03.matplotlib-tutorial.py

import numpy as np
import matplotlib.pyplot as plt


rg = np.random.default_rng(1)
mu, sigma = 2, 0.5
v = rg.normal(mu, sigma, 10000)
# Plot a normalized histogram with 50 bins
plt.hist(v, bins=50, density=True)       # matplotlib version (plot)
# Compute the histogram with numpy and then plot it
(n, bins) = np.histogram(v, bins=50, density=True)  # NumPy version (no plot)
plt.plot(.5 * (bins[1:] + bins[:-1]), n)


# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the open 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', linthresh=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)
# 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()