Installation | API | Data Formats | Paper |
PyTorch implementation of the masknmf framework for {calcium, voltage, glutamate} imaging analysis. Supports GPU-accelerated:
- Motion Correction
- Compression and Denoising
- Signal Demixing
- High-performance visualization
In a conda or python venv, do the following:
#1. First install the version of PyTorch you want: https://pytorch.org/get-started/locally/
#2. Clone the repo
git clone https://github.com/apasarkar/masknmf-toolbox.git
pip install -e ".[notebook]"See the notebooks folder for demos on how to use the motion correction, compression, and demixing APIs.
Support currently provided for
- multipage .tiff files
- hdf5 files.
Support for other formats can be easily added by defining a data loader class that implements LazyDataLoader.
If you use this method, please cite the accompanying paper
maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. (2023). A. Pasarkar*, I. Kinsella, P. Zhou, M. Wu, D. Pan, J.L. Fan, Z. Wang, L. Abdeladim, D.S. Peterka, H. Adesnik, N. Ji, L. Paninski.