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# scipy.ndimage.interpolation.affine_transform

`scipy.ndimage.interpolation.``affine_transform`(input, matrix, offset=0.0, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True)

  Apply an affine transformation.

  The given matrix and offset are used to find for each point in the output the corresponding coordinates in the input by an affine transformation. The value of the input at those coordinates is determined by spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode.

  Parameters : | input : ndarray The input array. matrix : ndarray The matrix must be two-dimensional or can also be given as a one-dimensional sequence or array. In the latter case, it is assumed that the matrix is diagonal. A more efficient algorithms is then applied that exploits the separability of the problem. offset : float or sequence, optional The offset into the array where the transform is applied. If a float, offset is the same for each axis. If a sequence, offset should contain one value for each axis. output_shape : tuple of ints, optional 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.
  Returns : | return_value : ndarray or None The transformed input. If output is given as a parameter, None is returned.

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