Neuroscience data pipeline for reproducible research used by Loren Frank Lab, UCSF
Project description
nwb_datajoint
The Frank lab Datajoint pipeline is designed to facilitate data storage, analysis, and sharing.
Setup
Installing packages
-
Clone this repository:
git clone https://github.com/LorenFrankLab/nwb_datajoint.git
-
Set up and activate a conda environment from
environment.yml
:cd nwb_datajoint conda env create -f environment.yml conda activate nwb_datajoint
-
Install this repository:
# This is preferred to `python setup.py develop` # Note the dot (.) at the end pip install -e .
Setting up database access
-
Ask Loren or Eric to set up an account for you on the Frank lab database (
lmf-db.cin.ucsf.edu
). Note that you have to be connected to UCSF LAN to access this server. -
Add the following environment variables (e.g. in
~/.bashrc
). This example assumes that you are interacting with the database on a computer that has mountedstelmo
at/stelmo
.export NWB_DATAJOINT_BASE_DIR="/stelmo/nwb/" export SPIKE_SORTING_STORAGE_DIR="/stelmo/nwb/spikesorting" # where output of spike sorting will be sorted export DJ_SUPPORT_FILEPATH_MANAGEMENT="TRUE" export KACHERY_P2P_API_HOST="typhoon" export KACHERY_P2P_API_PORT="14747" export KACHERY_TEMP_DIR="/stelmo/nwb/tmp"
-
Configure DataJoint. When your account is created, you will be given a temporary password. You can change your password and set up external stores. You should need to run these only once.
Finally, open up a python console and import nwb_datajoint
to check that the setup has worked.
Tutorials
The tutorials are in the form of Jupyter Notebooks and can be found in the notebooks
directory. We strongly recommend opening them in the context of jupyterlab
. Start with these tutorials:
0_intro.ipynb
: general introduction to the database1_spikesorting.ipynb
: how to run spike sorting
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