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.

NiLearn is a Python package for fast and easy statistical learning on NeuroImaging data with a focus on fMRI data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. Parietal is actively supporting the project with the full-time involvement of Kshitij Chawla.

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.

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.

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.

Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: (i) transparent disk-caching of the output values and lazy re-evaluation (memoize pattern). (ii) easy simple parallel computing.
It is used internally by other libraries such as scikit-learn, nilearn and MNE-Python.

Past collaborations

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)

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