Analysing Nitrogen-Water Instabilities Using Python

A Manim Presentation Project

This repository contains a presentation for the scientific computing course project at OIST, using Manim Community - the mathematical animation engine made famous by 3Blue1Brown.

---

📋 Table of Contents

• Overview
• Prerequisites
• Installation
• Project Structure
• Usage
• Rendering Individual Scenes
• Creating the Complete Presentation
• Editing Guide
• Troubleshooting
• Contact

---

🔬 Overview

This presentation demonstrates a computational pipeline for analyzing liquid nitrogen-water instability patterns using Python. The project showcases:

• Computer Vision: Circle detection using Hough transforms and geometric algorithms
• Coordinate Transformations: Converting Cartesian grids to polar coordinates
• Signal Processing: Angular binning and intensity profile analysis
• Data Visualization

Presentation Duration: ~3 minutes
Target Audience: Technical/scientific community
Key Technologies: Python, OpenCV, SciPy, NumPy, Manim

---

📦 Prerequisites

Before running this project, ensure you have:

Required Software

• Python 3.8+ - Download here
• FFmpeg - For video rendering
  • Windows: Download from gyan.dev
  • Mac: brew install ffmpeg
  • Linux: sudo apt install ffmpeg
• LaTeX (Optional) - For mathematical symbols
  • Windows: MiKTeX
  • Mac: brew install --cask mactex
  • Linux: sudo apt install texlive-full
  • Note: Current version uses Text instead of MathTex, so LaTeX is optional

Python Packages

All required packages are listed in requirements.txt

---

🚀 Installation

1. Get the Project Files

Option A: Clone from GitHub

git clone https://github.com/ayman-alashkar/SC_presentation.git
cd SC_presentation

Option B: Download ZIP

1. Click the green "Code" button on GitHub
2. Select "Download ZIP"
3. Extract the ZIP file to your desired location
4. Open terminal/command prompt in that folder

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Verify Installation

manim --version

Should display: Manim Community v0.18.x or similar

---

📁 Project Structure

SC_presentation/
│
├── scenes/                          # Scene source files
│   ├── scene1.py                    # Introduction with titles
│   ├── scene2.py                    # Circle detection method
│   ├── scene3.py                    # Polar transformation
│   └── scene4.py                    # Results and conclusions
│
├── data/                            # Experimental data
│   └── d4_T20_1.JPG                 # LN2-water experimental image
│
├── media/                           # Generated videos (created after rendering)
│   └── videos/
│       ├── scene1/
│       ├── scene2/
│       ├── scene3/
│       └── scene4/
│
├── merge_list.txt                   # FFmpeg concat file for merging
├── requirements.txt                 # Python dependencies
├── README.md                        # This file
└── COMPLETE_PRESENTATION.mp4        # Final merged video (after rendering)

---

🎬 Usage

Quick Start (First Time)

1. Activate virtual environment (if not already active)

   # Windows
   venv\Scripts\activate
   
   # Mac/Linux
   source venv/bin/activate

2. Render a test scene (low quality, fast)

   manim -pql scenes/scene1.py IntroSceneWithTitles

3. Check the output

   • Navigate to: media/videos/scene1/480p15/
   • Open IntroSceneWithTitles.mp4
   • If it plays, you're all set! 🎉

---

🎥 Rendering Individual Scenes

Scene 1: Introduction

manim -pql scenes/scene1.py IntroSceneWithTitles

Duration: ~30-35 seconds
Content: Title card, experimental setup, sampling strategy, signal unwrapping

Scene 2: Circle Detection

manim -pql scenes/scene2.py CircleDetectionScene

Duration: ~42-45 seconds
Content: Hough circles, 3-point geometric fallback method

Scene 3: Polar Transformation

manim -pql scenes/scene3.py PolarTransformScene

Duration: ~70-75 seconds
Content: Cartesian to polar grid transformation, angular binning

Scene 4: Results

manim -pql scenes/scene4.py ResultsScene

Duration: ~25-30 seconds
Content: Intensity vs angle plot, peak detection, conclusions

---

🎞️ Creating the Complete Presentation

Method 1: Using FFmpeg (Recommended - Fastest)

Step 1: Render all scenes in high quality

manim -pqh scenes/scene1.py IntroSceneWithTitles
manim -pqh scenes/scene2.py CircleDetectionScene
manim -pqh scenes/scene3.py PolarTransformScene
manim -pqh scenes/scene4.py ResultsScene

Step 2: Verify merge_list.txt has correct paths

file 'media/videos/scene1/1080p60/IntroSceneWithTitles.mp4'
file 'media/videos/scene2/1080p60/CircleDetectionScene.mp4'
file 'media/videos/scene3/1080p60/PolarTransformScene.mp4'
file 'media/videos/scene4/1080p60/ResultsScene.mp4'

Step 3: Merge all scenes

ffmpeg -f concat -safe 0 -i merge_list.txt -c copy COMPLETE_PRESENTATION.mp4

Result: COMPLETE_PRESENTATION.mp4 in your project root!

