semvec/pypi-packages huggingface dataset:
otf-api 0.15.4
Python OrangeTheory Fitness API Client
Requires Python: >=3.11

Simple API client for interacting with the OrangeTheory Fitness APIs.

Review the [documentation](https://otf-api.readthedocs.io/en/stable/).


This library allows access to the OrangeTheory API to retrieve workouts and performance data, class schedules, studio information, and bookings.

## Installation
```bash
pip install otf-api
```

## Overview

To use the API, you need to create an instance of the `Otf` class. This will authenticate you with the API and allow you to make requests. When the `Otf` object is created it automatically grabs your member details and home studio, to simplify the process of making requests.

You can either pass an `OtfUser` object to the `OtfClass` or you can pass nothing and allow it to prompt you for your username and password.

You can also export environment variables `OTF_EMAIL` and `OTF_PASSWORD` to get these from the environment.

```python
from otf_api import Otf, OtfUser

otf = Otf()

# OR

otf = Otf(user=OtfUser(<email_address>,<password>))

```
wx4 0.1.1
Add your description here
Requires Python: >=3.13

# 微信自动化工具

这是一个基于Python的微信桌面客户端自动化工具，使用uiautomation库实现对微信4.x界面的控制，可以帮助用户自动化执行微信的各种操作。

## 项目介绍

该工具通过UI自动化技术控制微信桌面客户端4.x，实现以下功能：

- 自动发送文本消息
- 获取聊天记录

该工具适用于需要自动化处理微信消息、批量操作或监控微信的场景，如自动客服、消息监控、自动回复等。

## 安装指南

### 前提条件

- Python 3.13+
- 微信桌面客户端4.x版本(不可以使用4.0.6+，包括4.0.6)
- Windows操作系统

### 安装步骤

#### 使用pip(可能要使用官方源)
```bash
pip install wx4
```

3. 确保微信桌面客户端已安装并能正常登录

## 使用示例

### 初始化微信控制

```python
from wx4.wx4 import WeChat

# 创建微信控制实例
wx = WeChat()
```

### 发送消息

```python
# 发送消息到当前聊天窗口
wx.SendMsg("你好，这是一条自动发送的消息")
```

### 获取消息

```python
# 获取当前聊天窗口的所有消息
messages = wx.GetAllMessage()
for msg in messages:
    print(f"发送者: {msg.sender}, 内容: {msg.content}")
```
###
```python
# 更新消息(没有将无法获取)
wx.get_new_message()
```
### 主要类

- `WeChat`: 微信控制的主类，提供各种微信操作的方法
- `MSG`: 表示一条微信消息，包含发送者、内容和索引信息


### 注意事项

- 使用前请确保微信已登录
- 部分功能可能受微信版本更新影响
- 使用自动化工具时请遵守微信的使用条款,如果出现任何问题与作者无关
gravify 2.0.0
A library to interface with Gravatar profiles and avatars.
Requires Python: >=3.10

# Gravify

**Gravify** is a Python library to interface with Gravatar profiles and avatars.

```python
>>> from gravify import AvatarGenerator
>>> AvatarGenerator(size=500).generate_url('hello@example.com')
'https://gravatar.com/avatar/1753bdb368271a785887ddbfb926164f2f7c6a88f609c07ff0401c5572955206?s=500'
```

[![Total Downloads](https://img.shields.io/pepy/dt/gravify)][pypi]
[![Supported Versions](https://img.shields.io/pypi/pyversions/gravify.svg)][pypi]
[![Testing Status](https://img.shields.io/github/actions/workflow/status/bsoyka/gravify/test.yml?branch=main&label=tests)][testing]
[![Coverage](https://img.shields.io/codecov/c/github/bsoyka/gravify)][codecov]
[![GitHub last commit](https://img.shields.io/github/last-commit/bsoyka/gravify)][github]

## Installation and usage

Gravify is [available on PyPI][pypi].
Install it with your preferred package manager:

```sh
$ uv add gravify
$ pip install gravify
```

Gravify officially supports Python 3.10+.

