matplotlib
diff --git a/‎galleries/examples/images_contours_and_fields/specgram_demo.py
Lines changed: 14 additions & 8 deletions b/‎galleries/examples/images_contours_and_fields/specgram_demo.py
Lines changed: 14 additions & 8 deletions
diff --git a/‎galleries/examples/lines_bars_and_markers/cohere.py
Lines changed: 3 additions & 4 deletions b/‎galleries/examples/lines_bars_and_markers/cohere.py
Lines changed: 3 additions & 4 deletions
diff --git a/‎galleries/examples/lines_bars_and_markers/csd_demo.py
Lines changed: 7 additions & 8 deletions b/‎galleries/examples/lines_bars_and_markers/csd_demo.py
Lines changed: 7 additions & 8 deletions
diff --git a/‎galleries/examples/lines_bars_and_markers/psd_demo.py
Lines changed: 9 additions & 7 deletions b/‎galleries/examples/lines_bars_and_markers/psd_demo.py
Lines changed: 9 additions & 7 deletions
diff --git a/‎galleries/examples/lines_bars_and_markers/spectrum_demo.py
Lines changed: 17 additions & 17 deletions b/‎galleries/examples/lines_bars_and_markers/spectrum_demo.py
Lines changed: 17 additions & 17 deletions
diff --git a/‎galleries/examples/lines_bars_and_markers/xcorr_acorr_demo.py
Lines changed: 5 additions & 3 deletions b/‎galleries/examples/lines_bars_and_markers/xcorr_acorr_demo.py
Lines changed: 5 additions & 3 deletions
@@ -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
@@ -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)
 
@@ -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()
 
 # %%
 
@@ -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
@@ -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()
@@ -1,16 +1,14 @@
 """
-========
-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)
@@ -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()
@@ -1,9 +1,9 @@
 """
-========
-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
@@ -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()
@@ -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
 
@@ -1,6 +1,6 @@
 """
 ========================
-Spectrum Representations
+Spectrum representations
 ========================
 
 The plots show different spectrum representations of a sine signal with
@@ -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()
@@ -1,7 +1,7 @@
 """
-================================
-Cross- and Auto-Correlation Demo
-================================
+===========================
+Cross- and auto-correlation
+===========================
 
 Example use of cross-correlation (`~.Axes.xcorr`) and auto-correlation
 (`~.Axes.acorr`) plots.
@@ -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()