semvec/pypi-packages huggingface dataset:
ncrvi 2025.8.9.0
Check rudimentary information

ncrvi
=====

Determine the number of components returning version information

Example usage
-------------

.. code-block:: bash

    $ ncrvi -h
    usage: ncrvi [-h] [-w INITIAL_WAIT] [-p POWER_ON_WAIT] [-P POWER_OFF_WAIT] [-N HOW_OFTEN] [-s SETTLING_DELAY] [-e EXPECTED_COMPONENTS] [-u USER] {this,that}

    Determine the number of firmware components that report version info

    positional arguments:
      {this,that}           the target to work on

    options:
      -h, --help            show this help message and exit
      -w, --initial-wait INITIAL_WAIT
                            number of seconds to wait before starting the tests (default: 1.0)
      -p, --power-on-wait POWER_ON_WAIT
                            number of seconds to wait before powering on the target (default: 5.0)
      -P, --power-off-wait POWER_OFF_WAIT
                            number of seconds to wait before powering off the target (default: 0.0)
      -N, --how-often HOW_OFTEN
                            how often to run the show (default: 10)
      -s, --settling-delay SETTLING_DELAY
                            how long to wait (in seconds) before reaching for the stars (default: 2.0)
      -e, --expected-components EXPECTED_COMPONENTS
                            the success criterion (default: 20)
      -u, --user USER       the user to book the target, etc. (default: None)

Installation
------------

The `project <https://pypi.org/project/ncrvi/>`_ is on PyPI, so simply run

.. code-block:: bash

    $ python -m pip install ncrvi
sdss-lvmnps 1.2.1
A library and actor to communicate with an SDSS-V LVM network power switch
Requires Python: <4.0,>=3.10

# lvmnps

![Versions](https://img.shields.io/badge/python->3.10-blue)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Documentation Status](https://readthedocs.org/projects/lvmnps/badge/?version=latest)](https://lvmnps.readthedocs.io/en/latest/?badge=latest)
[![Test](https://github.com/sdss/lvmnps/actions/workflows/test.yml/badge.svg)](https://github.com/sdss/lvmnps/actions/workflows/test.yml)
[![Docker](https://github.com/sdss/lvmnps/actions/workflows/docker.yml/badge.svg)](https://github.com/sdss/lvmnps/actions/workflows/docker.yml)
[![codecov](https://codecov.io/gh/sdss/lvmnps/branch/main/graph/badge.svg?token=M0RPGO77JH)](https://codecov.io/gh/sdss/lvmnps)

LVM Network Power Switch

## Features

- CLU Actor based interface
- Supports [Digital Loggers Web Power](https://www.digital-loggers.com/lpc7.html).
- Supports [NetIO](https://shop.netio.eu/netio-power-sockets/powerpdu-4ps--iec-320-c13-switched-power-distribution-unit/) power supplies.


## Installation

```console
pip install sdss-lvmnps
```

Or to install for development

```console
git clone https://github.com/sdss/lvmnps
cd lvmnps
poetry install
```
api-doc-mcp-server 1.0.7
An MCP tool for automatically generating API documentation that can be integrated with Cursor
Requires Python: >=3.8

# API文档生成MCP工具

这是一个用于自动化生成API文档的MCP工具，可集成到Cursor中使用。该工具能够编排整个API文档生成工作流，从分析项目结构到最终生成完整的API文档。

## 功能特点

- 自动编排API文档生成工作流
- 分析项目结构，识别API路由及相关代码文件
- 提取API信息（路由、参数、响应结构等）
- 生成Markdown格式的API文档
- 检查文档完整性并自动补充遗漏内容

## 工作流说明

1. **开始工作流** - 启动API文档生成流程
2. **分析项目结构** - 识别项目中的API路由、控制器、认证拦截器和过滤器
3. **检索API路由** - 定位所有相关的代码文件
4. **阅读代码文件** - 提取API路由、参数和响应信息
5. **编写API文档** - 根据提取的信息生成Markdown格式文档
6. **检查文档完整性** - 确保所有API都已包含在文档中
7. **补充遗漏内容** - 如有遗漏，继续编写直到完成
8. **工作流结束** - 完成整个文档生成过程

