From 5f756c36675e5191f2b19d2581dcf1a4b0991729 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 12:03:22 -0500 Subject: [PATCH 01/27] First commit for codespace compatability --- .devcontainer/Dockerfile | 48 +++++++++++++++++++++ .devcontainer/devcontainer.json | 30 +++++++++++++ .devcontainer/docker-compose.yaml | 25 +++++++++++ .gitignore | 2 - .markdownlint.yaml | 2 + .vscode/launch.json | 16 +++++++ .vscode/settings.json | 15 +++++++ CHANGELOG.md | 9 ++++ CONTRIBUTING.md | 2 +- Dockerfile | 15 ------- cspell.json | 70 +++++++++++++++++++++++++++---- docker-compose-build.yaml | 26 ------------ docker-compose-db.yaml | 15 +++++++ element_array_ephys/__init__.py | 23 +++++----- element_array_ephys/version.py | 2 +- notebooks/tutorial_pipeline.py | 32 ++++++++++++++ requirements.txt | 10 ----- requirements_dev.txt | 3 -- setup.py | 37 +++++++++++----- 19 files changed, 292 insertions(+), 90 deletions(-) create mode 100644 .devcontainer/Dockerfile create mode 100644 .devcontainer/devcontainer.json create mode 100644 .devcontainer/docker-compose.yaml create mode 100644 .vscode/launch.json create mode 100644 .vscode/settings.json delete mode 100644 Dockerfile delete mode 100644 docker-compose-build.yaml create mode 100644 docker-compose-db.yaml create mode 100644 notebooks/tutorial_pipeline.py delete mode 100644 requirements.txt delete mode 100644 requirements_dev.txt diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 00000000..1d10e278 --- /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 IMAGING_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..52b23ad4 --- /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/.gitignore b/.gitignore index e25ed045..8d8c0177 100644 --- a/.gitignore +++ b/.gitignore @@ -116,13 +116,11 @@ dj_local_conf_old.json **/#*# **/.#* -docker-compose.y*ml # include !docs/docker-compose.yaml # vscode settings -.vscode *.code-workspace # exports/notes 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/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/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/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py new file mode 100644 index 00000000..d5026bce --- /dev/null +++ b/notebooks/tutorial_pipeline.py @@ -0,0 +1,32 @@ +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_id as session +from element_session.session_with_id 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__) +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"], + }, ) From 5e2d7bef950f70007dc418c9975c22b3488c95a1 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 12:10:11 -0500 Subject: [PATCH 02/27] Enable devcontainer builds in CICD --- .github/workflows/release.yaml | 18 +++++++++--------- .github/workflows/test.yaml | 4 ++-- 2 files changed, 11 insertions(+), 11 deletions(-) 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: From 6c6afe4778466b26dbf846c84d1d77daf8672ca7 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 12:10:58 -0500 Subject: [PATCH 03/27] Black formatting --- element_array_ephys/probe.py | 8 +++++--- element_array_ephys/readers/spikeglx.py | 5 +---- notebooks/tutorial_pipeline.py | 1 + 3 files changed, 7 insertions(+), 7 deletions(-) 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/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py index d5026bce..1e3d2f46 100644 --- a/notebooks/tutorial_pipeline.py +++ b/notebooks/tutorial_pipeline.py @@ -25,6 +25,7 @@ def get_ephys_root_data_dir(): 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__) From dcd768a0e7bc799b9da968ed8831738d1facbee1 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 12:41:19 -0500 Subject: [PATCH 04/27] Allow build step in docker-compose --- .devcontainer/docker-compose.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 52b23ad4..7a8c2e14 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -3,10 +3,10 @@ services: app: cpus: 4 mem_limit: 8g - # build: - # context: .. - # dockerfile: ./.devcontainer/Dockerfile - image: datajoint/element_array_ephys:latest + build: + context: .. + dockerfile: ./.devcontainer/Dockerfile + # image: datajoint/element_array_ephys:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: From 1bea230d0789be6632c8dbb78139d9a2b8f92421 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Wed, 25 Oct 2023 17:53:40 +0000 Subject: [PATCH 05/27] Update root data dir env --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 1d10e278..2d0e9261 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -41,7 +41,7 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV IMAGING_ROOT_DATA_DIR /workspaces/element-array-ephys/example_data +ENV EPHYS_ROOT_DATA_DIR /workspaces/element-array-ephys/example_data ENV DATABASE_PREFIX neuro_ USER vscode From f5ab71d8abfcfe973d9792e91307ed705d56f54b Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:01:59 +0000 Subject: [PATCH 06/27] Update `.gitignore` to include Codespaces --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 8d8c0177..3f5c0d84 100644 --- a/.gitignore +++ b/.gitignore @@ -125,3 +125,6 @@ dj_local_conf_old.json # exports/notes temp* + +# Codespaces +example_data/ \ No newline at end of file From caf5c9109d43e373c262d3757c0bc3edd54d416f Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 16:23:44 -0500 Subject: [PATCH 07/27] Add tutorial notebook --- notebooks/tutorial.ipynb | 2624 ++++++++++++++++++++++++++++++++ notebooks/tutorial_pipeline.py | 2 +- 2 files changed, 2625 insertions(+), 1 deletion(-) create mode 100644 notebooks/tutorial.ipynb diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb new file mode 100644 index 00000000..9e7e40a9 --- /dev/null +++ b/notebooks/tutorial.ipynb @@ -0,0 +1,2624 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DataJoint Element Tutorial: Array Electrophysiology\n", + "\n", + "Welcome to the DataJoint Element tutorial for Array Electrophysiology! Dive into the world of automated analyses and organization of extracellular array electrophysiology data.\n", + "\n", + "**In this tutorial, we will cover:**\n", + "- The basics:\n", + " - Differentiating between an Element and a pipeline.\n", + " - How to plot the pipeline with `dj.Diagram`.\n", + "- Hands-on interactions with the pipeline:\n", + " - Inserting data into tables.\n", + " - Querying table contents.\n", + " - Fetching table contents.\n", + " - Running the pipeline for your experiments.\n", + "- A walk-through:\n", + " - Processing NeuroPixels ephys data acquired with OpenEphys and 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", + "+ **Element**: A modular chunk of related tables.\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", + "+ **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", + "+ **Table**: A structured set of data held within the database. It consists of rows and columns, much like an Excel spreadsheet.\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 the database. Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\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": [ + "[2023-03-20 21:44:02,597][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", + "[2023-03-20 21:44:02,607][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-03-20 21:44:02,614][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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", + " value = getattr(object, key)\n", + "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", + " value = getattr(object, key)\n", + "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", + " value = getattr(object, key)\n" + ] + }, + { + "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", + "ephys.ClusteringTask\n", + "\n", + "\n", + "ephys.ClusteringTask\n", + "\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.EphysRecording.EphysFile\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig\n", + "\n", + "\n", + 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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 add data based on existing 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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'subject : varchar(8) \\n---\\nsubject_nickname=\"\" : varchar(64) \\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 5, + "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": 6, + "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": 6, + "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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'-> subject.Subject\\nsession_datetime : datetime \\n'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(session.Session.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # " + ] + }, + "execution_count": 8, + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = dict(subject=\"subject5\", session_datetime=datetime.datetime.now())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject5 2023-03-20 21:\n", + " (Total: 1)" + ] + }, + "execution_count": 9, + "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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

