NiBabies is an open-source software pipeline designed to process anatomical and functional magnetic resonance imaging data, designed and optimized for human infants between 0-2 years old.

NiReports contains the two main components of the visual reporting system of NiPreps: 1) reportlets, visualizations for assessing the quality of a particular processing step within the neuroimaging pipeline, and 2) assemblers, end-user write out reportlets to a predetermined folder.

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fMRIPrep is a fMRI data preprocessing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skull-stripping, etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, and surface or volume-based statistics.

MRtrix3 provides a large suite of tools for image processing, analysis and visualisation, with a focus on the analysis of white matter using diffusion-weighted MRI.

Connectome Workbench is a visualization and discovery tool used to map neuroimaging data, especially data generated by the Human Connectome Project. It allows exploration of data and activity on the surface, as well as in the volume of the brain.

TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional MRI data.

This package provides a fast implementation of the Levenshtein distance with C++ and Cython.

This module can be used to extract or replace keywords in sentences, based on the FlashText algorithm.

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's clinical data warehouse. The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe. We also provide some fictitious templates and a script to generate a synthetic dataset.

WORLD Vocoder is a fast and high-quality vocoder which parameterizes speech into three components:

• f0: Pitch contour

• sp: Harmonic spectral envelope

• ap: Aperiodic spectral envelope

It can also (re)synthesize speech using these features.

PyRuSH is the python implementation of RuSH, which is originally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.

This package provides tools for unsupervised and semi-supervised morphological segmentation.

This package provides Python bindings for the simstring text similarity matching library.

seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

Extremely fast spelling checker and suggester in Python.

The following algorithms are supported currently:

• Edit-distance

• Editex

• Soundex

• Caverphone 1.0 and 2.0

• Typox

All the above algorithms use an underlying Trie-based dictionary for efficient storage and fast computation.

PyFastNER is the Python implementation of FastNER. It uses hash function to process multiple rules at the same time. Similar to FastNER, PyFastNER supports token-based rules and character-based rules.

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the NLP and IR communities.

This package provides a Python implementation of IAMsystem algorithm, a fast dictionary-based approach for semantic annotation, a.k.a entity linking.

Quicksectx is a simple, fast and no-dependency Python implementation of interval search, adapted from the bx-python project.

This package provides a module for OpenTURNS to formulate and solve robust optimization problems.

This package provides a bridge between OpenTURNS and FMUs, based on PyFMI.

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.