dartsort 0.5.1

DARTsort

dartsort

:warning: Work in progress code repository

We do not currently recommend DARTsort for production spike sorting purposes. We are in the process of implementing a robust and documented pipeline in src/dartsort, and we will update this page accordingly.

A workflow described in our preprint (https://www.biorxiv.org/content/10.1101/2023.08.11.553023v1) is in uhd_pipeline.py, which is implemented using the legacy code in src/spike_psvae.

Suggested install steps

If you already have a Python environment set up, you can install dartsort with

# get all dependencies (includes visualization and GPU packages); remove [gpu] if you don't have one
$ pip install dartsort[full,gpu]

If you don't already have Python and PyTorch 2 installed, we recommend doing this with the Miniforge distribution of conda. You can find info and installers for your platform at Miniforge's GitHub repository. After installing Miniforge, conda will be available on your computer for installing Python packages, as well as the newer and faster conda replacement tool mamba. We recommend using mamba instead of conda below, since the installation tends to be a lot faster with mamba. You can build a conda environment called dartsort with

$ mamba env create -f environment.yml
$ mamba activate dartsort

after cloning this repository. Next, visit https://pytorch.org/get-started/locally/ and follow the PyTorch install instructions for your specific OS and hardware needs.

Finish with the pip command above. Or, to work on the code, use:

(dartsort) $ pip install -e ./[full]
(dartsort) $ pytest tests/*