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Zhongwei Li
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skills/matplotlib/scripts/plot_template.py
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401
skills/matplotlib/scripts/plot_template.py
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#!/usr/bin/env python3
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"""
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Matplotlib Plot Template
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Comprehensive template demonstrating various plot types and best practices.
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Use this as a starting point for creating publication-quality visualizations.
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Usage:
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python plot_template.py [--plot-type TYPE] [--style STYLE] [--output FILE]
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Plot types:
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line, scatter, bar, histogram, heatmap, contour, box, violin, 3d, all
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.gridspec import GridSpec
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import argparse
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def set_publication_style():
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"""Configure matplotlib for publication-quality figures."""
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plt.rcParams.update({
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'figure.figsize': (10, 6),
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'figure.dpi': 100,
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'savefig.dpi': 300,
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'savefig.bbox': 'tight',
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'font.size': 11,
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'axes.labelsize': 12,
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'axes.titlesize': 14,
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'xtick.labelsize': 10,
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'ytick.labelsize': 10,
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'legend.fontsize': 10,
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'lines.linewidth': 2,
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'axes.linewidth': 1.5,
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})
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def generate_sample_data():
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"""Generate sample data for demonstrations."""
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np.random.seed(42)
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x = np.linspace(0, 10, 100)
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y1 = np.sin(x)
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y2 = np.cos(x)
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scatter_x = np.random.randn(200)
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scatter_y = np.random.randn(200)
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categories = ['A', 'B', 'C', 'D', 'E']
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bar_values = np.random.randint(10, 100, len(categories))
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hist_data = np.random.normal(0, 1, 1000)
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matrix = np.random.rand(10, 10)
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X, Y = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))
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Z = np.sin(np.sqrt(X**2 + Y**2))
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return {
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'x': x, 'y1': y1, 'y2': y2,
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'scatter_x': scatter_x, 'scatter_y': scatter_y,
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'categories': categories, 'bar_values': bar_values,
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'hist_data': hist_data, 'matrix': matrix,
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'X': X, 'Y': Y, 'Z': Z
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}
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def create_line_plot(data, ax=None):
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"""Create line plot with best practices."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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ax.plot(data['x'], data['y1'], label='sin(x)', linewidth=2, marker='o',
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markevery=10, markersize=6)
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ax.plot(data['x'], data['y2'], label='cos(x)', linewidth=2, linestyle='--')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_title('Line Plot Example')
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ax.legend(loc='best', framealpha=0.9)
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ax.grid(True, alpha=0.3, linestyle='--')
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# Remove top and right spines for cleaner look
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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if ax is None:
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return fig
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return ax
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def create_scatter_plot(data, ax=None):
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"""Create scatter plot with color and size variations."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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# Color based on distance from origin
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colors = np.sqrt(data['scatter_x']**2 + data['scatter_y']**2)
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sizes = 50 * (1 + np.abs(data['scatter_x']))
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scatter = ax.scatter(data['scatter_x'], data['scatter_y'],
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c=colors, s=sizes, alpha=0.6,
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cmap='viridis', edgecolors='black', linewidth=0.5)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_title('Scatter Plot Example')
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ax.grid(True, alpha=0.3, linestyle='--')
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# Add colorbar
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cbar = plt.colorbar(scatter, ax=ax)
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cbar.set_label('Distance from origin')
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if ax is None:
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return fig
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return ax
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def create_bar_chart(data, ax=None):
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"""Create bar chart with error bars and styling."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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x_pos = np.arange(len(data['categories']))
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errors = np.random.randint(5, 15, len(data['categories']))
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bars = ax.bar(x_pos, data['bar_values'], yerr=errors,
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color='steelblue', edgecolor='black', linewidth=1.5,
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capsize=5, alpha=0.8)
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# Color bars by value
