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skills/matplotlib/references/api_reference.md
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skills/matplotlib/references/api_reference.md
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# Matplotlib API Reference
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This document provides a quick reference for the most commonly used matplotlib classes and methods.
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## Core Classes
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### Figure
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The top-level container for all plot elements.
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**Creation:**
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```python
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fig = plt.figure(figsize=(10, 6), dpi=100, facecolor='white')
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fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))
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fig, axes = plt.subplots(2, 2, figsize=(12, 10))
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```
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**Key Methods:**
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- `fig.add_subplot(nrows, ncols, index)` - Add a subplot
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- `fig.add_axes([left, bottom, width, height])` - Add axes at specific position
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- `fig.savefig(filename, dpi=300, bbox_inches='tight')` - Save figure
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- `fig.tight_layout()` - Adjust spacing to prevent overlaps
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- `fig.suptitle(title)` - Set figure title
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- `fig.legend()` - Create figure-level legend
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- `fig.colorbar(mappable)` - Add colorbar to figure
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- `plt.close(fig)` - Close figure to free memory
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**Key Attributes:**
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- `fig.axes` - List of all axes in the figure
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- `fig.dpi` - Resolution in dots per inch
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- `fig.figsize` - Figure dimensions in inches (width, height)
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### Axes
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The actual plotting area where data is visualized.
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**Creation:**
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```python
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fig, ax = plt.subplots() # Single axes
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ax = fig.add_subplot(111) # Alternative method
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```
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**Plotting Methods:**
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**Line plots:**
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- `ax.plot(x, y, **kwargs)` - Line plot
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- `ax.step(x, y, where='pre'/'mid'/'post')` - Step plot
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- `ax.errorbar(x, y, yerr, xerr)` - Error bars
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**Scatter plots:**
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- `ax.scatter(x, y, s=size, c=color, marker='o', alpha=0.5)` - Scatter plot
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**Bar charts:**
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- `ax.bar(x, height, width=0.8, align='center')` - Vertical bar chart
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- `ax.barh(y, width)` - Horizontal bar chart
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**Statistical plots:**
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- `ax.hist(data, bins=10, density=False)` - Histogram
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- `ax.boxplot(data, labels=None)` - Box plot
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- `ax.violinplot(data)` - Violin plot
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**2D plots:**
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- `ax.imshow(array, cmap='viridis', aspect='auto')` - Display image/matrix
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- `ax.contour(X, Y, Z, levels=10)` - Contour lines
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- `ax.contourf(X, Y, Z, levels=10)` - Filled contours
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- `ax.pcolormesh(X, Y, Z)` - Pseudocolor plot
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**Filling:**
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- `ax.fill_between(x, y1, y2, alpha=0.3)` - Fill between curves
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- `ax.fill_betweenx(y, x1, x2)` - Fill between vertical curves
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**Text and annotations:**
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- `ax.text(x, y, text, fontsize=12)` - Add text
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- `ax.annotate(text, xy=(x, y), xytext=(x2, y2), arrowprops={})` - Annotate with arrow
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**Customization Methods:**
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**Labels and titles:**
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- `ax.set_xlabel(label, fontsize=12)` - Set x-axis label
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- `ax.set_ylabel(label, fontsize=12)` - Set y-axis label
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- `ax.set_title(title, fontsize=14)` - Set axes title
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**Limits and scales:**
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- `ax.set_xlim(left, right)` - Set x-axis limits
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- `ax.set_ylim(bottom, top)` - Set y-axis limits
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- `ax.set_xscale('linear'/'log'/'symlog')` - Set x-axis scale
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- `ax.set_yscale('linear'/'log'/'symlog')` - Set y-axis scale
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**Ticks:**
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- `ax.set_xticks(positions)` - Set x-tick positions
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- `ax.set_xticklabels(labels)` - Set x-tick labels
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- `ax.tick_params(axis='both', labelsize=10)` - Customize tick appearance
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**Grid and spines:**
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- `ax.grid(True, alpha=0.3, linestyle='--')` - Add grid
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- `ax.spines['top'].set_visible(False)` - Hide top spine
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- `ax.spines['right'].set_visible(False)` - Hide right spine
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**Legend:**
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- `ax.legend(loc='best', fontsize=10, frameon=True)` - Add legend
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- `ax.legend(handles, labels)` - Custom legend
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**Aspect and layout:**
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- `ax.set_aspect('equal'/'auto'/ratio)` - Set aspect ratio
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- `ax.invert_xaxis()` - Invert x-axis
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- `ax.invert_yaxis()` - Invert y-axis
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### pyplot Module
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High-level interface for quick plotting.
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**Figure creation:**
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- `plt.figure()` - Create new figure
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- `plt.subplots()` - Create figure and axes
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- `plt.subplot()` - Add subplot to current figure
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**Plotting (uses current axes):**
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- `plt.plot()` - Line plot
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- `plt.scatter()` - Scatter plot
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- `plt.bar()` - Bar chart
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- `plt.hist()` - Histogram
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- (All axes methods available)
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**Display and save:**
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- `plt.show()` - Display figure
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- `plt.savefig()` - Save figure
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- `plt.close()` - Close figure
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**Style:**
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- `plt.style.use(style_name)` - Apply style sheet
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- `plt.style.available` - List available styles
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**State management:**
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- `plt.gca()` - Get current axes
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- `plt.gcf()` - Get current figure
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- `plt.sca(ax)` - Set current axes
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- `plt.clf()` - Clear current figure
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- `plt.cla()` - Clear current axes
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## Line and Marker Styles
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### Line Styles
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- `'-'` or `'solid'` - Solid line
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- `'--'` or `'dashed'` - Dashed line
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- `'-.'` or `'dashdot'` - Dash-dot line
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- `':'` or `'dotted'` - Dotted line
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- `''` or `' '` or `'None'` - No line
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### Marker Styles
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- `'.'` - Point marker
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- `'o'` - Circle marker
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- `'v'`, `'^'`, `'<'`, `'>'` - Triangle markers
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- `'s'` - Square marker
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- `'p'` - Pentagon marker
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- `'*'` - Star marker
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- `'h'`, `'H'` - Hexagon markers
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- `'+'` - Plus marker
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- `'x'` - X marker
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- `'D'`, `'d'` - Diamond markers
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### Color Specifications
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**Single character shortcuts:**
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- `'b'` - Blue
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- `'g'` - Green
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- `'r'` - Red
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- `'c'` - Cyan
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- `'m'` - Magenta
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- `'y'` - Yellow
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- `'k'` - Black
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- `'w'` - White
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**Named colors:**
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- `'steelblue'`, `'coral'`, `'teal'`, etc.
