Jan 7, 2025 · Jan 7, 2025
diff --git a/doc/sphinxext/gallery_order.py b/doc/sphinxext/gallery_order.py
    "color_demo",
    #  pies
    "pie_features", "pie_demo2",
    # scales
    "scales",  # Scales overview

    # **Plot Types
    # Basic
diff --git a/galleries/examples/scales/scales.py b/galleries/examples/scales/scales.py

 Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.

 The last two examples are examples ofusing the ``'function'`` scale by
 supplying forward and inverse functions for the scale transformation.
 See `matplotlib.scale` for a full list ofbuilt-in scales, and
 :doc:`/gallery/scales/custom_scale` for how to create your own scale.
 """

 import matplotlib.pyplot as plt
 import numpy as np

 from matplotlib.ticker import FixedLocator, NullFormatter
 x = np.arange(400)
 y = np.linspace(0.002, 1, 400)

 # Fixing random state for reproducibility
 np.random.seed(19680801)

 # make up some data in the interval ]0, 1[
 y = np.random.normal(loc=0.5, scale=0.4, size=1000)
 y = y[(y > 0) & (y < 1)]
 y.sort()
 x = np.arange(len(y))

 # plot with various axes scales
 fig, axs = plt.subplots(3, 2, figsize=(6, 8), layout='constrained')

 # linear
 ax = axs[0, 0]
 ax.plot(x, y)
 ax.set_yscale('linear')
 ax.set_title('linear')
 ax.grid(True)

 axs[0, 0].plot(x, y)
 axs[0, 0].set_yscale('linear')
 axs[0, 0].set_title('linear')
 axs[0, 0].grid(True)

 # log
 ax = axs[0, 1]
 ax.plot(x, y)
 ax.set_yscale('log')
 ax.set_title('log')
 ax.grid(True)
 axs[0, 1].plot(x, y)
 axs[0, 1].set_yscale('log')
 axs[0, 1].set_title('log')
 axs[0, 1].grid(True)

 axs[1, 0].plot(x, y - y.mean())
 axs[1, 0].set_yscale('symlog', linthresh=0.02)
 axs[1, 0].set_title('symlog')
 axs[1, 0].grid(True)

 # symmetric log
 ax = axs[1, 1]
 ax.plot(x, y - y.mean())
 ax.set_yscale('symlog', linthresh=0.02)
 ax.set_title('symlog')
 ax.grid(True)
 axs[1, 1].plot(x, y)
 axs[1, 1].set_yscale('logit')
 axs[1, 1].set_title('logit')
 axs[1, 1].grid(True)

 # logit
 ax = axs[1, 0]
 ax.plot(x, y)
 ax.set_yscale('logit')
 ax.set_title('logit')
 ax.grid(True)
 axs[2, 0].plot(x, y - y.mean())
 axs[2, 0].set_yscale('asinh', linear_width=0.01)
 axs[2, 0].set_title('asinh')
 axs[2, 0].grid(True)


 # Function x**(1/2)
    return x**2


 ax = axs[2, 0]
 ax.plot(x, y)
 ax.set_yscale('function', functions=(forward, inverse))
 ax.set_title('function: $x^{1/2}$')
 ax.grid(True)
 ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)**2))
 ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)))


 # Function Mercator transform
 def forward(a):
    a = np.deg2rad(a)
    return np.rad2deg(np.log(np.abs(np.tan(a) + 1.0 / np.cos(a))))


 def inverse(a):
    a = np.deg2rad(a)
    return np.rad2deg(np.arctan(np.sinh(a)))

 ax = axs[2, 1]

 t = np.arange(0, 170.0, 0.1)
 s = t / 2.

 ax.plot(t, s, '-', lw=2)

 ax.set_yscale('function', functions=(forward, inverse))
 ax.set_title('function: Mercator')
 ax.grid(True)
 ax.set_xlim([0, 180])
 ax.yaxis.set_minor_formatter(NullFormatter())
 ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 90, 10)))
 axs[2, 1].plot(x, y)
 axs[2, 1].set_yscale('function', functions=(forward, inverse))
 axs[2, 1].set_title('function: $x^{1/2}$')
 axs[2, 1].grid(True)
 axs[2, 1].set_yticks(np.arange(0, 1.2, 0.2))

 plt.show()

 #
 #    - `matplotlib.axes.Axes.set_xscale`
 #    - `matplotlib.axes.Axes.set_yscale`
 #    - `matplotlib.axis.Axis.set_major_locator`
 #    - `matplotlib.scale.LinearScale`
 #    - `matplotlib.scale.LogScale`
 #    - `matplotlib.scale.SymmetricalLogScale`
Original file line number	Diff line number	Diff line change
Expand Up		@@ -86,6 +86,8 @@ def __call__(self, item):
		"color_demo",
		# pies
		"pie_features", "pie_demo2",
		# scales
		"scales", # Scales overview

		# **Plot Types
		# Basic
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -5,56 +5,42 @@

		Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.

