semvec/pypi-packages huggingface dataset:
xwformats 0.1.0.1
Convenience wrapper for exonware-xwformats - provides 'import xwformats' alias
License: MIT
Requires Python: >=3.12
Keywords: avro, enterprise, exonware, formats, hdf5, netcdf, parquet, protobuf, scientific, serialization, thrift

# xwformats - Enterprise Serialization Formats

**Company:** eXonware.com  
**Author:** Eng. Muhammad AlShehri  
**Email:** connect@exonware.com  
**Version:** 0.1.0.1
**Date:** 02-Nov-2025

---

## Overview

Extended serialization format library providing heavyweight enterprise formats for specialized domains.

**Note:** This library is separated from `xwsystem` to keep the core lightweight and fast.

---

## What's Inside

### 18 Enterprise Formats (~87 MB):

**Schema Formats (7):**
- Protocol Buffers
- Apache Avro
- Apache Parquet
- Apache Thrift
- Apache ORC
- Cap'n Proto
- FlatBuffers

**Scientific Formats (5):**
- HDF5
- Feather
- Zarr
- NetCDF
- MATLAB MAT

**Database Formats (3):**
- LMDB
- GraphDB (Neo4j, Dgraph)
- LevelDB

**Binary Formats (2):**
- BSON
- UBJSON

**Text Formats (1):**
- XML (enterprise features)

---

## Installation

```bash
# Standard installation
pip install exonware-xwformats

# With xwsystem
pip install exonware-xwsystem exonware-xwformats

# Or use xwsystem[lazy] for auto-installation
pip install exonware-xwsystem[lazy]
```

---

## Usage

```python
from exonware.xwformats import (
    ParquetSerializer,
    ProtobufSerializer,
    Hdf5Serializer,
    # ... all enterprise formats
)

# Or through xwsystem
from exonware.xwsystem import XWIO

io = XWIO()
io.serialize(data, format="parquet")  # Auto-discovers xwformats!
```

---

## Why Separate from xwsystem?

**Performance:**
- xwsystem: ~5 MB (core formats only)
- xwformats: ~87 MB (enterprise formats)
- Install only what you need!

**Startup Time:**
- xwsystem alone: ~0.1s (20x faster!)
- With xwformats: ~2s (when actually used)

**Coverage:**
- xwsystem: 14 core formats (80%+ use cases)
- xwformats: 18 enterprise formats (specialized needs)

---

## License

MIT License - See LICENSE file

---

**Part of the eXonware ecosystem**
exonware-xwformats 0.1.0.1
Enterprise serialization formats for xwsystem - Parquet, Avro, HDF5, NetCDF, and 14 more heavyweight formats
License: MIT
Requires Python: >=3.12
Keywords: avro, enterprise, exonware, formats, hdf5, netcdf, parquet, protobuf, scientific, serialization, thrift

# xwformats - Enterprise Serialization Formats

**Company:** eXonware.com  
**Author:** Eng. Muhammad AlShehri  
**Email:** connect@exonware.com  
**Version:** 0.1.0.1
**Date:** 02-Nov-2025

---

## Overview

Extended serialization format library providing heavyweight enterprise formats for specialized domains.

**Note:** This library is separated from `xwsystem` to keep the core lightweight and fast.

---

## What's Inside

### 18 Enterprise Formats (~87 MB):

**Schema Formats (7):**
- Protocol Buffers
- Apache Avro
- Apache Parquet
- Apache Thrift
- Apache ORC
- Cap'n Proto
- FlatBuffers

**Scientific Formats (5):**
- HDF5
- Feather
- Zarr
- NetCDF
- MATLAB MAT

**Database Formats (3):**
- LMDB
- GraphDB (Neo4j, Dgraph)
- LevelDB

**Binary Formats (2):**
- BSON
- UBJSON

**Text Formats (1):**
- XML (enterprise features)

---

## Installation

```bash
# Standard installation
pip install exonware-xwformats

# With xwsystem
pip install exonware-xwsystem exonware-xwformats

# Or use xwsystem[lazy] for auto-installation
pip install exonware-xwsystem[lazy]
```

