| Name | typed-cache JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | A framework for caching data locally. It features explicit caching with priority support for type hints. |
| upload_time | 2024-10-19 09:13:31 |
| maintainer | None |
| docs_url | None |
| author | panhaoyu |
| requires_python | <4.0,>=3.6 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# typed-cache
`typed-cache` is a lightweight framework designed for caching data locally, supporting explicit caching with a focus on Python's type hints. It simplifies the process of persisting data to disk by allowing easy integration into custom data classes, with automatic loading and manual saving of data.
## Features
- **Type Hinting**: Enhances code clarity and safety through the use of Python's type hints.
- **Automatic Caching**: Automatically loads cached data on initialization if the file exists.
- **Manual Data Saving**: Allows for explicit control over when data is saved via the `save()` method.
- **File Integrity Checks**: Ensures that cache files use the correct `.pickle` extension and manages errors properly.
- **Python 3.6+ Compatibility**: Works with Python 3.6 and above, making it accessible for most environments.
## Requirements
- Python 3.6+
- Standard libraries: `pickle`, `pathlib`
## Installation
To install `typed-cache`, you can use either `pip` or `poetry`:
### Using `pip`:
```bash
pip install typed-cache
```
### Using `poetry`:
```bash
poetry add typed-cache
```
Simply choose your preferred package manager to install the library.
## Usage
Here's a simple script demonstrating how to use `typed-cache` to persist and retrieve data in a custom class:
```python
from dataclasses import dataclass
from pathlib import Path
from typed_cache import TypedCache
@dataclass
class Settings(TypedCache):
volume: int = 50
brightness: float = 0.8
username: str = "user"
# Specify cache file path
cache_path = Path.home() / 'settings_cache.pickle'
# Initialize settings, modify, and save
settings = Settings(path=cache_path)
settings.volume = 70
settings.save() # Saves the modified settings to 'settings_cache.pickle'
# On subsequent runs, the previous settings will be automatically loaded
loaded_settings = Settings(path=cache_path)
print(f"Loaded volume: {loaded_settings.volume}") # Output: Loaded volume: 70
```
### Key Functions:
1. **Initialization**: When you instantiate your class with a cache file path, `typed-cache` will automatically attempt to load data from that file.
2. **Saving**: Call `save()` on your object to persist the current state to the cache file.
3. **Loading**: Upon reinitialization with the same cache path, previously saved data will be loaded automatically.
## Exception Handling
- **`ValueError`**: Raised if the cache file does not have a `.pickle` extension.
- **`FileNotFoundError`**: Raised if trying to load a cache file that does not exist.
## Contributing
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.
## License
This project is licensed under the MIT License.
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"description": "# typed-cache\n\n`typed-cache` is a lightweight framework designed for caching data locally, supporting explicit caching with a focus on Python's type hints. It simplifies the process of persisting data to disk by allowing easy integration into custom data classes, with automatic loading and manual saving of data.\n\n## Features\n\n- **Type Hinting**: Enhances code clarity and safety through the use of Python's type hints.\n- **Automatic Caching**: Automatically loads cached data on initialization if the file exists.\n- **Manual Data Saving**: Allows for explicit control over when data is saved via the `save()` method.\n- **File Integrity Checks**: Ensures that cache files use the correct `.pickle` extension and manages errors properly.\n- **Python 3.6+ Compatibility**: Works with Python 3.6 and above, making it accessible for most environments.\n\n## Requirements\n\n- Python 3.6+\n- Standard libraries: `pickle`, `pathlib`\n\n## Installation\n\nTo install `typed-cache`, you can use either `pip` or `poetry`:\n\n### Using `pip`:\n```bash\npip install typed-cache\n```\n\n### Using `poetry`:\n```bash\npoetry add typed-cache\n```\n\nSimply choose your preferred package manager to install the library.\n\n## Usage\n\nHere's a simple script demonstrating how to use `typed-cache` to persist and retrieve data in a custom class:\n\n```python\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nfrom typed_cache import TypedCache\n\n\n@dataclass\nclass Settings(TypedCache):\n volume: int = 50\n brightness: float = 0.8\n username: str = \"user\"\n\n\n# Specify cache file path\ncache_path = Path.home() / 'settings_cache.pickle'\n\n# Initialize settings, modify, and save\nsettings = Settings(path=cache_path)\nsettings.volume = 70\nsettings.save() # Saves the modified settings to 'settings_cache.pickle'\n\n# On subsequent runs, the previous settings will be automatically loaded\nloaded_settings = Settings(path=cache_path)\nprint(f\"Loaded volume: {loaded_settings.volume}\") # Output: Loaded volume: 70\n```\n\n### Key Functions:\n\n1. **Initialization**: When you instantiate your class with a cache file path, `typed-cache` will automatically attempt to load data from that file.\n2. **Saving**: Call `save()` on your object to persist the current state to the cache file.\n3. **Loading**: Upon reinitialization with the same cache path, previously saved data will be loaded automatically.\n\n## Exception Handling\n\n- **`ValueError`**: Raised if the cache file does not have a `.pickle` extension.\n- **`FileNotFoundError`**: Raised if trying to load a cache file that does not exist.\n\n## Contributing\n\nContributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.\n\n## License\n\nThis project is licensed under the MIT License.",
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