## API文档内容要求

生成的API文档包含以下信息：

- API地址
- API所属类路径
- API功能描述
- API是否需要鉴权
- 参数字段说明
- 响应数据结构和字段说明
- 请求响应示例

## 安装和运行

### 环境要求

- Python 3.8 或更高版本
- pip 包管理器

### 安装步骤

可以通过多种方式安装和使用本工具：

#### 方式一：通过pip安装

```bash
pip install api-doc-mcp-server
```

#### 方式二：使用uvx直接运行（推荐）

```bash
uvx --from api-doc-mcp-server api_doc_mcp_server
```

#### 方式三：从源码运行

1. 克隆或下载项目代码：
   ```bash
   git clone <项目地址>
   cd api-doc-mcp-server
   ```

2. 安装依赖：
   ```bash
   pip install -r requirements.txt
   ```

## 在Cursor中使用

### 简单配置方式

在Cursor项目根目录下添加 [mcp.json](file:///D:/codes/pycharm_worksapce/api-doc-mcp-server/mcp.json) 配置文件，内容如下：

```json
{
  "mcpServers": {
    "api-doc-mcp-server": {
      "command": "uvx",
      "args": [
        "--from", "api-doc-mcp-server", "api_doc_mcp_server"
      ]
    }
  }
}
```

这种方式利用 `uvx` 工具直接运行包而无需预先安装，是最便捷的使用方式。

### 传统配置方式

如果你已经通过pip安装了工具，可以使用以下配置：

```json
{
  "mcpServers": {
    "api-doc-mcp-server": {
      "command": "api-doc-mcp-server"
    }
  }
}
```

### 服务模式运行（可选）

如果你希望以服务模式运行工具（适用于需要同时在多个项目中使用的情况），可以按以下步骤操作：

1. 从源码运行服务：
   ```bash
   uvx --from api-doc-mcp-server api_doc_mcp_server serve
   ```

2. 在Cursor项目根目录下添加 [mcp.json](file:///D:/codes/pycharm_worksapce/api-doc-mcp-server/mcp.json) 配置文件，内容如下：
   ```json
   {
     "mcpServers": {
       "api-doc-mcp-server": {
         "url": "http://127.0.0.1:8000/sse"
       }
     }
   }
   ```

### 详细使用步骤

1. 在Cursor中打开需要生成API文档的项目
2. 重启Cursor以加载配置
3. 启动"API文档生成工具"工作流
4. 工具将自动分析项目结构并指导Cursor完成文档生成

## 发布信息

- 当前版本：v1.0.2
- 发布日期：2025-08-09
- 详细发布说明请查看 [RELEASE.md](file:///D:/codes/pycharm_worksapce/api-doc-mcp-server/RELEASE.md)

## 注意事项

- 该工具作为指挥和监督角色，代码检索和解析工作由Cursor AI完成
- 过程尽量减少人工参与，实现自动化生成
- 生成的文档格式为Markdown，便于查看和维护
- stdio模式下无需单独启动服务，工具会按需启动
- 服务模式下需要保持终端窗口运行以确保服务正常
- 使用uvx方式需要系统已安装uv工具

## 故障排除

如果遇到问题，请尝试以下解决方案：

1. 确保所有依赖已正确安装
2. 检查端口8000是否被其他程序占用（仅服务模式）
3. 确认Cursor中的MCP工具配置正确
4. 查看终端输出的日志信息以定位问题
5. 如果使用uvx方式，确保已安装uv工具：`pip install uv`
typeman 0.1.2
Add your description here
Requires Python: >=3.11

# Typeman
A terminal‑based typing-speed test CLI tool that shows **real-time feedback**, **WPM**, and **accuracy** in a beautiful, live interface built with **Typer** and **Rich**.

