions .. autoclass:: neo.io.NeuroScopeIO .. autoattribute:: extensions .. autoclass:: neo.io.NeuroshareIO .. autoattribute:: extensions .. autoclass:: neo.io.NixIO .. autoattribute:: extensions .. autoclass:: neo.io.NWBIO .. autoattribute:: extensions .. autoclass:: neo.io.OpenEphysIO .. autoattribute:: extensions .. autoclass:: neo.io.OpenEphysBinaryIO .. autoattribute:: extensions .. autoclass:: neo.io.PhyIO .. autoattribute:: extensions .. autoclass:: neo.io.PickleIO .. autoattribute:: extensions .. autoclass:: neo.io.PlexonIO .. autoattribute:: extensions .. autoclass:: neo.io.Plexon2IO .. autoattribute:: extensions .. autoclass:: neo.io.RawBinarySignalIO .. autoattribute:: extensions .. autoclass:: neo.io.RawMCSIO .. autoattribute:: extensions .. autoclass:: Spike2IO .. autoattribute:: extensions .. autoclass:: SpikeGadgetsIO .. autoattribute:: extensions .. autoclass:: SpikeGLXIO .. autoattribute:: extensions .. autoclass:: neo.io.StimfitIO .. autoattribute:: extensions .. autoclass:: neo.io.TdtIO .. autoattribute:: extensions .. autoclass:: neo.io.TiffIO .. autoattribute:: extensions .. autoclass:: neo.io.WinEdrIO .. autoattribute:: extensions .. autoclass:: neo.io.WinWcpIO .. autoattribute:: extensions """ import pathlib from collections import Counter import importlib.util # check if neuroshare library exists # if it is present, use the neuroshareapiio to load neuroshare files # if it is not present, use the neurosharectypesio to load files neuroshare_spec = importlib.util.find_spec("neuroshare") if neuroshare_spec is not None: from neo.io.neuroshareapiio import NeuroshareapiIO as NeuroshareIO else: from neo.io.neurosharectypesio import NeurosharectypesIO as NeuroshareIO from neo.io.alphaomegaio import AlphaOmegaIO from neo.io.asciiimageio import AsciiImageIO from neo.io.asciisignalio import AsciiSignalIO from neo.io.asciispiketrainio import AsciiSpikeTrainIO from neo.io.axographio import AxographIO from neo.io.axonaio import AxonaIO from neo.io.axonio import AxonIO from neo.io.biocamio import BiocamIO from neo.io.blackrockio import BlackrockIO from neo.io.blkio import BlkIO from neo.io.bci2000io import BCI2000IO from neo.io.brainvisionio import BrainVisionIO from neo.io.brainwaredamio import BrainwareDamIO from neo.io.brainwaref32io import BrainwareF32IO from neo.io.brainwaresrcio import BrainwareSrcIO from neo.io.cedio import CedIO from neo.io.edfio import EDFIO from neo.io.elanio import ElanIO from neo.io.elphyio import ElphyIO from neo.io.exampleio import ExampleIO from neo.io.igorproio import IgorIO from neo.io.intanio import IntanIO from neo.io.klustakwikio import KlustaKwikIO from neo.io.kwikio import KwikIO from neo.io.mearecio import MEArecIO from neo.io.maxwellio import MaxwellIO from neo.io.medio import MedIO from neo.io.micromedio import MicromedIO from neo.io.neomatlabio import NeoMatlabIO from neo.io.nestio import NestIO from neo.io.neuralynxio import NeuralynxIO from neo.io.neuroexplorerio import NeuroExplorerIO from neo.io.neuronexusio import NeuroNexusIO from neo.io.neuroscopeio import NeuroScopeIO from neo.io.nixio import NixIO from neo.io.nixio_fr import NixIO as NixIOFr from neo.io.nwbio import NWBIO from neo.io.openephysio import OpenEphysIO from neo.io.openephysbinaryio import OpenEphysBinaryIO from neo.io.phyio import PhyIO from neo.io.pickleio import PickleIO from neo.io.plexonio import PlexonIO from neo.io.plexon2io import Plexon2IO from neo.io.rawbinarysignalio import RawBinarySignalIO from neo.io.rawmcsio import RawMCSIO from neo.io.spike2io import Spike2IO from neo.io.spikegadgetsio import SpikeGadgetsIO from neo.io.spikeglxio import SpikeGLXIO from neo.io.stimfitio import StimfitIO from neo.io.tdtio import TdtIO from neo.io.tiffio import TiffIO from neo.io.winedrio import WinEdrIO from neo.io.winwcpio import WinWcpIO iolist = [ AlphaOmegaIO, AsciiImageIO, AsciiSignalIO, AsciiSpikeTrainIO, AxographIO, AxonaIO, AxonIO, BCI2000IO, BiocamIO, BlackrockIO, BlkIO, BrainVisionIO, BrainwareDamIO, BrainwareF32IO, BrainwareSrcIO, CedIO, EDFIO, ElanIO, ElphyIO, ExampleIO, IgorIO, IntanIO, KlustaKwikIO, KwikIO, MEArecIO, MaxwellIO, MedIO, MicromedIO, NixIO, NixIOFr, NeoMatlabIO, NestIO, NeuralynxIO, NeuroExplorerIO, NeuroNexusIO, NeuroScopeIO, NeuroshareIO, NWBIO, OpenEphysIO, OpenEphysBinaryIO, PhyIO, PickleIO, PlexonIO, Plexon2IO, RawBinarySignalIO, RawMCSIO, Spike2IO, SpikeGadgetsIO, SpikeGLXIO, StimfitIO, TdtIO, TiffIO, WinEdrIO, WinWcpIO, ] # for each supported extension list the ios supporting it io_by_extension = {} for current_io in iolist: # do not use `io` as variable name here as this overwrites the module io for extension in current_io.extensions: extension = extension.lower() # extension handling should not be case sensitive io_by_extension.setdefault(extension, []).append(current_io) def get_io(file_or_folder, *args, **kwargs): """ Return a Neo IO instance, guessing the type based on the filename suffix. """ ios = list_candidate_ios(file_or_folder) for io in ios: try: return io(file_or_folder, *args, **kwargs) except: continue raise IOError(f"Could not identify IO for {file_or_folder}") def list_candidate_ios(file_or_folder, ignore_patterns=["*.ini", "README.txt", "README.md"]): """ Identify neo IO that can potentially load data in the file or folder Parameters ---------- file_or_folder (str, pathlib.Path) Path to the file or folder to load ignore_patterns (list) List of patterns to ignore when scanning for known formats. See pathlib.PurePath.match(). Default: ['ini'] Returns ------- list List of neo io classes that are associated with the file extensions detected """ file_or_folder = pathlib.Path(file_or_folder) if file_or_folder.is_file(): suffix = file_or_folder.suffix[1:].lower() if suffix not in io_by_extension: raise ValueError(f"{suffix} is not a supported format of any IO.") return io_by_extension[suffix] elif file_or_folder.is_dir(): # scan files in folder to determine io type filenames = [f for f in file_or_folder.glob("**/*") if f.is_file()] # keep only relevant filenames filenames = [f for f in filenames if f.suffix and not any([f.match(p) for p in ignore_patterns])] # if no files are found in the folder, check subfolders # this is necessary for nested-folder based formats like spikeglx if not filenames: filenames = [f for f in file_or_folder.glob("**/*") if f.is_file()] # keep only relevant filenames filenames = [f for f in filenames if f.suffix and not any([f.match(p) for p in ignore_patterns])] # if only file prefix was provided, e.g /mydatafolder/session1- # to select all files sharing the `session1-` prefix elif file_or_folder.parent.exists(): filenames = list(file_or_folder.parent.glob(file_or_folder.name + "*")) # if filenames empty and suffix is provided then non-existent file # may be written in current dir. So run check for io if len(filenames) == 0 and file_or_folder.suffix: suffix = file_or_folder.suffix[1:].lower() if suffix not in io_by_extension: raise ValueError(f"{suffix} is not a supported format of any IO.") return io_by_extension[suffix] # If non-existent file in non-existent dir is given check if this # structure could be created with an io writing the file elif file_or_folder.suffix: suffix = file_or_folder.suffix[1:].lower() if suffix not in io_by_extension: raise ValueError(f"{suffix} is not a supported format of any IO.") return io_by_extension[suffix] else: raise ValueError(f"{file_or_folder} does not contain data files of a supported format") # find the io that fits the best with the files contained in the folder potential_ios = [] for filename in filenames: for suffix in filename.suffixes: suffix = suffix[1:].lower() if suffix in io_by_extension: potential_ios.extend(io_by_extension[suffix]) if not potential_ios: raise ValueError(f"Could not determine IO to load {file_or_folder}") # return ios ordered by number of files supported counter = Counter(potential_ios).most_common() return [io for io, count in counter] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/alphaomegaio.py0000644000175100017510000000102515154507213016220 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.alphaomegarawio import AlphaOmegaRawIO class AlphaOmegaIO(AlphaOmegaRawIO, BaseFromRaw): """Class for reading data from AlphaOmega MPX file""" __doc__ = AlphaOmegaRawIO.__doc__ def __init__(self, filename, lsx_files=None, prune_channels=True): AlphaOmegaRawIO.__init__( self, dirname=filename, lsx_files=lsx_files, prune_channels=prune_channels, ) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/asciiimageio.py0000644000175100017510000000571515154507213016227 0ustar00runnerrunnerimport numpy as np from .baseio import BaseIO from neo.core import ImageSequence, Segment, Block class AsciiImageIO(BaseIO): """ IO class for reading ImageSequence in a text file *Usage*: >>> from neo import io >>> import quantities as pq >>> r = io.AsciiImageIO(file_name='File_asciiimage_1.txt',nb_frame=511, nb_row=100, ... nb_column=100,units='mm', sampling_rate=1.0*pq.Hz, ... spatial_scale=1.0*pq.mm) >>> block = r.read_block() read block creating segment returning block >>> block Block with 1 segments file_origin: 'File_asciiimage_1.txt # segments (N=1) 0: Segment with 1 imagesequences # analogsignals (N=0) """ name = "AsciiImage IO" description = "Neo IO module for optical imaging data stored as a folder of TIFF images." _prefered_signal_group_mode = "group-by-same-units" is_readable = True is_writable = False supported_objects = [Block, Segment, ImageSequence] readable_objects = supported_objects writeable_object = [] support_lazy = False read_params = {} write_params = {} extensions = [] mode = "file" def __init__( self, file_name=None, nb_frame=None, nb_row=None, nb_column=None, units=None, sampling_rate=None, spatial_scale=None, **kwargs, ): BaseIO.__init__(self, file_name, **kwargs) self.nb_frame = nb_frame self.nb_row = nb_row self.nb_column = nb_column self.units = units self.sampling_rate = sampling_rate self.spatial_scale = spatial_scale def read_block(self, lazy=False, **kwargs): file = open(self.filename, "r") data = file.read() print("read block") liste_value = [] record = [] for i in range(len(data)): if data[i] == "\n" or data[i] == "\t": t = "".join(str(e) for e in record) liste_value.append(t) record = [] else: record.append(data[i]) data = [] nb = 0 for i in range(self.nb_frame): data.append([]) for y in range(self.nb_row): data[i].append([]) for x in range(self.nb_column): data[i][y].append(liste_value[nb]) nb += 1 image_sequence = ImageSequence( np.array(data, dtype="float"), units=self.units, sampling_rate=self.sampling_rate, spatial_scale=self.spatial_scale, ) file.close() print("creating segment") segment = Segment(file_origin=self.filename) segment.imagesequences = [image_sequence] block = Block(file_origin=self.filename) block.segments.append(segment) print("returning block") return block ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/asciisignalio.py0000644000175100017510000004120515154507213016414 0ustar00runnerrunner""" Class for reading/writing analog signals in a text file. Each column represents an AnalogSignal. All AnalogSignals have the same sampling rate. Covers many cases when parts of a file can be viewed as a CSV format. Supported : Read/Write Author: sgarcia """ import csv import os import json import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import AnalogSignal, IrregularlySampledSignal, Segment, Block class AsciiSignalIO(BaseIO): """ Class for reading signals in generic ascii format. Columns represent signals. They all share the same sampling rate. The sampling rate is externally known or the first column could hold the time vector. Usage: >>> from neo import io >>> r = io.AsciiSignalIO(filename='File_asciisignal_2.txt') >>> seg = r.read_segment() >>> print seg.analogsignals [ AnalogSignal sampling_rate = 1.0 / np.mean(np.diff(sig[:, self.timecolumn])) / self.time_units else: # not equally spaced --> IrregularlySampledSignal sampling_rate = None t_start = sig[0, self.timecolumn] * self.time_units if self.signal_group_mode == "all-in-one": channel_index_annotation = self.usecols or np.arange(sig.shape[1]) channel_index_annotation = np.asarray(channel_index_annotation) if self.timecolumn is not None: mask = list(range(sig.shape[1])) if self.timecolumn >= 0: mask.remove(self.timecolumn) else: # allow negative column index mask.remove(sig.shape[1] + self.timecolumn) signal = sig[:, mask] channel_index_annotation = channel_index_annotation[mask] else: signal = sig if sampling_rate is None: irr_sig = IrregularlySampledSignal( signal[:, self.timecolumn] * self.time_units, signal * self.units, name="multichannel" ) seg.irregularlysampledsignals.append(irr_sig) else: ana_sig = AnalogSignal( signal * self.units, sampling_rate=sampling_rate, t_start=t_start, name="multichannel" ) ana_sig.array_annotate(channel_index=channel_index_annotation) seg.analogsignals.append(ana_sig) else: if self.timecolumn is not None and self.timecolumn < 0: time_col = sig.shape[1] + self.timecolumn else: time_col = self.timecolumn for i in range(sig.shape[1]): if time_col == i: continue signal = sig[:, i] * self.units if sampling_rate is None: irr_sig = IrregularlySampledSignal( sig[:, time_col] * self.time_units, signal, t_start=t_start, channel_index=i, name="Column %d" % i, ) seg.irregularlysampledsignals.append(irr_sig) else: ana_sig = AnalogSignal( signal, sampling_rate=sampling_rate, t_start=t_start, channel_index=i, name="Column %d" % i ) seg.analogsignals.append(ana_sig) seg.check_relationships() return seg def read_metadata(self): """ Read IO parameters from an associated JSON file """ # todo: also read annotations if self.metadata_filename is None: candidate = os.path.splitext(self.filename)[0] + "_about.json" if os.path.exists(candidate): self.metadata_filename = candidate else: return {} if os.path.exists(self.metadata_filename): with open(self.metadata_filename) as fp: metadata = json.load(fp) for key in "sampling_rate", "t_start": if key in metadata: metadata[key] = pq.Quantity(metadata[key]["value"], metadata[key]["units"]) for key in "units", "time_units": if key in metadata: metadata[key] = pq.Quantity(1, metadata[key]) return metadata else: return {} def write_segment(self, segment): """ Write a segment and AnalogSignal in a text file. """ # todo: check all analog signals have the same length, physical dimensions # and sampling rates l = [] if self.timecolumn is not None: if self.timecolumn != 0: raise NotImplementedError("Only column 0 currently supported for writing times") l.append(segment.analogsignals[0].times[:, np.newaxis].rescale(self.time_units)) # check signals are compatible (size, sampling rate), otherwise we # can't/shouldn't concatenate them # also set sampling_rate, t_start, units, time_units from signal(s) signal0 = segment.analogsignals[0] for attr in ("sampling_rate", "units", "shape"): val0 = getattr(signal0, attr) for signal in segment.analogsignals[1:]: val1 = getattr(signal, attr) if val0 != val1: raise Exception("Signals being written have different " + attr) setattr(self, attr, val0) # todo t_start, time_units self.time_units = signal0.times.units self.t_start = min(sig.t_start for sig in segment.analogsignals) for anaSig in segment.analogsignals: l.append(anaSig.rescale(self.units).magnitude) sigs = np.concatenate(l, axis=1) # print sigs.shape np.savetxt(self.filename, sigs, delimiter=self.delimiter) if self.metadata_filename is not None: self.write_metadata() def write_block(self, block): """ Can only write blocks containing a single segment. """ # in future, maybe separate segments by a blank link, or a "magic" comment if len(block.segments) > 1: raise ValueError( "Can only write blocks containing a single segment." f" This block contains {len(block.segments)} segments." ) self.write_segment(block.segments[0]) def write_metadata(self, metadata_filename=None): """ Write IO parameters to an associated JSON file """ # todo: also write annotations metadata = { "filename": self.filename, "delimiter": self.delimiter, "usecols": self.usecols, "skiprows": self.skiprows, "timecolumn": self.timecolumn, "sampling_rate": { "value": float(self.sampling_rate.magnitude), "units": self.sampling_rate.dimensionality.string, }, "t_start": {"value": float(self.t_start.magnitude), "units": self.t_start.dimensionality.string}, "units": self.units.dimensionality.string, "time_units": self.time_units.dimensionality.string, "method": self.method, "signal_group_mode": self.signal_group_mode, } if metadata_filename is None: if self.metadata_filename is None: self.metadata_filename = os.path.splitext(self.filename) + "_about.json" else: self.metadata_filename = metadata_filename with open(self.metadata_filename, "w") as fp: json.dump(metadata, fp) return self.metadata_filename ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/asciispiketrainio.py0000644000175100017510000000716715154507213017321 0ustar00runnerrunner""" Classe for reading/writing SpikeTrains in a text file. It is the simple case where different spiketrains are written line by line. Supported : Read/Write Author: sgarcia """ import os import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import Segment, SpikeTrain, NeoReadWriteError class AsciiSpikeTrainIO(BaseIO): """ Class for reading/writing SpikeTrains in a text file. Each Spiketrain is a line. Usage: >>> from neo import io >>> r = io.AsciiSpikeTrainIO( filename = 'File_ascii_spiketrain_1.txt') >>> seg = r.read_segment() >>> print seg.spiketrains # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE [>> import neo >>> r = neo.io.AxographIO(filename=filename) >>> blk = r.read_block(signal_group_mode='split-all') >>> display(blk) >>> # get signals >>> seg_index = 0 # episode number >>> sigs = [sig for sig in blk.segments[seg_index].analogsignals ... if sig.name in channel_names] >>> display(sigs) >>> # get event markers (same for all segments/episodes) >>> ev = blk.segments[0].events[0] >>> print([ev for ev in zip(ev.times, ev.labels)]) >>> # get interval bars (same for all segments/episodes) >>> ep = blk.segments[0].epochs[0] >>> print([ep for ep in zip(ep.times, ep.durations, ep.labels)]) >>> # get notes >>> print(blk.annotations['notes']) """ name = "AxographIO" description = "This IO reads .axgd/.axgx files created with AxoGraph" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename="", force_single_segment=False): AxographRawIO.__init__(self, filename, force_single_segment) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/axonaio.py0000644000175100017510000000054115154507213015232 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.axonarawio import AxonaRawIO class AxonaIO(AxonaRawIO, BaseFromRaw): name = "Axona IO" description = "Read raw continuous data (.bin and .set files)" def __init__(self, filename): AxonaRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/axonio.py0000644000175100017510000000761415154507213015101 0ustar00runnerrunnerimport quantities as pq from neo.io.basefromrawio import BaseFromRaw from neo.rawio.axonrawio import AxonRawIO from neo.core import Segment, AnalogSignal class AxonIO(AxonRawIO, BaseFromRaw): """ Class for reading data from pCLAMP and AxoScope files (.abf version 1 and 2), developed by Molecular device/Axon technologies. - abf = Axon binary file - atf is a text file based format from axon that could be read by AsciiIO (but this file is less efficient.) Here an important note from erikli@github for user who want to get the : With Axon ABF2 files, the information that you need to recapitulate the original stimulus waveform (both digital and analog) is contained in multiple places. - `AxonIO._axon_info['protocol']` -- things like number of samples in episode - `AxonIO.axon_info['section']['ADCSection']` | `AxonIO.axon_info['section']['DACSection']` -- things about the number of channels and channel properties - `AxonIO._axon_info['protocol']['nActiveDACChannel']` -- bitmask specifying which DACs are actually active - `AxonIO._axon_info['protocol']['nDigitalEnable']` -- bitmask specifying which set of Epoch timings should be used to specify the duration of digital outputs - `AxonIO._axon_info['dictEpochInfoPerDAC']` -- dict of dict. First index is DAC channel and second index is Epoch number (i.e. information about Epoch A in Channel 2 would be in `AxonIO._axon_info['dictEpochInfoPerDAC'][2][0]`) - `AxonIO._axon_info['EpochInfo']` -- list of dicts containing information about each Epoch's digital out pattern. Digital out is a bitmask with least significant bit corresponding to Digital Out 0 - `AxonIO._axon_info['listDACInfo']` -- information about DAC name, scale factor, holding level, etc - `AxonIO._t_starts` -- start time of each sweep in a unified time basis - `AxonIO._sampling_rate` The current AxonIO.read_protocol() method utilizes a subset of these. In particular I know it doesn't consider `nDigitalEnable`, `EpochInfo`, or `nActiveDACChannel` and it doesn't account for different types of Epochs offered by Clampex/pClamp other than discrete steps (such as ramp, pulse train, etc and encoded by `nEpochType` in the EpochInfoPerDAC section). I'm currently parsing a superset of the properties used by read_protocol() in my analysis scripts, but that code still doesn't parse the full information and isn't in a state where it could be committed and I can't currently prioritize putting together all the code that would parse the full set of data. The `AxonIO._axon_info['EpochInfo']` section doesn't currently exist. """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): AxonRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) def read_protocol(self): """ Read the protocol waveform of the file, if present; function works with ABF2 only. Protocols can be reconstructed from the ABF1 header. Returns ------- segments : list of segments Segments, one for every episode, with list of analog signls (one for every DAC). """ sigs_by_segments, sig_names, sig_units = self.read_raw_protocol() segments = [] for seg_index, sigs in enumerate(sigs_by_segments): seg = Segment(index=seg_index) t_start = self._t_starts[seg_index] * pq.s for c, sig in enumerate(sigs): ana_sig = AnalogSignal( sig, sampling_rate=self._sampling_rate * pq.Hz, t_start=t_start, name=sig_names[c], units=sig_units[c], ) seg.analogsignals.append(ana_sig) segments.append(seg) return segments ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/basefromrawio.py0000644000175100017510000003422215154507213016437 0ustar00runnerrunner""" BaseFromRaw ====== BaseFromRaw implement a bridge between the new neo.rawio API and the neo.io legacy that give neo.core object. The neo.rawio API is more restricted and limited and does not cover tricky cases with asymetrical tree of neo object. But if a format is done in neo.rawio the neo.io is done for free by inheritance of this class. Furthermore, IOs that inherit this BaseFromRaw also have the ability to lazy load with proxy objects. """ import collections import warnings import numpy as np from neo import logging_handler from neo.core import AnalogSignal, Block, Epoch, Event, IrregularlySampledSignal, Group, Segment, SpikeTrain from neo.io.baseio import BaseIO from neo.io.proxyobjects import ( AnalogSignalProxy, SpikeTrainProxy, EventProxy, EpochProxy, ensure_signal_units, check_annotations, ensure_second, proxyobjectlist, ) import quantities as pq class BaseFromRaw(BaseIO): """ This implement generic reader on top of RawIO reader. Arguments depend on `mode` (dir or file) File case:: reader = BlackRockIO(filename='FileSpec2.3001.nev') Dir case:: reader = NeuralynxIO(dirname='Cheetah_v5.7.4/original_data') Other arguments are IO specific. """ is_readable = True is_writable = False supported_objects = [Block, Segment, AnalogSignal, SpikeTrain, Group, Event, Epoch] readable_objects = [Block, Segment] writeable_objects = [] support_lazy = True name = "BaseIO" description = "" extensions = [] mode = "file" _prefered_signal_group_mode = "group-by-same-units" # 'split-all' def __init__(self, *args, **kargs): BaseIO.__init__(self, *args, **kargs) self.parse_header() def read_block( self, block_index=0, lazy=False, create_group_across_segment=None, signal_group_mode=None, load_waveforms=False ): """ Reads one block of data from a file Parameters ---------- block_index: int, default: 0 In the case of multiple blocks, the block_index specifies which block to read lazy: bool, default: False Whether to read the block lazily (True) or load into memory (False) create_group_across_segment: bool | dict | None, default: None If True : * Create a neo.Group to group AnalogSignal segments * Create a neo.Group to group SpikeTrain across segments * Create a neo.Group to group Event across segments * Create a neo.Group to group Epoch across segments With a dict the behavior can be controlled more finely * for example: create_group_across_segment = { 'AnalogSignal': True, 'SpikeTrain': False, ...} signal_group_mode: 'split-all' | 'group-by-same-units' | None, default: None This control behavior for grouping channels in AnalogSignal. * 'split-all': each channel will be give an AnalogSignal * 'group-by-same-units' all channel sharing the same quantity units are grouped in a 2D AnalogSignal By default None since the default is dependant on the IO load_waveforms: bool, default: False Determines whether SpikeTrains.waveforms is created Returns ------- bl: neo.core.Block The block of data """ if signal_group_mode is None: signal_group_mode = self._prefered_signal_group_mode l = ["AnalogSignal", "SpikeTrain", "Event", "Epoch"] if create_group_across_segment is None: # @andrew @ julia @michael ? # I think here the default None could give this create_group_across_segment = { "AnalogSignal": True, # because mimic the old ChannelIndex for AnalogSignals "SpikeTrain": False, # False by default because can create too many object for simulation "Event": False, # not implemented yet "Epoch": False, # not implemented yet } elif isinstance(create_group_across_segment, bool): # bool to dict v = create_group_across_segment create_group_across_segment = {k: v for k in l} elif isinstance(create_group_across_segment, dict): # put False to missing keys create_group_across_segment = {k: create_group_across_segment.get(k, False) for k in l} else: raise ValueError("create_group_across_segment must be bool or dict") # annotations bl_annotations = dict(self.raw_annotations["blocks"][block_index]) bl_annotations.pop("segments") bl_annotations = check_annotations(bl_annotations) bl = Block(**bl_annotations) # Group for AnalogSignals coming from signal_streams if create_group_across_segment["AnalogSignal"]: signal_streams = self.header["signal_streams"] sub_streams = self.get_sub_signal_streams(signal_group_mode) sub_stream_groups = [] for sub_stream in sub_streams: stream_index, inner_stream_channels, name = sub_stream group = Group(name=name, stream_id=signal_streams[stream_index]["id"]) bl.groups.append(group) sub_stream_groups.append(group) if create_group_across_segment["SpikeTrain"]: spike_channels = self.header["spike_channels"] st_groups = [] for c in range(spike_channels.size): group = Group(name=f"SpikeTrain group {c}") group.annotate(unit_name=spike_channels[c]["name"]) group.annotate(unit_id=spike_channels[c]["id"]) bl.groups.append(group) st_groups.append(group) if create_group_across_segment["Event"]: # @andrew @ julia @michael : # Do we need this ? I guess yes raise NotImplementedError() if create_group_across_segment["Epoch"]: # @andrew @ julia @michael : # Do we need this ? I guess yes raise NotImplementedError() # Read all segments for seg_index in range(self.segment_count(block_index)): seg = self.read_segment( block_index=block_index, seg_index=seg_index, lazy=lazy, signal_group_mode=signal_group_mode, load_waveforms=load_waveforms, ) bl.segments.append(seg) # create link between group (across segment) and data objects for seg in bl.segments: if create_group_across_segment["AnalogSignal"]: for c, anasig in enumerate(seg.analogsignals): sub_stream_groups[c].add(anasig) if create_group_across_segment["SpikeTrain"]: for c, sptr in enumerate(seg.spiketrains): st_groups[c].add(sptr) bl.check_relationships() return bl def read_segment( self, block_index=0, seg_index=0, lazy=False, signal_group_mode=None, load_waveforms=False, time_slice=None, strict_slicing=True, ): """ Reads one segment of the data file Parameters ---------- block_index: int, default: 0 In the case of a multiblock dataset, this specifies which block to read seg_index: int, default: 0 In the case of a multisegment block, this specifies which segmeent to read lazy: bool, default: False Whether to lazily load the segment (True) or to load the segment into memory (False) signal_group_mode: 'split-all' | 'group-by-same-units' | None, default: None This control behavior for grouping channels in AnalogSignal. * 'split-all': each channel will be give an AnalogSignal * 'group-by-same-units' all channel sharing the same quantity units are grouped in a 2D AnalogSignal load_waveforms: bool, default: False Determines whether SpikeTrains.waveforms is created time_slice: tuple[quantity.Quantities | None] | None, default: None Whether to take a time slice of the data * None: indicates from beginning of the segment to the end of the segment * tuple: (t_start, t_stop) with t_start and t_stop being quantities in seconds * tuple: (None, t_stop) indicates the beginning of the segment to t_stop * tuple: (t_start, None) indicates from t_start to the end of the segment strict_slicing: bool, default: True Control if an error is raised or not when t_start or t_stop is outside of the real time range of the segment. Returns ------- seg: neo.core.Segment Returns the desired segment based on the parameters given """ if lazy: if time_slice is not None: raise ValueError("For lazy=True you must specify a time_slice when LazyObject.load(time_slice=...)") if load_waveforms: raise ValueError( "For lazy=True you must specify load_waveforms when SpikeTrain.load(load_waveforms=...)" ) if signal_group_mode is None: signal_group_mode = self._prefered_signal_group_mode # annotations seg_annotations = self.raw_annotations["blocks"][block_index]["segments"][seg_index].copy() for k in ("signals", "spikes", "events"): seg_annotations.pop(k) seg_annotations = check_annotations(seg_annotations) seg = Segment(index=seg_index, **seg_annotations) # AnalogSignal signal_streams = self.header["signal_streams"] sub_streams = self.get_sub_signal_streams(signal_group_mode) for sub_stream in sub_streams: stream_index, inner_stream_channels, name = sub_stream anasig = AnalogSignalProxy( rawio=self, stream_index=stream_index, inner_stream_channels=inner_stream_channels, block_index=block_index, seg_index=seg_index, ) anasig.name = name if not lazy: # ... and get the real AnalogSignal if not lazy anasig = anasig.load(time_slice=time_slice, strict_slicing=strict_slicing) seg.analogsignals.append(anasig) # SpikeTrain and waveforms (optional) spike_channels = self.header["spike_channels"] for spike_channel_index in range(len(spike_channels)): # make a proxy... sptr = SpikeTrainProxy( rawio=self, spike_channel_index=spike_channel_index, block_index=block_index, seg_index=seg_index ) if not lazy: # ... and get the real SpikeTrain if not lazy sptr = sptr.load(time_slice=time_slice, strict_slicing=strict_slicing, load_waveforms=load_waveforms) # TODO magnitude_mode='rescaled'/'raw' seg.spiketrains.append(sptr) # Events/Epoch event_channels = self.header["event_channels"] for chan_ind in range(len(event_channels)): if event_channels["type"][chan_ind] == b"event": e = EventProxy(rawio=self, event_channel_index=chan_ind, block_index=block_index, seg_index=seg_index) if not lazy: e = e.load(time_slice=time_slice, strict_slicing=strict_slicing) seg.events.append(e) elif event_channels["type"][chan_ind] == b"epoch": e = EpochProxy(rawio=self, event_channel_index=chan_ind, block_index=block_index, seg_index=seg_index) if not lazy: e = e.load(time_slice=time_slice, strict_slicing=strict_slicing) seg.epochs.append(e) seg.check_relationships() return seg def get_sub_signal_streams(self, signal_group_mode="group-by-same-units"): """ When signal streams don't have homogeneous SI units across channels, they have to be split in sub streams to construct AnalogSignal objects with unique units. For backward compatibility (neo version <= 0.5) sub-streams can also be used to generate one AnalogSignal per channel. """ signal_streams = self.header["signal_streams"] signal_channels = self.header["signal_channels"] sub_streams = [] for stream_index in range(len(signal_streams)): stream_id = signal_streams[stream_index]["id"] stream_name = signal_streams[stream_index]["name"] mask = signal_channels["stream_id"] == stream_id channels = signal_channels[mask] if signal_group_mode == "group-by-same-units": # this does not keep the original order _, idx = np.unique(channels["units"], return_index=True) all_units = channels["units"][np.sort(idx)] if len(all_units) == 1: # no substream # None iwill be transform as slice later inner_stream_channels = None name = stream_name sub_stream = (stream_index, inner_stream_channels, name) sub_streams.append(sub_stream) else: for units in all_units: (inner_stream_channels,) = np.nonzero(channels["units"] == units) chan_names = channels[inner_stream_channels]["name"] name = "Channels: (" + " ".join(chan_names) + ")" sub_stream = (stream_index, inner_stream_channels, name) sub_streams.append(sub_stream) elif signal_group_mode == "split-all": # mimic all neo <= 0.5 behavior for i, channel in enumerate(channels): inner_stream_channels = [i] name = channels[i]["name"] sub_stream = (stream_index, inner_stream_channels, name) sub_streams.append(sub_stream) else: raise (NotImplementedError) return sub_streams ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/baseio.py0000644000175100017510000002416215154507213015043 0ustar00runnerrunner""" baseio ====== Classes ------- BaseIO - abstract class which should be overridden, managing how a file will load/write its data If you want a model for developing a new IO start from exampleIO. """ from __future__ import annotations from pathlib import Path from collections.abc import Sequence import logging from neo import logging_handler from neo.core import ( AnalogSignal, Block, Epoch, Event, Group, IrregularlySampledSignal, ChannelView, Segment, SpikeTrain, ImageSequence, RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest, NeoReadWriteError, ) read_error = "This type is not supported by this file format for reading" write_error = "This type is not supported by this file format for writing" class BaseIO: """ Generic class to handle all the file read/write methods for the key objects of the core class. This template is file-reading/writing oriented but it can also handle data read from/written to a database such as TDT sytem tanks or SQLite files. This is an abstract class that will be subclassed for each format The key methods of the class are: - ``read()`` - Read the whole object structure, return a list of Block objects - ``read_block(lazy=True, **params)`` - Read Block object from file with some parameters - ``read_segment(lazy=True, **params)`` - Read Segment object from file with some parameters - ``read_spiketrainlist(lazy=True, **params)`` - Read SpikeTrainList object from file with some parameters - ``write()`` - Write the whole object structure - ``write_block(**params)`` - Write Block object to file with some parameters - ``write_segment(**params)`` - Write Segment object to file with some parameters - ``write_spiketrainlist(**params)`` - Write SpikeTrainList object to file with some parameters The class can also implement these methods: - ``read_XXX(lazy=True, **params)`` - ``write_XXX(**params)`` where XXX could be one of the objects supported by the IO Each class is able to declare what can be accessed or written directly discribed by **readable_objects** and **readable_objects**. The object types can be one of the classes defined in neo.core (Block, Segment, AnalogSignal, ...) Each class does not necessary support all the whole neo hierarchy but part of it. This is described with **supported_objects**. All IOs must support at least Block with a read_block() ** start a new IO ** If you want to implement your own file format, you should create a class that will inherit from this BaseFile class and implement the previous methods. See ExampleIO in exampleio.py """ is_readable = False is_writable = False supported_objects = [] readable_objects = [] writeable_objects = [] support_lazy = False read_params = {} write_params = {} name = "BaseIO" description = "" extensions = [] mode = "file" # or 'fake' or 'dir' or 'database' def __init__(self, filename: str | Path = None, **kargs): self.filename = str(filename) # create a logger for the IO class fullname = self.__class__.__module__ + "." + self.__class__.__name__ self.logger = logging.getLogger(fullname) # create a logger for 'neo' and add a handler to it if it doesn't # have one already. # (it will also not add one if the root logger has a handler) corename = self.__class__.__module__.split(".")[0] corelogger = logging.getLogger(corename) rootlogger = logging.getLogger() if not corelogger.handlers and not rootlogger.handlers: corelogger.addHandler(logging_handler) ######## General read/write methods ####################### def read(self, lazy: bool = False, **kargs): """ Return all data from the file as a list of Blocks Parameters ---------- lazy: bool, default: False Whether to lazily load the data (True) or to load into memory (False) kargs: dict IO specific additional arguments Returns ------ block_list: list[neo.core.Block] Returns all the data from the file as Blocks """ if lazy and not self.support_lazy: raise NeoReadWriteError("This IO module does not support lazy loading") if Block in self.readable_objects: if hasattr(self, "read_all_blocks") and callable(getattr(self, "read_all_blocks")): return self.read_all_blocks(lazy=lazy, **kargs) return [self.read_block(lazy=lazy, **kargs)] elif Segment in self.readable_objects: bl = Block(name="One segment only") seg = self.read_segment(lazy=lazy, **kargs) bl.segments.append(seg) bl.check_relationships() return [bl] else: raise NotImplementedError def write(self, bl, **kargs): """ Writes a given block if IO supports writing Parameters ---------- bl: neo.core.Block The neo Block to be written kargs: dict IO specific additional arguments """ if Block in self.writeable_objects: if isinstance(bl, Sequence): if not hasattr(self, "write_all_blocks"): raise NeoReadWriteError(f"{self.__class__.__name__} does not offer to store a sequence of blocks") self.write_all_blocks(bl, **kargs) else: self.write_block(bl, **kargs) elif Segment in self.writeable_objects: if len(bl.segments) != 1: raise NeoReadWriteError( f"{self.__class__.__name__} is based on segment so if you try to write a block it " + "must contain only one Segment" ) self.write_segment(bl.segments[0], **kargs) else: raise NotImplementedError ######## All individual read methods ####################### def read_block(self, **kargs): if Block not in self.readable_objects: raise NeoReadWriteError(read_error) def read_segment(self, **kargs): if Segment not in self.readable_objects: raise NeoReadWriteError(read_error) def read_spiketrain(self, **kargs): if SpikeTrain not in self.readable_objects: raise NeoReadWriteError(read_error) def read_analogsignal(self, **kargs): if AnalogSignal not in self.readable_objects: raise NeoReadWriteError(read_error) def read_imagesequence(self, **kargs): if ImageSequence not in self.readable_objects: raise NeoReadWriteError(read_error) def read_rectangularregionofinterest(self, **kargs): if RectangularRegionOfInterest not in self.readable_objects: raise NeoReadWriteError(read_error) def read_circularregionofinterest(self, **kargs): if CircularRegionOfInterest not in self.readable_objects: raise NeoReadWriteError(read_error) def read_polygonregionofinterest(self, **kargs): if PolygonRegionOfInterest not in self.readable_objects: raise NeoReadWriteError(read_error) def read_irregularlysampledsignal(self, **kargs): if IrregularlySampledSignal not in self.readable_objects: raise NeoReadWriteError(read_error) def read_channelview(self, **kargs): if ChannelView not in self.readable_objects: raise NeoReadWriteError(read_error) def read_event(self, **kargs): if Event not in self.readable_objects: raise NeoReadWriteError(read_error) def read_epoch(self, **kargs): if Epoch not in self.readable_objects: raise NeoReadWriteError(read_error) def read_group(self, **kargs): if Group not in self.readable_objects: raise NeoReadWriteError(read_error) ######## All individual write methods ####################### def write_block(self, bl, **kargs): if Block not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_segment(self, seg, **kargs): if Segment not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_spiketrain(self, sptr, **kargs): if SpikeTrain not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_analogsignal(self, anasig, **kargs): if AnalogSignal not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_imagesequence(self, imseq, **kargs): if ImageSequence not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_rectangularregionofinterest(self, rectroi, **kargs): if RectangularRegionOfInterest not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_circularregionofinterest(self, circroi, **kargs): if CircularRegionOfInterest not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_polygonregionofinterest(self, polyroi, **kargs): if PolygonRegionOfInterest not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_irregularlysampledsignal(self, irsig, **kargs): if IrregularlySampledSignal not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_channelview(self, chv, **kargs): if ChannelView not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_event(self, ev, **kargs): if Event not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_epoch(self, ep, **kargs): if Epoch not in self.writeable_objects: raise NeoReadWriteError(write_error) def write_group(self, group, **kargs): if Group not in self.writeable_objects: raise NeoReadWriteError(write_error) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/bci2000io.py0000644000175100017510000000064015154507213015163 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.bci2000rawio import BCI2000RawIO class BCI2000IO(BCI2000RawIO, BaseFromRaw): """Class for reading data from a BCI2000 .dat file, either version 1.0 or 1.1""" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): BCI2000RawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/biocamio.py0000644000175100017510000000060115154507213015353 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.biocamrawio import BiocamRawIO class BiocamIO(BiocamRawIO, BaseFromRaw): __doc__ = BiocamRawIO.__doc__ mode = "file" def __init__(self, filename, fill_gaps_strategy=None): BiocamRawIO.__init__(self, filename=filename, fill_gaps_strategy=fill_gaps_strategy) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/blackrockio.py0000644000175100017510000000120315154507213016053 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.blackrockrawio import BlackrockRawIO class BlackrockIO(BlackrockRawIO, BaseFromRaw): """ Supplementary class for reading BlackRock data using only a single nsx file. """ name = "Blackrock IO for single nsx" description = ( "This IO reads a pair of corresponding nev and nsX files of the Blackrock " "" + "(Cerebus) recording system." ) _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename, **kargs): BlackrockRawIO.__init__(self, filename=filename, **kargs) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/blkio.py0000644000175100017510000003207215154507213014700 0ustar00runnerrunnerimport struct import os import math import numpy as np from .baseio import BaseIO from neo.core import ImageSequence, Segment, Block class BlkIO(BaseIO): """ Neo IO module for optical imaging data stored as BLK file *Usage*: >>> from neo import io >>> import quantities as pq >>> r = io.BlkIO("file_blk_1.BLK",units='V',sampling_rate=1.0*pq.Hz, ... spatial_scale=1.0*pq.Hz) >>> block = r.read_block() reading the header reading block returning block >>> block Block with 6 segments file_origin: 'file_blk_1.BLK' # segments (N=6) 0: Segment with 1 imagesequences description: 'stim nb:0' # analogsignals (N=0) 1: Segment with 1 imagesequences description: 'stim nb:1' # analogsignals (N=0) 2: Segment with 1 imagesequences description: 'stim nb:2' # analogsignals (N=0) 3: Segment with 1 imagesequences description: 'stim nb:3' # analogsignals (N=0) 4: Segment with 1 imagesequences description: 'stim nb:4' # analogsignals (N=0) 5: Segment with 1 imagesequences description: 'stim nb:5' # analogsignals (N=0) Many thanks to Thomas Deneux for the MATLAB code on which this was based. """ name = "BLK IO" description = "Neo IO module for optical imaging data stored as BLK file" _prefered_signal_goup_mode = "group-by-same-units" is_readable = True is_wirtable = False supported_objects = [Block, Segment, ImageSequence] readble_objects = supported_objects support_lazy = False read_params = {} write_params = {} extensions = [] mode = "file" def __init__(self, file_name=None, units=None, sampling_rate=None, spatial_scale=None, **kwargs): BaseIO.__init__(self, file_name, **kwargs) self.units = units self.sampling_rate = sampling_rate self.spatial_scale = spatial_scale def read(self, lazy=False, **kwargs): """ Return all data from the file as a list of Blocks """ if lazy: raise ValueError("This IO module does not support lazy loading") return [ self.read_block( lazy=lazy, units=self.units, sampling_rate=self.sampling_rate, spatial_scale=self.spatial_scale, **kwargs, ) ] def read_block(self, lazy=False, **kargs): def read(name, type, nb, dictionary, file): if type == "int32": # dictionary[name] = int.from_bytes(file.read(4), byteorder=sys.byteorder, signed=True) dictionary[name] = struct.unpack("i", file.read(4))[0] if type == "float32": dictionary[name] = struct.unpack("f", file.read(4))[0] if type == "uint8": l = [] for i in range(nb): l.append(chr(struct.unpack("B", file.read(1))[0])) dictionary[name] = l if type == "uint16": l = [] for i in range(nb): l.append((struct.unpack("H", file.read(2)))[0]) dictionary[name] = l if type == "short": dictionary[name] = struct.unpack("h", file.read(2))[0] return dictionary def read_header(file_name): file = open(file_name, "rb") i = [ ["file_size", "int32", 1], ["checksum_header", "int32", 1], ["check_data", "int32", 1], ["lenheader", "int32", 1], ["versionid", "float32", 1], ["filetype", "int32", 1], ["filesubtype", "int32", 1], ["datatype", "int32", 1], ["sizeof", "int32", 1], ["framewidth", "int32", 1], ["frameheight", "int32", 1], ["nframesperstim", "int32", 1], ["nstimuli", "int32", 1], ["initialxbinfactor", "int32", 1], ["initialybinfactor", "int32", 1], ["xbinfactor", "int32", 1], ["ybinfactor", "int32", 1], ["username", "uint8", 32], ["recordingdate", "uint8", 16], ["x1roi", "int32", 1], ["y1roi", "int32", 1], ["x2roi", "int32", 1], ["y2roi", "int32", 1], ["stimoffs", "int32", 1], ["stimsize", "int32", 1], ["frameoffs", "int32", 1], ["framesize", "int32", 1], ["refoffs", "int32", 1], ["refsize", "int32", 1], ["refwidth", "int32", 1], ["refheight", "int32", 1], ["whichblocks", "uint16", 16], ["whichframe", "uint16", 16], ["loclip", "int32", 1], ["hiclip", "int32", 1], ["lopass", "int32", 1], ["hipass", "int32", 1], ["operationsperformed", "uint8", 64], ["magnification", "float32", 1], ["gain", "uint16", 1], ["wavelength", "uint16", 1], ["exposuretime", "int32", 1], ["nrepetitions", "int32", 1], ["acquisitiondelay", "int32", 1], ["interstiminterval", "int32", 1], ["creationdate", "uint8", 16], ["datafilename", "uint8", 64], ["orareserved", "uint8", 256], ] dic = {} for x in i: dic = read(name=x[0], type=x[1], nb=x[2], dictionary=dic, file=file) if dic["filesubtype"] == 13: i = [ ["includesrefframe", "int32", 1], ["temp", "uint8", 128], ["ntrials", "int32", 1], ["scalefactors", "int32", 1], ["cameragain", "short", 1], ["ampgain", "short", 1], ["samplingrate", "short", 1], ["average", "short", 1], ["exposuretime", "short", 1], ["samplingaverage", "short", 1], ["presentaverage", "short", 1], ["framesperstim", "short", 1], ["trialsperblock", "short", 1], ["sizeofanalogbufferinframes", "short", 1], ["cameratrials", "short", 1], ["filler", "uint8", 106], ["dyedaqreserved", "uint8", 106], ] for x in i: dic = read(name=x[0], type=x[1], nb=x[2], dictionary=dic, file=file) # nottested # p.listofstimuli=temp(1:max(find(temp~=0)))'; % up to first non-zero stimulus dic["listofstimuli"] = dic["temp"][0 : np.argwhere(x != 0).max(0)] else: i = [ ["includesrefframe", "int32", 1], ["listofstimuli", "uint8", 256], ["nvideoframesperdataframe", "int32", 1], ["ntrials", "int32", 1], ["scalefactor", "int32", 1], ["meanampgain", "float32", 1], ["meanampdc", "float32", 1], ["vdaqreserved", "uint8", 256], ] for x in i: dic = read(name=x[0], type=x[1], nb=x[2], dictionary=dic, file=file) i = [["user", "uint8", 256], ["comment", "uint8", 256], ["refscalefactor", "int32", 1]] for x in i: dic = read(name=x[0], type=x[1], nb=x[2], dictionary=dic, file=file) dic["actuallength"] = os.stat(file_name).st_size file.close() return dic # start of the reading process nblocks = 1 print("reading the header") header = read_header(self.filename) nstim = header["nstimuli"] ni = header["framewidth"] nj = header["frameheight"] nfr = header["nframesperstim"] lenh = header["lenheader"] framesize = header["framesize"] filesize = header["file_size"] dtype = header["datatype"] gain = header["meanampgain"] dc = header["meanampdc"] scalefactor = header["scalefactor"] # [["dtype","nbytes","datatype","type_out"],[...]] l = [ [11, 1, "uchar", "uint8", "B"], [12, 2, "ushort", "uint16", "H"], [13, 4, "ulong", "uint32", "I"], [14, 4, "float", "single", "f"], ] for i in l: if dtype == i[0]: nbytes, datatype, type_out, struct_type = i[1], i[2], i[3], i[4] if framesize != ni * nj * nbytes: print("BAD HEADER!!! framesize does not match framewidth*frameheight*nbytes!") framesize = ni * nj * nbytes if (filesize - lenh) > (framesize * nfr * nstim): nfr2 = nfr + 1 includesrefframe = True else: nfr2 = nfr includesrefframe = False nbin = nblocks conds = [i for i in range(1, nstim + 1)] ncond = len(conds) data = [[[np.zeros((ni, nj, nfr), type_out)] for x in range(ncond)] for i in range(nbin)] for k in range(1, nbin + 1): print("reading block") bin = np.arange(math.floor((k - 1 / nbin * nblocks) + 1), math.floor((k / nbin * nblocks) + 1)) sbin = bin.size for j in range(1, sbin + 1): file = open(self.filename, "rb") for i in range(1, ncond + 1): framestart = conds[i - 1] * nfr2 - nfr offset = framestart * ni * nj * nbytes + lenh file.seek(offset, 0) a = [(struct.unpack(struct_type, file.read(nbytes)))[0] for m in range(ni * nj * nfr)] a = np.reshape(np.array(a, dtype=type_out, order="F"), (ni * nj, nfr), order="F") a = np.reshape(a, (ni, nj, nfr), order="F") if includesrefframe: # not tested framestart = (conds[i] - 1) * nfr2 offset = framestart * ni * nj * nbytes + lenh file.seek(offset) ref = [(struct.unpack(struct_type, file.read(nbytes)))[0] for m in range(ni * nj)] ref = np.array(ref, dtype=type_out) for y in range(len(ref)): ref[y] *= scalefactor ref = np.reshape(ref, (ni, nj)) b = np.tile(ref, [1, 1, nfr]) for y in range(len(a)): b.append([]) for x in range(len(a[y])): b[y + 1].append([]) for frame in range(len(a[y][x])): b[y + 1][x][frame] = (a[y][x][frame] / gain) - (scalefactor * dc / gain) a = b if sbin == 1: data[k - 1][i - 1] = a else: # not tested for y in range(len(a)): for x in range(len(a[y])): a[y][x] /= sbin data[k - 1][i - 1] = data[k - 1][i - 1] + a / sbin file.close() # data format [block][stim][width][height][frame]] # data structure should be [block][stim][frame][width][height] in order to be easy to use with neo # each file is a block # each stim could be a segment # then an image sequence [frame][width][height] # image need to be rotated # changing order of data for compatibility # [block][stim][width][height][frame]] # to # [block][stim][frame][width][height] for block in range(len(data)): for stim in range(len(data[block])): a = [] for frame in range(header["nframesperstim"]): a.append([]) for width in range(len(data[block][stim])): a[frame].append([]) for height in range(len(data[block][stim][width])): a[frame][width].append(data[block][stim][width][height][frame]) # rotation of data to be the same as thomas deneux screenshot a[frame] = np.rot90(np.fliplr(a[frame])) data[block][stim] = a block = Block(file_origin=self.filename) for stim in range(len(data[0])): image_sequence = ImageSequence( data[0][stim], units=self.units, sampling_rate=self.sampling_rate, spatial_scale=self.spatial_scale ) segment = Segment(file_origin=self.filename, description=("stim nb:" + str(stim))) segment.imagesequences = [image_sequence] for key in header: block.annotations[key] = header[key] block.segments.append(segment) print("returning block") return block ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/brainvisionio.py0000644000175100017510000000063615154507213016454 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.brainvisionrawio import BrainVisionRawIO class BrainVisionIO(BrainVisionRawIO, BaseFromRaw): """Class for reading data from the BrainVision product.""" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): BrainVisionRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/brainwaredamio.py0000644000175100017510000001731415154507213016566 0ustar00runnerrunner""" Class for reading from Brainware DAM files DAM files are binary files for holding raw data. They are broken up into sequence of Segments, each containing a single raw trace and parameters. The DAM file does NOT contain a sampling rate, nor can it be reliably calculated from any of the parameters. You can calculate it from the "sweep length" attribute if it is present, but it isn't always present. It is more reliable to get it from the corresponding SRC file or F32 file if you have one. The DAM file also does not divide up data into Blocks, so only a single Block is returned.. Brainware was developed by Dr. Jan Schnupp and is availabe from Tucker Davis Technologies, Inc. http://www.tdt.com/downloads.htm Neither Dr. Jan Schnupp nor Tucker Davis Technologies, Inc. had any part in the development of this code The code is implemented with the permission of Dr. Jan Schnupp Author: Todd Jennings """ # import needed core python modules import os import os.path # numpy and quantities are already required by neo import numpy as np import quantities as pq # needed core neo modules from neo.core import AnalogSignal, Block, Group, Segment # need to subclass BaseIO from neo.io.baseio import BaseIO class BrainwareDamIO(BaseIO): """ Class for reading Brainware raw data files with the extension '.dam'. The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block. Note: The file format does not contain a sampling rate. The sampling rate is set to 1 Hz, but this is arbitrary. If you have a corresponding .src or .f32 file, you can get the sampling rate from that. It may also be possible to infer it from the attributes, such as "sweep length", if present. Usage: >>> from neo.io.brainwaredamio import BrainwareDamIO >>> damfile = BrainwareDamIO(filename='multi_500ms_mulitrep_ch1.dam') >>> blk1 = damfile.read() >>> blk2 = damfile.read_block() >>> print blk1.segments >>> print blk1.segments[0].analogsignals >>> print blk1.units >>> print blk1.units[0].name >>> print blk2 >>> print blk2[0].segments """ is_readable = True # This class can only read data is_writable = False # write is not supported # This class is able to directly or indirectly handle the following objects # You can notice that this greatly simplifies the full Neo object hierarchy supported_objects = [Block, Group, Segment, AnalogSignal] readable_objects = [Block] writeable_objects = [] has_header = False is_streameable = False # This is for GUI stuff: a definition for parameters when reading. # This dict should be keyed by object (`Block`). Each entry is a list # of tuple. The first entry in each tuple is the parameter name. The # second entry is a dict with keys 'value' (for default value), # and 'label' (for a descriptive name). # Note that if the highest-level object requires parameters, # common_io_test will be skipped. read_params = {Block: []} # do not support write so no GUI stuff write_params = None name = "Brainware DAM File" extensions = ["dam"] mode = "file" def __init__(self, filename=None): """ Arguments: filename: the filename """ BaseIO.__init__(self) self._path = filename self._filename = os.path.basename(filename) self._fsrc = None def read_block(self, lazy=False, **kargs): """ Reads a block from the raw data file "fname" generated with BrainWare """ assert not lazy, "Do not support lazy" # there are no keyargs implemented to so far. If someone tries to pass # them they are expecting them to do something or making a mistake, # neither of which should pass silently if kargs: raise NotImplementedError("This method does not have any " "arguments implemented yet") self._fsrc = None block = Block(file_origin=self._filename) # create the objects to store other objects gr = Group(file_origin=self._filename) # load objects into their containers block.groups.append(gr) # open the file with open(self._path, "rb") as fobject: # while the file is not done keep reading segments while True: seg = self._read_segment(fobject) # if there are no more Segments, stop if not seg: break # store the segment and signals block.segments.append(seg) gr.analogsignals.append(seg.analogsignals[0]) # remove the file object self._fsrc = None block.check_relationships() return block # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # IMPORTANT!!! # These are private methods implementing the internal reading mechanism. # Due to the way BrainWare DAM files are structured, they CANNOT be used # on their own. Calling these manually will almost certainly alter your # position in the file in an unrecoverable manner, whether they throw # an exception or not. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- def _read_segment(self, fobject): """ Read a single segment with a single analogsignal Returns the segment or None if there are no more segments """ try: # float64 -- start time of the AnalogSignal t_start = np.fromfile(fobject, dtype=np.float64, count=1)[0] except IndexError: # if there are no more Segments, return return False # int16 -- index of the stimulus parameters seg_index = np.fromfile(fobject, dtype=np.int16, count=1)[0].tolist() # int16 -- number of stimulus parameters numelements = np.fromfile(fobject, dtype=np.int16, count=1)[0] # read the name strings for the stimulus parameters paramnames = [] for _ in range(numelements): # unit8 -- the number of characters in the string numchars = np.fromfile(fobject, dtype=np.uint8, count=1)[0] # char * numchars -- a single name string name = np.fromfile(fobject, dtype=np.uint8, count=numchars) # exclude invalid characters name = str(name[name >= 32].view("c").tobytes()) # add the name to the list of names paramnames.append(name) # float32 * numelements -- the values for the stimulus parameters paramvalues = np.fromfile(fobject, dtype=np.float32, count=numelements) # combine parameter names and the parameters as a dict params = dict(zip(paramnames, paramvalues)) # int32 -- the number elements in the AnalogSignal numpts = np.fromfile(fobject, dtype=np.int32, count=1)[0] # int16 * numpts -- the AnalogSignal itself signal = np.fromfile(fobject, dtype=np.int16, count=numpts) sig = AnalogSignal( signal.astype(np.float32) * pq.mV, t_start=t_start * pq.d, file_origin=self._filename, sampling_period=1.0 * pq.s, copy=None, ) # Note: setting the sampling_period to 1 s is arbitrary # load the AnalogSignal and parameters into a new Segment seg = Segment(file_origin=self._filename, index=seg_index, **params) seg.analogsignals = [sig] return seg ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/brainwaref32io.py0000644000175100017510000002176615154507213016425 0ustar00runnerrunner""" Class for reading from Brainware F32 files F32 files are simplified binary files for holding spike data. Unlike SRC files, F32 files carry little metadata. This also means, however, that the file format does not change, unlike SRC files whose format changes periodically (although ideally SRC files are backwards-compatible). Each F32 file only holds a single Block. The only metadata stored in the file is the length of a single repetition of the stimulus and the values of the stimulus parameters (but not the names of the parameters). Brainware was developed by Dr. Jan Schnupp and is availabe from Tucker Davis Technologies, Inc. http://www.tdt.com/downloads.htm Neither Dr. Jan Schnupp nor Tucker Davis Technologies, Inc. had any part in the development of this code The code is implemented with the permission of Dr. Jan Schnupp Author: Todd Jennings """ # import needed core python modules from os import path # numpy and quantities are already required by neo import numpy as np import quantities as pq # needed core neo modules from neo.core import Block, Group, Segment, SpikeTrain, NeoReadWriteError # need to subclass BaseIO from neo.io.baseio import BaseIO class BrainwareF32IO(BaseIO): """ Class for reading Brainware Spike ReCord files with the extension '.f32' The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block. The read_all_blocks method automatically reads all Blocks. It will automatically close the file after reading. The read_next_block method will return one Block each time it is called. It will automatically close the file and reset to the first Block after reading the last block. Call the close method to close the file and reset this method back to the first Block. The isopen property tells whether the file is currently open and reading or closed. Note 1: There is always only one Group. Usage: >>> from neo.io.brainwaref32io import BrainwareF32IO >>> f32file = BrainwareF32IO(filename='multi_500ms_mulitrep_ch1.f32') >>> blk1 = f32file.read() >>> blk2 = f32file.read_block() >>> print blk1.segments >>> print blk1.segments[0].spiketrains >>> print blk1.units >>> print blk1.units[0].name >>> print blk2 >>> print blk2[0].segments """ is_readable = True # This class can only read data is_writable = False # write is not supported # This class is able to directly or indirectly handle the following objects # You can notice that this greatly simplifies the full Neo object hierarchy supported_objects = [Block, Group, Segment, SpikeTrain] readable_objects = [Block] writeable_objects = [] has_header = False is_streameable = False # This is for GUI stuff: a definition for parameters when reading. # This dict should be keyed by object (`Block`). Each entry is a list # of tuple. The first entry in each tuple is the parameter name. The # second entry is a dict with keys 'value' (for default value), # and 'label' (for a descriptive name). # Note that if the highest-level object requires parameters, # common_io_test will be skipped. read_params = {Block: []} # does not support write so no GUI stuff write_params = None name = "Brainware F32 File" extensions = ["f32"] mode = "file" def __init__(self, filename=None): """ Arguments: filename: the filename """ BaseIO.__init__(self) self._path = filename self._filename = path.basename(filename) self._fsrc = None self._blk = None self.__unit_group = None self.__t_stop = None self.__params = None self.__seg = None self.__spiketimes = None def read_block(self, lazy=False, **kargs): """ Reads a block from the simple spike data file "fname" generated with BrainWare """ if lazy: raise NeoReadWriteError("This IO does not support lazy reading") # there are no keyargs implemented to so far. If someone tries to pass # them they are expecting them to do something or making a mistake, # neither of which should pass silently if kargs: raise NotImplementedError("This method does not have any " "argument implemented yet") self._fsrc = None self._blk = Block(file_origin=self._filename) block = self._blk # create the objects to store other objects self.__unit_group = Group(file_origin=self._filename) block.groups.append(self.__unit_group) # initialize values self.__t_stop = None self.__params = None self.__seg = None self.__spiketimes = None # open the file with open(self._path, "rb") as self._fsrc: res = True # while the file is not done keep reading segments while res: res = self.__read_id() block.check_relationships() # cleanup attributes self._fsrc = None self._blk = None self.__t_stop = None self.__params = None self.__seg = None self.__spiketimes = None return block # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # IMPORTANT!!! # These are private methods implementing the internal reading mechanism. # Due to the way BrainWare DAM files are structured, they CANNOT be used # on their own. Calling these manually will almost certainly alter your # position in the file in an unrecoverable manner, whether they throw # an exception or not. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- def __read_id(self): """ Read the next ID number and do the appropriate task with it. Returns nothing. """ try: # float32 -- ID of the first data sequence objid = np.fromfile(self._fsrc, dtype=np.float32, count=1)[0] except IndexError: # if we have a previous segment, save it self.__save_segment() # if there are no more Segments, return return False if objid == -2: self.__read_condition() elif objid == -1: self.__read_segment() else: self.__spiketimes.append(objid) return True def __read_condition(self): """ Read the parameter values for a single stimulus condition. Returns nothing. """ # float32 -- SpikeTrain length in ms self.__t_stop = np.fromfile(self._fsrc, dtype=np.float32, count=1)[0] # float32 -- number of stimulus parameters numelements = int(np.fromfile(self._fsrc, dtype=np.float32, count=1)[0]) # [float32] * numelements -- stimulus parameter values paramvals = np.fromfile(self._fsrc, dtype=np.float32, count=numelements).tolist() # organize the parameers into a dictionary with arbitrary names paramnames = [f"Param{i}" for i in range(len(paramvals))] self.__params = dict(zip(paramnames, paramvals)) def __read_segment(self): """ Setup the next Segment. Returns nothing. """ # if we have a previous segment, save it self.__save_segment() # create the segment self.__seg = Segment(file_origin=self._filename, **self.__params) # create an empy array to save the spike times # this needs to be converted to a SpikeTrain before it can be used self.__spiketimes = [] def __save_segment(self): """ Write the segment to the Block if it exists """ # if this is the beginning of the first condition, then we don't want # to save, so exit # but set __seg from None to False so we know next time to create a # segment even if there are no spike in the condition if self.__seg is None: self.__seg = False return if not self.__seg: # create dummy values if there are no SpikeTrains in this condition self.__seg = Segment(file_origin=self._filename, **self.__params) self.__spiketimes = [] times = pq.Quantity(self.__spiketimes, dtype=np.float32, units=pq.ms) train = SpikeTrain(times, t_start=0 * pq.ms, t_stop=self.__t_stop * pq.ms, file_origin=self._filename) self.__seg.spiketrains = [train] self.__unit_group.spiketrains.append(train) self._blk.segments.append(self.__seg) # set an empty segment # from now on, we need to set __seg to False rather than None so # that if there is a condition with no SpikeTrains we know # to create an empty Segment self.__seg = False ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/brainwaresrcio.py0000755000175100017510000015267015154507213016624 0ustar00runnerrunner""" Class for reading from Brainware SRC files SRC files are binary files for holding spike data. They are broken up into nested data sequences of different types, with each type of sequence identified by a unique ID number. This allows new versions of sequences to be included without breaking backwards compatibility, since new versions can just be given a new ID number. The ID numbers and the format of the data they contain were taken from the Matlab-based reader function supplied with BrainWare. The python code, however, was implemented from scratch in Python using Python idioms. There are some situations where BrainWare data can overflow the SRC file, resulting in a corrupt file. Neither BrainWare nor the Matlab-based reader can read such files. This software, however, will try to recover the data, and in most cases can do so successfully. Each SRC file can hold the equivalent of multiple Neo Blocks. Brainware was developed by Dr. Jan Schnupp and is availabe from Tucker Davis Technologies, Inc. http://www.tdt.com/downloads.htm Neither Dr. Jan Schnupp nor Tucker Davis Technologies, Inc. had any part in the development of this code The code is implemented with the permission of Dr. Jan Schnupp Note when porting ChannelIndex/Unit to Group (Samuel Garcia). The ChannelIndex was used as group of units. To avoid now a "group of group" each units is directly a "Group"'. Author: Todd Jennings """ # import needed core python modules from datetime import datetime, timedelta from itertools import chain import logging import os.path # numpy and quantities are already required by neo import numpy as np import quantities as pq # needed core neo modules from neo.core import Block, Event, Group, Segment, SpikeTrain # need to subclass BaseIO from neo.io.baseio import BaseIO LOGHANDLER = logging.StreamHandler() class BrainwareSrcIO(BaseIO): """ Class for reading Brainware Spike ReCord files with the extension '.src' The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block. The read_all_blocks method automatically reads all Blocks. It will automatically close the file after reading. The read_next_block method will return one Block each time it is called. It will automatically close the file and reset to the first Block after reading the last block. Call the close method to close the file and reset this method back to the first Block. The _isopen property tells whether the file is currently open and reading or closed. Note 1: The first Unit in each Group is always UnassignedSpikes, which has a SpikeTrain for each Segment containing all the spikes not assigned to any Unit in that Segment. Note 2: The first Segment in each Block is always Comments, which stores all comments as an Event object. Note 3: The parameters from the BrainWare table for each condition are stored in the Segment annotations. If there are multiple repetitions of a condition, each repetition is stored as a separate Segment. Note 4: There is always only one Group. Usage: >>> from neo.io.brainwaresrcio import BrainwareSrcIO >>> srcfile = BrainwareSrcIO(filename='multi_500ms_mulitrep_ch1.src') >>> blk1 = srcfile.read() >>> blk2 = srcfile.read_block() >>> blks = srcfile.read_all_blocks() >>> print blk1.segments >>> print blk1.segments[0].spiketrains >>> print blk1.groups >>> print blk1.groups[0].name >>> print blk2 >>> print blk2[0].segments >>> print blks >>> print blks[0].segments """ is_readable = True # This class can only read data is_writable = False # write is not supported # This class is able to directly or indirectly handle the following objects supported_objects = [Block, Group, Segment, SpikeTrain, Event] readable_objects = [Block] writeable_objects = [] has_header = False is_streameable = False # This is for GUI stuff: a definition for parameters when reading. # This dict should be keyed by object (`Block`). Each entry is a list # of tuple. The first entry in each tuple is the parameter name. The # second entry is a dict with keys 'value' (for default value), # and 'label' (for a descriptive name). # Note that if the highest-level object requires parameters, # common_io_test will be skipped. read_params = {Block: []} # does not support write so no GUI stuff write_params = None name = "Brainware SRC File" extensions = ["src"] mode = "file" def __init__(self, filename=None): """ Arguments: filename: the filename """ BaseIO.__init__(self) # log the __init__ self.logger.info("__init__") # this stores the filename of the current object, exactly as it is # provided when the instance is initialized. self._filename = filename # this store the filename without the path self._file_origin = filename # This stores the file object for the current file self._fsrc = None # This stores the current Block self._blk = None # This stores the current Segment for easy access # It is equivalent to self._blk.segments[-1] self._seg0 = None # this stores a dictionary of the Block's Group (Units) by name, # making it easier and faster to retrieve Units by name later # UnassignedSpikes and Units accessed by index are not stored here self._unitdict = {} # this stores the current Unit self._unit0 = None # if the file has a list with negative length, the rest of the file's # list lengths are unreliable, so we need to store this value for the # whole file self._damaged = False # this stores an empty SpikeTrain which is used in various places. self._default_spiketrain = None @property def _isopen(self): """ This property tells whether the SRC file associated with the IO object is open. """ return self._fsrc is not None def _opensrc(self): """ Open the file if it isn't already open. """ # if the file isn't already open, open it and clear the Blocks if not self._fsrc or self._fsrc.closed: self._fsrc = open(self._filename, "rb") # figure out the filename of the current file self._file_origin = os.path.basename(self._filename) def close(self): """ Close the currently-open file and reset the current reading point. """ self.logger.info("close") if self._isopen and not self._fsrc.closed: self._fsrc.close() # we also need to reset all per-file attributes self._damaged = False self._fsrc = None self._seg0 = None self._file_origin = None self._lazy = False self._default_spiketrain = None def read_block(self, lazy=False, **kargs): """ Reads the first Block from the Spike ReCording file "filename" generated with BrainWare. If you wish to read more than one Block, please use read_all_blocks. """ assert not lazy, "Do not support lazy" # there are no keyargs implemented to so far. If someone tries to pass # them they are expecting them to do something or making a mistake, # neither of which should pass silently if kargs: raise NotImplementedError("This method does not have any " "arguments implemented yet") blockobj = self.read_next_block() self.close() return blockobj def read_next_block(self, **kargs): """ Reads a single Block from the Spike ReCording file "filename" generated with BrainWare. Each call of read will return the next Block until all Blocks are loaded. After the last Block, the file will be automatically closed and the progress reset. Call the close method manually to reset back to the first Block. """ # there are no keyargs implemented to so far. If someone tries to pass # them they are expecting them to do something or making a mistake, # neither of which should pass silently if kargs: raise NotImplementedError("This method does not have any " "arguments implemented yet") self._opensrc() # create _default_spiketrain here for performance reasons self._default_spiketrain = self._init_default_spiketrain.copy() self._default_spiketrain.file_origin = self._file_origin # create the Block and the contents all Blocks of from IO share self._blk = Block(file_origin=self._file_origin) self._seg0 = Segment(name="Comments", file_origin=self._file_origin) self._unit0 = Group(name="UnassignedSpikes", elliptic=[], boundaries=[], timestamp=[], max_valid=[]) self._blk.groups.append(self._unit0) self._blk.segments.append(self._seg0) # this actually reads the contents of the Block result = [] while hasattr(result, "__iter__"): try: result = self._read_by_id() except: self.close() raise # since we read at a Block level we always do this self._blk.check_relationships() # put the Block in a local object so it can be gargabe collected blockobj = self._blk # reset the per-Block attributes self._blk = None self._unitdict = {} # combine the comments into one big event self._combine_segment_events(self._seg0) # result is None iff the end of the file is reached, so we can # close the file # this notification is not helpful if using the read method with # cascading, since the user will know it is done when the method # returns a value if result is None: self.logger.info("Last Block read. Closing file.") self.close() return blockobj def read_all_blocks(self, lazy=False, **kargs): """ Reads all Blocks from the Spike ReCording file "filename" generated with BrainWare. The progress in the file is reset and the file closed then opened again prior to reading. The file is automatically closed after reading completes. """ # there are no keyargs implemented to so far. If someone tries to pass # them they are expecting them to do something or making a mistake, # neither of which should pass silently assert not lazy, "Do not support lazy" if kargs: raise NotImplementedError("This method does not have any " "argument implemented yet") self.close() self._opensrc() # Read each Block. # After the last Block self._isopen is set to False, so this make a # good way to determine when to stop blocks = [] while self._isopen: try: blocks.append(self.read_next_block()) except: self.close() raise return blocks def _convert_timestamp(self, timestamp, start_date=datetime(1899, 12, 30)): """ _convert_timestamp(timestamp, start_date) - convert a timestamp in brainware src file units to a python datetime object. start_date defaults to 1899.12.30 (ISO format), which is the start date used by all BrainWare SRC data Blocks so far. If manually specified it should be a datetime object or any other object that can be added to a timedelta object. """ # datetime + timedelta = datetime again. try: timestamp = convert_brainwaresrc_timestamp(timestamp, start_date) except OverflowError as err: timestamp = start_date self.logger.exception("_convert_timestamp overflow") return timestamp # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # All methods from here on are private. They are not intended to be used # on their own, although methods that could theoretically be called on # their own are marked as such. All private methods could be renamed, # combined, or split at any time. All private methods prefixed by # "__read" or "__skip" will alter the current place in the file. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- def _read_by_id(self): """ Reader for generic data BrainWare SRC files are broken up into data sequences that are identified by an ID code. This method determines the ID code and calls the method to read the data sequence with that ID code. See the _ID_DICT attribute for a dictionary of code/method pairs. IMPORTANT!!! This is the only private method that can be called directly. The rest of the private methods can only safely be called by this method or by other private methods, since they depend on the current position in the file. """ try: # uint16 -- the ID code of the next sequence seqid = np.fromfile(self._fsrc, dtype=np.uint16, count=1).item() except ValueError: # return a None if at EOF. Other methods use None to recognize # an EOF return None # using the seqid, get the reader function from the reader dict readfunc = self._ID_DICT.get(seqid) if readfunc is None: if seqid <= 0: # return if end-of-sequence ID code. This has to be 0. # just calling "return" will return a None which is used as an # EOF indicator return 0 else: # return a warning if the key is invalid # (this is consistent with the official behavior, # even the official reference files have invalid keys # when using the official reference reader matlab # scripts self.logger.warning("unknown ID: %s", seqid) return [] try: # run the function to get the data return readfunc(self) except (EOFError, UnicodeDecodeError) as err: # return a warning if the EOF is reached in the middle of a method self.logger.exception("Premature end of file") return None # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # These are helper methods. They don't read from the file, so it # won't harm the reading process to call them, but they are only relevant # when used in other private methods. # # These are tuned to the particular needs of this IO class, they are # unlikely to work properly if used with another file format. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- def _assign_sequence(self, data_obj): """ _assign_sequence(data_obj) - Try to guess where an unknown sequence should go based on its class. Warning are issued if this method is used since manual reorganization may be needed. """ if isinstance(data_obj, Group): self.logger.warning("Unknown Group found, adding to Group list") self._blk.groups.append(data_obj) if data_obj.name: self._unitdict[data_obj.name] = data_obj elif isinstance(data_obj, Segment): self.logger.warning("Unknown Segment found, " "adding to Segments list") self._blk.segments.append(data_obj) elif isinstance(data_obj, Event): self.logger.warning("Unknown Event found, " "adding to comment Events list") self._seg0.events.append(data_obj) elif isinstance(data_obj, SpikeTrain): self.logger.warning("Unknown SpikeTrain found, " "adding to the UnassignedSpikes Unit") self._unit0.spiketrains.append(data_obj) elif hasattr(data_obj, "__iter__") and not isinstance(data_obj, str): for sub_obj in data_obj: self._assign_sequence(sub_obj) else: if self.logger.isEnabledFor(logging.WARNING): self.logger.warning("Unrecognized sequence of type %s found, " "skipping", type(data_obj)) _default_datetime = datetime(1, 1, 1) _default_t_start = pq.Quantity(0.0, units=pq.ms, dtype=np.float32) _init_default_spiketrain = SpikeTrain( times=pq.Quantity([], units=pq.ms, dtype=np.float32), t_start=pq.Quantity(0, units=pq.ms, dtype=np.float32), t_stop=pq.Quantity(1, units=pq.ms, dtype=np.float32), waveforms=pq.Quantity([[[]]], dtype=np.int8, units=pq.mV), dtype=np.float32, copy=None, timestamp=_default_datetime, respwin=np.array([], dtype=np.int32), dama_index=-1, trig2=pq.Quantity([], units=pq.ms, dtype=np.uint8), side="", ) def _combine_events(self, events): """ _combine_events(events) - combine a list of Events with single events into one long Event """ if not events: event = Event( times=pq.Quantity([], units=pq.s), labels=np.array([], dtype="U"), senders=np.array([], dtype="S"), t_start=0, ) return event times = [] labels = [] senders = [] for event in events: times.append(event.times.magnitude) if event.labels.shape == (1,): labels.append(event.labels[0]) else: raise AssertionError( "This single event has multiple labels in an array with " f"shape {event.labels.shape} instead of a single label." ) senders.append(event.annotations["sender"]) times = np.array(times, dtype=np.float32) t_start = times.min() times = pq.Quantity(times - t_start, units=pq.d).rescale(pq.s) labels = np.array(labels, dtype="U") senders = np.array(senders) event = Event(times=times, labels=labels, t_start=t_start.tolist(), senders=senders) return event def _combine_segment_events(self, segment): """ _combine_segment_events(segment) Combine all Events in a segment. """ event = self._combine_events(segment.events) event_t_start = event.annotations.pop("t_start") segment.rec_datetime = self._convert_timestamp(event_t_start) segment.events = [event] def _combine_spiketrains(self, spiketrains): """ _combine_spiketrains(spiketrains) - combine a list of SpikeTrains with single spikes into one long SpikeTrain """ if not spiketrains: return self._default_spiketrain.copy() if hasattr(spiketrains[0], "waveforms") and len(spiketrains) == 1: train = spiketrains[0] return train if hasattr(spiketrains[0], "t_stop"): # workaround for bug in some broken files istrain = [hasattr(utrain, "waveforms") for utrain in spiketrains] if not all(istrain): goodtrains = [itrain for i, itrain in enumerate(spiketrains) if istrain[i]] badtrains = [itrain for i, itrain in enumerate(spiketrains) if not istrain[i]] spiketrains = goodtrains + [self._combine_spiketrains(badtrains)] spiketrains = [itrain for itrain in spiketrains if itrain.size > 0] if not spiketrains: return self._default_spiketrain.copy() # get the times of the spiketrains and combine them waveforms = [itrain.waveforms for itrain in spiketrains] rawtrains = np.array(np.concatenate(spiketrains, axis=1)) times = pq.Quantity(rawtrains, units=pq.ms) lens1 = np.array([wave.shape[1] for wave in waveforms]) lens2 = np.array([wave.shape[2] for wave in waveforms]) if lens1.max() != lens1.min() or lens2.max() != lens2.min(): lens1 = lens1.max() - lens1 lens2 = lens2.max() - lens2 waveforms = [ np.pad(waveform, ((0, 0), (0, len1), (0, len2)), "constant") for waveform, len1, len2 in zip(waveforms, lens1, lens2) ] waveforms = np.concatenate(waveforms, axis=0) # extract the trig2 annotation trig2 = np.array(np.concatenate([itrain.annotations["trig2"] for itrain in spiketrains], axis=1)) trig2 = pq.Quantity(trig2, units=pq.ms) elif hasattr(spiketrains[0], "units"): return self._combine_spiketrains([spiketrains]) else: times, waveforms, trig2 = zip(*spiketrains) times = np.concatenate(times, axis=0) # get the times of the SpikeTrains and combine them times = pq.Quantity(times, units=pq.ms) # get the waveforms of the SpikeTrains and combine them # these should be a 3D array with the first axis being the spike, # the second axis being the recording channel (there is only one), # and the third axis being the actual waveform waveforms = np.concatenate(waveforms, axis=0) # extract the trig2 annotation trig2 = pq.Quantity(np.hstack(trig2), units=pq.ms) if not times.size: return self._default_spiketrain.copy() # get the maximum time t_stop = times[-1] * 2.0 waveforms = pq.Quantity(waveforms, units=pq.mV) train = SpikeTrain( times=times, copy=None, t_start=self._default_t_start.copy(), t_stop=t_stop, file_origin=self._file_origin, waveforms=waveforms, timestamp=self._default_datetime, respwin=np.array([], dtype=np.int32), dama_index=-1, trig2=trig2, side="", ) return train # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # IMPORTANT!!! # These are private methods implementing the internal reading mechanism. # Due to the way BrainWare SRC files are structured, they CANNOT be used # on their own. Calling these manually will almost certainly alter your # position in the file in an unrecoverable manner, whether they throw # an exception or not. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- def __read_str(self, numchars=1, utf=None): """ Read a string of a specific length. """ rawstr = np.fromfile(self._fsrc, dtype="S%s" % numchars, count=1).item() return rawstr.decode("utf-8") def __read_annotations(self): """ Read the stimulus grid properties. ------------------------------------------------------------------- Returns a dictionary containing the parameter names as keys and the parameter values as values. The returned object must be added to the Block. ID: 29109 """ # int16 -- number of stimulus parameters numelements = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] if not numelements: return {} # [data sequence] * numelements -- parameter names names = [] for i in range(numelements): # {skip} = byte (char) -- skip one byte self._fsrc.seek(1, 1) # uint8 -- length of next string numchars = np.fromfile(self._fsrc, dtype=np.uint8, count=1).item() # if there is no name, make one up if not numchars: name = "param%s" % i else: # char * numchars -- parameter name string name = self.__read_str(numchars) # if the name is already in there, add a unique number to it # so it isn't overwritten if name in names: name = name + str(i) names.append(name) # float32 * numelements -- an array of parameter values values = np.fromfile(self._fsrc, dtype=np.float32, count=numelements) # combine the names and values into a dict # the dict will be added to the annotations annotations = dict(zip(names, values)) return annotations def __read_annotations_old(self): """ Read the stimulus grid properties. Returns a dictionary containing the parameter names as keys and the parameter values as values. ------------------------------------------------ The returned objects must be added to the Block. This reads an old version of the format that does not store paramater names, so placeholder names are created instead. ID: 29099 """ # int16 * 14 -- an array of parameter values values = np.fromfile(self._fsrc, dtype=np.int16, count=14) # create dummy names and combine them with the values in a dict # the dict will be added to the annotations params = ["param%s" % i for i in range(len(values))] annotations = dict(zip(params, values)) return annotations def __read_comment(self): """ Read a single comment. The comment is stored as an Event in Segment 0, which is specifically for comments. ---------------------- Returns an empty list. The returned object is already added to the Block. No ID number: always called from another method """ # float64 -- timestamp (number of days since dec 30th 1899) time = np.fromfile(self._fsrc, dtype=np.double, count=1)[0] # int16 -- length of next string numchars1 = np.fromfile(self._fsrc, dtype=np.int16, count=1).item() # char * numchars -- the one who sent the comment sender = self.__read_str(numchars1) # int16 -- length of next string numchars2 = np.fromfile(self._fsrc, dtype=np.int16, count=1).item() # char * numchars -- comment text text = self.__read_str(numchars2, utf=False) comment = Event( times=pq.Quantity(time, units=pq.d), labels=[text], sender=sender, file_origin=self._file_origin ) self._seg0.events.append(comment) return [] def __read_list(self): """ Read a list of arbitrary data sequences It only says how many data sequences should be read. These sequences are then read by their ID number. Note that lists can be nested. If there are too many sequences (for instance if there are a large number of spikes in a Segment) then a negative number will be returned for the number of data sequences to read. In this case the method tries to guess. This also means that all future list data sequences have unreliable lengths as well. ------------------------------------------- Returns a list of objects. Whether these objects need to be added to the Block depends on the object in question. There are several data sequences that have identical formats but are used in different situations. That means this data sequences has multiple ID numbers. ID: 29082 ID: 29083 ID: 29091 ID: 29093 """ # int16 -- number of sequences to read numelements = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] # {skip} = bytes * 4 (int16 * 2) -- skip four bytes self._fsrc.seek(4, 1) if numelements == 0: return [] if not self._damaged and numelements < 0: self._damaged = True self.logger.error("Negative sequence count %s, file damaged", numelements) if not self._damaged: # read the sequences into a list seq_list = [self._read_by_id() for _ in range(numelements)] else: # read until we get some indication we should stop seq_list = [] # uint16 -- the ID of the next sequence seqidinit = np.fromfile(self._fsrc, dtype=np.uint16, count=1)[0] # {rewind} = byte * 2 (int16) -- move back 2 bytes, i.e. go back to # before the beginning of the seqid self._fsrc.seek(-2, 1) while 1: # uint16 -- the ID of the next sequence seqid = np.fromfile(self._fsrc, dtype=np.uint16, count=1)[0] # {rewind} = byte * 2 (int16) -- move back 2 bytes, i.e. go # back to before the beginning of the seqid self._fsrc.seek(-2, 1) # if we come across a new sequence, we are at the end of the # list so we should stop if seqidinit != seqid: break # otherwise read the next sequence seq_list.append(self._read_by_id()) return seq_list def __read_segment(self): """ Read an individual Segment. A Segment contains a dictionary of parameters, the length of the recording, a list of Units with their Spikes, and a list of Spikes not assigned to any Unit. The unassigned spikes are always stored in Unit 0, which is exclusively for storing these spikes. ------------------------------------------------- Returns the Segment object created by the method. The returned object is already added to the Block. ID: 29106 """ # (data_obj) -- the stimulus parameters for this segment annotations = self._read_by_id() annotations["feature_type"] = -1 annotations["go_by_closest_unit_center"] = False annotations["include_unit_bounds"] = False # (data_obj) -- SpikeTrain list of unassigned spikes # these go in the first Unit since it is for unassigned spikes unassigned_spikes = self._read_by_id() self._unit0.spiketrains.extend(unassigned_spikes) # read a list of units and grab the second return value, which is the # SpikeTrains from this Segment (if we use the Unit we will get all the # SpikeTrains from that Unit, resuling in duplicates if we are past # the first Segment trains = self._read_by_id() if not trains: if unassigned_spikes: # if there are no assigned spikes, # just use the unassigned spikes trains = zip(unassigned_spikes) else: # if there are no spiketrains at all, # create an empty spike train trains = [[self._default_spiketrain.copy()]] elif hasattr(trains[0], "dtype"): # workaround for some broken files trains = [unassigned_spikes + [self._combine_spiketrains([trains])]] else: # get the second element from each returned value, # which is the actual SpikeTrains trains = [unassigned_spikes] + [train[1] for train in trains] # re-organize by sweeps trains = zip(*trains) # int32 -- SpikeTrain length in ms spiketrainlen = pq.Quantity(np.fromfile(self._fsrc, dtype=np.int32, count=1)[0], units=pq.ms) segments = [] for train in trains: # create the Segment and add everything to it segment = Segment(file_origin=self._file_origin, **annotations) segment.spiketrains = train self._blk.segments.append(segment) segments.append(segment) for itrain in train: # use the SpikeTrain length to figure out the stop time # t_start is always 0 so we can ignore it itrain.t_stop = spiketrainlen return segments def __read_segment_list(self): """ Read a list of Segments with comments. Since comments can occur at any point, whether a recording is happening or not, it is impossible to reliably assign them to a specific Segment. For this reason they are always assigned to Segment 0, which is exclusively used to store comments. -------------------------------------------------------- Returns a list of the Segments created with this method. The returned objects are already added to the Block. ID: 29112 """ # uint8 -- number of electrode channels in the Segment numchannels = np.fromfile(self._fsrc, dtype=np.uint8, count=1)[0] # [list of sequences] -- individual Segments segments = self.__read_list() while not hasattr(segments[0], "spiketrains"): segments = list(chain(*segments)) # char -- "side of brain" info side = self.__read_str(1) # int16 -- number of comments numelements = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] # comment_obj * numelements -- comments about the Segments # we don't know which Segment specifically, though for _ in range(numelements): self.__read_comment() # store what side of the head we are dealing with for segment in segments: for spiketrain in segment.spiketrains: spiketrain.annotations["side"] = side return segments def __read_segment_list_v8(self): """ Read a list of Segments with comments. This is version 8 of the data sequence. This is the same as __read_segment_list_var, but can also contain one or more arbitrary sequences. The class makes an attempt to assign the sequences when possible, and warns the user when this happens (see the _assign_sequence method) -------------------------------------------------------- Returns a list of the Segments created with this method. The returned objects are already added to the Block. ID: 29117 """ # segment_collection_var -- this is based off a segment_collection_var segments = self.__read_segment_list_var() # uint16 -- the ID of the next sequence seqid = np.fromfile(self._fsrc, dtype=np.uint16, count=1)[0] # {rewind} = byte * 2 (int16) -- move back 2 bytes, i.e. go back to # before the beginning of the seqid self._fsrc.seek(-2, 1) if seqid in self._ID_DICT: # if it is a valid seqid, read it and try to figure out where # to put it self._assign_sequence(self._read_by_id()) else: # otherwise it is a Unit list self.__read_unit_list() # {skip} = byte * 2 (int16) -- skip 2 bytes self._fsrc.seek(2, 1) return segments def __read_segment_list_v9(self): """ Read a list of Segments with comments. This is version 9 of the data sequence. This is the same as __read_segment_list_v8, but contains some additional annotations. These annotations are added to the Segment. -------------------------------------------------------- Returns a list of the Segments created with this method. The returned objects are already added to the Block. ID: 29120 """ # segment_collection_v8 -- this is based off a segment_collection_v8 segments = self.__read_segment_list_v8() # uint8 feature_type = np.fromfile(self._fsrc, dtype=np.uint8, count=1)[0] # uint8 go_by_closest_unit_center = np.fromfile(self._fsrc, dtype=np.bool8, count=1)[0] # uint8 include_unit_bounds = np.fromfile(self._fsrc, dtype=np.bool8, count=1)[0] # create a dictionary of the annotations annotations = { "feature_type": feature_type, "go_by_closest_unit_center": go_by_closest_unit_center, "include_unit_bounds": include_unit_bounds, } # add the annotations to each Segment for segment in segments: segment.annotations.update(annotations) return segments def __read_segment_list_var(self): """ Read a list of Segments with comments. This is the same as __read_segment_list, but contains information regarding the sampling period. This information is added to the SpikeTrains in the Segments. -------------------------------------------------------- Returns a list of the Segments created with this method. The returned objects are already added to the Block. ID: 29114 """ # float32 -- DA conversion clock period in microsec sampling_period = pq.Quantity(np.fromfile(self._fsrc, dtype=np.float32, count=1), units=pq.us)[0] # segment_collection -- this is based off a segment_collection segments = self.__read_segment_list() # add the sampling period to each SpikeTrain for segment in segments: for spiketrain in segment.spiketrains: spiketrain.sampling_period = sampling_period return segments def __read_spike_fixed(self, numpts=40): """ Read a spike with a fixed waveform length (40 time bins) ------------------------------------------- Returns the time, waveform and trig2 value. The returned objects must be converted to a SpikeTrain then added to the Block. ID: 29079 """ # float32 -- spike time stamp in ms since start of SpikeTrain time = np.fromfile(self._fsrc, dtype=np.float32, count=1) # int8 * 40 -- spike shape -- use numpts for spike_var waveform = np.fromfile(self._fsrc, dtype=np.int8, count=numpts).reshape(1, 1, numpts) # uint8 -- point of return to noise trig2 = np.fromfile(self._fsrc, dtype=np.uint8, count=1) return time, waveform, trig2 def __read_spike_fixed_old(self): """ Read a spike with a fixed waveform length (40 time bins) This is an old version of the format. The time is stored as ints representing 1/25 ms time steps. It has no trigger information. ------------------------------------------- Returns the time, waveform and trig2 value. The returned objects must be converted to a SpikeTrain then added to the Block. ID: 29081 """ # int32 -- spike time stamp in ms since start of SpikeTrain time = np.fromfile(self._fsrc, dtype=np.int32, count=1) / 25.0 time = time.astype(np.float32) # int8 * 40 -- spike shape # This needs to be a 3D array, one for each channel. BrainWare # only ever has a single channel per file. waveform = np.fromfile(self._fsrc, dtype=np.int8, count=40).reshape(1, 1, 40) # create a dummy trig2 value trig2 = np.array([-1], dtype=np.uint8) return time, waveform, trig2 def __read_spike_var(self): """ Read a spike with a variable waveform length ------------------------------------------- Returns the time, waveform and trig2 value. The returned objects must be converted to a SpikeTrain then added to the Block. ID: 29115 """ # uint8 -- number of points in spike shape numpts = np.fromfile(self._fsrc, dtype=np.uint8, count=1)[0] # spike_fixed is the same as spike_var if you don't read the numpts # byte and set numpts = 40 return self.__read_spike_fixed(numpts) def __read_spiketrain_indexed(self): """ Read a SpikeTrain This is the same as __read_spiketrain_timestamped except it also contains the index of the Segment in the dam file. The index is stored as an annotation in the SpikeTrain. ------------------------------------------------- Returns a SpikeTrain object with multiple spikes. The returned object must be added to the Block. ID: 29121 """ # int32 -- index of the analogsignalarray in corresponding .dam file dama_index = np.fromfile(self._fsrc, dtype=np.int32, count=1)[0] # spiketrain_timestamped -- this is based off a spiketrain_timestamped spiketrain = self.__read_spiketrain_timestamped() # add the property to the dict spiketrain.annotations["dama_index"] = dama_index return spiketrain def __read_spiketrain_timestamped(self): """ Read a SpikeTrain This SpikeTrain contains a time stamp for when it was recorded The timestamp is stored as an annotation in the SpikeTrain. ------------------------------------------------- Returns a SpikeTrain object with multiple spikes. The returned object must be added to the Block. ID: 29110 """ # float64 -- timeStamp (number of days since dec 30th 1899) timestamp = np.fromfile(self._fsrc, dtype=np.double, count=1)[0] # convert to datetime object timestamp = self._convert_timestamp(timestamp) # seq_list -- spike list # combine the spikes into a single SpikeTrain spiketrain = self._combine_spiketrains(self.__read_list()) # add the timestamp spiketrain.annotations["timestamp"] = timestamp return spiketrain def __read_unit(self): """ Read all SpikeTrains from a single Segment and Unit This is the same as __read_unit_unsorted except it also contains information on the spike sorting boundaries. ------------------------------------------------------------------ Returns a single Unit and a list of SpikeTrains from that Unit and current Segment, in that order. The SpikeTrains must be returned since it is not possible to determine from the Unit which SpikeTrains are from the current Segment. The returned objects are already added to the Block. The SpikeTrains must be added to the current Segment. ID: 29116 """ # same as unsorted Unit unit, trains = self.__read_unit_unsorted() # float32 * 18 -- Unit boundaries (IEEE 32-bit floats) unit.annotations["boundaries"] = [np.fromfile(self._fsrc, dtype=np.float32, count=18)] # uint8 * 9 -- boolean values indicating elliptic feature boundary # dimensions unit.annotations["elliptic"] = [np.fromfile(self._fsrc, dtype=np.uint8, count=9)] return unit, trains def __read_unit_list(self): """ A list of a list of Units ----------------------------------------------- Returns a list of Units modified in the method. The returned objects are already added to the Block. No ID number: only called by other methods """ # this is used to figure out which Units to return maxunit = 1 # int16 -- number of time slices numelements = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] # {sequence} * numelements1 -- the number of lists of Units to read for i in range(numelements): # {skip} = byte * 2 (int16) -- skip 2 bytes self._fsrc.seek(2, 1) # double max_valid = np.fromfile(self._fsrc, dtype=np.double, count=1)[0] # int16 - the number of Units to read numunits = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] # update tha maximum Unit so far maxunit = max(maxunit, numunits + 1) # if there aren't enough Units, create them # remember we need to skip the UnassignedSpikes Unit if numunits > len(self._blk.groups) + 1: for ind1 in range(len(self._blk.groups), numunits + 1): unit = Group( name="unit%s" % ind1, file_origin=self._file_origin, elliptic=[], boundaries=[], timestamp=[], max_valid=[], ) self._blk.groups.append(unit) # {Block} * numelements -- Units for ind1 in range(numunits): # get the Unit with the given index # remember we need to skip the UnassignedSpikes Unit unit = self._blk.groups[ind1 + 1] # {skip} = byte * 2 (int16) -- skip 2 bytes self._fsrc.seek(2, 1) # int16 -- a multiplier for the elliptic and boundaries # properties numelements3 = np.fromfile(self._fsrc, dtype=np.int16, count=1)[0] # uint8 * 10 * numelements3 -- boolean values indicating # elliptic feature boundary dimensions elliptic = np.fromfile(self._fsrc, dtype=np.uint8, count=10 * numelements3) # float32 * 20 * numelements3 -- feature boundaries boundaries = np.fromfile(self._fsrc, dtype=np.float32, count=20 * numelements3) unit.annotations["elliptic"].append(elliptic) unit.annotations["boundaries"].append(boundaries) unit.annotations["max_valid"].append(max_valid) return self._blk.groups[1:maxunit] def __read_unit_list_timestamped(self): """ A list of a list of Units. This is the same as __read_unit_list, except that it also has a timestamp. This is added ad an annotation to all Units. ----------------------------------------------- Returns a list of Units modified in the method. The returned objects are already added to the Block. ID: 29119 """ # double -- time zero (number of days since dec 30th 1899) timestamp = np.fromfile(self._fsrc, dtype=np.double, count=1)[0] # convert to to days since UNIX epoc time: timestamp = self._convert_timestamp(timestamp) # sorter -- this is based off a sorter units = self.__read_unit_list() for unit in units: unit.annotations["timestamp"].append(timestamp) return units def __read_unit_old(self): """ Read all SpikeTrains from a single Segment and Unit This is the same as __read_unit_unsorted except it also contains information on the spike sorting boundaries. This is an old version of the format that used 48-bit floating-point numbers for the boundaries. These cannot easily be read and so are skipped. ------------------------------------------------------------------ Returns a single Unit and a list of SpikeTrains from that Unit and current Segment, in that order. The SpikeTrains must be returned since it is not possible to determine from the Unit which SpikeTrains are from the current Segment. The returned objects are already added to the Block. The SpikeTrains must be added to the current Segment. ID: 29107 """ # same as Unit unit, trains = self.__read_unit_unsorted() # bytes * 108 (float48 * 18) -- Unit boundaries (48-bit floating # point numbers are not supported so we skip them) self._fsrc.seek(108, 1) # uint8 * 9 -- boolean values indicating elliptic feature boundary # dimensions unit.annotations["elliptic"] = np.fromfile(self._fsrc, dtype=np.uint8, count=9).tolist() return unit, trains def __read_unit_unsorted(self): """ Read all SpikeTrains from a single Segment and Unit This does not contain Unit boundaries. ------------------------------------------------------------------ Returns a single Unit and a list of SpikeTrains from that Unit and current Segment, in that order. The SpikeTrains must be returned since it is not possible to determine from the Unit which SpikeTrains are from the current Segment. The returned objects are already added to the Block. The SpikeTrains must be added to the current Segment. ID: 29084 """ # {skip} = bytes * 2 (uint16) -- skip two bytes self._fsrc.seek(2, 1) # uint16 -- number of characters in next string numchars = np.fromfile(self._fsrc, dtype=np.uint16, count=1).item() # char * numchars -- ID string of Unit name = self.__read_str(numchars) # int32 -- SpikeTrain length in ms # int32 * 4 -- response and spon period boundaries parts = np.fromfile(self._fsrc, dtype=np.int32, count=5) t_stop = pq.Quantity(parts[0].astype("float32"), units=pq.ms) respwin = parts[1:] # (data_obj) -- list of SpikeTrains spikeslists = self._read_by_id() # use the Unit if it already exists, otherwise create it if name in self._unitdict: unit = self._unitdict[name] else: unit = Group( name=name, file_origin=self._file_origin, elliptic=[], boundaries=[], timestamp=[], max_valid=[] ) self._blk.groups.append(unit) self._unitdict[name] = unit # convert the individual spikes to SpikeTrains and add them to the Unit trains = [self._combine_spiketrains(spikes) for spikes in spikeslists] unit.spiketrains.extend(trains) for train in trains: train.t_stop = t_stop.copy() train.annotations["respwin"] = respwin.copy() return unit, trains def __skip_information(self): """ Read an information sequence. This is data sequence is skipped both here and in the Matlab reference implementation. ---------------------- Returns an empty list Nothing is created so nothing is added to the Block. ID: 29113 """ # {skip} char * 34 -- display information self._fsrc.seek(34, 1) return [] def __skip_information_old(self): """ Read an information sequence This is data sequence is skipped both here and in the Matlab reference implementation This is an old version of the format ---------------------- Returns an empty list. Nothing is created so nothing is added to the Block. ID: 29100 """ # {skip} char * 4 -- display information self._fsrc.seek(4, 1) return [] # This dictionary maps the numeric data sequence ID codes to the data # sequence reading functions. # # Since functions are first-class objects in Python, the functions returned # from this dictionary are directly callable. # # If new data sequence ID codes are added in the future please add the code # here in numeric order and the method above in alphabetical order # # The naming of any private method may change at any time _ID_DICT = { 29079: __read_spike_fixed, 29081: __read_spike_fixed_old, 29082: __read_list, 29083: __read_list, 29084: __read_unit_unsorted, 29091: __read_list, 29093: __read_list, 29099: __read_annotations_old, 29100: __skip_information_old, 29106: __read_segment, 29107: __read_unit_old, 29109: __read_annotations, 29110: __read_spiketrain_timestamped, 29112: __read_segment_list, 29113: __skip_information, 29114: __read_segment_list_var, 29115: __read_spike_var, 29116: __read_unit, 29117: __read_segment_list_v8, 29119: __read_unit_list_timestamped, 29120: __read_segment_list_v9, 29121: __read_spiketrain_indexed, } def convert_brainwaresrc_timestamp(timestamp, start_date=datetime(1899, 12, 30)): """ convert_brainwaresrc_timestamp(timestamp, start_date) - convert a timestamp in brainware src file units to a python datetime object. start_date defaults to 1899.12.30 (ISO format), which is the start date used by all BrainWare SRC data Blocks so far. If manually specified it should be a datetime object or any other object that can be added to a timedelta object. """ # datetime + timedelta = datetime again. return start_date + timedelta(days=timestamp) if __name__ == "__main__": # run this when calling the file directly as a benchmark from neo.test.iotest.test_brainwaresrcio import FILES_TO_TEST from neo.test.iotest.common_io_test import url_for_tests from neo.test.iotest.tools import ( create_local_temp_dir, download_test_file, get_test_file_full_path, make_all_directories, ) shortname = BrainwareSrcIO.