scipy.ndimage.standard_deviation#
- scipy.ndimage.standard_deviation(input, labels=None, index=None)[source]#
Calculate the standard deviation of the values of an N-D image array, optionally at specified sub-regions.
- Parameters
- inputarray_like
N-D image data to process.
- labelsarray_like, optional
Labels to identify sub-regions in input. If not None, must be same shape as input.
- indexint or sequence of ints, optional
labels to include in output. If None (default), all values where labels is non-zero are used.
- Returns
- standard_deviationfloat or ndarray
Values of standard deviation, for each sub-region if labels and index are specified.
Examples
>>> a = np.array([[1, 2, 0, 0], ... [5, 3, 0, 4], ... [0, 0, 0, 7], ... [9, 3, 0, 0]]) >>> from scipy import ndimage >>> ndimage.standard_deviation(a) 2.7585095613392387
Features to process can be specified using labels and index:
>>> lbl, nlbl = ndimage.label(a) >>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1)) array([ 1.479, 1.5 , 3. ])
If no index is given, non-zero labels are processed:
>>> ndimage.standard_deviation(a, lbl) 2.4874685927665499