<img width="959" height="599" alt="image" src="https://github.com/user-attachments/assets/7e5a5aca-63c9-4cb1-aad3-76c85c52b1fe" />


## ✨ Why Typeman?

- Fast, interactive, and cross-platform 🧭  
- Live-color feedback: correct keystrokes turn **green**, mistakes in **red**  
- Custom cursor highlights typing position  
- Uses a built-in 10k word list for realistic, daily vocabulary  
- No external dependencies beyond Python packages  


## 🚀 Installation

**Create a virtual environment using [uv](https://docs.astral.sh/uv/getting-started/installation/)**

```bash
uv venv .type
```

**Activate the virtual environment**

```bash
source .type/bin/activate
```

**Install Typeman into your isolated `.type` virtual environment**

```bash
uv pip install typeman
```

### Alternatively, if you prefer traditional tools

```bash
python3 -m venv .venv      # Create a virtual environment
source .venv/bin/activate
pip install typeman        # Standard pip install
```

## Usage

### 🔸 Run a 30-second typing test (seconds must be 0–60)
```bash
typeman 30 
```

## Demo

https://github.com/user-attachments/assets/5f087a16-bcae-463c-b1e1-ec984ab858ee
pymsch 0.0.15
A package for creating mindustry schematic files
Requires Python: >=3.10

 # pymsch

 a package for generating mindustry schematic files with python code

 here's a basic example:
 ```py
 from pymsch import Schematic, Block, Content

 schem = Schematic()
 schem.set_tag('name', 'Example Schematic')
 schem.set_tag('description', 'A description for the schematic')

 schem.add_block(Block(Content.COPPER_WALL, 0, 0, None, 0))

 schem.write_clipboard()
 ```
 This makes a schematic with a single copper wall, and outputs it to your clipboard to import into the game
avi-tools 1.0.9
Avi's Python toolkit including async function manager, variable database, and terminal utilities.
Requires Python: >=3.8


### `README.md`

````markdown
# avi_tools

**avi_tools** is a modular Python toolkit by Avi Twil that includes:

- Asynchronous function management
- A folder-based variable database
- Terminal utilities including colors, monoid operations, infix expressions, and file helpers

This toolkit is designed to be lightweight, simple to use, and helpful for scripting, automation, and educational purposes.

## Installation

```bash
pip install avi_tools
````

## Modules Overview

* `avi_tools.async_funcs_manager`: Manage async functions with wrappers and async runners.
* `avi_tools.variabledb`: Lightweight file-based key-value store.
* `avi_tools.twillkit`: Functional tools including Monoid, Infix, terminal colors, file helpers, and more.

## Usage Examples

### 1. Async Function Manager

```python
from avi_tools.async_funcs_manager import CapsFunc, FuncsToAsync, AsyncRunner

def hello(name):
    print(f"Hello, {name}")

def add(a, b):
    return a + b

caps_hello = CapsFunc("hello", hello, "Twill")
caps_add = CapsFunc("add", add, 3, 4)

funcs = FuncsToAsync([caps_hello, caps_add])
runner = AsyncRunner(funcs)
runner.run_all()
```

### 2. Variable Database

```python
from avi_tools.variabledb import VariableDB

db = VariableDB("my_db")
db.save("counter", 42)
value = db.load("counter")
print(value)  # 42
print("counter" in db)  # True
```

### 3. Terminal Colors

```python
from avi_tools.twillkit import Colors

print(f"{Colors.RED}This is red text{Colors.ENDC}")
print(f"{Colors.GREEN}This is green text{Colors.ENDC}")
```

### 4. Monoid and Infix Operators

```python
from avi_tools.twillkit import Monoid, Infix

m = Monoid(1, 2, 3)
n = Monoid(4, 5)
combined = m + n
print(combined)  # Monoid(1, 2, 3, 4, 5)

