detect highest peaks automatically from noisy data python

Question

Is there any way to detect the highest peaks using a python library without setting any parameter?. I'm developing a user interface and I want the algorithm to be able to detect highest peaks automatically...

I want it to be able to detect these peaks in picture below: graph here

Data looks like this:

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Answer 1

The task you are describing could be treated like anomaly/outlier detection.

One possible solution is to use a Z-score transformation and treat every value with a z score above a certain threshold as an outlier. Because there is no clear definition of an outlier it won't be able to detect such peaks without setting any parameters (threshold).

One possible solution could be:

import numpy as np

def detect_outliers(data):

    outliers = []
    d_mean = np.mean(data)
    d_std = np.std(data)
    threshold = 3 # this defines what you would consider a peak (outlier)

    for point in data:
        z_score = (point - d_mean)/d_std
        if np.abs(z_score) > threshold:
            outliers.append(point)
    return outliers

# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))

If you only want to detect peaks, remove the np.abs function call from the code.

This code snippet is based on a Medium Post, which also provides another way of detecting outliers.