semvec/pypi-packages huggingface dataset:
omnisoot 0.2.10
a computational tool for modeling soot
Requires Python: >=3.7
Keywords: computational, soot

## OMNISOOT
Omnisoot is an object-oriented computational tool for the simulation of the gas phase synthesis of nanoparticles including soot (Carbon Black), graphene, nickel, and carbon nanotubes in reactors. Omnisoot can be used for scientific research or process design and optimization of commercial reactors. It accommodates a variety of particle dynamics, inception, growth, and oxidation models that can be combined and used for target simulations in Plug Flow, Constant Volume, Partially Stirred, and Pressure reactors. Reaction mechanisms are processed by [Cantera](https://cantera.org/) that is used to compute thermodynamic and kinetics properties of the gas mixture. A series of examples are also available in the form of Jupyter notebooks and Python scripts [here](https://github.com/mohammadadib-cu/omnisoot-cv).
Omnisoot has been registered for copyright on 31 May 2023 by [Canadian Intellectual Property Office (CIPO)](https://www.canada.ca/en/services/business/ip.html) under regirstration number 1203139.
rapidpro-api 0.1.1
A library for extracting and transforming data from RapidPro
Requires Python: >=3.6
Keywords: rapidpro, data extraction, data transformation, rapidpro-api

# RapidPro API Data analysis library 

This is a Python library for interacting with the RapidPro API. This library focuses in extracting data from one or multiple RapidPro workspaces and saving it in ready to analyze datasets from RapidPro workspaces. 

It is thought to be run in data pipelines.



### Installation

pip install rapidpro-api


### Development

First clone the repository:

```bash
git clone https://github.com/unicef/magasin-rapidpro-paquet 
```

Then create a virtual environment and install the dependencies:

```bash
cd rapidpro-api
python3 -m venv venv
source venv/bin/activate
pip install -e .[dev]

```

## Running the tests

```bash
pytest
```
To run the tests with coverage, you can use the following command:

```bash
pytest --cov=rapidpro_api
```

To run the tests with coverage and see which lines are missing, you can use the following command:
```bash
pytest --cov=rapidpro_api --cov-report=term-missing
```

## License
Apache2 license. See [LICENSE](LICENSE) for details.
flask-api-starter 1.1.3
A scalable Flask API starter with authentication, blueprints, DB setup, full project generator, and documentation setup using swaggerUI.
Requires Python: >=3.9

# flask-api-starter

A scalable Flask API starter with authentication (JWT), blueprints, database setup, environment configuration, and a CLI project generator.

Usage:
1. Installation:
   ```
   pip install flask-api-starter
   ```

2. Create a new project:
   ```
   flask-api-starter new myproject
   ```

3. In the generated project:
   ```
   cd myproject
   pip install -r requirements.txt
   python run.py
   ```

This package contains a `template/` directory that will be copied into new projects by the CLI.
lfm-universal 5.0.1
LFM V5.0 - Physics-Based Inference & Geometric Pruning Engine
Requires Python: >=3.7

The official implementation of the Luton Field Model (LFM). Includes the V3 Stability Lock and V5 Axiomatic Derivation Engine. MANDATORY: Requires LFM_AXIOM_ACCEPTED=TRUE environment variable.
minicc 0.3.2
Mini Claude Code - 极简教学版 AI 编程助手
Requires Python: >=3.13
Keywords: agent, ai, claude, coding-assistant, pydantic-ai, tui

# MiniCC

极简教学版 AI 编程助手（TUI），参考 Claude Code 的交互形态，用更少的代码把核心机制讲清楚：工具调用、事件驱动 UI、子代理与 MCP。

> v0.3.x 起对外只保证 **TUI 行为** 稳定，内部模块/API 允许 breaking change。

## 已实现能力（当前）

### 基础 Coding 能力

- 文件相关：读/写/精确替换编辑（`read_file` / `write_file` / `edit_file`）
- 代码检索：glob + 内容搜索（`glob_files` / `grep_search`）
- Shell：前台执行与后台任务（`bash` / `bash_output` / `kill_shell`）
- 任务列表：模型可写 todo，TUI 实时展示（`todo_write`）

### 子代理（SubAgent）

- `task(wait=True)`：默认等待子代理完成并返回结果，主 Agent 可以直接整合继续推理
- `task(wait=False)`：后台启动多个子任务
- `wait_subagents()`：等待所有后台子任务结束并汇总输出

### MCP（Model Context Protocol）

- **启动阶段预加载** MCP servers 与 toolsets（不再运行中懒加载）
- toolsets 会注入主 Agent 与子代理（避免每次创建重复加载）
- 缺少可选依赖时默认降级为空；可用 `MINICC_MCP_STRICT=1` 强制启动失败（适合 CI/严格环境）

### TUI 体验（Textual）

- 流式输出 + 自动滚动到末尾
- 工具调用行：直接消费 `agent.run_stream_events()` 的 ToolCall/ToolResult 事件（running/completed/failed）
- 多行输入：`Ctrl+J` 换行；`Enter` 发送
- `@` 引用文件：输入 `@` + 片段弹出候选，`↑/↓` 选择，`Enter/Tab` 插入路径
- ask_user 面板：工具向用户发起选择题/多选题并阻塞等待

