semvec/pypi-packages huggingface dataset:
VL53L3CX 1.0.1
VL53L3CX distance sensor driver for Raspberry Pi


# VL53L3CX Python

  Simple library for interacting with VL53L3CX sensors via python. This project is based on the [VL53L0X_rasp_python](https://github.com/johnbryanmoore/VL53L0X_rasp_python) project by John Bryan Moore. 

Connect your VL35L3CX module of choice via V+, GND, I2C clock, I2C data and xshut if you are using more then one sensor. The default address is 0x29. If you are using more sensors, drive the xshut high for all sensors but one, change address, then drive next xshut low, change address and repeat the process until all sensors have unique addresses.

*(Note that there is no support for multiplexers as I originally built this for my own use and later I thought to myself the library might come in handy for someone and it is probably worth sharing.)* 

  

# Installing

  

```
pip install VL53L3CX
```

  

# Usage

Look into examples to get basic gist of the usage. There are two simple python scripts - distance and change-address that probably cover all the usage cases you would ever need.

**Basic functions are:**  
`VL53L3CX.open()` - starts I2C  
`VL53L3CX.start_ranging()` - starts the sensor itself  
`VL53L3CX.stop_ranging()` - stops the sensor  
`VL53L3CX.get_distance()` - returns the measured distance in millimeters, should be called with `if VL53L3CX.is_ranging_ready():`  
`VL53L3CX.is_ranging_ready()` - returns true if sensor is ready to be read  
`VL53L3CX.change_address(new_address)` - changes the current I2C address to a new one specified  

**Not properly tested bonus functions I didn't end up using:**  
`VL53L3CX.set_distance_mode(mode)` - set distance mode to 1-short, 2-medium, 3-long  
`VL53L3CX.set_timing_budget(timing_budget)` - in microseconds  
`VL53L3CX.wait_for_data()` - interrupt the program until data is ready
mcp-server-akshare 1.0.0
Model Context Protocol server for AkShare financial data APIs
Requires Python: >=3.8
Keywords: akshare, financial-data, mcp, model-context-protocol

# MCP Server AkShare

一个基于 Model Context Protocol (MCP) 的 AkShare 金融数据接口服务器。

## 安装

### 方式 1: 使用 uvx (推荐)

```bash
uvx mcp-server-akshare
```

### 方式 2: 使用 pip

```bash
pip install mcp-server-akshare
mcp-server-akshare
```

### 方式 3: 从源码安装

```bash
git clone <repository-url>
cd mcp-server-akshare
pip install -e .
```

## MCP 客户端配置

### Claude Desktop

将以下配置添加到你的 Claude Desktop 配置文件中：

```json
{
  "mcpServers": {
    "akshare": {
      "command": "uvx",
      "args": ["mcp-server-akshare"]
    }
  }
}
```

### 其他 MCP 客户端

```json
{
  "command": "mcp-server-akshare",
  "args": [],
  "timeout": 30,
  "transportType": "stdio"
}
```

## 可用工具

### 核心工具

- `akshare_list_functions`: 列出所有可用的 AkShare 函数
- `akshare_get_categories`: 获取所有函数分类
- `akshare_call_function`: 调用任意 AkShare 函数
- `akshare_get_function_info`: 获取函数详细信息

### 常用函数快捷工具

- `akshare_stock_zh_a_hist`: 获取A股历史数据
- `akshare_stock_info_a_code_name`: 获取股票基本信息
- `akshare_macro_china_gdp`: 获取GDP数据
- `akshare_futures_main_sina`: 获取期货数据
- `akshare_bond_zh_hs_cov_daily`: 获取债券数据

## 使用示例

### 获取股票历史数据

```json
{
  "tool": "akshare_stock_zh_a_hist",
  "arguments": {
    "symbol": "000001",
    "period": "daily",
    "start_date": "20240101",
    "end_date": "20241231"
  }
}
```

