About#

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The Python Sparse data Analysis Package (PySAP) was developed as part of COSMIC, a multi-disciplinary collaboration between NeuroSpin, experts in biomedical imaging, and CosmoStat, experts in astrophysical image processing. The package was designed to provide state-of-the-art signal processing tools for a variety of imaging domains such as astronomy, electron tomography and magnetic resonance imaging (MRI). The first release of PySAP was presented in Farrens et al. [2020].

Structure#

PySAP Schema

Illustration of the structure of the PySAP package [Farrens et al., 2020]. The SPARSE2D and ModOpt core libraries are represented in orange and red, respectively. The various plug-in applications appear in blue.#

PySAP is comprised of several core modules, namely:

ModOpt

  • Sparse2D: a collection of sparse image transforms written in C++

  • ModOpt: a library of modular optimisation algorithms

  • Application-specific plug-ins

PySAP provides Python bindings to the C++ libraries, shared tools and a common interface for all of the plug-ins.

Plug-ins#

PySAP plug-ins are application-specific tools designed for a given imaging domain. Plug-ins combine ModOpt algorithms with image transforms to solve complex inverse problems.

Note

New plug-ins can be developed using the PySAP plug-in template.

The plug-ins currently available in PySAP are:

Contributors#

You can find a list of PySAP contributors here.