semvec/pypi-packages huggingface dataset:
mftsccs 0.1.12
A Python library for Concept-Connection System (CCS) - offline-first knowledge graph management
License: MIT
Requires Python: >=3.8
Keywords: ccs, library, utilities

# CCS

A Python library for CCS functionality.

## Installation

### From source (development mode)

```bash
# Clone the repository
git clone https://github.com/yourusername/ccs.git
cd ccs

# Install in development mode
pip install -e .

# Install with development dependencies
pip install -e ".[dev]"
```

### From PyPI (once published)

```bash
pip install ccs
```

## Usage

```python
# Import the main functions and classes
from ccs import example_function, ExampleClass, helper_function

# Use example_function
result = example_function("hello")
print(result)  # Output: Processed: hello

# Use ExampleClass
obj = ExampleClass("my_instance")
obj.process("some data")
print(obj)  # Output: ExampleClass(name='my_instance', data=some data)

# Use helper_function
first_item = helper_function([1, 2, 3])
print(first_item)  # Output: 1
```

### Import specific modules

```python
from ccs.core import ExampleClass
from ccs.utils import flatten_list, validate_input

# Flatten nested lists
flat = flatten_list([[1, 2], [3, 4]])
print(flat)  # Output: [1, 2, 3, 4]

# Validate input types
is_valid = validate_input("hello", str)
print(is_valid)  # Output: True
```

## Project Structure

```
CCS-Python/
├── pyproject.toml          # Package configuration
├── README.md               # This file
├── src/
│   └── ccs/                # Main package
│       ├── __init__.py     # Package initialization & exports
│       ├── core.py         # Core functionality
│       └── utils.py        # Utility functions
├── tests/                  # Test suite
│   ├── __init__.py
│   ├── test_core.py
│   └── test_utils.py
└── docs/                   # Documentation (optional)
```

## Development

### Running tests

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=ccs
```

### Code formatting

```bash
# Format code with black
black src/ tests/

# Lint with ruff
ruff check src/ tests/
```

### Type checking

```bash
mypy src/
```

## Building & Publishing

### Build the package

```bash
pip install build
python -m build
```

This creates distribution files in the `dist/` directory:
- `ccs-0.1.0.tar.gz` (source distribution)
- `ccs-0.1.0-py3-none-any.whl` (wheel distribution)

### Publish to PyPI

```bash
pip install twine

# Upload to TestPyPI first (recommended)
twine upload --repository testpypi dist/*

# Upload to PyPI
twine upload dist/*
```

## License

MIT License - see LICENSE file for details.
yunetas 0.5.8
TUI (Terminal User Interface) of Yunetas
License: MIT
Requires Python: >=3.7

# TUI for yunetas

> This is the TUI (Terminal User Interface) for [Yuneta Simplified](https://yuneta.io)

<a href="https://yuneta.io/">
    <img src="https://github.com/artgins/yunetas/blob/main/docs/doc.yuneta.io/_static/yuneta-image.svg?raw=true" alt="Icon" width="200" /> <!-- Adjust the width as needed -->
</a>

[Yuneta Simplified](https://yuneta.io) is a development framework about messages and services, based on 
[Event-driven](https://en.wikipedia.org/wiki/Event-driven_programming), 
[Automata-based](https://en.wikipedia.org/wiki/Automata-based_programming) 
and [Object-oriented](https://en.wikipedia.org/wiki/Object-oriented_programming) 
programming.

Yuneta is based in functions, but it manages a system of virtual classes 
defined by programmatically and schematically.  

All his philosophy is based on that virtual classes (namely GClass or class G).

All architecture done by configuration, based in schema,
easy to see by human eye. 
Of course, you have an API functions to change configuration and data in real time. 

For [Linux](https://en.wikipedia.org/wiki/Linux) and [RTOS/ESP32](https://www.espressif.com/en/products/sdks/esp-idf). 

Versions in C, Javascript and (TODO) Python.

For more details, see [doc.yuneta.io](https://doc.yuneta.io) 



[pypi-badge]: https://img.shields.io/pypi/v/yunetas


# How build this package


## Install pdm

This package use `pdm` to build and publish.

