Mar 12, 2025 · Dec 14, 2024 · Dec 14, 2024 · Dec 14, 2024 · Dec 14, 2024 · Dec 14, 2024
diff --git a/galleries/examples/scales/log_demo.py b/galleries/examples/scales/log_demo.py
 """
 ========
 LogDemo
 ========
 =========
 Logscale
 =========

 Examples of plots with logarithmic axes.

 You can set the x/y axes to be logarithmic by passing "log" to `~.Axes.set_xscale` /
 `~.Axes.set_yscale`.

 Convenience functions ``semilogx``, ``semilogy``, and ``loglog``
 ----------------------------------------------------------------
 Since plotting data on semi-logarithmic or double-logarithmic scales is very common,
 the functions `~.Axes.semilogx`, `~.Axes.semilogy`, and `~.Axes.loglog` are shortcuts
 for setting the scale and plotting data; e.g. ``ax.semilogx(x, y)`` is equivalent to
 ``ax.set_xscale('log'); ax.plot(x, y)``.
 """

 import matplotlib.pyplot as plt
 import numpy as np

 # Data for plotting
 t = np.arange(0.01, 20.0, 0.01)

 # Create figure
 fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

 # log y axis
 ax1.semilogy(t, np.exp(-t / 5.0))
 ax1.set(title='semilogy')
 fig, (ax1, ax2, ax3) = plt.subplots(1, 3, layout='constrained', figsize=(7, 7/3))
 # log x axis
 t = np.arange(0.01, 10.0, 0.01)
 ax1.semilogx(t, np.sin(2 * np.pi * t))
 ax1.set(title='semilogx')
 ax1.grid()
 ax1.grid(which="minor", color="0.9")

 # log x axis
 ax2.semilogx(t, np.sin(2 * np.pi * t))
 ax2.set(title='semilogx')
 # log y axis
 x = np.arange(4)
 ax2.semilogy(4*x, 10**x, 'o--')
 ax2.set(title='semilogy')
 ax2.grid()
 ax2.grid(which="minor", color="0.9")

 # log x and y axis
 ax3.loglog(t, 20 *np.exp(-t / 10.0))
 ax3.set_xscale('log', base=2)
 ax3.set(title='loglog base 2 on x')
 x =np.array([1, 10, 100, 1000])
 ax3.loglog(x, 5 * x, 'o--')
 ax3.set(title='loglog')
 ax3.grid()
 ax3.grid(which="minor", color="0.9")

 # %%
 # Logarithms with other bases
 # ---------------------------
 # By default, the log scale is to the base 10. One can change this via the *base*
 # parameter.
 fig, ax = plt.subplots()
 ax.bar(["L1 cache", "L2 cache", "L3 cache", "RAM", "SSD"],
       [32, 1_000, 32_000, 16_000_000, 512_000_000])
 ax.set_yscale('log', base=2)
 ax.set_yticks([1, 2**10, 2**20, 2**30], labels=['kB', 'MB', 'GB', 'TB'])
 ax.set_title("Typical memory sizes")
 ax.yaxis.grid()

 # %%
 # Dealing with negative values
 # ----------------------------
 # Non-positive values cannot be displayed on a log scale. The scale has two options
 # to handle these. Either mask the values so that they are ignored, or clip them
 # to a small positive value. Which one is more suited depends on the type of the
 # data and the visualization.
 #
 # The following example contains errorbars going negative. If we mask these values,
 # the bar vanishes, which is not desirable. In contrast, clipping makes the value
 # small positive (but well below the used scale) so that the error bar is drawn
 # to the edge of the Axes.
 x = np.linspace(0.0, 2.0, 10)
 y = 10**x
 yerr = 1.75 + 0.75*y

 # With errorbars: clip non-positive values
 # Use new data for plotting
 x = 10.0**np.linspace(0.0, 2.0, 20)
 y = x**2.0
 fig, (ax1, ax2) = plt.subplots(1, 2, layout="constrained", figsize=(6, 3))
 fig.suptitle("errorbars going negative")
 ax1.set_yscale("log", nonpositive='mask')
 ax1.set_title('nonpositive="mask"')
 ax1.errorbar(x, y, yerr=yerr, fmt='o', capsize=5)

 ax4.set_xscale("log", nonpositive='clip')
 ax4.set_yscale("log", nonpositive='clip')
 ax4.set(title='Errorbars go negative')
 ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
 # ylim must be set after errorbar to allow errorbar to autoscale limits
 ax4.set_ylim(bottom=0.1)
 ax2.set_yscale("log", nonpositive='clip')
 ax2.set_title('nonpositive="clip"')
 ax2.errorbar(x, y, yerr=yerr, fmt='o', capsize=5)

 fig.tight_layout()
 plt.show()
diff --git a/galleries/examples/scales/semilogx_demo.py b/galleries/examples/scales/semilogx_demo.py
Original file line number	Diff line number	Diff line change
		@@ -1,47 +1,83 @@
		"""
		========
		LogDemo
		========
		=========
		Logscale
		=========

		Examples of plots with logarithmic axes.

