semvec/pypi-packages huggingface dataset:
qec-bb 1.1.1
A package for quantum error correction with bivariate bicycle codes
Requires Python: >=3.8

# QEC Bivariate Bicycle Codes

A Python package for quantum error correction using bivariate bicycle codes.

## Installation

```bash
pip install qec-bb
```

## Usage

### Basic Usage Example

```python
from qec_bb import BBCode

# Create a new BBCode
l = 8
m = 10
A = ['i', 'x1', 'y7', 'x6.y5']
B = ['x0', 'x5', 'y6.x7', 'x.y5']

code = BBCode(l, m, A, B)

# Find the parameters of the code
# distance_method = 4 uses bposd to calculate the distance (recommended)
n, k, d = code.generate_bb_code(distance_method=4)
print(f"Code parameters: [{n}, {k}, {d}]")

# Get parity-check matrices
H_x, H_z = code.create_parity_check_matrices()

# Create Tanner Graph
G = code.make_graph()
```

### Optimized Version for Multiple Codes

```python
from qec_bb import BBCodeOptimised
from qec_bb.helpers import ProposeParameters

# Set up parameters
l = 10
m = 8
num_codes = 50

# Create an instance of BBCodeOptimised
code = BBCodeOptimised(l, m)

# Help generate random A and B expressions
parameters = ProposeParameters(l, m)

# Generate codes
for _ in range(num_codes):
    A = parameters.draw_bivariate_monomials(num_monomials=3)
    B = parameters.draw_bivariate_monomials(num_monomials=3)
    
    code.set_expressions(A_expression=A, B_expression=B)
    n, k, d = code.find_distance(distance_method=4)
    
    print(f"Code parameters: [{n}, {k}, {d}]")
```

## Documentation

For detailed documentation, please visit our [documentation site](https://github.com/vanshjjw/qec-bivariate-bicycle/blob/master/src/usage.md).

## Features

- Create and analyze bivariate bicycle quantum error correction codes
- Multiple distance calculation methods
- Optimized implementations for generating multiple codes
- Tanner graph representation of codes
- Polynomial helpers for working with bivariate polynomials

## License

MIT License
jweather-mcp-server 0.1.0
一个提供全国天气预报查询功能的模型上下文协议（Model Context Protocol）服务器。该服务器使大型语言模型（LLMs）能够获取全国城市、地区的天气预报情况。
Requires Python: >=3.10

# Juhe Weather MCP Server

一个提供全国天气预报查询功能的模型上下文协议（Model Context Protocol）服务器。该服务器使大型语言模型（LLMs）能够获取全国城市、地区的天气预报情况。

## Components

### Tools

服务器实现了一个工具:

- query_weather: 根据城市、地区、区县名称查询当地实时天气预报情况.
  - 需要传入 "city"（城市、区县等名称）作为必须的字符串参数。
```
async def query_weather(
    city: str = Field(description="查询的城市名称，如北京、上海、广州、深圳、泰顺等；城市或区县或地区名使用简写，严格按照规范填写，否则会导致查询失败")
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
```


## Install
This server requires Python 3.10 or higher. Install dependencies using uv (recommended) or pip

### Using uv (recommended)
When using [uv](https://docs.astral.sh/uv/) no specific installation is needed. We will use [uvx](https://docs.astral.sh/uv/guides/tools/) to directly run jweather-mcp-server.

```bash
uvx jweather-mcp-server
```

### Using PIP
Alternatively you can install jweather-mcp-server via pip:
```
pip install jweather-mcp-server
```
After installation, you can run it as a script using:
```
python -m jweather_mcp_server
```

### Configuration

#### Environment Variables
`JUHE_WEATHER_API_KEY`: 聚合数据的天气预报查询API密钥。获取：[https://www.juhe.cn/docs/api/id/73](https://www.juhe.cn/docs/api/id/73)

```
JUHE_WEATHER_API_KEY=your_api_key
```

#### Configure For CLINE

<details>
  <summary>Using uvx</summary>

  ```
  "mcpServers": {
    "jweather-mcp-server": {
      "command": "uvx",
      "args": [
        "jweather-mcp-server"
      ],
      "env": {
        "JUHE_WEATHER_API_KEY": "your_api_key"
      }
    }
  }
  ```
</details>

<details>
  <summary>Using pip installation</summary>

  ```
  "mcpServers": {
    "jweather-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "jmobile_location_mcp_server"
      ],
      "env": {
        "JUHE_WEATHER_API_KEY": "your_api_key"
      }
    }
  }
  ```
</details>

