semvec/pypi-packages huggingface dataset:
generalized-matth 0.0.0.1
Calcalating Matthew Correlation Coefficient for multi class problems

Generalized Matthew Corrlelation  Coefficient (MCC)

This package offers tools for calcualting (MCC)  
https://towardsdatascience.com/the-best-classification-metric-youve-never-heard-of-the-matthews-correlation-coefficient-3bf50a2f3e9a

The purpose was taking the knwon MCC and extending it to work on multi-class problems
In the init you can do one of the following:
1  Leave the avg_type empty - It will run the ggeneralized Matthew
2  Put 2 which will work similarly to clause 1
3 Put 3 which will run case The gneralized F1
certbot-access-server 0.1.2
OpenVPN Access Server Installer plugin for Certbot
Requires Python: >=3.6

certbot-access-server
=====================
OpenVPN Access Server Installer plugin for Certbot

The plugin automates installation of plugin, generated by certbot, to the
OpenVPN Access Server system.

Named Arguments
---------------
==================================================  ===================================
``--certbot-access-server:as-installer-socket``     Path to the socket OpenVPN Access Server
                                                    is listening at
                                                    (Default:
                                                    /usr/local/openvpn_as/etc/sock/sagent.localroot)
``--certbot-access-server:as-installer-path-only``  Set path to certificate to
                                                    OpenVPN Access Server instead of
                                                    certificate itself
                                                    (Default: false)
==================================================  ===================================

Installation
------------
``certbot-access-server`` requires Python 3.6+ to run.

.. code:: bash

    pip3 install certbot-access-server

Examples
--------
To acquire and install a single certificate for example.com, using certbot's
standalone webserver:

.. code-block:: bash

    certbot \
        --standalone
        --installer as_installer \
        -d example.com

Notes
-----
The plugin requires running instance of OpenVPN Access Server to work
magent 0.2.4
Multi-Agent Reinforcement Learning environments with very large numbers of agents
Requires Python: >=3.7
Keywords: Reinforcement Learning,game,RL,AI

<p align="center">
    <img src="readme.png" width="500px"/>
</p>

MAgent is a library for creating 2D environments with very large numbers of agents for conducting research in Multi-Agent Reinforcement Learning. These can look like this:

<img src="magent-graph-1.gif" width="200"><img src="magent-graph-2.gif" width="200">

This is a maintained fork from the original repo- https://github.com/geek-ai/MAgent. The code is significantly cleaned up in many aspects and some unfortunate names have been fixed, but there are no major differences.

This maintainenace was primarily done in the service of the [PettingZoo library](https://github.com/Farama-Foundation/PettingZoo). PettingZoo is the easiest way to use environments in this library, and has comprehensive documentation of them.

## Requirements
MAgent supports Linux and macOS and Python 3.6+


## Install instructions
You can simply use `pip install magent`


If you use this in your research, please cite the original paper:

```
@inproceedings{zheng2018magent,
  title={MAgent: A many-agent reinforcement learning platform for artificial collective intelligence},
  author={Zheng, Lianmin and Yang, Jiacheng and Cai, Han and Zhou, Ming and Zhang, Weinan and Wang, Jun and Yu, Yong},
  booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}
```

If you wish to cite this repo with it's modifications specifically, please cite:

```
@misc{magent2020,
  author = {Terry, Jordan K and Black, Benjamin and Jayakumar, Mario},
  title = {MAgent},
  year = {2020},
  publisher = {GitHub},
  note = {GitHub repository},
  howpublished = {\url{https://github.com/Farama-Foundation/MAgent}}
}
```
nqdm 0.4.12
NQDM -- An extension of TQDM which enables you to loop over multiple objects simultaneously, and specify the depth of iteration for each object. It is just pure Python magic, no extra libraries needed. It is customizable, minimal and open source.
Requires Python: >=3.8

# NQDM

<img width="390" alt="logo1" src="https://user-images.githubusercontent.com/46201716/117867669-f2032e80-b298-11eb-89dc-9f57bd0affb5.png">

A more generalised implementation to TQDM-progress bars, 
which simulates a single loop for multiple loops and returns 
multiple elements at the same time. It can iterate over the deeper levels. It is compatible with many data types, 
is customizable and beginner-friendly.

### Installing

Install it using the terminal

```
pip install nqdm
```


Then import it as follows

```
from nqdm import nqdm
```

## Get Started

Please check *main.ipynb* for use cases 

## Built With

* [tqdm](https://github.com/tqdm/tqdm) - The progress bar

## Authors

* **Yamac Eren Ay** - *Initial work* - [yamaceay](https://github.com/yamaceay)

## License

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

## Special Thanks to:

* **Stanislav Kosorin** - [stano45](https://github.com/stano45) 
* **Ori Toledo** - *Logo design* - [oritoledo](https://github.com/oritoledo)

## Acknowledgments

It's worth mentioning that this module is built on top of [TQDM](https://tqdm.github.io/),  I would like to
thank them for making this process of creating progress bars easy and flexible.
captain-project 0.1.16
Conservation Area Prioritization Through Artificial INtelligence
Requires Python: >=3.8

# The Captain Project

## Conservation Prioritization Through Artificial Intelligence

The CAPTAIN method is described in [this paper](https://www.nature.com/articles/s41893-022-00851-6) and released under [this license](https://github.com/captain-project/captain-project/blob/main/CAPTAIN-License.pdf).

