diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 00000000..2d0e9261 --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,48 @@ +FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f + +ENV PATH /usr/local/bin:$PATH +ENV PYTHON_VERSION 3.9.17 + +RUN \ + adduser --system --disabled-password --shell /bin/bash vscode && \ + # install docker + apt-get update && \ + apt-get install ca-certificates curl gnupg lsb-release -y && \ + mkdir -m 0755 -p /etc/apt/keyrings && \ + curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg && \ + echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null && \ + apt-get update && \ + apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin -y && \ + usermod -aG docker vscode && \ + apt-get clean + +RUN \ + # dev setup + apt update && \ + apt-get install sudo git bash-completion graphviz default-mysql-client s3fs procps -y && \ + usermod -aG sudo vscode && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ + pip install --no-cache-dir --upgrade black pip nbconvert && \ + echo '. /etc/bash_completion' >> /home/vscode/.bashrc && \ + echo 'export PS1="\[\e[32;1m\]\u\[\e[m\]@\[\e[34;1m\]\H\[\e[m\]:\[\e[33;1m\]\w\[\e[m\]$ "' >> /home/vscode/.bashrc && \ + apt-get clean + +COPY ./ /tmp/element-array-ephys/ + +RUN \ + # pipeline dependencies + apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \ + pip install --no-cache-dir -e /tmp/element-array-ephys[elements,tests] && \ + # clean up + rm -rf /tmp/element-array-ephys && \ + apt-get clean + +ENV DJ_HOST fakeservices.datajoint.io +ENV DJ_USER root +ENV DJ_PASS simple + +ENV EPHYS_ROOT_DATA_DIR /workspaces/element-array-ephys/example_data +ENV DATABASE_PREFIX neuro_ + +USER vscode +CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 00000000..bf939e88 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,30 @@ +{ + "name": "Environment + Data", + "dockerComposeFile": "docker-compose.yaml", + "service": "app", + "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}", + "remoteEnv": { + "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" + }, + "onCreateCommand": "mkdir -p ${EPHYS_ROOT_DATA_DIR} && pip install -e .", + "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${EPHYS_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", + "hostRequirements": { + "cpus": 4, + "memory": "8gb", + "storage": "32gb" + }, + "forwardPorts": [ + 3306 + ], + "customizations": { + "settings": { + "python.pythonPath": "/usr/local/bin/python" + }, + "vscode": { + "extensions": [ + "ms-python.python@2023.8.0", + "ms-toolsai.jupyter@2023.3.1201040234" + ] + } + } +} \ No newline at end of file diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml new file mode 100644 index 00000000..7a8c2e14 --- /dev/null +++ b/.devcontainer/docker-compose.yaml @@ -0,0 +1,25 @@ +version: "3" +services: + app: + cpus: 4 + mem_limit: 8g + build: + context: .. + dockerfile: ./.devcontainer/Dockerfile + # image: datajoint/element_array_ephys:latest + extra_hosts: + - fakeservices.datajoint.io:127.0.0.1 + environment: + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-array-ephys-benchmark/v2 + devices: + - /dev/fuse + cap_add: + - SYS_ADMIN + security_opt: + - apparmor:unconfined + volumes: + - ..:/workspaces/element-array-ephys:cached + - docker_data:/var/lib/docker # persist docker images + privileged: true # only because of dind +volumes: + docker_data: diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 9ae4ef02..4a5f2cb5 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -16,12 +16,12 @@ jobs: uses: datajoint/.github/.github/workflows/mkdocs_release.yaml@main permissions: contents: write - # devcontainer-build: - # uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main - # devcontainer-publish: - # needs: - # - devcontainer-build - # uses: datajoint/.github/.github/workflows/devcontainer-publish.yaml@main - # secrets: - # DOCKERHUB_USERNAME: ${{secrets.DOCKERHUB_USERNAME}} - # DOCKERHUB_TOKEN: ${{secrets.DOCKERHUB_TOKEN_FOR_ELEMENTS}} \ No newline at end of file + devcontainer-build: + uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main + devcontainer-publish: + needs: + - devcontainer-build + uses: datajoint/.github/.github/workflows/devcontainer-publish.yaml@main + secrets: + DOCKERHUB_USERNAME: ${{secrets.DOCKERHUB_USERNAME}} + DOCKERHUB_TOKEN: ${{secrets.DOCKERHUB_TOKEN_FOR_ELEMENTS}} \ No newline at end of file diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index acaddca0..e5e6a07a 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -4,8 +4,8 @@ on: pull_request: workflow_dispatch: jobs: - # devcontainer-build: - # uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main + devcontainer-build: + uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main tests: runs-on: ubuntu-latest strategy: diff --git a/.gitignore b/.gitignore index e25ed045..3f5c0d84 100644 --- a/.gitignore +++ b/.gitignore @@ -116,14 +116,15 @@ dj_local_conf_old.json **/#*# **/.#* -docker-compose.y*ml # include !docs/docker-compose.yaml # vscode settings -.vscode *.code-workspace # exports/notes temp* + +# Codespaces +example_data/ \ No newline at end of file diff --git a/.markdownlint.yaml b/.markdownlint.yaml index 7420359b..ac52a8aa 100644 --- a/.markdownlint.yaml +++ b/.markdownlint.yaml @@ -11,6 +11,8 @@ MD030: false # Number of spaces after a list MD033: # HTML elements allowed allowed_elements: - "br" + - "figure" + - "figcaption" MD034: false # Permit bare URLs MD031: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling MD046: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 00000000..2b2502c6 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false + } + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..e1160fae --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,15 @@ +{ + "editor.formatOnPaste": false, + "editor.formatOnSave": true, + "editor.rulers": [ + 88 + ], + "python.formatting.provider": "black", + "[python]": { + "editor.defaultFormatter": null + }, + "[markdown]": { + "editor.defaultFormatter": "disable" + }, + "files.autoSave": "off" +} \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md index 35f7120e..001e9e13 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,13 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. +## [0.3.0] - 2023-10-25 + ++ Add - DevContainer for codespaces ++ Add - `tutorial_pipeline.py` ++ Add - 60 min tutorial using Jupyter Notebooks, short demo notebook ++ Update - General improvements to increase consistency with other DataJoint Elements + ## [0.2.11] - 2023-06-29 + Update - Improve kilosort triggering routine - better logging, remove temporary files, robust resumable processing @@ -126,6 +133,8 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and + Add - Probe table supporting: Neuropixels probes 1.0 - 3A, 1.0 - 3B, 2.0 - SS, 2.0 - MS +[0.3.0]: https://github.com/datajoint/element-array-ephys/releases/tag/0.3.0 +[0.2.11]: https://github.com/datajoint/element-array-ephys/releases/tag/0.2.11 [0.2.10]: https://github.com/datajoint/element-array-ephys/releases/tag/0.2.10 [0.2.9]: https://github.com/datajoint/element-array-ephys/releases/tag/0.2.9 [0.2.8]: https://github.com/datajoint/element-array-ephys/releases/tag/0.2.8 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e04d1708..2bd0f498 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,5 +1,5 @@ # Contribution Guidelines This project follows the -[DataJoint Contribution