__name__.lower().strip("io") local_test_dir = create_local_temp_dir(shortname) url = url_for_tests + shortname FILES_TO_TEST.remove("long_170s_1rep_1clust_ch2.src") make_all_directories(FILES_TO_TEST, local_test_dir) download_test_file(FILES_TO_TEST, local_test_dir, url) for path in get_test_file_full_path(ioclass=BrainwareSrcIO, filename=FILES_TO_TEST, directory=local_test_dir): ioobj = BrainwareSrcIO(path) ioobj.read_all_blocks(lazy=False) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/cedio.py0000644000175100017510000000047115154507213014661 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.cedrawio import CedRawIO class CedIO(CedRawIO, BaseFromRaw): __doc__ = CedRawIO.__doc__ def __init__(self, filename, entfile=None, posfile=None): CedRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/edfio.py0000644000175100017510000000122215154507213014657 0ustar00runnerrunner""" IO for reading edf and edf+ files using pyedflib PyEDFLib https://pyedflib.readthedocs.io https://github.com/holgern/pyedflib EDF Format Specifications: https://www.edfplus.info/ Author: Julia Sprenger """ from neo.io.basefromrawio import BaseFromRaw from neo.rawio.edfrawio import EDFRawIO class EDFIO(EDFRawIO, BaseFromRaw): """ IO for reading edf and edf+ files. """ name = "EDF IO" description = "IO for reading EDF and EDF+ files" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename=""): EDFRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/elanio.py0000644000175100017510000000206015154507213015041 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.elanrawio import ElanRawIO class ElanIO(ElanRawIO, BaseFromRaw): """ Class for reading data from Elan. Elan is software for studying time-frequency maps of EEG data. Elan is developed in Lyon, France, at INSERM U821 https://elan.lyon.inserm.fr Args: filename (string) : Full path to the .eeg file entfile (string) : Full path to the .ent file (optional). If None, the path to the ent file is inferred from the filename by adding the ".ent" extension to it posfile (string) : Full path to the .pos file (optional). If None, the path to the pos file is inferred from the filename by adding the ".pos" extension to it """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename, entfile=None, posfile=None): ElanRawIO.__init__(self, filename=filename, entfile=entfile, posfile=posfile) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/elphyio.py0000644000175100017510000046001015154507213015246 0ustar00runnerrunner""" README ===================================================================================== This is the implementation of the NEO IO for Elphy files. IO dependencies: - NEO - types - numpy - quantities Quick reference: ===================================================================================== Class ElphyIO() with methods read_block() and write_block() are implemented. This classes represent the way to access and produce Elphy files from NEO objects. As regards reading an existing Elphy file, start by initializing a IO class with it: >>> import neo >>> r = neo.io.ElphyIO( filename="Elphy.DAT" ) >>> r Read the file content into NEO object Block: >>> bl = r.read_block() >>> bl Now you can then read all Elphy data as NEO objects: >>> b1.segments [, , , ] >>> bl.segments[0].analogsignals[0] These functions return NEO objects, completely "detached" from the original Elphy file. Changes to the runtime objects will not cause any changes in the file. Having already existing NEO structures, it is possible to write them as an Elphy file. For example, given a segment: >>> s = neo.Segment() filled with other NEO structures: >>> import numpy as np >>> import quantities as pq >>> a = AnalogSignal( signal=np.random.rand(300), t_start=42*pq.ms) >>> s.analogsignals.append( a ) and added to a newly created NEO Block: >>> bl = neo.Block() >>> bl.segments.append( s ) Then, it's easy to create an Elphy file: >>> r = neo.io.ElphyIO( filename="ElphyNeoTest.DAT" ) >>> r.write_block( bl ) Author: Thierry Brizzi Domenico Guarino """ # python commons: from datetime import datetime from math import gcd from os import path import re import struct from time import time # note neo.core needs only numpy and quantities import numpy as np import quantities as pq # I need to subclass BaseIO from neo.io.baseio import BaseIO # to import from core from neo.core import Block, Segment, AnalogSignal, Event, SpikeTrain, NeoReadWriteError # -------------------------------------------------------- # OBJECTS class ElphyScaleFactor: """ Useful to retrieve real values from integer ones that are stored in an Elphy file : ``scale`` : compute the actual value of a sample with this following formula : ``delta`` * value + ``offset`` """ def __init__(self, delta, offset): self.delta = delta self.offset = offset def scale(self, value): return value * self.delta + self.offset class BaseSignal: """ A descriptor storing main signal properties : ``layout`` : the :class:``ElphyLayout` object that extracts data from a file. ``episode`` : the episode in which the signal has been acquired. ``sampling_frequency`` : the sampling frequency of the analog to digital converter. ``sampling_period`` : the sampling period of the analog to digital converter computed from sampling_frequency. ``t_start`` : the start time of the signal acquisition. ``t_stop`` : the end time of the signal acquisition. ``duration`` : the duration of the signal acquisition computed from t_start and t_stop. ``n_samples`` : the number of sample acquired during the recording computed from the duration and the sampling period. ``name`` : a label to identify the signal. ``data`` : a property triggering data extraction. """ def __init__(self, layout, episode, sampling_frequency, start, stop, name=None): self.layout = layout self.episode = episode self.sampling_frequency = sampling_frequency self.sampling_period = 1 / sampling_frequency self.t_start = start self.t_stop = stop self.duration = self.t_stop - self.t_start self.n_samples = int(self.duration / self.sampling_period) self.name = name @property def data(self): raise NotImplementedError("must be overloaded in subclass") class ElphySignal(BaseSignal): """ Subclass of :class:`BaseSignal` corresponding to Elphy's analog channels : ``channel`` : the identifier of the analog channel providing the signal. ``units`` : an array containing x and y coordinates units. ``x_unit`` : a property to access the x-coordinates unit. ``y_unit`` : a property to access the y-coordinates unit. ``data`` : a property that delegate data extraction to the ``get_signal_data`` function of the ```layout`` object. """ def __init__(self, layout, episode, channel, x_unit, y_unit, sampling_frequency, start, stop, name=None): super().__init__(layout, episode, sampling_frequency, start, stop, name) self.channel = channel self.units = [x_unit, y_unit] def __str__(self): return f"{self.layout.file.name} ep_{self.episode} ch_{self.channel} [{self.x_unit}, {self.y_unit}]" def __repr__(self): return self.__str__() @property def x_unit(self): """ Return the x-coordinate of the signal. """ return self.units[0] @property def y_unit(self): """ Return the y-coordinate of the signal. """ return self.units[1] @property def data(self): return self.layout.get_signal_data(self.episode, self.channel) class ElphyTag(BaseSignal): """ Subclass of :class:`BaseSignal` corresponding to Elphy's tag channels : ``number`` : the identifier of the tag channel. ``x_unit`` : the unit of the x-coordinate. """ def __init__(self, layout, episode, number, x_unit, sampling_frequency, start, stop, name=None): super().__init__(layout, episode, sampling_frequency, start, stop, name) self.number = number self.units = [x_unit, None] def __str__(self): return f"{self.layout.file.name} : ep_{self.episode} tag_ch_{ self.number} [{self.x_unit}]" def __repr__(self): return self.__str__() @property def x_unit(self): """ Return the x-coordinate of the signal. """ return self.units[0] @property def data(self): return self.layout.get_tag_data(self.episode, self.number) @property def channel(self): return self.number class ElphyEvent: """ A descriptor that store a set of events properties : ``layout`` : the :class:``ElphyLayout` object that extracts data from a file. ``episode`` : the episode in which the signal has been acquired. ``number`` : the identifier of the channel. ``x_unit`` : the unit of the x-coordinate. ``n_events`` : the number of events. ``name`` : a label to identify the event. ``times`` : a property triggering event times extraction. """ def __init__(self, layout, episode, number, x_unit, n_events, ch_number=None, name=None): self.layout = layout self.episode = episode self.number = number self.x_unit = x_unit self.n_events = n_events self.name = name self.ch_number = ch_number def __str__(self): return f"{self.layout.file.name} : ep_{self.episode} evt_ch_{self.number} [{self.x_unit}]" def __repr__(self): return self.__str__() @property def channel(self): return self.number @property def times(self): return self.layout.get_event_data(self.episode, self.number) @property def data(self): return self.times class ElphySpikeTrain(ElphyEvent): """ A descriptor that store spiketrain properties : ``wf_samples`` : number of samples composing waveforms. ``wf_sampling_frequency`` : sampling frequency of waveforms. ``wf_sampling_period`` : sampling period of waveforms. ``wf_units`` : the units of the x and y coordinates of waveforms. ``t_start`` : the time before the arrival of the spike which corresponds to the starting time of a waveform. ``name`` : a label to identify the event. ``times`` : a property triggering event times extraction. ``waveforms`` : a property triggering waveforms extraction. """ def __init__( self, layout, episode, number, x_unit, n_events, wf_sampling_frequency, wf_samples, unit_x_wf, unit_y_wf, t_start, name=None, ): super().__init__(layout, episode, number, x_unit, n_events, name) self.wf_samples = wf_samples self.wf_sampling_frequency = wf_sampling_frequency if not wf_sampling_frequency: raise ValueError("bad sampling frequency") self.wf_sampling_period = 1.0 / wf_sampling_frequency self.wf_units = [unit_x_wf, unit_y_wf] self.t_start = t_start @property def x_unit_wf(self): """ Return the x-coordinate of waveforms. """ return self.wf_units[0] @property def y_unit_wf(self): """ Return the y-coordinate of waveforms. """ return self.wf_units[1] @property def times(self): return self.layout.get_spiketrain_data(self.episode, self.number) @property def waveforms(self): return self.layout.get_waveform_data(self.episode, self.number) if self.wf_samples else None # -------------------------------------------------------- # BLOCKS class BaseBlock: """ Represent a chunk of file storing metadata or raw data. A convenient class to break down the structure of an Elphy file to several building blocks : ``layout`` : the layout containing the block. ``identifier`` : the label that identified the block. ``size`` : the size of the block. ``start`` : the file index corresponding to the starting byte of the block. ``end`` : the file index corresponding to the ending byte of the block NB : Subclassing this class is a convenient way to set the properties using polymorphism rather than a conditional structure. By this way each :class:`BaseBlock` type know how to iterate through the Elphy file and store interesting data. """ def __init__(self, layout, identifier, start, size): self.layout = layout self.identifier = identifier self.size = size self.start = start self.end = self.start + self.size - 1 class ElphyBlock(BaseBlock): """ A subclass of :class:`BaseBlock`. Useful to store the location and size of interesting data within a block : ``parent_block`` : the parent block containing the block. ``header_size`` : the size of the header permitting the identification of the type of the block. ``data_offset`` : the file index located after the block header. ``data_size`` : the size of data located after the header. ``sub_blocks`` : the sub-blocks contained by the block. """ def __init__(self, layout, identifier, start, size, fixed_length=None, size_format="i", parent_block=None): super().__init__(layout, identifier, start, size) # a block may be a sub-block of another block self.parent_block = parent_block # pascal language store strings in 2 different ways # ... first, if in the program the size of the string is # specified (fixed) then the file stores the length # of the string and allocate a number of bytes equal # to the specified size # ... if this size is not specified the length of the # string is also stored but the file allocate dynamically # a number of bytes equal to the actual size of the string l_ident = len(self.identifier) if fixed_length: l_ident += fixed_length - l_ident self.header_size = l_ident + 1 + type_dict[size_format] # starting point of data located in the block self.data_offset = self.start + self.header_size self.data_size = self.size - self.header_size # a block may have sub-blocks # it is to subclasses to initialize # this property self.sub_blocks = list() def __repr__(self): return f"{self.identifier} : size = {self.size}, start = {self.start}, end = {self.end}" def add_sub_block(self, block): """ Append a block to the sub-block list. """ self.sub_blocks.append(block) class FileInfoBlock(ElphyBlock): """ Base class of all subclasses whose the purpose is to extract user file info stored into an Elphy file : ``header`` : the header block relative to the block. ``file`` : the file containing the block. NB : User defined metadata are not really practical. An Elphy script must know the order of metadata storage to know exactly how to retrieve these data. That's why it is necessary to subclass and reproduce elphy script commands to extract metadata relative to a protocol. Consequently managing a new protocol implies to refactor the file info extraction. """ def __init__(self, layout, identifier, start, size, fixed_length=None, size_format="i", parent_block=None): super().__init__(layout, identifier, start, size, fixed_length, size_format, parent_block=parent_block) self.header = None self.file = self.layout.file def get_protocol_and_version(self): """ Return a tuple useful to identify the kind of protocol that has generated a file during data acquisition. """ raise Exception("must be overloaded in a subclass") def get_user_file_info(self): """ Return a dictionary containing all user file info stored in the file. """ raise Exception("must be overloaded in a subclass") def get_sparsenoise_revcor(self): """ Return 'REVCOR' user file info. This method is common to :class:`ClassicFileInfo` and :class:`MultistimFileInfo` because the last one is able to store this kind of metadata. """ header = dict() header["n_div_x"] = read_from_char(self.file, "h") header["n_div_y"] = read_from_char(self.file, "h") header["gray_levels"] = read_from_char(self.file, "h") header["position_x"] = read_from_char(self.file, "ext") header["position_y"] = read_from_char(self.file, "ext") header["length"] = read_from_char(self.file, "ext") header["width"] = read_from_char(self.file, "ext") header["orientation"] = read_from_char(self.file, "ext") header["expansion"] = read_from_char(self.file, "h") header["scotoma"] = read_from_char(self.file, "h") header["seed"] = read_from_char(self.file, "h") # dt_on and dt_off may not exist in old revcor formats rollback = self.file.tell() header["dt_on"] = read_from_char(self.file, "ext") if header["dt_on"] is None: self.file.seek(rollback) rollback = self.file.tell() header["dt_off"] = read_from_char(self.file, "ext") if header["dt_off"] is None: self.file.seek(rollback) return header class ClassicFileInfo(FileInfoBlock): """ Extract user file info stored into an Elphy file corresponding to sparse noise (revcor), moving bar and flashbar protocols. """ def detect_protocol_from_name(self, path): pattern = r"\d{4}(\d+|\D)\D" codes = {"r": "sparsenoise", "o": "movingbar", "f": "flashbar", "m": "multistim"} # here just for assertion filename = path.split(path)[1] match = re.search(pattern, path) if hasattr(match, "end"): code = codes.get(path[match.end() - 1].lower(), None) if code == "m": raise ValueError("multistim file detected") return code elif "spt" in filename.lower(): return "spontaneousactivity" else: return None def get_protocol_and_version(self): if self.layout and self.layout.info_block: self.file.seek(self.layout.info_block.data_offset) version = self.get_title() if version in ["REVCOR1", "REVCOR2", "REVCOR + PAIRING"]: name = "sparsenoise" elif version in ["BARFLASH"]: name = "flashbar" elif version in ["ORISTIM", "ORISTM", "ORISTM1", "ORITUN"]: name = "movingbar" else: name = self.detect_protocol_from_name(self.file.name) self.file.seek(0) return name, version return None, None def get_title(self): title_length, title = struct.unpack("= 2: name = None version = None else: if center == 2: name = "sparsenoise" elif center == 3: name = "densenoise" elif center == 4: name = "densenoise" elif center == 5: name = "grating" else: name = None version = None self.file.seek(0) return name, version return None, None def get_title(self): title_length = read_from_char(self.file, "B") (title,) = struct.unpack(f"<{title_length}s", self.file.read(title_length)) if hasattr(title, "decode"): title = title.decode() self.file.seek(self.file.tell() + 255 - title_length) return title def get_user_file_info(self): header = dict() if self.layout and self.layout.info_block: # go to the info_block sub_block = self.layout.info_block self.file.seek(sub_block.data_offset) # get the first four parameters acqLGN = read_from_char(self.file, "i") center = read_from_char(self.file, "i") surround = read_from_char(self.file, "i") # store info in the header header["acqLGN"] = acqLGN header["center"] = center header["surround"] = surround if not (header["surround"] >= 2): header.update(self.get_center_header(center)) self.file.seek(0) return header def get_center_header(self, code): # get file info corresponding # to the executed protocol # for the center first ... if code == 0: return self.get_sparsenoise_revcor() elif code == 2: return self.get_sparsenoise_center() elif code == 3: return self.get_densenoise_center(True) elif code == 4: return self.get_densenoise_center(False) elif code == 5: return dict() # return self.get_grating_center() else: return dict() def get_surround_header(self, code): # then the surround if code == 2: return self.get_sparsenoise_surround() elif code == 3: return self.get_densenoise_surround(True) elif code == 4: return self.get_densenoise_surround(False) elif code == 5: raise NotImplementedError() return self.get_grating_center() else: return dict() def get_center_surround(self, center, surround): header = dict() header["stim_center"] = self.get_center_header(center) header["stim_surround"] = self.get_surround_header(surround) return header def get_sparsenoise_center(self): header = dict() header["title"] = self.get_title() header["number_of_sequences"] = read_from_char(self.file, "i") header["pretrigger_duration"] = read_from_char(self.file, "ext") header["n_div_x"] = read_from_char(self.file, "h") header["n_div_y"] = read_from_char(self.file, "h") header["gray_levels"] = read_from_char(self.file, "h") header["position_x"] = read_from_char(self.file, "ext") header["position_y"] = read_from_char(self.file, "ext") header["length"] = read_from_char(self.file, "ext") header["width"] = read_from_char(self.file, "ext") header["orientation"] = read_from_char(self.file, "ext") header["expansion"] = read_from_char(self.file, "h") header["scotoma"] = read_from_char(self.file, "h") header["seed"] = read_from_char(self.file, "h") header["luminance_1"] = read_from_char(self.file, "ext") header["luminance_2"] = read_from_char(self.file, "ext") header["dt_count"] = read_from_char(self.file, "i") dt_array = list() for _ in range(0, header["dt_count"]): dt_array.append(read_from_char(self.file, "ext")) header["dt_on"] = dt_array if dt_array else None header["dt_off"] = read_from_char(self.file, "ext") return header def get_sparsenoise_surround(self): header = dict() header["title_surround"] = self.get_title() header["gap"] = read_from_char(self.file, "ext") header["n_div_x"] = read_from_char(self.file, "h") header["n_div_y"] = read_from_char(self.file, "h") header["gray_levels"] = read_from_char(self.file, "h") header["expansion"] = read_from_char(self.file, "h") header["scotoma"] = read_from_char(self.file, "h") header["seed"] = read_from_char(self.file, "h") header["luminance_1"] = read_from_char(self.file, "ext") header["luminance_2"] = read_from_char(self.file, "ext") header["dt_on"] = read_from_char(self.file, "ext") header["dt_off"] = read_from_char(self.file, "ext") return header def get_densenoise_center(self, is_binary): header = dict() header["stimulus_type"] = "B" if is_binary else "T" header["title"] = self.get_title() _tmp = read_from_char(self.file, "i") header["number_of_sequences"] = _tmp if _tmp < 0 else None rollback = self.file.tell() header["stimulus_duration"] = read_from_char(self.file, "ext") if header["stimulus_duration"] is None: self.file.seek(rollback) header["pretrigger_duration"] = read_from_char(self.file, "ext") header["n_div_x"] = read_from_char(self.file, "h") header["n_div_y"] = read_from_char(self.file, "h") header["position_x"] = read_from_char(self.file, "ext") header["position_y"] = read_from_char(self.file, "ext") header["length"] = read_from_char(self.file, "ext") header["width"] = read_from_char(self.file, "ext") header["orientation"] = read_from_char(self.file, "ext") header["expansion"] = read_from_char(self.file, "h") header["seed"] = read_from_char(self.file, "h") header["luminance_1"] = read_from_char(self.file, "ext") header["luminance_2"] = read_from_char(self.file, "ext") header["dt_on"] = read_from_char(self.file, "ext") header["dt_off"] = read_from_char(self.file, "ext") return header def get_densenoise_surround(self, is_binary): header = dict() header["title_surround"] = self.get_title() header["gap"] = read_from_char(self.file, "ext") header["n_div_x"] = read_from_char(self.file, "h") header["n_div_y"] = read_from_char(self.file, "h") header["expansion"] = read_from_char(self.file, "h") header["seed"] = read_from_char(self.file, "h") header["luminance_1"] = read_from_char(self.file, "ext") header["luminance_2"] = read_from_char(self.file, "ext") header["dt_on"] = read_from_char(self.file, "ext") header["dt_off"] = read_from_char(self.file, "ext") return header def get_grating_center(self): pass def get_grating_surround(self): pass class Header(ElphyBlock): """ A convenient subclass of :class:`Block` to store Elphy file header properties. NB : Subclassing this class is a convenient way to set the properties of the header using polymorphism rather than a conditional structure. """ def __init__(self, layout, identifier, size, fixed_length=None, size_format="i"): super().__init__(layout, identifier, 0, size, fixed_length, size_format) class Acquis1Header(Header): """ A subclass of :class:`Header` used to identify the 'ACQUIS1/GS/1991' format. Whereas more recent format, the header contains all data relative to episodes, channels and traces : ``n_channels`` : the number of acquisition channels. ``nbpt`` and ``nbptEx`` : parameters useful to compute the number of samples by episodes. ``tpData`` : the data format identifier used to compute sample size. ``x_unit`` : the x-coordinate unit for all channels in an episode. ``y_units`` : an array containing y-coordinate units for each channel in the episode. ``dX`` and ``X0`` : the scale factors necessary to retrieve the actual times relative to each sample in a channel. ``dY_ar`` and ``Y0_ar``: arrays of scale factors necessary to retrieve the actual values relative to samples. ``continuous`` : a boolean telling if the file has been acquired in continuous mode. ``preSeqI`` : the size in bytes of the data preceding raw data. ``postSeqI`` : the size in bytes of the data preceding raw data. ``dat_length`` : the length in bytes of the data in the file. ``sample_size`` : the size in bytes of a sample. ``n_samples`` : the number of samples. ``ep_size`` : the size in bytes of an episode. ``n_episodes`` : the number of recording sequences store in the file. NB : The size is read from the file, the identifier is a string containing 15 characters and the size is encoded as small integer. See file 'FicDefAc1.pas' to identify the parsed parameters. """ def __init__(self, layout): fileobj = layout.file super().__init__(layout, "ACQUIS1/GS/1991", 1024, 15, "h") # parse the header to store interesting data about episodes and channels fileobj.seek(18) # extract episode properties n_channels = read_from_char(fileobj, "B") if (n_channels < 1) or (n_channels > 16): raise ValueError(f"`n_channels` must be between 1 and 15. It is currently {n_channels}") nbpt = read_from_char(fileobj, "h") l_xu, x_unit = struct.unpack("= self.end: tagShift = 0 else: tagShift = read_from_char(layout.file, "B") # setup object properties self.n_channels = n_channels self.nbpt = nbpt self.tpData = tpData self.x_unit = xu[0:l_xu] self.dX = dX self.X0 = X0 self.y_units = y_units[0:n_channels] self.dY_ar = dY_ar[0:n_channels] self.Y0_ar = Y0_ar[0:n_channels] self.continuous = continuous if self.continuous: self.preSeqI = 0 self.postSeqI = 0 else: self.preSeqI = preSeqI self.postSeqI = postSeqI self.varEp = varEp self.withTags = withTags if not self.withTags: self.tagShift = 0 else: if tagShift == 0: self.tagShift = 4 else: self.tagShift = tagShift self.sample_size = type_dict[types[self.tpData]] self.dat_length = self.layout.file_size - self.layout.data_offset if self.continuous: if self.n_channels > 0: self.n_samples = self.dat_length / (self.n_channels * self.sample_size) else: self.n_samples = 0 else: self.n_samples = self.nbpt self.ep_size = self.preSeqI + self.postSeqI + self.n_samples * self.sample_size * self.n_channels self.n_episodes = self.dat_length / self.ep_size if (self.n_samples != 0) else 0 class DAC2GSEpisodeBlock(ElphyBlock): """ Subclass of :class:`Block` useful to store data corresponding to 'DAC2SEQ' blocks stored in the DAC2/GS/2000 format. ``n_channels`` : the number of acquisition channels. ``nbpt`` : the number of samples by episodes. ``tpData`` : the data format identifier used to compute the sample size. ``x_unit`` : the x-coordinate unit for all channels in an episode. ``y_units`` : an array containing y-coordinate units for each channel in the episode. ``dX`` and ``X0`` : the scale factors necessary to retrieve the actual times relative to each sample in a channel. ``dY_ar`` and ``Y0_ar``: arrays of scale factors necessary to retrieve the actual values relative to samples. ``postSeqI`` : the size in bytes of the data preceding raw data. NB : see file 'FdefDac2.pas' to identify the parsed parameters. """ def __init__(self, layout, identifier, start, size, fixed_length=None, size_format="i"): main = layout.main_block n_channels, nbpt, tpData, postSeqI = struct.unpack(" 0] for data_block in blocks: self.file.seek(data_block.start) raw = self.file.read(data_block.size)[0:expected_size] databytes = np.frombuffer(raw, dtype=dtype) chunks.append(databytes) # concatenate all chunks and return # the specified slice if len(chunks) > 0: databytes = np.concatenate(chunks) return databytes[start:end] else: return np.array([]) def reshape_bytes(self, databytes, reshape, datatypes, order="<"): """ Reshape a numpy array containing a set of databytes. """ if not datatypes and len(datatypes) == len(reshape): raise NeoReadWriteError("The `datatypes` are not well-defined") l_bytes = len(databytes) # create the mask for each shape shape_mask = list() for shape in reshape: for _ in np.arange(1, shape + 1): shape_mask.append(shape) # create a set of masks to extract data bit_masks = list() for shape in reshape: bit_mask = list() for value in shape_mask: bit = 1 if (value == shape) else 0 bit_mask.append(bit) bit_masks.append(np.array(bit_mask)) # extract data n_samples = l_bytes / np.sum(reshape) data = np.empty([len(reshape), n_samples], dtype=(int, int)) for index, bit_mask in enumerate(bit_masks): tmp = self.filter_bytes(databytes, bit_mask) tp = f"{order}{datatypes[index]}{reshape[index]}" data[index] = np.frombuffer(tmp, dtype=tp) return data.T def filter_bytes(self, databytes, bit_mask): """ Detect from a bit mask which bits to keep to recompose the signal. """ n_bytes = len(databytes) mask = np.ones(n_bytes, dtype=int) np.putmask(mask, mask, bit_mask) to_keep = np.where(mask > 0)[0] return databytes.take(to_keep) def load_channel_data(self, ep, ch): """ Return a numpy array containing the list of bytes corresponding to the specified episode and channel. """ # memorise the sample size and symbol sample_size = self.sample_size(ep, ch) sample_symbol = self.sample_symbol(ep, ch) # create a bit mask to define which # sample to keep from the file bit_mask = self.create_bit_mask(ep, ch) # load all bytes contained in an episode data_blocks = self.get_data_blocks(ep) databytes = self.load_bytes(data_blocks) raw = self.filter_bytes(databytes, bit_mask) # reshape bytes from the sample size dt = np.dtype(numpy_map[sample_symbol]) dt.newbyteorder("<") return np.frombuffer(raw.reshape([int(len(raw) / sample_size), sample_size]), dt) def apply_op(self, np_array, value, op_type): """ A convenient function to apply an operator over all elements of a numpy array. """ if op_type == "shift_right": return np_array >> value elif op_type == "shift_left": return np_array << value elif op_type == "mask": return np_array & value else: return np_array def get_tag_mask(self, tag_ch, tag_mode): """ Return a mask useful to retrieve bits that encode a tag channel. """ if tag_mode == 1: tag_mask = 0b01 if (tag_ch == 1) else 0b10 elif tag_mode in [2, 3]: ar_mask = np.zeros(16, dtype=int) ar_mask[tag_ch - 1] = 1 st = "0b" + "".join(np.array(np.flipud(ar_mask), dtype=str)) tag_mask = eval(st) return tag_mask def load_encoded_tags(self, ep, tag_ch): """ Return a numpy array containing bytes corresponding to the specified episode and channel. """ tag_mode = self.tag_mode(ep) tag_mask = self.get_tag_mask(tag_ch, tag_mode) if tag_mode in [1, 2]: # digidata or itc mode # available for all formats ch = self.get_channel_for_tags(ep) raw = self.load_channel_data(ep, ch) return self.apply_op(raw, tag_mask, "mask") elif tag_mode == 3: # cyber k mode # only available for DAC2 objects format # store bytes corresponding to the blocks # containing tags in a numpy array and reshape # it to have a set of tuples (time, value) ck_blocks = self.get_blocks_of_type(ep, "RCyberTag") databytes = self.load_bytes(ck_blocks) raw = self.reshape_bytes(databytes, reshape=(4, 2), datatypes=("u", "u"), order="<") # keep only items that are compatible # with the specified tag channel raw[:, 1] = self.apply_op(raw[:, 1], tag_mask, "mask") # computing numpy.diff is useful to know # how many times a value is maintained # and necessary to reconstruct the # compressed signal ... repeats = np.array(np.diff(raw[:, 0]), dtype=int) data = np.repeat(raw[:-1, 1], repeats, axis=0) # ... note that there is always # a transition at t=0 for synchronisation # purpose, consequently it is not necessary # to complete with zeros when the first # transition arrive ... return data def load_encoded_data(self, ep, ch): """ Get encoded value of raw data from the elphy file. """ tag_shift = self.tag_shift(ep) data = self.load_channel_data(ep, ch) if tag_shift: return self.apply_op(data, tag_shift, "shift_right") else: return data def get_signal_data(self, ep, ch): """ Return a numpy array containing all samples of a signal, acquired on an Elphy analog channel, formatted as a list of (time, value) tuples. """ # get data from the file y_data = self.load_encoded_data(ep, ch) x_data = np.arange(0, len(y_data)) # create a recarray data = np.recarray(len(y_data), dtype=[("x", b_float), ("y", b_float)]) # put in the recarray the scaled data x_factors = self.x_scale_factors(ep, ch) y_factors = self.y_scale_factors(ep, ch) data["x"] = x_factors.scale(x_data) data["y"] = y_factors.scale(y_data) return data def get_tag_data(self, ep, tag_ch): """ Return a numpy array containing all samples of a signal, acquired on an Elphy tag channel, formatted as a list of (time, value) tuples. """ # get data from the file y_data = self.load_encoded_tags(ep, tag_ch) x_data = np.arange(0, len(y_data)) # create a recarray data = np.recarray(len(y_data), dtype=[("x", b_float), ("y", b_int)]) # put in the recarray the scaled data factors = self.x_tag_scale_factors(ep) data["x"] = factors.scale(x_data) data["y"] = y_data return data class Acquis1Layout(ElphyLayout): """ A subclass of :class:`ElphyLayout` to know how the 'ACQUIS1/GS/1991' format is organised. Extends :class:`ElphyLayout` to store the offset used to retrieve directly raw data : ``data_offset`` : an offset to jump directly to the raw data. """ def __init__(self, fileobj, data_offset): super().__init__(fileobj) self.data_offset = data_offset self.data_blocks = None def get_blocks_end(self): return self.data_offset def is_continuous(self): return self.header.continuous def get_episode_blocks(self): raise NotImplementedError() def set_info_block(self): i_blks = self.get_blocks_of_type("USER INFO") if len(i_blks) >= 2: raise ValueError(f"There are too many info blocks: {i_blks}") if len(i_blks): self.info_block = i_blks[0] def set_data_blocks(self): data_blocks = list() size = self.header.n_samples * self.header.sample_size * self.header.n_channels for ep in range(0, self.header.n_episodes): start = self.data_offset + ep * self.header.ep_size + self.header.preSeqI data_blocks.append(DummyDataBlock(self, "Acquis1Data", start, size)) self.data_blocks = data_blocks def get_data_blocks(self, ep): return [self.data_blocks[ep - 1]] @property def n_episodes(self): return self.header.n_episodes def n_channels(self, episode): return self.header.n_channels def n_tags(self, episode): return 0 def tag_mode(self, ep): return 0 def tag_shift(self, ep): return 0 def get_channel_for_tags(self, ep): return None @property def no_analog_data(self): return True if (self.n_episodes == 0) else self.header.no_analog_data def sample_type(self, ep, ch): return self.header.tpData def sampling_period(self, ep, ch): return self.header.dX def n_samples(self, ep, ch): return self.header.n_samples def x_tag_scale_factors(self, ep): return ElphyScaleFactor(self.header.dX, self.header.X0) def x_scale_factors(self, ep, ch): return ElphyScaleFactor(self.header.dX, self.header.X0) def y_scale_factors(self, ep, ch): dY = self.header.dY_ar[ch - 1] Y0 = self.header.Y0_ar[ch - 1] # TODO: see why this kind of exception exists if dY is None or Y0 is None: raise Exception(f"bad Y-scale factors for episode {ep} channel {ch}") return ElphyScaleFactor(dY, Y0) def x_unit(self, ep, ch): return self.header.x_unit def y_unit(self, ep, ch): return self.header.y_units[ch - 1] @property def ep_size(self): return self.header.ep_size @property def file_duration(self): return self.header.dX * self.n_samples def get_tag(self, episode, tag_channel): return None def create_channel_mask(self, ep): return np.arange(1, self.header.n_channels + 1) class DAC2GSLayout(ElphyLayout): """ A subclass of :class:`ElphyLayout` to know how the 'DAC2 / GS / 2000' format is organised. Extends :class:`ElphyLayout` to store the offset used to retrieve directly raw data : ``data_offset`` : an offset to jump directly after the 'MAIN' block where 'DAC2SEQ' blocks start. ``main_block```: a shortcut to access 'MAIN' block. ``episode_blocks`` : a shortcut to access blocks corresponding to episodes. """ def __init__(self, fileobj, data_offset): super().__init__(fileobj) self.data_offset = data_offset self.main_block = None self.episode_blocks = None def get_blocks_end(self): return self.file_size # data_offset def is_continuous(self): main_block = self.main_block return main_block.continuous if main_block else False def get_episode_blocks(self): raise NotImplementedError() def set_main_block(self): main_block = self.get_blocks_of_type("MAIN") self.main_block = main_block[0] if main_block else None def set_episode_blocks(self): ep_blocks = self.get_blocks_of_type("DAC2SEQ") self.episode_blocks = ep_blocks if ep_blocks else None def set_info_block(self): i_blks = self.get_blocks_of_type("USER INFO") if len(i_blks) >= 2: raise ValueError(f"There are too many info blocks: {i_blks}") if len(i_blks): self.info_block = i_blks[0] def set_data_blocks(self): data_blocks = list() identifier = "DAC2GSData" size = self.main_block.n_samples * self.main_block.sample_size * self.main_block.n_channels if not self.is_continuous(): blocks = self.get_blocks_of_type("DAC2SEQ") for block in blocks: start = block.start + self.main_block.preSeqI data_blocks.append(DummyDataBlock(self, identifier, start, size)) else: start = self.blocks[-1].end + 1 + self.main_block.preSeqI data_blocks.append(DummyDataBlock(self, identifier, start, size)) self.data_blocks = data_blocks def get_data_blocks(self, ep): return [self.data_blocks[ep - 1]] def episode_block(self, ep): return self.main_block if self.is_continuous() else self.episode_blocks[ep - 1] def tag_mode(self, ep): return 1 if self.main_block.withTags else 0 def tag_shift(self, ep): return self.main_block.tagShift def get_channel_for_tags(self, ep): return 1 def sample_type(self, ep, ch): return self.main_block.tpData def sample_size(self, ep, ch): size = super().sample_size(ep, ch) if size != 2: raise ValueError(f"sample size is always 2 bytes for DAC2/GS/2000 format, but size is calculated as {size}") return size def sampling_period(self, ep, ch): block = self.episode_block(ep) return block.dX def x_tag_scale_factors(self, ep): block = self.episode_block(ep) return ElphyScaleFactor( block.dX, block.X0, ) def x_scale_factors(self, ep, ch): block = self.episode_block(ep) return ElphyScaleFactor( block.dX, block.X0, ) def y_scale_factors(self, ep, ch): block = self.episode_block(ep) return ElphyScaleFactor(block.dY_ar[ch - 1], block.Y0_ar[ch - 1]) def x_unit(self, ep, ch): block = self.episode_block(ep) return block.x_unit def y_unit(self, ep, ch): block = self.episode_block(ep) return block.y_units[ch - 1] def n_samples(self, ep, ch): return self.main_block.n_samples def ep_size(self, ep): return self.main_block.ep_size @property def n_episodes(self): return self.main_block.n_episodes def n_channels(self, episode): return self.main_block.n_channels def n_tags(self, episode): return 2 if self.main_block.withTags else 0 @property def file_duration(self): return self.main_block.dX * self.n_samples def get_tag(self, episode, tag_channel): if episode not in range(1, self.n_episodes + 1): raise ValueError(f"`episode` must be within {range(1 + self.n_episodes + 1)} and is {episode}") # there are none or 2 tag channels if self.tag_mode(episode) == 1: if tag_channel not in range(1, 3): raise ValueError("DAC2/GS/2000 format support only 2 tag channels") block = self.episode_block(episode) t_stop = self.main_block.n_samples * block.dX return ElphyTag(self, episode, tag_channel, block.x_unit, 1.0 / block.dX, 0, t_stop) else: return None def n_tag_samples(self, ep, tag_channel): return self.main_block.n_samples def get_tag_data(self, episode, tag_channel): # memorise some useful properties block = self.episode_block(episode) sample_size = self.sample_size(episode, tag_channel) sample_symbol = self.sample_symbol(episode, tag_channel) # create a bit mask to define which # sample to keep from the file channel_mask = self.create_channel_mask(episode) bit_mask = self.create_bit_mask(channel_mask, 1) # get bytes from the file data_block = self.data_blocks[episode - 1] n_bytes = data_block.size self.file.seek(data_block.start) databytes = np.frombuffer(self.file.read(n_bytes), " 0)[0] raw = databytes.take(to_keep) raw = raw.reshape([len(raw) / sample_size, sample_size]) # create a recarray containing data dt = np.dtype(numpy_map[sample_symbol]) dt.newbyteorder("<") tag_mask = 0b01 if (tag_channel == 1) else 0b10 y_data = np.frombuffer(raw, dt) & tag_mask x_data = np.arange(0, len(y_data)) * block.dX + block.X0 data = np.recarray(len(y_data), dtype=[("x", b_float), ("y", b_int)]) data["x"] = x_data data["y"] = y_data return data def create_channel_mask(self, ep): return np.arange(1, self.main_block.n_channels + 1) class DAC2Layout(ElphyLayout): """ A subclass of :class:`ElphyLayout` to know how the Elphy format is organised. Whereas other formats storing raw data at the end of the file, 'DAC2 objects' format spreads them over multiple blocks : ``episode_blocks`` : a shortcut to access blocks corresponding to episodes. """ def __init__(self, fileobj): super().__init__(fileobj) self.episode_blocks = None def get_blocks_end(self): return self.file_size def is_continuous(self): ep_blocks = [k for k in self.blocks if k.identifier.startswith("B_Ep")] if ep_blocks: ep_block = ep_blocks[0] ep_sub_block = ep_block.sub_blocks[0] return ep_sub_block.continuous else: return False def set_episode_blocks(self): self.episode_blocks = [k for k in self.blocks if str(k.identifier).startswith("B_Ep")] def set_info_block(self): # in fact the file info are contained into a single sub-block with an USR identifier i_blks = self.get_blocks_of_type("B_Finfo") if len(i_blks) >= 2: raise ValueError(f"There are too many info blocks: {i_blks}") if len(i_blks): i_blk = i_blks[0] sub_blocks = i_blk.sub_blocks if len(sub_blocks): self.info_block = sub_blocks[0] def set_data_blocks(self): data_blocks = list() blocks = self.get_blocks_of_type("RDATA") for block in blocks: start = block.data_start size = block.end + 1 - start data_blocks.append(DummyDataBlock(self, "RDATA", start, size)) self.data_blocks = data_blocks def get_data_blocks(self, ep): return self.group_blocks_of_type(ep, "RDATA") def group_blocks_of_type(self, ep, identifier): ep_blocks = list() blocks = [k for k in self.get_blocks_stored_in_episode(ep) if k.identifier == identifier] for block in blocks: start = block.data_start size = block.end + 1 - start ep_blocks.append(DummyDataBlock(self, identifier, start, size)) return ep_blocks def get_blocks_stored_in_episode(self, ep): data_blocks = [k for k in self.blocks if k.identifier == "RDATA"] n_ep = self.n_episodes blk_1 = self.episode_block(ep) blk_2 = self.episode_block((ep + 1) % n_ep) i_1 = self.blocks.index(blk_1) i_2 = self.blocks.index(blk_2) if (blk_1 == blk_2) or (i_2 < i_1): return [k for k in data_blocks if self.blocks.index(k) > i_1] else: return [k for k in data_blocks if self.blocks.index(k) in range(i_1, i_2)] def set_cyberk_blocks(self): ck_blocks = list() blocks = self.get_blocks_of_type("RCyberTag") for block in blocks: start = block.data_start size = block.end + 1 - start ck_blocks.append(DummyDataBlock(self, "RCyberTag", start, size)) self.ck_blocks = ck_blocks def episode_block(self, ep): return self.episode_blocks[ep - 1] @property def n_episodes(self): return len(self.episode_blocks) def analog_index(self, episode): """ Return indices relative to channels used for analog signals. """ block = self.episode_block(episode) tag_mode = block.ep_block.tag_mode an_index = np.where(np.array(block.ks_block.k_sampling) > 0) if tag_mode == 2: an_index = an_index[:-1] return an_index def n_channels(self, episode): """ Return the number of channels used for analog signals but also events. NB : in Elphy this 2 kinds of channels are not differenciated. """ block = self.episode_block(episode) tag_mode = block.ep_block.tag_mode n_channels = len(block.ks_block.k_sampling) return n_channels if tag_mode != 2 else n_channels - 1 def n_tags(self, episode): block = self.episode_block(episode) tag_mode = block.ep_block.tag_mode tag_map = {0: 0, 1: 2, 2: 16, 3: 16} return tag_map.get(tag_mode, 0) def n_events(self, episode): """ Return the number of channels dedicated to events. """ block = self.episode_block(episode) return block.ks_block.k_sampling.count(0) def n_spiketrains(self, episode): spk_blocks = [k for k in self.blocks if k.identifier == "RSPK"] return spk_blocks[0].n_evt_channels if spk_blocks else 0 def sub_sampling(self, ep, ch): """ Return the sub-sampling factor for the specified episode and channel. """ block = self.episode_block(ep) return block.ks_block.k_sampling[ch - 1] if block.ks_block else 1 def aggregate_size(self, block, ep): ag_size = 0 for ch in range(1, len(block.ks_block.k_sampling)): if block.ks_block.k_sampling[ch - 1] != 0: ag_size += self.sample_size(ep, ch) return ag_size def n_samples(self, ep, ch): block = self.episode_block(ep) if not block.ep_block.continuous: return block.ep_block.nbpt / self.sub_sampling(ep, ch) else: # for continuous case there isn't any place # in the file that contains the number of # samples unlike the episode case ... data_blocks = self.get_data_blocks(ep) total_size = np.sum([k.size for k in data_blocks]) # count the number of samples in an # aggregate and compute its size in order # to determine the size of an aggregate ag_count = self.aggregate_sample_count(block) ag_size = self.aggregate_size(block, ep) n_ag = total_size / ag_size # the number of samples is equal # to the number of aggregates ... n_samples = n_ag n_chunks = total_size % ag_size # ... but not when there exists # a incomplete aggregate at the # end of the file, consequently # the preeceeding computed number # of samples must be incremented # by one only if the channel map # to a sample in the last aggregate # ... maybe this last part should be # deleted because the n_chunks is always # null in continuous mode if n_chunks: last_ag_size = total_size - n_ag * ag_count size = 0 for i in range(0, ch): size += self.sample_size(ep, i + 1) if size <= last_ag_size: n_samples += 1 return n_samples def sample_type(self, ep, ch): block = self.episode_block(ep) return block.kt_block.k_types[ch - 1] if block.kt_block else block.ep_block.tpData def sampling_period(self, ep, ch): block = self.episode_block(ep) return block.ep_block.dX * self.sub_sampling(ep, ch) def x_tag_scale_factors(self, ep): block = self.episode_block(ep) return ElphyScaleFactor(block.ep_block.dX, block.ep_block.X0) def x_scale_factors(self, ep, ch): block = self.episode_block(ep) return ElphyScaleFactor( block.ep_block.dX * block.ks_block.k_sampling[ch - 1], block.ep_block.X0, ) def y_scale_factors(self, ep, ch): block = self.episode_block(ep) return ElphyScaleFactor(block.ch_block.dY_ar[ch - 1], block.ch_block.Y0_ar[ch - 1]) def x_unit(self, ep, ch): block = self.episode_block(ep) return block.ep_block.x_unit def y_unit(self, ep, ch): block = self.episode_block(ep) return block.ch_block.y_units[ch - 1] def tag_mode(self, ep): block = self.episode_block(ep) return block.ep_block.tag_mode def tag_shift(self, ep): block = self.episode_block(ep) return block.ep_block.tag_shift def get_channel_for_tags(self, ep): block = self.episode_block(ep) tag_mode = self.tag_mode(ep) if tag_mode == 1: ks = np.array(block.ks_block.k_sampling) mins = np.where(ks == ks.min())[0] + 1 return mins[0] elif tag_mode == 2: return block.ep_block.n_channels else: return None def aggregate_sample_count(self, block): """ Return the number of sample in an aggregate. """ # compute the least common multiple # for channels having block.ks_block.k_sampling[ch] > 0 lcm0 = 1 for i in range(0, block.ep_block.n_channels): if block.ks_block.k_sampling[i] > 0: lcm0 = least_common_multiple(lcm0, block.ks_block.k_sampling[i]) # sum quotients lcm / KSampling count = 0 for i in range(0, block.ep_block.n_channels): if block.ks_block.k_sampling[i] > 0: count += int(lcm0 / block.ks_block.k_sampling[i]) return count def create_channel_mask(self, ep): """ Return the minimal pattern of channel numbers representing the succession of channels in the multiplexed data. It is useful to do the mapping between a sample stored in the file and its relative channel. NB : This function has been converted from the 'TseqBlock.BuildMask' method of the file 'ElphyFormat.pas' stored in Elphy source code. """ block = self.episode_block(ep) ag_count = self.aggregate_sample_count(block) mask_ar = np.zeros(ag_count, dtype="i") ag_size = 0 i = 0 k = 0 while k < ag_count: for j in range(0, block.ep_block.n_channels): if (block.ks_block.k_sampling[j] != 0) and (i % block.ks_block.k_sampling[j] == 0): mask_ar[k] = j + 1 ag_size += self.sample_size(ep, j + 1) k += 1 if k >= ag_count: break i += 1 return mask_ar def get_signal(self, episode, channel): block = self.episode_block(episode) k_sampling = np.array(block.ks_block.k_sampling) evt_channels = np.where(k_sampling == 0)[0] if channel not in evt_channels: return super().get_signal(episode, channel) else: k_sampling[channel - 1] = -1 return self.get_event(episode, channel, k_sampling) def get_tag(self, episode, tag_channel): """ Return a :class:`ElphyTag` which is a descriptor of the specified event channel. """ if episode not in range(1, self.n_episodes + 1): raise ValueError(f"`episode` must be in {range(1, self.n_episodes + 1)} and is {episode}") # there are none, 2 or 16 tag # channels depending on tag_mode tag_mode = self.tag_mode(episode) if tag_mode: block = self.episode_block(episode) x_unit = block.ep_block.x_unit # verify the validity of the tag channel if tag_mode == 1: if tag_channel not in range(1, 3): raise ValueError("Elphy format support only 2 tag channels for tag_mode == 1") elif tag_mode == 2 or tag_mode == 3: if tag_channel not in range(1, 17): raise ValueError("Elphy format support only 16 tag channels for tag_mode == 2 or tag_mode == 3") smp_period = block.ep_block.dX smp_freq = 1.0 / smp_period if tag_mode != 3: ch = self.get_channel_for_tags(episode) n_samples = self.n_samples(episode, ch) t_stop = (n_samples - 1) * smp_freq else: # get the max of n_samples multiplied by the sampling # period done on every analog channels in order to avoid # the selection of a channel without concrete signals t_max = list() for ch in self.analog_index(episode): n_samples = self.n_samples(episode, ch) factors = self.x_scale_factors(episode, ch) chtime = n_samples * factors.delta t_max.append(chtime) time_max = max(t_max) # as (n_samples_tag - 1) * dX_tag # and time_max = n_sample_tag * dX_tag # it comes the following duration t_stop = time_max - smp_period return ElphyTag(self, episode, tag_channel, x_unit, smp_freq, 0, t_stop) else: return None def get_event(self, ep, ch, marked_ks): """ Return a :class:`ElphyEvent` which is a descriptor of the specified event channel. """ if ep not in range(1, self.n_episodes + 1): raise ValueError(f"`ep` must be in {range(1, self.n_episodes + 1)}, but is {ep}") if ch not in range(1, self.n_channels + 1): raise ValueError(f"`ch` must be in {range(1, self.n_channels + 1)}, but is {ch}") # find the event channel number evt_channel = np.where(marked_ks == -1)[0][0] if evt_channel not in range(1, self.n_events(ep) + 1): raise ValueError(f"`evt_channel` must be in {range(1, self.n_events(ep) + 1)}, but is {evt_channel}") block = self.episode_block(ep) ep_blocks = self.get_blocks_stored_in_episode(ep) evt_blocks = [k for k in ep_blocks if k.identifier == "REVT"] n_events = np.sum([k.n_events[evt_channel - 1] for k in evt_blocks], dtype=int) x_unit = block.ep_block.x_unit return ElphyEvent(self, ep, evt_channel, x_unit, n_events, ch_number=ch) def load_encoded_events(self, episode, evt_channel, identifier): """ Return times stored as a 4-bytes integer in the specified event channel. """ data_blocks = self.group_blocks_of_type(episode, identifier) ep_blocks = self.get_blocks_stored_in_episode(episode) evt_blocks = [k for k in ep_blocks if k.identifier == identifier] # compute events on each channel n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0) pre_events = np.sum(n_events[0 : evt_channel - 1], dtype=int) start = pre_events end = start + n_events[evt_channel - 1] expected_size = 4 * np.sum(n_events, dtype=int) return self.load_bytes(data_blocks, dtype=" 0: name = names[episode - 1] start = name.size + 1 - name.data_size + 1 end = name.end - name.start + 1 chars = self.load_bytes([name], dtype="uint8", start=start, end=end, expected_size=name.size).tolist() # print "chars[%s:%s]: %s" % (start,end,chars) episode_name = "".join([chr(k) for k in chars]) return episode_name def get_event_data(self, episode, evt_channel): """ Return times contained in the specified event channel. This function is triggered when the 'times' property of an :class:`ElphyEvent` descriptor instance is accessed. """ times = self.load_encoded_events(episode, evt_channel, "REVT") block = self.episode_block(episode) return times * block.ep_block.dX / len(block.ks_block.k_sampling) def get_spiketrain(self, episode, electrode_id): """ Return a :class:`Spike` which is a descriptor of the specified spike channel. """ if episode not in range(1, self.n_episodes + 1): raise ValueError(f"`episode` must be in {range(1, self.n_episodes + 1)}, but is {episode}") if electrode_id not in range(1, self.n_spiketrains(episode) + 1): raise ValueError( f"`eletrodee_id` must be in {range(1, self.n_spiketrains(episode)+1)}, but is {electrode_id}" ) # get some properties stored in the episode sub-block block = self.episode_block(episode) x_unit = block.ep_block.x_unit x_unit_wf = getattr(block.ep_block, "x_unit_wf", None) y_unit_wf = getattr(block.ep_block, "y_unit_wf", None) # number of spikes in the entire episode spk_blocks = [k for k in self.blocks if k.identifier == "RSPK"] n_events = np.sum([k.n_events[electrode_id - 1] for k in spk_blocks], dtype=int) # number of samples in a waveform wf_sampling_frequency = 1.0 / block.ep_block.dX wf_blocks = [k for k in self.blocks if k.identifier == "RspkWave"] if wf_blocks: wf_samples = wf_blocks[0].wavelength t_start = wf_blocks[0].pre_trigger * block.ep_block.dX else: wf_samples = 0 t_start = 0 return ElphySpikeTrain( self, episode, electrode_id, x_unit, n_events, wf_sampling_frequency, wf_samples, x_unit_wf, y_unit_wf, t_start, ) def get_spiketrain_data(self, episode, electrode_id): """ Return times contained in the specified spike channel. This function is triggered when the 'times' property of an :class:`Spike` descriptor instance is accessed. NB : The 'RSPK' block is not actually identical to the 'EVT' one, because all units relative to a time are stored directly after all event times, 1 byte for each. This function doesn't return these units. But, they could be retrieved from the 'RspkWave' block with the 'get_waveform_data function' """ block = self.episode_block(episode) times = self.load_encoded_spikes(episode, electrode_id, "RSPK") return times * block.ep_block.dX def load_encoded_waveforms(self, episode, electrode_id): """ Return times on which waveforms are defined and a numpy recarray containing all the data stored in the RspkWave block. """ # load data corresponding to the RspkWave block identifier = "RspkWave" data_blocks = self.group_blocks_of_type(episode, identifier) databytes = self.load_bytes(data_blocks) # select only data corresponding # to the specified spk_channel ep_blocks = self.get_blocks_stored_in_episode(episode) wf_blocks = [k for k in ep_blocks if k.identifier == identifier] wf_samples = wf_blocks[0].wavelength events = np.sum([k.n_spikes for k in wf_blocks], dtype=int, axis=0) n_events = events[electrode_id - 1] pre_events = np.sum(events[0 : electrode_id - 1], dtype=int) start = pre_events end = start + n_events # data must be reshaped before dtype = [ # the time of the spike arrival ("elphy_time", "u4", (1,)), ("device_time", "u4", (1,)), # the identifier of the electrode # would also be the 'trodalness' # but this tetrode devices are not # implemented in Elphy ("channel_id", "u2", (1,)), # the 'category' of the waveform ("unit_id", "u1", (1,)), # do not used ("dummy", "u1", (13,)), # samples of the waveform ("waveform", "i2", (wf_samples,)), ] x_start = wf_blocks[0].pre_trigger x_stop = wf_samples - x_start return np.arange(-x_start, x_stop), np.frombuffer(databytes, dtype=dtype)[start:end] def get_waveform_data(self, episode, electrode_id): """ Return waveforms corresponding to the specified spike channel. This function is triggered when the ``waveforms`` property of an :class:`Spike` descriptor instance is accessed. """ block = self.episode_block(episode) times, databytes = self.load_encoded_waveforms(episode, electrode_id) (n_events,) = databytes.shape wf_samples = databytes["waveform"].shape[1] dtype = [("time", float), ("electrode_id", int), ("unit_id", int), ("waveform", float, (wf_samples, 2))] data = np.empty(n_events, dtype=dtype) data["electrode_id"] = databytes["channel_id"][:, 0] data["unit_id"] = databytes["unit_id"][:, 0] data["time"] = databytes["elphy_time"][:, 0] * block.ep_block.dX data["waveform"][:, :, 0] = times * block.ep_block.dX data["waveform"][:, :, 1] = databytes["waveform"] * block.ep_block.dY_wf + block.ep_block.Y0_wf return data def get_rspk_data(self, spk_channel): """ Return times stored as a 4-bytes integer in the specified event channel. """ evt_blocks = self.get_blocks_of_type("RSPK") # compute events on each channel n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0) # sum of array values up to spk_channel-1!!!! pre_events = np.sum(n_events[0:spk_channel], dtype=int) start = pre_events + (7 + len(n_events)) # rspk header end = start + n_events[spk_channel] expected_size = 4 * np.sum(n_events, dtype=int) # constant return self.load_bytes(evt_blocks, dtype="= layout.data_offset)): block = self.factory.create_block(layout, offset) # create the sub blocks if it is DAC2 objects format # this is only done for B_Ep and B_Finfo blocks for # DAC2 objects format, maybe it could be useful to # spread this to other block types. # if isinstance(header, DAC2Header) and (block.identifier in ['B_Ep']) : if isinstance(header, DAC2Header) and (block.identifier in ["B_Ep", "B_Finfo"]): sub_offset = block.data_offset while sub_offset < block.start + block.size: sub_block = self.factory.create_sub_block(block, sub_offset) block.add_sub_block(sub_block) sub_offset += sub_block.size # set up some properties of some DAC2Layout sub-blocks if isinstance(sub_block, (DAC2EpSubBlock, DAC2AdcSubBlock, DAC2KSampSubBlock, DAC2KTypeSubBlock)): block.set_episode_block() block.set_channel_block() block.set_sub_sampling_block() block.set_sample_size_block() # SpikeTrain # if isinstance(header, DAC2Header) and (block.identifier in ['RSPK']) : # print "\nElphyFile.create_layout() - RSPK" # print "ElphyFile.create_layout() - n_events",block.n_events # print "ElphyFile.create_layout() - n_evt_channels",block.n_evt_channels layout.add_block(block) offset += block.size # set up as soon as possible the shortcut # to the main block of a DAC2GSLayout if not detect_main and isinstance(layout, DAC2GSLayout) and isinstance(block, DAC2GSMainBlock): layout.set_main_block() detect_main = True # detect if the file is continuous when # the 'MAIN' block has been parsed if not detect_continuous: is_continuous = isinstance(header, DAC2GSHeader) and layout.is_continuous() # set up the shortcut to blocks corresponding # to episodes, only available for DAC2Layout # and also DAC2GSLayout if not continuous if isinstance(layout, DAC2Layout) or (isinstance(layout, DAC2GSLayout) and not layout.is_continuous()): layout.set_episode_blocks() layout.set_data_blocks() # finally set up the user info block of the layout layout.set_info_block() self.file.seek(0) return layout def is_continuous(self): return self.layout.is_continuous() @property def n_episodes(self): """ Return the number of recording sequences. """ return self.layout.n_episodes def n_channels(self, episode): """ Return the number of recording channels involved in data acquisition and relative to the specified episode : ``episode`` : the recording sequence identifier. """ return self.layout.n_channels(episode) def n_tags(self, episode): """ Return the number of tag channels relative to the specified episode : ``episode`` : the recording sequence identifier. """ return self.layout.n_tags(episode) def n_events(self, episode): """ Return the number of event channels relative to the specified episode : ``episode`` : the recording sequence identifier. """ return self.layout.n_events(episode) def n_spiketrains(self, episode): """ Return the number of event channels relative to the specified episode : ``episode`` : the recording sequence identifier. """ return self.layout.n_spiketrains(episode) def n_waveforms(self, episode): """ Return the number of waveform channels : """ return self.layout.n_waveforms(episode) def get_signal(self, episode, channel): """ Return the signal or event descriptor relative to the specified episode and channel : ``episode`` : the recording sequence identifier. ``channel`` : the analog channel identifier. NB : For 'DAC2 objects' format, it could be also used to retrieve events. """ return self.layout.get_signal(episode, channel) def get_tag(self, episode, tag_channel): """ Return the tag descriptor relative to the specified episode and tag channel : ``episode`` : the recording sequence identifier. ``tag_channel`` : the tag channel identifier. NB : There isn't any tag channels for 'Acquis1' format. ElphyTag channels appeared after 'DAC2/GS/2000' release. They are also present in 'DAC2 objects' format. """ return self.layout.get_tag(episode, tag_channel) def get_event(self, episode, evt_channel): """ Return the event relative the specified episode and event channel. `episode`` : the recording sequence identifier. ``tag_channel`` : the tag channel identifier. """ return self.layout.get_event(episode, evt_channel) def get_spiketrain(self, episode, electrode_id): """ Return the spiketrain relative to the specified episode and electrode_id. ``episode`` : the recording sequence identifier. ``electrode_id`` : the identifier of the electrode providing the spiketrain. NB : Available only for 'DAC2 objects' format. This descriptor can return the times of a spiketrain and waveforms relative to each of these times. """ return self.layout.get_spiketrain(episode, electrode_id) @property def comments(self): raise NotImplementedError() def get_user_file_info(self): """ Return user defined file metadata. """ if not self.layout.info_block: return dict() else: return self.layout.info_block.get_user_file_info() @property def episode_info(self, ep_number): raise NotImplementedError() def get_signals(self): """ Get all available analog or event channels stored into an Elphy file. """ signals = list() for ep in range(1, self.n_episodes + 1): for ch in range(1, self.n_channels(ep) + 1): signal = self.get_signal(ep, ch) signals.append(signal) return signals def get_tags(self): """ Get all available tag channels stored into an Elphy file. """ tags = list() for ep in range(1, self.n_episodes + 1): for tg in range(1, self.n_tags(ep) + 1): tag = self.get_tag(ep, tg) tags.append(tag) return tags def get_spiketrains(self): """ Get all available spiketrains stored into an Elphy file. """ spiketrains = list() for ep in range(1, self.n_episodes + 1): for ch in range(1, self.n_spiketrains(ep) + 1): spiketrain = self.get_spiketrain(ep, ch) spiketrains.append(spiketrain) return spiketrains def get_rspk_spiketrains(self): """ Get all available spiketrains stored into an Elphy file. """ spiketrains = list() spk_blocks = self.layout.get_blocks_of_type("RSPK") for bl in spk_blocks: # print "ElphyFile.get_spiketrains() - identifier:",bl.identifier for ch in range(0, bl.n_evt_channels): spiketrain = self.layout.get_rspk_data(ch) spiketrains.append(spiketrain) return spiketrains def get_names(self): com_blocks = list() com_blocks = self.layout.get_blocks_of_type("COM") return com_blocks # -------------------------------------------------------- class ElphyIO(BaseIO): """ Class for reading from and writing to an Elphy file. It enables reading: - :class:`Block` - :class:`Segment` - :class:`Event` - :class:`SpikeTrain` Usage: >>> from neo import io >>> r = io.ElphyIO(filename='ElphyExample.DAT') >>> seg = r.read_block() >>> print(seg.analogsignals) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE >>> print(seg.spiketrains) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE >>> print(seg.events) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE >>> print(anasig._data_description) >>> anasig = r.read_analogsignal() >>> bl = Block() >>> # creating segments, their contents and append to bl >>> r.write_block( bl ) """ is_readable = True # This class can read data is_writable = False # This class can write data # This class is able to directly or indirectly handle the following objects supported_objects = [Block, Segment, AnalogSignal, SpikeTrain] # This class can return a Block readable_objects = [Block] # This class is not able to write objects writeable_objects = [] has_header = False is_streameable = False # This is for GUI stuff : a definition for parameters when reading. # This dict should be keyed by object (`Block`). Each entry is a list # of tuple. The first entry in each tuple is the parameter name. The # second entry is a dict with keys 'value' (for default value), # and 'label' (for a descriptive name). # Note that if the highest-level object requires parameters, # common_io_test will be skipped. read_params = {} # do not supported write so no GUI stuff write_params = {} name = "Elphy IO" extensions = ["DAT"] # mode can be 'file' or 'dir' or 'fake' or 'database' mode = "file" # internal serialized representation of neo data serialized = None def __init__(self, filename=None): """ Arguments: filename : the filename to read """ BaseIO.__init__(self) self.filename = filename self.elphy_file = ElphyFile(self.filename) def read_block( self, lazy=False, ): """ Return :class:`Block`. Parameters ---------- lazy : bool Postpone actual reading of the file. """ if lazy: raise NeoReadWriteError("This IO does not support lazy reading") # basic block = Block(name=None) # get analog and tag channels try: self.elphy_file.open() except Exception as e: self.elphy_file.close() raise Exception(f"cannot open file {self.filename} : { e}") # create a segment containing all analog, # tag and event channels for the episode if self.elphy_file.n_episodes in [None, 0]: print("File '{self.filename}' appears to have no episodes") return block for episode in range(1, self.elphy_file.n_episodes + 1): segment = self.read_segment(episode) block.segments.append(segment) # close file self.elphy_file.close() # result return block def write_block(self, block): """ Write a given Neo Block to an Elphy file, its structure being, for example: Neo -> Elphy -------------------------------------------------------------- Block File Segment Episode Block (B_Ep) AnalogSignalArray Episode Descriptor (Ep + Adc + Ksamp + Ktype) multichannel RDATA (with a ChannelMask multiplexing channels) 2D NumPy Array ... AnalogSignalArray AnalogSignal AnalogSignal ... ... SpikeTrain Event Block (RSPK) SpikeTrain ... Arguments:: block: the block to be saved """ # Serialize Neo structure into Elphy file # each analog signal will be serialized as elphy Episode Block (with its subblocks) # then all spiketrains will be serialized into an Rspk Block (an Event Block with addons). # Serialize (and size) all Neo structures before writing them to file # Since to write each Elphy Block is required to know in advance its size, # which includes that of its subblocks, it is necessary to # serialize first the lowest structures. # Iterate over block structures elphy_limit = 256 All = "" # print "\n\n--------------------------------------------\n" # print "write_block() - n_segments:",len(block.segments) for seg in block.segments: analogsignals = 0 # init nbchan = 0 nbpt = 0 chls = 0 Dxu = 1e-8 # 0.0000001 Rxu = 1e8 # 10000000.0 X0uSpk = 0.0 CyberTime = 0.0 aa_units = [] NbEv = [] serialized_analog_data = "" serialized_spike_data = "" # AnalogSignals # Neo signalarrays are 2D numpy array where each row is an array of samples for a # channel: # signalarray A = [[ 1, 2, 3, 4 ], # [ 5, 6, 7, 8 ]] # signalarray B = [[ 9, 10, 11, 12 ], # [ 13, 14, 15, 16 ]] # Neo Segments can have more than one signalarray. # To be converted in Elphy analog channels they need to be all in a 2D array, not in # several 2D arrays. # Concatenate all analogsignalarrays into one and then flatten it. # Elphy RDATA blocks contain Fortran styled samples: # 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, 4, 8, 12, 16 # AnalogSignalArrays -> analogsignals # get the first to have analogsignals with the right shape # Annotations for analogsignals array come as a list of int being source ids # here, put each source id on a separate dict entry in order to have a matching # afterwards idx = 0 annotations = dict() # get all the others # print "write_block() - n_analogsignals:",len(seg.analogsignals) # print "write_block() - n_analogsignalarrays:",len(seg.analogsignalarrays) for asigar in seg.analogsignalarrays: idx, annotations = self.get_annotations_dict( annotations, "analogsignal", asigar.annotations.items(), asigar.name, idx ) # array structure _, chls = asigar.shape # units for _ in range(chls): aa_units.append(asigar.units) Dxu = asigar.sampling_period Rxu = asigar.sampling_rate if isinstance(analogsignals, np.ndarray): analogsignals = np.hstack((analogsignals, asigar)) else: analogsignals = asigar # first time # collect and reshape all analogsignals if isinstance(analogsignals, np.ndarray): # transpose matrix since in Neo channels are column-wise while in Elphy are # row-wise analogsignals = analogsignals.T # get dimensions nbchan, nbpt = analogsignals.shape # serialize AnalogSignal analog_data_fmt = "<" + str(analogsignals.size) + "f" # serialized flattened numpy channels in 'F'ortran style analog_data_64 = analogsignals.flatten("F") # elphy normally uses float32 values (for performance reasons) analog_data = np.array(analog_data_64, dtype=np.float32) serialized_analog_data += struct.pack(analog_data_fmt, *analog_data) # SpikeTrains # Neo spiketrains are stored as a one-dimensional array of times # [ 0.11, 1.23, 2.34, 3.45, 4.56, 5.67, 6.78, 7.89 ... ] # These are converted into Elphy Rspk Block which will contain all of them # RDATA + NbVeV:integer for the number of channels (spiketrains) # + NbEv:integer[] for the number of event per channel # followed by the actual arrays of integer containing spike times # spiketrains = seg.spiketrains # ... but consider elphy loading limitation: NbVeV = len(seg.spiketrains) # print "write_block() - n_spiketrains:",NbVeV if len(seg.spiketrains) > elphy_limit: NbVeV = elphy_limit # serialize format spiketrain_data_fmt = "<" spiketrains = [] for idx, train in enumerate(seg.spiketrains[:NbVeV]): # print "write_block() - train.size:", train.size,idx # print "write_block() - train:", train fake, annotations = self.get_annotations_dict( annotations, "spiketrain", train.annotations.items(), "", idx ) # annotations.update( dict( [("spiketrain-"+str(idx), # train.annotations['source_id'])] ) ) # print "write_block() - train[%s].annotation['source_id']:%s" # "" % (idx,train.annotations['source_id']) # total number of events format + blackrock sorting mark (0 for neo) spiketrain_data_fmt += str(train.size) + "i" + str(train.size) + "B" # get starting time X0uSpk = train.t_start.item() CyberTime = train.t_stop.item() # count number of events per train NbEv.append(train.size) # multiply by sampling period train = train * Rxu # all flattened spike train # blackrock acquisition card also adds a byte for each event to sort it spiketrains.extend([spike.item() for spike in train] + [0 for _ in range(train.size)]) # Annotations # print annotations # using DBrecord elphy block, they will be available as values in elphy environment # separate keys and values in two separate serialized strings ST_sub = "" st_fmt = "" st_data = [] BUF_sub = "" serialized_ST_data = "" serialized_BUF_data = "" for key in sorted(annotations.iterkeys()): # take all values, get their type and concatenate fmt = "" data = [] value = annotations[key] if isinstance(value, (int, np.int32, np.int64)): # elphy type 2 fmt = " 0 else f"episode {str(episode + 1)}" segment = Segment(name=name) # create an analog signal for # each channel in the episode for channel in range(1, self.elphy_file.n_channels(episode) + 1): signal = self.elphy_file.get_signal(episode, channel) x_unit = signal.x_unit.strip().decode() analog_signal = AnalogSignal( signal.data["y"], units=signal.y_unit, t_start=signal.t_start * getattr(pq, signal.x_unit.strip().decode()), t_stop=signal.t_stop * getattr(pq, signal.x_unit.strip().decode()), # sampling_rate = signal.sampling_frequency * pq.kHz, sampling_period=signal.sampling_period * getattr(pq, x_unit), channel_name=f"episode {int(episode + 1)}, channel { int(channel + 1)}", ) segment.analogsignals.append(analog_signal) # create a spiketrain for each # spike channel in the episode # in case of multi-electrode # acquisition context n_spikes = self.elphy_file.n_spiketrains(episode) # print "read_segment() - n_spikes:",n_spikes if n_spikes > 0: for spk in range(1, n_spikes + 1): spiketrain = self.read_spiketrain(episode, spk) segment.spiketrains.append(spiketrain) # segment return segment def read_event(self, episode, evt): """ Internal method used to return a list of elphy :class:`EventArray` acquired from event channels. Parameters ---------- episode : int Number of elphy episode, roughly corresponding to a segment. evt : int Index of the event. """ event = self.elphy_file.get_event(episode, evt) neo_event = Event(times=event.times * pq.s, channel_name=f"episode {episode + 1}, event channel {evt + 1}") return neo_event def read_spiketrain(self, episode, spk): """ Internal method used to return an elphy object :class:`SpikeTrain`. Parameters ---------- episode : int Number of elphy episode, roughly corresponding to a segment. spk : int Index of the spike array. """ block = self.elphy_file.layout.episode_block(episode) spike = self.elphy_file.get_spiketrain(episode, spk) spikes = spike.times * pq.s # print "read_spiketrain() - spikes: %s" % (len(spikes)) # print "read_spiketrain() - spikes:",spikes dct = { "times": spikes, # check "t_start": block.ep_block.X0_wf if block.ep_block.X0_wf < spikes[0] else spikes[0], "t_stop": block.ep_block.cyber_time if block.ep_block.cyber_time > spikes[-1] else spikes[-1], "units": "s", # special keywords to identify the # electrode providing the spiketrain # event though it is redundant with # waveforms "label": f"episode {episode}, electrode {spk}", "electrode_id": spk, } # new spiketrain return SpikeTrain(**dct) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/exampleio.py0000644000175100017510000000161415154507213015561 0ustar00runnerrunner""" neo.io has been split into a 2-level API: * neo.io: this API gives Neo objects * neo.rawio: this API gives raw data as they are in files. Developers are encourage to use neo.rawio. When this is done the neo.io can be implemented trivially using code like shown in this file. Author: sgarcia """ from neo.io.basefromrawio import BaseFromRaw from neo.rawio.examplerawio import ExampleRawIO class ExampleIO(ExampleRawIO, BaseFromRaw): name = "example IO" description = "Fake IO" # This is an inportant choice when there are several channels. # 'split-all' : 1 AnalogSignal each 1 channel # 'group-by-same-units' : one 2D AnalogSignal for each group of channel with same units _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename=""): ExampleRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/igorproio.py0000644000175100017510000001632415154507213015613 0ustar00runnerrunner""" Class for reading data created by IGOR Pro (WaveMetrics, Inc., Portland, OR, USA) Depends on: igor2 (https://pypi.python.org/pypi/igor2/) Supported: Read Author: Andrew Davison Also contributing: Rick Gerkin """ from warnings import warn import pathlib import quantities as pq from neo.io.baseio import BaseIO from neo.core import Block, Segment, AnalogSignal, NeoReadWriteError class IgorIO(BaseIO): """ Class for reading Igor Binary Waves (.ibw) or Packed Experiment (.pxp) files written by WaveMetrics’ IGOR Pro software. It requires the `igor2` Python package. Usage: >>> from neo import io >>> r = io.IgorIO(filename='...ibw') """ is_readable = True # This class can only read data is_writable = False # write is not supported supported_objects = [Block, Segment, AnalogSignal] readable_objects = [Block, Segment, AnalogSignal] writeable_objects = [] has_header = False is_streameable = False name = "igorpro" extensions = ["ibw", "pxp"] mode = "file" def __init__(self, filename=None, parse_notes=None): """ Arguments: filename: the filename parse_notes: (optional) A function which will parse the 'notes' field in the file header and return a dictionary which will be added to the object annotations. """ BaseIO.__init__(self) filename = pathlib.Path(filename) if filename.suffix[1:] not in self.extensions: raise NotImplementedError(f"Only the following extensions are supported: {self.extensions}") self.filename = filename self.extension = filename.suffix[1:] self.parse_notes = parse_notes self._filesystem = None def read_block(self, lazy=False): if lazy: raise NeoReadWriteError("This IO does not support lazy reading") block = Block(file_origin=str(self.filename)) block.segments.append(self.read_segment(lazy=lazy)) return block def read_segment(self, lazy=False): import igor2.packed as pxp from igor2.record.wave import WaveRecord if lazy: raise NeoReadWriteError("This IO does not support lazy mode") segment = Segment(file_origin=str(self.filename)) if self.extension == "pxp": if not self._filesystem: _, self.filesystem = pxp.load(str(self.filename)) def callback(dirpath, key, value): if isinstance(value, WaveRecord): signal = self._wave_to_analogsignal(value.wave["wave"], dirpath) segment.analogsignals.append(signal) pxp.walk(self.filesystem, callback) else: segment.analogsignals.append(self.read_analogsignal(lazy=lazy)) return segment def read_analogsignal(self, path=None, lazy=False): import igor2.binarywave as bw import igor2.packed as pxp if lazy: raise NeoReadWriteError("This IO does not support lazy mode") if self.extension == "ibw": data = bw.load(str(self.filename)) version = data["version"] if version > 5: raise IOError(f"Igor binary wave file format version {version} " "is not supported.") elif self.extension == "pxp": if type(path) is not str: raise TypeError("A colon-separated Igor-style path must be provided.") if not self._filesystem: _, self.filesystem = pxp.load(str(self.filename)) path = path.split(":") location = self.filesystem["root"] for element in path: if element != "root": location = location[element.encode("utf8")] data = location.wave return self._wave_to_analogsignal(data["wave"], []) def _wave_to_analogsignal(self, content, dirpath): if "padding" in content: if content["padding"].size != 0: raise NeoReadWriteError("Cannot handle non-empty padding") signal = content["wData"] note = content["note"] header = content["wave_header"] name = str(header["bname"].decode("utf-8")) units = "".join([x.decode() for x in header["dataUnits"]]) if "xUnits" in header: # "xUnits" is used in Versions 1, 2, 3 of .pxp files time_units = "".join([x.decode() for x in header["xUnits"]]) elif "dimUnits" in header: # Version 5 uses "dimUnits" # see https://github.com/AFM-analysis/igor2/blob/43fccf51714661fb96372e8119c59e17ce01f683/igor2/binarywave.py#L501 _time_unit_structure = header["dimUnits"].ravel() # For the files we've seen so far, the first element of _time_unit_structure contains the units. # If someone has a file for which this assumption does not hold an Exception will be raised. if not all([element == b"" for element in _time_unit_structure[1:]]): raise Exception( "Neo cannot yet handle the units in this file. " "Please create a new issue in the Neo issue tracker at " "https://github.com/NeuralEnsemble/python-neo/issues/new/choose" ) time_units = _time_unit_structure[0].decode() else: time_units = "" if len(time_units) == 0: time_units = "s" try: t_start = pq.Quantity(header["hsB"], time_units) except KeyError: t_start = pq.Quantity(header["sfB"][0], time_units) try: sampling_period = pq.Quantity(header["hsA"], time_units) except KeyError: sampling_period = pq.Quantity(header["sfA"][0], time_units) if self.parse_notes: try: annotations = self.parse_notes(note) except ValueError: warn("Couldn't parse notes field.") annotations = {"note": note} else: annotations = {"note": note} annotations["igor_path"] = ":".join(item.decode("utf-8") for item in dirpath) signal = AnalogSignal( signal, units=units, copy=None, t_start=t_start, sampling_period=sampling_period, name=name, file_origin=str(self.filename), **annotations, ) return signal # the following function is to handle the annotations in the # Igor data files from the Blue Brain Project NMC Portal def key_value_string_parser(itemsep=";", kvsep=":"): """ Parses a string into a dict. Parameters ---------- itemsep : str Character which separates items kvsep : str Character which separates the key and value within an item Returns ------- callable a function which takes the string to be parsed as the sole argument and returns a dict. Examples -------- >>> parse = key_value_string_parser(itemsep=";", kvsep=":") >>> parse("a:2;b:3") {'a': 2, 'b': 3} """ def parser(s): items = s.split(itemsep) return dict(item.split(kvsep, 1) for item in items if item) return parser ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/intanio.py0000644000175100017510000000054115154507213015235 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.intanrawio import IntanRawIO class IntanIO(IntanRawIO, BaseFromRaw): __doc__ = IntanRawIO.__doc__ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): IntanRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/klustakwikio.py0000644000175100017510000004101715154507213016320 0ustar00runnerrunner""" Reading and writing from KlustaKwik-format files. Ref: http://klusters.sourceforge.net/UserManual/data-files.html Supported : Read, Write Author : Chris Rodgers TODO: * When reading, put the Unit into the RCG, RC hierarchy * When writing, figure out how to get group and cluster if those annotations weren't set. Consider removing those annotations if they are redundant. * Load features in addition to spiketimes. """ import re import logging from pathlib import Path import shutil # note neo.core need only numpy and quantities import numpy as np # I need to subclass BaseIO from neo.io.baseio import BaseIO from neo.core import Block, Segment, Group, SpikeTrain # Pasted version of feature file format spec """ The Feature File Generic file name: base.fet.n Format: ASCII, integer values The feature file lists for each spike the PCA coefficients for each electrode, followed by the timestamp of the spike (more features can be inserted between the PCA coefficients and the timestamp). The first line contains the number of dimensions. Assuming N1 spikes (spike1...spikeN1), N2 electrodes (e1...eN2) and N3 coefficients (c1...cN3), this file looks like: nbDimensions c1_e1_spk1 c2_e1_spk1 ... cN3_e1_spk1 c1_e2_spk1 ... cN3_eN2_spk1 timestamp_spk1 c1_e1_spk2 c2_e1_spk2 ... cN3_e1_spk2 c1_e2_spk2 ... cN3_eN2_spk2 timestamp_spk2 ... c1_e1_spkN1 c2_e1_spkN1 ... cN3_e1_spkN1 c1_e2_spkN1 ... cN3_eN2_spkN1 timestamp_spkN1 The timestamp is expressed in multiples of the sampling interval. For instance, for a 20kHz recording (50 microsecond sampling interval), a timestamp of 200 corresponds to 200x0.000050s=0.01s from the beginning of the recording session. Notice that the last line must end with a newline or carriage return. """ class KlustaKwikIO(BaseIO): """Reading and writing from KlustaKwik-format files.""" # Class variables demonstrating capabilities of this IO is_readable = True is_writable = True # This IO can only manipulate objects relating to spike times supported_objects = [Block, SpikeTrain] # Keep things simple by always returning a block readable_objects = [Block] # And write a block writeable_objects = [Block] # Not sure what these do, if anything has_header = False is_streameable = False # GUI params read_params = {} # GUI params write_params = {} # The IO name and the file extensions it uses name = "KlustaKwik" extensions = ["fet", "clu", "res", "spk"] # Operates on directories mode = "dir" def __init__(self, dirname, sampling_rate=30000.0): """Create a new IO to operate on a directory dirname : the directory to contain the files sampling_rate : in Hz, necessary because the KlustaKwik files stores data in samples. """ BaseIO.__init__(self) self.dirname = Path(dirname) # in case no basename is provided if self.dirname.is_dir(): self.session_dir = self.dirname else: self.session_dir = self.dirname.parent self.basename = self.dirname.name self.sampling_rate = float(sampling_rate) # error check if not self.session_dir.is_dir(): raise ValueError("dirname must be in an existing directory") # initialize a helper object to parse filenames self._fp = FilenameParser(dirname=self.session_dir, basename=self.basename) def read_block(self, lazy=False): """Returns a Block containing spike information. There is no obvious way to infer the segment boundaries from raw spike times, so for now all spike times are returned in one big segment. The way around this would be to specify the segment boundaries, and then change this code to put the spikes in the right segments. """ assert not lazy, "Do not support lazy" # Create block and segment to hold all the data block = Block() # Search data directory for KlustaKwik files. # If nothing found, return empty block self._fetfiles = self._fp.read_filenames("fet") self._clufiles = self._fp.read_filenames("clu") if len(self._fetfiles) == 0: return block # Create a single segment to hold all of the data seg = Segment(name="seg0", index=0, file_origin=str(self.session_dir / self.basename)) block.segments.append(seg) # Load spike times from each group and store in a dict, keyed # by group number self.spiketrains = dict() for group in sorted(self._fetfiles.keys()): # Load spike times fetfile = self._fetfiles[group] spks, features = self._load_spike_times(fetfile) # Load cluster ids or generate if group in self._clufiles: clufile = self._clufiles[group] uids = self._load_unit_id(clufile) else: # unclustered data, assume all zeros uids = np.zeros(spks.shape, dtype=np.int32) # error check if len(spks) != len(uids): raise ValueError("lengths of fet and clu files are different") # Create Group for each cluster unique_unit_ids = np.unique(uids) for unit_id in sorted(unique_unit_ids): # Initialize the unit u = Group(name=(f"unit {unit_id} from group {group}"), index=unit_id, group=group) # Initialize a new SpikeTrain for the spikes from this unit st = SpikeTrain( times=spks[uids == unit_id] / self.sampling_rate, units="sec", t_start=0.0, t_stop=spks.max() / self.sampling_rate, name=(f"unit {unit_id} from group {group}"), ) st.annotations["cluster"] = unit_id st.annotations["group"] = group # put features in if len(features) != 0: st.annotations["waveform_features"] = features # Link u.add(st) seg.spiketrains.append(st) block.check_relationships() return block # Helper hidden functions for reading def _load_spike_times(self, fetfilename): """Reads and returns the spike times and features""" with open(fetfilename, mode="r") as f: # Number of clustering features is integer on first line nbFeatures = int(f.readline().strip()) # Each subsequent line consists of nbFeatures values, followed by # the spike time in samples. names = [f"fet{n}" for n in range(nbFeatures)] names.append("spike_time") # Load into recarray data = np.genfromtxt(fetfilename, names=names, skip_header=1, delimiter=" ") # get features features = np.array([data[f"fet{n}"] for n in range(nbFeatures)]) # Return the spike_time column return data["spike_time"], features.transpose() def _load_unit_id(self, clufilename): """Reads and return the cluster ids as int32""" with open(clufilename, mode="r") as f: # Number of clusters on this tetrode is integer on first line nbClusters = int(f.readline().strip()) # Read each cluster name as a string cluster_names = f.readlines() # Convert names to integers # I think the spec requires cluster names to be integers, but # this code could be modified to support string names which are # auto-numbered. try: cluster_ids = [int(name) for name in cluster_names] except ValueError: raise ValueError(f"Could not convert cluster name to integer in {clufilename}") # convert to numpy array and error check cluster_ids = np.array(cluster_ids, dtype=np.int32) if len(np.unique(cluster_ids)) != nbClusters: logging.warning( f"warning: I got {len(np.unique(cluster_ids))} clusters instead of {nbClusters} in {clufilename}" ) return cluster_ids # writing functions def write_block(self, block): """Write spike times and unit ids to disk. Currently descends hierarchy from block to segment to spiketrain. Then gets group and cluster information from spiketrain. Then writes the time and cluster info to the file associated with that group. The group and cluster information are extracted from annotations, eg `sptr.annotations['group']`. If no cluster information exists, it is assigned to cluster 0. Note that all segments are essentially combined in this process, since the KlustaKwik format does not allow for segment boundaries. As implemented currently, does not use the `Unit` object at all. We first try to use the sampling rate of each SpikeTrain, or if this is not set, we use `self.sampling_rate`. If the files already exist, backup copies are created by appending the filenames with a "~". """ # set basename if self.basename is None: logging.warning("warning: no basename provided, using `basename`") self.basename = "basename" # First create file handles for each group which will be stored self._make_all_file_handles(block) # We'll detect how many features belong in each group self._group2features = {} # Iterate through segments in this block for seg in block.segments: # Write each spiketrain of the segment for st in seg.spiketrains: # Get file handles for this spiketrain using its group group = self.st2group(st) fetfilehandle = self._fetfilehandles[group] clufilehandle = self._clufilehandles[group] # Get the id to write to clu file for this spike train cluster = self.st2cluster(st) # Choose sampling rate to convert to samples try: sr = st.annotations["sampling_rate"] except KeyError: sr = self.sampling_rate # Convert to samples spike_times_in_samples = np.rint(np.array(st) * sr).astype(np.int64) # Try to get features from spiketrain try: all_features = st.annotations["waveform_features"] except KeyError: # Use empty all_features = [[] for _ in range(len(spike_times_in_samples))] all_features = np.asarray(all_features) if all_features.ndim != 2: raise ValueError("waveform features should be 2d array") # Check number of features we're supposed to have try: n_features = self._group2features[group] except KeyError: # First time through .. set number of features n_features = all_features.shape[1] self._group2features[group] = n_features # and write to first line of file fetfilehandle.write(f"{n_features}\n") if n_features != all_features.shape[1]: raise ValueError( "inconsistent number of features: " + f"supposed to be {n_features} but I got {all_features.shape[1]}" ) # Write features and time for each spike for stt, features in zip(spike_times_in_samples, all_features): # first features for val in features: fetfilehandle.write(str(val)) fetfilehandle.write(" ") # now time fetfilehandle.write(f"{stt}\n") # and cluster id clufilehandle.write(f"{cluster}\n") # We're done, so close the files self._close_all_files() # Helper functions for writing def st2group(self, st): # Not sure this is right so make it a method in case we change it try: return st.annotations["group"] except KeyError: return 0 def st2cluster(self, st): # Not sure this is right so make it a method in case we change it try: return st.annotations["cluster"] except KeyError: return 0 def _make_all_file_handles(self, block): """Get the tetrode (group) of each neuron (cluster) by descending the hierarchy through segment and block. Store in a dict {group_id: list_of_clusters_in_that_group} """ group2clusters = {} for seg in block.segments: for st in seg.spiketrains: group = self.st2group(st) cluster = self.st2cluster(st) if group in group2clusters: if cluster not in group2clusters[group]: group2clusters[group].append(cluster) else: group2clusters[group] = [cluster] # Make new file handles for each group self._fetfilehandles, self._clufilehandles = {}, {} for group, clusters in group2clusters.items(): self._new_group(group, nbClusters=len(clusters)) def _new_group(self, id_group, nbClusters): # generate filenames fetfilename = self.session_dir / (self.basename + (f".fet.{id_group}")) clufilename = self.session_dir / (self.basename + (f".clu.{id_group}")) # back up before overwriting if fetfilename.exists(): shutil.copyfile(fetfilename, fetfilename + "~") if clufilename.exists(): shutil.copyfile(clufilename, clufilename + "~") # create file handles self._fetfilehandles[id_group] = open(fetfilename, mode="w") self._clufilehandles[id_group] = open(clufilename, mode="w") # write out first line # self._fetfilehandles[id_group].write("0\n") # Number of features self._clufilehandles[id_group].write(f"{nbClusters}\n") def _close_all_files(self): for val in self._fetfilehandles.values(): val.close() for val in self._clufilehandles.values(): val.close() class FilenameParser: """Simple class to interpret user's requests into KlustaKwik filenames""" def __init__(self, dirname, basename=None): """Initialize a new parser for a directory containing files dirname: directory containing files basename: basename in KlustaKwik format spec If basename is left None, then files with any basename in the directory will be used. An error is raised if files with multiple basenames exist in the directory. """ self.dirname = Path(dirname).absolute() self.basename = basename # error check if not self.dirname.is_dir(): raise ValueError("dirname must be a directory") def read_filenames(self, typestring="fet"): """Returns filenames in the data directory matching the type. Generally, `typestring` is one of the following: 'fet', 'clu', 'spk', 'res' Returns a dict {group_number: filename}, e.g.: { 0: 'basename.fet.0', 1: 'basename.fet.1', 2: 'basename.fet.2'} 'basename' can be any string not containing whitespace. Only filenames that begin with "basename.typestring." and end with a sequence of digits are valid. The digits are converted to an integer and used as the group number. """ all_filenames = self.dirname.glob("*") # Fill the dict with valid filenames d = {} for v in all_filenames: # Test whether matches format, ie ends with digits split_fn = v.name m = re.search(rf"^(.*)\.{typestring}\.(\d+)$", split_fn) if m is not None: # get basename from first hit if not specified if self.basename is None: self.basename = m.group(1) # return files with correct basename if self.basename == m.group(1): # Key the group number to the filename # This conversion to int should always work since only # strings of digits will match the regex tetn = int(m.group(2)) d[tetn] = v return d ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/kwikio.py0000644000175100017510000001450315154507213015074 0ustar00runnerrunner""" Class for reading data from a .kwik dataset Depends on: scipy phy Supported: Read Author: Mikkel E. Lepperød @CINPLA """ # TODO: writing to file import numpy as np import quantities as pq import os # I need to subclass BaseIO from neo.io.baseio import BaseIO # to import from core from neo.core import Segment, SpikeTrain, AnalogSignal, Block, Group, NeoReadWriteError import importlib.metadata from packaging.version import Version, parse numpy_version = parse(importlib.metadata.version("numpy")) class KwikIO(BaseIO): """ Class for "reading" experimental data from a .kwik file. Generates a :class:`Segment` with a :class:`AnalogSignal` """ is_readable = True # This class can only read data is_writable = False # write is not supported supported_objects = [Block, Segment, SpikeTrain, AnalogSignal, Group] # This class can return either a Block or a Segment # The first one is the default ( self.read ) # These lists should go from highest object to lowest object because # common_io_test assumes it. readable_objects = [Block] # This class is not able to write objects writeable_objects = [] has_header = False is_streameable = False name = "Kwik" description = "This IO reads experimental data from a .kwik dataset" extensions = ["kwik"] mode = "file" def __init__(self, filename): """ Arguments: filename : the filename """ if numpy_version >= Version("2.3.0"): deprecation_msg = ( "As the kwik package is no longer actively maintained it only works with NumPy < 2.3.0 and your environment " f"has {numpy_version}. Downgrade your NumPy version to use this IO" ) raise NeoReadWriteError(deprecation_msg) from klusta import kwik BaseIO.__init__(self) self.filename = os.path.abspath(filename) model = kwik.KwikModel(self.filename) # TODO this group is loaded twice self.models = [kwik.KwikModel(self.filename, channel_group=grp) for grp in model.channel_groups] def read_block( self, lazy=False, get_waveforms=True, cluster_group=None, raw_data_units="uV", get_raw_data=False, ): """ Reads a block with segments and groups Parameters ---------- get_waveforms: bool, default = False Wether or not to get the waveforms get_raw_data: bool, default = False Wether or not to get the raw traces raw_data_units: str, default = "uV" SI units of the raw trace according to voltage_gain given to klusta cluster_group: str, default = None Which clusters to load, possibilities are "noise", "unsorted", "good", if None all is loaded. """ assert not lazy, "Do not support lazy" blk = Block() seg = Segment(file_origin=self.filename) blk.segments += [seg] for model in self.models: group_id = model.channel_group group_meta = {"group_id": group_id} group_meta.update(model.metadata) chx = Group(name=f"channel group #{group_id}", index=model.channels, **group_meta) blk.groups.append(chx) clusters = model.spike_clusters for cluster_id in model.cluster_ids: meta = model.cluster_metadata[cluster_id] if cluster_group is None: pass elif cluster_group != meta: continue sptr = self.read_spiketrain( cluster_id=cluster_id, model=model, get_waveforms=get_waveforms, raw_data_units=raw_data_units ) sptr.annotations.update({"cluster_group": meta, "group_id": model.channel_group}) unit = Group(cluster_group=meta, group_id=model.channel_group, name=f"unit #{cluster_id}") unit.add(sptr) chx.add(unit) seg.spiketrains.append(sptr) if get_raw_data: ana = self.read_analogsignal(model, units=raw_data_units) chx.add(ana) seg.analogsignals.append(ana) seg.duration = model.duration * pq.s blk.check_relationships() return blk def read_analogsignal(self, model, units="uV", lazy=False): """ Reads analogsignals Parameters ---------- units: str, default = "uV" SI units of the raw trace according to voltage_gain given to klusta """ assert not lazy, "Do not support lazy" arr = model.traces[:] * model.metadata["voltage_gain"] ana = AnalogSignal( arr, sampling_rate=model.sample_rate * pq.Hz, units=units, file_origin=model.metadata["raw_data_files"] ) return ana def read_spiketrain(self, cluster_id, model, lazy=False, get_waveforms=True, raw_data_units=None): """ Reads sorted spiketrains Parameters ---------- get_waveforms: bool, default = False Wether or not to get the waveforms cluster_id: int, Which cluster to load, according to cluster id from klusta model: klusta.kwik.KwikModel A KwikModel object obtained by klusta.kwik.KwikModel(fname) """ try: if not (cluster_id in model.cluster_ids): raise ValueError except ValueError: print(f"Exception: cluster_id ({cluster_id}) not found !! ") return clusters = model.spike_clusters idx = np.nonzero(clusters == cluster_id) if get_waveforms: w = model.all_waveforms[idx] # klusta: num_spikes, samples_per_spike, num_chans = w.shape w = w.swapaxes(1, 2) w = pq.Quantity(w, raw_data_units) else: w = None if model.duration > 0.0: t_stop = model.duration else: t_stop = np.max(model.spike_times[idx]) sptr = SpikeTrain( times=model.spike_times[idx], t_stop=t_stop, waveforms=w, units="s", sampling_rate=model.sample_rate * pq.Hz, file_origin=self.filename, **{"cluster_id": cluster_id}, ) return sptr ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/maxwellio.py0000644000175100017510000000044415154507213015577 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.maxwellrawio import MaxwellRawIO class MaxwellIO(MaxwellRawIO, BaseFromRaw): mode = "file" def __init__(self, filename): MaxwellRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/mearecio.py0000644000175100017510000000071615154507213015364 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.mearecrawio import MEArecRawIO class MEArecIO(MEArecRawIO, BaseFromRaw): __doc__ = MEArecRawIO.__doc__ mode = "file" def __init__(self, filename, load_spiketrains=True, load_analogsignal=True): MEArecRawIO.__init__( self, filename=filename, load_spiketrains=load_spiketrains, load_analogsignal=load_analogsignal ) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/medio.py0000644000175100017510000000264515154507213014700 0ustar00runnerrunner""" IO for reading MED datasets using dhn-med-py library. dhn-med-py https://medformat.org https://pypi.org/project/dhn-med-py/ MED Format Specifications: https://medformat.org Author: Dan Crepeau, Matt Stead """ from neo.io.basefromrawio import BaseFromRaw from neo.rawio.medrawio import MedRawIO class MedIO(MedRawIO, BaseFromRaw): """ IO for reading MED datasets. """ name = "MED IO" description = "IO for reading MED datasets" _prefered_signal_group_mode = "group-by-same-units" mode = "dir" def __init__(self, dirname=None, password=None, keep_original_times=False): MedRawIO.__init__(self, dirname=dirname, password=password, keep_original_times=keep_original_times) """ Initialise IO instance Parameters ---------- dirname : str Directory containing data files password : str MED sessions can be optionally encrypted with a password. Default: None keep_original_times : bool Preserve original time stamps as in data files. By default datasets are shifted to begin at t_start = 0. When set to True, timestamps will be returned as UTC (seconds since midnight 1 Jan 1970). Default: False """ BaseFromRaw.__init__(self, dirname) def close(self): MedRawIO.close(self) def __del__(self): MedRawIO.__del__(self) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/micromedio.py0000644000175100017510000000071615154507213015727 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.micromedrawio import MicromedRawIO from neo.core import Segment, AnalogSignal, Epoch, Event class MicromedIO(MicromedRawIO, BaseFromRaw): """Class for reading/writing data from Micromed files (.trc).""" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): MicromedRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neomatlabio.py0000644000175100017510000005140015154507213016066 0ustar00runnerrunner""" Module for reading/writing Neo objects in MATLAB format (.mat) versions 5 to 7.2. This module is a bridge for MATLAB users who want to adopt the Neo object representation. The nomenclature is the same but using Matlab structs and cell arrays. With this module MATLAB users can use neo.io to read a format and convert it to .mat. Supported : Read/Write Author: sgarcia, Robert Pröpper """ from collections.abc import Mapping from datetime import datetime import re import numpy as np import quantities as pq from packaging.version import Version from neo.io.baseio import BaseIO from neo.core import ( Block, Segment, AnalogSignal, IrregularlySampledSignal, Event, Epoch, SpikeTrain, Group, ImageSequence, ChannelView, RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest, objectnames, class_by_name, NeoReadWriteError, ) from neo.core.regionofinterest import RegionOfInterest from neo.core.baseneo import _container_name def get_classname_from_container_name(container_name, struct): if container_name == "regionsofinterest": if "radius" in struct._fieldnames: return "CircularRegionOfInterest" elif "vertices" in struct._fieldnames: return "PolygonRegionOfInterest" else: return "RectangularRegionOfInterest" else: for classname in objectnames: if _container_name(classname) == container_name: return classname PY_NONE = "Py_None" class NeoMatlabIO(BaseIO): """ Class for reading/writing Neo objects in MATLAB format (.mat) versions 5 to 7.2. This module is a bridge for MATLAB users who want to adopt the Neo object representation. The nomenclature is the same but using Matlab structs and cell arrays. With this module MATLAB users can use neo.io to read a format and convert it to .mat. Rules of conversion: * Neo classes are converted to MATLAB structs. e.g., a Block is a struct with attributes "name", "file_datetime", ... * Neo one_to_many relationships are cellarrays in MATLAB. e.g., ``seg.analogsignals[2]`` in Python Neo will be ``seg.analogsignals{3}`` in MATLAB. * Quantity attributes are represented by 2 fields in MATLAB. e.g., ``anasig.t_start = 1.5 * s`` in Python will be ``anasig.t_start = 1.5`` and ``anasig.t_start_unit = 's'`` in MATLAB. * classes that inherit from Quantity (AnalogSignal, SpikeTrain, ...) in Python will have 2 fields (array and units) in the MATLAB struct. e.g.: ``AnalogSignal( [1., 2., 3.], 'V')`` in Python will be ``anasig.array = [1. 2. 3]`` and ``anasig.units = 'V'`` in MATLAB. 1 - **Scenario 1: create data in MATLAB and read them in Python** This MATLAB code generates a block:: block = struct(); block.segments = { }; block.name = 'my block with matlab'; for s = 1:3 seg = struct(); seg.name = strcat('segment ',num2str(s)); seg.analogsignals = { }; for a = 1:5 anasig = struct(); anasig.signal = rand(100,1); anasig.signal_units = 'mV'; anasig.t_start = 0; anasig.t_start_units = 's'; anasig.sampling_rate = 100; anasig.sampling_rate_units = 'Hz'; seg.analogsignals{a} = anasig; end seg.spiketrains = { }; for t = 1:7 sptr = struct(); sptr.times = rand(30,1)*10; sptr.times_units = 'ms'; sptr.t_start = 0; sptr.t_start_units = 'ms'; sptr.t_stop = 10; sptr.t_stop_units = 'ms'; seg.spiketrains{t} = sptr; end event = struct(); event.times = [0, 10, 30]; event.times_units = 'ms'; event.labels = ['trig0'; 'trig1'; 'trig2']; seg.events{1} = event; epoch = struct(); epoch.times = [10, 20]; epoch.times_units = 'ms'; epoch.durations = [4, 10]; epoch.durations_units = 'ms'; epoch.labels = ['a0'; 'a1']; seg.epochs{1} = epoch; block.segments{s} = seg; end save 'myblock.mat' block -V7 This code reads it in Python:: import neo r = neo.io.NeoMatlabIO(filename='myblock.mat') bl = r.read_block() print bl.segments[1].analogsignals[2] print bl.segments[1].spiketrains[4] 2 - **Scenario 2: create data in Python and read them in MATLAB** This Python code generates the same block as in the previous scenario:: import neo import quantities as pq from scipy import rand, array bl = neo.Block(name='my block with neo') for s in range(3): seg = neo.Segment(name='segment' + str(s)) bl.segments.append(seg) for a in range(5): anasig = neo.AnalogSignal(rand(100)*pq.mV, t_start=0*pq.s, sampling_rate=100*pq.Hz) seg.analogsignals.append(anasig) for t in range(7): sptr = neo.SpikeTrain(rand(40)*pq.ms, t_start=0*pq.ms, t_stop=10*pq.ms) seg.spiketrains.append(sptr) ev = neo.Event([0, 10, 30]*pq.ms, labels=array(['trig0', 'trig1', 'trig2'])) ep = neo.Epoch([10, 20]*pq.ms, durations=[4, 10]*pq.ms, labels=array(['a0', 'a1'])) seg.events.append(ev) seg.epochs.append(ep) from neo.io.neomatlabio import NeoMatlabIO w = NeoMatlabIO(filename='myblock.mat') w.write_block(bl) This MATLAB code reads it:: load 'myblock.mat' block.name block.segments{2}.analogsignals{3}.signal block.segments{2}.analogsignals{3}.signal_units block.segments{2}.analogsignals{3}.t_start block.segments{2}.analogsignals{3}.t_start_units 3 - **Scenario 3: conversion** This Python code converts a Spike2 file to MATLAB:: from neo import Block from neo.io import Spike2IO, NeoMatlabIO r = Spike2IO(filename='spike2.smr') w = NeoMatlabIO(filename='convertedfile.mat') blocks = r.read() w.write(blocks[0]) """ is_readable = True is_writable = True supported_objects = [ Block, Segment, AnalogSignal, IrregularlySampledSignal, Epoch, Event, SpikeTrain, Group, ImageSequence, ChannelView, RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest, ] readable_objects = [Block] writeable_objects = [Block] has_header = False is_streameable = False read_params = {Block: []} write_params = {Block: []} name = "neomatlab" extensions = ["mat"] mode = "file" def __init__(self, filename=None): """ This class read/write neo objects in matlab 5 to 7.2 format. Arguments: filename : the filename to read """ import scipy.version if Version(scipy.version.version) < Version("0.12.0"): raise ImportError( "your scipy version is too old to support " + "MatlabIO, you need at least 0.12.0. " + f"You have {scipy.version.version}" ) BaseIO.__init__(self) self.filename = filename self._refs = {} def read_block(self, lazy=False): """ Arguments: """ import scipy.io if lazy: raise NeoReadWriteError(f"This IO does not support lazy reading") d = scipy.io.loadmat(self.filename, struct_as_record=False, squeeze_me=True, mat_dtype=True) if "block" not in d: self.logger.exception("No block in " + self.filename) return None bl_struct = d["block"] bl = self.create_ob_from_struct(bl_struct, "Block") self._resolve_references(bl) bl.check_relationships() return bl def write_block(self, bl, **kargs): """ Arguments: bl: the block to be saved kargs: extra keyword arguments broadcasted to scipy.io.savemat """ import scipy.io bl_struct = self.create_struct_from_obj(bl) for seg in bl.segments: seg_struct = self.create_struct_from_obj(seg) bl_struct["segments"].append(seg_struct) for container_name in seg._child_containers: for child_obj in getattr(seg, container_name): child_struct = self.create_struct_from_obj(child_obj) seg_struct[container_name].append(child_struct) for group in bl.groups: group_structure = self.create_struct_from_obj(group) bl_struct["groups"].append(group_structure) for container_name in group._child_containers: for child_obj in getattr(group, container_name): if isinstance(child_obj, (ChannelView, RegionOfInterest)): child_struct = self.create_struct_from_view(child_obj) group_structure[container_name].append(child_struct) else: group_structure[container_name].append(id(child_obj)) scipy.io.savemat(self.filename, {"block": bl_struct}, oned_as="row", **kargs) def _get_matlab_value(self, ob, attrname): units = None if hasattr(ob, "_quantity_attr") and ob._quantity_attr == attrname: units = ob.dimensionality.string value = ob.magnitude else: try: value = getattr(ob, attrname) except AttributeError: value = ob[attrname] if isinstance(value, pq.Quantity): units = value.dimensionality.string value = value.magnitude elif isinstance(value, datetime): value = str(value) elif isinstance(value, Mapping): new_value = {} for key in value: subvalue, subunits = self._get_matlab_value(value, key) if subvalue is not None: new_value[key] = subvalue if subunits: new_value[f"{key}_units"] = subunits elif attrname == "annotations": # In general we don't send None to MATLAB # but we make an exception for annotations. # However, we have to save then retrieve some # special value as actual `None` is ignored by default. new_value[key] = PY_NONE value = new_value return value, units def create_struct_from_obj(self, ob): struct = {"neo_id": id(ob)} # relationship for childname in getattr(ob, "_child_containers", []): supported_containers = [_container_name(subob.