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- See full list: https://matplotlib.org/stable/gallery/color/named_colors.html
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**Other formats:**
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- Hex: `'#FF5733'`
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- RGB tuple: `(0.1, 0.2, 0.3)`
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- RGBA tuple: `(0.1, 0.2, 0.3, 0.5)`
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## Common Parameters
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### Plot Function Parameters
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```python
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ax.plot(x, y,
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color='blue', # Line color
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linewidth=2, # Line width
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linestyle='--', # Line style
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marker='o', # Marker style
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markersize=8, # Marker size
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markerfacecolor='red', # Marker fill color
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markeredgecolor='black',# Marker edge color
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markeredgewidth=1, # Marker edge width
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alpha=0.7, # Transparency (0-1)
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label='data', # Legend label
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zorder=2, # Drawing order
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rasterized=True # Rasterize for smaller file size
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)
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```
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### Scatter Function Parameters
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```python
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ax.scatter(x, y,
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s=50, # Size (scalar or array)
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c='blue', # Color (scalar, array, or sequence)
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marker='o', # Marker style
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cmap='viridis', # Colormap (if c is numeric)
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alpha=0.5, # Transparency
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edgecolors='black', # Edge color
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linewidths=1, # Edge width
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vmin=0, vmax=1, # Color scale limits
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label='data' # Legend label
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)
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```
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### Text Parameters
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```python
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ax.text(x, y, text,
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fontsize=12, # Font size
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fontweight='normal', # 'normal', 'bold', 'heavy', 'light'
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fontstyle='normal', # 'normal', 'italic', 'oblique'
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fontfamily='sans-serif',# Font family
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color='black', # Text color
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alpha=1.0, # Transparency
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ha='center', # Horizontal alignment: 'left', 'center', 'right'
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va='center', # Vertical alignment: 'top', 'center', 'bottom', 'baseline'
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rotation=0, # Rotation angle in degrees
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bbox=dict( # Background box
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facecolor='white',
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edgecolor='black',
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boxstyle='round'
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)
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)
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```
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## rcParams Configuration
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Common rcParams settings for global customization:
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```python
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# Font settings
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plt.rcParams['font.family'] = 'sans-serif'
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plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
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plt.rcParams['font.size'] = 12
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# Figure settings
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plt.rcParams['figure.figsize'] = (10, 6)
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plt.rcParams['figure.dpi'] = 100
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plt.rcParams['figure.facecolor'] = 'white'
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plt.rcParams['savefig.dpi'] = 300
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plt.rcParams['savefig.bbox'] = 'tight'
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# Axes settings
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plt.rcParams['axes.labelsize'] = 14
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plt.rcParams['axes.titlesize'] = 16
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plt.rcParams['axes.grid'] = True
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plt.rcParams['axes.grid.alpha'] = 0.3
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# Line settings
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plt.rcParams['lines.linewidth'] = 2
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plt.rcParams['lines.markersize'] = 8
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# Tick settings
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plt.rcParams['xtick.labelsize'] = 10
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plt.rcParams['ytick.labelsize'] = 10
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plt.rcParams['xtick.direction'] = 'in' # 'in', 'out', 'inout'
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plt.rcParams['ytick.direction'] = 'in'
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# Legend settings
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plt.rcParams['legend.fontsize'] = 12
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plt.rcParams['legend.frameon'] = True
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plt.rcParams['legend.framealpha'] = 0.8
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# Grid settings
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plt.rcParams['grid.alpha'] = 0.3
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plt.rcParams['grid.linestyle'] = '--'
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```
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## GridSpec for Complex Layouts
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```python
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from matplotlib.gridspec import GridSpec
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fig = plt.figure(figsize=(12, 8))
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gs = GridSpec(3, 3, figure=fig, hspace=0.3, wspace=0.3)
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# Span multiple cells
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ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns
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ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column
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ax3 = fig.add_subplot(gs[1, 1:]) # Middle row, last two columns
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ax4 = fig.add_subplot(gs[2, 1]) # Bottom row, middle column
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ax5 = fig.add_subplot(gs[2, 2]) # Bottom row, right column
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```
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## 3D Plotting
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```python
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from mpl_toolkits.mplot3d import Axes3D
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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# Plot types
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ax.plot(x, y, z) # 3D line
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ax.scatter(x, y, z) # 3D scatter
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ax.plot_surface(X, Y, Z) # 3D surface
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ax.plot_wireframe(X, Y, Z) # 3D wireframe
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ax.contour(X, Y, Z) # 3D contour
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ax.bar3d(x, y, z, dx, dy, dz) # 3D bar
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# Customization
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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ax.view_init(elev=30, azim=45) # Set viewing angle
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```
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## Animation
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```python
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from matplotlib.animation import FuncAnimation
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fig, ax = plt.subplots()
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line, = ax.plot([], [])
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def init():
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ax.set_xlim(0, 2*np.pi)
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ax.set_ylim(-1, 1)
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return line,
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def update(frame):
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x = np.linspace(0, 2*np.pi, 100)
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y = np.sin(x + frame/10)
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line.set_data(x, y)
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return line,
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anim = FuncAnimation(fig, update, init_func=init,
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frames=100, interval=50, blit=True)
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# Save animation
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anim.save('animation.gif', writer='pillow', fps=20)
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anim.save('animation.mp4', writer='ffmpeg', fps=20)
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```
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## Image Operations
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```python
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# Read and display image
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img = plt.imread('image.png')
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ax.imshow(img)
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# Display matrix as image
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ax.imshow(matrix, cmap='viridis', aspect='auto',
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interpolation='nearest', origin='lower')
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# Colorbar
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cbar = plt.colorbar(im, ax=ax)
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cbar.set_label('Values')
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# Image extent (set coordinates)
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ax.imshow(img, extent=[x_min, x_max, y_min, y_max])
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```
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## Event Handling
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```python
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# Mouse click event
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def on_click(event):
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if event.inaxes:
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print(f'Clicked at x={event.xdata:.2f}, y={event.ydata:.2f}')
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fig.canvas.mpl_connect('button_press_event', on_click)
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# Key press event
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def on_key(event):
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print(f'Key pressed: {event.key}')
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fig.canvas.mpl_connect('key_press_event', on_key)
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```
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## Useful Utilities
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```python
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# Get current axis limits
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xlims = ax.get_xlim()
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ylims = ax.get_ylim()
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# Set equal aspect ratio
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ax.set_aspect('equal', adjustable='box')
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# Share axes between subplots
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fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
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# Twin axes (two y-axes)
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ax2 = ax1.twinx()
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# Remove tick labels
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ax.set_xticklabels([])
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ax.set_yticklabels([])
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# Scientific notation
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ax.ticklabel_format(style='scientific', axis='y', scilimits=(0,0))
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# Date formatting
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import matplotlib.dates as mdates
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
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```
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563
skills/matplotlib/references/common_issues.md
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skills/matplotlib/references/common_issues.md
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# Matplotlib Common Issues and Solutions
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Troubleshooting guide for frequently encountered matplotlib problems.
