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Zhongwei Li
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skills/matplotlib/references/styling_guide.md
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skills/matplotlib/references/styling_guide.md
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# Matplotlib Styling Guide
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Comprehensive guide for styling and customizing matplotlib visualizations.
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## Colormaps
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### Colormap Categories
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**1. Perceptually Uniform Sequential**
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Best for ordered data that progresses from low to high values.
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- `viridis` (default, colorblind-friendly)
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- `plasma`
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- `inferno`
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- `magma`
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- `cividis` (optimized for colorblind viewers)
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**Usage:**
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```python
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im = ax.imshow(data, cmap='viridis')
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scatter = ax.scatter(x, y, c=values, cmap='plasma')
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```
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**2. Sequential**
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Traditional colormaps for ordered data.
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- `Blues`, `Greens`, `Reds`, `Oranges`, `Purples`
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- `YlOrBr`, `YlOrRd`, `OrRd`, `PuRd`
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- `BuPu`, `GnBu`, `PuBu`, `YlGnBu`
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**3. Diverging**
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Best for data with a meaningful center point (e.g., zero, mean).
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- `coolwarm` (blue to red)
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- `RdBu` (red-blue)
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- `RdYlBu` (red-yellow-blue)
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- `RdYlGn` (red-yellow-green)
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- `PiYG`, `PRGn`, `BrBG`, `PuOr`, `RdGy`
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**Usage:**
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```python
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# Center colormap at zero
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im = ax.imshow(data, cmap='coolwarm', vmin=-1, vmax=1)
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```
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**4. Qualitative**
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Best for categorical/nominal data without inherent ordering.
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- `tab10` (10 distinct colors)
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- `tab20` (20 distinct colors)
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- `Set1`, `Set2`, `Set3`
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- `Pastel1`, `Pastel2`
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- `Dark2`, `Accent`, `Paired`
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**Usage:**
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```python
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colors = plt.cm.tab10(np.linspace(0, 1, n_categories))
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for i, category in enumerate(categories):
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ax.plot(x, y[i], color=colors[i], label=category)
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```
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**5. Cyclic**
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Best for cyclic data (e.g., phase, angle).
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- `twilight`
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- `twilight_shifted`
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- `hsv`
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### Colormap Best Practices
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1. **Avoid `jet` colormap** - Not perceptually uniform, misleading
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2. **Use perceptually uniform colormaps** - `viridis`, `plasma`, `cividis`
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3. **Consider colorblind users** - Use `viridis`, `cividis`, or test with colorblind simulators
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4. **Match colormap to data type**:
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- Sequential: increasing/decreasing data
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- Diverging: data with meaningful center
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- Qualitative: categories
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5. **Reverse colormaps** - Add `_r` suffix: `viridis_r`, `coolwarm_r`
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### Creating Custom Colormaps
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```python
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from matplotlib.colors import LinearSegmentedColormap
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# From color list
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colors = ['blue', 'white', 'red']
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n_bins = 100
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cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
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# From RGB values
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colors = [(0, 0, 1), (1, 1, 1), (1, 0, 0)] # RGB tuples
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cmap = LinearSegmentedColormap.from_list('custom', colors)
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# Use the custom colormap
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ax.imshow(data, cmap=cmap)
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```
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### Discrete Colormaps
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```python
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import matplotlib.colors as mcolors
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# Create discrete colormap from continuous
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cmap = plt.cm.viridis
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bounds = np.linspace(0, 10, 11)
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norm = mcolors.BoundaryNorm(bounds, cmap.N)
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im = ax.imshow(data, cmap=cmap, norm=norm)
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```
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## Style Sheets
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### Using Built-in Styles
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```python
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# List available styles
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print(plt.style.available)
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# Apply a style
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plt.style.use('seaborn-v0_8-darkgrid')
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# Apply multiple styles (later styles override earlier ones)
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plt.style.use(['seaborn-v0_8-whitegrid', 'seaborn-v0_8-poster'])
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# Temporarily use a style
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with plt.style.context('ggplot'):
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fig, ax = plt.subplots()
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ax.plot(x, y)
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```
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### Popular Built-in Styles
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- `default` - Matplotlib's default style
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- `classic` - Classic matplotlib look (pre-2.0)
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- `seaborn-v0_8-*` - Seaborn-inspired styles
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- `seaborn-v0_8-darkgrid`, `seaborn-v0_8-whitegrid`
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- `seaborn-v0_8-dark`, `seaborn-v0_8-white`
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- `seaborn-v0_8-ticks`, `seaborn-v0_8-poster`, `seaborn-v0_8-talk`
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- `ggplot` - ggplot2-inspired style
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- `bmh` - Bayesian Methods for Hackers style
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- `fivethirtyeight` - FiveThirtyEight style
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- `grayscale` - Grayscale style
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### Creating Custom Style Sheets
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Create a file named `custom_style.mplstyle`:
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```
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# custom_style.mplstyle
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# Figure
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figure.figsize: 10, 6
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figure.dpi: 100
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figure.facecolor: white
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# Font
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font.family: sans-serif
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font.sans-serif: Arial, Helvetica
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font.size: 12
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# Axes
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axes.labelsize: 14
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axes.titlesize: 16
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axes.facecolor: white
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axes.edgecolor: black
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axes.linewidth: 1.5
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axes.grid: True
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axes.axisbelow: True
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# Grid
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grid.color: gray
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grid.linestyle: --
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grid.linewidth: 0.5
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grid.alpha: 0.3
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# Lines
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lines.linewidth: 2
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lines.markersize: 8
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# Ticks
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xtick.labelsize: 10
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ytick.labelsize: 10
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xtick.direction: in
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ytick.direction: in
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xtick.major.size: 6
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ytick.major.size: 6
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xtick.minor.size: 3
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ytick.minor.size: 3
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# Legend
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legend.fontsize: 12
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legend.frameon: True
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legend.framealpha: 0.8
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legend.fancybox: True
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# Savefig
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savefig.dpi: 300
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savefig.bbox: tight
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savefig.facecolor: white
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```
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Load and use:
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```python
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plt.style.use('path/to/custom_style.mplstyle')
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```
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## rcParams Configuration
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### Global Configuration
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```python
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import matplotlib.pyplot as plt
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# Configure globally
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plt.rcParams['figure.figsize'] = (10, 6)
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plt.rcParams['font.size'] = 12
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plt.rcParams['axes.labelsize'] = 14
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# Or update multiple at once
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plt.rcParams.update({
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'figure.figsize': (10, 6),
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'font.size': 12,
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'axes.labelsize': 14,
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'axes.titlesize': 16,
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'lines.linewidth': 2
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})
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```
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### Temporary Configuration
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```python
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# Context manager for temporary changes
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with plt.rc_context({'font.size': 14, 'lines.linewidth': 2.5}):
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fig, ax = plt.subplots()
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ax.plot(x, y)
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```
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### Common rcParams
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**Figure settings:**
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```python
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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['figure.edgecolor'] = 'white'
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plt.rcParams['figure.autolayout'] = False
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plt.rcParams['figure.constrained_layout.use'] = True
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```
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**Font settings:**
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```python
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plt.rcParams['font.family'] = 'sans-serif'
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plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'DejaVu Sans']
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plt.rcParams['font.size'] = 12
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plt.rcParams['font.weight'] = 'normal'
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```
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**Axes settings:**
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```python
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plt.rcParams['axes.facecolor'] = 'white'
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plt.rcParams['axes.edgecolor'] = 'black'
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plt.rcParams['axes.linewidth'] = 1.5
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plt.rcParams['axes.grid'] = True
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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.labelweight'] = 'normal'
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plt.rcParams['axes.spines.top'] = True
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plt.rcParams['axes.spines.right'] = True
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```
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**Line settings:**
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```python
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plt.rcParams['lines.linewidth'] = 2
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plt.rcParams['lines.linestyle'] = '-'
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plt.rcParams['lines.marker'] = 'None'
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plt.rcParams['lines.markersize'] = 6
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```
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**Save settings:**
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```python
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plt.rcParams['savefig.dpi'] = 300
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plt.rcParams['savefig.format'] = 'png'
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plt.rcParams['savefig.bbox'] = 'tight'
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plt.rcParams['savefig.pad_inches'] = 0.1
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plt.rcParams['savefig.transparent'] = False
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```
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## Color Palettes
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### Named Color Sets
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```python
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# Tableau colors
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tableau_colors = plt.cm.tab10.colors
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# CSS4 colors (subset)
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css_colors = ['steelblue', 'coral', 'teal', 'goldenrod', 'crimson']
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# Manual definition
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custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
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```
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### Color Cycles
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```python
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# Set default color cycle
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from cycler import cycler
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colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
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plt.rcParams['axes.prop_cycle'] = cycler(color=colors)
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# Or combine color and line style
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plt.rcParams['axes.prop_cycle'] = cycler(color=colors) + cycler(linestyle=['-', '--', ':', '-.'])
