The indexed_gzip project is a Python extension which aims to provide a drop-in replacement for the built-in Python gzip.GzipFile class, the IndexedGzipFile. indexed_gzip was written to allow fast random access of compressed NIFTI image files (for which GZIP is the de-facto compression standard), but will work with any GZIP file.

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

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.

This package provides the version schemes used for packaging software from the NiPreps organization.

SDCFlows (Susceptibility Distortion Correction workFlows) is a Python library of NiPype-based workflows to preprocess B0 mapping data, estimate the corresponding fieldmap and finally correct for susceptibility distortions. Susceptibility-derived distortions are typically displayed by images acquired with EPI MR schemes.

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

Nifti_clib is a set of I/O libraries for reading and writing files in the nifti-1, nifti-2, and (to some degree) cifti file formats. These are binary file formats for storing medical image data, e.g. MRI and fMRI brain images.

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.

{dcmstack

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

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 utilities for feature analysis, preprocessing and visualization of image quality metrics generated by MRIQC.

Framework to represent structured directories in python as FileTree objects. File trees can be read in from simple text files describing the directory structure. This is particularly useful for pipelines with large number of input, output, and intermediate files. It can also be used to visualise the data in structured directories using FSLeyes or file-tree on the command line.

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.

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.

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.

NiFreeze is a flexible framework for volume-to-volume motion estimation and correction in d/fMRI and PET, and eddy-current-derived distortion estimation in dMRI.

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).

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

This package provides processing pipelines for structural MRI.

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

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

This module provides simple, consistent access to package resources.