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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 the development and maintenance of scikit-learn with the full-time involvement of Guillaume Lemaitre, Jérémie du Boisberranger, Chiara Marmo, and Olivier Grisel thanks to the funding of the members of the scikit-learn consortium at Fondation Inria.

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 Guillaume Favelier and Richard Höchenberger who actively develops MNE-BIDS.

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.

Parietal team members also contribute regularly to the Open Source projects they use on a daily basis, for instance: the pickle, concurrent.futures and multiprocessing modules of the standard library of Python, NumPy, SciPy, Dask, etc.

Past collaborations

Parietal team members have also been involved in the development of:

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