Guidelines](https://datajoint.com/docs/community/contribute/). +[DataJoint Contribution Guidelines](https://datajoint.com/docs/about/contribute/). Please reference the link for more full details. diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index c144a405..00000000 --- a/Dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -ARG PY_VER -ARG DISTRO -ARG IMAGE -ARG PKG_NAME -ARG PKG_VERSION - -FROM datajoint/${IMAGE}:py${PY_VER}-${DISTRO} -COPY --chown=anaconda:anaconda ./requirements.txt ./setup.py \ - /main/ -COPY --chown=anaconda:anaconda ./${PKG_NAME} /main/${PKG_NAME} -RUN \ - cd /main && \ - pip install . && \ - rm -R /main/* -WORKDIR /main diff --git a/README.md b/README.md index 9324328f..b59fda94 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -[![PyPI version](https://badge.fury.io/py/element-array-ephys.svg)](http://badge.fury.io/py/element-array-ephys) - # DataJoint Element for Extracellular Electrophysiology DataJoint Element for extracellular array electrophysiology that processes data @@ -12,7 +10,8 @@ Kilosort](https://github.com/MouseLand/pykilosort) spike sorting software. DataJ Elements collectively standardize and automate data collection and analysis for neuroscience experiments. Each Element is a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements -to assemble a fully functional pipeline. +to assemble a fully functional pipeline. This repository also provides a tutorial +environment and notebooks to learn the pipeline. ## Experiment flowchart @@ -22,19 +21,66 @@ to assemble a fully functional pipeline. ![datajoint](https://raw.githubusercontent.com/datajoint/element-array-ephys/main/images/attached_array_ephys_element_acute.svg) ++ We have designed variations of the pipeline to handle different use cases. Displayed + above is the `ephys_acute` schema for acute electrophysiology recordings. Details on + all `ephys` schemas can be found in the documentation. ## Getting Started -+ Install from PyPI ++ Please fork this repository. ++ Clone the repository to your computer. +```bash + git clone https://github.com//element-array-ephys.git +``` + ++ Install with `pip`: ```bash - pip install element-array-ephys + pip install -e . ``` - -+ [Interactive tutorial on GitHub Codespaces](https://github.com/datajoint/workflow-array-ephys#interactive-tutorial) ++ [Interactive tutorial on GitHub + Codespaces](https://github.com/datajoint/element-array-ephys#interactive-tutorial) + [Documentation](https://datajoint.com/docs/elements/element-array-ephys) ## Support -+ If you need help getting started or run into any errors, please contact our team by email at support@datajoint.com. ++ If you need help getting started or run into any errors, please contact our team by + email at support@datajoint.com. + +## Interactive Tutorial + ++ The easiest way to learn about DataJoint Elements is to use the tutorial notebooks within the included interactive environment configured using [Dev Container](https://containers.dev/). + +### Launch Environment + +Here are some options that provide a great experience: + +- (*recommended*) Cloud-based Environment + - Launch using [GitHub Codespaces](https://github.com/features/codespaces) using the `+` option which will `Create codespace on main` in the codebase repository on your fork with default options. For more control, see the `...` where you may create `New with options...`. + - Build time for a codespace is a few minutes. This is done infrequently and cached for convenience. + - Start time for a codespace is less than 1 minute. This will pull the built codespace from cache when you need it. + - *Tip*: Each month, GitHub renews a [free-tier](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts) quota of compute and storage. Typically we run into the storage limits before anything else since Codespaces consume storage while stopped. It is best to delete Codespaces when not actively in use and recreate when needed. We'll soon be creating prebuilds to avoid larger build times. Once any portion of your quota is reached, you will need to wait for it to be reset at the end of your cycle or add billing info to your GitHub account to handle overages. + - *Tip*: GitHub auto names the codespace but you can rename the codespace so that it is easier to identify later. + +- Local Environment + > *Note: Access to example data is currently limited to MacOS and Linux due to the s3fs utility. Windows users are recommended to use the above environment.* + - Install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) + - Install [Docker](https://docs.docker.com/get-docker/) + - Install [VSCode](https://code.visualstudio.com/) + - Install the VSCode [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) + - `git clone` the codebase repository and open it in VSCode + - Use the `Dev Containers extension` to `Reopen in Container` (More info is in the `Getting started` included with the extension.) + +You will know your environment has finished loading once you either see a terminal open related to `Running postStartCommand` with a final message of `Done` or the `README.md` is opened in `Preview`. + +Once the environment has launched, please run the following command in the terminal: +``` +MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d +``` + +### Instructions + +1. We recommend you start by navigating to the `notebooks` directory on the left panel and go through the `tutorial.ipynb` Jupyter notebook. Execute the cells in the notebook to begin your walk through of the tutorial. + +2. Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to `Stop Current Codespace` but when running Dev Container on your own machine the equivalent option is `Reopen folder locally`. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity. Once the Codespace is no longer being used, we recommend deleting the Codespace. diff --git a/cspell.json b/cspell.json index 524ca0da..cb07a240 100644 --- a/cspell.json +++ b/cspell.json @@ -4,7 +4,9 @@ "version": "0.2", // Version of the setting file. Always 0.2 "language": "en", // language - current active spelling language "enabledLanguageIds": [ - "markdown", "yaml", "python" + "markdown", + "yaml", + "python" ], // flagWords - list of words to be always considered incorrect // This is useful for offensive words and common spelling errors. @@ -22,6 +24,7 @@ "Andreas", "apmeta", "arange", + "arithmatex", "asarray", "astype", "autocorrelogram", @@ -29,79 +32,111 @@ "bbins", "bdist", "Binarize", + "bouton", "Brody", + "Bruker", + "bshift", "Buccino", "catgt", "cbar", "cbin", "cdat", - "Chans", "chans", + "Chans", "chns", "Clust", "clusterings", "cmap", + "cnmf", "correlogram", "correlograms", "curations", "DANDI", "decomp", + "deconvolution", "DISTRO", "djbase", "dtype", "ecephys", + "Eftychios", "electrophysiogical", "elif", "Ephys", + "fluo", + "fneu", + "Fneu", "gblcar", "gfix", + "Giovannucci", + "Hakan", "hdmf", "HHMI", "hstack", "ibllib", "ifnull", + "imax", "Imax", "IMAX", - "imax", "imec", "imread", "imro", "imrotbl", "imshow", + "Inan", "inlinehilite", + "iplane", "ipynb", "ipywidgets", + "iscell", + "Kavli", "kcoords", "Klusta", "Kwik", "lfmeta", "linenums", + "masky", + "mathjax", "mdict", "Mesoscale", + "mesoscope", "mkdocs", "mkdocstrings", "Moser", "mtscomp", - "Nchan", + "nblocks", "nchan", + "Nchan", + "nchannels", "ndarray", + "ndepths", "ndim", "ndimage", "Neuralynx", "NEURO", "neuroconv", "Neurodata", + "Neurolabware", + "neuropil", + "Neuropil", "Neuropix", "neuropixel", "NeuroPixels", + "nfields", + "nframes", + "npix", + "nplanes", + "nrois", + "NTNU", "nwbfile", "NWBHDF", "oebin", "openephys", - "openephys", "openpyxl", + "Pachitariu", + "paramsets", "phylog", "plotly", + "Pnevmatikakis", "PSTH", "pykilosort", "pymdownx", @@ -109,48 +144,67 @@ "pyopenephys", "pyplot", "pytest", + "quantile", "Reimer", "repolarization", "Roboto", + "roidetect", + "rois", + "ROIs", "RRID", "Rxiv", "Sasaki", + "sbxreader", "scipy", "sdist", - "Siegle", "sess", "SGLX", "Shen", + "Siegle", "Sitonic", "spikeglx", "spkcount", + "spks", "Stereotaxic", + "Sutter", "tcat", "tickvals", "tofile", "Tolias", "tqdm", + "usecs", + "usedb", + "Vidrio's", "vline", - "Vmax", "vmax", + "Vmax", + "voxel", "xanchor", "xaxes", "xaxis", + "xblock", "xcoords", "xcorr", "xlabel", "xlim", + "xoff", + "xpix", "XPOS", "xtick", + "yanchor", "Yatsenko", "yaxes", "yaxis", + "yblock", "ycoord", "ycoords", "ylabel", "ylim", + "yoff", + "ypix", "YPOS", "yref", - "yticks" + "yticks", + "zpix" ] } diff --git a/docker-compose-build.yaml b/docker-compose-build.yaml deleted file mode 100644 index 81984c7c..00000000 --- a/docker-compose-build.yaml +++ /dev/null @@ -1,26 +0,0 @@ -# PY_VER=3.8 IMAGE=djbase DISTRO=alpine PKG_NAME=$(python -c "print([p for p in __import__('setuptools').find_packages() if '.' not in p][0])") PKG_VERSION=$(cat ${PKG_NAME}/version.py | awk -F\' '/__version__/ {print $2}') HOST_UID=$(id -u) docker-compose -f docker-compose-build.yaml up --exit-code-from element --build -# -# Intended for updating dependencies and docker image. -# Used to build release artifacts. -version: "2.4" -services: - element: - build: - context: . - args: - - PY_VER - - DISTRO - - IMAGE - - PKG_NAME - - PKG_VERSION - image: datajoint/${PKG_NAME}:${PKG_VERSION} - user: ${HOST_UID}:anaconda - volumes: - - .:/main - command: - - sh - - -lc - - | - set -e - rm -R build dist *.egg-info || echo "No prev build" - python setup.py bdist_wheel sdist diff --git a/docker-compose-db.yaml b/docker-compose-db.yaml new file mode 100644 index 00000000..1d453c89 --- /dev/null +++ b/docker-compose-db.yaml @@ -0,0 +1,15 @@ +# MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build +version: "3" +services: + db: + restart: always + image: datajoint/mysql:${MYSQL_VER} + environment: + - MYSQL_ROOT_PASSWORD=${DJ_PASS} + ports: + - "3306:3306" + healthcheck: + test: [ "CMD", "mysqladmin", "ping", "-h", "localhost" ] + timeout: 15s + retries: 10 + interval: 15s diff --git a/element_array_ephys/__init__.py b/element_array_ephys/__init__.py index 3a0e5af6..99acf32a 100644 --- a/element_array_ephys/__init__.py +++ b/element_array_ephys/__init__.py @@ -1,22 +1,19 @@ -""" -isort:skip_file -""" - -import logging import os - import datajoint as dj +if "custom" not in dj.config: + dj.config["custom"] = {} -__all__ = ["ephys", "get_logger"] - -dj.config["enable_python_native_blobs"] = True +# overwrite dj.config['custom'] values with environment variables if available +dj.config["custom"]["database.prefix"] = os.getenv( + "DATABASE_PREFIX", dj.config["custom"].get("database.prefix", "") +) -def get_logger(name): - log = logging.getLogger(name) - log.setLevel(os.getenv("LOGLEVEL", "INFO")) - return log +dj.config["custom"]["ephys_root_data_dir"] = os.getenv( + "EPHYS_ROOT_DATA_DIR", dj.config["custom"].get("ephys_root_data_dir", "") +) +db_prefix = dj.config["custom"].get("database.prefix", "") from . import ephys_acute as ephys diff --git a/element_array_ephys/ephys_acute.py b/element_array_ephys/ephys_acute.py index 9b7b5c8d..0a213db8 100644 --- a/element_array_ephys/ephys_acute.py +++ b/element_array_ephys/ephys_acute.py @@ -10,10 +10,10 @@ import pandas as pd from element_interface.utils import dict_to_uuid, find_full_path, find_root_directory -from . import ephys_report, get_logger, probe +from . import ephys_report, probe from .readers import kilosort, openephys, spikeglx -log = get_logger(__name__) +log = dj.logger schema = dj.schema() diff --git a/element_array_ephys/ephys_chronic.py b/element_array_ephys/ephys_chronic.py index 61c325a9..10a492fd 100644 --- a/element_array_ephys/ephys_chronic.py +++ b/element_array_ephys/ephys_chronic.py @@ -9,10 +9,10 @@ import pandas as pd from element_interface.utils import dict_to_uuid, find_full_path, find_root_directory -from . import ephys_report, get_logger, probe +from . import ephys_report, probe from .readers import kilosort, openephys, spikeglx -log = get_logger(__name__) +log = dj.logger schema = dj.schema() diff --git a/element_array_ephys/ephys_no_curation.py b/element_array_ephys/ephys_no_curation.py index bc297044..4d052169 100644 --- a/element_array_ephys/ephys_no_curation.py +++ b/element_array_ephys/ephys_no_curation.py @@ -10,10 +10,10 @@ import pandas as pd from element_interface.utils import dict_to_uuid, find_full_path, find_root_directory -from . import ephys_report, get_logger, probe +from . import ephys_report, probe from .readers import kilosort, openephys, spikeglx -log = get_logger(__name__) +log = dj.logger schema = dj.schema() diff --git a/element_array_ephys/probe.py b/element_array_ephys/probe.py index 1d517cf3..f0aa4ddd 100644 --- a/element_array_ephys/probe.py +++ b/element_array_ephys/probe.py @@ -107,10 +107,12 @@ def create_neuropixels_probe(probe_type: str = "neuropixels 1.0 - 3A"): npx_probes_config["neuropixels 2.0 - MS"] = npx_probes_config["NP2010"] probe_type = {"probe_type": probe_type} - probe_params = dict(zip( + probe_params = dict( + zip( probe_geometry.geom_param_names, - npx_probes_config[probe_type["probe_type"]] - )) + npx_probes_config[probe_type["probe_type"]], + ) + ) electrode_layouts = probe_geometry.build_npx_probe( **{**probe_params, **probe_type} ) diff --git a/element_array_ephys/readers/spikeglx.py b/element_array_ephys/readers/spikeglx.py index 819b7b1a..6d9ba4f6 100644 --- a/element_array_ephys/readers/spikeglx.py +++ b/element_array_ephys/readers/spikeglx.py @@ -451,10 +451,7 @@ def _transform_geom_to_shank(self): from . import probe_geometry probe_params = dict( - zip( - probe_geometry.geom_param_names, - probe_geometry.M[self.probe_PN] - ) + zip(probe_geometry.geom_param_names, probe_geometry.M[self.probe_PN]) ) probe_params["probe_type"] = self.probe_PN elec_pos_df = probe_geometry.build_npx_probe(**probe_params) diff --git a/element_array_ephys/version.py b/element_array_ephys/version.py index 122aedf1..6669fd89 100644 --- a/element_array_ephys/version.py +++ b/element_array_ephys/version.py @@ -1,2 +1,2 @@ """Package metadata.""" -__version__ = "0.2.11" +__version__ = "0.3.0" diff --git a/images/diagram_flowchart.drawio b/images/diagram_flowchart.drawio index 410a33b2..b259ce3e 100644 --- a/images/diagram_flowchart.drawio +++ b/images/diagram_flowchart.drawio @@ -1 +1,78 @@ 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 \ No newline at end of file + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/images/diagram_flowchart.svg b/images/diagram_flowchart.svg index 5840de98..e8b1df4b 100644 --- a/images/diagram_flowchart.svg +++ b/images/diagram_flowchart.svg @@ -1,3 +1,4 @@ + -
Generate quality
control metrics
Generate quality...
Optionally
curate units
Optionally...
Acquire Neuropixels data with OpenEphys
 or SpikeGLX
Acquire Neuropixels...
Enter metadata
into pipeline
Enter metadata...
Process with
Kilosort
Process with...
Visualize