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\n", + " Path to the data directory for a session\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet session_dir \n", + "+----------+ +------------+ +------------+\n", + "subject5 2023-03-20 21: subject5/sessi\n", + " (Total: 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.SessionDirectory.insert1(dict(**session_key, session_dir=\"subject5/session1\"))\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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'# Represent a physical probe with unique identification\\nprobe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\\n---\\n-> probe.ProbeType\\nprobe_comment=\"\" : varchar(1000) \\n'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(probe.Probe.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Represent a physical probe with unique identification\n", + "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\n", + "---\n", + "probe_type : varchar(32) # e.g. neuropixels_1.0\n", + "probe_comment=\"\" : varchar(1000) # " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probe.Probe.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Represent a physical probe with unique identification\n", + "
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\n", + " unique identifier for this model of probe (e.g. serial number)\n", + "
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\n", + " " + ], + "text/plain": [ + "*probe probe_type probe_comment \n", + "+-----------+ +------------+ +------------+\n", + "714000838 neuropixels 1. \n", + " (Total: 1)" + ] + }, + "execution_count": 15, + "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": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'# Probe insertion implanted into an animal for a given session.\\n-> session.Session\\ninsertion_number : tinyint unsigned \\n---\\n-> probe.Probe\\n'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ProbeInsertion.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ProbeInsertion.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Probe insertion implanted into an animal for a given session.\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num probe \n", + "+----------+ +------------+ +------------+ +-----------+\n", + "subject5 2023-03-20 21: 1 714000838 \n", + " (Total: 1)" + ] + }, + "execution_count": 18, + "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()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Populate\n", + "\n", + "### Automatically populate tables\n", + "\n", + "`ephys.EphysRecording` is the first table in the pipeline that can be populated automatically.\n", + "If a table contains a part table, this part table is also populated during the\n", + "`populate()` call. `populate()` takes several arguments including the a session\n", + "key. This key restricts `populate()` to performing the operation on the session\n", + "of interest rather than all possible sessions which could be a time-intensive\n", + "process for databases with lots of entries.\n", + "\n", + "Let's view the `ephys.EphysRecording` and its part table\n", + "`ephys.EphysRecording.EphysFile` and populate both through a single `populate()`\n", + "call." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Ephys recording from a probe insertion for a given session.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "---\n", + "electrode_config_hash : uuid # \n", + "acq_software : varchar(24) # \n", + "sampling_rate : float # (Hz)\n", + "recording_datetime : datetime # datetime of the recording from this probe\n", + "recording_duration : float # (seconds) duration of the recording from this probe" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Paths of files of a given EphysRecording round.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "file_path : varchar(255) # filepath relative to root data directory" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording.EphysFile.heading" + ] + }, + { + "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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\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": 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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file_path

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recording_datetime

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subject52023-03-20 21:44:0918d4cc6d8-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 2023-03-20 21: 1 8d4cc6d8-a02d- SpikeGLX 30000.0 2018-07-03 20: 338.666 \n", + " (Total: 1)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.EphysRecording()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Paths of files of a given EphysRecording round.\n", + "
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file_path

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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num *file_path \n", + "+----------+ +------------+ +------------+ +------------+\n", + "subject5 2023-03-20 21: 1 subject5/sessi\n", + " (Total: 1)" + ] + }, + "execution_count": 25, + "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": 26, + "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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ClusteringParamSet.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "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": 27, + "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": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'# Manual table for defining a clustering task ready to be run\\n-> ephys.EphysRecording\\n-> ephys.ClusteringParamSet\\n---\\nclustering_output_dir=\"\" : varchar(255) # clustering output directory relative to the clustering root data directory\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # \\'load\\': load computed analysis results, \\'trigger\\': trigger computation\\n'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.ClusteringTask.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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": 29, + "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 is 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": 30, + "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=\"subject5/session1/probe_1/ks2.1_01\",\n", + " )\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice we set the `task_mode` to `load`. Let's call populate on the `Clustering`\n", + "table in the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clustering: 100%|██████████| 1/1 [00:00<00:00, 15.88it/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": 32, + "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": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ephys.Curation.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "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": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "CuratedClustering: 100%|██████████| 1/1 [00:02<00:00, 2.22s/it]\n", + "LFP: 100%|██████████| 1/1 [00:20<00:00, 20.91s/it]\n", + "WaveformSet: 100%|██████████| 1/1 [05:47<00:00, 347.33s/it]\n" + ] + } + ], + "source": [ + "ephys.CuratedClustering.populate(session_key, display_progress=True)\n", + "ephys.LFP.populate(session_key, display_progress=True)\n", + "ephys.WaveformSet.populate(session_key, display_progress=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've populated the tables in this workflow, there are one of\n", + "several next steps. If you have an existing workflow for\n", + "aligning waveforms to behavior data or other stimuli, you can easily\n", + "invoke `element-event` or define your custom DataJoint tables to extend the\n", + "pipeline.\n", + "\n", + "In this tutorial, we will do some exploratory analysis by fetching the data from the database and creating a few plots.\n", + "\n", + "## Query\n", + "\n", + "This section focuses on working with data that is already in the\n", + "database. \n", + "\n", + "DataJoint queries allow you to view and import data from the database into a python\n", + "variable using the `fetch()` method. \n", + "\n", + "There are several important features supported by `fetch()`:\n", + "- By default, an empty `fetch()` imports a list of dictionaries containing all\n", + " attributes of all entries in the table that is queried.