learning-code-consistency

name	learning-code_consistency
description	Verify consistency between .py, .ipynb, and .md files for lab assignments. Use when (1) user asks to check consistency, (2) mentions "一致" or "consistency", (3) before lab submission.

Learning Code Consistency Checker

Objectives

• Verify code logic matches between .py and .ipynb files
• Ensure .md documentation data matches code output
• Validate all screenshots/images are referenced correctly

Instructions

1. Compare .py and .ipynb Files

Check these elements:

• Function definitions (names, parameters, logic)
• Import statements
• Main execution flow
• Output statements (print, plots)
• File paths (especially image save paths)

Common differences to ignore:

• Jupyter cell structure vs sequential execution
• Interactive outputs (plt.show() in .ipynb vs plt.savefig() in .py)
• Cell magic commands (%matplotlib inline)

2. Verify .md Documentation Against Code

Data consistency checks:

• Statistical values (α, R², counts, percentages)
• Top N results (word frequencies, rankings)
• Image filenames match actual generated files
• Screenshot descriptions match code output

How to verify:

1. Run the .py file and capture output
2. Compare printed values with .md content
3. Check [Image Data] markers in output for easy matching
4. Verify image paths are relative and correct

3. Image Reference Validation

Check:

• All images in .md exist in the specified directory
• Image paths are relative (e.g., images/xxx.png)
• No absolute paths (e.g., C:\Users\...)
• No broken image links

4. Report Format

Provide a structured report:

## Consistency Check Report

### ✅ .py vs .ipynb

- Functions: Identical
- Logic: Consistent
- Outputs: Match (accounting for interactive differences)

### ✅ .md vs Code Output

- Statistical values: All match
- Image references: All valid
- Data points verified: [list key values]

### ⚠️ Issues Found

- [Issue 1]: Description and fix
- [Issue 2]: Description and fix

### 📋 Summary

Ready for submission: Yes/No

Validation Steps

1. Code execution test: Run .py file successfully
2. Output comparison: Match printed values with .md
3. Image check: Verify all images exist and display correctly
4. Cross-reference: Ensure .ipynb produces same results as .py

Common Issues

• Different random seeds: Ensure reproducibility with random.seed() or np.random.seed()
• Path differences: Use relative paths consistently
• Rounding differences: Check decimal places match in .md
• Missing images: Verify all plots are saved before referencing