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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)