mask_classifications

mcalf.utils.smooth.mask_classifications(class_map, vmin=None, vmax=None, reduce=True)[source]

Mask 2D and 3D arrays of classifications.

If 3D, also reduces to 2D by selecting the most common classification along the first dimension.

Parameters
  • class_map (numpy.ndarray[int], ndim=2 or 3) – Array of classifications. If reduce is True (default) and the array is three-dimensional, it is assumed that the first dimension is time, and a time average classification will be calculated. The time average is the most common positive (valid) classification at each pixel.

  • vmin (int, optional, default=None) – Minimum classification integer to include. Must be greater or equal to zero. Defaults to min positive integer in class_map. Classifications below this value will be set to -1.

  • vmax (int, optional, default=None) – Maximum classification integer to include. Must be greater than zero. Defaults to max positive integer in class_map. Classifications above this value will be set to -1.

  • reduce (bool, optional, default=True) – Whether to perform the time average described in class_map info.

Returns

  • class_map (numpy.ndarray[int], ndim=2) – class_map with values between vmin and vmax averaged along the first dimension.

  • vmin (int) – Updated vmin value.

  • vmax (int) – Updated vmax value.

See also

mcalf.visualisation.plot_class_map

Plot a map of the classifications.