__name__) for subob in self.supported_objects] if childname in supported_containers: struct[childname] = [] # attributes all_attrs = list(ob._all_attrs) if hasattr(ob, "annotations"): all_attrs.append(("annotations", type(ob.annotations))) for attr in all_attrs: attrname, attrtype = attr[0], attr[1] attr_value, attr_units = self._get_matlab_value(ob, attrname) if attr_value is not None: struct[attrname] = attr_value if attr_units: struct[attrname + "_units"] = attr_units return struct def create_struct_from_view(self, ob): # for "view" objects (ChannelView and RegionOfInterest), we just store # a reference to the object (AnalogSignal, ImageSequence) that the view # points to struct = self.create_struct_from_obj(ob) obj_name = ob._necessary_attrs[0][0] # this is fragile, better to add an attribute _view_attr viewed_obj = getattr(ob, obj_name) struct[obj_name] = id(viewed_obj) struct["viewed_classname"] = viewed_obj.__class__.__name__ return struct def create_ob_from_struct(self, struct, classname): cl = class_by_name[classname] # ~ if is_quantity: if hasattr(cl, "_quantity_attr"): quantity_attr = cl._quantity_attr arr = getattr(struct, quantity_attr) # ~ data_complement = dict(units=str(struct.units)) data_complement = dict(units=str(getattr(struct, quantity_attr + "_units"))) if "sampling_rate" in (at[0] for at in cl._necessary_attrs): # put fake value for now, put correct value later data_complement["sampling_rate"] = 0 * pq.kHz try: len(arr) except TypeError: # strange scipy.io behavior: if len is 1 we get a float arr = np.array(arr) arr = arr.reshape((-1,)) # new view with one dimension if "t_stop" in (at[0] for at in cl._necessary_attrs): if len(arr) > 0: data_complement["t_stop"] = arr.max() else: data_complement["t_stop"] = 0.0 if "t_start" in (at[0] for at in cl._necessary_attrs): if len(arr) > 0: data_complement["t_start"] = arr.min() else: data_complement["t_start"] = 0.0 if "spatial_scale" in (at[0] for at in cl._necessary_attrs): if len(arr) > 0: data_complement["spatial_scale"] = arr else: data_complement["spatial_scale"] = 1.0 if "times" in (at[0] for at in cl._necessary_attrs) and quantity_attr != "times": # handle IrregularlySampledSignal times = getattr(struct, "times") data_complement["time_units"] = getattr(struct, "times_units") ob = cl(times, arr, **data_complement) else: ob = cl(arr, **data_complement) elif cl.is_view: kwargs = {} for i, attr in enumerate(cl._necessary_attrs): value = getattr(struct, attr[0]) if i == 0: # this is a bit hacky, should really add an attribute _view_attr to ChannelView and RegionOfInterest if not isinstance(value, int): # object id raise TypeError(f"value must be int not of type {type(value)}") kwargs[attr[0]] = _Ref(identifier=value, target_class_name=struct.viewed_classname) else: if attr[1] == np.ndarray and isinstance(value, int): value = np.array([value]) kwargs[attr[0]] = value ob = cl(**kwargs) else: ob = cl() for attrname in struct._fieldnames: # check children if attrname in getattr(ob, "_child_containers", []): child_struct = getattr(struct, attrname) try: # try must only surround len() or other errors are captured child_len = len(child_struct) except TypeError: # strange scipy.io behavior: if len is 1 there is no len() child_struct = [child_struct] child_len = 1 for c in range(child_len): child_class_name = get_classname_from_container_name(attrname, child_struct[c]) if classname == "Group": if child_class_name == ("ChannelView") or "RegionOfInterest" in child_class_name: child = self.create_ob_from_struct(child_struct[c], child_class_name) else: child = _Ref(child_struct[c], child_class_name) else: child = self.create_ob_from_struct(child_struct[c], child_class_name) getattr(ob, attrname.lower()).append(child) continue # attributes if attrname.endswith("_units") or attrname == "units": # linked with another field continue if hasattr(cl, "_quantity_attr") and cl._quantity_attr == attrname: continue if cl.is_view and attrname in ( "obj", "index", "image_sequence", "x", "y", "radius", "width", "height", "vertices", ): continue item = getattr(struct, attrname) attributes = cl._necessary_attrs + cl._recommended_attrs + (("annotations", dict),) dict_attributes = dict([(a[0], a[1:]) for a in attributes]) if attrname in dict_attributes: attrtype = dict_attributes[attrname][0] if attrtype == datetime: m = r"(\d+)-(\d+)-(\d+) (\d+):(\d+):(\d+).(\d+)" r = re.findall(m, str(item)) if len(r) == 1: item = datetime(*[int(e) for e in r[0]]) else: item = None elif attrtype == np.ndarray: dt = dict_attributes[attrname][2] try: item = item.astype(dt) except AttributeError: # it seems arrays of length 1 are stored as scalars item = np.array([item], dtype=dt) elif attrtype == pq.Quantity: ndim = dict_attributes[attrname][1] units = str(getattr(struct, attrname + "_units")) if ndim == 0: item = pq.Quantity(item, units) else: item = pq.Quantity(item, units) elif attrtype == dict: new_item = {} for fn in item._fieldnames: value = getattr(item, fn) if value == PY_NONE: value = None new_item[fn] = value item = new_item else: item = attrtype(item) setattr(ob, attrname, item) neo_id = getattr(struct, "neo_id", None) if neo_id: setattr(ob, "_id", neo_id) return ob def _resolve_references(self, bl): if bl.groups: obj_lookup = {} for ob in bl.children_recur: if hasattr(ob, "_id"): obj_lookup[ob._id] = ob for grp in bl.groups: for container_name in grp._child_containers: container = getattr(grp, container_name) for i, item in enumerate(container): if isinstance(item, _Ref): if not isinstance(item.identifier, (int, np.integer)): raise TypeError( f"item.identifier must be either int or np.integer not of type {type(item.identifier)}" ) # A reference to an object that already exists container[i] = obj_lookup[item.identifier] else: # ChannelView and RegionOfInterest if not item.is_view: raise TypeError(f"`item` must be a view") if not isinstance(item.obj, _Ref): raise TypeError(f"`item.obj` must be a {_Ref} and is of type {type(item.obj)}") item.obj = obj_lookup[item.obj.identifier] class _Ref: def __init__(self, identifier, target_class_name): self.identifier = identifier if target_class_name: self.target_cls = class_by_name[target_class_name] else: self.target_cls = None @property def proxy_for(self): return self.target_cls @property def data_children_recur(self): return [] @property def container_children_recur(self): return [] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/nestio.py0000644000175100017510000007172615154507213015112 0ustar00runnerrunner""" Class for reading output files from NEST simulations ( http://www.nest-simulator.org/ ). Tested with NEST2.10.0 Depends on: numpy, quantities Supported: Read Authors: Julia Sprenger, Maximilian Schmidt, Johanna Senk """ # needed for Python3 compatibility import os.path import warnings from datetime import datetime import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import Block, Segment, SpikeTrain, AnalogSignal value_type_dict = {"V": pq.mV, "I": pq.pA, "g": pq.CompoundUnit("10^-9*S"), "no type": pq.dimensionless} class NestIO(BaseIO): """ Class for reading NEST output files. GDF files for the spike data and DAT files for analog signals are possible. Usage: >>> from neo.io.nestio import NestIO >>> files = ['membrane_voltages-1261-0.dat', 'spikes-1258-0.gdf'] >>> r = NestIO(filenames=files) >>> seg = r.read_segment(gid_list=[], t_start=400 * pq.ms, t_stop=600 * pq.ms, id_column_gdf=0, time_column_gdf=1, id_column_dat=0, time_column_dat=1, value_columns_dat=2) """ is_readable = True # class supports reading, but not writing is_writable = False supported_objects = [SpikeTrain, AnalogSignal, Segment, Block] readable_objects = [SpikeTrain, AnalogSignal, Segment, Block] has_header = False is_streameable = False write_params = None # writing is not supported name = "nest" extensions = ["gdf", "dat"] mode = "file" def __init__(self, filenames=None): """ Parameters ---------- filenames: string or list of strings, default=None The filename or list of filenames to load. """ if isinstance(filenames, str): filenames = [filenames] self.filenames = filenames self.avail_formats = {} self.avail_IOs = {} for filename in filenames: path, ext = os.path.splitext(filename) ext = ext.strip(".") if ext in self.extensions: if ext in self.avail_IOs: raise ValueError( f'Received multiple files with "{ext}" ' "extension. Can only load single file of " "this type." ) self.avail_IOs[ext] = ColumnIO(filename) self.avail_formats[ext] = path def __read_analogsignals( self, gid_list, time_unit, t_start=None, t_stop=None, sampling_period=None, id_column=0, time_column=1, value_columns=2, value_types=None, value_units=None, ): """ Internal function called by read_analogsignal() and read_segment(). """ if "dat" not in self.avail_formats: raise ValueError("Can not load analogsignals. No DAT file " "provided.") # checking gid input parameters gid_list, id_column = self._check_input_gids(gid_list, id_column) # checking time input parameters t_start, t_stop = self._check_input_times(t_start, t_stop, mandatory=False) # checking value input parameters value_columns, value_types, value_units = self._check_input_values_parameters( value_columns, value_types, value_units ) # defining standard column order for internal usage # [id_column, time_column, value_column1, value_column2, ...] column_ids = [id_column, time_column] + value_columns for i, cid in enumerate(column_ids): if cid is None: column_ids[i] = -1 # assert that no single column is assigned twice column_list = [id_column, time_column] + value_columns column_list_no_None = [c for c in column_list if c is not None] if len(np.unique(column_list_no_None)) < len(column_list_no_None): raise ValueError( "One or more columns have been specified to contain " "the same data. Columns were specified to {column_list_no_None}." "" ) # extracting condition and sorting parameters for raw data loading condition, condition_column, sorting_column = self._get_conditions_and_sorting( id_column, time_column, gid_list, t_start, t_stop ) # loading raw data columns data = self.avail_IOs["dat"].get_columns( column_ids=column_ids, condition=condition, condition_column=condition_column, sorting_columns=sorting_column, ) sampling_period = self._check_input_sampling_period(sampling_period, time_column, time_unit, data) analogsignal_list = [] # extracting complete gid list for anasig generation if (gid_list == []) and id_column is not None: gid_list = np.unique(data[:, id_column]) # generate analogsignals for each neuron ID for i in gid_list: selected_ids = self._get_selected_ids(i, id_column, time_column, t_start, t_stop, time_unit, data) # extract starting time of analogsignal if (time_column is not None) and data.size: anasig_start_time = data[selected_ids[0], 1] * time_unit else: # set t_start equal to sampling_period because NEST starts # recording only after 1 sampling_period anasig_start_time = 1.0 * sampling_period # create one analogsignal per value column requested for v_id, value_column in enumerate(value_columns): signal = data[selected_ids[0] : selected_ids[1], value_column] # create AnalogSignal objects and annotate them with # the neuron ID analogsignal_list.append( AnalogSignal( signal * value_units[v_id], sampling_period=sampling_period, t_start=anasig_start_time, id=i, type=value_types[v_id], ) ) # check for correct length of analogsignal assert analogsignal_list[-1].t_stop == anasig_start_time + len(signal) * sampling_period return analogsignal_list def __read_spiketrains(self, gdf_id_list, time_unit, t_start, t_stop, id_column, time_column, **args): """ Internal function for reading multiple spiketrains at once. This function is called by read_spiketrain() and read_segment(). """ if "gdf" not in self.avail_IOs: raise ValueError("Can not load spiketrains. No GDF file provided.") # assert that the file contains spike times if time_column is None: raise ValueError("Time column is None. No spike times to " "be read in.") gdf_id_list, id_column = self._check_input_gids(gdf_id_list, id_column) t_start, t_stop = self._check_input_times(t_start, t_stop, mandatory=True) # assert that no single column is assigned twice if id_column == time_column: raise ValueError("One or more columns have been specified to " "contain the same data.") # defining standard column order for internal usage # [id_column, time_column, value_column1, value_column2, ...] column_ids = [id_column, time_column] for i, cid in enumerate(column_ids): if cid is None: column_ids[i] = -1 condition, condition_column, sorting_column = self._get_conditions_and_sorting( id_column, time_column, gdf_id_list, t_start, t_stop ) data = self.avail_IOs["gdf"].get_columns( column_ids=column_ids, condition=condition, condition_column=condition_column, sorting_columns=sorting_column, ) # create a list of SpikeTrains for all neuron IDs in gdf_id_list # assign spike times to neuron IDs if id_column is given if id_column is not None: if (gdf_id_list == []) and id_column is not None: gdf_id_list = np.unique(data[:, id_column]) spiketrain_list = [] for nid in gdf_id_list: selected_ids = self._get_selected_ids(nid, id_column, time_column, t_start, t_stop, time_unit, data) times = data[selected_ids[0] : selected_ids[1], time_column] spiketrain_list.append( SpikeTrain(times, units=time_unit, t_start=t_start, t_stop=t_stop, id=nid, **args) ) # if id_column is not given, all spike times are collected in one # spike train with id=None else: train = data[:, time_column] spiketrain_list = [SpikeTrain(train, units=time_unit, t_start=t_start, t_stop=t_stop, id=None, **args)] return spiketrain_list def _check_input_times(self, t_start, t_stop, mandatory=True): """ Checks input times for existence and setting default values if necessary. t_start: pq.quantity.Quantity, start time of the time range to load. t_stop: pq.quantity.Quantity, stop time of the time range to load. mandatory: bool, if True times can not be None and an error will be raised. if False, time values of None will be replaced by -infinity or infinity, respectively. default: True. """ if t_stop is None: if mandatory: raise ValueError("No t_start specified.") else: t_stop = np.inf * pq.s if t_start is None: if mandatory: raise ValueError("No t_stop specified.") else: t_start = -np.inf * pq.s for time in (t_start, t_stop): if not isinstance(time, pq.quantity.Quantity): raise TypeError(f"Time value ({time}) is not a quantity.") return t_start, t_stop def _check_input_values_parameters(self, value_columns, value_types, value_units): """ Checks value parameters for consistency. value_columns: int, column id containing the value to load. value_types: list of strings, type of values. value_units: list of units of the value columns. Returns adjusted list of [value_columns, value_types, value_units] """ if value_columns is None: raise ValueError("No value column provided.") if isinstance(value_columns, int): value_columns = [value_columns] if value_types is None: value_types = ["no type"] * len(value_columns) elif isinstance(value_types, str): value_types = [value_types] # translating value types into units as far as possible if value_units is None: short_value_types = [vtype.split("_")[0] for vtype in value_types] if not all([svt in value_type_dict for svt in short_value_types]): raise ValueError(f"Can not interpret value types " f'"{value_types}"') value_units = [value_type_dict[svt] for svt in short_value_types] # checking for same number of value types, units and columns if not (len(value_types) == len(value_units) == len(value_columns)): raise ValueError( "Length of value types, units and columns does " f"not match ({len(value_types)},{len(value_units)},{len(value_columns)})" ) if not all([isinstance(vunit, pq.UnitQuantity) for vunit in value_units]): raise ValueError("No value unit or standard value type specified.") return value_columns, value_types, value_units def _check_input_gids(self, gid_list, id_column): """ Checks gid values and column for consistency. gid_list: list of int or None, gid to load. id_column: int, id of the column containing the gids. Returns adjusted list of [gid_list, id_column]. """ if gid_list is None: gid_list = [gid_list] if None in gid_list and id_column is not None: raise ValueError( "No neuron IDs specified but file contains " f"neuron IDs in column {str(id_column)}. Specify empty list to " "retrieve spiketrains of all neurons." "" ) if gid_list != [None] and id_column is None: raise ValueError(f"Specified neuron IDs to be {gid_list}, but no ID column " "specified.") return gid_list, id_column def _check_input_sampling_period(self, sampling_period, time_column, time_unit, data): """ Checks sampling period, times and time unit for consistency. sampling_period: pq.quantity.Quantity, sampling period of data to load. time_column: int, column id of times in data to load. time_unit: pq.quantity.Quantity, unit of time used in the data to load. data: numpy array, the data to be loaded / interpreted. Returns pq.quantities.Quantity object, the updated sampling period. """ if sampling_period is None: if time_column is not None: data_sampling = np.unique(np.diff(sorted(np.unique(data[:, 1])))) if len(data_sampling) > 1: raise ValueError(f"Different sampling distances found in " "data set ({data_sampling})") else: dt = data_sampling[0] else: raise ValueError("Can not estimate sampling rate without time " "column id provided.") sampling_period = pq.CompoundUnit(str(dt) + "*" + time_unit.units.u_symbol) elif not isinstance(sampling_period, pq.UnitQuantity): raise ValueError("sampling_period is not specified as a unit.") return sampling_period def _get_conditions_and_sorting(self, id_column, time_column, gid_list, t_start, t_stop): """ Calculates the condition, condition_column and sorting_column based on other parameters supplied for loading the data. id_column: int, id of the column containing gids. time_column: int, id of the column containing times. gid_list: list of int, gid to be loaded. t_start: pq.quantity.Quantity, start of the time range to be loaded. t_stop: pq.quantity.Quantity, stop of the time range to be loaded. Returns updated [condition, condition_column, sorting_column]. """ condition, condition_column = None, None sorting_column = [] curr_id = 0 if (gid_list != [None]) and (gid_list is not None): if gid_list != []: def condition(x): return x in gid_list condition_column = id_column sorting_column.append(curr_id) # Sorting according to gids first curr_id += 1 if time_column is not None: sorting_column.append(curr_id) # Sorting according to time curr_id += 1 elif t_start != -np.inf and t_stop != np.inf: warnings.warn("Ignoring t_start and t_stop parameters, because no " "time column id is provided.") if sorting_column == []: sorting_column = None else: sorting_column = sorting_column[::-1] return condition, condition_column, sorting_column def _get_selected_ids(self, gid, id_column, time_column, t_start, t_stop, time_unit, data): """ Calculates the data range to load depending on the selected gid and the provided time range (t_start, t_stop) gid: int, gid to be loaded. id_column: int, id of the column containing gids. time_column: int, id of the column containing times. t_start: pq.quantity.Quantity, start of the time range to load. t_stop: pq.quantity.Quantity, stop of the time range to load. time_unit: pq.quantity.Quantity, time unit of the data to load. data: numpy array, data to load. Returns list of selected gids """ gid_ids = np.array([0, data.shape[0]]) if id_column is not None: gid_ids = np.array( [np.searchsorted(data[:, 0], gid, side="left"), np.searchsorted(data[:, 0], gid, side="right")] ) gid_data = data[gid_ids[0] : gid_ids[1], :] # select only requested time range id_shifts = np.array([0, 0]) if time_column is not None: id_shifts[0] = np.searchsorted(gid_data[:, 1], t_start.rescale(time_unit).magnitude, side="left") id_shifts[1] = ( np.searchsorted(gid_data[:, 1], t_stop.rescale(time_unit).magnitude, side="left") - gid_data.shape[0] ) selected_ids = gid_ids + id_shifts return selected_ids def read_block( self, gid_list=None, time_unit=pq.ms, t_start=None, t_stop=None, sampling_period=None, id_column_dat=0, time_column_dat=1, value_columns_dat=2, id_column_gdf=0, time_column_gdf=1, value_types=None, value_units=None, lazy=False, ): assert not lazy, "Do not support lazy" seg = self.read_segment( gid_list, time_unit, t_start, t_stop, sampling_period, id_column_dat, time_column_dat, value_columns_dat, id_column_gdf, time_column_gdf, value_types, value_units, ) blk = Block(file_origin=seg.file_origin, file_datetime=seg.file_datetime) blk.segments.append(seg) return blk def read_segment( self, gid_list=None, time_unit=pq.ms, t_start=None, t_stop=None, sampling_period=None, id_column_dat=0, time_column_dat=1, value_columns_dat=2, id_column_gdf=0, time_column_gdf=1, value_types=None, value_units=None, lazy=False, ): """ Reads a Segment which contains SpikeTrain(s) with specified neuron IDs from the GDF data. Arguments ---------- gid_list : list, default: None A list of GDF IDs of which to return SpikeTrain(s). gid_list must be specified if the GDF file contains neuron IDs, the default None then raises an error. Specify an empty list [] to retrieve the spike trains of all neurons. time_unit : Quantity (time), optional, default: quantities.ms The time unit of recorded time stamps in DAT as well as GDF files. t_start : Quantity (time), optional, default: 0 * pq.ms Start time of SpikeTrain. t_stop : Quantity (time), default: None Stop time of SpikeTrain. t_stop must be specified, the default None raises an error. sampling_period : Quantity (frequency), optional, default: None Sampling period of the recorded data. id_column_dat : int, optional, default: 0 Column index of neuron IDs in the DAT file. time_column_dat : int, optional, default: 1 Column index of time stamps in the DAT file. value_columns_dat : int, optional, default: 2 Column index of the analog values recorded in the DAT file. id_column_gdf : int, optional, default: 0 Column index of neuron IDs in the GDF file. time_column_gdf : int, optional, default: 1 Column index of time stamps in the GDF file. value_types : str, optional, default: None Nest data type of the analog values recorded, eg.'V_m', 'I', 'g_e' value_units : Quantity (amplitude), default: None The physical unit of the recorded signal values. lazy : bool, optional, default: False Returns ------- seg : Segment The Segment contains one SpikeTrain and one AnalogSignal for each ID in gid_list. """ assert not lazy, "Do not support lazy" if isinstance(gid_list, tuple): if gid_list[0] > gid_list[1]: raise ValueError("The second entry in gid_list must be " "greater or equal to the first entry.") gid_list = range(gid_list[0], gid_list[1] + 1) # __read_xxx() needs a list of IDs if gid_list is None: gid_list = [None] # create an empty Segment seg = Segment(file_origin=",".join(self.filenames)) seg.file_datetime = datetime.fromtimestamp(os.stat(self.filenames[0]).st_mtime) # todo: rather than take the first file for the timestamp, we should take the oldest # in practice, there won't be much difference # Load analogsignals and attach to Segment if "dat" in self.avail_formats: seg.analogsignals = self.__read_analogsignals( gid_list, time_unit, t_start, t_stop, sampling_period=sampling_period, id_column=id_column_dat, time_column=time_column_dat, value_columns=value_columns_dat, value_types=value_types, value_units=value_units, ) if "gdf" in self.avail_formats: seg.spiketrains = self.__read_spiketrains( gid_list, time_unit, t_start, t_stop, id_column=id_column_gdf, time_column=time_column_gdf ) return seg def read_analogsignal( self, gid=None, time_unit=pq.ms, t_start=None, t_stop=None, sampling_period=None, id_column=0, time_column=1, value_column=2, value_type=None, value_unit=None, lazy=False, ): """ Reads an AnalogSignal with specified neuron ID from the DAT data. Arguments ---------- gid : int, default: None The GDF ID of the returned SpikeTrain. gdf_id must be specified if the GDF file contains neuron IDs, the default None then raises an error. Specify an empty list [] to retrieve the spike trains of all neurons. time_unit : Quantity (time), optional, default: quantities.ms The time unit of recorded time stamps. t_start : Quantity (time), optional, default: 0 * pq.ms Start time of SpikeTrain. t_stop : Quantity (time), default: None Stop time of SpikeTrain. t_stop must be specified, the default None raises an error. sampling_period : Quantity (frequency), optional, default: None Sampling period of the recorded data. id_column : int, optional, default: 0 Column index of neuron IDs. time_column : int, optional, default: 1 Column index of time stamps. value_column : int, optional, default: 2 Column index of the analog values recorded. value_type : str, optional, default: None Nest data type of the analog values recorded, eg.'V_m', 'I', 'g_e'. value_unit : Quantity (amplitude), default: None The physical unit of the recorded signal values. lazy : bool, optional, default: False Returns ------- spiketrain : SpikeTrain The requested SpikeTrain object with an annotation 'id' corresponding to the gdf_id parameter. """ assert not lazy, "Do not support lazy" # __read_spiketrains() needs a list of IDs return self.__read_analogsignals( [gid], time_unit, t_start, t_stop, sampling_period=sampling_period, id_column=id_column, time_column=time_column, value_columns=value_column, value_types=value_type, value_units=value_unit, )[0] def read_spiketrain( self, gdf_id=None, time_unit=pq.ms, t_start=None, t_stop=None, id_column=0, time_column=1, lazy=False, **args ): """ Reads a SpikeTrain with specified neuron ID from the GDF data. Arguments ---------- gdf_id : int, default: None The GDF ID of the returned SpikeTrain. gdf_id must be specified if the GDF file contains neuron IDs. Providing [] loads all available IDs. time_unit : Quantity (time), optional, default: quantities.ms The time unit of recorded time stamps. t_start : Quantity (time), default: None Start time of SpikeTrain. t_start must be specified. t_stop : Quantity (time), default: None Stop time of SpikeTrain. t_stop must be specified. id_column : int, optional, default: 0 Column index of neuron IDs. time_column : int, optional, default: 1 Column index of time stamps. lazy : bool, optional, default: False Returns ------- spiketrain : SpikeTrain The requested SpikeTrain object with an annotation 'id' corresponding to the gdf_id parameter. """ assert not lazy, "Do not support lazy" if (not isinstance(gdf_id, int)) and gdf_id is not None: raise ValueError("gdf_id has to be of type int or None.") if gdf_id is None and id_column is not None: raise ValueError("No neuron ID specified but file contains " "neuron IDs in column " + str(id_column) + ".") return self.__read_spiketrains([gdf_id], time_unit, t_start, t_stop, id_column, time_column, **args)[0] class ColumnIO: """ Class for reading an ASCII file containing multiple columns of data. """ def __init__(self, filename): """ filename: string, path to ASCII file to read. """ self.filename = filename # read the first line to check the data type (int or float) of the data f = open(self.filename) line = f.readline() additional_parameters = {} if "." not in line: additional_parameters["dtype"] = np.int32 self.data = np.loadtxt(self.filename, **additional_parameters) if len(self.data.shape) == 1: self.data = self.data[:, np.newaxis] def get_columns(self, column_ids="all", condition=None, condition_column=None, sorting_columns=None): """ column_ids : 'all' or list of int, the ids of columns to extract. condition : None or function, which is applied to each row to evaluate if it should be included in the result. Needs to return a bool value. condition_column : int, id of the column on which the condition function is applied to sorting_columns : int or list of int, column ids to sort by. List entries have to be ordered by increasing sorting priority! Returns ------- numpy array containing the requested data. """ if column_ids == [] or column_ids == "all": column_ids = range(self.data.shape[-1]) if isinstance(column_ids, (int, float)): column_ids = [column_ids] column_ids = np.array(column_ids) if column_ids is not None: if max(column_ids) >= len(self.data) - 1: raise ValueError( f"Can not load column ID {max(column_ids)}. File contains " f"only {len(self.data)} columns" ) if sorting_columns is not None: if isinstance(sorting_columns, int): sorting_columns = [sorting_columns] if max(sorting_columns) >= self.data.shape[1]: raise ValueError( f"Can not sort by column ID {max(sorting_columns)}. File contains " f"only {self.data.shape[1]} columns" ) # Starting with whole dataset being selected for return selected_data = self.data # Apply filter condition to rows if condition and (condition_column is None): raise ValueError("Filter condition provided, but no " "condition_column ID provided") elif (condition_column is not None) and (condition is None): warnings.warn("Condition column ID provided, but no condition " "given. No filtering will be performed.") elif (condition is not None) and (condition_column is not None): condition_function = np.vectorize(condition) mask = condition_function(selected_data[:, condition_column]).astype(bool) selected_data = selected_data[mask, :] # Apply sorting if requested if sorting_columns is not None: values_to_sort = selected_data[:, sorting_columns].T ordered_ids = np.lexsort(tuple(values_to_sort[i] for i in range(len(values_to_sort)))) selected_data = selected_data[ordered_ids, :] # Select only requested columns selected_data = selected_data[:, column_ids] return selected_data ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neuralynxio.py0000644000175100017510000000615615154507213016161 0ustar00runnerrunner""" Class for reading data from Neuralynx files. This IO supports NCS, NEV and NSE file formats. Depends on: numpy Supported: Read Author: Julia Sprenger, Carlos Canova """ import warnings from neo.io.basefromrawio import BaseFromRaw from neo.rawio.neuralynxrawio.neuralynxrawio import NeuralynxRawIO class NeuralynxIO(NeuralynxRawIO, BaseFromRaw): """ Class for reading data from Neuralynx files. This IO supports NCS, NEV, NSE and NTT file formats. NCS contains signals for one channel NEV contains events NSE contains spikes and waveforms for mono electrodes NTT contains spikes and waveforms for tetrodes """ _prefered_signal_group_mode = "group-by-same-units" mode = "dir" def __init__( self, dirname, use_cache=False, cache_path="same_as_resource", include_filenames=None, exclude_filenames=None, keep_original_times=False, filename=None, exclude_filename=None, ): """ Initialise IO instance Parameters ---------- dirname : str Directory containing data files filename : str Deprecated and will be removed. Please use `include_filenames` instead Name of a single ncs, nse, nev, or ntt file to include in dataset. Will be ignored, if dirname is provided. use_cache : bool, optional Cache results of initial file scans for faster loading in subsequent runs. Default: False cache_path : str, optional Folder path to use for cache files. Default: 'same_as_resource' exclude_filename: None, Deprecated and will be removed. Please use `exclude_filenames` instead include_filenames: str or list Filename or list of filenames to be included. This can be absolute path or path relative to dirname. exclude_filenames: str or list Filename or list of filenames to be excluded. Expects base filenames without directory path. keep_original_times : bool Preserve original time stamps as in data files. By default datasets are shifted to begin at t_start = 0*pq.second. Default: False """ if filename is not None: warnings.warn("Deprecated and will be removed. Please use `include_filenames` instead") include_filenames = [filename] if exclude_filename is not None: warnings.warn("Deprecated and will be removed. Please use `exclude_filenames` instead") exclude_filenames = exclude_filename NeuralynxRawIO.__init__( self, dirname=dirname, include_filenames=include_filenames, exclude_filenames=exclude_filenames, keep_original_times=keep_original_times, use_cache=use_cache, cache_path=cache_path, ) if self.rawmode == "one-dir": BaseFromRaw.__init__(self, dirname) elif self.rawmode == "multiple-files": BaseFromRaw.__init__(self, include_filenames=include_filenames) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neuroexplorerio.py0000644000175100017510000000064415154507213017041 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.neuroexplorerrawio import NeuroExplorerRawIO class NeuroExplorerIO(NeuroExplorerRawIO, BaseFromRaw): """Class for reading data from NeuroExplorer (.nex)""" _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): NeuroExplorerRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neuronexusio.py0000644000175100017510000000057715154507213016350 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.neuronexusrawio import NeuroNexusRawIO class NeuroNexusIO(NeuroNexusRawIO, BaseFromRaw): __doc__ = NeuroNexusRawIO.__doc__ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): NeuroNexusRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neuroscopeio.py0000644000175100017510000000070115154507213016304 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.neuroscoperawio import NeuroScopeRawIO class NeuroScopeIO(NeuroScopeRawIO, BaseFromRaw): """ Reading from Neuroscope format files. Ref: http://neuroscope.sourceforge.net/ """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): NeuroScopeRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neuroshareapiio.py0000644000175100017510000004617115154507213017002 0ustar00runnerrunner""" Class for "reading" data from Neuroshare compatible files (check neuroshare.org) It runs through the whole file and searches for: analog signals, spike cutouts, and trigger events (without duration) Depends on: Neuroshare API 0.9.1, numpy 1.6.1, quantities 0.10.1 Supported: Read Author: Andre Maia Chagas """ # note neo.core needs only numpy and quantities import numpy as np import quantities as pq import os # check to see if the neuroshare bindings are properly imported try: import neuroshare as ns except ImportError as err: print(err) # print('\n neuroshare library not found, loading data will not work!' ) # print('\n be sure to install the library found at:') # print('\n www.http://pythonhosted.org/neuroshare/') else: pass # print('neuroshare library successfully imported') # import BaseIO from neo.io.baseio import BaseIO # import objects from neo.core from neo.core import Segment, AnalogSignal, SpikeTrain, Event, Epoch # create an object based on BaseIO class NeuroshareapiIO(BaseIO): # setting some class parameters is_readable = True # This class can only read data is_writable = False # write is not supported supported_objects = [Segment, AnalogSignal, SpikeTrain, Event, Epoch] has_header = False is_streameable = False readable_objects = [Segment, AnalogSignal, SpikeTrain, Event, Epoch] # This class is not able to write objects writeable_objects = [] # # This is for GUI stuff : a definition for parameters when reading. # # This dict should be keyed by object (`Block`). Each entry is a list # # of tuple. The first entry in each tuple is the parameter name. The # # second entry is a dict with keys 'value' (for default value), # # and 'label' (for a descriptive name). # # Note that if the highest-level object requires parameters, # # common_io_test will be skipped. read_params = { Segment: [ ("segment_duration", {"value": 0.0, "label": "Segment size (s.)"}), ("t_start", {"value": 0.0, "label": "start reading (s.)"}), # ("num_analogsignal", # {'value" : 8, "label" : "Number of recording points"}), # ("num_spiketrain_by_channel', # {"value" : 3, "label" : "Num of spiketrains"}), ], } # # do not supported write so no GUI stuff write_params = None name = "Neuroshare" extensions = ["mcd"] # This object operates on neuroshare files mode = "file" def __init__(self, filename=None, dllname=None): """ Arguments: filename : the filename dllname: the path of the library to use for reading The init function will run automatically upon calling of the class, as in: test = MultichannelIO(filename = filetoberead.mcd), therefore the first operations with the file are set here, so that the user doesn't have to remember to use another method, than the ones defined in the NEO library """ BaseIO.__init__(self) self.filename = str(filename) # set the flags for each event type eventID = 1 analogID = 2 epochID = 3 # if a filename was given, create a dictionary with information that will # be needed later on. if self.filename is not None: if dllname is not None: # converting to string to also accept pathlib objects dllpath = str(dllname) name = os.path.splitext(os.path.basename(dllpath))[0] library = ns.Library(name, dllpath) else: library = None self.fd = ns.File(self.filename, library=library) # get all the metadata from file self.metadata = self.fd.metadata_raw # get sampling rate self.metadata["sampRate"] = 1.0 / self.metadata["TimeStampResolution"] # hz # create lists and array for electrode, spike cutouts and trigger channels self.metadata["elecChannels"] = list() self.metadata["elecChanId"] = list() self.metadata["num_analogs"] = 0 self.metadata["spkChannels"] = list() self.metadata["spkChanId"] = list() self.metadata["num_spkChans"] = 0 self.metadata["triggers"] = list() self.metadata["triggersId"] = list() self.metadata["num_trigs"] = 0 self.metadata["digital epochs"] = list() self.metadata["digiEpochId"] = list() self.metadata["num_digiEpochs"] = 0 # loop through all entities in file to get the indexes for each entity # type, so that one can run through the indexes later, upon reading the # segment for entity in self.fd.entities: # if entity is analog and not the digital line recording # (stored as analog in neuroshare files) if entity.entity_type == analogID and entity.label[0:4] != "digi": # get the electrode number self.metadata["elecChannels"].append(entity.label[-4:]) # get the electrode index self.metadata["elecChanId"].append(entity.id) # increase the number of electrodes found self.metadata["num_analogs"] += 1 # if the entity is a event entitiy and a trigger if entity.entity_type == eventID and entity.label[0:4] == "trig": # get the digital bit/trigger number self.metadata["triggers"].append(entity.label[0:4] + entity.label[-4:]) # get the digital bit index self.metadata["triggersId"].append(entity.id) # increase the number of triggers found self.metadata["num_trigs"] += 1 # if the entity is non triggered digital values with duration if entity.entity_type == eventID and entity.label[0:4] == "digi": # get the digital bit number self.metadata["digital epochs"].append(entity.label[-5:]) # get the digital bit index self.metadata["digiEpochId"].append(entity.id) # increase the number of triggers found self.metadata["num_digiEpochs"] += 1 # if the entity is spike cutouts if entity.entity_type == epochID and entity.label[0:4] == "spks": self.metadata["spkChannels"].append(entity.label[-4:]) self.metadata["spkChanId"].append(entity.id) self.metadata["num_spkChans"] += 1 # function to create a block and read in a segment # def create_block(self, # # ): # # blk=Block(name = self.fileName+"_segment:", # file_datetime = str(self.metadata_raw["Time_Day"])+"/"+ # str(self.metadata_raw["Time_Month"])+"/"+ # str(self.metadata_raw["Time_Year"])+"_"+ # str(self.metadata_raw["Time_Hour"])+":"+ # str(self.metadata_raw["Time_Min"])) # # blk.rec_datetime = blk.file_datetime # return blk # create function to read segment def read_segment( self, lazy=False, # all following arguments are decided by this IO and are free t_start=0.0, segment_duration=0.0, ): """ Return a Segment containing all analog and spike channels, as well as all trigger events. Parameters ---------- segment_duration : float The size in seconds of the segment. num_analogsignal : int Number of AnalogSignal in this segment. num_spiketrain : int Number of SpikeTrain in this segment. """ assert not lazy, "Do not support lazy" # if no segment duration is given, use the complete file if segment_duration == 0.0: segment_duration = float(self.metadata["TimeSpan"]) # if the segment duration is bigger than file, use the complete file if segment_duration >= float(self.metadata["TimeSpan"]): segment_duration = float(self.metadata["TimeSpan"]) # if the time sum of start point and segment duration is bigger than # the file time span, cap it at the end if segment_duration + t_start > float(self.metadata["TimeSpan"]): segment_duration = float(self.metadata["TimeSpan"]) - t_start # create an empty segment seg = Segment(name="segment from the NeuroshareapiIO") # read nested analosignal if self.metadata["num_analogs"] == 0: print("no analog signals in this file!") else: # run through the number of analog channels found at the __init__ function for i in range(self.metadata["num_analogs"]): # create an analog signal object for each channel found ana = self.read_analogsignal( channel_index=self.metadata["elecChanId"][i], segment_duration=segment_duration, t_start=t_start ) # add analog signal read to segment object seg.analogsignals += [ana] # read triggers (in this case without any duration) for i in range(self.metadata["num_trigs"]): # create event object for each trigger/bit found eva = self.read_event( channel_index=self.metadata["triggersId"][i], segment_duration=segment_duration, t_start=t_start, ) # add event object to segment seg.events += [eva] # read epochs (digital events with duration) for i in range(self.metadata["num_digiEpochs"]): # create event object for each trigger/bit found epa = self.read_epoch( channel_index=self.metadata["digiEpochId"][i], segment_duration=segment_duration, t_start=t_start, ) # add event object to segment seg.epochs += [epa] # read nested spiketrain # run through all spike channels found for i in range(self.metadata["num_spkChans"]): # create spike object sptr = self.read_spiketrain( channel_index=self.metadata["spkChanId"][i], segment_duration=segment_duration, t_start=t_start ) # add the spike object to segment seg.spiketrains += [sptr] seg.check_relationships() return seg """ With this IO AnalogSignal can be accessed directly with its channel number """ def read_analogsignal( self, lazy=False, # channel index as given by the neuroshare API channel_index=0, # time in seconds to be read segment_duration=0.0, # time in seconds to start reading from t_start=0.0, ): assert not lazy, "Do not support lazy" # some controls: # if no segment duration is given, use the complete file if segment_duration == 0.0: segment_duration = float(self.metadata["TimeSpan"]) # if the segment duration is bigger than file, use the complete file if segment_duration >= float(self.metadata["TimeSpan"]): segment_duration = float(self.metadata["TimeSpan"]) # get the analog object sig = self.fd.get_entity(channel_index) # get the units (V, mV etc) sigUnits = sig.units # get the electrode number chanName = sig.label[-4:] # transform t_start into index (reading will start from this index) startat = int(t_start * self.metadata["sampRate"]) # get the number of bins to read in bins = int(segment_duration * self.metadata["sampRate"]) # if the number of bins to read is bigger than # the total number of bins, read only till the end of analog object if startat + bins > sig.item_count: bins = sig.item_count - startat # read the data from the sig object sig, _, _ = sig.get_data(index=startat, count=bins) # store it to the 'AnalogSignal' object anasig = AnalogSignal( sig, units=sigUnits, sampling_rate=self.metadata["sampRate"] * pq.Hz, t_start=t_start * pq.s, t_stop=(t_start + segment_duration) * pq.s, channel_index=channel_index, ) # annotate from which electrode the signal comes from anasig.annotate(info=f"signal from channel {chanName}") return anasig # function to read spike trains def read_spiketrain(self, lazy=False, channel_index=0, segment_duration=0.0, t_start=0.0): """ Function to read in spike trains. This API still does not support read in of specific channels as they are recorded. rather the fuunction gets the entity set by 'channel_index' which is set in the __init__ function (all spike channels) """ assert not lazy, "Do not support lazy" # sampling rate sr = self.metadata["sampRate"] # create a list to store spiketrain times times = list() # get the spike data from a specific channel index tempSpks = self.fd.get_entity(channel_index) # transform t_start into index (reading will start from this index) startat = tempSpks.get_index_by_time(t_start, 0) # zero means closest index to value # get the last index to read, using segment duration and t_start # -1 means last index before time endat = tempSpks.get_index_by_time(float(segment_duration + t_start), -1) numIndx = endat - startat # get the end point using segment duration # create a numpy empty array to store the waveforms waveforms = np.array(np.zeros([numIndx, 1, tempSpks.max_sample_count])) # loop through the data from the specific channel index for i in range(startat, endat, 1): # get cutout, timestamp, cutout duration, and spike unit tempCuts, timeStamp, duration, unit = tempSpks.get_data(i) # save the cutout in the waveform matrix waveforms[i, 0, :] = tempCuts[0] # append time stamp to list times.append(timeStamp) # create a spike train object spiketr = SpikeTrain( times, units=pq.s, t_stop=t_start + segment_duration, t_start=t_start * pq.s, name="spikes from electrode" + tempSpks.label[-3:], waveforms=waveforms * pq.volt, sampling_rate=sr * pq.Hz, file_origin=self.filename, annotate=("channel_index:" + str(channel_index)), ) return spiketr def read_event(self, lazy=False, channel_index=0, t_start=0.0, segment_duration=0.0): """function to read digital timestamps. this function only reads the event onset. to get digital event durations, use the epoch function (to be implemented).""" assert not lazy, "Do not support lazy" # create temporary empty lists to store data tempNames = list() tempTimeStamp = list() # get entity from file trigEntity = self.fd.get_entity(channel_index) # transform t_start into index (reading will start from this index) startat = trigEntity.get_index_by_time(t_start, 0) # zero means closest index to value # get the last index to read, using segment duration and t_start endat = trigEntity.get_index_by_time(float(segment_duration + t_start), -1) # -1 means last index before time # numIndx = endat-startat # run through specified intervals in entity for i in range(startat, endat + 1, 1): # trigEntity.item_count): # get in which digital bit was the trigger detected tempNames.append(trigEntity.label[-8:]) # get the time stamps of onset events tempData, onOrOff = trigEntity.get_data(i) # if this was an onset event, save it to the list # on triggered recordings it seems that only onset events are # recorded. On continuous recordings both onset(==1) # and offset(==255) seem to be recorded if onOrOff == 1: # append the time stamp to them empty list tempTimeStamp.append(tempData) # create an event array eva = Event( labels=np.array(tempNames, dtype="U"), times=np.array(tempTimeStamp) * pq.s, file_origin=self.filename, description="the trigger events (without durations)", ) return eva def read_epoch(self, lazy=False, channel_index=0, t_start=0.0, segment_duration=0.0): """function to read digital timestamps. this function reads the event onset and offset and outputs onset and duration. to get only onsets use the event array function""" assert not lazy, "Do not support lazy" # create temporary empty lists to store data tempNames = list() tempTimeStamp = list() durations = list() # get entity from file digEntity = self.fd.get_entity(channel_index) # transform t_start into index (reading will start from this index) startat = digEntity.get_index_by_time(t_start, 0) # zero means closest index to value # get the last index to read, using segment duration and t_start # -1 means last index before time endat = digEntity.get_index_by_time(float(segment_duration + t_start), -1) # run through entity using only odd "i"s for i in range(startat, endat + 1, 1): if i % 2 == 1: # get in which digital bit was the trigger detected tempNames.append(digEntity.label[-8:]) # get the time stamps of even events tempData, onOrOff = digEntity.get_data(i - 1) # if this was an onset event, save it to the list # on triggered recordings it seems that only onset events are # recorded. On continuous recordings both onset(==1) # and offset(==255) seem to be recorded # if onOrOff == 1: # append the time stamp to them empty list tempTimeStamp.append(tempData) # get time stamps of odd events tempData1, onOrOff = digEntity.get_data(i) # if onOrOff == 255: # pass durations.append(tempData1 - tempData) epa = Epoch( file_origin=self.filename, times=np.array(tempTimeStamp) * pq.s, durations=np.array(durations) * pq.s, labels=np.array(tempNames, dtype="U"), description="digital events with duration", ) return epa ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/neurosharectypesio.py0000644000175100017510000004121115154507213017526 0ustar00runnerrunner""" NeuroshareIO is a wrap with ctypes of neuroshare DLLs. Neuroshare is a C API for reading neural data. Neuroshare also provides a Matlab and a Python API on top of that. Neuroshare is an open source API but each dll is provided directly by the vendor. The neo user have to download separtatly the dll on neurosharewebsite: http://neuroshare.sourceforge.net/ For some vendors (Spike2/CED , Clampfit/Abf, ...), neo.io also provides pure Python Neo users you should prefer them of course :) Supported : Read Author: sgarcia """ import sys import ctypes import os import warnings import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import Segment, AnalogSignal, SpikeTrain, Event ns_OK = 0 # Function successful ns_LIBERROR = -1 # Generic linked library error ns_TYPEERROR = -2 # Library unable to open file type ns_FILEERROR = -3 # File access or read error ns_BADFILE = -4 # Invalid file handle passed to function ns_BADENTITY = -5 # Invalid or inappropriate entity identifier specified ns_BADSOURCE = -6 # Invalid source identifier specified ns_BADINDEX = -7 # Invalid entity index specified class NeuroshareError(Exception): def __init__(self, lib, errno): self.lib = lib self.errno = errno pszMsgBuffer = ctypes.create_string_buffer(256) self.lib.ns_GetLastErrorMsg(pszMsgBuffer, ctypes.c_uint32(256)) errstr = f"{errno}: {pszMsgBuffer.value}" Exception.__init__(self, errstr) class DllWithError: def __init__(self, lib): self.lib = lib def __getattr__(self, attr): f = getattr(self.lib, attr) return self.decorate_with_error(f) def decorate_with_error(self, f): def func_with_error(*args): errno = f(*args) if errno != ns_OK: raise NeuroshareError(self.lib, errno) return errno return func_with_error class NeurosharectypesIO(BaseIO): """ Class for reading file trougth neuroshare API. The user need the DLLs in the path of the file format. Usage: >>> from neo import io >>> r = io.NeuroshareIO(filename='a_file', dllname=the_name_of_dll) >>> seg = r.read_segment(import_neuroshare_segment=True) >>> print seg.analogsignals # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE [>> print seg.spiketrains [] >>> print seg.events [] Note: neuroshare.ns_ENTITY_EVENT: are converted to neo.EventArray neuroshare.ns_ENTITY_ANALOG: are converted to neo.AnalogSignal neuroshare.ns_ENTITY_NEURALEVENT: are converted to neo.SpikeTrain neuroshare.ns_ENTITY_SEGMENT: is something between serie of small AnalogSignal and Spiketrain with associated waveforms. It is arbitrarily converted as SpikeTrain. """ is_readable = True is_writable = False supported_objects = [Segment, AnalogSignal, Event, SpikeTrain] readable_objects = [Segment] writeable_objects = [] has_header = False is_streameable = False read_params = {Segment: []} write_params = None name = "neuroshare" extensions = ["mcd"] mode = "file" def __init__(self, filename="", dllname=""): """ Arguments: filename: the file to read ddlname: the name of neuroshare dll to be used for this file """ BaseIO.__init__(self) self.dllname = dllname self.filename = str(filename) def read_segment(self, import_neuroshare_segment=True, lazy=False): """ Arguments: import_neuroshare_segment: import neuroshare segment as SpikeTrain with associated waveforms or not imported at all. """ assert not lazy, "Do not support lazy" seg = Segment( file_origin=os.path.basename(self.filename), ) if self.dllname == "": warnings.warn("No neuroshare dll provided. Can not load data.") return Segment() if sys.platform.startswith("win"): neuroshare = ctypes.windll.LoadLibrary(self.dllname) elif sys.platform.startswith("linux"): neuroshare = ctypes.cdll.LoadLibrary(self.dllname) neuroshare = DllWithError(neuroshare) # elif sys.platform.startswith('darwin'): # API version info = ns_LIBRARYINFO() neuroshare.ns_GetLibraryInfo(ctypes.byref(info), ctypes.sizeof(info)) seg.annotate(neuroshare_version=str(info.dwAPIVersionMaj) + "." + str(info.dwAPIVersionMin)) # open file hFile = ctypes.c_uint32(0) neuroshare.ns_OpenFile(ctypes.c_char_p(bytes(self.filename, "utf-8")), ctypes.byref(hFile)) fileinfo = ns_FILEINFO() neuroshare.ns_GetFileInfo(hFile, ctypes.byref(fileinfo), ctypes.sizeof(fileinfo)) # read all entities for dwEntityID in range(fileinfo.dwEntityCount): entityInfo = ns_ENTITYINFO() neuroshare.ns_GetEntityInfo(hFile, dwEntityID, ctypes.byref(entityInfo), ctypes.sizeof(entityInfo)) # EVENT if entity_types[entityInfo.dwEntityType] == "ns_ENTITY_EVENT": pEventInfo = ns_EVENTINFO() neuroshare.ns_GetEventInfo(hFile, dwEntityID, ctypes.byref(pEventInfo), ctypes.sizeof(pEventInfo)) if pEventInfo.dwEventType == 0: # TEXT pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 1: # CVS pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 2: # 8bit pData = ctypes.c_byte(0) elif pEventInfo.dwEventType == 3: # 16bit pData = ctypes.c_int16(0) elif pEventInfo.dwEventType == 4: # 32bit pData = ctypes.c_int32(0) pdTimeStamp = ctypes.c_double(0.0) pdwDataRetSize = ctypes.c_uint32(0) times = [] labels = [] for dwIndex in range(entityInfo.dwItemCount): neuroshare.ns_GetEventData( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), ctypes.byref(pData), ctypes.sizeof(pData), ctypes.byref(pdwDataRetSize), ) times.append(pdTimeStamp.value) labels.append(str(pData.value)) times = times * pq.s labels = np.array(labels, dtype="U") ea = Event(name=str(entityInfo.szEntityLabel), times=times, labels=labels) seg.events.append(ea) # analog if entity_types[entityInfo.dwEntityType] == "ns_ENTITY_ANALOG": pAnalogInfo = ns_ANALOGINFO() neuroshare.ns_GetAnalogInfo(hFile, dwEntityID, ctypes.byref(pAnalogInfo), ctypes.sizeof(pAnalogInfo)) dwIndexCount = entityInfo.dwItemCount pdwContCount = ctypes.c_uint32(0) pData = np.zeros((entityInfo.dwItemCount,), dtype="float64") total_read = 0 while total_read < entityInfo.dwItemCount: dwStartIndex = ctypes.c_uint32(total_read) dwStopIndex = ctypes.c_uint32(entityInfo.dwItemCount - total_read) neuroshare.ns_GetAnalogData( hFile, dwEntityID, dwStartIndex, dwStopIndex, ctypes.byref(pdwContCount), pData[total_read:].ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) total_read += pdwContCount.value try: signal = pq.Quantity(pData, units=pAnalogInfo.szUnits.decode()) unit_annotation = None except LookupError: signal = pq.Quantity(pData, units="dimensionless") unit_annotation = pAnalogInfo.szUnits.decode() # t_start dwIndex = 0 pdTime = ctypes.c_double(0) neuroshare.ns_GetTimeByIndex(hFile, dwEntityID, dwIndex, ctypes.byref(pdTime)) anaSig = AnalogSignal( signal, sampling_rate=pAnalogInfo.dSampleRate * pq.Hz, t_start=pdTime.value * pq.s, name=str(entityInfo.szEntityLabel), ) anaSig.annotate(probe_info=str(pAnalogInfo.szProbeInfo)) if unit_annotation is not None: anaSig.annotate(units=unit_annotation) seg.analogsignals.append(anaSig) # segment if entity_types[entityInfo.dwEntityType] == "ns_ENTITY_SEGMENT" and import_neuroshare_segment: pdwSegmentInfo = ns_SEGMENTINFO() if not str(entityInfo.szEntityLabel).startswith("spks"): continue neuroshare.ns_GetSegmentInfo( hFile, dwEntityID, ctypes.byref(pdwSegmentInfo), ctypes.sizeof(pdwSegmentInfo) ) nsource = pdwSegmentInfo.dwSourceCount pszMsgBuffer = ctypes.create_string_buffer(" " * 256) neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256) for dwSourceID in range(pdwSegmentInfo.dwSourceCount): pSourceInfo = ns_SEGSOURCEINFO() neuroshare.ns_GetSegmentSourceInfo( hFile, dwEntityID, dwSourceID, ctypes.byref(pSourceInfo), ctypes.sizeof(pSourceInfo) ) pdTimeStamp = ctypes.c_double(0.0) dwDataBufferSize = pdwSegmentInfo.dwMaxSampleCount * pdwSegmentInfo.dwSourceCount pData = np.zeros((dwDataBufferSize), dtype="float64") pdwSampleCount = ctypes.c_uint32(0) pdwUnitID = ctypes.c_uint32(0) nsample = int(dwDataBufferSize) times = np.empty((entityInfo.dwItemCount), dtype="f") waveforms = np.empty((entityInfo.dwItemCount, nsource, nsample), dtype="f") for dwIndex in range(entityInfo.dwItemCount): neuroshare.ns_GetSegmentData( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), dwDataBufferSize * 8, ctypes.byref(pdwSampleCount), ctypes.byref(pdwUnitID), ) times[dwIndex] = pdTimeStamp.value waveforms[dwIndex, :, :] = pData[: nsample * nsource].reshape(nsample, nsource).transpose() sptr = SpikeTrain( times=pq.Quantity(times, units="s"), t_stop=times.max(), waveforms=pq.Quantity(waveforms, units=str(pdwSegmentInfo.szUnits)), left_sweep=nsample / 2.0 / float(pdwSegmentInfo.dSampleRate) * pq.s, sampling_rate=float(pdwSegmentInfo.dSampleRate) * pq.Hz, name=str(entityInfo.szEntityLabel), ) seg.spiketrains.append(sptr) # neuralevent if entity_types[entityInfo.dwEntityType] == "ns_ENTITY_NEURALEVENT": pNeuralInfo = ns_NEURALINFO() neuroshare.ns_GetNeuralInfo(hFile, dwEntityID, ctypes.byref(pNeuralInfo), ctypes.sizeof(pNeuralInfo)) pData = np.zeros((entityInfo.dwItemCount,), dtype="float64") dwStartIndex = 0 dwIndexCount = entityInfo.dwItemCount neuroshare.ns_GetNeuralData( hFile, dwEntityID, dwStartIndex, dwIndexCount, pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) ) times = pData * pq.s t_stop = times.max() sptr = SpikeTrain( times, t_stop=t_stop, name=str(entityInfo.szEntityLabel), ) seg.spiketrains.append(sptr) # close neuroshare.ns_CloseFile(hFile) seg.check_relationships() return seg # neuroshare structures class ns_FILEDESC(ctypes.Structure): _fields_ = [ ("szDescription", ctypes.c_char * 32), ("szExtension", ctypes.c_char * 8), ("szMacCodes", ctypes.c_char * 8), ("szMagicCode", ctypes.c_char * 16), ] class ns_LIBRARYINFO(ctypes.Structure): _fields_ = [ ("dwLibVersionMaj", ctypes.c_uint32), ("dwLibVersionMin", ctypes.c_uint32), ("dwAPIVersionMaj", ctypes.c_uint32), ("dwAPIVersionMin", ctypes.c_uint32), ("szDescription", ctypes.c_char * 64), ("szCreator", ctypes.c_char * 64), ("dwTime_Year", ctypes.c_uint32), ("dwTime_Month", ctypes.c_uint32), ("dwTime_Day", ctypes.c_uint32), ("dwFlags", ctypes.c_uint32), ("dwMaxFiles", ctypes.c_uint32), ("dwFileDescCount", ctypes.c_uint32), ("FileDesc", ns_FILEDESC * 16), ] class ns_FILEINFO(ctypes.Structure): _fields_ = [ ("szFileType", ctypes.c_char * 32), ("dwEntityCount", ctypes.c_uint32), ("dTimeStampResolution", ctypes.c_double), ("dTimeSpan", ctypes.c_double), ("szAppName", ctypes.c_char * 64), ("dwTime_Year", ctypes.c_uint32), ("dwTime_Month", ctypes.c_uint32), ("dwReserved", ctypes.c_uint32), ("dwTime_Day", ctypes.c_uint32), ("dwTime_Hour", ctypes.c_uint32), ("dwTime_Min", ctypes.c_uint32), ("dwTime_Sec", ctypes.c_uint32), ("dwTime_MilliSec", ctypes.c_uint32), ("szFileComment", ctypes.c_char * 256), ] class ns_ENTITYINFO(ctypes.Structure): _fields_ = [ ("szEntityLabel", ctypes.c_char * 32), ("dwEntityType", ctypes.c_uint32), ("dwItemCount", ctypes.c_uint32), ] entity_types = { 0: "ns_ENTITY_UNKNOWN", 1: "ns_ENTITY_EVENT", 2: "ns_ENTITY_ANALOG", 3: "ns_ENTITY_SEGMENT", 4: "ns_ENTITY_NEURALEVENT", } class ns_EVENTINFO(ctypes.Structure): _fields_ = [ ("dwEventType", ctypes.c_uint32), ("dwMinDataLength", ctypes.c_uint32), ("dwMaxDataLength", ctypes.c_uint32), ("szCSVDesc", ctypes.c_char * 128), ] class ns_ANALOGINFO(ctypes.Structure): _fields_ = [ ("dSampleRate", ctypes.c_double), ("dMinVal", ctypes.c_double), ("dMaxVal", ctypes.c_double), ("szUnits", ctypes.c_char * 16), ("dResolution", ctypes.c_double), ("dLocationX", ctypes.c_double), ("dLocationY", ctypes.c_double), ("dLocationZ", ctypes.c_double), ("dLocationUser", ctypes.c_double), ("dHighFreqCorner", ctypes.c_double), ("dwHighFreqOrder", ctypes.c_uint32), ("szHighFilterType", ctypes.c_char * 16), ("dLowFreqCorner", ctypes.c_double), ("dwLowFreqOrder", ctypes.c_uint32), ("szLowFilterType", ctypes.c_char * 16), ("szProbeInfo", ctypes.c_char * 128), ] class ns_SEGMENTINFO(ctypes.Structure): _fields_ = [ ("dwSourceCount", ctypes.c_uint32), ("dwMinSampleCount", ctypes.c_uint32), ("dwMaxSampleCount", ctypes.c_uint32), ("dSampleRate", ctypes.c_double), ("szUnits", ctypes.c_char * 32), ] class ns_SEGSOURCEINFO(ctypes.Structure): _fields_ = [ ("dMinVal", ctypes.c_double), ("dMaxVal", ctypes.c_double), ("dResolution", ctypes.c_double), ("dSubSampleShift", ctypes.c_double), ("dLocationX", ctypes.c_double), ("dLocationY", ctypes.c_double), ("dLocationZ", ctypes.c_double), ("dLocationUser", ctypes.c_double), ("dHighFreqCorner", ctypes.c_double), ("dwHighFreqOrder", ctypes.c_uint32), ("szHighFilterType", ctypes.c_char * 16), ("dLowFreqCorner", ctypes.c_double), ("dwLowFreqOrder", ctypes.c_uint32), ("szLowFilterType", ctypes.c_char * 16), ("szProbeInfo", ctypes.c_char * 128), ] class ns_NEURALINFO(ctypes.Structure): _fields_ = [ ("dwSourceEntityID", ctypes.c_uint32), ("dwSourceUnitID", ctypes.c_uint32), ("szProbeInfo", ctypes.c_char * 128), ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/nixio.py0000644000175100017510000015767115154507213014743 0ustar00runnerrunner# Copyright (c) 2016, German Neuroinformatics Node (G-Node) # Achilleas Koutsou # # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted under the terms of the BSD License. See # LICENSE file in the root of the Project. """ Module for reading data from files in the NIX format. Author: Achilleas Koutsou This IO supports both writing and reading of NIX files. Reading is supported only if the NIX file was created using this IO. Details on how the Neo object tree is mapped to NIX, as well as details on behaviours specific to this IO, can be found on the wiki of the G-Node fork of Neo: https://github.com/G-Node/python-neo/wiki """ from datetime import date, time, datetime from collections.abc import Iterable from collections import OrderedDict import itertools from uuid import uuid4 import warnings from packaging.version import Version import importlib.util import importlib.metadata import quantities as pq import numpy as np from .baseio import BaseIO from ..core import ( Block, Segment, AnalogSignal, IrregularlySampledSignal, Epoch, Event, SpikeTrain, ImageSequence, ChannelView, Group, ) from ..io.proxyobjects import BaseProxy from .. import __version__ as neover datetime_types = (date, time, datetime) EMPTYANNOTATION = "EMPTYLIST" ARRAYANNOTATION = "ARRAYANNOTATION" DATETIMEANNOTATION = "DATETIME" DATEANNOTATION = "DATE" TIMEANNOTATION = "TIME" MIN_NIX_VER = Version("1.5.0") datefmt = "%Y-%m-%d" timefmt = "%H:%M:%S.%f" datetimefmt = datefmt + "T" + timefmt def stringify(value): if value is None: return value if isinstance(value, bytes): value = value.decode() return str(value) def create_quantity(values, unitstr): if "*" in unitstr: unit = pq.CompoundUnit(stringify(unitstr)) else: unit = unitstr return pq.Quantity(values, unit) def units_to_string(pqunit): dim = str(pqunit.dimensionality) if dim.startswith("(") and dim.endswith(")"): return dim.strip("()") return dim def dt_to_nix(dt): """ Converts date, time, and datetime objects to an ISO string representation appropriate for storing in NIX. Returns the converted value and the annotation type definition for converting back to the original value type. """ if isinstance(dt, datetime): return dt.strftime(datetimefmt), DATETIMEANNOTATION if isinstance(dt, date): return dt.strftime(datefmt), DATEANNOTATION if isinstance(dt, time): return dt.strftime(timefmt), TIMEANNOTATION # Unknown: returning as is return dt def dt_from_nix(nixdt, annotype): """ Inverse function of 'dt_to_nix()'. Requires the stored annotation type to distinguish between the three source types (date, time, and datetime). """ if annotype == DATEANNOTATION: dt = datetime.strptime(nixdt, datefmt) return dt.date() if annotype == TIMEANNOTATION: dt = datetime.strptime(nixdt, timefmt) return dt.time() if annotype == DATETIMEANNOTATION: dt = datetime.strptime(nixdt, datetimefmt) return dt # Unknown type: older (or newer) IO version? # Returning as is to avoid data loss. return nixdt def check_nix_version(): nixio_spec = importlib.util.find_spec("nixio") if nixio_spec is None: raise ImportError( "Failed to import NIX. " "The NixIO requires the Python package for NIX " "(nixio on PyPi). Try `pip install nixio`." ) nixverstr = importlib.metadata.version("nixio") try: nixver = Version(nixverstr) except ValueError: warnings.warn( f"Could not understand NIX Python version {nixverstr}. " f"The NixIO requires version {MIN_NIX_VER} of the Python package for NIX. " "The IO may not work correctly." ) return if nixver < MIN_NIX_VER: raise Exception( "NIX version not supported. " f"The NixIO requires version {MIN_NIX_VER} or higher of the Python package " f"for NIX. Found version {nixverstr}" ) class NixIO(BaseIO): """ Class for reading and writing NIX files. """ is_readable = True is_writable = True supported_objects = [ Block, Segment, Group, ChannelView, AnalogSignal, IrregularlySampledSignal, Epoch, Event, SpikeTrain, ] readable_objects = [Block] writeable_objects = [Block] name = "NIX" extensions = ["h5", "nix"] mode = "file" def __init__(self, filename, mode="rw"): """ Initialise IO instance and NIX file. :param filename: Full path to the file """ check_nix_version() import nixio BaseIO.__init__(self, filename) self.filename = str(filename) if mode == "ro": filemode = nixio.FileMode.ReadOnly elif mode == "rw": filemode = nixio.FileMode.ReadWrite elif mode == "ow": filemode = nixio.FileMode.Overwrite else: raise ValueError( f"Invalid mode specified '{mode}'. " "Valid modes: 'ro' (ReadOnly)', 'rw' (ReadWrite)," " 'ow' (Overwrite)." ) self.nix_file = nixio.File.open(self.filename, filemode) if self.nix_file.mode == nixio.FileMode.ReadOnly: self._file_version = "0.5.2" if "neo" in self.nix_file.sections: self._file_version = self.nix_file.sections["neo"]["version"] elif self.nix_file.mode == nixio.FileMode.ReadWrite: if "neo" in self.nix_file.sections: self._file_version = self.nix_file.sections["neo"]["version"] else: self._file_version = "0.5.2" filemd = self.nix_file.create_section("neo", "neo.metadata") filemd["version"] = self._file_version else: # new file filemd = self.nix_file.create_section("neo", "neo.metadata") filemd["version"] = neover self._file_version = neover self._block_read_counter = 0 # helper maps self._neo_map = dict() self._ref_map = dict() self._signal_map = dict() self._view_map = dict() # _names_ok is used to guard against name check duplication self._names_ok = False def __enter__(self): return self def __exit__(self, *args): self.close() def read_all_blocks(self, lazy=False): if lazy: raise Exception("Lazy loading is not supported for NixIO") return list(self._nix_to_neo_block(blk) for blk in self.nix_file.blocks) def read_block(self, index=None, nixname=None, neoname=None, lazy=False): """ Loads a Block from the NIX file along with all contained child objects and returns the equivalent Neo Block. The Block to read can be specified in one of three ways: - Index (position) in the file - Name of the NIX Block (see [...] for details on the naming) - Name of the original Neo Block If no arguments are specified, the first Block is returned and consecutive calls to the function return the next Block in the file. After all Blocks have been loaded this way, the function returns None. If more than one argument is specified, the precedence order is: index, nixname, neoname Note that Neo objects can be anonymous or have non-unique names, so specifying a Neo name may be ambiguous. See also :meth:`NixIO.iter_blocks`. :param index: The position of the Block to be loaded (creation order) :param nixname: The name of the Block in NIX :param neoname: The name of the original Neo Block """ if lazy: raise Exception("Lazy loading is not supported for NixIO") nix_block = None if index is not None: nix_block = self.nix_file.blocks[index] elif nixname is not None: nix_block = self.nix_file.blocks[nixname] elif neoname is not None: for blk in self.nix_file.blocks: if "neo_name" in blk.metadata and blk.metadata["neo_name"] == neoname: nix_block = blk break else: raise KeyError(f"Block with Neo name '{neoname}' does not exist") else: index = self._block_read_counter if index >= len(self.nix_file.blocks): return None nix_block = self.nix_file.blocks[index] self._block_read_counter += 1 return self._nix_to_neo_block(nix_block) def iter_blocks(self): """ Returns an iterator which can be used to consecutively load and convert all Blocks from the NIX File. """ for blk in self.nix_file.blocks: yield self._nix_to_neo_block(blk) def _nix_to_neo_block(self, nix_block): neo_attrs = self._nix_attr_to_neo(nix_block) neo_block = Block(**neo_attrs) neo_block.rec_datetime = datetime.fromtimestamp(nix_block.created_at) # descend into Groups groups_to_resolve = [] for grp in nix_block.groups: if grp.type == "neo.segment": newseg = self._nix_to_neo_segment(grp) neo_block.segments.append(newseg) elif grp.type == "neo.group": newgrp, parent_name = self._nix_to_neo_group(grp) if parent_name is not None: raise ValueError(f"`parent_name` must be None and is {parent_name}") neo_block.groups.append(newgrp) elif grp.type == "neo.subgroup": newgrp, parent_name = self._nix_to_neo_group(grp) groups_to_resolve.append((newgrp, parent_name)) else: raise Exception("Unexpected group type") # link subgroups to parents for newgrp, parent_name in groups_to_resolve: parent = self._neo_map[parent_name] parent.groups.append(newgrp) # find free floating (Groupless) signals and spiketrains blockdas = self._group_signals(nix_block.data_arrays) for name, das in blockdas.items(): if name not in self._neo_map: if das[0].type == "neo.analogsignal": self._nix_to_neo_analogsignal(das) elif das[0].type == "neo.irregularlysampledsignal": self._nix_to_neo_irregularlysampledsignal(das) elif das[0].type == "neo.imagesequence": self._nix_to_neo_imagesequence(das) for mt in nix_block.multi_tags: if mt.type == "neo.spiketrain" and mt.name not in self._neo_map: self._nix_to_neo_spiketrain(mt) # create object links neo_block.check_relationships() # reset maps self._neo_map = dict() self._ref_map = dict() self._signal_map = dict() self._view_map = dict() return neo_block def _nix_to_neo_segment(self, nix_group): neo_attrs = self._nix_attr_to_neo(nix_group) neo_segment = Segment(**neo_attrs) neo_segment.rec_datetime = datetime.fromtimestamp(nix_group.created_at) self._neo_map[nix_group.name] = neo_segment # this will probably get all the DAs anyway, but if we change any part # of the mapping to add other kinds of DataArrays to a group, such as # MultiTag positions and extents, this filter will be necessary dataarrays = list( filter( lambda da: da.type in ( "neo.analogsignal", "neo.irregularlysampledsignal", "neo.imagesequence", ), nix_group.data_arrays, ) ) dataarrays = self._group_signals(dataarrays) # descend into DataArrays for name, das in dataarrays.items(): if das[0].type == "neo.analogsignal": newasig = self._nix_to_neo_analogsignal(das) neo_segment.analogsignals.append(newasig) elif das[0].type == "neo.irregularlysampledsignal": newisig = self._nix_to_neo_irregularlysampledsignal(das) neo_segment.irregularlysampledsignals.append(newisig) elif das[0].type == "neo.imagesequence": new_imgseq = self._nix_to_neo_imagesequence(das) neo_segment.imagesequences.append(new_imgseq) # descend into MultiTags for mtag in nix_group.multi_tags: if mtag.type == "neo.event": newevent = self._nix_to_neo_event(mtag) neo_segment.events.append(newevent) elif mtag.type == "neo.epoch": newepoch = self._nix_to_neo_epoch(mtag) neo_segment.epochs.append(newepoch) elif mtag.type == "neo.spiketrain": newst = self._nix_to_neo_spiketrain(mtag) neo_segment.spiketrains.append(newst) return neo_segment def _nix_to_neo_group(self, nix_group): neo_attrs = self._nix_attr_to_neo(nix_group) parent_name = neo_attrs.pop("neo_parent", None) neo_group = Group(**neo_attrs) self._neo_map[nix_group.name] = neo_group dataarrays = list( filter( lambda da: da.type in ( "neo.analogsignal", "neo.irregularlysampledsignal", "neo.imagesequence", ), nix_group.data_arrays, ) ) dataarrays = self._group_signals(dataarrays) # descend into DataArrays for name in dataarrays: obj = self._neo_map[name] neo_group.add(obj) # descend into MultiTags for mtag in nix_group.multi_tags: if mtag.type == "neo.channelview" and mtag.name not in self._neo_map: self._nix_to_neo_channelview(mtag) obj = self._neo_map[mtag.name] neo_group.add(obj) return neo_group, parent_name def _nix_to_neo_channelview(self, nix_mtag): neo_attrs = self._nix_attr_to_neo(nix_mtag) index = nix_mtag.positions (nix_name,) = self._group_signals(nix_mtag.references).keys() obj = self._neo_map[nix_name] neo_chview = ChannelView(obj, index, **neo_attrs) self._neo_map[nix_mtag.name] = neo_chview return neo_chview def _nix_to_neo_analogsignal(self, nix_da_group): """ Convert a group of NIX DataArrays to a Neo AnalogSignal. This method expects a list of data arrays that all represent the same, multidimensional Neo AnalogSignal object. :param nix_da_group: a list of NIX DataArray objects :return: a Neo AnalogSignal object """ neo_attrs = self._nix_attr_to_neo(nix_da_group[0]) metadata = nix_da_group[0].metadata neo_attrs["nix_name"] = metadata.name # use the common base name unit = nix_da_group[0].unit signaldata = np.array([d[:] for d in nix_da_group]).transpose() signaldata = create_quantity(signaldata, unit) timedim = self._get_time_dimension(nix_da_group[0]) sampling_period = create_quantity(timedim.sampling_interval, timedim.unit) # t_start should have been added to neo_attrs via the NIX # object's metadata. This may not be present since in older # versions, we didn't store t_start in the metadata when it # wasn't necessary, such as when the timedim.offset and unit # did not require rescaling. if "t_start" in neo_attrs: t_start = neo_attrs["t_start"] del neo_attrs["t_start"] else: t_start = create_quantity(timedim.offset, timedim.unit) neo_signal = AnalogSignal(signal=signaldata, sampling_period=sampling_period, t_start=t_start, **neo_attrs) self._neo_map[neo_attrs["nix_name"]] = neo_signal # all DAs reference the same sources srcnames = list(src.name for src in nix_da_group[0].sources) for n in srcnames: if n not in self._ref_map: self._ref_map[n] = list() self._ref_map[n].append(neo_signal) return neo_signal def _nix_to_neo_imagesequence(self, nix_da_group): """ Convert a group of NIX DataArrays to a Neo ImageSequence. This method expects a list of data arrays that all represent the same, multidimensional Neo ImageSequence object. :param nix_da_group: a list of NIX DataArray objects :return: a Neo ImageSequence object """ neo_attrs = self._nix_attr_to_neo(nix_da_group[0]) metadata = nix_da_group[0].metadata neo_attrs["nix_name"] = metadata.name # use the common base name unit = nix_da_group[0].unit imgseq = np.array([d[:] for d in nix_da_group]).transpose() sampling_rate = neo_attrs["sampling_rate"] del neo_attrs["sampling_rate"] spatial_scale = neo_attrs["spatial_scale"] del neo_attrs["spatial_scale"] if "t_start" in neo_attrs: t_start = neo_attrs["t_start"] del neo_attrs["t_start"] else: t_start = 0.0 * pq.ms neo_seq = ImageSequence( image_data=imgseq, sampling_rate=sampling_rate, spatial_scale=spatial_scale, units=unit, t_start=t_start, **neo_attrs, ) self._neo_map[neo_attrs["nix_name"]] = neo_seq # all DAs reference the same sources srcnames = list(src.name for src in nix_da_group[0].sources) for n in srcnames: if n not in self._ref_map: self._ref_map[n] = list() self._ref_map[n].append(neo_seq) return neo_seq def _nix_to_neo_irregularlysampledsignal(self, nix_da_group): """ Convert a group of NIX DataArrays to a Neo IrregularlySampledSignal. This method expects a list of data arrays that all represent the same, multidimensional Neo IrregularlySampledSignal object. :param nix_da_group: a list of NIX DataArray objects :return: a Neo IrregularlySampledSignal object """ neo_attrs = self._nix_attr_to_neo(nix_da_group[0]) metadata = nix_da_group[0].metadata neo_attrs["nix_name"] = metadata.name # use the common base name unit = nix_da_group[0].unit signaldata = np.array([d[:] for d in nix_da_group]).transpose() signaldata = create_quantity(signaldata, unit) timedim = self._get_time_dimension(nix_da_group[0]) times = create_quantity(timedim.ticks, timedim.unit) neo_signal = IrregularlySampledSignal(signal=signaldata, times=times, **neo_attrs) self._neo_map[neo_attrs["nix_name"]] = neo_signal # all DAs reference the same sources srcnames = list(src.name for src in nix_da_group[0].sources) for n in srcnames: if n not in self._ref_map: self._ref_map[n] = list() self._ref_map[n].append(neo_signal) return neo_signal def _nix_to_neo_event(self, nix_mtag): neo_attrs = self._nix_attr_to_neo(nix_mtag) time_unit = nix_mtag.positions.unit times = create_quantity(nix_mtag.positions, time_unit) labels = np.array(nix_mtag.positions.dimensions[0].labels, dtype="U") neo_event = Event(times=times, labels=labels, **neo_attrs) self._neo_map[nix_mtag.name] = neo_event return neo_event def _nix_to_neo_epoch(self, nix_mtag): neo_attrs = self._nix_attr_to_neo(nix_mtag) time_unit = nix_mtag.positions.unit times = create_quantity(nix_mtag.positions, time_unit) durations = create_quantity(nix_mtag.extents, nix_mtag.extents.unit) if len(nix_mtag.positions.dimensions[0].labels) > 0: labels = np.array(nix_mtag.positions.dimensions[0].labels, dtype="U") else: labels = None neo_epoch = Epoch(times=times, durations=durations, labels=labels, **neo_attrs) self._neo_map[nix_mtag.name] = neo_epoch return neo_epoch def _nix_to_neo_spiketrain(self, nix_mtag): neo_attrs = self._nix_attr_to_neo(nix_mtag) time_unit = nix_mtag.positions.unit times = create_quantity(nix_mtag.positions, time_unit) neo_spiketrain = SpikeTrain(times=times, **neo_attrs) if nix_mtag.features: wfda = nix_mtag.features[0].data wftime = self._get_time_dimension(wfda) neo_spiketrain.waveforms = create_quantity(wfda, wfda.unit) interval_units = wftime.unit neo_spiketrain.sampling_period = create_quantity(wftime.sampling_interval, interval_units) left_sweep_units = wftime.unit if "left_sweep" in wfda.metadata: neo_spiketrain.left_sweep = create_quantity(wfda.metadata["left_sweep"], left_sweep_units) self._neo_map[nix_mtag.name] = neo_spiketrain srcnames = list(src.name for src in nix_mtag.sources) for n in srcnames: if n not in self._ref_map: self._ref_map[n] = list() self._ref_map[n].append(neo_spiketrain) return neo_spiketrain def write_all_blocks(self, neo_blocks, use_obj_names=False): """ Convert all ``neo_blocks`` to the NIX equivalent and write them to the file. :param neo_blocks: List (or iterable) containing Neo blocks :param use_obj_names: If True, will not generate unique object names but will instead try to use the name of each Neo object. If these are not unique, an exception will be raised. """ if use_obj_names: self._use_obj_names(neo_blocks) self._names_ok = True for bl in neo_blocks: self.write_block(bl, use_obj_names) def write_block(self, block, use_obj_names=False): """ Convert the provided Neo Block to a NIX Block and write it to the NIX file. :param block: Neo Block to be written :param use_obj_names: If True, will not generate unique object names but will instead try to use the name of each Neo object. If these are not unique, an exception will be raised. """ if use_obj_names: if not self._names_ok: # _names_ok guards against check duplication # If it's False, it means write_block() was called directly self._use_obj_names([block]) if "nix_name" in block.annotations: nix_name = block.annotations["nix_name"] else: nix_name = f"neo.block.{self._generate_nix_name()}" block.annotate(nix_name=nix_name) if nix_name in self.nix_file.blocks: nixblock = self.nix_file.blocks[nix_name] del self.nix_file.blocks[nix_name] del self.nix_file.sections[nix_name] nixblock = self.nix_file.create_block(nix_name, "neo.block") nixblock.metadata = self.nix_file.create_section(nix_name, "neo.block.metadata") metadata = nixblock.metadata neoname = block.name if block.name is not None else "" metadata["neo_name"] = neoname nixblock.definition = block.description if block.rec_datetime: nix_rec_dt = int(block.rec_datetime.timestamp()) nixblock.force_created_at(nix_rec_dt) if block.file_datetime: fdt, annotype = dt_to_nix(block.file_datetime) fdtprop = metadata.create_property("file_datetime", fdt) fdtprop.definition = annotype if block.annotations: for k, v in block.annotations.items(): self._write_property(metadata, k, v) # descend into Segments for seg in block.segments: self._write_segment(seg, nixblock) # descend into Neo Groups for group in block.groups: self._write_group(group, nixblock) def _write_channelview(self, chview, nixblock, nixgroup): """ Convert the provided Neo ChannelView to a NIX MultiTag and write it to the NIX file. :param chx: The Neo ChannelView to be written :param nixblock: NIX Block where the MultiTag will be created """ if "nix_name" in chview.annotations: nix_name = chview.annotations["nix_name"] else: nix_name = f"neo.channelview.{self._generate_nix_name()}" chview.annotate(nix_name=nix_name) # create a new data array if this channelview was not saved yet if not nix_name in self._view_map: channels = nixblock.create_data_array(f"{nix_name}.index", "neo.channelview.index", data=chview.index) nixmt = nixblock.create_multi_tag(nix_name, "neo.channelview", positions=channels) nixmt.metadata = nixgroup.metadata.create_section(nix_name, "neo.channelview.metadata") metadata = nixmt.metadata neoname = chview.name if chview.name is not None else "" metadata["neo_name"] = neoname nixmt.definition = chview.description if chview.annotations: for k, v in chview.annotations.items(): self._write_property(metadata, k, v) self._view_map[nix_name] = nixmt # link tag to the data array for the ChannelView's signal if not ("nix_name" in chview.obj.annotations and chview.obj.annotations["nix_name"] in self._signal_map): # the following restriction could be relaxed later # but for a first pass this simplifies my mental model raise Exception("Need to save signals before saving views") nix_name = chview.obj.annotations["nix_name"] nixmt.references.extend(self._signal_map[nix_name]) else: nixmt = self._view_map[nix_name] nixgroup.multi_tags.append(nixmt) def _write_segment(self, segment, nixblock): """ Convert the provided Neo Segment to a NIX Group and write it to the NIX file. :param segment: Neo Segment to be written :param nixblock: NIX Block where the Group will be created """ if "nix_name" in segment.annotations: nix_name = segment.annotations["nix_name"] else: nix_name = f"neo.segment.{self._generate_nix_name()}" segment.annotate(nix_name=nix_name) nixgroup = nixblock.create_group(nix_name, "neo.segment") nixgroup.metadata = nixblock.metadata.create_section(nix_name, "neo.segment.metadata") metadata = nixgroup.metadata neoname = segment.name if segment.name is not None else "" metadata["neo_name"] = neoname nixgroup.definition = segment.description if segment.rec_datetime: nix_rec_dt = int(segment.rec_datetime.strftime("%s")) nixgroup.force_created_at(nix_rec_dt) if segment.file_datetime: fdt, annotype = dt_to_nix(segment.file_datetime) fdtprop = metadata.create_property("file_datetime", fdt) fdtprop.definition = annotype if segment.annotations: for k, v in segment.annotations.items(): self._write_property(metadata, k, v) # write signals, events, epochs, and spiketrains for asig in segment.analogsignals: self._write_analogsignal(asig, nixblock, nixgroup) for isig in segment.irregularlysampledsignals: self._write_irregularlysampledsignal(isig, nixblock, nixgroup) for event in segment.events: self._write_event(event, nixblock, nixgroup) for epoch in segment.epochs: self._write_epoch(epoch, nixblock, nixgroup) for spiketrain in segment.spiketrains: self._write_spiketrain(spiketrain, nixblock, nixgroup) for imagesequence in segment.imagesequences: self._write_imagesequence(imagesequence, nixblock, nixgroup) def _write_group(self, neo_group, nixblock, parent=None): """ Convert the provided Neo Group to a NIX Group and write it to the NIX file. :param neo_group: Neo Group to be written :param nixblock: NIX Block where the NIX Group will be created :param parent: for sub-groups, the parent Neo Group """ if parent: label = "neo.subgroup" # note that the use of a different label for top-level groups and sub-groups is not # strictly necessary, the presence of the "neo_parent" annotation is sufficient. # However, I think it adds clarity and helps in debugging and testing. else: label = "neo.group" if "nix_name" in neo_group.annotations: nix_name = neo_group.annotations["nix_name"] else: nix_name = f"{label}.{self._generate_nix_name()}" neo_group.annotate(nix_name=nix_name) nixgroup = nixblock.create_group(nix_name, label) nixgroup.metadata = nixblock.metadata.create_section(nix_name, f"{label}.metadata") metadata = nixgroup.metadata neoname = neo_group.name if neo_group.name is not None else "" metadata["neo_name"] = neoname if parent: metadata["neo_parent"] = parent.annotations["nix_name"] nixgroup.definition = neo_group.description if neo_group.annotations: for k, v in neo_group.annotations.items(): self._write_property(metadata, k, v) # link signals and image sequences objnames = [] for obj in itertools.chain( neo_group.analogsignals, neo_group.irregularlysampledsignals, neo_group.imagesequences, ): if not ("nix_name" in obj.annotations and obj.annotations["nix_name"] in self._signal_map): # the following restriction could be relaxed later # but for a first pass this simplifies my mental model raise Exception("Orphan signals/image sequences cannot be stored, needs to belong to a Segment") objnames.append(obj.annotations["nix_name"]) for name in objnames: for da in self._signal_map[name]: nixgroup.data_arrays.append(da) # link events, epochs and spiketrains objnames = [] for obj in itertools.chain( neo_group.events, neo_group.epochs, neo_group.spiketrains, ): if not ("nix_name" in obj.annotations and obj.annotations["nix_name"] in nixblock.multi_tags): # the following restriction could be relaxed later # but for a first pass this simplifies my mental model raise Exception("Orphan epochs/events/spiketrains cannot be stored, needs to belong to a Segment") objnames.append(obj.annotations["nix_name"]) for name in objnames: mt = nixblock.multi_tags[name] nixgroup.multi_tags.append(mt) # save channel views for chview in neo_group.channelviews: self._write_channelview(chview, nixblock, nixgroup) # save sub-groups for subgroup in neo_group.groups: self._write_group(subgroup, nixblock, parent=neo_group) def _write_analogsignal(self, anasig, nixblock, nixgroup): """ Convert the provided ``anasig`` (AnalogSignal) to a list of NIX DataArray objects and write them to the NIX file. All DataArray objects created from the same AnalogSignal have their metadata section point to the same object. :param anasig: The Neo AnalogSignal to be written :param nixblock: NIX Block where the DataArrays will be created :param nixgroup: NIX Group where the DataArrays will be attached """ if "nix_name" in anasig.annotations: nix_name = anasig.annotations["nix_name"] else: nix_name = f"neo.analogsignal.{self._generate_nix_name()}" anasig.annotate(nix_name=nix_name) if f"{nix_name}.0" in nixblock.data_arrays and nixgroup: # AnalogSignal is in multiple Segments. # Append DataArrays to Group and return. dalist = list() for idx in itertools.count(): daname = f"{nix_name}.{idx}" if daname in nixblock.data_arrays: dalist.append(nixblock.data_arrays[daname]) else: break nixgroup.data_arrays.extend(dalist) return if isinstance(anasig, BaseProxy): data = np.transpose(anasig.load()[:].magnitude) else: data = np.transpose(anasig[:].magnitude) parentmd = nixgroup.metadata if nixgroup else nixblock.metadata metadata = parentmd.create_section(nix_name, "neo.analogsignal.metadata") nixdas = list() for idx, row in enumerate(data): daname = f"{nix_name}.{idx}" da = nixblock.create_data_array(daname, "neo.analogsignal", data=row) da.metadata = metadata da.definition = anasig.description da.unit = units_to_string(anasig.units) sampling_period = anasig.sampling_period.magnitude.item() timedim = da.append_sampled_dimension(sampling_period) timedim.unit = units_to_string(anasig.sampling_period.units) tstart = anasig.t_start metadata["t_start"] = tstart.magnitude.item() metadata.props["t_start"].unit = units_to_string(tstart.units) timedim.offset = tstart.rescale(timedim.unit).magnitude.item() timedim.label = "time" nixdas.append(da) if nixgroup: nixgroup.data_arrays.append(da) neoname = anasig.name if anasig.name is not None else "" metadata["neo_name"] = neoname if anasig.annotations: for k, v in anasig.annotations.items(): self._write_property(metadata, k, v) if anasig.array_annotations: for k, v in anasig.array_annotations.items(): p = self._write_property(metadata, k, v) p.type = ARRAYANNOTATION self._signal_map[nix_name] = nixdas def _write_imagesequence(self, imgseq, nixblock, nixgroup): """ Convert the provided ``imgseq`` (ImageSequence) to a list of NIX DataArray objects and write them to the NIX file. All DataArray objects created from the same ImageSequence have their metadata section point to the same object. :param anasig: The Neo ImageSequence to be written :param nixblock: NIX Block where the DataArrays will be created :param nixgroup: NIX Group where the DataArrays will be attached """ if "nix_name" in imgseq.annotations: nix_name = imgseq.annotations["nix_name"] else: nix_name = f"neo.imagesequence.{self._generate_nix_name()}" imgseq.annotate(nix_name=nix_name) if f"{nix_name}.0" in nixblock.data_arrays and nixgroup: dalist = list() for idx in itertools.count(): daname = f"{nix_name}.{idx}" if daname in nixblock.data_arrays: dalist.append(nixblock.data_arrays[daname]) else: break nixgroup.data_arrays.extend(dalist) return if isinstance(imgseq, BaseProxy): data = np.transpose(imgseq.load()[:].magnitude) else: data = np.transpose(imgseq[:].magnitude) parentmd = nixgroup.metadata if nixgroup else nixblock.metadata metadata = parentmd.create_section(nix_name, "neo.imagesequence.metadata") nixdas = list() for idx, row in enumerate(data): daname = f"{nix_name}.{idx}" da = nixblock.create_data_array(daname, "neo.imagesequence", data=row) da.metadata = metadata da.definition = imgseq.description da.unit = units_to_string(imgseq.units) metadata["sampling_rate"] = imgseq.sampling_rate.magnitude.item() units = imgseq.sampling_rate.units metadata.props["sampling_rate"].unit = units_to_string(units) metadata["spatial_scale"] = imgseq.spatial_scale.magnitude.item() units = imgseq.spatial_scale.units metadata.props["spatial_scale"].unit = units_to_string(units) metadata["t_start"] = imgseq.t_start.magnitude.item() units = imgseq.t_start.units metadata.props["t_start"].unit = units_to_string(units) nixdas.append(da) if nixgroup: nixgroup.data_arrays.append(da) neoname = imgseq.name if imgseq.name is not None else "" metadata["neo_name"] = neoname if imgseq.annotations: for k, v in imgseq.annotations.items(): self._write_property(metadata, k, v) self._signal_map[nix_name] = nixdas def _write_irregularlysampledsignal(self, irsig, nixblock, nixgroup): """ Convert the provided ``irsig`` (IrregularlySampledSignal) to a list of NIX DataArray objects and write them to the NIX file at the location. All DataArray objects created from the same IrregularlySampledSignal have their metadata section point to the same object. :param irsig: The Neo IrregularlySampledSignal to be written :param nixblock: NIX Block where the DataArrays will be created :param nixgroup: NIX Group where the DataArrays will be attached """ if "nix_name" in irsig.annotations: nix_name = irsig.annotations["nix_name"] else: nix_name = f"neo.irregularlysampledsignal.{self._generate_nix_name()}" irsig.annotate(nix_name=nix_name) if f"{nix_name}.0" in nixblock.data_arrays and nixgroup: # IrregularlySampledSignal is in multiple Segments. # Append DataArrays to Group and return. dalist = list() for idx in itertools.count(): daname = f"{nix_name}.{idx}" if daname in nixblock.data_arrays: dalist.append(nixblock.data_arrays[daname]) else: break nixgroup.data_arrays.extend(dalist) return if isinstance(irsig, BaseProxy): data = np.transpose(irsig.load()[:].magnitude) else: data = np.transpose(irsig[:].magnitude) parentmd = nixgroup.metadata if nixgroup else nixblock.metadata metadata = parentmd.create_section(nix_name, "neo.irregularlysampledsignal.metadata") nixdas = list() for idx, row in enumerate(data): daname = f"{nix_name}.{idx}" da = nixblock.create_data_array(daname, "neo.irregularlysampledsignal", data=row) da.metadata = metadata da.definition = irsig.description da.unit = units_to_string(irsig.units) timedim = da.append_range_dimension(irsig.times.magnitude) timedim.unit = units_to_string(irsig.times.units) timedim.label = "time" nixdas.append(da) if nixgroup: nixgroup.data_arrays.append(da) neoname = irsig.name if irsig.name is not None else "" metadata["neo_name"] = neoname if irsig.annotations: for k, v in irsig.annotations.items(): self._write_property(metadata, k, v) if irsig.array_annotations: for k, v in irsig.array_annotations.items(): p = self._write_property(metadata, k, v) p.type = ARRAYANNOTATION self._signal_map[nix_name] = nixdas def _write_event(self, event, nixblock, nixgroup): """ Convert the provided Neo Event to a NIX MultiTag and write it to the NIX file. :param event: The Neo Event to be written :param nixblock: NIX Block where the MultiTag will be created :param nixgroup: NIX Group where the MultiTag will be attached """ if "nix_name" in event.annotations: nix_name = event.annotations["nix_name"] else: nix_name = f"neo.event.{self._generate_nix_name()}" event.annotate(nix_name=nix_name) if nix_name in nixblock.multi_tags: # Event is in multiple Segments. Append to Group and return. mt = nixblock.multi_tags[nix_name] nixgroup.multi_tags.append(mt) return if isinstance(event, BaseProxy): event = event.load() times = event.times.magnitude units = units_to_string(event.times.units) labels = event.labels timesda = nixblock.create_data_array(f"{nix_name}.times", "neo.event.times", data=times) timesda.unit = units nixmt = nixblock.create_multi_tag(nix_name, "neo.event", positions=timesda) nixmt.metadata = nixgroup.metadata.create_section(nix_name, "neo.event.metadata") metadata = nixmt.metadata labeldim = timesda.append_set_dimension() labeldim.labels = labels neoname = event.name if event.name is not None else "" metadata["neo_name"] = neoname nixmt.definition = event.description if event.annotations: for k, v in event.annotations.items(): self._write_property(metadata, k, v) if event.array_annotations: for k, v in event.array_annotations.items(): p = self._write_property(metadata, k, v) p.type = ARRAYANNOTATION nixgroup.multi_tags.append(nixmt) # reference all AnalogSignals and IrregularlySampledSignals in Group for da in nixgroup.data_arrays: if da.type in ("neo.analogsignal", "neo.irregularlysampledsignal"): nixmt.references.append(da) def _write_epoch(self, epoch, nixblock, nixgroup): """ Convert the provided Neo Epoch to a NIX MultiTag and write it to the NIX file. :param epoch: The Neo Epoch to be written :param nixblock: NIX Block where the MultiTag will be created :param nixgroup: NIX Group where the MultiTag will be attached """ if "nix_name" in epoch.annotations: nix_name = epoch.annotations["nix_name"] else: nix_name = f"neo.epoch.{self._generate_nix_name()}" epoch.annotate(nix_name=nix_name) if nix_name in nixblock.multi_tags: # Epoch is in multiple Segments. Append to Group and return. mt = nixblock.multi_tags[nix_name] nixgroup.multi_tags.append(mt) return if isinstance(epoch, BaseProxy): epoch = epoch.load() times = epoch.times.magnitude tunits = units_to_string(epoch.times.units) durations = epoch.durations.magnitude dunits = units_to_string(epoch.durations.units) timesda = nixblock.create_data_array(f"{nix_name}.times", "neo.epoch.times", data=times) timesda.unit = tunits nixmt = nixblock.create_multi_tag(nix_name, "neo.epoch", positions=timesda) durada = nixblock.create_data_array(f"{nix_name}.durations", "neo.epoch.durations", data=durations) durada.unit = dunits nixmt.extents = durada nixmt.metadata = nixgroup.metadata.create_section(nix_name, "neo.epoch.metadata") metadata = nixmt.metadata labeldim = timesda.append_set_dimension() labeldim.labels = epoch.labels neoname = epoch.name if epoch.name is not None else "" metadata["neo_name"] = neoname nixmt.definition = epoch.description if epoch.annotations: for k, v in epoch.annotations.items(): self._write_property(metadata, k, v) if epoch.array_annotations: for k, v in epoch.array_annotations.items(): p = self._write_property(metadata, k, v) p.type = ARRAYANNOTATION nixgroup.multi_tags.append(nixmt) # reference all AnalogSignals and IrregularlySampledSignals in Group for da in nixgroup.data_arrays: if da.type in ("neo.analogsignal", "neo.irregularlysampledsignal"): nixmt.references.append(da) def _write_spiketrain(self, spiketrain, nixblock, nixgroup): """ Convert the provided Neo SpikeTrain to a NIX MultiTag and write it to the NIX file. :param spiketrain: The Neo SpikeTrain to be written :param nixblock: NIX Block where the MultiTag will be created :param nixgroup: NIX Group where the MultiTag will be attached """ import nixio if "nix_name" in spiketrain.annotations: nix_name = spiketrain.annotations["nix_name"] else: nix_name = f"neo.spiketrain.{self._generate_nix_name()}" spiketrain.annotate(nix_name=nix_name) if nix_name in nixblock.multi_tags and nixgroup: # SpikeTrain is in multiple Segments. Append to Group and return. mt = nixblock.multi_tags[nix_name] nixgroup.multi_tags.append(mt) return if isinstance(spiketrain, BaseProxy): spiketrain = spiketrain.load(load_waveforms=True) times = spiketrain.times.magnitude tunits = units_to_string(spiketrain.times.units) waveforms = spiketrain.waveforms timesda = nixblock.create_data_array(f"{nix_name}.times", "neo.spiketrain.times", data=times) timesda.unit = tunits nixmt = nixblock.create_multi_tag(nix_name, "neo.spiketrain", positions=timesda) parentmd = nixgroup.metadata if nixgroup else nixblock.metadata nixmt.metadata = parentmd.create_section(nix_name, "neo.spiketrain.metadata") metadata = nixmt.metadata neoname = spiketrain.name if spiketrain.name is not None else "" metadata["neo_name"] = neoname nixmt.definition = spiketrain.description self._write_property(metadata, "t_start", spiketrain.t_start) self._write_property(metadata, "t_stop", spiketrain.t_stop) if spiketrain.annotations: for k, v in spiketrain.annotations.items(): self._write_property(metadata, k, v) if spiketrain.array_annotations: for k, v in spiketrain.array_annotations.items(): p = self._write_property(metadata, k, v) p.type = ARRAYANNOTATION if nixgroup: nixgroup.multi_tags.append(nixmt) if waveforms is not None: wfdata = list(wf.magnitude for wf in list(wfgroup for wfgroup in spiketrain.waveforms)) wfunits = units_to_string(spiketrain.waveforms.units) wfda = nixblock.create_data_array(f"{nix_name}.waveforms", "neo.waveforms", data=wfdata) wfda.unit = wfunits wfda.metadata = nixmt.metadata.create_section(wfda.name, "neo.waveforms.metadata") nixmt.create_feature(wfda, nixio.LinkType.Indexed) # TODO: Move time dimension first for PR #457 # https://github.com/NeuralEnsemble/python-neo/pull/457 wfda.append_set_dimension() wfda.append_set_dimension() wftime = wfda.append_sampled_dimension(spiketrain.sampling_period.magnitude.item()) wftime.unit = units_to_string(spiketrain.sampling_period.units) wftime.label = "time" if spiketrain.left_sweep is not None: self._write_property(wfda.metadata, "left_sweep", spiketrain.left_sweep) @staticmethod def _generate_nix_name(): return uuid4().hex def _write_property(self, section, name, v): """ Create a metadata property with a given name and value on the provided metadata section. :param section: The metadata section to hold the new property :param name: The name of the property :param v: The value to write :return: The newly created property """ import nixio if isinstance(v, datetime_types): value, annotype = dt_to_nix(v) prop = section.create_property(name, value) prop.definition = annotype elif isinstance(v, str): if len(v): section.create_property(name, v) else: section.create_property(name, nixio.DataType.String) elif isinstance(v, bytes): section.create_property(name, v.decode()) elif isinstance(v, Iterable): values = [] unit = None definition = None # handling (quantity) arrays with only a single element if hasattr(v, "ndim") and v.ndim == 0: values = v.item() # handling empty arrays or lists elif (hasattr(v, "size") and (v.size == 0)) or (len(v) == 0): # NIX supports empty properties but dtype must be specified # Defaulting to String and using definition to signify empty # iterable as opposed to empty string values = nixio.DataType.String definition = EMPTYANNOTATION else: for item in v: if isinstance(item, str): item = item elif isinstance(item, pq.Quantity): current_unit = str(item.dimensionality) if unit is None: unit = current_unit elif unit != current_unit: raise ValueError(f"Inconsistent units detected for " f"property {name}: {v}") item = item.magnitude.item() elif isinstance(item, Iterable): self.logger.warn( "Multidimensional arrays and nested " "containers are not currently " "supported when writing to NIX." ) return None else: item = item values.append(item) if hasattr(v, "dimensionality"): unit = str(v.dimensionality) section.create_property(name, values) section.props[name].unit = unit section.props[name].definition = definition elif type(v).__module__ == "numpy": section.create_property(name, v.item()) else: section.create_property(name, v) return section.props[name] @staticmethod def _nix_attr_to_neo(nix_obj): """ Reads common attributes and metadata from a NIX object and populates a dictionary with Neo-compatible attributes and annotations. Common attributes: neo_name, nix_name, description, file_datetime (if applicable). Metadata: For properties that specify a 'unit', a Quantity object is created. """ import nixio neo_attrs = dict() neo_attrs["nix_name"] = nix_obj.name neo_attrs["description"] = stringify(nix_obj.definition) if nix_obj.metadata: for prop in nix_obj.metadata.inherited_properties(): values = list(prop.values) if not len(values): if prop.definition == EMPTYANNOTATION: values = list() elif prop.data_type == nixio.DataType.String: values = "" elif len(values) == 1: values = values[0] if prop.unit: units = prop.unit values = create_quantity(values, units) if prop.definition in (DATEANNOTATION, TIMEANNOTATION, DATETIMEANNOTATION): values = dt_from_nix(values, prop.definition) if prop.type == ARRAYANNOTATION: if "array_annotations" in neo_attrs: neo_attrs["array_annotations"][prop.name] = values else: neo_attrs["array_annotations"] = {prop.name: values} else: neo_attrs[prop.name] = values # since the 'neo_name' NIX property becomes the actual object's name, # there's no reason to keep it in the annotations neo_attrs["name"] = stringify(neo_attrs.pop("neo_name", None)) return neo_attrs @staticmethod def _group_signals(dataarrays): """ Groups data arrays that were generated by the same Neo Signal object. The collection can contain both AnalogSignals and IrregularlySampledSignals. :param dataarrays: A collection of DataArray objects to group :return: A dictionary mapping a base name to a list of DataArrays which belong to the same Signal """ # now start grouping groups = OrderedDict() for da in dataarrays: basename = ".".join(da.name.split(".")[:-1]) if basename not in groups: groups[basename] = list() groups[basename].append(da) return groups @staticmethod def _get_time_dimension(obj): for dim in obj.dimensions: if hasattr(dim, "label") and dim.label == "time": return dim return None def _use_obj_names(self, blocks): errmsg = "use_obj_names enabled: found conflict or anonymous object" allobjs = [] def check_unique(objs): names = list(o.name for o in objs) if None in names or "" in names: raise ValueError(names) if len(names) != len(set(names)): self._names_ok = False raise ValueError(names) # collect objs if ok allobjs.extend(objs) try: check_unique(blocks) except ValueError as exc: raise ValueError(f"{errmsg} in Blocks") from exc for blk in blocks: try: # Segments check_unique(blk.segments) except ValueError as exc: raise ValueError(f"{errmsg} at Block '{blk.name}' > segments") from exc # collect all signals in all segments signals = [] # collect all events, epochs, and spiketrains in all segments eests = [] for seg in blk.segments: signals.extend(seg.analogsignals) signals.extend(seg.irregularlysampledsignals) signals.extend(seg.imagesequences) eests.extend(seg.events) eests.extend(seg.epochs) eests.extend(seg.spiketrains) try: # AnalogSignals and IrregularlySampledSignals check_unique(signals) except ValueError as exc: raise ValueError(f"{errmsg} in Signal names of Block '{blk.name}'") from exc try: # Events, Epochs, and SpikeTrains check_unique(eests) except ValueError as exc: raise ValueError(f"{errmsg} in Event, Epoch, and Spiketrain names of Block '{blk.name}'") from exc # groups groups = [] for grp in blk.groups: groups.extend(list(grp.walk())) try: check_unique(groups) except ValueError as exc: raise ValueError(f"{errmsg} in Group names of Block '{blk.name}'") from exc # names are OK: assign annotations for o in allobjs: o.annotations["nix_name"] = o.name def close(self): """ Closes the open nix file and resets maps. """ if hasattr(self, "nix_file") and self.nix_file and self.nix_file.is_open(): self.nix_file.close() self.nix_file = None self._neo_map = None self._ref_map = None self._signal_map = None self._view_map = None self._block_read_counter = None def __del__(self): self.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/nixio_fr.py0000644000175100017510000000110415154507213015405 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.nixrawio import NIXRawIO # This class subjects to limitations when there are multiple asymmetric blocks class NixIO(NIXRawIO, BaseFromRaw): name = "NIX IO" _prefered_signal_group_mode = "group-by-same-units" _prefered_units_group_mode = "all-in-one" def __init__(self, filename): NIXRawIO.__init__(self, filename) BaseFromRaw.__init__(self, filename) def __enter__(self): return self def __exit__(self, *args): self.header = None self.file.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/nwbio.py0000644000175100017510000011060715154507213014717 0ustar00runnerrunner""" NWBIO ===== IO class for reading data from a Neurodata Without Borders (NWB) dataset Documentation : https://www.nwb.org/ Depends on: h5py, nwb, dateutil Supported: Read, Write Python API - https://pynwb.readthedocs.io Sample datasets from CRCNS - https://crcns.org/NWB Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders """ from __future__ import absolute_import, division import json import logging import os from collections import defaultdict from itertools import chain from json.decoder import JSONDecodeError import numpy as np import quantities as pq from neo.core import Segment, SpikeTrain, Epoch, Event, AnalogSignal, IrregularlySampledSignal, Block, ImageSequence from neo.io.baseio import BaseIO from neo.io.proxyobjects import ( AnalogSignalProxy as BaseAnalogSignalProxy, EventProxy as BaseEventProxy, EpochProxy as BaseEpochProxy, SpikeTrainProxy as BaseSpikeTrainProxy, ) logger = logging.getLogger("Neo") GLOBAL_ANNOTATIONS = ( "session_start_time", "identifier", "timestamps_reference_time", "experimenter", "experiment_description", "session_id", "institution", "keywords", "notes", "pharmacology", "protocol", "related_publications", "slices", "source_script", "source_script_file_name", "data_collection", "surgery", "virus", "stimulus_notes", "lab", "session_description", "rec_datetime", ) POSSIBLE_JSON_FIELDS = ("source_script", "description") prefix_map = {1e9: "giga", 1e6: "mega", 1e3: "kilo", 1: "", 1e-3: "milli", 1e-6: "micro", 1e-9: "nano", 1e-12: "pico"} def try_json_field(content): """ Try to interpret a string as JSON data. If successful, return the JSON data (dict or list) If unsuccessful, return the original string """ try: return json.loads(content) except JSONDecodeError: return content def get_class(module, name): """ Given a module path and a class name, return the class object """ import pynwb module_path = module.split(".") if len(module_path) != 2: raise ValueError( f"`module_path` must be 2, not {module_path}" ) # todo: handle the general case where this isn't 2 return getattr(getattr(pynwb, module_path[1]), name) def statistics(block): # todo: move this to be a property of Block """ Return simple statistics about a Neo Block. """ stats = { "SpikeTrain": {"count": 0}, "AnalogSignal": {"count": 0}, "IrregularlySampledSignal": {"count": 0}, "Epoch": {"count": 0}, "Event": {"count": 0}, } for segment in block.segments: stats["SpikeTrain"]["count"] += len(segment.spiketrains) stats["AnalogSignal"]["count"] += len(segment.analogsignals) stats["IrregularlySampledSignal"]["count"] += len(segment.irregularlysampledsignals) stats["Epoch"]["count"] += len(segment.epochs) stats["Event"]["count"] += len(segment.events) return stats def get_units_conversion(signal, timeseries_class): """ Given a quantity array and a TimeSeries subclass, return the conversion factor and the expected units """ # it would be nice if the expected units was an attribute of the PyNWB class if "CurrentClamp" in timeseries_class.__name__: expected_units = pq.volt elif "VoltageClamp" in timeseries_class.