It helps developers working in continuous integration (CI) environments by providing essential information about the CI server. It can determine if the code is running on a CI server,identify the specific server,and detect if a pull request is being tested.

AFNI, Analysis of Functional NeuroImages is a suite of programs for looking at and analyzing MRI brain images at all stages of analysis (planning, setting up acquisition, preprocessing, analysis, quality control and statistical analysis).

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.

This package provides programs to perform EM based segmentation of images in nifti or analyse format.

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.

elastix is an image registration toolbox based on ITK. The software consists of a collection of algorithms that are commonly used to perform (medical) image registration: the task of finding a spatial transformation, mapping one image (the fixed image) to another (the moving image), by optimizing relevant image similarity metrics. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting.

fsleyes_props is a framework for event-driven programming using Python descriptors, similar in functionality to, and influenced by Enthought Traits.

This package provides processing pipelines for structural MRI.

The fsleyes-widgets package contains a collection of GUI widgets and utilities, based on wxPython, which are used by fsleyes-props and FSLeyes.

Nilearn enables approachable and versatile analyses of brain volumes and surfaces. It provides statistical and machine-learning tools, with instructive documentation & open community.

pybids provides a set of tools for working with BIDS datasets. The BIDS standard aims at organizing and describing neuroimaging data in a uniform way in order to facilitate data sharing within the scientific community.

A Python implementation of the moving average principal components analysis methods for functional MRI data translated from the MATLAB-based GIFT package.

DIPY is the paragon 3D/4D+ medical imaging library in Python. It contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

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.

This module provides simple, consistent access to package resources.

FSL is a comprehensive library of analysis tools for FMRI, MRI and diffusion brain imaging data. FSLeyes is a GUI for visualizing and editing brain images, from different sources and platforms.

heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.

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.

ANTs is a C++ library available through the command line that computes high-dimensional mappings to capture the statistics of brain structure and function. It allows one to organize, visualize and statistically explore large biomedical image sets.

This package provides Python tools for working with the BIDS schema.

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.

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.

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

niworkflows is capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM).