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Aug 29, 2023
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[Doc] Improve DSP-related examples #26622

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22 changes: 14 additions & 8 deletions galleries/examples/images_contours_and_fields/specgram_demo.py
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@@ -1,9 +1,9 @@
"""
================
Spectrogram Demo
================
===========
Spectrogram
===========

Demo of a spectrogram plot (`~.axes.Axes.specgram`).
Plotting a spectrogram using `~.Axes.specgram`.
"""
import matplotlib.pyplot as plt
import numpy as np
Expand All @@ -12,7 +12,7 @@
np.random.seed(19680801)

dt = 0.0005
t = np.arange(0.0, 20.0, dt)
t = np.arange(0.0, 20.5, dt)
s1 = np.sin(2 * np.pi * 100 * t)
s2 = 2 * np.sin(2 * np.pi * 400 * t)

Expand All @@ -24,16 +24,22 @@

x = s1 + s2 + nse # the signal
NFFT = 1024 # the length of the windowing segments
Fs = int(1.0 / dt) # the sampling frequency
Fs = 1/dt # the sampling frequency

fig, (ax1, ax2) = plt.subplots(nrows=2)
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
ax1.plot(t, x)
Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
ax1.set_ylabel('Signal')

Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs)
# The `specgram` method returns 4 objects. They are:
# - Pxx: the periodogram
# - freqs: the frequency vector
# - bins: the centers of the time bins
# - im: the .image.AxesImage instance representing the data in the plot
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('Frequency (Hz)')
ax2.set_xlim(0, 20)

plt.show()

# %%
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7 changes: 3 additions & 4 deletions galleries/examples/lines_bars_and_markers/cohere.py
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Expand Up @@ -3,7 +3,7 @@
Plotting the coherence of two signals
=====================================

An example showing how to plot the coherence of two signals.
An example showing how to plot the coherence of two signals using `~.Axes.cohere`.
"""
import matplotlib.pyplot as plt
import numpy as np
Expand All @@ -20,15 +20,14 @@
s1 = np.sin(2 * np.pi * 10 * t) + nse1
s2 = np.sin(2 * np.pi * 10 * t) + nse2

fig, axs = plt.subplots(2, 1)
fig, axs = plt.subplots(2, 1, layout='constrained')
axs[0].plot(t, s1, t, s2)
axs[0].set_xlim(0, 2)
axs[0].set_xlabel('Time')
axs[0].set_xlabel('Time (s)')
axs[0].set_ylabel('s1 and s2')
axs[0].grid(True)

cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
axs[1].set_ylabel('Coherence')

fig.tight_layout()
plt.show()
15 changes: 7 additions & 8 deletions galleries/examples/lines_bars_and_markers/csd_demo.py
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"""
========
CSD Demo
========
============================
Cross spectral density (CSD)
============================

Compute the cross spectral density of two signals
Plot the cross spectral density (CSD) of two signals using `~.Axes.csd`.
"""
import matplotlib.pyplot as plt
import numpy as np

fig, (ax1, ax2) = plt.subplots(2, 1)
# make a little extra space between the subplots
fig.subplots_adjust(hspace=0.5)
fig, (ax1, ax2) = plt.subplots(2, 1, layout='constrained')

dt = 0.01
t = np.arange(0, 30, dt)
Expand All @@ -32,10 +30,11 @@

ax1.plot(t, s1, t, s2)
ax1.set_xlim(0, 5)
ax1.set_xlabel('Time')
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('s1 and s2')
ax1.grid(True)

cxy, f = ax2.csd(s1, s2, 256, 1. / dt)
ax2.set_ylabel('CSD (dB)')

plt.show()
16 changes: 9 additions & 7 deletions galleries/examples/lines_bars_and_markers/psd_demo.py
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"""
========
Psd Demo
========
============================
Power spectral density (PSD)
============================

Plotting Power Spectral Density (PSD) in Matplotlib.
Plotting power spectral density (PSD) using `~.Axes.psd`.

