Numpy Array: First occurence of N consecutive values smaller than threshold

Approach #1 :

Here's a vectorized NumPy way -

def start_valid_island(a, thresh, window_size):
    m = a<thresh
    me = np.r_[False,m,False]
    idx = np.flatnonzero(me[:-1]!=me[1:])
    lens = idx[1::2]-idx[::2]
    return idx[::2][(lens >= window_size).argmax()]

Sample runs -

In [44]: a
Out[44]: array([ 1,  4,  5,  7,  1,  2,  2,  4, 10])

In [45]: start_valid_island(a, thresh=3, window_size=3)
Out[45]: 4

In [46]: a[:3] = 1

In [47]: start_valid_island(a, thresh=3, window_size=3)
Out[47]: 0

Approach #2 :

With SciPy's binary-erosion -

from scipy.ndimage.morphology import binary_erosion

def start_valid_island_v2(a, thresh, window_size):
    m = a<thresh
    k = np.ones(window_size,dtype=bool)
    return binary_erosion(m,k,origin=-(window_size//2)).argmax()

Approach #3 :

To complete the set, here's a loopy one based on short-ciruiting and using the efficiency of numba -

from numba import njit

@njit
def start_valid_island_v3(a, thresh, window_size):
    n = len(a)
    out = None
    for i in range(n-window_size+1):
        found = True
        for j in range(window_size):
            if a[i+j]>=thresh:
                found = False
                break
        if found:
            out = i
            break
    return out

Timings -

In [142]: np.random.seed(0)
     ...: a = np.random.randint(0,10,(100000000))

In [145]: %timeit start_valid_island(a, thresh=3, window_size=3)
1 loop, best of 3: 810 ms per loop

In [146]: %timeit start_valid_island_v2(a, thresh=3, window_size=3)
1 loop, best of 3: 1.27 s per loop

In [147]: %timeit start_valid_island_v3(a, thresh=3, window_size=3)
1000000 loops, best of 3: 608 ns per loop