__name__: expected_units = pq.ampere else: # todo: warn that we don't handle this subclass yet expected_units = signal.units return float((signal.units / expected_units).simplified.magnitude), expected_units def time_in_seconds(t): return float(t.rescale("second")) def _decompose_unit(unit): """ Given a quantities unit object, return a base unit name and a conversion factor. Example: >>> _decompose_unit(pq.mV) ('volt', 0.001) """ if not isinstance(unit, pq.quantity.Quantity): raise TypeError(f"`unit` must be of type pq.quantity.Quantity and not type {type(unit)}") if unit.magnitude != 1: raise ValueError(f"The magnitude of the `unit` must be 1 not {unit.magnitude}") conversion = 1.0 def _decompose(unit): dim = unit.dimensionality if len(dim) != 1: raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre uq, n = list(dim.items())[0] if n != 1: raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 uq_def = uq.definition return float(uq_def.magnitude), uq_def conv, unit2 = _decompose(unit) while conv != 1: conversion *= conv unit = unit2 conv, unit2 = _decompose(unit) return list(unit.dimensionality.keys())[0].name, conversion def _recompose_unit(base_unit_name, conversion): """ Given a base unit name and a conversion factor, return a quantities unit object Example: >>> _recompose_unit("ampere", 1e-9) UnitCurrent('nanoampere', 0.001 * uA, 'nA') """ unit_name = None for cf in prefix_map: # conversion may have a different float precision to the keys in # prefix_map, so we can't just use `prefix_map[conversion]` if abs(conversion - cf) / cf < 1e-6: unit_name = prefix_map[cf] + base_unit_name if unit_name is None: raise ValueError(f"Can't handle this conversion factor: {conversion}") if unit_name[-1] == "s": # strip trailing 's', e.g. "volts" --> "volt" unit_name = unit_name[:-1] try: return getattr(pq, unit_name) except AttributeError: logger.warning(f"Can't handle unit '{unit_name}'. Returning dimensionless") return pq.dimensionless class NWBIO(BaseIO): """ Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file from Neo """ supported_objects = [ Block, Segment, AnalogSignal, IrregularlySampledSignal, SpikeTrain, Epoch, Event, ImageSequence, ] readable_objects = supported_objects writeable_objects = supported_objects has_header = False support_lazy = True name = "NeoNWB IO" description = "This IO reads/writes experimental data from/to an .nwb dataset" extensions = ["nwb"] mode = "one-file" is_readable = True is_writable = True is_streameable = False def __init__(self, filename, mode="r", **annotations): """ Arguments: filename : the filename """ import pynwb BaseIO.__init__(self, filename=filename) self.filename = filename self.blocks_written = 0 self.nwb_file_mode = mode self._blocks = {} self.annotations = annotations self._io_nwb = None def read_all_blocks(self, lazy=False, **kwargs): """ Load all blocks in the file. """ import pynwb if self.nwb_file_mode not in ("r",): raise ValueError("`mode` at init needs to be set to 'r' to read files") self._io_nwb = pynwb.NWBHDF5IO( self.filename, mode=self.nwb_file_mode, load_namespaces=True ) # Open a file with NWBHDF5IO try: self._file = self._io_nwb.read() except ValueError: print("Error: Unable to read this version of NWB file.") print("Please convert to a later NWB format.") raise self.global_block_metadata = {} for annotation_name in GLOBAL_ANNOTATIONS: value = getattr(self._file, annotation_name, None) if value is not None: if annotation_name in POSSIBLE_JSON_FIELDS: value = try_json_field(value) self.global_block_metadata[annotation_name] = value if "session_description" in self.global_block_metadata: self.global_block_metadata["description"] = self.global_block_metadata["session_description"] self.global_block_metadata["file_origin"] = self.filename if "session_start_time" in self.global_block_metadata: self.global_block_metadata["rec_datetime"] = self.global_block_metadata["session_start_time"] if "file_create_date" in self.global_block_metadata: self.global_block_metadata["file_datetime"] = self.global_block_metadata["rec_datetime"] self._blocks = {} self._read_acquisition_group(lazy=lazy) self._read_stimulus_group(lazy) self._read_units(lazy=lazy) self._read_epochs_group(lazy) return list(self._blocks.values()) def read_block(self, lazy=False, block_index=0, **kargs): """ Load the first block in the file. """ return self.read_all_blocks(lazy=lazy)[block_index] def _get_segment(self, block_name, segment_name): # If we've already created a Block with the given name return it, # otherwise create it now and store it in self._blocks. # If we've already created a Segment in the given block, return it, # otherwise create it now and return it. if block_name in self._blocks: block = self._blocks[block_name] else: block = Block(name=block_name, **self.global_block_metadata) self._blocks[block_name] = block segment = None for seg in block.segments: if segment_name == seg.name: segment = seg break if segment is None: segment = Segment(name=segment_name) block.segments.append(segment) return segment def _read_epochs_group(self, lazy): if self._file.epochs is not None: try: # NWB files created by Neo store the segment, block and epoch names as extra # columns segment_names = self._file.epochs.segment[:] block_names = self._file.epochs.block[:] epoch_names = self._file.epochs._name[:] except AttributeError: epoch_names = None if epoch_names is not None: unique_epoch_names = np.unique(epoch_names) for epoch_name in unique_epoch_names: (index,) = np.where((epoch_names == epoch_name)) epoch = EpochProxy(self._file.epochs, epoch_name, index) if not lazy: epoch = epoch.load() segment_name = np.unique(segment_names[index]) block_name = np.unique(block_names[index]) if segment_name.size != block_name.size == 1: raise ValueError("the `segment_name` and the `block_name` should be the same") segment = self._get_segment(block_name[0], segment_name[0]) segment.epochs.append(epoch) else: epoch = EpochProxy(self._file.epochs) if not lazy: epoch = epoch.load() segment = self._get_segment("default", "default") segment.epochs.append(epoch) def _read_timeseries_group(self, group_name, lazy): import pynwb group = getattr(self._file, group_name) for timeseries in group.values(): try: # NWB files created by Neo store the segment and block names in the comments field hierarchy = json.loads(timeseries.comments) except JSONDecodeError: # For NWB files created with other applications, we put everything in a single # segment in a single block # todo: investigate whether there is a reliable way to create multiple segments, # e.g. using Trial information block_name = "default" segment_name = "default" else: block_name = hierarchy["block"] segment_name = hierarchy["segment"] segment = self._get_segment(block_name, segment_name) if isinstance(timeseries, pynwb.misc.AnnotationSeries): event = EventProxy(timeseries, group_name) if not lazy: event = event.load() segment.events.append(event) elif timeseries.rate: # AnalogSignal signal = AnalogSignalProxy(timeseries, group_name) if not lazy: signal = signal.load() segment.analogsignals.append(signal) else: # IrregularlySampledSignal signal = AnalogSignalProxy(timeseries, group_name) if not lazy: signal = signal.load() segment.irregularlysampledsignals.append(signal) def _read_units(self, lazy): if self._file.units: for id in range(len(self._file.units)): try: # NWB files created by Neo store the segment and block names as extra columns segment_name = self._file.units.segment[id] block_name = self._file.units.block[id] except AttributeError: # For NWB files created with other applications, we put everything in a single # segment in a single block segment_name = "default" block_name = "default" segment = self._get_segment(block_name, segment_name) spiketrain = SpikeTrainProxy(self._file.units, id) if not lazy: spiketrain = spiketrain.load() segment.spiketrains.append(spiketrain) def _read_acquisition_group(self, lazy): self._read_timeseries_group("acquisition", lazy) def _read_stimulus_group(self, lazy): self._read_timeseries_group("stimulus", lazy) def _build_global_annotations(self, blocks): annotations = defaultdict(set) for annotation_name in GLOBAL_ANNOTATIONS: if annotation_name in self.annotations: annotations[annotation_name] = self.annotations[annotation_name] else: for block in blocks: if annotation_name in block.annotations: try: annotations[annotation_name].add(block.annotations[annotation_name]) except TypeError: if annotation_name in POSSIBLE_JSON_FIELDS: encoded = json.dumps(block.annotations[annotation_name]) annotations[annotation_name].add(encoded) else: raise if annotation_name in annotations: if len(annotations[annotation_name]) > 1: raise NotImplementedError(f"We don't yet support multiple values for {annotation_name}") # take single value from set (annotations[annotation_name],) = annotations[annotation_name] if "identifier" not in annotations: annotations["identifier"] = str(self.filename) if "session_description" not in annotations: annotations["session_description"] = blocks[0].description or str(self.filename) # need to use str() here because self.filename may be a pathlib path object # todo: concatenate descriptions of multiple blocks if different if annotations.get("session_start_time", None) is None: if "rec_datetime" in annotations: annotations["session_start_time"] = annotations["rec_datetime"] else: raise Exception("Writing to NWB requires an annotation 'session_start_time'") return annotations def write_all_blocks(self, blocks, validate=True, **kwargs): """ Write list of blocks to the file """ import pynwb global_annotations = self._build_global_annotations(blocks) self._nwbfile = pynwb.NWBFile(**global_annotations) if sum(statistics(block)["SpikeTrain"]["count"] for block in blocks) > 0: self._nwbfile.add_unit_column("_name", "the name attribute of the SpikeTrain") # nwbfile.add_unit_column('_description', # 'the description attribute of the SpikeTrain') self._nwbfile.add_unit_column("segment", "the name of the Neo Segment to which the SpikeTrain belongs") self._nwbfile.add_unit_column("block", "the name of the Neo Block to which the SpikeTrain belongs") if sum(statistics(block)["Epoch"]["count"] for block in blocks) > 0: self._nwbfile.add_epoch_column("_name", "the name attribute of the Epoch") # nwbfile.add_epoch_column('_description', 'the description attribute of the Epoch') self._nwbfile.add_epoch_column("segment", "the name of the Neo Segment to which the Epoch belongs") self._nwbfile.add_epoch_column("block", "the name of the Neo Block to which the Epoch belongs") for i, block in enumerate(blocks): self._write_block(block) if self.nwb_file_mode not in ("w",): raise ValueError("mode must be 'w' in order to write files") # possibly expand to 'a'ppend later if self.nwb_file_mode == "w" and os.path.exists(self.filename): os.remove(self.filename) io_nwb = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode) io_nwb.write(self._nwbfile) io_nwb.close() if validate: self.validate_file() def validate_file(self): import pynwb with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: errors = pynwb.validate(io_validate, namespace="core") if errors: raise Exception(f"Errors found when validating {self.filename}") def write_block(self, block, **kwargs): """ Write a single Block to the file :param block: Block to be written """ return self.write_all_blocks([block], **kwargs) def _write_block(self, block): """ Write a Block to the file :param block: Block to be written """ electrodes = self._write_electrodes(self._nwbfile, block) if not block.name: block.name = f"block{self.blocks_written}" for i, segment in enumerate(block.segments): if segment.block is not block: raise TypeError(f"segment.block must be block it is {segment.block}") if not segment.name: segment.name = f"{block.name} segment{i}" self._write_segment(self._nwbfile, segment, electrodes) self.blocks_written += 1 def _write_electrodes(self, nwbfile, block): # this handles only icephys_electrode for now electrodes = {} devices = {} for segment in block.segments: for signal in chain(segment.analogsignals, segment.irregularlysampledsignals): if "nwb_electrode" in signal.annotations: elec_meta = signal.annotations["nwb_electrode"].copy() if elec_meta["name"] not in electrodes: # todo: check for consistency if the name is already there if elec_meta["device"]["name"] in devices: device = devices[elec_meta["device"]["name"]] else: device = self._nwbfile.create_device(**elec_meta["device"]) devices[elec_meta["device"]["name"]] = device elec_meta.pop("device") electrodes[elec_meta["name"]] = self._nwbfile.create_icephys_electrode( device=device, **elec_meta ) return electrodes def _write_segment(self, nwbfile, segment, electrodes): # maybe use NWB trials to store Segment metadata? for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): if signal.segment is not segment: raise TypeError(f"signal.segment must be segment and is {signal.segment}") if hasattr(signal, "name"): signal.name = f"{segment.name} {signal.name} {i}" logging.warning(f"Warning signal name exists. New name: {signal.name}") else: signal.name = f"{segment.name} analogsignal{signal.name} {i}" self._write_signal(self._nwbfile, signal, electrodes) for i, train in enumerate(segment.spiketrains): if train.segment is not segment: raise TypeError(f"train.segment must be segment and is {train.segment}") if not train.name: train.name = f"{segment.name} spiketrain{i}" self._write_spiketrain(self._nwbfile, train) for i, event in enumerate(segment.events): if event.segment is not segment: raise TypeError(f"event.segment mst be segment and is {event.segment}") if hasattr(event, "name"): event.name = f"{segment.name} {event.name} {i}" logging.warning(f"Warning event name exists. New name: {event.name}") else: event.name = f"{segment.name} event{event.name} {i}" self._write_event(self._nwbfile, event) for i, epoch in enumerate(segment.epochs): if not epoch.name: epoch.name = f"{segment.name} epoch{i}" self._write_epoch(self._nwbfile, epoch) def _write_signal(self, nwbfile, signal, electrodes): import pynwb hierarchy = {"block": signal.segment.block.name, "segment": signal.segment.name} if "nwb_neurodata_type" in signal.annotations: timeseries_class = get_class(*signal.annotations["nwb_neurodata_type"]) else: timeseries_class = pynwb.TimeSeries # default additional_metadata = {name[4:]: value for name, value in signal.annotations.items() if name.startswith("nwb:")} if "nwb_electrode" in signal.annotations: electrode_name = signal.annotations["nwb_electrode"]["name"] additional_metadata["electrode"] = electrodes[electrode_name] if timeseries_class != pynwb.TimeSeries: conversion, units = get_units_conversion(signal, timeseries_class) additional_metadata["conversion"] = conversion else: units = signal.units if hasattr(signal, "proxy_for") and signal.proxy_for in [AnalogSignal, IrregularlySampledSignal]: signal = signal.load() if issubclass(timeseries_class, pynwb.icephys.PatchClampSeries): if signal.shape[1] != 1: raise ValueError( "To store patch clamp data in NWB, please ensure that each AnalogSignal" f"contains only one channel. The current signal has {signal.shape[1]} channels." ) # see https://github.com/NeurodataWithoutBorders/pynwb/issues/1300 data = signal.ravel() # convert to 1D else: data = signal if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") tS = timeseries_class( name=signal.name, starting_time=time_in_seconds(signal.t_start), data=data, unit=units.dimensionality.string, rate=float(sampling_rate), comments=json.dumps(hierarchy), **additional_metadata, ) # todo: try to add array_annotations via "control" attribute elif isinstance(signal, IrregularlySampledSignal): tS = timeseries_class( name=signal.name, data=data, unit=units.dimensionality.string, timestamps=signal.times.rescale("second").magnitude, comments=json.dumps(hierarchy), **additional_metadata, ) else: raise TypeError( f"signal has type { signal.__class__.__name__}, should be AnalogSignal or IrregularlySampledSignal" ) nwb_group = signal.annotations.get("nwb_group", "acquisition") add_method_map = {"acquisition": self._nwbfile.add_acquisition, "stimulus": self._nwbfile.add_stimulus} if nwb_group in add_method_map: add_time_series = add_method_map[nwb_group] else: raise NotImplementedError(f"NWB group '{nwb_group}' not yet supported") add_time_series(tS) return tS def _write_spiketrain(self, nwbfile, spiketrain): segment = spiketrain.segment if hasattr(spiketrain, "proxy_for") and spiketrain.proxy_for is SpikeTrain: spiketrain = spiketrain.load() self._nwbfile.add_unit( spike_times=spiketrain.rescale("s").magnitude, obs_intervals=[[float(spiketrain.t_start.rescale("s")), float(spiketrain.t_stop.rescale("s"))]], _name=spiketrain.name, # _description=spiketrain.description, segment=segment.name, block=segment.block.name, ) # todo: handle annotations (using add_unit_column()?) # todo: handle Neo Units # todo: handle spike waveforms, if any (see SpikeEventSeries) return self._nwbfile.units def _write_event(self, nwbfile, event): import pynwb segment = event.segment if hasattr(event, "proxy_for") and event.proxy_for == Event: event = event.load() hierarchy = {"block": segment.block.name, "segment": segment.name} tS_evt = pynwb.misc.AnnotationSeries( name=event.name, data=event.labels, timestamps=event.times.rescale("second").magnitude, description=event.description or "", comments=json.dumps(hierarchy), ) self._nwbfile.add_acquisition(tS_evt) return tS_evt def _write_epoch(self, nwbfile, epoch): segment = epoch.segment if hasattr(epoch, "proxy_for") and epoch.proxy_for == Epoch: epoch = epoch.load() for t_start, duration, label in zip( epoch.rescale("s").magnitude, epoch.durations.rescale("s").magnitude, epoch.labels ): self._nwbfile.add_epoch( t_start, t_start + duration, [label], [], _name=epoch.name, segment=segment.name, block=segment.block.name, ) return self._nwbfile.epochs def close(self): if self._io_nwb: self._io_nwb.close() class AnalogSignalProxy(BaseAnalogSignalProxy): common_metadata_fields = ( # fields that are the same for all TimeSeries subclasses "comments", "description", "unit", "starting_time", "timestamps", "rate", "data", "starting_time_unit", "timestamps_unit", "electrode", "stream_id", ) def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.units = timeseries.unit if timeseries.conversion: self.units = _recompose_unit(timeseries.unit, timeseries.conversion) if timeseries.starting_time is not None: self.t_start = timeseries.starting_time * pq.s else: self.t_start = timeseries.timestamps[0] * pq.s if timeseries.rate: self.sampling_rate = timeseries.rate * pq.Hz else: self.sampling_rate = None self.name = timeseries.name self.annotations = {"nwb_group": nwb_group} self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): self.annotations["notes"] = self.description if "name" in self.annotations: self.annotations.pop("name") self.description = None self.shape = self._timeseries.data.shape if len(self.shape) == 1: self.shape = (self.shape[0], 1) metadata_fields = list(timeseries.__nwbfields__) for field_name in self.__class__.common_metadata_fields: # already handled try: metadata_fields.remove(field_name) except ValueError: pass for field_name in metadata_fields: value = getattr(timeseries, field_name) if value is not None: self.annotations[f"nwb:{field_name}"] = value self.annotations["nwb_neurodata_type"] = (timeseries.__class__.__module__, timeseries.__class__.__name__) if hasattr(timeseries, "electrode"): # todo: once the Group class is available, we could add electrode metadata # to a Group containing all signals that share that electrode # This would reduce the amount of redundancy (repeated metadata in every signal) electrode_metadata = {"device": {}} metadata_fields = list(timeseries.electrode.__class__.__nwbfields__) + ["name"] metadata_fields.remove("device") # needs special handling for field_name in metadata_fields: value = getattr(timeseries.electrode, field_name) if value is not None: electrode_metadata[field_name] = value for field_name in timeseries.electrode.device.__class__.__nwbfields__: value = getattr(timeseries.electrode.device, field_name) if value is not None: electrode_metadata["device"][field_name] = value self.annotations["nwb_electrode"] = electrode_metadata def load(self, time_slice=None, strict_slicing=True): """ Load AnalogSignalProxy args: :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ i_start, i_stop, sig_t_start = None, None, self.t_start if time_slice: if self.sampling_rate is None: i_start, i_stop = np.searchsorted(self._timeseries.timestamps, time_slice) else: i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) signal = self._timeseries.data[i_start:i_stop] if self.sampling_rate is None: return IrregularlySampledSignal( self._timeseries.timestamps[i_start:i_stop] * pq.s, signal, units=self.units, t_start=sig_t_start, sampling_rate=self.sampling_rate, name=self.name, description=self.description, array_annotations=None, **self.annotations, ) # todo: timeseries.control / control_description else: return AnalogSignal( signal, units=self.units, t_start=sig_t_start, sampling_rate=self.sampling_rate, name=self.name, description=self.description, array_annotations=None, **self.annotations, ) # todo: timeseries.control / control_description class EventProxy(BaseEventProxy): def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.name = timeseries.name self.annotations = {"nwb_group": nwb_group} self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): self.annotations.update(self.description) self.description = None self.shape = self._timeseries.data.shape def load(self, time_slice=None, strict_slicing=True): """ Load EventProxy args: :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ if time_slice: raise NotImplementedError("todo") else: times = self._timeseries.timestamps[:] labels = self._timeseries.data[:] return Event(times * pq.s, labels=labels, name=self.name, description=self.description, **self.annotations) class EpochProxy(BaseEpochProxy): def __init__(self, time_intervals, epoch_name=None, index=None): """ :param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods :param epoch_name: (str) Name of the epoch object :param index: (np.array, slice) Slice object or array of bool values masking time_intervals to be used. In case of an array it has to have the same shape as `time_intervals`. """ self._time_intervals = time_intervals if index is not None: self._index = index self.shape = (index.sum(),) else: self._index = slice(None) self.shape = (len(time_intervals),) self.name = epoch_name def load(self, time_slice=None, strict_slicing=True): """ Load EpochProxy args: :param time_slice: None or tuple of the time slice expressed with quantities. None is all of the intervals. :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ if time_slice: raise NotImplementedError("todo") else: start_times = self._time_intervals.start_time[self._index] stop_times = self._time_intervals.stop_time[self._index] durations = stop_times - start_times labels = self._time_intervals.tags[self._index] return Epoch(times=start_times * pq.s, durations=durations * pq.s, labels=labels, name=self.name) class SpikeTrainProxy(BaseSpikeTrainProxy): def __init__(self, units_table, id): """ :param units_table: A Units table (see https://pynwb.readthedocs.io/en/stable/pynwb.misc.html#pynwb.misc.Units) :param id: the cell/unit ID (integer) """ self._units_table = units_table self.id = id self.units = pq.s obs_intervals = units_table.get_unit_obs_intervals(id) if len(obs_intervals) == 0: t_start, t_stop = None, None elif len(obs_intervals) == 1: t_start, t_stop = obs_intervals[0] else: raise NotImplementedError("Can't yet handle multiple observation intervals") self.t_start = t_start * pq.s self.t_stop = t_stop * pq.s self.annotations = {"nwb_group": "acquisition"} try: # NWB files created by Neo store the name as an extra column self.name = units_table._name[id] except AttributeError: self.name = None self.shape = None # no way to get this without reading the data def load(self, time_slice=None, strict_slicing=True): """ Load SpikeTrainProxy args: :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire spike train. :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ interval = None if time_slice: interval = (float(t) for t in time_slice) # convert from quantities spike_times = self._units_table.get_unit_spike_times(self.id, in_interval=interval) return SpikeTrain( spike_times * self.units, self.t_stop, units=self.units, # sampling_rate=array(1.) * Hz, t_start=self.t_start, # waveforms=None, # left_sweep=None, name=self.name, # file_origin=None, # description=None, # array_annotations=None, **self.annotations, ) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/openephysbinaryio.py0000644000175100017510000000057715154507213017354 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.openephysbinaryrawio import OpenEphysBinaryRawIO class OpenEphysBinaryIO(OpenEphysBinaryRawIO, BaseFromRaw): _prefered_signal_group_mode = "group-by-same-units" mode = "dir" def __init__(self, dirname): OpenEphysBinaryRawIO.__init__(self, dirname=dirname) BaseFromRaw.__init__(self, dirname) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/openephysio.py0000644000175100017510000000054115154507213016136 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.openephysrawio import OpenEphysRawIO class OpenEphysIO(OpenEphysRawIO, BaseFromRaw): _prefered_signal_group_mode = "group-by-same-units" mode = "dir" def __init__(self, dirname): OpenEphysRawIO.__init__(self, dirname=dirname) BaseFromRaw.__init__(self, dirname) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/phyio.py0000644000175100017510000000062515154507213014727 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.phyrawio import PhyRawIO class PhyIO(PhyRawIO, BaseFromRaw): name = "Phy IO" description = "Phy IO" mode = "dir" def __init__(self, dirname, load_amplitudes=False, load_pcs=False): PhyRawIO.__init__(self, dirname=dirname, load_amplitudes=load_amplitudes, load_pcs=load_pcs) BaseFromRaw.__init__(self, dirname) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/pickleio.py0000644000175100017510000000257015154507213015377 0ustar00runnerrunner""" Module for reading/writing data from/to Python pickle format. Class: PickleIO Supported: Read/Write Authors: Andrew Davison """ import pickle from neo.io.baseio import BaseIO from neo.core import Block, Segment, AnalogSignal, SpikeTrain, NeoReadWriteError class PickleIO(BaseIO): """ A class for reading and writing Neo data from/to the Python "pickle" format. Note that files in this format may not be readable if using a different version of Neo to that used to create the file. It should therefore not be used for long-term storage, but rather for intermediate results in a pipeline. """ is_readable = True is_writable = True has_header = False is_streameable = False # TODO - correct spelling to "is_streamable" # should extend to other classes. supported_objects = [Block, Segment, AnalogSignal, SpikeTrain] readable_objects = supported_objects writeable_objects = supported_objects mode = "file" name = "Python pickle file" extensions = ["pkl", "pickle"] def read_block(self, lazy=False): if lazy: raise NeoReadWriteError("This IO does not support lazy reading") with open(self.filename, "rb") as fp: block = pickle.load(fp) return block def write_block(self, block): with open(self.filename, "wb") as fp: pickle.dump(block, fp) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/plexon2io.py0000644000175100017510000000071615154507213015517 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.plexon2rawio import Plexon2RawIO class Plexon2IO(Plexon2RawIO, BaseFromRaw): """ Class for reading data from Plexon PL2 files The IO is based on the Plexon2RawIO, see comments for memory optimization in neo.rawio.plexon2rawio.Plexon2RawIO """ def __init__(self, filename): Plexon2RawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/plexonio.py0000644000175100017510000000111215154507213015424 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.plexonrawio import PlexonRawIO class PlexonIO(PlexonRawIO, BaseFromRaw): """ Class for reading the old data format from Plexon acquisition system (.plx) Note that Plexon now use a new format PL2 which is NOT supported by this IO. Compatible with versions 100 to 106. Other versions have not been tested. """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): PlexonRawIO.__init__(self, filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/proxyobjects.py0000644000175100017510000006455615154507213016347 0ustar00runnerrunner""" Here a list of proxy object that can be used when lazy=True at neo.io level. This idea is to be able to postpone that real in memory loading for objects that contains big data (AnalogSIgnal, SpikeTrain, Event, Epoch). The implementation rely on neo.rawio, so it will available only for neo.io that ineherits neo.rawio. """ import logging import numpy as np import quantities as pq from neo.core.baseneo import BaseNeo from neo.core import AnalogSignal, Epoch, Event, SpikeTrain from neo.core.dataobject import ArrayDict logger = logging.getLogger("Neo") class BaseProxy(BaseNeo): def __init__(self, array_annotations=None, **annotations): # this for py27 str vs py3 str in neo attributes ompatibility annotations = check_annotations(annotations) if "file_origin" not in annotations: # the str is to make compatible with neo_py27 where attribute # used to be str so raw bytes annotations["file_origin"] = str(self._rawio.source_name()) if array_annotations is None: array_annotations = {} for k, v in array_annotations.items(): array_annotations[k] = np.asarray(v) # clean array annotations that are not 1D # TODO remove this once multi-dimensional array_annotations are possible array_annotations = {k: v for k, v in array_annotations.items() if v.ndim == 1} # this mock the array annotations to avoid inherits DataObject self.array_annotations = ArrayDict(self.shape[-1]) self.array_annotations.update(array_annotations) BaseNeo.__init__(self, **annotations) def load(self, time_slice=None, **kwargs): # should be implemented by subclass raise NotImplementedError def time_slice(self, t_start, t_stop): """ Load the proxy object within the specified time range. Has the same call signature as AnalogSignal.time_slice, Epoch.time_slice, etc. """ return self.load(time_slice=(t_start, t_stop)) class AnalogSignalProxy(BaseProxy): """ This object mimic AnalogSignal except that it does not have the signals array itself. All attributes and annotations are here. The goal is to postpone the loading of data into memory when reading a file with the new lazy load system based on neo.rawio. This object must not be constructed directly but is given neo.io when lazy=True instead of a true AnalogSignal. The AnalogSignalProxy is able to load: * only a slice of time * only a subset of channels * have an internal raw magnitude identic to the file (int16) with a pq.CompoundUnit(). Usage: >>> proxy_anasig = AnalogSignalProxy(rawio=self.reader, global_channel_indexes=None, block_index=0, seg_index=0) >>> anasig = proxy_anasig.load() >>> slice_of_anasig = proxy_anasig.load(time_slice=(1.*pq.s, 2.*pq.s)) >>> some_channel_of_anasig = proxy_anasig.load(channel_indexes=[0,5,10]) """ _parent_objects = ("Segment",) _necessary_attrs = (("sampling_rate", pq.Quantity, 0), ("t_start", pq.Quantity, 0)) _recommended_attrs = BaseNeo._recommended_attrs proxy_for = AnalogSignal def __init__(self, rawio=None, stream_index=None, inner_stream_channels=None, block_index=0, seg_index=0): # stream_index: indicate the stream stream_id can be retreive easily # inner_stream_channels: are channel index inside the stream None means all channels # if inner_stream_channels is not None: # * then this is a "substream" # * handle the case where channels have different units inside a stream # * is related to BaseFromRaw.get_sub_signal_streams() self._rawio = rawio self._block_index = block_index self._seg_index = seg_index self._stream_index = stream_index if inner_stream_channels is None: inner_stream_channels = slice(inner_stream_channels) self._inner_stream_channels = inner_stream_channels signal_streams = self._rawio.header["signal_streams"] stream_id = signal_streams[stream_index]["id"] signal_channels = self._rawio.header["signal_channels"] (global_inds,) = np.nonzero(signal_channels["stream_id"] == stream_id) self._nb_total_chann_in_stream = global_inds.size self._global_channel_indexes = global_inds[inner_stream_channels] self._nb_chan = self._global_channel_indexes.size sig_chans = signal_channels[self._global_channel_indexes] if np.unique(sig_chans["units"]).size != 1: raise ValueError("Channel do not have same units") if np.unique(sig_chans["dtype"]).size != 1: raise TypeError("Channel do not have same dtype") if np.unique(sig_chans["sampling_rate"]).size != 1: raise ValueError("Channel do not have same sampling_rate") self.units = ensure_signal_units(sig_chans["units"][0]) self.dtype = sig_chans["dtype"][0] self.sampling_rate = sig_chans["sampling_rate"][0] * pq.Hz self.sampling_period = 1.0 / self.sampling_rate sigs_size = self._rawio.get_signal_size(block_index=block_index, seg_index=seg_index, stream_index=stream_index) self.shape = (sigs_size, self._nb_chan) self.t_start = self._rawio.get_signal_t_start(block_index, seg_index, stream_index) * pq.s # magnitude_mode='raw' is supported only if all offset=0 # and all gain are the same support_raw_magnitude = np.all(sig_chans["gain"] == sig_chans["gain"][0]) and np.all(sig_chans["offset"] == 0.0) if support_raw_magnitude: str_units = ensure_signal_units(sig_chans["units"][0]).units.dimensionality.string gain0 = sig_chans["gain"][0] self._raw_units = pq.CompoundUnit(f"{gain0}*{str_units}") else: self._raw_units = None # retrieve annotations and array annotations seg_ann = self._rawio.raw_annotations["blocks"][block_index]["segments"][seg_index] annotations = seg_ann["signals"][stream_index].copy() array_annotations = annotations.pop("__array_annotations__") array_annotations = {k: v[inner_stream_channels] for k, v in array_annotations.items()} BaseProxy.__init__(self, array_annotations=array_annotations, **annotations) @property def duration(self): """Signal duration""" return self.shape[0] / self.sampling_rate @property def t_stop(self): """Time when signal ends""" return self.t_start + self.duration def _time_slice_indices(self, time_slice, strict_slicing=True): """ Calculate the start and end indices for the slice. Also returns t_start """ if time_slice is None: i_start, i_stop = None, None sig_t_start = self.t_start else: sr = self.sampling_rate t_start, t_stop = time_slice if t_start is None: i_start = None sig_t_start = self.t_start else: t_start = ensure_second(t_start) if strict_slicing: if t_start < self.t_start or t_start > self.t_stop: raise ValueError("`t_start` is outside of the time range") else: t_start = max(t_start, self.t_start) # the i_start is rounded to the nearest sample i_start = np.rint((t_start - self.t_start).magnitude * sr.magnitude).astype(np.int64) # this is needed to get the real t_start of the first sample # because it does not necessary match what is demanded sig_t_start = self.t_start + i_start / sr if t_stop is None: i_stop = None else: t_stop = ensure_second(t_stop) if strict_slicing: if t_stop < self.t_start or t_stop > self.t_stop: raise ValueError("`t_stop` is outside of the time range") else: t_stop = min(t_stop, self.t_stop) # calculate duration demanded then round it to nearest sample number # add this to i_start to get i_stop delta = (t_stop - t_start) * sr i_stop = i_start + int(np.rint(delta.simplified.magnitude)) return i_start, i_stop, sig_t_start def load(self, time_slice=None, strict_slicing=True, channel_indexes=None, magnitude_mode="rescaled"): """ *Args*: :time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :channel_indexes: None or list. Channels to load. None is all channels Be carefull that channel_indexes represent the local channel index inside the AnalogSignal and not the global_channel_indexes like in rawio. :magnitude_mode: 'rescaled' or 'raw'. For instance if the internal dtype is int16: * **rescaled** give [1.,2.,3.]*pq.uV and the dtype is float32 * **raw** give [10, 20, 30]*pq.CompoundUnit('0.1*uV') The CompoundUnit with magnitude_mode='raw' is usefull to postpone the scaling when needed and having an internal dtype=int16 but it less intuitive when you don't know so well quantities. :strict_slicing: True by default. Control if an error is raise or not when one of time_slice member (t_start or t_stop) is outside the real time range of the segment. """ # fixed_chan_indexes is channel index (or slice) in the stream # channel_indexes is channel index (or slice) in the substream if isinstance(self._inner_stream_channels, slice): if self._inner_stream_channels == slice(None): # sub stream is the entire stream if channel_indexes is None: fixed_chan_indexes = None else: fixed_chan_indexes = channel_indexes else: # sub stream is part of stream with slice if channel_indexes is None: fixed_chan_indexes = self._inner_stream_channels else: global_inds = np.arange(self._nb_total_chann_in_stream) fixed_chan_indexes = global_inds[self._inner_stream_channels][channel_indexes] else: # sub stream is part of stream with indexes if channel_indexes is None: fixed_chan_indexes = self._inner_stream_channels else: fixed_chan_indexes = self._inner_stream_channels[channel_indexes] i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) raw_signal = self._rawio.get_analogsignal_chunk( block_index=self._block_index, seg_index=self._seg_index, i_start=i_start, i_stop=i_stop, stream_index=self._stream_index, channel_indexes=fixed_chan_indexes, ) # if slice in channel : change name and array_annotations if raw_signal.shape[1] != self._nb_chan: name = "slice of " + self.name channel_indexes2 = channel_indexes if channel_indexes2 is None: channel_indexes2 = slice(None) array_annotations = {k: v[channel_indexes2] for k, v in self.array_annotations.items()} else: name = self.name array_annotations = self.array_annotations if magnitude_mode == "raw": if self._raw_units is None: raise ValueError( "raw magnitude is not supported if the gain are not the same for all channels or offset is not 0" ) sig = raw_signal units = self._raw_units elif magnitude_mode == "rescaled": # dtype is float32 when internally it is float32 or int16 if self.dtype == "float64": dtype = "float64" else: dtype = "float32" sig = self._rawio.rescale_signal_raw_to_float( raw_signal, dtype=dtype, stream_index=self._stream_index, channel_indexes=fixed_chan_indexes ) units = self.units else: raise ValueError(f"Invalid magnitude_mode {magnitude_mode}. Accepted values are " f'"rescaled" and "raw"') anasig = AnalogSignal( sig, units=units, copy=None, t_start=sig_t_start, sampling_rate=self.sampling_rate, name=name, file_origin=self.file_origin, description=self.description, array_annotations=array_annotations, **self.annotations, ) return anasig class SpikeTrainProxy(BaseProxy): """ This object mimic SpikeTrain except that it does not have the spike time nor waveforms. All attributes and annotations are here. The goal is to postpone the loading of data into memory when reading a file with the new lazy load system based on neo.rawio. This object must not be constructed directly but is given neo.io when lazy=True instead of a true SpikeTrain. The SpikeTrainProxy is able to load: * only a slice of time * load wveforms or not. * have an internal raw magnitude identic to the file (generally the ticks of clock in int64) or the rescale to seconds. Usage: >>> proxy_sptr = SpikeTrainProxy(rawio=self.reader, unit_channel=0, block_index=0, seg_index=0,) >>> sptr = proxy_sptr.load() >>> slice_of_sptr = proxy_sptr.load(time_slice=(1.*pq.s, 2.*pq.s)) """ _parent_objects = ("Segment", "Unit") _quantity_attr = "times" _necessary_attrs = (("t_start", pq.Quantity, 0), ("t_stop", pq.Quantity, 0)) _recommended_attrs = () proxy_for = SpikeTrain def __init__(self, rawio=None, spike_channel_index=None, block_index=0, seg_index=0): self._rawio = rawio self._block_index = block_index self._seg_index = seg_index self._spike_channel_index = spike_channel_index nb_spike = self._rawio.spike_count( block_index=block_index, seg_index=seg_index, spike_channel_index=spike_channel_index ) self.shape = (nb_spike,) self.t_start = self._rawio.segment_t_start(block_index, seg_index) * pq.s self.t_stop = self._rawio.segment_t_stop(block_index, seg_index) * pq.s seg_ann = self._rawio.raw_annotations["blocks"][block_index]["segments"][seg_index] annotations = seg_ann["spikes"][spike_channel_index].copy() array_annotations = annotations.pop("__array_annotations__") h = self._rawio.header["spike_channels"][spike_channel_index] wf_sampling_rate = h["wf_sampling_rate"] if not np.isnan(wf_sampling_rate) and wf_sampling_rate > 0: self.sampling_rate = wf_sampling_rate * pq.Hz self.left_sweep = (h["wf_left_sweep"] / self.sampling_rate).rescale("s") self._wf_units = ensure_signal_units(h["wf_units"]) else: self.sampling_rate = None self.left_sweep = None BaseProxy.__init__(self, array_annotations=array_annotations, **annotations) def load(self, time_slice=None, strict_slicing=True, magnitude_mode="rescaled", load_waveforms=False): """ *Args*: :time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :strict_slicing: True by default. Control if an error is raise or not when one of time_slice member (t_start or t_stop) is outside the real time range of the segment. :magnitude_mode: 'rescaled' or 'raw'. :load_waveforms: bool load waveforms or not. """ t_start, t_stop = consolidate_time_slice(time_slice, self.t_start, self.t_stop, strict_slicing) _t_start, _t_stop = prepare_time_slice(time_slice) spike_timestamps = self._rawio.get_spike_timestamps( block_index=self._block_index, seg_index=self._seg_index, spike_channel_index=self._spike_channel_index, t_start=_t_start, t_stop=_t_stop, ) if magnitude_mode == "raw": # we must modify a bit the neo.rawio interface to also read the spike_timestamps # underlying clock wich is not always same as sigs raise (NotImplementedError) elif magnitude_mode == "rescaled": dtype = "float64" spike_times = self._rawio.rescale_spike_timestamp(spike_timestamps, dtype=dtype) units = "s" if load_waveforms: if self.sampling_rate is None: raise ValueError("There are no waveforms") raw_wfs = self._rawio.get_spike_raw_waveforms( block_index=self._block_index, seg_index=self._seg_index, spike_channel_index=self._spike_channel_index, t_start=_t_start, t_stop=_t_stop, ) if magnitude_mode == "rescaled": float_wfs = self._rawio.rescale_waveforms_to_float( raw_wfs, dtype="float32", spike_channel_index=self._spike_channel_index ) waveforms = pq.Quantity(float_wfs, units=self._wf_units, dtype="float32") elif magnitude_mode == "raw": # could code also CompundUnit here but it is over killed # so we used dimentionless waveforms = pq.Quantity(raw_wfs, units="", dtype=raw_wfs.dtype) else: waveforms = None sptr = SpikeTrain( spike_times, t_stop, units=units, dtype=dtype, t_start=t_start, copy=None, sampling_rate=self.sampling_rate, waveforms=waveforms, left_sweep=self.left_sweep, name=self.name, file_origin=self.file_origin, description=self.description, **self.annotations, ) if time_slice is None: sptr.array_annotate(**self.array_annotations) else: # TODO handle array_annotations with time_slice pass return sptr class _EventOrEpoch(BaseProxy): _parent_objects = ("Segment",) _quantity_attr = "times" def __init__(self, rawio=None, event_channel_index=None, block_index=0, seg_index=0): self._rawio = rawio self._block_index = block_index self._seg_index = seg_index self._event_channel_index = event_channel_index nb_event = self._rawio.event_count( block_index=block_index, seg_index=seg_index, event_channel_index=event_channel_index ) self.shape = (nb_event,) self.t_start = self._rawio.segment_t_start(block_index, seg_index) * pq.s self.t_stop = self._rawio.segment_t_stop(block_index, seg_index) * pq.s # both necessary attr and annotations annotations = {} for k in ("name", "id"): annotations[k] = self._rawio.header["event_channels"][event_channel_index][k] seg_ann = self._rawio.raw_annotations["blocks"][block_index]["segments"][seg_index] ann = seg_ann["events"][event_channel_index] annotations = ann.copy() array_annotations = annotations.pop("__array_annotations__") BaseProxy.__init__(self, array_annotations=array_annotations, **annotations) def load(self, time_slice=None, strict_slicing=True): """ *Args*: :time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :strict_slicing: True by default. Control if an error is raise or not when one of time_slice member (t_start or t_stop) is outside the real time range of the segment. """ t_start, t_stop = consolidate_time_slice(time_slice, self.t_start, self.t_stop, strict_slicing) _t_start, _t_stop = prepare_time_slice(time_slice) timestamp, durations, labels = self._rawio.get_event_timestamps( block_index=self._block_index, seg_index=self._seg_index, event_channel_index=self._event_channel_index, t_start=_t_start, t_stop=_t_stop, ) dtype = "float64" times = self._rawio.rescale_event_timestamp(timestamp, dtype=dtype) units = "s" if durations is not None: durations = self._rawio.rescale_epoch_duration(durations, dtype=dtype) * pq.s h = self._rawio.header["event_channels"][self._event_channel_index] if h["type"] == b"event": ret = Event( times=times, labels=labels, units="s", name=self.name, file_origin=self.file_origin, description=self.description, **self.annotations, ) elif h["type"] == b"epoch": ret = Epoch( times=times, durations=durations, labels=labels, units="s", name=self.name, file_origin=self.file_origin, description=self.description, **self.annotations, ) if time_slice is None: ret.array_annotate(**self.array_annotations) else: # TODO handle array_annotations with time_slice pass return ret class EventProxy(_EventOrEpoch): """ This object mimic Event except that it does not have the times nor labels. All other attributes and annotations are here. The goal is to postpone the loading of data into memory when reading a file with the new lazy load system based on neo.rawio. This object must not be constructed directly but is given neo.io when lazy=True instead of a true Event. The EventProxy is able to load: * only a slice of time Usage: >>> proxy_event = EventProxy(rawio=self.reader, event_channel_index=0, block_index=0, seg_index=0,) >>> event = proxy_event.load() >>> slice_of_event = proxy_event.load(time_slice=(1.*pq.s, 2.*pq.s)) """ _necessary_attrs = (("times", pq.Quantity, 1), ("labels", np.ndarray, 1, np.dtype("U"))) proxy_for = Event class EpochProxy(_EventOrEpoch): """ This object mimic Epoch except that it does not have the times nor labels nor durations. All other attributes and annotations are here. The goal is to postpone the loading of data into memory when reading a file with the new lazy load system based on neo.rawio. This object must not be constructed directly but is given neo.io when lazy=True instead of a true Epoch. The EpochProxy is able to load: * only a slice of time Usage: >>> proxy_epoch = EpochProxy(rawio=self.reader, event_channel_index=0, block_index=0, seg_index=0,) >>> epoch = proxy_epoch.load() >>> slice_of_epoch = proxy_epoch.load(time_slice=(1.*pq.s, 2.*pq.s)) """ _necessary_attrs = ( ("times", pq.Quantity, 1), ("durations", pq.Quantity, 1), ("labels", np.ndarray, 1, np.dtype("U")), ) proxy_for = Epoch proxyobjectlist = [AnalogSignalProxy, SpikeTrainProxy, EventProxy, EpochProxy] unit_convert = { "Volts": "V", "volts": "V", "Volt": "V", "volt": "V", " Volt": "V", "microV": "uV", # note that "micro" and "mu" are two different characters in Unicode # although they mostly look the same. Here we accept both. "µV": "uV", "μV": "uV", } def ensure_signal_units(units): # test units units = units.replace(" ", "") if units in unit_convert: units = unit_convert[units] try: units = pq.Quantity(1, units) except: logger.warning(f'Units "{units}" can not be converted to a quantity. Using dimensionless ' "instead") units = "" units = pq.Quantity(1, units) return units def check_annotations(annotations): # force type to str for some keys # imposed for tests for k in ("name", "description", "file_origin"): if k in annotations: annotations[k] = str(annotations[k]) if "coordinates" in annotations: # some rawio expose some coordinates in annotations but is not standardized # (x, y, z) or polar, at the moment it is more resonable to remove them annotations.pop("coordinates") return annotations def ensure_second(v): if isinstance(v, float): return v * pq.s elif isinstance(v, pq.Quantity): return v.rescale("s") elif isinstance(v, int): return float(v) * pq.s def prepare_time_slice(time_slice): """ This give clean time slice but keep None for calling rawio slice """ if time_slice is None: t_start, t_stop = None, None else: t_start, t_stop = time_slice if t_start is not None: t_start = ensure_second(t_start).rescale("s").magnitude if t_stop is not None: t_stop = ensure_second(t_stop).rescale("s").magnitude return (t_start, t_stop) def consolidate_time_slice(time_slice, seg_t_start, seg_t_stop, strict_slicing): """ This give clean time slice in quantity for t_start/t_stop of object None is replace by seg limit. """ if time_slice is None: t_start, t_stop = None, None else: t_start, t_stop = time_slice if t_start is None: t_start = seg_t_start else: if strict_slicing: if t_start < seg_t_start or t_start > seg_t_stop: raise ValueError("`t_start` is outside of the time range") else: t_start = max(t_start, seg_t_start) t_start = ensure_second(t_start) if t_stop is None: t_stop = seg_t_stop else: if strict_slicing: if t_stop < seg_t_start or t_stop > seg_t_stop: raise ValueError("`t_stop` is outside of the time range") else: t_stop = min(t_stop, seg_t_stop) t_stop = ensure_second(t_stop) return (t_start, t_stop) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/rawbinarysignalio.py0000644000175100017510000000733015154507213017323 0ustar00runnerrunner""" Class for reading/writing data in a raw binary interleaved compact file. Sampling rate, units, number of channel and dtype must be externally known. This generic format is quite widely used in old acquisition systems and is quite universal for sharing data. Supported : Read/Write Author: sgarcia """ import numpy as np from neo.core import Segment, AnalogSignal, NeoReadWriteError from neo.io.basefromrawio import BaseFromRaw from neo.rawio.rawbinarysignalrawio import RawBinarySignalRawIO class RawBinarySignalIO(RawBinarySignalRawIO, BaseFromRaw): """ Class for reading/writing data in a raw binary interleaved compact file. **Important release note** Since the version neo 0.6.0 and the neo.rawio API, argmuents of the IO (dtype, nb_channel, sampling_rate) must be given at the __init__ and not at read_segment() because there is no read_segment() in neo.rawio classes. So now the usage is: >>>>r = io.RawBinarySignalIO(filename='file.raw', dtype='int16', nb_channel=16, sampling_rate=10000.) """ _prefered_signal_group_mode = "group-by-same-units" is_readable = True is_writable = True supported_objects = [Segment, AnalogSignal] readable_objects = [Segment] writeable_objects = [Segment] def __init__( self, filename, dtype="int16", sampling_rate=10000.0, nb_channel=2, signal_gain=1.0, signal_offset=0.0, bytesoffset=0, ): RawBinarySignalRawIO.__init__( self, filename=filename, dtype=dtype, sampling_rate=sampling_rate, nb_channel=nb_channel, signal_gain=signal_gain, signal_offset=signal_offset, bytesoffset=bytesoffset, ) BaseFromRaw.__init__(self, filename) def write_segment(self, segment): """ **Arguments** segment : the segment to write. Only analog signals will be written. Support only 2 cases: * segment.analogsignals have one 2D AnalogSignal * segment.analogsignals have several 1D AnalogSignal with same length/sampling_rate/dtype """ if self.bytesoffset: raise NotImplementedError("bytesoffset values other than 0 " + "not supported") anasigs = segment.analogsignals if len(anasigs) == 0: raise NeoReadWriteError("No AnalogSignal to write") anasig0 = anasigs[0] if len(anasigs) == 1 and anasig0.ndim == 2: numpy_sigs = anasig0.magnitude else: if anasig0.ndim != 1 and (anasig0.ndim == 2 and anasig0.shape[1] != 1): raise NeoReadWriteError("Incorrect analogsignal shaping for write") # all AnaologSignal from Segment must have the same length/sampling_rate/dtype for anasig in anasigs[1:]: if anasig.shape != anasig0.shape: raise ValueError("The shape of one of the analog signals does not match") if anasig.sampling_rate != anasig0.sampling_rate: raise ValueError("The sampling_rate of one of the analog signals does not match") if anasig.dtype != anasig0.dtype: raise ValueError("The dtype of one the analog signals does not match") numpy_sigs = np.empty((anasig0.size, len(anasigs))) for i, anasig in enumerate(anasigs): numpy_sigs[:, i] = anasig.magnitude.flatten() numpy_sigs -= self.signal_offset numpy_sigs /= self.signal_gain numpy_sigs = numpy_sigs.astype(self.dtype) with open(self.filename, "wb") as f: f.write(numpy_sigs.tobytes()) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/rawmcsio.py0000644000175100017510000000050515154507213015420 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.rawmcsrawio import RawMCSRawIO class RawMCSIO(RawMCSRawIO, BaseFromRaw): _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): RawMCSRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/spike2io.py0000644000175100017510000000052715154507213015325 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.spike2rawio import Spike2RawIO class Spike2IO(Spike2RawIO, BaseFromRaw): _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename, **kargs): Spike2RawIO.__init__(self, filename=filename, **kargs) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/spikegadgetsio.py0000644000175100017510000000052315154507213016576 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.spikegadgetsrawio import SpikeGadgetsRawIO class SpikeGadgetsIO(SpikeGadgetsRawIO, BaseFromRaw): __doc__ = SpikeGadgetsRawIO.__doc__ def __init__(self, filename): SpikeGadgetsRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/spikeglxio.py0000644000175100017510000000074615154507213015761 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.spikeglxrawio import SpikeGLXRawIO class SpikeGLXIO(SpikeGLXRawIO, BaseFromRaw): __doc__ = SpikeGLXRawIO.__doc__ mode = "dir" def __init__(self, dirname, load_sync_channel=False, load_channel_location=False): SpikeGLXRawIO.__init__( self, dirname=dirname, load_sync_channel=load_sync_channel, load_channel_location=load_channel_location ) BaseFromRaw.__init__(self, dirname) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/stimfitio.py0000644000175100017510000001210615154507213015603 0ustar00runnerrunner""" README =============================================================================== This is an adapter to represent stfio objects as neo objects. stfio is a standalone file i/o Python module that ships with the Stimfit program (http://www.stimfit.org). It's a Python wrapper around Stimfit's file i/o library (libstfio) that natively provides support for the following file types: - ABF (Axon binary file format; pClamp 6--9) - ABF2 (Axon binary file format 2; pClamp 10+) - ATF (Axon text file format) - AXGX/AXGD (Axograph X file format) - CFS (Cambridge electronic devices filing system) - HEKA (HEKA binary file format) - HDF5 (Hierarchical data format 5; only hdf5 files written by Stimfit or stfio are supported) In addition, libstfio can use the biosig file i/o library as an additional file handling backend (http://biosig.sourceforge.net/), extending support to more than 30 additional file formats (http://pub.ist.ac.at/~schloegl/biosig/TESTED). Based on exampleio.py and axonio.py from neo.io 08 Feb 2014, C. Schmidt-Hieber, University College London """ import importlib.util import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import Block, Segment, AnalogSignal, NeoReadWriteError class StimfitIO(BaseIO): """ Class for converting a stfio Recording to a Neo object. Provides a standardized representation of the data as defined by the neo project; this is useful to explore the data with an increasing number of electrophysiology software tools that rely on the Neo standard. stfio is a standalone file i/o Python module that ships with the Stimfit program (http://www.stimfit.org). It is a Python wrapper around Stimfit's file i/o library (libstfio) that natively provides support for the following file types: - ABF (Axon binary file format; pClamp 6--9) - ABF2 (Axon binary file format 2; pClamp 10+) - ATF (Axon text file format) - AXGX/AXGD (Axograph X file format) - CFS (Cambridge electronic devices filing system) - HEKA (HEKA binary file format) - HDF5 (Hierarchical data format 5; only hdf5 files written by Stimfit or stfio are supported) In addition, libstfio can use the biosig file i/o library as an additional file handling backend (http://biosig.sourceforge.net/), extending support to more than 30 additional file formats (http://pub.ist.ac.at/~schloegl/biosig/TESTED). Example usage: >>> import neo >>> neo_obj = neo.io.StimfitIO("file.abf") or >>> import stfio >>> stfio_obj = stfio.read("file.abf") >>> neo_obj = neo.io.StimfitIO(stfio_obj) """ is_readable = True is_writable = False supported_objects = [Block, Segment, AnalogSignal] readable_objects = [Block] writeable_objects = [] has_header = False is_streameable = False read_params = {Block: []} write_params = None name = "Stimfit" extensions = ["abf", "dat", "axgx", "axgd", "cfs"] mode = "file" def __init__(self, filename=None): """ Arguments: filename : Either a filename or a stfio Recording object """ # We need this module, so try importing now so that it fails on # instantiation rather than read_block stfio_spec = importlib.util.find_spec("stfio") if stfio_spec is None: raise ImportError("stfio must be installed to use StimfitIO") BaseIO.__init__(self) if hasattr(filename, "lower"): self.filename = filename self.stfio_rec = None else: self.stfio_rec = filename self.filename = None def read_block(self, lazy=False): import stfio if lazy: raise NeoReadWriteError("This IO does not support lazy reading") if self.filename is not None: self.stfio_rec = stfio.read(self.filename) bl = Block() bl.description = self.stfio_rec.file_description bl.annotate(comment=self.stfio_rec.comment) try: bl.rec_datetime = self.stfio_rec.datetime except: bl.rec_datetime = None dt = np.round(self.stfio_rec.dt * 1e-3, 9) * pq.s # ms to s sampling_rate = 1.0 / dt t_start = 0 * pq.s # iterate over sections first: for j, recseg in enumerate(self.stfio_rec[0]): seg = Segment(index=j) length = len(recseg) # iterate over channels: for i, recsig in enumerate(self.stfio_rec): name = recsig.name unit = recsig.yunits try: pq.Quantity(1, unit) except: unit = "" signal = pq.Quantity(recsig[j], unit) anaSig = AnalogSignal( signal, sampling_rate=sampling_rate, t_start=t_start, name=str(name), channel_index=i ) seg.analogsignals.append(anaSig) bl.segments.append(seg) t_start = t_start + length * dt bl.check_relationships() return bl ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/tdtio.py0000644000175100017510000000113315154507213014715 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.tdtrawio import TdtRawIO class TdtIO(TdtRawIO, BaseFromRaw): """ Class for reading data from from Tucker Davis TTank format. Terminology: TDT holds data with tanks (actually a directory). And tanks hold sub blocks (sub directories). Tanks correspond to Neo Blocks and TDT blocks correspond to Neo Segments. """ _prefered_signal_group_mode = "group-by-same-units" mode = "dir" def __init__(self, dirname): TdtRawIO.__init__(self, dirname=dirname) BaseFromRaw.__init__(self, dirname) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/tiffio.py0000644000175100017510000001270215154507213015056 0ustar00runnerrunner""" Neo IO module for optical imaging data stored as a folder of TIFF images. """ import glob import re import numpy as np from neo.core import ImageSequence, Segment, Block from .baseio import BaseIO class TiffIO(BaseIO): """ Neo IO module for optical imaging data stored as a folder of TIFF images. Parameters ---------- directory_path: Path | str | None, default: None The path to the folder containing tiff images units: Quantity units | None, default: None the units for creating the ImageSequence sampling_rate: Quantity Units | None, default: None The sampling rate spatial_scale: Quantity unit | None, default: None The scale of the images origin: Literal['top-left'| 'bottom-left'], default: 'top-left' Whether to use the python default origin for images which is upper left corner ('top-left') as orgin or to use a bottom left corner as orgin ('bottom-left') Note that plotting functions like matplotlib.pyplot.imshow expect upper left corner. **kwargs: dict The standard neo annotation kwargs Examples -------- >>> from neo import io >>> import quantities as pq >>> r = io.TiffIO("dir_tiff",spatial_scale=1.0*pq.mm, units='V', ... sampling_rate=1.0*pq.Hz) >>> block = r.read_block() read block creating segment returning block >>> block Block with 1 segments file_origin: 'test' # segments (N=1) 0: Segment with 1 imagesequences annotations: {'tiff_file_names': ['file_tif_1_.tiff', 'file_tif_2.tiff', 'file_tif_3.tiff', 'file_tif_4.tiff', 'file_tif_5.tiff', 'file_tif_6.tiff', 'file_tif_7.tiff', 'file_tif_8.tiff', 'file_tif_9.tiff', 'file_tif_10.tiff', 'file_tif_11.tiff', 'file_tif_12.tiff', 'file_tif_13.tiff', 'file_tif_14.tiff']} # analogsignals (N=0) """ name = "TIFF IO" description = "Neo IO module for optical imaging data stored as a folder of TIFF images." _prefered_signal_group_mode = "group-by-same-units" is_readable = True is_writable = False supported_objects = [Block, Segment, ImageSequence] readable_objects = supported_objects writeable_objects = [] support_lazy = False read_params = {} write_params = {} extensions = [] mode = "dir" def __init__( self, directory_path=None, units=None, sampling_rate=None, spatial_scale=None, origin="top-left", **kwargs, ): # this block is because people might be confused about the PIL -> pillow change # between python2 -> python3 (both with namespace PIL) try: import PIL except ImportError: raise ImportError("To use TiffIO you must first `pip install pillow`") if origin != "top-left" and origin != "bottom-left": raise ValueError("`origin` must be either `top-left` or `bottom-left`") BaseIO.__init__(self, directory_path, **kwargs) self.units = units self.sampling_rate = sampling_rate self.spatial_scale = spatial_scale self.origin = origin def read_block(self, lazy=False, **kwargs): import PIL # to sort file def natural_sort(l): convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [convert(c) for c in re.split("([0-9]+)", key)] return sorted(l, key=alphanum_key) # find all the images in the given directory file_name_list = [] # name of extensions to track types = ["*.tif", "*.tiff"] for file in types: file_name_list.append(glob.glob(self.filename + "/" + file)) # flatten list file_name_list = [item for sublist in file_name_list for item in sublist] # delete path in the name of file file_name_list = [file_name[len(self.filename) + 1 : :] for file_name in file_name_list] # sorting file file_name_list = natural_sort(file_name_list) list_data_image = [] for file_name in file_name_list: data = np.array(PIL.Image.open(self.filename + "/" + file_name)).astype(np.float32) if self.origin == "bottom-left": data = np.flip(data, axis=-2) list_data_image.append(data) list_data_image = np.array(list_data_image) if len(list_data_image.shape) == 4: list_data_image = [] for file_name in file_name_list: image = PIL.Image.open(self.filename + "/" + file_name).convert("L") data = np.array(image).astype(np.float32) if self.origin == "bottom-left": data = np.flip(data, axis=-2) list_data_image.append(data) print("read block") image_sequence = ImageSequence( np.stack(list_data_image), units=self.units, sampling_rate=self.sampling_rate, spatial_scale=self.spatial_scale, ) print("creating segment") segment = Segment(file_origin=self.filename) segment.annotate(tiff_file_names=file_name_list) segment.imagesequences = [image_sequence] block = Block(file_origin=self.filename) block.segments.append(segment) print("returning block") return block ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/tools.py0000644000175100017510000000442415154507213014740 0ustar00runnerrunner""" Tools for IO coder: * Creating RecordingChannel and making links with AnalogSignals and SPikeTrains """ from collections.abc import MutableSequence from neo.core import ( AnalogSignal, Block, Epoch, Event, IrregularlySampledSignal, Group, ChannelView, Segment, SpikeTrain, ) class LazyList(MutableSequence): """An enhanced list that can load its members on demand. Behaves exactly like a regular list for members that are Neo objects. Each item should contain the information that ``load_lazy_cascade`` needs to load the respective object. """ _container_objects = {Block, Segment, Group} _neo_objects = _container_objects.union( [AnalogSignal, Epoch, Event, ChannelView, IrregularlySampledSignal, SpikeTrain] ) def __init__(self, io, lazy, items=None): """ :param io: IO instance that can load items. :param lazy: Lazy parameter with which the container object using the list was loaded. :param items: Optional, initial list of items. """ if items is None: self._data = [] else: self._data = items self._lazy = lazy self._io = io def __getitem__(self, index): item = self._data.__getitem__(index) if isinstance(index, slice): return LazyList(self._io, item) if type(item) in self._neo_objects: return item loaded = self._io.load_lazy_cascade(item, self._lazy) self._data[index] = loaded return loaded def __delitem__(self, index): self._data.__delitem__(index) def __len__(self): return self._data.__len__() def __setitem__(self, index, value): self._data.__setitem__(index, value) def insert(self, index, value): self._data.insert(index, value) def append(self, value): self._data.append(value) def reverse(self): self._data.reverse() def extend(self, values): self._data.extend(values) def remove(self, value): self._data.remove(value) def __str__(self): return "<" + self.__class__.__name__ + ">" + self._data.__str__() def __repr__(self): return "<" + self.__class__.__name__ + ">" + self._data.__repr__() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/winedrio.py0000644000175100017510000000077315154507213015423 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.winedrrawio import WinEdrRawIO class WinEdrIO(WinEdrRawIO, BaseFromRaw): """ Class for reading data from WinEdr, a software tool written by John Dempster. WinEdr is free: http://spider.science.strath.ac.uk/sipbs/software.htm """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): WinEdrRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/io/winwcpio.py0000644000175100017510000000077315154507213015442 0ustar00runnerrunnerfrom neo.io.basefromrawio import BaseFromRaw from neo.rawio.winwcprawio import WinWcpRawIO class WinWcpIO(WinWcpRawIO, BaseFromRaw): """ Class for reading data from WinWCP, a software tool written by John Dempster. WinWCP is free: http://spider.science.strath.ac.uk/sipbs/software.htm """ _prefered_signal_group_mode = "group-by-same-units" def __init__(self, filename): WinWcpRawIO.__init__(self, filename=filename) BaseFromRaw.__init__(self, filename) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5420525 neo-0.14.4/neo/rawio/0000755000175100017510000000000015154507227013741 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/__init__.py0000644000175100017510000001753315154507213016056 0ustar00runnerrunner""" :mod:`neo.rawio` provides classes for reading electrophysiological data files with a low-level API :attr:`neo.rawio.rawiolist` provides a list of successfully imported rawio classes. Functions: .. autofunction:: neo.rawio.get_rawio Classes: * :attr:`AlphaOmegaRawIO` * :attr:`AxographRawIO` * :attr:`AxonaRawIO` * :attr:`AxonRawIO` * :attr:`BiocamRawIO` * :attr:`BlackrockRawIO` * :attr:`BrainVisionRawIO` * :attr:`CedRawIO` * :attr:`EDFRawIO` * :attr:`ElanRawIO` * :attr:`IntanRawIO` * :attr:`MaxwellRawIO` * :attr:`MedRawIO` * :attr:`MEArecRawIO` * :attr:`MicromedRawIO` * :attr:`NeuralynxRawIO` * :attr:`NeuroExplorerRawIO` * :attr:`NeuroNexusRawIO` * :attr:`NeuroScopeRawIO` * :attr:`NIXRawIO` * :attr:`OpenEphysRawIO` * :attr:`OpenEphysBinaryRawIO` * :attr:`PhyRawIO` * :attr:`PlexonRawIO` * :attr:`Plexon2RawIO` * :attr:`RawBinarySignalRawIO` * :attr:`RawMCSRawIO` * :attr:`Spike2RawIO` * :attr:`SpikeGadgetsRawIO` * :attr:`SpikeGLXRawIO` * :attr:`TdtRawIO` * :attr:`WinEdrRawIO` * :attr:`WinWcpRawIO` .. autoclass:: neo.rawio.AlphaOmegaRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.AxographRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.AxonaRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.AxonRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.BiocamRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.BlackrockRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.BrainVisionRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.CedRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.EDFRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.ElanRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.IntanRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.MaxwellRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.MedRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.MEArecRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.MicromedRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.NeuralynxRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.NeuroExplorerRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.NeuroNexusRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.NeuroScopeRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.NIXRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.OpenEphysRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.OpenEphysBinaryRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.PhyRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.PlexonRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.Plexon2RawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.RawBinarySignalRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.RawMCSRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.Spike2RawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.SpikeGadgetsRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.SpikeGLXRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.TdtRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.WinEdrRawIO .. autoattribute:: extensions .. autoclass:: neo.rawio.WinWcpRawIO .. autoattribute:: extensions """ from pathlib import Path from collections import Counter from neo.rawio.alphaomegarawio import AlphaOmegaRawIO from neo.rawio.axographrawio import AxographRawIO from neo.rawio.axonarawio import AxonaRawIO from neo.rawio.axonrawio import AxonRawIO from neo.rawio.biocamrawio import BiocamRawIO from neo.rawio.blackrockrawio import BlackrockRawIO from neo.rawio.brainvisionrawio import BrainVisionRawIO from neo.rawio.cedrawio import CedRawIO from neo.rawio.edfrawio import EDFRawIO from neo.rawio.elanrawio import ElanRawIO from neo.rawio.examplerawio import ExampleRawIO from neo.rawio.intanrawio import IntanRawIO from neo.rawio.maxwellrawio import MaxwellRawIO from neo.rawio.mearecrawio import MEArecRawIO from neo.rawio.medrawio import MedRawIO from neo.rawio.micromedrawio import MicromedRawIO from neo.rawio.neuralynxrawio import NeuralynxRawIO from neo.rawio.neuroexplorerrawio import NeuroExplorerRawIO from neo.rawio.neuronexusrawio import NeuroNexusRawIO from neo.rawio.neuroscoperawio import NeuroScopeRawIO from neo.rawio.nixrawio import NIXRawIO from neo.rawio.openephysrawio import OpenEphysRawIO from neo.rawio.openephysbinaryrawio import OpenEphysBinaryRawIO from neo.rawio.phyrawio import PhyRawIO from neo.rawio.plexonrawio import PlexonRawIO from neo.rawio.plexon2rawio import Plexon2RawIO from neo.rawio.rawbinarysignalrawio import RawBinarySignalRawIO from neo.rawio.rawmcsrawio import RawMCSRawIO from neo.rawio.spike2rawio import Spike2RawIO from neo.rawio.spikegadgetsrawio import SpikeGadgetsRawIO from neo.rawio.spikeglxrawio import SpikeGLXRawIO from neo.rawio.tdtrawio import TdtRawIO from neo.rawio.winedrrawio import WinEdrRawIO from neo.rawio.winwcprawio import WinWcpRawIO rawiolist = [ AlphaOmegaRawIO, AxographRawIO, AxonaRawIO, AxonRawIO, BiocamRawIO, BlackrockRawIO, BrainVisionRawIO, CedRawIO, EDFRawIO, ElanRawIO, IntanRawIO, MicromedRawIO, MaxwellRawIO, MEArecRawIO, MedRawIO, NeuralynxRawIO, NeuroExplorerRawIO, NeuroNexusRawIO, NeuroScopeRawIO, NIXRawIO, OpenEphysRawIO, OpenEphysBinaryRawIO, PhyRawIO, PlexonRawIO, Plexon2RawIO, RawBinarySignalRawIO, RawMCSRawIO, Spike2RawIO, SpikeGadgetsRawIO, SpikeGLXRawIO, TdtRawIO, WinEdrRawIO, WinWcpRawIO, ] def get_rawio_class(filename_or_dirname): """Legacy function for returning class guess from file extension DEPRECATED """ import warnings warnings.warn( "get_rawio_class is deprecated and will be removed in 0.15.0. " "In the future please use `get_rawio`" ) return get_rawio(filename_or_dirname) def get_rawio(filename_or_dirname, exclusive_rawio: bool = True): """ Return a neo.rawio class guess from file extension. Parameters ---------- filename_or_dirname : str | Path The filename or directory name to check for file suffixes that can be read by Neo. This can also be used to check whether a rawio could read a not-yet written file exclusive_rawio: bool, default: True Whether to return a rawio if there is only one rawio capable of reading the file. If this doesn't exist will return None. If set to False it will return all possible rawios organized by the most likely rawio. Returns ------- possibles: neo.RawIO | None | list[neo.RawIO] If exclusive_rawio is True, returns the single RawIO that can read a file/set of files or None. If exclusive_rawio is False it will return all possible RawIOs (organized by most likely) that could read the file or files. """ filename_or_dirname = Path(filename_or_dirname) # if filename_or_dirname doesn't exist then user may just be checking if # neo can read their file or they give a real file if not filename_or_dirname.exists() or filename_or_dirname.is_file(): ext = Path(filename_or_dirname).suffix ext_list = [ext[1:]] else: ext_list = list({filename.suffix[1:] for filename in filename_or_dirname.glob("*") if filename.is_file()}) possibles = [] for ext in ext_list: for rawio in rawiolist: if any(ext.lower() == ext2.lower() for ext2 in rawio.extensions): possibles.append(rawio) if len(possibles) == 1 and exclusive_rawio: return possibles[0] elif exclusive_rawio: return None else: possibles = [io[0] for io in Counter(possibles).most_common()] return possibles ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/alphaomegarawio.py0000644000175100017510000015375015154507213017461 0ustar00runnerrunner""" This module implements file reader for AlphaOmega MPX file format version 4. This module expect default channel names from the AlphaOmega record system (RAW ###, SPK ###, LFP ###, AI ###,…). This module reads all *.mpx files in a directory (not recursively) by default. If you provide a list of *.lsx files only the *.mpx files referenced by those *.lsx files will be loaded. The specifications are mostly extracted from the "AlphaRS User Manual V1.0.1.pdf" manual provided with the AlphaRS hardware. The specifications are described in the chapter 6: ALPHARS FILE FORMAT. See at the end of this file for file format blocks description. Some informations missing from the file specifications were kindly provided by AlphaOmega engineers. .. note:: Not a lot of memory optimization effort was put into this module. You should expect a big memory usage when loading data with this module Author: Thomas Perret """ import io import mmap import struct from collections import defaultdict from datetime import datetime from pathlib import Path, PureWindowsPath import numpy as np from .baserawio import ( BaseRawIO, _signal_buffer_dtype, _signal_stream_dtype, _signal_channel_dtype, _spike_channel_dtype, _event_channel_dtype, ) from neo.core import NeoReadWriteError class AlphaOmegaRawIO(BaseRawIO): """ AlphaOmega MPX file format 4 reader. Handles several segments. A segment is a continuous recording (when recording starts/stops). Only files in current `dirname` are loaded, subfolders are not explored. Parameters ---------- dirname: str | Path The folder from which the data will be loaded lsx_files: list[str] | None, default: None List of lsx files in `dirname` referencing mpx files to load (optional) If None all mpx files will be read prune_channels: bool, default: True If True removes the empty channels Notes ----- Because channels must be gathered into coherent streams, channels names **must** be the default channel names in AlphaRS or Alpha LAB SNR software. """ extensions = ["lsx", "mpx"] rawmode = "one-dir" STREAM_CHANNELS = ( ("Accelerometer", "ACC", "ACC"), ("Spike Train", "SPK", "SPK"), ("Local Field Potential", "LFP", "LFP"), ("Raw Analog", "RAW", "RAW"), ("Analog Input", "AI", "AI"), ("User Defined", "UD", "UD"), ) EVENT_CHANNELS = ( ("TTL", "TTL", "TTL"), ("Digital Output", "DOUT", "DOUT"), ("User Event", "UD", "UD"), ("Stim Marker", "StimMarker", "StimMarker"), ("Digital Port", "InPort", "InPort"), ("Internal Detection", "Internal Detection", "Internal Detection"), ) def __init__(self, dirname="", lsx_files=None, prune_channels=True): super().__init__(dirname=dirname) self.dirname = Path(dirname) self._lsx_files = lsx_files self._mpx_files = None self._prune_channels = prune_channels self._opened_files = {} self._ignore_unknown_datablocks = True # internal debug property if self.dirname.is_dir(): self._explore_folder() else: raise ValueError(f"{self.dirname} is not a folder") def _explore_folder(self): """ If class was instantiated with lsx_files (list of .lsx files), load only the files referenced in these lsx files otherwise, load all *.mpx files in `dirname`. It does not explores the subfolders. """ filenames = [] if self._lsx_files is not None: for index_file in self._lsx_files: index_file = self.dirname / index_file with open(index_file, "r") as f: for line in f: # a line is a Microsoft Windows path. As we cannot # instantiate a WindowsPath on other OS than MS # Windows, we use the PureWindowsPath class filename = PureWindowsPath(line.strip()) filename = self.dirname / filename.name if not filename.is_file(): self.logger.warning(f"File {filename} does not exist") else: filenames.append(filename) else: # Load all mpx. Filter in only files in case there's a folder with # .mpx extension filenames = list(filter(lambda x: x.is_file(), self.dirname.glob("*.mpx"))) if not filenames: self.logger.error(f"Found no AlphaOmega *.mpx files in {self.dirname}") else: # Sorting lexicographically should be enough to load the files in # correct order. This could improve slightly loading performances filenames.sort() self._mpx_files = filenames def _source_name(self): return str(self.dirname) def _read_file_datablocks(self, filename, prune_channels=True): """Read datablocks from AlphaOmega MPX file version 4. :param filename: the MPX filename to read datablocks from :type filename: Path-like object or str :param prune_channels: Remove references to channels and ports which doesn't contain any data recorded. Be careful when using this option with multiple-file data since it could theoretically leads to exception raised when data recorded in further files are merged into the first file pruned from these channels. :type prune_channels: bool """ continuous_analog_channels = {} segmented_analog_channels = {} digital_channels = {} channel_type = {} stream_data_channels = {} ports = {} events = [] unknown_datablocks = [] with open(filename, "rb") as f: length, block_type = HeaderType.unpack(f.read(HeaderType.size)) # First block is always type h and size 60 if block_type != b"h": try: bt = block_type.decode() self.logger.error("First block must be of type h not type {bt}") except UnicodeDecodeError: self.logger.error( (f"First block must be of type h not type " f"{int.from_bytes(bt, 'little')} (int format)") ) raise Exception("First block of AlphaOmega MPX file format must be of type h") if not length == 60: self.logger.error("First block must be of size 60 (got size {length})") raise Exception("First block of AlphaOmega MPX file format must be of size 60") ( next_block, version, hour, minute, second, hsecond, day, month, year, dayofweek, minimum_time, maximum_time, erase_count, map_version, application_name, resource_version, reserved, ) = SDataHeader.unpack(f.read(SDataHeader.size)) if map_version != 4: self.logger.error("Only map version 4 is supported") raise Exception("Only AlphaOmega MPX file format 4 is supported") resource_version = "".join([str(b) for b in resource_version]) try: resource_version = int(resource_version) except ValueError: self.logger.error(f"m_ResourceVersion should be an integer (got: {resource_version}") metadata = { "application_version": version, "application_name": decode_string(application_name), "record_date": datetime(year, month, day, hour, minute, second, 10000 * hsecond), "start_time": minimum_time, "stop_time": maximum_time, "erase_count": erase_count, "map_version": map_version, "resource_version": resource_version, "max_sample_rate": 0, } pos = 0 while True: pos += length f.seek(pos) header_bytes = f.read(HeaderType.size) length, block_type = HeaderType.unpack(header_bytes) if length == 65535: # The stop condition for reading MPX datablocks is base on # the # length of the block: if the block has length 65535 # (or -1 in signed integer value) we know we have reached # the end of the file # We could also check that we are at the end of the file # after this block break if block_type == b"h": self.logger.error("Type h block must exist only at the beginning of file") raise Exception("AlphaOmega MPX file format must not have type h block after first block") if block_type == b"2": ( next_block, is_analog, is_input, channel_number, *spike_color, ) = Type2DataBlock.unpack(f.read(Type2DataBlock.size)) if is_analog and is_input: ( mode, amplitude, sample_rate, spike_count, mode_spike, ) = SDefAnalog.unpack(f.read(SDefAnalog.size)) mode_spike_mode = (mode_spike & 0b1111000000000000) >> 12 mode_spike_master = mode_spike_mode == 1 mode_spike_slave = mode_spike_mode == 2 mode_spike_linked_channel = mode_spike & 0b0000111111111111 mode_spike = { "mode_spike_orig": mode_spike, "mode_spike_master": mode_spike_master, "mode_spike_slave": mode_spike_slave, "mode_spike_linked_channel": mode_spike_linked_channel, } metadata["max_sample_rate"] = max(metadata["max_sample_rate"], sample_rate * 1000) if amplitude <= 5: # This is true for any logging software for map # version >4 (specs say only for ALab SNR but AO # engineer says it's true for any software) amplitude = 1250000 / 2**15 if mode == 0: # continuous analog channel definition block if channel_number in channel_type: raise NeoReadWriteError( f"channel_number {channel_number} must not be in channel_type {channel_type}" ) channel_type[channel_number] = "continuous_analog" duration, total_gain_100 = SDefContinAnalog.unpack(f.read(SDefContinAnalog.size)) name_length = length - 38 name = get_name(f, name_length) if channel_number in continuous_analog_channels: raise NeoReadWriteError( f"`channel_number` {channel_number} must not be in continous_analog_channels {continuous_analog_channels}" ) continuous_analog_channels[channel_number] = { "spike_color": spike_color, "bit_resolution": amplitude, "sample_rate": sample_rate * 1000, "spike_count": spike_count, "mode_spike": mode_spike, "duration": duration, "gain": total_gain_100 / 100, "name": name, "positions": defaultdict(list), } elif mode == 1: # segmented analog channel definition block if channel_number in channel_type: raise NeoReadWriteError( f"`channel_number` {channel_number} must not be in channel_type {channel_type}" ) channel_type[channel_number] = "segmented_analog" ( pre_trig_ms, post_trig_ms, level_value, trg_mode, yes_rms, total_gain_100, ) = SDefLevelAnalog.unpack(f.read(SDefLevelAnalog.size)) name_length = length - 48 name = get_name(f, name_length) if channel_number in segmented_analog_channels: raise NeoReadWriteError( f"`channel_number` {channel_number} must be in segmented_analog_channels {segmented_analog_channels}" ) segmented_analog_channels[channel_number] = { "spike_color": spike_color, "bit_resolution": amplitude, "sample_rate": sample_rate * 1000, "spike_count": spike_count, "mode_spike": mode_spike, "pre_trig_duration": pre_trig_ms / 1000, "post_trig_duration": post_trig_ms / 1000, "level_value": level_value, "trg_mode": trg_mode, "automatic_level_base_rms": yes_rms, "gain": total_gain_100 / 100, "name": name, "positions": defaultdict(list), } else: self.logger.error(f"Unknown type 2 analog block mode: {mode}") continue elif is_analog == 0 and is_input == 1: # digital input channel definition if channel_number in channel_type: raise NeoReadWriteError( f"`channel_number` {channel_number} must not be in channel_type {channel_type}" ) channel_type[channel_number] = "digital" ( sample_rate, save_trigger, duration, prev_status, ) = SDefDigitalInput.unpack(f.read(SDefDigitalInput.size)) metadata["max_sample_rate"] = max(metadata["max_sample_rate"], sample_rate * 1000) if channel_number in digital_channels: raise NeoReadWriteError( f"`channel_number {channel_number} must not be in digital_channels {digital_channels}" ) name_length = length - 30 name = get_name(f, name_length) digital_channels[channel_number] = { "spike_color": spike_color, "sample_rate": sample_rate * 1000, "save_trigger": save_trigger, "duration": duration, "prev_status": prev_status, "name": name, "samples": [], } else: self.logger.error(f"Unknown type 2 block: analog={is_analog}, input={is_input}") continue elif block_type == b"S": # stream data definition block next_block, channel_number, sample_rate = SDefStream.unpack(f.read(SDefStream.size)) metadata["max_sample_rate"] = max(metadata["max_sample_rate"], sample_rate * 1000) if channel_number in channel_type: raise NeoReadWriteError( f"`channel_number` {channel_number} must not be in channel_type {channel_type}" ) channel_type[channel_number] = "stream_data" name_length = length - 18 name = get_name(f, name_length) stream_data_channels[channel_number] = { "sample_rate": sample_rate * 1000, "name": name, } elif block_type == b"b": # digital input/output port definition block board_number, port, sample_rate, prev_value = SDefPortX.unpack(f.read(SDefPortX.size)) metadata["max_sample_rate"] = max(metadata["max_sample_rate"], sample_rate * 1000) if port in channel_type: raise NeoReadWriteError(f"`port` {port} must not be in channel_type {channel_type}") channel_type[port] = "port" name_length = length - 18 name = get_name(f, name_length) ports[port] = { "board_number": board_number, "sample_rate": sample_rate * 1000, "prev_value": prev_value, "name": name, "samples": [], } elif block_type == b"5": # channel data block unit_number, channel_number = SDataChannel.unpack(f.read(SDataChannel.size)) if channel_number not in channel_type: raise ValueError(f"`channel_number` must be in channel_type {channel_type}") unit_number = int.from_bytes(unit_number, "little") if "analog" in channel_type[channel_number]: data_length = (length - 10) / 2 # why do we even have this check? if int(data_length) != data_length: raise ValueError(f"The data length must be equal to the int of the data length") data_length = int(data_length) data_start = f.tell() f.seek(2 * data_length, io.SEEK_CUR) if channel_type[channel_number].startswith("continuous"): if channel_number not in continuous_analog_channels: raise NeoReadWriteError( f"The `channel_number` must be in the continuous_analog_channels {continuous_analog_channels}" ) continuous_analog_channels[channel_number]["positions"][filename].append( ( SDataChannel_sample_id.unpack(f.read(SDataChannel_sample_id.size))[0], data_start, data_length, ) ) elif channel_type[channel_number].startswith("segmented"): if channel_number not in segmented_analog_channels: raise NeoReadWriteError( f"The `channel_number` must be in the segmented_analog_channels {segmented_analog_channels}" ) if unit_number > 0 and unit_number <= 4: segmented_analog_channels[channel_number]["positions"][filename].append( ( SDataChannel_sample_id.unpack(f.read(SDataChannel_sample_id.size))[0], data_start, data_length, ) ) elif unit_number == 0: segmented_analog_channels[channel_number]["positions"][filename].append( ( SDataChannel_sample_id.unpack(f.read(SDataChannel_sample_id.size))[0], data_start, data_length, ) ) else: self.logger.error(f"Unknown unit_number={unit_number} in channel data block") continue elif channel_type[channel_number] == "digital": if channel_number not in digital_channels: raise NeoReadWriteError( f"The `channel_number` must be in the digital_channels {digital_channels}" ) sample_number, value = SDataChannelDigital.unpack(f.read(SDataChannelDigital.size)) digital_channels[channel_number]["samples"].append( ( sample_number, value, ) ) elif channel_type[channel_number] == "port": if channel_number not in ports: raise NeoReadWriteError(f"The `channel_number` must be in ports {ports}") # specifications says that for ports it should be "= segment["metadata"]["record_date"] and 0 <= (s["metadata"]["start_time"] - segment["metadata"]["stop_time"]) <= factor_period / segment["metadata"]["max_sample_rate"] and s is not segment ] if len(possible_same_segments) not in (0, 1): self.logger.error( f"Cannot merge segments. Found {len(possible_same_segments)} segments following segment: {segment['metadata']}" ) continue if possible_same_segments: existing_merges = [s for segs in segments_to_merge for s in segs] if existing_merges: existing_merges.sort(key=lambda x: x["metadata"]["start_time"]) segment = existing_merges[0] segments_to_merge.append((segment, possible_same_segments[0])) for segment, segment_to_merge in segments_to_merge: sample_rate = segment["metadata"]["max_sample_rate"] sample_rate_merge = segment_to_merge["metadata"]["max_sample_rate"] if sample_rate != sample_rate_merge: self.logger.error( f"Segment to merge has sample_rate={sample_rate_merge}, expected {sample_rate}. Continuing anyway." ) segment["metadata"]["stop_time"] = segment_to_merge["metadata"]["stop_time"] segment["metadata"]["filenames"].extend(segment_to_merge["metadata"]["filenames"]) for stream in segment_to_merge["streams"]: for channel_id in segment_to_merge["streams"][stream]: try: channel = segment["streams"][stream][channel_id] except KeyError: # there can potentially have segment without stream for this # channel but the segment to merge has stream for this channel segment["streams"][stream][channel_id] = segment_to_merge["streams"][stream][channel_id] else: for f in segment_to_merge["streams"][stream][channel_id]["positions"]: channel["positions"][f].extend( segment_to_merge["streams"][stream][channel_id]["positions"][f] ) for channel_id in segment_to_merge["events"]: try: channel = segment["events"][channel_id] except KeyError: # it's possible that the first segment doesn't record any # port channel data and the port could have been pruned segment["events"][channel_id] = segment_to_merge["events"][channel_id] else: channel["samples"].extend(segment_to_merge["events"][channel_id]["samples"]) for channel_id in segment_to_merge["spikes"]: try: channel = segment["spikes"][channel_id] except KeyError: # there can potentially have segment without spike for this # channel but the segment to merge has spikes for this channel segment["spikes"][channel_id] = segment_to_merge["spikes"][channel_id] else: for f in segment_to_merge["spikes"][channel_id]["positions"]: channel["positions"][f].extend(segment_to_merge["spikes"][channel_id]["positions"][f]) segment["ao_events"].extend(segment_to_merge["ao_events"]) for channel_id in segment_to_merge["stream_data"]: # To be honest, I have no idea what is a # stream_data_channels so let's just overwrite it here segment["stream_data"][channel_id] = segment_to_merge["stream_data"][channel_id] self._segments.remove(segment_to_merge) def _parse_header(self): segments = [] for i, filename in enumerate(self._mpx_files): metadata, cac, sac, dc, ct, sd, p, e, ub = self._read_file_datablocks(filename, self._prune_channels) metadata["filenames"] = [filename] streams = {} for stream_name, channel_name_start, stream_id in self.STREAM_CHANNELS: channels = {i: c for i, c in cac.items() if c["name"].startswith(channel_name_start)} streams[stream_id] = channels events = dc.copy() events.update(p) segment = { "metadata": metadata, "streams": streams, "events": events, "spikes": sac, "ao_events": e, "stream_data": sd, } segments.append(segment) self._segments = segments # We merge segments after having loading all the files because they # could be loaded in any order self._merge_segments() buffer_id = "" signal_streams = set( (stream_name, stream_id, buffer_id) for segment in self._segments for stream in segment["streams"] for stream_name, _, stream_id in self.STREAM_CHANNELS if stream_id == stream and segment["streams"][stream] ) signal_streams = list(signal_streams) signal_streams.sort(key=lambda x: x[1]) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) signal_buffers = np.array([], dtype=_signal_buffer_dtype) buffer_id = "" signal_channels = set( ( channel["name"], channel_id, channel["sample_rate"], np.dtype(np.short).name, "uV", channel["gain"] / channel["bit_resolution"], 0, stream_id, buffer_id, ) for segment in self._segments for stream_id in segment["streams"] for channel_id, channel in segment["streams"][stream_id].items() ) signal_channels = list(signal_channels) signal_channels.sort(key=lambda x: (x[7], x[0])) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) spike_channels = set( ( c["name"], i, "uV", c["gain"] / c["bit_resolution"], 0, round(c["pre_trig_duration"] * c["sample_rate"]), c["sample_rate"], ) for segment in self._segments for i, c in segment["spikes"].items() ) spike_channels = list(spike_channels) spike_channels.sort(key=lambda x: x[0]) spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = set( (event["name"], i, "event") for segment in self._segments for i, event in segment["events"].items() ) event_channels = list(event_channels) event_channels.sort(key=lambda x: x[1]) event_channels = np.array(event_channels, dtype=_event_channel_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [len(self._segments)] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() bl_ann = self.raw_annotations["blocks"][0] bl_ann["name"] = "Block #{}{}".format(0, " from lsx file(s) {self._lsx_files}" if self._lsx_files else "") bl_ann["file_origin"] = ( "\n".join(str(self.dirname / f) for f in self._lsx_files) if self._lsx_files else bl_ann["file_origin"] ) bl_ann["rec_datetime"] = self._segments[0]["metadata"]["record_date"] for seg_index, segment in enumerate(self._segments): seg_ann = bl_ann["segments"][seg_index] seg_ann["name"] = f"Seg #{seg_index} Block #0" seg_ann["file_origin"] = "\n".join(str(f) for f in self._segments[seg_index]["metadata"]["filenames"]) seg_ann["rec_datetime"] = self._segments[seg_index]["metadata"]["record_date"] for c_index, c in enumerate(seg_ann["signals"]): c = c.copy() c["file_origin"] = "\n".join( set( str(f) for channels in self._segments[seg_index]["streams"][c["stream_id"]].values() for f in channels["positions"] ) ) seg_ann["signals"][c_index] = c for e_index, e in enumerate(seg_ann["events"]): e = e.copy() e["file_origin"] = seg_ann["file_origin"] seg_ann["events"][e_index] = e # We open files and create mmap objects for filename in self._mpx_files: if filename not in self._opened_files: self._opened_files[filename] = {} self._opened_files[filename]["file"] = filename.open(mode="rb") self._opened_files[filename]["mmap"] = mmap.mmap( self._opened_files[filename]["file"].fileno(), 0, access=mmap.ACCESS_READ, ) def __del__(self): # To be sure we close the file when object is deleted. Be aware that the # __del__ method is not necessarily called when interpreter exits so we # could still leave file opened. This is probably bad… for filename in self._opened_files: self._opened_files[filename]["mmap"].close() self._opened_files[filename]["file"].close() if hasattr(super(), "__del__"): super().__del__() def _segment_t_start(self, block_index, seg_index): return self._segments[seg_index]["metadata"]["start_time"] def _segment_t_stop(self, block_index, seg_index): return self._segments[seg_index]["metadata"]["stop_time"] def _get_signal_size(self, block_index, seg_index, stream_index): stream_id = self.header["signal_streams"][stream_index]["id"] sizes = [ sum(sample[2] for sample_by_file in channel["positions"].values() for sample in sample_by_file) for channel in self._segments[seg_index]["streams"][stream_id].values() ] if not all(s == sizes[0] for s in sizes): raise NeoReadWriteError("The sizes of signals must be the same to get signal size") return sizes[0] def _get_signal_t_start(self, block_index, seg_index, stream_index): return self._segment_t_start(block_index, seg_index) def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if i_start is None: i_start = 0 signal_size = self._get_signal_size(block_index, seg_index, stream_index) if i_stop is None or i_stop > signal_size: i_stop = signal_size stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id channel_ids = self.header["signal_channels"][mask]["id"][channel_indexes].flatten() # the data refers to timestamp (see docstrings) which does not start at # 0 but at time_start (see type H docstring) first_pos = [] for channel_id in channel_ids: channel_id = int(channel_id) first_pos.append( min( p[0] for f in self._segments[seg_index]["streams"][stream_id][channel_id]["positions"].values() for p in f ) ) # now we need to get all file data block indexes for the signal and # timestamp we want file_chunks = defaultdict(list) for i, channel_id in enumerate(channel_ids): channel_id = int(channel_id) effective_start = i_start + first_pos[i] effective_stop = i_stop + first_pos[i] for filename, positions in self._segments[seg_index]["streams"][stream_id][channel_id]["positions"].items(): file_chunks[filename].extend( [(i, *p) for p in positions if p[0] + p[2] > effective_start and p[0] < effective_stop] ) # we almost surely loaded more than asked (because the blocks are not # contiguous) so we need to know where to cut the results slices_channels = [] for channel_index, channel_id in enumerate(channel_ids): channel_id = int(channel_id) min_pos = ( min(p[0] for f in file_chunks.values() for i, *p in f if i == channel_index) - first_pos[channel_index] ) max_pos = ( max(p[0] + p[2] for f in file_chunks.values() for i, *p in f if i == channel_index) - first_pos[channel_index] ) slices_channels.append((min_pos, max_pos)) min_size = min(s[0] for s in slices_channels) max_size = max(s[1] for s in slices_channels) sigs = np.ndarray((max_size - min_size, len(channel_ids)), dtype=np.short) for filename in file_chunks: # we sort by chunk position in the file because we want to optimize # IO access and possibly read in sequential access. This is mainly # true for hard drives but shouldn't hurt flash memory file_chunks[filename].sort(key=lambda x: x[2]) for filename in file_chunks: for channel_index, chunk_index, file_position, chunk_size in file_chunks[filename]: sig_offset = chunk_index - first_pos[channel_index] - min_size sigs[sig_offset : sig_offset + chunk_size, channel_index] = np.frombuffer( self._opened_files[filename]["mmap"], dtype=np.short, count=chunk_size, offset=file_position, ) return sigs[i_start - min_size : i_stop - min_size, :] def _spike_count(self, block_index, seg_index, spike_channel_index): spike_id = int(self.header["spike_channels"]["id"][spike_channel_index]) nb_spikes = sum(len(f) for f in self._segments[seg_index]["spikes"][spike_id]["positions"].values()) return nb_spikes def _get_spike_timestamps(self, block_index, seg_index, spike_channel_index, t_start, t_stop): if self._spike_count(block_index, seg_index, spike_channel_index): spike_id = int(self.header["spike_channels"]["id"][spike_channel_index]) spikes = self._segments[seg_index]["spikes"][spike_id] if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) effective_start = t_start * spikes["sample_rate"] effective_stop = t_stop * spikes["sample_rate"] timestamps = np.array( [p[0] for f in spikes["positions"].values() for p in f if effective_start <= p[0] <= effective_stop] ) else: timestamps = np.array([], dtype=np.uint32) return timestamps def _rescale_spike_timestamp(self, spike_timestamps, dtype): # let's hope every spike channels have the same sampling rate sample_rate = int(self.header["spike_channels"]["wf_sampling_rate"][0]) spike_timestamps = spike_timestamps.astype(dtype) / sample_rate return spike_timestamps def _get_spike_raw_waveforms(self, block_index, seg_index, spike_channel_index, t_start, t_stop): spike_id = int(self.header["spike_channels"]["id"][spike_channel_index]) # nb_spikes = self._spike_count(block_index, seg_index, spike_channel_index) nb_spikes = self._get_spike_timestamps(block_index, seg_index, spike_channel_index, t_start, t_stop).size spikes = self._segments[seg_index]["spikes"][spike_id] spike_length = {p[2] for f in spikes["positions"].values() for p in f} if len(spike_length) != 1: raise ValueError(f"The len of `spike_length` must be 1 not {len(spike_length)}") spike_length = spike_length.pop() waveforms = np.ndarray((nb_spikes, spike_length), dtype=np.short) if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) effective_start = t_start * spikes["sample_rate"] effective_stop = t_stop * spikes["sample_rate"] i = 0 for filename in spikes["positions"]: for timestamp, file_position, length in spikes["positions"][filename]: if effective_start <= timestamp <= effective_stop: waveforms[i, :length] = np.frombuffer( self._opened_files[filename]["mmap"], dtype=np.short, count=length, offset=file_position, ) i += 1 waveforms.shape = nb_spikes, 1, spike_length return waveforms def _event_count(self, block_index, seg_index, event_channel_index): event_id = int(self.header["event_channels"]["id"][event_channel_index]) try: nb_events = len(self._segments[seg_index]["events"][event_id]["samples"]) except KeyError: # No event in this segment nb_events = 0 return nb_events def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): if self._event_count(block_index, seg_index, event_channel_index): event_id = int(self.header["event_channels"]["id"][event_channel_index]) event = self._segments[seg_index]["events"][event_id] timestamps = np.array([s[0] for s in event["samples"]], dtype=np.uint32) if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) effective_start = t_start * event["sample_rate"] effective_stop = t_stop * event["sample_rate"] mask = (effective_start <= timestamps) & (timestamps <= effective_stop) timestamps = timestamps[mask] labels = np.array([s[1] for s in event["samples"]], dtype="U") labels = labels[mask] else: timestamps = np.array([], dtype=np.uint32) labels = np.array([], dtype="U") return timestamps, None, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_id = int(self.header["event_channels"]["id"][event_channel_index]) for segment in self._segments: if event_id in segment["events"]: event = segment["events"][event_id] break event_times = event_timestamps.astype(dtype) / event["sample_rate"] return event_times def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): pass def decode_string(encoded_string): """According to AlphaOmega engineers, all strings are NULL terminated and ASCII encoded""" return encoded_string[: encoded_string.find(b"\x00")].decode("ascii") def get_name(f, name_length): """Helper function to read a string from opened binary file""" return decode_string(struct.unpack(f"<{name_length}s", f.read(name_length))[0]) HeaderType = struct.Struct(" 100) - alignment byte: ignore - reserved (long): not used """ Type2DataBlock = struct.Struct(">> import logging >>> r = AxographRawIO(filename) >>> r.logger.setLevel(logging.DEBUG) >>> r.parse_header() Background and Terminology -------------------------- Acquisition modes: AxoGraph can operate in two main data acquisition modes: - Episodic "protocol-driven" acquisition mode, in which the program records from specified signal channels for a fixed duration each time a trigger is detected. Each trigger-activated recording is called an "episode". From files acquired in this mode, AxographRawIO creates multiple Neo Segments, one for each episode, unless force_single_segment=True. - Continuous "chart recorder" acquisition mode, in which it creates a continuous recording that can be paused and continued by the user whenever they like. From files acquired in this mode, AxographRawIO creates a single Neo Segment. "Episode": analogous to a Neo Segment See descriptions of acquisition modes above and of groups below. "Column": analogous to a Quantity array A column is a 1-dimensional array of data, stored in any one of a number of data types (e.g., scaled ints or floats). In the oldest version of the AxoGraph file format, even time was stored as a 1-dimensional array. In newer versions, time is stored as a special type of "column" that is really just a starting time and a sampling period. Column data appears in series in the file, i.e., all of the first column's data appears before the second column's. As an aside, because of this design choice AxoGraph cannot write data to disk as it is collected but must store it all in memory until data acquisition ends. This also affected how file slicing was implemented for this RawIO: Instead of using a single memmap to address into a 2-dimensional block of data, AxographRawIO constructs multiple 1-dimensional memmaps, one for each column, each with its own offset. Each column's data array is preceded by a header containing the column title, which normally contains the units (e.g., "Current (nA)"). Data recorded in episodic acquisition mode will contain a repeating sequence of column names, where each repetition corresponds to an episode (e.g., "Time", "Column A", "Column B", "Column A", "Column B", etc.). AxoGraph offers a spreadsheet view for viewing all column data. "Trace": analogous to a single-channel Neo AnalogSignal A trace is a 2-dimensional series. Raw data is not stored in the part of the file concerned with traces. Instead, in the header for each trace are indexes pointing to two data columns, defined earlier in the file, corresponding to the trace's x and y data. These indexes can be changed in AxoGraph under the "Assign X and Y Columns" tool, though doing so may violate assumptions made by AxographRawIO. For time series data collected under the usual data acquisition modes that has not been modified after collection by the user, the x-index always points to the time column; one trace exists for each non-time column, with the y-index pointing to that column. Traces are analogous to AnalogSignals in Neo. However, for simplicity of implementation, AxographRawIO does not actually check the pairing of columns in the trace headers. Instead it assumes the default pairing described above when it creates signal channels while scanning through columns. Older versions of the AxoGraph file format lack trace headers entirely, so this is the most general solution. Trace headers contain additional information about the series, such as plot style, which is parsed by AxographRawIO and made available in self.info['trace_header_info_list'] but is otherwise unused. "Group": analogous to a Neo ChannelIndex for matching channels across Segments A group is a collection of one or more traces. Like traces, raw data is not stored in the part of the file concerned with groups. Instead, each trace header contains an index pointing to the group it is assigned to. Group assignment of traces can be changed in AxoGraph under the "Group Traces" tool, or by using the "Merge Traces" or "Separate Traces" commands, though doing so may violate assumptions made by AxographRawIO. Files created in episodic acquisition mode contain multiple traces per group, one for each episode. In that mode, a group corresponds to a signal channel and is analogous to a ChannelIndex in Neo; the traces within the group represent the time series recorded for that channel across episodes and are analogous to AnalogSignals from multiple Segments in Neo. In contrast, files created in continuous acquisition mode contain one trace per group, each corresponding to a signal channel. In that mode, groups and traces are basically conceptually synonymous, though the former can still be thought of as analogous to ChannelIndexes in Neo for a single-Segment. Group headers are only consulted by AxographRawIO to determine if is safe to interpret a file as episodic and therefore translatable to multiple Segments in Neo. Certain criteria have to be met, such as all groups containing equal numbers of traces and each group having homogeneous signal parameters. If trace grouping was modified by the user after data acquisition, this may result in the file being interpreted as non-episodic. Older versions of the AxoGraph file format lack group headers entirely, so these files are never deemed safe to interpret as episodic, even if the column names follow a repeating sequence as described above. "Tag" / "Event marker": analogous to a Neo Event In continuous acquisition mode, the user can press a hot key to tag a moment in time with a short label. Additionally, if the user stops or restarts data acquisition in this mode, a tag is created automatically with the label "Stop" or "Start", respectively. These are displayed by AxoGraph as event markers. AxographRawIO will organize all event markers into a single Neo Event channel with the name "AxoGraph Tags". In episodic acquisition mode, the tag hot key behaves differently. The current episode number is recorded in a user-editable notes section of the file, made available by AxographRawIO in self.info['notes']. Because these do not correspond to moments in time, they are not processed into Neo Events. "Interval bar": analogous to a Neo Epoch After data acquisition, the user can annotate an AxoGraph file with horizontal, labeled bars called interval bars that span a specified period of time. These are not episode specific. AxographRawIO will organize all interval bars into a single Neo Epoch channel with the name "AxoGraph Intervals". """ from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) import os from datetime import datetime from io import open, BufferedReader from struct import unpack, calcsize import numpy as np class AxographRawIO(BaseRawIO): """ RawIO class for reading AxoGraph files (.axgd, .axgx) Parameters ---------- filename: str File name of the AxoGraph file to read. force_single_segment: bool, default: False Episodic files are normally read as multi-Segment Neo objects. This parameter can force AxographRawIO to put all signals into a single Segment. Examples -------- >>> import neo >>> reader = neo.rawio.AxographRawIO(filename=filename) >>> reader.parse_header() >>> print(reader) >>> # get signals >>> raw_chunk = reader.get_analogsignal_chunk(block_index=0, ... seg_index=0, ... i_start=0, ... i_stop=1024, ... channel_names=channel_names) >>> float_chunk = r.rescale_signal_raw_to_float(raw_chunk, ... dtype='float64', ... channel_names=channel_names) >>> print(float_chunk) >>> # get event markers >>> ev_raw_times, _, ev_labels = reader.get_event_timestamps(event_channel_index=0) >>> ev_times = reader.rescale_event_timestamp(ev_raw_times, dtype='float64') >>> print([ev for ev in zip(ev_times, ev_labels)]) >>> # get interval bars >>> ep_raw_times, ep_raw_durations, ep_labels = reader.get_event_timestamps(event_channel_index=1) >>> ep_times = reader.rescale_event_timestamp(ep_raw_times, dtype='float64') >>> ep_durations = reader.rescale_epoch_duration(ep_raw_durations, dtype='float64') >>> print([ep for ep in zip(ep_times, ep_durations, ep_labels)]) >>> # get notes >>> print(reader.info['notes']) >>> # get other miscellaneous info >>> print(reader.info) """ name = "AxographRawIO" description = "This IO reads .axgd/.axgx files created with AxoGraph" extensions = ["axgd", "axgx", ""] rawmode = "one-file" def __init__(self, filename, force_single_segment=False): BaseRawIO.