Peak-finding Algorithm for Python/SciPy
Finding peaks in data is a common task in signal processing and analysis. While it is possible to implement a peak-finding algorithm manually, it is often more convenient to use an existing library function.
One such function is scipy.signal.find_peaks. This function takes a signal as input and returns the indices of the peaks. It can be used for both 1D and 2D signals.
find_peaks has a number of parameters that control its behavior. These parameters include:
In addition to these parameters, find_peaks also has a number of advanced parameters, such as height and prominence. These parameters can be used to fine-tune the peak-finding algorithm for specific applications.
To use find_peaks, simply call the function with the signal as the first argument. The function will return a tuple containing the indices of the peaks and a dictionary containing the values of the advanced parameters.
Here is an example of how to use find_peaks to find peaks in a 1D signal:
<code class="python">import numpy as np from scipy.signal import find_peaks x = np.sin(2*np.pi*100*np.arange(1000)/1000) peaks, _ = find_peaks(x) plt.plot(x) plt.plot(peaks, x[peaks], "xr") plt.show()</code>
This code will plot the signal and the detected peaks. As you can see, the find_peaks function is able to accurately identify the peaks in the signal.
find_peaks is a versatile and powerful peak-finding algorithm that can be used for a wide range of applications. It is easy to use and provides a number of advanced parameters for fine-tuning the peak-finding process.
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