The fslpy package is a collection of utilities and data abstractions used within FSL and by FSLeyes.

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.

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

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.

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

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.

The Insight Toolkit (ITK) is a toolkit for N-dimensional scientific image processing, segmentation, and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

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

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.

Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages.

Convert data from DICOM and organise the resulting NIfTI files into BIDS.

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.

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

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

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.

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

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.

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.

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

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

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.

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

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

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