Jupyter notebooks demonstrating the main functionality of the library are [available here](https://github.com/captain-project/notebooks).

## License

This software is licensed jointly by Daniele Silvestro, Stefano Goria, Thomas Sterner, and Alexandre Antonelli on the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License terms and conditions set forth in [LICENSE.md](LICENSE.md). By downloading the software, you are agreeing to the terms and conditions.
If you wish to use the software in any other way, or for any other purpose, than as permitted by the terms and conditions, please contact:

CAPTAIN development team
captain.project.team@gmail.com.
nonebot-plugin-ShuYing-setu 0.1
A setu plugin for NoneBot

# nonebot-plugin-ShuYing-setu

这是一个由书樱编写的 获取色图 Nonebot插件

## issue

目前使用的是 [API - 书樱寄语](api.sakurapuare.com/pic/])API，可能会存在访问速度慢等问题。

## 使用

本插件使用关键词识别：

以 `色图`、`setu`、`来点色图`、`涩图` 开头的消息均会被认为为获取色图消息

如果此插件出现 R-18 内容，请[联系书樱寄语](https://sakurapuare.com/contact)
nonebot-plugin-ShuYing-hitokoto 0.1
A hitokoto plugin for NoneBot

# ShuYing-hitokoto

这是一个由书樱编写的 一言 Nonebot插件

## issue

右对齐由于腾讯蛋疼的排版设计，还无法完美对齐

## 使用

本插件使用关键词识别：

以 `一言`、`hitokoto` 开头的消息均会被认为为获取一言消息
AVOScript 0.11.5
little language just4fun
Requires Python: >=3.10
Keywords: language,avocat,avoscript,script language

<div align="center">

# AVOScript

| ![CodeExample][] | ![CLIExample][] |
|:----------------:|:---------------:|
|   Code example   |   CLI Example   |

### little language just for fun
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/avoscript?style=flat-square)](https://pypi.project/avoscript)
[![PyPI](https://img.shields.io/pypi/v/avoscript?style=flat-square)](https://pypi.org/project/avoscript)

</div>

## Getting Started!
### Install
- via pip
  ```bash
  pip install avoscript --upgrade
  ```
- via git
  ```bash
  git clone https://github.com/ethosa/avoscipt
  cd avoscript
  pip install requirements.txt
  ```

### CLI
```bash
python avos.py -h
```
#### Flags
- `-v`/`--version` AVOScript version
  ```bash
  python avos.py -v
  ```
- `-i`/`--interactive` interactive mode
  ```bash
  python avos.py -i
  ```
- `-s`/`--script` execute script
  ```bash
  python avos.py -s "var a = 1; echo(a);"
  ```
- `-f`/`--file` execute code from file
  ```bash
  python avos.py -f tests/test_code.avo
  ```

#### Package Manager
- install package
  ```bash
  python avos.py add package-name
  ```
- install package without packages fetching
  ```bash
  python avos.py add package-name -nf
  python avos.py add package-name --no-fetch
  ```
- update packages data
  ```bash
  python avos.py upd
  ```

<div align="center">

| [Wiki][] | [AvailableStatements][] | [Playground][] |
|----------|-------------------------|----------------|

</div>

[Wiki]:https://github.com/Ethosa/avoscript/wiki
[AvailableStatements]:https://github.com/Ethosa/avoscript/wiki/Available-Statements
[Playground]:https://ethosa.github.io/avoscript

[CodeExample]:https://user-images.githubusercontent.com/49402667/187029351-875205bf-67f7-4dac-bed7-ca2746d65ad4.png
[CLIExample]:https://user-images.githubusercontent.com/49402667/187029349-4ee9a53e-3ad6-44f4-b2c0-be1d9cc71257.png
ShuYing-hitokoto 0.1
A hitokoto plugin for NoneBot

# ShuYing-hitokoto
 
这是一个由书樱编写的 一言 Nonebot插件

## issue

右对齐由于腾讯蛋疼的排版设计，还无法完美对齐

## 使用

本插件使用关键词识别：

以 `一言`、`hitokoto` 开头的消息均会被认为为获取一言消息
task5 0.1.0
task 5
Requires Python: >=3.10,<4.0

# task_5

# Requirements

  * Python 3.7

> This code makes use of the `f"..."` or [f-string
> syntax](https://www.python.org/dev/peps/pep-0498/). This syntax was
> introduced in Python 3.6.


# Sample Execution & Output

If run without command line arguments, using

```
./precisionEstimate
```

the following usage message will be displayed.

```
Usage: ./estimatePrecision.py num_execs [arbitrary precision]
```

If run using

```
./precisionEstimate 1000000
```

output *simliar* to

```
           float|  3.7247|2.220446049250313e-16
 float-type-hint|  3.6731|2.220446049250313e-16
      Decimal-28| 31.8602|0.999999999999999999999999999
```

will be generated. Note that the `float` and `float-type-hint` lines may vary.

---

An optional precision command line argument can be supplied to change the
arbitrary precision used by the Python `decimal` module. For example:

```
./precisionEstimate 1000000 16
```

will generate output similar to

```
           float| 0.3979|2.220446049250313e-16
 float-type-hint| 0.4053|2.220446049250313e-16
      Decimal-16| 3.1643|0.999999999999999
```