Concatenate range arrays given start, stop numbers in a vectorized way – NumPy

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Think I have cracked it finally with a cumsum trick for a vectorized solution –

def create_ranges(a):
    l = a[:,1] - a[:,0]
    clens = l.cumsum()
    ids = np.ones(clens[-1],dtype=int)
    ids[0] = a[0,0]
    ids[clens[:-1]] = a[1:,0] - a[:-1,1]+1
    out = ids.cumsum()
    return out

Sample runs –

In [416]: a = np.array([[4,7],[10,16],[11,18]])

In [417]: create_ranges(a)
Out[417]: array([ 4,  5,  6, 10, 11, 12, 13, 14, 15, 11, 12, 13, 14, 15, 16, 17])

In [425]: a = np.array([[-2,4],[-5,2],[11,12]])

In [426]: create_ranges(a)
Out[426]: array([-2, -1,  0,  1,  2,  3, -5, -4, -3, -2, -1,  0,  1, 11])

If we are given starts and stops as two 1D arrays, we just need to use those in place of the first and second columns. For completeness, here’s the complete code –

def create_ranges(starts, ends):
    l = ends - starts
    clens = l.cumsum()
    ids = np.ones(clens[-1],dtype=int)
    ids[0] = starts[0]
    ids[clens[:-1]] = starts[1:] - ends[:-1]+1
    out = ids.cumsum()
    return out

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