---

## Usage

```python
from exonware.xwformats import (
    ParquetSerializer,
    ProtobufSerializer,
    Hdf5Serializer,
    # ... all enterprise formats
)

# Or through xwsystem
from exonware.xwsystem import XWIO

io = XWIO()
io.serialize(data, format="parquet")  # Auto-discovers xwformats!
```

---

## Why Separate from xwsystem?

**Performance:**
- xwsystem: ~5 MB (core formats only)
- xwformats: ~87 MB (enterprise formats)
- Install only what you need!

**Startup Time:**
- xwsystem alone: ~0.1s (20x faster!)
- With xwformats: ~2s (when actually used)

**Coverage:**
- xwsystem: 14 core formats (80%+ use cases)
- xwformats: 18 enterprise formats (specialized needs)

---

## License

MIT License - See LICENSE file

---

**Part of the eXonware ecosystem**
vnpy_ctabacktester 1.3.0
CTA strategy backtesting application for VeighNa quant trading framework.
License: MIT
Requires Python: >=3.10
Keywords: algotrading, investment, quant, quantitative, trading

# VeighNa框架的CTA投研模块

<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-1.3.0-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

图形化CTA策略回测模块，基于用户友好的图形界面实现简洁易用的数据下载、历史回测和参数优化等投研功能。

## 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_ctabacktester
```


或者下载源代码后，解压后在cmd中运行：

```
pip install .
```
eco-num-extract 2025.12.22085323
eco-num-extract turns natural science text into structured numeric data using llmatch-guided outputs.
License: MIT
Requires Python: >=3.9

# eco-num-extract
[![PyPI version](https://badge.fury.io/py/eco-num-extract.svg)](https://badge.fury.io/py/eco-num-extract)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/eco-num-extract)](https://pepy.tech/project/eco-num-extract)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


Extract structured numerical data from natural phenomenon descriptions using AI-powered pattern matching.

## Overview
A Python package that converts qualitative ecological/natural descriptions into structured quantitative insights. Uses `llmatch-messages` to enforce numerical output schemas from LLM responses.

## Installation

```bash
pip install eco_num_extract
```

## Usage

### Basic Usage
```python
from eco_num_extract import eco_num_extract

# Default usage with LLM7
response = eco_num_extract(
    user_input="The forest had 120 trees with 45% canopy cover and 3.2m average height"
)
```

### Custom LLM Integration
Pass your preferred LLM instance (OpenAI, Anthropic, etc.):

```python
from langchain_openai import ChatOpenAI
from eco_num_extract import eco_num_extract

llm = ChatOpenAI()
response = eco_num_extract(
    user_input="Sample text",
    llm=llm  # Your custom LLM instance
)
```

## Parameters

- `user_input` (str): Textual description containing numerical patterns
- `api_key` (str, optional): LLM7 API key (defaults to environment variable)
- `llm` (BaseChatModel, optional): Custom LLM instance (defaults to ChatLLM7)

## Features
- Regex-enforced numerical output structure
- Supports any LLM via LangChain interface
- Environment variable fallback for API keys
- Free tier compatible with LLM7

## Rate Limits
LLM7 free tier provides sufficient throughput. For higher limits:
```python
# Via environment variable
os.environ["LLM7_API_KEY"] = "your_key"

# Or direct parameter
eco_num_extract(api_key="your_key")
```

## Getting Started
1. Install package
2. Get free API key at [LLM7 Token Dashboard](https://token.llm7.io/)
3. Process your ecological descriptions

## Issues
Report problems at [GitHub Issues](https://github.com/chigwell/eco-num-extract/issues)

## Author
Eugene Evstafev - [GitHub](https://github.com/chigwell) | [Email](mailto:hi@euegne.plus)