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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.