<commit_before>.. _stateoftheart:

============================
A quick overview of features
============================

Here are just a few of the state-of-the-art :ref:`technologies <examples>` and algorithms which are provided in Dipy_:

- Reconstruction algorithms e.g. CSD, DSI, GQI, DTI, QBI and SHORE.
- Fiber tracking algorithms e.g. Deterministic, Probabilistic.
- Simple interactive visualization of ODFs and streamlines.
- Apply different operations on streamlines.
- Simplify large datasets of streamlines using QuickBundles clustering.
- Reslice datasets with anisotropic voxels to isotropic.
- Calculate distances/correspondences between streamlines.
- Deal with huge streamline datasets without memory restrictions (.dpy).

With the help of some external tools you can also:

- Read many different file formats e.g. Trackvis or Nifti (with nibabel).
- Examine your datasets interactively (with ipython).

For more information on specific algorithms we recommend starting from  looking at Dipy's :ref:`gallery <examples>` of examples.
<commit_msg>DOC: Small correction in stateoftheart.rst<commit_after>.. _stateoftheart:

============================
A quick overview of features
============================

Here are just a few of the state-of-the-art :ref:`technologies <examples>` and algorithms which are provided in Dipy_:

- Reconstruction algorithms e.g. CSD, DSI, GQI, DTI, QBI and SHORE.
- Fiber tracking algorithms e.g. Deterministic, Probabilistic.
- Simple interactive visualization of ODFs and streamlines.
- Apply different operations on streamlines.
- Simplify large datasets of streamlines using QuickBundles clustering.
- Reslice datasets with anisotropic voxels to isotropic.
- Calculate distances/correspondences between streamlines.
- Deal with huge streamline datasets without memory restrictions (.dpy).

With the help of some external tools you can also:

- Read many different file formats e.g. Trackvis or Nifti (with nibabel).
- Examine your datasets interactively (with ipython).

For more information on specific algorithms we recommend starting by looking at Dipy's :ref:`gallery <examples>` of examples.

{'fraction_non_alphanumeric': 0.11245353159851301, 'fraction_numerical': 0.0, 'mean_word_length': 4.997214484679666, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5177608', 'n_tokens_mistral': 589, 'n_tokens_neox': 530, 'n_words': 252}