diff --git a/README.md b/README.md index 4b24b46d..4c841679 100644 --- a/README.md +++ b/README.md @@ -1,282 +1,51 @@ -# Pipeline for extracellular electrophysiology using Neuropixels probe and kilosort clustering method +# DataJoint Workflow - Array Electrophysiology -Build a full ephys pipeline using the canonical pipeline elements +Workflow for extracellular array electrophysiology data acquired with a polytrode probe (e.g. +Neuropixels, Neuralynx) using the `SpikeGLX` or `OpenEphys` acquisition software and processed +with MATLAB- or python-based `Kilosort` spike sorting software. + +A complete electrophysiology workflow can be built using the DataJoint Elements. + [element-lab](https://github.com/datajoint/element-lab) + [element-animal](https://github.com/datajoint/element-animal) + [element-session](https://github.com/datajoint/element-session) + [element-array-ephys](https://github.com/datajoint/element-array-ephys) This repository provides demonstrations for: -1. Set up a workflow using different elements (see [workflow_array_ephys/pipeline.py](workflow_array_ephys/pipeline.py)) -2. Ingestion of data/metadata based on: - + predefined file/folder structure and naming convention - + predefined directory lookup methods (see [workflow_array_ephys/paths.py](workflow_array_ephys/paths.py)) -3. Ingestion of clustering results (built-in routine from the ephys element) - +1. Set up a workflow using DataJoint Elements (see +[workflow_array_ephys/pipeline.py](workflow_array_ephys/pipeline.py)) +2. Ingestion of data/metadata based on a predefined file structure, file naming +convention, and directory lookup methods (see +[workflow_array_ephys/paths.py](workflow_array_ephys/paths.py)). +3. Ingestion of clustering results. -## Pipeline Architecture +## Workflow architecture -The electrophysiology pipeline presented here uses pipeline components from 4 DataJoint Elements, -`element-lab`, `element-animal`, `element-session` and `element-array-ephys`, assembled together to form a fully functional workflow. +The electrophysiology workflow presented here uses components from 4 DataJoint +Elements (`element-lab`, `element-animal`, `element-session`, +`element-array-ephys`) assembled together to form a fully functional workflow. ### element-lab -![element-lab](https://github.com/datajoint/element-lab/raw/main/images/element_lab_diagram.svg) +![element-lab]( +https://github.com/datajoint/element-lab/raw/main/images/element_lab_diagram.svg) ### element-animal -![element-animal](https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg) +![element-animal]( +https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg) ### assembled with element-array-ephys ![element-array-ephys](images/attached_array_ephys_element.svg) -## Installation instruction - -### Step 1 - clone this project - -Clone this repository from [here](https://github.com/datajoint/workflow-array-ephys) - -+ Launch a new terminal and change directory to where you want to clone the repository to - ``` - cd C:/Projects - ``` -+ Clone the repository: - ``` - git clone https://github.com/datajoint/workflow-array-ephys - ``` -+ Change directory to `workflow-array-ephys` - ``` - cd workflow-array-ephys - ``` - -### Step 2 - Setup virtual environment -It is highly recommended (though not strictly required) to create a virtual environment to run the pipeline. - -+ You can install with `virtualenv` or `conda`. Below are the commands for `virtualenv`. - -+ If `virtualenv` not yet installed, run `pip install --user virtualenv` - -+ To create a new virtual environment named `venv`: - ``` - virtualenv venv - ``` - -+ To activated the virtual environment: - + On Windows: - ``` - .\venv\Scripts\activate - ``` - - + On Linux/macOS: - ``` - source venv/bin/activate - ``` - -### Step 3 - Install this repository - -From the root of the cloned repository directory: - ``` - pip install -e . - ``` - -Note: the `-e` flag will install this repository in editable mode, -in case there's a need to modify the code (e.g. the `pipeline.py` or `paths.py` scripts). -If no such modification required, using `pip install .