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Merge branch 'master' into tiffio-fix
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‎.github/workflows/caches_cron_job.yml‎

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jobs:
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create-conda-env-cache-if-missing:
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name: Caching conda env
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runs-on: "ubuntu-latest"
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strategy:
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fail-fast: true
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defaults:
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# by default run in bash mode (required for conda usage)
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run:
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shell: bash -l {0}
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steps:
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- uses: actions/checkout@v3
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- name: Get current year-month
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id: date
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run: |
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echo "date=$(date +'%Y-%m')" >> $GITHUB_OUTPUT
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- name: Get current dependencies hash
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id: dependencies
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run: |
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echo "hash=${{hashFiles('**/pyproject.toml', '**/environment_testing.yml')}}" >> $GITHUB_OUTPUT
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- uses: actions/cache@v3
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# the cache for python package is reset:
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# * every month
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# * when package dependencies change
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id: cache-conda-env
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with:
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path: /usr/share/miniconda/envs/neo-test-env
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key: ${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-${{ steps.date.outputs.date }}
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- name: Cache found?
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run: echo "Cache-hit == ${{steps.cache-conda-env.outputs.cache-hit == 'true'}}"
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# activate environment if not restored from cache
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- uses: conda-incubator/[email protected]
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if: steps.cache-conda-env.outputs.cache-hit != 'true'
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with:
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activate-environment: neo-test-env
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environment-file: environment_testing.yml
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python-version: 3.9
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- name: Create the conda environment to be cached
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if: steps.cache-conda-env.outputs.cache-hit != 'true'
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# create conda env, configure git and install pip, neo and test dependencies from master
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# for PRs that change dependencies, this environment will be updated in the test workflow
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run: |
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git config --global user.email "neo_ci@fake_mail.com"
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git config --global user.name "neo CI"
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python -m pip install -U pip # Official recommended way
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pip install --upgrade -e .[test]
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# create-conda-env-cache-if-missing:
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# name: Caching conda env
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# runs-on: "ubuntu-latest"
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# strategy:
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# fail-fast: true
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# defaults:
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# # by default run in bash mode (required for conda usage)
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# run:
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# shell: bash -l {0}
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# steps:
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# - uses: actions/checkout@v3
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# - name: Get current year-month
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# id: date
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# run: |
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# echo "date=$(date +'%Y-%m')" >> $GITHUB_OUTPUT
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# - name: Get current dependencies hash
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# id: dependencies
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# run: |
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# echo "hash=${{hashFiles('**/pyproject.toml', '**/environment_testing.yml')}}" >> $GITHUB_OUTPUT
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# - uses: actions/cache@v3
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# # the cache for python package is reset:
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# # * every month
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# # * when package dependencies change
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# id: cache-conda-env
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# with:
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# path: /usr/share/miniconda/envs/neo-test-env
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# key: ${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-${{ steps.date.outputs.date }}
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# - name: Cache found?
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# run: echo "Cache-hit == ${{steps.cache-conda-env.outputs.cache-hit == 'true'}}"
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# # activate environment if not restored from cache
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# - uses: conda-incubator/[email protected]
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# if: steps.cache-conda-env.outputs.cache-hit != 'true'
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# with:
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# activate-environment: neo-test-env
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# environment-file: environment_testing.yml
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# python-version: 3.9
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# - name: Create the conda environment to be cached
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# if: steps.cache-conda-env.outputs.cache-hit != 'true'
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# # create conda env, configure git and install pip, neo and test dependencies from master
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# # for PRs that change dependencies, this environment will be updated in the test workflow
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# run: |
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# git config --global user.email "neo_ci@fake_mail.com"
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# git config --global user.name "neo CI"
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# python -m pip install -U pip # Official recommended way
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# pip install --upgrade -e .[test]
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create-data-cache-if-missing:
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name: Caching data env