__init__(self) self.filename = filename self.force_single_segment = force_single_segment def _parse_header(self): self.header = {} self._scan_axograph_file() if not self.force_single_segment and self._safe_to_treat_as_episodic(): self.logger.debug("Will treat as episodic") self._convert_to_multi_segment() else: self.logger.debug("Will not treat as episodic") self.logger.debug("") self._generate_minimal_annotations() blk_annotations = self.raw_annotations["blocks"][0] blk_annotations["format_ver"] = self.info["format_ver"] blk_annotations["comment"] = self.info["comment"] if "comment" in self.info else None blk_annotations["notes"] = self.info["notes"] if "notes" in self.info else None blk_annotations["rec_datetime"] = self._get_rec_datetime() # modified time is not ideal but less prone to # cross-platform issues than created time (ctime) blk_annotations["file_datetime"] = datetime.fromtimestamp(os.path.getmtime(self.filename)) def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): # same for all segments return self._t_start def _segment_t_stop(self, block_index, seg_index): # same for all signals in all segments t_stop = self._t_start + len(self._raw_signals[seg_index][0]) * self._sampling_period return t_stop ### # signal and channel zone def _get_signal_size(self, block_index, seg_index, stream_index): # same for all signals in all segments return len(self._raw_signals[seg_index][0]) def _get_signal_t_start(self, block_index, seg_index, stream_index): # same for all signals in all segments return self._t_start def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if channel_indexes is None or np.all(channel_indexes == slice(None, None, None)): channel_indexes = range(self.signal_channels_count(stream_index)) raw_signals = [ self._raw_signals[seg_index][channel_index][slice(i_start, i_stop)] for channel_index in channel_indexes ] raw_signals = np.array(raw_signals).T # loads data into memory return raw_signals ### # spiketrain and unit zone def _spike_count(self, block_index, seg_index, unit_index): # not supported return None def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): # not supported return None def _rescale_spike_timestamp(self, spike_timestamps, dtype): # not supported return None ### # spike waveforms zone def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): # not supported return None ### # event and epoch zone def _event_count(self, block_index, seg_index, event_channel_index): # Retrieve size of either event or epoch channel: # event_channel_index: 0 AxoGraph Tags, 1 AxoGraph Intervals # AxoGraph tags can only be inserted in continuous data acquisition # mode. When the tag hot key is pressed in episodic acquisition mode, # the notes are updated with the current episode number instead of an # instantaneous event marker being created. This means that Neo-like # Events cannot be generated by AxoGraph for multi-Segment (episodic) # files. Furthermore, Neo-like Epochs (interval markers) are not # episode specific. For these reasons, this function ignores seg_index. return self._raw_event_epoch_timestamps[event_channel_index].size def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): # Retrieve either event or epoch data, unscaled: # event_channel_index: 0 AxoGraph Tags, 1 AxoGraph Intervals # AxoGraph tags can only be inserted in continuous data acquisition # mode. When the tag hot key is pressed in episodic acquisition mode, # the notes are updated with the current episode number instead of an # instantaneous event marker being created. This means that Neo-like # Events cannot be generated by AxoGraph for multi-Segment (episodic) # files. Furthermore, Neo-like Epochs (interval markers) are not # episode specific. For these reasons, this function ignores seg_index. timestamps = self._raw_event_epoch_timestamps[event_channel_index] durations = self._raw_event_epoch_durations[event_channel_index] labels = self._event_epoch_labels[event_channel_index] if durations is None: # events if t_start is not None: # keep if event occurs after t_start ... keep = timestamps >= int(t_start / self._sampling_period) timestamps = timestamps[keep] labels = labels[keep] if t_stop is not None: # ... and before t_stop keep = timestamps <= int(t_stop / self._sampling_period) timestamps = timestamps[keep] labels = labels[keep] else: # epochs if t_start is not None: # keep if epoch ends after t_start ... keep = timestamps + durations >= int(t_start / self._sampling_period) timestamps = timestamps[keep] durations = durations[keep] labels = labels[keep] if t_stop is not None: # ... and starts before t_stop keep = timestamps <= int(t_stop / self._sampling_period) timestamps = timestamps[keep] durations = durations[keep] labels = labels[keep] return timestamps, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): # Scale either event or epoch start times from sample index to seconds # (t_start shouldn't be added) event_times = event_timestamps.astype(dtype) * self._sampling_period return event_times def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): # Scale epoch durations from samples to seconds epoch_durations = raw_duration.astype(dtype) * self._sampling_period return epoch_durations ### # multi-segment zone def _safe_to_treat_as_episodic(self): """ The purpose of this function is to determine if the file contains any irregularities in its grouping of traces such that it cannot be treated as episodic. Even "continuous" recordings can be treated as single-episode recordings and could be identified as safe by this function. Recordings in which the user has changed groupings to create irregularities should be caught by this function. """ # First check: Old AxoGraph file formats do not contain enough metadata # to know for certain that the file is episodic. if self.info["format_ver"] < 3: self.logger.debug("Cannot treat as episodic because old format " "contains insufficient metadata") return False # Second check: If the file is episodic, it should report that it # contains more than 1 episode. if "n_episodes" not in self.info: self.logger.debug("Cannot treat as episodic because episode " "metadata is missing or could not be parsed") return False if self.info["n_episodes"] == 1: self.logger.debug("Cannot treat as episodic because file reports " "one episode") return False # Third check: If the file is episodic, groups of traces should all # contain the same number of traces, one for each episode. This is # generally true of "continuous" (single-episode) recordings as well, # which normally have 1 trace per group. if "group_header_info_list" not in self.info: self.logger.debug("Cannot treat as episodic because group " "metadata is missing or could not be parsed") return False if "trace_header_info_list" not in self.info: self.logger.debug("Cannot treat as episodic because trace " "metadata is missing or could not be parsed") return False group_id_to_col_indexes = {} for group_id in self.info["group_header_info_list"]: col_indexes = [] for trace_header in self.info["trace_header_info_list"].values(): if trace_header["group_id_for_this_trace"] == group_id: col_indexes.append(trace_header["y_index"]) group_id_to_col_indexes[group_id] = col_indexes n_traces_by_group = {k: len(v) for k, v in group_id_to_col_indexes.items()} all_groups_have_same_number_of_traces = len(np.unique(list(n_traces_by_group.values()))) == 1 if not all_groups_have_same_number_of_traces: self.logger.debug("Cannot treat as episodic because groups differ " "in number of traces") return False # Fourth check: The number of traces in each group should equal # n_episodes. n_traces_per_group = np.unique(list(n_traces_by_group.values())) if n_traces_per_group != self.info["n_episodes"]: self.logger.debug( "Cannot treat as episodic because n_episodes " "does not match number of traces per group" ) return False # Fifth check: If the file is episodic, all traces within a group # should have identical signal channel parameters (e.g., name, units) # except for their unique ids. This too is generally true of # "continuous" (single-episode) files, which normally have 1 trace per # group. signal_channels_with_ids_dropped = self.header["signal_channels"][ [n for n in self.header["signal_channels"].dtype.names if n != "id"] ] group_has_uniform_signal_parameters = {} for group_id, col_indexes in group_id_to_col_indexes.items(): # subtract 1 from indexes in next statement because time is not # included in signal_channels signal_params_for_group = np.array(signal_channels_with_ids_dropped[np.array(col_indexes) - 1]) group_has_uniform_signal_parameters[group_id] = len(np.unique(signal_params_for_group)) == 1 all_groups_have_uniform_signal_parameters = np.all(list(group_has_uniform_signal_parameters.values())) if not all_groups_have_uniform_signal_parameters: self.logger.debug("Cannot treat as episodic because some groups " "have heterogeneous signal parameters") return False # all checks passed self.logger.debug("Can treat as episodic") return True def _convert_to_multi_segment(self): """ Reshape signal headers and signal data for an episodic file """ self.header["nb_segment"] = [self.info["n_episodes"]] # drop repeated signal headers self.header["signal_channels"] = self.header["signal_channels"].reshape(self.info["n_episodes"], -1)[0] # reshape signal memmap list new_sig_memmaps = [] n_channels = len(self.header["signal_channels"]) sig_memmaps = self._raw_signals[0] for first_index in np.arange(0, len(sig_memmaps), n_channels): new_sig_memmaps.append(sig_memmaps[first_index : first_index + n_channels]) self._raw_signals = new_sig_memmaps self.logger.debug(f"New number of segments: {self.info['n_episodes']}") return def _get_rec_datetime(self): """ Determine the date and time at which the recording was started from automatically generated notes. How these notes should be parsed differs depending on whether the recording was obtained in episodic or continuous acquisition mode. """ rec_datetime = None date_string = "" time_string = "" datetime_string = "" if "notes" not in self.info: return None for note_line in self.info["notes"].split("\n"): # episodic acquisition mode if note_line.startswith("Created on "): date_string = note_line.strip("Created on ") if note_line.startswith("Start data acquisition at "): time_string = note_line.strip("Start data acquisition at ") # continuous acquisition mode if note_line.startswith("Created : "): datetime_string = note_line.strip("Created : ") if date_string and time_string: datetime_string = " ".join([date_string, time_string]) if datetime_string: try: rec_datetime = datetime.strptime(datetime_string, "%a %b %d %Y %H:%M:%S") except ValueError: pass return rec_datetime def _scan_axograph_file(self): """ This function traverses the entire AxoGraph file, constructing memmaps for signals and collecting channel information and other metadata """ self.info = {} with open(self.filename, "rb") as fid: f = StructFile(fid) self.logger.debug(f"filename: {self.filename}") self.logger.debug("") # the first 4 bytes are always a 4-character file type identifier # - for early versions of AxoGraph, this identifier was 'AxGr' # - starting with AxoGraph X, the identifier is 'axgx' header_id = f.read(4).decode("utf-8") self.info["header_id"] = header_id if header_id not in ["AxGr", "axgx"]: raise NeoReadWriteError(f'File "{self.filename}" is not an AxoGraph binary file! Use other neo reader') self.logger.debug(f"{header_id=}") # the next two numbers store the format version number and the # number of data columns to follow # - for 'AxGr' files, these numbers are 2-byte unsigned short ints # - for 'axgx' files, these numbers are 4-byte long ints # - the 4-character identifier changed from 'AxGr' to 'axgx' with # format version 3 if header_id == "AxGr": format_ver, n_cols = f.read_f("HH") if format_ver != 1 and format_ver != 2: raise ValueError( f'Mismatch between header identifier "{header_id}" and format ' f'version "{format_ver}"!' ) elif header_id == "axgx": format_ver, n_cols = f.read_f("ll") if format_ver < 3: raise ValueError( f'Mismatch between header identifier "{header_id}" and format ' f'version "{format_ver}"!' ) else: raise NotImplementedError(f'unimplemented file header identifier "{header_id}"!') self.info["format_ver"] = format_ver self.info["n_cols"] = n_cols self.logger.debug(f"format_ver: {format_ver}") self.logger.debug(f"n_cols: {n_cols}") self.logger.debug("") ############################################## # BEGIN COLUMNS sig_memmaps = [] sig_channels = [] for i in range(n_cols): self.logger.debug(f"== COLUMN INDEX {i} ==") ############################################## # NUMBER OF DATA POINTS IN COLUMN n_points = f.read_f("l") self.logger.debug(f"n_points: {n_points}") ############################################## # COLUMN TYPE # depending on the format version, data columns may have a type # - prior to version 3, column types did not exist and data was # stored in a fixed pattern # - beginning with version 3, several data types are available # as documented in AxoGraph_ReadWrite.h if format_ver == 1 or format_ver == 2: col_type = None elif format_ver >= 3: col_type = f.read_f("l") else: raise NotImplementedError(f'unimplemented file format version "{format_ver}"!') self.logger.debug(f"col_type: {col_type}") ############################################## # COLUMN NAME AND UNITS # depending on the format version, column titles are stored # differently # - prior to version 3, column titles were stored as # fixed-length 80-byte Pascal strings # - beginning with version 3, column titles are stored as # variable-length strings (see StructFile.read_string for # details) if format_ver == 1 or format_ver == 2: title = f.read_f("80p").decode("utf-8") elif format_ver >= 3: title = f.read_f("S") else: raise NotImplementedError(f'unimplemented file format version "{format_ver}"!') self.logger.debug(f"title: {title}") # units are given in parentheses at the end of a column title, # unless units are absent if len(title.split()) > 0 and title.split()[-1][0] == "(" and title.split()[-1][-1] == ")": name = " ".join(title.split()[:-1]) units = title.split()[-1].strip("()") else: name = title units = "" self.logger.debug(f"name: {name}") self.logger.debug(f"units: {units}") ############################################## # COLUMN DTYPE, SCALE, OFFSET if format_ver == 1: # for format version 1, all columns are arrays of floats dtype = "f" gain, offset = 1, 0 # data is neither scaled nor off-set elif format_ver == 2: # for format version 2, the first column is a "series" of # regularly spaced values specified merely by a first value # and an increment, and all subsequent columns are arrays # of shorts with a scaling factor if i == 0: # series first_value, increment = f.read_f("ff") self.logger.debug(f"interval: {increment}, freq: {1 / increment}") self.logger.debug(f"start: {first_value}, end: {first_value + increment * (n_points - 1)}") # assume this is the time column t_start, sampling_period = first_value, increment self.info["t_start"] = t_start self.info["sampling_period"] = sampling_period self.logger.debug("") continue # skip memmap, chan info for time col else: # scaled short dtype = "h" gain, offset = f.read_f("f"), 0 # data is scaled without offset elif format_ver >= 3: # for format versions 3 and later, the column type # determines how the data should be read # - column types 1, 2, 3, and 8 are not defined in # AxoGraph_ReadWrite.h # - column type 9 is different from the others in that it # represents regularly spaced values # (such as times at a fixed frequency) specified by a # first value and an increment, without storing a large # data array if col_type == 9: # series first_value, increment = f.read_f("dd") self.logger.debug(f"interval: {increment}, freq: {1 / increment}") self.logger.debug("start: {first_value}, end: {first_value + increment * (n_points - 1)}") if i == 0: # assume this is the time column t_start, sampling_period = first_value, increment self.info["t_start"] = t_start self.info["sampling_period"] = sampling_period self.logger.debug("") continue # skip memmap, chan info for time col else: raise NotImplementedError( "series data are supported only for the first " "data column (time)!" ) elif col_type == 4: # short dtype = "h" gain, offset = 1, 0 # data neither scaled nor off-set elif col_type == 5: # long dtype = "l" gain, offset = 1, 0 # data neither scaled nor off-set elif col_type == 6: # float dtype = "f" gain, offset = 1, 0 # data neither scaled nor off-set elif col_type == 7: # double dtype = "d" gain, offset = 1, 0 # data neither scaled nor off-set elif col_type == 10: # scaled short dtype = "h" gain, offset = f.read_f("dd") # data scaled w/ offset else: raise NotImplementedError(f'unimplemented column type "{col_type}"!') else: raise NotImplementedError(f'unimplemented file format version "{format_ver}"!') ############################################## # COLUMN MEMMAP AND CHANNEL INFO # create a memory map that allows accessing parts of the file # without loading it all into memory array = np.memmap(self.filename, mode="r", dtype=f.byte_order + dtype, offset=f.tell(), shape=n_points) # advance the file position to after the data array f.seek(array.nbytes, 1) if i == 0: # assume this is the time column containing n_points values # verify times are spaced regularly diffs = np.diff(array) increment = np.median(diffs) max_frac_step_deviation = np.max(np.abs(diffs / increment - 1)) tolerance = 1e-3 if max_frac_step_deviation > tolerance: self.logger.debug( "largest proportional deviation " "from median step size in the first " "column exceeds the tolerance " "of " + str(tolerance) + ":" " " + str(max_frac_step_deviation) ) raise ValueError("first data column (assumed to be " "time) is not regularly spaced") first_value = array[0] self.logger.debug(f"interval: {increment}, freq: {1 / increment}") self.logger.debug(f"start: {first_value}, end: {first_value + increment * (n_points - 1)}") t_start, sampling_period = first_value, increment self.info["t_start"] = t_start self.info["sampling_period"] = sampling_period self.logger.debug("") continue # skip saving memmap, chan info for time col else: # not a time column self.logger.debug(f"gain: {gain}, offset: {offset}") self.logger.debug("initial data: {array[:5] * gain + offset}") # channel_info will be cast to _signal_channel_dtype buffer_id = "" stream_id = "0" channel_info = ( name, str(i), 1 / sampling_period, f.byte_order + dtype, units, gain, offset, stream_id, buffer_id, ) self.logger.debug("channel_info: {channel_info}") self.logger.debug("") sig_memmaps.append(array) sig_channels.append(channel_info) # END COLUMNS ############################################## # initialize lists for events and epochs raw_event_timestamps = [] raw_epoch_timestamps = [] raw_epoch_durations = [] event_labels = [] epoch_labels = [] # the remainder of the file may contain metadata, events and epochs try: ############################################## # COMMENT self.logger.debug("== COMMENT ==") comment = f.read_f("S") self.info["comment"] = comment self.logger.debug(comment if comment else "no comment!") self.logger.debug("") ############################################## # NOTES self.logger.debug("== NOTES ==") notes = f.read_f("S") self.info["notes"] = notes self.logger.debug(notes if notes else "no notes!") self.logger.debug("") ############################################## # TRACES self.logger.debug("== TRACES ==") n_traces = f.read_f("l") self.info["n_traces"] = n_traces self.logger.debug(f"n_traces: {n_traces}") self.logger.debug("") trace_header_info_list = {} group_ids = [] for i in range(n_traces): # AxoGraph traces are 1-indexed in GUI, so use i+1 below self.logger.debug(f"== TRACE #{i+1} ==") trace_header_info = {} if format_ver < 6: # before format version 6, there was only one version # of the header, and version numbers were not provided trace_header_info["trace_header_version"] = 1 else: # for format versions 6 and later, the header version # must be read trace_header_info["trace_header_version"] = f.read_f("l") if trace_header_info["trace_header_version"] == 1: TraceHeaderDescription = TraceHeaderDescriptionV1 elif trace_header_info["trace_header_version"] == 2: TraceHeaderDescription = TraceHeaderDescriptionV2 else: raise NotImplementedError( f'unimplemented trace header version `{trace_header_info["trace_header_version"]}`!' ) for key, fmt in TraceHeaderDescription: trace_header_info[key] = f.read_f(fmt) # AxoGraph traces are 1-indexed in GUI, so use i+1 below trace_header_info_list[i + 1] = trace_header_info group_ids.append(trace_header_info["group_id_for_this_trace"]) self.logger.debug(trace_header_info) self.logger.debug("") self.info["trace_header_info_list"] = trace_header_info_list ############################################## # GROUPS self.logger.debug("== GROUPS ==") n_groups = f.read_f("l") self.info["n_groups"] = n_groups group_ids = np.sort(list(set(group_ids))) # remove duplicates and sort if n_groups != len(group_ids): raise ValueError(f"expected group_ids to have length {n_groups}: {group_ids}") self.logger.debug(f"n_groups: {n_groups}") self.logger.debug(f"group_ids: {group_ids}") self.logger.debug("") group_header_info_list = {} for i in group_ids: # AxoGraph groups are 0-indexed in GUI, so use i below self.logger.debug(f"== GROUP #{i} ==") group_header_info = {} if format_ver < 6: # before format version 6, there was only one version # of the header, and version numbers were not provided group_header_info["group_header_version"] = 1 else: # for format versions 6 and later, the header version # must be read group_header_info["group_header_version"] = f.read_f("l") if group_header_info["group_header_version"] == 1: GroupHeaderDescription = GroupHeaderDescriptionV1 else: raise NotImplementedError( f'unimplemented group header version `{group_header_info["group_header_version"]}`!' ) for key, fmt in GroupHeaderDescription: group_header_info[key] = f.read_f(fmt) # AxoGraph groups are 0-indexed in GUI, so use i below group_header_info_list[i] = group_header_info self.logger.debug(group_header_info) self.logger.debug("") self.info["group_header_info_list"] = group_header_info_list ############################################## # UNKNOWN self.logger.debug(">> UNKNOWN 1 <<") # 36 bytes of undeciphered data (types here are guesses) unknowns = f.read_f("9l") self.logger.debug(unknowns) self.logger.debug("") ############################################## # EPISODES self.logger.debug("== EPISODES ==") # a subset of episodes can be selected for "review", or # episodes can be paged through one by one, and the indexes of # those currently in review appear in this list episodes_in_review = [] n_episodes = f.read_f("l") self.info["n_episodes"] = n_episodes for i in range(n_episodes): episode_bool = f.read_f("Z") if episode_bool: episodes_in_review.append(i + 1) self.info["episodes_in_review"] = episodes_in_review self.logger.debug(f"n_episodes: {n_episodes}") self.logger.debug(f"episodes_in_review: {episodes_in_review}") if format_ver == 5: # the test file for version 5 contains this extra list of # episode indexes with unknown purpose old_unknown_episode_list = [] n_episodes2 = f.read_f("l") for i in range(n_episodes2): episode_bool = f.read_f("Z") if episode_bool: old_unknown_episode_list.append(i + 1) self.logger.debug(f"old_unknown_episode_list: {old_unknown_episode_list}") if n_episodes2 != n_episodes: self.logger.debug(f"n_episodes2 ({n_episodes2}) and n_episodes ({n_episodes}) " "differ!") # another list of episode indexes with unknown purpose unknown_episode_list = [] n_episodes3 = f.read_f("l") for i in range(n_episodes3): episode_bool = f.read_f("Z") if episode_bool: unknown_episode_list.append(i + 1) self.logger.debug(f"unknown_episode_list: {unknown_episode_list}") if n_episodes3 != n_episodes: self.logger.debug(f"n_episodes3 ({n_episodes3,}) and n_episodes ({n_episodes}) " "differ!") # episodes can be masked to be removed from the pool of # reviewable episodes completely until unmasked, and the # indexes of those currently masked appear in this list masked_episodes = [] n_episodes4 = f.read_f("l") for i in range(n_episodes4): episode_bool = f.read_f("Z") if episode_bool: masked_episodes.append(i + 1) self.info["masked_episodes"] = masked_episodes self.logger.debug(f"masked_episodes: {masked_episodes}") if n_episodes4 != n_episodes: self.logger.debug(f"n_episodes4 ({n_episodes4}) and n_episodes ({n_episodes}) " "differ!") self.logger.debug("") ############################################## # UNKNOWN self.logger.debug(">> UNKNOWN 2 <<") # 68 bytes of undeciphered data (types here are guesses) unknowns = f.read_f("d 9l d 4l") self.logger.debug(unknowns) self.logger.debug("") ############################################## # FONTS if format_ver >= 6: font_categories = ["axis titles", "axis labels (ticks)", "notes", "graph title"] else: # would need an old version of AxoGraph to determine how it # used these settings font_categories = ["everything (?)"] font_settings_info_list = {} for i in font_categories: self.logger.debug(f"== FONT SETTINGS FOR {i} ==") font_settings_info = {} for key, fmt in FontSettingsDescription: font_settings_info[key] = f.read_f(fmt) # I don't know why two arbitrary values were selected to # represent this switch, but it seems they were # - setting1 could contain other undeciphered data as a # bitmask, like setting2 if font_settings_info["setting1"] not in [ FONT_BOLD, FONT_NOT_BOLD, ]: raise ValueError( f"expected setting1 ({ font_settings_info['setting1']}) to have value FONT_BOLD " f"({FONT_BOLD}) or FONT_NOT_BOLD ({FONT_NOT_BOLD})" ) # size is stored 10 times bigger than real value font_settings_info["size"] = font_settings_info["size"] / 10.0 font_settings_info["bold"] = bool(font_settings_info["setting1"] == FONT_BOLD) font_settings_info["italics"] = bool(font_settings_info["setting2"] & FONT_ITALICS) font_settings_info["underline"] = bool(font_settings_info["setting2"] & FONT_UNDERLINE) font_settings_info["strikeout"] = bool(font_settings_info["setting2"] & FONT_STRIKEOUT) font_settings_info_list[i] = font_settings_info self.logger.debug(font_settings_info) self.logger.debug("") self.info["font_settings_info_list"] = font_settings_info_list ############################################## # X-AXIS SETTINGS self.logger.debug("== X-AXIS SETTINGS ==") x_axis_settings_info = {} for key, fmt in XAxisSettingsDescription: x_axis_settings_info[key] = f.read_f(fmt) self.info["x_axis_settings_info"] = x_axis_settings_info self.logger.debug(x_axis_settings_info) self.logger.debug("") ############################################## # UNKNOWN self.logger.debug(">> UNKNOWN 3 <<") # 108 bytes of undeciphered data (types here are guesses) unknowns = f.read_f("8l 3d 13l") self.logger.debug(unknowns) self.logger.debug("") ############################################## # EVENTS / TAGS self.logger.debug("=== EVENTS / TAGS ===") n_events, n_events_again = f.read_f("ll") self.info["n_events"] = n_events self.logger.debug(f"n_events: {n_events}") # event / tag timing is stored as an index into time raw_event_timestamps = [] event_labels = [] for i in range(n_events_again): event_index = f.read_f("l") raw_event_timestamps.append(event_index) n_events_yet_again = f.read_f("l") for i in range(n_events_yet_again): title = f.read_f("S") event_labels.append(title) event_list = [] for event_label, event_index in zip(event_labels, raw_event_timestamps): # t_start shouldn't be added here event_time = event_index * sampling_period event_list.append({"title": event_label, "index": event_index, "time": event_time}) self.info["event_list"] = event_list for event in event_list: self.logger.debug(event) self.logger.debug("") ############################################## # UNKNOWN self.logger.debug(">> UNKNOWN 4 <<") # 28 bytes of undeciphered data (types here are guesses) unknowns = f.read_f("7l") self.logger.debug(unknowns) self.logger.debug("") ############################################## # EPOCHS / INTERVAL BARS self.logger.debug("=== EPOCHS / INTERVAL BARS ===") n_epochs = f.read_f("l") self.info["n_epochs"] = n_epochs self.logger.debug(f"n_epochs: {n_epochs}") epoch_list = [] for i in range(n_epochs): epoch_info = {} for key, fmt in EpochInfoDescription: epoch_info[key] = f.read_f(fmt) epoch_list.append(epoch_info) self.info["epoch_list"] = epoch_list # epoch / interval bar timing and duration are stored in # seconds, so here they are converted to (possibly non-integer) # indexes into time to fit into the procrustean beds of # _rescale_event_timestamp and _rescale_epoch_duration raw_epoch_timestamps = [] raw_epoch_durations = [] epoch_labels = [] for epoch in epoch_list: raw_epoch_timestamps.append(epoch["t_start"] / sampling_period) raw_epoch_durations.append((epoch["t_stop"] - epoch["t_start"]) / sampling_period) epoch_labels.append(epoch["title"]) self.logger.debug(epoch) self.logger.debug("") ############################################## # UNKNOWN self.logger.debug(">> UNKNOWN 5 (includes y-axis plot ranges) <<") # lots of undeciphered data rest_of_the_file = f.read() self.logger.debug(rest_of_the_file) self.logger.debug("") self.logger.debug("End of file reached (expected)") except EOFError as e: if format_ver == 1 or format_ver == 2: # for format versions 1 and 2, metadata like graph display # information was stored separately in the "resource fork" # of the file, so reaching the end of the file before all # metadata is parsed is expected self.logger.debug("End of file reached (expected)") pass else: # for format versions 3 and later, there should be metadata # stored at the end of the file, so warn that something may # have gone wrong, but try to continue anyway self.logger.warning( "End of file reached unexpectedly " "while parsing metadata, will attempt " "to continue" ) self.logger.debug(e, exc_info=True) pass except UnicodeDecodeError as e: # warn that something went wrong with reading a string, but try # to continue anyway self.logger.warning( "Problem decoding text while parsing " "metadata, will ignore any remaining " "metadata and attempt to continue" ) self.logger.debug(e, exc_info=True) pass self.logger.debug("") ############################################## # RAWIO HEADER # event_channels will be cast to _event_channel_dtype event_channels = [] event_channels.append(("AxoGraph Tags", "", "event")) event_channels.append(("AxoGraph Intervals", "", "epoch")) if len(sig_channels) > 0: signal_streams = [("Signals", "0", "")] else: signal_streams = [] signal_buffers = [] # organize header self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = np.array(signal_buffers, dtype=_signal_buffer_dtype) self.header["signal_streams"] = np.array(signal_streams, dtype=_signal_stream_dtype) self.header["signal_channels"] = np.array(sig_channels, dtype=_signal_channel_dtype) self.header["event_channels"] = np.array(event_channels, dtype=_event_channel_dtype) self.header["spike_channels"] = np.array([], dtype=_spike_channel_dtype) ############################################## # DATA OBJECTS # organize data self._sampling_period = sampling_period self._t_start = t_start self._raw_signals = [sig_memmaps] # first index is seg_index self._raw_event_epoch_timestamps = [np.array(raw_event_timestamps), np.array(raw_epoch_timestamps)] self._raw_event_epoch_durations = [None, np.array(raw_epoch_durations)] self._event_epoch_labels = [np.array(event_labels, dtype="U"), np.array(epoch_labels, dtype="U")] class StructFile(BufferedReader): """ A container for the file buffer with some added convenience functions for reading AxoGraph files """ def __init__(self, *args, **kwargs): # As far as I've seen, every AxoGraph file uses big-endian encoding, # regardless of the system architecture on which it was created, but # here I provide means for controlling byte ordering in case a counter # example is found. self.byte_order = kwargs.pop("byte_order", ">") if self.byte_order == ">": # big-endian self.utf_16_decoder = "utf-16-be" elif self.byte_order == "<": # little-endian self.utf_16_decoder = "utf-16-le" else: # unspecified self.utf_16_decoder = "utf-16" super().__init__(*args, **kwargs) def read_and_unpack(self, fmt): """ Calculate the number of bytes corresponding to the format string, read in that number of bytes, and unpack them according to the format string """ try: return unpack(self.byte_order + fmt, self.read(calcsize(self.byte_order + fmt))) except Exception as e: if e.args[0].startswith("unpack requires a buffer of"): raise EOFError(e) else: raise def read_string(self): """ The most common string format in AxoGraph files is a variable length string with UTF-16 encoding, preceded by a 4-byte integer (long) specifying the length of the string in bytes. Unlike a Pascal string ('p' format), these strings are not stored in a fixed number of bytes with padding at the end. This function reads in one of these variable length strings """ # length may be -1, 0, or a positive integer length = self.read_and_unpack("l")[0] if length > 0: return self.read(length).decode(self.utf_16_decoder) else: return "" def read_bool(self): """ AxoGraph files encode each boolean as 4-byte integer (long) with value 1 = True, 0 = False. This function reads in one of these booleans. """ return bool(self.read_and_unpack("l")[0]) def read_f(self, fmt, offset=None): """ This function is a wrapper for read_and_unpack that adds compatibility with two new format strings: 'S': a variable length UTF-16 string, readable with read_string 'Z': a boolean encoded as a 4-byte integer, readable with read_bool This method does not implement support for numbers before the new format strings, such as '3Z' to represent 3 bools (use 'ZZZ' instead). """ if offset is not None: self.seek(offset) # place commas before and after each instance of S or Z for special in ["S", "Z"]: fmt = fmt.replace(special, "," + special + ",") # split S and Z into isolated strings fmt = fmt.split(",") # construct a tuple of unpacked data data = () for subfmt in fmt: if subfmt == "S": data += (self.read_string(),) elif subfmt == "Z": data += (self.read_bool(),) else: data += self.read_and_unpack(subfmt) if len(data) == 1: return data[0] else: return data FONT_BOLD = 75 # mysterious arbitrary constant FONT_NOT_BOLD = 50 # mysterious arbitrary constant FONT_ITALICS = 1 FONT_UNDERLINE = 2 FONT_STRIKEOUT = 4 TraceHeaderDescriptionV1 = [ # documented in AxoGraph_ReadWrite.h ("x_index", "l"), ("y_index", "l"), ("err_bar_index", "l"), ("group_id_for_this_trace", "l"), ("hidden", "Z"), # AxoGraph_ReadWrite.h incorrectly states "shown" instead ("min_x", "d"), ("max_x", "d"), ("min_positive_x", "d"), ("x_is_regularly_spaced", "Z"), ("x_increases_monotonically", "Z"), ("x_interval_if_regularly_spaced", "d"), ("min_y", "d"), ("max_y", "d"), ("min_positive_y", "d"), ("trace_color", "xBBB"), ("display_joined_line_plot", "Z"), ("line_thickness", "d"), ("pen_style", "l"), ("display_symbol_plot", "Z"), ("symbol_type", "l"), ("symbol_size", "l"), ("draw_every_data_point", "Z"), ("skip_points_by_distance_instead_of_pixels", "Z"), ("pixels_between_symbols", "l"), ("display_histogram_plot", "Z"), ("histogram_type", "l"), ("histogram_bar_separation", "l"), ("display_error_bars", "Z"), ("display_pos_err_bar", "Z"), ("display_neg_err_bar", "Z"), ("err_bar_width", "l"), ] # documented in AxoGraph_ReadWrite.h # - only one difference exists between versions 1 and 2 TraceHeaderDescriptionV2 = list(TraceHeaderDescriptionV1) # make a copy TraceHeaderDescriptionV2.insert(3, ("neg_err_bar_index", "l")) GroupHeaderDescriptionV1 = [ # undocumented and reverse engineered ("title", "S"), ("unknown1", "h"), # 2 bytes of undeciphered data (types are guesses) ("units", "S"), ("unknown2", "hll"), # 10 bytes of undeciphered data (types are guesses) ] FontSettingsDescription = [ # undocumented and reverse engineered ("font", "S"), ("size", "h"), # divide this 2-byte integer by 10 to get font size ("unknown1", "5b"), # 5 bytes of undeciphered data (types are guesses) ("setting1", "B"), # includes bold setting ("setting2", "B"), # italics, underline, strikeout specified in bitmap ] XAxisSettingsDescription = [ # undocumented and reverse engineered ("unknown1", "3l2d"), # 28 bytes of undeciphered data (types are guesses) ("plotted_x_range", "dd"), ("unknown2", "d"), # 8 bytes of undeciphered data (types are guesses) ("auto_x_ticks", "Z"), ("x_minor_ticks", "d"), ("x_major_ticks", "d"), ("x_axis_title", "S"), ("unknown3", "h"), # 2 bytes of undeciphered data (types are guesses) ("units", "S"), ("unknown4", "h"), # 2 bytes of undeciphered data (types are guesses) ] EpochInfoDescription = [ # undocumented and reverse engineered ("title", "S"), ("t_start", "d"), ("t_stop", "d"), ("y_pos", "d"), ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/axonarawio.py0000644000175100017510000005644115154507213016470 0ustar00runnerrunner""" This class reads .set and .bin file data from the Axona acquisition system. File format overview: http://space-memory-navigation.org/DacqUSBFileFormats.pdf In brief: data.set - setup file containing all hardware setups related to the trial data.bin - raw data file There are many other data formats from Axona, which we do not consider (yet). These are derivative from the raw continuous data (.bin) and could in principle be extracted from it (see file format overview for details). Author: Steffen Buergers, Julia Sprenger """ import datetime import pathlib import re import numpy as np from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class AxonaRawIO(BaseRawIO): """ Class for reading raw, continuous data from the Axona dacqUSB system Parameters ---------- filename: str The name of the *.bin file containing the data Notes ----- http://space-memory-navigation.org/DacqUSBFileFormats.pdf The raw data is saved in .bin binary files with an accompanying .set file about the recording setup (see the above manual for details). Examples -------- >>> import neo.rawio >>> r = neo.rawio.AxonaRawIO(filename=os.path.join(dir_name, base_filename)) >>> r.parse_header() >>> print(r) >>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=0, i_stop=1024, channel_names=channel_names) >>> float_chunk = reader.rescale_signal_raw_to_float(raw_chunk, dtype='float64', channel_indexes=[0, 3, 6]) """ extensions = ["bin", "set"] + [str(i) for i in range(1, 33)] # Never used? rawmode = "multi-file" # In the .bin file, channels are arranged in a strange order. # This list takes a channel index as input and returns the actual # offset for the channel in the memory map (self._raw_signals). channel_memory_offset = [ 32, 33, 34, 35, 36, 37, 38, 39, 0, 1, 2, 3, 4, 5, 6, 7, 40, 41, 42, 43, 44, 45, 46, 47, 8, 9, 10, 11, 12, 13, 14, 15, 48, 49, 50, 51, 52, 53, 54, 55, 16, 17, 18, 19, 20, 21, 22, 23, 56, 57, 58, 59, 60, 61, 62, 63, 24, 25, 26, 27, 28, 29, 30, 31, ] def __init__(self, filename): BaseRawIO.__init__(self) # Accepting filename with arbitrary suffix as input self.filename = pathlib.Path(filename).with_suffix("") self.set_file = self.filename.with_suffix(".set") self.bin_file = None self.tetrode_files = [] # set file is mandatory for any recording if not self.set_file.exists(): raise ValueError( f'Could not locate ".set" file. ' f'{self.filename.with_suffix(".set")} does not ' f"exist." ) # detecting available files if self.filename.with_suffix(".bin").exists(): self.bin_file = self.filename.with_suffix(".bin") for i in range(1, 33): unit_file = self.filename.with_suffix(f".{i}") if unit_file.exists(): self.tetrode_files.append(unit_file) else: break def _source_name(self): return self.filename def _parse_header(self): """ Read important information from .set header file, create memory map to raw data (.bin file) and prepare header dictionary in neo format. """ unit_dtype = np.dtype([("spiketimes", ">i4"), ("samples", "int8", (50,))]) # Utility collection of file parameters (general info and header data) params = { "bin": { "filename": self.bin_file, "bytes_packet": 432, "bytes_data": 384, "bytes_head": 32, "bytes_tail": 16, "data_type": "int16", "header_size": 0, # bin files don't contain a file header "header_encoding": None, }, "set": {"filename": self.set_file, "header_encoding": "cp1252"}, "unit": {"data_type": unit_dtype, "tetrode_ids": [], "header_encoding": "cp1252"}, } self.file_parameters = params # SCAN SET FILE set_dict = self.get_header_parameters(self.set_file, "set") params["set"]["file_header"] = set_dict params["set"]["sampling_rate"] = int(set_dict["rawRate"]) # SCAN BIN FILE signal_buffers = [] signal_streams = [] signal_channels = [] if self.bin_file: bin_dict = self.file_parameters["bin"] # add derived parameters from bin file bin_dict["num_channels"] = len(self.get_active_tetrode()) * 4 num_tot_packets = int(self.bin_file.stat().st_size / bin_dict["bytes_packet"]) bin_dict["num_total_packets"] = num_tot_packets bin_dict["num_total_samples"] = num_tot_packets * 3 # Create np.memmap to .bin file self._raw_signals = np.memmap( self.bin_file, dtype=self.file_parameters["bin"]["data_type"], mode="r", offset=self.file_parameters["bin"]["header_size"], ) signal_streams = self._get_signal_streams_header() signal_channels = self._get_signal_chan_header() # SCAN TETRODE FILES # In this IO one tetrode corresponds to one unit as spikes are not # sorted yet. self._raw_spikes = [] spike_channels = [] if self.tetrode_files: for i, tetrode_file in enumerate(self.tetrode_files): # collecting tetrode specific parameters and dtype conversions tdict = self.get_header_parameters(tetrode_file, "unit") tdict["filename"] = tetrode_file tdict["num_chans"] = int(tdict["num_chans"]) tdict["num_spikes"] = int(tdict["num_spikes"]) tdict["header_size"] = len(self.get_header_bstring(tetrode_file)) # memory mapping spiking data spikes = np.memmap( tetrode_file, dtype=self.file_parameters["unit"]["data_type"], mode="r", offset=tdict["header_size"], shape=(tdict["num_spikes"], 4), ) self._raw_spikes.append(spikes) unit_name = f"tetrode {i + 1}" unit_id = f"{i + 1}" wf_units = "dimensionless" wf_gain = 1 wf_offset = 0.0 # left sweep information is only stored in set file wf_left_sweep = int(self.file_parameters["set"]["file_header"]["pretrigSamps"]) # Extract waveform sample rate # 1st priority source: Spike2msMode (0 -> 48kHz; 1 -> 24kHz) # 2nd priority source: tetrode sample rate spikemode_to_sr = {0: 48000, 1: 24000} # spikemode->rate in Hz sm = self.file_parameters["set"]["file_header"].get("Spike2msMode", -1) wf_sampling_rate = spikemode_to_sr.get(int(sm), None) if wf_sampling_rate is None: wf_sampling_rate = self._to_hz(tdict["sample_rate"], dtype=float) spike_channels.append( (unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate) ) self.file_parameters["unit"]["tetrode_ids"].append(i + 1) self.file_parameters["unit"][i + 1] = tdict # propagate common tetrode parameters to global unit level units_dict = self.file_parameters["unit"] ids = units_dict["tetrode_ids"] copied_keys = [] if ids: for key, value in units_dict[ids[0]].items(): # copy key-value pair if present across all tetrodes if all([key in units_dict[t] for t in ids]) and all([units_dict[t][key] == value for t in ids]): self.file_parameters["unit"][key] = value copied_keys.append(key) # remove key from individual tetrode parameters for key in copied_keys: for t in ids: self.file_parameters["unit"][t].pop(key) # Create RawIO header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = np.array(signal_buffers, dtype=_signal_buffer_dtype) self.header["signal_streams"] = np.array(signal_streams, dtype=_signal_stream_dtype) self.header["signal_channels"] = np.array(signal_channels, dtype=_signal_channel_dtype) self.header["spike_channels"] = np.array(spike_channels, dtype=_spike_channel_dtype) self.header["event_channels"] = np.array([], dtype=_event_channel_dtype) # Annotations self._generate_minimal_annotations() # Adding custom annotations bl_ann = self.raw_annotations["blocks"][0] seg_ann = bl_ann["segments"][0] seg_ann["rec_datetime"] = self.read_datetime() if len(seg_ann["signals"]): seg_ann["signals"][0]["__array_annotations__"]["tetrode_id"] = [ tetr for tetr in self.get_active_tetrode() for _ in range(4) ] if len(seg_ann["spikes"]): # adding segment annotations seg_keys = ["experimenter", "comments", "sw_version"] for seg_key in seg_keys: if seg_key in self.file_parameters["unit"]: seg_ann[seg_key] = self.file_parameters["unit"][seg_key] def _get_signal_streams_header(self): # create signals stream information (we always expect a single stream) return np.array([("stream 0", "0", "")], dtype=_signal_stream_dtype) def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): t_stop = 0.0 if "num_total_packets" in self.file_parameters["bin"]: sr = self.file_parameters["set"]["sampling_rate"] t_stop = self.file_parameters["bin"]["num_total_samples"] / sr if self.file_parameters["unit"]["tetrode_ids"]: # get tetrode recording durations in seconds if "duration" not in self.file_parameters["unit"]: raise ValueError("Can not determine common tetrode recording" "duration.") tetrode_duration = float(self.file_parameters["unit"]["duration"]) t_stop = max(t_stop, tetrode_duration) return t_stop def _get_signal_size(self, block_index, seg_index, channel_indexes=None): if "num_total_packets" in self.file_parameters["bin"]: return self.file_parameters["bin"]["num_total_samples"] else: return 0 def _get_signal_t_start(self, block_index, seg_index, stream_index): return 0.0 def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): """ Return raw (continuous) signals as 2d numpy array (time x chan). Note that block_index and seg_index are always 1 (regardless of input). Raw data is in a single vector np.memmap with the following structure: Each byte packet (432 bytes) has header (32 bytes), footer (16 bytes) and three samples of 2 bytes each for 64 channels (384 bytes), which are jumbled up in a strange order. Each channel is remapped to a certain position (see get_channel_offset), and a channel's samples are allocated as follows (example for channel 7): sample 1: 32b (head) + 2*38b (remappedID) and 2*38b + 1b (2nd byte) sample 2: 32b (head) + 128 (all chan. 1st entry) + 2*38b and ... sample 3: 32b (head) + 128*2 (all channels 1st and 2nd entry) + ... """ bin_dict = self.file_parameters["bin"] # Set default values if i_start is None: i_start = 0 if i_stop is None: i_stop = bin_dict["num_total_samples"] if channel_indexes is None: channel_indexes = [i for i in range(bin_dict["num_channels"])] elif isinstance(channel_indexes, slice): channel_indexes = self._get_active_channels() num_samples = i_stop - i_start # Create base index vector for _raw_signals for time period of interest num_packets_oi = (num_samples + 2) // 3 offset = i_start // 3 * (bin_dict["bytes_packet"] // 2) rem = i_start % 3 raw_samples = np.arange(num_packets_oi + 1, dtype=np.uint32) sample1 = raw_samples * (bin_dict["bytes_packet"] // 2) + bin_dict["bytes_head"] // 2 + offset sample2 = sample1 + 64 sample3 = sample2 + 64 sig_ids = np.empty((sample1.size + sample2.size + sample3.size,), dtype=sample1.dtype) sig_ids[0::3] = sample1 sig_ids[1::3] = sample2 sig_ids[2::3] = sample3 sig_ids = sig_ids[rem : (rem + num_samples)] # Read one channel at a time raw_signals = np.ndarray(shape=(num_samples, len(channel_indexes)), dtype=bin_dict["data_type"]) for i, ch_idx in enumerate(channel_indexes): chan_offset = self.channel_memory_offset[ch_idx] raw_signals[:, i] = self._raw_signals[sig_ids + chan_offset] return raw_signals def _spike_count(self, block_index, seg_index, unit_index): tetrode_id = unit_index raw_spikes = self._raw_spikes[tetrode_id] nb_tetrode_spikes = raw_spikes.shape[0] # also take into account last, potentially incomplete set of spikes nb_unit_spikes = int(np.ceil(nb_tetrode_spikes)) return nb_unit_spikes def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): if block_index != 0: raise ValueError("`block_index must be 0") if seg_index != 0: raise ValueError("`seg_index` must be 0") tetrode_id = unit_index raw_spikes = self._raw_spikes[tetrode_id] # spike times are repeated for each contact -> use only first contact unit_spikes = raw_spikes["spiketimes"][:, 0] # slice spike times only if needed if t_start is None and t_stop is None: return unit_spikes if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) mask = self._get_temporal_mask(t_start, t_stop, tetrode_id) return unit_spikes[mask] def _rescale_spike_timestamp(self, spike_timestamps, dtype): spike_times = spike_timestamps.astype(dtype) spike_times /= self._to_hz(self.file_parameters["unit"]["timebase"], dtype=int) return spike_times def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): if block_index != 0: raise ValueError("`block_index must be 0") if seg_index != 0: raise ValueError("`seg_index` must be 0") tetrode_id = unit_index waveforms = self._raw_spikes[tetrode_id]["samples"] # slice timestamps / waveforms only when necessary if t_start is None and t_stop is None: return waveforms if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) mask = self._get_temporal_mask(t_start, t_stop, tetrode_id) waveforms = waveforms[mask] return waveforms def get_header_bstring(self, file): """ Scan file for the occurrence of 'data_start' and return the header as byte string Parameters ---------- file (str or path): file to be loaded Returns ------- str: header byte content """ header = b"" with open(file, "rb") as f: for bin_line in f: if b"data_start" in bin_line: header += b"data_start" break else: header += bin_line return header # ------------------ HELPER METHODS -------------------- # This is largely based on code by Geoff Barrett from the Hussaini lab: # https://github.com/GeoffBarrett/BinConverter # Adapted or modified by Steffen Buergers, Julia Sprenger def _get_temporal_mask(self, t_start, t_stop, tetrode_id): # Convenience function for creating a temporal mask given # start time (t_start) and stop time (t_stop) # Used by _get_spike_raw_waveforms and _get_spike_timestamps # spike times are repeated for each contact -> use only first contact raw_spikes = self._raw_spikes[tetrode_id] unit_spikes = raw_spikes["spiketimes"][:, 0] # convert t_start and t_stop to sampling frequency # Note: this assumes no time offset! unit_params = self.file_parameters["unit"] lim0 = t_start * self._to_hz(unit_params["timebase"], dtype=int) lim1 = t_stop * self._to_hz(unit_params["timebase"], dtype=int) mask = (unit_spikes >= lim0) & (unit_spikes <= lim1) return mask def get_header_parameters(self, file, file_type): """ Extract header parameters as dictionary keys and following phrases as values (strings). Parameters ---------- file (str or path): file to be loaded file_type (str): type of file to be loaded ('set' or 'unit') Returns ------- header (dict): dictionary with keys being the parameters that were found & values being strings of the data. EXAMPLE self.get_header_parameters('file.set', 'set') """ params = {} encoding = self.file_parameters[file_type]["header_encoding"] header_string = self.get_header_bstring(file).decode(encoding) # omit the last line as this contains only `data_start` key for line in header_string.splitlines()[:-1]: key, value = line.split(" ", 1) params[key] = value return params def get_active_tetrode(self): """ Returns the ID numbers of the active tetrodes as a list. E.g.: [1,2,3,4] for a recording with 4 tetrodes (16 channels). """ active_tetrodes = [] for key, status in self.file_parameters["set"]["file_header"].items(): # The pattern to look for is collectMask_X Y, # where X is the tetrode number, and Y is 1 or 0 if key.startswith("collectMask_"): if int(status): tetrode_id = int(key.strip("collectMask_")) active_tetrodes.append(tetrode_id) return active_tetrodes def _get_active_channels(self): """ Returns the ID numbers of the active channels as a list. E.g.: [20,21,22,23] for tetrode 6 active. """ active_tetrodes = self.get_active_tetrode() active_channels = [] for tetrode in active_tetrodes: chans = self._get_channel_from_tetrode(tetrode) active_channels.append(chans) return np.concatenate(active_channels) def _get_channel_from_tetrode(self, tetrode): """ This function will take the tetrode number and return the Axona channel numbers, i.e. Tetrode 1 = Ch0-Ch3, Tetrode 2 = Ch4-Ch7, etc. """ return np.arange(0, 4) + 4 * (int(tetrode) - 1) def read_datetime(self): """ Creates datetime object (y, m, d, h, m, s) from .set file header """ date_str = self.file_parameters["set"]["file_header"]["trial_date"] time_str = self.file_parameters["set"]["file_header"]["trial_time"] # extract core date string date_str = re.findall(r"\d+\s\w+\s\d{4}$", date_str)[0] return datetime.datetime.strptime(date_str + ", " + time_str, "%d %b %Y, %H:%M:%S") def _get_channel_gain(self, bytes_per_sample=2): """ This is actually not the gain_ch value from the .set file, but the conversion factor from raw data to uV. Formula for conversion to uV: 1000 * adc_fullscale_mv / (gain_ch * max-value), with max_value = 2**(8 * bytes_per_sample - 1) Adapted from https://github.com/CINPLA/pyxona/blob/stable/pyxona/core.py """ gain_list = [] adc_fm = int(self.file_parameters["set"]["file_header"]["ADC_fullscale_mv"]) for key, value in self.file_parameters["set"]["file_header"].items(): if key.startswith("gain_ch"): gain_list.append(np.float32(value)) max_value = 2 ** (8 * bytes_per_sample - 1) gain_list = [1000 * adc_fm / (gain * max_value) for gain in gain_list] return gain_list def _get_signal_chan_header(self): """ Returns a 1 dimensional np.array of tuples with one entry per channel that recorded data. Each tuple contains the following information: channel name (1a, 1b, 1c, 1d, 2a, 2b, ...; num=tetrode, letter=elec), channel id (1, 2, 3, 4, 5, ... N), sampling rate, data type (int16), unit (uV), gain, offset, stream id """ active_tetrode_set = self.get_active_tetrode() num_active_tetrode = len(active_tetrode_set) elec_per_tetrode = 4 letters = ["a", "b", "c", "d"] dtype = self.file_parameters["bin"]["data_type"] units = "uV" gain_list = self._get_channel_gain() offset = 0 # What is the offset? first_channel = (active_tetrode_set[0] - 1) * elec_per_tetrode sig_channels = [] for itetr in range(num_active_tetrode): for ielec in range(elec_per_tetrode): cntr = (itetr * elec_per_tetrode) + ielec + first_channel ch_name = f"{itetr + active_tetrode_set[0]}{letters[ielec]}" chan_id = str(cntr) gain = gain_list[cntr] stream_id = "0" buffer_id = "" # the sampling rate information is stored in the set header # and not in the bin file sr = self.file_parameters["set"]["sampling_rate"] sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) return np.array(sig_channels, dtype=_signal_channel_dtype) def _to_hz(self, param, dtype=float): return dtype(param.replace(" hz", "")) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/axonrawio.py0000644000175100017510000015543115154507213016326 0ustar00runnerrunner""" Class for reading data from pCLAMP and AxoScope files (.abf version 1 and 2), developed by Molecular device/Axon technologies. - abf = Axon binary file - atf is a text file based format from axon that could be read by AsciiIO (but this file is less efficient.) This code is a port of abfload and abf2load written in Matlab (BSD-2-Clause licence) by : - Copyright (c) 2009, Forrest Collman, fcollman@princeton.edu - Copyright (c) 2004, Harald Hentschke and available here: http://www.mathworks.com/matlabcentral/fileexchange/22114-abf2load The StringsSection parsing (parse_axon_soup) now relies on an idea presented in pyABF MIT License Copyright (c) 2018 Scott W Harden written by Scott Harden. His unofficial documentation for the formats is here: https://swharden.com/pyabf/abf2-file-format/ strings section: [uModifierNameIndex, uCreatorNameIndex, uProtocolPathIndex, lFileComment, lADCCChannelNames, lADCUnitsIndex lDACChannelNameIndex, lDACUnitIndex, lDACFilePath, nLeakSubtractADC] ['', 'Clampex', '', 'C:/path/protocol.pro', 'some comment', 'IN 0', 'mV', 'IN 1', 'mV', 'Cmd 0', 'pA', 'Cmd 1', 'pA', 'Cmd 2', 'mV', 'Cmd 3', 'mV'] Information on abf 1 and 2 formats is available here: http://www.moleculardevices.com/pages/software/developer_info.html This file supports the old (ABF1) and new (ABF2) format. ABF1 (clampfit <=9) and ABF2 (clampfit >10) All possible mode are possible : - event-driven variable-length mode 1 -> return several Segments per Block - event-driven fixed-length mode 2 or 5 -> return several Segments - gap free mode -> return one (or several) Segment in the Block Supported : Read Author: Samuel Garcia, JS Nowacki Note: j.s.nowacki@gmail.com has a C++ library with SWIG bindings which also reads abf files - would be good to cross-check """ import struct import datetime from io import open, BufferedReader import numpy as np from .baserawio import ( BaseRawWithBufferApiIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from neo.core import NeoReadWriteError class AxonRawIO(BaseRawWithBufferApiIO): """ Class for Class for reading data from pCLAMP and AxoScope files (.abf version 1 and 2) Parameters ---------- filename: str, default: '' The *.abf file to be read Notes ----- This code is a port of abfload and abf2load written in Matlab (BSD-2-Clause licence) by Copyright (c) 2009, Forrest Collman, fcollman@princeton.edu Copyright (c) 2004, Harald Hentschke Examples -------- >>> import neo.rawio >>> reader = neo.rawio.AxonRawIO(filename='mydata.abf') >>> reader.parse_header() >>> print(reader) """ extensions = ["abf"] rawmode = "one-file" def __init__(self, filename=""): BaseRawWithBufferApiIO.__init__(self) self.filename = filename def _parse_header(self): info = self._axon_info = parse_axon_soup(self.filename) version = info["fFileVersionNumber"] # file format if info["nDataFormat"] == 0: sig_dtype = np.dtype("i2") elif info["nDataFormat"] == 1: sig_dtype = np.dtype("f4") if version < 2.0: nbchannel = info["nADCNumChannels"] head_offset = info["lDataSectionPtr"] * BLOCKSIZE + info["nNumPointsIgnored"] * sig_dtype.itemsize totalsize = info["lActualAcqLength"] elif version >= 2.0: nbchannel = info["sections"]["ADCSection"]["llNumEntries"] head_offset = info["sections"]["DataSection"]["uBlockIndex"] * BLOCKSIZE totalsize = info["sections"]["DataSection"]["llNumEntries"] # 3 possible modes if version < 2.0: mode = info["nOperationMode"] elif version >= 2.0: mode = info["protocol"]["nOperationMode"] if mode not in [1, 2, 3, 5]: raise NeoReadWriteError(f"Mode {mode} is not currently supported in Neo") # event-driven variable-length mode (mode 1) # event-driven fixed-length mode (mode 2 or 5) # gap free mode (mode 3) can be in several episodes # read sweep pos if version < 2.0: nbepisod = info["lSynchArraySize"] offset_episode = info["lSynchArrayPtr"] * BLOCKSIZE elif version >= 2.0: nbepisod = info["sections"]["SynchArraySection"]["llNumEntries"] offset_episode = info["sections"]["SynchArraySection"]["uBlockIndex"] * BLOCKSIZE if nbepisod > 0: episode_array = np.memmap( self.filename, [("offset", "i4"), ("len", "i4")], "r", shape=nbepisod, offset=offset_episode ) else: episode_array = np.empty(1, [("offset", "i4"), ("len", "i4")]) episode_array[0]["len"] = totalsize episode_array[0]["offset"] = 0 # sampling_rate if version < 2.0: self._sampling_rate = 1.0 / (info["fADCSampleInterval"] * nbchannel * 1.0e-6) elif version >= 2.0: self._sampling_rate = 1.0e6 / info["protocol"]["fADCSequenceInterval"] # one sweep = one segment nb_segment = episode_array.size stream_id = "0" buffer_id = "0" # Get raw data by segment # self._raw_signals = {} self._t_starts = {} self._buffer_descriptions = {0: {}} self._stream_buffer_slice = {stream_id: None} pos = 0 for seg_index in range(nb_segment): length = episode_array[seg_index]["len"] if version < 2.0: fSynchTimeUnit = info["fSynchTimeUnit"] elif version >= 2.0: fSynchTimeUnit = info["protocol"]["fSynchTimeUnit"] if (fSynchTimeUnit != 0) and (mode == 1): length /= fSynchTimeUnit self._buffer_descriptions[0][seg_index] = {} self._buffer_descriptions[0][seg_index][buffer_id] = { "type": "raw", "file_path": str(self.filename), "dtype": str(sig_dtype), "order": "C", "file_offset": head_offset + pos * sig_dtype.itemsize, "shape": (int(length // nbchannel), int(nbchannel)), } pos += length t_start = float(episode_array[seg_index]["offset"]) if fSynchTimeUnit == 0: t_start = t_start / self._sampling_rate else: t_start = t_start * fSynchTimeUnit * 1e-6 self._t_starts[seg_index] = t_start # Create channel header if version < 2.0: channel_ids = [chan_num for chan_num in info["nADCSamplingSeq"] if chan_num >= 0] else: channel_ids = list(range(nbchannel)) signal_channels = [] adc_nums = [] for chan_index, chan_id in enumerate(channel_ids): if version < 2.0: name = info["sADCChannelName"][chan_id].replace(b" ", b"") units = safe_decode_units(info["sADCUnits"][chan_id]) adc_num = info["nADCPtoLChannelMap"][chan_id] elif version >= 2.0: ADCInfo = info["listADCInfo"][chan_id] name = ADCInfo["ADCChNames"].replace(b" ", b"") units = safe_decode_units(ADCInfo["ADCChUnits"]) adc_num = ADCInfo["nADCNum"] adc_nums.append(adc_num) if info["nDataFormat"] == 0: # int16 gain/offset if version < 2.0: gain = info["fADCRange"] gain /= info["fInstrumentScaleFactor"][chan_id] gain /= info["fSignalGain"][chan_id] gain /= info["fADCProgrammableGain"][chan_id] gain /= info["lADCResolution"] if info["nTelegraphEnable"][chan_id] == 0: pass elif info["nTelegraphEnable"][chan_id] == 1: gain /= info["fTelegraphAdditGain"][chan_id] else: self.logger.warning("ignoring buggy nTelegraphEnable") offset = info["fInstrumentOffset"][chan_id] offset -= info["fSignalOffset"][chan_id] elif version >= 2.0: gain = info["protocol"]["fADCRange"] gain /= info["listADCInfo"][chan_id]["fInstrumentScaleFactor"] gain /= info["listADCInfo"][chan_id]["fSignalGain"] gain /= info["listADCInfo"][chan_id]["fADCProgrammableGain"] gain /= info["protocol"]["lADCResolution"] if info["listADCInfo"][chan_id]["nTelegraphEnable"]: gain /= info["listADCInfo"][chan_id]["fTelegraphAdditGain"] offset = info["listADCInfo"][chan_id]["fInstrumentOffset"] offset -= info["listADCInfo"][chan_id]["fSignalOffset"] else: gain, offset = 1.0, 0.0 signal_channels.append( (name, str(chan_id), self._sampling_rate, sig_dtype, units, gain, offset, stream_id, buffer_id) ) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) # one unique signal stream and buffer signal_buffers = np.array([("Signals", buffer_id)], dtype=_signal_buffer_dtype) signal_streams = np.array([("Signals", stream_id, buffer_id)], dtype=_signal_stream_dtype) # only one events channel : tag # In ABF timstamps are not attached too any particular segment # so each segment acess all event timestamps = [] labels = [] comments = [] for i, tag in enumerate(info["listTag"]): timestamps.append(tag["lTagTime"]) labels.append(str(tag["nTagType"])) comments.append(str(clean_string(tag["sComment"]))) self._raw_ev_timestamps = np.array(timestamps) self._ev_labels = np.array(labels, dtype="U") self._ev_comments = np.array(comments, dtype="U") event_channels = [("Tag", "", "event")] event_channels = np.array(event_channels, dtype=_event_channel_dtype) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # fille into header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [nb_segment] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # insert some annotation at some place self._generate_minimal_annotations() bl_annotations = self.raw_annotations["blocks"][0] bl_annotations["rec_datetime"] = info["rec_datetime"] bl_annotations["abf_version"] = version for seg_index in range(nb_segment): seg_annotations = bl_annotations["segments"][seg_index] seg_annotations["abf_version"] = version signal_an = self.raw_annotations["blocks"][0]["segments"][seg_index]["signals"][0] nADCNum = np.array([adc_nums[c] for c in range(signal_channels.size)]) signal_an["__array_annotations__"]["nADCNum"] = nADCNum for c in range(event_channels.size): ev_ann = seg_annotations["events"][c] ev_ann["comments"] = self._ev_comments def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): return self._t_starts[seg_index] def _segment_t_stop(self, block_index, seg_index): sig_size = self.get_signal_size(block_index, seg_index, 0) t_stop = self._t_starts[seg_index] + sig_size / self._sampling_rate return t_stop def _get_signal_t_start(self, block_index, seg_index, stream_index): return self._t_starts[seg_index] def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): return self._buffer_descriptions[block_index][seg_index][buffer_id] def _event_count(self, block_index, seg_index, event_channel_index): return self._raw_ev_timestamps.size def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): # In ABF timestamps are not attached too any particular segment # so each segment accesses all events timestamp = self._raw_ev_timestamps labels = self._ev_labels durations = None if t_start is not None: keep = timestamp >= int(t_start * self._sampling_rate) timestamp = timestamp[keep] labels = labels[keep] if t_stop is not None: keep = timestamp <= int(t_stop * self._sampling_rate) timestamp = timestamp[keep] labels = labels[keep] return timestamp, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = event_timestamps.astype(dtype) / self._sampling_rate return event_times def read_raw_protocol(self): """ Read stimulus waveforms (DAC output protocols) from ABF2 files. Purpose ------- In electrophysiology experiments, the DAC (Digital-to-Analog Converter) outputs generate stimulus waveforms (voltage steps in voltage-clamp, current injections in current-clamp). This function reconstructs these commanded output waveforms from the protocol definition stored in the file, NOT from recorded data. Why This Matters ---------------- - Recorded data shows what was MEASURED (ADC inputs: membrane voltage, current, etc.) - Protocol waveforms show what was COMMANDED (DAC outputs: stimulus patterns) - Having both allows you to analyze the relationship between stimulus and response Limitations ----------- - ABF2 files only: ABF1 files don't store protocol sections, though protocols can theoretically be reconstructed from ABF1 headers - Waveforms are RECONSTRUCTED from epoch definitions, not recorded data - Some complex protocols may not reconstruct perfectly (see TODO in code) Returns ------- sigs_by_segments : list of list of numpy.ndarray Outer list: one entry per episode/sweep (length = nEpi) Inner list: one entry per DAC channel (length = nDAC) Arrays: stimulus waveform samples (length = nSam samples per episode) Structure: sigs_by_segments[episode_idx][DAC_idx] = 1D array Example for 3 episodes, 2 DAC channels: sigs_by_segments[0][0] -> Episode 0, DAC 0 waveform (nSam samples) sigs_by_segments[0][1] -> Episode 0, DAC 1 waveform (nSam samples) sigs_by_segments[1][0] -> Episode 1, DAC 0 waveform (nSam samples) ... sig_names : list of str DAC channel names (length = nDAC) Example: ['Cmd 0', 'Cmd 1'] sig_units : list of str DAC channel units (length = nDAC) Example: ['mV', 'pA'] Example Usage ------------- >>> reader = AxonRawIO(filename='mydata.abf') >>> reader.parse_header() >>> waveforms, names, units = reader.read_raw_protocol() >>> # Get DAC 0 waveform for episode 2: >>> dac0_ep2 = waveforms[2][0] >>> print(f"{names[0]} waveform in {units[0]}: {dac0_ep2}") Notes ----- Waveforms are constructed from: - Holding levels (fDACHoldingLevel) - Epoch definitions (EpochPerDACSection) describing step changes - Episode number (waveforms can change across episodes via increment parameters) Author: JS Nowacki """ info = self._axon_info if info["fFileVersionNumber"] < 2.0: raise IOError("Protocol section is only present in ABF2 files.") nADC = info["sections"]["ADCSection"]["llNumEntries"] # Number of ADC channels nDAC = info["sections"]["DACSection"]["llNumEntries"] # Number of DAC channels nSam = int(info["protocol"]["lNumSamplesPerEpisode"] / nADC) # Number of samples per episode nEpi = info["lActualEpisodes"] # Actual number of episodes # Make a list of segments with analog signals with just holding levels # List of segments relates to number of episodes, as for recorded data sigs_by_segments = [] for epiNum in range(nEpi): # One analog signal for each DAC in segment (episode) signals = [] for DACNum in range(nDAC): sig = np.ones(nSam) * info["listDACInfo"][DACNum]["fDACHoldingLevel"] # If there are epoch infos for this DAC if DACNum in info["dictEpochInfoPerDAC"]: # Save last sample index i_last = int(nSam * 15625 / 10**6) # TODO guess for first holding # Go over EpochInfoPerDAC and change the analog signal # according to the epochs epochInfo = info["dictEpochInfoPerDAC"][DACNum] for epochNum, epoch in epochInfo.items(): i_begin = i_last i_end = i_last + epoch["lEpochInitDuration"] + epoch["lEpochDurationInc"] * epiNum dif = i_end - i_begin sig[i_begin:i_end] = np.ones(dif) * ( epoch["fEpochInitLevel"] + epoch["fEpochLevelInc"] * epiNum ) i_last += epoch["lEpochInitDuration"] + epoch["lEpochDurationInc"] * epiNum signals.append(sig) sigs_by_segments.append(signals) sig_names = [] sig_units = [] for DACNum in range(nDAC): name = info["listDACInfo"][DACNum]["DACChNames"].decode("utf-8") units = safe_decode_units(info["listDACInfo"][DACNum]["DACChUnits"]) sig_names.append(name) sig_units.append(units) return sigs_by_segments, sig_names, sig_units def parse_axon_soup(filename): """ Parse ABF file header and return metadata dict. The strategy here differs from the original script under Matlab. In the original script for ABF2, it completes the header with information that is located in other structures. Returns info with sub dicts depending on version: ABF1: listTag ABF2: sections, protocol, listTags, listADCInfo, listDACInfo, dictEpochInfoPerDAC Parameters ---------- filename : str Path to the ABF file Returns ------- dict or None Header dictionary with file metadata, or None if file signature is invalid """ with open(filename, "rb") as fid: f = StructFile(fid) signature = f.read(4) if signature == b"ABF ": return _parse_abf_v1(f, headerDescriptionV1) elif signature == b"ABF2": return _parse_abf_v2(f, headerDescriptionV2) else: return None def _parse_abf_v1(f, header_description): """ Parse ABF 1.x files (pCLAMP 6-9). Overview -------- ABF 1.x uses a single, large, fixed-length header (~5KB) that contains EVERYTHING you need to parse the file. Think of it as a "road map" where every piece of metadata and every pointer is at a known, fixed location. Key Design Principle: DIRECT ACCESS ------------------------------------ Unlike ABF 2.x which uses indirection (Section Index -> Sections), ABF 1.x puts all metadata directly in the header at fixed byte offsets. Example: To get the number of channels: Simply read 2 bytes at offset 120 -> nADCNumChannels Example: To find the data section: Read lDataSectionPtr at offset 40 -> multiply by 512 -> data location Limitations: - Maximum 16 ADC channels (arrays are fixed size in header) - Strings are fixed-length (384 bytes for protocol path, 10 bytes per channel name, etc.) - Cannot add new features without breaking file format Benefits: - Simple to parse (read header, done!) - Fast (direct memory mapping, no indirection) - Everything in one place Historical Context: ABF 1.x merged the older CLAMPEX and FETCHEX file formats into a single, more versatile format for electrophysiology data. Parsing Order (as implemented in this function): ------------------------------------------------ 1. Read entire fixed header (bytes 0-5119) -> all metadata available 2. Parse Tag section (using lTagSectionPtr from header) 3. Adjust protocol path formatting 4. Compute datetime from header fields Format: Fixed header with static field offsets Block Size: 512 bytes Header Fields: See headerDescriptionV1 for complete field list File Layout: +----------------------------------------------------------------------+ | HEADER SECTION (Blocks 0-9, ~5KB) | +----------------------------------------------------------------------+ | Contains all metadata for the entire file, including acquisition | | parameters and pointers to all other data sections: | | - File signature, version, operation mode | | - Pointers to data/tag/synch sections (in block numbers) | | - Channel configuration (up to 16 ADC channels) | | - Sampling parameters, gains, offsets | | - Protocol path and timing information | | See headerDescriptionV1 for all fields (offset, format, name) | +----------------------------------------------------------------------+ | TAG SECTION (at lTagSectionPtr x 512) | +----------------------------------------------------------------------+ | User-defined markers/comments during acquisition | | Each tag: 64 bytes (time, comment, type, voice tag) | | See TagInfoDescription for structure | +----------------------------------------------------------------------+ | SYNCH ARRAY SECTION (Physical disk layout) | +----------------------------------------------------------------------+ | Purpose: Index of episodes/sweeps within the continuous data stream | | Critical for episodic acquisition modes (mode 2, 5) | | | | Electrophysiology Context: | | - Episode/Sweep = Single experimental trial or stimulation cycle | | - In voltage-clamp: one voltage step protocol execution | | - In current-clamp: one current injection sequence | | - Data contains multiple episodes concatenated in data section | | | | Neo Mapping: | | - Each episode becomes one neo.Segment | | - offset = starting sample index in the continuous data array | | - len = number of samples in this episode (all channels combined) | | | | Start Position: | | Byte = lSynchArrayPtr x 512 | | Total entries = lSynchArraySize (number of episodes/segments) | | | | Each Entry Structure (8 bytes): | | Offset Size Type Field Description | | 0 4 int32 offset Episode start position (samples)| | 4 4 int32 len Episode length (samples) | | | | Example: 3 episodes, 3 channels, 5000 samples per channel per ep: | | | | Disk Offset | Content | | -------------|-------------------------------------------------- | | +0 bytes | Episode 0 offset (4 bytes, int32) = 0 | | +4 bytes | Episode 0 length (4 bytes, int32) = 15000 | | +8 bytes | Episode 1 offset (4 bytes, int32) = 15000 | | +12 bytes | Episode 1 length (4 bytes, int32) = 15000 | | +16 bytes | Episode 2 offset (4 bytes, int32) = 30000 | | +20 bytes | Episode 2 length (4 bytes, int32) = 15000 | | | | Note: len = samples_per_channel x nADCNumChannels (interleaved) | +----------------------------------------------------------------------+ | DATA SECTION (Physical disk layout) | +----------------------------------------------------------------------+ | Start Position: | | Byte = (lDataSectionPtr x 512) + (nNumPointsIgnored x item_size) | | item_size = 2 bytes if nDataFormat=0 (int16) | | item_size = 4 bytes if nDataFormat=1 (float32) | | | | Example: 3 channels (Ch0, Ch1, Ch2), int16 format: | | | | Disk Offset | Content | | -------------|-------------------------------------------------- | | +0 bytes | Sample 0, Channel 0 (2 bytes, int16) | | +2 bytes | Sample 0, Channel 1 (2 bytes, int16) | | +4 bytes | Sample 0, Channel 2 (2 bytes, int16) | | +6 bytes | Sample 1, Channel 0 (2 bytes, int16) | | +8 bytes | Sample 1, Channel 1 (2 bytes, int16) | | +10 bytes | Sample 1, Channel 2 (2 bytes, int16) | | +12 bytes | Sample 2, Channel 0 (2 bytes, int16) | | +14 bytes | Sample 2, Channel 1 (2 bytes, int16) | | +16 bytes | Sample 2, Channel 2 (2 bytes, int16) | | ... continues for lActualAcqLength total samples | | | | Pattern: Channels are interleaved within each time point | | [Ch0,Ch1,Ch2] [Ch0,Ch1,Ch2] [Ch0,Ch1,Ch2] ... | +----------------------------------------------------------------------+ Parameters ---------- f : StructFile File object positioned after reading the signature header_description : list List of (key, offset, fmt) tuples describing header structure Returns ------- dict Header dictionary with file metadata """ # construct dict header = {} for key, offset, fmt in header_description: val = f.read_f(fmt, offset=offset) if len(val) == 1: header[key] = val[0] else: header[key] = np.array(val) # correction of version number and starttime header["lFileStartTime"] += header["nFileStartMillisecs"] * 0.001 # tags listTag = [] for i in range(header["lNumTagEntries"]): f.seek(header["lTagSectionPtr"] + i * 64) tag = {} for key, fmt in TagInfoDescription: val = f.read_f(fmt) if len(val) == 1: tag[key] = val[0] else: tag[key] = np.array(val) listTag.append(tag) header["listTag"] = listTag # protocol name formatting header["sProtocolPath"] = clean_string(header["sProtocolPath"]) header["sProtocolPath"] = header["sProtocolPath"].replace(b"\\", b"/") # date and time YY = 1900 MM = 1 DD = 1 hh = int(header["lFileStartTime"] / 3600.0) mm = int((header["lFileStartTime"] - hh * 3600) / 60) ss = header["lFileStartTime"] - hh * 3600 - mm * 60 ms = int(np.mod(ss, 1) * 1e6) ss = int(ss) header["rec_datetime"] = datetime.datetime(YY, MM, DD, hh, mm, ss, ms) return header def _parse_abf_v2(f, header_description): """ Parse ABF 2.x files (pCLAMP 10+, 2006-present). Overview -------- ABF 2.x uses a "table of contents" architecture, similar to a book or database. Think of it as three layers: 1. MAIN HEADER (bytes 0-75): Cover page with basic file info 2. SECTION INDEX (bytes 76-827): Table of contents - WHERE everything is 3. SECTIONS (variable locations): The actual data chapters Key Design Principle: INDIRECTION ---------------------------------- Unlike ABF 1.x where everything is at fixed offsets, ABF 2.x uses a two-step lookup process: Step 1: Read Section Index to find WHERE a section lives Step 2: Jump to that location and read the section data Example: To read ADC channel info: 1. Read sections['ADCSection'] -> {uBlockIndex: 50, uBytes: 128, llNumEntries: 3} 2. Jump to byte (50 x 512) and read 3 entries of 128 bytes each Critical Feature: CENTRAL STRING POOL -------------------------------------- All strings (channel names, units, paths) are stored ONCE in StringsSection. Other sections store only INTEGER INDICES pointing to strings, not the strings themselves. This is why StringsSection must be parsed before ADC/DAC sections. Example: StringsSection contains: ['Clampex', 'IN 0', 'mV', 'IN 1', 'pA', ...] ADCInfo[0] has lADCChannelNameIndex = 1 -> name is strings[1] = 'IN 0' ADCInfo[0] has lADCUnitsIndex = 2 -> units is strings[2] = 'mV' Benefits of This Architecture: - Variable channel/epoch counts (no hard limits like ABF 1.x's 16 channels) - Efficient storage (strings not duplicated) - Extensible (new section types don't break old parsers) Parsing Order (as implemented in this function): ------------------------------------------------ 1. Read Main Header (bytes 0-75) -> basic file metadata 2. Read Section Index (bytes 76-827) -> build 'sections' dict 3. Parse StringsSection -> build 'strings' array (MUST be before step 4!) 4. Parse ADCSection -> use string indices to get channel names/units 5. Parse ProtocolSection -> acquisition parameters 6. Parse TagSection -> user markers 7. Parse DACSection -> use string indices to get output channel info 8. Parse EpochPerDACSection -> waveform definitions 9. Parse EpochSection -> epoch metadata 10. Compute datetime from header fields Parsing Note: This is a reverse-engineering effort. Official method uses Axon's ABFFIO.DLL. Some parts (e.g., Strings parsing) are brittle. Format: Section-based with table of contents Block Size: 512 bytes Header Fields: See headerDescriptionV2 for main header fields File Layout: +----------------------------------------------------------------------+ | MAIN HEADER (Bytes 0-75) | +----------------------------------------------------------------------+ | Minimal header with file metadata and version info | | See headerDescriptionV2 for complete field list | +----------------------------------------------------------------------+ | SECTION INDEX (Bytes 76-827) - THE KEY TO PARSING | +----------------------------------------------------------------------+ | Table of contents: 18 sections x 16 bytes per entry | | Each entry: uBlockIndex, uBytes, llNumEntries | | Section names defined in sectionNames: | | ProtocolSection, ADCSection, DACSection, EpochSection, | | StringsSection, DataSection, TagSection, SynchArraySection, etc. | +----------------------------------------------------------------------+ | PROTOCOL SECTION (at sections['ProtocolSection'] location) | +----------------------------------------------------------------------+ | Core acquisition settings (512 bytes): operation mode, sample rate, | | resolution, etc. See protocolInfoDescription for fields | +----------------------------------------------------------------------+ | STRINGS SECTION (at sections['StringsSection'] location) | +----------------------------------------------------------------------+ | CENTRAL STRING REPOSITORY: All strings (channel names, units, | | protocol paths) stored here. Other sections store indices, not | | strings directly. This is fundamental to ABF 2.x design. | | Format: \x00\x00[str1]\x00[str2]\x00... | | Accessed via indices like lADCChannelNameIndex | +----------------------------------------------------------------------+ | ADC SECTION (at sections['ADCSection'] location) | +----------------------------------------------------------------------+ | Per-channel input configs: 128 bytes per entry | | Contains: gains, offsets, telegraph settings, string indices | | See ADCInfoDescription for fields | +----------------------------------------------------------------------+ | DAC SECTION (at sections['DACSection'] location) | +----------------------------------------------------------------------+ | Per-channel output configs: 256 bytes per entry | | Contains: holding levels, scale factors, waveform settings, | | string indices. See DACInfoDescription for fields | +----------------------------------------------------------------------+ | EPOCH PER DAC SECTION (at sections['EpochPerDACSection'] location) | +----------------------------------------------------------------------+ | Waveform epoch definitions: 48 bytes per entry | | Defines stimulus waveforms per DAC channel | | See EpochPerDACDescription for fields | +----------------------------------------------------------------------+ | TAG SECTION (at sections['TagSection'] location) | +----------------------------------------------------------------------+ | User-defined markers/comments during acquisition | | Each tag: 64 bytes. See TagInfoDescription for structure | +----------------------------------------------------------------------+ | SYNCH ARRAY SECTION (Physical disk layout) | +----------------------------------------------------------------------+ | Purpose: Index of episodes/sweeps within the continuous data stream | | Critical for episodic acquisition modes (mode 2, 5) | | | | Electrophysiology Context: | | - Episode/Sweep = Single experimental trial or stimulation cycle | | - In voltage-clamp: one voltage step protocol execution | | - In current-clamp: one current injection sequence | | - Data contains multiple episodes concatenated in data section | | | | Neo Mapping: | | - Each episode becomes one neo.Segment | | - offset = starting sample index in the continuous data array | | - len = number of samples in this episode (all channels combined) | | | | Start Position: | | Byte = sections['SynchArraySection']['uBlockIndex'] x 512 | | Total entries = sections['SynchArraySection']['llNumEntries'] | | (number of episodes/segments) | | | | Each Entry Structure (8 bytes): | | Offset Size Type Field Description | | 0 4 int32 offset Episode start position (samples)| | 4 4 int32 len Episode length (samples) | | | | Example: 3 episodes, 3 channels, 5000 samples per channel per ep: | | | | Disk Offset | Content | | -------------|-------------------------------------------------- | | +0 bytes | Episode 0 offset (4 bytes, int32) = 0 | | +4 bytes | Episode 0 length (4 bytes, int32) = 15000 | | +8 bytes | Episode 1 offset (4 bytes, int32) = 15000 | | +12 bytes | Episode 1 length (4 bytes, int32) = 15000 | | +16 bytes | Episode 2 offset (4 bytes, int32) = 30000 | | +20 bytes | Episode 2 length (4 bytes, int32) = 15000 | | | | Note: len = samples_per_channel x num_channels (interleaved) | | num_channels from ADCSection llNumEntries | +----------------------------------------------------------------------+ | DATA SECTION (Physical disk layout) | +----------------------------------------------------------------------+ | Start Position: | | Byte = sections['DataSection']['uBlockIndex'] x 512 | | Total samples = sections['DataSection']['llNumEntries'] | | item_size = 2 bytes if nDataFormat=0 (int16) | | item_size = 4 bytes if nDataFormat=1 (float32) | | | | Example: 3 channels (Ch0, Ch1, Ch2), int16 format: | | | | Disk Offset | Content | | -------------|-------------------------------------------------- | | +0 bytes | Sample 0, Channel 0 (2 bytes, int16) | | +2 bytes | Sample 0, Channel 1 (2 bytes, int16) | | +4 bytes | Sample 0, Channel 2 (2 bytes, int16) | | +6 bytes | Sample 1, Channel 0 (2 bytes, int16) | | +8 bytes | Sample 1, Channel 1 (2 bytes, int16) | | +10 bytes | Sample 1, Channel 2 (2 bytes, int16) | | +12 bytes | Sample 2, Channel 0 (2 bytes, int16) | | +14 bytes | Sample 2, Channel 1 (2 bytes, int16) | | +16 bytes | Sample 2, Channel 2 (2 bytes, int16) | | ... continues for llNumEntries total samples | | | | Pattern: Channels are interleaved within each time point | | [Ch0,Ch1,Ch2] [Ch0,Ch1,Ch2] [Ch0,Ch1,Ch2] ... | +----------------------------------------------------------------------+ Note: All section locations calculated as: uBlockIndex x 512 Parameters ---------- f : StructFile File object positioned after reading the signature header_description : list List of (key, offset, fmt) tuples describing header structure Returns ------- dict Header dictionary with file metadata """ # construct dict header = {} for key, offset, fmt in header_description: val = f.read_f(fmt, offset=offset) if len(val) == 1: header[key] = val[0] else: header[key] = np.array(val) # correction of version number and starttime n = header["fFileVersionNumber"] header["fFileVersionNumber"] = n[3] + 0.1 * n[2] + 0.01 * n[1] + 0.001 * n[0] header["lFileStartTime"] = header["uFileStartTimeMS"] * 0.001 # sections sections = {} for s, sectionName in enumerate(sectionNames): uBlockIndex, uBytes, llNumEntries = f.read_f("IIl", offset=76 + s * 16) sections[sectionName] = {} sections[sectionName]["uBlockIndex"] = uBlockIndex sections[sectionName]["uBytes"] = uBytes sections[sectionName]["llNumEntries"] = llNumEntries header["sections"] = sections # strings sections # hack for reading channels names and units # this section is not very detailed and so the code # not very robust. f.seek(sections["StringsSection"]["uBlockIndex"] * BLOCKSIZE) big_string = f.read(sections["StringsSection"]["uBytes"]) # this idea comes from pyABF https://github.com/swharden/pyABF # previously we searched for clampex, Clampex etc, but this was # brittle. pyABF believes that looking for the \x00\x00 is more # robust. We find these values, replace mu->u, then split into # a set of strings indexed_string = big_string[big_string.rfind(b"\x00\x00") :] # replace mu -> u for easy display indexed_string = indexed_string.replace(b"\xb5", b"\x75") # we need to remove one of the \x00 to have the indices be # the correct order indexed_string = indexed_string.split(b"\x00")[1:] strings = indexed_string # ADC sections header["listADCInfo"] = [] for i in range(sections["ADCSection"]["llNumEntries"]): # read ADCInfo f.seek(sections["ADCSection"]["uBlockIndex"] * BLOCKSIZE + sections["ADCSection"]["uBytes"] * i) ADCInfo = {} for key, fmt in ADCInfoDescription: val = f.read_f(fmt) if len(val) == 1: ADCInfo[key] = val[0] else: ADCInfo[key] = np.array(val) ADCInfo["ADCChNames"] = strings[ADCInfo["lADCChannelNameIndex"]] ADCInfo["ADCChUnits"] = strings[ADCInfo["lADCUnitsIndex"]] header["listADCInfo"].append(ADCInfo) # protocol sections protocol = {} f.seek(sections["ProtocolSection"]["uBlockIndex"] * BLOCKSIZE) for key, fmt in protocolInfoDescription: val = f.read_f(fmt) if len(val) == 1: protocol[key] = val[0] else: protocol[key] = np.array(val) header["protocol"] = protocol header["sProtocolPath"] = strings[header["uProtocolPathIndex"]] # tags listTag = [] for i in range(sections["TagSection"]["llNumEntries"]): f.seek(sections["TagSection"]["uBlockIndex"] * BLOCKSIZE + sections["TagSection"]["uBytes"] * i) tag = {} for key, fmt in TagInfoDescription: val = f.read_f(fmt) if len(val) == 1: tag[key] = val[0] else: tag[key] = np.array(val) listTag.append(tag) header["listTag"] = listTag # DAC sections header["listDACInfo"] = [] for i in range(sections["DACSection"]["llNumEntries"]): # read DACInfo f.seek(sections["DACSection"]["uBlockIndex"] * BLOCKSIZE + sections["DACSection"]["uBytes"] * i) DACInfo = {} for key, fmt in DACInfoDescription: val = f.read_f(fmt) if len(val) == 1: DACInfo[key] = val[0] else: DACInfo[key] = np.array(val) DACInfo["DACChNames"] = strings[DACInfo["lDACChannelNameIndex"]] DACInfo["DACChUnits"] = strings[DACInfo["lDACChannelUnitsIndex"]] header["listDACInfo"].append(DACInfo) # EpochPerDAC sections # header['dictEpochInfoPerDAC'] is dict of dicts: # - the first index is the DAC number # - the second index is the epoch number # It has to be done like that because data may not exist # and may not be in sorted order header["dictEpochInfoPerDAC"] = {} for i in range(sections["EpochPerDACSection"]["llNumEntries"]): # read DACInfo f.seek(sections["EpochPerDACSection"]["uBlockIndex"] * BLOCKSIZE + sections["EpochPerDACSection"]["uBytes"] * i) EpochInfoPerDAC = {} for key, fmt in EpochInfoPerDACDescription: val = f.read_f(fmt) if len(val) == 1: EpochInfoPerDAC[key] = val[0] else: EpochInfoPerDAC[key] = np.array(val) DACNum = EpochInfoPerDAC["nDACNum"] EpochNum = EpochInfoPerDAC["nEpochNum"] # Checking if the key exists, if not, the value is empty # so we have to create empty dict to populate if DACNum not in header["dictEpochInfoPerDAC"]: header["dictEpochInfoPerDAC"][DACNum] = {} header["dictEpochInfoPerDAC"][DACNum][EpochNum] = EpochInfoPerDAC # Epoch sections header["EpochInfo"] = [] for i in range(sections["EpochSection"]["llNumEntries"]): # read EpochInfo f.seek(sections["EpochSection"]["uBlockIndex"] * BLOCKSIZE + sections["EpochSection"]["uBytes"] * i) EpochInfo = {} for key, fmt in EpochInfoDescription: val = f.read_f(fmt) if len(val) == 1: EpochInfo[key] = val[0] else: EpochInfo[key] = np.array(val) header["EpochInfo"].append(EpochInfo) # date and time YY = int(header["uFileStartDate"] / 10000) MM = int((header["uFileStartDate"] - YY * 10000) / 100) DD = int(header["uFileStartDate"] - YY * 10000 - MM * 100) hh = int(header["uFileStartTimeMS"] / 1000.0 / 3600.0) mm = int((header["uFileStartTimeMS"] / 1000.0 - hh * 3600) / 60) ss = header["uFileStartTimeMS"] / 1000.0 - hh * 3600 - mm * 60 ms = int(np.mod(ss, 1) * 1e6) ss = int(ss) header["rec_datetime"] = datetime.datetime(YY, MM, DD, hh, mm, ss, ms) return header class StructFile(BufferedReader): def read_f(self, fmt, offset=None): if offset is not None: self.seek(offset) return struct.unpack(fmt, self.read(struct.calcsize(fmt))) def clean_string(s): s = s.rstrip(b"\x00") s = s.rstrip(b" ") return s def safe_decode_units(s): s = s.replace(b" ", b"") s = s.replace(b"\xb5", b"u") # \xb5 is µ s = s.replace(b"\xb0", b"\xc2\xb0") # \xb0 is ° s = s.decode("utf-8") return s BLOCKSIZE = 512 headerDescriptionV1 = [ ("fFileSignature", 0, "4s"), ("fFileVersionNumber", 4, "f"), ("nOperationMode", 8, "h"), ("lActualAcqLength", 10, "i"), ("nNumPointsIgnored", 14, "h"), ("lActualEpisodes", 16, "i"), ("lFileStartTime", 24, "i"), ("lDataSectionPtr", 40, "i"), ("lTagSectionPtr", 44, "i"), ("lNumTagEntries", 48, "i"), ("lSynchArrayPtr", 92, "i"), ("lSynchArraySize", 96, "i"), ("nDataFormat", 100, "h"), ("nADCNumChannels", 120, "h"), ("fADCSampleInterval", 122, "f"), ("fSynchTimeUnit", 130, "f"), ("lNumSamplesPerEpisode", 138, "i"), ("lPreTriggerSamples", 142, "i"), ("lEpisodesPerRun", 146, "i"), ("fADCRange", 244, "f"), ("lADCResolution", 252, "i"), ("nFileStartMillisecs", 366, "h"), ("nADCPtoLChannelMap", 378, "16h"), ("nADCSamplingSeq", 410, "16h"), ("sADCChannelName", 442, "10s" * 16), ("sADCUnits", 602, "8s" * 16), ("fADCProgrammableGain", 730, "16f"), ("fInstrumentScaleFactor", 922, "16f"), ("fInstrumentOffset", 986, "16f"), ("fSignalGain", 1050, "16f"), ("fSignalOffset", 1114, "16f"), ("nDigitalEnable", 1436, "h"), ("nActiveDACChannel", 1440, "h"), ("nDigitalHolding", 1584, "h"), ("nDigitalInterEpisode", 1586, "h"), ("nDigitalValue", 2588, "10h"), ("lDACFilePtr", 2048, "2i"), ("lDACFileNumEpisodes", 2056, "2i"), ("fDACCalibrationFactor", 2074, "4f"), ("fDACCalibrationOffset", 2090, "4f"), ("nWaveformEnable", 2296, "2h"), ("nWaveformSource", 2300, "2h"), ("nInterEpisodeLevel", 2304, "2h"), ("nEpochType", 2308, "20h"), ("fEpochInitLevel", 2348, "20f"), ("fEpochLevelInc", 2428, "20f"), ("lEpochInitDuration", 2508, "20i"), ("lEpochDurationInc", 2588, "20i"), ("nTelegraphEnable", 4512, "16h"), ("fTelegraphAdditGain", 4576, "16f"), ("sProtocolPath", 4898, "384s"), ] headerDescriptionV2 = [ ("fFileSignature", 0, "4s"), ("fFileVersionNumber", 4, "4b"), ("uFileInfoSize", 8, "I"), ("lActualEpisodes", 12, "I"), ("uFileStartDate", 16, "I"), ("uFileStartTimeMS", 20, "I"), ("uStopwatchTime", 24, "I"), ("nFileType", 28, "H"), ("nDataFormat", 30, "H"), ("nSimultaneousScan", 32, "H"), ("nCRCEnable", 34, "H"), ("uFileCRC", 36, "I"), ("FileGUID", 40, "I"), ("uCreatorVersion", 56, "I"), ("uCreatorNameIndex", 60, "I"), ("uModifierVersion", 64, "I"), ("uModifierNameIndex", 68, "I"), ("uProtocolPathIndex", 72, "I"), ] sectionNames = [ "ProtocolSection", "ADCSection", "DACSection", "EpochSection", "ADCPerDACSection", "EpochPerDACSection", "UserListSection", "StatsRegionSection", "MathSection", "StringsSection", "DataSection", "TagSection", "ScopeSection", "DeltaSection", "VoiceTagSection", "SynchArraySection", "AnnotationSection", "StatsSection", ] protocolInfoDescription = [ ("nOperationMode", "h"), ("fADCSequenceInterval", "f"), ("bEnableFileCompression", "b"), ("sUnused1", "3s"), ("uFileCompressionRatio", "I"), ("fSynchTimeUnit", "f"), ("fSecondsPerRun", "f"), ("lNumSamplesPerEpisode", "i"), ("lPreTriggerSamples", "i"), ("lEpisodesPerRun", "i"), ("lRunsPerTrial", "i"), ("lNumberOfTrials", "i"), ("nAveragingMode", "h"), ("nUndoRunCount", "h"), ("nFirstEpisodeInRun", "h"), ("fTriggerThreshold", "f"), ("nTriggerSource", "h"), ("nTriggerAction", "h"), ("nTriggerPolarity", "h"), ("fScopeOutputInterval", "f"), ("fEpisodeStartToStart", "f"), ("fRunStartToStart", "f"), ("lAverageCount", "i"), ("fTrialStartToStart", "f"), ("nAutoTriggerStrategy", "h"), ("fFirstRunDelayS", "f"), ("nChannelStatsStrategy", "h"), ("lSamplesPerTrace", "i"), ("lStartDisplayNum", "i"), ("lFinishDisplayNum", "i"), ("nShowPNRawData", "h"), ("fStatisticsPeriod", "f"), ("lStatisticsMeasurements", "i"), ("nStatisticsSaveStrategy", "h"), ("fADCRange", "f"), ("fDACRange", "f"), ("lADCResolution", "i"), ("lDACResolution", "i"), ("nExperimentType", "h"), ("nManualInfoStrategy", "h"), ("nCommentsEnable", "h"), ("lFileCommentIndex", "i"), ("nAutoAnalyseEnable", "h"), ("nSignalType", "h"), ("nDigitalEnable", "h"), ("nActiveDACChannel", "h"), ("nDigitalHolding", "h"), ("nDigitalInterEpisode", "h"), ("nDigitalDACChannel", "h"), ("nDigitalTrainActiveLogic", "h"), ("nStatsEnable", "h"), ("nStatisticsClearStrategy", "h"), ("nLevelHysteresis", "h"), ("lTimeHysteresis", "i"), ("nAllowExternalTags", "h"), ("nAverageAlgorithm", "h"), ("fAverageWeighting", "f"), ("nUndoPromptStrategy", "h"), ("nTrialTriggerSource", "h"), ("nStatisticsDisplayStrategy", "h"), ("nExternalTagType", "h"), ("nScopeTriggerOut", "h"), ("nLTPType", "h"), ("nAlternateDACOutputState", "h"), ("nAlternateDigitalOutputState", "h"), ("fCellID", "3f"), ("nDigitizerADCs", "h"), ("nDigitizerDACs", "h"), ("nDigitizerTotalDigitalOuts", "h"), ("nDigitizerSynchDigitalOuts", "h"), ("nDigitizerType", "h"), ] ADCInfoDescription = [ ("nADCNum", "h"), ("nTelegraphEnable", "h"), ("nTelegraphInstrument", "h"), ("fTelegraphAdditGain", "f"), ("fTelegraphFilter", "f"), ("fTelegraphMembraneCap", "f"), ("nTelegraphMode", "h"), ("fTelegraphAccessResistance", "f"), ("nADCPtoLChannelMap", "h"), ("nADCSamplingSeq", "h"), ("fADCProgrammableGain", "f"), ("fADCDisplayAmplification", "f"), ("fADCDisplayOffset", "f"), ("fInstrumentScaleFactor", "f"), ("fInstrumentOffset", "f"), ("fSignalGain", "f"), ("fSignalOffset", "f"), ("fSignalLowpassFilter", "f"), ("fSignalHighpassFilter", "f"), ("nLowpassFilterType", "b"), ("nHighpassFilterType", "b"), ("fPostProcessLowpassFilter", "f"), ("nPostProcessLowpassFilterType", "c"), ("bEnabledDuringPN", "b"), ("nStatsChannelPolarity", "h"), ("lADCChannelNameIndex", "i"), ("lADCUnitsIndex", "i"), ] TagInfoDescription = [ ("lTagTime", "i"), ("sComment", "56s"), ("nTagType", "h"), ("nVoiceTagNumber_or_AnnotationIndex", "h"), ] DACInfoDescription = [ ("nDACNum", "h"), ("nTelegraphDACScaleFactorEnable", "h"), ("fInstrumentHoldingLevel", "f"), ("fDACScaleFactor", "f"), ("fDACHoldingLevel", "f"), ("fDACCalibrationFactor", "f"), ("fDACCalibrationOffset", "f"), ("lDACChannelNameIndex", "i"), ("lDACChannelUnitsIndex", "i"), ("lDACFilePtr", "i"), ("lDACFileNumEpisodes", "i"), ("nWaveformEnable", "h"), ("nWaveformSource", "h"), ("nInterEpisodeLevel", "h"), ("fDACFileScale", "f"), ("fDACFileOffset", "f"), ("lDACFileEpisodeNum", "i"), ("nDACFileADCNum", "h"), ("nConditEnable", "h"), ("lConditNumPulses", "i"), ("fBaselineDuration", "f"), ("fBaselineLevel", "f"), ("fStepDuration", "f"), ("fStepLevel", "f"), ("fPostTrainPeriod", "f"), ("fPostTrainLevel", "f"), ("nMembTestEnable", "h"), ("nLeakSubtractType", "h"), ("nPNPolarity", "h"), ("fPNHoldingLevel", "f"), ("nPNNumADCChannels", "h"), ("nPNPosition", "h"), ("nPNNumPulses", "h"), ("fPNSettlingTime", "f"), ("fPNInterpulse", "f"), ("nLTPUsageOfDAC", "h"), ("nLTPPresynapticPulses", "h"), ("lDACFilePathIndex", "i"), ("fMembTestPreSettlingTimeMS", "f"), ("fMembTestPostSettlingTimeMS", "f"), ("nLeakSubtractADCIndex", "h"), ("sUnused", "124s"), ] EpochInfoPerDACDescription = [ ("nEpochNum", "h"), ("nDACNum", "h"), ("nEpochType", "h"), ("fEpochInitLevel", "f"), ("fEpochLevelInc", "f"), ("lEpochInitDuration", "i"), ("lEpochDurationInc", "i"), ("lEpochPulsePeriod", "i"), ("lEpochPulseWidth", "i"), ("sUnused", "18s"), ] EpochInfoDescription = [ ("nEpochNum", "h"), ("nDigitalValue", "h"), ("nDigitalTrainValue", "h"), ("nAlternateDigitalValue", "h"), ("nAlternateDigitalTrainValue", "h"), ("bEpochCompression", "b"), ("sUnused", "21s"), ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/baserawio.py0000644000175100017510000021202315154507213016262 0ustar00runnerrunner""" baserawio ====== Classes ------- BaseRawIO abstract class which should be overridden to write a RawIO. RawIO is a low level API in neo that provides fast access to the raw data. When possible, all IOs should/implement this level following these guidelines: * internal use of memmap (or hdf5) * fast reading of the header (do not read the complete file) * neo tree object is symmetric and logical: same channel/units/event along all block and segments. For this level, datasets of recordings are mapped as follows: A channel refers to a physical channel of a recording in an experiment. It is identified by a channel_id. Recordings from a channel consist of sections of samples which are recorded contiguously in time; in other words, a section of a channel has a specific sampling_rate, start_time, and length (and thus also stop_time, which is the time of the sample which would lie one sampling interval beyond the last sample present in that section). A stream consists of a set of channels which all have the same structure of their sections of recording and the same data type of samples. Each stream has a unique stream_id and has a name, which does not need to be unique. A stream thus has multiple channels which all have the same sampling rate and are on the same clock, have the same sections with t_starts and lengths, and the same data type for their samples. The samples in a stream can thus be retrieved as an Numpy array, a chunk of samples. Channels within a stream can be accessed be either their channel_id, which must be unique within a stream, or by their channel_index, which is a 0 based index to all channels within the stream. Note that a single channel of recording may be represented within multiple streams, and such is the case for RawIOs which may have both unfiltered and filtered or downsampled versions of the signals from a single recording channel. In such a case, a single channel and channel_id may be represented by a different channel_index within different streams. Lists of channel_indexes are often convenient to pass around a selection of channels within a stream. At the neo.io level, one AnalogSignal with multiple channels can be created for each stream. Such an AnalogSignal may have multiple Segments, with each segment containing the sections from each channel with the same t_start and length. Such multiple Segments for a RawIO will have the same sampling rate. It is thus possible to retrieve the t_start and length of the sections of the channels for a Block and Segment of a stream. So this handles **only** one simplified but very frequent case of dataset: * Only one channel set for AnalogSignal stable along Segment * Only one channel set for SpikeTrain stable along Segment * AnalogSignal have all the same sampling_rate across all Segment * t_start/t_stop are the same for many object (SpikeTrain, Event) inside a Segment Signal channels are handled by group of "stream". One stream will result at neo.io level in one AnalogSignal with multiple channels. A helper class `neo.io.basefromrawio.BaseFromRaw` transforms a RawIO to neo legacy IO. In short all "neo.rawio" classes are also "neo.io" with lazy reading capability. With this API the IO have an attributes `header` with necessary keys. This `header` attribute is done in `_parse_header(...)` method. See ExampleRawIO as example. BaseRawIO also implements a possible persistent cache system that can be used by some RawIOs to avoid a very long parse_header() call. The idea is that some variable or vector can be stored somewhere (near the file, /tmp, any path) for use across multiple constructions of a RawIO for a given set of data. """ from __future__ import annotations import logging import numpy as np import os import sys from neo import logging_handler from .utils import get_memmap_chunk_from_opened_file possible_raw_modes = [ "one-file", "multi-file", "one-dir", ] # 'multi-dir', 'url', 'other' error_header = "Header is not read yet, do parse_header() first" _signal_buffer_dtype = [ ("name", "U64"), # not necessarily unique ("id", "U64"), # must be unique ] # To be left an empty array if the concept of buffer is undefined for a reader. _signal_stream_dtype = [ ("name", "U128"), # not necessarily unique ("id", "U64"), # must be unique ( "buffer_id", "U64", ), # should be "" (empty string) when the stream is not nested under a buffer or the buffer is undefined for some reason. ] _signal_channel_dtype = [ ("name", "U64"), # not necessarily unique ("id", "U64"), # must be unique ("sampling_rate", "float64"), ("dtype", "U16"), ("units", "U64"), ("gain", "float64"), ("offset", "float64"), ("stream_id", "U64"), ("buffer_id", "U64"), ] # TODO for later: add t_start and length in _signal_channel_dtype # this would simplify all t_start/t_stop stuff for each RawIO class _common_sig_characteristics = ["sampling_rate", "dtype", "stream_id"] _spike_channel_dtype = [ ("name", "U64"), ("id", "U64"), # for waveform ("wf_units", "U64"), ("wf_gain", "float64"), ("wf_offset", "float64"), ("wf_left_sweep", "int64"), ("wf_sampling_rate", "float64"), ] # in rawio event and epoch are handled the same way # except, that duration is `None` for events _event_channel_dtype = [ ("name", "U64"), ("id", "U64"), ("type", "S5"), # epoch or event ] class BaseRawIO: """ Generic class to handle. """ name = "BaseRawIO" description = "" extensions = [] rawmode = None # one key from possible_raw_modes # TODO Why multi-file would have a single filename is confusing here - shouldn't # the name of this argument be filenames_list or filenames_base or similar? # # When rawmode=='one-file' kargs MUST contains 'filename' the filename # When rawmode=='multi-file' kargs MUST contains 'filename' one of the filenames. # When rawmode=='one-dir' kargs MUST contains 'dirname' the dirname. def __init__(self, use_cache: bool = False, cache_path: str = "same_as_resource", **kargs): """ init docstring should be filled out at the rawio level so the user knows whether to input filename or dirname. """ # create a logger for the IO class fullname = self.__class__.__module__ + "." + self.__class__.__name__ self.logger = logging.getLogger(fullname) # Create a logger for 'neo' and add a handler to it if it doesn't have one already. # (it will also not add one if the root logger has a handler) corename = self.__class__.__module__.split(".")[0] corelogger = logging.getLogger(corename) rootlogger = logging.getLogger() if not corelogger.handlers and not rootlogger.handlers: corelogger.addHandler(logging_handler) self.use_cache = use_cache if use_cache: self.setup_cache(cache_path) else: self._cache = None self.header = None self.is_header_parsed = False self._has_buffer_description_api = False def has_buffer_description_api(self) -> bool: """ Return if the reader handle the buffer API. If True then the reader support internally `get_analogsignal_buffer_description()` """ return self._has_buffer_description_api def parse_header(self): """ Parses the header of the file(s) to allow for faster computations for all other functions """ # this must create # self.header['nb_block'] # self.header['nb_segment'] # self.header['signal_buffers'] # self.header['signal_streams'] # self.header['signal_channels'] # self.header['spike_channels'] # self.header['event_channels'] self._parse_header() self._check_stream_signal_channel_characteristics() self.is_header_parsed = True def source_name(self): """Return fancy name of file source""" return self._source_name() def __repr__(self): txt = f"{self.__class__.__name__}: {self.source_name()}\n" if self.header is not None: nb_block = self.block_count() txt += f"nb_block: {nb_block}\n" nb_seg = [self.segment_count(i) for i in range(nb_block)] txt += f"nb_segment: {nb_seg}\n" # signal streams v = [ s["name"] + f" (chans: {self.signal_channels_count(i)})" for i, s in enumerate(self.header["signal_streams"]) ] v = pprint_vector(v) txt += f"signal_streams: {v}\n" for k in ("signal_channels", "spike_channels", "event_channels"): ch = self.header[k] v = pprint_vector(self.header[k]["name"]) txt += f"{k}: {v}\n" return txt def _repr_html_(self): """ HTML representation for the raw recording base. Returns ------- html : str The HTML representation as a string. """ html = [] html.append('
') # Header html.append(f'

{self.__class__.__name__}: {self.source_name()}

') if self.is_header_parsed: # Basic info nb_block = self.block_count() html.append(f"

nb_block: {nb_block}

") nb_seg = [self.segment_count(i) for i in range(nb_block)] html.append(f"

nb_segment: {nb_seg}

") # CSS for tables - using only black, white, and gray colors html.append(""" """) # Signal Streams signal_streams = self.header["signal_streams"] if signal_streams.size > 0: html.append("
") html.append("Signal Streams") html.append('
') html.append('') html.append("") html.append("") for i, stream in enumerate(signal_streams): html.append("") html.append(f'') html.append(f'') html.append(f'') html.append(f"") html.append("") html.append("
NameIDBuffer IDChannel Count
{stream["name"]}{stream["id"]}{stream["buffer_id"]}{self.signal_channels_count(i)}
") html.append("
") html.append("
") # Signal Channels signal_channels = self.header["signal_channels"] if signal_channels.size > 0: html.append("
") html.append("Signal Channels") html.append('
') html.append('') html.append( "" ) html.append("") for channel in signal_channels: html.append("") html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append("") html.append("
NameIDSampling RateData TypeUnitsGainOffsetStream IDBuffer ID
{channel["name"]}{channel["id"]}{channel["sampling_rate"]}{channel["dtype"]}{channel["units"]}{channel["gain"]}{channel["offset"]}{channel["stream_id"]}{channel["buffer_id"]}
") html.append("
") html.append("
") # Spike Channels spike_channels = self.header["spike_channels"] if spike_channels.size > 0: html.append("
") html.append("Spike Channels") html.append('
') html.append('') html.append( "" ) html.append("") for channel in spike_channels: html.append("") html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append(f'') html.append("") html.append("
NameIDWF UnitsWF GainWF OffsetWF Left SweepWF Sampling Rate
{channel["name"]}{channel["id"]}{channel["wf_units"]}{channel["wf_gain"]}{channel["wf_offset"]}{channel["wf_left_sweep"]}{channel["wf_sampling_rate"]}
") html.append("
") html.append("
") # Event Channels event_channels = self.header["event_channels"] if event_channels.size > 0: html.append("
") html.append("Event Channels") html.append('
') html.append('') html.append("") html.append("") for channel in event_channels: html.append("") html.append(f'') html.append(f'') html.append( f'' ) html.append("") html.append("
NameIDType
{channel["name"]}{channel["id"]}{channel["type"].decode("utf-8") if isinstance(channel["type"], bytes) else channel["type"]}
") html.append("
") html.append("
") else: html.append("

Call parse_header() to load the reader data.