		The last two examples are examples ofusing the ``'function'`` scale by
		supplying forward and inverse functions for the scale transformation.
		See `matplotlib.scale` for a full list ofbuilt-in scales, and
		:doc:`/gallery/scales/custom_scale` for how to create your own scale.
		"""

		import matplotlib.pyplot as plt
		import numpy as np

		from matplotlib.ticker import FixedLocator, NullFormatter
		x = np.arange(400)
		y = np.linspace(0.002, 1, 400)

		# Fixing random state for reproducibility
		np.random.seed(19680801)

		# make up some data in the interval ]0, 1[
		y = np.random.normal(loc=0.5, scale=0.4, size=1000)
		y = y[(y > 0) & (y < 1)]
		y.sort()
		x = np.arange(len(y))

		# plot with various axes scales
		fig, axs = plt.subplots(3, 2, figsize=(6, 8), layout='constrained')

		# linear
		ax = axs[0, 0]
		ax.plot(x, y)
		ax.set_yscale('linear')
		ax.set_title('linear')
		ax.grid(True)

		axs[0, 0].plot(x, y)
		axs[0, 0].set_yscale('linear')
		axs[0, 0].set_title('linear')
		axs[0, 0].grid(True)

		# log
		ax = axs[0, 1]
		ax.plot(x, y)
		ax.set_yscale('log')
		ax.set_title('log')
		ax.grid(True)
		axs[0, 1].plot(x, y)
		axs[0, 1].set_yscale('log')
		axs[0, 1].set_title('log')
		axs[0, 1].grid(True)

		axs[1, 0].plot(x, y - y.mean())
		axs[1, 0].set_yscale('symlog', linthresh=0.02)
		axs[1, 0].set_title('symlog')
		axs[1, 0].grid(True)

		# symmetric log
		ax = axs[1, 1]
		ax.plot(x, y - y.mean())
		ax.set_yscale('symlog', linthresh=0.02)
		ax.set_title('symlog')
		ax.grid(True)
		axs[1, 1].plot(x, y)
		axs[1, 1].set_yscale('logit')
		axs[1, 1].set_title('logit')
		axs[1, 1].grid(True)

		# logit
		ax = axs[1, 0]
		ax.plot(x, y)
		ax.set_yscale('logit')
		ax.set_title('logit')
		ax.grid(True)
		axs[2, 0].plot(x, y - y.mean())
		axs[2, 0].set_yscale('asinh', linear_width=0.01)
		axs[2, 0].set_title('asinh')
		axs[2, 0].grid(True)


		# Function x**(1/2)
Expand All		@@ -66,38 +52,11 @@ def inverse(x):
		return x**2


		ax = axs[2, 0]
		ax.plot(x, y)
		ax.set_yscale('function', functions=(forward, inverse))
		ax.set_title('function: $x^{1/2}$')
		ax.grid(True)
		ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)**2))
		ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)))


		# Function Mercator transform
		def forward(a):
		a = np.deg2rad(a)
		return np.rad2deg(np.log(np.abs(np.tan(a) + 1.0 / np.cos(a))))


		def inverse(a):
		a = np.deg2rad(a)
		return np.rad2deg(np.arctan(np.sinh(a)))

		ax = axs[2, 1]

		t = np.arange(0, 170.0, 0.1)
		s = t / 2.

		ax.plot(t, s, '-', lw=2)

		ax.set_yscale('function', functions=(forward, inverse))
		ax.set_title('function: Mercator')
		ax.grid(True)
		ax.set_xlim([0, 180])
		ax.yaxis.set_minor_formatter(NullFormatter())
		ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 90, 10)))
		axs[2, 1].plot(x, y)
		axs[2, 1].set_yscale('function', functions=(forward, inverse))
		axs[2, 1].set_title('function: $x^{1/2}$')
		axs[2, 1].grid(True)
		axs[2, 1].set_yticks(np.arange(0, 1.2, 0.2))

		plt.show()

Expand All		@@ -110,7 +69,6 @@ def inverse(a):
		#
		# - `matplotlib.axes.Axes.set_xscale`
		# - `matplotlib.axes.Axes.set_yscale`
		# - `matplotlib.axis.Axis.set_major_locator`
		# - `matplotlib.scale.LinearScale`
		# - `matplotlib.scale.LogScale`
		# - `matplotlib.scale.SymmetricalLogScale`
Expand Down