weekly 0.1.7

A comprehensive Python project quality analyzer that provides actionable next steps for improving your project

Weekly - Project Quality Analyzer

Weekly is a comprehensive Python project quality analyzer that helps developers maintain high code quality by automatically detecting issues and suggesting improvements. It analyzes various aspects of your Python projects and generates actionable reports with clear next steps.

✨ Features

• 🧪 Test Coverage Analysis: Check test coverage and test configuration
• 📚 Documentation Check: Verify README, LICENSE, CHANGELOG, and API docs
• 🔄 CI/CD Integration: Detect CI/CD configuration and best practices
• 📦 Dependency Analysis: Identify outdated or vulnerable dependencies
• 🛠️ Code Quality: Check for code style, formatting, and common issues
• 📊 Interactive Reports: Generate detailed reports in multiple formats (JSON, Markdown, Text, HTML)
• 🔍 Extensible Architecture: Easy to add custom checkers and rules
• 🚀 Fast and Lightweight: Minimal dependencies, fast analysis
• 🔄 Git Integration: Works seamlessly with Git repositories
• 🔍 Multi-Repo Scanning: Scan multiple Git repositories in a directory structure
• 📅 Date-based Filtering: Only analyze repositories with recent changes
• 📑 HTML Reports: Beautiful, interactive HTML reports with drill-down capabilities
• 🔒 Security Checks: Identify potential security issues in your code
• 📈 Trend Analysis: Track code quality metrics over time

🔍 Git Repository Scanning

Weekly can scan multiple Git repositories in a directory structure and generate comprehensive reports for each one, plus a summary report.

Basic Usage

# Scan all Git repositories in ~/github
weekly scan ~/github

# Only show repositories with changes in the last 7 days (default)
weekly scan ~/github --since "7 days ago"

# Specify a custom output directory
weekly scan ~/github -o ./weekly-reports

# Run with 8 parallel jobs for faster scanning
weekly scan ~/github -j 8

# Generate JSON reports instead of HTML
weekly scan ~/github --format json

Example Output

🔍 Scanning Git repositories in /Users/username/github...
✅ Scan complete! Generated reports for 3 repositories.
📊 Summary report: weekly-reports/summary.html

✅ org1/repo1: 5 checks
   ✓ style: Passed
   ✓ code_quality: Passed
   ✓ dependencies: 2 outdated packages found
   ✓ docs: Documentation is 85% complete
   ✓ tests: 92% test coverage

Command Options

Usage: weekly scan [OPTIONS] [ROOT_DIR]

  Scan multiple Git repositories and generate reports.

  ROOT_DIR: Directory containing Git repositories (default: current directory)

Options:
  -o, --output PATH      Output directory for reports (default: ./weekly-reports)
  -s, --since TEXT        Only include repositories with changes since this date (e.g., "7 days ago", "2023-01-01")
  --recursive / --no-recursive  Scan directories recursively (default: True)
  -j, --jobs INTEGER      Number of parallel jobs (default: 4)
  -f, --format [html|json|markdown]  Output format (default: html)
  --summary-only          Only generate a summary report, not individual reports
  -v, --verbose           Show detailed output
  --help                  Show this message and exit.

Programmatic Usage

from pathlib import Path
from datetime import datetime, timedelta
from weekly import GitScanner

# Create a scanner instance
scanner = GitScanner(
    root_dir=Path.home() / "github",
    output_dir="weekly-reports",
    since=datetime.now() - timedelta(days=7),
    recursive=True,
    jobs=4
)

# Run the scan
results = scanner.scan_all()

# Process results
for result in results:
    print(f"{result.repo.org}/{result.repo.name}:")
    for name, check in result.results.items():
        status = "✓" if check.is_ok else "✗"
        print(f"  {status} {name}: {check.message}")

🚀 Installation

Using pip

pip install weekly

Using Poetry (recommended)

poetry add weekly

For Development

# Clone the repository
git clone https://github.com/wronai/weekly.git
cd weekly

# Install with Poetry
poetry install --with dev

# Install pre-commit hooks
pre-commit install

# Activate the virtual environment
poetry shell

Usage

Basic Usage

Analyze a Python project:

weekly analyze /path/to/your/project

Command Line Options

Usage: weekly analyze [OPTIONS] PROJECT_PATH

  Analyze a Python project and provide quality insights.

