semvec/pypi-packages huggingface dataset:
get-wrapped 0.1.3
Create your own Wrapped-style summary from any dataset
License: MIT
Requires Python: >=3.13

# get-wrapped

Turn your structured data into a **yearly Wrapped-style summary**, just like your favorite music recaps — but for **any dataset**. Works seamlessly with both **OpenAI** and **Anthropic** models.

---

## Features

- Generate engaging, punchy recaps from **any structured data**.
- Provider-agnostic: works with **OpenAI** or **Anthropic** automatically.
- Simple, library-first API: just pass a summary dictionary.
- Easy integration with **pandas** or any analytics pipeline.

---

## Installation

Install from PyPI:

```bash
pip install get-wrapped
```

---

## Environment Variables

Set your model credentials (Not needed if you pass in your model credentials):

**Anthropic Example:**

```bash
export MODEL_API_URL="https://api.anthropic.com/v1/complete"
export MODEL_API_KEY="sk-xxxx"
export MODEL_NAME="claude-sonnet-4"      # optional
```

**Or OpenAI Example:**

```bash
export MODEL_API_URL="https://api.openai.com/v1/chat/completions"
export MODEL_API_KEY="sk-xxxx"
export MODEL_NAME="gpt-4.1-mini"  # optional
```

---
## Usage

```python
from get_wrapped import generate_wrapped

summary = {
    "rows": 5,
    "columns": {
        "activity": {"top_values": {"run": 3, "bike": 2}, "dtype": "object", "nulls": 0},
        "minutes": {"min": 20, "max": 60, "mean": 35, "sum": 175, "dtype": "float64", "nulls": 0},
    },
}

# Example summary (or pandas dataframe)
# summary = pd.DataFrame({
#     "activity": ["run", "run", "run", "bike", "bike"],
#     "minutes": [20, 30, 35, 30, 60]
# })

wrapped_text = generate_wrapped(summary)
print(wrapped_text)
```

## Output - 
```
# 🏃‍♀️ Your Activity Recap

## **Total Sessions** 📊
5 activities logged

## **Time Investment** ⏱️
175 total minutes of activity
Average session: 35 minutes

## **Activity Breakdown** 🔥
**Running** dominated with 3 sessions
**Biking** rounded out with 2 sessions

## **Session Range** 📈
Shortest: 20 minutes
Longest: 60 minutes

## **The Verdict** ✨
You kept it consistent with a solid mix of cardio activities! 🎯

```

---

## How it works

1. Provide a **structured summary** of your dataset.  
2. The library generates a **prompt** and sends it to your LLM (OpenAI or Anthropic).  
3. Receive a **fun, readable Wrapped-style recap** strictly based on your data.
4. Bring your own API key—or let the library pick it up from your local environment.

---

## Development

Clone the repository:


```bash
git clone https://github.com/kkumar/get-wrapped.git
cd get-wrapped
uv sync
uv run python examples/run_wrapped.py
```

---

## License

MIT License — see [LICENSE](LICENSE) for details.
vnpy_ctp 6.7.11.2
CTP gateway for vn.py quant trading framework.
License: MIT
Requires Python: >=3.10
Keywords: quant, quantitative, investment, trading, algotrading, ctp

# VeighNa框架的CTP底层接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-6.7.11.2-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows|linux|macos-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.10|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CTP期货版的6.7.11接口封装开发，接口中自带的是【穿透式实盘、评测环境合并】的dll文件。

## 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_ctp
```

或者下载源代码后，解压后在cmd中运行：

```
pip install .
```

使用源代码安装时需要进行C++编译，因此在执行上述命令之前请确保已经安装了【Visual Studio（Windows）】、【GCC（Linux）】、【XCode（Mac）】编译器。

如果需要以**开发模式**安装到当前Python环境，可以使用下述命令：

```
pip install -e . --no-build-isolation --config-settings=build-dir=.\vnpy_ctp\api
```

## 使用

以脚本方式启动（script/run.py）：

```
from vnpy.event import EventEngine
from vnpy.trader.engine import MainEngine
from vnpy.trader.ui import MainWindow, create_qapp

from vnpy_ctp import CtpGateway


def main():
    """主入口函数"""
    qapp = create_qapp()

    event_engine = EventEngine()
    main_engine = MainEngine(event_engine)
    main_engine.add_gateway(CtpGateway)
    
    main_window = MainWindow(main_engine, event_engine)
    main_window.showMaximized()

    qapp.exec()


if __name__ == "__main__":
    main()
```

## Mac系统支持

由于新版本CTP的Mac系统API项目结构发生了较大变化，改为了使用framework目录的结构，因此无法再直接从PyPI下载预编译好的wheel二进制包进行安装。

用户需要克隆（或下载）本仓库的源代码到本地后自行编译安装，具体命令如下：

```
git clone https://github.com/vnpy/vnpy_ctp.git

cd vnpy_ctp

pip3 install .
```

相关注意事项如下：

源码编译需要依赖XCode开发工具中的C++编译器，请务必先安装好。