Annotating Matplotlib Plots with Coordinate Systems

Introduction

Matplotlib is a powerful visualization tool that allows users to create a wide variety of plots and charts. Annotations are an important feature of Matplotlib that allow users to add text and arrows to their plots. In this tutorial, we will learn how to use different coordinate systems for annotations.

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Import Libraries

The first step is to import the necessary libraries. We will be using the matplotlib.pyplot library to create our plot and annotations.

import matplotlib.pyplot as plt
import numpy as np

Create Data

Next, we will create some data to plot. We will be using the numpy library to create a sine wave.

x = np.arange(0, 10, 0.005)
y = np.exp(-x/2.) * np.sin(2*np.pi*x)

Create the Plot

Now, we will create the plot using the matplotlib.pyplot library. We will set the x and y limits of the plot and then plot the data.

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)

Transform Coordinates

The next step is to transform the coordinates of the data and the display. We will use the ax.transData method to transform the data coordinates and the figure pixels coordinate system to transform the display coordinates.

xdata, ydata = 5, 0
xdisplay, ydisplay = ax.transData.transform((xdata, ydata))

Add Annotations

The final step is to add annotations to the plot. We will use the ax.annotate method to add text and arrows to the plot. We will also use the bbox and arrowprops parameters to style the annotations.

bbox = dict(boxstyle="round", fc="0.8")
arrowprops = dict(
    arrowstyle="->",
    connectionstyle="angle,angleA=0,angleB=90,rad=10")

offset = 72
ax.annotate(
    f'data = ({xdata:.1f}, {ydata:.1f})',
    (xdata, ydata),
    xytext=(-2*offset, offset), textcoords='offset points',
    bbox=bbox, arrowprops=arrowprops)
ax.annotate(
    f'display = ({xdisplay:.1f}, {ydisplay:.1f})',
    xy=(xdisplay, ydisplay), xycoords='figure pixels',
    xytext=(0.5*offset, -offset), textcoords='offset points',
    bbox=bbox, arrowprops=arrowprops)

Show the Plot

The final step is to show the plot using the plt.show() method.

plt.show()

Summary

In this tutorial, we learned how to use different coordinate systems for annotations in Matplotlib. We created a plot, transformed the data and display coordinates, and added annotations to the plot using the ax.annotate method. Annotations are an important feature of Matplotlib that allow users to add context and information to their plots.