```shell
    pip install pdm
    pip install cement
    pip install plumbum
    pip install fastapi
    pip install "uvicorn[standard]"
    pip install "typer[all]"
```

## Build and publish
```shell
  # Firstly change the version (explained below)
  # Next go to source root folder
  pdm build
  pdm publish --username __token__ --password <your-api-token> # (me: the full command is saved in publish-tui_yunetas.sh)
```

## Install the package in editable mode using pip from the source root folder:

```shell
  pip install -e .
```

## Change the version

> Edit the `__version__.py` file and change the variable `__version__`.
Then [build and publish](build-and-publish)
Topsis-Kshitiz-102303748 0.1.2
TOPSIS CLI with automatic categorical handling
License: MIT
Requires Python: >=3.8
Keywords: topsis, decision-making, mcdm, multi-criteria, ranking

# Topsis-Kshitiz-102303748

TOPSIS command-line tool with automatic handling of categorical columns (ordinal detection + label encoding fallback).

## Installation

```bash
pip install Topsis-Kshitiz-102303748
```

## Quick Start

```bash
topsis-kshitiz-102303748 input.csv "1,1,2,1" "+,+,-,+" output.csv
```

## Features

- ✅ Multi-criteria decision analysis using TOPSIS
- ✅ Automatic categorical-to-numeric conversion
- ✅ Comprehensive input validation
- ✅ Clear error messages
- ✅ Support for any number of criteria

## Basic Usage

```bash
topsis-kshitiz-102303748 <InputFile.csv> <Weights> <Impacts> <OutputFile.csv>
```

**Example:**

```bash
topsis-kshitiz-102303748 data.csv "1,1,1,2,1" "+,+,-,+,+" results.csv
```

## Input Format

CSV file with:

- First column: Name/identifier
- Remaining columns: Criteria (numeric or categorical)
- Minimum 3 columns required

## Parameters

- **Weights**: Comma-separated numbers (e.g., "1,2,1")
- **Impacts**: '+' (higher is better) or '-' (lower is better)
- Both must match the number of criteria columns

## For Developers

### Build from source

```bash
pip install build twine
python -m build
```

### Upload to PyPI

```bash
twine upload --repository testpypi dist/*
twine upload dist/*
```

## License

MIT License

## Version

0.1.2
gpflib 3.1.25
Grid-based Parsing Framework
License: MIT
Requires Python: >=3.6

# GPF简介

gpflib库提供了一种**基于网格的自然语言结构分析框架（Grid based Parsing Framework，GPF）**。该框架中，语言结构分析被泛化为识别语言单元和建立语言单元之间的关系、为语言单元和关系赋予属性的过程。通过gpflib，用户可以实现可控的自然语言结构计算。

GPF具有以下特点：

1. 利用网格结构分析语言单元和关系，通过键值方式对其属性进行描述和计算；
2. 采用数据表表示不同类型的知识；
3. 借助有限状态自动机剖析语言的具体应用场景；
4. 定义了数据交换标准接口，可以利用深度学习方法的结果，在网格中进行融合计算。

GPF具有良好的包容性，该框架以符号计算为主导，由它接收输入、输出结果，具有更好的可控性、可扩展性和可解释性。


# 说明文档

GPF说明文档及使用示例： http://gpf.blcu.edu.cn.

# 参考书目

[《自然语言结构计算——GPF结构分析框架》](https://item.jd.com/13574545.html)——荀恩东著

# 引用

如果在工作中使用了 GPF，您可以引用：

> 荀恩东.自然语言结构计算——GPF结构分析框架[M].北京:人民邮电出版社.2022
vxsched 20260121
基于事件驱动的轻量级任务调度器
License: MIT
Requires Python: >=3.10

# VXSched

VXSched 是一个轻量级的 Python 事件调度器，支持多种触发器（Cron、间隔、一次性）和事件驱动的架构。它旨在简化定时任务和事件处理的管理。

## 特性

*   **轻量级**: 单文件实现，易于集成。
*   **灵活的触发器**:
    *   `CronTrigger`: 支持标准 Cron 表达式。
    *   `IntervalTrigger`: 支持固定时间间隔执行。
    *   `OnceTrigger`: 支持特定时间点的一次性执行。
*   **事件驱动**: 基于事件和处理器的设计，解耦任务逻辑。
*   **装饰器支持**: 使用装饰器轻松注册事件处理器。
*   **配置管理**: 内置只读配置管理和持久化参数存储。
*   **CLI 工具**: 提供命令行工具快速初始化和运行项目。

## 依赖

*   `pydantic`
*   `vxutils` (需要确保该模块在路径中)

## 快速开始

### 1. 初始化项目

使用 `init` 命令初始化配置目录和示例文件：