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## Display and Backend Issues
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### Issue: Plots Not Showing
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**Problem:** `plt.show()` doesn't display anything
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**Solutions:**
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```python
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# 1. Check if backend is properly set (for interactive use)
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import matplotlib
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print(matplotlib.get_backend())
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# 2. Try different backends
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matplotlib.use('TkAgg') # or 'Qt5Agg', 'MacOSX'
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import matplotlib.pyplot as plt
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# 3. In Jupyter notebooks, use magic command
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%matplotlib inline # Static images
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# or
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%matplotlib widget # Interactive plots
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# 4. Ensure plt.show() is called
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plt.plot([1, 2, 3])
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plt.show()
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```
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### Issue: "RuntimeError: main thread is not in main loop"
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**Problem:** Interactive mode issues with threading
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**Solution:**
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```python
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# Switch to non-interactive backend
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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# Or turn off interactive mode
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plt.ioff()
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```
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### Issue: Figures Not Updating Interactively
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**Problem:** Changes not reflected in interactive windows
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**Solution:**
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```python
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# Enable interactive mode
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plt.ion()
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# Draw after each change
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plt.plot(x, y)
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plt.draw()
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plt.pause(0.001) # Brief pause to update display
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```
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## Layout and Spacing Issues
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### Issue: Overlapping Labels and Titles
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**Problem:** Labels, titles, or tick labels overlap or get cut off
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||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Constrained layout (RECOMMENDED)
|
||||
fig, ax = plt.subplots(constrained_layout=True)
|
||||
|
||||
# Solution 2: Tight layout
|
||||
fig, ax = plt.subplots()
|
||||
plt.tight_layout()
|
||||
|
||||
# Solution 3: Adjust margins manually
|
||||
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
|
||||
|
||||
# Solution 4: Save with bbox_inches='tight'
|
||||
plt.savefig('figure.png', bbox_inches='tight')
|
||||
|
||||
# Solution 5: Rotate long tick labels
|
||||
ax.set_xticklabels(labels, rotation=45, ha='right')
|
||||
```
|
||||
|
||||
### Issue: Colorbar Affects Subplot Size
|
||||
|
||||
**Problem:** Adding colorbar shrinks the plot
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Solution 1: Use constrained layout
|
||||
fig, ax = plt.subplots(constrained_layout=True)
|
||||
im = ax.imshow(data)
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
# Solution 2: Manually specify colorbar dimensions
|
||||
from mpl_toolkits.axes_grid1 import make_axes_locatable
|
||||
divider = make_axes_locatable(ax)
|
||||
cax = divider.append_axes("right", size="5%", pad=0.05)
|
||||
plt.colorbar(im, cax=cax)
|
||||
|
||||
# Solution 3: For multiple subplots, share colorbar
|
||||
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
|
||||
for ax in axes:
|
||||
im = ax.imshow(data)
|
||||
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95)
|
||||
```
|
||||
|
||||
### Issue: Subplots Too Close Together
|
||||
|
||||
**Problem:** Multiple subplots overlapping
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Solution 1: Use constrained_layout
|
||||
fig, axes = plt.subplots(2, 2, constrained_layout=True)
|
||||
|
||||
# Solution 2: Adjust spacing with subplots_adjust
|
||||
fig, axes = plt.subplots(2, 2)
|
||||
plt.subplots_adjust(hspace=0.4, wspace=0.4)
|
||||
|
||||
# Solution 3: Specify spacing in tight_layout
|
||||
plt.tight_layout(h_pad=2.0, w_pad=2.0)
|
||||
```
|
||||
|
||||
## Memory and Performance Issues
|
||||
|
||||
### Issue: Memory Leak with Multiple Figures
|
||||
|
||||
**Problem:** Memory usage grows when creating many figures
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Close figures explicitly
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(x, y)
|
||||
plt.savefig('plot.png')
|
||||
plt.close(fig) # or plt.close('all')
|
||||
|
||||
# Clear current figure without closing
|
||||
plt.clf()
|
||||
|
||||
# Clear current axes
|
||||
plt.cla()
|
||||
```
|
||||
|
||||
### Issue: Large File Sizes
|
||||
|
||||
**Problem:** Saved figures are too large
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Reduce DPI
|
||||
plt.savefig('figure.png', dpi=150) # Instead of 300
|
||||
|
||||
# Solution 2: Use rasterization for complex plots
|
||||
ax.plot(x, y, rasterized=True)
|
||||
|
||||
# Solution 3: Use vector format for simple plots
|
||||
plt.savefig('figure.pdf') # or .svg
|
||||
|
||||
# Solution 4: Compress PNG
|
||||
plt.savefig('figure.png', dpi=300, optimize=True)
|
||||
```
|
||||
|
||||
### Issue: Slow Plotting with Large Datasets
|
||||
|
||||
**Problem:** Plotting takes too long with many points
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Downsample data
|
||||
from scipy.signal import decimate
|
||||
y_downsampled = decimate(y, 10) # Keep every 10th point
|
||||
|
||||
# Solution 2: Use rasterization
|
||||
ax.plot(x, y, rasterized=True)
|
||||
|
||||
# Solution 3: Use line simplification
|
||||
ax.plot(x, y)
|
||||
for line in ax.get_lines():
|
||||
line.set_rasterized(True)
|
||||
|
||||
# Solution 4: For scatter plots, consider hexbin or 2d histogram
|
||||
ax.hexbin(x, y, gridsize=50, cmap='viridis')
|
||||
```
|
||||
|
||||
## Font and Text Issues
|
||||
|
||||
### Issue: Font Warnings
|
||||
|
||||
**Problem:** "findfont: Font family [...] not found"
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Use available fonts
|
||||
from matplotlib.font_manager import findfont, FontProperties
|
||||
print(findfont(FontProperties(family='sans-serif')))
|
||||
|
||||
# Solution 2: Rebuild font cache
|
||||
import matplotlib.font_manager
|
||||
matplotlib.font_manager._rebuild()
|
||||
|
||||
# Solution 3: Suppress warnings
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# Solution 4: Specify fallback fonts
|
||||
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'sans-serif']
|
||||
```
|
||||
|
||||
### Issue: LaTeX Rendering Errors
|
||||
|
||||
**Problem:** Math text not rendering correctly
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Use raw strings with r prefix
|
||||
ax.set_xlabel(r'$\alpha$') # Not '\alpha'
|
||||
|
||||
# Solution 2: Escape backslashes in regular strings
|
||||
ax.set_xlabel('$\\alpha$')
|
||||
|
||||
# Solution 3: Disable LaTeX if not installed
|
||||
plt.rcParams['text.usetex'] = False
|
||||
|
||||
# Solution 4: Use mathtext instead of full LaTeX
|
||||
# Mathtext is always available, no LaTeX installation needed
|
||||
ax.text(x, y, r'$\int_0^\infty e^{-x} dx$')
|
||||
```
|
||||
|
||||
### Issue: Text Cut Off or Outside Figure
|
||||
|
||||
**Problem:** Labels or annotations appear outside figure bounds
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Use bbox_inches='tight'
|
||||
plt.savefig('figure.png', bbox_inches='tight')
|
||||
|
||||
# Solution 2: Adjust figure bounds
|
||||
plt.subplots_adjust(left=0.15, right=0.85, top=0.85, bottom=0.15)
|
||||
|
||||
# Solution 3: Clip text to axes
|
||||
ax.text(x, y, 'text', clip_on=True)
|
||||
|
||||
# Solution 4: Use constrained_layout
|
||||
fig, ax = plt.subplots(constrained_layout=True)
|
||||
```
|
||||
|
||||
## Color and Colormap Issues
|
||||
|
||||
### Issue: Colorbar Not Matching Plot
|
||||
|
||||
**Problem:** Colorbar shows different range than data
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Explicitly set vmin and vmax
|
||||
im = ax.imshow(data, vmin=0, vmax=1, cmap='viridis')
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
# Or use the same norm for multiple plots
|
||||
import matplotlib.colors as mcolors
|
||||
norm = mcolors.Normalize(vmin=data.min(), vmax=data.max())
|
||||
im1 = ax1.imshow(data1, norm=norm, cmap='viridis')
|
||||
im2 = ax2.imshow(data2, norm=norm, cmap='viridis')
|
||||
```
|
||||
|
||||
### Issue: Colors Look Wrong
|
||||
|
||||
**Problem:** Unexpected colors in plots
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Check color specification format
|
||||
ax.plot(x, y, color='blue') # Correct
|
||||
ax.plot(x, y, color=(0, 0, 1)) # Correct RGB
|
||||
ax.plot(x, y, color='#0000FF') # Correct hex
|
||||
|
||||
# Solution 2: Verify colormap exists
|
||||
print(plt.colormaps()) # List available colormaps
|
||||
|
||||
# Solution 3: For scatter plots, ensure c shape matches
|
||||
ax.scatter(x, y, c=colors) # colors should have same length as x, y
|
||||
|
||||
# Solution 4: Check if alpha is set correctly
|
||||
ax.plot(x, y, alpha=1.0) # 0=transparent, 1=opaque
|
||||
```
|
||||
|
||||
### Issue: Reversed Colormap
|
||||
|
||||
**Problem:** Colormap direction is backwards
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Add _r suffix to reverse any colormap
|
||||
ax.imshow(data, cmap='viridis_r')
|
||||
```
|
||||
|
||||
## Axis and Scale Issues
|
||||
|
||||
### Issue: Axis Limits Not Working
|
||||
|
||||
**Problem:** `set_xlim` or `set_ylim` not taking effect
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Set after plotting
|
||||
ax.plot(x, y)
|
||||
ax.set_xlim(0, 10)
|
||||
ax.set_ylim(-1, 1)
|
||||
|
||||
# Solution 2: Disable autoscaling
|
||||
ax.autoscale(False)
|
||||
ax.set_xlim(0, 10)
|
||||
|
||||
# Solution 3: Use axis method
|
||||
ax.axis([xmin, xmax, ymin, ymax])
|
||||
```
|
||||
|
||||
### Issue: Log Scale with Zero or Negative Values
|
||||
|
||||
**Problem:** ValueError when using log scale with data ≤ 0
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Filter out non-positive values
|
||||
mask = (data > 0)
|
||||
ax.plot(x[mask], data[mask])
|
||||
ax.set_yscale('log')
|
||||
|
||||
# Solution 2: Use symlog for data with positive and negative values
|
||||
ax.set_yscale('symlog')
|
||||
|
||||
# Solution 3: Add small offset
|
||||
ax.plot(x, data + 1e-10)
|
||||
ax.set_yscale('log')
|
||||
```
|
||||
|
||||
### Issue: Dates Not Displaying Correctly
|
||||
|
||||
**Problem:** Date axis shows numbers instead of dates
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
import matplotlib.dates as mdates
|
||||
import pandas as pd
|
||||
|
||||
# Convert to datetime if needed
|
||||
dates = pd.to_datetime(date_strings)
|
||||
|
||||
ax.plot(dates, values)
|
||||
|
||||
# Format date axis
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
|
||||
ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
|
||||
plt.xticks(rotation=45)
|
||||
```
|
||||
|
||||
## Legend Issues
|
||||
|
||||
### Issue: Legend Covers Data
|
||||
|
||||
**Problem:** Legend obscures important parts of plot
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Use 'best' location
|
||||
ax.legend(loc='best')
|
||||
|
||||
# Solution 2: Place outside plot area
|
||||
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
|
||||
|
||||
# Solution 3: Make legend semi-transparent
|
||||
ax.legend(framealpha=0.7)
|
||||
|
||||
# Solution 4: Put legend below plot
|
||||
ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3)
|
||||
```
|
||||
|
||||
### Issue: Too Many Items in Legend
|
||||
|
||||
**Problem:** Legend is cluttered with many entries
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Only label selected items
|
||||
for i, (x, y) in enumerate(data):
|
||||
label = f'Data {i}' if i % 5 == 0 else None
|
||||
ax.plot(x, y, label=label)
|
||||
|
||||
# Solution 2: Use multiple columns
|
||||
ax.legend(ncol=3)
|
||||
|
||||
# Solution 3: Create custom legend with fewer entries
|
||||
from matplotlib.lines import Line2D
|
||||
custom_lines = [Line2D([0], [0], color='r'),
|
||||
Line2D([0], [0], color='b')]
|
||||
ax.legend(custom_lines, ['Category A', 'Category B'])
|
||||
|
||||
# Solution 4: Use separate legend figure
|
||||
fig_leg = plt.figure(figsize=(3, 2))
|
||||
ax_leg = fig_leg.add_subplot(111)
|
||||
ax_leg.legend(*ax.get_legend_handles_labels(), loc='center')
|
||||
ax_leg.axis('off')
|
||||
```
|
||||
|
||||
## 3D Plot Issues
|
||||
|
||||
### Issue: 3D Plots Look Flat
|
||||
|
||||
**Problem:** Difficult to perceive depth in 3D plots
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Adjust viewing angle
|
||||
ax.view_init(elev=30, azim=45)
|
||||
|
||||
# Solution 2: Add gridlines
|
||||
ax.grid(True)
|
||||
|
||||
# Solution 3: Use color for depth
|
||||
scatter = ax.scatter(x, y, z, c=z, cmap='viridis')
|
||||
|
||||
# Solution 4: Rotate interactively (if using interactive backend)
|
||||
# User can click and drag to rotate
|
||||
```
|
||||
|
||||
### Issue: 3D Axis Labels Cut Off
|
||||
|
||||
**Problem:** 3D axis labels appear outside figure
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
ax.plot_surface(X, Y, Z)
|
||||
|
||||
# Add padding
|
||||
fig.tight_layout(pad=3.0)
|
||||
|
||||
# Or save with tight bounding box
|
||||
plt.savefig('3d_plot.png', bbox_inches='tight', pad_inches=0.5)
|
||||
```
|
||||
|
||||
## Image and Colorbar Issues
|
||||
|
||||
### Issue: Images Appear Flipped
|
||||
|
||||
**Problem:** Image orientation is wrong
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Set origin parameter
|
||||
ax.imshow(img, origin='lower') # or 'upper' (default)
|
||||
|
||||
# Or flip array
|
||||
ax.imshow(np.flipud(img))
|
||||
```
|
||||
|
||||
### Issue: Images Look Pixelated
|
||||
|
||||
**Problem:** Image appears blocky when zoomed
|
||||
|
||||
**Solutions:**
|
||||
```python
|
||||
# Solution 1: Use interpolation
|
||||
ax.imshow(img, interpolation='bilinear')
