8000 DOC: Update scales overview by timhoffm · Pull Request #29406 · matplotlib/matplotlib · GitHub
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DOC: Update scales overview #29406

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Jan 7, 2025
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DOC: Update scales overview #29406

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2 changes: 2 additions & 0 deletions doc/sphinxext/gallery_order.py
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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
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100 changes: 29 additions & 71 deletions galleries/examples/scales/scales.py
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Expand Up @@ -5,56 +5,42 @@

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

The last two examples are examples of using the ``'function'`` scale by
supplying forward and inverse functions for the scale transformation.
See `matplotlib.scale` for a full list of built-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`
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