Scales#

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.

import matplotlib.pyplot as plt
import numpy as np

from matplotlib.ticker import FixedLocator, NullFormatter

# 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)


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

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


# Function x**(1/2)
def forward(x):
    return x**(1/2)


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)))

plt.show()
linear, log, logit, symlog, function: $x^{1/2}$, function: Mercator

Total running time of the script: (0 minutes 1.963 seconds)

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