Matplotlib Figures

# Matplotlib

Overview

Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.

When to Use This Skill

• This skill should be used when:
• Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
• Generating scientific or statistical visualizations
• Customizing plot appearance (colors, styles, labels, legends)
• Creating multi-panel figures with subplots
• Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
• Building interactive plots or animations
• Working with 3D visualizations
• Integrating plots into Jupyter notebooks or GUI applications

Core Concepts

The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:

1. Figure - The top-level container for all plot elements
2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
3. Artist - Everything visible on the figure (lines, text, ticks, etc.)
4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels

Two Interfaces

1. pyplot Interface (Implicit, MATLAB-style) ```python import matplotlib.pyplot as plt

• plt.plot([1, 2, 3, 4])
• plt.ylabel('some numbers')
• plt.show()
• ```
• Convenient for quick, simple plots
• Maintains state automatically
• Good for interactive work and simple scripts

2. Object-Oriented Interface (Explicit) ```python import matplotlib.pyplot as plt

• fig, ax = plt.subplots()
• ax.plot([1, 2, 3, 4])
• ax.set_ylabel('some numbers')
• plt.show()
• ```
• Recommended for most use cases
• More explicit control over figure and axes
• Better for complex figures with multiple subplots
• Easier to maintain and debug

Common Workflows

1. Basic Plot Creation

Single plot workflow: ```python import matplotlib.pyplot as plt import numpy as np

# Create figure and axes (OO interface - RECOMMENDED) fig, ax = plt.subplots(figsize=(10, 6))

# Generate and plot data x = np.linspace(0, 2*np.pi, 100) ax.plot(x, np.sin(x), label='sin(x)') ax.plot(x, np.cos(x), label='cos(x)')

# Customize ax.set_xlabel('x') ax.set_ylabel('y') ax.set_title('Trigonometric Functions') ax.legend() ax.grid(True, alpha=0.3)

# Save and/or display plt.savefig('plot.png', dpi=300, bbox_inches='tight') plt.show() ```

2. Multiple Subplots

Creating subplot layouts: ```python # Method 1: Regular grid fig, axes = plt.subplots(2, 2, figsize=(12, 10)) axes[0, 0].plot(x, y1) axes[0, 1].scatter(x, y2) axes[1, 0].bar(categories, values) axes[1, 1].hist(data, bins=30)

# Method 2: Mosaic layout (more flexible) fig, axes = plt.subplot_mosaic([['left', 'right_top'], ['left', 'right_bottom']], figsize=(10, 8)) axes['left'].plot(x, y) axes['right_top'].scatter(x, y) axes['right_bottom'].hist(data)

# Method 3: GridSpec (maximum control) from matplotlib.gridspec import GridSpec fig = plt.figure(figsize=(12, 8)) gs = GridSpec(3, 3, figure=fig) ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column ax3 = fig.add_subplot(gs[1:, 1:]) # Bottom two rows, last two columns ```

3. Plot Types and Use Cases

Line plots - Time series, continuous data, trends ```python ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue') ```

Scatter plots - Relationships between variables, correlations ```python ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis') ```

Bar charts - Categorical comparisons ```python ax.bar(categories, values, color='steelblue', edgecolor='black') # For horizontal bars: ax.barh(categories, values) ```

Histograms - Distributions ```python ax.hist(data, bins=30, edgecolor='black', alpha=0.7) ```

Heatmaps - Matrix data, correlations ```python im = ax.imshow(matrix, cmap='coolwarm', aspect='auto') plt.colorbar(im, ax=ax) ```

Contour plots - 3D data on 2D plane ```python contour = ax.contour(X, Y, Z, levels=10) ax.clabel(contour, inline=True, fontsize=8) ```

Box plots - Statistical distributions ```python ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C']) ```

Violin plots - Distribution densities ```python ax.violinplot([data1, data2, data3], positions=[1, 2, 3]) ```

For comprehensive plot type examples and variations, refer to `references/plot_types.md`.

4. Styling and Customization

• Color specification methods:
• Named colors: `'red'`, `'blue'`, `'steelblue'`
• Hex codes: `'#FF5733'`
• RGB tuples: `(0.1, 0.2, 0.3)`
• Colormaps: `cmap='viridis'`, `cmap='plasma'`, `cmap='coolwarm'`

Using style sheets: ```python plt.style.use('seaborn-v0_8-darkgrid') # Apply predefined style # Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc. print(plt.style.available) # List all available styles ```

Customizing with rcParams: ```python plt.rcParams['font.size'] = 12 plt.rcParams['axes.labelsize'] = 14 plt.rcParams['axes.titlesize'] = 16 plt.rcParams['xtick.labelsize'] = 10 plt.rcParams['ytick.labelsize'] = 10 plt.rcParams['legend.fontsize'] = 12 plt.rcParams['figure.titlesize'] = 18 ```

Text and annotations: ```python ax.text(x, y, 'annotation', fontsize=12, ha='center') ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1), arrowprops=dict(arrowstyle='->', color='red')) ```

For detailed styling options and colormap guidelines, see `references/styling_guide.md`.

