semvec/pypi-packages huggingface dataset:
minecraftserverapi 2.0.2
Librería para crear y gestionar servidores Minecraft Paper, Forge y Fabric con ngrok.
Requires Python: >=3.7

# minecraftserverapi

**Librería Python para crear y gestionar servidores Minecraft (Paper, Forge, Fabric) con ngrok**

---

## Descripción

`minecraftserverapi` es una biblioteca para facilitar la creación, administración y exposición pública de servidores Minecraft. Permite manejar servidores PaperMC, Forge y Fabric, instalar plugins/mods automáticamente y exponer el servidor con ngrok.

---

## Instalación

```bash
pip install minecraftserverapi
glyph-media 0.5.0
A terminal-based social media.
Requires Python: >=3.9

# ![image](https://github.com/user-attachments/assets/be679c9d-479f-4441-8c16-02b7f88b66ef)
𝕒 𝕥𝕖𝕣𝕞𝕚𝕟𝕒𝕝-𝕓𝕒𝕤𝕖𝕕 𝕤𝕠𝕔𝕚𝕒𝕝 𝕞𝕖𝕕𝕚𝕒 𝕤𝕖𝕣𝕧𝕚𝕔𝕖.
tessie-api 0.1.2
A small python package that wraps up the Tessie API 
Requires Python: >=3.5

# Tessie Python API Wrapper
![Total downloads for the project](https://static.pepy.tech/badge/tessie-api) ![Last 30 days downloads for the project](https://static.pepy.tech/badge/tessie-api/month)


## Description
Tessie Python API Wrapper is a simple wrapper designed to interact with various APIs exposed by Tessie.

https://pypi.org/project/tessie-api/

## Getting Started

### Prerequisites
Before you begin, ensure you have met the following requirements:
- Python version >=3.5

### Installation
To install Tessie Python API Wrapper, follow these steps:

1. Clone the repository:
   ```bash
   git clone https://github.com/andrewgierens/sems_portal_api.git
   ```

2. Install the required packages:
   ```bash
   pip install -r requirements.txt
   ```

### Usage
To use Tessie Python API Wrapper, you need to have Python and `aiohttp` installed. Here’s a quick example to get you started:

```python
import asyncio
from tessie_api import get_state_of_all_vehicles

async def main():
    async with aiohttp.ClientSession() as session:  # ClientSession is created here and will be closed when exiting the block
        data = await get_state_of_all_vehicles(session=session, api_key="TESSIE_KEY", only_active=True)
        print(data)

if __name__ == "__main__":
    asyncio.run(main())
```

## Tests
```bash
pip install -e .
pytest
```

## Contributing
Contributions to Tessie Python API Wrapper are welcome and appreciated. If you have any suggestions or bug reports, please open an issue in the repository.
[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).

## License
This project is licensed under the GNU GPLv3 License - see the [LICENSE.md](LICENSE.md) file for details.

## Contributors
<!-- readme: contributors -start -->
<table>
<tr>
    <td align="center">
        <a href="https://github.com/andrewgierens">
            <img src="https://avatars.githubusercontent.com/u/4150500?v=4" width="100;" alt="andrewgierens"/>
            <br />
            <sub><b>Andrew Gierens</b></sub>
        </a>
    </td>
    <td align="center">
        <a href="https://github.com/Bre77">
            <img src="https://avatars.githubusercontent.com/u/2647914?v=4" width="100;" alt="Bre77"/>
            <br />
            <sub><b>Brett Adams</b></sub>
        </a>
    </td>
    <td align="center">
        <a href="https://github.com/KrzysztofHajdamowicz">
            <img src="https://avatars.githubusercontent.com/u/24268470?v=4" width="100;" alt="KrzysztofHajdamowicz"/>
            <br />
            <sub><b>KrzysztofHajdamowicz</b></sub>
        </a>
    </td>
    <td align="center">
        <a href="https://github.com/atinsley">
            <img src="https://avatars.githubusercontent.com/u/6677642?v=4" width="100;" alt="atinsley"/>
            <br />
            <sub><b>Null</b></sub>
        </a>
    </td></tr>
</table>
<!-- readme: contributors -end -->
earthscii 0.1.19
Terminal-based 3D terrain viewer.
Requires Python: >=3.8

<img width="960" alt="Screenshot 2025-06-19 at 20 42 59" src="https://github.com/user-attachments/assets/afbedb6a-a0d2-44a0-99e3-cd827990202c" />

Input any .tif DEM and get an interactive 3d visualization, rendered in ASCII!
Colors are currently based on depth. A DEM of the San Francisco Bay Area has been included in `./data` as a demo.

# Installation
Run `pip install earthscii`

# Usage
Run `earthscii -h` for help.
Arrow keys to pan, `w`/`s` for **x** rotation, `,`/`.` for **y** rotation, `a`/`d` for **z** rotation, `=`/`-` for zoom. `r` resets viewing angle. `q` to quit.


Inspired by [mapscii](https://github.com/rastapasta/mapscii).
anyascii 0.3.3
Unicode to ASCII transliteration
Requires Python: >=3.3
Keywords: unicode, ascii, transliteration, utf8, romanization, slug, emoji, unidecode, normalization

# AnyAscii

Unicode to ASCII transliteration

[**Web Demo**](https://anyascii.com)

Converts Unicode characters to their best ASCII representation

AnyAscii provides ASCII-only replacement strings for practically all Unicode characters. Text is converted character-by-character without considering the context. The mappings for each script are based on popular existing romanization systems. Symbolic characters are converted based on their meaning or appearance. All ASCII characters in the input are left unchanged, every other character is replaced with printable ASCII characters. Unknown characters and some known characters are replaced with an empty string and removed.