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colors = plt.cm.viridis(data['bar_values'] / data['bar_values'].max())
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for bar, color in zip(bars, colors):
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bar.set_facecolor(color)
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ax.set_xlabel('Category')
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ax.set_ylabel('Values')
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ax.set_title('Bar Chart Example')
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ax.set_xticks(x_pos)
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ax.set_xticklabels(data['categories'])
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ax.grid(True, axis='y', alpha=0.3, linestyle='--')
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# Remove top and right 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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if ax is None:
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return fig
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return ax
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def create_histogram(data, ax=None):
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"""Create histogram with density overlay."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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n, bins, patches = ax.hist(data['hist_data'], bins=30, density=True,
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alpha=0.7, edgecolor='black', color='steelblue')
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# Overlay theoretical normal distribution
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from scipy.stats import norm
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mu, std = norm.fit(data['hist_data'])
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x_theory = np.linspace(data['hist_data'].min(), data['hist_data'].max(), 100)
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ax.plot(x_theory, norm.pdf(x_theory, mu, std), 'r-', linewidth=2,
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label=f'Normal fit (μ={mu:.2f}, σ={std:.2f})')
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ax.set_xlabel('Value')
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ax.set_ylabel('Density')
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ax.set_title('Histogram with Normal Fit')
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ax.legend()
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ax.grid(True, axis='y', alpha=0.3, linestyle='--')
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if ax is None:
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return fig
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return ax
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def create_heatmap(data, ax=None):
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"""Create heatmap with colorbar and annotations."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 8), constrained_layout=True)
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im = ax.imshow(data['matrix'], cmap='coolwarm', aspect='auto',
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vmin=0, vmax=1)
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# Add colorbar
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cbar = plt.colorbar(im, ax=ax)
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cbar.set_label('Value')
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# Optional: Add text annotations
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# for i in range(data['matrix'].shape[0]):
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# for j in range(data['matrix'].shape[1]):
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# text = ax.text(j, i, f'{data["matrix"][i, j]:.2f}',
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# ha='center', va='center', color='black', fontsize=8)
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ax.set_xlabel('X Index')
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ax.set_ylabel('Y Index')
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ax.set_title('Heatmap Example')
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if ax is None:
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return fig
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return ax
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def create_contour_plot(data, ax=None):
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"""Create contour plot with filled contours and labels."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 8), constrained_layout=True)
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# Filled contours
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contourf = ax.contourf(data['X'], data['Y'], data['Z'],
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levels=20, cmap='viridis', alpha=0.8)
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# Contour lines
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contour = ax.contour(data['X'], data['Y'], data['Z'],
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levels=10, colors='black', linewidths=0.5, alpha=0.4)
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# Add labels to contour lines
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ax.clabel(contour, inline=True, fontsize=8)
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# Add colorbar
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cbar = plt.colorbar(contourf, ax=ax)
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cbar.set_label('Z value')
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_title('Contour Plot Example')
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ax.set_aspect('equal')
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if ax is None:
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return fig
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return ax
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def create_box_plot(data, ax=None):
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"""Create box plot comparing distributions."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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# Generate multiple distributions
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box_data = [np.random.normal(0, std, 100) for std in range(1, 5)]
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bp = ax.boxplot(box_data, labels=['Group 1', 'Group 2', 'Group 3', 'Group 4'],
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patch_artist=True, showmeans=True,
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boxprops=dict(facecolor='lightblue', edgecolor='black'),
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medianprops=dict(color='red', linewidth=2),
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meanprops=dict(marker='D', markerfacecolor='green', markersize=8))
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ax.set_xlabel('Groups')
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ax.set_ylabel('Values')
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ax.set_title('Box Plot Example')
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ax.grid(True, axis='y', alpha=0.3, linestyle='--')
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if ax is None:
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return fig
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return ax
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def create_violin_plot(data, ax=None):
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"""Create violin plot showing distribution shapes."""