5. Saving Figures

Export to various formats: ```python # High-resolution PNG for presentations/papers plt.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')

# Vector format for publications (scalable) plt.savefig('figure.pdf', bbox_inches='tight') plt.savefig('figure.svg', bbox_inches='tight')

# Transparent background plt.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True) ```

• Important parameters:
• `dpi`: Resolution (300 for publications, 150 for web, 72 for screen)
• `bbox_inches='tight'`: Removes excess whitespace
• `facecolor='white'`: Ensures white background (useful for transparent themes)
• `transparent=True`: Transparent background

6. Working with 3D Plots

```python from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d')

# Surface plot ax.plot_surface(X, Y, Z, cmap='viridis')

# 3D scatter ax.scatter(x, y, z, c=colors, marker='o')

# 3D line plot ax.plot(x, y, z, linewidth=2)

# Labels ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label') ```

Best Practices

1. Interface Selection - **Use the object-oriented interface** (fig, ax = plt.subplots()) for production code - Reserve pyplot interface for quick interactive exploration only - Always create figures explicitly rather than relying on implicit state

2. Figure Size and DPI - Set figsize at creation: `fig, ax = plt.subplots(figsize=(10, 6))` - Use appropriate DPI for output medium: - Screen/notebook: 72-100 dpi - Web: 150 dpi - Print/publications: 300 dpi

3. Layout Management - Use `constrained_layout=True` or `tight_layout()` to prevent overlapping elements - `fig, ax = plt.subplots(constrained_layout=True)` is recommended for automatic spacing

4. Colormap Selection - **Sequential** (viridis, plasma, inferno): Ordered data with consistent progression - **Diverging** (coolwarm, RdBu): Data with meaningful center point (e.g., zero) - **Qualitative** (tab10, Set3): Categorical/nominal data - Avoid rainbow colormaps (jet) - they are not perceptually uniform

5. Accessibility - Use colorblind-friendly colormaps (viridis, cividis) - Add patterns/hatching for bar charts in addition to colors - Ensure sufficient contrast between elements - Include descriptive labels and legends

6. Performance - For large datasets, use `rasterized=True` in plot calls to reduce file size - Use appropriate data reduction before plotting (e.g., downsample dense time series) - For animations, use blitting for better performance

7. Code Organization ```python # Good practice: Clear structure def create_analysis_plot(data, title): """Create standardized analysis plot.""" fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)

# Plot data ax.plot(data['x'], data['y'], linewidth=2)

# Customize ax.set_xlabel('X Axis Label', fontsize=12) ax.set_ylabel('Y Axis Label', fontsize=12) ax.set_title(title, fontsize=14, fontweight='bold') ax.grid(True, alpha=0.3)

return fig, ax

# Use the function fig, ax = create_analysis_plot(my_data, 'My Analysis') plt.savefig('analysis.png', dpi=300, bbox_inches='tight') ```

Quick Reference Scripts

This skill includes helper scripts in the `scripts/` directory:

`plot_template.py` Template script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.

Usage: ```bash python scripts/plot_template.py ```

`style_configurator.py` Interactive utility to configure matplotlib style preferences and generate custom style sheets.

Usage: ```bash python scripts/style_configurator.py ```

Detailed References

For comprehensive information, consult the reference documents:

• `references/plot_types.md` - Complete catalog of plot types with code examples and use cases
• `references/styling_guide.md` - Detailed styling options, colormaps, and customization
• `references/api_reference.md` - Core classes and methods reference
• `references/common_issues.md` - Troubleshooting guide for common problems

Integration with Other Tools

• Matplotlib integrates well with:
• NumPy/Pandas - Direct plotting from arrays and DataFrames
• Seaborn - High-level statistical visualizations built on matplotlib
• Jupyter - Interactive plotting with `%matplotlib inline` or `%matplotlib widget`
• GUI frameworks - Embedding in Tkinter, Qt, wxPython applications

Common Gotchas

1. Overlapping elements: Use `constrained_layout=True` or `tight_layout()`
2. State confusion: Use OO interface to avoid pyplot state machine issues
3. Memory issues with many figures: Close figures explicitly with `plt.close(fig)`
4. Font warnings: Install fonts or suppress warnings with `plt.rcParams['font.sans-serif']`
5. DPI confusion: Remember that figsize is in inches, not pixels: `pixels = dpi * inches`

Additional Resources

• Official documentation: https://matplotlib.org/
• Gallery: https://matplotlib.org/stable/gallery/index.html
• Cheatsheets: https://matplotlib.org/cheatsheets/
• Tutorials: https://matplotlib.org/stable/tutorials/index.html