Signal Processing Basics
Signal processing is a key area in scientific computing, focusing on the analysis, manipulation, and interpretation of signals—such as audio, electrical, or sensor data. In Python, the scipy.signal submodule provides a comprehensive set of tools for signal processing tasks. These tools include filtering, peak detection, convolution, and more, making it possible to process real-world signals efficiently and accurately.
1234567891011121314151617181920212223242526import numpy as np import matplotlib.pyplot as plt from scipy.signal import butter, filtfilt # Create a time array and a noisy signal t = np.linspace(0, 1.0, 200) clean_signal = np.sin(2 * np.pi * 5 * t) noise = np.random.normal(0, 0.5, t.shape) noisy_signal = clean_signal + noise # Design a low-pass Butterworth filter b, a = butter(N=4, Wn=0.2) # Apply the filter to the noisy signal filtered_signal = filtfilt(b, a, noisy_signal) # Plot the results plt.figure(figsize=(10, 4)) plt.plot(t, noisy_signal, label="Noisy Signal") plt.plot(t, filtered_signal, label="Filtered Signal", linewidth=2) plt.legend() plt.xlabel("Time [s]") plt.ylabel("Amplitude") plt.title("Low-pass Filtering with scipy.signal") plt.tight_layout() plt.show()
123456789101112131415161718192021import numpy as np import matplotlib.pyplot as plt from scipy.signal import find_peaks # Generate a signal with peaks x = np.linspace(0, 6 * np.pi, 200) signal = np.sin(x) + 0.5 * np.random.randn(200) # Find peaks in the signal peaks, _ = find_peaks(signal, height=0) # Plot signal and detected peaks plt.figure(figsize=(10, 4)) plt.plot(x, signal, label="Signal") plt.plot(x[peaks], signal[peaks], "x", label="Peaks", markersize=10) plt.xlabel("x") plt.ylabel("Amplitude") plt.title("Peak Detection with scipy.signal.find_peaks") plt.legend() plt.tight_layout() plt.show()
Filtering is a common operation in signal processing that removes unwanted components, such as noise, from a signal. Using filters like the Butterworth filter, you can isolate the frequencies of interest, enhancing the quality of your data for analysis. Peak detection, on the other hand, helps you identify local maxima within a signal—these correspond to significant events or features. In scientific applications, filtering and peak detection are essential for tasks such as analyzing experimental measurements, detecting anomalies, and extracting useful information from complex data.
1. Which SciPy submodule is used for signal processing?
2. What is the purpose of filtering a signal?
3. How can you detect peaks in a signal using SciPy?
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Signal processing is a key area in scientific computing, focusing on the analysis, manipulation, and interpretation of signals—such as audio, electrical, or sensor data. In Python, the scipy.signal submodule provides a comprehensive set of tools for signal processing tasks. These tools include filtering, peak detection, convolution, and more, making it possible to process real-world signals efficiently and accurately.
1234567891011121314151617181920212223242526import numpy as np import matplotlib.pyplot as plt from scipy.signal import butter, filtfilt # Create a time array and a noisy signal t = np.linspace(0, 1.0, 200) clean_signal = np.sin(2 * np.pi * 5 * t) noise = np.random.normal(0, 0.5, t.shape) noisy_signal = clean_signal + noise # Design a low-pass Butterworth filter b, a = butter(N=4, Wn=0.2) # Apply the filter to the noisy signal filtered_signal = filtfilt(b, a, noisy_signal) # Plot the results plt.figure(figsize=(10, 4)) plt.plot(t, noisy_signal, label="Noisy Signal") plt.plot(t, filtered_signal, label="Filtered Signal", linewidth=2) plt.legend() plt.xlabel("Time [s]") plt.ylabel("Amplitude") plt.title("Low-pass Filtering with scipy.signal") plt.tight_layout() plt.show()
123456789101112131415161718192021import numpy as np import matplotlib.pyplot as plt from scipy.signal import find_peaks # Generate a signal with peaks x = np.linspace(0, 6 * np.pi, 200) signal = np.sin(x) + 0.5 * np.random.randn(200) # Find peaks in the signal peaks, _ = find_peaks(signal, height=0) # Plot signal and detected peaks plt.figure(figsize=(10, 4)) plt.plot(x, signal, label="Signal") plt.plot(x[peaks], signal[peaks], "x", label="Peaks", markersize=10) plt.xlabel("x") plt.ylabel("Amplitude") plt.title("Peak Detection with scipy.signal.find_peaks") plt.legend() plt.tight_layout() plt.show()
Filtering is a common operation in signal processing that removes unwanted components, such as noise, from a signal. Using filters like the Butterworth filter, you can isolate the frequencies of interest, enhancing the quality of your data for analysis. Peak detection, on the other hand, helps you identify local maxima within a signal—these correspond to significant events or features. In scientific applications, filtering and peak detection are essential for tasks such as analyzing experimental measurements, detecting anomalies, and extracting useful information from complex data.
1. Which SciPy submodule is used for signal processing?
2. What is the purpose of filtering a signal?
3. How can you detect peaks in a signal using SciPy?
Grazie per i tuoi commenti!
