Created
April 4, 2012 00:55
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cut function for numpy
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########################################### | |
# Note: this requires PR # 245 in numpy | |
# https://github.com/numpy/numpy/pull/245 | |
# | |
# Author: Skipper Seabold | |
# License: BSD | |
import numpy as np | |
def cut(x, bins, right=True): | |
""" | |
Return indices of half-open bins to which each value of `x` belongs. | |
Parameters | |
---------- | |
x : array-like | |
Input array to be binned. It has to be 1-dimensional. | |
bins : int or sequence of scalars | |
If `bins` is an int, it defines the number of equal-width bins in the | |
range of `x`. The range of `x`, however, is extended by .1% on each | |
side to include the min or max values of `x`. If `bins` is a sequence | |
it defines the bin edges allowing for non-uniform bin width. | |
right : bool | |
Indicates whether the bins include the rightmost edge or not. If | |
right == True (the default), then the bins [1,2,3,4] indicate | |
(1,2], (2,3], (3,4]. | |
Returns | |
------- | |
out : ndarray of ints | |
Output array of indices, of same shape as `x`. | |
""" | |
if not np.iterable(bins): | |
if np.isscalar(bins) and bins < 1: | |
raise ValueError("`bins` should be a positive integer.") | |
if x.size == 0: | |
# handle empty arrays. Can't determine range, so use 0-1. | |
range = (0, 1) | |
else: | |
range = (x.min(), x.max()) | |
mn, mx = [mi+0.0 for mi in range] | |
if mn == mx: | |
mn -= 0.5 | |
mx += 0.5 | |
bins = np.linspace(mn, mx, bins+1, endpoint=True) | |
bins[0] -= .1*mn | |
bins[-1] += .1*mx | |
else: | |
bins = np.asarray(bins) | |
if (np.diff(bins) < 0).any(): | |
raise AttributeError( | |
'bins must increase monotonically.') | |
return np.digitize(x, bins, right) |
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