") html.append("
") return "\n".join(html) def _generate_minimal_annotations(self): """ Helper function that generates a nested dict for annotations. Must be called when these are Ok after self.header is done and thus when these functions return the correct values: * block_count() * segment_count() * signal_streams_count() * signal_channels_count() * spike_channels_count() * event_channels_count() There are several sources and kinds of annotations that will be forwarded to the neo.io level and used to enrich neo objects: * annotations of objects common across segments * signal_streams > neo.AnalogSignal annotations * signal_channels > neo.AnalogSignal array_annotations split by stream * spike_channels > neo.SpikeTrain * event_channels > neo.Event and neo.Epoch * annotations that depend of the block_id/segment_id of the object: * nested in raw_annotations['blocks'][block_index]['segments'][seg_index]['signals'] Usage after a call to this function we can do this to populate more annotations: raw_annotations['blocks'][block_index][ 'nickname'] = 'super block' raw_annotations['blocks'][block_index] ['segments']['important_key'] = 'important value' raw_annotations['blocks'][block_index] ['segments'][seg_index] ['signals']['nickname'] = 'super signals stream' raw_annotations['blocks'][block_index] ['segments'][seg_index] ['signals']['__array_annotations__'] ['channels_quality'] = ['bad', 'good', 'medium', 'good'] raw_annotations['blocks'][block_index] ['segments'][seg_index] ['spikes'][spike_chan]['nickname'] = 'super neuron' raw_annotations['blocks'][block_index] ['segments'][seg_index] ['spikes'][spike_chan] ['__array_annotations__']['spike_amplitudes'] = [-1.2, -10., ...] raw_annotations['blocks'][block_index] ['segments'][seg_index] ['events'][ev_chan]['nickname'] = 'super trigger' raw_annotations['blocks'][block_index] ['segments'][seg_index] ['events'][ev_chan] Z['__array_annotations__']['additional_label'] = ['A', 'B', 'A', 'C', ...] Theses annotations will be used at the neo.io API directly in objects. Standard annotation like name/id/file_origin are already generated here. """ signal_streams = self.header["signal_streams"] signal_channels = self.header["signal_channels"] spike_channels = self.header["spike_channels"] event_channels = self.header["event_channels"] # use for AnalogSignal.annotations and AnalogSignal.array_annotations signal_stream_annotations = [] for c in range(signal_streams.size): stream_id = signal_streams[c]["id"] channels = signal_channels[signal_channels["stream_id"] == stream_id] d = {} d["name"] = signal_streams["name"][c] d["stream_id"] = stream_id d["file_origin"] = self._source_name() d["__array_annotations__"] = {} for key in ("name", "id"): values = np.array([channels[key][chan] for chan in range(channels.size)]) d["__array_annotations__"]["channel_" + key + "s"] = values signal_stream_annotations.append(d) # used for SpikeTrain.annotations and SpikeTrain.array_annotations spike_annotations = [] for c in range(spike_channels.size): # use for Unit.annotations d = {} d["name"] = spike_channels["name"][c] d["id"] = spike_channels["id"][c] d["file_origin"] = self._source_name() d["__array_annotations__"] = {} spike_annotations.append(d) # used for Event/Epoch.annotations and Event/Epoch.array_annotations event_annotations = [] for c in range(event_channels.size): # not used in neo.io at the moment could useful one day d = {} d["name"] = event_channels["name"][c] d["id"] = event_channels["id"][c] d["file_origin"] = self._source_name() d["__array_annotations__"] = {} event_annotations.append(d) # duplicate this signal_stream_annotations/spike_annotations/event_annotations # across blocks and segments and create annotations ann = {} ann["blocks"] = [] for block_index in range(self.block_count()): d = {} d["file_origin"] = self.source_name() d["segments"] = [] ann["blocks"].append(d) for seg_index in range(self.segment_count(block_index)): d = {} d["file_origin"] = self.source_name() # copy nested d["signals"] = signal_stream_annotations.copy() d["spikes"] = spike_annotations.copy() d["events"] = event_annotations.copy() ann["blocks"][block_index]["segments"].append(d) self.raw_annotations = ann def _repr_annotations(self): txt = "Raw annotations\n" for block_index in range(self.block_count()): bl_a = self.raw_annotations["blocks"][block_index] txt += f"*Block {block_index}\n" for k, v in bl_a.items(): if k in ("segments",): continue txt += f" -{k}: {v}\n" for seg_index in range(self.segment_count(block_index)): seg_a = bl_a["segments"][seg_index] txt += f" *Segment {seg_index}\n" for k, v in seg_a.items(): if k in ( "signals", "spikes", "events", ): continue txt += f" -{k}: {v}\n" # annotations by channels for spikes/events/epochs for child in ( "signals", "events", "spikes", ): if child == "signals": n = self.header["signal_streams"].shape[0] else: n = self.header[child[:-1] + "_channels"].shape[0] for c in range(n): neo_name = {"signals": "AnalogSignal", "spikes": "SpikeTrain", "events": "Event/Epoch"}[child] txt += f" *{neo_name} {c}\n" child_a = seg_a[child][c] for k, v in child_a.items(): if k == "__array_annotations__": continue txt += f" -{k}: {v}\n" for k, values in child_a["__array_annotations__"].items(): values = ", ".join([str(v) for v in values[:4]]) values = "[ " + values + " ..." txt += f" -{k}: {values}\n" return txt def print_annotations(self): """Print formatted raw_annotations""" print(self._repr_annotations()) def block_count(self): """Returns the number of blocks""" return self.header["nb_block"] def segment_count(self, block_index: int): """ Returns count of segments for a given block Parameters ---------- block_index: int The index of the block to do the segment count for Returns ------- count: int The number of segments for a given block """ return self.header["nb_segment"][block_index] def signal_streams_count(self): """Return the number of signal streams. Same for all Blocks and Segments. """ return len(self.header["signal_streams"]) def signal_channels_count(self, stream_index: int): """Returns the number of signal channels for a given stream. This number is the same for all Blocks and Segments. Parameters ---------- stream_index: int the stream index in which to count the signal channels Returns ------- count: int the number of signal channels of a given stream """ stream_id = self.header["signal_streams"][stream_index]["id"] channels = self.header["signal_channels"] channels = channels[channels["stream_id"] == stream_id] return len(channels) def spike_channels_count(self): """Return the number of unit (aka spike) channels. Same for all Blocks and Segments. """ return len(self.header["spike_channels"]) def event_channels_count(self): """Return the number of event/epoch channels. Same for all Blocks and Segments. """ return len(self.header["event_channels"]) def segment_t_start(self, block_index: int, seg_index: int): """ Global t_start of a Segment Shared by all objects except for AnalogSignal. Parameters ---------- block_index: int The index of the block to find the segment t_start seg_index: int The index of the segment within the block_index in which to find the t_start Returns ------- t_start: float the time of global t_start of a segment within a block """ return self._segment_t_start(block_index, seg_index) def segment_t_stop(self, block_index, seg_index): """ Global t_stop of a Segment in s. Shared by all objects except for AnalogSignal. Parameters ---------- block_index: int The index of the block to find the segment t_start seg_index: int The index of the segment within the block_index in which to find the t_start Returns ------- t_stop: float the time of global t_stop of a segment within a block """ return self._segment_t_stop(block_index, seg_index) ### # signal and channel zone def _check_stream_signal_channel_characteristics(self): """ Check that all channels that belonging to the same stream_id have the same stream id and _common_sig_characteristics. These presently includes: * sampling_rate * units * dtype """ signal_streams = self.header["signal_streams"] signal_channels = self.header["signal_channels"] if signal_streams.size > 0: if signal_channels.size < 1: raise ValueError("Signal stream exists but there are no signal channels") for stream_index in range(signal_streams.size): stream_id = signal_streams[stream_index]["id"] mask = signal_channels["stream_id"] == stream_id characteristics = signal_channels[mask][_common_sig_characteristics] unique_characteristics = np.unique(characteristics) if unique_characteristics.size != 1: raise ValueError( f"Some channels in stream_id {stream_id} " f"do not have the same {_common_sig_characteristics} {unique_characteristics}" ) # also check that channel_id is unique inside a stream channel_ids = signal_channels[mask]["id"] if np.unique(channel_ids).size != channel_ids.size: raise ValueError(f"signal_channels do not have unique ids for stream {stream_index}") self._several_channel_groups = signal_streams.size > 1 def channel_name_to_index(self, stream_index: int, channel_names: list[str]): """ Inside a stream, transform channel_names to channel_indexes. Based on self.header['signal_channels'] channel_indexes are zero-based offsets within the stream Parameters ---------- stream_index: int The stream in which to convert channel_names to their respective channel_indexes channel_names: list[str] The channel names to convert to channel_indexes Returns ------- channel_indexes: np.array[int] the channel_indexes associated with the given channel_ids """ stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] chan_names = list(signal_channels["name"]) if signal_channels.size != np.unique(chan_names).size: raise ValueError("Channel names are not unique") channel_indexes = np.array([chan_names.index(name) for name in channel_names]) return channel_indexes def channel_id_to_index(self, stream_index: int, channel_ids: list[str]): """ Inside a stream, transform channel_ids to channel_indexes. Based on self.header['signal_channels'] channel_indexes are zero-based offsets within the stream Parameters ---------- stream_index: int the stream index in which to convert the channel_ids to channel_indexes channel_ids: list[str] the list of channel_ids to convert to channel_indexes Returns ------- channel_indexes: np.array[int] the channel_indexes associated with the given channel_ids """ # unique ids is already checked in _check_stream_signal_channel_characteristics stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] chan_ids = list(signal_channels["id"]) channel_indexes = np.array([chan_ids.index(chan_id) for chan_id in channel_ids]) return channel_indexes def _get_channel_indexes( self, stream_index: int, channel_indexes: list[int] | None, channel_names: list[str] | None, channel_ids: list[str] | None, ): """ Select channel_indexes for a stream based on channel_indexes/channel_names/channel_ids depending on which one is not None. Parameters ---------- stream_index: int, the stream index in which to get channel_indexes channel_indexes: list[int] | None the channel_indexes desired channel_names: list[str] | None the names of the channels to be converted to channel_indexes. Give this or channel_ids for conversion channel_ids: list[str] | None the ids of the channels to be converted to channel_indexes. Give this or channel_names for conversion Returns ------- channel_indexes: np.array[int] The desired channel_indexes for functions requiring channel_indexes """ if channel_indexes is None and channel_names is not None: channel_indexes = self.channel_name_to_index(stream_index, channel_names) elif channel_indexes is None and channel_ids is not None: channel_indexes = self.channel_id_to_index(stream_index, channel_ids) return channel_indexes def _get_stream_index_from_arg(self, stream_index_arg: int | None): """ Verifies the desired stream_index exists Parameters ---------- stream_index_arg: int | None, default: None The stream_index to verify If None checks if only one stream exists and then returns 0 if it is single stream Returns ------- stream_index: int The stream_index to be used for function requiring a stream_index """ if stream_index_arg is None: if self.header["signal_streams"].size != 1: raise ValueError("stream_index must be given for files with multiple streams") stream_index = 0 else: if stream_index_arg < 0 or stream_index_arg >= self.header["signal_streams"].size: raise ValueError(f"stream_index must be between 0 and {self.header['signal_streams'].size}") stream_index = stream_index_arg return stream_index def get_signal_size(self, block_index: int, seg_index: int, stream_index: int | None = None): """ Retrieves the length of a single section of the channels in a stream. Parameters ---------- block_index: int The desired block in which to get a signal size seg_index: int The desired segment of the block in which to get the signal size stream_index: int | None, default: None The optional stream index in which to determine signal size This is required for data with multiple streams Returns ------- signal_size: int The number of samples for a given signal within the desired block, segment, and stream """ stream_index = self._get_stream_index_from_arg(stream_index) return self._get_signal_size(block_index, seg_index, stream_index) def get_signal_t_start(self, block_index: int, seg_index: int, stream_index: int | None = None): """ Retrieves the t_start of a single section of the channels in a stream. Parameters ---------- block_index: int The desired block in which to get a t_start seg_index: int The desired segment of the block in which to get the t_start stream_index: int | None, default: None The optional stream index in which to determine t_start This is required for data with multiple streams Returns ------- signal_t_start: float The start time for a given signal within the desired block, segment, and stream """ stream_index = self._get_stream_index_from_arg(stream_index) return self._get_signal_t_start(block_index, seg_index, stream_index) def get_signal_sampling_rate(self, stream_index: int | None = None): """ Retrieves the sampling rate for a stream and all channels withinin that stream. Parameters ---------- stream_index: int | None, default: None The desired stream index in which to get the sampling_rate This is required for data with multiple streams Returns ------- sr: float The sampling rate of a given stream and all channels in that stream """ stream_index = self._get_stream_index_from_arg(stream_index) stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] sr = signal_channels[0]["sampling_rate"] return float(sr) def get_analogsignal_chunk( self, block_index: int = 0, seg_index: int = 0, i_start: int | None = None, i_stop: int | None = None, stream_index: int | None = None, channel_indexes: list[int] | None = None, channel_names: list[str] | None = None, channel_ids: list[str] | None = None, prefer_slice: bool = False, ): """ Returns a chunk of raw signal as a Numpy array. Parameters ---------- block_index: int, default: 0 The block with the desired analog signal seg_index: int, default: 0 The segment containing the desired analog signal i_start: int | None, default: None The index of the first sample (not time) of the desired analog signal i_stop: int | None, default: None The index of one past the last sample (not time) of the desired analog signal stream_index: int | None, default: None The index of the stream containing the channels to assess for the analog signal This is required for data with multiple streams channel_indexes: list[int] | np.array[int]| slice | None, default: None The list of indexes of channels to retrieve One of channel_indexes, channel_names, or channel_ids must be given channel_names: list[str] | None, default: None The list of channel names to retrieve One of channel_indexes, channel_names, or channel_ids must be given channel_ids: list[str] | None, default: None The list of channel_ids to retrieve One of channel_indexes, channel_names, or channel_ids must be given Returns ------- raw_chunk: np.array (n_samples, n_channels) The array with the raw signal samples Notes ----- Rows are the samples and columns are the channels The channels are chosen either by channel_names, if provided, otherwise by channel_ids, if provided, otherwise by channel_indexes, if provided, otherwise all channels are selected. Examples -------- # tetrode with 1 sec recording at sampling_rate = 1000. Hz >>> rawio_reader.parse_header() >>> raw_sigs = rawio_reader.get_analogsignal_chunk(block_index=2, seg_index=0, stream_index=0) >>> raw_sigs.shape (1000,4) # 1000 samples by 4 channels >>> raw_sigs.dtype 'int16' # returns the dtype from the recording itself # If we only want one electrode >>> raw_sigs_one_electrode = rawio_reader.get_analogsignal_chunk(block_index=2, seg_index=0, stream_index=0, channel_indexes=[0]) >>> raw_sigs_one_electrode.shape (1000,1) """ signal_streams = self.header["signal_streams"] signal_channels = self.header["signal_channels"] no_signal_streams = signal_streams.size == 0 no_channels = signal_channels.size == 0 if no_signal_streams or no_channels: error_message = ( "get_analogsignal_chunk can't be called on a file with no signal streams or channels." "Double check that your file contains signal streams and channels." ) raise AttributeError(error_message) stream_index = self._get_stream_index_from_arg(stream_index) channel_indexes = self._get_channel_indexes(stream_index, channel_indexes, channel_names, channel_ids) # some check on channel_indexes if isinstance(channel_indexes, list): channel_indexes = np.asarray(channel_indexes) if isinstance(channel_indexes, np.ndarray): if channel_indexes.dtype == "bool": if self.signal_channels_count(stream_index) != channel_indexes.size: raise ValueError( "If channel_indexes is a boolean it must have be the same length as the " f"number of channels {self.signal_channels_count(stream_index)}" ) (channel_indexes,) = np.nonzero(channel_indexes) if prefer_slice and isinstance(channel_indexes, np.ndarray): # Check if channel_indexes are contiguous and transform to slice argument if possible. # This is useful for memmap or hdf5 where providing a slice causes a lazy read, # rather than a list of indexes that make a copy (like numpy.take()). if np.all(np.diff(channel_indexes) == 1): channel_indexes = slice(channel_indexes[0], channel_indexes[-1] + 1) raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes) return raw_chunk def rescale_signal_raw_to_float( self, raw_signal: np.ndarray, dtype: np.dtype = "float32", stream_index: int | None = None, channel_indexes: list[int] | None = None, channel_names: list[str] | None = None, channel_ids: list[str] | None = None, ): """ Rescales a chunk of raw signals which are provided as a Numpy array. These are normally returned by a call to get_analogsignal_chunk. Parameters ---------- raw_signal: np.array (n_samples, n_channels) The numpy array of samples with columns being samples for a single channel dtype: np.dype, default: "float32" The datatype for returning scaled samples, must be acceptable by the numpy dtype constructor stream_index: int | None, default: None The index of the stream containing the channels to assess channel_indexes: list[int], np.array[int], slice | None, default: None The list of indexes of channels to retrieve channel_names: list[str] | None, default: None The list of channel names to retrieve channel_ids: list[str] | None, default: None list of channel_ids to retrieve Returns ------- float_signal: np.array (n_samples, n_channels) The rescaled signal Notes ----- The channels are specified either by channel_names, if provided, otherwise by channel_ids, if provided, otherwise by channel_indexes, if provided, otherwise all channels are selected. These are rawio dependent because rescaling of the NumPy array requires the offset and gain stored within the header of the rawio Examples -------- # Once we have a `raw_sigs` using rawio.get_analogsignal_chunk() we can convert to voltages with a desired dtype # If we used `stream_index=0` with `get_analogsignal_chunk` we use `stream_index=0` here >>> float_sigs = rawio_reader.rescale_signal_raw_to_float(raw_signal=raw_sigs, dtype='float32', stream_index=0) >>> float_sigs.dtype 'float32' >>> float_sigs.shape (1000,4) >>> float_sigs.shape == raw_sigs.shape True """ stream_index = self._get_stream_index_from_arg(stream_index) channel_indexes = self._get_channel_indexes(stream_index, channel_indexes, channel_names, channel_ids) if channel_indexes is None: channel_indexes = slice(None) stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id channels = self.header["signal_channels"][mask] if channel_indexes is None: channel_indexes = slice(None) channels = channels[channel_indexes] float_signal = raw_signal.astype(dtype) if np.any(channels["gain"] != 1.0): float_signal *= channels["gain"] if np.any(channels["offset"] != 0.0): float_signal += channels["offset"] return float_signal # spiketrain and unit zone def spike_count(self, block_index: int = 0, seg_index: int = 0, spike_channel_index: int = 0): """ Returns the spike count for a given block, segment, and spike_channel_index Parameters ---------- block_index: int, default: 0 The block with the desired segment to assess seg_index: int, default: 0 The segment containing the desired section to assess spike_channel_index: int, default: 0 The spike_channel_index for assessing spike_count Returns ------- spike_count: int The number of spikes in the block and segment """ return self._spike_count(block_index, seg_index, spike_channel_index) def get_spike_timestamps( self, block_index: int = 0, seg_index: int = 0, spike_channel_index: int = 0, t_start: float | None = None, t_stop: float | None = None, ): """ Returns the spike_timestamps in samples (see note for dtype) Parameters ---------- block_index: int, default: 0 The block containing the section to get the spike timestamps seg_index: int, default: 0 The segment containing the section to get the spike timestamps spike_channel_index: int, default: 0 The channel in which to collect spike timestamps t_start: float | None, default: None The time in seconds for the start of the section to get spike timestamps None indicates to start at the beginning of the segment t_stop: float | None, default: None The time in seconds for the end of the section to get spike timestamps None indicates to end at the end of the segment Returns ------- timestamp: np.array The spike timestamps Notes ----- The timestamp datatype is as close to the format itself. Sometimes float/int32/int64. Sometimes it is the index on the signal but not always. The conversion to second or index_on_signal is done outside this method. Examples -------- # to look at block 1, segment 0, and channel 3 on a tetrode from 10 # seconds to 30 seconds we would do: >>> timestamps = rawio_reader.get_spike_timestamps(block_index=1, seg_index=0, spike_channel_index=3, t_start=10, t_stop=30) """ timestamp = self._get_spike_timestamps(block_index, seg_index, spike_channel_index, t_start, t_stop) return timestamp def rescale_spike_timestamp(self, spike_timestamps: np.ndarray, dtype: np.dtype = "float64"): """ Rescale spike timestamps from samples to seconds. Parameters ---------- spike_timestamps: np.ndarray The array containing the spike_timestamps to convert dtype: np.dtype, default: "float64" The dtype in which to convert the spike time in seconds. Must be accepted by the numpy.dtype constructor Returns ------- scaled_spike_timestamps: np.array The spiketimes in seconds Examples -------- # After running `get_spike_timestamps` and returning timestamps we can do the following: >>> scaled_spike_timestamps = rawio_reader.rescale_spike_timestamps(spike_timestamps=timestamps, dtype='float64') """ return self._rescale_spike_timestamp(spike_timestamps, dtype) # spiketrain waveform zone def get_spike_raw_waveforms( self, block_index: int = 0, seg_index: int = 0, spike_channel_index: int = 0, t_start: float | None = None, t_stop: float | None = None, ): """ Gets the waveforms for one channel within one segment of one block Parameters ---------- block_index: int, default: 0 The block containing the desired set of waveform data seg_index: int, default: 0 The segment containing the desired set of waveform data spike_channel_index: int, default: 0 The channel index on which to get waveform data t_start: float | None, default: None The time in seconds for the start of the section to get waveforms None indicates to start at the beginning of the segment t_stop: float | None, default: None The time in seconds for the end of the section to waveforms None indicates to end at the end of the segment Returns ------- wf: np.ndarray (nb_spike, nb_channel, nb_sample)) A NumPy array of spikes, channels and samples """ wf = self._get_spike_raw_waveforms(block_index, seg_index, spike_channel_index, t_start, t_stop) return wf def rescale_waveforms_to_float( self, raw_waveforms: np.ndarray, dtype: np.dtype = "float32", spike_channel_index: int = 0 ): """ Rescale waveforms to based on the rawio's waveform gain and waveform offset Parameters ---------- raw_waveforms: np.ndarray The array containing the spike_timestamps to convert dtype: np.dtype, default: "float64" The dtype in which to convert the spike time to. Must be accepted by the numpy.dtype constructor spike_channel_index: int, default: 0 The channel index of the desired channel to rescale Returns ------- float_waveforms: np.ndarray (nb_spikes, nb_channels, nb_samples) The scaled waveforms to the dtype specified by dtype """ wf_gain = self.header["spike_channels"]["wf_gain"][spike_channel_index] wf_offset = self.header["spike_channels"]["wf_offset"][spike_channel_index] float_waveforms = raw_waveforms.astype(dtype) if wf_gain != 1.0: float_waveforms *= wf_gain if wf_offset != 0.0: float_waveforms += wf_offset return float_waveforms # event and epoch zone def event_count(self, block_index: int = 0, seg_index: int = 0, event_channel_index: int = 0): """ Returns the count of events for a particular block, segment, and channel_index Parameters ---------- block_index: int, default: 0 The block in which to count the events seg_index: int, default: 0 The segment within the block given by block_index in which to count events event_channel_index: int, default: 0 The index of the channel in which to count events Returns ------- n_events: int The number of events in the given block, segment, and event_channel_index """ return self._event_count(block_index, seg_index, event_channel_index) def get_event_timestamps( self, block_index: int = 0, seg_index: int = 0, event_channel_index: int = 0, t_start: float | None = None, t_stop: float | None = None, ): """ Returns the event timestamps along with their labels and durations Parameters ---------- block_index: int, default: 0 The block in which to count the events seg_index: int, default: 0 The segment within the block given by block_index in which to count events event_channel_index: int, default: 0 The index of the channel in which to count events t_start: float | None, default: None The time in seconds for the start of the section to get waveforms None indicates to start at the beginning of the segment t_stop: float | None, default: None The time in seconds for the end of the section to waveforms None indicates to end at the end of the segment Returns ------- timestamp: np.array The timestamps of events (in samples) durations: np.array The durations of each event labels: np.array The labels of the events Notes ----- The timestamp datatype is as close to the format itself. Sometimes float/int32/int64. Sometimes it is the index on the signal but not always. The conversion to second or index_on_signal is done outside this method. Examples -------- # A given rawio reader that generates events data. For this example we will # look at Block 0, Segment 1, on Channel 1, with a start time at the beginning # of the segment and an end time of 5 minutes (300 s) >>> event_timestamps, durations, labels = rawio_reader.get_event_timestamps(block_index=0, seg_index=1, event_channel_index=1, t_start=None, t_stop=300) """ timestamp, durations, labels = self._get_event_timestamps( block_index, seg_index, event_channel_index, t_start, t_stop ) return timestamp, durations, labels def rescale_event_timestamp( self, event_timestamps: np.ndarray, dtype: np.dtype = "float64", event_channel_index: int = 0 ): """ Rescale event timestamps to seconds. Parameters ---------- event_timestamps: np.ndarray The array containing the event timestamps to convert dtype: np.dtype, default: "float64" The dtype in which to convert the event time in seconds. Must be accepted by the numpy.dtype constructor event_channel_index: int, default: 0 The channel index for scaling the events Returns ------- scaled_event_timestamps: np.array The scaled event timestamps in seconds Examples -------- # Using the event_timestamps from the `get_event_timestamps` function we can then scale from samples into # seconds using this `rescale_event_timestamp`. We use the same event_channel_index as used during the # `get_event_timestamps` >>> event_timestamps_seconds = rawio_reader.rescale_event_timestamp(event_timestamps=event_timestamps, dtype='float64', event_channel_index=1) """ return self._rescale_event_timestamp(event_timestamps, dtype, event_channel_index) def rescale_epoch_duration( self, raw_duration: np.ndarray, dtype: np.dtype = "float64", event_channel_index: int = 0 ): """ Rescales the epoch duration from samples to seconds Parameters ---------- raw_duration: np.ndarray The array containing the epoch times in samples dtype: np.dtype, default: "float64" The dtype in which to convert the spike time in seconds. Must be accepted by the numpy.dtype constructor event_channel_index: int, default: 0 The channel on which to index for scaling epochs Returns ------- scaled_epoch_durations: np.array The scaled epoch durations in seconds Examples -------- # In this example we use the durations obtained from running `get_event_timestamps` >>> duration_seconds = rawio_reader.rescale_epoch_duration(raw_durations=durations, dtype='float64', event_channel_index=0) """ return self._rescale_epoch_duration(raw_duration, dtype, event_channel_index) def setup_cache(self, cache_path: "home" | "same_as_resource", **init_kargs): try: import joblib except ImportError: raise ImportError("Using the RawIO cache needs joblib to be installed") if self.rawmode in ("one-file", "multi-file"): resource_name = self.filename elif self.rawmode == "one-dir": resource_name = self.dirname else: raise (NotImplementedError) if cache_path == "home": if sys.platform.startswith("win"): dirname = os.path.join(os.environ["APPDATA"], "neo_rawio_cache") elif sys.platform.startswith("darwin"): dirname = "~/Library/Application Support/neo_rawio_cache" else: dirname = os.path.expanduser("~/.config/neo_rawio_cache") dirname = os.path.join(dirname, self.__class__.__name__) if not os.path.exists(dirname): os.makedirs(dirname) elif cache_path == "same_as_resource": dirname = os.path.dirname(resource_name) else: if not os.path.exists(cache_path): raise ValueError("cache_path does not exist use 'home' or 'same_as_resource' to make this auto") # the hash of the resource (dir of file) is done with filename+datetime # TODO make something more sophisticated when rawmode='one-dir' that use all # filename and datetime d = dict(ressource_name=resource_name, mtime=os.path.getmtime(resource_name)) hash = joblib.hash(d, hash_name="md5") # name is constructed from the resource_name and the hash name = f"{os.path.basename(resource_name)}_{hash}" self.cache_filename = os.path.join(dirname, name) if os.path.exists(self.cache_filename): self.logger.warning(f"Use existing cache file {self.cache_filename}") self._cache = joblib.load(self.cache_filename) else: self.logger.warning(f"Create cache file {self.cache_filename}") self._cache = {} self.dump_cache() def add_in_cache(self, **kargs): if not self.use_cache: raise ValueError("Can not use add_in_cache if not using cache") self._cache.update(kargs) self.dump_cache() def dump_cache(self): if not self.use_cache: raise ValueError("Can not use dump_cache if not using cache") joblib.dump(self._cache, self.cache_filename) ################## # Functions to be implemented in IO below here def _parse_header(self): raise (NotImplementedError) # must call # self._generate_empty_annotations() def _source_name(self): raise (NotImplementedError) def _segment_t_start(self, block_index: int, seg_index: int): raise (NotImplementedError) def _segment_t_stop(self, block_index: int, seg_index: int): raise (NotImplementedError) ### # signal and channel zone def _get_signal_size(self, block_index: int, seg_index: int, stream_index: int): """ Return the size of a set of AnalogSignals indexed by channel_indexes. All channels indexed must have the same size and t_start. """ raise (NotImplementedError) def _get_signal_t_start(self, block_index: int, seg_index: int, stream_index: int): """ Return the t_start of a set of AnalogSignals indexed by channel_indexes. All channels indexed must have the same size and t_start. """ raise (NotImplementedError) def _get_analogsignal_chunk( self, block_index: int, seg_index: int, i_start: int | None, i_stop: int | None, stream_index: int, channel_indexes: list[int] | None, ): """ Return the samples from a set of AnalogSignals indexed by stream_index and channel_indexes (local index inner stream). RETURNS ------- array of samples, with each requested channel in a column """ raise (NotImplementedError) ### # spiketrain and unit zone def _spike_count(self, block_index: int, seg_index: int, spike_channel_index: int): raise (NotImplementedError) def _get_spike_timestamps( self, block_index: int, seg_index: int, spike_channel_index: int, t_start: float | None, t_stop: float | None ): raise (NotImplementedError) def _rescale_spike_timestamp(self, spike_timestamps: np.ndarray, dtype: np.dtype): raise (NotImplementedError) ### # spike waveforms zone def _get_spike_raw_waveforms( self, block_index: int, seg_index: int, spike_channel_index: int, t_start: float | None, t_stop: float | None ): raise (NotImplementedError) ### # event and epoch zone def _event_count(self, block_index: int, seg_index: int, event_channel_index: int): raise (NotImplementedError) def _get_event_timestamps( self, block_index: int, seg_index: int, event_channel_index: int, t_start: float | None, t_stop: float | None ): raise (NotImplementedError) def _rescale_event_timestamp(self, event_timestamps: np.ndarray, dtype: np.dtype): raise (NotImplementedError) def _rescale_epoch_duration(self, raw_duration: np.ndarray, dtype: np.dtype): raise (NotImplementedError) ### # buffer api zone # must be implemented if has_buffer_description_api=True def get_analogsignal_buffer_description(self, block_index: int = 0, seg_index: int = 0, buffer_id: str = None): if not self.has_buffer_description_api: raise ValueError("This reader do not support buffer_description API") descr = self._get_analogsignal_buffer_description(block_index, seg_index, buffer_id) return descr def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): raise (NotImplementedError) class BaseRawWithBufferApiIO(BaseRawIO): """ Generic class for reader that support "buffer api". In short reader that are internally based on: * np.memmap * hdf5 In theses cases _get_signal_size and _get_analogsignal_chunk are totaly generic and do not need to be implemented in the class. For this class sub classes must implements theses two dict: * self._buffer_descriptions[block_index][seg_index] = buffer_description * self._stream_buffer_slice[buffer_id] = None or slicer o indices """ def __init__(self, *arg, **kwargs): super().__init__(*arg, **kwargs) self._has_buffer_description_api = True def _get_signal_size(self, block_index, seg_index, stream_index): buffer_id = self.header["signal_streams"][stream_index]["buffer_id"] buffer_desc = self.get_analogsignal_buffer_description(block_index, seg_index, buffer_id) # time_axis indicates which dimension is time: # time_axis=0: shape is (time, channels) # time_axis=1: shape is (channels, time) time_axis = buffer_desc.get("time_axis", 0) return buffer_desc["shape"][time_axis] def _get_analogsignal_chunk( self, block_index: int, seg_index: int, i_start: int | None, i_stop: int | None, stream_index: int, channel_indexes: list[int] | None, ): stream_id = self.header["signal_streams"][stream_index]["id"] buffer_id = self.header["signal_streams"][stream_index]["buffer_id"] buffer_slice = self._stream_buffer_slice[stream_id] buffer_desc = self.get_analogsignal_buffer_description(block_index, seg_index, buffer_id) # Get time_axis to determine which dimension is time time_axis = buffer_desc.get("time_axis", 0) i_start = i_start or 0 i_stop = i_stop or buffer_desc["shape"][time_axis] if buffer_desc["type"] == "raw": if time_axis == 0: # MULTIPLEXED: time_axis=0 means (time, channels) layout # open files on demand and keep reference to opened file if not hasattr(self, "_memmap_analogsignal_buffers"): self._memmap_analogsignal_buffers = {} if block_index not in self._memmap_analogsignal_buffers: self._memmap_analogsignal_buffers[block_index] = {} if seg_index not in self._memmap_analogsignal_buffers[block_index]: self._memmap_analogsignal_buffers[block_index][seg_index] = {} if buffer_id not in self._memmap_analogsignal_buffers[block_index][seg_index]: fid = open(buffer_desc["file_path"], mode="rb") self._memmap_analogsignal_buffers[block_index][seg_index][buffer_id] = fid else: fid = self._memmap_analogsignal_buffers[block_index][seg_index][buffer_id] num_channels = buffer_desc["shape"][1] raw_sigs = get_memmap_chunk_from_opened_file( fid, num_channels, i_start, i_stop, np.dtype(buffer_desc["dtype"]), file_offset=buffer_desc["file_offset"], ) elif time_axis == 1: # VECTORIZED: time_axis=1 means shape is (channels, time) # Data is stored as [all_samples_ch1, all_samples_ch2, ...] dtype = np.dtype(buffer_desc["dtype"]) num_channels = buffer_desc["shape"][0] # shape is (channels, time) num_samples = i_stop - i_start total_samples_per_channel = buffer_desc["shape"][1] # shape is (channels, time) # Determine which channels to read if channel_indexes is None: chan_indices = np.arange(num_channels) else: chan_indices = np.asarray(channel_indexes) raw_sigs = np.empty((num_samples, len(chan_indices)), dtype=dtype) for i, chan_idx in enumerate(chan_indices): offset = buffer_desc["file_offset"] + chan_idx * total_samples_per_channel * dtype.itemsize channel_data = np.memmap( buffer_desc["file_path"], dtype=dtype, mode="r", offset=offset, shape=(total_samples_per_channel,), ) raw_sigs[:, i] = channel_data[i_start:i_stop] # Channel slicing already done above, so skip later channel_indexes slicing channel_indexes = None else: raise ValueError(f"time_axis must be 0 or 1, got {time_axis}") elif buffer_desc["type"] == "hdf5": # open files on demand and keep reference to opened file if not hasattr(self, "_hdf5_analogsignal_buffers"): self._hdf5_analogsignal_buffers = {} if block_index not in self._hdf5_analogsignal_buffers: self._hdf5_analogsignal_buffers[block_index] = {} if seg_index not in self._hdf5_analogsignal_buffers[block_index]: self._hdf5_analogsignal_buffers[block_index][seg_index] = {} if buffer_id not in self._hdf5_analogsignal_buffers[block_index][seg_index]: import h5py h5file = h5py.File(buffer_desc["file_path"], mode="r") self._hdf5_analogsignal_buffers[block_index][seg_index][buffer_id] = h5file else: h5file = self._hdf5_analogsignal_buffers[block_index][seg_index][buffer_id] hdf5_path = buffer_desc["hdf5_path"] full_raw_sigs = h5file[hdf5_path] time_axis = buffer_desc.get("time_axis", 0) if time_axis == 0: raw_sigs = full_raw_sigs[i_start:i_stop, :] elif time_axis == 1: raw_sigs = full_raw_sigs[:, i_start:i_stop].T else: raise RuntimeError("Should never happen") if buffer_slice is not None: raw_sigs = raw_sigs[:, buffer_slice] else: raise NotImplementedError() # this is a pre slicing when the stream do not contain all channels (for instance spikeglx when load_sync_channel=False) if buffer_slice is not None: raw_sigs = raw_sigs[:, buffer_slice] # channel slice requested if channel_indexes is not None: raw_sigs = raw_sigs[:, channel_indexes] return raw_sigs def __del__(self): if hasattr(self, "_memmap_analogsignal_buffers"): for block_index in self._memmap_analogsignal_buffers.keys(): for seg_index in self._memmap_analogsignal_buffers[block_index].keys(): for buffer_id, fid in self._memmap_analogsignal_buffers[block_index][seg_index].items(): fid.close() del self._memmap_analogsignal_buffers if hasattr(self, "_hdf5_analogsignal_buffers"): for block_index in self._hdf5_analogsignal_buffers.keys(): for seg_index in self._hdf5_analogsignal_buffers[block_index].keys(): for buffer_id, h5_file in self._hdf5_analogsignal_buffers[block_index][seg_index].items(): h5_file.close() del self._hdf5_analogsignal_buffers def pprint_vector(vector, lim: int = 8): vector = np.asarray(vector) if vector.ndim != 1: raise ValueError(f"`vector` must have a dimension of 1 and not {vector.ndim}") if len(vector) > lim: part1 = ", ".join(e for e in vector[: lim // 2]) part2 = " , ".join(e for e in vector[-lim // 2 :]) txt = f"[{part1} ... {part2}]" else: part1 = ", ".join(e for e in vector) txt = f"[{part1}]" return txt ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/bci2000rawio.py0000644000175100017510000004411715154507213016416 0ustar00runnerrunner""" BCI2000RawIO is a class to read BCI2000 .dat files. https://www.bci2000.org/mediawiki/index.php/Technical_Reference:BCI2000_File_Format Note : BCI2000RawIO cannot implemented using has_buffer_description_api because the buffer is not compact. The buffer of signals is not compact (has some interleaved state uint in between channels) """ import numpy as np import re try: from urllib.parse import unquote except ImportError: from urllib import url2pathname as unquote from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from neo.core import NeoReadWriteError class BCI2000RawIO(BaseRawIO): """ Class for reading data from a BCI2000 .dat file, either version 1.0 or 1.1 Parameters ---------- filename: str, default: '' The *.dat recorded by BCI2000 """ extensions = ["dat"] rawmode = "one-file" def __init__(self, filename=""): BaseRawIO.__init__(self) self.filename = filename self._my_events = None def _source_name(self): return self.filename def _parse_header(self): file_info, state_defs, param_defs = parse_bci2000_header(self.filename) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] # one unique stream but no buffer because channels are not compact stream_id = "0" buffer_id = "" signal_buffers = np.array([], dtype=_signal_buffer_dtype) signal_streams = np.array([("Signals", stream_id, buffer_id)], dtype=_signal_stream_dtype) self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams sig_channels = [] for chan_ix in range(file_info["SourceCh"]): if "ChannelNames" in param_defs and isinstance(param_defs["ChannelNames"]["value"], (list, tuple)): ch_name = param_defs["ChannelNames"]["value"][chan_ix] else: ch_name = "ch" + str(chan_ix) chan_id = str(chan_ix + 1) sr = param_defs["SamplingRate"]["value"] # Hz dtype = file_info["DataFormat"] units = "uV" gain = param_defs["SourceChGain"]["value"][chan_ix] if isinstance(gain, str): r = re.findall(r"(\d+)(\D+)", gain) # some files have strange units attached to gain # in that case it is ignored if len(r) == 1: gain = r[0][0] gain = float(gain) offset = param_defs["SourceChOffset"]["value"][chan_ix] if isinstance(offset, str): offset = float(offset) sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) self.header["signal_channels"] = np.array(sig_channels, dtype=_signal_channel_dtype) self.header["spike_channels"] = np.array([], dtype=_spike_channel_dtype) # creating event channel for each state variable event_channels = [] for st_ix, st_tup in enumerate(state_defs): event_channels.append((st_tup[0], "ev_" + str(st_ix), "event")) self.header["event_channels"] = np.array(event_channels, dtype=_event_channel_dtype) # Add annotations. # Generates basic annotations in nested dict self.raw_annotations self._generate_minimal_annotations() self.raw_annotations["blocks"][0].update({"file_info": file_info, "param_defs": param_defs}) event_annotations = self.raw_annotations["blocks"][0]["segments"][0]["events"] for ev_ix, ev_dict in enumerate(event_annotations): ev_dict.update( { "length": state_defs[ev_ix][1], "startVal": state_defs[ev_ix][2], "bytePos": state_defs[ev_ix][3], "bitPos": state_defs[ev_ix][4], } ) import time time_formats = ["%a %b %d %H:%M:%S %Y", "%Y-%m-%dT%H:%M:%S"] try: self._global_time = time.mktime(time.strptime(param_defs["StorageTime"]["value"], time_formats[0])) except: self._global_time = time.mktime(time.strptime(param_defs["StorageTime"]["value"], time_formats[1])) # Save variables to make it easier to load the binary data. self._read_info = { "header_len": file_info["HeaderLen"], "n_chans": file_info["SourceCh"], "sample_dtype": {"int16": np.int16, "int32": np.int32, "float32": np.float32}.get(file_info["DataFormat"]), "state_vec_len": file_info["StatevectorLen"], "sampling_rate": param_defs["SamplingRate"]["value"], } # Calculate the dtype for a single timestamp of data. This contains the data + statevector self._read_info["line_dtype"] = [ ("raw_vector", self._read_info["sample_dtype"], self._read_info["n_chans"]), ("state_vector", np.uint8, self._read_info["state_vec_len"]), ] import os self._read_info["n_samps"] = int( (os.stat(self.filename).st_size - file_info["HeaderLen"]) / np.dtype(self._read_info["line_dtype"]).itemsize ) # memmap is fast so we can get the data ready for reading now. self._memmap = np.memmap( self.filename, dtype=self._read_info["line_dtype"], offset=self._read_info["header_len"], mode="r" ) def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): return self._read_info["n_samps"] / self._read_info["sampling_rate"] def _get_signal_size(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index` must be 0") return self._read_info["n_samps"] def _get_signal_t_start(self, block_index, seg_index, channel_indexes): return 0.0 def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if stream_index != 0: raise ValueError("`stream_index` must be 0") if i_start is None: i_start = 0 if i_stop is None: i_stop = self._read_info["n_samps"] if (i_start < 0) or (i_start > self._read_info["n_samps"]): raise ValueError("`i_start` is outside of data range") if (i_stop < 0) or (i_stop > self._read_info["n_samps"]): raise ValueError("`i_stop` is outside of data range") if channel_indexes is None: channel_indexes = np.arange(self.header["signal_channels"].size) return self._memmap["raw_vector"][i_start:i_stop, channel_indexes] def _spike_count(self, block_index, seg_index, unit_index): return 0 def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): return None def _rescale_spike_timestamp(self, spike_timestamps, dtype): return None def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): return None def _event_count(self, block_index, seg_index, event_channel_index): return self._event_arrays_list[event_channel_index][0].shape[0] def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): # Return 3 numpy arrays: timestamp, durations, labels # durations must be None for 'event' # label must a dtype ='U' ts, dur, labels = self._event_arrays_list[event_channel_index] # seg_t_start = self._segment_t_start(block_index, seg_index) keep = np.ones(ts.shape, dtype=bool) if t_start is not None: keep = np.logical_and(keep, ts >= t_start) if t_stop is not None: keep = np.logical_and(keep, ts <= t_stop) return ts[keep], dur[keep], labels[keep] def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = (event_timestamps / float(self._read_info["sampling_rate"])).astype(dtype) return event_times def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): durations = (raw_duration / float(self._read_info["sampling_rate"])).astype(dtype) return durations @property def _event_arrays_list(self): if self._my_events is None: event_annotations = self.raw_annotations["blocks"][0]["segments"][0]["events"] self._my_events = [] for event_channel_index in range(self.event_channels_count()): sd = event_annotations[event_channel_index] ev_times = durs = vals = np.array([]) # Skip these big but mostly useless (?) states. if sd["name"] not in ["SourceTime", "StimulusTime"]: # Determine which bytes of self._memmap['state_vector'] are needed. nbytes = int(np.ceil((sd["bitPos"] + sd["length"]) / 8)) byte_slice = slice(sd["bytePos"], sd["bytePos"] + nbytes) # Then determine how to mask those bytes to get only the needed bits. bit_mask = np.array([255] * nbytes, dtype=np.uint8) bit_mask[0] &= 255 & (255 << sd["bitPos"]) # Fix the mask for the first byte extra_bits = 8 - (sd["bitPos"] + sd["length"]) % 8 bit_mask[-1] &= 255 & (255 >> extra_bits) # Fix the mask for the last byte # When converting to an int, we need to know which integer type it will become n_max_bytes = 1 << (nbytes - 1).bit_length() view_type = {1: np.int8, 2: np.int16, 4: np.int32, 8: np.int64}.get(n_max_bytes) # Slice and mask the data masked_byte_array = self._memmap["state_vector"][:, byte_slice] & bit_mask # Convert byte array to a vector of ints: # pad to give even columns then view as larger int type state_vec = np.pad(masked_byte_array, (0, n_max_bytes - nbytes), "constant").view(dtype=view_type) state_vec = np.right_shift(state_vec, sd["bitPos"])[:, 0] # In the state vector, find 'events' whenever the state changes st_ch_ix = np.where(np.hstack((0, np.diff(state_vec))) != 0)[0] # event inds if len(st_ch_ix) > 0: ev_times = st_ch_ix durs = np.asarray([None] * len(st_ch_ix)) # np.hstack((np.diff(st_ch_ix), len(state_vec) - st_ch_ix[-1])) vals = np.char.mod("%d", state_vec[st_ch_ix]) # event val, string'd self._my_events.append([ev_times, durs, vals.astype("U")]) return self._my_events def parse_bci2000_header(filename): # typically we want parameter values in Hz, seconds, or microvolts. scales_dict = { "hz": 1, "khz": 1000, "mhz": 1000000, "uv": 1, "muv": 1, "mv": 1000, "v": 1000000, "s": 1, "us": 0.000001, "mus": 0.000001, "ms": 0.001, "min": 60, "sec": 1, "usec": 0.000001, "musec": 0.000001, "msec": 0.001, } def rescale_value(param_val, data_type): unit_str = "" if param_val.lower().startswith("0x"): param_val = int(param_val, 16) elif data_type in ["int", "float"]: matches = re.match(r"(-*\d+)(\w*)", param_val) if matches is not None: # Can be None for % in def, min, max vals param_val, unit_str = matches.group(1), matches.group(2) param_val = int(param_val) if data_type == "int" else float(param_val) if len(unit_str) > 0: param_val *= scales_dict.get(unit_str.lower(), 1) else: param_val = unquote(param_val) return param_val, unit_str def parse_dimensions(param_list): num_els = param_list.pop(0) # Sometimes the number of elements isn't given, # but the list of element labels is wrapped with {} if num_els == "{": num_els = param_list.index("}") el_labels = [unquote(param_list.pop(0)) for x in range(num_els)] param_list.pop(0) # Remove the '}' else: num_els = int(num_els) el_labels = [str(ix) for ix in range(num_els)] return num_els, el_labels with open(filename, "rb") as fid: # Parse the file header (plain text) # The first line contains basic information which we store in a dictionary. temp = fid.readline().decode("utf8").split() keys = [k.rstrip("=") for k in temp[::2]] vals = temp[1::2] # Insert default version and format file_info = {"BCI2000V": 1.0, "DataFormat": "int16"} file_info.update(**dict(zip(keys, vals))) # From string to float/int file_info["BCI2000V"] = float(file_info["BCI2000V"]) for k in ["HeaderLen", "SourceCh", "StatevectorLen"]: if k in file_info: file_info[k] = int(file_info[k]) # The next lines contain state vector definitions. temp = fid.readline().decode("utf8").strip() if temp != "[ State Vector Definition ]": raise NeoReadWriteError(f"State definitions not found in header {filename}") state_defs = [] state_def_dtype = [("name", "a64"), ("length", int), ("startVal", int), ("bytePos", int), ("bitPos", int)] while True: temp = fid.readline().decode("utf8").strip() if len(temp) == 0 or temp[0] == "[": # Presence of '[' signifies new section. break temp = temp.split() state_defs.append((temp[0], int(temp[1]), int(temp[2]), int(temp[3]), int(temp[4]))) state_defs = np.array(state_defs, dtype=state_def_dtype) # The next lines contain parameter definitions. # There are many, and their formatting can be complicated. if temp != "[ Parameter Definition ]": raise NeoReadWriteError(f"Parameter definitions not found in header {filename}") param_defs = {} while True: temp = fid.readline().decode("utf8") if fid.tell() >= file_info["HeaderLen"]: # End of header. break if len(temp.strip()) == 0: continue # Skip empty lines # Everything after the '//' is a comment. temp = temp.strip().split("//", 1) param_def = {"comment": temp[1].strip() if len(temp) > 1 else ""} # Parse the parameter definition. Generally it is sec:cat:name dtype name param_value+ temp = temp[0].split() param_def.update({"section_category_name": [unquote(x) for x in temp.pop(0).split(":")]}) dtype = temp.pop(0) param_name = unquote(temp.pop(0).rstrip("=")) # Parse the rest. Parse method depends on the dtype param_value, units = None, None if dtype in ("int", "float"): param_value = temp.pop(0) if param_value == "auto": param_value = np.nan units = "" else: param_value, units = rescale_value(param_value, dtype) elif dtype in ("string", "variant"): param_value = unquote(temp.pop(0)) elif dtype.endswith("list"): # e.g., intlist, stringlist, floatlist, list dtype = dtype[:-4] # The list parameter values will begin with either # an int to specify the number of elements # or a list of labels surrounded by { }. num_elements, element_labels = parse_dimensions(temp) # This will pop off info. param_def.update({"element_labels": element_labels}) pv_un = [rescale_value(pv, dtype) for pv in temp[:num_elements]] if len(pv_un) > 0: param_value, units = zip(*pv_un) else: param_value, units = np.nan, "" temp = temp[num_elements:] # Sometimes an element list will be a list of ints even though # the element_type is '' (str)... # This usually happens for known parameters, such as SourceChOffset, # that can be dealt with explicitly later. elif dtype.endswith("matrix"): dtype = dtype[:-6] # The parameter values will be preceded by two dimension descriptors, # first rows then columns. Each dimension might be described by an # int or a list of labels surrounded by {} n_rows, row_labels = parse_dimensions(temp) n_cols, col_labels = parse_dimensions(temp) param_def.update({"row_labels": row_labels, "col_labels": col_labels}) param_value = [] units = [] for row_ix in range(n_rows): cols = [] for col_ix in range(n_cols): col_val, _units = rescale_value(temp[row_ix * n_cols + col_ix], dtype) cols.append(col_val) units.append(_units) param_value.append(cols) temp = temp[n_rows * n_cols :] param_def.update({"value": param_value, "units": units, "dtype": dtype}) # At the end of the parameter definition, we might get # default, min, max values for the parameter. temp.reverse() if len(temp): param_def.update({"max_val": rescale_value(temp.pop(0), dtype)}) if len(temp): param_def.update({"min_val": rescale_value(temp.pop(0), dtype)}) if len(temp): param_def.update({"default_val": rescale_value(temp.pop(0), dtype)}) param_defs.update({param_name: param_def}) # End parameter block # Outdent to close file return file_info, state_defs, param_defs ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/biocamrawio.py0000644000175100017510000004463215154507213016613 0ustar00runnerrunner""" Class for reading data from a 3-brain Biocam system. See: https://www.3brain.com/products/single-well/biocam-x Authors: Alessio Buccino, Robert Wolff """ import json import numpy as np from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) import numpy as np import json import warnings from neo.core import NeoReadWriteError class BiocamRawIO(BaseRawIO): """ Class for reading data from a Biocam h5 file. Parameters ---------- filename: str, default: '' The *.h5 file to be read fill_gaps_strategy: "zeros" | "synthetic_noise" | None, default: None The strategy to fill the gaps in the data when using event-based compression. If None and the file is event-based compressed, you need to specify a fill gaps strategy: * "zeros": the gaps are filled with unsigned 0s (2048). This value is the "0" of the unsigned 12 bits representation of the data. * "synthetic_noise": the gaps are filled with synthetic noise. Examples -------- >>> import neo.rawio >>> r = neo.rawio.BiocamRawIO(filename='biocam.h5') >>> r.parse_header() >>> print(r) >>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=0, i_stop=1024, channel_names=channel_names) >>> float_chunk = r.rescale_signal_raw_to_float(raw_chunk, dtype='float64', channel_indexes=[0, 3, 6]) """ extensions = ["h5", "brw"] rawmode = "one-file" def __init__(self, filename="", fill_gaps_strategy="zeros"): BaseRawIO.__init__(self) self.filename = filename self._fill_gaps_strategy = fill_gaps_strategy def _source_name(self): return self.filename def _parse_header(self): self._header_dict = open_biocam_file_header(self.filename) self._num_channels = self._header_dict["num_channels"] self._num_frames = self._header_dict["num_frames"] self._sampling_rate = self._header_dict["sampling_rate"] self._filehandle = self._header_dict["file_handle"] self._read_function = self._header_dict["read_function"] self._channels = self._header_dict["channels"] gain = self._header_dict["gain"] offset = self._header_dict["offset"] # buffer concept cannot be used in this reader because of too complicated dtype across version signal_buffers = np.array([], dtype=_signal_stream_dtype) signal_streams = np.array([("Signals", "0", "")], dtype=_signal_stream_dtype) sig_channels = [] for c, chan in enumerate(self._channels): ch_name = f"ch{chan[0]}-{chan[1]}" chan_id = str(c + 1) sr = self._sampling_rate # Hz dtype = "uint16" units = "uV" gain = gain offset = offset stream_id = "0" buffer_id = "" sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) # No events event_channels = [] event_channels = np.array(event_channels, dtype=_event_channel_dtype) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = sig_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() def _segment_t_start(self, block_index, seg_index): all_starts = [[0.0]] return all_starts[block_index][seg_index] def _segment_t_stop(self, block_index, seg_index): t_stop = self._num_frames / self._sampling_rate all_stops = [[t_stop]] return all_stops[block_index][seg_index] def _get_signal_size(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index` must be 0") return self._num_frames def _get_signal_t_start(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index must be 0") return self._segment_t_start(block_index, seg_index) def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if i_start is None: i_start = 0 if i_stop is None: i_stop = self._num_frames # read functions are different based on the version of biocam if self._read_function is readHDF5t_brw4_sparse: if self._fill_gaps_strategy is None: raise ValueError("Please set `fill_gaps_strategy` to 'zeros' or 'synthetic_noise'.") if self._fill_gaps_strategy == "synthetic_noise": warnings.warn( "Event-based compression : gaps will be filled with synthetic noise. " "Set `fill_gaps_strategy` to 'zeros' to fill gaps with 0s." ) use_synthetic_noise = True elif self._fill_gaps_strategy == "zeros": use_synthetic_noise = False else: raise ValueError("`fill_gaps_strategy` must be 'zeros' or 'synthetic_noise'") data = self._read_function( self._filehandle, i_start, i_stop, self._num_channels, use_synthetic_noise=use_synthetic_noise ) else: data = self._read_function(self._filehandle, i_start, i_stop, self._num_channels) # older style data returns array of (n_samples, n_channels), should be a view # but if memory issues come up we should doublecheck out how the file is being stored if data.ndim > 1: if channel_indexes is None: channel_indexes = slice(None) sig_chunk = data[:, channel_indexes] # newer style data returns an initial flat array (n_samples * n_channels) # we iterate through channels rather than slicing # Due to the fact that Neo and SpikeInterface tend to prefer slices we need to add # some careful checks around slicing of None in the case we need to iterate through # channels. First check if None. Then check if slice and only if slice check that it is slice(None) else: if channel_indexes is None: channel_indexes = [ch for ch in range(self._num_channels)] elif isinstance(channel_indexes, slice): start = channel_indexes.start or 0 stop = channel_indexes.stop or self._num_channels step = channel_indexes.step or 1 channel_indexes = [ch for ch in range(start, stop, step)] sig_chunk = np.zeros((i_stop - i_start, len(channel_indexes)), dtype=data.dtype) # iterate through channels to prevent loading all channels into memory which can cause # memory exhaustion. See https://github.com/SpikeInterface/spikeinterface/issues/3303 for index, channel_index in enumerate(channel_indexes): sig_chunk[:, index] = data[channel_index :: self._num_channels] return sig_chunk def open_biocam_file_header(filename) -> dict: """Open a Biocam hdf5 file, read and return the recording info, pick the correct method to access raw data, and return this to the caller Parameters ---------- filename: str The file to be parsed Returns ------- dict The information necessary to read a biocam file (gain, n_samples, n_channels, etc).""" import h5py rf = h5py.File(filename, "r") if "3BRecInfo" in rf.keys(): # brw v3.x # Read recording variables rec_vars = rf.require_group("3BRecInfo/3BRecVars/") bit_depth = rec_vars["BitDepth"][0] max_uv = rec_vars["MaxVolt"][0] min_uv = rec_vars["MinVolt"][0] num_frames = rec_vars["NRecFrames"][0] sampling_rate = rec_vars["SamplingRate"][0] signal_inv = rec_vars["SignalInversion"][0] # Get the actual number of channels used in the recording file_format = rf["3BData"].attrs.get("Version", None) format_100 = False if file_format == 100: num_channels = len(rf["3BData/Raw"][0]) format_100 = True elif file_format in (101, 102) or file_format is None: num_channels = int(rf["3BData/Raw"].shape[0] / num_frames) else: raise NeoReadWriteError("Unknown data file format.") # get channels channels = rf["3BRecInfo/3BMeaStreams/Raw/Chs"][:] # determine correct function to read data if format_100: if signal_inv == 1: read_function = readHDF5t_100 elif signal_inv == -1: read_function = readHDF5t_100_i else: raise NeoReadWriteError("Unknown signal inversion") else: if signal_inv == 1: read_function = readHDF5t_101 elif signal_inv == -1: read_function = readHDF5t_101_i else: raise NeoReadWriteError("Unknown signal inversion") gain = (max_uv - min_uv) / (2**bit_depth) offset = min_uv return dict( file_handle=rf, num_frames=num_frames, sampling_rate=sampling_rate, num_channels=num_channels, channels=channels, file_format=file_format, signal_inv=signal_inv, read_function=read_function, gain=gain, offset=offset, ) else: # brw v4.x # Read recording variables experiment_settings = json.JSONDecoder().decode(rf["ExperimentSettings"][0].decode()) max_uv = experiment_settings["ValueConverter"]["MaxAnalogValue"] min_uv = experiment_settings["ValueConverter"]["MinAnalogValue"] max_digital = experiment_settings["ValueConverter"]["MaxDigitalValue"] min_digital = experiment_settings["ValueConverter"]["MinDigitalValue"] scale_factor = experiment_settings["ValueConverter"]["ScaleFactor"] sampling_rate = experiment_settings["TimeConverter"]["FrameRate"] num_frames = rf["TOC"][-1, -1] num_channels = None well_ID = None for well_ID in rf: if well_ID.startswith("Well_"): num_channels = len(rf[well_ID]["StoredChIdxs"]) if "Raw" in rf[well_ID]: if len(rf[well_ID]["Raw"]) % num_channels: raise NeoReadWriteError( f"Length of raw data array is not multiple of channel number in {well_ID}" ) num_frames = len(rf[well_ID]["Raw"]) // num_channels break elif "EventsBasedSparseRaw" in rf[well_ID]: # Not sure how to check for this with sparse data pass if num_channels is not None: num_channels_x = num_channels_y = int(np.sqrt(num_channels)) else: raise NeoReadWriteError("No Well found in the file") if num_channels_x * num_channels_y != num_channels: raise NeoReadWriteError(f"Cannot determine structure of the MEA plate with {num_channels} channels") channels = 1 + np.concatenate(np.transpose(np.meshgrid(range(num_channels_x), range(num_channels_y)))) gain = scale_factor * (max_uv - min_uv) / (max_digital - min_digital) offset = min_uv if "Raw" in rf[well_ID]: read_function = readHDF5t_brw4 elif "EventsBasedSparseRaw" in rf[well_ID]: read_function = readHDF5t_brw4_sparse return dict( file_handle=rf, num_frames=num_frames, sampling_rate=sampling_rate, num_channels=num_channels, channels=channels, read_function=read_function, gain=gain, offset=offset, ) ###################################################################### # Helper functions to obtain the raw data split by Biocam version. # return the full array for the old datasets def readHDF5t_100(rf, t0, t1, nch): return rf["3BData/Raw"][t0:t1] def readHDF5t_100_i(rf, t0, t1, nch): return 4096 - rf["3BData/Raw"][t0:t1] # return flat array that we will iterate through def readHDF5t_101(rf, t0, t1, nch): return rf["3BData/Raw"][nch * t0 : nch * t1] def readHDF5t_101_i(rf, t0, t1, nch): return 4096 - rf["3BData/Raw"][nch * t0 : nch * t1] def readHDF5t_brw4(rf, t0, t1, nch): for key in rf: if key.startswith("Well_"): return rf[key]["Raw"][nch * t0 : nch * t1] def readHDF5t_brw4_sparse(rf, t0, t1, nch, use_synthetic_noise=False): # noise_std = None start_frame = t0 num_frames = t1 - t0 for well_ID in rf: if well_ID.startswith("Well_"): break # initialize an empty (fill with zeros) data collection data = np.zeros((nch, num_frames), dtype=np.uint16) if not use_synthetic_noise: # Will read as 0s after 12 bits signed conversion data.fill(2048) else: # fill the data collection with Gaussian noise if requested data = generate_synthetic_noise(rf, data, well_ID, start_frame, num_frames) # , std=noise_std) # fill the data collection with the decoded event based sparse raw data data = decode_event_based_raw_data(rf, data, well_ID, start_frame, num_frames) return data.T def decode_event_based_raw_data(rf, data, well_ID, start_frame, num_frames): # Source: Documentation by 3Brain # https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/src/master/biocam/documentation_brw_4.x_bxr_3.x_bcmp_1.x_in_brainwave_5.x_v1.1.3.pdf # collect the TOCs toc = np.array(rf["TOC"]) events_toc = np.array(rf[well_ID]["EventsBasedSparseRawTOC"]) # from the given start position and duration in frames, localize the corresponding event positions # using the TOC toc_start_idx = np.searchsorted(toc[:, 1], start_frame) toc_end_idx = min(np.searchsorted(toc[:, 1], start_frame + num_frames, side="right") + 1, len(toc) - 1) events_start_pos = events_toc[toc_start_idx] events_end_pos = events_toc[toc_end_idx] # decode all data for the given well ID and time interval binary_data = rf[well_ID]["EventsBasedSparseRaw"][events_start_pos:events_end_pos] binary_data_length = len(binary_data) pos = 0 while pos < binary_data_length: ch_idx = int.from_bytes(binary_data[pos : pos + 4], byteorder="little") pos += 4 ch_data_length = int.from_bytes(binary_data[pos : pos + 4], byteorder="little") pos += 4 ch_data_pos = pos while pos < ch_data_pos + ch_data_length: from_inclusive = int.from_bytes(binary_data[pos : pos + 8], byteorder="little") pos += 8 to_exclusive = int.from_bytes(binary_data[pos : pos + 8], byteorder="little") pos += 8 range_data_pos = pos for j in range(from_inclusive, to_exclusive): if j >= start_frame + num_frames: break if j >= start_frame: data[ch_idx][j - start_frame] = int.from_bytes( binary_data[range_data_pos : range_data_pos + 2], byteorder="little" ) range_data_pos += 2 pos += (to_exclusive - from_inclusive) * 2 return data def generate_synthetic_noise(rf, data, well_ID, start_frame, num_frames): # Source: Documentation by 3Brain # https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/src/master/biocam/documentation_brw_4.x_bxr_3.x_bcmp_1.x_in_brainwave_5.x_v1.1.3.pdf # collect the TOCs toc = np.array(rf["TOC"]) noise_toc = np.array(rf[well_ID]["NoiseTOC"]) # from the given start position in frames, localize the corresponding noise positions # using the TOC toc_start_idx = np.searchsorted(toc[:, 1], start_frame) noise_start_pos = noise_toc[toc_start_idx] noise_end_pos = noise_start_pos for i in range(toc_start_idx + 1, len(noise_toc)): next_pos = noise_toc[i] if next_pos > noise_start_pos: noise_end_pos = next_pos break if noise_end_pos == noise_start_pos: for i in range(toc_start_idx - 1, 0, -1): previous_pos = noise_toc[i] if previous_pos < noise_start_pos: noise_end_pos = noise_start_pos noise_start_pos = previous_pos break # obtain the noise info at the start position noise_ch_idx = rf[well_ID]["NoiseChIdxs"][noise_start_pos:noise_end_pos] noise_mean = rf[well_ID]["NoiseMean"][noise_start_pos:noise_end_pos] noise_std = rf[well_ID]["NoiseStdDev"][noise_start_pos:noise_end_pos] noise_length = noise_end_pos - noise_start_pos noise_info = {} mean_collection = [] std_collection = [] for i in range(1, noise_length): noise_info[noise_ch_idx[i]] = [noise_mean[i], noise_std[i]] mean_collection.append(noise_mean[i]) std_collection.append(noise_std[i]) # calculate the median mean and standard deviation of all channels to be used for # invalid channels median_mean = np.median(mean_collection) median_std = np.median(std_collection) # fill with Gaussian noise for ch_idx in range(len(data)): if ch_idx in noise_info: data[ch_idx] = np.array( np.random.normal(noise_info[ch_idx][0], noise_info[ch_idx][1], num_frames), dtype=np.uint16 ) else: data[ch_idx] = np.array(np.random.normal(median_mean, median_std, num_frames), dtype=np.uint16) # Assuming a 12-bit unsigned to signed conversion downstream, the baseline should read at 2048 raw # However, synthetic noise is generated with a baseline of 0, which requires an offset of + 2048 # See issue #1743 data = data + 2048 return data ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/blackrockrawio.py0000644000175100017510000033446615154507213017323 0ustar00runnerrunner""" Module for reading data from files in the Blackrock in raw format. This work is based on: * Chris Rodgers - first version * Michael Denker, Lyuba Zehl - second version * Samuel Garcia - third version * Lyuba Zehl, Michael Denker - fourth version * Samuel Garcia, Julia Srenger - fifth version * Chadwick Boulay - FileSpec 3.0 and 3.0-PTP * Heberto Mayorquin - Time segmentation fixes, reporting and refactoring This IO supports reading only. This IO is able to read: * the nev file which contains spikes * ns1, ns2, .., ns6 files that contain signals at different sampling rates This IO can handle the following Blackrock file specifications: * 2.1 * 2.2 * 2.3 * 3.0 * 3.0 with PTP timestamps (Gemini systems) The neural data channels are 1 - 128. The analog inputs are 129 - 144. (129 - 137 AC coupled, 138 - 144 DC coupled) spike- and event-data; 30000 Hz in NEV file. "ns1": "analog data: 500 Hz", "ns2": "analog data: 1000 Hz", "ns3": "analog data: 2000 Hz", "ns4": "analog data: 10000 Hz", "ns5": "analog data: 30000 Hz", "ns6": "analog data: 30000 Hz (no digital filter)" The possible file extensions of the Cerebus system and their content: ns1: contains analog data; sampled at 500 Hz (+ digital filters) ns2: contains analog data; sampled at 1000 Hz (+ digital filters) ns3: contains analog data; sampled at 2000 Hz (+ digital filters) ns4: contains analog data; sampled at 10000 Hz (+ digital filters) ns5: contains analog data; sampled at 30000 Hz (+ digital filters) ns6: contains analog data; sampled at 30000 Hz (no digital filters) nev: contains spike- and event-data; sampled at 30000 Hz sif: contains institution and patient info (XML) ccf: contains Cerebus configurations TODO: * videosync events (file spec 2.3) * tracking events (file spec 2.3) * buttontrigger events (file spec 2.3) * config events (file spec 2.3) * check left sweep settings of Blackrock * check nsx offsets (file spec 2.1) * add info of nev ext header (NSASEXEX) to non-neural events (file spec 2.1 and 2.2) * read sif file information * read ccf file information * fix reading of periodic sampling events (non-neural event type) (file spec 2.1 and 2.2) """ import datetime import os import re import warnings import math import numpy as np import quantities as pq from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from neo.core import NeoReadWriteError class BlackrockRawIO(BaseRawIO): """ Class for reading data in from a file set recorded by the Blackrock (Cerebus) recording system. Upon initialization, the class is linked to the available set of Blackrock files. Parameters ---------- filename: str, default: '' File name (without extension) of the set of Blackrock files to associate with. Any .nsX or .nev, .sif, or .ccf extensions are ignored when parsing this parameter. nsx_override: str | None, default: None File name of the .nsX files (without extension). If None, filename is used. nev_override: str | None, default: None File name of the .nev file (without extension). If None, filename is used. nsx_to_load: int | list | 'max' | 'all' | None, default: None IDs of nsX file from which to load data, e.g., if set to 5 only data from the ns5 file are loaded. If 'all', then all nsX will be loaded. Contrary to previous version of the IO (<0.7), nsx_to_load must be set at the init before parse_header(). load_nev: bool, default: True Load (or not) events/spikes by ignoring or not the nev file. gap_tolerance_ms : float | None, default: None Maximum acceptable gap size in milliseconds for automatic segmentation. **Default behavior (None)**: If timestamp gaps are detected, an error is raised with a detailed gap report. This ensures users are aware of data discontinuities. **Opt-in segmentation**: Provide a value to automatically segment data at gaps larger than this threshold. Gaps smaller than the threshold are ignored (data treated as continuous). Examples: - None (default): Error on any detected gaps - 1.0: Tolerate gaps up to 1 ms, segment on larger gaps - 10.0: Tolerate gaps up to 10 ms (filters buffer artifacts) - 100.0: Tolerate gaps up to 100 ms (only major pauses create segments) Applies to: - PTP format (v3.0-ptp): Gaps in per-sample timestamps - Standard format (v2.2/2.3/3.0): Gaps between data blocks Notes ----- * Note: This routine will handle files according to specification 2.1, 2.2, 2.3, 3.0 and 3.0-ptp. Recording pauses that may occur in file specifications 2.2 and 2.3 are automatically extracted and the data set is split into different segments. * The Blackrock data format consists not of a single file, but a set of different files. This constructor associates itself with a set of files that constitute a common data set. By default, all files belonging to the file set have the same base name, but different extensions. However, by using the override parameters, individual filenames can be set. Examples -------- >>> import neo.rawio >>> # Inspect a set of file consisting of files FileSpec2.3001.ns5 and FileSpec2.3001.nev >>> reader = neo.rawio.BlackrockRawIO(filename='FileSpec2.3001', nsx_to_load=5) >>> reader.parse_header() >>> print(reader) """ extensions = ["ns" + str(_) for _ in range(1, 7)] extensions.extend(["nev", "sif", "ccf"]) # 'sif', 'ccf' not yet supported rawmode = "multi-file" # We need to document the origin of this value main_sampling_rate = 30000.0 def __init__( self, filename=None, nsx_override=None, nev_override=None, nsx_to_load=None, load_nev=True, verbose=False, gap_tolerance_ms=None, ): BaseRawIO.__init__(self) self.gap_tolerance_ms = gap_tolerance_ms self.filename = str(filename) # remove extension from base _filenames for ext in self.extensions: if self.filename.endswith(os.path.extsep + ext): self.filename = self.filename.replace(os.path.extsep + ext, "") self.nsx_to_load = nsx_to_load # remove extensions from overrides self._filenames = {} if nsx_override: self._filenames["nsx"] = re.sub(os.path.extsep + "ns[1,2,3,4,5,6]$", "", nsx_override) else: self._filenames["nsx"] = self.filename if nev_override: self._filenames["nev"] = re.sub(os.path.extsep + "nev$", "", nev_override) else: self._filenames["nev"] = self.filename self._filenames["sif"] = self.filename self._filenames["ccf"] = self.filename # check which files are available self._avail_files = dict.fromkeys(self.extensions, False) self._avail_nsx = [] for ext in self.extensions: if ext.startswith("ns"): file2check = "".join([self._filenames["nsx"], os.path.extsep, ext]) else: file2check = "".join([self._filenames[ext], os.path.extsep, ext]) if os.path.exists(file2check): self._avail_files[ext] = True if ext.startswith("ns"): self._avail_nsx.append(int(ext[-1])) if not load_nev: self._avail_files["nev"] = False if not self._avail_files["nev"] and not self._avail_nsx: raise IOError("No Blackrock files found in specified path") # These dictionaries are used internally to map the file specification # revision of the nsx and nev files to one of the reading routines # NEV self._waveform_size = { "2.1": self._get_waveform_size_spec_v21, "2.2": self._get_waveform_size_spec_v21, "2.3": self._get_waveform_size_spec_v22_30, "3.0": self._get_waveform_size_spec_v22_30, } self._channel_labels = { "2.1": self._get_channel_labels_spec_v21, "2.2": self._get_channel_labels_spec_v22_30, "2.3": self._get_channel_labels_spec_v22_30, "3.0": self._get_channel_labels_spec_v22_30, } self._nonneural_evdicts = { "2.1": self._get_nonneural_evdicts_spec_v21_22, "2.2": self._get_nonneural_evdicts_spec_v21_22, "2.3": self._get_nonneural_evdicts_spec_v23, "3.0": self._get_nonneural_evdicts_spec_v23, } self._comment_evdict = { "2.1": self._get_comment_evdict_spec_v21_22, "2.2": self._get_comment_evdict_spec_v21_22, "2.3": self._get_comment_evdict_spec_v21_22, "3.0": self._get_comment_evdict_spec_v21_22, } def _parse_header(self): event_channels = [] spike_channels = [] signal_buffers = [] signal_streams = [] signal_channels = [] # Step1 NEV file if self._avail_files["nev"]: # Load file spec and headers of available # read nev file specification self._nev_spec = self._extract_nev_file_spec() # read nev headers nev_filename = f"{self._filenames['nev']}.nev" self._nev_basic_header, self._nev_ext_header = self._read_nev_header(self._nev_spec, nev_filename) self.nev_data = self._read_nev_data(self._nev_spec, nev_filename) spikes, spike_segment_ids = self.nev_data["Spikes"] # scan all channel to get number of Unit spike_channels = [] self.internal_unit_ids = [] # pair of chan['packet_id'], spikes['unit_class_nb'] for i in range(len(self._nev_ext_header[b"NEUEVWAV"])): # electrode_id values are stored at uint16 which can overflow when # multiplying by 1000 below. We convert to a regular python int which # won't overflow channel_id = int(self._nev_ext_header[b"NEUEVWAV"]["electrode_id"][i]) chan_mask = spikes["packet_id"] == channel_id chan_spikes = spikes[chan_mask] # all `unit_class_nb` is uint8. Also will have issues with overflow # cast this to python int all_unit_id = np.unique(chan_spikes["unit_class_nb"]) for u, unit_id in enumerate(all_unit_id): unit_id = int(unit_id) self.internal_unit_ids.append((channel_id, unit_id)) name = f"ch{channel_id}#{unit_id}" _id = f"Unit {1000 * channel_id + unit_id}" wf_gain = self._nev_params("digitization_factor")[channel_id] / 1000.0 wf_offset = 0.0 wf_units = "uV" # TODO: Double check if this is the correct assumption (10 samples) # default value: threshold crossing after 10 samples of waveform wf_left_sweep = 10 wf_sampling_rate = self.main_sampling_rate spike_channels.append((name, _id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) # scan events # NonNeural: serial and digital input events_data, event_segment_ids = self.nev_data["NonNeural"] ev_dict = self._nonneural_evdicts[self._nev_spec](events_data) if "Comments" in self.nev_data: comments_data, comments_segment_ids = self.nev_data["Comments"] ev_dict.update(self._comment_evdict[self._nev_spec](comments_data)) for ev_name in ev_dict: event_channels.append((ev_name, "", "event")) # TODO: TrackingEvents # TODO: ButtonTrigger # TODO: VideoSync # Step2 NSX file # Load file spec and headers of available nsx files self._nsx_spec = {} self._nsx_basic_header = {} self._nsx_ext_header = {} self._nsx_data_header = {} self._nsx_sampling_frequency = {} # Read headers for nsx_nb in self._avail_nsx: spec_version = self._nsx_spec[nsx_nb] = self._extract_nsx_file_spec(nsx_nb) # read nsx headers self._nsx_basic_header[nsx_nb], self._nsx_ext_header[nsx_nb] = self._read_nsx_header(spec_version, nsx_nb) # The Blackrock defines period as the number of 1/30_000 seconds between data points # E.g. it is 1 for 30_000, 3 for 10_000, etc nsx_period = self._nsx_basic_header[nsx_nb]["period"] sampling_rate = 30_000.0 / nsx_period self._nsx_sampling_frequency[nsx_nb] = float(sampling_rate) # Parase data packages for nsx_nb in self._avail_nsx: # The only way to know if it is the Precision Time Protocol of file spec 3.0 # is to check for nanosecond timestamp resolution. is_ptp_variant = ( "timestamp_resolution" in self._nsx_basic_header[nsx_nb].dtype.names and self._nsx_basic_header[nsx_nb]["timestamp_resolution"] == 1_000_000_000 ) if is_ptp_variant: data_header_spec = "3.0-ptp" else: data_header_spec = spec_version # nsx_to_load can be either int, list, 'max', 'all' (aka None) # here make a list only if self.nsx_to_load is None or self.nsx_to_load == "all": self.nsx_to_load = list(self._avail_nsx) elif self.nsx_to_load == "max": if len(self._avail_nsx): self.nsx_to_load = [max(self._avail_nsx)] else: self.nsx_to_load = [] elif isinstance(self.nsx_to_load, int): self.nsx_to_load = [self.nsx_to_load] elif isinstance(self.nsx_to_load, list): pass else: raise (ValueError("nsx_to_load is wrong")) missing_nsx_files = [nsx_nb for nsx_nb in self.nsx_to_load if nsx_nb not in self._avail_nsx] if missing_nsx_files: missing_list = ", ".join(f"ns{nsx_nb}" for nsx_nb in missing_nsx_files) raise FileNotFoundError( f"Requested NSX file(s) not found: {missing_list}. Available NSX files: {self._avail_nsx}" ) # check that all files come from the same specification all_spec = [self._nsx_spec[nsx_nb] for nsx_nb in self.nsx_to_load] if self._avail_files["nev"]: all_spec.append(self._nev_spec) if not all(all_spec[0] == spec for spec in all_spec): raise NeoReadWriteError("Files don't have the same internal version") if len(self.nsx_to_load) > 0 and self._nsx_spec[self.nsx_to_load[0]] == "2.1" and not self._avail_files["nev"]: pass # Because rescaling to volts requires information from nev file (dig_factor) # Remove if raw loading becomes possible # raise IOError("For loading Blackrock file version 2.1 .nev files are required!") self.nsx_datas = {} # Keep public attribute for backward compatibility but let's use the private one and maybe deprecate this at some point self.sig_sampling_rates = { nsx_number: self._nsx_sampling_frequency[nsx_number] for nsx_number in self.nsx_to_load } if len(self.nsx_to_load) > 0: for nsx_nb in self.nsx_to_load: basic_header = self._nsx_basic_header[nsx_nb] spec_version = self._nsx_spec[nsx_nb] # The only way to know if it is the Precision Time Protocol of file spec 3.0 # is to check for nanosecond timestamp resolution. is_ptp_variant = ( "timestamp_resolution" in basic_header.dtype.names and basic_header["timestamp_resolution"] == 1_000_000_000 ) if is_ptp_variant: data_spec = "3.0-ptp" else: data_spec = spec_version # Parse data blocks (creates memmap, extracts data+timestamps) data_blocks = self._parse_nsx_data(data_spec, nsx_nb) # Segment the data (analyzes gaps, reports issues) segments = self._segment_nsx_data(data_blocks, nsx_nb) # Store in existing structures for backward compatibility self._nsx_data_header[nsx_nb] = { seg_idx: {k: v for k, v in seg.items() if k != "data"} for seg_idx, seg in segments.items() } self.nsx_datas[nsx_nb] = {seg_idx: seg["data"] for seg_idx, seg in segments.items()} # Match NSX and NEV segments for v2.3 if self._avail_files["nev"]: self._match_nsx_and_nev_segment_ids(nsx_nb) sr = self._nsx_sampling_frequency[nsx_nb] if spec_version in ["2.2", "2.3", "3.0"]: ext_header = self._nsx_ext_header[nsx_nb] elif spec_version == "2.1": # v2.1 has no extended headers - construct from NEV digitization factors ext_header = self._build_nsx_v21_ext_header(nsx_nb) if len(ext_header) > 0: # in blackrock : one stream per buffer so same id buffer_id = stream_id = str(nsx_nb) stream_name = f"nsx{nsx_nb}" signal_buffers.append((stream_name, buffer_id)) signal_streams.append((stream_name, stream_id, buffer_id)) for i, chan in enumerate(ext_header): if spec_version in ["2.2", "2.3", "3.0"]: ch_name = chan["electrode_label"].decode() ch_id = str(chan["electrode_id"]) units = chan["units"].decode() elif spec_version == "2.1": ch_name = chan["labels"] ch_id = str(self._nsx_ext_header[nsx_nb][i]["electrode_id"]) units = chan["units"] sig_dtype = "int16" # max_analog_val/min_analog_val/max_digital_val/min_analog_val are int16!!!!! # dangerous situation so cast to float everyone if np.isnan(float(chan["min_analog_val"])): gain = 1 offset = 0 else: gain = (float(chan["max_analog_val"]) - float(chan["min_analog_val"])) / ( float(chan["max_digital_val"]) - float(chan["min_digital_val"]) ) offset = -float(chan["min_digital_val"]) * gain + float(chan["min_analog_val"]) buffer_id = stream_id = str(nsx_nb) signal_channels.append((ch_name, ch_id, sr, sig_dtype, units, gain, offset, stream_id, buffer_id)) # check nb segment per nsx nb_segments_for_nsx = [len(self.nsx_datas[nsx_nb]) for nsx_nb in self.nsx_to_load] if not all(nb == nb_segments_for_nsx[0] for nb in nb_segments_for_nsx): raise NeoReadWriteError("Segment nb not consistent across nsX files") self._nb_segment = nb_segments_for_nsx[0] self._delete_empty_segments() # t_start/t_stop for segment are given by nsx limits or nev limits self._sigs_t_starts = {nsx_nb: [] for nsx_nb in self.nsx_to_load} self._seg_t_starts, self._seg_t_stops = [], [] for data_bl in range(self._nb_segment): t_stop = 0.0 for nsx_nb in self.nsx_to_load: spec = self._nsx_spec[nsx_nb] if "timestamp_resolution" in self._nsx_basic_header[nsx_nb].dtype.names: ts_res = self._nsx_basic_header[nsx_nb]["timestamp_resolution"] elif spec == "2.1": ts_res = 30_000 # v2.1 always uses 30kHz timestamp resolution else: ts_res = 30_000 period = self._nsx_basic_header[nsx_nb]["period"] sec_per_samp = period / 30_000 # Maybe 30_000 should be ['sample_resolution'] length = self.nsx_datas[nsx_nb][data_bl].shape[0] timestamps = self._nsx_data_header[nsx_nb][data_bl]["timestamp"] if timestamps is None: # V2.1 format has no timestamps t_start = 0.0 t_stop = max(t_stop, length / self._nsx_sampling_frequency[nsx_nb]) elif hasattr(timestamps, "size") and timestamps.size == length: # FileSpec 3.0 with PTP -- use the per-sample timestamps t_start = timestamps[0] / ts_res t_stop = max(t_stop, timestamps[-1] / ts_res + sec_per_samp) else: # Standard format with scalar timestamp t_start = timestamps / ts_res t_stop = max(t_stop, t_start + length / self._nsx_sampling_frequency[nsx_nb]) self._sigs_t_starts[nsx_nb].append(t_start) if self._avail_files["nev"]: max_nev_time = 0 for k, (data, ev_ids) in self.nev_data.items(): segment_mask = ev_ids == data_bl if data[segment_mask].size > 0: t = data[segment_mask][-1]["timestamp"] / self._nev_basic_header["timestamp_resolution"] max_nev_time = max(max_nev_time, t) if max_nev_time > t_stop: t_stop = max_nev_time min_nev_time = max_nev_time for k, (data, ev_ids) in self.nev_data.items(): segment_mask = ev_ids == data_bl if data[segment_mask].size > 0: t = data[segment_mask][0]["timestamp"] / self._nev_basic_header["timestamp_resolution"] min_nev_time = min(min_nev_time, t) if min_nev_time < t_start: t_start = min_nev_time self._seg_t_starts.append(t_start) self._seg_t_stops.append(float(t_stop)) else: # When only nev is available, only segments that are documented in nev can be detected max_nev_times = {} min_nev_times = {} # Find maximal and minimal time for each nev segment for k, (data, ev_ids) in self.nev_data.items(): for i in np.unique(ev_ids): curr_data = data[ev_ids == i] if curr_data.size > 0: if max(curr_data["timestamp"]) >= max_nev_times.get(i, 0): max_nev_times[i] = max(curr_data["timestamp"]) if min(curr_data["timestamp"]) <= min_nev_times.get(i, max_nev_times[i]): min_nev_times[i] = min(curr_data["timestamp"]) # Calculate t_start and t_stop for each segment in seconds resolution = self._nev_basic_header["timestamp_resolution"] self._seg_t_starts = [v / float(resolution) for k, v in sorted(min_nev_times.items())] self._seg_t_stops = [v / float(resolution) for k, v in sorted(max_nev_times.items())] self._nb_segment = len(self._seg_t_starts) self._sigs_t_starts = [None] * self._nb_segment # finalize header spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = np.array(event_channels, dtype=_event_channel_dtype) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) signal_buffers = np.array(signal_buffers, dtype=_signal_buffer_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [self._nb_segment] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels rec_datetime = self._nev_params("rec_datetime") if self._avail_files["nev"] else None # Put annotations at some places for compatibility # with previous BlackrockIO version self._generate_minimal_annotations() block_ann = self.raw_annotations["blocks"][0] block_ann["description"] = "Block of data from Blackrock file set." block_ann["file_origin"] = self.filename block_ann["name"] = "Blackrock Data Block" block_ann["rec_datetime"] = rec_datetime block_ann["avail_file_set"] = [k for k, v in self._avail_files.items() if v] block_ann["avail_nsx"] = self._avail_nsx block_ann["avail_nev"] = self._avail_files["nev"] # 'sif' and 'ccf' files not yet supported # block_ann['avail_sif'] = self._avail_files['sif'] # block_ann['avail_ccf'] = self._avail_files['ccf'] block_ann["rec_pauses"] = False for seg_index in range(self._nb_segment): seg_ann = block_ann["segments"][seg_index] seg_ann["file_origin"] = self.filename seg_ann["name"] = f"Segment {seg_index}" seg_ann["description"] = "Segment containing data from t_start to t_stop" if seg_index == 0: # if more than 1 segment means pause # so datetime is valid only for seg_index=0 seg_ann["rec_datetime"] = rec_datetime for c in range(signal_streams.size): sig_ann = seg_ann["signals"][c] stream_id = signal_streams["id"][c] nsx_nb = int(stream_id) sig_ann["description"] = f"AnalogSignal from nsx{nsx_nb}" sig_ann["file_origin"] = self._filenames["nsx"] + ".ns" + str(nsx_nb) sig_ann["nsx"] = nsx_nb # handle signal array annotations from nsx header if self._nsx_spec[nsx_nb] in ["2.2", "2.3"] and nsx_nb in self._nsx_ext_header: mask = signal_channels["stream_id"] == stream_id channels = signal_channels[mask] nsx_header = self._nsx_ext_header[nsx_nb] for key in ( "physical_connector", "connector_pin", "hi_freq_corner", "lo_freq_corner", "hi_freq_order", "lo_freq_order", "hi_freq_type", "lo_freq_type", ): values = [] for chan_id in channels["id"]: chan_id = int(chan_id) idx = list(nsx_header["electrode_id"]).index(chan_id) values.append(nsx_header[key][idx]) values = np.array(values) sig_ann["__array_annotations__"][key] = values for c in range(spike_channels.size): st_ann = seg_ann["spikes"][c] channel_id, unit_id = self.internal_unit_ids[c] st_ann["channel_id"] = channel_id st_ann["unit_id"] = unit_id if unit_id == 0: st_ann["unit_classification"] = "unclassified" elif 1 <= unit_id <= 16: st_ann["unit_classification"] = "sorted" elif unit_id == 255: st_ann["unit_classification"] = "noise" else: # 17-254 are reserved st_ann["unit_classification"] = "reserved" st_ann["unit_tag"] = st_ann["unit_classification"] st_ann["description"] = f"SpikeTrain channel_id: {channel_id}, unit_id: {unit_id}" st_ann["file_origin"] = self._filenames["nev"] + ".nev" if self._avail_files["nev"]: ev_dict = self._nonneural_evdicts[self._nev_spec](events_data) if "Comments" in self.nev_data: ev_dict.update(self._comment_evdict[self._nev_spec](comments_data)) color_codes = ["#{:08X}".format(code) for code in comments_data["color"]] color_codes = np.array(color_codes, dtype="S9") for c in range(event_channels.size): # Next line makes ev_ann a reference to seg_ann['events'][c] ev_ann = seg_ann["events"][c] name = event_channels["name"][c] ev_ann["description"] = ev_dict[name]["desc"] ev_ann["file_origin"] = self._filenames["nev"] + ".nev" if name == "comments": ev_ann["color_codes"] = color_codes def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): return self._seg_t_starts[seg_index] def _segment_t_stop(self, block_index, seg_index): return self._seg_t_stops[seg_index] def _get_signal_size(self, block_index, seg_index, stream_index): stream_id = self.header["signal_streams"][stream_index]["id"] nsx_nb = int(stream_id) memmap_data = self.nsx_datas[nsx_nb][seg_index] return memmap_data.shape[0] def _get_signal_t_start(self, block_index, seg_index, stream_index): stream_id = self.header["signal_streams"][stream_index]["id"] nsx_nb = int(stream_id) return self._sigs_t_starts[nsx_nb][seg_index] def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): stream_id = self.header["signal_streams"][stream_index]["id"] nsx_nb = int(stream_id) memmap_data = self.nsx_datas[nsx_nb][seg_index] if channel_indexes is None: channel_indexes = slice(None) sig_chunk = memmap_data[i_start:i_stop, channel_indexes] return sig_chunk def _get_blackrock_timestamps(self, block_index, seg_index, i_start, i_stop, stream_index): """ Return timestamps in seconds for analog signal samples in the given range. The behavior depends on the file format: - **PTP format (FileSpec 3.0-ptp):** Each packet in the file contains exactly one sample with its own PTP hardware timestamp at nanosecond resolution. This method returns those actual timestamps converted to seconds. Because each NSx file (e.g. ns2 at 1kHz, ns6 at 30kHz) stores its own independent PTP packets, every sample has a real timestamp. Note that PTP timestamps exhibit natural clock jitter at the nanosecond scale, so consecutive sample intervals are not perfectly uniform. - **Standard formats (FileSpec 2.2, 2.3, 3.0 non-PTP):** Each data block has a single scalar timestamp for its first sample. All subsequent samples within the block are interpolated as ``t_start + sample_index / sampling_rate``, assuming uniform spacing. - **FileSpec 2.1:** No timestamps are stored in the file. All samples are interpolated from ``t_start=0`` using the sampling rate. Parameters ---------- block_index : int Block index. seg_index : int Segment index. i_start : int | None First sample index. None means 0. i_stop : int | None Stop sample index (exclusive). None means end of segment. stream_index : int Stream index. Returns ------- timestamps : np.ndarray (float64) Timestamps in seconds for each sample in [i_start, i_stop). """ stream_id = self.header["signal_streams"][stream_index]["id"] nsx_nb = int(stream_id) # Resolve None to concrete indices size = self.nsx_datas[nsx_nb][seg_index].shape[0] i_start = i_start if i_start is not None else 0 i_stop = i_stop if i_stop is not None else size # Check if this segment has per-sample timestamps (PTP format) raw_timestamps = self._nsx_data_header[nsx_nb][seg_index]["timestamp"] if isinstance(raw_timestamps, np.ndarray) and raw_timestamps.size == size: # PTP: real hardware timestamps ts_res = float(self._nsx_basic_header[nsx_nb]["timestamp_resolution"]) return raw_timestamps[i_start:i_stop].astype("float64") / ts_res else: # Non-PTP: reconstruct from t_start + index / sampling_rate t_start = self._sigs_t_starts[nsx_nb][seg_index] sr = self._nsx_sampling_frequency[nsx_nb] return t_start + np.arange(i_start, i_stop, dtype="float64") / sr def _spike_count(self, block_index, seg_index, unit_index): channel_id, unit_id = self.internal_unit_ids[unit_index] all_spikes = self.nev_data["Spikes"][0] mask = (all_spikes["packet_id"] == channel_id) & (all_spikes["unit_class_nb"] == unit_id) if self._nb_segment == 1: # very fast nb = int(np.sum(mask)) else: # must clip in time time range timestamp = all_spikes[mask]["timestamp"] sl = self._get_timestamp_slice(timestamp, seg_index, None, None) timestamp = timestamp[sl] nb = timestamp.size return nb def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): channel_id, unit_id = self.internal_unit_ids[unit_index] all_spikes, event_segment_ids = self.nev_data["Spikes"] # select by channel_id and unit_id mask = ( (all_spikes["packet_id"] == channel_id) & (all_spikes["unit_class_nb"] == unit_id) & (event_segment_ids == seg_index) ) unit_spikes = all_spikes[mask] timestamp = unit_spikes["timestamp"] sl = self._get_timestamp_slice(timestamp, seg_index, t_start, t_stop) timestamp = timestamp[sl] return timestamp def _get_timestamp_slice(self, timestamp, seg_index, t_start, t_stop): if self._nb_segment > 1: # we must clip event in seg time limits if t_start is None: t_start = self._seg_t_starts[seg_index] if t_stop is None: t_stop = self._seg_t_stops[seg_index] + 1 / float(self._nev_basic_header["timestamp_resolution"]) if t_start is None: ind_start = None else: ts = math.ceil(t_start * self._nev_basic_header["timestamp_resolution"]) ind_start = np.searchsorted(timestamp, ts) if t_stop is None: ind_stop = None else: ts = int(t_stop * self._nev_basic_header["timestamp_resolution"]) ind_stop = np.searchsorted(timestamp, ts) # +1 sl = slice(ind_start, ind_stop) return sl def _rescale_spike_timestamp(self, spike_timestamps, dtype): spike_times = spike_timestamps.astype(dtype) spike_times /= self._nev_basic_header["timestamp_resolution"] return spike_times def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): channel_id, unit_id = self.internal_unit_ids[unit_index] all_spikes, event_segment_ids = self.nev_data["Spikes"] mask = ( (all_spikes["packet_id"] == channel_id) & (all_spikes["unit_class_nb"] == unit_id) & (event_segment_ids == seg_index) ) unit_spikes = all_spikes[mask] wf_dtype = self._nev_params("waveform_dtypes")[channel_id] wf_size = self._nev_params("waveform_size")[channel_id] wf_byte_size = np.dtype(wf_dtype).itemsize * wf_size dt1 = [ ("extra", "S{}".format(unit_spikes["waveform"].dtype.itemsize - wf_byte_size)), ("ch_waveform", "S{}".format(wf_byte_size)), ] waveforms = unit_spikes["waveform"].view(dt1)["ch_waveform"].flatten().view(wf_dtype) waveforms = waveforms.reshape(int(unit_spikes.size), 1, int(wf_size)) timestamp = unit_spikes["timestamp"] sl = self._get_timestamp_slice(timestamp, seg_index, t_start, t_stop) waveforms = waveforms[sl] return waveforms def _event_count(self, block_index, seg_index, event_channel_index): name = self.header["event_channels"]["name"][event_channel_index] if name == "comments": events_data, event_segment_ids = self.nev_data["Comments"] ev_dict = self._comment_evdict[self._nev_spec](events_data)[name] else: events_data, event_segment_ids = self.nev_data["NonNeural"] ev_dict = self._nonneural_evdicts[self._nev_spec](events_data)[name] mask = ev_dict["mask"] & (event_segment_ids == seg_index) if self._nb_segment == 1: # very fast nb = int(np.sum(mask)) else: # must clip in time time range timestamp = events_data[ev_dict["mask"]]["timestamp"] sl = self._get_timestamp_slice(timestamp, seg_index, None, None) timestamp = timestamp[sl] nb = timestamp.size return nb def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): name = self.header["event_channels"]["name"][event_channel_index] if name == "comments": events_data, event_segment_ids = self.nev_data["Comments"] ev_dict = self._comment_evdict[self._nev_spec](events_data)[name] # If immediate decoding is desired: encoding = {0: "latin_1", 1: "utf_16", 255: "latin_1"} labels = [data[ev_dict["field"]].decode(encoding[data["char_set"]]) for data in events_data] labels = np.array(labels, dtype="U") else: events_data, event_segment_ids = self.nev_data["NonNeural"] ev_dict = self._nonneural_evdicts[self._nev_spec](events_data)[name] labels = events_data[ev_dict["field"]].astype("U") mask = ev_dict["mask"] & (event_segment_ids == seg_index) timestamp = events_data[mask]["timestamp"] labels = labels[mask] # time clip sl = self._get_timestamp_slice(timestamp, seg_index, t_start, t_stop) timestamp = timestamp[sl] labels = labels[sl] durations = None return timestamp, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): ev_times = event_timestamps.astype(dtype) ev_times /= self._nev_basic_header["timestamp_resolution"] return ev_times ################################################### ################################################### # Above here code from Lyuba Zehl, Michael Denker # coming from previous BlackrockIO def _extract_nsx_file_spec(self, nsx_nb): """ Extract file specification from an .nsx file. """ filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Header structure of files specification 2.2 and higher. For files 2.1 # and lower, the entries ver_major and ver_minor are not supported. dt0 = [("file_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8")] nsx_file_id = np.fromfile(filename, count=1, dtype=dt0)[0] if nsx_file_id["file_id"].decode() == "NEURALSG": spec = "2.1" elif nsx_file_id["file_id"].decode() in ["NEURALCD", "BRSMPGRP"]: spec = f"{nsx_file_id['ver_major']}.{nsx_file_id['ver_minor']}" else: raise IOError("Unsupported NSX file type.") return spec def _extract_nev_file_spec(self): """ Extract file specification from an .nev file """ filename = f"{self._filenames['nev']}.nev" # Header structure of files specification 2.2 and higher. For files 2.1 # and lower, the entries ver_major and ver_minor are not supported. dt0 = [("file_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8")] nev_file_id = np.fromfile(filename, count=1, dtype=dt0)[0] if nev_file_id["file_id"].decode() in ["NEURALEV", "BREVENTS"]: spec = f"{nev_file_id['ver_major']}.{nev_file_id['ver_minor']}" else: raise IOError(f"NEV file type {nev_file_id['file_id'].decode()} is not supported") return spec def _read_nsx_header(self, spec, nsx_nb): """ Extract nsx header information for any specification version. Parameters ---------- spec : str The specification version (e.g., "2.1", "2.2", "2.3", "3.0") nsx_nb : int The NSX file number (e.g., 5 for ns5) Returns ------- nsx_basic_header : numpy structured array Basic header information nsx_ext_header : numpy memmap Extended header information """ # Construct filename filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Get basic header structure for this spec basic_header_dtype = NSX_BASIC_HEADER_TYPES[spec] nsx_basic_header = np.fromfile(filename, count=1, dtype=basic_header_dtype)[0] # Get extended header structure for this spec ext_header_dtype = NSX_EXT_HEADER_TYPES[spec] offset_dt0 = np.dtype(basic_header_dtype).itemsize channel_count = int(nsx_basic_header["channel_count"]) nsx_ext_header = np.memmap(filename, shape=channel_count, offset=offset_dt0, dtype=ext_header_dtype, mode="r") return nsx_basic_header, nsx_ext_header def _read_nsx_dataheader(self, spec, nsx_nb, offset): """ Reads data header following the given offset of an nsx file. Parameters ---------- spec : str The specification version (e.g., "2.2", "2.3", "3.0") nsx_nb : int The NSX file number offset : int Offset position in the file """ filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Get data header structure for this spec data_header_dtype = NSX_DATA_HEADER_TYPES[spec] if data_header_dtype is None: return None # v2.1 has no data headers nsx_basic_header = np.memmap(filename, dtype=data_header_dtype, shape=1, offset=offset, mode="r")[0] return nsx_basic_header def _parse_nsx_data(self, spec, nsx_nb): """ Parse NSX data blocks from file and extract data with timestamps. This is the main router function for NSX data parsing. It creates a memory-mapped view of the file internally and extracts data blocks with their associated timestamps. The function handles three different NSX file format variants, each with different internal structure for storing data and timestamps. NSX FILE FORMAT VARIANTS ======================== STANDARD FORMAT (v2.2, v2.3, v3.0 non-PTP) ------------------------------------------ File structure: ┌─────────────────────────────────────────────� │ BASIC HEADER (fixed size) │ � File metadata │ - file_id, version, period, etc. │ ├─────────────────────────────────────────────┤ │ EXTENDED HEADER (per channel) │ � Channel info │ - electrode_id, label, units, etc. │ ├─────────────────────────────────────────────┤ │ DATA BLOCK 1 HEADER │ � Block metadata │ - header_flag=1 │ │ - timestamp (scalar, e.g., 0) │ │ - nb_data_points (e.g., 1000) │ ├─────────────────────────────────────────────┤ │ DATA BLOCK 1 DATA │ � Actual samples │ - 1000 samples x N channels │ │ - int16 values │ ├─────────────────────────────────────────────┤ │ DATA BLOCK 2 HEADER │ � Next block │ - header_flag=1 │ │ - timestamp (scalar, e.g., 30000) │ │ - nb_data_points (e.g., 1000) │ ├─────────────────────────────────────────────┤ │ DATA BLOCK 2 DATA │ │ - 1000 samples x N channels │ └─────────────────────────────────────────────┘ Key characteristics: - Headers are EXPLICIT and SPARSE (only at block boundaries) - Each block has ONE scalar timestamp for ALL samples in that block - Reader LOOPS through file, finding headers PTP FORMAT (v3.0 with Precision Time Protocol) ----------------------------------------------- File structure: ┌─────────────────────────────────────────────� │ BASIC HEADER (fixed size) │ │ - timestamp_resolution = 1,000,000,000 │ � Nanosecond precision! ├─────────────────────────────────────────────┤ │ EXTENDED HEADER (per channel) │ ├─────────────────────────────────────────────┤ │ PACKET 0: │ � Each sample = packet │ - reserved (1 byte) │ │ - timestamp (8 bytes, e.g., 1000) │ │ - num_data_points (always 1) │ │ - samples (N channels x int16) │ ├─────────────────────────────────────────────┤ │ PACKET 1: │ │ - reserved │ │ - timestamp (e.g., 1033) │ │ - num_data_points (1) │ │ - samples (N channels x int16) │ ├─────────────────────────────────────────────┤ │ PACKET 2: │ │ - reserved │ │ - timestamp (e.g., 1066) │ │ - num_data_points (1) │ │ - samples (N channels x int16) │ ├─────────────────────────────────────────────┤ │ ...thousands more packets... │ │ PACKET 500: │ │ - timestamp (e.g., 50000) │ � BIG GAP! │ ... │ └─────────────────────────────────────────────┘ Key characteristics: - NO separate headers and data - they're INTERLEAVED - EVERY sample has its own timestamp (per-sample nanosecond precision) - File is ONE CONTINUOUS ARRAY of uniform packets V2.1 FORMAT ----------- Simplest format: - Single continuous data block - No timestamps in data section - No multiple blocks (no pause/resume support) Parameters ---------- spec : str File specification version ("2.1", "2.2", "2.3", "3.0", "3.0-ptp") nsx_nb : int NSX file number (e.g., 5 for ns5 file) Returns ------- dict Dictionary mapping block index to block information: { block_idx: { "data": np.ndarray, View into memory-mapped file with shape (samples, channels) "timestamps": scalar, np.ndarray, or None, - Standard format: scalar (one timestamp per block) - PTP format: array (one timestamp per sample) - v2.1 format: None (no timestamps) # Additional metadata as needed }, ... } Notes ----- - This function creates the file memmap internally - Data views are created using np.ndarray with buffer parameter (memory efficient) - Returned data is NOT YET SEGMENTED (segmentation happens in a separate step) - For standard format, each block from the file is one dict entry - For PTP format, all data is in a single block (block_idx=0) """ if spec == "2.1": return self._parse_nsx_data_v21(nsx_nb) elif spec == "3.0-ptp": return self._parse_nsx_data_v30_ptp(nsx_nb) else: # 2.2, 2.3, 3.0 standard return self._parse_nsx_data_v22_v30(spec, nsx_nb) def _parse_nsx_data_v21(self, nsx_nb): """ Parse v2.1 NSX data blocks. V2.1 format is the simplest: - Single continuous data block - No timestamps - No pause/resume support Returns ------- dict {0: {"data": np.ndarray, "timestamps": None}} """ filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Create file memmap file_memmap = np.memmap(filename, dtype="uint8", mode="r") # Calculate header size and data points for v2.1 channels = int(self._nsx_basic_header[nsx_nb]["channel_count"]) bytes_in_headers = ( self._nsx_basic_header[nsx_nb].dtype.itemsize + self._nsx_ext_header[nsx_nb].dtype.itemsize * channels ) filesize = self._get_file_size(filename) num_samples = int((filesize - bytes_in_headers) / (2 * channels) - 1) offset = bytes_in_headers # Create data view into memmap data = np.ndarray(shape=(num_samples, channels), dtype="int16", buffer=file_memmap, offset=offset) return { 0: { "data": data, "timestamps": None, } } def _parse_nsx_data_v22_v30(self, spec, nsx_nb): """ Parse standard format NSX data blocks (v2.2, 2.3, 3.0). Standard format has: - Explicit block headers in file - Each block has scalar timestamp - Multiple blocks when recording paused/resumed Returns ------- dict {block_idx: {"data": np.ndarray, "timestamps": scalar}, ...} """ filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Create file memmap file_memmap = np.memmap(filename, dtype="uint8", mode="r") # Get file parameters filesize = self._get_file_size(filename) channels = int(self._nsx_basic_header[nsx_nb]["channel_count"]) current_offset = int(self._nsx_basic_header[nsx_nb]["bytes_in_headers"]) data_blocks = {} block_idx = 0 # Loop through file, reading block headers while current_offset < filesize: # Read header at current position header = self._read_nsx_dataheader(spec, nsx_nb, current_offset) if header["header_flag"] != 1: raise ValueError( f"Invalid NSX data block header at offset {current_offset:#x} " f"in ns{nsx_nb} file. Expected header_flag=1, got {header['header_flag']}." ) num_samples = int(header["nb_data_points"]) data_offset = current_offset + header.dtype.itemsize timestamp = header["timestamp"] # Create data view into memmap for this block data = np.ndarray(shape=(num_samples, channels), dtype="int16", buffer=file_memmap, offset=data_offset) data_blocks[block_idx] = { "data": data, "timestamps": timestamp, } # Jump to next block data_size_bytes = num_samples * channels * 2 # int16 = 2 bytes current_offset = data_offset + data_size_bytes block_idx += 1 return data_blocks def _parse_nsx_data_v30_ptp(self, nsx_nb): """ Parse PTP format NSX data (v3.0 with Precision Time Protocol). PTP format has: - Interleaved structure (timestamp + sample per packet) - Array of timestamps (one per sample) - Continuous data (segmentation inferred from timestamp gaps) Returns ------- dict {0: {"data": np.ndarray, "timestamps": np.ndarray}} """ filename = f"{self._filenames['nsx']}.ns{nsx_nb}" # Get file parameters filesize = self._get_file_size(filename) header_size = int(self._nsx_basic_header[nsx_nb]["bytes_in_headers"]) channel_count = int(self._nsx_basic_header[nsx_nb]["channel_count"]) # Create structured memmap (timestamp + samples per packet) ptp_dt = NSX_DATA_HEADER_TYPES["3.0-ptp"](channel_count) npackets = int((filesize - header_size) / np.dtype(ptp_dt).itemsize) file_memmap = np.memmap(filename, dtype=ptp_dt, shape=npackets, offset=header_size, mode="r") # Verify this is truly PTP (all packets should have 1 sample) if not np.all(file_memmap["num_data_points"] == 1): # Not actually PTP! Fall back to standard format return self._parse_nsx_data_v22_v30("3.0", nsx_nb) # Extract data and timestamps from structured array data = file_memmap["samples"] timestamps = file_memmap["timestamps"] return { 0: { "data": data, "timestamps": timestamps, } } def _format_gap_report(self, gap_indices, timestamps_in_seconds, time_differences, nsx_nb): """ Format a detailed gap report showing where timestamp discontinuities occur. Parameters ---------- gap_indices : np.ndarray Indices where gaps were detected timestamps_in_seconds : np.ndarray All timestamps converted to seconds time_differences : np.ndarray Time differences between consecutive timestamps nsx_nb : int NSX file number for the report Returns ------- str Formatted gap report with table """ # Calculate gap details gap_durations_seconds = time_differences[gap_indices] gap_durations_ms = gap_durations_seconds * 1000 gap_positions_seconds = timestamps_in_seconds[gap_indices] - timestamps_in_seconds[0] # Build gap detail table gap_detail_lines = [ f"| {index:>15,} | {pos:>21.6f} | {dur:>21.3f} |\n" for index, pos, dur in zip(gap_indices, gap_positions_seconds, gap_durations_ms) ] return ( f"Gap Report for ns{nsx_nb}:\n" f"Found {len(gap_indices)} timestamp gaps (detection threshold: 2 x sampling period)\n\n" "Gap Details:\n" "+-----------------+-----------------------+-----------------------+\n" "| Sample Index | Sample at (Seconds) | Gap Size (ms) |\n" "+-----------------+-----------------------+-----------------------+\n" + "".join(gap_detail_lines) + "+-----------------+-----------------------+-----------------------+\n" ) def _segment_nsx_data(self, data_blocks_dict, nsx_nb): """ Segment NSX data based on timestamp gaps. Takes the data blocks returned by _parse_nsx_data() and creates segments. Segmentation logic depends on the file format: - Standard format (multiple blocks): Each block IS a segment - PTP format (single block with timestamp array): Detect gaps in timestamps - V2.1 format (no timestamps): Single segment Parameters ---------- data_blocks_dict : dict Dictionary from _parse_nsx_data(): {block_idx: {"data": np.ndarray, "timestamps": scalar/array/None}} nsx_nb : int NSX file number Returns ------- dict { seg_idx: { "data": np.ndarray, "timestamps": scalar, array, or None, "nb_data_points": int, "header": int or None, "offset_to_data_block": None (deprecated but kept for compatibility) }, ... } """ segments = {} # Case 1: Multiple blocks (Standard format) - each block is a segment if len(data_blocks_dict) > 1: for block_idx, block_info in data_blocks_dict.items(): segments[block_idx] = { "data": block_info["data"], "timestamp": block_info["timestamps"], # Use singular for backward compatibility "nb_data_points": block_info["data"].shape[0], "header": 1, # Standard format has headers "offset_to_data_block": None, # Not needed (have data directly) } # Case 2: Single block - check if PTP (array timestamps) or simple (no timestamps) elif len(data_blocks_dict) == 1: block_info = data_blocks_dict[0] data = block_info["data"] timestamps = block_info["timestamps"] # PTP format: array of timestamps - need to detect gaps if isinstance(timestamps, np.ndarray): # Analyze timestamp gaps sampling_rate = self._nsx_sampling_frequency[nsx_nb] # Detection threshold: use strict 2x sampling period to find ALL gaps detection_threshold = 2.0 / sampling_rate timestamps_sampling_rate = self._nsx_basic_header[nsx_nb]["timestamp_resolution"] timestamps_in_seconds = timestamps / timestamps_sampling_rate time_differences = np.diff(timestamps_in_seconds) gap_indices = np.argwhere(time_differences > detection_threshold).flatten() # If gaps found, check user's tolerance if len(gap_indices) > 0: gap_report = self._format_gap_report(gap_indices, timestamps_in_seconds, time_differences, nsx_nb) # Error by default - user must opt-in to segmentation if self.gap_tolerance_ms is None: raise ValueError( f"Detected {len(gap_indices)} timestamp gaps in ns{nsx_nb} file.