\n", + "- **`fetch1()`**, on the other hand, imports a dictionary containing all attributes of\n", + " one of the entries in the table. By default, if a table has multiple entries,\n", + " `fetch1()` imports the first entry in the table.\n", + "- Both `fetch()` and `fetch1()` accept table attributes as an argument to query\n", + " that particular attribute. For example `fetch1('fps')` will fetch the first\n", + " value of the `fps` attribute if it exists in the table.\n", + "- Recommended best practice is to **restrict** queries by primary key attributes of the\n", + " table to ensure the accuracy of imported data.\n", + " - The most common restriction for entries in DataJoint tables is performed\n", + " using the `&` operator. For example to fetch all session start times belonging to\n", + " `subject1`, a possible query could be `subject1_sessions =\n", + " (session.Session & \"subject = 'subject1'\").fetch(\"session_datetime\")`. \n", + "- `fetch()` can also be used to obtain the primary keys of a table. To fetch the primary\n", + " keys of a table use `.fetch(\"KEY\")` syntax.\n", + "\n", + "Let's walk through these concepts of querying by moving from simple to more\n", + "complex queries." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_average = (ephys.LFP & \"insertion_number = '1'\").fetch1(\"lfp_mean\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the query above, we fetch a single `lfp_mean` attribute from the `LFP` table.\n", + "We also restrict the query to insertion number 1.\n", + "\n", + "Let's go ahead and plot the LFP mean." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": 37, + "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": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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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": 39, + "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": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject5',\n", + " 'session_datetime': datetime.datetime(2023, 3, 20, 21, 44, 9),\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': 186,\n", + " 'cluster_quality_label': 'good',\n", + " 'spike_count': 18,\n", + " 'spike_times': array([ 48.74336667, 67.63773333, 69.05666667, 83.11203333,\n", + " 94.1978 , 108.0454 , 110.36893333, 122.16836667,\n", + " 149.41143333, 152.55593333, 182.84816667, 236.8929 ,\n", + " 270.40613333, 292.4432 , 299.99216667, 321.74016667,\n", + " 326.5434 , 329.26373333]),\n", + " 'spike_sites': array([186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186,\n", + " 186, 186, 186, 186, 186]),\n", + " 'spike_depths': array([2144.38644037, 2240.44959257, 2286.37081289, 2177.07452433,\n", + " 2227.42382471, 2143.86346649, 2235.74014979, 2236.04354982,\n", + " 2236.06676094, 2299.53408387, 2222.6567435 , 2165.88562393,\n", + " 2165.55087743, 2254.93847903, 2241.71132163, 2057.9714868 ,\n", + " 2272.82484323, 2229.42266297]),\n", + " 'peak_electrode_waveform': array([-343.61979167, -345.703125 , -344.79166667, -341.53645833,\n", + " -339.0625 , -340.88541667, -342.1875 , -339.97395833,\n", + " -338.02083333, -336.97916667, -338.41145833, -341.53645833,\n", + " -343.48958333, -344.53125 , -341.796875 , -337.36979167,\n", + " -339.32291667, -336.58854167, -338.80208333, -337.63020833,\n", + " -336.97916667, -338.02083333, -338.02083333, -333.984375 ,\n", + " -324.86979167, -317.05729167, -313.93229167, -309.765625 ,\n", + " -304.296875 , -300.13020833, -300.65104167, -301.43229167,\n", + " -306.11979167, -311.58854167, -316.40625 , -320.57291667,\n", + " -333.203125 , -339.58333333, -340.625 , -337.63020833,\n", + " -344.140625 , -350.91145833, -356.11979167, -363.80208333,\n", + " -375. , -379.42708333, -382.68229167, -389.97395833,\n", + " -391.796875 , -393.61979167, -390.625 , -395.96354167,\n", + " -400.52083333, -399.73958333, -395.18229167, -392.96875 ,\n", + " -395.05208333, -391.796875 , -392.31770833, -387.36979167,\n", + " -387.23958333, -387.890625 , -384.375 , -383.33333333])}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_data" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "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)\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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 cell and proceed with the rest of the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": "3.9.16" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "ff52d424e56dd643d8b2ec122f40a2e279e94970100b4e6430cb9025a65ba4cf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py index 1e3d2f46..5a62f8df 100644 --- a/notebooks/tutorial_pipeline.py +++ b/notebooks/tutorial_pipeline.py @@ -7,7 +7,7 @@ from element_lab.lab import Device as Equipment from element_lab.lab import User as Experimenter from element_session import session_with_id as session -from element_session.session_with_id import Session +from element_session.session_with_datetime import Session import element_interface import pathlib From 41909257e34b37c9197943dc75d855c31f9cda89 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Wed, 25 Oct 2023 17:05:16 -0500 Subject: [PATCH 08/27] Upsdate tutorial markdown --- notebooks/tutorial.ipynb | 210 +++++++++------------------------------ 1 file changed, 48 insertions(+), 162 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 9e7e40a9..a6060417 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -1125,50 +1125,6 @@ "multielectrode probe. " ] }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'# Represent a physical probe with unique identification\\nprobe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\\n---\\n-> probe.ProbeType\\nprobe_comment=\"\" : varchar(1000) \\n'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(probe.Probe.describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Represent a physical probe with unique identification\n", - "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\n", - "---\n", - "probe_type : varchar(32) # e.g. neuropixels_1.0\n", - "probe_comment=\"\" : varchar(1000) # " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "probe.Probe.heading" - ] - }, { "cell_type": "code", "execution_count": 15, @@ -1293,7 +1249,7 @@ } ], "source": [ - "ephys.ProbeInsertion.describe()" + "print(ephys.ProbeInsertion.describe())" ] }, { @@ -1428,7 +1384,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1436,69 +1391,17 @@ "\n", "### Automatically populate tables\n", "\n", - "`ephys.EphysRecording` is the first table in the pipeline that can be populated automatically.\n", - "If a table contains a part table, this part table is also populated during the\n", - "`populate()` call. `populate()` takes several arguments including the a session\n", - "key. This key restricts `populate()` to performing the operation on the session\n", - "of interest rather than all possible sessions which could be a time-intensive\n", - "process for databases with lots of entries.\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", - "Let's view the `ephys.EphysRecording` and its part table\n", - "`ephys.EphysRecording.EphysFile` and populate both through a single `populate()`\n", - "call." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Ephys recording from a probe insertion for a given session.\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "---\n", - "electrode_config_hash : uuid # \n", - "acq_software : varchar(24) # \n", - "sampling_rate : float # (Hz)\n", - "recording_datetime : datetime # datetime of the recording from this probe\n", - "recording_duration : float # (seconds) duration of the recording from this probe" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ephys.EphysRecording.heading" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Paths of files of a given EphysRecording round.\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "file_path : varchar(255) # filepath relative to root data directory" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ephys.EphysRecording.EphysFile.heading" + "- **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 on unrelated data.