semvec/pypi-packages huggingface dataset:
uniswap-fetcher-rs 0.1.11
A Rust-based library to fetch and decode Uniswap V3 pool events, designed to be used as a Python module.
License: MIT
Keywords: uniswap, ethereum, rust, python, pyo3

# Uniswap Fetcher

Uniswap Fetcher is a Rust-based library that provides functionalities to fetch and decode Uniswap V3 pool events. It is designed to be used as a Python module using PyO3.

## Features

- Fetch pool events by token pairs within a specified block range.
- Fetch pool events by token pairs within a specified time range.
- Fetch pool created events between two timestamps.
- Get block number range for a given timestamp range.

## Prerequisites

- Rust and Cargo: Install from [rustup.rs](https://rustup.rs/)
- Python 3.10 or higher
- An Ethereum node RPC URL

## Installation

1. Clone the repository:

    ```sh
    git clone https://github.com/nestlest/uniswap-fetcher-rs.git
    cd uniswap-fetcher-rs
    ```

2. Build the Python module:

    ```sh
    maturin develop
    ```

    This will build the Rust code and install the Python module locally.

## Usage

### Python

```python
from uniswap_fetcher_rs import UniswapFetcher

# Initialize the fetcher with the RPC URL
fetcher = UniswapFetcher("http://localhost:8545")

# Define token pairs and block range
token_pairs = [("0xaea46a60368a7bd060eec7df8cba43b7ef41ad85", "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", 3000)]
from_block = 12345678
to_block = 12345778

# Fetch pool events by token pairs
events = fetcher.get_pool_events_by_token_pairs(token_pairs, from_block, to_block)
print(events)

# Define time range
start_timestamp = 1633046400  # Example start timestamp
end_timestamp = 1633132800    # Example end timestamp

# Fetch pool data by token pairs within the specified time range
pool_data = fetcher.fetch_pool_data(token_pairs, start_timestamp, end_timestamp)
print(pool_data)

# Fetch pool created events between two timestamps
pool_created_events = fetcher.get_pool_created_events_between_two_timestamps(start_timestamp, end_timestamp)
print(pool_created_events)
```
To use the library directly in Rust, you can call the asynchronous functions provided in the `lib.rs` file.
## Testing
To run the tests, use the following command:
```sh
cargo test
```
## License
This project is licensed under the MIT License. See the [MIT License](LICENSE) file for details.
arcadia-sdk 0.3.8
arcadia is a platform for ml developers to host and get paid for their proprietary ml models.
License: MIT
Requires Python: <4.0,>=3.13

# Arcadia SDK

<p align="center">
  <!-- You could add a logo image here -->
  <h3 align="center">Deploy and Monetize ML Models with Ease</h3>
</p>

Arcadia is a powerful platform that enables machine learning developers to seamlessly deploy, manage, and monetize their models. With our SDK, you can transform your ML models into production-ready APIs in minutes.

## ✨ Features

- 🚀 **One-Click Deployment**: Deploy models to production with a single command
- 💰 **Built-in Monetization**: Set pricing and start earning from your models instantly
- 📊 **Advanced Monitoring**: Track performance, usage metrics, and revenue in real-time
- 🔒 **Enterprise-Grade Security**: SOC 2 compliant with end-to-end encryption
- 🔄 **Version Control**: Manage multiple versions of your models effortlessly
- 📈 **Auto-scaling**: Handle varying loads with automatic scaling

## 🚀 Quick Start

### Installation

```bash
pip install arcadia-sdk
```

### Usage

```python
from arcadia import Model, predict

@predict
def my_model(input_data):
    # Your model logic here
    return prediction

Model("my-model").deploy()
```

### Documentation

For more detailed documentation, please refer to the [Arcadia Documentation](https://docs.arcadia.dev).

### Support

For support, please refer to the [Arcadia Support](https://support.arcadia.dev).

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
achibdatalib 0.1.0
A library for data manipulation and analysis.
License: MIT
Requires Python: >=3.9

# DataLib

A library for data manipulation and analysis.

DataLib is a comprehensive data analysis library that provides functionalities for data loading, cleaning, processing, visualization, classification, regression, and advanced analysis.

## Installation

You can install DataLib using `pip`:

```sh
pip install achibdatalib
```

## Dependencies

DataLib requires the following dependencies:

- pandas>=1.3.0
- numpy>=1.21.0,<2.0.0
- scikit-learn>=0.24.2
- pytest>=6.2.4

## Usage

Here is an example of how to use DataLib:

```python
import pandas as pd
from DataLib.data_lib import DataLib

# Load and clean data
df = DataLib.load_and_clean_data('data.csv')

# Describe data
description = DataLib.describe_data(df)
print(description)

# Visualize data
DataLib.visualize_data(df, 'column_name')

# Perform classification
classification_results = DataLib.perform_classification(df, 'target_column')
print(classification_results)

# Perform regression
regression_results = DataLib.perform_regression(df, 'target_column')
print(regression_results)

# Perform advanced analysis
advanced_analysis_results = DataLib.perform_advanced_analysis(df, 'target_column')
print(advanced_analysis_results)

# Execute full analysis workflow
results = DataLib.analyze_data_engine('data.csv', 'target_column')
print(results)
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License.
awmt-basic-python-packge 0.1.1
A simple template package
License: MIT
Requires Python: <4.0,>=3.9

# AWMT_BasicPythonPackage
Template for creating PyPI hosted python packages

This repo is intended to be used as a template for new AWMT python projects 

## Release Notes

For detailed release notes see:

[Releases Page](https://github.com/andowt/AWMT_BasicPythonPackage/releases)


## Manual Buildling Instructiibs

### Build Tools
Poetry was selected based on this article suggesting it has the best support and is most widely adopted:

https://www.pyopensci.org/python-package-guide/package-structure-code/python-package-build-tools.html

Repo structure has been adopted from Poetry docs here:

https://python-poetry.org/docs/basic-usage/#project-setup

Poetry can be installed as documented here:

https://python-poetry.org/docs/#installation

### Unit tests
Pytest has been chosen for unit testing based on its scalability and flexability for different applications - see here for more: https://builtin.com/data-science/pytest-vs-unittest

pytest can be installed using

```
pip install pytest
```

### Uploading to PyPI
Install twine using

```
pip install twine
```

Run the command

```
poetry build
```

Test upload to PyPI

```
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
```


Test install from PyPI

```
pip install -i https://test.pypi.org/simple/ AWMT_BasicPythonPackage
```

Full Upload to PyPI

```
twine upload dist/*
```

View Project

```
https://pypi.org/project/<package_name>
```

Full install

```
pip install <package_name>
```