` is sufficient - -### Step 4 - Jupyter Notebook -+ Register an IPython kernel with Jupyter - ``` - ipython kernel install --name=workflow-array-ephys - ``` - -### Step 5 - Configure the `dj_local_conf.json` - -We provided a tutorial notebook [01-configuration](notebooks/01-configuration.ipynb) to guide the configuration. - -At the root of the repository folder, -create a new file `dj_local_conf.json` with the following template: - -```json -{ - "database.host": "", - "database.user": "", - "database.password": "", - "loglevel": "INFO", - "safemode": true, - "display.limit": 7, - "display.width": 14, - "display.show_tuple_count": true, - "custom": { - "database.prefix": "", - "ephys_root_data_dir": ["Full path to root directory of raw data", - "Full path to root directory of processed data"] - } -} -``` - -+ Specify database's `hostname`, `username`, and `password` properly. - -+ Specify a `database.prefix` to create the schemas. - -+ Setup your data directory (`ephys_root_data_dir`) following the convention described below. - - -### Installation complete - -+ At this point the setup of this workflow is complete. - -## Directory structure and file naming convention - -The workflow presented here is designed to work with the directory structure and file naming convention as followed - -+ The `ephys_root_data_dir` is configurable in the `dj_local_conf.json`, under `custom/ephys_root_data_dir` variable - -+ The `subject` directory names must match the identifiers of your subjects in the [subjects.csv](./user_data/subjects.csv) script - -+ The `session` directories can have any naming convention - -+ Each session can have multiple probes, the `probe` directories must match the following naming convention: - - `*[0-9]` (where `[0-9]` is a one digit number specifying the probe number) - -+ Each `probe` directory should contain: - - + One neuropixels meta file, with the following naming convention: - - `*[0-9].ap.meta` - - + Potentially one Kilosort output folder - -``` -root_data_dir/ -└───subject1/ -│ └───session0/ -│ │ └───imec0/ -│ │ │ │ *imec0.ap.meta -│ │ │ └───ksdir/ -│ │ │ │ spike_times.npy -│ │ │ │ templates.npy -│ │ │ │ ... -│ │ └───imec1/ -│ │ │ *imec1.ap.meta -│ │ └───ksdir/ -│ │ │ spike_times.npy -│ │ │ templates.npy -│ │ │ ... -│ └───session1/ -│ │ │ ... -└───subject2/ -│ │ ... -``` - -We provide an example data set to run through this workflow. The instruction of data downloading is in the notebook [00-data-download](notebooks/00-data-download-optional.ipynb). - - -## Running this workflow - -For new users, we recommend using the following two notebooks to run through the workflow. -+ [03-process](notebooks/03-process.ipynb) -+ [04-automate](notebooks/04-automate-optional.ipynb) - -Here is a general instruction: - -Once you have your data directory configured with the above convention, -populating the pipeline with your data amounts to these 3 steps: - -1. Insert meta information (e.g. subjects, sessions, etc.) - modify: - + user_data/subjects.csv - + user_data/sessions.csv - -2. Import session data - run: - ``` - python workflow_array_ephys/ingest.py - ``` - -3. Import clustering data and populate downstream analyses - run: - ``` - python workflow_array_ephys/populate.py - ``` - -+ For inserting new subjects, sessions or new analysis parameters, step 1 needs to be re-executed. - -+ Rerun step 2 and 3 every time new sessions or clustering data become available. - -+ In fact, step 2 and 3 can be executed as scheduled jobs that will automatically process any data newly placed into the `ephys_root_data_dir`. - -## Interacting with the DataJoint pipeline and exploring data - -For new users, we recommend using our notebook [05-explore](notebooks/05-explore.ipynb) to interact with the pipeline. - -Here