\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" ] }, { @@ -2131,26 +2034,6 @@ "downstream processing. Let's view the attributes to get a better understanding. " ] }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'# Manual table for defining a clustering task ready to be run\\n-> ephys.EphysRecording\\n-> ephys.ClusteringParamSet\\n---\\nclustering_output_dir=\"\" : varchar(255) # clustering output directory relative to the clustering root data directory\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # \\'load\\': load computed analysis results, \\'trigger\\': trigger computation\\n'" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ephys.ClusteringTask.describe()" - ] - }, { "cell_type": "code", "execution_count": 29, @@ -2187,7 +2070,7 @@ "+ `paramset_idx` \n", "+ `task_mode` \n", "\n", - "The `paramset_idx` attribute is tracks\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", @@ -2215,15 +2098,6 @@ ")" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice we set the `task_mode` to `load`. Let's call populate on the `Clustering`\n", - "table in the pipeline." - ] - }, { "cell_type": "code", "execution_count": 31, @@ -2335,34 +2209,46 @@ "\n", "In this tutorial, we will do some exploratory analysis by fetching the data from the database and creating a few plots.\n", "\n", - "## Query\n", + "## Querying Data\n", + "\n", + "DataJoint provides a powerful querying system, allowing you to retrieve and work with data stored in your database seamlessly. In this section, we'll explore the fundamental querying concepts.\n", + "\n", + "#### What is a Query?\n", + "\n", + "- A query is essentially a request for data. With DataJoint, you can craft specific queries to fetch data that meets your criteria from the database.\n", + "\n", + "#### The `fetch()` Method\n", + "\n", + "- The primary method for retrieving data from a DataJoint table is `fetch()`.\n", + "- **Default Behavior**: Without any arguments, `fetch()` returns a list of dictionaries. Each dictionary corresponds to an entry in the table.\n", + " \n", + "#### The `fetch1()` Method\n", + "\n", + "- For tables with a single entry or when you're only interested in the first entry, use `fetch1()`.\n", + "- **Default Behavior**: It returns a dictionary of attributes for that one entry.\n", + "\n", + "#### Specific Attributes\n", + "\n", + "- Both `fetch()` and `fetch1()` can be made more specific by providing attributes.\n", + "- Example: `fetch1('fps')` will retrieve only the `fps` attribute from the first entry.\n", + "\n", + "#### Restricting Queries\n", + "\n", + "- Often, you don't want to fetch everything. Instead, you might want data related to a specific subject or session.\n", + "- DataJoint uses the `&` operator to restrict queries.\n", + "- Example: To get all session times for `subject5`, you might use:\n", + " ```python\n", + " subject1_times = (session.Session & \"subject = 'subject1'\").fetch(\"session_datetime\")\n", + " ```\n", "\n", - "This section focuses on working with data that is already in the\n", - "database. \n", + "#### Fetching Primary Keys\n", "\n", - "DataJoint queries allow you to view and import data from the database into a python\n", - "variable using the `fetch()` method. \n", + "- Sometimes, you just need the primary keys of entries.\n", + "- Use the `fetch(\"KEY\")` syntax for this. For instance, `(session.Session).fetch(\"KEY\")`.\n", "\n", - "There are several important features supported by `fetch()`:\n", - "- By default, an empty `fetch()` imports a list of dictionaries containing all\n", - " attributes of all entries in the table that is queried.\n", - "- **`fetch1()`**, on the other hand, imports a dictionary containing all attributes of\n", - " one of the entries in the table. By default, if a table has multiple entries,\n", - " `fetch1()` imports the first entry in the table.\n", - "- Both `fetch()` and `fetch1()` accept table attributes as an argument to query\n", - " that particular attribute. For example `fetch1('fps')` will fetch the first\n", - " value of the `fps` attribute if it exists in the table.\n", - "- Recommended best practice is to **restrict** queries by primary key attributes of the\n", - " table to ensure the accuracy of imported data.\n", - " - The most common restriction for entries in DataJoint tables is performed\n", - " using the `&` operator. For example to fetch all session start times belonging to\n", - " `subject1`, a possible query could be `subject1_sessions =\n", - " (session.Session & \"subject = 'subject1'\").fetch(\"session_datetime\")`. \n", - "- `fetch()` can also be used to obtain the primary keys of a table. To fetch the primary\n", - " keys of a table use `.fetch(\"KEY\")` syntax.\n", + "#### Let's Dive In!\n", "\n", - "Let's walk through these concepts of querying by moving from simple to more\n", - "complex queries." + "Now that we've established the basics, let's delve deeper into querying with some practical examples." ] }, { From 38c50fbaad8cc2a3d3d717ed4c2d5a577fa908e9 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Thu, 26 Oct 2023 14:57:07 -0500 Subject: [PATCH 09/27] Markdown improvements in tutorial --- notebooks/tutorial.ipynb | 57 ++++++++++++++++++++++++++++------------ 1 file changed, 40 insertions(+), 17 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index a6060417..a6c89e67 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -7,19 +7,21 @@ "source": [ "# DataJoint Element Tutorial: Array Electrophysiology\n", "\n", - "Welcome to the DataJoint Element tutorial for Array Electrophysiology! Dive into the world of automated analyses and organization of extracellular array electrophysiology data.\n", + "Welcome to the tutorial for DataJoint's open-source data pipeline for NeuroPixels Array\n", + "Electrophysiology. This tutorial aims to provide a comprehensive understanding of the\n", + "open-source data pipeline created using `element-array-ephys` for processing\n", + "and analyzing extracellular electrophysiology datasets. \n", "\n", "**In this tutorial, we will cover:**\n", "- The basics:\n", - " - Differentiating between an Element and a pipeline.\n", - " - How to plot the pipeline with `dj.Diagram`.\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 data into tables.\n", + " - Inserting real data into tables.\n", " - Querying table contents.\n", " - Fetching table contents.\n", - " - Running the pipeline for your experiments.\n", "- A walk-through:\n", - " - Processing NeuroPixels ephys data acquired with OpenEphys and sorted with Kilosort.\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", @@ -51,10 +53,13 @@ "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", - "+ **Element**: A modular chunk of related tables.\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", - "+ **Table**: A structured set of data held within the database. It consists of rows and columns, much like an Excel spreadsheet.\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", @@ -67,7 +72,9 @@ "| 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 the database. Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\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." ] @@ -622,7 +629,7 @@ "source": [ "## Understanding Table Types in DataJoint\n", "\n", - "In the previous section, 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", + "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", @@ -650,7 +657,7 @@ "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 add data based on existing data.\n", + "- **Populate**: Automatically compute and insert data.\n", "- **Query**: Search and filter data.\n", "- **Fetch**: Retrieve data for further analysis or visualization.\n", "\n", @@ -692,7 +699,7 @@ } ], "source": [ - "subject.Subject.describe()" + "print(subject.Subject.describe())" ] }, { @@ -1399,7 +1406,7 @@ "\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 on unrelated data.\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" ] @@ -2448,10 +2455,20 @@ ] }, { - "attachments": {}, "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", @@ -2471,12 +2488,18 @@ "dj.conn()\n", "```\n", "\n", - "- Run this code cell and proceed with the rest of the notebook." + "- Run this code block above and proceed with the rest of the notebook." ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], "source": [] } ], From b2180c457e86303ac816bd0acb94c99fb1097821 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 20:07:16 +0000 Subject: [PATCH 10/27] Update `get_logger` to `dj.logger` --- element_array_ephys/ephys_acute.