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