 
Visualize...

 Export & publish

 
 
Export & publish...
Synchronize data modalities
& exploratory
analysis
Synchronize data mo...Text is not SVG - cannot display \ No newline at end of file +
Generate quality
control metrics
Generate quality...
Optionally
curate units
Optionally...
Acquire Neuropixels data with OpenEphys
 or SpikeGLX
Acquire Neuropixels...
Enter metadata
into pipeline
Enter metadata...
Process with
Kilosort
Process with...
Visualize



 
Visualize...

 Export & publish

 
 
Export & publish...
Synchronize data modalities
& exploratory
analysis
Synchronize data mo...Text is not SVG - cannot display \ No newline at end of file diff --git a/notebooks/demo_prepare.ipynb b/notebooks/demo_prepare.ipynb new file mode 100644 index 00000000..74057ba4 --- /dev/null +++ b/notebooks/demo_prepare.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Demo Preparation Notebook\n", + "\n", + "**Please Note**: This notebook (`demo_prepare.ipynb`) and `demo_run.ipynb` are **NOT** intended to be used as learning materials. To gain\n", + "a thorough understanding of the DataJoint workflow for extracellular electrophysiology, please\n", + "see the [`tutorial`](./tutorial.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Runs in about 45s\n", + "import datajoint as dj\n", + "import datetime\n", + "from tutorial_pipeline import subject, session, probe, ephys\n", + "from element_array_ephys import ephys_report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.insert1(\n", + " dict(subject=\"subject5\", subject_birth_date=\"2023-01-01\", sex=\"U\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = dict(subject=\"subject5\", session_datetime=\"2023-01-01 00:00:00\")\n", + "\n", + "session.Session.insert1(session_key)\n", + "\n", + "session.SessionDirectory.insert1(dict(session_key, session_dir=\"raw/subject5/session1\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "probe.Probe.insert1(dict(probe=\"714000838\", probe_type=\"neuropixels 1.0 - 3B\"))\n", + "\n", + "ephys.ProbeInsertion.insert1(\n", + " dict(\n", + " session_key,\n", + " insertion_number=1,\n", + " probe=\"714000838\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "populate_settings = {\"display_progress\": True}\n", + "\n", + "ephys.EphysRecording.populate(**populate_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kilosort_params = {\n", + " \"fs\": 30000,\n", + " \"fshigh\": 150,\n", + " \"minfr_goodchannels\": 0.1,\n", + " \"Th\": [10, 4],\n", + " \"lam\": 10,\n", + " \"AUCsplit\": 0.9,\n", + " \"minFR\": 0.02,\n", + " \"momentum\": [20, 400],\n", + " \"sigmaMask\": 30,\n", + " \"ThPr\": 8,\n", + " \"spkTh\": -6,\n", + " \"reorder\": 1,\n", + " \"nskip\": 25,\n", + " \"GPU\": 1,\n", + " \"Nfilt\": 1024,\n", + " \"nfilt_factor\": 4,\n", + " \"ntbuff\": 64,\n", + " \"whiteningRange\": 32,\n", + " \"nSkipCov\": 25,\n", + " \"scaleproc\": 200,\n", + " \"nPCs\": 3,\n", + " \"useRAM\": 0,\n", + "}\n", + "\n", + "ephys.ClusteringParamSet.insert_new_params(\n", + " clustering_method=\"kilosort2\",\n", + " paramset_idx=1,\n", + " params=kilosort_params,\n", + " paramset_desc=\"Spike sorting using Kilosort2\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.ClusteringTask.insert1(\n", + " dict(\n", + " session_key,\n", + " insertion_number=1,\n", + " paramset_idx=1,\n", + " task_mode=\"load\", # load or trigger\n", + " clustering_output_dir=\"processed/subject5/session1/probe_1/kilosort2-5_1\",\n", + " )\n", + ")\n", + "\n", + "ephys.Clustering.populate(**populate_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clustering_key = (ephys.ClusteringTask & session_key).fetch1(\"KEY\")\n", + "ephys.Curation().create1_from_clustering_task(clustering_key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Runs in about 12m\n", + "ephys.CuratedClustering.populate(**populate_settings)\n", + "ephys.WaveformSet.populate(**populate_settings)\n", + "ephys_report.ProbeLevelReport.populate(**populate_settings)\n", + "ephys_report.UnitLevelReport.populate(**populate_settings)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drop schemas\n", + "- Schemas are not typically dropped in a production workflow with real data in it.\n", + "- At the developmental phase, it might be required for the table redesign.\n", + "- When dropping all schemas is needed, the following is the dependency order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def drop_databases(databases):\n", + " import pymysql.err\n", + "\n", + " conn = dj.conn()\n", + "\n", + " with dj.config(safemode=False):\n", + " for database in databases:\n", + " schema = dj.Schema(f'{dj.config[\"custom\"][\"database.prefix\"]}{database}')\n", + " while schema.list_tables():\n", + " for table in schema.list_tables():\n", + " try:\n", + " conn.query(f\"DROP TABLE `{schema.database}`.`{table}`\")\n", + " except pymysql.err.OperationalError:\n", + " print(f\"Can't drop `{schema.database}`.`{table}`. Retrying...\")\n", + " schema.drop()\n", + "\n", + "\n", + "# drop_databases(databases=['analysis', 'trial', 'event', 'ephys_report', 'ephys', 'probe', 'session', 'subject', 'project', 'lab'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/demo_run.ipynb b/notebooks/demo_run.ipynb new file mode 100644 index 00000000..348a3c43 --- /dev/null +++ b/notebooks/demo_run.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DataJoint Workflow for Neuropixels Analysis\n", + "\n", + "+ This notebook demonstrates using the open-source DataJoint Element to build a workflow for extracellular electrophysiology.\n", + "+ For a detailed tutorial, please see the [tutorial notebook](./tutorial.ipynb)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from tutorial_pipeline import subject, session, probe, ephys\n", + "from element_array_ephys.plotting.widget import main" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " dj.Diagram(subject.Subject)\n", + " + dj.Diagram(session.Session)\n", + " + dj.Diagram(probe)\n", + " + dj.Diagram(ephys)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize processed data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main(ephys)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For an in-depth tutorial please see the [tutorial notebook](./tutorial.ipynb)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3p10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "ff52d424e56dd643d8b2ec122f40a2e279e94970100b4e6430cb9025a65ba4cf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb new file mode 100644 index 00000000..5ccc0b4f --- /dev/null +++ b/notebooks/tutorial.ipynb @@ -0,0 +1,2802 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DataJoint Elements for NeuroPixels Array Electrophysiology\n", + "\n", + "This tutorial aims to provide a comprehensive understanding of the open-source data pipeline created using `element-array-ephys` for processing and analyzing extracellular electrophysiology datasets. \n", + "\n", + "**In this tutorial, we will cover:**\n", + "- The basics:\n", + " - Differentiating between an Element, module, schema, table, and pipeline.\n", + " - How to plot an overview of the pipeline with `dj.Diagram`.\n", + "- Hands-on interactions with the pipeline:\n", + " - Inserting real data into tables.\n", + " - Querying table contents.\n", + " - Fetching table contents.\n", + "- A walk-through:\n", + " - Processing NeuroPixels ephys data acquired with OpenEphys and spike sorted with Kilosort.\n", + "\n", + "**Additional Resources:**\n", + "- [Interactive Tutorials](https://github.com/datajoint/datajoint-tutorials) on `datajoint-python`: Dive deep into DataJoint's fundamentals.\n", + "- [*`datajoint-python`* Documentation](https://datajoint.com/docs/core/datajoint-python/): Comprehensive documentation on DataJoint for Python.\n", + "- [Element Array Electrophysiology Documentation](https://datajoint.com/docs/elements/element-array-ephys/): Detailed guide on the DataJoint Element for Array Electrophysiology.\n", + "\n", + "Before we jump into the core concepts, let's ensure we have all the necessary packages imported." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "import datetime\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combine multiple Elements into a pipeline\n", + "\n", + "Each DataJoint Element is a modular set of tables that can be combined into a complete\n", + "pipeline. Here are the definitions for clarity:\n", + "\n", + "+ **Module**: In Python, a module is a file containing definitions and statements. In the context of DataJoint, modules often define and structure related database tables.\n", + "+ **Table**: A structured set of data held within the database. It consists of rows and\n", + " columns, much like an Excel spreadsheet.\n", + "+ **Schema**: Think of a schema as a container or namespace within the database where related tables are grouped together. It helps organize and manage the database structure.\n", + "+ **Element**: A modular set of related tables. \n", + "\n", + "---\n", + "\n", + "Each Element contains 1 or more modules, and each module declares its own schema in the database.\n", + "\n", + "This tutorial pipeline is assembled from four DataJoint Elements.\n", + "\n", + "| Element | Source Code | Documentation | Description |\n", + "| -- | -- | -- | -- |\n", + "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", + "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General animal metadata and surgery information. |\n", + "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", + "| Element Array Ephys | [Link](https://github.com/datajoint/element-array-ephys) | [Link](https://datajoint.com/docs/elements/element-array-ephys) | NeuroPixels Array Electrophysiology analysis with Kilosort. |\n", + "\n", + "By importing the modules for the first time, the schemas and tables will be created in\n", + "the database. Once created, importing modules will not create schemas and tables\n", + "again, but will allow access to existing schemas/tables.\n", + "\n", + "The Elements are imported and activated within the `tutorial_pipeline` script." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-26 21:39:21,831][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", + "[2023-10-26 21:39:21,833][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-26 21:39:21,840][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + } + ], + "source": [ + "from tutorial_pipeline import (\n", + " lab,\n", + " subject,\n", + " session,\n", + " probe,\n", + " ephys,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each Python module (e.g. `subject`) contains a schema object that enables interaction with the schema in the database." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Schema `neuro_subject`" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Python classes in the module correspond to a table in the database server." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+---------+ +------------+ +-----+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diagram\n", + "\n", + "Let's plot the diagram of tables within multiple schemas and their dependencies using `dj.Diagram()`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "ephys.ClusteringMethod\n", + "\n", + "\n", + "ephys.ClusteringMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringParamSet\n", + "\n", + "\n", + "ephys.ClusteringParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringMethod->ephys.ClusteringParamSet\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType\n", + "\n", + "\n", + "probe.ProbeType\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType.Electrode\n", + "\n", + "\n", + "probe.ProbeType.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.ProbeType.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "probe.Probe\n", + "\n", + "\n", + "probe.Probe\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.Probe\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig\n", + "\n", + "\n", + "probe.ElectrodeConfig\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.ElectrodeConfig\n", + "\n", + "\n", + "\n", + "\n", + "ephys.InsertionLocation\n", + "\n", + "\n", + "ephys.InsertionLocation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType.Electrode->probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion\n", + "\n", + "\n", + "ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP.Electrode\n", + "\n", + "\n", + "ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Curation\n", + "\n", + "\n", + "ephys.Curation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering\n", + "\n", + "\n", + "ephys.CuratedClustering\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Curation->ephys.CuratedClustering\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringTask\n", + "\n", + "\n", + "ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringParamSet->ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet\n", + "\n", + "\n", + "ephys.WaveformSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet->ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "\n", + "ephys.AcquisitionSoftware\n", + "\n", + "\n", + "ephys.AcquisitionSoftware\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording\n", + "\n", + "\n", + "ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.AcquisitionSoftware->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusterQualityLabel\n", + "\n", + "\n", + "ephys.ClusterQualityLabel\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusterQualityLabel->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.InsertionLocation\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics\n", + "\n", + "\n", + "ephys.QualityMetrics\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics->ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics->ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Clustering\n", + "\n", + "\n", + "ephys.Clustering\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringTask->ephys.Clustering\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.WaveformSet\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.QualityMetrics\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Clustering->ephys.Curation\n", + "\n", + "\n", + "\n", + "\n", + "probe.Probe->ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig->probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP->ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.LFP\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " dj.Diagram(subject.Subject)\n", + " + dj.Diagram(session.Session)\n", + " + dj.Diagram(probe)\n", + " + dj.Diagram(ephys)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Table Types in DataJoint\n", + "\n", + "In the previous cell, we visualized the relationships between various tables in our pipeline using `dj.Diagram`. As you might have noticed, tables have different colors and shapes. This is because, in DataJoint, tables can be of different types, each serving a unique purpose.\n", + "\n", + "| Table tier | Color and shape | Description | Practical Example |\n", + "| -- | -- | -- | -- |\n", + "| **Manual table** | Green box | Data entered manually, either by hand or with external helper scripts. | A table containing data about individual subjects, like their birth date or sex. |\n", + "| **Lookup table** | Gray box | Small tables containing general, non-changing information or settings. | A table containing available experimental protocols or animal species. |\n", + "| **Imported table** | Blue oval | Data automatically ingested but requiring external data. | A table that pulls data from an external file or dataset. |\n", + "| **Computed table** | Red circle | Data computed entirely within the pipeline. | A table calculating metrics or statistics from previously stored data. |\n", + "| **Part table** | Plain text | Tables associated with a master table, sharing its tier. | A subtable containing specific measurements for each subject in a master subject table. |\n", + "\n", + "### Order matters!\n", + "\n", + "The arrangement of tables in the `dj.Diagram` is not arbitrary. It represents the flow of data and dependencies:\n", + "\n", + "- **Higher-up Tables**: These are typically your starting point. For instance, before you can insert data about an experimental session, you need to have data about the subject of that session.\n", + "- **Dependencies**: Tables connected by a line have dependencies. For example, before\n", + " you can populate data about an experiment's results, you need to insert data about the\n", + " experiment setup.\n", + "\n", + "**Quick Check**: Based on the diagram, which tables do you think we would insert data into first?\n", + "\n", + "---\n", + "\n", + "## Interacting with DataJoint Pipelines\n", + "\n", + "DataJoint offers a powerful set of commands that allow us to interact with the pipeline:\n", + "\n", + "- **Insert**: Manually add data to a table.\n", + "- **Populate**: Automatically compute and insert data.\n", + "- **Query**: Search and filter data.\n", + "- **Fetch**: Retrieve data for further analysis or visualization.\n", + "\n", + "In the upcoming sections, we'll get hands-on with these commands. Let's start by\n", + "understanding how to manually insert data into our pipeline!\n", + "\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insert entries into manual tables\n", + "\n", + "Manual tables serve as the foundation upon which our pipeline builds. By entering data\n", + "here, we lay the groundwork for subsequent automated analyses.\n", + "\n", + "Let's start with the first table in the schema diagram (i.e. `subject.Subject` table).\n", + "\n", + "To know what data to insert into the table, we can view its dependencies and attributes using the `.describe()` and `.heading` functions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subject : varchar(8) \n", + "---\n", + "subject_nickname=\"\" : varchar(64) \n", + "sex : enum('M','F','U') \n", + "subject_birth_date : date \n", + "subject_description=\"\" : varchar(1024) \n", + "\n" + ] + } + ], + "source": [ + "print(subject.Subject.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "---\n", + "subject_nickname=\"\" : varchar(64) # \n", + "sex : enum('M','F','U') # \n", + "subject_birth_date : date # \n", + "subject_description=\"\" : varchar(1024) # " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cells above show all attributes of the subject table.\n", + "We will insert data into the\n", + "`subject.Subject` table. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject5 U 2023-01-01 \n", + " (Total: 1)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject.insert1(\n", + " dict(subject=\"subject5\", subject_birth_date=\"2023-01-01\", sex=\"U\")\n", + ")\n", + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's repeat the steps above for the `Session` table and see how the output varies between\n", + "`.describe` and `.heading`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> subject.Subject\n", + "session_datetime : datetime \n", + "\n" + ] + } + ], + "source": [ + "print(session.Session.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.Session.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that `describe`, displays the table's structure and highlights its dependencies, such as its reliance on the `Subject` table. These dependencies represent foreign key references, linking data across tables.\n", + "\n", + "On the other hand, `heading` provides an exhaustive list of the table's attributes. This\n", + "list includes both the attributes declared in this table and any inherited from upstream\n", + "tables.\n", + "\n", + "With this understanding, let's move on to insert a session associated with our subject.\n", + "\n", + "We will insert into the `session.Session` table by passing a dictionary to the `insert1` method." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = dict(subject=\"subject5\", session_datetime=\"2023-01-01 00:00:00\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject5 2023-01-01 00:\n", + " (Total: 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.Session.insert1(session_key)\n", + "session.Session()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Introducing the `SessionDirectory` Table\n", + "\n", + "Every experimental session produces a set of data files. The `SessionDirectory` table's\n", + "purpose is to locate these files. It references a directory path relative to a root\n", + "directory, defined in `dj.config[\"custom\"]`. More\n", + "information about `dj.config` is provided in the [User\n", + "Guide](https://datajoint.com/docs/elements/user-guide/)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

