Plotting Exponential Decay with Matplotlib Semilog

Introduction

This tutorial will guide you through how to assign a log scale for the x-axis using matplotlib.axes.Axes.semilogx in Python Matplotlib. A logarithmic scale is useful when the data you want to plot spans several orders of magnitude. In this tutorial, we will use an example of plotting exponential decay as a function of time.

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Skills Graph

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Import the necessary libraries

We will use numpy and matplotlib libraries in this tutorial.

import matplotlib.pyplot as plt
import numpy as np

Generate data

We will generate data for exponential decay function np.exp(-t / 5.0) using numpy library.

dt = 0.01
t = np.arange(dt, 20.0, dt)

Create a plot and set x-axis to logarithmic scale

We create a figure and axes object using subplots() method. We then plot the exponential decay function using semilogx() method and set the x-axis to a logarithmic scale using set_xscale() method. We also add a grid to the plot using grid() method.

fig, ax = plt.subplots()

ax.semilogx(t, np.exp(-t / 5.0))
ax.set_xscale('log')
ax.grid()

Show the plot

We use show() method to display the plot.

plt.show()

Summary

In this tutorial, we learned how to assign a logarithmic scale to the x-axis using matplotlib.axes.Axes.semilogx method. We also learned how to generate data for an exponential decay function and add a grid to the plot.