scipy.ndimage.histogram

scipy.ndimage.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])