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# nipype.interfaces.nipy.utils module

## Similarity

Link to code

> Bases: NipyBaseInterface

> 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 since version 0.10.0: Use `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()

> volume1a pathlike object or string representing an existing file

>   3D volume.

> volume2a pathlike object or string representing an existing file

>   3D volume.

> mask1a pathlike object or string representing an existing file

>   3D volume.

> mask2a pathlike object or string representing an existing file

>   3D volume.

> metric‘cc’ or ‘cr’ or ‘crl1’ or ‘mi’ or ‘nmi’ or ‘slr’ or a callable value

>   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. (Nipype default value: `None`)

> similaritya float

>   Similarity between volume 1 and 2.