Take sum of numpy array based on a boolean array

Question

I have two numpy arrays prods andindex

prods = np.asarray([ 0.5 ,  0.25,  1.98,  2.4 ,  2.1 ,  0.6 ])
index = np.asarray([False,  True,  True, False, False,  True], dtype=bool)

I need to calculate the sum of the values in prods array using the index array. The output I want to is

res = [0.75, 1.98, 5.1]

The first True in index array is preceded by a False, so I take the first two elements from prods(.5,.25) and sum them up(0.75). The second True in index has no preceding False (since its preceded by a True, the False at position zero doesn't count), so I simply output 1.98 in this case. The third True is preceded by two False, so I take those values from prods array (2.4,2.1,0.6) and sum them up. Any ideas on how to do this?

I basically need something like np.cumsum but I need to return the cumulative sum every time a True occurs in index and reset the cumulative sum value to zero.

MSeifert · Accepted Answer · 2017-08-11 11:46:25Z

3

You could use np.split and using np.where of your index array as positions to split:

>>> [arr.sum() for arr in np.split(prods, np.where(index)[0]+1)[:-1]]
[0.75, 1.98, 5.0999999999999996]

The last one isn't exactly 5.1 because of floating point precision. If you don't want to use Fractions or Decimals there's nothing you can do about that.

You could also use np.add.reduceat here:

>>> np.add.reduceat(prods, np.append([0], (np.where(index)[0]+1)[:-1]))
array([ 0.75,  1.98,  5.1 ])

edited Aug 11, 2017 at 11:46

answered Aug 11, 2017 at 11:38

MSeifert

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