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```
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### Palette Generation
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```python
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# Evenly spaced colors from colormap
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n_colors = 5
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colors = plt.cm.viridis(np.linspace(0, 1, n_colors))
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# Use in plot
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for i, (x, y) in enumerate(data):
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ax.plot(x, y, color=colors[i])
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```
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## Typography
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### Font Configuration
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```python
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# Set font family
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plt.rcParams['font.family'] = 'serif'
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plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif']
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# Or sans-serif
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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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# Or monospace
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plt.rcParams['font.family'] = 'monospace'
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plt.rcParams['font.monospace'] = ['Courier New', 'DejaVu Sans Mono']
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```
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### Font Properties in Text
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```python
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from matplotlib import font_manager
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# Specify font properties
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ax.text(x, y, 'Text',
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fontsize=14,
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fontweight='bold', # 'normal', 'bold', 'heavy', 'light'
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fontstyle='italic', # 'normal', 'italic', 'oblique'
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fontfamily='serif')
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# Use specific font file
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prop = font_manager.FontProperties(fname='path/to/font.ttf')
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ax.text(x, y, 'Text', fontproperties=prop)
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```
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### Mathematical Text
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```python
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# LaTeX-style math
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ax.set_title(r'$\alpha > \beta$')
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ax.set_xlabel(r'$\mu \pm \sigma$')
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ax.text(x, y, r'$\int_0^\infty e^{-x} dx = 1$')
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# Subscripts and superscripts
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ax.set_ylabel(r'$y = x^2 + 2x + 1$')
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ax.text(x, y, r'$x_1, x_2, \ldots, x_n$')
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# Greek letters
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ax.text(x, y, r'$\alpha, \beta, \gamma, \delta, \epsilon$')
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```
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### Using Full LaTeX
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```python
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# Enable full LaTeX rendering (requires LaTeX installation)
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plt.rcParams['text.usetex'] = True
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plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath}'
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ax.set_title(r'\textbf{Bold Title}')
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ax.set_xlabel(r'Time $t$ (s)')
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```
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## Spines and Grids
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### Spine Customization
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```python
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# Hide specific spines
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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# Move spine position
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ax.spines['left'].set_position(('outward', 10))
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ax.spines['bottom'].set_position(('data', 0))
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# Change spine color and width
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ax.spines['left'].set_color('red')
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ax.spines['bottom'].set_linewidth(2)
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```
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### Grid Customization
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```python
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# Basic grid
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ax.grid(True)
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# Customized grid
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ax.grid(True, which='major', linestyle='--', linewidth=0.8, alpha=0.3)
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ax.grid(True, which='minor', linestyle=':', linewidth=0.5, alpha=0.2)
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# Grid for specific axis
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ax.grid(True, axis='x') # Only vertical lines
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ax.grid(True, axis='y') # Only horizontal lines
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# Grid behind or in front of data
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ax.set_axisbelow(True) # Grid behind data
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```
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## Legend Customization
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### Legend Positioning
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```python
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# Location strings
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ax.legend(loc='best') # Automatic best position
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ax.legend(loc='upper right')
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ax.legend(loc='upper left')
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ax.legend(loc='lower right')
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ax.legend(loc='lower left')
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ax.legend(loc='center')
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ax.legend(loc='upper center')
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ax.legend(loc='lower center')
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ax.legend(loc='center left')
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ax.legend(loc='center right')
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# Precise positioning (bbox_to_anchor)
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ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # Outside plot area
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ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3) # Below plot
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```
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### Legend Styling
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```python
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ax.legend(
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fontsize=12,
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frameon=True, # Show frame
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framealpha=0.9, # Frame transparency
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fancybox=True, # Rounded corners
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shadow=True, # Shadow effect
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ncol=2, # Number of columns
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title='Legend Title', # Legend title
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title_fontsize=14, # Title font size
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edgecolor='black', # Frame edge color
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facecolor='white' # Frame background color
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)
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```
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### Custom Legend Entries
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```python
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from matplotlib.lines import Line2D
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# Create custom legend handles
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custom_lines = [Line2D([0], [0], color='red', lw=2),
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Line2D([0], [0], color='blue', lw=2, linestyle='--'),
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Line2D([0], [0], marker='o', color='w', markerfacecolor='green', markersize=10)]
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ax.legend(custom_lines, ['Label 1', 'Label 2', 'Label 3'])
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```
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## Layout and Spacing
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||||
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### Constrained Layout
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||||
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```python
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# Preferred method (automatic adjustment)
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fig, axes = plt.subplots(2, 2, constrained_layout=True)
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||||
```
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### Tight Layout
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||||
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```python
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# Alternative method
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fig, axes = plt.subplots(2, 2)
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plt.tight_layout(pad=1.5, h_pad=2.0, w_pad=2.0)
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||||
```
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### Manual Adjustment
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||||
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||||
```python
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# Fine-grained control
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||||
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
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hspace=0.3, wspace=0.4)
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||||
```
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||||
## Professional Publication Style
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||||
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||||
Example configuration for publication-quality figures:
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||||
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||||
```python
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||||
# Publication style configuration
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||||
plt.rcParams.update({
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||||
# Figure
|
||||
'figure.figsize': (8, 6),
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||||
'figure.dpi': 100,
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||||
'savefig.dpi': 300,
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||||
'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