sound-copy/orientation_processor

AI Summary: Implement batch processing logic to skip files already processed and log a summary of the processing. This involves checking for existing processed files (likely using a log or database) and only processing new or unprocessed files. A summary log should be generated at the end detailing the number of files processed and any errors encountered.

AI Summary: Set up a pre-commit hook using either `ruff` or `black` for linting and code style enforcement. This ensures consistent code formatting before commits are made.

AI Summary: Create a README.md file for the project that includes instructions for installation, a quick start guide, and a list of available Make targets.

AI Summary: Create sample MIDI and JSON files and place them in an `/examples` directory. This is to provide example data for a project.

AI Summary: Extract the metadata fields `channel_name`, `channel_color`, and `musical_aspects` from a data source. This likely involves parsing and processing data to identify and isolate these specific pieces of information.

AI Summary: Implement unit tests for the project using pytest and pytest-cov to establish a green baseline. This involves writing tests to cover the existing codebase and ensuring that all tests pass.

AI Summary: Set up GitHub Actions workflows for continuous integration and continuous deployment (CI/CD). This involves configuring automated testing and linting processes within the GitHub Actions environment.

AI Summary: Implement schema validation using Pydantic models for the project. This involves defining Pydantic models to represent the data structures and using them to validate data against the defined schemas.

AI Summary: Implement robust error handling for edge cases in a music project, specifically addressing empty tracks, notes with zero duration, and invalid velocity values. This involves preventing crashes and providing informative error messages or graceful fallback behaviors.

AI Summary: Integrate the `default_plot.py` file's functionality into the existing `plots.py` and `io.py` files. This likely involves merging plotting functions and potentially refactoring code for better organization and maintainability.

AI Summary: Implement logging functionality using the `loguru` library, including a verbose mode controlled by the `-v` or `--verbose` command-line flags. This involves integrating `loguru` into the existing project and adding command-line argument parsing to control the log level.

AI Summary: Implement robust error handling throughout the project, ensuring graceful skips for non-critical errors and providing clear, informative messages to the user for all errors.

AI Summary: Create a `requirements.txt` file listing all project dependencies and optionally create a `Dockerfile` for reproducible environment setup.

AI Summary: Implement command-line interface (CLI) parsing for the `stage2.py` script. The script should accept the following arguments: `--help`, `--config`, `--input-dir`, and `--output-dir`.

AI Summary: Implement a configuration system using a root `config.yaml` file and allowing command-line overrides for flexibility. This involves creating the YAML parsing and handling logic, as well as integrating CLI argument parsing to allow users to modify settings from the command line.

AI Summary: Add a GitHub Actions status badge to the project's README file to visually display the build status.