"""

=============

Pyplot Scales

=============

Create plots on different scales. Here a linear, a logarithmic, a symmetric

logarithmic and a logit scale are shown. For further examples also see the

:ref:`scales_examples` section of the gallery.

"""

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import NullFormatter # useful for `logit` scale

# 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

plt.figure()

# linear

plt.subplot(221)

plt.plot(x, y)

plt.yscale('linear')

plt.title('linear')

plt.grid(True)

# log

plt.subplot(222)

plt.plot(x, y)

plt.yscale('log')

plt.title('log')

plt.grid(True)

# symmetric log

plt.subplot(223)

plt.plot(x, y - y.mean())

plt.yscale('symlog', linthreshy=0.01)

plt.title('symlog')

plt.grid(True)

# logit

plt.subplot(224)

plt.plot(x, y)

plt.yscale('logit')

plt.title('logit')

plt.grid(True)

# Format the minor tick labels of the y-axis into empty strings with

# `NullFormatter`, to avoid cumbering the axis with too many labels.

plt.gca().yaxis.set_minor_formatter(NullFormatter())

# Adjust the subplot layout, because the logit one may take more space

# than usual, due to y-tick labels like "1 - 10^{-3}"

plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,

wspace=0.35)

plt.show()

#############################################################################

#

# ------------

#

# References

# """"""""""

#

# The use of the following functions, methods, classes and modules is shown

# in this example:

import matplotlib

matplotlib.pyplot.subplot

matplotlib.pyplot.subplots_adjust

matplotlib.pyplot.gca

matplotlib.pyplot.yscale

matplotlib.ticker.NullFormatter

matplotlib.axis.Axis.set_minor_formatter