oryx-wat-scraper 0.1.0

Python scraper for Oryx equipment loss data, matching the R script approach

oryx-wat-scraper

Python scraper for Oryx equipment loss data, matching the R script approach from scrape_oryx.

This package scrapes equipment loss data directly from the Oryx blog post and generates CSV files in the same format as the oryx_data repository.

Features

• ✅ CSV Output: Generates CSV files matching oryx_data format
• ✅ Type-safe: Full type hints with dataclasses
• ✅ Context Manager: Proper resource cleanup
• ✅ Well-tested: Comprehensive test suite
• ✅ Modern Python: Requires Python 3.10+

Installation

pip install oryx-wat-scraper

Or using uv:

uv add oryx-wat-scraper

Or using poetry:

poetry add oryx-wat-scraper

Quick Start

Python API

from oryx_wat_scraper import OryxScraper

# Initialize the scraper
scraper = OryxScraper()

# Scrape data
data = scraper.scrape()

# Generate CSV files (matching oryx_data format)
scraper.scrape_to_csv('outputfiles')

# Or get JSON
json_data = scraper.scrape_to_json('output.json')

# Close when done
scraper.close()

Context Manager

from oryx_wat_scraper import OryxScraper

with OryxScraper() as scraper:
    # Scrape specific countries
    data = scraper.scrape(countries=['russia', 'ukraine'])

    # Generate CSV files
    scraper.scrape_to_csv('outputfiles')

Command Line

# Generate CSV files
oryx-scraper --csv

# Save to JSON
oryx-scraper -o output.json

# Scrape specific countries
oryx-scraper --csv --countries russia ukraine

# Custom output directory
oryx-scraper --csv --output-dir my_output

Output Formats

CSV Files (matching oryx_data format)

daily_count.csv (columns: country, equipment_type, destroyed, abandoned, captured, damaged, type_total, date_recorded)

country,equipment_type,destroyed,abandoned,captured,damaged,type_total,date_recorded
russia,T-62M,154,5,34,1,194,2024-01-15
ukraine,T-72,45,2,8,0,55,2024-01-15

totals_by_type.csv (columns: country, type, destroyed, abandoned, captured, damaged, total)

country,type,destroyed,abandoned,captured,damaged,total
russia,T-62M,154,5,34,1,194
ukraine,T-72,45,2,8,0,55

JSON Output

{
  "url": "https://www.oryxspioenkop.com/...",
  "date_scraped": "2024-01-15",
  "total_entries": 1000,
  "daily_count": [...],
  "totals_by_type": [...]
}

API Reference

OryxScraper

Main scraper class.

Methods

• scrape(countries: List[str] | None = None) -> Dict: Scrape data for specified countries
• scrape_to_csv(output_dir: str = 'outputfiles') -> Dict: Scrape and save to CSV files
• scrape_to_json(output_file: str | None = None, indent: int = 2) -> str: Scrape and return/save as JSON
• close(): Close the HTTP client

Models

• EquipmentEntry: Individual equipment entry with status
• SystemEntry: Individual system entry with status

Exceptions

• OryxScraperError: Base exception
• OryxScraperNetworkError: Network errors
• OryxScraperParseError: HTML parsing errors
• OryxScraperValidationError: Data validation errors

Development

Setup

# Clone the repository
git clone https://github.com/wat-suite/oryx-wat-scraper.git
cd oryx-wat-scraper

# Install with uv
uv sync --dev

# Install pre-commit hooks
uv run pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=oryx_wat_scraper --cov-report=html

# Run specific test file
pytest tests/test_client.py

Running CI Checks Locally

You can run the same checks that CI runs locally:

# Run all checks
black --check .
ruff check .
mypy oryx_wat_scraper
pytest

Code Quality

# Format code
black .

# Lint code
ruff check .

# Type check
mypy oryx_wat_scraper

Make Commands

make install-dev  # Install with dev dependencies
make test         # Run tests
make lint         # Run linters
make format       # Format code
make type-check   # Type check
make clean        # Clean build artifacts

Releasing

This project uses GitHub Releases to trigger PyPI publishing. The workflow automatically publishes to PyPI when a final (non-pre-release) GitHub Release is created.

Release Workflow

1. Update Version

Update the version in pyproject.toml:

[project]
version = "0.2.0"  # Update to your new version

Follow Semantic Versioning:

• MAJOR (1.0.0): Breaking changes
• MINOR (0.1.0): New features, backwards compatible
• PATCH (0.0.1): Bug fixes, backwards compatible

2. Update Changelog

Update CHANGELOG.md with the changes for this version.

3. Commit and Push Changes

git add pyproject.toml CHANGELOG.md
git commit -m "chore: bump version to 0.2.0"
git push origin main

4. Create a Git Tag

Create a tag matching the version (with or without 'v' prefix):

# Option 1: Tag with 'v' prefix
git tag v0.2.0

# Option 2: Tag without prefix
git tag 0.2.0

# Push the tag
git push origin v0.2.0

Important: The tag version must match the version in pyproject.toml exactly (excluding the 'v' prefix if used).

5. Create GitHub Release

Go to the GitHub Releases page and click "Draft a new release":

For Pre-Release (Testing):

• Tag: Select the tag you just created (e.g., v0.2.0)
• Release title: v0.2.0 (or your version)
• Description: Copy from CHANGELOG.md or write release notes
• ☑️ Set as a pre-release: Check this box
• Click "Publish release"

Pre-releases are not published to PyPI. Use them for testing before the final release.

For Final Release (Publishing to PyPI):

• Tag: Select the tag you just created (e.g., v0.2.0)
• Release title: v0.2.0 (or your version)
• Description: Copy from CHANGELOG.md or write release notes
• ☐ Set as a pre-release: Leave this unchecked
• Click "Publish release"

The GitHub Actions workflow will:

1. Verify the tag version matches pyproject.toml
2. Build the package
3. Check the package with twine
4. Publish to PyPI (only for final releases, not pre-releases)

Workflow Summary

1. Update version in pyproject.toml
2. Update CHANGELOG.md
3. Commit and push changes
4. Create and push git tag
5. Create GitHub Release (pre-release or final)
   └─> Pre-release: Testing only, not published to PyPI
   └─> Final release: Automatically published to PyPI

Troubleshooting

• Version mismatch error: Ensure the tag version (without 'v' prefix) exactly matches pyproject.toml version
• Pre-release published: Pre-releases are intentionally skipped. Create a final release to publish to PyPI
• Workflow not triggered: Ensure the release is "Published" (not "Draft") and the tag exists

Based On

This scraper is based on the R script approach from:

• scrape_oryx - R script for scraping Oryx data
• oryx_data - Processed CSV data repository

Contributing

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

Development Workflow

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass and code is formatted (black . && ruff check . && pytest)
6. Commit your changes (following Conventional Commits)
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Commit Message Guidelines

This project follows Conventional Commits. Commit messages should be formatted as:

<type>(<scope>): <subject>

<body>

<footer>

Types:

• feat: New feature
• fix: Bug fix
• docs: Documentation changes
• style: Code style changes (formatting, etc.)
• refactor: Code refactoring
• test: Adding or updating tests
• chore: Maintenance tasks

Code Style

• This project uses Black for code formatting
• Ruff is used for linting
• mypy is used for type checking
• All code must pass linting and type checking

License

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

Support

If you encounter any issues or have questions, please open an issue on GitHub.

Changelog

See CHANGELOG.md for a list of changes and version history.

Acknowledgments

• Oryx for documenting equipment losses
• scarnecchia for the R script and data processing
• All contributors who help improve this library