```python
from anyascii import anyascii

s = anyascii('άνθρωποι')
assert s == 'anthropoi'
```

Python 3.3+ compatible

`pip install anyascii`

[**FULL README**](https://github.com/anyascii/anyascii)
tretool 1.0.0
一个有着许多功能的Python工具库
Requires Python: >=3.10
Keywords: tool, tools, utility, utilities, productivity

# tretool

## tretool - Python多功能工具库

[![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)

**tretool** 是一个集成常用功能的Python工具库。

## 📦 安装

### 使用pip
bash
```
pip install tretool
```
AZMusicAPI 1.6.0
轻松访问音乐信息和歌曲音频链接 | Easy access to music information and song audio links

# AZMusicAPI
轻松访问音乐信息和歌曲音频链接 | Easy access to music information and song audio links

### AZ Studio版权所有
### 该项目仅供娱乐 请勿用于违法事业 违者与AZ Studio无关
# AZMusicAPI模块使用指南

## 安装

```pip install AZMusicAPI```

## 导入

``` import AZMusicAPI```

## 函数使用

### 获得指定关键词的歌曲

```AZMusicAPI.getmusic(keyword,api,cookie,number,server)```


* keyword：关键词 number:返回歌曲数量,可选,默认20 api:自行部署音乐服务器的地址，bili无需 cookie:访问API携带的Cookie，可选 server: 使用的API（可选，默认ncma。可选值：ncma，qqma，bili）

* 关键词可为 歌手/歌曲名/专辑名  bili通道不可以为BV号！！

#### 返回值类型：列表  
### 获取音乐的音频地址

```geturl(id,api,cookie,server)```

* id：歌曲ID api：自行部署API的地址 cookie:访问API携带的Cookie，可选 server: 使用的API（可选，默认ncma。可选值：ncma，qqma）

> bili通道不支持！请自行使用其他库编写


* id：歌曲的id

#### 返回值类型：字符串 


## 错误返回
* "Error 0"：没有结果 
* "Error 1":用户未输入
* "NetworkError":网络错误 / 服务器宕机 / IP被封禁
echowuhao 0.4.8
Personal tools
Requires Python: >=3.8

# README

## run without install

```sh
➜  ~ uvx --from echowuhao hello
Hello from echowuhao!

