semvec/pypi-packages huggingface dataset:
multifuncpy 0.1.1
A simple async HTTP client based on aiohttp
Requires Python: >=3.7

# AsyncHttpClient

一个简单易用的异步HTTP客户端，基于aiohttp封装，支持链式调用。

## 特性

- 异步HTTP请求支持
- 链式调用API
- 自动会话管理
- 超时控制
- 统一的错误处理
- 支持自定义headers
- 支持JSON数据

## 安装
```bash
pip install AsyncHttpClient
```

## 快速开始

```python
import asyncio
from async_http_client import AsyncHttpClient

async def main():
    async with AsyncHttpClient() as client:
        # GET请求示例
        response = await client.get('https://api.example.com/users')
        data = await response.json()
        print(data)

        # POST请求示例
        response = await client.post(
            'https://api.example.com/users',
            json_data={"name": "test"}
        )
        result = await response.json()
        print(result)

asyncio.run(main())

```

## API参考

### AsyncHttpClient
#### 主要方法：
- get(url, **kwargs) - 发送GET请求
- post(url, data=None, json_data=None, **kwargs) - 发送POST请求
- put(url, data=None, json_data=None, **kwargs) - 发送PUT请求
- delete(url, data=None, json_data=None,**kwargs) - 发送DELETE请求

## 高级用法
```python
from datetime import timedelta
from async_http_client import AsyncHttpClient

# 自定义配置
client = AsyncHttpClient(
    base_url='https://api.example.com',
    timeout=timedelta(seconds=30),
    headers={'Authorization': 'Bearer token123'}
)

async def main():
    # 使用上下文管理器
    async with client:
        response = await client.get('/users')
        data = await response.json()

```

## 依赖
- Python >= 3.7
- aiohttp

## 许可证
MIT License

## 贡献
欢迎提交Issue和Pull Request！
IntegraCord 0.1.1
IntegraCord that allows you to connect to the application as a 'Webhook' without connecting to the Discord gateway.

# IntegraCord

IntegraCord that allows you to connect to the application as a "Webhook" without connecting to the Discord gateway.

## Setup
1. Download repository `pip install IntegraCord`.
2. Create an `.env` file for configuration and enter:
```env
BOT_TOKEN=TOKEN # The bot token is needed for integration with the discord API, e.g. channel creation and so on
APPLICATION_ID=ID
APPLICATION_PUBLIC_KEY=KEY
```
3. Create a startup file, e.g. main.py, and enter this script:
```py
from fastapi import FastAPI, Request
from IntegraCord.lib.webhook import webhook
from IntegraCord.lib.handler import InteractionHandler
from IntegraCord.lib.commands import register_global_commands
from IntegraCord.lib.utils import send_message

import asyncio

app = FastAPI()
handler = InteractionHandler()

async def setup_commands():
    await register_global_commands(handler)

@handler.command(name="ping", description="ping pong")
async def ping_command(payload):
    return send_message('pong')

@app.post("/interactions")
async def interactions(request: Request):
    payload = await webhook.validate_request(request)

    if payload.get("type") == 1:
        return await webhook.handle_ping(payload)

    return await handler.handle_interaction(payload)

if __name__ == "__main__":
    import uvicorn
    asyncio.run(setup_commands())
    uvicorn.run(app, host="0.0.0.0", port=8000)
```
4. Run and wait for the script to register the commands, and open the local network.
5. It is necessary to give your own endpoint to the application panel on the discord website, so if you have `ngrok` then run the command `ngrok http 0.0.0.0:8000`. If you do not have one then install it.
6. If all goes according to plan, copy the displayed link in the console from ngrok and paste it into the `Interactions Endpoint URL`, as in the following screenshot:
![image](https://github.com/user-attachments/assets/d86056ca-c40d-4cdd-8e2a-a2158ca0e71a)

## Additional notes
The library is under development, all the points mentioned in the Setup should make your bot responsive to ngrok integrations. If you don't see the command on your own server, reset Discord (CTRL + R).
sciencespy 1.0.0
Python code developed during the SCIENCES project
Requires Python: >=3.7

# SCIENCES

This repository is used to manage research of the SCIENCES project in accordance to the FAIR principle. As soon as the research being conducted in the SCIENCES project will be published in international journals, all data and sources
contained in the repository will be publically available.

# Overall structure

The SCIENCES repository consists of the following folders, each of them having a different responsability in the research data management of the project:
- [data](#data): store data being collected during the SCIENCES project
- [doc](#documentation): contains the documentation used for various purposes of the SCIENCES project
- [notebooks](#notebooks): contains Jupyter notebooks to demonstrate the interoperability and reproducibility of results
- [src](#sources): contains sources that were used to produce results of the SCIENCES project using data stored in [data](#data) as an input
- [tst](#testing): contains unit tests for procedures implemented in [src](#sources)

# Data

The **data** folder is used to store and manage data being collected during the SCIENCES project. The data are used as an input of the methodology developed in the project, and are necessary to the reproducability of results.

# Documentation

The **doc** contains various documents that were used for different purposes in the SCIENCES project. It includes documents related to the administration of the project ([admin](##administration)), papers used during the literature review 
([literature](##literature)), ...

## Administration

Various administrative aspects of the SCIENCES project can be found in this folder, including:
- The career development plan
- ...

## Literature

In this folder, one can find all the papers that were reviewed during the SCIENCES project. The folder also contains Excel files to have an overall vision of achievements and limitations of the research described in each paper. 
There one Excel file per study conducted during the SCIENCES project.

# Notebooks

This folder contains a serie of [Jupyter notebooks](https://jupyter.org/) to show the interoperability and reproducibility of results obtained during the SCIENCES project. The notebooks can either be run locally or in
the [Google Colab](https://colab.research.google.com/) environment.

# Sources

This folder contains several Python modules that were used to produce results of the SCIENCES project.

# Testing

In this folder, one can find various unit tests for most of procedures implemented in Python modules.