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# interfaces.nipy.utils

## Similarity

Link to code

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.

### 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()

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
            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