I have a question related to finding maxima or more preciseley discontinuities in a numpy array?
My exemplary data looks for example like this
a = np.array([3,4,5,8,7,6,5,4,1])
In general, I am interested in every maximum/jump in the data. For array a, I want to detect the 8 since it is a maximum (growing numbers on the left side and decreasing numbers on the right) and the value of 4, since the data drops after this value. Until now, I have used scipy.signal.argrelextrema
with np.greater to detect maxima, but I am not able to detect these jumps/discontinuities. For the data I am looking at, only a jump towards smaller values can occur not the opposite. Is there an easy pythonic way to detect these jumps?
Let's try this:
threshold = 1
a = np.array([3, 4, 5, 8, 7, 6, 5, 4, 1])
discontinuities_idx = np.where(abs(np.diff(a))>threshold)[0] + 1
np.diff(a) gives the difference between every component of a:
>>> array([ 1, 1, 3, -1, -1, -1, -1, -3])
From then np.where(abs(np.diff(a))>threshold)[0] is applied to find where detected discontinuities are (above user specified threshold in terms of absolute difference). Finally, you may add +1 to compensate for n=1 difference idx if needed (see np.diff kwargs) depending on which side of the discontinuities you need to be.
>>> discontinuities_idx
>>> array([3, 8])
>>> a[discontinuities_idx]
>>> array([8, 1])