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* * Matplotlib Reference](#)\\n\\nMatplotlib provides plotting functions related to spectral analysis and signal processing, commonly used in scientific computing and engineering visualization.\\n\\n## Function Overview\\n\\n| Function | Description |\\n| --- | --- |\\n| acorr() | Plot auto-correlation |\\n| xcorr() | Plot cross-correlation |\\n| psd() | Plot Power Spectral Density |\\n| csd() | Plot Cross Spectral Density |\\n| specgram() | Plot spectrogram / time-frequency diagram |\\n| cohere() | Plot coherence |\\n| angle_spectrum() | Plot angle spectrum |\\n| magnitude_spectrum() | Plot magnitude spectrum |\\n| phase_spectrum() | Plot phase spectrum |\\n\\n## Function Definitions\\n\\n### acorr() / xcorr()\\n\\nmatplotlib.pyplot.acorr(x, *, detrend=<function detrend_none>, maxlags=10, **kwargs) matplotlib.pyplot.xcorr(x, y, *, detrend=<function detrend_none>, maxlags=10, normed=True, **kwargs)\\n### psd() / csd()\\n\\nmatplotlib.pyplot.psd(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, **kwargs) matplotlib.pyplot.csd(x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, **kwargs)\\n### specgram()\\n\\nmatplotlib.pyplot.specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, **kwargs)\\n### angle_spectrum() / magnitude_spectrum() / phase_spectrum()\\n\\nmatplotlib.pyplot.angle_spectrum(x, Fs=None, Fc=None, **kwargs) matplotlib.pyplot.magnitude_spectrum(x, Fs=None, Fc=None, **kwargs) matplotlib.pyplot.phase_spectrum(x, Fs=None, Fc=None, **kwargs)\\n| Common Parameters | Description |\\n| --- | --- |\\n| Fs | Sampling frequency (Hz), default 2 |\\n| NFFT | Number of FFT points, affects frequency resolution |\\n| noverlap | Number of overlapping points in the window |\\n| window | Window function, default hanning window |\\n| detrend | Detrend method: 'none', 'mean', 'linear' |\\n\\n* * *\\n\\n## Usage Examples\\n\\n### Example 1: Auto-correlation and Cross-correlation\\n\\n## Instance\\n\\nimport matplotlib.pyplot as plt\\n\\nimport numpy as np\\n\\nnp.random.seed(42)\\n\\n t = np.linspace(0,10,500)\\n\\n# Noisy Sine Wave\\n\\n sig = np.sin(2 * np.pi * 2 * t) + np.random.randn(500) * 0.3\\n\\nfig,(ax1, ax2)= plt.subplots(1,2, figsize=(12,4),\\n\\n layout='constrained')\\n\\n# Autocorrelation\\n\\n ax1.acorr(sig, maxlags=100, color='steelblue')\\n\\n ax1.set_title('acorr() - Auto-correlation of sin(4πt) + noise')\\n\\n ax1.set_xlabel('Lag')\\n\\n# Cross-correlation (Signal with a Delayed Version of Itself)\\n\\n delayed = np.roll(sig,20)\\n\\n ax2.xcorr(sig, delayed, maxlags=100, color='coral')\\n\\n ax2.set_title('xcorr() - Cross-correlation (lag=20)')\\n\\n ax2.set_xlabel('Lag')\\n\\nplt.show()\\n\\n### Example 2: Power Spectral Density\\n\\n## Instance\\n\\nimport matplotlib.pyplot as plt\\n\\nimport numpy as np\\n\\n# generategenerateSampling Rate 100Hz thesignal(10Hz + 25Hz sine wave)\\n\\n Fs =100# Sampling Rate\\n\\n t = np.arange(0,5,1/Fs)\\n\\n sig = np.sin(2*np.pi*10*t) + 0.5*np.sin(2*np.pi*25*t)\\n\\n + np.random.randn(len(t))*0.5\\n\\nfig,(ax1, ax2)= plt.subplots(1,2, figsize=(12,4),\\n\\n layout='constrained')\\n\\n# PSD\\n\\n ax1.psd(sig, NFFT=256, Fs=Fs, color='steelblue')\\n\\n ax1.set_title('psd() - Power Spectral Density')\\n\\n# Spectrogram\\n\\n ax2.specgram(sig, NFFT=128, Fs=Fs, noverlap=64,\\n\\ncmap='viridis')\\n\\n ax2.set_title('specgram() - Spectrogram')\\n\\n ax2.set_xlabel('Time (s)')\\n\\n ax2.set_ylabel('Frequency (Hz)')\\n\\nplt.show()\\n\\n### Example 3: Magnitude Spectrum and Phase Spectrum\\n\\n## Instance\\n\\nimport matplotlib.pyplot as plt\\n\\nimport numpy as np\\n\\nFs =200\\n\\n t = np.arange(0,2,1/Fs)\\n\\n sig = np.sin(2*np.pi*20*t) + 0.5*np.sin(2*np.pi*50*t)\\n\\nfig, axes = plt.subplots(2,2, figsize=(10,8),\\n\\n layout='constrained')\\n\\n# Original Signal\\n\\n axes[0,0].plot(t[:100], sig[:100])\\n\\n axes[0,0].set_title('Original Signal')\\n\\n axes[0,0].set_xlabel('Time (s)')\\n\\n# Magnitude Spectrum\\n\\n axes[0,1].magnitude_spectrum(sig, Fs=Fs, color='steelblue')\\n\\n axes[0,1].set_title('magnitude_spectrum()')\\n\\n# Angle Spectrum\\n\\n axes[1,0].angle_spectrum(sig, Fs=Fs, color='coral')\\n\\n axes[1,0].set_title('angle_spectrum()')\\n\\n# Phase Spectrum\\n\\n axes[1,1].phase_spectrum(sig, Fs=Fs, color='green')\\n\\n axes[1,1].set_title('phase_spectrum()')\\n\\nplt.show()\\n\\nprint("tutorial: spectrum analysis displayed")\\n\\n[ Matplotlib Reference](#)