|
||||
# Options: 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', etc.
|
||||
|
||||
# Solution 2: Increase DPI when saving
|
||||
plt.savefig('figure.png', dpi=300)
|
||||
|
||||
# Solution 3: Use vector format if appropriate
|
||||
plt.savefig('figure.pdf')
|
||||
```
|
||||
|
||||
## Common Errors and Fixes
|
||||
|
||||
### "TypeError: 'AxesSubplot' object is not subscriptable"
|
||||
|
||||
**Problem:** Trying to index single axes
|
||||
```python
|
||||
# Wrong
|
||||
fig, ax = plt.subplots()
|
||||
ax[0].plot(x, y) # Error!
|
||||
|
||||
# Correct
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(x, y)
|
||||
```
|
||||
|
||||
### "ValueError: x and y must have same first dimension"
|
||||
|
||||
**Problem:** Data arrays have mismatched lengths
|
||||
```python
|
||||
# Check shapes
|
||||
print(f"x shape: {x.shape}, y shape: {y.shape}")
|
||||
|
||||
# Ensure they match
|
||||
assert len(x) == len(y), "x and y must have same length"
|
||||
```
|
||||
|
||||
### "AttributeError: 'numpy.ndarray' object has no attribute 'plot'"
|
||||
|
||||
**Problem:** Calling plot on array instead of axes
|
||||
```python
|
||||
# Wrong
|
||||
data.plot(x, y)
|
||||
|
||||
# Correct
|
||||
ax.plot(x, y)
|
||||
# or for pandas
|
||||
data.plot(ax=ax)
|
||||
```
|
||||
|
||||
## Best Practices to Avoid Issues
|
||||
|
||||
1. **Always use the OO interface** - Avoid pyplot state machine
|
||||
```python
|
||||
fig, ax = plt.subplots() # Good
|
||||
ax.plot(x, y)
|
||||
```
|
||||
|
||||
2. **Use constrained_layout** - Prevents overlap issues
|
||||
```python
|
||||
fig, ax = plt.subplots(constrained_layout=True)
|
||||
```
|
||||
|
||||
3. **Close figures explicitly** - Prevents memory leaks
|
||||
```python
|
||||
plt.close(fig)
|
||||
```
|
||||
|
||||
4. **Set figure size at creation** - Better than resizing later
|
||||
```python
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
```
|
||||
|
||||
5. **Use raw strings for math text** - Avoids escape issues
|
||||
```python
|
||||
ax.set_xlabel(r'$\alpha$')
|
||||
```
|
||||
|
||||
6. **Check data shapes before plotting** - Catch size mismatches early
|
||||
```python
|
||||
assert len(x) == len(y)
|
||||
```
|
||||
|
||||
7. **Use appropriate DPI** - 300 for print, 150 for web
|
||||
```python
|
||||
plt.savefig('figure.png', dpi=300)
|
||||
```
|
||||
|
||||
8. **Test with different backends** - If display issues occur
|
||||
```python
|
||||
import matplotlib
|
||||
matplotlib.use('TkAgg')
|
||||
```
|
||||
476
skills/matplotlib/references/plot_types.md
Normal file
476
skills/matplotlib/references/plot_types.md
Normal file
@@ -0,0 +1,476 @@
|
||||
# Matplotlib Plot Types Guide
|
||||
|
||||
Comprehensive guide to different plot types in matplotlib with examples and use cases.
|
||||
|
||||
## 1. Line Plots
|
||||
|
||||
**Use cases:** Time series, continuous data, trends, function visualization
|
||||
|
||||
### Basic Line Plot
|
||||
```python
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
ax.plot(x, y, linewidth=2, label='Data')
|
||||
ax.set_xlabel('X axis')
|
||||
ax.set_ylabel('Y axis')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Multiple Lines
|
||||
```python
|
||||
ax.plot(x, y1, label='Dataset 1', linewidth=2)
|
||||
ax.plot(x, y2, label='Dataset 2', linewidth=2, linestyle='--')
|
||||
ax.plot(x, y3, label='Dataset 3', linewidth=2, linestyle=':')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Line with Markers
|
||||
```python
|
||||
ax.plot(x, y, marker='o', markersize=8, linestyle='-',
|
||||
linewidth=2, markerfacecolor='red', markeredgecolor='black')
|
||||
```
|
||||
|
||||
### Step Plot
|
||||
```python
|
||||
ax.step(x, y, where='mid', linewidth=2, label='Step function')
|
||||
# where options: 'pre', 'post', 'mid'
|
||||
```
|
||||
|
||||
### Error Bars
|
||||
```python
|
||||
ax.errorbar(x, y, yerr=error, fmt='o-', linewidth=2,
|
||||
capsize=5, capthick=2, label='With uncertainty')
|
||||
```
|
||||
|
||||
## 2. Scatter Plots
|
||||
|
||||
**Use cases:** Correlations, relationships between variables, clusters, outliers
|
||||
|
||||
### Basic Scatter
|
||||
```python
|
||||
ax.scatter(x, y, s=50, alpha=0.6)
|
||||
```
|
||||
|
||||
### Sized and Colored Scatter
|
||||
```python
|
||||
scatter = ax.scatter(x, y, s=sizes*100, c=colors,
|
||||
cmap='viridis', alpha=0.6, edgecolors='black')
|
||||
plt.colorbar(scatter, ax=ax, label='Color variable')
|
||||
```
|
||||
|
||||
### Categorical Scatter
|
||||
```python
|
||||
for category in categories:
|
||||
mask = data['category'] == category
|
||||
ax.scatter(data[mask]['x'], data[mask]['y'],
|
||||
label=category, s=50, alpha=0.7)
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
## 3. Bar Charts
|
||||
|
||||
**Use cases:** Categorical comparisons, discrete data, counts
|
||||
|
||||
### Vertical Bar Chart
|
||||
```python
|
||||
ax.bar(categories, values, color='steelblue',
|
||||
edgecolor='black', linewidth=1.5)
|
||||
ax.set_ylabel('Values')
|
||||
```
|
||||
|
||||
### Horizontal Bar Chart
|
||||
```python
|
||||
ax.barh(categories, values, color='coral',
|
||||
edgecolor='black', linewidth=1.5)
|
||||
ax.set_xlabel('Values')
|
||||
```
|
||||
|
||||
### Grouped Bar Chart
|
||||
```python
|
||||
x = np.arange(len(categories))
|
||||
width = 0.35
|
||||
|
||||
ax.bar(x - width/2, values1, width, label='Group 1')
|
||||
ax.bar(x + width/2, values2, width, label='Group 2')
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(categories)
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Stacked Bar Chart
|
||||
```python
|
||||
ax.bar(categories, values1, label='Part 1')
|
||||
ax.bar(categories, values2, bottom=values1, label='Part 2')
|
||||
ax.bar(categories, values3, bottom=values1+values2, label='Part 3')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Bar Chart with Error Bars
|
||||
```python
|
||||
ax.bar(categories, values, yerr=errors, capsize=5,
|
||||
color='steelblue', edgecolor='black')
|
||||
```
|
||||
|
||||
### Bar Chart with Patterns
|
||||
```python
|
||||
bars1 = ax.bar(x - width/2, values1, width, label='Group 1',
|
||||
color='white', edgecolor='black', hatch='//')
|
||||
bars2 = ax.bar(x + width/2, values2, width, label='Group 2',
|
||||
color='white', edgecolor='black', hatch='\\\\')
|
||||
```
|
||||
|
||||
## 4. Histograms
|
||||
|
||||
