fig add_axes

Guri Bee

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19-10-2024

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Mastering the Matplotlib Canvas: A Deep Dive into fig.add_axes()

Matplotlib, the go-to plotting library in Python, offers a plethora of tools for creating compelling visualizations. One powerful feature is fig.add_axes(), a method that allows you to add custom axes to your figures, enhancing the flexibility and control you have over your plots. This article dives deep into fig.add_axes(), revealing its capabilities and exploring its practical applications.

Understanding fig.add_axes(): Your Canvas for Customization

At its core, fig.add_axes() provides a way to add new axes (or plotting regions) to your Matplotlib figure. It acts like a painter adding multiple frames onto a canvas. Here's how it works:

import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # [left, bottom, width, height]
ax2 = fig.add_axes([0.2, 0.5, 0.3, 0.3]) # [left, bottom, width, height]

# Plot data on each axis
ax1.plot([1, 2, 3], [4, 5, 6])
ax2.plot([1, 2, 3], [6, 5, 4])

plt.show()

In this example, fig.add_axes() is called twice to create two axes:

1. ax1: Occupies the majority of the figure with coordinates [0.1, 0.1, 0.8, 0.8]. This means it starts at 10% of the figure's width and height, spans 80% of the width and 80% of the height.
2. ax2: Located inside ax1, it has coordinates [0.2, 0.5, 0.3, 0.3], signifying a smaller plot within the larger one.

Key Points:

• Coordinates: The arguments passed to fig.add_axes() represent the position and size of the new axes relative to the figure, ranging from 0 to 1.
• Customization: You can create axes of different sizes, positions, and even overlap them, achieving complex and insightful visualizations.

Beyond Basic Plots: Exploring fig.add_axes() Applications

1. Insets: fig.add_axes() is a versatile tool for creating insets, magnifying specific regions of your primary plot. This can be particularly useful for highlighting details or specific areas of interest.

# Example using "fig.add_axes()" for an inset
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

fig, ax1 = plt.subplots()

ax1.plot(x, y, label='Main Plot')

# Define the inset axes
ax2 = fig.add_axes([0.6, 0.6, 0.3, 0.3])

# Zoom in on a specific region
ax2.plot(x, y)
ax2.set_xlim([5, 6])
ax2.set_ylim([0.5, 1])
ax2.set_title('Zoomed Region')

plt.show()

2. Multiple Y-Axes: For comparing data with different scales, fig.add_axes() can be used to create multiple y-axes, enhancing the visualization's clarity and making comparisons easier.

# Example using "fig.add_axes()" for multiple y-axes
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = 100 * np.cos(x)

fig, ax1 = plt.subplots()

ax1.plot(x, y1, label='Data 1')
ax1.set_ylabel('Data 1 (sin(x))', color='blue')

# Create a second y-axis with a different scale
ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.8], sharex=ax1, frameon=False)
ax2.plot(x, y2, label='Data 2', color='red')
ax2.set_ylabel('Data 2 (100 * cos(x))', color='red')

# Adjust the ticks and labels for the second y-axis
ax2.tick_params(axis='y', labelcolor='red')

plt.show()

3. Custom Plot Layouts: fig.add_axes() opens up possibilities for creating custom layouts beyond the standard grid-based layouts provided by plt.subplots(). You can arrange axes in different ways, incorporating unique designs for specific visualization needs.

Understanding the GitHub Context:

Many users on GitHub utilize fig.add_axes() for various plotting purposes. The code snippets shared on GitHub often showcase specific use cases, such as creating insets, adding multiple y-axes, or even creating entirely custom plot layouts.

Conclusion:

fig.add_axes() empowers you to take control of your Matplotlib figures. By mastering this tool, you can create visually engaging and informative plots, going beyond standard configurations and crafting visualizations tailored to your specific needs. The vast library of examples and discussions on GitHub serves as a valuable resource for exploring the diverse possibilities of fig.add_axes(), pushing the boundaries of your Matplotlib creations.