### 列出股票相关函数

```json
{
  "tool": "akshare_list_functions",
  "arguments": {
    "category": "stock"
  }
}
```

### 调用任意函数

```json
{
  "tool": "akshare_call_function",
  "arguments": {
    "function_name": "stock_zh_a_hist",
    "parameters": {
      "symbol": "000001",
      "period": "daily"
    }
  }
}
```

## 支持的数据类别

- **stock**: 股票数据
- **futures**: 期货数据  
- **bond**: 债券数据
- **macro**: 宏观经济数据
- **energy**: 能源数据
- **crypto**: 加密货币数据
- **forex**: 外汇数据
- **commodity**: 商品数据
- **real_estate**: 房地产数据
- **news**: 新闻数据
- **other**: 其他数据

## 开发说明

项目结构：
```
akshare_mcp/
├── src/akshare_mcp/
│   ├── __init__.py
│   ├── server.py      # MCP服务器主程序
│   └── wrapper.py     # AkShare接口包装器
├── main.py            # 启动脚本
├── requirements.txt   # 依赖列表
└── README.md         # 说明文档
```

核心组件：
- `AkShareWrapper`: 自动发现和包装AkShare函数
- `AkShareMCPServer`: MCP协议服务器实现
modact 1.0.1
Multi-Objective Design of Actuators
Requires Python: >=3.8

# MODAct

Python package for the Multi-Objective Design of electro-mechanical Actuators
that is used to derive 20 benchmark problems for constrained multi-objective
optimization.

For more information about the framework, please refer to the associate
publication:

C. Picard and J. Schiffmann, “Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 2, pp. 234-246, April 2021, doi: [10.1109/TEVC.2020.3020046](https://ieeexplore.ieee.org/document/9179777).

If you use MODAct in your research, we would appreciate a citation.

## Installation

`modact` has a few requirements listed in `requirements.txt`. In particular,
[python-fcl](https://github.com/BerkeleyAutomation/python-fcl) needs to be
installed along with the required `fcl` shared library.

The easiest way to get started is to build a Docker image.

```bash
docker build -t modact .
```

Otherwise, users can install fcl through their package manager (apt, brew, vcpkg)
and then run:

```bash
pip install -r requirements.txt
pip install .
```

## Usage

Each benchmark problem is in a self-contained object:

```python
import modact.problems as pb

# Create problem
cs1 = pb.get_problem('cs1')

# Get search bounds
xl, xu = cs1.bounds()

# Objective weights: -1 --> minimization / 1 --> maximization
cs1.weights  # (-1, 1)
# Constraints weights: -1 --> g(x) >= 0 / 1 --> g(x) <= 0
cs1.c_weights  # (-1, -1, -1, -1, -1, -1, -1)

# To evaluate a vector
f, g = cs1(xl)
```

Note that the output of the function call is not per se automatically converted
to a minimization problem. The `weights` and `c_weights` tuples need to be used.
An example of how this is done is given in the adapter for pymoo:
`modact.interfaces.pymoo`.

Usage examples are shown in the `scripts` folder. In particular, optimization
example using [pymoo](https://github.com/msu-coinlab/pymoo) are given.

Interfaces form different languages (C++ and MATLAB) to python are provided in
the `interfaces` folder.

The best-known Pareto fronts approximations of the 20 problems can be downloaded
here: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3824302.svg)](https://doi.org/10.5281/zenodo.3824302)
mk-append-to-head 0.0.1
Append some string to a MkDocs page's <head>.
Requires Python: >=3.9

# mk-append-to-head
Append some string to a MkDocs page's &lt;head>.

## Installation

```bash
pip install mk-append-to-head
```

## Configuration

In MkDocs configuration file:

```yaml title="mkdocs.yml"
plugins:
- mk-append-to-head:
    append_str: <script>console.log(1)</script>
    pages: ['Home'] # optional
```

Leaving `pages` empty will append the `append_str` to all pages.