# Web3 Data Center
Web3 Data Center is a Python package that integrates multiple APIs to provide comprehensive blockchain data analysis capabilities. It offers a unified interface to access data from various sources, making it easier for developers to gather and analyze blockchain-related information.
## Features
- Integration with multiple blockchain data providers (GeckoTerminal, GMGN, Birdeye, Solscan, GoPlus, DexScreener)
- Asynchronous API calls for improved performance
- Persistent file-based caching system for optimized data retrieval
- Caching mechanism to reduce API calls and improve response times
- Support for multiple blockchains (Ethereum, Solana, and more)
- Token information retrieval (price, market cap, holders, etc.)
- Transaction analysis
- Token security checks
## Installation
You can install Web3 Data Center using pip:
```bash
pip install data_center
```
## Quick Start
Here's a simple example of how to use Web3 Data Center:
```python
import asyncio
from web3_data_center import DataCenter
async def main():
data_center = DataCenter()
# Get token info
token_address = "CzLSujWBLFsSjncfkh59rUFqvafWcY5tzedWJSuypump" # Wrapped SOL
token_info = await data_center.get_token_info(token_address)
print(f"Token Info: {token_info}")
# Get top holders
top_holders = await data_center.get_top_holders(token_address, limit=10)
print(f"Top 10 Holders: {top_holders}")
asyncio.run(main())
```
## Caching System
Web3 Data Center includes a robust file-based caching system to improve performance and reduce API calls. The cache is stored in `~/.web3_data_center/cache/` and is automatically managed.
### Cached Operations
The following operations are cached by default:
- Root funder lookups (24-hour cache)
- Funding path queries (24-hour cache)
- Funding relationship checks (24-hour cache)
### Using the Cache
The cache is automatically used when calling the relevant methods. You can also use the caching decorator for your own functions:
```python
from web3_data_center import file_cache
@file_cache(namespace="my_cache", ttl=3600) # 1-hour cache
async def my_function():
# Your code here
pass
```
### Cache Management
To clear the cache for a specific function:
```python
data_center.get_root_funder.cache_clear()
```
To get the cache directory:
```python
from web3_data_center import get_cache_dir
cache_dir = get_cache_dir()
```
The cache automatically manages:
- Entry expiration (TTL-based)
- Size limits
- Cleanup of old entries
## Documentation
For detailed documentation, please refer to the [docs](./docs) directory.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Thanks to all the API providers that make this project possible.
- Special thanks to the open-source community for their invaluable tools and libraries.
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