**[Read the documentation][docs]** to learn how to use Gravify.

[codecov]: https://codecov.io/github/bsoyka/gravify

[docs]: https://gravify.readthedocs.io/

[github]: https://github.com/bsoyka/gravify

[license]: https://github.com/bsoyka/gravify/blob/master/LICENSE

[pypi]: https://pypi.org/project/gravify/

[testing]: https://github.com/bsoyka/gravify/actions/workflows/test.yml
gozargah-node-bridge 0.0.44
python package to connect your project with gozargah node go
Requires Python: >=3.12
Keywords: Gozargah, Gozargah-Node, Marzban, Marzban API, Marzban python

# gozargah_node_bridge_py
Library to connect and use https://github.com/M03ED/gozargah-node

# Usage
```shell
pip install gozargah-node-bridge
```
Library is fully async for both `gRPC` and `REST API` connection.

# Example
```python
import GozargahNodeBridge as Bridge
# or
import GozargahNodeBridge as GNB
```

## Creating Node
```python
node = Bridge.create_node(
    connection=Bridge.NodeType.GRPC, # GRPC or REST
    address=address, # Node ip address or domain
    port=port, 
    client_cert=client_cert_content, # client side ssl certificate as string
    client_key=client_key_content, # client side ssl key as string
    server_ca=server_ca_content, # server side ssl key as string
    extra={}, # a dictionary to hold node data on production, optional, default: None
    )
```

## Proto Structure
If you need to have access proto structure you can use:
```python
from GozargahNodeBridge.common import service_pb2 as service
```

## Create User
```python
user = Bridge.create_user(
    email="jeff", 
    proxies=Bridge.create_proxy(
        vmess_id="0d59268a-9847-4218-ae09-65308eb52e08", # UUID converted to str
        vless_id="0d59268a-9847-4218-ae09-65308eb52e08", # UUID converted to str
        vless_flow="",              # Valid vless flow if is set for user (str)
        trojan_password="",         # Trojan password (str)
        shadowsocks_password="",    # Shadowsocks password (str)
        shadowsocks_method="",      # Valid shadowsocks method supported by backend
        ), 
    inbounds=[]                     # List of outbound tag, List[str]
    )
```

## Methods
Before use any method you need to call start method and connect to node unless you will face `NodeAPIError` for every method
```python
await node.start(
    config=config,  # backend config as string 
    backend_type=0, # backend type , XRAY = 0
    users=[],       # list of users you want to add to this node, will be recheck in node with config
    timeout=20,
    )
```

### Get User Stats
```python
stats = await node.get_user_stats(
    email="noreply@donate.gozargah.pro",
    reset=True,
    timeout=10,
    )
```

### Health
Return a `Bridge.Health`
```python
health = await node.get_health()
```

### Logs
Return a `asyncio.Queue[str]` 
```python
logs = await node.get_logs()
```
onedevcommonfileextractor 1.0.7
Ferramenta para extração de conteúdo de arquivos
Requires Python: <4.0,>=3.13

# Library Content File Extractor

Extrator de conteúdo de arquivos com suporte a PDFs e integração com APIs de IA (Google Gemini e Azure OpenAI).

## Pré-requisitos

- Python 3.8+
- Poetry (para gerenciamento de dependências)

## Instalação

1. Instalação da Biblioteca
```bash
pip install onedevcommonfileextractor
```
ou, se estiver usando poetry
```bash
poetry add onedevcommonfileextractor
```

## Configuração das Variáveis

Ao instanciar a classe `ExtractorFile`, é necessário passar como parâmetro algumas configurações em `config: ConfigAI`.

### Variáveis Obrigatórias

| Variável | Descrição | Exemplo |
|----------|-----------|---------|
| `GOOGLE_API_KEY` | Chave da API Google Gemini | `your_google_api_key_here` |
| `OPENAI_API_URL` | URL da API Azure OpenAI | `https://your-resource.openai.azure.com` |
| `OPENAI_API_KEY` | Chave da API Azure OpenAI | `your_azure_openai_api_key_here` |
| `ENVIRONMENT` | Ambiente de execução | `development`, `production` |

### Obtenção das Chaves de API

- **Google API Key**: Acesse o [Google Cloud Console](https://console.cloud.google.com/) e habilite a API Gemini
- **Azure OpenAI**: Configure no [Azure Portal](https://portal.azure.com/) e obtenha as credenciais necessárias

## Uso

### Após a instalação da biblioteca instancie a classe `ExtractorFile`:

```bash
config = ConfigAI()
config.AZURE_API_VERSION = ""
config.AZURE_DEPLOYMENT_NAME = ""
config.AZURE_OPENAI_API_KEY = ""
config.AZURE_OPENAI_API_URL = ""
config.GOOGLE_API_KEY = ""
 
extractor = ExtractorFile(config)
```