## 快速开始

### 安装

```bash
# uv
uv pip install minicc

# pip
pip install minicc
```

### 配置 API Key

```bash
export ANTHROPIC_API_KEY="sk-ant-xxx"
# 或
export OPENAI_API_KEY="sk-xxx"
```

### 启动

```bash
minicc
# 或
python -m minicc
```

也可以直接运行（无需手动安装）：

```bash
uvx minicc
```

## 配置

### `~/.minicc/config.json`

```json
{
  "provider": "anthropic",
  "model": "claude-sonnet-4-20250514",
  "api_key": null,
  "base_url": null,
  "prompt_cache": {
    "instructions": false,
    "messages": false,
    "tool_definitions": false
  }
}
```

### MCP（可选）

配置文件位置优先级：

1. 工作目录下的 `.minicc/mcp.json`
2. 全局 `~/.minicc/mcp.json`

启用 MCP 需要安装可选依赖：

```bash
pip install "minicc[mcp]"
# 或
uv pip install "minicc[mcp]"
```

严格模式（配置错误/缺依赖时直接失败）：

```bash
export MINICC_MCP_STRICT=1
```

### 系统提示词

`~/.minicc/AGENTS.md`：自定义系统提示词（建议写清“可选方案/等待子代理/何时 ask_user”）。

## 快捷键

| 快捷键 | 功能 |
| --- | --- |
| Enter | 发送消息 |
| Ctrl+J | 输入框换行 |
| Ctrl+C | 退出 |
| Ctrl+L | 清屏 |
| Esc | 取消/关闭候选 |

## 项目结构（v0.3.x）

```
minicc/
├── cli.py       # 入口（仅启动 TUI）
├── core/        # 运行时/模型/事件总线/MCP 预加载
├── tools/       # 工具实现（按职责拆分）
└── tui/         # Textual TUI（消费 stream events + event_bus）
```

## 开发

```bash
git clone https://github.com/TokenRollAI/minicc.git
cd minicc
uv sync
uv run minicc
```

Textual 开发模式：

```bash
uv run textual run --dev minicc.tui.app:MiniCCApp
textual console
```

运行测试：

```bash
.venv/bin/pytest -q
```

## Roadmap（TODO）

- Slash Command：如 `/help`、`/clear`、`/model`、`/tasks`、`/mcp` 等
- 自定义 SubAgent：不同职责/不同系统提示词/不同工具集的 agent profile
- WebSearch / Fetch：联网检索与抓取（含缓存与引用）
- Skills：可复用技能包（如“重构”“修复测试”“写文档”等工作流）
- llmdoc：更完整的 Document-Driven Development（新增更多指南/参考/架构图）

## 文档（llmdoc）

- `llmdoc/index.md`：文档索引
- `llmdoc/guides/usage.md`：使用指南
- `llmdoc/guides/testing.md`：测试指南

## License

MIT
ismk 2025.12.18
Modified SMK library that supports defining workflows via strings without Snakefile
Requires Python: >=3.11
Keywords: workflow, pipeline

# ismk

ismk is a lightweight fork of Snakemake (v9.14) that makes workflows fileless by design.
Instead of forcing you to create a Snakefile on disk, ismk lets you define and run workflows directly from Python strings, entirely in-process.

This is especially handy for programmatic or dynamic workflow generation—think “build the Snakefile on the fly, run it immediately, and leave no files behind.”

Updates will be aligned with upstream major releases when needed, rather than tracking every minor version bump.

## What's different from upstream

### Added

- StringSourceFile class (sourcecache.py:105–142)
  - A SourceFile implementation that keeps Snakefile content in memory
  - Enables workflows to be sourced straight from strings

- Workflow.include_string() method (workflow.py:1667–1710)
  - Load Snakefile content directly from a string
  - Full Snakemake/Snakefile syntax supported
  - Optional debug mode to print compiled code for easier inspection

- Bug fix (api.py:610–612)
  - Fixes missing initialization of scheduler_settings when using scheduler='greedy' directly

### Removed

To stay focused on the core library experience, ismk strips out:

- Docs (Please read the official snakemake online docs)
- Some development tooling configs

Note: The tests/ module itself is kept to support Snakemake’s built-in testing features.

### Others

Performed some code formatting and minor refactoring.

## License

MIT License — same as upstream Snakemake.
uni-tokenizer 0.1.1
Fast BPE tokenizer/trainer with a Rust core and Python bindings
Requires Python: >=3.11

unitoken
=======

`unitoken` is a fast BPE tokenizer/trainer with a Rust core and optional Python bindings.