		You can set the x/y axes to be logarithmic by passing "log" to `~.Axes.set_xscale` /
		`~.Axes.set_yscale`.

		Convenience functions ``semilogx``, ``semilogy``, and ``loglog``
		----------------------------------------------------------------
		Since plotting data on semi-logarithmic or double-logarithmic scales is very common,
		the functions `~.Axes.semilogx`, `~.Axes.semilogy`, and `~.Axes.loglog` are shortcuts
		for setting the scale and plotting data; e.g. ``ax.semilogx(x, y)`` is equivalent to
		``ax.set_xscale('log'); ax.plot(x, y)``.
		"""

		import matplotlib.pyplot as plt
		import numpy as np

		# Data for plotting
		t = np.arange(0.01, 20.0, 0.01)

		# Create figure
		fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

		# log y axis
		ax1.semilogy(t, np.exp(-t / 5.0))
		ax1.set(title='semilogy')
		fig, (ax1, ax2, ax3) = plt.subplots(1, 3, layout='constrained', figsize=(7, 7/3))
		# log x axis
		t = np.arange(0.01, 10.0, 0.01)
		ax1.semilogx(t, np.sin(2 * np.pi * t))
		ax1.set(title='semilogx')
		ax1.grid()
		ax1.grid(which="minor", color="0.9")

		# log x axis
		ax2.semilogx(t, np.sin(2 * np.pi * t))
		ax2.set(title='semilogx')
		# log y axis
		x = np.arange(4)
		ax2.semilogy(4x, 10*x, 'o--')
		ax2.set(title='semilogy')
		ax2.grid()
		ax2.grid(which="minor", color="0.9")

		# log x and y axis
		ax3.loglog(t, 20 *np.exp(-t / 10.0))
		ax3.set_xscale('log', base=2)
		ax3.set(title='loglog base 2 on x')
		x =np.array([1, 10, 100, 1000])
		ax3.loglog(x, 5 * x, 'o--')
		ax3.set(title='loglog')
		ax3.grid()
		ax3.grid(which="minor", color="0.9")

		# %%
		# Logarithms with other bases
		# ---------------------------
		# By default, the log scale is to the base 10. One can change this via the base
		# parameter.
		fig, ax = plt.subplots()
		ax.bar(["L1 cache", "L2 cache", "L3 cache", "RAM", "SSD"],
		[32, 1_000, 32_000, 16_000_000, 512_000_000])
		ax.set_yscale('log', base=2)
		ax.set_yticks([1, 210, 220, 2**30], labels=['kB', 'MB', 'GB', 'TB'])
		ax.set_title("Typical memory sizes")
		ax.yaxis.grid()

		# %%
		# Dealing with negative values
		# ----------------------------
		# Non-positive values cannot be displayed on a log scale. The scale has two options
		# to handle these. Either mask the values so that they are ignored, or clip them
		# to a small positive value. Which one is more suited depends on the type of the
		# data and the visualization.
		#
		# The following example contains errorbars going negative. If we mask these values,
		# the bar vanishes, which is not desirable. In contrast, clipping makes the value
		# small positive (but well below the used scale) so that the error bar is drawn
		# to the edge of the Axes.
		x = np.linspace(0.0, 2.0, 10)
		y = 10**x
		yerr = 1.75 + 0.75*y

		# With errorbars: clip non-positive values
		# Use new data for plotting
		x = 10.0**np.linspace(0.0, 2.0, 20)
		y = x**2.0
		fig, (ax1, ax2) = plt.subplots(1, 2, layout="constrained", figsize=(6, 3))
		fig.suptitle("errorbars going negative")
		ax1.set_yscale("log", nonpositive='mask')
		ax1.set_title('nonpositive="mask"')
		ax1.errorbar(x, y, yerr=yerr, fmt='o', capsize=5)

		ax4.set_xscale("log", nonpositive='clip')
		ax4.set_yscale("log", nonpositive='clip')
		ax4.set(title='Errorbars go negative')
		ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
		# ylim must be set after errorbar to allow errorbar to autoscale limits
		ax4.set_ylim(bottom=0.1)
		ax2.set_yscale("log", nonpositive='clip')
		ax2.set_title('nonpositive="clip"')
		ax2.errorbar(x, y, yerr=yerr, fmt='o', capsize=5)

		fig.tight_layout()
		plt.show()