PaperOps - Academic Publication-Ready Plotting Library

PaperOps is a Python plotting library specifically designed for academic papers, providing chart templates and styles that meet top-tier journal standards. Whether using single-column or double-column layouts, PaperOps helps researchers quickly create professional and aesthetically pleasing academic figures.

✨ Key Features

📊 Diverse Chart Types

• Line Plots: Time series data and trend analysis
• Bar Charts: Single and multi-metric comparisons with grouped and horizontal layouts
• Scatter Plots: Correlation analysis with size and color mapping
• Heatmaps: Correlation matrices and confusion matrix visualization
• Pie Charts: Proportion distribution display

🎨 Professional Color Schemes

• Nature: Nature journal style colors
• Science: Science journal style colors
• IEEE: IEEE publication standard colors
• Academic: General academic style colors
• Colorblind: Colorblind-friendly color palette

📐 Flexible Layout Templates

• Single Column Layout: Suitable for most journal single-column figures
• Double Column Layout: Suitable for large figures spanning two columns
• Multiple Sizes: Small, Medium, Large

🔧 Smart Features

• Automatic Y-axis Limits: Multiple modes (auto, percentage, data_extend, zero_extend, custom)
• Smart Legend Positioning: Automatically finds optimal legend placement to avoid data overlap
• High-Quality Output: Automatically optimizes DPI and font sizes for print quality

🚀 Quick Start

Installation

pip install paperops

# or

uv add paperops

Basic Usage

import pandas as pd
import numpy as np
from paperops.core import AcademicPlotter

# Create sample data
data = pd.DataFrame({
    'x': range(10),
    'method_a': np.random.rand(10) * 100,
    'method_b': np.random.rand(10) * 100,
    'method_c': np.random.rand(10) * 100
})

# Create plotter
plotter = AcademicPlotter(
    layout="single",           # Single column layout
    size="medium",            # Medium size
    color_scheme="nature"     # Nature journal colors
)

# Create line plot
fig, ax = plotter.line_plot(
    data=data,
    x='x',
    y=['method_a', 'method_b', 'method_c'],
    fig_name="Performance Comparison",
    xlabel="Time Steps",
    ylabel="Performance Score",
    save_path="comparison.pdf"
)

📚 Detailed Usage Examples

1. Bar Chart - Algorithm Performance Comparison

# Single metric comparison
algorithms_data = pd.DataFrame({
    'algorithm': ['SVM', 'Random Forest', 'Neural Network', 'XGBoost'],
    'accuracy': [0.85, 0.92, 0.89, 0.94]
})

plotter = AcademicPlotter(layout="single", color_scheme="science")

fig, ax = plotter.bar_plot(
    data=algorithms_data,
    x='algorithm',
    y='accuracy',
    fig_name="Algorithm Accuracy Comparison",
    xlabel="Algorithm",
    ylabel="Accuracy",
    ylim_mode="percentage",  # Y-axis limited to 0-1
    save_path="algorithm_comparison.pdf"
)

2. Grouped Bar Chart - Multi-Metric Comparison

# Multi-metric comparison
multi_metrics_data = pd.DataFrame({
    'algorithm': ['SVM', 'Random Forest', 'Neural Network', 'XGBoost'],
    'accuracy': [0.85, 0.92, 0.89, 0.94],
    'precision': [0.83, 0.90, 0.87, 0.92],
    'recall': [0.81, 0.88, 0.85, 0.90]
})

fig, ax = plotter.bar_plot(
    data=multi_metrics_data,
    x='algorithm',
    y=['accuracy', 'precision', 'recall'],
    fig_name="Multi-Metric Algorithm Comparison",
    xlabel="Algorithm",
    ylabel="Score",
    legend=True,
    ylim_mode="percentage",
    save_path="multi_metric_comparison.pdf"
)

3. Scatter Plot - Training Time vs Accuracy

scatter_data = pd.DataFrame({
    'training_time': np.random.exponential(2, 100),
    'accuracy': 0.5 + 0.4 * np.random.beta(2, 2, 100),
    'model_size': np.random.lognormal(0, 1, 100)
})

fig, ax = plotter.scatter_plot(
    data=scatter_data,
    x='training_time',
    y='accuracy',
    size='model_size',  # Point size mapped to model size
    fig_name="Training Time vs Accuracy",
    xlabel="Training Time (hours)",
    ylabel="Accuracy",
    save_path="time_accuracy_scatter.pdf"
)

4. Heatmap - Feature Correlation Matrix

# Create correlation matrix
correlation_matrix = np.random.randn(6, 6)
correlation_matrix = np.corrcoef(correlation_matrix)
correlation_df = pd.DataFrame(
    correlation_matrix,
    index=['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4', 'Feature 5', 'Feature 6'],
    columns=['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4', 'Feature 5', 'Feature 6']
)

plotter = AcademicPlotter(layout="double", color_scheme="science")

fig, ax = plotter.heatmap(
    data=correlation_df,
    fig_name="Feature Correlation Matrix",
    cmap="RdBu_r",
    annot=True,
    fmt=".2f",
    save_path="correlation_heatmap.pdf"
)

🎯 Advanced Features

Y-axis Limit Modes

PaperOps provides multiple Y-axis limit modes to adapt to different data types:

# Auto mode (default)
ylim_mode="auto"

# Percentage mode (0-1 or 0-100)
ylim_mode="percentage"

# Data extend mode (min to max*1.1)
ylim_mode="data_extend"

# Zero extend mode (0 to max*1.1)
ylim_mode="zero_extend"

# Custom mode
ylim_mode="custom"
ylim=(0, 100)

Legend Style Control

fig, ax = plotter.line_plot(
    data=data,
    x='x',
    y=['method_a', 'method_b'],
    legend=True,
    legend_location="upper right",  # Specify legend position
    legend_outside=False,           # Whether to place legend outside plot
    legend_style="clean"            # Legend style
)

Color Scheme Switching

# Switch color scheme at runtime
plotter.set_color_scheme("ieee")

# Or specify during initialization
plotter = AcademicPlotter(color_scheme="colorblind")

📁 Project Structure

paperops/
├── core.py          # Main plotting interface
├── plots.py         # Various chart implementations
├── styles.py        # Color schemes and styles
├── templates.py     # Layout templates
└── config.py        # Configuration options

🎨 Available Color Schemes

Scheme Name	Description	Use Case
nature	Nature journal style	Biology, medical papers
science	Science journal style	Physics, chemistry papers
ieee	IEEE standard colors	Engineering, computer science papers
academic	General academic style	All disciplines
colorblind	Colorblind-friendly colors	Accessibility considerations

📏 Layout Sizes

Layout	Small	Medium	Large
Single Column	3.5" × 2.6"	4.5" × 3.4"	5.5" × 4.1"
Double Column	7.0" × 2.6"	9.0" × 3.4"	11.0" × 4.1"

🔧 Dependencies

• Python 3.13+
• matplotlib >= 3.7.0
• seaborn >= 0.12.0
• numpy >= 1.24.0
• pandas >= 2.0.0
• scipy >= 1.10.0

🤝 Contributing

Issues and Pull Requests are welcome! Before contributing code, please ensure:

1. Code follows PEP 8 standards
2. Add appropriate test cases
3. Update relevant documentation

📄 License

This project is licensed under the APACHE 2.0 License - see the LICENSE file for details.

🙏 Acknowledgments

This repository was generated by Claude 4, aimed at providing high-quality plotting tools for academic researchers to make paper figure creation simpler and more efficient.

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