thechart/.github/instructions/copilot.instructions.md

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AI Coding Guidelines for TheChart Project

Project Overview

• Project Name: TheChart (Medication Tracker)
• Purpose: Desktop application for tracking medications and pathologies.
• Tech Stack: Python 3.x, Tkinter, Pandas, modular architecture.
• Key Features:
  • Add/edit/delete daily medication and pathology entries
  • Visual graphs and charts
  • Data export
  • Keyboard shortcuts
  • Theming support

Coding Guidelines

1. Code Style

• Follow PEP8 for Python code (indentation, naming, spacing).
• Use type hints for all function signatures and variables where possible.
• Use docstrings for all public methods and classes.
• Prefer f-strings for string formatting.
• Use snake_case for variables/functions, CamelCase for classes.
• Keep lines under 88 characters.
• Use descriptive names for variables and functions to enhance readability.
• Avoid global variables; use class attributes or method parameters instead.
• Use logging for debug/info messages instead of print statements.
• Use .venv/bin/activate.fish as the virtual environment activation script.
• The package manager is uv.
• Use ruff for linting and formatting.
• The terminal uses fish shell.

2. Architecture & Structure

• Maintain separation of concerns: UI, data management, and business logic in their respective modules.
• Use manager classes (e.g., DataManager, UIManager, ThemeManager) for encapsulating related functionality.
• UI elements and data columns must be generated dynamically based on current medicines/pathologies.
• New medicines/pathologies should not require changes to main logic—use dynamic lists and keys.
• Avoid hardcoding values; use configuration files or constants.
• Adopt a modular project structure following python best practices.

3. Error Handling

• Use try/except for operations that may fail (file I/O, data parsing).
• Show user-friendly error messages via messagebox dialogs.
• Log errors and important actions using the logger.

4. User Experience

• Always update the status bar and provide feedback for user actions.
• Use confirmation dialogs for destructive actions (e.g., deleting entries).
• Support keyboard shortcuts for all major actions.
• Keep the UI responsive and avoid blocking operations in the main thread.

5. Data Handling

• Use Pandas DataFrames for all data manipulation.
• Always check for duplicate dates before adding new entries.
• Store medicine doses as a string (e.g., "time:dose|time:dose") for each medicine.
• Support dynamic addition/removal of medicines and pathologies.

6. Testing & Robustness

• Validate all user input before saving.
• Ensure all UI elements are updated after data changes.
• Use batch operations for updating UI elements (e.g., clearing and repopulating the table).

7. Documentation

• Keep code well-commented and maintain clear docstrings.
• Document any non-obvious logic, especially dynamic UI/data handling.

8. Performance

• Use efficient methods for updating UI elements (e.g., batch delete/insert for Treeview).
• Avoid unnecessary data reloads or UI refreshes.
• Use multi-threading when appropriate.

When Generating or Reviewing Code

• Respect the modular structure—add new logic to the appropriate manager or window class.
• Do not hardcode medicine/pathology names—always use dynamic keys from the managers.
• Preserve user feedback (status bar, dialogs) for all actions.
• Maintain keyboard shortcut support for new features.
• Code Refactoring is allowed as long as it does not change the external behavior of the code.
• Ensure compatibility with the existing UI and data model.
• Write clear, concise, and maintainable code with proper type hints and docstrings.
• Avoid using deprecated imports or patterns.
• Remove any warnings or deprecation notices from the codebase.
• Replace legacy code.

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Summary: This project is a modular, extensible Tkinter application for tracking medication and pathology data. Code should be clean, dynamic, user-friendly, and robust, following PEP8 and the architectural patterns already established. All new features or changes should integrate seamlessly with the existing managers and UI paradigms, unless instructed otherwise.

Notes: A robust Python project directory structure is crucial for maintainability, scalability, and collaboration. Key best practices include:

Root Project Directory:
    Create a top-level directory for your project, typically named after the project itself.
Source Code (src/ or my_package/):
    Modern approach: Place all application source code within a src/ directory. This clearly separates source code from other project files.
    Alternative: If your project is a single package, the main package directory (e.g., my_package/) can reside directly under the root, containing your modules and __init__.py.
    Modularity: Break down your code into smaller, logical modules within this directory, each with a clear responsibility.
    __init__.py: Include an __init__.py file in every directory intended to be a Python package, marking it as importable.
Tests (tests/):
    Create a dedicated tests/ directory at the root level to house all your test files.
    Structure tests to mirror the application's module structure for easier navigation and understanding.
Documentation (docs/):
    Include a docs/ directory for project documentation, including usage guides, API references, and design documents.
Configuration (config/ or pyproject.toml):
    Use pyproject.toml for modern project configuration, including project metadata, dependencies, and tool configurations (linters, formatters, test runners).
    For application-specific or environment-dependent configurations, consider a config/ directory or environment variables.
Entry Point (main.py or cli.py):
    Designate a clear entry point for your application, often main.py or cli.py for command-line interfaces. This file should primarily orchestrate the application's flow and delegate logic to other modules.
Other Important Files:
    README.md: A comprehensive README at the root level providing project overview, installation instructions, and usage examples.
    LICENSE: A license file specifying the terms of use and distribution.
    .gitignore: For version control, specifying files and directories to be ignored by Git (e.g., virtual environments, compiled files, sensitive data).
    requirements.txt: (or managed via pyproject.toml): Lists project dependencies.
Virtual Environments:
    Utilize virtual environments (e.g., venv, conda) to isolate project dependencies and avoid conflicts. The virtual environment directory (e.g., .venv/) should be ignored by version control.