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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() From ce6e3bf8ee3968f5b1d1b97e1d7d238272b6c073 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 20:22:04 +0000 Subject: [PATCH 11/27] Use `session_with_datetime` for tutorial --- notebooks/tutorial_pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py index 5a62f8df..eb166faa 100644 --- a/notebooks/tutorial_pipeline.py +++ b/notebooks/tutorial_pipeline.py @@ -6,7 +6,7 @@ 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_id as session +from element_session import session_with_datetime as session from element_session.session_with_datetime import Session import element_interface import pathlib From 1717054c4eefb1176be2a54bc9e75b80508b62a0 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 20:46:00 +0000 Subject: [PATCH 12/27] Update Dockerfile and tutorial_pipeline to fix errors --- .devcontainer/Dockerfile | 2 +- notebooks/tutorial_pipeline.py | 13 +++++++++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 2d0e9261..9d9e17c2 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -41,7 +41,7 @@ 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 EPHYS_ROOT_DATA_DIR /workspaces/element-array-ephys/example_data/processed ENV DATABASE_PREFIX neuro_ USER vscode diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py index eb166faa..12e13a6b 100644 --- a/notebooks/tutorial_pipeline.py +++ b/notebooks/tutorial_pipeline.py @@ -30,4 +30,17 @@ def get_ephys_root_data_dir(): 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__) From d5430aa93b507baf4923acda3d3eb8663e480a23 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 21:22:19 +0000 Subject: [PATCH 13/27] Update root_data_dir in Dockerfile --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 9d9e17c2..2d0e9261 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -41,7 +41,7 @@ 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/processed +ENV EPHYS_ROOT_DATA_DIR /workspaces/element-array-ephys/example_data ENV DATABASE_PREFIX neuro_ USER vscode From 7bf9f9f3cf80f32963ed421221a4cd405aef6dd8 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 21:42:15 +0000 Subject: [PATCH 14/27] Completed tutorial --- notebooks/tutorial.ipynb | 1134 +++++++++++++++++++++++--------------- 1 file changed, 702 insertions(+), 432 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index a6c89e67..543ab984 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -88,9 +88,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-03-20 21:44:02,597][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", - "[2023-03-20 21:44:02,607][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-03-20 21:44:02,614][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + "[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" ] } ], @@ -113,9 +113,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Schema `neuro_subject`" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.schema" ] @@ -129,9 +140,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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"\n", - "\n", - "ephys.QualityMetrics.Waveform\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", + "\n", "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Waveform\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.WaveformSet.PeakWaveform\n", + "\n", "\n", "\n", "\n", @@ -429,186 +567,139 @@ "\n", "\n", "\n", - "\n", + "\n", "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Cluster\n", - "\n", - "\n", - "\n", - "\n", - "ephys.WaveformSet.Waveform\n", - "\n", - "\n", - "ephys.WaveformSet.Waveform\n", - "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "ephys.CuratedClustering.Unit->ephys.WaveformSet.Waveform\n", - "\n", - "\n", - "\n", - "\n", - "probe.Probe->ephys.ProbeInsertion\n", - "\n", - "\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", + "\n", "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.InsertionLocation\n", + "\n", "\n", - "\n", - "\n", - "ephys.AcquisitionSoftware->ephys.EphysRecording\n", - "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.EphysRecording\n", + "\n", "\n", "\n", - "\n", + "\n", "ephys.QualityMetrics\n", - "\n", - "\n", - "ephys.QualityMetrics\n", + "\n", + "\n", + "ephys.QualityMetrics\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ephys.QualityMetrics->ephys.QualityMetrics.Waveform\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "ephys.QualityMetrics->ephys.QualityMetrics.Cluster\n", - "\n", - "\n", - "\n", - "\n", - "probe.ElectrodeConfig.Electrode->ephys.CuratedClustering.Unit\n", - "\n", + "\n", "\n", - "\n", - "\n", - "ephys.LFP.Electrode\n", - "\n", - "\n", - "ephys.LFP.Electrode\n", + "\n", + "\n", + "ephys.Clustering\n", + "\n", + "\n", + "ephys.Clustering\n", "\n", "\n", "\n", - "\n", - "\n", - "probe.ElectrodeConfig.Electrode->ephys.LFP.Electrode\n", - "\n", - "\n", - "\n", - 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"\n", + "\n", "\n", - "ephys.WaveformSet->ephys.WaveformSet.Waveform\n", - "\n", + "probe.ElectrodeConfig->probe.ElectrodeConfig.Electrode\n", + "\n", "\n", - "\n", + "\n", "\n", - "ephys.ClusteringParamSet->ephys.ClusteringTask\n", - "\n", + "probe.ElectrodeConfig->ephys.EphysRecording\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->session.Session\n", - "\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", "\n", - "\n", - "\n", - "ephys.Clustering->ephys.Curation\n", - "\n", "\n", - "\n", - "\n", - "ephys.ClusterQualityLabel\n", - "\n", - "\n", - "ephys.ClusterQualityLabel\n", - "\n", + "\n", + "\n", + "ephys.LFP->ephys.LFP.Electrode\n", + "\n", "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.ClusteringTask\n", + "\n", "\n", - "\n", + "\n", "\n", - "ephys.ClusterQualityLabel->ephys.CuratedClustering.Unit\n", - "\n", + "ephys.EphysRecording->ephys.EphysRecording.EphysFile\n", + "\n", "\n", - "\n", + "\n", "\n", - "probe.ProbeType.Electrode->probe.ElectrodeConfig.Electrode\n", - "\n", + "ephys.EphysRecording->ephys.LFP\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -684,18 +775,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'subject : varchar(8) \\n---\\nsubject_nickname=\"\" : varchar(64) \\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "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": [ @@ -704,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -719,7 +813,7 @@ "subject_description=\"\" : varchar(1024) # " ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -740,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -761,9 +855,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -832,7 +928,7 @@ " (Total: 1)" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -854,18 +950,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'-> subject.Subject\\nsession_datetime : datetime \\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "-> subject.Subject\n", + "session_datetime : datetime \n", + "\n" + ] } ], "source": [ @@ -874,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -885,7 +980,7 @@ "session_datetime : datetime # " ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -912,16 +1007,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "session_key = dict(subject=\"subject5\", session_datetime=datetime.datetime.now())" + "session_key = dict(subject=\"subject5\", session_datetime=\"2023-01-01 00:00:00\")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -942,9 +1037,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -988,7 +1085,7 @@ " \n", " \n", " subject5\n", - "2023-03-20 21:44:09 \n", + "2023-01-01 00:00:00 \n", " \n", " \n", "

Total: 1

\n", @@ -997,11 +1094,11 @@ "text/plain": [ "*subject *session_datet\n", "+----------+ +------------+\n", - "subject5 2023-03-20 21:\n", + "subject5 2023-01-01 00:\n", " (Total: 1)" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1027,7 +1124,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1048,9 +1145,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1097,8 +1196,8 @@ " Path to the data directory for a session\n", " \n", " subject5\n", - "2023-03-20 21:44:09\n", - "subject5/session1 \n", + "2023-01-01 00:00:00\n", + "raw/subject5/session1 \n", " \n", " \n", "

Total: 1