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if ax is None:
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fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
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# Generate multiple distributions
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violin_data = [np.random.normal(0, std, 100) for std in range(1, 5)]
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parts = ax.violinplot(violin_data, positions=range(1, 5),
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showmeans=True, showmedians=True)
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# Customize colors
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for pc in parts['bodies']:
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pc.set_facecolor('lightblue')
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pc.set_alpha(0.7)
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pc.set_edgecolor('black')
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ax.set_xlabel('Groups')
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ax.set_ylabel('Values')
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ax.set_title('Violin Plot Example')
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ax.set_xticks(range(1, 5))
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ax.set_xticklabels(['Group 1', 'Group 2', 'Group 3', 'Group 4'])
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ax.grid(True, axis='y', alpha=0.3, linestyle='--')
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if ax is None:
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return fig
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return ax
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def create_3d_plot():
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"""Create 3D surface plot."""
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from mpl_toolkits.mplot3d import Axes3D
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fig = plt.figure(figsize=(12, 9))
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ax = fig.add_subplot(111, projection='3d')
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# Generate data
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X = np.linspace(-5, 5, 50)
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Y = np.linspace(-5, 5, 50)
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X, Y = np.meshgrid(X, Y)
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Z = np.sin(np.sqrt(X**2 + Y**2))
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# Create surface plot
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surf = ax.plot_surface(X, Y, Z, cmap='viridis',
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edgecolor='none', alpha=0.9)
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# Add colorbar
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fig.colorbar(surf, ax=ax, shrink=0.5)
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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.set_title('3D Surface Plot Example')
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# Set viewing angle
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ax.view_init(elev=30, azim=45)
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plt.tight_layout()
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return fig
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def create_comprehensive_figure():
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"""Create a comprehensive figure with multiple subplots."""
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data = generate_sample_data()
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fig = plt.figure(figsize=(16, 12), constrained_layout=True)
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gs = GridSpec(3, 3, figure=fig)
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# Create subplots
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ax1 = fig.add_subplot(gs[0, :2]) # Line plot - top left, spans 2 columns
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create_line_plot(data, ax1)
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ax2 = fig.add_subplot(gs[0, 2]) # Bar chart - top right
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create_bar_chart(data, ax2)
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ax3 = fig.add_subplot(gs[1, 0]) # Scatter plot - middle left
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create_scatter_plot(data, ax3)
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ax4 = fig.add_subplot(gs[1, 1]) # Histogram - middle center
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create_histogram(data, ax4)
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ax5 = fig.add_subplot(gs[1, 2]) # Box plot - middle right
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create_box_plot(data, ax5)
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ax6 = fig.add_subplot(gs[2, :2]) # Contour plot - bottom left, spans 2 columns
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create_contour_plot(data, ax6)
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ax7 = fig.add_subplot(gs[2, 2]) # Heatmap - bottom right
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create_heatmap(data, ax7)
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fig.suptitle('Comprehensive Matplotlib Template', fontsize=18, fontweight='bold')
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return fig
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def main():
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"""Main function to run the template."""
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parser = argparse.ArgumentParser(description='Matplotlib plot template')
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parser.add_argument('--plot-type', type=str, default='all',
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choices=['line', 'scatter', 'bar', 'histogram', 'heatmap',
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'contour', 'box', 'violin', '3d', 'all'],
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help='Type of plot to create')
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parser.add_argument('--style', type=str, default='default',
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help='Matplotlib style to use')
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parser.add_argument('--output', type=str, default='plot.png',
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help='Output filename')
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args = parser.parse_args()
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# Set style
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if args.style != 'default':
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plt.style.use(args.style)
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else:
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set_publication_style()
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# Generate data
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data = generate_sample_data()
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# Create plot based on type
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plot_functions = {
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'line': create_line_plot,
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'scatter': create_scatter_plot,
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'bar': create_bar_chart,
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'histogram': create_histogram,
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'heatmap': create_heatmap,
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'contour': create_contour_plot,
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'box': create_box_plot,
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'violin': create_violin_plot,
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}
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if args.plot_type == '3d':
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fig = create_3d_plot()
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elif args.plot_type == 'all':
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fig = create_comprehensive_figure()
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else:
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fig = plot_functions[args.plot_type](data)
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# Save figure
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plt.savefig(args.output, dpi=300, bbox_inches='tight')
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print(f"Plot saved to {args.output}")
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# Display
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plt.show()
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if __name__ == "__main__":
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main()
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