semvec/pypi-packages huggingface dataset:
Hawc2Models 0.0.4
Pip installable hawc2 models
Requires Python: >=3.7

# HAWC2 Models

Collection of pip-installable hawc2 models.

## Installation

```
pip install hawc2models
```


# Example


```python
from hawc2models import IEA22MW, DTU10MW

# IEA 22MW Reference turbine
htc = IEA22MW(folder='IEA-22-280-RWT')

# specific version
htc = IEA22MW(folder='IEA-22-280-RWT_v1.0.1', version='refs/tags/v1.0.1')

# DTU10MW Reference wind turbine
htc = DTU10MW(folder='DTU10MW')

```





# Background
 
 
## Current status

We have reference models in different locations, e.g:
 
https://www.hawc2.dk/models
https://gitlab.windenergy.dtu.dk/rwts/dtu-10mw-rwt (frza’s model source)
https://gitlab.windenergy.dtu.dk/hawc-reference-models (rink’s group with models (e.g. a copy of dtu10mw) and utils)
https://github.com/IEAWindSystems (IEA3.4, 10, 15, 22)
 
Moreover, we have copies of the dtu10mw in the test_files folder of wetb, pytest_hawc2, hawc2lib, etc.
 
The models have very different sets of sensors. IEA22MW, for instance, has ~2000 aerodynamic sensors, which is just slowing down simulations for most users.
 
dtu10mw.py (https://gitlab.windenergy.dtu.dk/HAWC2/pytest_hawc2/-/blob/master/test_files/DTU10MW/dtu10mw.py?ref_type=heads) contains functionality to 
- Set tilt, cone, yaw
- Set fixed pitch and rotor speed
- Make blades stiff
- Make blades straight
- Set aero methods: aero_calc, induction, tiploss, dynstall
- Set wind: wsp, tint, turb_format, shear
- Set gravity
 
https://gitlab.windenergy.dtu.dk/hawc-reference-models has functionality to:
- Clone hawc2binary
- Clone/download dlls
  - https://gitlab.windenergy.dtu.dk/OpenLAC/control-binary/control-win32.git
  -	towerclearance_mblade.dll from artifact
  -	nrel-5mw-discon from artifact
- base_to_hs2
- base_to_step
- base_to_turb
- base_to_test
 
## Vision
- From roadmap
  - Reference model with full IEC 61400 DLB data set + post-processing
  - Example-model-library (exercises/walk-throughs)
  - Comparisons with measurements
- From users’ perspective:
  - One place to find all (up-to-date) HAWC2 reference models or links to source repositories?
  - Model similarity? (same kind of output, wind, aerodynamic, tiploss, tower shadow, turbulence, controllers?)
  - Newest controllers?
  - Including hs2 section
  - Step and turb?
  - Picture of model
  - DLB Reference loads?
  - Our and/or users comments/issues regarding the models? e.g.
	DTU10MW has aerodynamic discontinuties along blade due to change of profiles
	IEA22MW uses 100 aerodynamic calculation points because…”
- From test framework perspective
  - Pip installable
  - Download models from source repositories. Consistent or newest version?
  - Update dll’s?
  - Features as in dtu10mw.py. For all or just a subset?
- Other considerations
  - Should we try to change/fix the source repositories (e.g. reduce the number of sensors of the IEA22MW) or just provide a website with “our” versions?

## Open questions

- Where should model source files be stored
- How to obtain/download control dlls
- How to handle updates of control dlls and models
- What is a standard model (wsp, turbulence, controller/fixed)
- Which standard sensors should exist
aec-audio-processing-fork 1.0.1.dev1
AEC(Acoustic Echo Cancellation) Audio Processing Module
Requires Python: >=3.11
Keywords: acoustic echo cancellation, webrtc audioprocessing, voice activity detection, noise suppression, automatic gain control

# AEC Audio Processing Module

A Python module for real-time audio processing using WebRTC technology, optimized and maintained by TheDeveloper. This package provides an easy-to-use interface for audio processing capabilities including echo cancellation, noise suppression, and automatic gain control.

## Features

- **Acoustic Echo Cancellation (AEC)**: Removes echo from audio streams
- **Noise Suppression (NS)**: Reduces background noise
- **Automatic Gain Control (AGC)**: Automatically adjusts audio levels

## Requirements
+ swig
+ meson
+ compile toolchain
+ python3

## Installation

You can install the package directly from PyPI:

```bash
pip install aec-audio-processing
```

## Basic Usage

### Simple Audio Processing
```python
from aec_audio_processing  import AudioProcessor

# Initialize with all features enabled
ap = AudioProcessor(enable_aec=True, enable_ns=True, enable_agc=True)
ap.set_stream_format(16000, 1)      # 16kHz mono

# Process 10ms of audio data
audio_10ms = b'\0' * 160 * 2        # 10ms, 16000 sample rate, 16 bits, 1 channel
audio_out = ap.process_stream(audio_10ms)

# Check if voice was detected
if ap.has_voice():
    print("Voice detected!")
```