Install
-------

Rust:

```bash
cargo add unitoken
```

Python (wheels via PyPI):

```bash
pip install uni-tokenizer
```

Quickstart (Python)
-------------------

```python
from uni_tokenizer import BpeTrainer, BpeEncoder

trainer = BpeTrainer(["
"])  # first token is treated as EOT
trainer.add_words({"hello": 10, "world": 7})
trainer.train(vocab_size=256)
trainer.save("demo")

enc = BpeEncoder.load("demo")
ids = enc.encode_word("hello")
```

Building from source
--------------------

This project uses `maturin` for the Python extension module.

```bash
maturin develop
```
pytest-html5 1.6
the best report for pytest
Requires Python: >=3.6

![img](pytest_html5/sttc/pytest-html5.jpg)
### 作者的话🍭

我是一个颜值控，同时我也喜欢原生报告的简约风格，

或许只有我知道，这个库被严重低估了。

放眼整个ui自动化领域，有哪个库能把测试报告做得简约优雅的。

- 原生的吗？原生的一堆兼容性问题还巨丑！

- allure报告吗？fuck！它即繁琐又依赖java，它最不应该被推荐但却人尽皆知。


- testrunner吗？真的很丑好吗！


- 其它的如poium？它只能搭配固定的库来用，一点也不开源！



我找了很久，一个好看又简约的都没有，于是我自己写一个，一个专属于只要有pytest就能用的测试报告。

几年过去了，从私有仓库变开源了，依旧一个能打的都没有。

这个过程真的写的我想哭，我完全是一个人在战斗，我自己用pyecharts解决了图表各种问题，为了不断优化多模块时的图像展示，我调试了好久好久。还有利用反射解决了无需传递driver，我自己都佩服我自己。为了兼容原生报告，我要深入了解pytest的注册机制，这才趋于完美的。

pytest-html5搭配seliky，让你的自动化之旅如虎添翼。



### 怎么用💢💢

首先卸载掉以前安装的html报告插件

然后安装它！pip install pytest-html5

然后呢，没有然后，pytest-html5是非侵入式的，不需要你额外写任何配置，不需要conftest，你怎么用pytest-html的，你就怎么用pytest-html5


### 效果展示

![img](pytest_html5/sttc/example.jpg)
pyOpenSourceProjects 0.5.0
Check python OpenSource Projects to follow standards for README, github workflow and pyprojec.toml - creates badges
Requires Python: >=3.10

# pyOpenSourceProjects

Helper Library to organize open source Projects

| | |
| :--- | :--- |
| **PyPi** | [![PyPI Status](https://img.shields.io/pypi/v/pyOpenSourceProjects.svg)](https://pypi.python.org/pypi/pyOpenSourceProjects/) [![License](https://img.shields.io/github/license/WolfgangFahl/pyOpenSourceProjects.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![pypi](https://img.shields.io/pypi/pyversions/pyOpenSourceProjects)](https://pypi.org/project/pyOpenSourceProjects/) [![format](https://img.shields.io/pypi/format/pyOpenSourceProjects)](https://pypi.org/project/pyOpenSourceProjects/) [![downloads](https://img.shields.io/pypi/dd/pyOpenSourceProjects)](https://pypi.org/project/pyOpenSourceProjects/) |
| **GitHub** | [![Github Actions Build](https://github.com/WolfgangFahl/pyOpenSourceProjects/actions/workflows/build.yml/badge.svg)](https://github.com/WolfgangFahl/pyOpenSourceProjects/actions/workflows/build.yml) [![Release](https://img.shields.io/github/v/release/WolfgangFahl/pyOpenSourceProjects)](https://github.com/WolfgangFahl/pyOpenSourceProjects/releases) [![Contributors](https://img.shields.io/github/contributors/WolfgangFahl/pyOpenSourceProjects)](https://github.com/WolfgangFahl/pyOpenSourceProjects/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/WolfgangFahl/pyOpenSourceProjects)](https://github.com/WolfgangFahl/pyOpenSourceProjects/commits/) [![GitHub issues](https://img.shields.io/github/issues/WolfgangFahl/pyOpenSourceProjects.svg)](https://github.com/WolfgangFahl/pyOpenSourceProjects/issues) [![GitHub closed issues](https://img.shields.io/github/issues-closed/WolfgangFahl/pyOpenSourceProjects.svg)](https://github.com/WolfgangFahl/pyOpenSourceProjects/issues/?q=is%3Aissue+is%3Aclosed) |
| **Code** | [![style-black](https://img.shields.io/badge/%20style-black-000000.svg)](https://github.com/psf/black) [![imports-isort](https://img.shields.io/badge/%20imports-isort-%231674b1)](https://pycqa.github.io/isort/) |
| **Docs** | [![API Docs](https://img.shields.io/badge/API-Documentation-blue)](https://WolfgangFahl.github.io/pyOpenSourceProjects/) [![formatter-docformatter](https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter) [![style-google](https://img.shields.io/badge/%20style-google-3666d6.svg)](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings) |

## Documentation
[Wiki](https://wiki.bitplan.com/index.php/PyOpenSourceProjects)

### Authors
* [Tim Holzheim](https://www.semantic-mediawiki.org/wiki/Tim_Holzheim)
* [Wolfgang Fahl](http://www.bitplan.com/Wolfgang_Fahl)