is a general instruction: - - -+ Connect to database and import tables - ``` - from workflow_array_ephys.pipeline import * - ``` - -+ View ingested/processed data - ``` - subject.Subject() - session.Session() - ephys.ProbeInsertion() - ephys.EphysRecording() - ephys.Clustering() - ephys.Clustering.Unit() - ``` - -+ If required to drop all schemas, the following is the dependency order. Also refer to [06-drop](notebooks/06-drop-optional.ipynb) - ``` - from workflow_array_ephys.pipeline import * - - ephys.schema.drop() - probe.schema.drop() - session.schema.drop() - subject.schema.drop() - lab.schema.drop() - ``` - - -## Developer Guide - -### Development mode installation - -This method allows you to modify the source code for `workflow-array-ephys`, `element-array-ephys`, `element-animal`, `element-session`, and `element-lab`. - -+ Launch a new terminal and change directory to where you want to clone the repositories - ``` - cd C:/Projects - ``` -+ Clone the repositories - ``` - git clone https://github.com/datajoint/element-lab - git clone https://github.com/datajoint/element-animal - git clone https://github.com/datajoint/element-session - git clone https://github.com/datajoint/element-array-ephys - git clone https://github.com/datajoint/workflow-array-ephys - ``` -+ Install each package with the `-e` option - ``` - pip install -e ./element-lab - pip install -e ./element-animal - pip install -e ./element-session - pip install -e ./element-array-ephys - pip install -e ./workflow-array-ephys - ``` - -### Running tests - -1. Download the test dataset to your local machine -(note the directory where the dataset is saved at - e.g. `/tmp/testset`) - -2. Create an `.env` file with the following content: - - > TEST_DATA_DIR=/tmp/testset - - (replace `/tmp/testset` with the directory where you have the test dataset downloaded to) +## Installation instructions -3. Run: ++ The installation instructions can be found at [datajoint-elements/install.md]( + https://github.com/datajoint/datajoint-elements/blob/main/install.md). +## Interacting with the DataJoint workflow - docker-compose -f docker-compose-test.yaml up --build ++ Please refer to the following workflow-specific +[Jupyter notebooks](/notebooks) for an in-depth explanation of how to run the +workflow ([03-process.ipynb](notebooks/03-process.ipynb)) and explore the data +([05-explore.ipynb](notebooks/05-explore.ipynb)). \ No newline at end of file diff --git a/notebooks/02-workflow-structure-optional.ipynb b/notebooks/02-workflow-structure-optional.ipynb index 13183660..34fa7f1a 100644 --- a/notebooks/02-workflow-structure-optional.ipynb +++ b/notebooks/02-workflow-structure-optional.ipynb @@ -282,315 +282,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", - 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"\n\n%3\n\n\n`neuro_lab`.`#skull_reference`\n\n`neuro_lab`.`#skull_reference`\n\n\nlab.LabMembership\n\n\nlab.LabMembership\n\n\n\n\nlab.Location\n\n\nlab.Location\n\n\n\n\nlab.Source\n\n\nlab.Source\n\n\n\n\nlab.Protocol\n\n\nlab.Protocol\n\n\n\n\nlab.Project\n\n\nlab.Project\n\n\n\n\nlab.ProjectUser\n\n\nlab.ProjectUser\n\n\n\n\nlab.Project->lab.ProjectUser\n\n\n\nlab.Lab\n\n\nlab.Lab\n\n\n\n\nlab.Lab->lab.LabMembership\n\n\n\nlab.Lab->lab.Location\n\n\n\nlab.User\n\n\nlab.User\n\n\n\n\nlab.User->lab.LabMembership\n\n\n\nlab.User->lab.ProjectUser\n\n\n\nlab.UserRole\n\n\nlab.UserRole\n\n\n\n\nlab.UserRole->lab.LabMembership\n\n\n\nlab.ProtocolType\n\n\nlab.ProtocolType\n\n\n\n\nlab.ProtocolType->lab.Protocol\n\n\n\n", "text/plain": [ "" ] @@ -2188,7 +519,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "+ [`subject`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc." + "+ [`animal`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc." ] }, { @@ -2198,246 +529,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", 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"text/plain": [ "" ] @@ -2490,53 +582,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", - "\n", - "\n", - "" - ], + "image/svg+xml": "\n\n%3\n\n\nsession.SessionDirectory\n\n\nsession.SessionDirectory\n\n\n\n\nsession.ProjectSession\n\n\nsession.ProjectSession\n\n\n\n\nsession.Session\n\n\nsession.Session\n\n\n\n\nsession.Session->session.SessionDirectory\n\n\n\nsession.Session->session.ProjectSession\n\n\n\n", "text/plain": [ "" ] @@ -2587,414 +633,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - 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"text/plain": [ "" ] diff --git a/notebooks/05-explore.ipynb b/notebooks/05-explore.ipynb index dc3d8e51..b897b778 100644 --- a/notebooks/05-explore.ipynb +++ b/notebooks/05-explore.ipynb @@ -68,457 +68,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "`u24_ephys_lab`.`#skull_reference`\n", - "\n", - "`u24_ephys_lab`.`#skull_reference`\n", - "\n", - "\n", - "ephys.InsertionLocation\n", - "\n", - "\n", - "ephys.InsertionLocation\n", - "\n", - "\n", - "\n", - "\n", - "`u24_ephys_lab`.`#skull_reference`->ephys.InsertionLocation\n", - "\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", - "\n", - "ephys.AcquisitionSoftware\n", - "\n", - "\n", - "\n", - "\n", - "ephys.EphysRecording\n", - "\n", - "\n", - "ephys.EphysRecording\n", - "\n", - "\n", - "\n", - "\n", - "ephys.AcquisitionSoftware->ephys.EphysRecording\n", - "\n", - "\n", - "\n", - "ephys.ClusterQualityLabel\n", - "\n", - "\n", - "ephys.ClusterQualityLabel\n", - "\n", - "\n", 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"\n\n%3\n\n\n`u24_ephys_lab`.`#skull_reference`\n\n`u24_ephys_lab`.`#skull_reference`\n\n\nephys.InsertionLocation\n\n\nephys.InsertionLocation\n\n\n\n\n`u24_ephys_lab`.`#skull_reference`->ephys.InsertionLocation\n\n\n\nephys.AcquisitionSoftware\n\n\nephys.AcquisitionSoftware\n\n\n\n\nephys.EphysRecording\n\n\nephys.EphysRecording\n\n\n\n\nephys.AcquisitionSoftware->ephys.EphysRecording\n\n\n\nephys.ClusterQualityLabel\n\n\nephys.ClusterQualityLabel\n\n\n\n\nephys.CuratedClustering.Unit\n\n\nephys.CuratedClustering.Unit\n\n\n\n\nephys.ClusterQualityLabel->ephys.CuratedClustering.Unit\n\n\n\nephys.ClusteringMethod\n\n\nephys.ClusteringMethod\n\n\n\n\nephys.ClusteringParamSet\n\n\nephys.ClusteringParamSet\n\n\n\n\nephys.ClusteringMethod->ephys.ClusteringParamSet\n\n\n\nephys.ClusteringTask\n\n\nephys.ClusteringTask\n\n\n\n\nephys.ClusteringParamSet->ephys.ClusteringTask\n\n\n\nephys.Clustering\n\n\nephys.Clustering\n\n\n\n\nephys.Curation\n\n\nephys.Curation\n\n\n\n\nephys.Clustering->ephys.Curation\n\n\n\nephys.CuratedClustering\n\n\nephys.CuratedClustering\n\n\n\n\nephys.CuratedClustering->ephys.CuratedClustering.Unit\n\n\n\nephys.WaveformSet\n\n\nephys.WaveformSet\n\n\n\n\nephys.CuratedClustering->ephys.WaveformSet\n\n\n\nephys.WaveformSet.PeakWaveform\n\n\nephys.WaveformSet.PeakWaveform\n\n\n\n\nephys.CuratedClustering.Unit->ephys.WaveformSet.PeakWaveform\n\n\n\nephys.WaveformSet.Waveform\n\n\nephys.WaveformSet.Waveform\n\n\n\n\nephys.CuratedClustering.Unit->ephys.WaveformSet.Waveform\n\n\n\nephys.EphysRecording.EphysFile\n\n\nephys.EphysRecording.EphysFile\n\n\n\n\nephys.EphysRecording->ephys.EphysRecording.EphysFile\n\n\n\nephys.LFP\n\n\nephys.LFP\n\n\n\n\nephys.EphysRecording->ephys.LFP\n\n\n\nephys.EphysRecording->ephys.ClusteringTask\n\n\n\nephys.LFP.Electrode\n\n\nephys.LFP.Electrode\n\n\n\n\nephys.LFP->ephys.LFP.Electrode\n\n\n\nephys.WaveformSet->ephys.WaveformSet.PeakWaveform\n\n\n\nephys.WaveformSet->ephys.WaveformSet.Waveform\n\n\n\nephys.ClusteringTask->ephys.Clustering\n\n\n\nephys.Curation->ephys.CuratedClustering\n\n\n\nephys.ProbeInsertion\n\n\nephys.ProbeInsertion\n\n\n\n\nephys.ProbeInsertion->ephys.EphysRecording\n\n\n\nephys.ProbeInsertion->ephys.InsertionLocation\n\n\n\nlab.Project\n\n\nlab.Project\n\n\n\n\nsession.ProjectSession\n\n\nsession.ProjectSession\n\n\n\n\nlab.Project->session.ProjectSession\n\n\n\nephys.probe.ElectrodeConfig\n\n\nephys.probe.ElectrodeConfig\n\n\n\n\nephys.probe.ElectrodeConfig->ephys.EphysRecording\n\n\n\nephys.probe.ElectrodeConfig.Electrode\n\n\nephys.probe.ElectrodeConfig.Electrode\n\n\n\n\nephys.probe.ElectrodeConfig->ephys.probe.ElectrodeConfig.Electrode\n\n\n\nephys.probe.ElectrodeConfig.Electrode->ephys.CuratedClustering.Unit\n\n\n\nephys.probe.ElectrodeConfig.Electrode->ephys.LFP.Electrode\n\n\n\nephys.probe.ElectrodeConfig.Electrode->ephys.WaveformSet.Waveform\n\n\n\nephys.probe.Probe\n\n\nephys.probe.Probe\n\n\n\n\nephys.probe.Probe->ephys.ProbeInsertion\n\n\n\nsession.Session\n\n\nsession.Session\n\n\n\n\nsession.Session->ephys.ProbeInsertion\n\n\n\nsession.Session->session.ProjectSession\n\n\n\nsession.SessionDirectory\n\n\nsession.SessionDirectory\n\n\n\n\nsession.Session->session.SessionDirectory\n\n\n\nsubject.Subject\n\n\nsubject.Subject\n\n\n\n\nsubject.Subject->session.Session\n\n\n\n", "text/plain": [ "" ] @@ -1976,7 +1526,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -2228,7 +1778,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/requirements.txt b/requirements.txt index 280be238..05062520 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ datajoint>=0.13.0 -element-array-ephys -element-lab -element-animal -element-session -element-data-loader @ git+https://github.com/datajoint/element-data-loader.git -ipykernel \ No newline at end of file +element-array-ephys==0.1.0b0 +element-lab==0.1.0b0 +element-animal==0.1.0b0 +element-session==0.1.0b0 +element-interface @ git+https://github.com/datajoint/element-interface.git +ipykernel==6.0.1 \ No newline at end of file diff --git a/tests/test_ingest.py b/tests/test_ingest.py index 20f2fa1f..5dfba93c 100644 --- a/tests/test_ingest.py +++ b/tests/test_ingest.py @@ -31,7 +31,7 @@ def test_ingest_sessions(pipeline, sessions_csv, ingest_sessions): def test_find_valid_full_path(pipeline, sessions_csv): - from element_data_loader.utils import find_full_path + from element_interface.utils import find_full_path get_ephys_root_data_dir = pipeline['get_ephys_root_data_dir'] @@ -52,7 +52,7 @@ def test_find_valid_full_path(pipeline, sessions_csv): def test_find_root_directory(pipeline, sessions_csv): - from element_data_loader.utils import find_root_directory + from element_interface.utils import find_root_directory get_ephys_root_data_dir = pipeline['get_ephys_root_data_dir'] @@ -73,7 +73,7 @@ def test_find_root_directory(pipeline, sessions_csv): def test_paramset_insert(kilosort_paramset, pipeline): ephys = pipeline['ephys'] - from element_data_loader.utils import dict_to_uuid + from element_interface.utils import dict_to_uuid method, desc, paramset_hash = (ephys.ClusteringParamSet & {'paramset_idx': 0}).fetch1( 'clustering_method', 'paramset_desc', 'param_set_hash') diff --git a/workflow_array_ephys/ingest.py b/workflow_array_ephys/ingest.py index 2cf59658..3a909ae4 100644 --- a/workflow_array_ephys/ingest.py +++ b/workflow_array_ephys/ingest.py @@ -6,7 +6,7 @@ from workflow_array_ephys.paths import get_ephys_root_data_dir from element_array_ephys.readers import spikeglx, openephys -import element_data_loader.utils +import element_interface.utils def ingest_subjects(subject_csv_path='./user_data/subjects.csv'): # -------------- Insert new "Subject" -------------- @@ -28,7 +28,7 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv'): session_list, session_dir_list, probe_list, probe_insertion_list = [], [], [], [] for sess in input_sessions: - session_dir = element_data_loader.utils.find_full_path( + session_dir = element_interface.utils.find_full_path( get_ephys_root_data_dir(), sess['session_dir']) session_datetimes, insertions = [], [] diff --git a/workflow_array_ephys/paths.py b/workflow_array_ephys/paths.py index cb46bdea..c86329b6 100644 --- a/workflow_array_ephys/paths.py +++ b/workflow_array_ephys/paths.py @@ -1,5 +1,4 @@ import datajoint as dj -import pathlib def get_ephys_root_data_dir():