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Merge pull request #1626 from zm711/update-copyright
0.14.0 release notes
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‎codemeta.json

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"license": "https://spdx.org/licenses/BSD-3-Clause",
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"codeRepository": "https://github.com/NeuralEnsemble/python-neo",
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"contIntegration": "https://github.com/NeuralEnsemble/python-neo/actions",
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"dateModified": "2024-10-14",
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"downloadUrl": "https://files.pythonhosted.org/packages/e0/0d/e973b7e8464b6f1d88022c46040f203d93c0b080af0e33702bb11873dbbb/neo-0.13.4.tar.gz",
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"dateModified": "2025-01-17",
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"downloadUrl": "",
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"issueTracker": "https://github.com/NeuralEnsemble/python-neo/issues",
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"name": "Neo",
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"version": "0.13.4",
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"version": "0.14.0",
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"identifier": "RRID:SCR_000634",
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"description": "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.\n\nThe 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.\n\nNeo 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).\n\nNeo 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.",
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"applicationCategory": "neuroscience",
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"releaseNotes": "https://neo.readthedocs.io/en/latest/releases/0.13.4.html",
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"releaseNotes": "https://neo.readthedocs.io/en/latest/releases/0.14.0.html",
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"funding": "https://cordis.europa.eu/project/id/945539",
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"developmentStatus": "active",
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"referencePublication": "https://doi.org/10.3389/fninf.2014.00010",
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{ "@type": "Person", "givenName": "Xin", "familyName": "Niu"},
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{ "@type": "Person", "givenName": "Anthony", "familyName": "Pinto"},
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{ "@type": "Person", "givenName": "Chris", "familyName": "Heydrick"},
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{"@type": "Person", "givenName": "Nikhil", "familyName": "Chandra"}
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{"@type": "Person", "givenName": "Nikhil", "familyName": "Chandra"},
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{"@type": "Person", "givenName": "Luigi", "familyName": "Petrucco"}
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]
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}

‎doc/source/authors.rst

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* Anthony Pinto [41]
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* Xin Niu
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* Nikhil Chandra [40]
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* Luigi Petrucco [42]
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1. Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292 - INSERM U1028 - Universite Claude Bernard Lyon 1
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2. Unité de Neuroscience, Information et Complexité, CNRS UPR 3293, Gif-sur-Yvette, France
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39. Massachusetts General Hospital, Department of Molecular Biology
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40. Plexon Inc.
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41. Paris Brain Institute
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42. Istituto Italiano di Tecnologia (IIT), Italy
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‎doc/source/releases.rst

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.. toctree::
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:maxdepth: 1
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releases/0.14.0.rst
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releases/0.13.4.rst
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releases/0.13.3.rst
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releases/0.13.2.rst

‎doc/source/releases/0.14.0.rst

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========================
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Neo 0.14.0 release notes
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========================
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17 January 2025
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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,
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and includes IO bug fixes with an eye toward a 1.0 release.
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See all `pull requests`_ included in this release and the `list of closed issues`_.
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Updated dependencies
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--------------------
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Neo now has a limit of NumPy >= 1.22.4
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CI Improvements
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---------------
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To ensure compatiblility between pre- and post- NumPy 2.0 the CI was changed to test on the lowest supported Python (3.9) and
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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.
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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
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with the caches getting corrupted.
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Testing of additional Python-NumPy combinations for core tests were added (NumPy 2.0 and 2.1 with their respective Python versions).
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Bug fixes and improvements in IO modules
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----------------------------------------
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Bug fixes and/or improvements have been made to :class:`NeuroNexusIO`, :class:`OpenEphysBinaryIO`, :class:`MicromedIO`, :class:`IntanIO` and :class:`SpikeGLX`.
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Acknowledgements
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----------------
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Thanks to Zach McKenzie, Heberto Mayorquin, Andrew Davison, Luigi Petrucco, Alessio Buccino, and Samuel Garcia.
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.. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.0
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.. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.0

‎examples/plot_igorio.py

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"""
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IgorProIO Demo
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===========================
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IgorProIO Demo (BROKEN)
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=======================
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"""
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# Downloaded from Human Brain Project Collaboratory
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# Digital Reconstruction of Neocortical Microcircuitry (nmc-portal)
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# http://microcircuits.epfl.ch/#/animal/8ecde7d1-b2d2-11e4-b949-6003088da632
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datafile_url = "https://microcircuits.epfl.ch/data/released_data/B95.zip"
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filename_zip = "B95.zip"
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filename = "grouped_ephys/B95/B95_Ch0_IDRest_107.ibw"
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urlretrieve(datafile_url, filename_zip)
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zip_ref = zipfile.ZipFile(filename_zip) # create zipfile object
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zip_ref.extract(path=".", member=filename) # extract file to dir
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zip_ref.close()
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######################################################
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# Once we have our data we can use `get_io` to find an
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# io (Igor in this case). Then we read the analogsignals
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# Finally we will make some nice plots
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reader = get_io(filename)
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signal = reader.read_analogsignal()
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plt.plot(signal.times, signal)
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plt.xlabel(signal.sampling_period.dimensionality)
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plt.ylabel(signal.dimensionality)
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plt.show()
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# NOTE: this dataset is not found as the link is broken.
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# datafile_url = "https://microcircuits.epfl.ch/data/released_data/B95.zip"
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# filename_zip = "B95.zip"
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# filename = "grouped_ephys/B95/B95_Ch0_IDRest_107.ibw"
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# urlretrieve(datafile_url, filename_zip)
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# zip_ref = zipfile.ZipFile(filename_zip) # create zipfile object
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# zip_ref.extract(path=".", member=filename) # extract file to dir
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# zip_ref.close()
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# ######################################################
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# # Once we have our data we can use `get_io` to find an
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# # io (Igor in this case). Then we read the analogsignals
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# # Finally we will make some nice plots
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# reader = get_io(filename)
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# signal = reader.read_analogsignal()
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# plt.plot(signal.times, signal)
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# plt.xlabel(signal.sampling_period.dimensionality)
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# plt.ylabel(signal.dimensionality)
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# plt.show()

‎pyproject.toml

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[project]
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name = "neo"
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version = "0.14.0.dev0"
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version = "0.14.0"
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authors = [{name = "Neo authors and contributors"}]
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description = "Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats"
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readme = "README.rst"
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Programming Language :: Python :: 3 :: Only",
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]
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# "dhn_med_py<2.0", # ci failing with 2.0 test future version when stable
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"pytest",
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"pytest-cov",
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# datalad # this dependency is covered by conda (environment_testing.yml)
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# datalad # this dependency is covered by conda (environment_testing.yml)-- maybe moving straight to ci
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"scipy>=1.0.0",
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"pyedflib",
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"h5py",

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