Introduction

Matplotlib is a powerful data visualization library in Python, and mastering its advanced text and annotation features can take your plots to the next level. In this guide, we'll explore various techniques to enhance your visualizations with custom text, arrows, and shapes.

1. Advanced Text Placement

1.1 Text Alignment and Rotation

Matplotlib offers fine-grained control over text placement and orientation. Let's start with a simple example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

ax.text(5, 5, "Center Aligned", ha='center', va='center')
ax.text(2, 8, "Left Aligned", ha='left', va='top')
ax.text(8, 2, "Right Aligned\nRotated", ha='right', va='bottom', rotation=45)

plt.show()

In this example, we use ha (horizontal alignment) and va (vertical alignment) to control text positioning. The rotation parameter allows us to tilt the text as needed.

1.2 Text with Custom Fonts and Styles

You can further customize text appearance using font properties:

from matplotlib import font_manager

custom_font = font_manager.FontProperties(family='serif', style='italic', weight='bold', size=16)

ax.text(5, 5, "Custom Font", fontproperties=custom_font)

2. Advanced Annotations

2.1 Arrows and Annotation Boxes

Annotations often require pointing to specific data points. Here's how to create sophisticated annotations with arrows:

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])

ax.annotate('Peak', xy=(2, 4), xytext=(3, 4.5),
            arrowprops=dict(facecolor='black', shrink=0.05),
            fontsize=16, ha='right', va='top')

plt.show()

This creates an arrow pointing to the peak of our plot with custom text.

2.2 Fancy Annotation Boxes

For more complex annotations, we can use fancy boxes:

from matplotlib.patches import FancyBboxPatch

ax.add_patch(FancyBboxPatch((0.2, 0.2), 0.3, 0.3,
                            boxstyle="round,pad=0.1",
                            fc=(1.0, 0.8, 1.0),
                            ec=(0.5, 0.0, 0.5)))

ax.text(0.35, 0.35, "Fancy\nBox", ha='center', va='center')

This creates a rounded box with custom colors and text inside.

3. Mathematical Expressions

Matplotlib supports LaTeX-style mathematical expressions:

ax.text(0.5, 0.5, r'$\sigma = \sqrt{\frac{1}{N}\sum_{i=1}^N (x_i - \mu)^2}$',
        fontsize=16)

Use the raw string (r'') and enclose the LaTeX expression in $ signs.

4. Text Effects

4.1 Text with Shadows

Add depth to your text with shadows:

from matplotlib.patheffects import withStroke

text = ax.text(0.5, 0.5, 'Shadow Effect', fontsize=20, ha='center', va='center')
text.set_path_effects([withStroke(linewidth=3, foreground='gray')])

4.2 Outlined Text

Create outlined text for better visibility:

from matplotlib.patheffects import Stroke, Normal

text = ax.text(0.5, 0.5, 'Outlined Text', fontsize=20, ha='center', va='center')
text.set_path_effects([Stroke(linewidth=3, foreground='black'), Normal()])

5. Dynamic Annotations

For interactive plots, you might want to add annotations dynamically:

def on_plot_hover(event):
    if event.inaxes:
        ax.texts.clear()  # Clear previous annotations
        ax.text(event.xdata, event.ydata, f'({event.xdata:.2f}, {event.ydata:.2f})',
                ha='right', va='top')
        plt.draw()

fig.canvas.mpl_connect('motion_notify_event', on_plot_hover)

This code snippet adds a hover effect, displaying coordinates as you move your mouse over the plot.

Conclusion

By leveraging these advanced text and annotation techniques, you can create more informative and visually appealing plots. Remember to experiment with different combinations to find what works best for your specific data and audience. Happy plotting!