Figure Templates for Publication-Quality Figure Panels

A collection of templates for creating publication-quality figures including bar plots, line plots, histograms, and multi-panel figure layouts.

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� Quick Start: Open in Google Colab

Click any link below to open directly in Google Colab - no installation needed!

Main Resources (Click to Open in Google Colab)

• 📖 Figure Templates - 🔵 Open in Colab - Template library with all plot types (line plots, histograms, error bars, dual-axis, shared-axis, broken-axis)
• 🎨 Demo Examples - 🔵 Open in Colab - Working examples showing how to combine multiple plots into one figure panel
• 📊 Publication Example - 🔵 Open in Colab - Real figure panel replicated from a research article

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📋 Purpose

This repository provides ready-to-use templates for:

• Bar plots with error bars and customizable styling
• Line plots with multiple datasets and fitted curves
• Histograms for distribution visualization
• Multi-panel figures combining multiple plot types
• Dual-axis plots (single X with multiple Y axes)
• Shared-axis plots (multiple subplots sharing axes)
• Broken Y-axis plots for data with large value ranges

All templates are designed to meet publication standards with professional styling, proper labeling, and high-quality output.

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📖 User Guide: Two Ways to Use Templates

Option 1: Google Colab (Recommended for Beginners - No Installation)

Step 1: Create a new notebook in Google Colab

• Go to colab.research.google.com
• Click "New notebook"

Step 2: Mount your Google Drive (to access your data files)

from google.colab import drive
drive.mount('/content/drive')

Step 3: Upload your data files to Google Drive

• Use the file explorer in Colab to upload CSV/data files
• Or organize them in a folder in your Google Drive

Step 4: Look at template examples

• Open the Figure Templates Guide to see how plots are created
• Copy the relevant section code into your notebook
• Update the data file paths to match your uploaded files
• Do necessary adjustments, take help from AI!

Step 5: Run and save your figure

• Execute the code cells
• Download the output images or use plt.savefig() to save

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Option 2: Local Installation (For Advanced Users)

Requirements:

• Python 3.7+
• Jupyter Notebook or VS Code
• matplotlib, numpy

Step 1: Download the Repository

Option A - Simple Download (Recommended if unfamiliar with Git):

1. Go to https://github.com/ab-zarif-razin/figure-template
2. Click the green Code button
3. Click Download ZIP
4. Extract the ZIP file to your computer
5. Open the folder in Jupyter Notebook or VS Code

Option B - Using Git (For Git users):

git clone https://github.com/ab-zarif-razin/figure-template.git
cd figure-template

Step 2: Create a new notebook in the repository folder

• Use Jupyter Notebook or VS Code
• Name it something like my_figures.ipynb

Step 3: Look at template examples

• Open template/figure_templates.ipynb to see how plots are created
• Look at template/demo_figure_panel.ipynb to see how to combine multiple figures
• Copy relevant code sections into your new notebook

Step 4: Update data paths and customize

• Point to your data files
• Adjust labels, colors, and styles as needed
• Do necessary adjustments, take help from AI!

Step 5: Save your figure

• Run your notebook
• Use plt.savefig() to save as PNG or PDF

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📊 Repository Structure

template/                          # Template notebooks and data files
├── figure_templates.ipynb         # Main template library with all plot types
├── demo_figure_panel.ipynb        # Complete example: combining multiple figures
├── BarPlot/                       # Bar plot example data
├── LinePlot/                      # Line plot example data
├── singleXsingleY/                # Single-axis plot templates
│   ├── ErrorBar/                  # Error bar data
│   └── Histogram/                 # Histogram data
├── singleXdoubleY/                # Dual Y-axis plot data
├── breakY/                        # Broken Y-axis plot data
└── sharedX/                       # Shared X-axis plot data

Example/                           # Real research figure examples
├── example_figure_panel.ipynb     # Publication figure panel
├── FigureA/ - FigureF/            # Figure data and images
└── aee5269_Figure_fig2_seq2_v2.jpg # Example output

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Contact

Nanoscale Heat Transfer Lab - North Carolina State University

For questions or custom templates, contact the repository owner.

Happy plotting! 📈