Here's a performant approach using numba:
from numba import njit
@njit
def n_ranges_nb(t1, t2):
a = np.arange(np.max(t2)+1)
n = (t2 - t1).sum()
out = np.zeros(n)
l, l_old = 0, 0
for i,j in zip(t1, t2):
l += j-i
out[l_old:l] = a[i:j]
l_old = l
return out
Checking with the same values as above:
t1 = np.array([0,13,22])
t2 = np.array([4,14,25])
n_ranges_nb(t1, t2+1)
# array([ 0., 1., 2., 3., 4., 13., 14., 22., 23., 24., 25.])
Lets check the timings:
d = 100
perfplot.show(
setup=lambda n: np.cumsum(np.random.randint(0, 50, n)),
kernels=[
lambda x: np.array([i for a, b in zip(x,x+d) for i in range(a,b+1)]),
lambda x: n_ranges_nb(x, x+d+1),
lambda x: create_ranges(x, x+d+1) # (from the dupe)
],
labels=['nested-list-comp', 'n_ranges_nb', 'create_ranges'],
n_range=[2**k for k in range(0, 18)],
xlabel='N'
)