**Use cases:** Distributions, frequency analysis
|
||||
|
||||
### Basic Histogram
|
||||
```python
|
||||
ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
|
||||
ax.set_xlabel('Value')
|
||||
ax.set_ylabel('Frequency')
|
||||
```
|
||||
|
||||
### Multiple Overlapping Histograms
|
||||
```python
|
||||
ax.hist(data1, bins=30, alpha=0.5, label='Dataset 1')
|
||||
ax.hist(data2, bins=30, alpha=0.5, label='Dataset 2')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Normalized Histogram (Density)
|
||||
```python
|
||||
ax.hist(data, bins=30, density=True, alpha=0.7,
|
||||
edgecolor='black', label='Empirical')
|
||||
|
||||
# Overlay theoretical distribution
|
||||
from scipy.stats import norm
|
||||
x = np.linspace(data.min(), data.max(), 100)
|
||||
ax.plot(x, norm.pdf(x, data.mean(), data.std()),
|
||||
'r-', linewidth=2, label='Normal fit')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### 2D Histogram (Hexbin)
|
||||
```python
|
||||
hexbin = ax.hexbin(x, y, gridsize=30, cmap='Blues')
|
||||
plt.colorbar(hexbin, ax=ax, label='Counts')
|
||||
```
|
||||
|
||||
### 2D Histogram (hist2d)
|
||||
```python
|
||||
h = ax.hist2d(x, y, bins=30, cmap='Blues')
|
||||
plt.colorbar(h[3], ax=ax, label='Counts')
|
||||
```
|
||||
|
||||
## 5. Box and Violin Plots
|
||||
|
||||
**Use cases:** Statistical distributions, outlier detection, comparing distributions
|
||||
|
||||
### Box Plot
|
||||
```python
|
||||
ax.boxplot([data1, data2, data3],
|
||||
labels=['Group A', 'Group B', 'Group C'],
|
||||
showmeans=True, meanline=True)
|
||||
ax.set_ylabel('Values')
|
||||
```
|
||||
|
||||
### Horizontal Box Plot
|
||||
```python
|
||||
ax.boxplot([data1, data2, data3], vert=False,
|
||||
labels=['Group A', 'Group B', 'Group C'])
|
||||
ax.set_xlabel('Values')
|
||||
```
|
||||
|
||||
### Violin Plot
|
||||
```python
|
||||
parts = ax.violinplot([data1, data2, data3],
|
||||
positions=[1, 2, 3],
|
||||
showmeans=True, showmedians=True)
|
||||
ax.set_xticks([1, 2, 3])
|
||||
ax.set_xticklabels(['Group A', 'Group B', 'Group C'])
|
||||
```
|
||||
|
||||
## 6. Heatmaps
|
||||
|
||||
**Use cases:** Matrix data, correlations, intensity maps
|
||||
|
||||
### Basic Heatmap
|
||||
```python
|
||||
im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
|
||||
plt.colorbar(im, ax=ax, label='Values')
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
```
|
||||
|
||||
### Heatmap with Annotations
|
||||
```python
|
||||
im = ax.imshow(matrix, cmap='coolwarm')
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
# Add text annotations
|
||||
for i in range(matrix.shape[0]):
|
||||
for j in range(matrix.shape[1]):
|
||||
text = ax.text(j, i, f'{matrix[i, j]:.2f}',
|
||||
ha='center', va='center', color='black')
|
||||
```
|
||||
|
||||
### Correlation Matrix
|
||||
```python
|
||||
corr = data.corr()
|
||||
im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
|
||||
plt.colorbar(im, ax=ax, label='Correlation')
|
||||
|
||||
# Set tick labels
|
||||
ax.set_xticks(range(len(corr)))
|
||||
ax.set_yticks(range(len(corr)))
|
||||
ax.set_xticklabels(corr.columns, rotation=45, ha='right')
|
||||
ax.set_yticklabels(corr.columns)
|
||||
```
|
||||
|
||||
## 7. Contour Plots
|
||||
|
||||
**Use cases:** 3D data on 2D plane, topography, function visualization
|
||||
|
||||
### Contour Lines
|
||||
```python
|
||||
contour = ax.contour(X, Y, Z, levels=10, cmap='viridis')
|
||||
ax.clabel(contour, inline=True, fontsize=8)
|
||||
plt.colorbar(contour, ax=ax)
|
||||
```
|
||||
|
||||
### Filled Contours
|
||||
```python
|
||||
contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis')
|
||||
plt.colorbar(contourf, ax=ax)
|
||||
```
|
||||
|
||||
### Combined Contours
|
||||
```python
|
||||
contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis', alpha=0.8)
|
||||
contour = ax.contour(X, Y, Z, levels=10, colors='black',
|
||||
linewidths=0.5, alpha=0.4)
|
||||
ax.clabel(contour, inline=True, fontsize=8)
|
||||
plt.colorbar(contourf, ax=ax)
|
||||
```
|
||||
|
||||
## 8. Pie Charts
|
||||
|
||||
**Use cases:** Proportions, percentages (use sparingly)
|
||||
|
||||
### Basic Pie Chart
|
||||
```python
|
||||
ax.pie(sizes, labels=labels, autopct='%1.1f%%',
|
||||
startangle=90, colors=colors)
|
||||
ax.axis('equal') # Equal aspect ratio ensures circular pie
|
||||
```
|
||||
|
||||
### Exploded Pie Chart
|
||||
```python
|
||||
explode = (0.1, 0, 0, 0) # Explode first slice
|
||||
ax.pie(sizes, explode=explode, labels=labels,
|
||||
autopct='%1.1f%%', shadow=True, startangle=90)
|
||||
ax.axis('equal')
|
||||
```
|
||||
|
||||
### Donut Chart
|
||||
```python
|
||||
ax.pie(sizes, labels=labels, autopct='%1.1f%%',
|
||||
wedgeprops=dict(width=0.5), startangle=90)
|
||||
ax.axis('equal')
|
||||
```
|
||||
|
||||
## 9. Polar Plots
|
||||
|
||||
**Use cases:** Cyclic data, directional data, radar charts
|
||||
|
||||
### Basic Polar Plot
|
||||
```python
|
||||
theta = np.linspace(0, 2*np.pi, 100)
|
||||
r = np.abs(np.sin(2*theta))
|
||||
|
||||
ax = plt.subplot(111, projection='polar')
|
||||
ax.plot(theta, r, linewidth=2)
|
||||
```
|
||||
|
||||
### Radar Chart
|
||||
```python
|
||||
categories = ['A', 'B', 'C', 'D', 'E']
|
||||
values = [4, 3, 5, 2, 4]
|
||||
|
||||
# Add first value to the end to close the polygon
|
||||
angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False)
|
||||
values_closed = np.concatenate((values, [values[0]]))
|
||||
angles_closed = np.concatenate((angles, [angles[0]]))
|
||||
|
||||
ax = plt.subplot(111, projection='polar')
|
||||
ax.plot(angles_closed, values_closed, 'o-', linewidth=2)
|
||||
ax.fill(angles_closed, values_closed, alpha=0.25)
|
||||
ax.set_xticks(angles)
|
||||
ax.set_xticklabels(categories)
|
||||
```
|
||||
|
||||
## 10. Stream and Quiver Plots
|
||||
|
||||
**Use cases:** Vector fields, flow visualization
|
||||
|
||||
### Quiver Plot (Vector Field)
|
||||
```python
|
||||
ax.quiver(X, Y, U, V, alpha=0.8)
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_aspect('equal')
|
||||
```
|
||||
|
||||
### Stream Plot
|
||||
```python
|
||||
ax.streamplot(X, Y, U, V, density=1.5, color='k', linewidth=1)
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_aspect('equal')
|
||||
```
|
||||
|
||||
## 11. Fill Between
|
||||
|
||||
**Use cases:** Uncertainty bounds, confidence intervals, areas under curves
|
||||
|
||||
