When visualizing data that varies across a very wide range, a logarithmic scale allows us to visualize variations that would otherwise be barely visible. In this recipe, we are going to show you how to manipulate the scaling system of a figure.
There are several ways to set up a logarithmic scale. The way it is done here works for any kind of figure and not only curve plots. In the following example, we set up a logarithmic scale that will apply to all plot elements:
import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(1, 10, 1024)
plt.yscale('log')
plt.plot(X, X, c = 'k', lw = 2., label = r'$f(x)=x$')
plt.plot(X, 10 ** X, c = '.75', ls = '--', lw = 2., label = r'$f(x)=e^x$')
plt.plot(X, np.log(X), c = '.75', lw = 2., label = r'$f(x)=\log(x)$')
plt.legend()
plt.show()Several curves are shown in the following figure, with the vertical axis using a logarithmic scale:
