semvec/pypi-packages huggingface dataset:
custom-commons 0.2.0
공통 모듈 패키지
Requires Python: >=3.6.8

# Common Modules

공통 모듈 패키지

## 설치

```bash
# GitLab에서 직접 설치
pip install git+ssh://git@gitlab.company.com/team/common_modules.git

# 또는 로컬에서 개발 모드 설치
pip install -e .
```

## 사용법

```python
from common_modules import get_connection, setup_logger, load_config
from common_modules.api import APIClient
```
vnpy-xtdata 2.0.1
Xtquant datafeed/pub interface.
Requires Python: >=3.11
Keywords: quant, quantitative, investment, trading, algotrading, data

<h1 align="center">VeighNa框架的迅投数据服务接口</h1> 

***

<p align="center">
    <img src ="https://img.shields.io/badge/version-2.0.0-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.11|3.12|3.13-blue.svg" />
    <img src ="https://img.shields.io/github/license/vnpy/vnpy.svg?color=orange"/>
</p>

## 说明

基于迅投XtQuant封装开发的实时行情和数据服务接口，支持以下中国金融市场的K线和Tick数据：

* 股票、基金、债券、ETF期权：
  * SSE：上海证券交易所
  * SZSE：深圳证券交易所
* 期货、期货期权：
  * CFFEX：中国金融期货交易所
  * SHFE：上海期货交易所
  * DCE：大连商品交易所
  * CZCE：郑州商品交易所
  * INE：上海国际能源交易中心
  * GFEX：广州期货交易所


## 安装

安装环境推荐基于4.0.0版本以上的【[**VeighNa Studio**](https://www.vnpy.com/)】。

直接使用pip命令：

```
pip install vnpy_xtdata
```


或者下载解压后在cmd中运行：

```
pip install .
```

## 使用

**Token连接**

1. 连接前请先确保xtquant模块可以正常加载（在[投研知识库](http://docs.thinktrader.net/)下载xtquant的安装包，解压后放置xtquant包到自己使用的Python环境的site_packages文件夹下）。
2. 登录[迅投研服务平台](https://xuntou.net/#/userInfo)，在【用户中心】-【个人设置】-【接口TOKEN】处获取Token。
3. 在VeighNa Trader的【全局配置】处进行数据服务配置：
    * datafeed.name：xt
    * datafeed.username：token
    * datafeed.password：填复制的Token

**客户端连接**

1. 连接请先登录迅投极速交易终端，同时确保xtquant模块可以正常加载（点击【下载Python库】-【Python库下载】，下载完成后拷贝“Python库路径”下Lib\site-packages文件夹中的xtquant包到自己使用的Python环境的site_packages文件夹下）。
2. 在Veighna Trader的【全局配置】处进行数据服务配置：
    * datafeed.name：xt
    * datafeed.username：client
    * datafeed.password：留空
3. 请注意以客户端方式连接时，需要保持迅投客户端的运行。
lunatv 0.1.2.dev0
A collection of tools for downloading M3U8 video streams.
Requires Python: >=3.14

# LunaTV Tools

A collection of Python scripts for downloading M3U8 video streams.

## Features

- **Fast Downloads**: The main script, `luna_downloader.py`, uses multi-threading to download video segments concurrently.
- **Automatic Merging**: Automatically merges the downloaded `.ts` segments into a single `.mp4` file using `ffmpeg`.
- **URL Utilities**: Includes other scripts for checking and handling stream URLs.

## Requirements

- Python 3.8+
- `ffmpeg`: Must be installed and available in your system's PATH.

## Setup & Installation

This project is packaged as a library and can be installed using `uv`.