### Fill Between Two Curves
|
||||
```python
|
||||
ax.plot(x, y, 'k-', linewidth=2, label='Mean')
|
||||
ax.fill_between(x, y - std, y + std, alpha=0.3,
|
||||
label='±1 std dev')
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
### Fill Between with Condition
|
||||
```python
|
||||
ax.plot(x, y1, label='Line 1')
|
||||
ax.plot(x, y2, label='Line 2')
|
||||
ax.fill_between(x, y1, y2, where=(y2 >= y1),
|
||||
alpha=0.3, label='y2 > y1', interpolate=True)
|
||||
ax.legend()
|
||||
```
|
||||
|
||||
## 12. 3D Plots
|
||||
|
||||
**Use cases:** Three-dimensional data visualization
|
||||
|
||||
### 3D Scatter
|
||||
```python
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
scatter = ax.scatter(x, y, z, c=colors, cmap='viridis',
|
||||
marker='o', s=50)
|
||||
plt.colorbar(scatter, ax=ax)
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_zlabel('Z')
|
||||
```
|
||||
|
||||
### 3D Surface Plot
|
||||
```python
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
surf = ax.plot_surface(X, Y, Z, cmap='viridis',
|
||||
edgecolor='none', alpha=0.9)
|
||||
plt.colorbar(surf, ax=ax)
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_zlabel('Z')
|
||||
```
|
||||
|
||||
### 3D Wireframe
|
||||
```python
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
ax.plot_wireframe(X, Y, Z, color='black', linewidth=0.5)
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_zlabel('Z')
|
||||
```
|
||||
|
||||
### 3D Contour
|
||||
```python
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
ax.contour(X, Y, Z, levels=15, cmap='viridis')
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
ax.set_zlabel('Z')
|
||||
```
|
||||
|
||||
## 13. Specialized Plots
|
||||
|
||||
### Stem Plot
|
||||
```python
|
||||
ax.stem(x, y, linefmt='C0-', markerfmt='C0o', basefmt='k-')
|
||||
ax.set_xlabel('X')
|
||||
ax.set_ylabel('Y')
|
||||
```
|
||||
|
||||
### Filled Polygon
|
||||
```python
|
||||
vertices = [(0, 0), (1, 0), (1, 1), (0, 1)]
|
||||
from matplotlib.patches import Polygon
|
||||
polygon = Polygon(vertices, closed=True, edgecolor='black',
|
||||
facecolor='lightblue', alpha=0.5)
|
||||
ax.add_patch(polygon)
|
||||
ax.set_xlim(-0.5, 1.5)
|
||||
ax.set_ylim(-0.5, 1.5)
|
||||
```
|
||||
|
||||
### Staircase Plot
|
||||
```python
|
||||
ax.stairs(values, edges, fill=True, alpha=0.5)
|
||||
```
|
||||
|
||||
### Broken Barh (Gantt-style)
|
||||
```python
|
||||
ax.broken_barh([(10, 50), (100, 20), (130, 10)], (10, 9),
|
||||
facecolors='tab:blue')
|
||||
ax.broken_barh([(10, 20), (50, 50), (120, 30)], (20, 9),
|
||||
facecolors='tab:orange')
|
||||
ax.set_ylim(5, 35)
|
||||
ax.set_xlim(0, 200)
|
||||
ax.set_xlabel('Time')
|
||||
ax.set_yticks([15, 25])
|
||||
ax.set_yticklabels(['Task 1', 'Task 2'])
|
||||
```
|
||||
|
||||
## 14. Time Series Plots
|
||||
|
||||
### Basic Time Series
|
||||
```python
|
||||
import pandas as pd
|
||||
import matplotlib.dates as mdates
|
||||
|
||||
ax.plot(dates, values, linewidth=2)
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
|
||||
ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
|
||||
plt.xticks(rotation=45)
|
||||
ax.set_xlabel('Date')
|
||||
ax.set_ylabel('Value')
|
||||
```
|
||||
|
||||
### Time Series with Shaded Regions
|
||||
```python
|
||||
ax.plot(dates, values, linewidth=2)
|
||||
# Shade weekends or specific periods
|
||||
ax.axvspan(start_date, end_date, alpha=0.2, color='gray')
|
||||
```
|
||||
|
||||
## Plot Selection Guide
|
||||
|
||||
| Data Type | Recommended Plot | Alternative Options |
|
||||
|-----------|-----------------|---------------------|
|
||||
| Single continuous variable | Histogram, KDE | Box plot, Violin plot |
|
||||
| Two continuous variables | Scatter plot | Hexbin, 2D histogram |
|
||||
| Time series | Line plot | Area plot, Step plot |
|
||||
| Categorical vs continuous | Bar chart, Box plot | Violin plot, Strip plot |
|
||||
| Two categorical variables | Heatmap | Grouped bar chart |
|
||||
| Three continuous variables | 3D scatter, Contour | Color-coded scatter |
|
||||
| Proportions | Bar chart | Pie chart (use sparingly) |
|
||||
| Distributions comparison | Box plot, Violin plot | Overlaid histograms |
|
||||
| Correlation matrix | Heatmap | Clustered heatmap |
|
||||
| Vector field | Quiver plot, Stream plot | - |
|
||||
| Function visualization | Line plot, Contour | 3D surface |
|
||||
589
skills/matplotlib/references/styling_guide.md
Normal file
589
skills/matplotlib/references/styling_guide.md
Normal file
@@ -0,0 +1,589 @@
|
||||
# Matplotlib Styling Guide
|
||||
|
||||
Comprehensive guide for styling and customizing matplotlib visualizations.
|
||||
|
||||
## Colormaps
|
||||
|
||||
### Colormap Categories
|
||||
|
||||
**1. Perceptually Uniform Sequential**
|
||||
Best for ordered data that progresses from low to high values.
|
||||
- `viridis` (default, colorblind-friendly)
|
||||
- `plasma`
|
||||
- `inferno`
|
||||
- `magma`
|
||||
- `cividis` (optimized for colorblind viewers)
|
||||
|
||||
**Usage:**
|
||||
```python
|
||||
im = ax.imshow(data, cmap='viridis')
|
||||
scatter = ax.scatter(x, y, c=values, cmap='plasma')
|
||||
```
|
||||
|
||||
**2. Sequential**
|
||||
Traditional colormaps for ordered data.
|
||||
- `Blues`, `Greens`, `Reds`, `Oranges`, `Purples`
|
||||
- `YlOrBr`, `YlOrRd`, `OrRd`, `PuRd`
|
||||
- `BuPu`, `GnBu`, `PuBu`, `YlGnBu`
|
||||
|
||||
**3. Diverging**
|
||||
Best for data with a meaningful center point (e.g., zero, mean).
|
||||
- `coolwarm` (blue to red)
|
||||
- `RdBu` (red-blue)
|
||||
- `RdYlBu` (red-yellow-blue)
|
||||
- `RdYlGn` (red-yellow-green)
|
||||
- `PiYG`, `PRGn`, `BrBG`, `PuOr`, `RdGy`
|
||||
|
||||
**Usage:**
|
||||
```python
|
||||
# Center colormap at zero
|
||||
im = ax.imshow(data, cmap='coolwarm', vmin=-1, vmax=1)
|
||||
```
|
||||
|
||||
**4. Qualitative**
|
||||
Best for categorical/nominal data without inherent ordering.
|
||||
- `tab10` (10 distinct colors)
|
||||
- `tab20` (20 distinct colors)
|
||||
- `Set1`, `Set2`, `Set3`
|
||||
- `Pastel1`, `Pastel2`
|
||||
- `Dark2`, `Accent`, `Paired`
|
||||
|
||||
**Usage:**
|
||||
```python
|
||||
colors = plt.cm.tab10(np.linspace(0, 1, n_categories))
|
||||
for i, category in enumerate(categories):
|
||||
ax.plot(x, y[i], color=colors[i], label=category)
|
||||
```
|
||||
|
||||
**5. Cyclic**
|
||||
Best for cyclic data (e.g., phase, angle).
