Finding Several Regions Of Interest In An Array

Say I have conducted an experiment where I've left a python program running for some long time and in that time I've taken several measurements of some quantity against time. Each

Solution 1:

Here is my two cents, based on exponential smoothing.

import itertools
A=np.array([0,1,0,0,-1,4,8,22,16,7,2,1,0,-1,-17,-20,-6,-1,0,1,0,2,1,0,8,-7,-1,0,0,1,0,1,-1,-17,-22,-40,16,1,3,14,17,19,8,2,0,1,3,2,3,1,0,0,-2,1,0,0,-1,22,4,0,-1,0])
B=np.hstack(([0,0],A,[0,0]))
B=np.asanyarray(zip(*[B[i:] for i inrange(5)]))
C=(B*[0.25,0.5,1,0.5,0.25]).mean(axis=1) #C is the 5-element sliding windows exponentially smoothed signal
D=[]
for item in itertools.groupby(enumerate(C), lambda x: abs(x[1])>1.5): 
    if item[0]:
        D.append(list(item[1])) #Get the indices where the signal are of magnitude >2. Change 1.5 to control the behavior.
E=[D[0]]
for item in D[1:]:
    if (item[0][0]-E[-1][-1][0]) <5: #Merge interesting regions if they are 5 or less indices apart. Change 5 to control the behavior.
        E[-1]=E[-1]+item
    else:
        E.append(item)
print [(item[0][0], item[-1][0]) for item in E]
[A[item[0][0]: item[-1][0]] for item in E if (item[-1][0]-item[0][0])>9] #Filter out the interesting regions <10 in length.