Matplotlib - Enabling LaTex Rendering in Annotations

What is LaTex Rendering?

LaTeX rendering refers to the process of converting LaTeX markup language which contains typesetting instructions and commands, into formatted output. This output is typically high-quality documents, mathematical formulas, scientific papers or technical reports with precise and consistent typography.

LaTeX rendering is widely used in academia, scientific research, technical documentation and publishing due to its robust typesetting capabilities and its ability to produce professional-looking documents.

Key Aspects of LaTeX Rendering

Typesetting − LaTeX is renowned for its superior typesetting capabilities ensuring professional-grade document formatting for academic and technical content.

Mathematical Formulas − LaTeX is extensively used for its exceptional support in typesetting complex mathematical equations by making it a preferred choice for academic and scientific publications.

Markup Language − LaTeX uses a markup language in which, where users write documents with plain text and include commands to specify formatting, structure and content.

Compilation − The LaTeX source code needs to be compiled using a LaTeX compiler such as pdflatex, xelatex, lualatex. During compilation the compiler interprets the LaTeX commands and generates the final output in various formats like PDF, DVI or PostScript.

Customization − LaTeX allows users to create custom styles, templates and packages by enabling precise control over document formatting and layout.

Benefits of LaTeX Rendering

Quality and Consistency − LaTeX ensures high-quality and consistent document formatting across various platforms and devices.

Mathematical Typesetting − It excels in handling complex mathematical notation and making it indispensable for scientific and mathematical content.

Cross-Platform Compatibility − LaTeX documents can be easily compiled and viewed on different operating systems.

Version Control − Plain text-based source files facilitate version control systems by making collaboration and document history management easier.

Enabling Latex Rendering

To enable LaTeX rendering for creating documents, equations or annotations we typically need the following.

LaTeX Installation

Install a LaTeX distribution like TeX Live, MiKTeX or MacTeX which includes the necessary LaTeX compiler and packages.

Text Editor

Choose a text editor or an integrated development environment (IDE) that supports LaTeX such as TeXstudio, TeXworks, Overleaf or editors like Sublime Text, VS Code or Atom with LaTeX plugins/extensions.

Write LaTeX Code

Create a .tex file and write LaTeX code using the appropriate commands and syntax to structure our document which include equations or format text.

Compilation

Use the LaTeX compiler to compile the .tex file into the desired output format such as PDF, DVI, PS. Run the appropriate command in the terminal or use the integrated features of our chosen editor/IDE.

For example in a terminal we might run the following code.

Example

pdflatex your_file.tex

Or within an editor/IDE there's often a Build or Compile button to initiate the compilation process.

LaTeX Rendering in Matplotlib for Annotations

For Matplotlib annotations using LaTeX for text formatting within plots we have to ensure the below.

Matplotlib Support − Matplotlib supports LaTeX for annotations by using LaTeX syntax within plt.annotate() or similar functions.

LaTeX Installation − Ensure we have a working LaTeX installation on our system that Matplotlib can access for rendering LaTeX text within annotations.

Correct Syntax − Use the correct LaTeX syntax r'$...$' within Matplotlib functions for annotations to render the desired LaTeX-formatted text.

By following the above mentioned steps we can enable LaTeX rendering for various purposes such as document creation, mathematical notation or annotations in visualization libraries like Matplotlib.

Enabling LaTex Rendering

In this example we are going to use the LaTex rendering in the annotations of the plot.

Example

import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4]
y = [2, 5, 7, 10]
plt.plot(x, y, 'o-', label='Data')
# Annotating a point with LaTeX-rendered text
plt.annotate(r'$\sum_{i=1}^{4} y_i$',   # LaTeX expression within the annotation
   xy=(x[2], y[2]),           # Coordinates of the annotation point
   xytext=(2.5, 6),           # Text position
   arrowprops=dict(facecolor='black', arrowstyle='->'),
   fontsize=12,
   color='green')
# Labeling axes and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Plot with LaTeX rendering in Annotation')
plt.legend()
plt.show()

Output

On executing the above code you will get the following output −

Example

Here this is another example of using the LaTex rendering in annotations of a plot.

import matplotlib.pyplot as plt
# Generating some data points
x = [1, 2, 3, 4]
y = [2, 5, 7, 10]
plt.plot(x, y, 'o-', label='Data')
# Annotating a point with LaTeX rendering
plt.annotate(r'\textbf{Max Value}', 
   xy=(x[y.index(max(y))], max(y)), 
   xytext=(2.5, 8),
   arrowprops=dict(facecolor='black', shrink=0.05),
   fontsize=12,
   color='white',
   bbox=dict(boxstyle='round,pad=0.3', edgecolor='red', facecolor='green'))
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Example Plot with LaTeX Annotation')
plt.legend()
plt.show()

Output

On executing the above code you will get the following output −

Change the axis tick font in a Matplotlib plot when rendering using LaTex

Here this is the example to change the axis tick font in matplotlib when rendering using LaTeX

Example

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
x = np.array([1, 2, 3, 4])
y = np.exp(x)
ax1 = plt.subplot()
ax1.set_xticks(x)
ax1.set_yticks(y)
ax1.plot(x, y, c="red")
ax1.set_xticklabels([r"$\bf{one}$", r"$\bf{two}$", r"$\bf{three}$", r"$\bf{four}$"], rotation=45)
ax1.set_yticklabels([r"$\bf{:.2f}$".format(y[0]), r"$\bf{:.2f}$".format(y[1]),
   r"$\bf{:.2f}$".format(y[2]), r"$\bf{:.2f}$".format(y[3])], rotation=45)
plt.tight_layout()
plt.show()

Output

On executing the above code you will get the following output −

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