# Filter by type
print(m.of_type(int))

# Infix usage
@Infix
def is_evenly_divisible(a, b):
    return a % b == 0

print(10 |is_evenly_divisible| 2)  # True
```

## Project Structure

```
avi_tools/
├── async_funcs_manager/
├── variabledb/
├── twillkit/
```

## License

MIT License

## Author

Avi Twil

GitHub: [https://github.com/avitwil/avi_tools](https://github.com/avitwil/avi_tools)
PyPI: [https://pypi.org/project/avi\_tools/](https://pypi.org/project/avi_tools/)

```

---
rbash 2.0.0
Remote Bash Shell with reverse SSHFS local directory mounting
Requires Python: >=3.8

# Remote Bash Shell (`remote-bash`)

[![PyPI version](https://badge.fury.io/py/remote-bash.svg)](https://badge.fury.io/py/remote-bash)  
[![Python Version](https://img.shields.io/pypi/pyversions/remote-bash.svg)](https://pypi.org/project/remote-bash/)  
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

---

rename to remtoe-bash

[![https://pypi.org/project/remote-bash/](https://pypi.org/project/remote-bash/)](https://pypi.org/project/remote-bash/)
ghidra-jupyter 1.7.4
A Jupyter kernel for Ghidra
Requires Python: >=3.9

# Ghidra-Jupyter

A Jupyter kernel (notebook & QtConsole) plugin for Ghidra.

Currently supporting [Kotlin-Jupyter](https://github.com/Kotlin/kotlin-jupyter).

For info and installation see the [github repo](https://github.com/GhidraJupyter/ghidra-jupyter-kotlin).
quarknet 0.1.2
QuarkNet is a lightweight Deep learning library that's built entirely with NumPy and supports the essentials needed to train fully-connected feedforward neural networks.
Requires Python: >=3.10

## QuarkNet ⚡ 

***A minimal deep learning library built from scratch with just NumPy.***

QuarkNet is a lightweight Deep learning library that's built entirely with NumPy and supports the essentials needed to train fully-connected feedforward neural networks.


### 🔸 Features

- Fully Connected Linear Layers (`Linear`)
- Activation Functions: `Tanh`, `ReLU`, `Sigmoid`
- Loss Functions: Mean Squared Error (`MSE`)
- Optimizer: Stochastic Gradient Descent (`SGD`)
- Mini-batch Training with shuffling
- Layer Stacking API for building networks
- Prediction & Evaluation support


### 🚀 Installation

**Create a virtual environment using [uv](https://docs.astral.sh/uv/getting-started/installation/)**

```bash
uv venv .venv
```

**Activate the virtual environment**

```bash
source .venv/bin/activate
```

**Install QuarkNet into your isolated `.venv` virtual environment**

```bash
uv pip install quarknet
```

### Alternatively, if you prefer traditional tools

```bash
python3 -m venv .venv      # Create a virtual environment
source .venv/bin/activate
pip install QuarkNet        # Standard pip install
```


### Solving the XOR Problem with QuarkNet ⚡

The XOR (exclusive OR) problem is a classic example in neural networks because it is non-linear and cannot be solved by a single linear layer.

```python
import numpy as np
from quarknet.nn import NeuralNet
from quarknet.layers import Linear
from quarknet.activations import Tanh
from quarknet.loss import MSE
from quarknet.optim import SGD

# XOR inputs
inputs = np.array([
    [0, 0],
    [0, 1],
    [1, 0],
    [1, 1]
])

# XOR outputs
targets = np.array([
    [0],
    [1], 
    [1],
    [0],
])

# Train the model
model.train(
    inputs=inputs,
    targets=targets,
    loss=MSE(),
    optimizer=SGD(),
    epochs=5000,
    batch_size=32,
    shuffle=True
)

# Make predictions
predictions = model.predict(inputs)
print("Predictions:\n", np.round(predictions))

```

**Output**

```bash
Predictions:
array(
    [
        [0.],
        [1.],
        [1.],
        [0.],
    ]
)
```