The PSD is a common plot in the field of signal processing. NumPy has
many useful libraries for computing a PSD. Below we demo a few examples
Expand All @@ -26,8 +26,10 @@
cnse = cnse[:len(t)]
s = 0.1 * np.sin(2 * np.pi * t) + cnse

fig, (ax0, ax1) = plt.subplots(2, 1)
fig, (ax0, ax1) = plt.subplots(2, 1, layout='constrained')
ax0.plot(t, s)
ax0.set_xlabel('Time (s)')
ax0.set_ylabel('Signal')
ax1.psd(s, 512, 1 / dt)

plt.show()
Expand Down Expand Up @@ -64,8 +66,8 @@
], layout='constrained')

axs['signal'].plot(t, y)
axs['signal'].set_xlabel('time [s]')
axs['signal'].set_ylabel('signal')
axs['signal'].set_xlabel('Time (s)')
axs['signal'].set_ylabel('Signal')

# Plot the PSD with different amounts of zero padding. This uses the entire
# time series at once
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34 changes: 17 additions & 17 deletions galleries/examples/lines_bars_and_markers/spectrum_demo.py
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"""
========================
Spectrum Representations
Spectrum representations
========================

The plots show different spectrum representations of a sine signal with
Expand All @@ -24,28 +24,28 @@

s = 0.1 * np.sin(4 * np.pi * t) + cnse # the signal

fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(7, 7))
fig = plt.figure(figsize=(7, 7), layout='constrained')
axs = fig.subplot_mosaic([["signal", "signal"],
["magnitude", "log_magnitude"],
["phase", "angle"]])

# plot time signal:
axs[0, 0].set_title("Signal")
axs[0, 0].plot(t, s, color='C0')
axs[0, 0].set_xlabel("Time")
axs[0, 0].set_ylabel("Amplitude")
axs["signal"].set_title("Signal")
axs["signal"].plot(t, s, color='C0')
axs["signal"].set_xlabel("Time (s)")
axs["signal"].set_ylabel("Amplitude")

# plot different spectrum types:
axs[1, 0].set_title("Magnitude Spectrum")
axs[1, 0].magnitude_spectrum(s, Fs=Fs, color='C1')
axs["magnitude"].set_title("Magnitude Spectrum")
axs["magnitude"].magnitude_spectrum(s, Fs=Fs, color='C1')

axs[1, 1].set_title("Log. Magnitude Spectrum")
axs[1, 1].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')
axs["log_magnitude"].set_title("Log. Magnitude Spectrum")
axs["log_magnitude"].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')

axs[2, 0].set_title("Phase Spectrum ")
axs[2, 0].phase_spectrum(s, Fs=Fs, color='C2')
axs["phase"].set_title("Phase Spectrum ")
axs["phase"].phase_spectrum(s, Fs=Fs, color='C2')

axs[2, 1].set_title("Angle Spectrum")
axs[2, 1].angle_spectrum(s, Fs=Fs, color='C2')
axs["angle"].set_title("Angle Spectrum")
axs["angle"].angle_spectrum(s, Fs=Fs, color='C2')

axs[0, 1].remove() # don't display empty ax

fig.tight_layout()
plt.show()
8 changes: 5 additions & 3 deletions galleries/examples/lines_bars_and_markers/xcorr_acorr_demo.py
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"""
================================
Cross- and Auto-Correlation Demo
================================
===========================
Cross- and auto-correlation
===========================

Example use of cross-correlation (`~.Axes.xcorr`) and auto-correlation
(`~.Axes.acorr`) plots.
Expand All @@ -17,9 +17,11 @@
fig, [ax1, ax2] = plt.subplots(2, 1, sharex=True)
ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)
ax1.grid(True)
ax1.set_title('Cross-correlation (xcorr)')

ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2)
ax2.grid(True)
ax2.set_title('Auto-correlation (acorr)')

plt.show()

Expand Down