Lecture Image Generator

Lecture Image Generator

Overview

Create lecture visuals as code, not as one-off notebook output. Keep generation scripts reproducible, lecture-local, and aligned with the existing lectures/*/generate_*.py patterns in this repository.

Read references/repo-patterns.md when you need concrete examples from existing lecture scripts. Copy or adapt assets/image_generation_template.py when starting a new generator from scratch.

Workflow

1. Inspect the lecture context

• Read the target lectures/XX/lecture.md before naming images or choosing layouts.
• Inspect existing generate_*.py files in the same lecture before introducing a new script.
• Reuse existing filenames when updating visuals so slide links stay stable.
• Use a new script only when the lecture has no suitable generator or when a topic is clearly separate.

2. Start from the lecture template or an existing script

• For a new generator, copy assets/image_generation_template.py into the lecture directory and rename it to a descriptive generate_*.py filename.
• For an existing generator, preserve the current structure unless it is actively blocking the task.
• Keep scripts runnable from the lecture directory with relative paths such as images/....

3. Keep generation deterministic and lecture-local

• Create the images/ directory in code if needed.
• Set a fixed random seed before any stochastic layout or sampling step.
• Prefer a shared helper for repeated save-and-style logic.
• Close figures after saving to avoid memory leaks in multi-image scripts.
• Print progress messages when generation is non-trivial.

4. Organize code around output images

• Use one function per distinct figure or figure family.
• Give each function a clear docstring that explains the teaching purpose of the image.
• Return reusable objects only when that materially simplifies related figures.
• Keep main() as the place that orchestrates all outputs for the script.

5. Match repository conventions

• Save into images/<descriptive-name>.png unless the lecture already uses another extension.
• Prefer descriptive filenames that map cleanly to slide content.
• Use NetworkX, matplotlib, and NumPy directly unless the lecture already depends on another library.
• Keep figure sizes and DPI appropriate for slide use, not paper publication.

6. Verify outputs before finishing

• Run the generator after editing it.
• Confirm that expected files were written under images/.
• If the task includes slide updates, verify that every referenced filename exists.
• If the task is primarily about slide layout or Marp authoring, switch to or combine with marp-lecture-author.

Patterns To Preserve

• Prefer simple, readable code over abstract helper layers.
• Keep each script understandable by an instructor editing it later.
• Use inline comments sparingly and only where the visualization logic is not obvious.
• Avoid notebook-only idioms or hidden state.

Outputs

• lectures/XX/generate_*.py
• lectures/XX/images/*.png
• Optional small adjustments to lectures/XX/lecture.md when image names or ordering change

Avoid

• Do not hardcode absolute paths.
• Do not save figures outside the lecture directory.
• Do not introduce a new generator file when a small edit to an existing one is enough.
• Do not change image filenames casually when slides already reference them.