‎.github/workflows/io-test.yml‎

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jobs:
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build-and-test:
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name: Test on (${{ inputs.os }}) (${{ matrix.python-version}})
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name: Test on (${{ inputs.os }}) (${{ matrix.python-version}}) (${{ matrix.numpy-version }})
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runs-on: ${{ inputs.os }}
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strategy:
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fail-fast: true
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matrix:
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python-version: ['3.9', '3.12']
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numpy-version: ['1.26', '2.0']
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defaults:
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# by default run in bash mode (required for conda usage)
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run:
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with:
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activate-environment: neo-test-env-${{ matrix.python-version }}
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python-version: "${{ matrix.python-version }}"
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- name: Get current dependencies hash
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id: dependencies
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run: |
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echo "hash=${{hashFiles('**/pyproject.toml', '**/environment_testing.yml')}}" >> $GITHUB_OUTPUT
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- uses: actions/cache/restore@v3
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# TEST NOT USING CACHE
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#- name: Get current dependencies hash
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# id: dependencies
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# run: |
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# echo "hash=${{hashFiles('**/pyproject.toml', '**/environment_testing.yml')}}" >> $GITHUB_OUTPUT
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#- uses: actions/cache/restore@v3
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# the cache for python package is reset:
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# * every month
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# * when package dependencies change
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id: cache-conda-env
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with:
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path: /usr/share/miniconda/envs/neo-test-env-${{ matrix.python-version }}
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key: ${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-${{ steps.date.outputs.date }}
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# id: cache-conda-env
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# with:
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# path: /usr/share/miniconda/envs/neo-test-env-${{ matrix.python-version }}
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# key: ${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-${{ steps.date.outputs.date }}
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# restore-keys match any key that starts with the restore-key
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restore-keys: |
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${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-
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${{ runner.os }}-conda-env-
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# restore-keys: |
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# ${{ runner.os }}-conda-env-${{ steps.dependencies.outputs.hash }}-
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# ${{ runner.os }}-conda-env-
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- name: Install testing dependencies
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# testing environment is only created from yml if no cache was found
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# restore-key hits should result in `cache-hit` == 'false'
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if: steps.cache-conda-env.outputs.cache-hit != 'true'
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#if: steps.cache-conda-env.outputs.cache-hit != 'true'
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run: |
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conda env update --name neo-test-env-${{ matrix.python-version }} --file environment_testing.yml --prune
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conda install datalad pip numpy=${{ matrix.numpy-version }} -c conda-forge
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# this command is for updating cache. We are resting removal.
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# conda env update --name neo-test-env-${{ matrix.python-version }} --file environment_testing.yml --prune
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- name: Configure git
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run: |
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- name: Install neo including dependencies
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# installation with dependencies is only required if no cache was found
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# restore-key hits should result in `cache-hit` == 'false'
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if: steps.cache-conda-env.outputs.cache-hit != 'true'
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# if: steps.cache-conda-env.outputs.cache-hit != 'true'
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run: |
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pip install --upgrade -e .
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pip install .[test]
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- name: Install neo without dependencies
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# TEST NOT USING CACHE
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#- name: Install neo without dependencies
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# only installing neo version to test as dependencies should be in cached conda env already
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if: steps.cache-conda-env.outputs.cache-hit == 'true'
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run: |
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pip install --no-dependencies -e .
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# if: steps.cache-conda-env.outputs.cache-hit == 'true'
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# run: |
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# pip install --no-dependencies -e .
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- name: Install wine
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run: |

‎LICENSE.txt‎

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Copyright (c) 2010-2024, Neo authors and contributors
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Copyright (c) 2010-2025, Neo authors and contributors
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All rights reserved.
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

‎README.rst‎

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To cite Neo in publications, see CITATION.txt
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:copyright: Copyright 2010-2024 by the Neo team, see doc/source/authors.rst.
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:copyright: Copyright 2010-2025 by the Neo team, see doc/source/authors.rst.
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:license: 3-Clause Revised BSD License, see LICENSE.txt for details.
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Funding

‎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": "2025-01-20",
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"downloadUrl": "https://files.pythonhosted.org/packages/3b/91/c3630d766b3b959f8e604d8d41580c78973ece5f1e070b13bd9755dba60b/neo-0.14.0.tar.gz",
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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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* Tommaso Lambresa [43]
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* Nina Kudryashova [37]
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* Rémi Proville [44]
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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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1. Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292 - INSERM U1028 - Université 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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3. University of California, Berkeley
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4. Laboratoire de Neurosciences Intégratives et Adaptatives, CNRS UMR 6149 - Université de Provence, Marseille, 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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43. University of Genoa, Italy
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44. AquiNeuro, SAS
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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,
8+
and includes IO bug fixes with an eye toward a 1.0 release.
9+
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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
16+
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CI Improvements
18+
---------------
19+
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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
21+
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
24+
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).
27+
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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

‎environment_testing.yml‎

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dependencies:
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- datalad
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- pip
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# temporary have this here for IO testing while we decide how to deal with
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# external packages not 2.0 ready
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- numpy=1.26.4

‎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
29-
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()
37-
plt.plot(signal.times, signal)
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plt.xlabel(signal.sampling_period.dimensionality)
39-
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
29+
# 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)
38+
# plt.xlabel(signal.sampling_period.dimensionality)
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# plt.ylabel(signal.dimensionality)
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# plt.show()

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