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 package 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 or part-time involvement of Guillaume Lemaitre, Joris Van den Bossche, Jérémie du Boisberranger and Olivier Grisel.
MNE-Python is the Python open source toolbox for processing and visualizing MEG and EEG data. It facilitates a wide range of data processing tasks, including artifact rejection, diagnostic visualizations, multivariate pattern analysis, source localization, time-frequency analysis and statistics. MNE-Python is developed by an international community of researchers from diverse laboratories. Parietal is actively supporting the project with the full-time involvement of Joan “Sik” Massich.
pySAP is a Python package related to sparsity and its application in astronomical or medical data analysis. This package binds the ‘sparse2d’ C++ library that allows sparse decomposition, denoising and deconvolution.
It is used internally by other libraries such as scikit-learn, nilearn and MNE-Python.
Parietal team members have also been involved in the development of:
- Mayavi (Python software for scientific 3D visualization)
- medInria (medical imaging and processing)
- The Tensor Toolkit (for DTI analysis)
- vtkINRIA3D (C++ library extending VTK)
- Nipy (generic fMRI Processing toolbox)
- PyXNAT (Python interface for XNAT)
- OpenMEEG (MEG/EEG forward problem)
- cogspaces (multi-study decoding for fMRI)
- modl (large-scale matrix factorization, with a sklearn API + fMRI usecases)