\n", + " \n", + "
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session_datetime

\n", + " \n", + "
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session_dir

\n", + " Path to the data directory for a session\n", + "
subject52023-01-01 00:00:00raw/subject5/session1
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet session_dir \n", + "+----------+ +------------+ +------------+\n", + "subject5 2023-01-01 00: raw/subject5/s\n", + " (Total: 1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.SessionDirectory.insert1(\n", + " dict(**session_key, session_dir=\"raw/subject5/session1\")\n", + ")\n", + "session.SessionDirectory()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the workflow diagram indicates, the tables in the `probe` schemas need to\n", + "contain data before the tables in the `ephys` schema accept any data. Let's\n", + "start by inserting into `probe.Probe`, a table containing metadata about a\n", + "multielectrode probe. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Represent a physical probe with unique identification\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
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probe

\n", + " unique identifier for this model of probe (e.g. serial number)\n", + "
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probe_type

\n", + " e.g. neuropixels_1.0\n", + "
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\n", + " " + ], + "text/plain": [ + "*probe probe_type probe_comment \n", + "+-----------+ +------------+ +------------+\n", + "714000838 neuropixels 1. \n", + " (Total: 1)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probe.Probe.insert1(\n", + " dict(probe=\"714000838\", probe_type=\"neuropixels 1.0 - 3B\")\n", + ") # this info could be achieve from neuropixels meta file.\n", + "probe.Probe()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The probe metadata is used by the downstream `ProbeInsertion` table which we\n", + "insert data into in the cells below:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Probe insertion implanted into an animal for a given session.\n", + "-> session.Session\n", + "insertion_number : tinyint unsigned \n", + "---\n", + "-> probe.Probe\n", + "\n" + ] + } + ], + "source": [ + "print(ephys.ProbeInsertion.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Probe insertion implanted into an animal for a given session.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "---\n", + "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ProbeInsertion.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Probe insertion implanted into an animal for a given session.\n", + "
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session_datetime

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insertion_number

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probe

\n", + " unique identifier for this model of probe (e.g. serial number)\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num probe \n", + "+----------+ +------------+ +------------+ +-----------+\n", + "subject5 2023-01-01 00: 1 714000838 \n", + " (Total: 1)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ProbeInsertion.insert1(\n", + " dict(\n", + " session_key,\n", + " insertion_number=1,\n", + " probe=\"714000838\",\n", + " )\n", + ") # probe, subject, session_datetime needs to follow the restrictions of foreign keys.\n", + "ephys.ProbeInsertion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Populate\n", + "\n", + "### Automatically populate tables\n", + "\n", + "In DataJoint, the `populate()` method is a powerful feature designed to fill tables based on the logic defined in the table's `make` method. Here's a breakdown of its functionality:\n", + "\n", + "- **Automation**: Instead of manually inserting data into each table, which can be error-prone and time-consuming, `populate()` automates the insertion based on the dependencies and relationships already established in the schema.\n", + "\n", + "- **Dependency Resolution**: Before populating a table, `populate()` ensures all its dependencies are populated. This maintains the integrity and consistency of the data.\n", + "\n", + "- **Part Tables**: If a table has part tables associated with it, calling `populate()` on the main table will also populate its part tables. This is especially useful in cases like `ephys.EphysRecording` and its part table `ephys.EphysRecording.EphysFile`, as they are closely linked in terms of data lineage.\n", + "\n", + "- **Restriction**: The `populate()` method can be restricted to specific entries. For instance, by providing a `session_key`, we're ensuring the method only operates on the data relevant to that particular session. This is both efficient and avoids unnecessary operations.\n", + "\n", + "In the upcoming cells, we'll make use of the `populate()` method to fill the `ephys.EphysRecording` table and its part table. Remember, while this operation is automated, it's essential to understand the underlying logic to ensure accurate and consistent data entry.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Ephys recording from a probe insertion for a given session.\n", + "
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electrode_config_hash

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acq_software

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sampling_rate

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recording_datetime

\n", + " datetime of the recording from this probe\n", + "
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recording_duration

\n", + " (seconds) duration of the recording from this probe\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num electrode_conf acq_software sampling_rate recording_date recording_dura\n", + "+---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Paths of files of a given EphysRecording round.\n", + "
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session_datetime

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file_path

\n", + " filepath relative to root data directory\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num *file_path \n", + "+---------+ +------------+ +------------+ +-----------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording.EphysFile()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "EphysRecording: 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]\n" + ] + } + ], + "source": [ + "ephys.EphysRecording.populate(session_key, display_progress=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's view the information was entered into each of these tables:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Ephys recording from a probe insertion for a given session.\n", + "
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subject52023-01-01 00:00:0018d4cc6d8-a02d-42c8-bf27-7459c39ea0eeSpikeGLX30000.02018-07-03 20:32:28338.666
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num electrode_conf acq_software sampling_rate recording_date recording_dura\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "subject5 2023-01-01 00: 1 8d4cc6d8-a02d- SpikeGLX 30000.0 2018-07-03 20: 338.666 \n", + " (Total: 1)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Paths of files of a given EphysRecording round.\n", + "
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subject

