# twat-cache
(work in progress)
A flexible caching utility package for Python functions that provides a unified interface for caching function results using various backends (memory, disk, SQL).
## Features
- Simple decorator interface for caching function results
- Multiple caching backends:
- Memory-based LRU cache (always available)
- SQL-based disk cache using `diskcache` (optional)
- Efficient array caching using `joblib` (optional)
- Automatic cache directory management
- Type hints and modern Python features
## Installation
```bash
pip install twat-cache
```
## Usage
Basic usage with default memory caching:
```python
from twat_cache import ucache
@ucache()
def expensive_computation(x):
# Results will be cached automatically
return x * x
result = expensive_computation(42) # Computed
result = expensive_computation(42) # Retrieved from cache
```
Using SQL-based disk cache:
```python
@ucache(folder_name="my_cache", use_sql=True)
def process_data(data):
# Results will be cached to disk using SQL backend
return data.process()
```
Using joblib for efficient array caching:
```python
import numpy as np
from twat_cache import ucache
@ucache(folder_name="array_cache")
def matrix_operation(arr):
# Large array operations will be efficiently cached
return np.dot(arr, arr.T)
```
## Cache Location
The package automatically manages cache directories using the following strategy:
1. If `platformdirs` is available, uses the platform-specific user cache directory
2. Otherwise, falls back to `~/.cache`
You can get the cache path programmatically:
```python
from twat_cache import get_cache_path
cache_dir = get_cache_path("my_cache")
```
## Dependencies
- Required: None (basic memory caching works without dependencies)
- Optional:
- `platformdirs`: For platform-specific cache directories
- `diskcache`: For SQL-based disk caching
- `joblib`: For efficient array caching
## License
MIT License
.
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