Return the indices of the bins to which each value in input array belongs.
Each index i returned is such that bins[i1] <= x < bins[i] if bins is monotonically increasing, or bins[i1] > x >= bins[i] if bins is monotonically decreasing. If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate. If right is True, then the right bin is closed so that the index i is such that bins[i1] < x <= bins[i] or bins[i1] >= x > bins[i]`` if bins is monotonically increasing or decreasing, respectively.
Parameters :  x : array_like
bins : array_like
right : bool, optional


Returns :  out : ndarray of ints

Raises :  ValueError :
TypeError :

Notes
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.
Examples
>>> x = np.array([0.2, 6.4, 3.0, 1.6])
>>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
>>> inds = np.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 = np.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = np.array([0,5,10,15,20])
>>> np.digitize(x,bins,right=True)
array([1, 2, 3, 4, 4])
>>> np.digitize(x,bins,right=False)
array([1, 3, 3, 4, 5])