### Configuration Options

```python
from aec_audio_processing  import AudioProcessor

# Initialize with specific features
ap = AudioProcessor(
    enable_aec=True,    # Echo cancellation
    enable_ns=True,     # Noise suppression  
    enable_agc=True,    # Automatic gain control
    enable_vad=True     # Voice activity detection
)

# Set audio format
ap.set_stream_format(
    sample_rate_in=16000,      # Input sample rate (Hz)
    channel_count_in=1,        # Input channels
    sample_rate_out=16000,     # Output sample rate (Hz) 
    channel_count_out=1        # Output channels
)

# Set reverse stream for echo cancellation
ap.set_reverse_stream_format(16000, 1)

# Set stream delay for echo cancellation
ap.set_stream_delay(50)  # 50ms delay


# Check feature status
print(f"AEC enabled: {ap.aec_enabled()}")
print(f"NS enabled: {ap.ns_enabled()}")
print(f"AGC enabled: {ap.agc_enabled()}")
```
lark-helper-mcp 0.1.0
A Model Context Protocol (MCP) server for Lark (Feishu) integration
Requires Python: >=3.12
Keywords: mcp, lark, feishu, claude, ai

# Lark Helper MCP Server

一个用于 [Lark (飞书)](https://www.larksuite.com/) 集成的 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 服务器。

## 功能特性

- 🚀 基于 FastMCP 构建的高性能 MCP 服务器
- 📱 支持向飞书用户和群聊发送文本消息
- 🔐 安全的环境变量配置管理
- 🛠 支持多种安装和运行方式

## 环境要求

- Python 3.12+
- 有效的飞书应用凭据（App ID 和 App Secret）

## 安装

### 方法一：使用 uvx（推荐）

```bash
# 临时运行
uvx lark-helper-mcp

# 或永久安装
uv tool install lark-helper-mcp
```

### 方法二：使用 pip

```bash
pip install lark-helper-mcp
```

### 方法三：开发安装

```bash
git clone <repository-url>
cd lark-helper-mcp
uv sync
uv run python main.py
```

## 配置

在运行之前，需要设置以下环境变量：

```bash
export LARK_APP_ID=your_app_id_here
export LARK_APP_SECRET=your_app_secret_here
```

或者创建 `.env` 文件：

```env
LARK_APP_ID=your_app_id_here
LARK_APP_SECRET=your_app_secret_here
```

### 获取飞书应用凭据

1. 访问 [飞书开放平台](https://open.feishu.cn/)
2. 创建一个新的应用或选择现有应用
3. 在应用详情页面获取 App ID 和 App Secret
4. 确保应用有发送消息的权限

## 使用方法

### 作为独立服务器运行

```bash
# 使用 uvx
uvx lark-helper-mcp

# 或使用已安装的命令
lark-helper-mcp

# 或使用 Python 模块
python -m lark_helper_mcp
```

### 在 Claude Desktop 中使用

在 Claude Desktop 的配置文件中添加：

```json
{
  "mcpServers": {
    "lark-helper": {
      "command": "uvx",
      "args": ["lark-helper-mcp"],
      "env": {
        "LARK_APP_ID": "your_app_id_here",
        "LARK_APP_SECRET": "your_app_secret_here"
      }
    }
  }
}
```

## 可用工具

### `send_text_message`

向指定的飞书用户或群聊发送文本消息。

**参数：**
- `receive_id` (string): 接收者 ID
- `receive_id_type` (string): 接收者 ID 类型，支持：
  - `user_id`: 用户 ID
  - `open_id`: Open ID
  - `chat_id`: 群聊 ID
- `text_content` (string): 要发送的文本内容

**示例使用：**
```python
# 发送消息给用户
send_text_message(
    receive_id="user123", 
    receive_id_type="user_id", 
    text_content="Hello from MCP!"
)

# 发送消息到群聊
send_text_message(
    receive_id="oc_chat456", 
    receive_id_type="chat_id", 
    text_content="团队通知：会议将在10分钟后开始"
)
```

## 开发

### 环境设置

```bash
# 克隆仓库
git clone <repository-url>
cd lark-helper-mcp

# 创建虚拟环境并安装依赖
uv venv
uv sync

# 运行开发服务器
uv run python main.py
```

### 代码质量检查

```bash
# 运行 linter
ruff check

# 格式化代码
ruff format
```

### 构建和发布

```bash
# 构建包
uv build

# 发布到 PyPI（需要配置 PyPI 凭据）
twine upload dist/*
```

## 许可证

MIT License - 详见 [LICENSE](LICENSE) 文件。

## 贡献

欢迎提交 Issue 和 Pull Request！

## 支持

如有问题，请在 [GitHub Issues](https://github.com/philoveritas/lark-helper-mcp/issues) 中提出。