.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.nipy.utils
=====================


.. _nipype.interfaces.nipy.utils.Similarity:


.. index:: Similarity

Similarity
----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/nipy/utils.py#L53>`__

Calculates similarity between two 3D volumes. Both volumes have to be in
the same coordinate system, same space within that coordinate system and
with the same voxel dimensions.

.. deprecated:: 0.10.0
   Use :py:class:`nipype.algorithms.metrics.Similarity` instead.

Example
~~~~~~~
>>> from nipype.interfaces.nipy.utils import Similarity
>>> similarity = Similarity()
>>> similarity.inputs.volume1 = 'rc1s1.nii'
>>> similarity.inputs.volume2 = 'rc1s2.nii'
>>> similarity.inputs.mask1 = 'mask.nii'
>>> similarity.inputs.mask2 = 'mask.nii'
>>> similarity.inputs.metric = 'cr'
>>> res = similarity.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        volume1: (an existing file name)
                3D volume
        volume2: (an existing file name)
                3D volume

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        mask1: (an existing file name)
                3D volume
        mask2: (an existing file name)
                3D volume
        metric: (u'cc' or u'cr' or u'crl1' or u'mi' or u'nmi' or u'slr' or a
                 callable value, nipype default value: None)
                str or callable
                Cost-function for assessing image similarity. If a string,
                one of 'cc': correlation coefficient, 'cr': correlation
                ratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual
                information, 'nmi': normalized mutual information, 'slr':
                supervised log-likelihood ratio. If a callable, it should
                take a two-dimensional array representing the image joint
                histogram as an input and return a float.

Outputs::

        similarity: (a float)
                Similarity between volume 1 and 2