\n", @@ -1107,17 +1206,17 @@ "text/plain": [ "*subject *session_datet session_dir \n", "+----------+ +------------+ +------------+\n", - "subject5 2023-03-20 21: subject5/sessi\n", + "subject5 2023-01-01 00: raw/subject5/s\n", " (Total: 1)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "session.SessionDirectory.insert1(dict(**session_key, session_dir=\"subject5/session1\"))\n", + "session.SessionDirectory.insert1(dict(**session_key, session_dir=\"raw/subject5/session1\"))\n", "session.SessionDirectory()" ] }, @@ -1134,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1155,9 +1254,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1218,7 +1319,7 @@ " (Total: 1)" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1241,18 +1342,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'# Probe insertion implanted into an animal for a given session.\\n-> session.Session\\ninsertion_number : tinyint unsigned \\n---\\n-> probe.Probe\\n'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" + "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": [ @@ -1261,7 +1364,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1275,7 +1378,7 @@ "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1286,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1307,9 +1410,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1359,7 +1464,7 @@ " unique identifier for this model of probe (e.g. serial number)\n", " \n", " subject5\n", - "2023-03-20 21:44:09\n", + "2023-01-01 00:00:00\n", "1\n", "714000838 \n", " \n", @@ -1370,11 +1475,11 @@ "text/plain": [ "*subject *session_datet *insertion_num probe \n", "+----------+ +------------+ +------------+ +-----------+\n", - "subject5 2023-03-20 21: 1 714000838 \n", + "subject5 2023-01-01 00: 1 714000838 \n", " (Total: 1)" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1413,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1434,9 +1539,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1510,7 +1617,7 @@ " (Total: 0)" ] }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1521,7 +1628,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1542,9 +1649,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1606,7 +1715,7 @@ " (Total: 0)" ] }, - "execution_count": 22, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1617,14 +1726,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "EphysRecording: 100%|██████████| 1/1 [00:00<00:00, 13.35it/s]\n" + "EphysRecording: 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]\n" ] } ], @@ -1642,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1663,9 +1772,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1727,7 +1838,7 @@ " (seconds) duration of the recording from this probe\n", " \n", " subject5\n", - "2023-03-20 21:44:09\n", + "2023-01-01 00:00:00\n", "1\n", "8d4cc6d8-a02d-42c8-bf27-7459c39ea0ee\n", "SpikeGLX\n", @@ -1742,11 +1853,11 @@ "text/plain": [ "*subject *session_datet *insertion_num electrode_conf acq_software sampling_rate recording_date recording_dura\n", "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "subject5 2023-03-20 21: 1 8d4cc6d8-a02d- SpikeGLX 30000.0 2018-07-03 20: 338.666 \n", + "subject5 2023-01-01 00: 1 8d4cc6d8-a02d- SpikeGLX 30000.0 2018-07-03 20: 338.666 \n", " (Total: 1)" ] }, - "execution_count": 24, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1757,7 +1868,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1778,9 +1889,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1830,9 +1943,9 @@ " filepath relative to root data directory\n", " \n", " subject5\n", - "2023-03-20 21:44:09\n", + "2023-01-01 00:00:00\n", "1\n", - "subject5/session1/probe_1/npx_g0_t0.imec.ap.meta \n", + "raw/subject5/session1/probe_1/npx_g0_t0.imec.ap.meta \n", " \n", " \n", "

Total: 1

\n", @@ -1841,11 +1954,11 @@ "text/plain": [ "*subject *session_datet *insertion_num *file_path \n", "+----------+ +------------+ +------------+ +------------+\n", - "subject5 2023-03-20 21: 1 subject5/sessi\n", + "subject5 2023-01-01 00: 1 raw/subject5/s\n", " (Total: 1)" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +1983,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1885,7 +1998,7 @@ "params : longblob # dictionary of all applicable parameters" ] }, - "execution_count": 26, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1896,7 +2009,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1917,9 +2030,11 @@ " }\n", " .Table tr:nth-child(odd){\n", " background: #ffffff;\n", + " color: #000000;\n", " }\n", " .Table tr:nth-child(even){\n", " background: #f3f1ff;\n", + " color: #000000;\n", " }\n", " /* Tooltip container */\n", " .djtooltip {\n", @@ -1988,7 +2103,7 @@ " (Total: 1)" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2043,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2059,7 +2174,7 @@ "task_mode=\"load\" : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation" ] }, - "execution_count": 29, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2090,7 +2205,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2100,21 +2215,21 @@ " insertion_number=1,\n", " paramset_idx=0,\n", " task_mode=\"load\", # load or trigger\n", - " clustering_output_dir=\"subject5/session1/probe_1/ks2.1_01\",\n", + " clustering_output_dir=\"processed/subject5/session1/probe_1/kilosort2-5_1\",\n", " )\n", ")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Clustering: 100%|██████████| 1/1 [00:00<00:00, 15.88it/s]\n" + "Clustering: 100%|██████████| 1/1 [00:00<00:00, 3.46it/s]\n" ] } ], @@ -2134,7 +2249,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2154,7 +2269,7 @@ "curation_note=\"\" : varchar(2000) # " ] }, - "execution_count": 32, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2165,7 +2280,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -2184,16 +2299,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "CuratedClustering: 100%|██████████| 1/1 [00:02<00:00, 2.22s/it]\n", - "LFP: 100%|██████████| 1/1 [00:20<00:00, 20.91s/it]\n", - "WaveformSet: 100%|██████████| 1/1 [05:47<00:00, 347.33s/it]\n" + "CuratedClustering: 0%| | 0/1 [00:00" ] @@ -2298,7 +2415,7 @@ "plt.plot(lfp_average)\n", "plt.title(\"Average LFP Waveform for Insertion 1\")\n", "plt.xlabel(\"Samples\")\n", - "plt.ylabel(\"microvolts (uV)\")" + "plt.ylabel(\"microvolts (uV)\");" ] }, { @@ -2320,7 +2437,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2334,12 +2451,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2353,12 +2470,12 @@ "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\")" + "plt.ylabel(\"Unit\");" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2370,54 +2487,211 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'subject': 'subject5',\n", - " 'session_datetime': datetime.datetime(2023, 3, 20, 21, 44, 9),\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': 186,\n", - " 'cluster_quality_label': 'good',\n", - " 'spike_count': 18,\n", - " 'spike_times': array([ 48.74336667, 67.63773333, 69.05666667, 83.11203333,\n", - " 94.1978 , 108.0454 , 110.36893333, 122.16836667,\n", - " 149.41143333, 152.55593333, 182.84816667, 236.8929 ,\n", - " 270.40613333, 292.4432 , 299.99216667, 321.74016667,\n", - " 326.5434 , 329.26373333]),\n", - " 'spike_sites': array([186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186,\n", - " 186, 186, 186, 186, 186]),\n", - " 'spike_depths': array([2144.38644037, 2240.44959257, 2286.37081289, 2177.07452433,\n", - " 2227.42382471, 2143.86346649, 2235.74014979, 2236.04354982,\n", - " 2236.06676094, 2299.53408387, 2222.6567435 , 2165.88562393,\n", - " 2165.55087743, 2254.93847903, 2241.71132163, 2057.9714868 ,\n", - " 2272.82484323, 2229.42266297]),\n", - " 'peak_electrode_waveform': array([-343.61979167, -345.703125 , -344.79166667, -341.53645833,\n", - " -339.0625 , -340.88541667, -342.1875 , -339.97395833,\n", - " -338.02083333, -336.97916667, -338.41145833, -341.53645833,\n", - " -343.48958333, -344.53125 , -341.796875 , -337.36979167,\n", - " -339.32291667, -336.58854167, -338.80208333, -337.63020833,\n", - " -336.97916667, -338.02083333, -338.02083333, -333.984375 ,\n", - " -324.86979167, -317.05729167, -313.93229167, -309.765625 ,\n", - " -304.296875 , -300.13020833, -300.65104167, -301.43229167,\n", - " -306.11979167, -311.58854167, -316.40625 , -320.57291667,\n", - " -333.203125 , -339.58333333, -340.625 , -337.63020833,\n", - " -344.140625 , -350.91145833, -356.11979167, -363.80208333,\n", - " -375. , -379.42708333, -382.68229167, -389.97395833,\n", - " -391.796875 , -393.61979167, -390.625 , -395.96354167,\n", - " -400.52083333, -399.73958333, -395.18229167, -392.96875 ,\n", - " -395.05208333, -391.796875 , -392.31770833, -387.36979167,\n", - " -387.23958333, -387.890625 , -384.375 , -383.33333333])}" + " '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, 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, 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962.92735677,\n", + " 915.56550832, 922.14542481, 894.27941318, 874.31413165,\n", + " 847.78323382, 960.73651726, 902.09721294, 899.97936727,\n", + " 936.76767365, 908.68471877, 931.97562897, 892.8563381 ,\n", + " 940.49920044, 925.87119487, 915.55667036, 786.01763842,\n", + " 867.61268444, 1007.66794229, 867.43220284, 941.51843395,\n", + " 955.66765617, 970.36344663, 894.82624021, 915.00922015,\n", + " 889.99169864, 878.28545616, 850.33714087, 928.43421969,\n", + " 939.52352775, 962.60336897, 903.36858487, 896.37548128,\n", + " 963.73199174, 789.82843798, 920.43722936, 828.40065648,\n", + " 943.98810503, 835.85766882, 841.76946105, 937.36801859,\n", + " 896.49642885, 967.15672796, 918.39713395, 