Lab_Pipelines

Config and pipeline definitions for spike sorting, trials, etc.

Conda environment setup

We have a Conda environment called gold_pipelines defined here in environment.yml. If you create this environment locally and activate it, you should be all set to work with pipelines here in this repo. This should meet dependenices for two main Python projects:

• Proceed (Docker-based pipelines)
• Pyramid (Data synthesis and trial files)

Here are some commands for setting up the Conda environment.

# get this repo
git clone https://github.com/TheGoldLab/Lab_Pipelines
cd Lab_Pipelines

# create a fresh environment -- can take several minutes
conda env create -f environment.yml

# if the environment already exists, you can make sure it's up to date like this:
conda env update -f environment.yml --prune

# you can also delete the environment and start fresh
conda remove -n gold_pipelines --all

# don't forget to ACTIVATE the environment before trying to use stuff
conda activate gold_pipelines

Related

Here are a few related repos that we've been working on for the Gold lab in 2022 / 2023.

this repo

• https://github.com/TheGoldLab/Lab_Pipelines

repose used indirectly from here

• https://github.com/benjamin-heasly/pyramid
• https://github.com/benjamin-heasly/proceed
• https://github.com/benjamin-heasly/plx-to-kilosort
• https://github.com/benjamin-heasly/kilosort3-docker
• https://github.com/benjamin-heasly/Kilosort
• https://github.com/benjamin-heasly/phy-to-fira

scenery we learned about, we might revisit

• https://github.com/benjamin-heasly/spikeglx-tools-poc
• https://github.com/benjamin-heasly/signac-poc