Skip to content
forked from dipy/dipy

DIPY is the paragon 3D/4D+ imaging library in Python. 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.

License

Notifications You must be signed in to change notification settings

scilus/hot_dipy

 
 

Repository files navigation

DIPY - Diffusion Imaging in Python

https://github.com/dipy/dipy/actions/workflows/test.yml/badge.svg?branch=master

DIPY [DIPYREF] is a python library for the analysis of MR diffusion imaging.

DIPY is for research only; please do not use results from DIPY for clinical decisions.

Website

Current information can always be found from the DIPY website - http://dipy.org

Mailing Lists

Please see the DIPY community list at https://mail.python.org/mailman3/lists/dipy.python.org/

Please see the users' forum at https://github.com/dipy/dipy/discussions

Please join the gitter chatroom here.

Code

You can find our sources and single-click downloads:

Installing DIPY

DIPY can be installed using pip:

pip install dipy

or using conda:

conda install -c conda-forge dipy

For detailed installation instructions, including instructions for installing from source, please read our installation documentation.

License

DIPY is licensed under the terms of the BSD license. Please see the LICENSE file.

Contributing

We welcome contributions from the community. Please read our Contributing guidelines.

Reference

[DIPYREF]E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, I. Nimmo-Smith and DIPY contributors, "DIPY, a library for the analysis of diffusion MRI data", Frontiers in Neuroinformatics, vol. 8, p. 8, Frontiers, 2014.

About

DIPY is the paragon 3D/4D+ imaging library in Python. 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.

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 91.2%
  • Cython 8.3%
  • Shell 0.1%
  • PowerShell 0.1%
  • Batchfile 0.1%
  • Makefile 0.1%
  • C 0.1%