Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/int-brain-lab/ibl-sorter
https://github.com/int-brain-lab/ibl-sorter
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/int-brain-lab/ibl-sorter
- Owner: int-brain-lab
- License: gpl-2.0
- Created: 2024-04-09T15:02:12.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-10-29T13:02:30.000Z (about 2 months ago)
- Last Synced: 2024-10-29T15:55:40.327Z (about 2 months ago)
- Language: Python
- Size: 859 KB
- Stars: 2
- Watchers: 23
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# 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](https://github.com/MouseLand/Kilosort), 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 run_spike_sorting_ibl, ibl_pykilosort_params, download_test_data
if __name__ == "__main__":
data_path = Path("/mnt/s0/spike_sorting/integration_tests") # path on which the raw data will be downloaded
scratch_dir = Path.home().joinpath("scratch",
'iblsorter') # temporary path on which intermediate raw data will be written, we highly recommend a SSD drive
ks_output_dir = Path("/mnt/s0/spike_sorting/outputs") # path containing the kilosort output unprocessed
alf_path = ks_output_dir.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 3.12 in October 2024.
### 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 11.8
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install cupy-cuda11x
pip install -e .
Installation for the latest cuda:
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.