  PROJECT_PATH: Path to the project directory (default: current directory)

Options:
  -f, --format [text|json|markdown]  Output format (default: text)
  -o, --output FILE                  Output file (default: stdout)
  --show-suggestions / --no-suggestions
                                      Show improvement suggestions (default: true)
  -v, --verbose                      Show detailed output
  --help                             Show this message and exit.

Examples

1. Analyze current directory and show results in the terminal:

   weekly analyze .
   

2. Generate a Markdown report:

   weekly analyze -f markdown -o report.md /path/to/project
   

3. Generate a JSON report for programmatic use:

   weekly analyze -f json -o report.json /path/to/project
   

Output Example

Text Output

📊 Weekly Project Analysis Report
================================================================================
Project: example-project
Generated: 2025-06-07 12:34:56

Summary:
--------------------------------------------------------------------------------
✅ 5 passed
⚠️  3 warnings
❌ 1 errors

Detailed Results:
--------------------------------------------------------------------------------
✅ Project Structure
  Found Python project with proper structure

✅ Dependencies
  All dependencies are properly specified
  
⚠️  Test Coverage
  Test coverage is below 80% (currently 65%)
  
  Suggestions:
    • Add more test cases to improve coverage
    • Consider using pytest-cov for coverage reporting

❌ Documentation
  Missing API documentation
  
  Suggestions:
    • Add docstrings to all public functions and classes
    • Consider using Sphinx or MkDocs for API documentation

Recommended Actions:
--------------------------------------------------------------------------------
1. Improve Test Coverage
   • Add unit tests for untested modules
   • Add integration tests for critical paths
   • Set up code coverage reporting in CI

2. Enhance Documentation
   • Add docstrings to all public APIs
   • Create API documentation using Sphinx or MkDocs
   • Add examples to the README

Programmatic Usage

from pathlib import Path
from weekly import analyze_project
from weekly.core.report import Report

# Analyze a project
report = analyze_project(Path("/path/to/your/project"))

# Get report as dictionary
report_data = report.to_dict()

# Get markdown report
markdown = report.to_markdown()

# Print summary
print(f"✅ {report.summary['success']} passed")
print(f"⚠️  {report.summary['warnings']} warnings")
print(f"❌ {report.summary['errors']} errors")

# Get suggestions
for suggestion in report.get_suggestions():
    print(f"\n{suggestion['title']}:")
    for item in suggestion['suggestions']:
        print(f"  • {item}")

### Most Active Files

- `src/main.py`: 12 changes
- `tests/test_main.py`: 8 changes
- `README.md`: 5 changes

### Languages Used

- `.py`: 15 files
- `.md`: 3 files
- `.json`: 2 files

## 📋 Next Steps

- [ ] Add tests for recent changes
- [ ] Refactor large files: src/utils.py, src/processor.py...

## 📜 Recent Commits

- `a1b2c3d` Fix bug in data processing (2023-05-15)
- `f4e5d6a` Add new feature X (2023-05-14)
- `b3c4d5e` Update documentation (2023-05-13)
- `c6d7e8f` Refactor module Y (2023-05-12)
- `d9e0f1a` Initial commit (2023-05-10)

*[View full history in the JSON file]*

Development

Setup

1. Clone the repository:

   git clone https://github.com/wronai/weekly.git
   cd weekly
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install development dependencies:

   pip install -e .[dev]
   

Running Tests

pytest

Code Style

This project uses:

• Black for code formatting
• isort for import sorting
• flake8 for linting
• mypy for type checking

Run all checks:

black .
isort .
flake8
mypy .

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with ❤️ by the WronAI team
• Inspired by various Git analysis tools