semvec/pypi-packages huggingface dataset:
matrix-agents-hub 0.1.3
A hub for managing AI agents
Requires Python: >=3.8

# Matrix Agents Hub

Matrix Agents Hub 是一个用于管理和与各种 AI agents 交互的 Python 包。

## 安装

```bash
pip install matrix-agents-hub
```

## 使用示例

```python
from matrix_agents_hub import AgentsHub, Agent

# 使用单个 Agent
agent = Agent(
    api_key="your-api-key",
    agent_name="agent-name"
)
response = agent.chat("你好")

# 使用 AgentsHub 管理多个 agents
agents = ["agent1", "agent2"]
hub = AgentsHub(agents)
response = hub.chat("agent1", "你好")

# 获取平台上的所有 agents
platform_agents = hub.get_platform_agents()
```

## 环境变量

- `MATRIX_API_KEY`: API密钥（必需）
- `MATRIX_BASE_URL`: 平台服务器地址（可选，默认为 http://localhost:8099）

## 开发

```bash
# 克隆仓库
git clone https://github.com/yourusername/matrix-agents-hub.git

# 安装开发依赖
pip install -e .

# 运行测试
python -m pytest tests/
```

## License

MIT
mark-scraper 0.3.0
Add your description here
Requires Python: >=3.10

# Mark Scraper
CLI to scrape webpages and return them in markdown format

```bash
mark-scraper http://example.com
# Example Domain
# ==============

# This domain is for use in illustrative examples in documents. You may use this
# domain in literature without prior coordination or asking for permission.

# [More information...](https://www.iana.org/domains/example)
```

# Installation
```bash
pipx install mark-scraper
# or
uv tool install mark-scraper
```

# TODO
Here is a list of features I would like to add to this project
- Also save html
    - `mark-scraper -h/--html example.com.html http://example.com > example.com.md`
- Take a screen shot of web page
    - `mark-scraper -s/--screenshot screenshot.png http://example.com > example.com.md`
- Add Page Caching
    - Will return from cache if exists `mark-scraper -c/--cache http://example.com > example.com.md`
    - Bust cache `mark-scraper -b/--bust-cache http://example.com > example.com.md`
    - Clear cache `rm -rf .mark-scraper/cache`
- Add log of pages scrapped

# Development
```bash
uv sync
uv run pytest
uv run mark-scraper http://example.com
```
102203658-Suvit-Kumar 1.0.2
A Python implementation of the TOPSIS method for multi-criteria decision making.
Requires Python: >=3.6

# TOPSIS - Technique for Order Preference by Similarity to Ideal Solution

TOPSIS is a multi-criteria decision analysis method. It is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution.

## Features
- Easy implementation of TOPSIS in Python.
- Suitable for ranking and decision-making tasks.
- Handles datasets with multiple criteria and alternatives.

## Installation
You can install the package using pip:
```bash
pip install 102203658_Suvit_Kumar

## Usage

from 102203658_Suvit_Kumar import topsis

# Example usage
criteria_weights = [0.4, 0.3, 0.3]
dataset = [
    [250, 16, 12],
    [200, 20, 8],
    [300, 12, 10],
]
impact = ['+', '+', '-']

ranked_alternatives = topsis(dataset, criteria_weights, impact)
print(ranked_alternatives)

## License
This project is licensed under the MIT License.
printextent 0.1.13
Printing made for the creativity.
Requires Python: >=3.6

# What is it?

printextent is a Python library for customizing print statements in a different variety of ways.

printextent makes it easier to accomplish! (Either by printing or returning the customized text!)

## Installation

Via pip:

```bash
pip install printextent
```
or
```bash
python -m pip install printextent --upgrade
```

## Examples

```python
import printextent

# prints out "Hello World! This is a test sentence!" in the color blue while also being bolded.
printextent.colorPrint("Hello World! This is a test sentence!", "Blue", "Bold")

# prints out "Hello World! This is a test sentence!" in a rainbow pattern.
printextent.colorPrint("Hello World! This is a test sentence!", "Rainbow")

# prints out "Hello World! This is a test sentence!" while under a typewriter effect.
printextent.typewritePrint("Hello World! This is a test sentence!")

# prints out "Hello World! This is a test sentence!" reversed. ("!ecnetnes tset a si sihT !dlroW olleH")
printextent.reversePrint("Hello World! This is a test sentence!")

# prints out a info message regarding an important message.
printextent.informUserPrint("This is an informational message that can let users know about a situation regarding something that the user must know.")

# prints out a warning message regarding an important message.
printextent.warnUserPrint("This is an informational message that can let users know about a situation regarding something that can go poetentially wrong.")

