:py:mod:`AFQ.recognition.cleaning`
==================================

.. py:module:: AFQ.recognition.cleaning


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   AFQ.recognition.cleaning.clean_by_orientation
   AFQ.recognition.cleaning.clean_bundle



Attributes
~~~~~~~~~~

.. autoapisummary::

   AFQ.recognition.cleaning.logger


.. py:data:: logger

   

.. py:function:: clean_by_orientation(streamlines, primary_axis, tol=None)

   
   Compute the cardinal orientation of each streamline


   :Parameters:

       **streamlines** : sequence of N by 3 arrays
           Where N is number of nodes in the array, the collection of
           streamlines to filter down to.

   :Returns:

       cleaned_idx, indicies of streamlines that passed cleaning
           ..













   ..
       !! processed by numpydoc !!

.. py:function:: clean_bundle(tg, n_points=100, clean_rounds=5, distance_threshold=3, length_threshold=4, min_sl=20, stat='mean', return_idx=False)

   
   Clean a segmented fiber group based on the Mahalnobis distance of
   each streamline


   :Parameters:

       **tg** : StatefulTractogram class instance or ArraySequence
           A whole-brain tractogram to be segmented.

       **n_points** : int, optional
           Number of points to resample streamlines to.
           Default: 100

       **clean_rounds** : int, optional.
           Number of rounds of cleaning based on the Mahalanobis distance from
           the mean of extracted bundles. Default: 5

       **distance_threshold** : float, optional.
           Threshold of cleaning based on the Mahalanobis distance (the units are
           standard deviations). Default: 3.

       **length_threshold: float, optional**
           Threshold for cleaning based on length (in standard deviations). Length
           of any streamline should not be *more* than this number of stdevs from
           the mean length.

       **min_sl** : int, optional.
           Number of streamlines in a bundle under which we will
           not bother with cleaning outliers. Default: 20.

       **stat** : callable or str, optional.
           The statistic of each node relative to which the Mahalanobis is
           calculated. Default: `np.mean` (but can also use median, etc.)

       **return_idx** : bool
           Whether to return indices in the original streamlines.
           Default: False.

       **Returns**
           ..

       **-------**
           ..

       **A StatefulTractogram class instance containing only the streamlines**
           ..

       **that have a Mahalanobis distance smaller than `clean_threshold` from**
           ..

       **the mean of each one of the nodes.**
           ..














   ..
       !! processed by numpydoc !!