IBL Spike Sorting

This is the implementation of the IBL spike sorting pipeline described on this white paper: (https://doi.org/10.6084/m9.figshare.19705522.v4). The clustering part is based on the original MATLAB version of Kilosort 2.5, written by Marius Pachitariu.

Usage

We provide a few datasets to explore parametrization and test on several brain regions. The smallest dataset is a 100 seconds excerpt to test the installation. Here is the minimal working example:

```python import shutil from pathlib import Path

from iblsorter.ibl import runspikesortingibl, iblpykilosortparams, downloadtest_data

if name == "main": datapath = Path("/mnt/s0/spikesorting/integrationtests") # path on which the raw data will be downloaded scratchdir = Path.home().joinpath("scratch", 'iblsorter') # temporary path on which intermediate raw data will be written, we highly recommend a SSD drive ksoutputdir = Path("/mnt/s0/spikesorting/outputs") # path containing the kilosort output unprocessed alfpath = ksoutputdir.joinpath( 'alf') # this is the output standardized as per IBL standards (SI units, ALF convention)

# download the integration test data from amazon s3 bucket
bin_file, meta_file = download_test_data(data_path)

# prepare and mop up folder architecture for consecutive runs
DELETE = True  # delete the intermediate run products, if False they'll be copied over to the output directory for debugging
shutil.rmtree(scratch_dir, ignore_errors=True)
scratch_dir.mkdir(exist_ok=True)
ks_output_dir.mkdir(parents=True, exist_ok=True)

# loads parameters and run
params = ibl_pykilosort_params(bin_file)
params['Th'] = [6, 3]

run_spike_sorting_ibl(bin_file, delete=DELETE, scratch_dir=scratch_dir,
                        ks_output_dir=ks_output_dir, alf_path=alf_path, log_level='INFO', params=params)

```

Installation

System Requirements

The code makes extensive use of the GPU via the CUDA framework. A high-end NVIDIA GPU with at least 8GB of memory is required. The solution has been deployed and tested on Cuda 12+ and Python 3.10, 3.11 and 3.12

Python environment

Only on Linux, first install fftw by running the following

sudo apt-get install -y libfftw3-dev

Navigate to the desired location for the repository and clone it

git clone https://github.com/int-brain-lab/ibl-sorter.git
cd ibl-sorter

Installation for cuda 11.x

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install cupy-cuda11x
pip install -e .

Installation for cuda 12.x (as of October 2024, check installation instructions from pytorch for the latest)

pip3 install torch torchvision torchaudio
pip install cupy-cuda12x
pip install -e .

Making sure the installation is successful and CUDA is available

Here we make sure that both cupy and torch are installed and that the CUDA framework is available.

python from iblsorter.utils import cuda_installation_test cuda_installation_test()

Then we can run the integration test.