\n" f"{gap_report}\n" f"To load this data, provide gap_tolerance_ms parameter to automatically " f"segment at gaps larger than the specified tolerance." ) # User provided tolerance - filter gaps and segment gap_tolerance_s = self.gap_tolerance_ms / 1000.0 significant_gap_mask = time_differences[gap_indices] > gap_tolerance_s significant_gap_indices = gap_indices[significant_gap_mask] # Use significant gaps for segmentation (no warning - user opted in) gap_indices = significant_gap_indices # Create segments based on gaps segment_starts = np.hstack((0, gap_indices + 1)) segment_boundaries = list(segment_starts) + [len(data)] for seg_idx, start in enumerate(segment_starts): end = segment_boundaries[seg_idx + 1] segments[seg_idx] = { "data": data[start:end], "timestamp": timestamps[start:end], # Use singular for backward compatibility "nb_data_points": end - start, "header": None, # PTP has no headers "offset_to_data_block": None, } # V2.1 or single block standard format: no segmentation needed else: segments[0] = { "data": data, "timestamp": timestamps, # Use singular for backward compatibility "nb_data_points": data.shape[0], "header": None, "offset_to_data_block": None, } return segments def _build_nsx_v21_ext_header(self, nsx_nb): """ Build extended header structure for v2.1 NSX files. v2.1 NSX files don't have extended headers with analog/digital ranges. We estimate these from the digitization factor in the NEV file. dig_factor = max_analog_val / max_digital_val We set max_digital_val = 1000, so max_analog_val = dig_factor dig_factor is in nV, so units are uV. Information from Kian Torab, Blackrock Microsystems. """ ext_header = [] for i, elid in enumerate(self._nsx_ext_header[nsx_nb]["electrode_id"]): # Get digitization factor from NEV if self._avail_files["nev"]: # Workaround for DigitalFactor overflow in buggy Cerebus systems # Fix from NPMK toolbox (openNEV, line 464, git rev d0a25eac902704a3a29fa5dfd3aed0744f4733ed) dig_factor = self._nev_params("digitization_factor")[elid] if dig_factor == 21516: dig_factor = 152592.547 units = "uV" else: dig_factor = float("nan") units = "" if i == 0: # Only warn once warnings.warn("Cannot rescale to voltage, raw data will be returned.", UserWarning) # Generate label if elid < 129: label = f"chan{elid}" else: label = f"ainp{(elid - 129 + 1)}" ext_header.append( { "labels": label, "units": units, "min_analog_val": -float(dig_factor), "max_analog_val": float(dig_factor), "min_digital_val": -1000, "max_digital_val": 1000, } ) return ext_header def _read_nev_header(self, spec, filename): """ Extract nev header information for any specification version. Parameters ---------- spec : str The specification version (e.g., "2.1", "2.2", "2.3", "3.0") filename : str The NEV filename to read from Returns ------- nev_basic_header : np.ndarray Basic header information nev_ext_header : dict Extended header information by packet ID """ # Note: This function only uses the passed parameters, not self attributes # This makes it easy to convert to @staticmethod later # basic header (same for all versions) dt0 = [ # Set to "NEURALEV" ("file_type_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8"), # Flags ("additionnal_flags", "uint16"), # File index of first data sample ("bytes_in_headers", "uint32"), # Number of bytes per data packet (sample) ("bytes_in_data_packets", "uint32"), # Time resolution of time stamps in Hz ("timestamp_resolution", "uint32"), # Sampling frequency of waveforms in Hz ("sample_resolution", "uint32"), ("year", "uint16"), ("month", "uint16"), ("weekday", "uint16"), ("day", "uint16"), ("hour", "uint16"), ("minute", "uint16"), ("second", "uint16"), ("millisecond", "uint16"), ("application_to_create_file", "S32"), ("comment_field", "S256"), # Number of extended headers ("nb_ext_headers", "uint32"), ] nev_basic_header = np.fromfile(filename, count=1, dtype=dt0)[0] shape = nev_basic_header["nb_ext_headers"] offset_dt0 = np.dtype(dt0).itemsize # This is the common structure of the beginning of extended headers dt1 = [("packet_id", "S8"), ("info_field", "S24")] raw_ext_header = np.memmap(filename, offset=offset_dt0, dtype=dt1, shape=shape, mode="r") # Get extended header types for this spec header_types = NEV_EXT_HEADER_TYPES_BY_SPEC[spec] # Parse extended headers by packet type # Strategy: view() entire array first, then mask for efficiency # Since all NEV extended header packets are fixed-width (32 bytes), temporarily # interpreting a "NEUEVWAV" packet as "ARRAYNME" structure is safe - the raw bytes # are just reinterpreted without copying. We immediately filter out mismatched packets # with the mask, keeping only those that actually belong to the current packet type. nev_ext_header = {} for packet_id, dtype_def in header_types.items(): mask = raw_ext_header["packet_id"] == packet_id nev_ext_header[packet_id] = raw_ext_header.view(dtype_def)[mask] return nev_basic_header, nev_ext_header def _read_nev_data(self, spec, filename): """ Extract nev data for any specification version. Parameters ---------- spec : str The specification version (e.g., "2.1", "2.2", "2.3", "3.0") filename : str The NEV filename to read from """ packet_size_bytes = self._nev_basic_header["bytes_in_data_packets"] header_size = self._nev_basic_header["bytes_in_headers"] if self._nev_basic_header["ver_major"] >= 3: ts_format = "uint64" header_skip = 10 else: ts_format = "uint32" header_skip = 6 # read all raw data packets and markers dt0 = [("timestamp", ts_format), ("packet_id", "uint16"), ("value", f"S{packet_size_bytes - header_skip}")] # expected number of data packets. We are not sure why, but it seems we can get partial data packets # based on blackrock's own code this is okay so applying an int to round down is necessary to obtain the # memory map of full packets and toss the partial packet. # See reference: https://github.com/BlackrockNeurotech/Python-Utilities/blob/fa75aa671680306788e10d3d8dd625f9da4ea4f6/brpylib/brpylib.py#L580-L587 data_packages_in_bytes = self._get_file_size(filename) - header_size n_packets = int(data_packages_in_bytes / packet_size_bytes) raw_data = np.memmap( filename, offset=header_size, dtype=dt0, shape=(n_packets,), mode="r", ) # Get packet identifiers and types directly from spec-based dictionaries packet_identifiers = NEV_PACKET_IDENTIFIERS_BY_SPEC[spec] data_types = NEV_PACKET_DATA_TYPES_BY_SPEC[spec] # Apply masks and create type definitions masks = {} types = {} for data_type, packet_id_spec in packet_identifiers.items(): if isinstance(packet_id_spec, tuple): # Range check (min, max) min_val, max_val = packet_id_spec masks[data_type] = (min_val <= raw_data["packet_id"]) & (raw_data["packet_id"] <= max_val) else: # Equality check masks[data_type] = raw_data["packet_id"] == packet_id_spec types[data_type] = data_types[data_type](packet_size_bytes) event_segment_ids = self._get_event_segment_ids(raw_data, masks, spec) # Extract data for each packet type using view-then-mask pattern # Strategy: reinterpret entire raw_data array with each packet type's structure, then filter # All NEV data packets are fixed-width, so temporarily viewing "Spikes" data as "Comments" # structure is safe - we immediately filter to keep only packets that actually match. # This avoids creating copies of large data arrays during the parsing process. data = {} for data_type in packet_identifiers: mask = masks[data_type] data[data_type] = (raw_data.view(types[data_type])[mask], event_segment_ids[mask]) return data def _get_reset_event_mask(self, raw_event_data, masks, spec): """ Extract mask for reset comment events in 2.3 .nev file """ if "Comments" not in masks: return np.zeros(len(raw_event_data), dtype=bool) restart_mask = np.logical_and( masks["Comments"], raw_event_data["value"] == b"\x00\x00\x00\x00\x00\x00critical load restart", ) # TODO: Fix hardcoded number of bytes return restart_mask def _get_event_segment_ids(self, raw_event_data, masks, spec): """ Construct array of corresponding segment ids for each event for nev version 2.3 """ if spec in ["2.1", "2.2"]: # No pause or reset mechanism present for file version 2.1 and 2.2 return np.zeros(len(raw_event_data), dtype=int) elif spec in ["2.3", "3.0"]: reset_ev_mask = self._get_reset_event_mask(raw_event_data, masks, spec) reset_ev_ids = np.where(reset_ev_mask)[0] # consistency check for monotone increasing time stamps # - Use logical comparator (instead of np.diff) to avoid unsigned dtype issues. # - Only consider handled/known event types. mask_handled = np.any([mask for mask in masks.values()], axis=0) jump_ids_handled = ( np.where( raw_event_data["timestamp"][mask_handled][1:] < raw_event_data["timestamp"][mask_handled][:-1] )[0] + 1 ) jump_ids = np.where(mask_handled)[0][jump_ids_handled] # jump ids in full set of events (incl. unhandled) overlap = np.isin(jump_ids, reset_ev_ids) if not all(overlap): # additional resets occurred without a reset event being stored additional_ids = jump_ids[np.invert(overlap)] warnings.warn( f"Detected {len(additional_ids)} undocumented segments within " f"nev data after timestamps {additional_ids}." "" ) reset_ev_ids = sorted(np.unique(np.concatenate((reset_ev_ids, jump_ids)))) event_segment_ids = np.zeros(len(raw_event_data), dtype=int) for reset_event_id in reset_ev_ids: event_segment_ids[reset_event_id:] += 1 self._nb_segment_nev = len(reset_ev_ids) + 1 return event_segment_ids else: raise ValueError(f"Unknown File Spec {spec}") def _match_nsx_and_nev_segment_ids(self, nsx_nb): """ Ensure matching ids of segments detected in nsx and nev file for version 2.3 """ # NSX required for matching, if not available, warn the user if not self._avail_nsx: warnings.warn( "No nsX available so it cannot be checked whether " "the segments in nev are all correct. Most importantly, " "recording pauses will not be detected", UserWarning, ) return # Only needs to be done for nev version 2.3 if self._nev_spec == "2.3": nsx_offset = self._nsx_data_header[nsx_nb][0]["timestamp"] # Multiples of 1/30.000s that pass between two nsX samples nsx_period = self._nsx_basic_header[nsx_nb]["period"] # NSX segments needed as dict and list nonempty_nsx_segments = {} list_nonempty_nsx_segments = [] # Counts how many segments CAN be created from nev nb_possible_nev_segments = self._nb_segment_nev # Nonempty segments are those containing at least 2 samples # These have to be able to be mapped to nev for k, v in sorted(self._nsx_data_header[nsx_nb].items()): if v["nb_data_points"] > 1: nonempty_nsx_segments[k] = v list_nonempty_nsx_segments.append(v) # Account for paused segments # This increases nev event segment ids if from the nsx an additional segment is found # If one new segment, i.e. that could not be determined from the nev was found, # all following ids need to be increased to account for the additional segment before for k, (data, ev_ids) in self.nev_data.items(): # Check all nonempty nsX segments for i, seg in enumerate(list_nonempty_nsx_segments[:]): # Last timestamp in this nsX segment # Not subtracting nsX offset from end because spike extraction might continue end_of_current_nsx_seg = ( seg["timestamp"] + seg["nb_data_points"] * self._nsx_basic_header[nsx_nb]["period"] ) mask_after_seg = (ev_ids == i) & (data["timestamp"] > end_of_current_nsx_seg + nsx_period) # Show warning if spikes do not fit any segment (+- 1 sampling 'tick') # Spike should belong to segment before mask_outside = (ev_ids == i) & ( data["timestamp"] < int(seg["timestamp"]) - int(nsx_offset) - int(nsx_period) ) if len(data[mask_outside]) > 0: warnings.warn(f"Spikes outside any segment. Detected on segment #{i}") ev_ids[mask_outside] -= 1 # If some nev data are outside of this nsX segment, increase their segment ids # and the ids of all following segments. They are checked for the next nsX # segment then. If they do not fit any of them, # a warning will be shown, indicating how far outside the segment spikes are # If they fit the next segment, more segments are possible in nev, # because a new one has been discovered if len(data[mask_after_seg]) > 0: # Warning if spikes are after last segment if i == len(list_nonempty_nsx_segments) - 1: # Get timestamp resolution from header (available for v2.2+) timestamp_resolution = self._nsx_basic_header[nsx_nb]["timestamp_resolution"] time_after_seg = ( data[mask_after_seg]["timestamp"][-1] - end_of_current_nsx_seg ) / timestamp_resolution warnings.warn(f"Spikes {time_after_seg}s after last segment.") # Break out of loop because it's the last iteration # and the spikes should stay connected to last segment break # If reset and no segment detected in nev, then these segments cannot be # distinguished in nev, which is a big problem # XXX 96 is an arbitrary number based on observations in available files elif list_nonempty_nsx_segments[i + 1]["timestamp"] - nsx_offset <= 96: # If not all definitely belong to the next segment, # then it cannot be distinguished where some belong if len(data[ev_ids == i]) != len(data[mask_after_seg]): raise ValueError("Some segments in nsX cannot be detected in nev") # Actual processing if no problem has occurred nb_possible_nev_segments += 1 ev_ids[ev_ids > i] += 1 ev_ids[mask_after_seg] += 1 # consistency check: same number of segments for nsx and nev data if nb_possible_nev_segments != len(nonempty_nsx_segments): raise NeoReadWriteError( f"Inconsistent ns{nsx_nb} and nev file. {nb_possible_nev_segments} " f"segments present in .nev file, but {len(nonempty_nsx_segments)} in " f"ns{nsx_nb} file." ) new_nev_segment_id_mapping = dict(zip(range(nb_possible_nev_segments), sorted(list(nonempty_nsx_segments)))) # replacing event ids by matched event ids in place for k, (data, ev_ids) in self.nev_data.items(): if len(ev_ids): ev_ids[:] = np.vectorize(new_nev_segment_id_mapping.__getitem__)(ev_ids) def _nev_params(self, param_name): """ Returns wanted nev parameter. """ nev_parameters = { "bytes_in_data_packets": self._nev_basic_header["bytes_in_data_packets"], "rec_datetime": datetime.datetime( year=self._nev_basic_header["year"], month=self._nev_basic_header["month"], day=self._nev_basic_header["day"], hour=self._nev_basic_header["hour"], minute=self._nev_basic_header["minute"], second=self._nev_basic_header["second"], microsecond=int(self._nev_basic_header["millisecond"]) * 1000, ), "max_res": self._nev_basic_header["timestamp_resolution"], "channel_ids": self._nev_ext_header[b"NEUEVWAV"]["electrode_id"], "channel_labels": self._channel_labels[self._nev_spec](), "event_unit": pq.CompoundUnit(f"1.0/{self._nev_basic_header['timestamp_resolution']} * s"), "nb_units": dict( zip( self._nev_ext_header[b"NEUEVWAV"]["electrode_id"], self._nev_ext_header[b"NEUEVWAV"]["nb_sorted_units"], ) ), "digitization_factor": dict( zip( self._nev_ext_header[b"NEUEVWAV"]["electrode_id"], self._nev_ext_header[b"NEUEVWAV"]["digitization_factor"], ) ), "data_size": self._nev_basic_header["bytes_in_data_packets"], "waveform_size": self._waveform_size[self._nev_spec](), "waveform_dtypes": self._get_waveforms_dtype(), "waveform_sampling_rate": self._nev_basic_header["sample_resolution"] * pq.Hz, "waveform_time_unit": pq.CompoundUnit(f"1.0/{self._nev_basic_header['sample_resolution']} * s"), "waveform_unit": pq.uV, } return nev_parameters[param_name] def _get_file_size(self, filename): """ Returns the file size in bytes for the given file. """ filebuf = open(filename, "rb") filebuf.seek(0, os.SEEK_END) file_size = int(filebuf.tell()) filebuf.close() return file_size def _get_min_time(self): """ Returns the smallest time that can be determined from the recording for use as the lower bound n in an interval [n,m). """ tp = [] if self._avail_files["nev"]: tp.extend(self._get_nev_rec_times()[0]) for nsx_i in self._avail_nsx: tp.extend(self._nsx_rec_times[self._nsx_spec[nsx_i]](nsx_i)[0]) return min(tp) def _get_max_time(self): """ Returns the largest time that can be determined from the recording for use as the upper bound m in an interval [n,m). """ tp = [] if self._avail_files["nev"]: tp.extend(self._get_nev_rec_times()[1]) for nsx_i in self._avail_nsx: tp.extend(self._nsx_rec_times[self._nsx_spec[nsx_i]](nsx_i)[1]) return max(tp) def _get_nev_rec_times(self): """ Extracts minimum and maximum time points from a nev file. """ filename = ".".join([self._filenames["nev"], "nev"]) dt = [("timestamp", "uint32")] offset = self._get_file_size(filename) - self._nev_params("bytes_in_data_packets") last_data_packet = np.memmap(filename, offset=offset, dtype=dt, mode="r")[0] n_starts = [0 * self._nev_params("event_unit")] n_stops = [last_data_packet["timestamp"] * self._nev_params("event_unit")] return n_starts, n_stops def _get_waveforms_dtype(self): """ Extracts the actual waveform dtype set for each channel. """ # Blackrock code giving the appropriate dtype conv = {0: "int8", 1: "int8", 2: "int16", 4: "int32"} # get all electrode ids from nev ext header all_el_ids = self._nev_ext_header[b"NEUEVWAV"]["electrode_id"] # get the dtype of waveform (this is stupidly complicated) if self._is_set(np.array(self._nev_basic_header["additionnal_flags"]), 0): dtype_waveforms = {k: "int16" for k in all_el_ids} else: # extract bytes per waveform waveform_bytes = self._nev_ext_header[b"NEUEVWAV"]["bytes_per_waveform"] # extract dtype for waveforms fro each electrode dtype_waveforms = dict(zip(all_el_ids, conv[waveform_bytes])) return dtype_waveforms def _get_channel_labels_spec_v21(self): """ Returns labels for all channels for file spec 2.1 """ elids = self._nev_ext_header[b"NEUEVWAV"]["electrode_id"] labels = [] for elid in elids: if elid < 129: labels.append(f"chan{elid}") else: labels.append(f"ainp{(elid - 12 +1 )}") return dict(zip(elids, labels)) def _get_channel_labels_spec_v22_30(self): """ Returns labels for all channels for file spec 2.2 and 2.3 """ elids = self._nev_ext_header[b"NEUEVWAV"]["electrode_id"] labels = self._nev_ext_header[b"NEUEVLBL"]["label"] return dict(zip(elids, labels)) if len(labels) > 0 else None def _get_waveform_size_spec_v21(self): """ Returns waveform sizes for all channels for file spec 2.1 and 2.2 """ wf_dtypes = self._get_waveforms_dtype() nb_bytes_wf = self._nev_basic_header["bytes_in_data_packets"] - 8 wf_sizes = {ch: int(nb_bytes_wf / np.dtype(dt).itemsize) for ch, dt in wf_dtypes.items()} return wf_sizes def _get_waveform_size_spec_v22_30(self): """ Returns waveform sizes for all channels for file spec 2.3 """ elids = self._nev_ext_header[b"NEUEVWAV"]["electrode_id"] spike_widths = self._nev_ext_header[b"NEUEVWAV"]["spike_width"] return dict(zip(elids, spike_widths)) def _get_left_sweep_waveforms(self): """ Returns left sweep of waveforms for each channel. Left sweep is defined as the time from the beginning of the waveform to the trigger time of the corresponding spike. """ # TODO: Double check if this is the actual setting for Blackrock wf_t_unit = self._nev_params("waveform_time_unit") all_ch = self._nev_params("channel_ids") # TODO: Double check if this is the correct assumption (10 samples) # default value: threshold crossing after 10 samples of waveform wf_left_sweep = {ch: 10 * wf_t_unit for ch in all_ch} # non-default: threshold crossing at center of waveform # wf_size = self._nev_params('waveform_size') # wf_left_sweep = dict( # [(ch, (wf_size[ch] / 2) * wf_t_unit) for ch in all_ch]) return wf_left_sweep def _get_nonneural_evdicts_spec_v21_22(self, data): """ Defines event types and the necessary parameters to extract them from a 2.1 and 2.2 nev file. """ # TODO: add annotations of nev ext header (NSASEXEX) to event types # digital events event_types = { "digital_input_port": { "name": "digital_input_port", "field": "digital_input", "mask": data["packet_insertion_reason"] == 1, "desc": "Events of the digital input port", }, "serial_input_port": { "name": "serial_input_port", "field": "digital_input", "mask": data["packet_insertion_reason"] == 129, "desc": "Events of the serial input port", }, } # analog input events via threshold crossings for ch in range(5): event_types.update( { f"analog_input_channel_{ch + 1}": { "name": f"analog_input_channel_{ch + 1}", "field": f"analog_input_channel_{ch + 1}", "mask": self._is_set(data["packet_insertion_reason"], ch + 1), "desc": f"Values of analog input channel {ch + 1} in mV " "(+/- 5000)", } } ) # TODO: define field and desc event_types.update( { "periodic_sampling_events": { "name": "periodic_sampling_events", "field": "digital_input", "mask": self._is_set(data["packet_insertion_reason"], 6), "desc": "Periodic sampling event of a certain frequency", } } ) return event_types def _delete_empty_segments(self): """ If there are empty segments (e.g. due to a reset or clock synchronization across two systems), these can be discarded. If this is done, all the data and data_headers need to be remapped to become a range starting from 0 again. Nev data are mapped accordingly to stay with their corresponding segment in the nsX data. """ # Discard empty segments removed_seg = [] for data_bl in range(self._nb_segment): keep_seg = True for nsx_nb in self.nsx_to_load: length = self.nsx_datas[nsx_nb][data_bl].shape[0] keep_seg = keep_seg and (length >= 2) if not keep_seg: removed_seg.append(data_bl) for nsx_nb in self.nsx_to_load: self.nsx_datas[nsx_nb].pop(data_bl) self._nsx_data_header[nsx_nb].pop(data_bl) # Keys need to be increasing from 0 to maximum in steps of 1 # To ensure this after removing empty segments, some keys need to be re mapped for i in removed_seg[::-1]: for j in range(i + 1, self._nb_segment): # remap nsx seg index for nsx_nb in self.nsx_to_load: data = self.nsx_datas[nsx_nb].pop(j) self.nsx_datas[nsx_nb][j - 1] = data data_header = self._nsx_data_header[nsx_nb].pop(j) self._nsx_data_header[nsx_nb][j - 1] = data_header # Also remap nev data, ev_ids are the equivalent to keys above if self._avail_files["nev"]: for k, (data, ev_ids) in self.nev_data.items(): ev_ids[ev_ids == j] -= 1 self._nb_segment -= 1 def _get_nonneural_evdicts_spec_v23(self, data): """ Defines event types and the necessary parameters to extract them from a 2.3 nev file. """ # digital events if not np.all(np.isin(data["packet_insertion_reason"], [1, 129])): # Blackrock spec gives reason==64 means PERIODIC, but never seen this live warnings.warn("Unknown event codes found", RuntimeWarning) event_types = { "digital_input_port": { "name": "digital_input_port", "field": "digital_input", "mask": self._is_set(data["packet_insertion_reason"], 0) & ~self._is_set(data["packet_insertion_reason"], 7), "desc": "Events of the digital input port", }, "serial_input_port": { "name": "serial_input_port", "field": "digital_input", "mask": self._is_set(data["packet_insertion_reason"], 0) & self._is_set(data["packet_insertion_reason"], 7), "desc": "Events of the serial input port", }, } return event_types def _get_comment_evdict_spec_v21_22(self, data): return { "comments": {"name": "comments", "field": "comment", "mask": data["packet_id"] == 65535, "desc": "Comments"} } def _is_set(self, flag, pos): """ Checks if bit is set at the given position for flag. If flag is an array, an array will be returned. """ return flag & (1 << pos) > 0 # Extended header types for different NEV file specifications # Structure: {spec: {packet_id: data_type_definition}} NEV_EXT_HEADER_TYPES_BY_SPEC = { "2.1": { b"NEUEVWAV": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("digitization_factor", "uint16"), ("energy_threshold", "uint16"), ("hi_threshold", "int16"), ("lo_threshold", "int16"), ("nb_sorted_units", "uint8"), ("bytes_per_waveform", "uint8"), ("unused", "S10"), ], b"ARRAYNME": [("packet_id", "S8"), ("electrode_array_name", "S24")], b"ECOMMENT": [("packet_id", "S8"), ("extra_comment", "S24")], b"CCOMMENT": [("packet_id", "S8"), ("continued_comment", "S24")], b"MAPFILE": [("packet_id", "S8"), ("mapFile", "S24")], b"NSASEXEV": [ ("packet_id", "S8"), ("frequency", "uint16"), ("digital_input_config", "uint8"), ("analog_channel_1_config", "uint8"), ("analog_channel_1_edge_detec_val", "uint16"), ("analog_channel_2_config", "uint8"), ("analog_channel_2_edge_detec_val", "uint16"), ("analog_channel_3_config", "uint8"), ("analog_channel_3_edge_detec_val", "uint16"), ("analog_channel_4_config", "uint8"), ("analog_channel_4_edge_detec_val", "uint16"), ("analog_channel_5_config", "uint8"), ("analog_channel_5_edge_detec_val", "uint16"), ("unused", "S6"), ], }, "2.2": { b"NEUEVWAV": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("digitization_factor", "uint16"), ("energy_threshold", "uint16"), ("hi_threshold", "int16"), ("lo_threshold", "int16"), ("nb_sorted_units", "uint8"), ("bytes_per_waveform", "uint8"), ("spike_width", "uint16"), ("unused", "S8"), ], b"ARRAYNME": [("packet_id", "S8"), ("electrode_array_name", "S24")], b"ECOMMENT": [("packet_id", "S8"), ("extra_comment", "S24")], b"CCOMMENT": [("packet_id", "S8"), ("continued_comment", "S24")], b"MAPFILE": [("packet_id", "S8"), ("mapFile", "S24")], b"NEUEVLBL": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("label", "S16"), ("unused", "S6"), ], b"NEUEVFLT": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ("unused", "S2"), ], b"DIGLABEL": [ ("packet_id", "S8"), ("label", "S16"), ("mode", "uint8"), ("unused", "S7"), ], b"NSASEXEV": [ ("packet_id", "S8"), ("frequency", "uint16"), ("digital_input_config", "uint8"), ("analog_channel_1_config", "uint8"), ("analog_channel_1_edge_detec_val", "uint16"), ("analog_channel_2_config", "uint8"), ("analog_channel_2_edge_detec_val", "uint16"), ("analog_channel_3_config", "uint8"), ("analog_channel_3_edge_detec_val", "uint16"), ("analog_channel_4_config", "uint8"), ("analog_channel_4_edge_detec_val", "uint16"), ("analog_channel_5_config", "uint8"), ("analog_channel_5_edge_detec_val", "uint16"), ("unused", "S6"), ], }, "2.3": { b"NEUEVWAV": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("digitization_factor", "uint16"), ("energy_threshold", "uint16"), ("hi_threshold", "int16"), ("lo_threshold", "int16"), ("nb_sorted_units", "uint8"), ("bytes_per_waveform", "uint8"), ("spike_width", "uint16"), ("unused", "S8"), ], b"ARRAYNME": [("packet_id", "S8"), ("electrode_array_name", "S24")], b"ECOMMENT": [("packet_id", "S8"), ("extra_comment", "S24")], b"CCOMMENT": [("packet_id", "S8"), ("continued_comment", "S24")], b"MAPFILE": [("packet_id", "S8"), ("mapFile", "S24")], b"NEUEVLBL": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("label", "S16"), ("unused", "S6"), ], b"NEUEVFLT": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ("unused", "S2"), ], b"DIGLABEL": [ ("packet_id", "S8"), ("label", "S16"), ("mode", "uint8"), ("unused", "S7"), ], b"VIDEOSYN": [ ("packet_id", "S8"), ("video_source_id", "uint16"), ("video_source", "S16"), ("frame_rate", "float32"), ("unused", "S2"), ], b"TRACKOBJ": [ ("packet_id", "S8"), ("trackable_type", "uint16"), ("trackable_id", "uint16"), ("point_count", "uint16"), ("video_source", "S16"), ("unused", "S2"), ], b"NSASEXEV": [ ("packet_id", "S8"), ("frequency", "uint16"), ("digital_input_config", "uint8"), ("analog_channel_1_config", "uint8"), ("analog_channel_1_edge_detec_val", "uint16"), ("analog_channel_2_config", "uint8"), ("analog_channel_2_edge_detec_val", "uint16"), ("analog_channel_3_config", "uint8"), ("analog_channel_3_edge_detec_val", "uint16"), ("analog_channel_4_config", "uint8"), ("analog_channel_4_edge_detec_val", "uint16"), ("analog_channel_5_config", "uint8"), ("analog_channel_5_edge_detec_val", "uint16"), ("unused", "S6"), ], }, "3.0": { # Version 3.0 uses the same structure as 2.3 b"NEUEVWAV": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("digitization_factor", "uint16"), ("energy_threshold", "uint16"), ("hi_threshold", "int16"), ("lo_threshold", "int16"), ("nb_sorted_units", "uint8"), ("bytes_per_waveform", "uint8"), ("spike_width", "uint16"), ("unused", "S8"), ], b"ARRAYNME": [("packet_id", "S8"), ("electrode_array_name", "S24")], b"ECOMMENT": [("packet_id", "S8"), ("extra_comment", "S24")], b"CCOMMENT": [("packet_id", "S8"), ("continued_comment", "S24")], b"MAPFILE": [("packet_id", "S8"), ("mapFile", "S24")], b"NEUEVLBL": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("label", "S16"), ("unused", "S6"), ], b"NEUEVFLT": [ ("packet_id", "S8"), ("electrode_id", "uint16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ("unused", "S2"), ], b"DIGLABEL": [ ("packet_id", "S8"), ("label", "S16"), ("mode", "uint8"), ("unused", "S7"), ], b"VIDEOSYN": [ ("packet_id", "S8"), ("video_source_id", "uint16"), ("video_source", "S16"), ("frame_rate", "float32"), ("unused", "S2"), ], b"TRACKOBJ": [ ("packet_id", "S8"), ("trackable_type", "uint16"), ("trackable_id", "uint16"), ("point_count", "uint16"), ("video_source", "S16"), ("unused", "S2"), ], b"NSASEXEV": [ ("packet_id", "S8"), ("frequency", "uint16"), ("digital_input_config", "uint8"), ("analog_channel_1_config", "uint8"), ("analog_channel_1_edge_detec_val", "uint16"), ("analog_channel_2_config", "uint8"), ("analog_channel_2_edge_detec_val", "uint16"), ("analog_channel_3_config", "uint8"), ("analog_channel_3_edge_detec_val", "uint16"), ("analog_channel_4_config", "uint8"), ("analog_channel_4_edge_detec_val", "uint16"), ("analog_channel_5_config", "uint8"), ("analog_channel_5_edge_detec_val", "uint16"), ("unused", "S6"), ], }, } # Packet identifiers for different NEV file specifications # Used to create masks that filter raw data packets by their packet ID field. # Single values indicate equality check, tuples (min, max) indicate range check. # According to NEV spec: packet IDs < 32768 identify channels, IDs >= 32768 are system events. NEV_PACKET_IDENTIFIERS_BY_SPEC = { "2.1": { "NonNeural": 0, "Spikes": (1, 255), # Packet IDs in this range identify spike events on electrodes }, "2.2": { "NonNeural": 0, "Spikes": (1, 255), # Packet IDs in this range identify spike events on electrodes }, "2.3": { "NonNeural": 0, "Spikes": (1, 2048), # Packet IDs in this range identify spike events on electrodes "Comments": 0xFFFF, "VideoSync": 0xFFFE, "TrackingEvents": 0xFFFD, "ButtonTrigger": 0xFFFC, "ConfigEvent": 0xFFFB, }, "3.0": { "NonNeural": 0, "Spikes": (1, 2048), # Packet IDs in this range identify spike events on electrodes "Comments": 0xFFFF, "VideoSync": 0xFFFE, "TrackingEvents": 0xFFFD, "ButtonTrigger": 0xFFFC, "ConfigEvent": 0xFFFB, }, } # Data types for different NEV file specifications # Structure: {spec: {data_type: lambda function that returns dtype definition}} NEV_PACKET_DATA_TYPES_BY_SPEC = { "2.1": { "NonNeural": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("packet_insertion_reason", "uint8"), ("reserved", "uint8"), ("digital_input", "uint16"), ("analog_input_channel_1", "int16"), ("analog_input_channel_2", "int16"), ("analog_input_channel_3", "int16"), ("analog_input_channel_4", "int16"), ("analog_input_channel_5", "int16"), ("unused", f"S{packet_size_bytes - 20}"), ], "Spikes": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("unit_class_nb", "uint8"), ("reserved", "uint8"), ("waveform", f"S{packet_size_bytes - 8}"), ], }, "2.2": { "NonNeural": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("packet_insertion_reason", "uint8"), ("reserved", "uint8"), ("digital_input", "uint16"), ("analog_input_channel_1", "int16"), ("analog_input_channel_2", "int16"), ("analog_input_channel_3", "int16"), ("analog_input_channel_4", "int16"), ("analog_input_channel_5", "int16"), ("unused", f"S{packet_size_bytes - 20}"), ], "Spikes": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("unit_class_nb", "uint8"), ("reserved", "uint8"), ("waveform", f"S{packet_size_bytes - 8}"), ], }, "2.3": { "NonNeural": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("packet_insertion_reason", "uint8"), ("reserved", "uint8"), ("digital_input", "uint16"), ("unused", f"S{packet_size_bytes - 10}"), ], "Spikes": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("unit_class_nb", "uint8"), ("reserved", "uint8"), ("waveform", f"S{packet_size_bytes - 8}"), ], "Comments": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("char_set", "uint8"), ("flag", "uint8"), ("color", "uint32"), ("comment", f"S{packet_size_bytes - 12}"), ], "VideoSync": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("video_file_nb", "uint16"), ("video_frame_nb", "uint32"), ("video_elapsed_time", "uint32"), ("video_source_id", "uint32"), ("unused", "int8", (packet_size_bytes - 20,)), ], "TrackingEvents": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("parent_id", "uint16"), ("node_id", "uint16"), ("node_count", "uint16"), ("point_count", "uint16"), ("tracking_points", "uint16", ((packet_size_bytes - 14) // 2,)), ], "ButtonTrigger": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("trigger_type", "uint16"), ("unused", "int8", (packet_size_bytes - 8,)), ], "ConfigEvent": lambda packet_size_bytes: [ ("timestamp", "uint32"), ("packet_id", "uint16"), ("config_change_type", "uint16"), ("config_changed", f"S{packet_size_bytes - 8}"), ], }, "3.0": { "NonNeural": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("packet_insertion_reason", "uint8"), ("dlen", "uint8"), ("digital_input", "uint16"), ("unused", f"S{packet_size_bytes - 14}"), ], "Spikes": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("unit_class_nb", "uint8"), ("dlen", "uint8"), ("waveform", f"S{packet_size_bytes - 12}"), ], "Comments": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("char_set", "uint8"), ("flag", "uint8"), ("color", "uint32"), ("comment", f"S{packet_size_bytes - 16}"), ], "VideoSync": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("video_file_nb", "uint16"), ("video_frame_nb", "uint32"), ("video_elapsed_time", "uint32"), ("video_source_id", "uint32"), ("unused", "int8", (packet_size_bytes - 24,)), ], "TrackingEvents": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("parent_id", "uint16"), ("node_id", "uint16"), ("node_count", "uint16"), ("point_count", "uint16"), ("tracking_points", "uint16", ((packet_size_bytes - 18) // 2,)), ], "ButtonTrigger": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("trigger_type", "uint16"), ("unused", "int8", (packet_size_bytes - 12,)), ], "ConfigEvent": lambda packet_size_bytes: [ ("timestamp", "uint64"), ("packet_id", "uint16"), ("config_change_type", "uint16"), ("config_changed", f"S{packet_size_bytes - 12}"), ], }, } # Basic header types for different NSX file specifications NSX_BASIC_HEADER_TYPES = { "2.1": [ ("file_id", "S8"), ("label", "S16"), ("period", "uint32"), ("channel_count", "uint32"), ], "2.2": [ ("file_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8"), ("bytes_in_headers", "uint32"), ("label", "S16"), ("comment", "S256"), ("period", "uint32"), ("timestamp_resolution", "uint32"), ("year", "uint16"), ("month", "uint16"), ("weekday", "uint16"), ("day", "uint16"), ("hour", "uint16"), ("minute", "uint16"), ("second", "uint16"), ("millisecond", "uint16"), ("channel_count", "uint32"), ], "2.3": [ ("file_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8"), ("bytes_in_headers", "uint32"), ("label", "S16"), ("comment", "S256"), ("period", "uint32"), ("timestamp_resolution", "uint32"), ("year", "uint16"), ("month", "uint16"), ("weekday", "uint16"), ("day", "uint16"), ("hour", "uint16"), ("minute", "uint16"), ("second", "uint16"), ("millisecond", "uint16"), ("channel_count", "uint32"), ], "3.0": [ ("file_id", "S8"), ("ver_major", "uint8"), ("ver_minor", "uint8"), ("bytes_in_headers", "uint32"), ("label", "S16"), ("comment", "S256"), ("period", "uint32"), ("timestamp_resolution", "uint32"), ("year", "uint16"), ("month", "uint16"), ("weekday", "uint16"), ("day", "uint16"), ("hour", "uint16"), ("minute", "uint16"), ("second", "uint16"), ("millisecond", "uint16"), ("channel_count", "uint32"), ], } # Extended header types for different NSX file specifications NSX_EXT_HEADER_TYPES = { "2.1": [ ("electrode_id", "uint32"), ], "2.2": [ ("type", "S2"), ("electrode_id", "uint16"), ("electrode_label", "S16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("min_digital_val", "int16"), ("max_digital_val", "int16"), ("min_analog_val", "int16"), ("max_analog_val", "int16"), ("units", "S16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ], "2.3": [ ("type", "S2"), ("electrode_id", "uint16"), ("electrode_label", "S16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("min_digital_val", "int16"), ("max_digital_val", "int16"), ("min_analog_val", "int16"), ("max_analog_val", "int16"), ("units", "S16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ], "3.0": [ ("type", "S2"), ("electrode_id", "uint16"), ("electrode_label", "S16"), ("physical_connector", "uint8"), ("connector_pin", "uint8"), ("min_digital_val", "int16"), ("max_digital_val", "int16"), ("min_analog_val", "int16"), ("max_analog_val", "int16"), ("units", "S16"), ("hi_freq_corner", "uint32"), ("hi_freq_order", "uint32"), ("hi_freq_type", "uint16"), ("lo_freq_corner", "uint32"), ("lo_freq_order", "uint32"), ("lo_freq_type", "uint16"), ], } # NSX Data Header Types by specification version # These define the structure of data block headers within NSX files NSX_DATA_HEADER_TYPES = { # Version 2.1 has no data headers - data is stored continuously after the main header "2.1": None, # Versions 2.2+ use data block headers with timestamp size based on major version "2.2": [("header_flag", "uint8"), ("timestamp", "uint32"), ("nb_data_points", "uint32")], "2.3": [("header_flag", "uint8"), ("timestamp", "uint32"), ("nb_data_points", "uint32")], "3.0": [("header_flag", "uint8"), ("timestamp", "uint64"), ("nb_data_points", "uint32")], # PTP variant has a completely different structure with samples embedded "3.0-ptp": lambda channel_count: [ ("reserved", "uint8"), ("timestamps", "uint64"), ("num_data_points", "uint32"), ("samples", "int16", (channel_count,)), ], } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/brainvisionrawio.py0000644000175100017510000003001515154507213017672 0ustar00runnerrunner""" Class for reading data from BrainVision product. This code was originally made by L. Pezard (2010), modified B. Burle and S. More. Author: Samuel Garcia """ import os import re import numpy as np from .baserawio import ( BaseRawWithBufferApiIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from .utils import get_memmap_shape from neo.core import NeoReadWriteError class BrainVisionRawIO(BaseRawWithBufferApiIO): """Class for reading BrainVision files Parameters ---------- filename: str, default: '' The *.vhdr file to load Examples -------- >>> import neo.rawio >>> reader = neo.rawio.BrainVisionRawIO(filename=data_filename) """ extensions = ["vhdr"] rawmode = "one-file" def __init__(self, filename=""): BaseRawWithBufferApiIO.__init__(self) self.filename = str(filename) def _parse_header(self): # Read header file (vhdr) vhdr_header = read_brainvsion_soup(self.filename) bname = os.path.basename(self.filename) marker_filename = self.filename.replace(bname, vhdr_header["Common Infos"]["MarkerFile"]) binary_filename = self.filename.replace(bname, vhdr_header["Common Infos"]["DataFile"]) marker_filename = self._ensure_filename(marker_filename, "marker", "MarkerFile") binary_filename = self._ensure_filename(binary_filename, "data", "DataFile") if vhdr_header["Common Infos"]["DataFormat"] != "BINARY": raise NeoReadWriteError( f"Only `BINARY` format has been implemented. Current Data Format is {vhdr_header['Common Infos']['DataFormat']}" ) # Store the data orientation for later use in reading self._data_orientation = vhdr_header["Common Infos"]["DataOrientation"] if self._data_orientation not in ("MULTIPLEXED", "VECTORIZED"): raise NeoReadWriteError( f"Data orientation must be either `MULTIPLEXED` or `VECTORIZED`. Current Orientation is {self._data_orientation}" ) nb_channel = int(vhdr_header["Common Infos"]["NumberOfChannels"]) sr = 1.0e6 / float(vhdr_header["Common Infos"]["SamplingInterval"]) self._sampling_rate = sr fmt = vhdr_header["Binary Infos"]["BinaryFormat"] fmts = { "INT_16": np.int16, "INT_32": np.int32, "IEEE_FLOAT_32": np.float32, } if fmt not in fmts: raise NeoReadWriteError(f"the fmt {fmt} is not implmented. Must be one of {fmts}") sig_dtype = np.dtype(fmts[fmt]) stream_id = "0" buffer_id = "0" self._buffer_descriptions = {0: {0: {}}} self._stream_buffer_slice = {} # time_axis indicates data layout: 0 for MULTIPLEXED (time, channels), 1 for VECTORIZED (channels, time) time_axis = 0 if self._data_orientation == "MULTIPLEXED" else 1 # Get shape - always returns (num_samples, num_channels) temp_shape = get_memmap_shape(binary_filename, sig_dtype, num_channels=nb_channel, offset=0) # For consistency with HDF5 pattern: when time_axis=1, shape should be (channels, time) if time_axis == 1: shape = (temp_shape[1], temp_shape[0]) # (num_channels, num_samples) else: shape = temp_shape # (num_samples, num_channels) self._buffer_descriptions[0][0][buffer_id] = { "type": "raw", "file_path": binary_filename, "dtype": str(sig_dtype), "order": "C", "file_offset": 0, "shape": shape, "time_axis": time_axis, } self._stream_buffer_slice[stream_id] = None signal_buffers = np.array([("Signals", "0")], dtype=_signal_buffer_dtype) signal_streams = np.array([("Signals", "0", "0")], dtype=_signal_stream_dtype) sig_channels = [] channel_infos = vhdr_header["Channel Infos"] for c in range(nb_channel): try: channel_desc = channel_infos[f"Ch{c+1}"] except KeyError: channel_desc = channel_infos[f"ch{c + 1}"] # split up channel description, handling default values cds = channel_desc.split(",") name = cds[0] if len(cds) >= 2: ref = cds[1] else: ref = "" if len(cds) >= 3: res = cds[2] else: res = "1.0" if len(cds) == 4: units = cds[3] else: units = "u" units = units.replace("µ", "u") # Brainvision spec for specific unicode chan_id = str(c + 1) if sig_dtype == np.int16 or sig_dtype == np.int32: gain = float(res) else: gain = 1 offset = 0 stream_id = "0" buffer_id = "0" sig_channels.append( (name, chan_id, self._sampling_rate, sig_dtype, units, gain, offset, stream_id, buffer_id) ) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # read all markers in memory all_info = read_brainvsion_soup(marker_filename)["Marker Infos"] ev_types = [] ev_timestamps = [] ev_labels = [] for i in range(len(all_info)): ev_type, ev_label, pos, size, channel = all_info[f"Mk{i+1}"].split(",")[:5] ev_types.append(ev_type) ev_timestamps.append(int(pos)) ev_labels.append(ev_label) ev_types = np.array(ev_types) ev_timestamps = np.array(ev_timestamps) ev_labels = np.array(ev_labels, dtype="U") # group them by types self._raw_events = [] event_channels = [] for c, ev_type in enumerate(np.unique(ev_types)): ind = ev_types == ev_type event_channels.append((ev_type, "", "event")) self._raw_events.append((ev_timestamps[ind], ev_labels[ind])) event_channels = np.array(event_channels, dtype=_event_channel_dtype) # fille into header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = sig_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() if "Coordinates" in vhdr_header: sig_annotations = self.raw_annotations["blocks"][0]["segments"][0]["signals"][0] all_coords = [] for c in range(sig_channels.size): coords = vhdr_header["Coordinates"][f"Ch{c+1}"] all_coords.append([float(v) for v in coords.split(",")]) all_coords = np.array(all_coords) for dim in range(all_coords.shape[1]): sig_annotations["__array_annotations__"][f"coordinates_{dim}"] = all_coords[:, dim] def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): sig_size = self.get_signal_size(block_index, seg_index, 0) t_stop = sig_size / self._sampling_rate return t_stop ### def _get_signal_t_start(self, block_index, seg_index, stream_index): return 0.0 ### def _spike_count(self, block_index, seg_index, unit_index): return 0 ### # event and epoch zone def _event_count(self, block_index, seg_index, event_channel_index): all_timestamps, all_label = self._raw_events[event_channel_index] return all_timestamps.size def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): timestamps, labels = self._raw_events[event_channel_index] if t_start is not None: keep = timestamps >= int(t_start * self._sampling_rate) timestamps = timestamps[keep] labels = labels[keep] if t_stop is not None: keep = timestamps <= int(t_stop * self._sampling_rate) timestamps = timestamps[keep] labels = labels[keep] durations = None return timestamps, durations, labels raise (NotImplementedError) def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = event_timestamps.astype(dtype) / self._sampling_rate return event_times def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): return self._buffer_descriptions[block_index][seg_index][buffer_id] def _ensure_filename(self, filename, kind, entry_name): if not os.path.exists(filename): # file not found, subsequent import stage would fail ext = os.path.splitext(filename)[1] # Check if we can fall back to a file with the same prefix as the .vhdr. # This can happen when users rename their files but forget to edit the # .vhdr file to fix the path reference to the binary and marker files, # in which case import will fail. These files come in triples, like: # myfile.vhdr, myfile.eeg and myfile.vmrk; this code will thus pick # the next best alternative. alt_name = self.filename.replace(".vhdr", ext) if os.path.exists(alt_name): self.logger.warning( f"The {kind} file {filename} was not found, but found a file whose " f"prefix matched the .vhdr ({os.path.basename(alt_name)}). Using " f"this file instead." ) filename = alt_name else: # we neither found the file referenced in the .vhdr file nor a file of # same name as header with the desired extension; most likely a file went # missing or was renamed in an inconsistent fashion; generate a useful # error message header_dname = os.path.dirname(self.filename) header_bname = os.path.basename(self.filename) referenced_bname = os.path.basename(filename) alt_bname = os.path.basename(alt_name) if alt_bname != referenced_bname: # this is only needed when the two candidate file names differ detail = ( f" is named either as per the {entry_name}={referenced_bname} " f"line in the .vhdr file, or" ) else: # we omit it if we can to make it less confusing detail = "" self.logger.error( f"Did not find the {kind} file associated with .vhdr (header) " f"file {header_bname!r} in folder {header_dname!r}.\n Please make " f"sure the file{detail} is named the same way as the .vhdr file, but " f"ending in {ext} (i.e. {alt_bname}).\n The import will likely fail, " f"but if it goes through, you can ignore this message (the check " f"can misfire on networked file systems)." ) return filename def read_brainvsion_soup(filename): with open(filename, "r", encoding="utf8") as f: section = None all_info = {} for line in f: line = line.strip("\n").strip("\r") if line.startswith("["): section = re.findall(r"\[([\S ]+)\]", line)[0] all_info[section] = {} continue if line.startswith(";"): continue if "=" in line and len(line.split("=")) == 2: k, v = line.split("=") all_info[section][k] = v return all_info ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/cedrawio.py0000644000175100017510000002207115154507213016105 0ustar00runnerrunner""" Class for reading data from CED (Cambridge Electronic Design) http://ced.co.uk/ This reads *.smrx (and *.smr) from spike2 and signal software. Note Spike2RawIO/Spike2IO is the old implementation in neo. It still works without any dependency and should be faster. Spike2IO only works for smr (32 bit) and not for smrx (64 bit) files. This implementation depends on the SONPY package: https://pypi.org/project/sonpy/ Please note that the SONPY package: * is NOT open source * internally uses a list instead of numpy.ndarray, potentially causing slow data reading * is maintained by CED Author : Samuel Garcia """ import numpy as np from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class CedRawIO(BaseRawIO): """ Class for reading data from CED (Cambridge Electronic Design) spike2. Parameters ---------- filename: str, default: '' The .smr or .smrx file to load take_ideal_sampling_rate: bool, default: False If true use the `GetIdealRate` function from sonpy package Notes ----- * This internally uses the sonpy package which is closed source. * This IO reads smr and smrx files """ extensions = ["smr", "smrx"] rawmode = "one-file" def __init__( self, filename="", take_ideal_sampling_rate=False, ): BaseRawIO.__init__(self) self.filename = filename self.take_ideal_sampling_rate = take_ideal_sampling_rate def _source_name(self): return self.filename def _parse_header(self): import sonpy self.smrx_file = sonpy.lib.SonFile(sName=str(self.filename), bReadOnly=True) smrx = self.smrx_file self._time_base = smrx.GetTimeBase() channel_infos = [] signal_channels = [] spike_channels = [] self._all_spike_ticks = {} for chan_ind in range(smrx.MaxChannels()): chan_type = smrx.ChannelType(chan_ind) chan_id = str(chan_ind) if chan_type == sonpy.lib.DataType.Adc: physical_chan = smrx.PhysicalChannel(chan_ind) divide = smrx.ChannelDivide(chan_ind) if self.take_ideal_sampling_rate: sr = smrx.GetIdealRate(chan_ind) else: sr = 1.0 / (smrx.GetTimeBase() * divide) max_time = smrx.ChannelMaxTime(chan_ind) first_time = smrx.FirstTime(chan_ind, 0, max_time) # max_times is included so +1 time_size = (max_time - first_time) / divide + 1 channel_infos.append((first_time, max_time, divide, time_size, sr)) gain = smrx.GetChannelScale(chan_ind) / 6553.6 offset = smrx.GetChannelOffset(chan_ind) units = smrx.GetChannelUnits(chan_ind) ch_name = smrx.GetChannelTitle(chan_ind) dtype = "int16" # set later after grouping stream_id = "0" buffer_id = "" signal_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) elif chan_type == sonpy.lib.DataType.AdcMark: # spike and waveforms : only spike times is used here ch_name = smrx.GetChannelTitle(chan_ind) first_time = smrx.FirstTime(chan_ind, 0, max_time) max_time = smrx.ChannelMaxTime(chan_ind) divide = smrx.ChannelDivide(chan_ind) # here we don't use filter (sonpy.lib.MarkerFilter()) so we get all marker wave_marks = smrx.ReadWaveMarks(chan_ind, int(max_time / divide), 0, max_time) # here we load in memory all spike once because the access is really slow # with the ReadWaveMarks spike_ticks = np.array([t.Tick for t in wave_marks]) spike_codes = np.array([t.Code1 for t in wave_marks]) unit_ids = np.unique(spike_codes) for unit_id in unit_ids: name = f"{ch_name}#{unit_id}" spike_chan_id = f"ch{chan_id}#{unit_id}" spike_channels.append((name, spike_chan_id, "", 1, 0, 0, 0)) mask = spike_codes == unit_id self._all_spike_ticks[spike_chan_id] = spike_ticks[mask] signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) # channels are grouped into stream if they have a common start, stop, size, divide and sampling_rate channel_infos = np.array( channel_infos, dtype=[("first_time", "i8"), ("max_time", "i8"), ("divide", "i8"), ("size", "i8"), ("sampling_rate", "f8")], ) unique_info = np.unique(channel_infos) self.stream_info = unique_info signal_streams = [] for i, info in enumerate(unique_info): stream_id = str(i) mask = channel_infos == info signal_channels["stream_id"][mask] = stream_id num_chans = np.sum(mask) stream_name = f"{stream_id} {num_chans}chans" buffer_id = "" signal_streams.append((stream_name, stream_id, buffer_id)) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) # buffer is unknown because using a close source API signal_buffers = np.array([], dtype=_signal_buffer_dtype) # spike channels not handled spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # event channels not handled event_channels = [] event_channels = np.array(event_channels, dtype=_event_channel_dtype) self._seg_t_start = np.inf self._seg_t_stop = -np.inf for info in self.stream_info: self._seg_t_start = min(self._seg_t_start, info["first_time"] * self._time_base) self._seg_t_stop = max(self._seg_t_stop, info["max_time"] * self._time_base) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() def _segment_t_start(self, block_index, seg_index): return self._seg_t_start def _segment_t_stop(self, block_index, seg_index): return self._seg_t_stop def _get_signal_size(self, block_index, seg_index, stream_index): size = self.stream_info[stream_index]["size"] return size def _get_signal_t_start(self, block_index, seg_index, stream_index): info = self.stream_info[stream_index] t_start = info["first_time"] * self._time_base return t_start def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if i_start is None: i_start = 0 if i_stop is None: i_stop = self.stream_info[stream_index]["size"] stream_id = self.header["signal_streams"]["id"][stream_index] signal_channels = self.header["signal_channels"] mask = signal_channels["stream_id"] == stream_id signal_channels = signal_channels[mask] if channel_indexes is not None: signal_channels = signal_channels[channel_indexes] num_chans = len(signal_channels) size = i_stop - i_start sigs = np.zeros((size, num_chans), dtype="int16") info = self.stream_info[stream_index] t_from = info["first_time"] + info["divide"] * i_start t_upto = info["first_time"] + info["divide"] * i_stop for i, chan_id in enumerate(signal_channels["id"]): chan_ind = int(chan_id) sig = self.smrx_file.ReadInts(chan=chan_ind, nMax=size, tFrom=t_from, tUpto=t_upto) sigs[:, i] = sig return sigs def _spike_count(self, block_index, seg_index, unit_index): unit_id = self.header["spike_channels"][unit_index]["id"] spike_ticks = self._all_spike_ticks[unit_id] return spike_ticks.size def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): unit_id = self.header["spike_channels"][unit_index]["id"] spike_ticks = self._all_spike_ticks[unit_id] if t_start is not None: tick_start = int(t_start / self._time_base) spike_ticks = spike_ticks[spike_ticks >= tick_start] if t_stop is not None: tick_stop = int(t_stop / self._time_base) spike_ticks = spike_ticks[spike_ticks <= tick_stop] return spike_ticks def _rescale_spike_timestamp(self, spike_timestamps, dtype): spike_times = spike_timestamps.astype(dtype) spike_times *= self._time_base return spike_times def _get_spike_raw_waveforms(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/edfrawio.py0000644000175100017510000002724515154507213016120 0ustar00runnerrunner""" RawIO for reading EDF and EDF+ files using pyedflib PyEDFLib https://pyedflib.readthedocs.io https://github.com/holgern/pyedflib EDF Format Specifications: https://www.edfplus.info/ Author: Julia Sprenger """ import numpy as np try: from pyedflib import EdfReader HAS_PYEDF = True except ImportError: HAS_PYEDF = False from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class EDFRawIO(BaseRawIO): """ Class for reading European Data Format files (EDF and EDF+). Currently only continuous EDF+ files (EDF+C) and original EDF files (EDF) are supported Parameters ---------- filename: str, default: '' The EDF+C file or EDF file to be loaded Examples -------- >>> import neo.rawio >>> r = neo.rawio.EDFRawIO(filename='file.edf') >>> r.parse_header() >>> print(r) >>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=0, i_stop=1024, stream_index=0, channel_indexes=range(10)) >>> float_chunk = reader.rescale_signal_raw_to_float(raw_chunk, dtype='float64', channel_indexes=[0, 3, 6]) """ extensions = ["edf"] rawmode = "one-file" def __init__(self, filename=""): if not HAS_PYEDF: raise ValueError("Requires pyedflib") BaseRawIO.__init__(self) # note that this filename is used in self._source_name self.filename = filename self.signal_headers = [] self.edf_header = {} def _source_name(self): return self.filename def _parse_header(self): # read basic header with open(self.filename, "rb") as f: f.seek(192) file_version_header = f.read(44).decode("ascii") # only accepting basic EDF files (no 'EDF+' in header) # or continuous EDF+ files ('EDF+C' in header) if ("EDF+" in file_version_header) and ("EDF+C" not in file_version_header): raise ValueError("Only continuous EDF+ files are currently supported.") self._open_reader() # load headers, signal information and self.edf_header = self.edf_reader.getHeader() self.signal_headers = self.edf_reader.getSignalHeaders() # add annotations to header self._edf_annotations = self.edf_reader.readAnnotations() self.signal_annotations = [[s, d, a] for s, d, a in zip(*self._edf_annotations)] # 1 stream = 1 sampling rate stream_characteristics = [] self.stream_idx_to_chidx = {} signal_channels = [] for ch_idx, sig_dict in enumerate(self.signal_headers): ch_name = sig_dict["label"] chan_id = ch_idx # pyedf >= 0.1.39 uses sample_frequency, pyedf < 0.1.39 uses sample_rate sr = sig_dict.get("sample_frequency") or sig_dict.get("sample_rate") # Hz dtype = "int16" # assume general int16 based on edf documentation units = sig_dict["dimension"] physical_range = sig_dict["physical_max"] - sig_dict["physical_min"] # number of digital steps resolved (+1 to account for '0') digital_range = sig_dict["digital_max"] - sig_dict["digital_min"] + 1 gain = physical_range / digital_range offset = -1 * sig_dict["digital_min"] * gain + sig_dict["physical_min"] # identify corresponding stream based on sampling rate if (sr,) not in stream_characteristics: stream_characteristics += [(sr,)] stream_id = stream_characteristics.index((sr,)) self.stream_idx_to_chidx.setdefault(stream_id, []).append(ch_idx) buffer_id = "" signal_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) # convert channel index lists to arrays for indexing self.stream_idx_to_chidx = {k: np.asarray(v) for k, v in self.stream_idx_to_chidx.items()} signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) signal_streams = [(f"stream ({sr} Hz)", i, "") for i, sr in enumerate(stream_characteristics)] signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) # no buffer handling here signal_buffers = np.array([], dtype=_signal_buffer_dtype) # no unit/epoch information contained in edf spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = [] event_channels.append(("Event", "event_channel", "event")) event_channels.append(("Epoch", "epoch_channel", "epoch")) event_channels = np.array(event_channels, dtype=_event_channel_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] # we only accept continuous edf files self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() # Add custom annotations for neo objects bl_ann = self.raw_annotations["blocks"][0] bl_ann["name"] = "EDF Data Block" bl_ann.update(self.edf_header) seg_ann = bl_ann["segments"][0] seg_ann["name"] = "Seg #0 Block #0" # extract keys for array_annotations common to all signals and not already used ignore_annotations = [ "label", "dimension", "sample_rate", "sample_frequency", "physical_min", "physical_max", "digital_min", "digital_max", ] array_keys = [] for k in self.signal_headers[0]: if k not in ignore_annotations and all([k in h for h in self.signal_headers]): array_keys.append(k) for array_key in array_keys: array_anno = {array_key: [h[array_key] for h in self.signal_headers]} seg_ann["signals"].append({"__array_annotations__": array_anno}) # We store the following attributes for rapid access without needing the reader self._t_stop = self.edf_reader.datarecord_duration * self.edf_reader.datarecords_in_file # use sample count of first signal in stream self._stream_index_samples = { stream_index: self.edf_reader.getNSamples()[chidx][0] for stream_index, chidx in self.stream_idx_to_chidx.items() } self._number_of_events = len(self.edf_reader.readAnnotations()[0]) self.close() def _get_stream_channels(self, stream_index): return self.header["signal_channels"][self.stream_idx_to_chidx[stream_index]] def _segment_t_start(self, block_index, seg_index): # no time offset provided by EDF format return 0.0 # in seconds def _segment_t_stop(self, block_index, seg_index): # this must return an float scale in second return self._t_stop def _get_signal_size(self, block_index, seg_index, stream_index): return self._stream_index_samples[stream_index] def _get_signal_t_start(self, block_index, seg_index, stream_index): return 0.0 # EDF does not provide temporal offset information def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): # only dealing with single block and segment edf files if block_index != 0: raise ValueError("`block_index` must be 0") if seg_index != 0: raise ValueError("`seg_index` must be 0") stream_channel_idxs = self.stream_idx_to_chidx[stream_index] # keep all channels of the stream if none are selected if channel_indexes is None: channel_indexes = slice(None) if i_start is None: i_start = 0 if i_stop is None: i_stop = self.get_signal_size(block_index=block_index, seg_index=seg_index, stream_index=stream_index) n = i_stop - i_start # raw_signals = self.edf_reader. am[channel_indexes, i_start:i_stop] selected_channel_idxs = stream_channel_idxs[channel_indexes] # load data into numpy array buffer data = [] self._open_reader() for i, channel_idx in enumerate(selected_channel_idxs): # use int32 for compatibility with pyedflib buffer = np.empty(n, dtype=np.int32) self.edf_reader.read_digital_signal(channel_idx, i_start, n, buffer) data.append(buffer) self._close_reader() # downgrade to int16 as this is what is used in the edf file format # use fortran (column major) order to be more efficient after transposing data = np.asarray(data, dtype=np.int16, order="F") # use dimensions (time, channel) data = data.T return data def _spike_count(self, block_index, seg_index, spike_channel_index): return None def _get_spike_timestamps(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None def _rescale_spike_timestamp(self, spike_timestamps, dtype): return None def _get_spike_raw_waveforms(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None def _event_count(self, block_index, seg_index, event_channel_index): return self._number_of_events def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): # these time should be already in seconds timestamps, durations, labels = self._edf_annotations if t_start is None: t_start = self.segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self.segment_t_stop(block_index, seg_index) # only consider events and epochs that overlap with t_start t_stop range time_mask = ((t_start < timestamps) & (timestamps < t_stop)) | ( (t_start < (timestamps + durations)) & ((timestamps + durations) < t_stop) ) # separate event from epoch times event_mask = durations[time_mask] == 0 if self.header["event_channels"]["type"][event_channel_index] == b"epoch": event_mask = ~event_mask durations = durations[time_mask][event_mask] elif self.header["event_channels"]["type"][event_channel_index] == b"event": durations = None times = timestamps[time_mask][event_mask] labels = np.asarray(labels[time_mask][event_mask], dtype="U") return times, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): return np.asarray(event_timestamps, dtype=dtype) def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): return np.asarray(raw_duration, dtype=dtype) def _open_reader(self): self.edf_reader = EdfReader(self.filename) def __enter__(self): return self def __del__(self): self._close_reader() def __exit__(self, exc_type, exc_val, ex_tb): self._close_reader() def close(self): """ Closes the file handler """ self._close_reader() def _close_reader(self): if hasattr(self, "edf_reader"): self.edf_reader.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/elanrawio.py0000644000175100017510000002526515154507213016301 0ustar00runnerrunner""" Class for reading data from Elan. Elan is software for studying time-frequency maps of EEG data. Elan is developed in Lyon, France, at INSERM U821 https://elan.lyon.inserm.fr An Elan dataset is separated into 3 files : - .eeg raw data file - .eeg.ent header file - .eeg.pos event file Author: Samuel Garcia """ import datetime import re import pathlib import numpy as np from .baserawio import ( BaseRawWithBufferApiIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from .utils import get_memmap_shape from neo.core import NeoReadWriteError class ElanRawIO(BaseRawWithBufferApiIO): """ Class for reading time-frequency EEG data maps from the Elan software Parameters ---------- filename: str | None, default: None The raw data to load (.eeg) entfile: str | None, default: None The header file to load (.eeg.ent) posfile: str | None, deafult: None The event file to load (.eeg.pos) Examples -------- >>> import neo.rawio >>> reader = neo.rawio.ElanRawIO(filename='data.eeg', entfile='header.eeg.ent', posfile='events.eeg.pos') >>> reader.parse_header() >>> print(reader) """ extensions = ["eeg"] rawmode = "one-file" def __init__(self, filename=None, entfile=None, posfile=None): BaseRawWithBufferApiIO.__init__(self) self.filename = pathlib.Path(filename) # check whether ent and pos files are defined # keep existing suffixes in the process of ent and pos filename # generation if entfile is None: entfile = self.filename.with_suffix(self.filename.suffix + ".ent") if posfile is None: posfile = self.filename.with_suffix(self.filename.suffix + ".pos") self.entfile = entfile self.posfile = posfile def _parse_header(self): with open(self.entfile, mode="rt", encoding="ascii", newline=None) as f: # version version = f.readline()[:-1] if version not in ["V2", "V3"]: raise NeoReadWriteError(f"Reading is only possible for V2 or V3 .eeg.ent files. But {version=}") # info info1 = f.readline()[:-1] info2 = f.readline()[:-1] # strange 2 line for datetime # line1 line = f.readline() r1 = re.findall(r"(\d+)-(\d+)-(\d+) (\d+):(\d+):(\d+)", line) r2 = re.findall(r"(\d+):(\d+):(\d+)", line) r3 = re.findall(r"(\d+)-(\d+)-(\d+)", line) YY, MM, DD, hh, mm, ss = (None,) * 6 if len(r1) != 0: DD, MM, YY, hh, mm, ss = r1[0] elif len(r2) != 0: hh, mm, ss = r2[0] elif len(r3) != 0: DD, MM, YY = r3[0] # line2 line = f.readline() r1 = re.findall(r"(\d+)-(\d+)-(\d+) (\d+):(\d+):(\d+)", line) r2 = re.findall(r"(\d+):(\d+):(\d+)", line) r3 = re.findall(r"(\d+)-(\d+)-(\d+)", line) if len(r1) != 0: DD, MM, YY, hh, mm, ss = r1[0] elif len(r2) != 0: hh, mm, ss = r2[0] elif len(r3) != 0: DD, MM, YY = r3[0] try: fulldatetime = datetime.datetime(int(YY), int(MM), int(DD), int(hh), int(mm), int(ss)) except: fulldatetime = None line = f.readline() line = f.readline() line = f.readline() # sampling rate sample line = f.readline() self._sampling_rate = 1.0 / float(line) # nb channel line = f.readline() nb_channel = int(line) - 2 channel_infos = [{} for c in range(nb_channel + 2)] # channel label for c in range(nb_channel + 2): channel_infos[c]["label"] = f.readline()[:-1] # channel kind for c in range(nb_channel + 2): channel_infos[c]["kind"] = f.readline()[:-1] # channel unit for c in range(nb_channel + 2): channel_infos[c]["units"] = f.readline()[:-1] # range for gain and offset for c in range(nb_channel + 2): channel_infos[c]["min_physic"] = float(f.readline()[:-1]) for c in range(nb_channel + 2): channel_infos[c]["max_physic"] = float(f.readline()[:-1]) for c in range(nb_channel + 2): channel_infos[c]["min_logic"] = float(f.readline()[:-1]) for c in range(nb_channel + 2): channel_infos[c]["max_logic"] = float(f.readline()[:-1]) # info filter info_filter = [] for c in range(nb_channel + 2): channel_infos[c]["info_filter"] = f.readline()[:-1] n = int(round(np.log(channel_infos[0]["max_logic"] - channel_infos[0]["min_logic"]) / np.log(2)) / 8) sig_dtype = np.dtype(">i" + str(n)) # unique buffer and stream stream_id = "0" buffer_id = "0" signal_buffers = np.array([("Signals", buffer_id)], dtype=_signal_buffer_dtype) signal_streams = np.array([("Signals", stream_id, buffer_id)], dtype=_signal_stream_dtype) sig_channels = [] for c, chan_info in enumerate(channel_infos): chan_name = chan_info["label"] chan_id = str(c) if c < len(channel_infos) - 2: # standard channels stream_id = "0" else: # last 2 channels are not included in the stream stream_id = "" gain = (chan_info["max_physic"] - chan_info["min_physic"]) / ( chan_info["max_logic"] - chan_info["min_logic"] ) offset = -chan_info["min_logic"] * gain + chan_info["min_physic"] sig_channels.append( ( chan_name, chan_id, self._sampling_rate, sig_dtype, chan_info["units"], gain, offset, stream_id, buffer_id, ) ) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) # raw data self._buffer_descriptions = {0: {0: {}}} self._stream_buffer_slice = {} shape = get_memmap_shape(self.filename, sig_dtype, num_channels=nb_channel + 2, offset=0) self._buffer_descriptions[0][0][buffer_id] = { "type": "raw", "file_path": self.filename, "dtype": sig_dtype, "order": "C", "file_offset": 0, "shape": shape, } self._stream_buffer_slice["0"] = slice(0, -2) # triggers with open(self.posfile, mode="rt", encoding="ascii", newline=None) as f: self._raw_event_timestamps = [] self._event_labels = [] self._reject_codes = [] for line in f.readlines(): r = re.findall(r" *(\d+)\s* *(\d+)\s* *(\d+) *", line) self._raw_event_timestamps.append(int(r[0][0])) self._event_labels.append(str(r[0][1])) self._reject_codes.append(str(r[0][2])) self._raw_event_timestamps = np.array(self._raw_event_timestamps, dtype="int64") self._event_labels = np.array(self._event_labels, dtype="U") self._reject_codes = np.array(self._reject_codes, dtype="U") event_channels = [] event_channels.append(("Trigger", "", "event")) event_channels = np.array(event_channels, dtype=_event_channel_dtype) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # fille into header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = sig_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # insert some annotation at some place self._generate_minimal_annotations() extra_info = dict(rec_datetime=fulldatetime, elan_version=version, info1=info1, info2=info2) block_annotations = self.raw_annotations["blocks"][0] block_annotations.update(extra_info) seg_annotations = self.raw_annotations["blocks"][0]["segments"][0] seg_annotations.update(extra_info) sig_annotations = self.raw_annotations["blocks"][0]["segments"][0]["signals"][0] for key in ("info_filter", "kind"): values = [channel_infos[c][key] for c in range(nb_channel)] sig_annotations["__array_annotations__"][key] = np.array(values) event_annotations = self.raw_annotations["blocks"][0]["segments"][0]["events"][0] event_annotations["__array_annotations__"]["reject_codes"] = self._reject_codes def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): sig_size = self.get_signal_size(block_index, seg_index, 0) t_stop = sig_size / self._sampling_rate return t_stop def _get_signal_t_start(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index` must be 0") return 0.0 def _spike_count(self, block_index, seg_index, unit_index): return 0 def _event_count(self, block_index, seg_index, event_channel_index): return self._raw_event_timestamps.size def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): timestamp = self._raw_event_timestamps labels = self._event_labels durations = None if t_start is not None: keep = timestamp >= int(t_start * self._sampling_rate) timestamp = timestamp[keep] labels = labels[keep] if t_stop is not None: keep = timestamp <= int(t_stop * self._sampling_rate) timestamp = timestamp[keep] labels = labels[keep] return timestamp, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = event_timestamps.astype(dtype) / self._sampling_rate return event_times def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): return self._buffer_descriptions[block_index][seg_index][buffer_id] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/examplerawio.py0000644000175100017510000004633415154507213017015 0ustar00runnerrunner""" ExampleRawIO is a class of a fake example. This is to be used when coding a new RawIO. Rules for creating a new class: 1. Step 1: Create the main class * Create a file in **neo/rawio/** that ends with "rawio.py" * Create the class that inherits from BaseRawIO * copy/paste all methods that need to be implemented. * code hard! The main difficulty is `_parse_header()`. In short you have to create a mandatory dict that contains channel information:: self.header = {} self.header['nb_block'] = 2 self.header['nb_segment'] = [2, 3] self.header['signal_streams'] = signal_streams self.header['signal_channels'] = signal_channels self.header['spike_channels'] = spike_channels self.header['event_channels'] = event_channels 2. Step 2: RawIO test: * create a file in neo/rawio/tests with the same name with "test_" prefix * copy paste neo/rawio/tests/test_examplerawio.py and do the same 3. Step 3: Create the neo.io class with the wrapper * Create a file in neo/io/ that ends with "io.py" * Create a class that inherits both your RawIO class and the BaseFromRaw class * copy/paste from neo/io/exampleio.py 4.Step 4: IO test * create a file in neo/test/iotest with the same name as previously with "test_" prefix * copy/paste from neo/test/iotest/test_exampleio.py """ from __future__ import annotations import numpy as np from pathlib import Path from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class ExampleRawIO(BaseRawIO): """ Class for "reading" fake data from an imaginary file. For the user, it gives access to raw data (signals, event, spikes) as they are in the (fake) file int16 and int64. For a developer, it is just an example showing guidelines for someone who wants to develop a new IO module. Two rules for developers: * Respect the :ref:`section-neo-rawio-API` * Follow the guidelines in :ref:`section-raw-io-recipe` This fake IO: * has 2 blocks * blocks have 2 and 3 segments * has 2 signals streams of 8 channels each (sample_rate = 10000) so 16 channels in total * has 3 spike_channels * has 2 event channels: one has *type=event*, the other has *type=epoch* Usage: >>> import neo.rawio >>> r = neo.rawio.ExampleRawIO(filename='itisafake.nof') >>> r.parse_header() >>> print(r) >>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=0, i_stop=1024, channel_names=channel_names) >>> float_chunk = reader.rescale_signal_raw_to_float(raw_chunk, dtype='float64', channel_indexes=[0, 3, 6]) >>> spike_timestamp = reader.spike_timestamps(spike_channel_index=0, t_start=None, t_stop=None) >>> spike_times = reader.rescale_spike_timestamp(spike_timestamp, 'float64') >>> ev_timestamps, _, ev_labels = reader.event_timestamps(event_channel_index=0) """ extensions = ["fake"] rawmode = "one-file" def __init__(self, filename: str | Path = ""): BaseRawIO.__init__(self) # note that this filename is ued in self._source_name self.filename = filename def _source_name(self): # this function is used by __repr__ # for general cases self.filename is good # But for URL you could mask some part of the URL to keep # the main part. return self.filename def _parse_header(self): # This is the central part of a RawIO # we need to collect from the original format all # information required for fast access # at any place in the file # In short `_parse_header()` can be slow but # `_get_analogsignal_chunk()` needs to be as fast as possible # create simulated signal streams and buffer information # a buffer is a group of channels that are in the same IO buffer for instance hdf5 or binary that can mapped to with np.memmap : this is optional machinery but must be specified as an empty list if not present # a stream is a set channels that need to be grouped : this is mandatory # very often streams are similar to buffer signal_buffers = [] signal_streams = [] for c in range(2): stream_id = f"{c}" buffer_id = f"{c}" signal_buffers.append((f"buffer {c}", buffer_id)) signal_streams.append((f"stream {c}", stream_id, buffer_id)) signal_buffers = np.array(signal_buffers, dtype=_signal_buffer_dtype) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) # create fake signal channels information # This is mandatory!!!! # gain/offset/units are really important because # the scaling to real value will be done with that # The real signal will be evaluated as `(raw_signal * gain + offset) * pq.Quantity(units)` signal_channels = [] for c in range(16): ch_name = f"ch{c}" # our channel id is c+1 just for fun # Note that chan_id should be related to # original channel id in the file format # so that the end user should not be confused when reading datasets chan_id = c + 1 sr = 10000.0 # Hz dtype = "int16" units = "uV" gain = 1000.0 / 2**16 offset = 0.0 # stream_id indicates how to group channels # channels inside a "stream" share the same characteristics # (sampling rate/dtype/t_start/units/...) stream_id = str(c // 8) # buffer_id indicates optionally if channels are in the same buffer # this is optional. In case the buffer concept does not apply then buffer_id must be "" buffer_id = str(c // 8) signal_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) # A stream can contain signals with different physical units. # Here, the two last channels will have different units (pA) # Since AnalogSignals must have consistent units across channels, # this stream will be split in 2 parts on the neo.io level and finally 3 AnalogSignals # will be generated per Segment. signal_channels[-2:]["units"] = "pA" # create fake unit channels # This is mandatory!!!! # Note that if there is no waveform at all in the file # then wf_units/wf_gain/wf_offset/wf_left_sweep/wf_sampling_rate # can be set to any value because _spike_raw_waveforms # will return None spike_channels = [] for c in range(3): unit_name = f"unit{c}" unit_id = f"#{c}" wf_units = "uV" wf_gain = 1000.0 / 2**16 wf_offset = 0.0 wf_left_sweep = 20 wf_sampling_rate = 10000.0 spike_channels.append((unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # creating event/epoch channel # This is mandatory!!!! # In RawIO epoch and event are dealt with in the same way. event_channels = [] event_channels.append(("Some events", "ev_0", "event")) event_channels.append(("Some epochs", "ep_1", "epoch")) event_channels = np.array(event_channels, dtype=_event_channel_dtype) # fill information into the header dict # This is mandatory!!!!! self.header = {} self.header["nb_block"] = 2 self.header["nb_segment"] = [2, 3] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # insert some annotations/array_annotations at some place # at neo.io level. IOs can add annotations # to any object. To keep this functionality with the wrapper # BaseFromRaw you can add annotations in a nested dict. # `_generate_minimal_annotations()` must be called to generate the nested # dict of annotations/array_annotations self._generate_minimal_annotations() # this pprint lines really help with understanding the nested (and sometimes complicated) dict # from pprint import pprint # pprint(self.raw_annotations) # Until here all mandatory operations for setting up a rawio are implemented. # The following lines provide additional, recommended annotations for the # final neo objects. for block_index in range(2): bl_ann = self.raw_annotations["blocks"][block_index] bl_ann["name"] = f"Block #{block_index}" bl_ann["block_extra_info"] = f"This is the block {block_index}" for seg_index in range([2, 3][block_index]): seg_ann = bl_ann["segments"][seg_index] seg_ann["name"] = f"Seg #{seg_index} Block #{block_index}" seg_ann["seg_extra_info"] = f"This is the seg {seg_index} of block {block_index}" for c in range(2): sig_an = seg_ann["signals"][c]["nickname"] = f"This stream {c} is from a subdevice" # add some array annotations (8 channels) sig_an = seg_ann["signals"][c]["__array_annotations__"]["impedance"] = np.random.rand(8) * 10000 for c in range(3): spiketrain_an = seg_ann["spikes"][c] spiketrain_an["quality"] = "Good!!" # add some array annotations num_spikes = self.spike_count(block_index, seg_index, c) spiketrain_an["__array_annotations__"]["amplitudes"] = np.random.randn(num_spikes) for c in range(2): event_an = seg_ann["events"][c] if c == 0: event_an["nickname"] = "Miss Event 0" # add some array annotations num_ev = self.event_count(block_index, seg_index, c) event_an["__array_annotations__"]["button"] = ["A"] * num_ev elif c == 1: event_an["nickname"] = "MrEpoch 1" def _segment_t_start(self, block_index: int, seg_index: int): # this must return a float scaled in seconds # this t_start will be shared by all objects in the segment # except AnalogSignal all_starts = [[0.0, 15.0], [0.0, 20.0, 60.0]] return all_starts[block_index][seg_index] def _segment_t_stop(self, block_index: int, seg_index: int): # this must return a float scaled in seconds all_stops = [[10.0, 25.0], [10.0, 30.0, 70.0]] return all_stops[block_index][seg_index] def _get_signal_size(self, block_index: int, seg_index: int, stream_index: int): # We generate fake data in which the two stream signals have the same shape # across all segments (10.0 seconds) # This is not the case for real data, instead you should return the signal # size depending on the block_index and segment_index # this must return an int = the number of samples # Note that channel_indexes can be ignored for most cases # except for the case of several sampling rates. return 100000 def _get_signal_t_start(self, block_index: int, seg_index: int, stream_index: int): # This give the t_start of a signal. # Very often this is equal to _segment_t_start but not # always. # this must return a float scaled in seconds # Note that channel_indexes can be ignored for most cases # except for the case of several sampling rates. # Here this is the same. # this is not always the case return self._segment_t_start(block_index, seg_index) def _get_analogsignal_chunk( self, block_index: int, seg_index: int, i_start: int | None, i_stop: int | None, stream_index: int, channel_indexes: np.ndarray | list | slice | None, ): # this must return a signal chunk in a signal stream # limited with i_start/i_stop (can be None) # channel_indexes can be None (=all channel in the stream) or a list or numpy.array # This must return a numpy array 2D (even with one channel). # This must return the original dtype. No conversion here. # This must be as fast as possible. # To speed up this call all preparatory calculations should be implemented # in _parse_header(). # Here we are lucky: our signals are always zeros!! # it is not always the case :) # internally signals are int16 # conversion to real units is done with self.header['signal_channels'] if i_start is None: i_start = 0 if i_stop is None: i_stop = 100000 if i_start < 0 or i_stop > 100000: # some checks raise IndexError("I don't like your jokes") if channel_indexes is None: nb_chan = 8 elif isinstance(channel_indexes, slice): channel_indexes = np.arange(8, dtype="int")[channel_indexes] nb_chan = len(channel_indexes) else: channel_indexes = np.asarray(channel_indexes) if any(channel_indexes < 0): raise IndexError("bad boy") if any(channel_indexes >= 8): raise IndexError("big bad wolf") nb_chan = len(channel_indexes) raw_signals = np.zeros((i_stop - i_start, nb_chan), dtype="int16") return raw_signals def _spike_count(self, block_index: int, seg_index: int, spike_channel_index: int): # Must return the nb of spikes for given (block_index, seg_index, spike_channel_index) # we are lucky: our units have all the same nb of spikes!! # it is not always the case nb_spikes = 20 return nb_spikes def _get_spike_timestamps( self, block_index: int, seg_index: int, spike_channel_index: int, t_start: float | None, t_stop: float | None ): # In our IO, timestamp are internally coded 'int64' and they # represent the index of the signals 10kHz # we are lucky: spikes have the same discharge in all segments!! # incredible neuron!! This is not always the case # the same clip t_start/t_start must be used in _spike_raw_waveforms() ts_start = self._segment_t_start(block_index, seg_index) * 10000 spike_timestamps = np.arange(0, 10000, 500) + ts_start if t_start is not None or t_stop is not None: # restrict spikes to given limits (in seconds) lim0 = int(t_start * 10000) lim1 = int(t_stop * 10000) mask = (spike_timestamps >= lim0) & (spike_timestamps <= lim1) spike_timestamps = spike_timestamps[mask] return spike_timestamps def _rescale_spike_timestamp(self, spike_timestamps: np.ndarray, dtype: np.dtype): # must rescale to seconds, a particular spike_timestamps # with a fixed dtype so the user can choose the precision they want. spike_times = spike_timestamps.astype(dtype) spike_times /= 10000.0 # because 10kHz return spike_times def _get_spike_raw_waveforms( self, block_index: int, seg_index: int, spike_channel_index: int, t_start: float | None, t_stop: float | None ): # this must return a 3D numpy array (nb_spike, nb_channel, nb_sample) # in the original dtype # this must be as fast as possible. # the same clip t_start/t_start must be used in _spike_timestamps() # If there there is no waveform supported in the # IO them _spike_raw_waveforms must return None # In our IO waveforms come from all channels # they are int16 # conversion to real units is done with self.header['spike_channels'] # Here, we have a realistic case: all waveforms are only noise. # it is not always the case # we get 20 spikes with a sweep of 50 (5ms) # trick to get how many spike in the slice ts = self._get_spike_timestamps(block_index, seg_index, spike_channel_index, t_start, t_stop) nb_spike = ts.size np.random.seed(2205) # a magic number (my birthday) waveforms = np.random.randint(low=-(2**4), high=2**4, size=nb_spike * 50, dtype="int16") waveforms = waveforms.reshape(nb_spike, 1, 50) return waveforms def _event_count(self, block_index: int, seg_index: int, event_channel_index: int): # event and spike are very similar # we have 2 event channels if event_channel_index == 0: # event channel return 6 elif event_channel_index == 1: # epoch channel return 10 def _get_event_timestamps( self, block_index: int, seg_index: int, event_channel_index: int, t_start: float | None, t_stop: float | None ): # the main difference between spike channel and event channel # is that for event channels we have 3D numpy array (timestamp, durations, labels) where # durations must be None for 'event' # label must a dtype ='U' # in our IO events are directly coded in seconds seg_t_start = self._segment_t_start(block_index, seg_index) if event_channel_index == 0: timestamp = np.arange(0, 6, dtype="float64") + seg_t_start durations = None labels = np.array(["trigger_a", "trigger_b"] * 3, dtype="U12") elif event_channel_index == 1: timestamp = np.arange(0, 10, dtype="float64") + 0.5 + seg_t_start durations = np.ones((10), dtype="float64") * 0.25 labels = np.array(["zoneX"] * 5 + ["zoneZ"] * 5, dtype="U12") if t_start is not None: keep = timestamp >= t_start timestamp, labels = timestamp[keep], labels[keep] if durations is not None: durations = durations[keep] if t_stop is not None: keep = timestamp <= t_stop timestamp, labels = timestamp[keep], labels[keep] if durations is not None: durations = durations[keep] return timestamp, durations, labels def _rescale_event_timestamp(self, event_timestamps: np.ndarray, dtype: np.dtype, event_channel_index: int): # must rescale to seconds for a particular event_timestamps # with a fixed dtype so the user can choose the precision they want. # really easy here because in our case it is already in seconds event_times = event_timestamps.astype(dtype) return event_times def _rescale_epoch_duration(self, raw_duration: np.ndarray, dtype: np.dtype, event_channel_index: int): # really easy here because in our case it is already in seconds durations = raw_duration.astype(dtype) return durations ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/intanrawio.py0000644000175100017510000017403115154507213016467 0ustar00runnerrunner""" Support for intan tech rhd and rhs files. These 2 formats are more or less the same but: * some variance in headers. * rhs amplifier is more complex because the optional DC channel RHS supported version 1.0 RHD supported version 1.0 1.1 1.2 1.3 2.0 3.0, 3.1 RHD headerless binary support 3.x RHS headerless binary support 3.x See: * http://intantech.com/files/Intan_RHD2000_data_file_formats.pdf * http://intantech.com/files/Intan_RHS2000_data_file_formats.pdf Author: Samuel Garcia (Initial), Zach McKenzie & Heberto Mayorquin (Updates) """ from pathlib import Path from packaging.version import Version import warnings import numpy as np from neo.core import NeoReadWriteError from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class IntanRawIO(BaseRawIO): """ Class for reading rhd and rhs Intan data Parameters ---------- filename: str, default: '' name of the 'rhd' or 'rhs' data/header file ignore_integrity_checks: bool, default: False If True, data that violates integrity assumptions will be loaded. At the moment the only integrity check we perform is that timestamps are continuous. Setting this to True will ignore this check and set the attribute `discontinuous_timestamps` to True if the timestamps are not continous. This attribute can be checked after parsing the header to see if the timestamps are continuous or not. Notes ----- * The Intan reader can handle two file formats 'rhd' and 'rhs'. It will automatically check for the file extension and will gather the header information based on the extension. Additionally it functions with RHS v 1.0 and v 3.x and RHD 1.0, 1.1, 1.2, 1.3, 2.0, 3.x files. * The Intan reader can also handle the headerless binary formats 'one-file-per-signal' and 'one-file-per-channel' which have a header file called 'info.rhd' or 'info.rhs' and a series of binary files with the '.dat' suffix * Intan files contain amplifier channels labeled 'A', 'B' 'C' or 'D' for the 512 recorder or 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' for the 1024 recorder system depending on the port in which they were recorded along (stored in stream_id '0') with the following additional streams. 0: 'RHD2000 amplifier channel' 1: 'RHD2000 auxiliary input channel', 2: 'RHD2000 supply voltage channel', 3: 'USB board ADC input channel', 4: 'USB board digital input channel', 5: 'USB board digital output channel' And for RHS: 0: 'RHS2000 amplfier channel' 3: 'USB board ADC input channel', 4: 'USB board ADC output channel', 5: 'USB board digital input channel', 6: 'USB board digital output channel', 10: 'DC Amplifier channel', 11: 'Stim channel', * We currently implement digital data demultiplexing so that if digital streams are requested they are returned as arrays of 1s and 0s. * We also do stim data decoding which returns the stim data as an int16 of appropriate magnitude. Please use `rescale_signal_raw_to_float` to obtain stim data in amperes. Examples -------- >>> import neo.rawio >>> # for a header-attached file >>> reader = neo.rawio.IntanRawIO(filename='data.rhd') >>> # for the other formats we point to the info.rhd >>> reader = neo.rawioIntanRawIO(filename='info.rhd') >>> reader.parse_header() >>> raw_chunk = reader.get_analogsignal_chunk(block_index=0, seg_index=0 stream_index=0) >>> float_chunk = reader.rescale_signal_raw_to_float(raw_chunk, stream_index=0) """ extensions = ["rhd", "rhs", "dat"] rawmode = "one-file" def __init__(self, filename="", ignore_integrity_checks=False): BaseRawIO.__init__(self) self.filename = Path(filename) self.ignore_integrity_checks = ignore_integrity_checks self.discontinuous_timestamps = False def _source_name(self): return self.filename def _parse_header(self): self.filename = Path(self.filename) # Input checks if not self.filename.is_file(): raise FileNotFoundError(f"{self.filename} does not exist") if not self.filename.suffix in [".rhd", ".rhs"]: raise ValueError(f"{self.filename} is not a valid Intan file. Expected .rhd or .rhs extension") # see comment below for RHD which explains the division between file types if self.filename.suffix == ".rhs": if self.filename.name == "info.rhs": if any((self.filename.parent / file).exists() for file in stream_name_to_file_name_rhs.values()): self.file_format = "one-file-per-signal" raw_file_paths_dict = create_one_file_per_signal_dict_rhs(dirname=self.filename.parent) else: self.file_format = "one-file-per-channel" raw_file_paths_dict = create_one_file_per_channel_dict_rhs(dirname=self.filename.parent) else: self.file_format = "header-attached" ( self._global_info, self._ordered_channel_info, memmap_data_dtype, header_size, self._block_size, channel_number_dict, ) = read_rhs(self.filename, self.file_format) # 3 possibilities for rhd files, one combines the header and the data in the same file with suffix `rhd` while # the other two separates the data from the header which is always called `info.rhd` # attached to the actual binary file with data elif self.filename.suffix == ".rhd": if self.filename.name == "info.rhd": # first we have one-file-per-signal which is where one neo stream/file is saved as .dat files if any((self.filename.parent / file).exists() for file in stream_name_to_file_name_rhd.values()): self.file_format = "one-file-per-signal" raw_file_paths_dict = create_one_file_per_signal_dict_rhd(dirname=self.filename.parent) # then there is one-file-per-channel where each channel in a neo stream is in its own .dat file else: self.file_format = "one-file-per-channel" raw_file_paths_dict = create_one_file_per_channel_dict_rhd(dirname=self.filename.parent) # finally the format with the header-attached to the binary file as one giant file else: self.file_format = "header-attached" ( self._global_info, self._ordered_channel_info, memmap_data_dtype, header_size, self._block_size, channel_number_dict, ) = read_rhd(self.filename, self.file_format) ############################################# # memmap the raw data for each format type ############################################# # if header-attached there is one giant memory-map if self.file_format == "header-attached": self._raw_data = np.memmap(self.filename, dtype=memmap_data_dtype, mode="r", offset=header_size) # for 'one-file-per-signal' we have one memory map / neo stream # based on https://github.com/NeuralEnsemble/python-neo/issues/1556 bug in versions 0.13.1, .2, .3 elif self.file_format == "one-file-per-signal": self._raw_data = {} for stream_name, stream_dtype in memmap_data_dtype.items(): num_channels = channel_number_dict[stream_name] file_path = raw_file_paths_dict[stream_name] size_in_bytes = file_path.stat().st_size dtype_size = np.dtype(stream_dtype).itemsize n_samples = size_in_bytes // (dtype_size * num_channels) signal_stream_memmap = np.memmap( file_path, dtype=stream_dtype, mode="r", shape=(n_samples, num_channels), order="C", ) self._raw_data[stream_name] = signal_stream_memmap # for one-file-per-channel we have one memory map / channel stored as a list / neo stream elif self.file_format == "one-file-per-channel": self._raw_data = {} for stream_name, stream_dtype in memmap_data_dtype.items(): channel_file_paths = raw_file_paths_dict[stream_name] channel_memmap_list = [np.memmap(fp, dtype=stream_dtype, mode="r") for fp in channel_file_paths] self._raw_data[stream_name] = channel_memmap_list # Data Integrity checks # strictness of check is controlled by ignore_integrity_checks # which is set at __init__ self._assert_timestamp_continuity() # signals signal_channels = [] # This is used to calculate bit masks self.native_channel_order = {} for chan_info in self._ordered_channel_info: name = chan_info["custom_channel_name"] channel_id = chan_info["native_channel_name"] sig_dtype = chan_info["dtype"] stream_id = str(chan_info["signal_type"]) # buffer will be handled later bepending the format buffer_id = "" signal_channels.append( ( name, channel_id, chan_info["sampling_rate"], sig_dtype, chan_info["units"], chan_info["gain"], chan_info["offset"], stream_id, buffer_id, ) ) self.native_channel_order[channel_id] = chan_info["native_order"] signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) stream_ids = np.unique(signal_channels["stream_id"]) signal_streams = np.zeros(stream_ids.size, dtype=_signal_stream_dtype) buffer_ids = [] # we need to sort the data because the string of stream_index 10 is mis-sorted. signal_streams["id"] = sorted(stream_ids, key=lambda x: int(x)) for stream_index, stream_id in enumerate(signal_streams["id"]): if self.filename.suffix == ".rhd": name = stream_id_to_stream_name_rhd.get(stream_id, "") else: name = stream_id_to_stream_name_rhs.get(stream_id, "") signal_streams["name"][stream_index] = name # zach I need you help here if self.file_format == "header-attached": buffer_id = "" elif self.file_format == "one-file-per-signal": buffer_id = stream_id buffer_ids.append(buffer_id) elif self.file_format == "one-file-per-channel": buffer_id = "" signal_streams["buffer_id"][stream_index] = buffer_id # set buffer_id to channels if buffer_id != "": mask = signal_channels["stream_id"] == stream_id signal_channels["buffer_id"][mask] = buffer_id # depending the format we can have buffer_id or not signal_buffers = np.zeros(len(buffer_ids), dtype=_signal_buffer_dtype) signal_buffers["id"] = buffer_ids self._max_sampling_rate = np.max(signal_channels["sampling_rate"]) # if header is attached we need to incorporate our block size to get signal length if self.file_format == "header-attached": self._max_sigs_length = self._raw_data.size * self._block_size # for one-file-per-signal we just take the size which will give n_samples for each # signal stream and then we just take the longest one elif self.file_format == "one-file-per-signal": self._max_sigs_length = max([raw_data.size for raw_data in self._raw_data.values()]) # for one-file-per-channel we do the same as for one-file-per-signal, but since they # are in a list we just take the first channel in each list of channels else: self._max_sigs_length = max([raw_data[0].size for raw_data in self._raw_data.values()]) # No events event_channels = [] event_channels = np.array(event_channels, dtype=_event_channel_dtype) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # fill into header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # Extract annotations from the format self._generate_minimal_annotations() bl_annotations = self.raw_annotations["blocks"][0] seg_annotations = bl_annotations["segments"][0] for signal_annotation in seg_annotations["signals"]: # Add global annotations signal_annotation["intan_version"] = ( f"{self._global_info['major_version']}." f"{self._global_info['minor_version']}" ) global_keys_to_skip = [ "major_version", "minor_version", "sampling_rate", "magic_number", "reference_channel", ] global_keys_to_annotate = set(self._global_info.keys()) - set(global_keys_to_skip) for key in global_keys_to_annotate: signal_annotation[key] = self._global_info[key] reference_channel = self._global_info.get("reference_channel", None) # Following the pdf specification reference_channel = "hardware" if reference_channel == "n/a" else reference_channel # Add channel annotations array_annotations = signal_annotation["__array_annotations__"] channel_ids = array_annotations["channel_ids"] # TODO refactor ordered channel dict to make this easier # Use this to find which elements of the ordered channels correspond to the current signal signal_type = int(signal_annotation["stream_id"]) channel_info = next((info for info in self._ordered_channel_info if info["signal_type"] == signal_type)) channel_keys_to_skip = [ "signal_type", "custom_channel_name", "native_channel_name", "gain", "offset", "channel_enabled", "dtype", "units", "sampling_rate", ] channel_keys_to_annotate = set(channel_info.keys()) - set(channel_keys_to_skip) properties_dict = {key: [] for key in channel_keys_to_annotate} for channel_id in channel_ids: matching_info = next( info for info in self._ordered_channel_info if info["native_channel_name"] == channel_id ) for key in channel_keys_to_annotate: properties_dict[key].append(matching_info[key]) for key in channel_keys_to_annotate: array_annotations[key] = properties_dict[key] def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): t_stop = self._max_sigs_length / self._max_sampling_rate return t_stop def _get_signal_size(self, block_index, seg_index, stream_index): stream_name = self.header["signal_streams"][stream_index]["name"][:] if self.file_format == "header-attached": stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] channel_ids = signal_channels["id"] stream_is_digital = stream_name in digital_stream_names field_name = stream_name if stream_is_digital else channel_ids[0] size = self._raw_data[field_name].size # one-file-per-signal is (n_samples, n_channels) elif self.file_format == "one-file-per-signal": size = self._raw_data[stream_name].shape[0] # one-file-per-channel is (n_samples) so pull from list else: size = self._raw_data[stream_name][0].size return size def _get_signal_t_start(self, block_index, seg_index, stream_index): return 0.0 def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if i_start is None: i_start = 0 if i_stop is None: i_stop = self._get_signal_size(block_index, seg_index, stream_index) if channel_indexes is None: channel_indexes = slice(None) if self.file_format == "header-attached": sigs_chunk = self._get_analogsignal_chunk_header_attached( i_start, i_stop, stream_index, channel_indexes, ) elif self.file_format == "one-file-per-signal": sigs_chunk = self._get_analogsignal_chunk_one_file_per_signal( i_start, i_stop, stream_index, channel_indexes, ) else: sigs_chunk = self._get_analogsignal_chunk_one_file_per_channel( i_start, i_stop, stream_index, channel_indexes, ) return sigs_chunk def _get_analogsignal_chunk_header_attached(self, i_start, i_stop, stream_index, channel_indexes): stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] channel_ids = signal_channels["id"][channel_indexes] stream_name = self.header["signal_streams"][stream_index]["name"][:] stream_is_digital = stream_name in digital_stream_names stream_is_stim = stream_name == "Stim channel" field_name = stream_name if stream_is_digital else channel_ids[0] shape = self._raw_data[field_name].shape dtype = self._raw_data[field_name].dtype # This is False for Temperature and timestamps multiple_samples_per_block = len(shape) == 2 # In the header attached case the data for each channel comes interleaved in blocks # To avoid unecessary memory access we can calculate the blocks we need to access beforehand: if multiple_samples_per_block: block_size = shape[1] block_start = i_start // block_size block_stop = i_stop // block_size + 1 sl0 = i_start % block_size sl1 = sl0 + (i_stop - i_start) if not stream_is_digital: # For all streams raw_data is a structured memmap with a field for each channel_id sigs_chunk = np.zeros((i_stop - i_start, len(channel_ids)), dtype=dtype) for chunk_index, channel_id in enumerate(channel_ids): data_chan = self._raw_data[channel_id] if multiple_samples_per_block: sigs_chunk[:, chunk_index] = data_chan[block_start:block_stop].flatten()[sl0:sl1] else: sigs_chunk[:, chunk_index] = data_chan[i_start:i_stop] if stream_is_stim: sigs_chunk = self._decode_current_from_stim_data(sigs_chunk, 0, sigs_chunk.shape[0]) else: # For digital data the channels come interleaved in a single field so we need to demultiplex digital_raw_data = self._raw_data[field_name].flatten() sigs_chunk = self._demultiplex_digital_data(digital_raw_data, channel_ids, i_start, i_stop) return sigs_chunk def _get_analogsignal_chunk_one_file_per_channel(self, i_start, i_stop, stream_index, channel_indexes): stream_name = self.header["signal_streams"][stream_index]["name"][:] signal_data_memmap_list = self._raw_data[stream_name] stream_is_stim = stream_name == "Stim channel" channel_indexes_are_slice = isinstance(channel_indexes, slice) if channel_indexes_are_slice: num_channels = len(signal_data_memmap_list) start = channel_indexes.start or 0 stop = channel_indexes.stop or num_channels step = channel_indexes.step or 1 channel_indexes = range(start, stop, step) # We get the dtype from the first channel first_channel_index = channel_indexes[0] dtype = signal_data_memmap_list[first_channel_index].dtype sigs_chunk = np.zeros((i_stop - i_start, len(channel_indexes)), dtype=dtype) for chunk_index, channel_index in enumerate(channel_indexes): channel_memmap = signal_data_memmap_list[channel_index] sigs_chunk[:, chunk_index] = channel_memmap[i_start:i_stop] # If this is stim data, we need to decode the current values if stream_is_stim: sigs_chunk = self._decode_current_from_stim_data(sigs_chunk, 0, sigs_chunk.shape[0]) return sigs_chunk def _get_analogsignal_chunk_one_file_per_signal(self, i_start, i_stop, stream_index, channel_indexes): stream_name = self.header["signal_streams"][stream_index]["name"][:] raw_data = self._raw_data[stream_name] stream_is_digital = stream_name in digital_stream_names stream_is_stim = stream_name == "Stim channel" if stream_is_digital: stream_id = self.header["signal_streams"][stream_index]["id"] mask = self.header["signal_channels"]["stream_id"] == stream_id signal_channels = self.header["signal_channels"][mask] channel_ids = signal_channels["id"][channel_indexes] output = self._demultiplex_digital_data(raw_data, channel_ids, i_start, i_stop) elif stream_is_stim: output = self._decode_current_from_stim_data(raw_data, i_start, i_stop) output = output[:, channel_indexes] else: output = raw_data[i_start:i_stop, channel_indexes] return output def _demultiplex_digital_data(self, raw_digital_data, channel_ids, i_start, i_stop): """ Demultiplex digital data by extracting individual channel values from packed 16-bit format. According to the Intan format, digital input/output data is stored with all 16 channels encoded bit-by-bit in each 16-bit word. This method extracts the specified digital channels from the packed format into separate uint16 arrays of 0 and 1. Parameters ---------- raw_digital_data : ndarray Raw digital data in packed 16-bit format where each bit represents a different channel. channel_ids : list or array List of channel identifiers to extract. Each channel_id must correspond to a digital input or output channel. i_start : int Starting sample index for demultiplexing. i_stop : int Ending sample index for demultiplexing (exclusive). Returns ------- ndarray Demultiplexed digital data with shape (i_stop-i_start, len(channel_ids)), containing 0 or 1 values for each requested channel. Notes ----- In the Intan format, digital channels are packed into 16-bit words where each bit position corresponds to a specific channel number. For example, with digital inputs 0, 4, and 5 set high and the rest low, the 16-bit word would be 2^0 + 2^4 + 2^5 = 1 + 16 + 32 = 49. The native_order property for each channel corresponds to its bit position in the packed word. """ dtype = np.uint16 # We fix this to match the memmap dtype output = np.zeros((i_stop - i_start, len(channel_ids)), dtype=dtype) for channel_index, channel_id in enumerate(channel_ids): native_order = self.native_channel_order[channel_id] mask = 1 << native_order demultiplex_data = np.bitwise_and(raw_digital_data, mask) > 0 output[:, channel_index] = demultiplex_data[i_start:i_stop].flatten() return output def _decode_current_from_stim_data(self, raw_stim_data, i_start, i_stop): """ Demultiplex stimulation data by extracting current values from packed 16-bit format. According to the Intan RHS data format, stimulation current is stored in the lower 9 bits of each 16-bit word: 8 bits for magnitude and 1 bit for sign. The upper bits contain flags for compliance limit, charge recovery, and amplifier settle. Parameters ---------- raw_stim_data : ndarray Raw stimulation data in packed 16-bit format. i_start : int Starting sample index for demultiplexing. i_stop : int Ending sample index for demultiplexing (exclusive). Returns ------- ndarray Demultiplexed stimulation current values in amperes, preserving the original array dimensions. The output values need to be multiplied by the stim_step_size parameter (from header) to obtain the actual current in amperes. Notes ----- Bit structure of each 16-bit stimulation word: - Bits 0-7: Current magnitude - Bit 8: Sign bit (1 = negative current) - Bits 9-13: Unused (always zero) - Bit 14: Amplifier settle flag (1 = activated) - Bit 15: Charge recovery flag (1 = activated) - Bit 16 (MSB): Compliance limit flag (1 = limit reached) The actual current value in amperes is obtained by multiplying the output by the 'stim_step_size' parameter from the file header. These scaled values can be obtained with the `rescale_signal_raw_to_float` function. """ # Get the relevant portion of the data data = raw_stim_data[i_start:i_stop] # Extract current value (bits 0-8) magnitude = np.bitwise_and(data, 0xFF) sign_bit = np.bitwise_and(np.right_shift(data, 8), 0x01) # Shift right by 8 bits to get the sign bit # Apply sign to current values # We need to cast to int16 to handle negative values correctly # The max value of 8 bits is 255 so the casting is safe as there are non-negative values magnitude = magnitude.astype(np.int16) current = np.where(sign_bit == 1, -magnitude, magnitude) # Note: If needed, other flag bits could be extracted as follows: # compliance_flag = np.bitwise_and(np.right_shift(data, 15), 0x01).astype(bool) # Bit 16 (MSB) # charge_recovery_flag = np.bitwise_and(np.right_shift(data, 14), 0x01).astype(bool) # Bit 15 # amp_settle_flag = np.bitwise_and(np.right_shift(data, 13), 0x01).astype(bool) # Bit 14 # These could be returned as a structured array or dictionary if needed return current def get_intan_timestamps(self, i_start=None, i_stop=None): """ Retrieves the sample indices from the Intan raw data within a specified range. Note that sample indices are called timestamps in the Intan format but they are in fact just sample indices. This function extracts the sample index timestamps from Intan files, which represent relative time points in sample units (not absolute time). These indices can be particularly useful when working with recordings that have discontinuities. Parameters ---------- i_start : int, optional The starting index from which to retrieve sample indices. If None, starts from 0. i_stop : int, optional The stopping index up to which to retrieve sample indices (exclusive). If None, retrieves all available indices from i_start onward. Returns ------- timestamps : ndarray The flattened array of sample indices within the specified range. Notes ----- - Sample indices can be converted to seconds by dividing by the sampling rate of the amplifier stream. - The function automatically handles different file formats: * header-attached: Timestamps are extracted directly from the timestamp field * one-file-per-signal: Timestamps are read from the timestamp stream * one-file-per-channel: Timestamps are read from the first channel in the timestamp stream - When recordings have discontinuities (indicated by the `discontinuous_timestamps` attribute being True), these indices allow for proper temporal alignment of the data. """ if i_start is None: i_start = 0 # Get the timestamps based on file format if self.file_format == "header-attached": timestamps = self._raw_data["timestamp"] elif self.file_format == "one-file-per-signal": timestamps = self._raw_data["timestamp"] elif self.file_format == "one-file-per-channel": timestamps = self._raw_data["timestamp"][0] # TODO if possible ensure that timestamps memmaps are always of correct shape to avoid memory copy here. timestamps = timestamps.flatten() if timestamps.ndim > 1 else timestamps if i_stop is None: return timestamps[i_start:] else: return timestamps[i_start:i_stop] def _assert_timestamp_continuity(self): """ Asserts the continuity of timestamps in the data. This method verifies that the timestamps in the raw data are sequential, indicating a continuous recording. If discontinuities are found, a flag is set to indicate potential data integrity issues, and an error is raised unless `ignore_integrity_checks` is True. Raises ------ NeoReadWriteError If timestamps are not continuous and `ignore_integrity_checks` is False. The error message includes a table detailing the discontinuities found. """ # check timestamp continuity timestamps = self.get_intan_timestamps() discontinuous_timestamps = np.diff(timestamps) != 1 timestamps_are_not_contiguous = np.any(discontinuous_timestamps) if timestamps_are_not_contiguous: # Mark a flag that can be checked after parsing the header to see if the timestamps are continuous or not self.discontinuous_timestamps = True if not self.ignore_integrity_checks: error_msg = ( "\nTimestamps are not continuous, likely due to a corrupted file or inappropriate file merge.