Finding local maxima using find_peaks

Question

I am using scipy.signal.find_peaks in order to try and find the maximum values for very fluctuating data. Using the following dataframe:

import pandas as pd
import numpy as np
from scipy.signal import find_peaks
Data = [95,95,95,95,95,95,95,95,94,94,94,94,94,94,94,94,229,444,457,387,280,188,236,181,183,183,185,186,189,190,190,190,179,165,151,151,161,214,213,213,214,213,212,195,179,160,158,155,114,98,164,346,229,39,134,149,194,1,153,171,187,185,104,102,100,90,90,92,92,92,93,93,93,93,93,93,94,94,94,94,94,11,1,11,11,70,182,104,58,60,134,115,99,97,99,98,98,97,97,97,97,97,97,97,97,97,96,96,96,96,96,96,96,96,96,96,96,96,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,94,94,94,94,94,94,94,94,94,94,94,94,94,94,94,93,93,152,206,221,286,326,341,360,377,391,392,393,393,393,394,406,418,420,422,422,408,389,345,329,276,224,166,113,-6,91,91,91,442,324,387,389,387,443,393,393,393,393,391,381,379,377,303,174,131,0,115,112,112,111,111,109,107,106,104,104,103,102,101,101,101,101,100,100,1,1,12,13,65,138,87]
df2 = pd.DataFrame(Data)


#convert to 1D array
number_column = df.loc[:,'Data']
numbers = number_column.values

#finding peaks for 1D array
peaks = find_peaks(numbers, height = 300, threshold = 1, distance = 5)
height = peaks[1]['peak_heights'] #list of heights of peaks
peak_pos = numbers[peaks[0]]
print(peaks)

#plot the peaks
fig = plt.figure()
ax = fig.subplots()
ax.plot(numbers)
ax.scatter(peak_pos, height,color = 'r', s = 25, label = 'Maxima')
ax.legend

I am getting the local extrema of 457, 346, 442, 443. However this is a system where I need to get the following values as the extrema: (457, 346, 422, 443)

When plotting my extrema I am geting this:

So my question is does anyone know how to get the correct extrema I need? I am just missing that 422 value and have been playing around with the settings but have not been successful.

user_5327068 · Answer

You should change the line peak_pos = numbers[peaks[0]] to peak_pos = peaks[0] because peaks[0] gives you the index of the peaks which are the actual x coordinates you want to pass to ax.scatter.

To get the peak at 422, we can set the threshold to None (so that you aren't constraining yourself by vertical distance to the neighbors), and make the distance larger, such as 10.

Then you can add the heights as text annotations:

import pandas as pd
import numpy as np
from scipy.signal import find_peaks
import matplotlib.pyplot as plt

Data = [95,95,95,95,95,95,95,95,94,94,94,94,94,94,94,94,229,444,457,387,280,188,236,181,183,183,185,186,189,190,190,190,179,165,151,151,161,214,213,213,214,213,212,195,179,160,158,155,114,98,164,346,229,39,134,149,194,1,153,171,187,185,104,102,100,90,90,92,92,92,93,93,93,93,93,93,94,94,94,94,94,11,1,11,11,70,182,104,58,60,134,115,99,97,99,98,98,97,97,97,97,97,97,97,97,97,96,96,96,96,96,96,96,96,96,96,96,96,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,94,94,94,94,94,94,94,94,94,94,94,94,94,94,94,93,93,152,206,221,286,326,341,360,377,391,392,393,393,393,394,406,418,420,422,422,408,389,345,329,276,224,166,113,-6,91,91,91,442,324,387,389,387,443,393,393,393,393,391,381,379,377,303,174,131,0,115,112,112,111,111,109,107,106,104,104,103,102,101,101,101,101,100,100,1,1,12,13,65,138,87]
df = pd.DataFrame({'Data':Data})

# convert to 1D array
number_column = df.loc[:,'Data']
numbers = number_column.values

#finding peaks for 1D array
# peaks = find_peaks(numbers, height = 300, threshold = 1, distance = 5)

peaks = find_peaks(numbers, height = 300, threshold = None, distance=10)
height = peaks[1]['peak_heights'] #list of heights of peaks
peak_pos = peaks[0]
print(peaks)

# plot the peaks
fig = plt.figure()
ax = fig.subplots()
ax.plot(numbers)
ax.scatter(peak_pos, height,color = 'r', s = 25, label = 'Maxima')
ax.legend

## add numbers as text annotations
for i, text in enumerate(height):
    if text.is_integer():
        ax.annotate(int(text), (peak_pos[i], height[i]), size=10)
    else:
        ax.annotate(text, (peak_pos[i], height[i]), size=10)
plt.show()

Finding local maxima using find_peaks

1 Answers