# Python port of KiloSort2
This is a Python port of the original MATLAB version of [Kilosort 2.5](https://github.com/MouseLand/Kilosort), written by Marius Pachitariu, with Neuropixel specific improvements and software engineering enhancements.
The modifications are described in [this white paper](https://doi.org/10.6084/m9.figshare.19705522.v3).
## 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.
### Doing the install using Anaconda (Linux)
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/pykilosort.git
cd pykilosort
Create a conda environment
conda env create -f pyks2.yml
conda activate pyks2
pip install -e .
## Usage
### Example
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 pykilosort.ibl import run_spike_sorting_ibl, ibl_pykilosort_params, download_test_data
data_path = Path("/mnt/s0/spike_sorting/integration_tests") # path on which the raw data will be downloaded
scratch_dir = Path.home().joinpath("scratch", 'pykilosort') # 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)
```
## Troubleshooting
### Managing CUDA Errors
Errors with the CUDA installation can sometimes be fixed by downgrading
the version of cudatoolkit installed. Currently tested versions are 9.2,
10.0, 10.2, 11.0 and 11.5
To check the current version run the following:
conda activate pyks2
conda list cudatoolkit
To install version 10.0 for example run the following
conda activate pyks2
conda remove cupy, cudatoolkit
conda install -c conda-forge cupy cudatoolkit=10.0
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"description": "# Python port of KiloSort2\n\nThis is a Python port of the original MATLAB version of [Kilosort 2.5](https://github.com/MouseLand/Kilosort), written by Marius Pachitariu, with Neuropixel specific improvements and software engineering enhancements.\nThe modifications are described in [this white paper](https://doi.org/10.6084/m9.figshare.19705522.v3).\n\n## Installation \n\n### System Requirements\n\nThe code makes extensive use of the GPU via the CUDA framework. A high-end NVIDIA GPU with at least 8GB of memory is required.\n\n\n### Doing the install using Anaconda (Linux)\n\nOnly on Linux, first install fftw by running the following \n \n sudo apt-get install -y libfftw3-dev\n\nNavigate to the desired location for the repository and clone it\n\n git clone https://github.com/int-brain-lab/pykilosort.git\n cd pykilosort\n\nCreate a conda environment\n\n conda env create -f pyks2.yml\n conda activate pyks2\n pip install -e .\n\n## Usage\n\n### Example\n\nWe provide a few datasets to explore parametrization and test on several brain regions.\nThe smallest dataset is a 100 seconds excerpt to test the installation. Here is the minimal working example:\n\n```python\nimport shutil\nfrom pathlib import Path\n\nfrom pykilosort.ibl import run_spike_sorting_ibl, ibl_pykilosort_params, download_test_data\n\ndata_path = Path(\"/mnt/s0/spike_sorting/integration_tests\") # path on which the raw data will be downloaded\nscratch_dir = Path.home().joinpath(\"scratch\", 'pykilosort') # temporary path on which intermediate raw data will be written, we highly recommend a SSD drive\nks_output_dir = Path(\"/mnt/s0/spike_sorting/outputs\") # path containing the kilosort output unprocessed\nalf_path = ks_output_dir.joinpath('alf') # this is the output standardized as per IBL standards (SI units, ALF convention)\n\n# download the integration test data from amazon s3 bucket\nbin_file, meta_file = download_test_data(data_path)\n\n# prepare and mop up folder architecture for consecutive runs\nDELETE = True # delete the intermediate run products, if False they'll be copied over to the output directory for debugging\nshutil.rmtree(scratch_dir, ignore_errors=True)\nscratch_dir.mkdir(exist_ok=True)\nks_output_dir.mkdir(parents=True, exist_ok=True)\n\n# loads parameters and run\nparams = ibl_pykilosort_params(bin_file)\nparams['Th'] = [6, 3]\nrun_spike_sorting_ibl(bin_file, delete=DELETE, scratch_dir=scratch_dir,\n ks_output_dir=ks_output_dir, alf_path=alf_path, log_level='INFO', params=params)\n```\n\n## Troubleshooting\n### Managing CUDA Errors\n\nErrors with the CUDA installation can sometimes be fixed by downgrading\nthe version of cudatoolkit installed. Currently tested versions are 9.2,\n10.0, 10.2, 11.0 and 11.5\n\nTo check the current version run the following:\n\n conda activate pyks2\n conda list cudatoolkit\n\nTo install version 10.0 for example run the following\n\n conda activate pyks2\n conda remove cupy, cudatoolkit\n conda install -c conda-forge cupy cudatoolkit=10.0\n",
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