emcd-appcore 1.7

Key Features ⭐

• 🚀 Async Application Initialization: Single prepare() function that sets up your entire application foundation with sensible defaults.

• 📁 Platform Directory Management: Automatic discovery and creation of platform-specific directories for configuration, data, and cache.

• ⚙️ TOML Configuration System: Hierarchical configuration loading with includes, template variables, and overrides. Can bring your own configuration system too.

• 🎯 Distribution Detection: Automatic detection of development vs production deployment modes with package introspection.

• 📝 Logging Configuration: Logging setup with plain and rich modes and environment variable overrides.

• 🔄 Resource Management: Integration with AsyncExitStack for proper cleanup of async resources.

• 🏷️ Safety: Full type annotations with immutable data structures for thread safety.

Installation 📦

Method: Install Python Package

Install via uv pip command:

uv pip install emcd-appcore

Or, install via pip:

pip install emcd-appcore

Optional Dependencies ⚙️

For CLI applications, install the cli extra which includes Tyro and Rich:

pip install emcd-appcore[cli]

For enhanced console output without CLI framework:

pip install emcd-appcore[rich]

Examples 💡

>>> import asyncio
>>> import contextlib
>>> import appcore
>>> async def main( ):
...     async with contextlib.AsyncExitStack( ) as exits:
...         auxdata = await appcore.prepare( exits )
...         print( f"App: {auxdata.application.name}" )
...         return auxdata.configuration
>>> # asyncio.run( main( ) )  # Returns configuration dictionary

>>> async def display_directories( ):
...     async with contextlib.AsyncExitStack( ) as exits:
...         application = appcore.ApplicationInformation(
...             name = 'my-app', publisher = 'MyCompany' )
...         auxdata = await appcore.prepare( exits, application = application )
...         dirs = auxdata.directories
...         print( f"Config: {dirs.user_config_path}" )
...         print( f"Data: {dirs.user_data_path}" )
...         print( f"Cache: {dirs.user_cache_path}" )
>>> # asyncio.run( display_directories( ) )

>>> import asyncio
>>> from appcore import cli, state
>>> class HelloCommand( cli.Command ):
...     async def execute( self, auxdata: state.Globals ) -> None:
...         print( f"Hello from {auxdata.application.name}!" )
>>> class MyApplication( cli.Application ):
...     async def execute( self, auxdata: state.Globals ) -> None:
...         command = HelloCommand( )
...         await command( auxdata )
>>> # asyncio.run( MyApplication( )( ) )

$ python -m appcore configuration --display.presentation json
$ python -m appcore environment
$ python -m appcore directories --display.target-file dirs.txt

Dependencies & Architecture 🏛️

Appcore is built on a foundation of proven, lightweight dependencies:

• Configuration: Uses standard library tomli for TOML parsing with accretive data structures that can grow but never shrink.

• Platform Integration: Leverages platformdirs for cross-platform directory discovery and aiofiles for async file operations.

• Logging Enhancement: Optional integration with Rich for enhanced console output with graceful fallbacks.

• Distribution Management: Uses importlib-metadata and importlib-resources for package introspection and resource handling.

The architecture emphasizes:

• Immutability: All configuration and state objects are immutable after creation, preventing accidental modifications.

• Async-First: Built from the ground up for async/await patterns with proper resource management.

• Dependency Injection: Configurable components that can be replaced or extended without modifying core functionality.

• Type Safety: Comprehensive type annotations for excellent IDE support and static analysis.

Contribution 🤝

Contribution to this project is welcome! However, it must follow the code of conduct for the project.

Please file bug reports and feature requests in the issue tracker or submit pull requests to improve the source code or documentation.

For development guidance and standards, please see the development guide.

Additional Indicia

Other Projects by This Author 🌟

• python-absence (absence on PyPI)

  🕳️ A Python library package which provides a sentinel for absent values - a falsey, immutable singleton that represents the absence of a value in contexts where None or False may be valid values.

• python-accretive (accretive on PyPI)

  🌌 A Python library package which provides accretive data structures - collections which can grow but never shrink.

• python-classcore (classcore on PyPI)

  🏭 A Python library package which provides foundational class factories and decorators for providing classes with attributes immutability and concealment and other custom behaviors.

• python-detextive (detextive on PyPI)

  🕵️ A Python library which provides consolidated text detection capabilities for reliable content analysis. Offers MIME type detection, character set detection, and line separator processing.

• python-dynadoc (dynadoc on PyPI)

  📝 A Python library package which bridges the gap between rich annotations and automatic documentation generation with configurable renderers and support for reusable fragments.

• python-falsifier (falsifier on PyPI)

  🎭 A very simple Python library package which provides a base class for falsey objects - objects that evaluate to False in boolean contexts.

• python-frigid (frigid on PyPI)

  🔒 A Python library package which provides immutable data structures - collections which cannot be modified after creation.

• python-icecream-truck (icecream-truck on PyPI)

  🍦 Flavorful Debugging - A Python library which enhances the powerful and well-known icecream package with flavored traces, configuration hierarchies, customized outputs, ready-made recipes, and more.

• python-librovore (librovore on PyPI)

  🐲 Documentation Search Engine - An intelligent documentation search and extraction tool that provides both a command-line interface for humans and an MCP (Model Context Protocol) server for AI agents. Search across Sphinx and MkDocs sites with fuzzy matching, extract clean markdown content, and integrate seamlessly with AI development workflows.

• python-mimeogram (mimeogram on PyPI)

  📨 A command-line tool for exchanging collections of files with Large Language Models - bundle multiple files into a single clipboard-ready document while preserving directory structure and metadata… good for code reviews, project sharing, and LLM interactions.