scipy.ndimage.histogram¶
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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.
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])