semvec/pypi-packages huggingface dataset:
mimic-replay 1.1.6
MIMIC installation script, which enables your application to communicate with your MIMIC server.
Requires Python: >=3.8

This installation package sets up and injects MIMIC's session replay recording functionality into your own application. Unfamiliar with MIMIC? Please visit our project links for more information!  

To properly utilize this installation package:
- Ensure your MIMIC backend is installed and running before proceeding.
- In the root folder of the desired application, run the installation script with `python3 -m mimic_replay.install` and follow the prompts.
mbodied-ai 0.0.1
Pydantic agents for interacting with various backends.
Requires Python: >=3.8

# agentic-ai

[![PyPI - Version](https://img.shields.io/pypi/v/agentic-ai.svg)](https://pypi.org/project/agentic-ai)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/agentic-ai.svg)](https://pypi.org/project/agentic-ai)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install agentic-ai
```

## License

`agentic-ai` is distributed under the terms of the [apache-2.0](https://spdx.org/licenses/apache-2.0.html) license.
agentic-ai 0.0.1
Pydantic agents for interacting with various backends.
Requires Python: >=3.8

# agentic-ai

[![PyPI - Version](https://img.shields.io/pypi/v/agentic-ai.svg)](https://pypi.org/project/agentic-ai)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/agentic-ai.svg)](https://pypi.org/project/agentic-ai)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install agentic-ai
```

## License

`agentic-ai` is distributed under the terms of the [apache-2.0](https://spdx.org/licenses/apache-2.0.html) license.
onebag 0.1.3
A collection of various helpers I use in a variety of projects
Requires Python: <4.0.0,>=3.8.0

# Onebag

---

I found myself often reusing the same helpers for a lot of my personal projects, so finally decided to gather them all together and make it easier for me to use them anywhere I wanted. The result is this Python package: Onebag

# Installation & Updating

---

Onebag can be installed with pip.
` pip install onebag`

# Usage

---

login_bot --> 
Logs a user into reddit and returns a `praw.reddit` instance.
- `file_path` should be the absolute path to a `login.txt` file.
- `login.txt` should take the following format  
  `<subreddit>:<user_agent>:<id>:<secret>:<refresh_token>`
Explanation of items:
```yaml
subreddit: The subreddit you plan on targeting
user_agent: A general user_agent that Reddit will see. ex. Reddit bot by /u/username
id: At least 14 character string listed under personal user script for desired application
secret: The client secret listed adjacent to secret for the application. Should be at least 27 characters
refresh_token: A generated refresh token. See https://praw.readthedocs.io/en/stable/tutorials/refresh_token.html#refresh-token
```

get_timestamp --> Returns a current timestamp in the `m/dd [hh:mm]` format. Especially useful for logging.


# Contributing

---

Feel free to open an issue if you encounter any bugs or have any major suggestions. Pull Requests are also welcome.
Response times may vary.

# License

---
[MIT](https://choosealicense.com/licenses/mit/)
smart-arrays 0.0.1
A wrapper around python lists that implement type safety and element-wise operations like numpy. Additionally, if the uncertainties package is installed, arrays of ufloats can be used
Requires Python: >=3.8
Keywords: arrays, numpy, science, typed

# Smart Arrays

I created this project for two reasons. For a college project I wanted to have the flexibility of numpy arrays (which I absolutely love), especialy their ability to do element-wise operations and type safety (in Python, when doing math, I hate it that if you use simple lists, any element can be of any type. I need type safety!), but didn't want to have to use a library so big. I also wanted to try to understand how would one come to implement something like numpy arrays' api. This library is the result.

Everything stems out of the class SmartArray, which is a simple wrapper around a list, that has static size and is type safe. There is also a SmartList, which is the same but its size is mutable. I'll provide a list below with all available objects

| Object name        | Fixed size? | Implements [collections.abc](https://docs.python.org/3/library/collections.abc.html) |
| ------------------ | ----------- | --------------------------- |
| SmartArray         | Yes         | Sequence[T]                 |
| SmartList          | No          | MutableSequence[T] (almost) |
| SmartArrayNumber   | Yes         | Set[C]                      |
| SmartListNumber    | No          | MutableSet[C]               |
| SmartArrayComplex  | Yes         | Set[complex]                |
| SmartListComplex   | No          | MutableSet[complex]         |
| SmartArrayFloat    | Yes         | Set[float]                  |
| SmartListFloat     | No          | MutableSet[float]           |
| SmartArrayInt      | Yes         | Set[int]                    |
| SmartListInt       | No          | MutableSet[int]             |
| SmartArrayBool     | Yes         | Set[bool]                   |
| SmartListBool      | No          | MutableSet[bool]            |
| UncertaintiesArray | Yes         | Set[ufloat] (almost)        |
| UncertaintiesList  | No          | MutableSet[ufloat] (almost) |

