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# scipy.ndimage.measurements.histogram

`scipy.ndimage.measurements.``histogram`(input, min, max, bins, labels=None, index=None)

  Calculate the histogram of the values of an array, optionally at labels.

  Histogram calculates the frequency of values in an array within bins determined by min, max, and bins. The labels and index keywords can limit the scope of the histogram to specified sub-regions within the array.

  Parameters: | input : array_like Data for which to calculate histogram. min, max : int Minimum and maximum values of range of histogram bins. bins : int Number of bins. labels : array_like, optional Labels for objects in input. If not None, must be same shape as input. index : int or sequence of ints, optional Label or labels for which to calculate histogram. If None, all values where label is greater than zero are used
  Returns: | hist : ndarray Histogram counts.

  Examples

      >>> a = np.array([[ 0.    ,  0.2146,  0.5962,  0.    ],
                        [ 0.    ,  0.7778,  0.    ,  0.    ],
                        [ 0.    ,  0.    ,  0.    ,  0.    ],
                        [ 0.    ,  0.    ,  0.7181,  0.2787],
                        [ 0.    ,  0.    ,  0.6573,  0.3094]])
      >>> from scipy import ndimage
      >>> ndimage.measurements.histogram(a, 0, 1, 10)
      array([13,  0,  2,  1,  0,  1,  1,  2,  0,  0])

  With labels and no indices, non-zero elements are counted:

      >>> lbl, nlbl = ndimage.label(a)
      >>> ndimage.measurements.histogram(a, 0, 1, 10, lbl)
      array([0, 0, 2, 1, 0, 1, 1, 2, 0, 0])

  Indices can be used to count only certain objects:

      >>> ndimage.measurements.histogram(a, 0, 1, 10, lbl, 2)
      array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0])