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conda create -n sabatini-datajoint -c conda-forge python=3.9 -y
conda activate sabatini-datajoint
conda install graphviz python-graphviz pydotplus ipykernel ipywidgets nb_conda_kernels jupyterlab
git clone https://github.com/bernardosabatinilab/sabatini-datajoint-pipeline
Navigate into cloned repository
cd sabatini-datajoint-pipeline/
pip install -r requirements.txt
pip install -e .
- This step of pip installing in -editable mode, must be rerun if you want to test with local changes
Create a copy of .example_dj_local_config.json, rename it to dj_local_conf.json and fill in database user/host/password credentials
Launch Jupyter Notebook/Lab and set kernel to the sabatini-datajoint conda environment
Your data ``/Inbox`` directory structure will need to be set up like the following:
| dlc_projects
| └── PROJECT_PATH
| Subject1
| ├── Session1
| ├── Imaging
| ├── scan0
| ├── 00001.tif
| ├── 00002.tif
| └── ...
| ├── Photometry
| ├── timeseries*.mat; data*.mat; .tdt
| └── .toml
| ├── Behavior
| ├── .toml
| └── .parquet, .csv
| ├── Ephys
| └── .bin, .lf, .meta
| ├── dlc_behavior_videos
| └── .avi
|
| ├── Session2
| └── ...
Note that the ``Subject`` is the top level directory, and all other data types are nested. You do not need to have all data types for each session.
For DLC related projects, the ``dlc_projects`` directory is expected to be in the Inbox directory *not* the subject directory.
- After making the code changes locally, run the following command to start the application:
docker compose -f webapps/sciviz/docker-compose.yaml up -d
- Access the application using the following URL in an incognito window: https://localhost/login and log in with your DataJoint Works credentials.
- When you have finished testing, please ensure to stop and remove the Docker container by running the following command:
docker compose -f webapps/sciviz/docker-compose.yaml down