.. _home:

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Diffusion Imaging In Python
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DIPY_ is a **free** and **open source** software project for computational neuroanatomy,
focusing mainly on **diffusion** *magnetic resonance imaging* (dMRI) analysis. It implements a
broad range of algorithms for denoising, registration, reconstruction, tracking, clustering,
visualization, and statistical analysis of MRI data.

**********
Highlights
**********

**DIPY 1.0.0** is now available. New features include:

- Critical :doc:`API changes <api_changes>`
- Large refactoring of tracking API.
- New denoising algorithm: MP-PCA.
- New Gibbs ringing removal.
- New interpolation module: ``dipy.core.interpolation``.
- New reconstruction models: MTMS-CSD, Mean Signal DKI.
- Increased coordinate systems consistency.
- New object to manage safely tractography data: StatefulTractogram
- New command line interface for downloading datasets: FetchFlow
- Horizon updated, medical visualization interface powered by QuickBundlesX.
- Removed all deprecated functions and parameters.
- Removed compatibility with Python 2.7.
- Updated minimum dependencies version (Numpy, Scipy).
- All tutorials updated to API changes and 3 new added.
- Large documentation update.
- Closed 289 issues and merged 98 pull requests.

See :ref:`Older Highlights <old_highlights>`.


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Announcements
*************
- DIPY Workshop - Titanium Edition (March 11-15, 2019) is now open for registration:

.. raw :: html

  <div style="width: 80% max-width=800px">
    <a href="https://workshop.dipy.org/" target="_blank"><img alt=" " class="align-center" src="_static/dipy-ws-header.png" style="width: 90%;max-height: 90%"></a>
  </div>


- :doc:`DIPY 1.0 <release_notes/release1.0>` released August 5, 2019.
- :doc:`DIPY 0.16 <release_notes/release0.16>` released March 10, 2019.
- :doc:`DIPY 0.15 <release_notes/release0.15>` released December 12, 2018.


See some of our :ref:`Past Announcements <old_news>`


***************
Getting Started
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Here is a quick snippet showing how to calculate `color FA` also known as the
DEC map. We use a Tensor model to reconstruct the datasets which are
saved in a Nifti file along with the b-values and b-vectors which are saved as
text files. Finally, we save our result as a Nifti file ::

    fdwi = 'dwi.nii.gz'
    fbval = 'dwi.bval'
    fbvec = 'dwi.bvec'

    from dipy.io.image import load_nifti, save_nifti
    from dipy.io import read_bvals_bvecs
    from dipy.core.gradients import gradient_table
    from dipy.reconst.dti import TensorModel

    data, affine = load_nifti(fdwi)
    bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
    gtab = gradient_table(bvals, bvecs)

    tenmodel = TensorModel(gtab)
    tenfit = tenmodel.fit(data)

    save_nifti('colorfa.nii.gz', tenfit.color_fa, affine)

As an exercise, you can try to calculate `color FA` with your datasets. You will need
to replace the filepaths `fdwi`, `fbval` and `fbvec`. Here is what
a slice should look like.

.. image:: _static/colorfa.png
    :align: center

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Next Steps
**********

You can learn more about how you to use DIPY_ with  your datasets by reading the examples in our :ref:`documentation`.

.. We need the following toctree directive to include the documentation
.. in the document hierarchy - see http://sphinx.pocoo.org/concepts.html
.. toctree::
   :hidden:

   documentation
   stateoftheart

*******
Support
*******

We acknowledge support from the following organizations:

- The department of Intelligent Systems Engineering of Indiana University.

- The National Institute of Biomedical Imaging and Bioengineering, NIH.

- The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the
  University of Washington eScience Institute Data Science Environment.

- Google supported DIPY through the Google Summer of Code Program during
  Summer 2015, 2016 and 2018.

- The International Neuroinformatics Coordination Facility.




.. include:: links_names.inc