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# scipy.ndimage.interpolation.geometric_transform

`scipy.ndimage.interpolation.``geometric_transform`(input, mapping, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True, extra_arguments=(), extra_keywords={})

  Apply an arbritrary geometric transform.

  The given mapping function is used to find, for each point in the output, the corresponding coordinates in the input. The value of the input at those coordinates is determined by spline interpolation of the requested order.

  Parameters : | input : array_like The input array. mapping : callable A callable object that accepts a tuple of length equal to the output array rank, and returns the corresponding input coordinates as a tuple of length equal to the input array rank. output_shape : tuple of ints Shape tuple. output : ndarray or dtype, optional The array in which to place the output, or the dtype of the returned array. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : str, optional Points outside the boundaries of the input are filled according to the given mode (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’. cval : scalar, optional Value used for points outside the boundaries of the input if `mode='constant'`. Default is 0.0 prefilter : bool, optional The parameter prefilter determines if the input is pre-filtered with spline_filter before interpolation (necessary for spline interpolation of order > 1). If False, it is assumed that the input is already filtered. Default is True. extra_arguments : tuple, optional Extra arguments passed to mapping. extra_keywords : dict, optional Extra keywords passed to mapping.
  Returns : | return_value : ndarray or None The filtered input. If output is given as a parameter, None is returned.

  See also

  `map_coordinates`, `affine_transform`, `spline_filter1d`

  Examples

      >>> a = np.arange(12.).reshape((4, 3))
      >>> def shift_func(output_coords):
      ...     return (output_coords[0] - 0.5, output_coords[1] - 0.5)
      ...
      >>> sp.ndimage.geometric_transform(a, shift_func)
      array([[ 0.   ,  0.   ,  0.   ],
             [ 0.   ,  1.362,  2.738],
             [ 0.   ,  4.812,  6.187],
             [ 0.   ,  8.263,  9.637]])

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