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.

See also

bincount: Counts number of occurrences of each value in array of non-negative ints.
histogram: Computes the histogram of a set of data.

Note

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.

Restriction

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])