## Debugging
You can use the MCP inspector to debug the server. For uvx installations:

```bash
npx @modelcontextprotocol/inspector uvx jweather-mcp-server 
```

Or if you've installed the package in a specific directory or are developing on it:

```bash
cd path/to/servers/src/jweather-mcp-server
npx @modelcontextprotocol/inspector uv run jweather-mcp-server
```

## Examples of Questions for Cline
1. "查询下苏州的天气"
2. "今天上海的天气如何？"
phenoscope 0.5.1
An image processing framework created for Ex-FAB NSF BioFoundry that aims to streamline the development of image processing pipelines for images analysis of phenotypes.
Requires Python: ~=3.12

<div style="background-color: white; display: inline-block; padding: 10px; border-radius: 5px;">
  <img src="docs/assets/logo_background_svg/PhenoScopeLogo.svg" alt="PhenoScope Logo" style="width: 200px; height: auto;">
</div>

# PhenoScope: A Python Framework for Bio-Image Analysis
![Development Status](https://img.shields.io/badge/status-Pre--Alpha-red)

A modular image processing framework developed at the NSF Ex-FAB BioFoundry.

---

## Overview
PhenoScope provides a modular toolkit designed to simplify and accelerate the development of bio-image analysis pipelines. 
Its structured architecture allows researchers and developers to seamlessly integrate custom computer vision modules, avoiding 
the need to build entirely new packages from scratch. Additionally, PhenoScope supports incorporating components from 
other existing image analysis tools into a cohesive, unified ecosystem.


## Installation

### Pip
```
pip install phenoscope
```

### Manual Installation
```  
git clone https://github.com/Xander-git/PhenoScope.git
cd PhenoScope
pip install -e .
```  

## Dev Installation
```  
git clone https://github.com/Xander-git/PhenoScope.git
cd PhenoScope
pip install -e ".[dev]"
```  

## Acknowledgements

### CellProfiler
PhenoScope has drawn inspiration and foundational concepts from [CellProfiler](https://github.com/CellProfiler/CellProfiler), 
an open-source software platform for bio-image analysis developed by the Broad Institute Imaging Platform. CellProfiler's modularity, 
pipeline-oriented design, and extensive documentation have significantly influenced the development approach and structure of PhenoScope.

**Reference:**
- [CellProfiler GitHub Repository](https://github.com/CellProfiler/CellProfiler)
- [CellProfiler Official Website](https://cellprofiler.org/)
lmdeployv1 0.0.9.4
LMDeploy api client
Requires Python: >=3.8

# 项目名称
LMDeploy API Client

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## 简介
LMDeploy API Client 是一个用于请求多模态大模型的部署服务的 Python API。它提供了一组方便的功能。

### 本地构建
1. 执行以下命令来构建项目：
cd windmill-endpoint/sdk/python/lmdeployv1 && make -f Makefile
2. 构建完成后，你可以在 `output/dist/` 目录下找到生成的lmdeployv1 whl文件
3. 执行以下命令来安装构建好的包：
pip install -r lmdeployv1-x.x.x-py3-none-any.whl

### 发布到pypi源
cd windmill-endpoint/sdk/python/lmdeployv1 && make -f Makefile publish_pypi

### 直接安装
你也可以直接安装已构建好的包：
- pip install lmdeployv1
- 如果需要再别的项目里引用本包 推荐使用：uv add lmdeployv1 或者 uv add lmdeployv1==0.0.9 [https://docs.astral.sh/uv/]

### 如何使用
```python
from lmdeployv1.client import LMDeployClient

client = LMDeployClient(
    endpoint="http://127.0.0.1:8312/ep-llm"
)
# 带鉴权版本，主要是需要有大模型使用权益的OrgID，UserID可填*或者等同于OrgID
auth_client = LMDeployClient(
    endpoint="http://127.0.0.1:8312/ep-llm",
    context={
        "OrgID":"XXXXXXXXXXX",
        "UserID":"XXXXXXXXXXX",
    }
)

from lmdeployv1.client import build_chat_messages
message = {"{image_url}": "Is there any cat in the image? only answer yes or no."}
client.chat_completion(
    messages=build_chat_messages(message),
    top_p=0.9,
    temperature=0.6,
    ....., # 其他参数
)

# 批量请求
from lmdeployv1.client import build_batch_chat_messages
from lmdeployv1.api import BatchChatCompletionRequest
client.batch_chat_completion(BatchChatCompletionRequest(
    messages=build_batch_chat_messages([message1, message2]),
    temperature=0.6,
    top_p=0.9,
    ....., # 其他参数
))
```