Method 2: Using Python Script

pip install moviepy
python merge_scenes.py

---

✏️ Editing Guide

Common Modifications

Change Colors

# Find:
color=BLUE

# Change to:
color=RED, color=GREEN, color=YELLOW, etc.

Adjust Timing

# Make animation faster:
run_time=2  →  run_time=1

# Add more pause for narration:
self.wait(2)  →  self.wait(4)

Modify Text

# Find:
Text("Thank You!", ...)

# Change to:
Text("Thanks for Watching!", ...)

Change Sizes

# Make text bigger:
font_size=36  →  font_size=48

# Make lines thicker:
stroke_width=3  →  stroke_width=6

Quality Settings

• -pql : Low quality (480p, 15fps) - Fast for testing
• -pqm : Medium quality (720p, 30fps) - Good balance
• -pqh : High quality (1080p, 60fps) - Final export

---

🔧 Troubleshooting

Issue: "manim: command not found"

Solution:

• Make sure virtual environment is activated (look for (venv) in terminal)
• Try: python -m manim instead of just manim

Issue: "FileNotFoundError" when rendering

Solution:

• Check file paths are correct
• Use forward slashes / not backslashes \
• Verify file exists: dir scenes\scene1.py

Issue: FFmpeg merge fails

Solution:

• Check merge_list.txt format (must have file keyword)
• Use forward slashes in paths
• Wrap paths in single quotes: file 'path/to/video.mp4'

Issue: LaTeX errors (if you enable MathTex)

Solution:

• Current version uses Text() instead of MathTex() - no LaTeX needed!
• If you want LaTeX: Install MiKTeX/MacTeX and restart VS Code

Issue: Video renders but is blank/black

Solution:

• Check image path in scene1.py: data/d4_T20_1.JPG
• Verify image exists in data folder
• Try absolute path if relative path doesn't work

---

📚 Resources

• Manim Documentation: https://docs.manim.community/
• Tutorial Videos: https://www.youtube.com/c/TheoremofBeethoven
• Example Gallery: https://docs.manim.community/en/stable/examples.html
• Community Discord: https://www.manim.community/discord/

---

🎯 Rendering Workflow

For Testing/Editing

# 1. Make changes to a scene file
# 2. Render quickly in low quality
manim -pql scenes/your_scene.py SceneClassName
# 3. Check output in media/videos/...
# 4. Repeat until satisfied

For Final Presentation

# 1. Render all scenes in high quality
manim -pqh scenes/scene1.py IntroSceneWithTitles
manim -pqh scenes/scene2.py CircleDetectionScene
manim -pqh scenes/scene3.py PolarTransformScene
manim -pqh scenes/scene4.py ResultsScene

# 2. Merge into complete video
ffmpeg -f concat -safe 0 -i merge_list.txt -c copy COMPLETE_PRESENTATION.mp4

# 3. Your final presentation is ready!

---

👨‍🔬 Credits

Created by: Ayman Alashkar Institution: CFF Unit - OIST (Okinawa Institute of Science and Technology) Topic: Nitrogen-Water Instability Pattern Analysis
Animation Framework: Manim Community Edition
Date: December 2025

---

📝 License

This project is created for educational and research purposes.

---

🤝 Contributing

If you find issues or want to improve this presentation:

1. Create a detailed issue describing the problem
2. Fork the repository
3. Make your changes
4. Submit a pull request

---

📧 Contact

For questions about this presentation or the research:

• Email: a.alashkar@oist.jp
• Research Group: CFF Unit

---

✨ Acknowledgments

Special thanks to:

• Professor Kenji Doya for guiding me through the Scientific Computing course
• Manim Community for the excellent animation framework
• 3Blue1Brown for inspiring mathematical visualization
• CFF Unit for supporting this research

---

Enjoy the presentation! 🎉