## DeepMass

 Authors: N. Jeffrey, F. Lanusse Language: Python Download: GitHub Description: Deep learning to reconstruct a Bayesian estimate of dark matter maps from weak lensing data Notes:

## DecGMCA

 Authors: M. Jiang Language: Python Download: Python Description: A toolbox for solving joint multichannel Deconvolution and Blind Source Separation (DBSS) Notes:

## DecGMCA

DecGMCA (Deconvolution Generalized Morphological Component Analysis) is a sparsity-based algorithm aiming at solving joint multichannel Deconvolution and Blind Source Separation (DBSS) problem.

For more details, please refer to the paper Joint Multichannel Deconvolution and Blind Source Separation (https://arxiv.org/abs/1703.02650)

## ModOpt

 Authors: S. Farrens, Z. Ramzi, Contributors Language: Python Download: GitHub Description: ModOpt is a series of Modular Optimisation tools for solving inverse problems. Notes: API documentation

## Installation

$pip install modopt ## Contributing If you want to contribute to ModOpt, be sure to review the contribution guidelines and follow to the code of conduct. ## PySAP  Authors: S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, Chaithya G. R., S. Starck, B. Sarthou, H. Cherkaoui, P.Ciuciu, J-L. Starck Language: Python Download: GitHub Description: PySAP (Python Sparse data Analysis Package) is a Python module for sparse data analysis. Notes: PySAP paper ## Installation The installation of PySAP has been extensively tested on Ubuntu and macOS, however we cannot guarantee it will work on every operating system (e.g. Windows). If you encounter any installation issues be sure to go through the following steps before opening a new issue: 1. Check that that all of the installed all the dependencies listed above have been installed. 2. Read through all of the documentation provided, including the troubleshooting suggestions. 3. Check if you problem has already been addressed in a previous issue. Further instructions are available here. ### From PyPi To install PySAP simply run: $ pip install python-pysap

Depending on your Python setup you may need to provide the --user option.

$pip install --user python-pysap ### Locally To build PySAP locally, clone the repository: $ git clone https://github.com/CEA-COSMIC/pysap.git

and run:

$python setup.py install or: $ python setup.py develop

As before, use the --user option if needed.

### macOS

Help with installation on macOS is available here.

### Linux

Please refer to the PyQtGraph homepage for issues regarding the installation of pyqtgraph.

## Contributing

If you want to contribute to pySAP, be sure to review the contribution guidelines and follow to the code of conduct.

## pyGMCALab

 Authors: J. Bobin, J.Rapin, C.Chenot, C.Kervazo Language: Python Download: Python Description: A toolbox for solving Blind Source Separation problems. Notes:

## GMCALab

GMCALab is a Python toolboxes that focus on solving Blind Source Separation problems from multichannel/multispectral/hyperspectral data. In essence, multichannel data provide different observations of the same physical phenomena (e.g. multiple wavelengths, ), which are modeled as a linear combination of unknown elementary components or sources:

$$\mathbf{Y} = \mathbf{A}\mathbf{S},$$

where $$\mathbf{Y}$$ is the data matrix, $$\mathbf{S}$$ is the source matrix, and $$\mathbf{A}$$ is the mixing matrix. The goal of blind source separation is to retrieve $$\mathbf{A}$$ and $$\mathbf{S}$$ from the knwoledge of the data only.

Generalized Morphological Component Analysis, a.k.a. GMCA, is a BSS method that enforces the sparsity of the sought-after sources:

$$\underset{\mathbf{A},~\mathbf{S}}{\text{argmin}}~\|\mathbf{Y}-\mathbf{A}\mathbf{S}\|_2^2+\|\mathbf{\Lambda}\odot\mathbf{S}\|_1,$$

Please check out the project's GitHub page.

It is worth noting that GMCA provides a very generic framework that has been extended to tackle different matrix factorization problems:

• Non-negative matrix factorization with nGMCA
• Separation of partially correlated sources with AMCA
• The decomposition of hyperspectral data with HypGMCA (available soon)
• The analysis of multichannel data in the presence of outliers with rAMCA at this location (updated the 14/06/16).
• Robust BSS in transformed domains with tr-rGMCA .

We are now developping a python-based toolbox coined pyGMCALab, which is available at this location.

## LGMCA

 Authors: J. Bobin Language: IDL Download: IDL Description: The scripts required to compute the CMB map from WMAP and Planck data Notes:

## LGMCA

Local-generalised morphological component analysis is an extension to GMCA. Similarly to GMCA it is a Blind Source Separation method which enforces sparsity. The novel aspect of LGMCA, however is that the mixing matrix changes across pixels allowing LMCA to deal with emissions sources which vary spatially.

Running LGMCA on the WMAP9 temperature products requires the main script and a selection of mandatory files, algorithm parameters and map parameters.

## MSVST-lab

 Authors: J. Fadili Language: Matlab Download: Homepage Description: A code for sparse representation-based image deconvolution with Poisson noise. Notes:

## GMCALab

 Authors: J. Bobin Language: Matlab and Python Download: Python | Matlab Description: A toolbox for solving Blind Source Separation problems. Notes:

## GMCALab

GMCALab is a set of Matlab toolboxes that focus on solving Blind Source Separation problems from multichannel/multispectral/hyperspectral data. In essence, multichannel data provide different observations of the same physical phenomena (e.g. multiple wavelengths, ), which are modeled as a linear combination of unknown elementary components or sources:

$$\mathbf{Y} = \mathbf{A}\mathbf{S},$$

where $$\mathbf{Y}$$ is the data matrix, $$\mathbf{S}$$ is the source matrix, and $$\mathbf{A}$$ is the mixing matrix. The goal of blind source separation is to retrieve $$\mathbf{A}$$ and $$\mathbf{S}$$ from the knwoledge of the data only.

Generalized Morphological Component Analysis, a.k.a. GMCA, is a BSS method that enforces the sparsity of the sought-after sources:

$$\underset{\mathbf{A},~\mathbf{S}}{\text{argmin}}~\|\mathbf{Y}-\mathbf{A}\mathbf{S}\|_2^2+\|\mathbf{\Lambda}\odot\mathbf{S}\|_1,$$

A lightweight Matlab/Octave version of the GMCALab toolbox is available at this location. Illustrations are provide here.

Please check out the project's GitHub page.

It is worth noting that GMCA provides a very generic framework that has been extended to tackle different matrix factorization problems:

• Non-negative matrix factorization with nGMCA
• Separation of partially correlated sources with AMCA
• The decomposition of hyperspectral data with HypGMCA (available soon)
• The analysis of multichannel data in the presence of outliers with rAMCA at this location (updated the 14/06/16).
• Robust BSS in transformed domains with tr-rGMCA .

We are now developping a python-based toolbox coined pyGMCALab, which is available at this location.