semvec/pypi-packages huggingface dataset:
pyfractionx 0.2.1
A custom fraction math library for Python
Requires Python: >=3.6

# Fraction Library ðŸ§®

A simple Python class to do arithmetic with fractions.

## Usage

```python
from fraction import Fraction

f1 = Fraction(1, 2)
f2 = Fraction(3, 4)

print(f1 + f2)  # Add
print(f1 - f2)  # Subtract
print(f1 * f2)  # Multiply
print(f1 / f2)  # Divide
#\x00 \x00p\x00y\x00f\x00r\x00a\x00c\x00t\x00i\x00o\x00n\x00x\x00
\x00
\x00
geedl 0.1.6
A Python package for Google Earth Engine tools
Requires Python: >=3.7

# GEEdl

**GEEdl** 是一个基于 Google Earth Engine (GEE) 的工具包，目前处于开发阶段。以下是工具包的主要功能规划：

## 功能规划

1. **GEE 基础函数的组合与本地环境的连接**  
   - 封装常用的 GEE 函数，简化遥感数据处理流程。
   - 支持 GEE 与本地环境的数据交互，集成并实现 ArcGIS 中的部分功能。

2. **GEE + PyTorch 深度学习支持**  
   - 集成深度学习功能，如 CNN、DNN 等。

3. **文献代码复现**  
   - 提供部分经典文献的代码复现和示例。

---


## 当前状态

### 开发中：

当前版本已实现以下基础功能：

- 计算任意年份、区域的 Landsat 和 MOD09A1 数据。
- 提供基础的分块函数，如矩形、六边形等。
- 实现其他一些简单的遥感数据处理功能。

高级功能正在持续开发中，包括：

- 高效的大规模数据计算支持。
- 批量处理样本、深度学习相关支持等。

---

## 欢迎贡献

欢迎社区开发者提交建议、功能需求或贡献代码。

---

## 文档

[现有功能文档（zh-CN）](https://gee-py.readthedocs.io/zh_CN/latest/)

---

## 安装方法

使用以下命令从 PyPI 安装 GEEdl：

```bash
pip install geedl
```

或者从 GitHub 安装最新版本：

```bash
pip install git+https://gitclone.com/github.com/gogo-zl/geedl.git
```

[PyPI 上的 GEEdl 项目页面](https://pypi.org/project/geedl/)

---

## 注意

- **版本不一致**：由于 GitHub 和 PyPI 上的版本可能不同步，建议在 GitHub 上获取最新的开发版本。如果你需要稳定版本，请从 PyPI 安装。
- **GitHub 版本**：可以通过 `pip install git+https://github.com/gogo-zl/geedl.git` 获取 GitHub 上的最新代码。

---

## 联系方式

- **作者**：gogo-zl  
- **GitHub 仓库**：[geedl](https://github.com/gogo-zl/geedl.git)

---

> 如有问题或建议，欢迎在 GitHub 仓库提交 Issue 反馈。
vevod-mcp 0.1.2
VolcEngine VOD MCP
Requires Python: >=3.11

# Volcengine MCP

Volcengine VOD 的 Model Context Protocol (MCP) Server 实现

## 项目简介

Volcengine VOD MCP是一个基于[Model Context Protocol](https://github.com/modelcontextprotocol/python-sdk)的 MCP Server，
它将 Volcengine VOD服务集成到LLM模型上下文中，使大模型能够直接操作和管理VOD资源。

## 功能特点

- 提供多种资源访问接口，便于LLM获取Volcengine VOD服务信息、视频资源等
- 实现了多个Volcengine功能的工具封装，包括上传媒资，剪辑视频等

## 安装

### 环境要求

- Python 3.13+
- 火山引擎账号及AccessKey/SecretKey

## 使用方法

### 在 Mcp Client 中集成

在 mcp client 中配置 mcp 服务, 配置的 MCP JSON：

```json
{
  "mcpServers": {
    "vevod": {
      "command": "uvx",
      "args": ["vevod-mcp"],
      "env": {
        "VOLCENGINE_ACCESS_KEY": "Your Volcengine AK",
        "VOLCENGINE_SECRET_KEY": "Your Volcengine SK"
      }
    }
  }
}
```

请在[火山引擎-视频点播-控制台](https://www.volcengine.com/product/vod)申请ACCESS_KEY、SECRET_KEY
hexa-mlops 0.2.1
Hexa MLOps package

# HexaMLOPS CLI


The  HexaMLOps command line tool allows data science teams to generate deployment files needed for platforms like Azure, Kubeflow, and more (coming soon).

By using our abstraction files, data science teams can easily generate deployment files that are compatible with the CLI or SDK of various Machine Learning (ML) platforms, enabling seamless operations on those platforms.


## Installation

