nlcpy.digitize

nlcpy.digitize(x, bins, right=False)

Return is the indices of the bins to which each value in input array belongs.

right	order of bins	returned index i satisfies
False	increasing	bins[i-1] <= x < bins[i]
True	increasing	bins[i-1] < x <= bins[i]
False	decreasing	bins[i-1] < x <= bins[i]
True	decreasing	bins[i-1] < x <= bins[i]

If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate.

Parameters

xarray_like

Input array to be binned.

binsarray_like

Array of bins. It has to be 1-dimensional and monotonic.

rightbool, optional

Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval does not include the right edge. The left bin end is open in this case, i.e., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins.

Returns

indicesndarray of ints

Output array of indices, of same shape as x.

参考

bincount

Counts number of occurrences of each value in array of non-negative ints.

histogram

Computes the histogram of a set of data.

注釈

If values in x are such that they fall outside the bin range, attempting to index bins with the indices that digitize() returns will result in an IndexError.

For monotonically _increasing_ bins, the following are equivalent:

vp.digitize(x, bins, right=True)

Note that as the order of the arguments are reversed, the side must be too.

制限事項

This function is the wrapper function to utilize numpy.digitize(). Calculations during this function perform on only Vector Host(Linux/x86).

Examples

>>> import nlcpy as vp
>>> x = vp.array([0.2, 6.4, 3.0, 1.6])
>>> bins = vp.array([0.0, 1.0, 2.5, 4.0, 10.0])
>>> inds = vp.digitize(x, bins)
>>> inds
array([1, 4, 3, 2])
>>> for n in range(x.size):
...     print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]])
...
0.0 <= 0.2 < 1.0
4.0 <= 6.4 < 10.0
2.5 <= 3.0 < 4.0
1.0 <= 1.6 < 2.5
>>> x = vp.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = vp.array([0, 5, 10, 15, 20])
>>> vp.digitize(x,bins,right=True)
array([1, 2, 3, 4, 4])
>>> vp.digitize(x,bins,right=False)
array([1, 3, 3, 4, 5])