HNCcorr algorithm for cell detection in calcium-imaging.
The HNCcorr algorithm identifies cell bodies in two-photon calcium imaging movies. We provide a Python 3 implementation as well as a legacy Matlab implementation. The software is freely available for non-commercial use. See license file for details.
The HNCcorr algorithm is described in our ArXiv paper:
Q Spaen, R Asín-Achá, and DS Hochbaum. (2017). HNCcorr: A novel combinatorial approach for cell identification in calcium-imaging movies. arXiv:1703.01999.
from hnccorr import HNCcorr, Movie from hnccorr.example import load_example_data movie = Movie( "Example movie", load_example_data() # downloads sample Neurofinder dataset ) H = HNCcorr.from_config() # Initialize HNCcorr with default configuration H.segment(movie) H.segmentations # List of identified cells H.segmentations_to_list() # Export list of cells (for Neurofinder)
See the quickstart guide for more details.
Installation Instructions for Python 3
You can install HNCcorr directly from the Python Package Index with pip:
pip install hnccorr
On Windows you may need to install a C-compiler for Python.
Installation Instructions for Matlab
The Matlab implementation was used to generate the results in the eNeuro manuscript and is now superseded by the Python implementation. The Matlab implementation is available in the
matlab folder. See the README file in the
matlab folder for instructions.
The documentation is hosted at ReadTheDocs.
The tests for HNCcorr use the
pytest package. You can execute them with the
pytest command in the main directory.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size hnccorr-2020.5.1-py3-none-any.whl (31.3 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size hnccorr-2020.5.1.tar.gz (33.7 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for hnccorr-2020.5.1-py3-none-any.whl