1.  **Create & Activate Virtual Environment**:
    ```sh
    # Create the virtual environment
    uv venv
    
    # Activate it (on macOS/Linux)
    source .venv/bin/activate
    ```

2.  **Install the `lunatv` package**:
    Installing the package in editable mode (`-e`) is recommended for development. This will also automatically install all dependencies from `pyproject.toml` and make the command-line tool available.
    ```sh
    uv pip install -e .
    ```

## Usage

After installation, a command-line tool `luna-downloader` will be available in your environment.

```sh
luna-downloader
```

The tool will then prompt you to enter the M3U8 URL and the desired output filename.
pvshadesim 0.0.9
A physical and electrical shade model using Shapely and PVMismatch.
Keywords: pvmismatch, PV, shading, mismatch, IV, shapely

# PVShadeSim
 A physical and electrical shade model using Shapely and PVMismatch.

![image](https://github.com/user-attachments/assets/979498f7-2e68-4a2d-9560-a97d184d83e6)

## Install & Setup
In a fresh Python virtual environment, simply run:

```
pip install pvshadesim
```

### Install using .whl file (Recommended)
Download files from Github and run the latest version of the .whl file using the command below from the parent folder.

```
pip install .\dist\pvshadesim-<version>-py3-none-any.whl
```

## Requirements

PVShadeSim requires Matplotlib, Seaborn, Plotly, NumPy, Pandas, Openpyxl, v_PVMismatch, Shapely and Dataframe_image.

## Current Maintainer at Maxeon Solar

@k-bsub
simt-emlite 1.2.1
API and CLI for communicating with the Emlite meters via EMOP
Requires Python: <4.0,>=3.13

# Simtricity Emlite Python APIs and CLI

This repository contains:

- API to connect to and send EMOP messages to Emlite meters
- a CLI (emop) for sending messages to the meters

## Configure

Configuration steps:

- create file ~/.simt/emlite.prod.env from Lastpass secret 'emop-cli-env-file (prod)'
- create file ~/.simt/emlite.qa.env from Lastpass secret 'emop-cli-env-file (qa)'
- ln -s ~/.simt/emlite.<qa|prod|custom>.env ~/.simt/emlite.env

NOTE:

- FLY_DNS_SERVER needs the DNS that wireguard uses (on Linux look under `resolvectl status` for the interface and DNS)

see also <https://www.notion.so/Emop-and-mediators-CLI-setup-834d32be5c794add8716399ab186abe8>

## Use

### emop

```sh
emop env_show
emop env_set prod

emop --help

emop prepay_balance EML2137555666
emop csq EML2137555666
emop profile_log_1 --timestamp 2024-07-19T00:00 EML2137555666
```

### Python API

You can also use the library directly in your Python scripts. The library offers two main client classes:

- `EmliteMediatorAPI`: The core client for standard meter operations (reads, basic writes).
- `EmlitePrepayAPI`: Extends the core API with prepay-specific functionality (balance, tokens, tariffs).

#### Initialization and Logging

The `EmliteMediatorAPI` constructor allows you to configure the connection and log verbosity.

```python
import logging
from simt_emlite.mediator.api_core import EmliteMediatorAPI

# Initialize the client
# The logging_level parameter controls the verbosity of the internal logger.
# It uses the standard python logging module levels (DEBUG, INFO, WARNING, ERROR).
client = EmliteMediatorAPI(
    mediator_address="localhost:50051",
    logging_level=logging.WARNING  # Set to logging.DEBUG for verbose output
)

# Example Usage
serial = "EML2137555666"
try:
    voltage = client.instantaneous_voltage(serial)
    print(f"Meter {serial} voltage: {voltage}V")
except Exception as e:
    print(f"Error: {e}")
```
akshare-one 0.4.0
Standardized interface for Chinese financial market data, built on AKShare with unified data formats and simplified APIs
Requires Python: >=3.10
Keywords: akshare, financial-data, stock-data, quant

<div align="center">
  <h1>AKShare One</h1>
  <div>
    <a href="README_zh.md">中文</a> | <strong>English</strong>
  </div>
</div>

**AKShare One** is a data interface for obtaining Chinese A-shares, based on [AKShare](https://github.com/akfamily/akshare). It aims to simplify AKShare's usage and unify input/output formats from different data sources, making it easier to pass data to LLM.

