graph_teaching

Python tutorial code and Manim animations for graph theory and network science teaching.

Current Dependencies

The Chapter 5 animation workflow is built and tested in the manim conda environment.

Core libraries and tools:

• Python 3
• Manim Community
• NumPy
• NetworkX
• pandas, used by the real-network data loaders and clustering comparison scripts
• openpyxl, used by pandas to read the C. elegans .xlsx data file
• FFmpeg and FFprobe
• LaTeX tools used by Manim for MathTex
• A Chinese font for bilingual captions, currently Noto Sans SC
• Pillow, used for generated video cover images

Audio and review helpers:

• gTTS, used by NetworkScience/Chapter5/video5_1/scripts/generate_chinese_audio.py
• openai-whisper, used for recorded Chinese narration transcription
• faster-whisper, used for recorded Chinese narration transcription when available
• Optional manim-voiceover[gtts] for future in-scene voiceover timing
• Optional ImageMagick for contact-sheet visual checks

Typical setup:

source /home/haotian/miniconda3/etc/profile.d/conda.sh
conda activate manim
pip install gTTS
pip install pandas openpyxl
pip install faster-whisper Pillow
pip install "manim-voiceover[gtts]"  # optional

For the recorded-audio transcription pass, openai-whisper was installed in the system Python with existing Torch dependencies:

python -m pip install --user --no-deps openai-whisper

Chapter 5 videos should render Manim scenes sequentially, because parallel renders can race while writing shared media/Tex files.

End References

Every finished movie should end with:

• Network Science book: https://www.networksciencebook.com/
• Course repository: https://github.com/haotianh9/graph_teaching

References

1. Network Science Book by Albert-Laszlo Barabasi
2. Dynamics and Bifurcation in Networks: Theory and Applications of Coupled Differential Equations by Martin Golubitsky and Ian Stewart