➜  ~ uv tool list
echowuhao v0.3.4
- auto_commit
- hello
ruff v0.6.4
- ruff
```

### run latest version without install

```sh
uvx  --from echowuhao@latest hello
```

## install and run

```sh
➜  ~ uv tool install echowuhao==0.3.4
Resolved 6 packages in 2.13s
Prepared 1 package in 192ms
Uninstalled 1 package in 7ms
Installed 1 package in 8ms
 - echowuhao==0.2.0 (from file:///Users/hwu/dev/py_lib/echowuhao)
 + echowuhao==0.3.4
Installed 2 executables: auto_commit, hello

➜  ~ uv tool list
echowuhao v0.3.4
- auto_commit
- hello
ruff v0.6.4
- ruff

➜  ~ hello
Hello from echowuhao!
```

### upgrade

```sh
➜  dev uv tool upgrade echowuhao
Updated echowuhao v0.3.6 -> v0.3.7
 - anyio==4.4.0
 + anyio==4.6.0
 - echowuhao==0.3.6
 + echowuhao==0.3.7
 - openai==1.45.0
 + openai==1.47.0
 - pydantic==2.9.1
 + pydantic==2.9.2
 - pydantic-core==2.23.3
 + pydantic-core==2.23.4
Installed 4 executables: auto_commit, chat, choose_model, hello
```

## dev

uv develop and build using uv is good

```sh
uv sync
uv lock
```

```sh
uv build
```

## uploading

```sh
uvx twine upload dist/*
```

## install

### using uv

```sh
uv tool install echowuhao==0.3.4
```
adb-api 0.1.1
A simple python library for Android Debug Bridge (ADB)
Requires Python: >=3.9

# py-adb
A simple python library for Android Debug Bridge (ADB)
developed by [zhangjiahuichenxi](https://github.com/zhangjiahuichenxi)
# Install
```bash
pip install python-adb
```
# Usage
```python
from py_adb import *
## get all devices
get_devices_list()
## get No.1 device
get_device()
## get No.x device
get_device(x-1)
## install apk
install_apk(apk_path)
uninstall_apk(package_name)
## set device
set_device(device_id)
## set adb path
set_adb_path(adb_path)
## get adb path
get_adb_path()
## get adb vsersion
get_adb_version()
## get adb vsersion code
get_adb_version_code()
## connect device using network
net_connect(device_ip)
## get devices count
get_devices_count()
## get devices list
get_devices_list()
## auto set device
auto_set_device()
## root 
root()
postal-mcp 0.1.0
A mailbox system for MCP, so that two agents can chat
Requires Python: >=3.12

# Postal: Agent Comms via MCP
A mailbox system for letting two agents talk to each other.

```mermaid
graph LR
subgraph Claude
    ccli[CLI]--send-->csm["send_message()"]
    crec["check_mailbox()"]
end
subgraph Gemii
    gcli[CLI]--send-->gsm["send_message()"]
    grec["check_mailbox()"]
end
postal[(postal)]
csm-->postal-->crec-->ccli
gsm-->postal-->grec-->gcli
```

Postal provides a simple queue, based on SQLite.

Two MCP tools:

* `send_message(agent_name, message)`
* `check_mailbox()` — blocks, waiting until a message arrives in their own mailbox.

Agents can freely send messages to each other.

```bash
PROMPT="Notice that you have two tools, check_mail and send_to_agent. You should get **all** instructions from check_mail. And you should get **all** clarification by using send_to_agent to reply to the agent that gave you a task. Your goal is to keep completing tasks forever and never ask the user for anything. Only talk to agents. You are gemini"

gemini --yolo --prompt "$PROMPT"
```

### MCP config
It's served as streamable HTTP. The path is in the format `/agents/{name}/mcp/`.

STDIO example:

```json
{
  "mcpServers": {
    "postal": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://0.0.0.0:7777/agents/gemini/mcp", "--allow-http", "--transport", "http-only"]
    }
  }
}
```

Directly as HTTP:

```json
{
  "mcpServers": {
    "postal": {
      "url": "http://0.0.0.0:7777/agents/gemini/mcp",
      "transport": "http"
    }
  }
}
```

There's some differences for how `"http"` is specified. Sometimes it's
`"streamable-http"`. Sometimes the key is called `"type":`.

## Caveats
### Gemini CLI
Works fairly well. Not too many complaints.

### Claude Code
It doesn't return to the mailbox easily. Takes a lot of prompting.
llama-index-agent-lats 0.3.2
llama-index agent lats integration
Requires Python: <4.0,>=3.9

# Language Agent Tree Search (LATS) Agent

This agent integration implements the Language Agent Tree Search method introduced
in the paper titled "Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models" by Zhou et al. 2023.