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session_datetime

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insertion_number

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file_path

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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num *file_path \n", + "+----------+ +------------+ +------------+ +------------+\n", + "subject5 2023-01-01 00: 1 raw/subject5/s\n", + " (Total: 1)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording.EphysFile()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're almost ready to spike sort the data with `kilosort`. An important step before\n", + "processing is managing the parameters which will be used in that step. To do so, we will\n", + "define the kilosort parameters in a dictionary and insert them into a DataJoint table\n", + "`ClusteringParamSet`. This table keeps track of all combinations of your spike sorting\n", + "parameters. You can choose which parameters are used during processing in a later step.\n", + "\n", + "Let's view the attributes and insert data into `ephys.ClusteringParamSet`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Parameter set to be used in a clustering procedure\n", + "paramset_idx : smallint # \n", + "---\n", + "clustering_method : varchar(16) # \n", + "paramset_desc : varchar(128) # \n", + "param_set_hash : uuid # \n", + "params : longblob # dictionary of all applicable parameters" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ClusteringParamSet.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Parameter set to be used in a clustering procedure\n", + "
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\n", + " " + ], + "text/plain": [ + "*paramset_idx clustering_met paramset_desc param_set_hash params \n", + "+------------+ +------------+ +------------+ +------------+ +--------+\n", + "0 kilosort2 Spike sorting de78cee1-526f- =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# insert clustering task manually\n", + "params_ks = {\n", + " \"fs\": 30000,\n", + " \"fshigh\": 150,\n", + " \"minfr_goodchannels\": 0.1,\n", + " \"Th\": [10, 4],\n", + " \"lam\": 10,\n", + " \"AUCsplit\": 0.9,\n", + " \"minFR\": 0.02,\n", + " \"momentum\": [20, 400],\n", + " \"sigmaMask\": 30,\n", + " \"ThPr\": 8,\n", + " \"spkTh\": -6,\n", + " \"reorder\": 1,\n", + " \"nskip\": 25,\n", + " \"GPU\": 1,\n", + " \"Nfilt\": 1024,\n", + " \"nfilt_factor\": 4,\n", + " \"ntbuff\": 64,\n", + " \"whiteningRange\": 32,\n", + " \"nSkipCov\": 25,\n", + " \"scaleproc\": 200,\n", + " \"nPCs\": 3,\n", + " \"useRAM\": 0,\n", + "}\n", + "ephys.ClusteringParamSet.insert_new_params(\n", + " clustering_method=\"kilosort2\",\n", + " paramset_idx=0,\n", + " params=params_ks,\n", + " paramset_desc=\"Spike sorting using Kilosort2\",\n", + ")\n", + "ephys.ClusteringParamSet()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've inserted kilosort parameters into the `ClusteringParamSet` table,\n", + "we're almost ready to sort our data. DataJoint uses a `ClusteringTask` table to\n", + "manage which `EphysRecording` and `ClusteringParamSet` should be used during processing. \n", + "\n", + "This table is important for defining several important aspects of\n", + "downstream processing. Let's view the attributes to get a better understanding. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Manual table for defining a clustering task ready to be run\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "paramset_idx : smallint # \n", + "---\n", + "clustering_output_dir=\"\" : varchar(255) # clustering output directory relative to the clustering root data directory\n", + "task_mode=\"load\" : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ClusteringTask.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `ClusteringTask` table contains two important attributes: \n", + "+ `paramset_idx` \n", + "+ `task_mode` \n", + "\n", + "The `paramset_idx` attribute tracks\n", + "your kilosort parameter sets. You can choose the parameter set using which \n", + "you want spike sort ephys data. For example, `paramset_idx=0` may contain\n", + "default parameters for kilosort processing whereas `paramset_idx=1` contains your custom parameters for sorting. This\n", + "attribute tells the `Processing` table which set of parameters you are processing in a given `populate()`.\n", + "\n", + "The `task_mode` attribute can be set to either `load` or `trigger`. When set to `load`,\n", + "running the processing step initiates a search for exisiting kilosort output files. When set to `trigger`, the\n", + "processing step will run kilosort on the raw data. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.ClusteringTask.insert1(\n", + " dict(\n", + " session_key,\n", + " insertion_number=1,\n", + " paramset_idx=0,\n", + " task_mode=\"load\", # load or trigger\n", + " clustering_output_dir=\"processed/subject5/session1/probe_1/kilosort2-5_1\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clustering: 100%|██████████| 1/1 [00:00<00:00, 3.46it/s]\n" + ] + } + ], + "source": [ + "ephys.Clustering.populate(session_key, display_progress=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While spike sorting is completed in the above step, you can optionally curate\n", + "the output of image processing using the `Curation` table. For this demo, we\n", + "will simply use the results of the spike sorting output from the `Clustering` task." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Manual curation procedure\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "paramset_idx : smallint # \n", + "curation_id : int # \n", + "---\n", + "curation_time : datetime # time of generation of this set of curated clustering results\n", + "curation_output_dir : varchar(255) # output directory of the curated results, relative to root data directory\n", + "quality_control : tinyint # has this clustering result undergone quality control?\n", + "manual_curation : tinyint # has manual curation been performed on this clustering result?\n", + "curation_note=\"\" : varchar(2000) # " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.Curation.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "clustering_key = (ephys.ClusteringTask & session_key).fetch1(\"KEY\")\n", + "ephys.Curation().create1_from_clustering_task(clustering_key)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the `Curation` table receives an entry, we can populate the remaining\n", + "tables in the workflow including `CuratedClustering`, `WaveformSet`, and `LFP`. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "CuratedClustering: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(lfp_average)\n", + "plt.title(\"Average LFP Waveform for Insertion 1\")\n", + "plt.xlabel(\"Samples\")\n", + "plt.ylabel(\"microvolts (uV)\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DataJoint queries are a highly flexible tool to manipulate and visualize your data.\n", + "After all, visualizing traces or generating rasters is likely just the start of\n", + "your analysis workflow. This can also make the queries seem more complex at\n", + "first. However, we'll walk through them slowly to simplify their content in this notebook. \n", + "\n", + "The examples below perform several operations using DataJoint queries:\n", + "- Fetch the primary key attributes of all units that are in `insertion_number=1`.\n", + "- Use **multiple restrictions** to fetch timestamps and create a raster plot.\n", + "- Use a **join** operation and **multiple restrictions** to fetch a waveform\n", + " trace, along with unit data to create a single waveform plot" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "insert_key = (ephys.ProbeInsertion & \"insertion_number = '1'\").fetch1(\"KEY\")\n", + "units, unit_spiketimes = (\n", + " ephys.CuratedClustering.Unit\n", + " & insert_key\n", + " & 'unit IN (\"6\",\"7\",\"9\",\"14\",\"15\",\"17\",\"19\")'\n", + ").fetch(\"unit\", \"spike_times\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.hstack(unit_spiketimes)\n", + "y = np.hstack([np.full_like(s, u) for u, s in zip(units, unit_spiketimes)])\n", + "plt.plot(x, y, \"|\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Unit\");" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "unit_key = (ephys.CuratedClustering.Unit & insert_key & \"unit = '15'\").fetch1(\"KEY\")\n", + "unit_data = (\n", + " ephys.CuratedClustering.Unit * ephys.WaveformSet.PeakWaveform & unit_key\n", + ").fetch1()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject5',\n", + " 'session_datetime': datetime.datetime(2023, 1, 1, 0, 0),\n", + " 'insertion_number': 1,\n", + " 'paramset_idx': 0,\n", + " 'curation_id': 1,\n", + " 'unit': 15,\n", + " 'electrode_config_hash': UUID('8d4cc6d8-a02d-42c8-bf27-7459c39ea0ee'),\n", + " 'probe_type': 'neuropixels 1.0 - 3A',\n", + " 'electrode': 92,\n", + " 'cluster_quality_label': 'noise',\n", + " 'spike_count': 292,\n", + " 'spike_times': array([ 1.02606667, 1.19973333, 1.5044 , 1.52283333,\n", + " 1.86786667, 1.8688 , 1.8806 , 1.88553333,\n", + " 2.0581 , 2.76826667, 2.89186667, 2.9311 ,\n", + " 4.8753 , 5.2964 , 7.02266667, 9.65273333,\n", + " 9.81373333, 10.22443333, 11.96146667, 15.07173333,\n", + " 15.08983333, 15.78326667, 21.30936667, 22.7549 ,\n", + " 23.5582 , 23.6582 , 23.87043333, 24.16726667,\n", + " 24.25456667, 24.2671 , 25.0249 , 27.89116667,\n", + " 35.3036 , 36.0171 , 40.00396667, 40.0873 ,\n", + " 41.015 , 42.70086667, 45.8682 , 47.9291 ,\n", + " 48.90843333, 49.37996667, 49.39596667, 49.4058 ,\n", + " 49.65926667, 49.68203333, 49.74273333, 51.52213333,\n", + " 52.41486667, 55.27623333, 55.54576667, 55.81213333,\n", + " 56.0544 , 56.29426667, 56.36896667, 56.3743 ,\n", + " 56.47403333, 56.7147 , 56.71683333, 60.764 ,\n", + " 61.5317 , 61.54046667, 61.7721 , 62.10233333,\n", + " 62.10726667, 62.2118 , 62.81173333, 