|
||||
- `twilight`
|
||||
- `twilight_shifted`
|
||||
- `hsv`
|
||||
|
||||
### Colormap Best Practices
|
||||
|
||||
1. **Avoid `jet` colormap** - Not perceptually uniform, misleading
|
||||
2. **Use perceptually uniform colormaps** - `viridis`, `plasma`, `cividis`
|
||||
3. **Consider colorblind users** - Use `viridis`, `cividis`, or test with colorblind simulators
|
||||
4. **Match colormap to data type**:
|
||||
- Sequential: increasing/decreasing data
|
||||
- Diverging: data with meaningful center
|
||||
- Qualitative: categories
|
||||
5. **Reverse colormaps** - Add `_r` suffix: `viridis_r`, `coolwarm_r`
|
||||
|
||||
### Creating Custom Colormaps
|
||||
|
||||
```python
|
||||
from matplotlib.colors import LinearSegmentedColormap
|
||||
|
||||
# From color list
|
||||
colors = ['blue', 'white', 'red']
|
||||
n_bins = 100
|
||||
cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
|
||||
|
||||
# From RGB values
|
||||
colors = [(0, 0, 1), (1, 1, 1), (1, 0, 0)] # RGB tuples
|
||||
cmap = LinearSegmentedColormap.from_list('custom', colors)
|
||||
|
||||
# Use the custom colormap
|
||||
ax.imshow(data, cmap=cmap)
|
||||
```
|
||||
|
||||
### Discrete Colormaps
|
||||
|
||||
```python
|
||||
import matplotlib.colors as mcolors
|
||||
|
||||
# Create discrete colormap from continuous
|
||||
cmap = plt.cm.viridis
|
||||
bounds = np.linspace(0, 10, 11)
|
||||
norm = mcolors.BoundaryNorm(bounds, cmap.N)
|
||||
im = ax.imshow(data, cmap=cmap, norm=norm)
|
||||
```
|
||||
|
||||
## Style Sheets
|
||||
|
||||
### Using Built-in Styles
|
||||
|
||||
```python
|
||||
# List available styles
|
||||
print(plt.style.available)
|
||||
|
||||
# Apply a style
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
# Apply multiple styles (later styles override earlier ones)
|
||||
plt.style.use(['seaborn-v0_8-whitegrid', 'seaborn-v0_8-poster'])
|
||||
|
||||
# Temporarily use a style
|
||||
with plt.style.context('ggplot'):
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(x, y)
|
||||
```
|
||||
|
||||
### Popular Built-in Styles
|
||||
|
||||
- `default` - Matplotlib's default style
|
||||
- `classic` - Classic matplotlib look (pre-2.0)
|
||||
- `seaborn-v0_8-*` - Seaborn-inspired styles
|
||||
- `seaborn-v0_8-darkgrid`, `seaborn-v0_8-whitegrid`
|
||||
- `seaborn-v0_8-dark`, `seaborn-v0_8-white`
|
||||
- `seaborn-v0_8-ticks`, `seaborn-v0_8-poster`, `seaborn-v0_8-talk`
|
||||
- `ggplot` - ggplot2-inspired style
|
||||
- `bmh` - Bayesian Methods for Hackers style
|
||||
- `fivethirtyeight` - FiveThirtyEight style
|
||||
- `grayscale` - Grayscale style
|
||||
|
||||
### Creating Custom Style Sheets
|
||||
|
||||
Create a file named `custom_style.mplstyle`:
|
||||
|
||||
```
|
||||
# custom_style.mplstyle
|
||||
|
||||
# Figure
|
||||
figure.figsize: 10, 6
|
||||
figure.dpi: 100
|
||||
figure.facecolor: white
|
||||
|
||||
# Font
|
||||
font.family: sans-serif
|
||||
font.sans-serif: Arial, Helvetica
|
||||
font.size: 12
|
||||
|
||||
# Axes
|
||||
axes.labelsize: 14
|
||||
axes.titlesize: 16
|
||||
axes.facecolor: white
|
||||
axes.edgecolor: black
|
||||
axes.linewidth: 1.5
|
||||
axes.grid: True
|
||||
axes.axisbelow: True
|
||||
|
||||
# Grid
|
||||
grid.color: gray
|
||||
grid.linestyle: --
|
||||
grid.linewidth: 0.5
|
||||
grid.alpha: 0.3
|
||||
|
||||
# Lines
|
||||
lines.linewidth: 2
|
||||
lines.markersize: 8
|
||||
|
||||
# Ticks
|
||||
xtick.labelsize: 10
|
||||
ytick.labelsize: 10
|
||||
xtick.direction: in
|
||||
ytick.direction: in
|
||||
xtick.major.size: 6
|
||||
ytick.major.size: 6
|
||||
xtick.minor.size: 3
|
||||
ytick.minor.size: 3
|
||||
|
||||
# Legend
|
||||
legend.fontsize: 12
|
||||
legend.frameon: True
|
||||
legend.framealpha: 0.8
|
||||
legend.fancybox: True
|
||||
|
||||
# Savefig
|
||||
savefig.dpi: 300
|
||||
savefig.bbox: tight
|
||||
savefig.facecolor: white
|
||||
```
|
||||
|
||||
Load and use:
|
||||
```python
|
||||
plt.style.use('path/to/custom_style.mplstyle')
|
||||
```
|
||||
|
||||
## rcParams Configuration
|
||||
|
||||
### Global Configuration
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
# Configure globally
|
||||
plt.rcParams['figure.figsize'] = (10, 6)
|
||||
plt.rcParams['font.size'] = 12
|
||||
plt.rcParams['axes.labelsize'] = 14
|
||||
|
||||
# Or update multiple at once
|
||||
plt.rcParams.update({
|
||||
'figure.figsize': (10, 6),
|
||||
'font.size': 12,
|
||||
'axes.labelsize': 14,
|
||||
'axes.titlesize': 16,
|
||||
'lines.linewidth': 2
|
||||
})
|
||||
```
|
||||
|
||||
### Temporary Configuration
|
||||
|
||||
```python
|
||||
# Context manager for temporary changes
|
||||
with plt.rc_context({'font.size': 14, 'lines.linewidth': 2.5}):
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(x, y)
|
||||
```
|
||||
|
||||
### Common rcParams
|
||||
|
||||
**Figure settings:**
|
||||
```python
|
||||
plt.rcParams['figure.figsize'] = (10, 6)
|
||||
plt.rcParams['figure.dpi'] = 100
|
||||
plt.rcParams['figure.facecolor'] = 'white'
|
||||
plt.rcParams['figure.edgecolor'] = 'white'
|
||||
plt.rcParams['figure.autolayout'] = False
|
||||
plt.rcParams['figure.constrained_layout.use'] = True
|
||||
```
|
||||
|
||||
**Font settings:**
|
||||
```python
|
||||
plt.rcParams['font.family'] = 'sans-serif'
|
||||
plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'DejaVu Sans']
|
||||
plt.rcParams['font.size'] = 12
|
||||
plt.rcParams['font.weight'] = 'normal'
|
||||
```
|
||||
|
||||
**Axes settings:**
|
||||
```python
|
||||
plt.rcParams['axes.facecolor'] = 'white'
|
||||
plt.rcParams['axes.edgecolor'] = 'black'
|
||||
plt.rcParams['axes.linewidth'] = 1.5
|
||||
plt.rcParams['axes.grid'] = True
|
||||
plt.rcParams['axes.labelsize'] = 14
|
||||
plt.rcParams['axes.titlesize'] = 16
|
||||
plt.rcParams['axes.labelweight'] = 'normal'
|
||||
plt.rcParams['axes.spines.top'] = True
|
||||
plt.rcParams['axes.spines.right'] = True
|
||||
```
|
||||
|
||||
**Line settings:**
|
||||
```python
|
||||
plt.rcParams['lines.linewidth'] = 2
|
||||
plt.rcParams['lines.linestyle'] = '-'
|
||||
plt.rcParams['lines.marker'] = 'None'
|
||||
plt.rcParams['lines.markersize'] = 6
|
||||
```
|
||||
|
||||
**Save settings:**
|
||||
```python
|
||||
plt.rcParams['savefig.dpi'] = 300
|
||||
plt.rcParams['savefig.format'] = 'png'
|
||||
plt.rcParams['savefig.bbox'] = 'tight'
|
||||
plt.rcParams['savefig.pad_inches'] = 0.1
|
||||
plt.rcParams['savefig.transparent'] = False
|
||||
```
|
||||
|
||||
## Color Palettes
|
||||
|
||||
### Named Color Sets
|
||||
|
||||
```python
|
||||
# Tableau colors
|
||||
tableau_colors = plt.cm.tab10.colors
|
||||
|
||||
# CSS4 colors (subset)
|
||||
css_colors = ['steelblue', 'coral', 'teal', 'goldenrod', 'crimson']
|
||||
|
||||
# Manual definition
|
||||
custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
|
||||
```
|
||||
|
||||
### Color Cycles
|
||||
|
||||
```python
|
||||
# Set default color cycle
|
||||
from cycler import cycler
|
||||
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
|
||||
plt.rcParams['axes.prop_cycle'] = cycler(color=colors)
|
||||
|
||||
# Or combine color and line style
|
||||
plt.rcParams['axes.prop_cycle'] = cycler(color=colors) + cycler(linestyle=['-', '--', ':', '-.'])