906.36795986,\n", + " 919.40916519, 875.89694182, 908.3418033 , 972.05674531,\n", + " 927.07275902, 940.77150366, 918.04680759, 981.59648505,\n", + " 940.32229921, 938.36235287, 849.04038544, 893.3058527 ,\n", + " 913.09883832, 912.35625832, 989.78190796, 879.34780591,\n", + " 928.81644221, 892.97322993, 898.70158737, 906.91746418,\n", + " 946.42526162, 914.08702993, 859.34034454, 907.18338275,\n", + " 989.30138781, 926.06769221, 838.08269605, 929.50654863,\n", + " 827.39586238, 927.36188021, 910.17661856, 935.41822322,\n", + " 885.63661049, 914.48018584, 895.93753147, 911.13864214,\n", + " 881.60996972, 938.20931097, 1015.75963624, 938.56188955,\n", + " 909.09481672, 867.81417174, 912.15092578, 907.0519042 ,\n", + " 913.43873474, 913.23570042, 983.89208176, 899.44708324,\n", + " 910.55603061, 950.67199374, 916.27718038, 886.16272254,\n", + " 975.57372749, 882.46972449, 908.99151954, 893.41760103,\n", + " 938.6441373 , 885.19738826, 947.44382361, 918.75218854,\n", + " 942.78614663, 968.52291541, 915.78656008, 989.00683215,\n", + " 855.88223229, 976.62553529, 919.25644927, 958.63970635,\n", + " 934.03540249, 926.45247121, 961.36373273, 926.55709697,\n", + " 896.6375551 , 897.47943897, 907.74803191, 954.04187795,\n", + " 882.10295293, 1004.09432843, 892.73726557, 846.13198111,\n", + " 929.42733278, 894.24531402, 921.97988827, 860.601478 ,\n", + " 912.55635483, 997.22339505, 985.88635074, 938.45775184,\n", + " 944.63766895, 942.18376197, 984.23087354, 922.40370934,\n", + " 965.34813049, 921.32552393, 969.81367405, 917.74503135,\n", + " 833.76599428, 894.97963584, 878.49090123, 864.91049261,\n", + " 985.04808527, 927.18133162, 844.64492657, 913.88047009,\n", + " 928.03561194, 930.79686847, 930.13055052, 839.5761256 ,\n", + " 943.37875897, 956.44476838, 931.39781252, 989.20932655,\n", + " 1006.29928459, 942.03146682, 901.72518137, 932.53880871,\n", + " 935.96943144, 925.53161728, 874.67439465, 951.48997974,\n", + " 960. , 874.14717137, 921.49562818, 927.26749515,\n", + " 926.41158172, 896.61260138, 938.6103718 , 959.64828575,\n", + " 935.22630845, 955.63103852, 950.8495808 , 888.45366602,\n", + " 912.06814044, 862.81354284, 875.05713562, 875.4100677 ,\n", + " 923.5426824 , 839.78562837, 889.74471045, 867.8332007 ,\n", + " 933.88736839, 868.07155277, 883.34738777, 913.12741673,\n", + " 915.77804797, 962.99775026, 932.9485248 , 930.37415413,\n", + " 944.21567324, 776.98228821, 936.37311401, 941.58975024,\n", + " 941.55965056, 864.51860126, 930.38329188, 912.78502395,\n", + " 947.32712079, 1011.13494089, 937.99624501, 916.59535343,\n", + " 873.82437868, 901.49630725, 846.11674006, 998.64016883,\n", + " 978.32805846, 876.27655411, 905.78089027, 827.25234516,\n", + " 1002.18813125, 898.65291068, 920.350764 , 874.17361952,\n", + " 938.70381451, 945.81921622, 874.98921047, 915.97985629,\n", + " 920.14460695, 965.25282342, 977.01542391, 863.18856888,\n", + " 907.62393082, 906.19584978, 851.37149337, 956.85804045,\n", + " 969.94521463, 912.02588788, 953.60047228, 819.53086148,\n", + " 911.99611929, 845.66361521, 962.24964999, 921.43338903,\n", + " 982.04267741, 931.1841306 , 896.56744607, 926.6958426 ,\n", + " 841.56778533, 961.77261899, 943.15601639, 786.18136435,\n", + " 864.98026608, 950.29718422, 930.39820918, 983.57154574,\n", + " 909.55498598, 925.76591071, 906.07808244, 964.41923255,\n", + " 911.17276099, 919.34859876, 906.11721418, 905.96844161,\n", + " 911.53683431, 958.97512493, 953.15452691, 889.09240389,\n", + " 957.98473324, 993.29170991, 928.99175736, 908.10563408]),\n", + " 'peak_electrode_waveform': array([-503.33636558, -502.734375 , -501.32973031, -500.34246575,\n", + " -499.73244863, -498.66491866, -494.41887842, -491.6015625 ,\n", + " -486.59300086, -481.55233305, -474.36055223, -466.79152397,\n", + " -458.98169949, -451.10766267, -442.41491866, -431.03328339,\n", + " -420.81549658, -410.91074486, -402.25010702, -395.59610445,\n", + " -389.25513699, -384.02985873, -381.67005565, -379.81592466,\n", + " -378.79655394, -380.26541096, -384.54355736, -392.41759418,\n", + " -401.11033818, -410.67797517, -420.13324058, -430.6640625 ,\n", + " -442.34267979, -452.71297089, -461.42979452, -469.67305223,\n", + " -478.64672517, -486.40036387, -493.83294092, -498.87360873,\n", + " -504.82127568, -509.58101455, -511.55554366, -514.19627568,\n", + " -515.77750428, -516.41160103, -517.3828125 , -518.16941353,\n", + " -517.26241438, -518.46639555, -517.76808647, -517.3828125 ,\n", + " -516.61226455, -514.32470034, -512.5187286 , -511.66791524,\n", + " -510.65657106, -507.74293664, -504.84535531, -503.02333048,\n", + " -501.93172089, -499.3552012 , -498.77729024, -495.78339041,\n", + " -495.42219606, -495.78339041, -494.30650685, -492.30789812,\n", + " -491.83433219, -492.14736729, -490.16481164, -489.69124572,\n", + " -488.84043236, -488.5354238 , -489.24175942, -487.68461045,\n", + " -486.48062928, -485.50941781, -484.23319777, -484.24925086,\n", + " -484.26530394, -483.3984375 ])}" ] }, - "execution_count": 40, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2428,12 +2702,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2451,7 +2725,7 @@ " unit_data[\"peak_electrode_waveform\"],\n", ")\n", "plt.xlabel(\"Time (ms)\")\n", - "plt.ylabel(r\"Voltage ($\\mu$V)\")" + "plt.ylabel(r\"Voltage ($\\mu$V)\");" ] }, { @@ -2494,11 +2768,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [] } @@ -2519,7 +2789,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.17" }, "orig_nbformat": 4, "vscode": { From d02d8a585b575ea60b01caae953df4176a40de01 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Thu, 26 Oct 2023 16:50:53 -0500 Subject: [PATCH 15/27] Add demo notebooks --- notebooks/demo_prepare.ipynb | 225 +++++++++++++++++++++++++++++++++++ notebooks/demo_run.ipynb | 136 +++++++++++++++++++++ 2 files changed, 361 insertions(+) create mode 100644 notebooks/demo_prepare.ipynb create mode 100644 notebooks/demo_run.ipynb diff --git a/notebooks/demo_prepare.ipynb b/notebooks/demo_prepare.ipynb new file mode 100644 index 00000000..3ed4d443 --- /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 and demo 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": "3.9.16" + }, + "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..46292471 --- /dev/null +++ b/notebooks/demo_run.ipynb @@ -0,0 +1,136 @@ +{ + "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": [ + "dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(probe) + dj.Diagram(ephys)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insert an entry in a manual table by calling the `insert()` method\n", + "\n", + "```python\n", + "subject.Subject.insert1(\n", + " dict(subject='subject1',\n", + " subject_birth_date='2023-01-01',\n", + " sex='U',\n", + " )\n", + ")\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Automatically process data with the `populate()` method\n", + "\n", + "+ Once data is inserted into manual tables, the `populate()` function automatically runs the ingestion and processing routines. \n", + "\n", + "+ For example, to run Kilosort processing in the `Clustering` table:\n", + "\n", + " ```python\n", + " ephys.Clustering.populate()\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": "3.9.16" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "ff52d424e56dd643d8b2ec122f40a2e279e94970100b4e6430cb9025a65ba4cf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1da15dbd1bc9d759fc2c3cf1dd6e1388f5645fad Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Thu, 26 Oct 2023 16:51:15 -0500 Subject: [PATCH 16/27] Update demo presentation notebook --- notebooks/demo_run.ipynb | 33 --------------------------------- 1 file changed, 33 deletions(-) diff --git a/notebooks/demo_run.ipynb b/notebooks/demo_run.ipynb index 46292471..d500dc61 100644 --- a/notebooks/demo_run.ipynb +++ b/notebooks/demo_run.ipynb @@ -47,39 +47,6 @@ "dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(probe) + dj.Diagram(ephys)" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Insert an entry in a manual table by calling the `insert()` method\n", - "\n", - "```python\n", - "subject.Subject.insert1(\n", - " dict(subject='subject1',\n", - " subject_birth_date='2023-01-01',\n", - " sex='U',\n", - " )\n", - ")\n", - "```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Automatically process data with the `populate()` method\n", - "\n", - "+ Once data is inserted into manual tables, the `populate()` function automatically runs the ingestion and processing routines. \n", - "\n", - "+ For example, to run Kilosort processing in the `Clustering` table:\n", - "\n", - " ```python\n", - " ephys.Clustering.populate()\n", - " ```" - ] - }, { "attachments": {}, "cell_type": "markdown", From 