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scipy.ndimage.

# median_filter

scipy.ndimage.median_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)

  Calculate a multidimensional median filter.

  Parameters:
    input array_like

      The input array.

    size scalar or tuple, optional

      See footprint, below. Ignored if footprint is given.

    footprint array, optional

      Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus `size=(n,m)` is equivalent to `footprint=np.ones((n,m))`. We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

    output array 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.

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

      The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘reflect’. Behavior for each valid value is as follows:

      ‘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.

      ‘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.

      ‘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.

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

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

      For consistency with the interpolation functions, the following mode names can also be used:

      ‘grid-mirror’

        This is a synonym for ‘reflect’.

      ‘grid-constant’

        This is a synonym for ‘constant’.

      ‘grid-wrap’

        This is a synonym for ‘wrap’.

    cval scalar, optional

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

    origin int or sequence, optional

      Controls the placement of the filter on the input array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the input array, different shifts can be specified along each axis.

    axes tuple of int or None, optional

      If None, input is filtered along all axes. Otherwise, input is filtered along the specified axes. When axes is specified, any tuples used for size, origin, and/or mode must match the length of axes. The ith entry in any of these tuples corresponds to the ith entry in axes.

  Returns:
    median_filter ndarray

      Filtered array. Has the same shape as input.

  See also

  `scipy.signal.medfilt2d`

  Notes

  For 2-dimensional images with `uint8`, `float32` or `float64` dtypes the specialised function `scipy.signal.medfilt2d` may be faster. It is however limited to constant mode with `cval=0`.

  The filter always returns the argument that would appear at index `n // 2` in a sorted array, where `n` is the number of elements in the footprint of the filter. Note that this differs from the conventional definition of the median when `n` is even. Also, this function does not support the `float16` dtype, behavior in the presence of NaNs is undefined, and memory consumption scales with `n**4`. For `float16` support, greater control over the definition of the filter, and to limit memory usage, consider using `vectorized_filter` with NumPy functions np.median or np.nanmedian.

  Examples

      >>> from scipy import ndimage, datasets
      >>> import matplotlib.pyplot as plt
      >>> fig = plt.figure()
      >>> plt.gray()  # show the filtered result in grayscale
      >>> ax1 = fig.add_subplot(121)  # left side
      >>> ax2 = fig.add_subplot(122)  # right side
      >>> ascent = datasets.ascent()
      >>> result = ndimage.median_filter(ascent, size=20)
      >>> ax1.imshow(ascent)
      >>> ax2.imshow(result)
      >>> plt.show()