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]])