Bk3_Ch12_01.py

###############
# Authored by Weisheng Jiang
# Book 3  |  From Basic Arithmetic to Machine Learning
# Published and copyrighted by Tsinghua University Press
# Beijing, China, 2022
###############

# Bk3_Ch12_01

import numpy as np
import matplotlib.pyplot as plt

x1 = np.linspace(0.1, 10, 100)
x = np.linspace(0.1, 1000, 100)

# x log scale
f1 = 10**x1
f2 = x1
f3 = np.log(x)

fig, ax = plt.subplots()
plt.plot(x1,f1,color = 'r')
plt.plot(x1,f2,color = 'k')
plt.plot(x,f3,color = 'b')

plt.xscale("log")
plt.ylim((-0.5,10))
plt.grid()
plt.tight_layout()
ax.set_box_aspect(1)

# y log scale
f1 = 10**x1
f2 = x1
f3 = np.log(x1)

fig, ax = plt.subplots()
plt.plot(x1,f1,color = 'r')
plt.plot(x1,f2,color = 'k')
plt.plot(x1,f3,color = 'b')

plt.yscale("log")
plt.ylim((0.1,1000))
plt.grid()
plt.tight_layout()
ax.set_box_aspect(1)


# x and y log scale
x_log = np.logspace(np.log(0.1), np.log(1000), num=100, 
                    endpoint=True, base=10.0)
f1 = 10**x_log
f2 = x_log
f3 = np.log(x_log)

fig, ax = plt.subplots()
plt.plot(x_log,f1,color = 'r')
plt.plot(x_log,f2,color = 'k')
plt.plot(x_log,f3,color = 'b')

plt.yscale("log")
plt.xscale("log")
plt.ylim((0.1,1000))
plt.xlim((0.1,1000))
plt.grid()
plt.tight_layout()
ax.set_box_aspect(1)