:py:mod:`AFQ.utils.volume`
==========================

.. py:module:: AFQ.utils.volume


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


Functions
~~~~~~~~~

.. autoapisummary::

   AFQ.utils.volume.transform_inverse_roi
   AFQ.utils.volume.patch_up_roi
   AFQ.utils.volume.density_map
   AFQ.utils.volume.dice_coeff



Attributes
~~~~~~~~~~

.. autoapisummary::

   AFQ.utils.volume.logger


.. py:data:: logger

   

.. py:function:: transform_inverse_roi(roi, mapping, bundle_name='ROI')

   
   After being non-linearly transformed, ROIs tend to have holes in them.
   We perform a couple of computational geometry operations on the ROI to
   fix that up.


   :Parameters:

       **roi** : Nifti1Image, str, ndarray
           The ROI to transform. Can be a path or image, which will be
           converted to an ndarray.

       **mapping** : DiffeomorphicMap object
           A mapping between DWI space and a template.

       **bundle_name** : str, optional
           Name of bundle, which may be useful for error messages.
           Default: None

   :Returns:

       ROI after dilation and hole-filling
           ..













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.. py:function:: patch_up_roi(roi, bundle_name='ROI')

   
   After being non-linearly transformed, ROIs tend to have holes in them.
   We perform a couple of computational geometry operations on the ROI to
   fix that up.


   :Parameters:

       **roi** : 3D binary array
           The ROI after it has been transformed.

       **bundle_name** : str, optional
           Name of bundle, which may be useful for error messages.
           Default: None

   :Returns:

       ROI after dilation and hole-filling
           ..













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.. py:function:: density_map(tractogram, n_sls=None, normalize=False)

   
   Create a streamline density map.
   based on:
   https://dipy.org/documentation/1.1.1./examples_built/streamline_formats/


   :Parameters:

       **tractogram** : StatefulTractogram
           Stateful tractogram whose streamlines are used to make
           the density map.

       **n_sls** : int or None, optional
           n_sls to randomly select to make the density map.
           If None, all streamlines are used.
           Default: None

       **normalize** : bool, optional
           Whether to normalize maximum values to 1.
           Default: False

   :Returns:

       Nifti1Image containing the density map.
           ..













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.. py:function:: dice_coeff(arr1, arr2, weighted=True)

   
   Compute Dice's coefficient between two images.


   :Parameters:

       **arr1** : Nifti1Image, str, ndarray
           One ndarray to compare. Can be a path or image, which will be
           converted to an ndarray.

       **arr2** : Nifti1Image, str, ndarray
           The other ndarray to compare. Can be a path or image, which will be
           converted to an ndarray.

       **weighted** : bool, optional
           Whether or not to weight the DICE coefficient as in [Cousineau2017]_.
           The weighted Dice coefficient is calculated by adding the sum of all
           values in arr1 where arr2 is nonzero to the sum of all values in arr2
           where arr1 is nonzero, then dividing that by the sum of all values in
           arr1 and arr2.
           Default: True

   :Returns:

       The dice similarity between the images.
           ..








   .. rubric:: Notes

   .. [R525e1fe6798e-1] Cousineau M, Jodoin PM, Morency FC, et al. A test-retest study on
          Parkinson's PPMI dataset yields statistically significant white
          matter fascicles. Neuroimage Clin. 2017;16:222-233. Published 2017
          Jul 25. doi:10.1016/j.nicl.2017.07.020





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