```bash
python vxsched.py init -t my_project -e
```

这将创建 `my_project` 目录，包含配置文件 `config.json` 和示例处理器 `mods/handlers.py`。

### 2. 编写处理器

在 `mods/handlers.py` 中定义你的事件处理器：

```python
import logging
from vxutils import loggerConfig
from vxsched import register, Event, Scheduler, INIT_EVENT, daily, every,APP

# 注册一个自定义事件处理器
@register("my_task")
def handle_my_task(app: Scheduler, event: Event):
    logging.info(f"Processing my_task: {event}")

# 在调度器启动时注册定时任务
@register(INIT_EVENT)
def on_init(app: Scheduler, event: Event):
    logging.info("Scheduler started!")
    
    # 每天 08:00 执行
    app.submit("my_task", trigger=daily("08:00:00"))
    
    # 每 3 秒执行一次
    app.submit(Event(type="my_task", data={"msg": "Hello"}), trigger=every(3))
    
if __name__ == "__main__":
    try:
        loggerConfig(level="INFO", filename="", force=True)
        APP.start()
        APP.submit(Event(type="test"))
        APP.wait()
    except KeyboardInterrupt:
        pass
    finally:
        APP.stop()

```

### 3. 运行调度器

使用 `run` 命令启动调度器：

```bash
python vxsched.py run -m my_project/mods
```

## API 参考

### 触发器装饰器

*   `@daily(time_str)`: 每天指定时间触发 (e.g., "08:00:00")。
*   `@weekly(time_str, day_of_week)`: 每周指定时间触发 (day_of_week: 0-6, 0 is Monday)。
*   `@every(interval)`: 每隔 `interval` 秒触发。
*   `@crontab(expression)`: 使用 Cron 表达式触发。
*   `@once(datetime)`: 在指定时间触发一次。

注意：这些函数返回 `Trigger` 对象，通常用于 `app.submit(..., trigger=...)`。

### 注册处理器

使用 `@register(event_type)` 装饰器将函数注册为事件处理器。

```python
@register("event_name")
def my_handler(app: Scheduler, event: Event):
    ...
```

### 提交任务

使用 `app.submit(event, trigger=...)` 提交任务。

*   `event`: 可以是 `Event` 对象或事件类型字符串。
*   `trigger`: (可选) 触发器对象。如果不提供，默认为立即执行一次。

```python
# 立即执行
app.submit("simple_task")

# 延迟/定时执行
app.submit("recurring_task", trigger=every(60))
```

## 内置事件

*   `INIT_EVENT` (`__INIT__`): 调度器启动时触发。用于初始化资源或提交初始定时任务。
*   `SHUTDOWN_EVENT` (`__SHUTDOWN__`): 调度器停止时触发。用于清理资源。

## 配置与参数

*   **Config**: `vxsched.json` 中的配置加载后为只读属性，通过 `app.config` 访问。
*   **Params**: `app.params` 用于存储需要在重启后持久化的运行时参数。使用 `app.params.set_params(key, value, persist=True)` 保存。