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 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.

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.

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.
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:

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