At Parietal, we believe that high-quality open-source scientific software is an important aspect of research in computational methods. For these reasons, we invest heavily in community-driven projects, some directly linked to Inria, and others living their own life. The different members of Parietal are committers to a variety of open-source projects and we hire research engineers and programmers dedicated to the projects.
scikit-learn is a Python module for machine learning. It is an open source (BSD-licensed) library that exposes many standard algorithms for supervised and unsupervised classification. It is shared by the python scientific community. Parietal is actively contributing to scikit-learn with the full-time involvement of Jaques Grobler and Olivier Grisel.
NiLearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
Mayavi is the most used scientific 3D visualization python software. It has been developed by Prabhu Ramachandran (IIT Bombay) and Gaël Varoquaux (Parietal, Inria Saclay). Mayavi can be used as a visualization tool, through an interactive command line or as a library. It is distributed under Linux through Ubuntu, Debian, Fedora and Mandriva, as well as in PythonXY and EPD Python scientific distributions. Mayavi is used by several software platforms, such as PDE solvers (fipy, sfepy), molecule visualization tools (pyrx.scripps.edu) and brain connectivity analysis tools (connectomeViewer).
PyHRF is a Python library to estimate the filter that relates neural activity to the blood oxygen-level dependent (BOLD) signal observed in functional MRI.
Parietal team members have also been involved in the development of: