How to Find Significant Peaks in Python Using SciPy\'s find_peaks Function?

Finding Peaks in Python/SciPy

Finding peaks in data is a common task in various fields, including signal processing, image analysis, and data analysis. Python provides several packages and functions for peak detection, including SciPy's scipy.signal.find_peaks function.

SciPy's Peak-Finding Algorithm

The find_peaks function takes a 1D array as input and returns the indices of the peaks. It employs a peak-finding algorithm that detects peaks based on several parameters:

• width: Minimum separation between peaks in samples.
• threshold: Minimum amplitude threshold for peak detection.
• distance: Minimum distance between consecutive peaks.
• prominence: Topographic prominence, which measures the relative height of a peak compared to its surroundings.

Prominence for Noise Rejection

The prominence parameter is particularly useful for distinguishing significant peaks from noise-induced peaks. Prominence is defined as the minimum height descent to get from the peak to any higher terrain. By setting a high prominence threshold, the algorithm can effectively filter out minor peaks caused by noise.

Example Usage

The following code demonstrates peak-finding in a noisy frequency-varying sinusoid using the find_peaks function:

<code class="python">import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import find_peaks

x = np.sin(2*np.pi*(2**np.linspace(2,10,1000))*np.arange(1000)/48000) + np.random.normal(0, 1, 1000) * 0.15
peaks_prominence, _ = find_peaks(x, prominence=1)

plt.plot(x)
plt.plot(peaks_prominence, x[peaks_prominence], "ob")
plt.legend(['Signal', 'Peaks (prominence)'])
plt.show()</code>

As demonstrated in the plot, the find_peaks function finds peaks with both high amplitude and prominence, effectively filtering out noise-induced peaks.

Other Peak-Finding Options

In addition to find_peaks, SciPy also provides other peak-finding functionality, such as peak_widths and argrelmax. These functions may be more suitable for specific applications or adjustments.

Conclusion

SciPy's scipy.signal.find_peaks function provides a robust and versatile solution for peak-finding in Python. Its adjustable parameters, including prominence, allow for customization to detect significant peaks in various types of data.

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