Find local maximums in numpy array

Question

I am looking to find the peaks in some gaussian smoothed data that I have. I have looked at some of the peak detection methods available but they require an input range over

Answer 1

>> import numpy as np
>> from scipy.signal import argrelextrema
>> a = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])
>> argrelextrema(a, np.greater)
array([ 4, 10, 17]),)
>> a[argrelextrema(a, np.greater)]
array([5, 3, 6])

If your input represents a noisy distribution, you can try smoothing it with NumPy convolve function.

Answer 2

If you can exclude maxima at the edges of the arrays you can always check if one elements is bigger than each of it's neighbors by checking:

import numpy as np
array = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])
# Check that it is bigger than either of it's neighbors exluding edges:
max = (array[1:-1] > array[:-2]) & (array[1:-1] > array[2:])
# Print these values
print(array[1:-1][max])
# Locations of the maxima
print(np.arange(1, array.size-1)[max])

Answer 3

There exists a bulit-in function argrelextrema that gets this task done:

import numpy as np
from scipy.signal import argrelextrema
    
a = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])

# determine the indices of the local maxima
max_ind = argrelextrema(a, np.greater)

# get the actual values using these indices
r = a[max_ind]  # array([5, 3, 6])

That gives you the desired output for r.

As of SciPy version 1.1, you can also use find_peaks. Below are two examples taken from the documentation itself.

Using the height argument, one can select all maxima above a certain threshold (in this example, all non-negative maxima; this can be very useful if one has to deal with a noisy baseline; if you want to find minima, just multiply you input by -1):

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np

x = electrocardiogram()[2000:4000]
peaks, _ = find_peaks(x, height=0)
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.plot(np.zeros_like(x), "--", color="gray")
plt.show()

Another extremely helpful argument is distance, which defines the minimum distance between two peaks:

peaks, _ = find_peaks(x, distance=150)
# difference between peaks is >= 150
print(np.diff(peaks))
# prints [186 180 177 171 177 169 167 164 158 162 172]

plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.show()

Answer 4

If your original data is noisy, then using statistical methods is preferable, as not all peaks are going to be significant. For your a array, a possible solution is to use double differentials:

peaks = a[1:-1][np.diff(np.diff(a)) < 0]
# peaks = array([5, 3, 6])