Here's one approach -
def start_stop(a, trigger_val):
# "Enclose" mask with sentients to catch shifts later on
mask = np.r_[False,np.equal(a, trigger_val),False]
# Get the shifting indices
idx = np.flatnonzero(mask[1:] != mask[:-1])
# Get the start and end indices with slicing along the shifting ones
return zip(idx[::2], idx[1::2]-1)
Sample run -
In [216]: mask = [1, 0, 0, 1, 1, 1, 0, 0]
In [217]: start_stop(mask, trigger_val=1)
Out[217]: [(0, 0), (3, 5)]
Use it to get the edges for 0s
-
In [218]: start_stop(mask, trigger_val=0)
Out[218]: [(1, 2), (6, 7)]
Timings on 100000x
scaled up datasize -
In [226]: mask = [1, 0, 0, 1, 1, 1, 0, 0]
In [227]: mask = np.repeat(mask,100000)
# Original soln
In [230]: %%timeit
...: segments = []
...: start = 0
...: for i in range(len(mask) - 1):
...: e1 = mask[i]
...: e2 = mask[i + 1]
...: if e1 == 0 and e2 == 1:
...: start = i + 1
...: elif e1 == 1 and e2 == 0:
...: segments.append((start, i))
1 loop, best of 3: 401 ms per loop
# @Yakym Pirozhenko's soln
In [231]: %%timeit
...: slices = np.ma.clump_masked(np.ma.masked_where(mask, mask))
...: result = [(s.start, s.stop - 1) for s in slices]
100 loops, best of 3: 4.8 ms per loop
In [232]: %timeit start_stop(mask, trigger_val=1)
1000 loops, best of 3: 1.41 ms per loop
与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…