# prints out a error message regarding an important message.
printextent.errorUserPrint("This is an informational message that can let users know about a situation regarding something that is wrong.")
```

## License

[MIT](https://choosealicense.com/licenses/mit/)
picodaq 0.1.0
Python library for picoDAQ data acquisition
Requires Python: >=3.8

# picodaq — Python library for picoDAQ data acquisition

## Introduction

Data acquisition is a central aspect of many types of scientific
experiments. Commonly, some measurement device outputs a time-varying
voltage which you would like to capture on a computer. With picodaq,
this can be as easy as

    with AnalogIn(channel=0, rate=50*kHz) as ai:
        data = ai.read(10*s)

(See below for fully worked examples.)

At present, the picodaq library supports our soon-to-be-released
picoDAQ hardware. Support for other popular hardware (including National
Instruments and Measurement Computing) is planned as well.
    
### Example of use



## Installation

As easy as

    pip install picodaq
    

## Fully worked examples

### Single-channel data acquisition

We weren't kidding! After

    from picodaq import *
    import matplotlib.pyplot as plt
    
you really can acquire 10 seconds of data, sampled at 50 kHz from
channel “0” of your data acquisition board simply by

    with AnalogIn(channel=0, rate=50*kHz) as ai:
        data = ai.read(10*s)

and plot the results with

    plt.plot(data)

### Multi-channel data acquisition

Of course, you can also capture several channels at once, and the
“read” method can be made to return time stamps so you can have time
in seconds rather than sample numbers on your x-axis:

    with AnalogIn(channels=[0,2], rate=50*kHz) as ai:
        data, time_s = ai.read(10*s, times=True)
    plt.plot(time_s, data)


## Development

Development of picodaq is on [github](https://github.com/wagenadl/picodaq).
botrun-keys-mgr 0.1.0
波特人 API 金鑰管理套件
Requires Python: <4.0,>=3.11

# botrun_keys_mgr

波特人 API 金鑰管理套件

## 概述

這是一個專門為波特人（botrun）用戶管理 API 金鑰申請和存取控制的 Python 套件。本套件整合了 Google 表單進行申請，並使用 Google 試算表進行金鑰管理。

## 前置需求

### Google Service Account 設定
1. 本套件需要使用 `create-vms@plant-hero.iam.gserviceaccount.com` service account 來存取 Google 試算表
2. 請確保您有此 service account 的金鑰檔案（JSON 格式）
3. 設定環境變數：
```bash
export GOOGLE_APPLICATION_CREDENTIALS="./keys/google_service_account_key.json"
```
注意：請將金鑰檔案放在專案的 `keys` 目錄下，並命名為 `google_service_account_key.json`

## 安裝方式

### 開發環境安裝

1. 確保您已安裝 Python 3.11+ 和 Poetry
2. Clone 此專案：
```bash
git clone [repository_url]
cd botrun_keys_mgr
```

3. 使用 Poetry 安裝依賴：
```bash
poetry install
```

### 一般安裝
```bash
pip install botrun-keys-mgr
```

## 使用方式

### 基本使用
```python
from botrun_keys_mgr import KeyManager

# 初始化金鑰管理器
key_manager = KeyManager()

# 查詢金鑰資訊
key_data = key_manager.get_key_info("your-api-key")

if key_data:
    print(f"申請單位：{key_data.department}")
    print(f"申請人：{key_data.applicant_name}")
    print(f"Email：{key_data.applicant_email}")
    print(f"申請事由：{key_data.purpose}")
    print(f"處理 RD：{key_data.processor}")
    print(f"發放時間：{key_data.key_time}")
    if key_data.note:
        print(f"備註：{key_data.note}")
else:
    print("找不到此金鑰資訊")
```

### KeyData 類別欄位
```python
from botrun_keys_mgr import KeyData

# KeyData 包含以下欄位：
key_data = KeyData(
    timestamp="2024/1/22 上午 8:55:54",  # 時間戳記
    department="申請單位",
    purpose="申請事由",
    applicant_name="申請人姓名",
    applicant_email="申請人Email",
    processor="處理RD",
    key_time="2024/1/22",  # 發 key 時間
    api_key="sk-xxxxxxxxxxxxxxxx",  # API Key
    note="備註（選填）"
)
```

## 開發與測試

### 執行主程式
1. 確保已正確設置 service account 金鑰：
   - 建立 `keys` 目錄
   - 將金鑰檔案放入 `keys/google_service_account_key.json`

2. 使用 Poetry 執行：
```bash
poetry run python -m botrun_keys_mgr
```

或在 VS Code 中：
1. 開啟命令選擇（F1 或 Cmd+Shift+P）
2. 選擇 "Run and Debug"
3. 選擇 "Python: Run Main Script"

主程式會：
- 連接到 Google Sheets API
- 讀取試算表內容
- 顯示讀取到的資料

### 執行測試
使用 Poetry 執行測試：
```bash
poetry run python -m unittest discover -s tests
```

或者在 VS Code 中：
1. 開啟命令選擇（F1 或 Cmd+Shift+P）
2. 選擇 "Testing: Focus on Python Test Explorer"
3. 點擊測試檔案旁的執行按鈕

## 功能特色

- 透過 Google 表單管理 API 金鑰申請
- 金鑰驗證與確認
- 追蹤使用者電子郵件和金鑰啟用時間
- 整合 Google 試算表作為資料儲存

## 申請流程

使用者可以透過以下流程申請 API 金鑰：

1. 透過[API 金鑰申請表單](https://docs.google.com/forms/d/1M8s6UDzkekVuEKzEATWAOkc-U0229QG0NV89QVMPTDQ/edit)提交申請
2. 填寫必要資訊：
   - 申請單位
   - 申請事由
   - 申請人姓名
   - 申請人電子郵件

## 金鑰管理

系統使用 [Google 試算表資料庫](https://docs.google.com/spreadsheets/d/1P4n8uaLljygnfcn_FUN7RMsfoI-wDFgqygq8dAfejBI/edit)追蹤和管理以下資訊：
- API 金鑰
- 關聯的電子郵件地址
- 金鑰啟用時間戳記

## 授權條款

[授權資訊待補充]