MRIQC extracts no-reference image quality metrics from structural (T1w and T2w), functional and diffusion MRI data.

migas (mee-gahs) is a Python client to facilitate communication with a migas server.

The nipype1-workflows repository contains legacy workflows from Nipype 1.x, showcasing nearly a decade of development in neuroimaging data processing and analysis.

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.

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.

NIPY provides a platform-independent Python environment for the analysis of functional brain imaging data.

The etelemetry Python client facilitates communication with the etelemetry server, providing version information and checking for critical bugs in projects. The client allows you to retrieve project details and compare versions to identify and warn about problematic versions.

CiftiLib is a C++ library for CIFTI-2 file reading/writing. It additionally supports CIFTI-1 files, and supports both on-disk and in-memory access. It also provides C++ code for reading and writing generic NIfTI-1 and NIfTI-2 files.

CIFTI (Connectivity Informatics Technology Initiative) standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties.

See http://www.nitrc.org/projects/cifti/ for more information.

This package provides a Pydantic schema for BIDS Stats Models.

This package provides an implementation of TRX, a tractography file format designed to facilitate dataset exchange, interoperability, and state-of-the-art analyses, acting as a community-driven replacement for the myriad existing file formats.

Nitime contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

dcm2niix is designed to convert neuroimaging data from the DICOM format to the NIfTI format. dcm2niix is also able to generate a BIDS JSON format sidecar which includes relevant information for brain scientists in a vendor agnostic and human readable form.

{dcmstack

Convert3d is a command-line tool for converting 3D images between common file formats. The tool also includes a growing list of commands for image manipulation, such as thresholding and resampling. The tool can also be used to obtain information about image files.

This package provides programs to perform rigid, affine and non-linear registration of 2D and 3D images stored as NIfTI or Analyze formats.

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

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.

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

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.

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

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

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

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

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.