|
||||
```
|
||||
|
||||
### Palette Generation
|
||||
|
||||
```python
|
||||
# Evenly spaced colors from colormap
|
||||
n_colors = 5
|
||||
colors = plt.cm.viridis(np.linspace(0, 1, n_colors))
|
||||
|
||||
# Use in plot
|
||||
for i, (x, y) in enumerate(data):
|
||||
ax.plot(x, y, color=colors[i])
|
||||
```
|
||||
|
||||
## Typography
|
||||
|
||||
### Font Configuration
|
||||
|
||||
```python
|
||||
# Set font family
|
||||
plt.rcParams['font.family'] = 'serif'
|
||||
plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif']
|
||||
|
||||
# Or sans-serif
|
||||
plt.rcParams['font.family'] = 'sans-serif'
|
||||
plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
|
||||
|
||||
# Or monospace
|
||||
plt.rcParams['font.family'] = 'monospace'
|
||||
plt.rcParams['font.monospace'] = ['Courier New', 'DejaVu Sans Mono']
|
||||
```
|
||||
|
||||
### Font Properties in Text
|
||||
|
||||
```python
|
||||
from matplotlib import font_manager
|
||||
|
||||
# Specify font properties
|
||||
ax.text(x, y, 'Text',
|
||||
fontsize=14,
|
||||
fontweight='bold', # 'normal', 'bold', 'heavy', 'light'
|
||||
fontstyle='italic', # 'normal', 'italic', 'oblique'
|
||||
fontfamily='serif')
|
||||
|
||||
# Use specific font file
|
||||
prop = font_manager.FontProperties(fname='path/to/font.ttf')
|
||||
ax.text(x, y, 'Text', fontproperties=prop)
|
||||
```
|
||||
|
||||
### Mathematical Text
|
||||
|
||||
```python
|
||||
# LaTeX-style math
|
||||
ax.set_title(r'$\alpha > \beta$')
|
||||
ax.set_xlabel(r'$\mu \pm \sigma$')
|
||||
ax.text(x, y, r'$\int_0^\infty e^{-x} dx = 1$')
|
||||
|
||||
# Subscripts and superscripts
|
||||
ax.set_ylabel(r'$y = x^2 + 2x + 1$')
|
||||
ax.text(x, y, r'$x_1, x_2, \ldots, x_n$')
|
||||
|
||||
# Greek letters
|
||||
ax.text(x, y, r'$\alpha, \beta, \gamma, \delta, \epsilon$')
|
||||
```
|
||||
|
||||
### Using Full LaTeX
|
||||
|
||||
```python
|
||||
# Enable full LaTeX rendering (requires LaTeX installation)
|
||||
plt.rcParams['text.usetex'] = True
|
||||
plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath}'
|
||||
|
||||
ax.set_title(r'\textbf{Bold Title}')
|
||||
ax.set_xlabel(r'Time $t$ (s)')
|
||||
```
|
||||
|
||||
## Spines and Grids
|
||||
|
||||
### Spine Customization
|
||||
|
||||
```python
|
||||
# Hide specific spines
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
|
||||
# Move spine position
|
||||
ax.spines['left'].set_position(('outward', 10))
|
||||
ax.spines['bottom'].set_position(('data', 0))
|
||||
|
||||
# Change spine color and width
|
||||
ax.spines['left'].set_color('red')
|
||||
ax.spines['bottom'].set_linewidth(2)
|
||||
```
|
||||
|
||||
### Grid Customization
|
||||
|
||||
```python
|
||||
# Basic grid
|
||||
ax.grid(True)
|
||||
|
||||
# Customized grid
|
||||
ax.grid(True, which='major', linestyle='--', linewidth=0.8, alpha=0.3)
|
||||
ax.grid(True, which='minor', linestyle=':', linewidth=0.5, alpha=0.2)
|
||||
|
||||
# Grid for specific axis
|
||||
ax.grid(True, axis='x') # Only vertical lines
|
||||
ax.grid(True, axis='y') # Only horizontal lines
|
||||
|
||||
# Grid behind or in front of data
|
||||
ax.set_axisbelow(True) # Grid behind data
|
||||
```
|
||||
|
||||
## Legend Customization
|
||||
|
||||
### Legend Positioning
|
||||
|
||||
```python
|
||||
# Location strings
|
||||
ax.legend(loc='best') # Automatic best position
|
||||
ax.legend(loc='upper right')
|
||||
ax.legend(loc='upper left')
|
||||
ax.legend(loc='lower right')
|
||||
ax.legend(loc='lower left')
|
||||
ax.legend(loc='center')
|
||||
ax.legend(loc='upper center')
|
||||
ax.legend(loc='lower center')
|
||||
ax.legend(loc='center left')
|
||||
ax.legend(loc='center right')
|
||||
|
||||
# Precise positioning (bbox_to_anchor)
|
||||
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # Outside plot area
|
||||
ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3) # Below plot
|
||||
```
|
||||
|
||||
### Legend Styling
|
||||
|
||||
```python
|
||||
ax.legend(
|
||||
fontsize=12,
|
||||
frameon=True, # Show frame
|
||||
framealpha=0.9, # Frame transparency
|
||||
fancybox=True, # Rounded corners
|
||||
shadow=True, # Shadow effect
|
||||
ncol=2, # Number of columns
|
||||
title='Legend Title', # Legend title
|
||||
title_fontsize=14, # Title font size
|
||||
edgecolor='black', # Frame edge color
|
||||
facecolor='white' # Frame background color
|
||||
)
|
||||
```
|
||||
|
||||
### Custom Legend Entries
|
||||
|
||||
```python
|
||||
from matplotlib.lines import Line2D
|
||||
|
||||
# Create custom legend handles
|
||||
custom_lines = [Line2D([0], [0], color='red', lw=2),
|
||||
Line2D([0], [0], color='blue', lw=2, linestyle='--'),
|
||||
Line2D([0], [0], marker='o', color='w', markerfacecolor='green', markersize=10)]
|
||||
|
||||
ax.legend(custom_lines, ['Label 1', 'Label 2', 'Label 3'])
|
||||
```
|
||||
|
||||
## Layout and Spacing
|
||||
|
||||
### Constrained Layout
|
||||
|
||||
```python
|
||||
# Preferred method (automatic adjustment)
|
||||
fig, axes = plt.subplots(2, 2, constrained_layout=True)
|
||||
```
|
||||
|
||||
### Tight Layout
|
||||
|
||||
```python
|
||||
# Alternative method
|
||||
fig, axes = plt.subplots(2, 2)
|
||||
plt.tight_layout(pad=1.5, h_pad=2.0, w_pad=2.0)
|
||||
```
|
||||
|
||||
### Manual Adjustment
|
||||
|
||||
```python
|
||||
# Fine-grained control
|
||||
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
|
||||
hspace=0.3, wspace=0.4)
|
||||
```
|
||||
|
||||
## Professional Publication Style
|
||||
|
||||
Example configuration for publication-quality figures:
|
||||
|
||||
```python
|
||||
# Publication style configuration
|
||||
plt.rcParams.update({
|
||||
# Figure
|
||||
'figure.figsize': (8, 6),
|
||||
'figure.dpi': 100,
|
||||
'savefig.dpi': 300,
|
||||
'savefig.bbox': 'tight',
|
||||
'savefig.pad_inches': 0.1,
|
||||
|
||||
# Font
|
||||
'font.family': 'sans-serif',
|
||||
'font.sans-serif': ['Arial', 'Helvetica'],
|
||||
'font.size': 11,
|
||||
|
||||
# Axes
|
||||
'axes.labelsize': 12,
|
||||
'axes.titlesize': 14,
|
||||
'axes.linewidth': 1.5,
|
||||
'axes.grid': False,
|
||||
'axes.spines.top': False,
|
||||
'axes.spines.right': False,
|
||||
|
||||
# Lines
|
||||
'lines.linewidth': 2,
|
||||
'lines.markersize': 8,
|
||||
|
||||
# Ticks
|
||||
'xtick.labelsize': 10,
|
||||
'ytick.labelsize': 10,
|
||||
'xtick.major.size': 6,
|
||||
'ytick.major.size': 6,
|
||||
'xtick.major.width': 1.5,
|
||||
'ytick.major.width': 1.5,
|
||||
'xtick.direction': 'in',
|
||||
'ytick.direction': 'in',
|
||||
|
||||
# Legend
|
||||
'legend.fontsize': 10,
|
||||
'legend.frameon': True,
|
||||
'legend.framealpha': 1.0,
|
||||
'legend.edgecolor': 'black'
|
||||
})
|
||||
```
|
||||
|
||||
## Dark Theme
|
||||
|
||||
```python
|
||||
# Dark background style
|
||||
plt.style.use('dark_background')
|
||||
|
||||
# Or manual configuration
|
||||
plt.rcParams.update({
|
||||
'figure.facecolor': '#1e1e1e',
|
||||
'axes.facecolor': '#1e1e1e',
|
||||
'axes.edgecolor': 'white',
|
||||
'axes.labelcolor': 'white',
|
||||
'text.color': 'white',
|
||||
'xtick.color': 'white',
|
||||
'ytick.color': 'white',
|
||||
'grid.color': 'gray',
|
||||
'legend.facecolor': '#1e1e1e',
|
||||
'legend.edgecolor': 'white'
|
||||
})
|
||||
```
|
||||
|
||||
## Color Accessibility
|
||||
|
||||
### Colorblind-Friendly Palettes
|
||||
|
||||
```python
|
||||
# Use colorblind-friendly colormaps
|
||||
colorblind_friendly = ['viridis', 'plasma', 'cividis']
|
||||
|
||||
# Colorblind-friendly discrete colors
|
||||
cb_colors = ['#0173B2', '#DE8F05', '#029E73', '#CC78BC',
|
||||
'#CA9161', '#949494', '#ECE133', '#56B4E9']
|
||||
|
||||
# Test with simulation tools or use these validated palettes
|
||||
```
|
||||
|
||||
### High Contrast
|
||||
|
||||
```python
|
||||
# Ensure sufficient contrast
|
||||
plt.rcParams['axes.edgecolor'] = 'black'
|
||||
plt.rcParams['axes.linewidth'] = 2
|
||||
plt.rcParams['xtick.major.width'] = 2
|
||||
plt.rcParams['ytick.major.width'] = 2
|
||||
```
|
||||
Reference in New Issue
Block a user