Make Numpy.where stop after first true encountered

If cump is a cumulative density function then it is monotonic, hence sorted. Rather than linearly scanning it, you are going to get best performance guarantees by binary searching it.

First we create some fake data to search over:

>>> import numpy as np
>>> cump = np.cumsum(np.random.rand(11))
>>> cump -= cump[0]
>>> cump /= cump[-1]
>>> cump
array([ 0.        ,  0.07570573,  0.1417473 ,  0.30536346,  0.36277835,
        0.47102093,  0.54456142,  0.6859625 ,  0.75270741,  0.84691162,  1.
   ])

Then we create some fake data to search for:

>>> sample = np.random.rand(5)
>>> sample
array([ 0.19597276,  0.37885803,  0.2096784 ,  0.57559965,  0.72175056])

And we finally search for it (and find it!):

>>> [np.where(_ < cump)[0][0] for _ in sample]
[3, 5, 3, 7, 8]
>>> np.searchsorted(cump, sample)
array([3, 5, 3, 7, 8], dtype=int64)