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scipy.ndimage.

# zoom

scipy.ndimage.zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0, prefilter=True, *, grid_mode=False)

  Zoom an array.

  The array is zoomed using spline interpolation of the requested order.

  Parameters:
    inputarray_like

      The input array.

    zoomfloat or sequence

      The zoom factor along the axes. If a float, `zoom` is the same for each axis. If a sequence, `zoom` should contain one value for each axis.

    outputarray or dtype, optional

      The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.

    orderint, optional

      The order of the spline interpolation, default is 3. The order has to be in the range 0-5.

    mode{‘reflect’, ‘grid-mirror’, ‘constant’, ‘grid-constant’, ‘nearest’, ‘mirror’, ‘grid-wrap’, ‘wrap’}, optional

      The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘constant’. Behavior for each valid value is as follows (see additional plots and details on boundary modes):

      ‘reflect’ (d c b a | a b c d | d c b a)

        The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

      ‘grid-mirror’

        This is a synonym for ‘reflect’.

      ‘constant’ (k k k k | a b c d | k k k k)

        The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. No interpolation is performed beyond the edges of the input.

      ‘grid-constant’ (k k k k | a b c d | k k k k)

        The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. Interpolation occurs for samples outside the input’s extent as well.

      ‘nearest’ (a a a a | a b c d | d d d d)

        The input is extended by replicating the last pixel.

      ‘mirror’ (d c b | a b c d | c b a)

        The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

      ‘grid-wrap’ (a b c d | a b c d | a b c d)

        The input is extended by wrapping around to the opposite edge.

      ‘wrap’ (d b c d | a b c d | b c a b)

        The input is extended by wrapping around to the opposite edge, but in a way such that the last point and initial point exactly overlap. In this case it is not well defined which sample will be chosen at the point of overlap.

    cvalscalar, optional

      Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

    prefilterbool, optional

      Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary float64 array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

    grid_modebool, optional

      If False, the distance from the pixel centers is zoomed. Otherwise, the distance including the full pixel extent is used. For example, a 1d signal of length 5 is considered to have length 4 when grid_mode is False, but length 5 when grid_mode is True. See the following visual illustration:

          | pixel 1 | pixel 2 | pixel 3 | pixel 4 | pixel 5 |
               |<-------------------------------------->|
                                  vs.
          |<----------------------------------------------->|

      The starting point of the arrow in the diagram above corresponds to coordinate location 0 in each mode.

  Returns:
    zoomndarray

      The zoomed input.

  Notes

  For complex-valued input, this function zooms the real and imaginary components independently.

  Added in version 1.6.0: Complex-valued support added.

  Examples

      >>> from scipy import ndimage, datasets
      >>> import matplotlib.pyplot as plt

      >>> fig = plt.figure()
      >>> ax1 = fig.add_subplot(121)  # left side
      >>> ax2 = fig.add_subplot(122)  # right side
      >>> ascent = datasets.ascent()
      >>> result = ndimage.zoom(ascent, 3.0)
      >>> ax1.imshow(ascent, vmin=0, vmax=255)
      >>> ax2.imshow(result, vmin=0, vmax=255)
      >>> plt.show()

      >>> print(ascent.shape)
      (512, 512)

      >>> print(result.shape)
      (1536, 1536)