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timhoffmmeeseeksmachine
timhoffm
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Backport PR #26622: [Doc] Improve DSP-related examples
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Lines changed: 55 additions & 47 deletions

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‎galleries/examples/images_contours_and_fields/specgram_demo.py‎

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"""
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================
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Spectrogram Demo
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================
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===========
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Spectrogram
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===========
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6-
Demo of a spectrogram plot (`~.axes.Axes.specgram`).
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Plotting a spectrogram using `~.Axes.specgram`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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np.random.seed(19680801)
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dt = 0.0005
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t = np.arange(0.0, 20.0, dt)
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t = np.arange(0.0, 20.5, dt)
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s1 = np.sin(2 * np.pi * 100 * t)
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s2 = 2 * np.sin(2 * np.pi * 400 * t)
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@@ -24,16 +24,22 @@
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x = s1 + s2 + nse # the signal
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NFFT = 1024 # the length of the windowing segments
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Fs = int(1.0 / dt) # the sampling frequency
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Fs = 1/dt # the sampling frequency
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fig, (ax1, ax2) = plt.subplots(nrows=2)
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fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
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ax1.plot(t, x)
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Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
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ax1.set_ylabel('Signal')
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Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs)
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# The `specgram` method returns 4 objects. They are:
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# - Pxx: the periodogram
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# - freqs: the frequency vector
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# - bins: the centers of the time bins
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# - im: the .image.AxesImage instance representing the data in the plot
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ax2.set_xlabel('Time (s)')
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ax2.set_ylabel('Frequency (Hz)')
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ax2.set_xlim(0, 20)
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plt.show()
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# %%

‎galleries/examples/lines_bars_and_markers/cohere.py‎

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Plotting the coherence of two signals
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=====================================
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6-
An example showing how to plot the coherence of two signals.
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An example showing how to plot the coherence of two signals using `~.Axes.cohere`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
@@ -20,15 +20,14 @@
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s1 = np.sin(2 * np.pi * 10 * t) + nse1
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s2 = np.sin(2 * np.pi * 10 * t) + nse2
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fig, axs = plt.subplots(2, 1)
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fig, axs = plt.subplots(2, 1, layout='constrained')
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axs[0].plot(t, s1, t, s2)
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axs[0].set_xlim(0, 2)
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axs[0].set_xlabel('Time')
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axs[0].set_xlabel('Time (s)')
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axs[0].set_ylabel('s1 and s2')
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axs[0].grid(True)
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cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
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axs[1].set_ylabel('Coherence')
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fig.tight_layout()
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plt.show()

‎galleries/examples/lines_bars_and_markers/csd_demo.py‎

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"""
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========
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CSD Demo
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========
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============================
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Cross spectral density (CSD)
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============================
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Compute the cross spectral density of two signals
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Plot the cross spectral density (CSD) of two signals using `~.Axes.csd`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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fig, (ax1, ax2) = plt.subplots(2, 1)
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# make a little extra space between the subplots
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fig.subplots_adjust(hspace=0.5)
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fig, (ax1, ax2) = plt.subplots(2, 1, layout='constrained')
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dt = 0.01
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t = np.arange(0, 30, dt)
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ax1.plot(t, s1, t, s2)
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ax1.set_xlim(0, 5)
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ax1.set_xlabel('Time')
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ax1.set_xlabel('Time (s)')
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ax1.set_ylabel('s1 and s2')
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ax1.grid(True)
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cxy, f = ax2.csd(s1, s2, 256, 1. / dt)
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ax2.set_ylabel('CSD (dB)')
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plt.show()

‎galleries/examples/lines_bars_and_markers/psd_demo.py‎

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"""
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========
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Psd Demo
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========
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============================
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Power spectral density (PSD)
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============================
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Plotting Power Spectral Density (PSD) in Matplotlib.
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Plotting power spectral density (PSD) using `~.Axes.psd`.
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The PSD is a common plot in the field of signal processing. NumPy has
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many useful libraries for computing a PSD. Below we demo a few examples
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cnse = cnse[:len(t)]
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s = 0.1 * np.sin(2 * np.pi * t) + cnse
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fig, (ax0, ax1) = plt.subplots(2, 1)
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fig, (ax0, ax1) = plt.subplots(2, 1, layout='constrained')
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ax0.plot(t, s)
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ax0.set_xlabel('Time (s)')
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ax0.set_ylabel('Signal')
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ax1.psd(s, 512, 1 / dt)
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plt.show()
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], layout='constrained')
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axs['signal'].plot(t, y)
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axs['signal'].set_xlabel('time [s]')
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axs['signal'].set_ylabel('signal')
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axs['signal'].set_xlabel('Time (s)')
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axs['signal'].set_ylabel('Signal')
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# Plot the PSD with different amounts of zero padding. This uses the entire
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# time series at once

‎galleries/examples/lines_bars_and_markers/spectrum_demo.py‎

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"""
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========================
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Spectrum Representations
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Spectrum representations
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========================
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The plots show different spectrum representations of a sine signal with
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s = 0.1 * np.sin(4 * np.pi * t) + cnse # the signal
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fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(7, 7))
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fig = plt.figure(figsize=(7, 7), layout='constrained')
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axs = fig.subplot_mosaic([["signal", "signal"],
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["magnitude", "log_magnitude"],
30+
["phase", "angle"]])
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# plot time signal:
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axs[0, 0].set_title("Signal")
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axs[0, 0].plot(t, s, color='C0')
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axs[0, 0].set_xlabel("Time")
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axs[0, 0].set_ylabel("Amplitude")
33+
axs["signal"].set_title("Signal")
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axs["signal"].plot(t, s, color='C0')
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axs["signal"].set_xlabel("Time (s)")
36+
axs["signal"].set_ylabel("Amplitude")
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# plot different spectrum types:
36-
axs[1, 0].set_title("Magnitude Spectrum")
37-
axs[1, 0].magnitude_spectrum(s, Fs=Fs, color='C1')
39+
axs["magnitude"].set_title("Magnitude Spectrum")
40+
axs["magnitude"].magnitude_spectrum(s, Fs=Fs, color='C1')
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39-
axs[1, 1].set_title("Log. Magnitude Spectrum")
40-
axs[1, 1].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')
42+
axs["log_magnitude"].set_title("Log. Magnitude Spectrum")
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axs["log_magnitude"].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')
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42-
axs[2, 0].set_title("Phase Spectrum ")
43-
axs[2, 0].phase_spectrum(s, Fs=Fs, color='C2')
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axs["phase"].set_title("Phase Spectrum ")
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axs["phase"].phase_spectrum(s, Fs=Fs, color='C2')
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45-
axs[2, 1].set_title("Angle Spectrum")
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axs[2, 1].angle_spectrum(s, Fs=Fs, color='C2')
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axs["angle"].set_title("Angle Spectrum")
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axs["angle"].angle_spectrum(s, Fs=Fs, color='C2')
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48-
axs[0, 1].remove() # don't display empty ax
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fig.tight_layout()
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plt.show()

‎galleries/examples/lines_bars_and_markers/xcorr_acorr_demo.py‎

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"""
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================================
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Cross- and Auto-Correlation Demo
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================================
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===========================
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Cross- and auto-correlation
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===========================
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Example use of cross-correlation (`~.Axes.xcorr`) and auto-correlation
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(`~.Axes.acorr`) plots.
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fig, [ax1, ax2] = plt.subplots(2, 1, sharex=True)
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ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)
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ax1.grid(True)
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ax1.set_title('Cross-correlation (xcorr)')
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ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2)
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ax2.grid(True)
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ax2.set_title('Auto-correlation (acorr)')
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plt.show()
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