63.15896667,\n", + " 65.11126667, 65.495 , 67.18373333, 77.59256667,\n", + " 79.2709 , 80.46186667, 82.1178 , 85.6568 ,\n", + " 86.52613333, 89.12126667, 89.46963333, 89.64663333,\n", + " 90.19103333, 92.0923 , 92.99573333, 93.36923333,\n", + " 93.68086667, 95.2097 , 97.96296667, 98.1067 ,\n", + " 98.69713333, 99.26963333, 99.28013333, 101.04216667,\n", + " 101.2002 , 101.3843 , 101.3975 , 101.40656667,\n", + " 102.00996667, 102.07066667, 102.17033333, 103.5861 ,\n", + " 104.68523333, 104.71643333, 105.2595 , 105.5166 ,\n", + " 105.54723333, 107.0848 , 109.82746667, 110.14853333,\n", + " 110.92203333, 111.2481 , 112.26 , 113.09466667,\n", + " 113.09846667, 113.1005 , 113.33513333, 118.842 ,\n", + " 118.96803333, 119.32606667, 119.64796667, 119.96313333,\n", + " 119.97343333, 120.19573333, 120.19693333, 120.21163333,\n", + " 121.1879 , 121.233 , 121.9611 , 122.40203333,\n", + " 123.1745 , 124.6798 , 124.68196667, 124.97396667,\n", + " 125.2205 , 125.87786667, 125.957 , 125.95803333,\n", + " 127.7149 , 136.8558 , 136.86413333, 138.49966667,\n", + " 139.49553333, 139.93163333, 139.9819 , 142.57013333,\n", + " 142.70053333, 143.343 , 143.40796667, 148.46066667,\n", + " 148.47023333, 148.89893333, 152.79766667, 153.2198 ,\n", + " 153.39096667, 153.62456667, 153.82253333, 153.82693333,\n", + " 153.8364 , 153.98863333, 155.4047 , 157.52706667,\n", + " 157.53233333, 163.1177 , 163.12516667, 163.46916667,\n", + " 163.69406667, 163.7119 , 166.6946 , 166.7006 ,\n", + " 170.78053333, 170.7891 , 170.79653333, 180.36596667,\n", + " 180.43416667, 182.72556667, 182.73706667, 182.76576667,\n", + " 184.24713333, 185.5131 , 185.7329 , 186.4198 ,\n", + " 186.48443333, 186.72036667, 186.95926667, 187.1402 ,\n", + " 187.16683333, 189.4036 , 193.27583333, 195.40946667,\n", + " 200.2427 , 203.03376667, 203.04076667, 205.84043333,\n", + " 206.1151 , 207.3402 , 210.95773333, 217.73583333,\n", + " 220.35 , 221.1235 , 227.7719 , 227.946 ,\n", + " 228.02456667, 231.2108 , 231.80003333, 233.48253333,\n", + " 236.8342 , 241.78993333, 243.6285 , 245.61546667,\n", + " 245.95573333, 246.04586667, 246.06206667, 247.12933333,\n", + " 248.17143333, 249.16873333, 251.13613333, 252.4734 ,\n", + " 253.2007 , 254.50943333, 255.22563333, 255.2502 ,\n", + " 255.5314 , 255.53423333, 255.7684 , 256.81196667,\n", + " 256.99066667, 256.9928 , 257.0588 , 257.06206667,\n", + " 257.0801 , 257.15523333, 257.27496667, 257.291 ,\n", + " 257.99483333, 259.6955 , 259.7061 , 259.71746667,\n", + " 263.59203333, 266.7052 , 266.70576667, 267.0376 ,\n", + " 267.35913333, 267.87316667, 268.36156667, 268.94556667,\n", + " 269.01516667, 269.63623333, 269.88553333, 270.16176667,\n", + " 270.39123333, 273.41633333, 273.43763333, 274.2084 ,\n", + " 276.36113333, 278.68053333, 279.50626667, 281.0338 ,\n", + " 281.9869 , 283.69233333, 285.0663 , 287.0179 ,\n", + " 289.5541 , 291.78113333, 292.16493333, 292.85823333,\n", + " 292.8702 , 295.93823333, 295.9514 , 295.9679 ,\n", + " 296.00623333, 296.0391 , 296.04393333, 298.9717 ,\n", + " 299.01556667, 299.0859 , 299.7463 , 306.65153333,\n", + " 307.38243333, 307.7771 , 311.80163333, 311.83026667,\n", + " 313.37816667, 314.948 , 317.10726667, 317.98363333,\n", + " 319.25856667, 321.01533333, 321.16426667, 330.15466667,\n", + " 330.73633333, 330.91536667, 332.83243333, 332.86266667]),\n", + " 'spike_sites': array([92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92,\n", + " 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 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"execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_data" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sampling_rate = (ephys.EphysRecording & insert_key).fetch1(\n", + " \"sampling_rate\"\n", + ") / 1000 # in kHz\n", + "plt.plot(\n", + " np.r_[: unit_data[\"peak_electrode_waveform\"].size] * 1 / sampling_rate,\n", + " unit_data[\"peak_electrode_waveform\"],\n", + ")\n", + "plt.xlabel(\"Time (ms)\")\n", + "plt.ylabel(r\"Voltage ($\\mu$V)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "Throughout this notebook, we've used DataJoint to work with database tables and keep\n", + "data organized and automate analyses to increase efficiency of data processing. We've\n", + "inserted data into tables, used queries to retrieve, manipulate, and visualize ephys data.\n", + "\n", + "Remember, this is just the beginning. As you grow familiar with DataJoint, you'll\n", + "uncover even more ways to harness its capabilities for your specific research needs. \n", + "\n", + "---\n", + "\n", + "To run this tutorial notebook on your own data, please use the following steps:\n", + "- Download the mysql-docker image for DataJoint and run the container according to the\n", + " instructions provide in the repository.\n", + "- Create a fork of this repository to your GitHub account.\n", + "- Clone the repository and open the files using your IDE.\n", + "- Add a code cell immediately after the first code cell in the notebook - we will setup\n", + " the local connection using this cell. In this cell, type in the following code. \n", + "\n", + "```python\n", + "import datajoint as dj\n", + "dj.config[\"database.host\"] = \"localhost\"\n", + "dj.config[\"database.user\"] = \"\"\n", + "dj.config[\"database.password\"] = \"\"\n", + "dj.config[\"custom\"] = {\"imaging_root_data_dir\": \"path/to/your/data/dir\",\n", + "\"database_prefix\": \"\"}\n", + "dj.config.save_local()\n", + "dj.conn()\n", + "```\n", + "\n", + "- Run this code block above and proceed with the rest of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3p10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "ff52d424e56dd643d8b2ec122f40a2e279e94970100b4e6430cb9025a65ba4cf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py new file mode 100644 index 00000000..2163d315 --- /dev/null +++ b/notebooks/tutorial_pipeline.py @@ -0,0 +1,48 @@ +import datajoint as dj +from element_animal import subject +from element_animal.subject import Subject +from element_array_ephys import db_prefix, probe, ephys_acute as ephys +from element_lab import lab +from element_lab.lab import Lab, Location, Project, Protocol, Source, User +from element_lab.lab import Device as Equipment +from element_lab.lab import User as Experimenter +from element_session import session_with_datetime as session +from element_session.session_with_datetime import Session +import element_interface +import pathlib + + +# Declare functions for retrieving data +def get_ephys_root_data_dir(): + """Retrieve ephys root data directory.""" + ephys_root_dirs = dj.config.get("custom", {}).get("ephys_root_data_dir", None) + if not ephys_root_dirs: + return None + elif isinstance(ephys_root_dirs, (str, pathlib.Path)): + return [ephys_root_dirs] + elif isinstance(ephys_root_dirs, list): + return ephys_root_dirs + else: + raise TypeError("`ephys_root_data_dir` must be a string, pathlib, or list") + + +# Activate schemas +lab.activate(db_prefix + "lab") +subject.activate(db_prefix + "subject", linking_module=__name__) +session.activate(db_prefix + "session", linking_module=__name__) + + +@lab.schema +class SkullReference(dj.Lookup): + definition = """ + skull_reference : varchar(60) + """ + contents = zip(["Bregma", "Lambda"]) + + +def get_session_directory(session_key): + session_directory = (session.SessionDirectory & session_key).fetch1("session_dir") + return pathlib.Path(session_directory) + + +ephys.activate(db_prefix + "ephys", db_prefix + "probe", linking_module=__name__) diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 528f6349..00000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -datajoint>=0.13 -element-interface>=0.4.0 -ipywidgets -openpyxl -plotly -pyopenephys>=1.1.6 -seaborn -scikit-image -spikeinterface -nbformat>=4.2.0 \ No newline at end of file diff --git a/requirements_dev.txt b/requirements_dev.txt deleted file mode 100644 index 0652d0e2..00000000 --- a/requirements_dev.txt +++ /dev/null @@ -1,3 +0,0 @@ -pre-commit -pytest -pytest-cov diff --git a/setup.py b/setup.py index 31b9be61..abd3a92d 100644 --- a/setup.py +++ b/setup.py @@ -1,19 +1,13 @@ from os import path - from setuptools import find_packages, setup -pkg_name = next(p for p in find_packages() if "." not in p) + +pkg_name = "element_array_ephys" here = path.abspath(path.dirname(__file__)) with open(path.join(here, "README.md"), "r") as f: long_description = f.read() -with open(path.join(here, "requirements.txt")) as f: - requirements = f.read().splitlines() - -with open(path.join(here, "requirements_dev.txt")) as f: - requirements_dev = f.read().splitlines() - with open(path.join(here, "element_array_ephys/export/nwb/requirements.txt")) as f: requirements_nwb = f.read().splitlines() @@ -23,7 +17,7 @@ setup( name=pkg_name.replace("_", "-"), version=__version__, # noqa F821 - description="DataJoint Element for Extracellular Array Electrophysiology", + description="Extracellular Array Electrophysiology DataJoint Element", long_description=long_description, long_description_content_type="text/markdown", author="DataJoint", @@ -32,7 +26,28 @@ url=f'https://github.com/datajoint/{pkg_name.replace("_", "-")}', keywords="neuroscience electrophysiology science datajoint", packages=find_packages(exclude=["contrib", "docs", "tests*"]), - extras_require={"dev": requirements_dev, "nwb": requirements_nwb}, scripts=[], - install_requires=requirements, + install_requires=[ + "datajoint>=0.13.0", + "ipykernel>=6.0.1", + "ipywidgets", + "openpyxl", + "plotly", + "seaborn", + "spikeinterface", + "scikit-image", + "nbformat>=4.2.0", + "pyopenephys>=1.1.6", + ], + extras_require={ + "elements": [ + "element-animal>=0.1.8", + "element-event>=0.2.3", + "element-interface>=0.4.0", + "element-lab>=0.3.0", + "element-session>=0.1.5", + ], + "nwb": ["dandi", "neuroconv[ecephys]", "pynwb"], + "tests": ["pre-commit", "pytest", "pytest-cov"], + }, )