Where T is any type and C is bound to complex. That means complex, float, int or bool. Also, if you have installed the [uncertainties](https://pythonhosted.org/uncertainties/) package, you can also use the UncertaintiesArray and UncertaintiesList. These work like SmartArrayNumber and SmartListNumber (except for some boolean, logic and binary operations) but their type is ufloat. That means that any arithmetical operation propagates the error, element-wise!

In utils I wrote some utility functions for these Arrays/Lists, for example way to save and load arrays to and from disk.

In stats I wrote functions to do some statistical analysis. 

I'm very busy with college, but I'll try to keep updating this to make it more friendly for users. :)
nonebot-plugin-chikari-economy 0.0.7
A economy plugin for nonebot 2

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
</div>

<div align="center">

# nonebot-plugin-chikari-economy

_✨ NoneBot 经济插件（库） ✨_


<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/owner/nonebot-plugin-chikari-economy.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-chikari-economy">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-chikari-economy.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">

</div>

## 📖 介绍

一个经济插件（库），可由其他插件调用，以便插件间的经济联动

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-chikari-economy

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot-plugin-chikari-economy
</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-chikari-economy
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-chikari-economy
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-chikari-economy
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_chikari_economy"]

</details>


## 🎉 使用
### 开发者

你可以通过以下方法使用本插件的功能

```
require("nonebot_plugin_chikari_economy")
import nonebot_plugin_chikari_economy
```

#### def_money_type
```
def def_money_type(id: str,name: str,description: str):
    """定义一种新的货币种类
    
    不使用此方法定义货币直接使用可能会出现意料之外的错误
    
    id相同时，新的定义会覆盖旧的定义，但已有的货币量不变

    Args:
        id (str): 货币的id，不同的id会视为不同的货币
        name (str): 货币的名字
        description (str): 货币的描述

    Returns:
        tuple: 一个二元组，应当为(name, description)
    """
```
#### set_money
```
def set_money(uid: str,id: str,value: float):
    """设置用户的某种货币数量

    Args:
        uid (str): 用户id
        id (str): 货币id
        value (float): 货币要设置成的值

    Returns:
        float: 货币设置后的值
    """
```
#### add_money
```
def add_money(uid: str,id: str,value: float):
    """增加（或减少）用户的某种货币数量

    Args:
        uid (str): 用户id
        id (str): 货币id
        value (float): 货币要增加（负数为减少）的值

    Returns:
        float: 货币增加（或减少）后的值
    """
```
#### inquire_money
```
def inquire_money(uid: str,id: str):
    """查询用户的某种货币数量

    Args:
        uid (str): 用户id
        id (str): 货币id

    Returns:
        float: 货币当前数量
    """
```

### 指令表
| 指令 | 权限 | 需要@ | 范围 | 说明 |
|:-----:|:----:|:----:|:----:|:----:|
| Chikari_economy_inquire_one_money | 群员 | 否 | 群聊 | 内置的单种货币查询指令，一般不建议使用 |
| Chikari_economy_inquire_all_money | 群员 | 否 | 群聊 | 内置的所有货币查询指令，一般不建议使用 |
sapython 1.1
Pacote usado para automações no SAP GUI

#Biblioteca para automações no SAP Gui

Funções disponíveis

open_sap(ogin, password, ambient_name, mandante, path_sap_gui)

close_process(name_process)

init_sap() - quando se utiliza sapscript deve rodar essa função para estabelecer a conexão com o SAP (como sap já aberto e logado)

create_file_time_exec(path_file) - caminho da pasta (Logs)

log_task_sucess(file_name_path, rpa_name, task_name)

log_erro(file_name_path, rpa_name)

log_sucess(file_name_path,rpa_name)