## ✨ Features

- 📊 Unified stock code formats across data sources
- 🏗️ Standardized return data structures
- 🛠️ Simplified API parameter design
- ⏱️ Automatic timestamp and adjustment handling

## 🚀 Core Features

| Function | Interface |
|------|------|
| Historical data | `get_hist_data` |
| Real-time quotes | `get_realtime_data` |
| Stock news | `get_news_data` |
| Financial data | `get_balance_sheet`/`get_income_statement`/`get_cash_flow` |
| Futures data | `get_futures_hist_data`/`get_futures_realtime_data` |
| Options data | `get_options_chain`/`get_options_realtime`/`get_options_hist` |
| Internal transactions | `get_inner_trade_data` |
| Basic stock info | `get_basic_info` |
| Financial metrics | `get_financial_metrics` |
| Technical indicators | See [indicators.py](src/akshare_one/indicators.py) |

## 📦 Quick Installation

```bash
pip install akshare-one
```

## 💻 Usage Example

```python
from akshare_one import get_hist_data
from akshare_one.indicators import get_sma

# Get historical data
df = get_hist_data(
    symbol="600000",
    interval="day",
    adjust="hfq"
)

# Calculate 20-day Simple Moving Average
df_sma = get_sma(df, window=20)
```

## 📚 Documentation

Full API documentation is now available on GitHub Pages:

https://zwldarren.github.io/akshare-one/
celescope 2.11.1
Single Cell Analysis Pipelines
Requires Python: >=3.6

# Introduction
CeleScope is a collection of bioinfomatics analysis pipelines to process single cell sequencing data generated with Singleron products. These pipelines take paired-end FASTQ files as input and generate output files which can be used for downstream data analysis as well as a summary of QC criteria.

# Mirror
If you have problems connecting to github, try:
https://gitee.com/singleron-rd/celescope/

# Documentation
[User guide](./doc/user_guide.md)

# Support
Please use the issue tracker for questions and malfunctions.

# About
CeleScope is currently developed by Singleron Biotechnologies.

[www.singleron.bio](https://www.singleron.bio)
geo-engine-python 0.1.3
SDK oficial para Geo-Engine: Rastreo y Geofencing
Requires Python: >=3.7

# 🐍 Geo-Engine Python SDK

Cliente oficial en **Python** para interactuar con la plataforma **Geo-Engine**.
Permite enviar ubicaciones en tiempo real, gestionar geocercas y consumir la API de Geo-Engine desde aplicaciones **Django**, **Flask**, scripts o entornos de **Data Science**.

Diseñado para ser **simple**, **rápido** y **fácil de integrar**.

---

## 🚀 Instalación

Instala el SDK usando `pip`:

```bash
pip install geo-engine-python
```

---

## ⚡ Uso Rápido

Envía la ubicación de un dispositivo en pocos pasos:

```python
from geo_engine import GeoEngine

# 1. Inicializar el cliente
# Por defecto se conecta al entorno de producción
client = GeoEngine(api_key="sk_live_123456")

# 2. Enviar ubicación
try:
    res = client.send_location(
        device_id="camion-01",
        lat=19.4326,
        lng=-99.1332
    )
    print(f"✅ Enviado: {res}")
except Exception as e:
    print(f"❌ Error: {e}")
```

---

## 🔧 Configuración Avanzada

Puedes personalizar la configuración para conectarte a otros entornos (por ejemplo, local o staging) o modificar URLs internas:

```python
client = GeoEngine(
    api_key="sk_test_...",
    ingest_url="http://localhost:8080",
    management_url="http://localhost:8081"
)
```

---

## 🧩 Casos de Uso Comunes

* 📍 **Tracking en tiempo real** de vehículos o dispositivos IoT
* 🚚 **Logística y última milla**
* 🗺️ **Geocercas** y eventos por entrada/salida
* 📊 **Análisis de datos geoespaciales** en notebooks o pipelines

---

## 🔐 Autenticación

El SDK utiliza **API Keys** para autenticarse.
Asegúrate de mantenerlas seguras y **no exponerlas en el frontend**.

```python
GeoEngine(api_key="sk_live_...")
```

---

## 🧪 Compatibilidad

* Python **3.8+**
* Funciona con:

  * Django
  * Flask
  * FastAPI
  * Scripts CLI
  * Jupyter / Data Science

---

## 📄 Licencia

MIT © Geo-Engine

---

## 🤝 Soporte

¿Tienes dudas o sugerencias?
Abre un issue o contáctanos en **[support@geoengine.dev](mailto:support@geoengine.dev)**

---