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
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])