\n" "To open the file anyway, initialize the reader with `ignore_integrity_checks=True`.\n\n" "Discontinuities Found:\n" "+-----------------+-----------------+-----------------+-----------------------+\n" "| Discontinuity | Previous | Next | Time Difference |\n" "| Index | (Frames) | (Frames) | (Seconds) |\n" "+-----------------+-----------------+-----------------+-----------------------+\n" ) amplifier_sampling_rate = self._global_info["sampling_rate"] for discontinuity_index in np.where(discontinuous_timestamps)[0]: prev_ts = timestamps[discontinuity_index] next_ts = timestamps[discontinuity_index + 1] time_diff = (next_ts - prev_ts) / amplifier_sampling_rate error_msg += ( f"| {discontinuity_index + 1:>15,} | {prev_ts:>15,} | {next_ts:>15,} | {time_diff:>21.6f} |\n" ) error_msg += "+-----------------+-----------------+-----------------+-----------------------+\n" raise NeoReadWriteError(error_msg) ################################### # Header reading helper functions def read_qstring(f): """ Reads the optional notes included in the Intan RHX software Parameters ---------- f: BinaryIO The file object Returns ------- txt: str The string """ length = np.fromfile(f, dtype="uint32", count=1)[0] if length == 0xFFFFFFFF or length == 0: return "" txt = f.read(length).decode("utf-16") return txt def read_variable_header(f, header: list): """ Reads the information from the binary file for the header info dict Parameters ---------- f: BinaryIO The file object header: list[tuple] The list of header sections along with their associated dtype Returns ------- info: dict The dictionary containing the information contained in the header """ info = {} for field_name, field_type in header: if field_type == "QString": field_value = read_qstring(f) else: field_value = np.fromfile(f, dtype=field_type, count=1)[0] info[field_name] = field_value return info ############### # RHS ZONE rhs_global_header = [ ("magic_number", "uint32"), # 0xD69127AC ("major_version", "int16"), ("minor_version", "int16"), ("sampling_rate", "float32"), ("dsp_enabled", "int16"), ("actual_dsp_cutoff_frequency", "float32"), ("actual_lower_bandwidth", "float32"), ("actual_lower_settle_bandwidth", "float32"), ("actual_upper_bandwidth", "float32"), ("desired_dsp_cutoff_frequency", "float32"), ("desired_lower_bandwidth", "float32"), ("desired_lower_settle_bandwidth", "float32"), ("desired_upper_bandwidth", "float32"), ("notch_filter_mode", "int16"), ("desired_impedance_test_frequency", "float32"), ("actual_impedance_test_frequency", "float32"), ("amp_settle_mode", "int16"), ("charge_recovery_mode", "int16"), ("stim_step_size", "float32"), ("recovery_current_limit", "float32"), ("recovery_target_voltage", "float32"), ("note1", "QString"), ("note2", "QString"), ("note3", "QString"), ("dc_amplifier_data_saved", "int16"), ("board_mode", "int16"), ("ref_channel_name", "QString"), ("nb_signal_group", "int16"), ] rhs_signal_group_header = [ ("signal_group_name", "QString"), ("signal_group_prefix", "QString"), ("signal_group_enabled", "int16"), ("channel_num", "int16"), ("amplified_channel_num", "int16"), ] rhs_signal_channel_header = [ ("native_channel_name", "QString"), ("custom_channel_name", "QString"), ("native_order", "int16"), ("custom_order", "int16"), ("signal_type", "int16"), ("channel_enabled", "int16"), ("chip_channel_num", "int16"), ("command_stream", "int16"), ("board_stream_num", "int16"), ("spike_scope_trigger_mode", "int16"), ("spike_scope_voltage_thresh", "int16"), ("spike_scope_digital_trigger_channel", "int16"), ("spike_scope_digital_edge_polarity", "int16"), ("electrode_impedance_magnitude", "float32"), ("electrode_impedance_phase", "float32"), ] stream_id_to_stream_name_rhs = { "0": "RHS2000 amplifier channel", "3": "USB board ADC input channel", "4": "USB board ADC output channel", "5": "USB board digital input channel", "6": "USB board digital output channel", "10": "DC Amplifier channel", "11": "Stim channel", } def read_rhs(filename, file_format: str): BLOCK_SIZE = 128 # sample per block with open(filename, mode="rb") as f: global_info = read_variable_header(f, rhs_global_header) # We use signal_type in the header as stream_id in neo with the following # complications: for rhs files in the header-attaached stream_id 0 also # contains information for stream_id 10 and stream_id 11 so we need to # break these up. See notes throughout code base; for timestamps we always # force them to be the last stream_id. stream_names = stream_id_to_stream_name_rhs.values() stream_name_to_channel_info_list = {name: [] for name in stream_names} if not file_format == "header-attached": memmap_data_dtype = {name: [] for name in stream_names} for g in range(global_info["nb_signal_group"]): group_info = read_variable_header(f, rhs_signal_group_header) if bool(group_info["signal_group_enabled"]): for c in range(group_info["channel_num"]): chan_info = read_variable_header(f, rhs_signal_channel_header) if chan_info["signal_type"] in (1, 2): error_msg = ( "signal_type of 1 or 2 does not exist for rhs files. If you have an rhs file " "with these formats open an issue on the python-neo github page" ) raise NeoReadWriteError(error_msg) if bool(chan_info["channel_enabled"]): stream_id = str(chan_info["signal_type"]) stream_name = stream_id_to_stream_name_rhs[stream_id] stream_name_to_channel_info_list[stream_name].append(chan_info) # Build a dictionary with channel count special_cases_for_counting = [ "DC Amplifier channel", "Stim channel", ] names_to_count = [name for name in stream_names if name not in special_cases_for_counting] channel_number_dict = {name: len(stream_name_to_channel_info_list[name]) for name in names_to_count} # Each DC amplifier channel has a corresponding RHS2000 amplifier channel channel_number_dict["DC Amplifier channel"] = channel_number_dict["RHS2000 amplifier channel"] if file_format == "one-file-per-channel": # There is a way to shut off saving amplifier data and only keeping the DC amplifier or shutting off all amplifier file saving, # so we need to count the number of files we find instead of relying on the header. raw_file_paths_dict = create_one_file_per_channel_dict_rhs(dirname=filename.parent) channel_number_dict["Stim channel"] = len(raw_file_paths_dict["Stim channel"]) # Moreover, even if the amplifier channels are in the header their files are dropped channel_number_dict["RHS2000 amplifier channel"] = len(raw_file_paths_dict["RHS2000 amplifier channel"]) else: channel_number_dict["Stim channel"] = channel_number_dict["RHS2000 amplifier channel"] header_size = f.tell() sr = global_info["sampling_rate"] # construct dtype by re-ordering channels by types ordered_channel_info = [] if file_format == "header-attached": memmap_data_dtype = [("timestamp", "int32", BLOCK_SIZE)] else: memmap_data_dtype["timestamp"] = "int32" channel_number_dict["timestamp"] = 1 if channel_number_dict["RHS2000 amplifier channel"] > 0: for chan_info in stream_name_to_channel_info_list["RHS2000 amplifier channel"]: chan_info["sampling_rate"] = sr chan_info["units"] = "uV" chan_info["gain"] = 0.195 if file_format == "header-attached": chan_info["offset"] = -32768 * 0.195 else: chan_info["offset"] = 0.0 if file_format == "header-attached": chan_info["dtype"] = "uint16" else: chan_info["dtype"] = "int16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16", BLOCK_SIZE)] else: memmap_data_dtype["RHS2000 amplifier channel"] = "int16" if bool(global_info["dc_amplifier_data_saved"]): # if we have dc amp we need to grab the correct number of channels # the channel number is the same as the count for amplifier data for chan_info in stream_name_to_channel_info_list["RHS2000 amplifier channel"]: chan_info_dc = dict(chan_info) name = chan_info["native_channel_name"] chan_info_dc["native_channel_name"] = name + "_DC" chan_info_dc["custom_channel_name"] = chan_info_dc["custom_channel_name"] + "_DC" chan_info_dc["sampling_rate"] = sr chan_info_dc["units"] = "mV" chan_info_dc["gain"] = 19.23 chan_info_dc["offset"] = -512 * 19.23 chan_info_dc["signal_type"] = 10 # put it in another group chan_info_dc["dtype"] = "uint16" ordered_channel_info.append(chan_info_dc) if file_format == "header-attached": memmap_data_dtype += [(name + "_DC", "uint16", BLOCK_SIZE)] else: memmap_data_dtype["DC Amplifier channel"] = "uint16" # I can't seem to get stim files to generate for one-file-per-channel # so ideally at some point we need test data to confirm this is true # based on what Heberto and I read in the docs # Add stim channels for chan_info in stream_name_to_channel_info_list["RHS2000 amplifier channel"]: # stim channel presence is not indicated in the header so for some formats each amplifier channel has a stim channel, but for other formats this isn't the case. if file_format == "one-file-per-channel": # Some amplifier channels don't have a corresponding stim channel, # so we need to make sure we don't add channel info for stim channels that don't exist. # In this case, if the stim channel has no data, there won't be a file for it. stim_file_paths = raw_file_paths_dict["Stim channel"] amplifier_native_name = chan_info["native_channel_name"] stim_file_exists = any([amplifier_native_name in stim_file.stem for stim_file in stim_file_paths]) if not stim_file_exists: continue chan_info_stim = dict(chan_info) name = chan_info["native_channel_name"] chan_info_stim["native_channel_name"] = name + "_STIM" chan_info_stim["custom_channel_name"] = chan_info_stim["custom_channel_name"] + "_STIM" chan_info_stim["sampling_rate"] = sr chan_info_stim["units"] = "A" # Amps chan_info_stim["gain"] = global_info["stim_step_size"] chan_info_stim["offset"] = 0.0 chan_info_stim["signal_type"] = 11 # put it in another group chan_info_stim["dtype"] = "int16" # this change is due to bit decoding see note below ordered_channel_info.append(chan_info_stim) # Note that the data on disk is uint16 but the data is # then decoded as int16 so the chan_info is int16 if file_format == "header-attached": memmap_data_dtype += [(name + "_STIM", "uint16", BLOCK_SIZE)] else: memmap_data_dtype["Stim channel"] = "uint16" # No supply or aux for rhs files (ie no stream_id 1 and 2) # We have an error above that requests test files to help if the spec is changed # in the future. for stream_name in ["USB board ADC input channel", "USB board ADC output channel"]: for chan_info in stream_name_to_channel_info_list[stream_name]: chan_info["sampling_rate"] = sr chan_info["units"] = "V" chan_info["gain"] = 0.0003125 chan_info["offset"] = -32768 * 0.0003125 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16", BLOCK_SIZE)] else: memmap_data_dtype[stream_name] = "uint16" for stream_name in ["USB board digital input channel", "USB board digital output channel"]: for chan_info in stream_name_to_channel_info_list[stream_name]: chan_info["sampling_rate"] = sr # arbitrary units are used to indicate that Intan does not # store raw voltages but only the boolean TTL state chan_info["units"] = "a.u." chan_info["gain"] = 1.0 chan_info["offset"] = 0.0 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) # Note that all the channels are packed in one buffer, so the data type only needs to be added once if len(stream_name_to_channel_info_list[stream_name]) > 0: if file_format == "header-attached": memmap_data_dtype += [(stream_name, "uint16", BLOCK_SIZE)] elif file_format == "one-file-per-channel": memmap_data_dtype[stream_name] = "uint16" elif file_format == "one-file-per-signal": memmap_data_dtype[stream_name] = "uint16" # per discussion with Intan developers before version 3 of their software the 'notch_filter_mode' # was a request for postprocessing to be done in one of their scripts. From version 3+ the notch # filter is now applied to the data in realtime and only the post notched amplifier data is # saved. if global_info["notch_filter_mode"] == 2 and global_info["major_version"] >= 3: global_info["notch_filter"] = "60Hz" elif global_info["notch_filter_mode"] == 1 and global_info["major_version"] >= 3: global_info["notch_filter"] = "50Hz" else: global_info["notch_filter"] = False if not file_format == "header-attached": # filter out dtypes without any values memmap_data_dtype = {k: v for (k, v) in memmap_data_dtype.items() if len(v) > 0} channel_number_dict = {k: v for (k, v) in channel_number_dict.items() if v > 0} return global_info, ordered_channel_info, memmap_data_dtype, header_size, BLOCK_SIZE, channel_number_dict ############### # RHD ZONE rhd_global_header_base = [ ("magic_number", "uint32"), # 0xC6912702 ("major_version", "int16"), ("minor_version", "int16"), ] rhd_global_header_part1 = [ ("sampling_rate", "float32"), ("dsp_enabled", "int16"), ("actual_dsp_cutoff_frequency", "float32"), ("actual_lower_bandwidth", "float32"), ("actual_upper_bandwidth", "float32"), ("desired_dsp_cutoff_frequency", "float32"), ("desired_lower_bandwidth", "float32"), ("desired_upper_bandwidth", "float32"), ("notch_filter_mode", "int16"), ("desired_impedance_test_frequency", "float32"), ("actual_impedance_test_frequency", "float32"), ("note1", "QString"), ("note2", "QString"), ("note3", "QString"), ] rhd_global_header_v11 = [ ("num_temp_sensor_channels", "int16"), ] rhd_global_header_v13 = [ ("eval_board_mode", "int16"), ] rhd_global_header_v20 = [ ("reference_channel", "QString"), ] rhd_global_header_final = [ ("nb_signal_group", "int16"), ] rhd_signal_group_header = [ ("signal_group_name", "QString"), ("signal_group_prefix", "QString"), ("signal_group_enabled", "int16"), ("channel_num", "int16"), ("amplified_channel_num", "int16"), ] rhd_signal_channel_header = [ ("native_channel_name", "QString"), ("custom_channel_name", "QString"), ("native_order", "int16"), ("custom_order", "int16"), ("signal_type", "int16"), ("channel_enabled", "int16"), ("chip_channel_num", "int16"), ("board_stream_num", "int16"), ("spike_scope_trigger_mode", "int16"), ("spike_scope_voltage_thresh", "int16"), ("spike_scope_digital_trigger_channel", "int16"), ("spike_scope_digital_edge_polarity", "int16"), ("electrode_impedance_magnitude", "float32"), ("electrode_impedance_phase", "float32"), ] stream_id_to_stream_name_rhd = { "0": "RHD2000 amplifier channel", "1": "RHD2000 auxiliary input channel", "2": "RHD2000 supply voltage channel", "3": "USB board ADC input channel", "4": "USB board digital input channel", "5": "USB board digital output channel", } def read_rhd(filename, file_format: str): """Function for reading the rhd file header Parameters ---------- filename: str | Path The filename of the *.rhd file file_format: 'header-attached' | 'one-file-per-signal' | 'one-file-per-channel' Whether the header is included with the rest of the data ('header-attached') Or as a standalone file ('one-file-per-signal' or 'one-file-per-channel') """ with open(filename, mode="rb") as f: global_info = read_variable_header(f, rhd_global_header_base) version = Version(f"{global_info['major_version']}.{global_info['minor_version']}") # the header size depends on the version :-( header = list(rhd_global_header_part1) # make a copy if version >= Version("1.1"): header = header + rhd_global_header_v11 else: global_info["num_temp_sensor_channels"] = 0 if version >= Version("1.3"): header = header + rhd_global_header_v13 else: global_info["eval_board_mode"] = 0 if version >= Version("2.0"): header = header + rhd_global_header_v20 else: global_info["reference_channel"] = "" header = header + rhd_global_header_final global_info.update(read_variable_header(f, header)) # read channel group and channel header stream_names = stream_id_to_stream_name_rhd.values() stream_name_to_channel_info_list = {name: [] for name in stream_names} if not file_format == "header-attached": memmap_data_dtype = {name: [] for name in stream_names} memmap_data_dtype["timestamp"] = [] for g in range(global_info["nb_signal_group"]): group_info = read_variable_header(f, rhd_signal_group_header) if bool(group_info["signal_group_enabled"]): for c in range(group_info["channel_num"]): chan_info = read_variable_header(f, rhd_signal_channel_header) if bool(chan_info["channel_enabled"]): stream_id = str(chan_info["signal_type"]) stream_name = stream_id_to_stream_name_rhd[stream_id] stream_name_to_channel_info_list[stream_name].append(chan_info) channel_number_dict = {name: len(stream_name_to_channel_info_list[name]) for name in stream_names} header_size = f.tell() sr = global_info["sampling_rate"] # construct the data block dtype and reorder channels if version >= Version("2.0"): BLOCK_SIZE = 128 else: BLOCK_SIZE = 60 # 256 channels ordered_channel_info = [] if version >= Version("1.2"): if file_format == "header-attached": memmap_data_dtype = [("timestamp", "int32", BLOCK_SIZE)] else: memmap_data_dtype["timestamp"] = "int32" channel_number_dict["timestamp"] = 1 else: if file_format == "header-attached": memmap_data_dtype = [("timestamp", "uint32", BLOCK_SIZE)] else: memmap_data_dtype["timestamp"] = "uint32" channel_number_dict["timestamp"] = 1 for chan_info in stream_name_to_channel_info_list["RHD2000 amplifier channel"]: chan_info["sampling_rate"] = sr chan_info["units"] = "uV" chan_info["gain"] = 0.195 if file_format == "header-attached": chan_info["offset"] = -32768 * 0.195 chan_info["dtype"] = "uint16" else: chan_info["offset"] = 0.0 chan_info["dtype"] = "int16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16", BLOCK_SIZE)] else: memmap_data_dtype["RHD2000 amplifier channel"] = "int16" for chan_info in stream_name_to_channel_info_list["RHD2000 auxiliary input channel"]: chan_info["sampling_rate"] = sr / 4.0 chan_info["units"] = "V" chan_info["gain"] = 0.0000374 chan_info["offset"] = 0.0 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16", BLOCK_SIZE // 4)] else: memmap_data_dtype["RHD2000 auxiliary input channel"] = "uint16" for chan_info in stream_name_to_channel_info_list["RHD2000 supply voltage channel"]: chan_info["sampling_rate"] = sr / BLOCK_SIZE chan_info["units"] = "V" chan_info["gain"] = 0.0000748 chan_info["offset"] = 0.0 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16")] else: memmap_data_dtype["RHD2000 supply voltage channel"] = "uint16" # temperature is not an official channel in the header for i in range(global_info["num_temp_sensor_channels"]): name = f"temperature_{i}" chan_info = {"native_channel_name": name, "signal_type": 20} chan_info["sampling_rate"] = sr / BLOCK_SIZE chan_info["units"] = "Celsius" chan_info["gain"] = 0.001 chan_info["offset"] = 0.0 chan_info["dtype"] = "int16" ordered_channel_info.append(chan_info) memmap_data_dtype += [(name, "int16")] # 3: USB board ADC input channel for chan_info in stream_name_to_channel_info_list["USB board ADC input channel"]: chan_info["sampling_rate"] = sr chan_info["units"] = "V" if global_info["eval_board_mode"] == 0: chan_info["gain"] = 0.000050354 chan_info["offset"] = 0.0 elif global_info["eval_board_mode"] == 1: chan_info["gain"] = 0.00015259 chan_info["offset"] = -32768 * 0.00015259 elif global_info["eval_board_mode"] == 13: chan_info["gain"] = 0.0003125 chan_info["offset"] = -32768 * 0.0003125 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) if file_format == "header-attached": name = chan_info["native_channel_name"] memmap_data_dtype += [(name, "uint16", BLOCK_SIZE)] else: memmap_data_dtype["USB board ADC input channel"] = "uint16" for stream_name in ["USB board digital input channel", "USB board digital output channel"]: for chan_info in stream_name_to_channel_info_list[stream_name]: chan_info["sampling_rate"] = sr # arbitrary units are used to indicate that Intan does not # store raw voltages but only the boolean TTL state chan_info["units"] = "a.u." chan_info["gain"] = 1.0 chan_info["offset"] = 0.0 chan_info["dtype"] = "uint16" ordered_channel_info.append(chan_info) # Note that all the channels are packed in one buffer, so the data type only needs to be added once if len(stream_name_to_channel_info_list[stream_name]) > 0: if file_format == "header-attached": memmap_data_dtype += [(stream_name, "uint16", BLOCK_SIZE)] elif file_format == "one-file-per-channel": memmap_data_dtype[stream_name] = "uint16" elif file_format == "one-file-per-signal": memmap_data_dtype[stream_name] = "uint16" # per discussion with Intan developers before version 3 of their software the 'notch_filter_mode' # was a request for postprocessing to be done in one of their scripts. From version 3+ the notch # filter is now applied to the data in realtime and only the post notched amplifier data is # saved. if global_info["notch_filter_mode"] == 2 and version >= Version("3.0"): global_info["notch_filter"] = "60Hz" elif global_info["notch_filter_mode"] == 1 and version >= Version("3.0"): global_info["notch_filter"] = "50Hz" else: global_info["notch_filter"] = False if not file_format == "header-attached": # filter out dtypes without any values memmap_data_dtype = {k: v for (k, v) in memmap_data_dtype.items() if len(v) > 0} channel_number_dict = {k: v for (k, v) in channel_number_dict.items() if v > 0} return global_info, ordered_channel_info, memmap_data_dtype, header_size, BLOCK_SIZE, channel_number_dict ########################################################################## # RHX Zone for Binary Files (note this is for version 3+ of Intan software) # This section provides all possible headerless binary files in both the rhs and rhd # formats. digital_stream_names = ["USB board digital input channel", "USB board digital output channel"] stream_name_to_file_name_rhd = { "RHD2000 amplifier channel": "amplifier.dat", "RHD2000 auxiliary input channel": "auxiliary.dat", "RHD2000 supply voltage channel": "supply.dat", "USB board ADC input channel": "analogin.dat", "USB board digital input channel": "digitalin.dat", "USB board digital output channel": "digitalout.dat", } def create_one_file_per_signal_dict_rhd(dirname): """Function for One File Per Signal Type Parameters ---------- dirname: pathlib.Path The folder to explore Returns ------- raw_files_paths_dict: dict A dict of all the file paths """ raw_file_paths_dict = {} for stream_name, file_name in stream_name_to_file_name_rhd.items(): if Path(dirname / file_name).is_file(): raw_file_paths_dict[stream_name] = Path(dirname / file_name) raw_file_paths_dict["timestamp"] = Path(dirname / "time.dat") return raw_file_paths_dict stream_name_to_file_name_rhs = { "RHS2000 amplifier channel": "amplifier.dat", "RHS2000 auxiliary input channel": "auxiliary.dat", "RHS2000 supply voltage channel": "supply.dat", "USB board ADC input channel": "analogin.dat", "USB board ADC output channel": "analogout.dat", "USB board digital input channel": "digitalin.dat", "USB board digital output channel": "digitalout.dat", } def create_one_file_per_signal_dict_rhs(dirname): """Function for One File Per Signal Type Parameters ---------- dirname: pathlib.Path The folder to explore Returns ------- raw_files_paths_dict: dict A dict of all the file paths """ raw_file_paths_dict = {} for stream_name, file_name in stream_name_to_file_name_rhs.items(): if Path(dirname / file_name).is_file(): raw_file_paths_dict[stream_name] = Path(dirname / file_name) raw_file_paths_dict["timestamp"] = Path(dirname / "time.dat") if Path(dirname / "dcamplifier.dat").is_file(): raw_file_paths_dict["DC Amplifier channel"] = Path(dirname / "dcamplifier.dat") if Path(dirname / "stim.dat").is_file(): raw_file_paths_dict["Stim channel"] = Path(dirname / "stim.dat") return raw_file_paths_dict # RHD Binary Files for One File Per Channel stream_name_to_file_prefix_rhd = { "RHD2000 amplifier channel": "amp", "RHD2000 auxiliary input channel": "aux", "RHD2000 supply voltage channel": "vdd", "USB board ADC input channel": "board-ANALOG-IN", "USB board digital input channel": "board-DIGITAL-IN", "USB board digital output channel": "board-DIGITAL-OUT", } def create_one_file_per_channel_dict_rhd(dirname): """Utility function for One File Per Channel Parameters ---------- dirname: pathlib.Path The folder to explore Returns ------- raw_files_paths_dict: dict A dict of all the file paths """ # Developer note, at the moment, the channels require to be n in order # See https://github.com/NeuralEnsemble/python-neo/issues/1599 fo file_names = dirname.glob("**/*.dat") files = [file for file in file_names if file.is_file()] raw_file_paths_dict = {} for stream_name, file_prefix in stream_name_to_file_prefix_rhd.items(): stream_files = [file for file in files if file_prefix in file.name] # Map file name to channel name amp-A-000.dat -> A-000 amp-B-006.dat -> B-006 etc file_path_to_channel_name = lambda x: "-".join(x.stem.split("-")[1:]) sorted_stream_files = sorted(stream_files, key=file_path_to_channel_name) raw_file_paths_dict[stream_name] = sorted_stream_files raw_file_paths_dict["timestamp"] = [Path(dirname / "time.dat")] return raw_file_paths_dict # RHS Binary Files for One File Per Channel stream_name_to_file_prefix_rhs = { "RHS2000 amplifier channel": "amp", "RHS2000 auxiliary input channel": "aux", "RHS2000 supply voltage channel": "vdd", "USB board ADC input channel": "board-ANALOG-IN", "USB board ADC output channel": "board-ANALOG-OUT", "USB board digital input channel": "board-DIGITAL-IN", "USB board digital output channel": "board-DIGITAL-OUT", } def create_one_file_per_channel_dict_rhs( dirname, ): """Utility function for One File Per Channel Parameters ---------- dirname: pathlib.Path The folder to explore Returns ------- raw_files_paths_dict: dict A dict of all the file paths """ file_names = dirname.glob("**/*.dat") files = [file for file in file_names if file.is_file()] raw_file_paths_dict = {} for stream_name, file_prefix in stream_name_to_file_prefix_rhs.items(): stream_files = [file for file in files if file_prefix in file.name] # Map file name to channel name amp-A-000.dat -> A-000 amp-B-006.dat -> B-006 etc file_path_to_channel_name = lambda x: "-".join(x.stem.split("-")[1:]) sorted_stream_files = sorted(stream_files, key=file_path_to_channel_name) raw_file_paths_dict[stream_name] = sorted_stream_files raw_file_paths_dict["timestamp"] = [Path(dirname / "time.dat")] raw_file_paths_dict["DC Amplifier channel"] = [file for file in files if "dc-" in file.name] # we can find the files, but I can see how to read them out of header # so for now we don't expose the stim files in one-file-per-channel raw_file_paths_dict["Stim channel"] = [file for file in files if "stim-" in file.name] return raw_file_paths_dict ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/maxwellrawio.py0000644000175100017510000002631415154507213017027 0ustar00runnerrunner""" Class for reading data from maxwell biosystem device: * MaxOne * MaxTwo https://www.mxwbio.com/resources/mea/ The implementation is a mix between: * the implementation in spikeextractors https://github.com/SpikeInterface/spikeextractors/blob/master/spikeextractors/extractors/maxwellextractors/maxwellextractors.py * the implementation in spyking-circus https://github.com/spyking-circus/spyking-circus/blob/master/circus/files/maxwell.py This implementation does not handle spikes at the moment. For maxtwo device, each well will be a different signal stream. Author : Samuel Garcia, Alessio Buccino, Pierre Yger """ import os from pathlib import Path import platform import warnings from urllib.request import urlopen import numpy as np from .baserawio import ( BaseRawWithBufferApiIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from neo.core import NeoReadWriteError class MaxwellRawIO(BaseRawWithBufferApiIO): """ Class for reading MaxOne or MaxTwo files. Parameters ---------- filename: str, default: '' The .h5 file to be loaded rec_name: str | None, default: None If the file has multiple recordings, specify the one to read. For 24-well plates, the rec_name needs to be specified since different well rows generate different recording ids. E.g., rec0001, rec0002, etc. """ extensions = ["h5"] rawmode = "one-file" def __init__(self, filename="", rec_name=None): BaseRawWithBufferApiIO.__init__(self) self.filename = filename self.rec_name = rec_name def _source_name(self): return self.filename def _parse_header(self): import h5py h5file = h5py.File(self.filename, mode="r") self.h5_file = h5file version = h5file["version"][0].decode() # create signal stream # one stream per well signal_streams = [] if int(version) == 20160704: self._old_format = True signal_streams.append(("well000", "well000", "well000")) elif int(version) > 20160704: # multi stream stream (one well is one stream) self._old_format = False well_ids = list(h5file["wells"].keys()) unique_rec_names = [] for well_name in well_ids: rec_names = list(h5file["wells"][well_name].keys()) for rec_name in rec_names: unique_rec_names.append(rec_name) # check consistency of rec_names unique_rec_names = np.unique(unique_rec_names) if len(unique_rec_names) > 1: if self.rec_name is None: raise ValueError( f"Detected multiple recording IDs across wells. " f"Please select a single recording using the `rec_name` parameter. " f"Possible rec_names: {unique_rec_names}" ) else: if self.rec_name not in unique_rec_names: raise NeoReadWriteError(f"rec_name {self.rec_name} not found") else: self.rec_name = unique_rec_names[0] # add streams that contain the selected rec_name for well_name in well_ids: rec_names = list(h5file["wells"][well_name].keys()) if self.rec_name in rec_names: signal_streams.append((well_name, well_name, well_name)) else: raise NotImplementedError(f"This version {version} is not supported") signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) # one stream per buffer signal_buffers = np.zeros(signal_streams.size, dtype=_signal_buffer_dtype) signal_buffers["id"] = signal_streams["id"] signal_buffers["name"] = signal_streams["name"] # create signal channels max_sig_length = 0 self._buffer_descriptions = {0: {0: {}}} self._stream_buffer_slice = {} sig_channels = [] well_indices_to_remove = [] for stream_index, stream_id in enumerate(signal_streams["id"]): if int(version) == 20160704: sr = 20000.0 settings = h5file["settings"] if "lsb" in settings: gain_uV = settings["lsb"][0] * 1e6 else: if "gain" not in settings: print("'gain' amd 'lsb' not found in settings. " "Setting gain to 512 (default)") gain = 512 else: gain = settings["gain"][0] gain_uV = 3.3 / (1024 * gain) * 1e6 hdf5_path = "sig" mapping = h5file["mapping"] ids = np.array(mapping["channel"]) ids = ids[ids >= 0] self._stream_buffer_slice[stream_id] = ids elif int(version) > 20160704: settings = h5file["wells"][stream_id][self.rec_name]["settings"] sr = settings["sampling"][0] if "lsb" in settings: gain_uV = settings["lsb"][0] * 1e6 else: if "gain" not in settings: print("'gain' amd 'lsb' not found in settings. " "Setting gain to 512 (default)") gain = 512 else: gain = settings["gain"][0] gain_uV = 3.3 / (1024 * gain) * 1e6 mapping = settings["mapping"] if "routed" in h5file["wells"][stream_id][self.rec_name]["groups"]: hdf5_path = f"/wells/{stream_id}/{self.rec_name}/groups/routed/raw" else: warnings.warn(f"No 'routed' group found for well {stream_id}") well_indices_to_remove.append(stream_index) continue self._stream_buffer_slice[stream_id] = None buffer_id = stream_id shape = h5file[hdf5_path].shape self._buffer_descriptions[0][0][buffer_id] = { "type": "hdf5", "file_path": str(self.filename), "hdf5_path": hdf5_path, "shape": shape, "time_axis": 1, } self._stream_buffer_slice[stream_id] = slice(None) channel_ids = np.array(mapping["channel"]) electrode_ids = np.array(mapping["electrode"]) mask = channel_ids >= 0 channel_ids = channel_ids[mask] electrode_ids = electrode_ids[mask] for i, chan_id in enumerate(channel_ids): elec_id = electrode_ids[i] ch_name = f"ch{chan_id} elec{elec_id}" offset_uV = 0 buffer_id = stream_id sig_channels.append( (ch_name, str(chan_id), sr, "uint16", "uV", gain_uV, offset_uV, stream_id, buffer_id) ) max_sig_length = max(max_sig_length, shape[1]) self._t_stop = max_sig_length / sr if len(well_indices_to_remove) > 0: signal_streams = np.delete(signal_streams, np.array(well_indices_to_remove)) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = [] event_channels = np.array(event_channels, dtype=_event_channel_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = sig_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() bl_ann = self.raw_annotations["blocks"][0] bl_ann["maxwell_version"] = version def _segment_t_start(self, block_index, seg_index): return 0.0 def _segment_t_stop(self, block_index, seg_index): return self._t_stop def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): return self._buffer_descriptions[block_index][seg_index][buffer_id] def _get_signal_t_start(self, block_index, seg_index, stream_index): return 0.0 def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): try: return super()._get_analogsignal_chunk( block_index, seg_index, i_start, i_stop, stream_index, channel_indexes ) except OSError as e: print("*" * 10) print(_hdf_maxwell_error) print("*" * 10) raise (e) _hdf_maxwell_error = """The MaxWell file compression requires a custom plugin. You can use the auto_install_maxwell_hdf5_compression_plugin() function or (if it fails) install it manually: Download the missing decompression library: https://share.mxwbio.com/d/7f2d1e98a1724a1b8b35 Then, link the decompression library by setting the `HDF5_PLUGIN_PATH` to its location, e.g. directly in Python via: os.environ['HDF5_PLUGIN_PATH'] = '/path/to/custom/hdf5/plugin/' or in your shell via: export HDF5_PLUGIN_PATH=/path/to/custom/hdf5/plugin/ You can also set the `HDF5_PLUGIN_PATH` environment variable in your shell configuration file (e.g. .bashrc, .bash_profile, .zshrc, etc.) to make it permanent. See https://mxw.bio/MxW_Doc_Installing_Decompression_Library_to_load_MaxLab_Live_Recordings for more details. """ def auto_install_maxwell_hdf5_compression_plugin(hdf5_plugin_path=None, force_download=True): if hdf5_plugin_path is None: hdf5_plugin_path = os.getenv("HDF5_PLUGIN_PATH", None) if hdf5_plugin_path is None: hdf5_plugin_path = Path.home() / "hdf5_plugin_path_maxwell" os.environ["HDF5_PLUGIN_PATH"] = str(hdf5_plugin_path) hdf5_plugin_path = Path(hdf5_plugin_path) hdf5_plugin_path.mkdir(exist_ok=True) if platform.system() == "Linux": remote_lib = "https://share.mxwbio.com/d/7f2d1e98a1724a1b8b35/files/?p=%2FLinux%2Flibcompression.so&dl=1" local_lib = hdf5_plugin_path / "libcompression.so" elif platform.system() == "Darwin": if platform.machine() == "arm64": remote_lib = "https://share.mxwbio.com/d/7f2d1e98a1724a1b8b35/files/?p=%2FMacOS%2FMac_arm64%2Flibcompression.dylib&dl=1" else: # Assuming x86_64 for MacOS remote_lib = "https://share.mxwbio.com/d/7f2d1e98a1724a1b8b35/files/?p=%2FMacOS%2FMac_x86_64%2Flibcompression.dylib&dl=1" local_lib = hdf5_plugin_path / "libcompression.dylib" elif platform.system() == "Windows": remote_lib = "https://share.mxwbio.com/d/7f2d1e98a1724a1b8b35/files/?p=%2FWindows%2Fcompression.dll&dl=1" local_lib = hdf5_plugin_path / "compression.dll" if not force_download and local_lib.is_file(): print(f"The h5 compression library for Maxwell is already located in {local_lib}!") return dist = urlopen(remote_lib) with open(local_lib, "wb") as f: f.write(dist.read()) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/mearecrawio.py0000644000175100017510000002001615154507213016603 0ustar00runnerrunner""" Class for reading data from a MEArec simulated data. See: https://mearec.readthedocs.io/en/latest/ https://github.com/alejoe91/MEArec https://link.springer.com/article/10.1007/s12021-020-09467-7 Author : Alessio Buccino """ from copy import deepcopy import numpy as np from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class MEArecRawIO(BaseRawIO): """ Class for "reading" simulated data from a MEArec file. Parameters ---------- filename : str, default: '' The filename of the MEArec file to read. load_spiketrains : bool, default: True Whether or not to load spike train data. load_analogsignal : bool, default: True Whether or not to load continuous recording data. Examples -------- >>> import neo.rawio >>> r = neo.rawio.MEArecRawIO(filename='mearec.h5', load_spiketrains=True) >>> r.parse_header() >>> print(r) >>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0, i_start=0, i_stop=1024, channel_names=channel_names) >>> float_chunk = reader.rescale_signal_raw_to_float(raw_chunk, dtype='float64', channel_indexes=[0, 3, 6]) >>> spike_timestamp = reader.spike_timestamps(unit_index=0, t_start=None, t_stop=None) >>> spike_times = reader.rescale_spike_timestamp(spike_timestamp, 'float64') """ extensions = ["h5"] rawmode = "one-file" def __init__(self, filename="", load_spiketrains=True, load_analogsignal=True): BaseRawIO.__init__(self) self.filename = filename self.load_spiketrains = load_spiketrains self.load_analogsignal = load_analogsignal def _source_name(self): return self.filename def _parse_header(self): load = ["channel_positions"] if self.load_analogsignal: load.append("recordings") if self.load_spiketrains: load.append("spiketrains") import MEArec as mr self._recgen = mr.load_recordings( recordings=self.filename, return_h5_objects=True, check_suffix=False, load=load, load_waveforms=False ) self.info_dict = deepcopy(self._recgen.info) self.channel_positions = self._recgen.channel_positions if self.load_analogsignal: self._recordings = self._recgen.recordings if self.load_spiketrains: self._spiketrains = self._recgen.spiketrains self._sampling_rate = self.info_dict["recordings"]["fs"] self.duration_seconds = self.info_dict["recordings"]["duration"] self._num_frames = int(self._sampling_rate * self.duration_seconds) self._num_channels = self.channel_positions.shape[0] self._dtype = self.info_dict["recordings"]["dtype"] signal_buffers = [("Signals", "0")] if self.load_analogsignal else [] signal_streams = [("Signals", "0", "0")] if self.load_analogsignal else [] signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) signal_buffers = np.array(signal_buffers, dtype=_signal_buffer_dtype) sig_channels = [] if self.load_analogsignal: for c in range(self._num_channels): ch_name = f"ch{c}" chan_id = str(c + 1) sr = self._sampling_rate # Hz dtype = self._dtype units = "uV" gain = 1.0 offset = 0.0 stream_id = "0" buffer_id = "0" sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, stream_id, buffer_id)) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) # creating units channels spike_channels = [] if self.load_spiketrains: for c in range(len(self._spiketrains)): unit_name = f"unit{c}" unit_id = f"#{c}" # if spiketrains[c].waveforms is not None: wf_units = "" wf_gain = 1.0 wf_offset = 0.0 wf_left_sweep = 0 wf_sampling_rate = self._sampling_rate spike_channels.append( (unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate) ) spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = [] event_channels = np.array(event_channels, dtype=_event_channel_dtype) self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [1] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = sig_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels self._generate_minimal_annotations() for block_index in range(1): bl_ann = self.raw_annotations["blocks"][block_index] bl_ann["mearec_info"] = self.info_dict def _segment_t_start(self, block_index, seg_index): all_starts = [[0.0]] return all_starts[block_index][seg_index] def _segment_t_stop(self, block_index, seg_index): t_stop = self._num_frames / self._sampling_rate all_stops = [[t_stop]] return all_stops[block_index][seg_index] def _get_signal_size(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index` must be 0") return self._num_frames def _get_signal_t_start(self, block_index, seg_index, stream_index): if stream_index != 0: raise ValueError("`stream_index` must be 0") return self._segment_t_start(block_index, seg_index) def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): if not self.load_analogsignal: raise AttributeError("Recordings not loaded. Set load_analogsignal=True in MEArecRawIO constructor") if i_start is None: i_start = 0 if i_stop is None: i_stop = self._num_frames if channel_indexes is None: channel_indexes = slice(self._num_channels) if isinstance(channel_indexes, slice): raw_signals = self._recordings[i_start:i_stop, channel_indexes] else: # sort channels because h5py neeeds sorted indexes if np.any(np.diff(channel_indexes) < 0): sorted_channel_indexes = np.sort(channel_indexes) sorted_idx = np.array([list(sorted_channel_indexes).index(ch) for ch in channel_indexes]) raw_signals = self._recordings[i_start:i_stop, sorted_channel_indexes] raw_signals = raw_signals[:, sorted_idx] else: raw_signals = self._recordings[i_start:i_stop, channel_indexes] return raw_signals def _spike_count(self, block_index, seg_index, unit_index): return len(self._spiketrains[unit_index]) def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): spike_timestamps = self._spiketrains[unit_index].times.magnitude if t_start is None: t_start = self._segment_t_start(block_index, seg_index) if t_stop is None: t_stop = self._segment_t_stop(block_index, seg_index) timestamp_idxs = np.where((spike_timestamps >= t_start) & (spike_timestamps < t_stop)) return spike_timestamps[timestamp_idxs] def _rescale_spike_timestamp(self, spike_timestamps, dtype): return spike_timestamps.astype(dtype) def _get_spike_raw_waveforms(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/medrawio.py0000644000175100017510000003773215154507213016131 0ustar00runnerrunner""" Class for reading MED (Multiscale Electrophysiology Data) Format. Uses the dhn-med-py python package, created by Dark Horse Neuro, Inc. Authors: Dan Crepeau, Matt Stead """ import numpy as np from .baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) class MedRawIO(BaseRawIO): """ Class for reading MED (Multiscale Electrophysiology Data) Format. Uses the dhn-med-py MED python package (version >= 1.0.0), created by Dark Horse Neuro, Inc. and medformat.org. Parameters ---------- dirname: str | Path | None, default: None The folder containing the data files to load password: str | None, default: None The password for the Med session keep_original_times: bool, default: False If True UTC timestamps are used and returned as seconds referenced to midnight 1 Jan 1970 If False timestamps are referenced to the beginning of the session with the beginning being 0 Notes ----- Currently reads the entire MED session. Every discontinuity is considered to be a new segment. Channels are grouped by sampling frequency, to create streams. In MED all channels will line up time-wise, so streams will span the entire recording, and continuous sections of those streams are divided up into segments. """ extensions = ["medd", "rdat", "ridx"] rawmode = "one-dir" def __init__(self, dirname=None, password=None, keep_original_times=False, **kargs): BaseRawIO.__init__(self, **kargs) import dhn_med_py from dhn_med_py import MedSession self.dirname = str(dirname) self.password = password self.keep_original_times = keep_original_times def _source_name(self): return self.dirname def _parse_header(self): import dhn_med_py from dhn_med_py import MedSession # Set a default password to improve compatibility and ease-of-use. # This password will be ignored if an unencrypted MED dataset is being used. if self.password is None: self.password = "L2_password" # Open the MED session (open data file pointers and read metadata files) self.sess = MedSession(self.dirname, self.password) # set the matrix calls to be "sample_major" as opposed to "channel_major" self.sess.set_major_dimension("sample") # find number of segments sess_contigua = self.sess.session_info["contigua"] self._nb_segment = len(sess_contigua) # find overall session start time. self._session_start_time = sess_contigua[0]["start_time"] # keep track of offset from metadata, if we are keeping original times. if not self.keep_original_times: self._session_time_offset = 0 - self._session_start_time else: self._session_time_offset = self.sess.session_info["metadata"]["recording_time_offset"] # find start/stop times of each segment self._seg_t_starts = [] self._seg_t_stops = [] for seg_idx in range(self._nb_segment): self._seg_t_starts.append(sess_contigua[seg_idx]["start_time"]) self._seg_t_stops.append(sess_contigua[seg_idx]["end_time"]) # find number of streams per segment self._stream_info = [] self._num_stream_info = 0 for chan_idx, chan_info in enumerate(self.sess.session_info["channels"]): chan_freq = chan_info["metadata"]["sampling_frequency"] # set MED session reference channel to be this channel, so the correct contigua is returned self.sess.set_reference_channel(chan_info["metadata"]["channel_name"]) contigua = self.sess.find_discontinuities() # find total number of samples in this channel chan_num_samples = 0 for seg_idx in range(len(contigua)): chan_num_samples += (contigua[seg_idx]["end_index"] - contigua[seg_idx]["start_index"]) + 1 # see if we need a new stream, or add channel to existing stream add_to_existing_stream_info = False for stream_info in self._stream_info: if chan_freq == stream_info["sampling_frequency"] and chan_num_samples == stream_info["num_samples"]: # found a match, so add it! add_to_existing_stream_info = True stream_info["chan_list"].append((chan_idx, chan_info["metadata"]["channel_name"])) stream_info["raw_chans"].append(chan_info["metadata"]["channel_name"]) break if not add_to_existing_stream_info: self._num_stream_info += 1 new_stream_info = { "sampling_frequency": chan_info["metadata"]["sampling_frequency"], "chan_list": [(chan_idx, chan_info["metadata"]["channel_name"])], "contigua": contigua, "raw_chans": [chan_info["metadata"]["channel_name"]], "num_samples": chan_num_samples, } self._stream_info.append(new_stream_info) self.num_channels_in_session = len(self.sess.session_info["channels"]) self.num_streams_in_session = self._num_stream_info signal_streams = [] signal_channels = [] # fill in signal_streams and signal_channels info for signal_stream_counter, stream_info in enumerate(self._stream_info): # get the stream start time, which is the start time of the first continuous section stream_start_time = (stream_info["contigua"][0]["start_time"] + self._session_time_offset) / 1e6 # create stream name/id with info that we now have name = f'stream (rate,#sample,t0): ({stream_info["sampling_frequency"]}, {stream_info["num_samples"]}, {stream_start_time})' stream_id = signal_stream_counter buffer_id = "" signal_streams.append((name, stream_id, buffer_id)) # add entry for signal_channels for each channel in a stream for chan in stream_info["chan_list"]: signal_channels.append( (chan[1], chan[0], stream_info["sampling_frequency"], "int32", "uV", 1, 0, stream_id, buffer_id) ) # the MED format is one dir per channel and so no buffer concept signal_buffers = np.array([], dtype=_signal_buffer_dtype) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) # no unit/epoch information contained in MED spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # Events event_channels = [] event_channels.append(("Event", "event_channel", "event")) event_channels.append(("Epoch", "epoch_channel", "epoch")) event_channels = np.array(event_channels, dtype=_event_channel_dtype) # Create Neo header structure self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [self._nb_segment] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # `_generate_minimal_annotations()` must be called to generate the nested # dict of annotations/array_annotations self._generate_minimal_annotations() # Add custom annotations for neo objects bl_ann = self.raw_annotations["blocks"][0] bl_ann["name"] = "MED Data Block" # The following adds all of the MED session_info to the block annotations, # which includes features like patient name, recording location, etc. bl_ann.update(self.sess.session_info) # Give segments unique names for i in range(self._nb_segment): seg_ann = bl_ann["segments"][i] seg_ann["name"] = "Seg #" + str(i) + " Block #0" # this pprint lines really help for understand the nested (and complicated sometimes) dict # from pprint import pprint # pprint(self.raw_annotations) def _segment_t_start(self, block_index, seg_index): return (self._seg_t_starts[seg_index] + self._session_time_offset) / 1e6 def _segment_t_stop(self, block_index, seg_index): return (self._seg_t_stops[seg_index] + self._session_time_offset) / 1e6 def _get_signal_size(self, block_index, seg_index, stream_index): stream_segment_contigua = self._stream_info[stream_index]["contigua"] return (stream_segment_contigua[seg_index]["end_index"] - stream_segment_contigua[seg_index]["start_index"]) + 1 def _get_signal_t_start(self, block_index, seg_index, stream_index): return (self._seg_t_starts[seg_index] + self._session_time_offset) / 1e6 def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): import dhn_med_py from dhn_med_py import MedSession # Correct for None start/stop inputs if i_start is None: i_start = 0 if i_stop is None: i_stop = self.get_signal_size(block_index=block_index, seg_index=seg_index, stream_index=stream_index) # Check for invalid start/stop inputs if i_start < 0 or i_stop > self.get_signal_size( block_index=block_index, seg_index=seg_index, stream_index=stream_index ): raise IndexError("MED read error: Too many samples requested!") if i_start > i_stop: raise IndexError("MED read error: i_start (" + i_start + ") is greater than i_stop (" + i_stop + ")") num_channels = 0 if channel_indexes is None: self.sess.set_channel_inactive("all") self.sess.set_channel_active(self._stream_info[stream_index]["raw_chans"]) num_channels = len(self._stream_info[stream_index]["raw_chans"]) self.sess.set_reference_channel(self._stream_info[stream_index]["raw_chans"][0]) # in the case we have a slice or we give an ArrayLike we need to iterate through the channels # in order to activate them. else: if isinstance(channel_indexes, slice): start = channel_indexes.start or 0 stop = channel_indexes.stop or len(self._stream_info[stream_index]["raw_chans"]) step = channel_indexes.step or 1 channel_indexes = [ch for ch in range(start, stop, step)] else: if any(channel_indexes < 0): raise IndexError(f"Can not index negative channels: {channel_indexes}") # Set all channels to be inactive, then selectively set some of them to be active self.sess.set_channel_inactive("all") for i, channel_idx in enumerate(channel_indexes): num_channels += 1 self.sess.set_channel_active(self._stream_info[stream_index]["raw_chans"][channel_idx]) self.sess.set_reference_channel(self._stream_info[stream_index]["raw_chans"][channel_indexes[0]]) # Return empty dataset if start/stop samples are equal if i_start == i_stop: raw_signals = np.zeros((0, num_channels), dtype="int32") return raw_signals # Otherwise, return the matrix 2D array returned by the MED library start_sample_offset = self._stream_info[stream_index]["contigua"][seg_index]["start_index"] self.sess.read_by_index(i_start + start_sample_offset, i_stop + start_sample_offset) raw_signals = np.empty((i_stop - i_start, num_channels), dtype=np.int32) for i, chan in enumerate(self.sess.data["channels"]): raw_signals[:, i] = chan["data"] return raw_signals def _spike_count(self, block_index, seg_index, spike_channel_index): return None def _get_spike_timestamps(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None def _rescale_spike_timestamp(self, spike_timestamps, dtype): return None def _get_spike_raw_waveforms(self, block_index, seg_index, spike_channel_index, t_start, t_stop): return None def _event_count(self, block_index, seg_index, event_channel_index): # there are no epochs to consider for MED in this interface if self.header["event_channels"]["type"][event_channel_index] == b"epoch": return 0 records = self.sess.get_session_records() # Find segment boundaries ts0 = (self.segment_t_start(block_index, seg_index) * 1e6) - self._session_time_offset ts1 = (self.segment_t_stop(block_index, seg_index) * 1e6) - self._session_time_offset # Only count Note and Neuralynx type records. count = 0 for record in records: if (record["type_string"] == "Note" or record["type_string"] == "NlxP") and ( record["start_time"] >= ts0 and record["start_time"] < ts1 ): count += 1 return count def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): # There are no epochs to consider for MED in this interface, # so just bail out if that's what's being asked for if self.header["event_channels"]["type"][event_channel_index] == b"epoch": return np.array([]), np.array([]), np.array([], dtype="U") if t_start is not None: start_time = (t_start * 1e6) - self._session_time_offset else: start_time = (self.segment_t_start(block_index, seg_index) * 1e6) - self._session_time_offset if t_stop is not None: end_time = (t_stop * 1e6) - self._session_time_offset else: end_time = (self.segment_t_stop(block_index, seg_index) * 1e6) - self._session_time_offset # Ask MED session for a list of events that match time parameters records = self.sess.get_session_records(start_time, end_time) # create a subset of only Note and Neuralynx type records records_subset = [] for record in records: if record["type_string"] == "Note" or record["type_string"] == "NlxP": records_subset.append(record) records = records_subset # if no records match our criteria, then we are done, output empty arrays if len(records) == 0: return np.array([]), np.array([]), np.array([], dtype="U") # inialize output arrays times = np.empty(shape=[len(records)]) durations = None labels = [] # populate output arrays of times and labels for i, record in enumerate(records): times[i] = (record["start_time"] + self._session_time_offset) / 1e6 if record["type_string"] == "Note": labels.append(record["text"]) elif record["type_string"] == "NlxP": labels.append("NlxP subport: " + str(record["subport"]) + " value: " + str(record["value"])) labels = np.asarray(labels, dtype="U") return times, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): return np.asarray(event_timestamps, dtype=dtype) def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): return np.asarray(raw_duration, dtype=dtype) def __del__(self): try: # Important to make sure the session is closed, since the MED library only allows # one session to be open at a time. self.sess.close() del self.sess except Exception: pass def close(self): try: # Important to make sure the session is closed, since the MED library only allows # one session to be open at a time. self.sess.close() except Exception: pass ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/micromedrawio.py0000644000175100017510000003017315154507213017153 0ustar00runnerrunner""" Class for reading/writing data from micromed (.trc). Inspired by the Matlab code for EEGLAB from Rami K. Niazy. Completed with matlab Guillaume BECQ code. Author: Samuel Garcia """ import datetime import struct import io import numpy as np from .baserawio import ( BaseRawWithBufferApiIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) from .utils import get_memmap_shape from neo.core import NeoReadWriteError class StructFile(io.BufferedReader): def read_f(self, fmt, offset=None): if offset is not None: self.seek(offset) return struct.unpack(fmt, self.read(struct.calcsize(fmt))) class MicromedRawIO(BaseRawWithBufferApiIO): """ Class for reading data from micromed (.trc). Parameters ---------- filename: str, default: None The *.trc file to be loaded """ extensions = ["trc", "TRC"] rawmode = "one-file" def __init__(self, filename=""): BaseRawWithBufferApiIO.__init__(self) self.filename = filename def _parse_header(self): with open(self.filename, "rb") as fid: f = StructFile(fid) # Name f.seek(64) surname = f.read(22).strip(b" ") firstname = f.read(20).strip(b" ") # Date day, month, year, hour, minute, sec = f.read_f("bbbbbb", offset=128) rec_datetime = datetime.datetime(year + 1900, month, day, hour, minute, sec) Data_Start_Offset, Num_Chan, Multiplexer, Rate_Min, Bytes = f.read_f("IHHHH", offset=138) sig_dtype = "u" + str(Bytes) # header version (header_version,) = f.read_f("b", offset=175) if header_version != 4: raise NotImplementedError(f"`header_version {header_version} is not implemented in neo yet") # area f.seek(176) zone_names = [ "ORDER", "LABCOD", "NOTE", "FLAGS", "TRONCA", "IMPED_B", "IMPED_E", "MONTAGE", "COMPRESS", "AVERAGE", "HISTORY", "DVIDEO", "EVENT A", "EVENT B", "TRIGGER", ] zones = {} for zname in zone_names: zname2, pos, length = f.read_f("8sII") zones[zname] = zname2, pos, length if zname != zname2.decode("ascii").strip(" "): raise NeoReadWriteError("expected the zone name to match") # "TRONCA" zone define segments zname2, pos, length = zones["TRONCA"] f.seek(pos) # this number avoid a infinite loop in case of corrupted TRONCA zone (seg_start!=0 and trace_offset!=0) max_segments = 100 self.info_segments = [] for i in range(max_segments): # 4 bytes u4 each seg_start = int(np.frombuffer(f.read(4), dtype="u4")[0]) trace_offset = int(np.frombuffer(f.read(4), dtype="u4")[0]) if seg_start == 0 and trace_offset == 0: break else: self.info_segments.append((seg_start, trace_offset)) if len(self.info_segments) == 0: # one unique segment = general case self.info_segments.append((0, 0)) nb_segment = len(self.info_segments) # Reading Code Info zname2, pos, length = zones["ORDER"] f.seek(pos) code = np.frombuffer(f.read(Num_Chan * 2), dtype="u2") # unique stream and buffer buffer_id = "0" stream_id = "0" units_code = {-1: "nV", 0: "uV", 1: "mV", 2: 1, 100: "percent", 101: "dimensionless", 102: "dimensionless"} signal_channels = [] sig_grounds = [] for c in range(Num_Chan): zname2, pos, length = zones["LABCOD"] # Force code[c] which is currently a uint16 (or u2) into a int to prevent integer overflow # for the following operation -- code[c] * 128 + 2. # An integer overflow below may have side - effects including but not limited # to having repeated channel names. f.seek(pos + int(code[c]) * 128 + 2, 0) chan_name = f.read(6).strip(b"\x00").decode("ascii") ground = f.read(6).strip(b"\x00").decode("ascii") sig_grounds.append(ground) logical_min, logical_max, logical_ground, physical_min, physical_max = f.read_f("iiiii") (k,) = f.read_f("h") units = units_code.get(k, "uV") factor = float(physical_max - physical_min) / float(logical_max - logical_min + 1) gain = factor offset = -logical_ground * factor f.seek(8, 1) (sampling_rate,) = f.read_f("H") sampling_rate *= Rate_Min chan_id = str(c) signal_channels.append( (chan_name, chan_id, sampling_rate, sig_dtype, units, gain, offset, stream_id, buffer_id) ) signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) self._stream_buffer_slice = {"0": slice(None)} signal_buffers = np.array([("Signals", buffer_id)], dtype=_signal_buffer_dtype) signal_streams = np.array([("Signals", stream_id, buffer_id)], dtype=_signal_stream_dtype) if np.unique(signal_channels["sampling_rate"]).size != 1: raise NeoReadWriteError("The sampling rates must be the same across signal channels") self._sampling_rate = float(np.unique(signal_channels["sampling_rate"])[0]) # memmap traces buffer full_signal_shape = get_memmap_shape( self.filename, sig_dtype, num_channels=Num_Chan, offset=Data_Start_Offset ) seg_limits = [trace_offset for seg_start, trace_offset in self.info_segments] + [full_signal_shape[0]] self._t_starts = [] self._buffer_descriptions = {0: {}} for seg_index in range(nb_segment): seg_start, trace_offset = self.info_segments[seg_index] self._t_starts.append(seg_start / self._sampling_rate) start = seg_limits[seg_index] stop = seg_limits[seg_index + 1] shape = (stop - start, Num_Chan) file_offset = Data_Start_Offset + (start * np.dtype(sig_dtype).itemsize * Num_Chan) self._buffer_descriptions[0][seg_index] = {} self._buffer_descriptions[0][seg_index][buffer_id] = { "type": "raw", "file_path": str(self.filename), "dtype": sig_dtype, "order": "C", "file_offset": file_offset, "shape": shape, } # Event channels event_channels = [] event_channels.append(("Trigger", "", "event")) event_channels.append(("Note", "", "event")) event_channels.append(("Event A", "", "epoch")) event_channels.append(("Event B", "", "epoch")) event_channels = np.array(event_channels, dtype=_event_channel_dtype) # Read trigger and notes self._raw_events = [] ev_dtypes = [ ("TRIGGER", [("start", "u4"), ("label", "u2")]), ("NOTE", [("start", "u4"), ("label", "S40")]), ("EVENT A", [("label", "u4"), ("start", "u4"), ("stop", "u4")]), ("EVENT B", [("label", "u4"), ("start", "u4"), ("stop", "u4")]), ] for zname, ev_dtype in ev_dtypes: zname2, pos, length = zones[zname] dtype = np.dtype(ev_dtype) rawevent = np.memmap(self.filename, dtype=dtype, mode="r", offset=pos, shape=length // dtype.itemsize) # important : all events timing are related to the first segment t_start self._raw_events.append([]) for seg_index in range(nb_segment): left_lim = seg_limits[seg_index] right_lim = seg_limits[seg_index + 1] keep = (rawevent["start"] >= left_lim) & (rawevent["start"] < right_lim) & (rawevent["start"] != 0) self._raw_events[-1].append(rawevent[keep]) # No spikes spike_channels = [] spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) # fille into header dict self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [nb_segment] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # insert some annotation at some place self._generate_minimal_annotations() bl_annotations = self.raw_annotations["blocks"][0] seg_annotations = bl_annotations["segments"][0] for d in (bl_annotations, seg_annotations): d["rec_datetime"] = rec_datetime d["firstname"] = firstname d["surname"] = surname d["header_version"] = header_version sig_annotations = self.raw_annotations["blocks"][0]["segments"][0]["signals"][0] sig_annotations["__array_annotations__"]["ground"] = np.array(sig_grounds) def _source_name(self): return self.filename def _segment_t_start(self, block_index, seg_index): return self._t_starts[seg_index] def _segment_t_stop(self, block_index, seg_index): duration = self.get_signal_size(block_index, seg_index, stream_index=0) / self._sampling_rate return duration + self.segment_t_start(block_index, seg_index) def _get_signal_t_start(self, block_index, seg_index, stream_index): assert stream_index == 0 return self._t_starts[seg_index] def _spike_count(self, block_index, seg_index, unit_index): return 0 def _event_count(self, block_index, seg_index, event_channel_index): n = self._raw_events[event_channel_index][seg_index].size return n def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): raw_event = self._raw_events[event_channel_index][seg_index] # important : all events timing are related to the first segment t_start seg_start0, _ = self.info_segments[0] if t_start is not None: keep = raw_event["start"] + seg_start0 >= int(t_start * self._sampling_rate) raw_event = raw_event[keep] if t_stop is not None: keep = raw_event["start"] + seg_start0 <= int(t_stop * self._sampling_rate) raw_event = raw_event[keep] timestamp = raw_event["start"] + seg_start0 if event_channel_index < 2: durations = None else: durations = raw_event["stop"] - raw_event["start"] try: labels = raw_event["label"].astype("U") except UnicodeDecodeError: # sometimes the conversion do not work : here a simple fix labels = np.array([e.decode("cp1252") for e in raw_event["label"]], dtype="U") return timestamp, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = event_timestamps.astype(dtype) / self._sampling_rate return event_times def _rescale_epoch_duration(self, raw_duration, dtype, event_channel_index): durations = raw_duration.astype(dtype) / self._sampling_rate return durations def _get_analogsignal_buffer_description(self, block_index, seg_index, buffer_id): return self._buffer_descriptions[block_index][seg_index][buffer_id] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1773309590.5429316 neo-0.14.4/neo/rawio/neuralynxrawio/0000755000175100017510000000000015154507227017030 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/neuralynxrawio/__init__.py0000644000175100017510000000053115154507213021133 0ustar00runnerrunner""" RawIO for reading data from Neuralynx files. This IO supports NCS, NEV, NSE and NTT file formats. NCS contains the sampled signal for one channel NEV contains events NSE contains spikes and waveforms for mono electrodes NTT contains spikes and waveforms for tetrodes """ from neo.rawio.neuralynxrawio.neuralynxrawio import NeuralynxRawIO ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/neuralynxrawio/ncssections.py0000644000175100017510000003553715154507213021745 0ustar00runnerrunner""" Objects to represent continguous sections of samples in a Neuralynx continuously sampled .Ncs file. These sections are considered contiguous in that they have a start time, a sampling rate, and a length. Defining these sections is complicated due to the physical structure of .Ncs files which contain both a header and a set of fixed length records, CscRecords. Each CscRecord has a start time in microseconds, a channel id (chan_id), a stated sampling frequency (sampFreq) which is rounded to the whole sample/s, and a number of valid samples (nb_valid), which may be less than the physical maximum of 512. In principle each of these parameters may vary in each record in the file; however, there are no known examples of .Ncs files where the chan_id or sampFreq varied from record to record. The header normally, though not always, contains a stated sampling frequency, which may be rounded to a whole number of samples per second or not. Finally, the actual sampling frequency used within a section may be slightly different than any of the two frequencies above (that in the records or that in the header). This often arises due to clock drift over the course of a longer recording combined with the fact that only whole microseconds are recorded in the CscRecords. This can easily be 0.01% amounting to 0.2 seconds over the course of a half hour recording. These files may often contain apparent gaps of time between the sequential CscRecords in a file. A gap is where the predicted time at the start of the next record, based on taking the start time of the record and then adding 1/sampling rate x nbValid, does not agree exactly with the start time given in the next record. These gaps vary from those of less than one microsecond, likely due to rounding of the start time, to gaps of many minutes, which may be introduced by the human operator stopping and starting recording during a recording session. The challenge addressed by the NcsSectionsFactory of this module is to separate those intended gaps from those introduced by a quirk of the hardware, recording software, or file format. """ import math import numpy as np from enum import IntEnum, auto class AcqType(IntEnum): PRE4 = auto() BML = auto() DIGITALLYNX = auto() DIGITALLYNXSX = auto() CHEETAH64 = auto() RAWDATAFILE = auto() CHEETAH560 = auto() ATLAS = auto() UNKNOWN = auto() class NcsSections: """ Contains information regarding the contiguous sections of records in an Ncs file. Methods of NcsSectionsFactory perform parsing of this information from an Ncs file and produce these where the sections are discontiguous in time and in temporal order. TODO: This class will likely need __ne__ to be useful in more sophisticated segment construction algorithms. """ def __init__(self): self.sects = [] self.sampFreqUsed = 0 # actual sampling frequency of samples self.microsPerSampUsed = 0 # microseconds per sample def __eq__(self, other): samp_eq = self.sampFreqUsed == other.sampFreqUsed micros_eq = self.microsPerSampUsed == other.microsPerSampUsed sects_eq = self.sects == other.sects return samp_eq and micros_eq and sects_eq def __hash__(self): return (f"{self.sampFreqUsed};{self.microsPerSampUsed};" f"{[s.__hash__() for s in self.sects]}").__hash__() def is_equivalent(self, other, rel_tol=0, abs_tol=0): if len(self.sects) != len(other.sects): return False else: # do not check for gaps if only a single section is present for sec_id in range(len(self.sects) - 1): if not self.sects[sec_id].is_equivalent(other.sects[sec_id], rel_tol=rel_tol, abs_tol=abs_tol): return False return True class NcsSection: """ Information regarding a single contiguous section or group of records in an Ncs file. Model is that times are closed on the left and open on the right. Record numbers are closed on both left and right, that is, inclusive of the last record. endTime should never be set less than startTime for comparison functions to work properly, though this is not enforced. """ _RECORD_SIZE = 512 # nb sample per signal record def __init__(self, startRec, startTime, endRec, endTime, n_samples): self.startRec = startRec # index of starting record self.startTime = startTime # starttime of first record self.endRec = endRec # index of last record (inclusive) self.endTime = endTime # end time of last record, that is, the end time of the last # sampling period contained in the last record of the section self.n_samples = n_samples # number of samples in record which are valid def __eq__(self, other): return ( self.startRec == other.startRec and self.startTime == other.startTime and self.endRec == other.endRec and self.endTime == other.endTime and self.n_samples == other.n_samples ) def __hash__(self): s = f"{self.startRec};{self.startTime};{self.endRec};{self.endTime};{self.n_samples}" return s.__hash__() def is_equivalent(self, other, rel_tol=0, abs_tol=0): eq_start = math.isclose(self.startTime, other.startTime, rel_tol=rel_tol, abs_tol=abs_tol) eq_end = math.isclose(self.endTime, other.endTime, rel_tol=rel_tol, abs_tol=abs_tol) return eq_start & eq_end def before_time(self, rhb): """ Determine if this section is completely before another section in time. """ return self.endTime < rhb.startTime def overlaps_time(self, rhb): """ Determine if this section overlaps another in time. """ return self.startTime <= rhb.endTime and self.endTime >= rhb.startTime def after_time(self, rhb): """ Determine if this section is completely after another section in time. """ return self.startTime >= rhb.endTime class NcsSectionsFactory: """ Class for factory methods which perform parsing of contiguous sections of records in Ncs files. Model for times is that times are rounded to nearest microsecond. Times from start of a sample until just before the next sample are included, that is, closed lower bound and open upper bound on intervals. A channel with no samples is empty and contains no time intervals. Moved here since algorithm covering all 3 header styles and types used is more complicated. """ _maxGapSampFrac = 0.2 # maximum fraction of a sampling interval between predicted # and actual record timestamps still considered within one section @staticmethod def get_freq_for_micros_per_samp(micros): """ Compute fractional sampling frequency, given microseconds per sample. """ return 1e6 / micros @staticmethod def get_micros_per_samp_for_freq(sampFr): """ Calculate fractional microseconds per sample, given the sampling frequency (Hz). """ return 1e6 / sampFr @staticmethod def calc_sample_time(sampFr, startTime, posn): """ Calculate time rounded to microseconds for sample given frequency, start time, and sample position. """ return round(startTime + NcsSectionsFactory.get_micros_per_samp_for_freq(sampFr) * posn) @staticmethod def _buildNcsSections(ncsMemMap, sampFreq, gapTolerance=0): """ Construct NcsSections with fast mode when no gaps or parsing the file to detect gaps. """ channel_id = ncsMemMap["channel_id"][0] ncsSects = NcsSections() # check if file is one block of records, which is often the case, and avoid full parse # need that last timestamp match the predicted one. predLastBlockStartTime = NcsSectionsFactory.calc_sample_time( sampFreq, ncsMemMap["timestamp"][0], NcsSection._RECORD_SIZE * (ncsMemMap.shape[0] - 1) ) if ( channel_id == ncsMemMap["channel_id"][-1] and ncsMemMap["sample_rate"][0] == ncsMemMap["sample_rate"][-1] and ncsMemMap["timestamp"][-1] == predLastBlockStartTime ): lastBlkEndTime = NcsSectionsFactory.calc_sample_time( sampFreq, ncsMemMap["timestamp"][-1], ncsMemMap["nb_valid"][-1] ) n_samples = NcsSection._RECORD_SIZE * (ncsMemMap.size - 1) + ncsMemMap["nb_valid"][-1] section0 = NcsSection( startRec=0, startTime=ncsMemMap["timestamp"][0], endRec=ncsMemMap.size - 1, endTime=lastBlkEndTime, n_samples=n_samples, ) ncsSects.sects.append(section0) else: # need to parse all data block to detect gaps # check when the predicted timestamp is outside the tolerance delta = (ncsMemMap["timestamp"][1:] - ncsMemMap["timestamp"][:-1]).astype(np.int64) delta_prediction = ((ncsMemMap["nb_valid"][:-1] / sampFreq) * 1e6).astype(np.int64) gap_inds = np.flatnonzero(np.abs(delta - delta_prediction) > gapTolerance) gap_inds += 1 sections_limits = [0] + gap_inds.tolist() + [len(ncsMemMap)] for i in range(len(gap_inds) + 1): start = sections_limits[i] stop = sections_limits[i + 1] duration = np.uint64(1e6 / sampFreq * ncsMemMap["nb_valid"][stop - 1]) ncsSects.sects.append( NcsSection( startRec=start, startTime=ncsMemMap["timestamp"][start], endRec=stop - 1, endTime=ncsMemMap["timestamp"][stop - 1] + duration, n_samples=np.sum(ncsMemMap["nb_valid"][start:stop]), ) ) return ncsSects @staticmethod def build_for_ncs_file(ncsMemMap, nlxHdr, gapTolerance=None, strict_gap_mode=True): """ Build an NcsSections object for an NcsFile, given as a memmap and NlxHeader, handling gap detection appropriately given the file type as specified by the header. Parameters ---------- ncsMemMap: memory map of file nlxHdr: NlxHeader from corresponding file. Returns ------- An NcsSections corresponding to the provided ncsMemMap and nlxHdr """ acqType = nlxHdr.type_of_recording() freq = nlxHdr["sampling_rate"] if acqType == AcqType.PRE4: # Old Neuralynx style with truncated whole microseconds for actual sampling. This # restriction arose from the sampling being based on a master 1 MHz clock. microsPerSampUsed = math.floor(NcsSectionsFactory.get_micros_per_samp_for_freq(freq)) sampFreqUsed = NcsSectionsFactory.get_freq_for_micros_per_samp(microsPerSampUsed) if gapTolerance is None: if strict_gap_mode: # this is the old behavior, maybe we could put 0.9 sample interval no ? gapTolerance = 0 else: gapTolerance = 0 ncsSects = NcsSectionsFactory._buildNcsSections(ncsMemMap, sampFreqUsed, gapTolerance=gapTolerance) ncsSects.sampFreqUsed = sampFreqUsed ncsSects.microsPerSampUsed = microsPerSampUsed elif acqType in [ AcqType.DIGITALLYNX, AcqType.DIGITALLYNXSX, AcqType.CHEETAH64, AcqType.CHEETAH560, AcqType.RAWDATAFILE, ]: # digital lynx style with fractional frequency and micros per samp determined from block times if gapTolerance is None: if strict_gap_mode: # this is the old behavior gapTolerance = round(NcsSectionsFactory._maxGapSampFrac * 1e6 / freq) else: # quarter of paquet size is tolerate gapTolerance = round(0.25 * NcsSection._RECORD_SIZE * 1e6 / freq) ncsSects = NcsSectionsFactory._buildNcsSections(ncsMemMap, freq, gapTolerance=gapTolerance) # take longer data block to compute reaal sampling rate # ind_max = np.argmax([section.n_samples for section in ncsSects.sects]) ind_max = np.argmax([section.endRec - section.startRec for section in ncsSects.sects]) section = ncsSects.sects[ind_max] if section.endRec != section.startRec: # need several data block sampFreqUsed = ( (section.n_samples - ncsMemMap["nb_valid"][section.endRec]) * 1e6 / (ncsMemMap["timestamp"][section.endRec] - section.startTime) ) else: sampFreqUsed = freq ncsSects.sampFreqUsed = sampFreqUsed ncsSects.microsPerSampUsed = NcsSectionsFactory.get_micros_per_samp_for_freq(sampFreqUsed) elif acqType == AcqType.BML or acqType == AcqType.ATLAS: # BML & ATLAS style with fractional frequency and micros per samp if strict_gap_mode: # this is the old behavior, maybe we could put 0.9 sample interval no ? gapTolerance = 0 else: # quarter of paquet size is tolerate gapTolerance = round(0.25 * NcsSection._RECORD_SIZE * 1e6 / freq) ncsSects = NcsSectionsFactory._buildNcsSections(ncsMemMap, freq, gapTolerance=gapTolerance) ncsSects.sampFreqUsed = freq ncsSects.microsPerSampUsed = NcsSectionsFactory.get_micros_per_samp_for_freq(freq) else: raise TypeError("Unknown Ncs file type from header.") return ncsSects @staticmethod def _verifySectionsStructure(ncsMemMap, ncsSects): """ Check that the record structure and timestamps for the ncsMemMap agrees with that in ncsSects. Provides a more rapid verification of structure than building a new NcsSections and checking equality. Parameters ---------- ncsMemMap: memmap of file to be checked ncsSects existing block structure to be checked Returns ------- true if all timestamps and block record starts and stops agree, otherwise false. """ for blki in range(0, len(ncsSects.sects)): if ncsMemMap["timestamp"][ncsSects.sects[blki].startRec] != ncsSects.sects[blki].startTime: return False ets = ncsMemMap["timestamp"][ncsSects.sects[blki].endRec] enb = ncsMemMap["nb_valid"][ncsSects.sects[blki].endRec] endTime = NcsSectionsFactory.calc_sample_time(ncsSects.sampFreqUsed, ets, enb) if endTime != ncsSects.sects[blki].endTime: return False return True ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1773309579.0 neo-0.14.4/neo/rawio/neuralynxrawio/neuralynxrawio.py0000644000175100017510000012734415154507213022477 0ustar00runnerrunner""" Class for reading data from Neuralynx files. This IO supports NCS, NEV, NSE and NTT file formats. NCS contains the sampled signal for one channel NEV contains events NSE contains spikes and waveforms for mono electrodes NTT contains spikes and waveforms for tetrodes All Neuralynx files contain a 16 kilobyte text header followed by 0 or more fixed length records. The format of the header has never been formally specified, however, the Neuralynx programs which write them have followed a set of conventions which have varied over the years. Additionally, other programs like Pegasus write files with somewhat varying headers. This variation requires parsing to determine the exact version and type which is handled within this RawIO by the NlxHeader class. Ncs files contain a series of 1044 byte records, each of which contains 512 16 byte samples and header information which includes a 64 bit timestamp in microseconds, a 16 bit channel number, the sampling frequency in integral Hz, and the number of the 512 samples which are considered valid samples (the remaining samples within the record are invalid). The Ncs file header usually contains a specification of the sampling frequency, which may be rounded to an integral number of Hz or may be fractional. The actual sampling frequency in terms of the underlying clock is physically determined by the spacing of the timestamps between records. These variations of header format and possible differences between the stated sampling frequency and actual sampling frequency can create apparent time discrepancies in .Ncs files. Additionally, the Neuralynx recording software can start and stop recording while continuing to write samples to a single .Ncs file, which creates larger gaps in the time sequences of the samples. This RawIO attempts to correct for these deviations where possible and present a single section of contiguous samples with one sampling frequency, t_start, and length for each .Ncs file. These sections are determined by the NcsSectionsFactory class. In the event the gaps are larger, this RawIO only provides the samples from the first section as belonging to one Segment. If .Ncs files are loaded these determine the Segments of data to be loaded. Events and spiking data outside of Segments defined by .Ncs files will be ignored. To access all time point data in a single Segment load a session excluding .Ncs files. Continuous data streams are ordered by descending sampling rate. This RawIO presents only a single Block. Author: Julia Sprenger, Carlos Canova, Samuel Garcia, Peter N. Steinmetz. """ from ..baserawio import ( BaseRawIO, _signal_channel_dtype, _signal_stream_dtype, _signal_buffer_dtype, _spike_channel_dtype, _event_channel_dtype, ) import numpy as np import os from pathlib import Path import copy import warnings from collections import namedtuple, OrderedDict from neo.rawio.neuralynxrawio.ncssections import NcsSection, NcsSectionsFactory from neo.rawio.neuralynxrawio.nlxheader import NlxHeader # Named tuple for stream identification keys StreamKey = namedtuple("StreamKey", ["sampling_rate", "input_range", "filter_params"]) class NeuralynxRawIO(BaseRawIO): """ Class for reading datasets recorded by Neuralynx. This version works with rawmode of one-dir for a single directory of files or one-file for a single file. Parameters ---------- dirname: str, default: '' Name of directory containing all files for a dataset. If provided, filename is ignored. But one of either dirname or filename is required. include_filenames: str | list | None, default: None Name of a single ncs, nse, nev or ntt file or list of such files. Will only include file names in the list. This can be plain filenames or fullpath or path relative to dirname. All files should be in a single path. exclude_filenames: str | list | None, default: None Name of a single ncs, nse, nev or ntt file or list of such files. Expects plain filenames (without directory path). None will search for all file types keep_original_times: bool, default: False If True, keep original start time as in files, Otherwise set 0 of time to first time in dataset strict_gap_mode: bool, default: True Detect gaps using strict mode or not. * strict_gap_mode = True then a gap is consider when timstamp difference between two consequtive data packet is more than one sample interval. * strict_gap_mode = False then a gap has an increased tolerance. Some new system with different clock need this option otherwise, too many gaps are detected Notes ----- * This IO supports NCS, NEV, NSE and NTT file formats (not NVT or NRD yet) * These variations of header format and possible differences between the stated sampling frequency and actual sampling frequency can create apparent time discrepancies in .Ncs files. Additionally, the Neuralynx recording software can start and stop recording while continuing to write samples to a single .Ncs file, which creates larger gaps in the time sequences of the samples. * This RawIO attempts to correct for these deviations where possible and present a single section of contiguous samples with one sampling frequency, t_start, and length for each .Ncs file. These sections are determined by the NcsSectionsFactory class. In the event the gaps are larger, this RawIO only provides the samples from the first section as belonging to one Segment. Examples -------- >>> import neo.rawio >>> reader = neo.rawio.NeuralynxRawIO(dirname='Cheetah_v5.5.1/original_data') >>> reader.parse_header() Inspect all files in the directory. >>> print(reader) Display all information about signal channels, units, segment size.... """ extensions = ["nse", "ncs", "nev", "ntt", "nvt", "nrd"] # nvt and nrd are not yet supported rawmode = "one-dir" _ncs_dtype = [ ("timestamp", "uint64"), ("channel_id", "uint32"), ("sample_rate", "uint32"), ("nb_valid", "uint32"), ("samples", "int16", NcsSection._RECORD_SIZE), ] # Filter parameter keys used for stream differentiation _filter_keys = [ "DSPLowCutFilterEnabled", "DspLowCutFrequency", "DspLowCutFilterType", "DspLowCutNumTaps", "DSPHighCutFilterEnabled", "DspHighCutFrequency", "DspHighCutFilterType", "DspHighCutNumTaps", "DspDelayCompensation", "DspFilterDelay_µs", ] def __init__( self, dirname="", include_filenames=None, exclude_filenames=None, keep_original_times=False, strict_gap_mode=True, filename=None, exclude_filename=None, **kargs, ): if not dirname: raise ValueError("`dirname` cannot be empty.") if filename is not None: include_filenames = [filename] warnings.warn( "`filename` is deprecated and will be removed in version 1.0. Please use `include_filenames` instead" ) if exclude_filename is not None: if isinstance(exclude_filename, str): exclude_filenames = [exclude_filename] else: exclude_filenames = exclude_filename warnings.warn( "`exclude_filename` is deprecated and will be removed in version 1.0. Please use `exclude_filenames` instead" ) if include_filenames is None: include_filenames = [] elif isinstance(include_filenames, str): include_filenames = [include_filenames] if exclude_filenames is None: exclude_filenames = [] elif isinstance(exclude_filenames, str): exclude_filenames = [exclude_filenames] if include_filenames: self.rawmode = "multiple-files" else: self.rawmode = "one-dir" self.dirname = dirname self.include_filenames = include_filenames self.exclude_filenames = exclude_filenames self.keep_original_times = keep_original_times self.strict_gap_mode = strict_gap_mode BaseRawIO.__init__(self, **kargs) def _source_name(self): return self.dirname def _parse_header(self): stream_channels = [] signal_channels = [] spike_channels = [] event_channels = [] self.ncs_filenames = OrderedDict() # (chan_name, chan_id): filename self.nse_ntt_filenames = OrderedDict() # (chan_name, chan_id): filename self.nev_filenames = OrderedDict() # chan_id: filename self.file_headers = OrderedDict() # filename: file header dict self._nev_memmap = {} self._spike_memmap = {} self.internal_unit_ids = [] # channel_index > ((channel_name, channel_id), unit_id) self.internal_event_ids = [] self._empty_ncs = [] # this list contains filenames of empty files self._empty_nev = [] self._empty_nse_ntt = [] # Explore the directory looking for ncs, nev, nse and ntt # and construct channels headers. signal_annotations = [] unit_annotations = [] event_annotations = [] # 1) Get file paths based on mode and validate existence for multiple-files mode if self.rawmode == "multiple-files": # For multiple-files mode, validate that all explicitly provided files exist file_paths = [] for filename in self.include_filenames: full_path = Path(self.dirname) / filename if not full_path.is_file(): raise ValueError( f"Provided Filename is not a file: " f"{full_path}. If you want to provide a " f"directory use the `dirname` keyword" ) file_paths.append(full_path) else: # one-dir mode # For one-dir mode, get all files from directory dir_path = Path(self.dirname) file_paths = [p for p in dir_path.iterdir() if p.is_file()] file_paths = sorted(file_paths, key=lambda p: p.name) # 2) Filter by exclude filenames file_paths = [fp for fp in file_paths if fp.name not in self.exclude_filenames] # 3) Filter to keep only files with correct extensions # Note: suffix[1:] removes the leading dot from file extension (e.g., ".ncs" -> "ncs") valid_file_paths = [fp for fp in file_paths if fp.suffix[1:].lower() in self.extensions] # Convert back to strings for backwards compatibility with existing code full_filenames = [str(fp) for fp in valid_file_paths] stream_props = {} # {(sampling_rate, n_samples, t_start): {stream_id: [filenames]} for filename in full_filenames: _, ext = os.path.splitext(filename) ext = ext[1:].lower() # remove dot and make lower case # Skip Ncs files with only header. Other empty file types # will have an empty dataset constructed later. if (os.path.getsize(filename) <= NlxHeader.HEADER_SIZE) and ext in ["ncs"]: self._empty_ncs.append(filename) continue # All file have more or less the same header structure info = NlxHeader(filename) self.file_headers[filename] = info chan_names = info["channel_names"] chan_ids = info["channel_ids"] for idx, chan_id in enumerate(chan_ids): chan_name = chan_names[idx] chan_uid = (chan_name, str(chan_id)) if ext == "ncs": # Calculate gain for this channel gain = info["bit_to_microVolt"][idx] if info.get("input_inverted", False): gain *= -1 # Build stream key from acquisition parameters sampling_rate = float(info["sampling_rate"]) # Get InputRange for this specific channel # Normalized by NlxHeader to always be a list input_range = info.get("InputRange") if isinstance(input_range, list): input_range = input_range[idx] if idx < len(input_range) else input_range[0] # Build filter parameters tuple filter_params = [] for key in self._filter_keys: if key in info: filter_params.append((key, info[key])) # Create stream key (channels with same key go in same stream) stream_key = StreamKey( sampling_rate=sampling_rate, input_range=input_range, filter_params=tuple(sorted(filter_params)), ) if stream_key not in stream_props: stream_props[stream_key] = { "stream_id": len(stream_props), "filenames": [filename], "channels": set(), } else: stream_props[stream_key]["filenames"].append(filename) stream_id = stream_props[stream_key]["stream_id"] stream_props[stream_key]["channels"].add((chan_name, str(chan_id))) # @zach @ramon : we need to discuss this split by channel buffer buffer_id = "" # a sampled signal channel units = "uV" offset = 0.0 signal_channels.append( ( chan_name, str(chan_id), info["sampling_rate"], "int16", units, gain, offset, stream_id, buffer_id, ) ) self.ncs_filenames[chan_uid] = filename keys = [ "DspFilterDelay_µs", "recording_opened", "FileType", "DspDelayCompensation", "recording_closed", "DspLowCutFilterType", "HardwareSubSystemName", "DspLowCutNumTaps", "DSPLowCutFilterEnabled", "HardwareSubSystemType", "DspHighCutNumTaps", "ADMaxValue", "DspLowCutFrequency", "DSPHighCutFilterEnabled", "RecordSize", "InputRange", "DspHighCutFrequency", "input_inverted", "NumADChannels", "DspHighCutFilterType", ] d = {k: info[k] for k in keys if k in info} signal_annotations.append(d) elif ext in ("nse", "ntt"): # nse and ntt are pretty similar except for the waveform shape. # A file can contain several unit_id (so several unit channel). assert chan_id not in self.nse_ntt_filenames, "Several nse or ntt files have the same unit_id!!!" self.nse_ntt_filenames[chan_uid] = filename data = self._get_file_map(filename) self._spike_memmap[chan_uid] = data unit_ids = np.unique(data["unit_id"]) for unit_id in unit_ids: # a spike channel for each (chan_id, unit_id) self.internal_unit_ids.append((chan_uid, unit_id)) unit_name = "ch{}#{}#{}".format(chan_name, chan_id, unit_id) unit_id = "{}".format(unit_id) wf_units = "uV" wf_gain = info["bit_to_microVolt"][idx] if info.get("input_inverted", False): wf_gain *= -1 wf_offset = 0.0 wf_left_sweep = -1 # NOT KNOWN wf_sampling_rate = info["sampling_rate"] spike_channels.append( ( unit_name, "{}".format(unit_id), wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate, ) ) unit_annotations.append(dict(file_origin=filename)) elif ext == "nev": # an event channel # each ('event_id', 'ttl_input') give a new event channel self.nev_filenames[chan_id] = filename if os.path.getsize(filename) <= NlxHeader.HEADER_SIZE: self._empty_nev.append(filename) data = np.zeros((0,), dtype=nev_dtype) internal_ids = [] else: data = self._get_file_map(filename) if data.shape[0] == 0: # empty file self._empty_nse_ntt.append(filename) internal_ids = np.unique(data[["event_id", "ttl_input"]]).tolist() for internal_event_id in internal_ids: if internal_event_id not in self.internal_event_ids: event_id, ttl_input = internal_event_id name = "{} event_id={} ttl={}".format(chan_name, event_id, ttl_input) event_channels.append((name, chan_id, "event")) self.internal_event_ids.append(internal_event_id) self._nev_memmap[chan_id] = data signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype) spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype) event_channels = np.array(event_channels, dtype=_event_channel_dtype) if signal_channels.size > 0: # Build DSP filter configuration registry: filter_id -> filter parameters dict # Extract unique filter configurations from stream keys unique_filter_tuples = {stream_key.filter_params for stream_key in stream_props.keys()} _dsp_filter_configurations = {i: dict(filt) for i, filt in enumerate(sorted(unique_filter_tuples))} # Create reverse mapping for looking up filter IDs during stream name construction seen_filters = {filt: i for i, filt in enumerate(sorted(unique_filter_tuples))} # Store DSP filter configurations as private instance attribute # Keeping private for now - may expose via annotations or public API in future self._dsp_filter_configurations = _dsp_filter_configurations # Order streams by sampling rate (high to low) # This is to keep some semblance of stability ordered_stream_keys = sorted(stream_props.keys(), reverse=True, key=lambda x: x.sampling_rate) stream_names = [] stream_ids = [] buffer_ids = [] for stream_id, stream_key in enumerate(ordered_stream_keys): # Format stream name using namedtuple fields dsp_filter_id = seen_filters[stream_key.filter_params] voltage_mv = int(stream_key.input_range / 1000) if stream_key.input_range is not None else 0 stream_name = ( f"stream{stream_id}_{int(stream_key.sampling_rate)}Hz_{voltage_mv}mVRange_DSPFilter{dsp_filter_id}" ) stream_names.append(stream_name) stream_ids.append(str(stream_id)) buffer_ids.append("") signal_streams = list(zip(stream_names, stream_ids, buffer_ids)) else: signal_streams = [] self._filter_configurations = {} signal_buffers = np.array([], dtype=_signal_buffer_dtype) signal_streams = np.array(signal_streams, dtype=_signal_stream_dtype) # set 2 attributes needed later for header in case there are no ncs files in dataset, # e.g. Pegasus self._timestamp_limits = None self._nb_segment = 1 stream_infos = {} # Read ncs files of each stream for gap detection and nb_segment computation. for stream_id in np.unique(signal_channels["stream_id"]): stream_channels = signal_channels[signal_channels["stream_id"] == stream_id] stream_chan_uids = zip(stream_channels["name"], stream_channels["id"]) stream_filenames = [self.ncs_filenames[chuid] for chuid in stream_chan_uids] _sigs_memmaps, ncsSegTimestampLimits, section_structure = self.scan_stream_ncs_files(stream_filenames) stream_infos[stream_id] = { "segment_sig_memmaps": _sigs_memmaps, "ncs_segment_infos": ncsSegTimestampLimits, "section_structure": section_structure, } # check if section structure across streams is compatible and merge infos ref_sec_structure = None for stream_id, stream_info in stream_infos.items(): ref_stream_id = list(stream_infos.keys())[0] ref_sec_structure = stream_infos[ref_stream_id]["section_structure"] sec_structure = stream_info["section_structure"] # check if section structure of streams are compatible # using tolerance of one data packet (512 samples) tolerance = 512 / min(ref_sec_structure.sampFreqUsed, sec_structure.sampFreqUsed) * 1e6 if not ref_sec_structure.is_equivalent(sec_structure, abs_tol=tolerance): ref_chan_ids = signal_channels[signal_channels["stream_id"] == ref_stream_id]["name"] chan_ids = signal_channels[signal_channels["stream_id"] == stream_id]["name"] raise ValueError( "Incompatible section structures across streams: " f"Stream id {ref_stream_id}:{ref_chan_ids} and " f"{stream_id}:{chan_ids}." ) if ref_sec_structure is not None: self._nb_segment = len(ref_sec_structure.sects) else: # Use only a single segment if no ncs data is present self._nb_segment = 1 def min_max_tuple(tuple1, tuple2): """Merge tuple by selecting min for first and max for 2nd entry""" mins, maxs = zip(tuple1, tuple2) result = (min(m for m in mins if m is not None), max(m for m in maxs if m is not None)) return result # merge stream mmemmaps since streams are compatible self._sigs_memmaps = [{} for seg_idx in range(self._nb_segment)] # time limits of integer timestamps in ncs files self._timestamp_limits = [(None, None) for seg_idx in range(self._nb_segment)] # time limits physical times in ncs files self._signal_limits = [(None, None) for seg_idx in range(self._nb_segment)] for stream_id, stream_info in stream_infos.items(): stream_mmaps = stream_info["segment_sig_memmaps"] for seg_idx, signal_dict in enumerate(stream_mmaps): self._sigs_memmaps[seg_idx].update(signal_dict) ncs_segment_info = stream_info["ncs_segment_infos"] for seg_idx, (t_start, t_stop) in enumerate(ncs_segment_info.timestamp_limits): self._timestamp_limits[seg_idx] = min_max_tuple(self._timestamp_limits[seg_idx], (t_start, t_stop)) for seg_idx in range(ncs_segment_info.nb_segment): t_start = ncs_segment_info.t_start[seg_idx] t_stop = ncs_segment_info.t_stop[seg_idx] self._signal_limits[seg_idx] = min_max_tuple(self._signal_limits[seg_idx], (t_start, t_stop)) # precompute signal lengths within segments self._sigs_length = [] if self._sigs_memmaps: for seg_idx, sig_container in enumerate(self._sigs_memmaps): self._sigs_length.append({}) for chan_uid, sig_infos in sig_container.items(): self._sigs_length[seg_idx][chan_uid] = int(sig_infos["nb_valid"].sum()) # Determine timestamp limits in nev, nse, ntt files by scanning them. ts0, ts1 = None, None for _data_memmap in (self._spike_memmap, self._nev_memmap): for _, data in _data_memmap.items(): ts = data["timestamp"] if ts.size == 0: continue if ts0 is None: ts0 = ts[0] ts1 = ts[-1] ts0 = min(ts0, ts[0]) ts1 = max(ts1, ts[-1]) # rescaling for comparison with signal times if ts0 is not None: timestamps_start, timestamps_stop = ts0 / 1e6, ts1 / 1e6 # decide on segment and global start and stop times based on files available if self._timestamp_limits is None: # case NO ncs but HAVE nev or nse -> single segment covering all spikes & events self._timestamp_limits = [(ts0, ts1)] self._seg_t_starts = [timestamps_start] self._seg_t_stops = [timestamps_stop] self.global_t_start = timestamps_start self.global_t_stop = timestamps_stop elif ts0 is not None: # case HAVE ncs AND HAVE nev or nse -> multi segments based on ncs segmentation # ignoring nev/nse/ntt time limits, loading only data within ncs segments global_events_limits = (timestamps_start, timestamps_stop) global_signal_limits = (self._signal_limits[0][0], self._signal_limits[-1][-1]) self.global_t_start, self.global_t_stop = min_max_tuple(global_events_limits, global_signal_limits) self._seg_t_starts = [limits[0] for limits in self._signal_limits] self._seg_t_stops = [limits[1] for limits in self._signal_limits] self._seg_t_starts[0] = self.global_t_start self._seg_t_stops[-1] = self.global_t_stop else: # case HAVE ncs but NO nev or nse -> self._seg_t_starts = [limits[0] for limits in self._signal_limits] self._seg_t_stops = [limits[1] for limits in self._signal_limits] self.global_t_start = self._signal_limits[0][0] self.global_t_stop = self._signal_limits[-1][-1] if self.keep_original_times: self.global_t_stop = self.global_t_stop - self.global_t_start self.global_t_start = 0 # fill header dictionary self.header = {} self.header["nb_block"] = 1 self.header["nb_segment"] = [self._nb_segment] self.header["signal_buffers"] = signal_buffers self.header["signal_streams"] = signal_streams self.header["signal_channels"] = signal_channels self.header["spike_channels"] = spike_channels self.header["event_channels"] = event_channels # Annotations self._generate_minimal_annotations() bl_annotations = self.raw_annotations["blocks"][0] for seg_index in range(self._nb_segment): seg_annotations = bl_annotations["segments"][seg_index] for stream_id in range(signal_streams.size): # one or no signal stream stream_ann = seg_annotations["signals"][stream_id] # handle array annotations for key in signal_annotations[0].keys(): values = [] # only collect values from channels belonging to current stream for d in np.where(signal_channels["stream_id"] == f"{stream_id}")[0]: value = signal_annotations[d][key] values.append(value) values = np.array(values) if values.ndim == 1: # 'InputRange': is 2D and make bugs stream_ann["__array_annotations__"][key] = values for c in range(spike_channels.size): unit_ann = seg_annotations["spikes"][c] unit_ann.update(unit_annotations[c]) for c in range(event_channels.size): # annotations for channel events event_id, ttl_input = self.internal_event_ids[c] chan_id = event_channels[c]["id"] ev_ann = seg_annotations["events"][c] ev_ann["file_origin"] = self.nev_filenames[chan_id] # ~ ev_ann['marker_id'] = # ~ ev_ann['nttl'] = # ~ ev_ann['digital_marker'] = # ~ ev_ann['analog_marker'] = @staticmethod def _get_file_map(filename): """ Create memory maps when needed see also https://github.com/numpy/numpy/issues/19340 """ filename = Path(filename) suffix = filename.suffix.lower()[1:] if suffix == "ncs": return np.memmap(filename, dtype=NeuralynxRawIO._ncs_dtype, mode="r", offset=NlxHeader.HEADER_SIZE) elif suffix in ["nse", "ntt"]: info = NlxHeader(filename) dtype = get_nse_or_ntt_dtype(info, suffix) # return empty map if file does not contain data if os.path.getsize(filename) <= NlxHeader.HEADER_SIZE: return np.zeros((0,), dtype=dtype) return np.memmap(filename, dtype=dtype, mode="r", offset=NlxHeader.HEADER_SIZE) elif suffix == "nev": return np.memmap(filename, dtype=nev_dtype, mode="r", offset=NlxHeader.HEADER_SIZE) else: raise ValueError(f"Unknown file suffix {suffix}") # Accessors for segment times which are offset by appropriate global start time def _segment_t_start(self, block_index, seg_index): return self._seg_t_starts[seg_index] - self.global_t_start def _segment_t_stop(self, block_index, seg_index): return self._seg_t_stops[seg_index] - self.global_t_start def _get_signal_size(self, block_index, seg_index, stream_index): stream_id = self.header["signal_streams"][stream_index]["id"] stream_mask = self.header["signal_channels"]["stream_id"] == stream_id signals = self.header["signal_channels"][stream_mask] if len(signals): sig = signals[0] return self._sigs_length[seg_index][(sig["name"], sig["id"])] else: raise ValueError( f"No signals present for block {block_index}, segment {seg_index}," f" stream {stream_index}" ) def _get_signal_t_start(self, block_index, seg_index, stream_index): stream_id = self.header["signal_streams"][stream_index]["id"] stream_mask = self.header["signal_channels"]["stream_id"] == stream_id # use first channel of stream as all channels in stream have a common t_start channel = self.header["signal_channels"][stream_mask][0] data = self._sigs_memmaps[seg_index][(channel["name"], channel["id"])] absolute_t_start = data["timestamp"][0] return absolute_t_start / 1e6 - self.global_t_start def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, stream_index, channel_indexes): """ Retrieve chunk of analog signal, a chunk being a set of contiguous samples. Parameters ---------- block_index: index of block in dataset, ignored as only 1 block in this implementation seg_index: index of segment to use i_start: sample index of first sample within segment to retrieve i_stop: sample index of last sample within segment to retrieve channel_indexes: list of channel indices to return data for Returns ------- array of samples, with each requested channel in a column """ if i_start is None: i_start = 0 if i_stop is None: i_stop = self.get_signal_size(block_index=block_index, seg_index=seg_index, stream_index=stream_index) block_start = i_start // NcsSection._RECORD_SIZE block_stop = i_stop // NcsSection._RECORD_SIZE + 1 sl0 = i_start % 512 sl1 = sl0 + (i_stop - i_start) if channel_indexes is None: channel_indexes = slice(None) stream_id = self.header["signal_streams"][stream_index]["id"] stream_mask = self.header["signal_channels"]["stream_id"] == stream_id channel_ids = self.header["signal_channels"][stream_mask][channel_indexes]["id"] channel_names = self.header["signal_channels"][stream_mask][channel_indexes]["name"] # create buffer for samples sigs_chunk = np.zeros((i_stop - i_start, len(channel_ids)), dtype="int16") for i, chan_uid in enumerate(zip(channel_names, channel_ids)): data = self._sigs_memmaps[seg_index][chan_uid] sub = data[block_start:block_stop] sigs_chunk[:, i] = sub["samples"].flatten()[sl0:sl1] return sigs_chunk def _spike_count(self, block_index, seg_index, unit_index): chan_uid, unit_id = self.internal_unit_ids[unit_index] data = self._spike_memmap[chan_uid] ts = data["timestamp"] ts0 = self.segment_t_start(block_index, seg_index) ts1 = self.segment_t_stop(block_index, seg_index) # rescale to integer sampling of time ts0 = int((ts0 + self.global_t_start) * 1e6) ts1 = int((ts1 + self.global_t_start) * 1e6) # only count spikes inside the timestamp limits, inclusive, and for the specified unit keep = (ts >= ts0) & (ts <= ts1) & (unit_id == data["unit_id"]) nb_spike = int(data[keep].size) return nb_spike def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): """ Extract timestamps within a Segment defined by ncs timestamps """ chan_uid, unit_id = self.internal_unit_ids[unit_index] data = self._spike_memmap[chan_uid] ts = data["timestamp"] ts0, ts1 = t_start, t_stop if ts0 is None: ts0 = self.segment_t_start(block_index, seg_index) if ts1 is None: ts1 = self.segment_t_stop(block_index, seg_index) # rescale to integer sampling of time ts0 = int((ts0 + self.global_t_start) * 1e6) ts1 = int((ts1 + self.global_t_start) * 1e6) keep = (ts >= ts0) & (ts <= ts1) & (unit_id == data["unit_id"]) timestamps = ts[keep] return timestamps def _rescale_spike_timestamp(self, spike_timestamps, dtype): spike_times = spike_timestamps.astype(dtype) spike_times /= 1e6 spike_times -= self.global_t_start return spike_times def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): chan_uid, unit_id = self.internal_unit_ids[unit_index] data = self._spike_memmap[chan_uid] ts = data["timestamp"] ts0, ts1 = t_start, t_stop if ts0 is None: ts0 = self.segment_t_start(block_index, seg_index) if ts1 is None: ts1 = self.segment_t_stop(block_index, seg_index) # rescale to integer sampling of time ts0 = int((ts0 + self.global_t_start) * 1e6) ts1 = int((ts1 + self.global_t_start) * 1e6) keep = (ts >= ts0) & (ts <= ts1) & (unit_id == data["unit_id"]) wfs = data[keep]["samples"] if wfs.ndim == 2: # case for nse waveforms = wfs[:, None, :] else: # case for ntt change (n, 32, 4) to (n, 4, 32) waveforms = wfs.swapaxes(1, 2) return waveforms def _event_count(self, block_index, seg_index, event_channel_index): event_id, ttl_input = self.internal_event_ids[event_channel_index] chan_id = self.header["event_channels"][event_channel_index]["id"] data = self._nev_memmap[chan_id] ts0 = self.segment_t_start(block_index, seg_index) ts1 = self.segment_t_stop(block_index, seg_index) # rescale to integer sampling of time ts0 = int((ts0 + self.global_t_start) * 1e6) ts1 = int((ts1 + self.global_t_start) * 1e6) ts = data["timestamp"] keep = (ts >= ts0) & (ts <= ts1) & (data["event_id"] == event_id) & (data["ttl_input"] == ttl_input) nb_event = int(data[keep].size) return nb_event def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): event_id, ttl_input = self.internal_event_ids[event_channel_index] chan_id = self.header["event_channels"][event_channel_index]["id"] data = self._nev_memmap[chan_id] ts0, ts1 = t_start, t_stop if ts0 is None: ts0 = self.segment_t_start(block_index, seg_index) if ts1 is None: ts1 = self.segment_t_stop(block_index, seg_index) # rescale to integer sampling of time ts0 = int((ts0 + self.global_t_start) * 1e6) ts1 = int((ts1 + self.global_t_start) * 1e6) ts = data["timestamp"] keep = (ts >= ts0) & (ts <= ts1) & (data["event_id"] == event_id) & (data["ttl_input"] == ttl_input) subdata = data[keep] timestamps = subdata["timestamp"] labels = subdata["event_string"].astype("U") durations = None return timestamps, durations, labels def _rescale_event_timestamp(self, event_timestamps, dtype, event_channel_index): event_times = event_timestamps.astype(dtype) event_times /= 1e6 event_times -= self.global_t_start return event_times def scan_stream_ncs_files(self, ncs_filenames): """ Given a list of ncs files, read their basic structure. Ncs files have to have common sampling_rate, number of packets and t_start (be part of a single stream) Parameters ---------- ncs_filenames: list List of ncs filenames to scan. Returns ------- memmaps [ {} for seg_index in range(self._nb_segment) ][chan_uid] seg_time_limits SegmentTimeLimits for sections in scanned Ncs files section_structure Section structure common to the ncs files Files will be scanned to determine the sections of records. If file is a single section of records, this scan is brief, otherwise it will check each record which may take some time. """ if len(ncs_filenames) == 0: return None, None, None # Build dictionary of chan_uid to associated NcsSections, memmap and NlxHeaders. Only # construct new NcsSections when it is different from that for the preceding file. chanSectMap = dict() sig_length = [] for ncs_filename in ncs_filenames: data = self._get_file_map(ncs_filename) nlxHeader = NlxHeader(ncs_filename) verify_sec_struct = NcsSectionsFactory._verifySectionsStructure if not chanSectMap or (not verify_sec_struct(data, chan_ncs_sections)): chan_ncs_sections = NcsSectionsFactory.build_for_ncs_file( data, nlxHeader, strict_gap_mode=self.strict_gap_mode ) # register file section structure for all contained channels for chan_uid in zip(nlxHeader["channel_names"], np.asarray(nlxHeader["channel_ids"], dtype=str)): chanSectMap[chan_uid] = [chan_ncs_sections, nlxHeader, ncs_filename] del data # Construct an inverse dictionary from NcsSections to list of associated chan_uids revSectMap = dict() for k, v in chanSectMap.items(): revSectMap.setdefault(v[0], []).append(k) # If there is only one NcsSections structure in the set of ncs files, there should only # be one entry. Otherwise this is presently unsupported. if len(revSectMap) > 1: raise IOError( f"ncs files have {len(revSectMap)} different sections " f"structures. Unsupported configuration to be handled with in a single " f"stream." ) seg_time_limits = SegmentTimeLimits( nb_segment=len(chan_ncs_sections.sects), t_start=[], t_stop=[], length=[], timestamp_limits=[] ) memmaps = [{} for seg_index in range(seg_time_limits.nb_segment)] # create segment with subdata block/t_start/t_stop/length for each channel for i, chan_uid in enumerate(chanSectMap.keys()): data = self._get_file_map(chanSectMap[chan_uid][2]) # create a memmap for each record section of the current file curSects = chanSectMap[chan_uid][0] for seg_index in range(len(curSects.sects)): curSect = curSects.sects[seg_index] subdata = data[curSect.startRec : (curSect.endRec + 1)] memmaps[seg_index][chan_uid] = subdata # create segment timestamp limits based on only NcsSections structure in use if i == 0: numSampsLastSect = subdata[-1]["nb_valid"] ts0 = subdata[0]["timestamp"] ts1 = NcsSectionsFactory.calc_sample_time( curSects.sampFreqUsed, subdata[-1]["timestamp"], numSampsLastSect ) seg_time_limits.timestamp_limits.append((ts0, ts1)) t_start = ts0 / 1e6 seg_time_limits.t_start.append(t_start) t_stop = ts1 / 1e6 seg_time_limits.t_stop.append(t_stop) # :NOTE: This should really be the total of nb_valid in records, but this # allows the last record of a section to be shorter, the most common case. # Have never seen a section of records with not full records before the last. length = (subdata.size - 1) * NcsSection._RECORD_SIZE + numSampsLastSect seg_time_limits.length.append(length) stream_section_structure = list(revSectMap.keys())[0] return memmaps, seg_time_limits, stream_section_structure # time limits for set of segments SegmentTimeLimits = namedtuple("SegmentTimeLimits", ["nb_segment", "t_start", "t_stop", "length", "timestamp_limits"]) nev_dtype = [ ("reserved", "\S+)" r"\s+(\(h:m:s\.ms\)|At Time:) (?P