matplotlib.github.com/examples/pyplots/pyplot_scales.py at master · abhiip/matplotlib.github.com · GitHub

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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(1)

# 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()

matplotlib.github.com/examples/pyplots/pyplot_scales.py at master · abhiip/matplotlib.github.com · GitHub

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