5d57ff2c1d12963391b3318d091a0ac1ca66db6d Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Thu, 26 Oct 2023 16:52:14 -0500 Subject: [PATCH 17/27] Black formatting --- notebooks/demo_run.ipynb | 7 ++++++- notebooks/tutorial.ipynb | 4 +++- notebooks/tutorial_pipeline.py | 2 ++ 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/notebooks/demo_run.ipynb b/notebooks/demo_run.ipynb index d500dc61..1e831e0c 100644 --- a/notebooks/demo_run.ipynb +++ b/notebooks/demo_run.ipynb @@ -44,7 +44,12 @@ "metadata": {}, "outputs": [], "source": [ - "dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(probe) + dj.Diagram(ephys)" + "(\n", + " dj.Diagram(subject.Subject)\n", + " + dj.Diagram(session.Session)\n", + " + dj.Diagram(probe)\n", + " + dj.Diagram(ephys)\n", + ")" ] }, { diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 543ab984..31e95118 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -1216,7 +1216,9 @@ } ], "source": [ - "session.SessionDirectory.insert1(dict(**session_key, session_dir=\"raw/subject5/session1\"))\n", + "session.SessionDirectory.insert1(\n", + " dict(**session_key, session_dir=\"raw/subject5/session1\")\n", + ")\n", "session.SessionDirectory()" ] }, diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py index 12e13a6b..2163d315 100644 --- a/notebooks/tutorial_pipeline.py +++ b/notebooks/tutorial_pipeline.py @@ -31,6 +31,7 @@ def get_ephys_root_data_dir(): subject.activate(db_prefix + "subject", linking_module=__name__) session.activate(db_prefix + "session", linking_module=__name__) + @lab.schema class SkullReference(dj.Lookup): definition = """ @@ -38,6 +39,7 @@ class SkullReference(dj.Lookup): """ contents = zip(["Bregma", "Lambda"]) + def get_session_directory(session_key): session_directory = (session.SessionDirectory & session_key).fetch1("session_dir") return pathlib.Path(session_directory) From 21fde1351084c6f73751dd47f0024c9b9e6487ad Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 26 Oct 2023 22:04:42 +0000 Subject: [PATCH 18/27] Complete demo notebooks --- notebooks/demo_prepare.ipynb | 6 +++--- notebooks/demo_run.ipynb | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/notebooks/demo_prepare.ipynb b/notebooks/demo_prepare.ipynb index 3ed4d443..74057ba4 100644 --- a/notebooks/demo_prepare.ipynb +++ b/notebooks/demo_prepare.ipynb @@ -6,7 +6,7 @@ "source": [ "### Demo Preparation Notebook\n", "\n", - "**Please Note**: This notebook and demo are NOT intended to be used as learning materials. To gain\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." ] @@ -20,7 +20,7 @@ "# Runs in about 45s\n", "import datajoint as dj\n", "import datetime\n", - "from tutorial.pipeline import subject, session, probe, ephys\n", + "from tutorial_pipeline import subject, session, probe, ephys\n", "from element_array_ephys import ephys_report" ] }, @@ -211,7 +211,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.17" }, "orig_nbformat": 4, "vscode": { diff --git a/notebooks/demo_run.ipynb b/notebooks/demo_run.ipynb index 1e831e0c..348a3c43 100644 --- a/notebooks/demo_run.ipynb +++ b/notebooks/demo_run.ipynb @@ -26,7 +26,7 @@ "outputs": [], "source": [ "import datajoint as dj\n", - "from tutorial.pipeline import subject, session, probe, ephys\n", + "from tutorial_pipeline import subject, session, probe, ephys\n", "from element_array_ephys.plotting.widget import main" ] }, @@ -94,7 +94,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.17" }, "orig_nbformat": 4, "vscode": { From 632839825214c6c66baa29cd7136bc0bc46f0f3a Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:10:35 -0500 Subject: [PATCH 19/27] Update diagram_flowchart.drawio --- images/diagram_flowchart.drawio | 79 ++++++++++++++++++++++++++++++++- 1 file changed, 78 insertions(+), 1 deletion(-) diff --git a/images/diagram_flowchart.drawio b/images/diagram_flowchart.drawio index 410a33b2..f94fb602 100644 --- a/images/diagram_flowchart.drawio +++ b/images/diagram_flowchart.drawio @@ -1 +1,78 @@ 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 \ No newline at end of file + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From bb2f507704b7b15bbe0f887dffee0c98018828e3 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:14:10 -0500 Subject: [PATCH 20/27] Updated diagram_flowchart.svg --- images/diagram_flowchart.svg | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/images/diagram_flowchart.svg b/images/diagram_flowchart.svg index 5840de98..3efdbb7b 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 From f9fc3ec002160fb0bfe36c579019637bd6ab285e Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:18:02 -0500 Subject: [PATCH 21/27] Update diagram_flowchart.svg --- images/diagram_flowchart.svg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/images/diagram_flowchart.svg b/images/diagram_flowchart.svg index 3efdbb7b..edd56e52 100644 --- a/images/diagram_flowchart.svg +++ b/images/diagram_flowchart.svg @@ -1,4 +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 From a08736cd58b2c43bdeb6e9d0d35b69ce818174f8 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:18:23 -0500 Subject: [PATCH 22/27] Update diagram_flowchart.drawio --- images/diagram_flowchart.drawio | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/images/diagram_flowchart.drawio b/images/diagram_flowchart.drawio index f94fb602..b259ce3e 100644 --- a/images/diagram_flowchart.drawio +++ b/images/diagram_flowchart.drawio @@ -1,6 +1,6 @@ - + - + @@ -25,7 +25,7 @@ - + From b9fd4a35f19587ea6527d968bb6b42e5afa880b2 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Mon, 30 Oct 2023 16:26:32 -0500 Subject: [PATCH 23/27] Update README + minor fixes --- README.md | 62 +++++++++++++++++++++--- element_array_ephys/ephys_chronic.py | 4 +- element_array_ephys/ephys_no_curation.py | 4 +- 3 files changed, 58 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 9324328f..61a42384 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. + +1. 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/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() From a376c90a6f495f84af9611b8e8293272892c3c29 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:34:18 -0500 Subject: [PATCH 24/27] Updated diagram_flowchart.svg --- images/diagram_flowchart.svg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/images/diagram_flowchart.svg b/images/diagram_flowchart.svg index edd56e52..e8b1df4b 100644 --- a/images/diagram_flowchart.svg +++ b/images/diagram_flowchart.svg @@ -1,4 +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 From d1657b2c57c1d8733cb88bce326511679a575fe3 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Mon, 30 Oct 2023 16:39:44 -0500 Subject: [PATCH 25/27] Fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 61a42384..b59fda94 100644 --- a/README.md +++ b/README.md @@ -83,4 +83,4 @@ MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d 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. -1. 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. +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. From 820b282e29eab4793c56ab99ac29ac897f3bdd33 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Fri, 3 Nov 2023 10:53:06 -0500 Subject: [PATCH 26/27] Fix typo in tutorial heading --- notebooks/tutorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 31e95118..9ca29593 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# DataJoint Element Tutorial: Array Electrophysiology\n", + "# DataJoint Elements Tutorial: Array Electrophysiology\n", "\n", "Welcome to the tutorial for DataJoint's open-source data pipeline for NeuroPixels Array\n", "Electrophysiology. This tutorial aims to provide a comprehensive understanding of the\n", From 733d2b1ee9702a6d4e391b6ce62e373534ffdd0a Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Thu, 9 Nov 2023 09:26:33 -0600 Subject: [PATCH 27/27] review PR tutorial --- notebooks/tutorial.ipynb | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 9ca29593..5ccc0b4f 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -5,12 +5,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# DataJoint Elements Tutorial: Array Electrophysiology\n", + "# DataJoint Elements for NeuroPixels Array Electrophysiology\n", "\n", - "Welcome to the tutorial for DataJoint's open-source data pipeline for NeuroPixels Array\n", - "Electrophysiology. This tutorial aims to provide a comprehensive understanding of the\n", - "open-source data pipeline created using `element-array-ephys` for processing\n", - "and analyzing extracellular electrophysiology datasets. \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",