Matplotlib

# 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