In the field of artificial intelligence, international competition is tough. So when researchers from CEA-Joliot and Irfu challenge start-ups and other companies specializing in AI, we cheer them on. Here’s a success story from the field of MRI reconstruction.
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:
- Check that that all of the installed all the dependencies listed above have been installed.
- Read through all of the documentation provided, including the troubleshooting suggestions.
- Check if you problem has already been addressed in a previous issue.
Further instructions are available here.
To install PySAP simply run:
$ pip install python-pysap
Depending on your Python setup you may need to provide the
$ pip install --user python-pysap
To build PySAP locally, clone the repository:
$ git clone https://github.com/CEA-COSMIC/pysap.git
$ python setup.py install
$ python setup.py develop
As before, use the
--user option if needed.
Help with installation on macOS is available here.
Please refer to the PyQtGraph homepage for issues regarding the installation of
We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.