scipy.ndimage.

histogram#

scipy.ndimage.histogram(input, min, max, bins, labels=None, index=None)[source]#

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:
inputarray_like

Data for which to calculate histogram.

min, maxint

Minimum and maximum values of range of histogram bins.

binsint

Number of bins.

labelsarray_like, optional

Labels for objects in input. If not None, must be same shape as input.

indexint 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:
histndarray

Histogram counts.

Notes

histogram has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library

CPU

GPU

NumPy

n/a

CuPy

n/a

PyTorch

JAX

⚠️ no JIT

Dask

⚠️ computes graph

n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> 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.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.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.histogram(a, 0, 1, 10, lbl, 2)
array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0])