### Executar a funcionalidade. 
## Para executar a funcionalidade usar o método `run`, com os parâmetros para a sua execução.

```bash
result = extractor.run(
    task_id=None,
    use_ai=True,
    ai_provider='google',
    extract_insurance_data=True,
    file_path=""
)

print(result)
```

## Funcionalidades

- Extração de conteúdo de PDFs
- Processamento com IA (Google Gemini e Azure OpenAI)
- Geração de saída em JSON
- Armazenamento seguro de dados
- Criptografia de informações sensíveis

## Licença

Este projeto está sob a licença [MIT](LICENSE).
homelink-home-assistant 0.0.8
API to interact with Homelink cloud for Home Assistant
Requires Python: >=3.12

# HomeLink SmartHome Home Assistant API

This project contains a python API to connect home assistant to HomeLink smarthome cloud

## Setup

This repo utilizes VS Code [Dev Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) so you don't need to alter your global python environment.

## Installing dependencies

Use `pip` to install required dependencies by running `pip install -r requirements.txt` from the project root.

## Updating dependencies

During the course of development, if new packages are added, it's recommended to update [`requirements.txt`](./requirements.txt) by running `pip freeze > requirements.txt`.
moist_thermodynamics 0.0.5
Constants and functions for the treatment of moist atmospheric thermodynamics.
Requires Python: >=3.10

# moist_thermodynamics

## Description
This repository contains a python module providing constants and functions used for the analysis of moist atmospheric thermodynamic processes.  An effort is made to be as accurate as possible given the assumption of a Rankine-Kirchoff fluid, i.e., zero condensate volume, perfect mixtures of perfect gases, perfect liquids and constancy of specific heats.  

In some cases even more exact treatments will be desired, for instance the more accurate specifications of the saturation vapor pressure are not consistent with the assumptions of a Rankine-Kirchoff fluid, but are useful references.  Those more generally interested in the most accurate treatment are referred to the [IAPWS](http://iapws.org), or the TEOS libraries, e.g., their sea-ice-air library is [available on github](https://github.com/TEOS-10/SIA-Fortran)

The functionality is not meant to be exhaustive, but to provide the basic tools to treate moist thermodynamics in a consistent manner and in ways that allow the tools to be easily incorporated into other programmes, or used as references for simpler more analytically tractable approximations.   

## Usage
Jupyter notebooks are provided in the examples directory to provide illustrative use cases and further information pursuant to choices made in structuring the code, and the functionality it enables.  This includes analyses used in ([Marquet and Stevens (2021)](https://journals.ametsoc.org/view/journals/atsc/79/4/JAS-D-21-0095.1.xml).

## References

Marquet, P., & Stevens, B. (2022). On Moist Potential Temperatures and Their Ability to Characterize Differences in the Properties of Air Parcels, Journal of the Atmospheric Sciences, 79(4), 1089-1103. ([open access pdf version](https://arxiv.org/pdf/2104.01376.pdf)) 


Romps, D.M. (2021), The Rankine–Kirchhoff approximations for moist thermodynamics. QJR Meteorol Soc, 147: 3493-3497. https://doi.org/10.1002/qj.4154

Siebesma, A., Bony, S., Jakob, C., & Stevens, B. (Eds.). (2020). Clouds and Climate: Climate Science's Greatest Challenge. Cambridge: Cambridge University Press. doi:10.1017/9781107447738

## Contributing
Code contributions are welcome.  Please suggest changes on a branch and make a merge request.  Format all changes using Black, and test docstrings using pytest before making merge requests.

## Authors and acknowledgment
The code was written by Bjorn Stevens with contributions from Lukas Kluft and Tobias Kölling. LK and TK also are thanked for expert input in setting up the repository and on how best to structure and code the libraries in ways that enourage intuitive use.  They, also along with Jiawei Bao, Geet George and Hauke Schulz are thanked for their feedback on the thermodynamic analysis.

## License
Copyright 2016-2021 MPI-M, Bjorn Stevens

Code subject to BSD-3-C, SPDX short identifier: BSD-3-Clause, see [license file](LICENSE.md)


## Contact
bjorn.stevens@mpimet.mpg.de