```bash
# Create a VENV
python3 -m venv VENV
. ./VENV/bin/activate
```

```bash
pip install hexa-mlops --upgrade # upgrade to last version
```



## Usage

```bash
$ hexa [engine_type] [ file_type ] [ command ] {parameters}
```

## Get Started

Please refer to the [User guide](docs/USERGUIDE.md) for in-depth instructions.

For usage and help content, pass in the `-h` parameter, for example:

```bash
$ hexa az -h
$ hexa general -h
```

## Testing

```bash
python -m build && pip install . && cd tests && python -m unittest
```
## Inputs
Primary input files are `configuration.yaml` and `training.yaml`

`configuration.yaml` example

```bash
# General fields
resource_group: xxx
workspace_name: xxx
location: xxx
experiment_name: hexa_mlops
tags: created_by:hexa_mlops

# Fields to generate files for training
training:  
  compute_name: hexa_compute
  max_instances: 2

  environment_name: hexa_env
  environment_version: 1
  environment_dependencies:
    - python=3.8
    - numpy=1.21.2
    - pip=21.2.4
    - scikit-learn=0.24.2
    - pip:
        - inference-schema[numpy-support]==1.3.0
        - xlrd==2.0.1
        - mlflow==2.6.0
        - azureml-mlflow==1.42.0
    source_code_path: ./src
# Fields to generate files for inference
inference:
    compute_name: inference_compute
    max_instances: 3
    environment_name: inference_env
    environment_version: 2
    endpoint_name: hexa_ml_endpoint
    deployment_name: hexa_ml_blue_deployment
```

`training.yaml` example
```bash
# Jobs within your training pipeline
run_name: training_example
steps:
  - name: download_data
    inputs:
      - data_config: 
          type: uri_file
          path: ./src/download_data/config_yaml
     
    outputs:
      - data_folder: 

  - name : data_prep
    inputs:
      - json_folder: download_data.outputs.data_folder
    outputs:
      - output_folder: 
          type: uri_folder
          mode: upload
 
```
## Outputs

Command to generate files which are used with Azure Machine Learning CLI for ML operation on Azure ML platform, for example:


- Training a model with `pipeline.yaml`
```bash
$ hexa az training_pipeline generate config.yaml pipeline.yaml training.yaml
```

- Deploy a model as an online endpoint with `online_deployment.yaml`
```bash
$ hexa az online_deployment generate config.yaml online_deployment.yaml
```
For other supported file types, check `-h` command
qipdsl 0.1.2
Quantum-Inspired Probabilistic Domain-Specific Language (CUDA-only)
Requires Python: >=3.6
Keywords: quantum, matrix, optimization, probabilistic, dsl, cuda, gpu

# QIP-DSL: Quantum-Inspired Probabilistic Domain-Specific Language (CUDA-only)

QIP-DSL is a high-performance Quantum-Inspired Probabilistic Domain-Specific Language designed for quantum simulation, optimization, and machine learning tasks. It leverages CUDA for GPU acceleration to provide exceptional performance for matrix operations and quantum-inspired algorithms.

## Features

- **CUDA-Accelerated Matrix Operations**: High-performance matrix operations optimized for NVIDIA GPUs
- **Quantum-Inspired Algorithms**: Implementations of quantum-inspired optimization and simulation algorithms
- **Entanglement Simulation**: Tools for simulating quantum entanglement and related phenomena
- **Probabilistic Sampling**: Efficient sampling from complex probability distributions

## Requirements

- CUDA 12.0 or higher
- NVIDIA GPU with Compute Capability 6.0 or higher
- Python 3.6 or higher

## Installation

```bash
pip install qipdsl
```

## Important Note

This package will **NOT** work on systems without CUDA. It will raise an error at runtime if no CUDA-capable devices are found.
cosine-utils 0.1.3
A package to compute cosine similarity between vectors
Requires Python: >=3.6

# Cosine Utils

This package provides a utility to compute cosine similarity between vectors as a percentage. It's implemented using Cython for better performance.

## Installation

You can install it via pip:

```bash
pip install cosine_utils

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.