Please refer to the following wiki page or read in the testing section below

Python translation of Constrained Non-negative Matrix Factorization algorithm for source extraction from calcium imaging data.

The code implements a method for simultaneous source extraction and spike inference from large scale calcium imaging movies. The code is suitable for the analysis of somatic imaging data. Implementation for the analysis of dendritic/axonal imaging data will be added in the future.

The algorithm is presented in more detail in

Pnevmatikakis, E.A., Soudry, D., Gao, Y., Machado, T., Merel, J., ... & Paninski, L. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89(2):285-299, http://dx.doi.org/10.1016/j.neuron.2015.11.037

Pnevmatikakis, E.A., Gao, Y., Soudry, D., Pfau, D., Lacefield, C., ... & Paninski, L. (2014). A structured matrix factorization framework for large scale calcium imaging data analysis. arXiv preprint arXiv:1409.2903. http://arxiv.org/abs/1409.2903

Andrea Giovannucci, Eftychios Pnevmatikakis, Center for Computational Biology, Simons Foundation, New York, NY

Valentina Staneva eScience Institute. University of Washinghton. Seattle, WA.

Johannes Friedrich Columbia University, New York, NY.

This repository contains a Python implementation of the spatio-temporal demixing, i.e., (source extraction) code for large scale calcium imaging data. Related code can be found in the following links:

• Source extraction with CNMF (this package)
• Group LASSO initialization and spatial CNMF

• Constrained deconvolution and source extraction with CNMF
• MCMC spike inference
• Group LASSO initialization and spatial CNMF

• SIMA: The constrained deconvolution method has been integrated with SIMA, a Python based library for calcium imaging data analysis.
• Thunder: The group LASSO initialization and spatial CNMF method has been integrated with Thunder, a library for large scale neural data analysis with Spark.

Download and install Anaconda (Python 2.7) http://docs.continuum.io/anaconda/install

```
git clone  https://github.com/simonsfoundation/CaImAn
cd CaImAn
git checkout dev
git pull
conda create -n CaImAn ipython --file requirements_conda.txt    
source activate CaImAn
pip install -r requirements_pip.txt
conda install -c menpo opencv3=3.1.0

To make the package available from everywhere:
```
export PYTHONPATH="/path/to/Constrained_NMF:$PYTHONPATH"
```

SCS:

if you get errors compiling scs when installing cvxpy you probably need to create a link to openblas or libgfortran in /usr/local/lib/, for instance:

sudo ln -s /Library/Frameworks/R.framework/Libraries/libgfortran.3.dylib /usr/local/lib/libgfortran.2.dylib

debian fortran compiler problems: if you get the error gcc: error trying to exec 'cc1plus': execvp: No such file or directory in ubuntu run or issues related to SCS type

sudo apt-get install g++ libatlas-base-dev gfortran  libopenblas-dev
conda install openblas atlas

if still there are issues try

export LD_LIBRARY_PATH=/path_to_your_home/anaconda2/lib/

if more problems try

conda install  atlas (only Ubuntu)
pip install 'tifffile>=0.7'
conda install accelerate
conda install openblas 

CVXOPT:

If you are on Windows and don't manage to install or compile cvxopt, a simple solution is to download the right binary there and install the library by typing:

pip install cvxopt-1.1.7-XXXX.whl

SINGLE PATCH

In case you used installation af point 1 above you will need to download the test files from https://github.com/agiovann/Constrained_NMF/releases/download/v0.3/Demo.zip

A. Go into the cloned folder, type python demo.py

B. Using the Spyder (type conda install spyder) IDE.

1. Unzip the file Demo.zip (you do not need this step if you installed dusing method 2 above, just enter the Constrained_NMF folder and you will find all the required files there).
2. Open the file demo.py with spyder
3. change the base_folder variable to point to the folder you just unzipped
3. Run the cells one by one inspecting the output
4. Remember to stop the cluster (last three lines of file). You can also stop it manually by typing in a terminal
'ipcluster stop'

C. Using notebook.

1. Unzip the file Demo.zip (you do not need this step if you installed dusing method 3 above, just enter the Constrained_NMF folder and you will find all the required files there).
2. type `ipython notebook`
3. open the notebook called demoCNMF.ipynb 
4. change the base_folder variable to point to the folder you just unzipped
5. and run cell by cell inspecting the result
6. Remember to stop the cluster (last three lines of file). You can also stop it manually by typing in a terminal
'ipcluster stop'

MULTI PATCH

• Download the two demo movies here (courtesy of Dr. Sue Ann Koay from the Tank Lab, Princeton Neuroscience Institute, Princeton. NJ). Unzip the folder. Then in Spyder open the file demo_patches.py, and change the base_folder variable to point to the folder you just unzipped.
• Run one by one the cells (delimited by '#%%')
• Inspect the results. The demo will start a cluster and process pathes of the movie (more details here) in parallel (cse.map_reduce.run_CNMF_patches). Afterwards, it will merge the results back together and proceed to firstly merge potentially overlaping components (cse.merge_components) from different patches, secondly to update the spatial extent of the joined spatial components (cse.spatial.update_spatial_components), and finally denoising the traces (cse.temporal.update_temporal_components). THe final bit is used for visualization.

Documentation of the code can be found here

The code uses the following libraries

• NumPy
• SciPy
• Matplotlib
• Scikit-Learn
• Tifffile For reading tiff files. Other choices can work there too.
• cvxpy for solving optimization problems
• ipyparallel for parallel processing

For the constrained deconvolution method (deconvolution.constrained_foopsi) various solvers can be used, each of which requires some additional packages:

1. 'cvxpy': (default) For this option, the following packages are needed:

• CVXOPT Required.
• CVXPY Required.

2. 'cvx': For this option, the following packages are needed:

• CVXOPT Required.
• PICOS Required.
• MOSEK Optional but strongly recommended for speed improvement, free for academic use.

3. 'spgl1': For this option, the SPGL1 python implementation is required. It is by default imported as a submodule. The original implementation can be found at (https://github.com/mpf/spgl1).

In general 'cvxpy' can be faster, when using the 'ECOS' or 'SCS' sovlers, which are included with the CVXPY installation. 'spgl1' can also be very fast but the python implementation is not as fast as in Matlab and not thoroughly tested.

Please use the gitter chat room (use the button above) for questions and comments and create an issue for any bugs you might encounter.

The implementation of this package is based on the matlab implementation which can be found here. Some of the Matlab features are currently lacking, but will be included in future releases.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

The package comes with a toolbox to manipulate movies written in Python, Calblitz. If you want to give it a try use the demo_pipeline.py file. Before that you need to install some packages:

pip install pims
conda install opencv
conda install h5py