Find large number of consecutive values fulfilling condition in a numpy array

Question

I have some audio data loaded in a numpy array and I wish to segment the data by finding silent parts, i.e. parts where the audio amplitude is below a certain threshold over

Answer 1

@joe-kington I've got about 20%-25% speed improvement over np.diff / np.nonzero solution by using argmax instead (see code below, condition is boolean)

def contiguous_regions(condition):
    idx = []
    i = 0
    while i < len(condition):
        x1 = i + condition[i:].argmax()
        try:
            x2 = x1 + condition[x1:].argmin()
        except:
            x2 = x1 + 1
        if x1 == x2:
            if condition[x1] == True:
                x2 = len(condition)
            else:
                break
        idx.append( [x1,x2] )
        i = x2
    return idx

Of course, your mileage may vary depending on your data.

Besides, I'm not entirely sure, but i guess numpy may optimize argmin/argmax over boolean arrays to stop searching on first True/False occurrence. That might explain it.