npx skills add ...
npx skills add davila7/claude-code-templates --skill matplotlib
npx skills add davila7/claude-code-templates --skill matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.
This skill should be used when:
Matplotlib uses a hierarchical structure of objects:
1. pyplot Interface (Implicit, MATLAB-style)
2. Object-Oriented Interface (Explicit)
Single plot workflow:
Creating subplot layouts:
Line plots - Time series, continuous data, trends
Scatter plots - Relationships between variables, correlations
Bar charts - Categorical comparisons
Histograms - Distributions
Heatmaps - Matrix data, correlations
Contour plots - 3D data on 2D plane
Box plots - Statistical distributions
Violin plots - Distribution densities
For comprehensive plot type examples and variations, refer to references/plot_types.md.
Color specification methods:
'red', 'blue', 'steelblue''#FF5733'(0.1, 0.2, 0.3)cmap='viridis', cmap='plasma', cmap='coolwarm'Using style sheets:
Customizing with rcParams:
Text and annotations:
For detailed styling options and colormap guidelines, see references/styling_guide.md.
Export to various formats:
Important parameters:
dpi: Resolution (300 for publications, 150 for web, 72 for screen)bbox_inches='tight': Removes excess whitespacefacecolor='white': Ensures white background (useful for transparent themes)transparent=True: Transparent backgroundfig, ax = plt.subplots(figsize=(10, 6))constrained_layout=True or tight_layout() to prevent overlapping elementsfig, ax = plt.subplots(constrained_layout=True) is recommended for automatic spacingrasterized=True in plot calls to reduce file sizeThis skill includes helper scripts in the scripts/ directory:
plot_template.pyTemplate script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.
Usage:
style_configurator.pyInteractive utility to configure matplotlib style preferences and generate custom style sheets.
Usage:
For comprehensive information, consult the reference documents:
references/plot_types.md - Complete catalog of plot types with code examples and use casesreferences/styling_guide.md - Detailed styling options, colormaps, and customizationreferences/api_reference.md - Core classes and methods referencereferences/common_issues.md - Troubleshooting guide for common problemsMatplotlib integrates well with:
%matplotlib inline or %matplotlib widgetconstrained_layout=True or tight_layout()plt.close(fig)plt.rcParams['font.sans-serif']pixels = dpi * inchesimport matplotlib.pyplot as plt
import numpy as np
# Create figure and axes (OO interface - RECOMMENDED)
fig, ax = plt.subplots(figsize=(10, 6))
# Generate and plot data
x = np.linspace(0, 2*np.pi, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')
# Customize
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)
# Save and/or display
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()# Method 1: Regular grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x, y1)
axes[0, 1].scatter(x, y2)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=30)
# Method 2: Mosaic layout (more flexible)
fig, axes = plt.subplot_mosaic([['left', 'right_top'],
['left', 'right_bottom']],
figsize=(10, 8))
axes['left'].plot(x, y)
axes['right_top'].scatter(x, y)
axes['right_bottom'].hist(data)
# Method 3: GridSpec (maximum control)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns
ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column
ax3 = fig.add_subplot(gs[1:, 1:]) # Bottom two rows, last two columnsax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)ax.hist(data, bins=30, edgecolor='black', alpha=0.7)im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])ax.violinplot([data1, data2, data3], positions=[1, 2, 3])plt.style.use('seaborn-v0_8-darkgrid') # Apply predefined style
# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
print(plt.style.available) # List all available stylesplt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.titlesize'] = 18ax.text(x, y, 'annotation', fontsize=12, ha='center')
ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
arrowprops=dict(arrowstyle='->', color='red'))# High-resolution PNG for presentations/papers
plt.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')
# Vector format for publications (scalable)
plt.savefig('figure.pdf', bbox_inches='tight')
plt.savefig('figure.svg', bbox_inches='tight')
# Transparent background
plt.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True)from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Surface plot
ax.plot_surface(X, Y, Z, cmap='viridis')
# 3D scatter
ax.scatter(x, y, z, c=colors, marker='o')
# 3D line plot
ax.plot(x, y, z, linewidth=2)
# Labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')# Good practice: Clear structure
def create_analysis_plot(data, title):
"""Create standardized analysis plot."""
fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
# Plot data
ax.plot(data['x'], data['y'], linewidth=2)
# Customize
ax.set_xlabel('X Axis Label', fontsize=12)
ax.set_ylabel('Y Axis Label', fontsize=12)
ax.set_title(title, fontsize=14, fontweight='bold')
ax.grid(True, alpha=0.3)
return fig, ax
# Use the function
fig, ax = create_analysis_plot(my_data, 'My Analysis')
plt.savefig('analysis.png', dpi=300, bbox_inches='tight')python scripts/plot_template.pypython scripts/style_configurator.py