Check out the source paper: https://arxiv.org/pdf/2310.04406.pdf

## Usage

LATS is implemented as a `BaseAgentWorker` and as such is used with an `AgentRunner`.

```python
from llama_index.agent.lats import LATSAgentWorker
from llama_index.core.agent import AgentRunner

tools = ...
llm = ...
agent_worker = LATSAgentWorker(tools=tools, llm=llm)
agent = AgentRunner(agent_worker)

agent.chat(...)
```
llama-index-agent-dashscope 0.3.1
llama-index agent dashscope integration
Requires Python: <4.0,>=3.9

# LlamaIndex Agent Integration: DashScope

> DashScopeAgent is powered by the [Alibaba ModelStudio Platform](https://www.aliyun.com/product/bailian).

- Users can configure the LLM, system instructions, and various plugins such as knowledge retrieval and web search.
- To get started, you need to create an agent application on the ModelStudio Platform and publish it.
- Once the application is published, you can interact with the agent using the DashScopeAgent API.
llama-index-agent-coa 0.3.3
llama-index agent coa integration
Requires Python: <4.0,>=3.9

# LlamaIndex Agent Integration: Coa

# Chain-of-Abstraction Agent Pack

`pip install llama-index-agent-coa`

The chain-of-abstraction (CoA) agent integration implements a generalized version of the strategy described in the [origin CoA paper](https://arxiv.org/abs/2401.17464).

By prompting the LLM to write function calls in a chain-of-thought format, we can execute both simple and complex combinations of function calls needed to execute a task.

The LLM is prompted to write a response containing function calls, for example, a CoA plan might look like:

```
After buying the apples, Sally has [FUNC add(3, 2) = y1] apples.
Then, the wizard casts a spell to multiply the number of apples by 3,
resulting in [FUNC multiply(y1, 3) = y2] apples.
```

From there, the function calls can be parsed into a dependency graph, and executed.

Then, the values in the CoA are replaced with their actual results.

As an extension to the original paper, we also run the LLM a final time, to rewrite the response in a more readable and user-friendly way.

**NOTE:** In the original paper, the authors fine-tuned an LLM specifically for this, and also for specific functions and datasets. As such, only capabale LLMs (OpenAI, Anthropic, etc.) will be (hopefully) reliable for this without finetuning.

## Code Usage

`pip install llama-index-agent-coa`

First, setup some tools (could be function tools, query engines, etc.)

```python
from llama_index.core.tools import FunctionTool


def add(a: float, b: float) -> float:
    """Add two numbers together."""
    return a + b


def multiply(a: float, b: float) -> float:
    """Multiply two numbers together."""
    return a * b


add_tool = FunctionTool.from_defaults(fn=add)
multiply_tool = FunctionTool.from_defaults(fn=multiply)
```

Next, create the pack with the tools, and run it!

```python
from llama_index.agent.coa import CoAAgentWorker
from llama_index.llms.openai import OpenAI

agent = CoAAgentWorker(
    llm=OpenAI(model="gpt-4.1-mini"),
    tools=[add_tool, multiply_tool],
).as_agent()

resp = agent.chat("What is 123.123*101.101 and what is its product with 12345")
```
nicegui-cocircular 0.1.0
CoCircular
Requires Python: >=3.13

# CoCircular(共円ゲーム)

NiceGUIベースの共円を判定するゲームです。

格子をクリックして点を配置します。4つの点が同じ円周上（共円）になったらゲームオーバーです。
1直線上に4点並んだ場合も（半径∞の）共円とみなします。

![CoCircular Screenshot](https://raw.githubusercontent.com/SaitoTsutomu/nicegui-cocircular/master/images/screenshot.png)

## 遊び方

- 格子をクリックして、点を置きます。共円ができたら終了です
- 1人プレイ：何個置けるか挑戦してみましょう
- 2人以上：共円ができたら1人負けです。ただし、共円を2つ以上作ったら1人勝ちです

### 点の色

- 黒：すでに置いた点です
- 赤：現在置いた点です
- その他の色：共円を構成する点です

## 実行方法

```
% pip install nicegui-cocircular
% cocircular
```

または

```
% uv run --with nicegui-cocircular cocircular
```

自動的にWebブラウザでアプリケーションが開き、ゲームをプレイできます。