Finding local maxima/minima with Numpy in a 1D numpy array

Question

Can you suggest a module function from numpy/scipy that can find local maxima/minima in a 1D numpy array? Obviously the simplest approach ever is to have a look at the neare

Answer 1

Update: I wasn't happy with gradient so I found it more reliable to use numpy.diff. Please let me know if it does what you want.

Regarding the issue of noise, the mathematical problem is to locate maxima/minima if we want to look at noise we can use something like convolve which was mentioned earlier.

import numpy as np
from matplotlib import pyplot

a=np.array([10.3,2,0.9,4,5,6,7,34,2,5,25,3,-26,-20,-29],dtype=np.float)

gradients=np.diff(a)
print gradients


maxima_num=0
minima_num=0
max_locations=[]
min_locations=[]
count=0
for i in gradients[:-1]:
        count+=1

    if ((cmp(i,0)>0) & (cmp(gradients[count],0)<0) & (i != gradients[count])):
        maxima_num+=1
        max_locations.append(count)     

    if ((cmp(i,0)<0) & (cmp(gradients[count],0)>0) & (i != gradients[count])):
        minima_num+=1
        min_locations.append(count)


turning_points = {'maxima_number':maxima_num,'minima_number':minima_num,'maxima_locations':max_locations,'minima_locations':min_locations}  

print turning_points

pyplot.plot(a)
pyplot.show()