semvec/pypi-packages huggingface dataset:
wirikiki 1.1.2
A tiny desktop wiki
Requires Python: >=3.10,<4.0

_Wirikiki_ aims at being a productivity oriented **markdown notebook**.
It's compatible with the most common features supported by GitHub's markdown and adds a few other.

Aims at

- fast / **small** footprint
- **simplicity** of use
- **uncluttered** UI supporting both **dark & bright modes**
- **advanced markdown support** via [markItDown](https://markitdown.netlify.app/) and plugins and [Marked](https://marked.js.org/) for the editor
- auto **archival** (using git)

# Installation

## Building

3 ways to get a runnable code:

- install a release (eg: using `pip`)
- `make dev`
- manual installation

```
sh ./makevueApps.sh
npm install
DIST=1 ./node_modules/.bin/rollup -c rollup.config.js
```

# Usage

## Create one wiki

```
wirikiki new myNewWiki
```

## Run the wiki

Go to the newly created folder "myNewWiki" and run:

```
wirikiki
```

## Zero-install mode

You can just open the html file to get a degraded experience, data will not be saved.

## Keyboard shortcuts

| Key            | Description                                     |
| -------------- | ----------------------------------------------- |
| **Escape**     | Close editor or modal, else toggles the sidebar |
| **Del**        | **D**elete current page                         |
| **E**          | **E**dit current note                           |
| **F**          | Search/**F**ind something                       |
| **N**          | **C**reate a new note                           |
| **Left/Right** | Switch to previous/next note                    |

# Advanced usage

Basic git support is provided, to enable it just type `git init` in the `myKB` folder.

```shell
cd myKB
git init
```

# Dependencies

- **Python 3**
  - if you want to run it **without the virtualenv** you will need the following python packages:
    - aiofiles
    - fastapi
    - uvicorn
- nodejs and npm (BUILD ONLY)
- inotify-tools (DEV ONLY)

# Developers note

This is built using
[Vue.js version 3](https://v3.vuejs.org/) for the front
and [FastAPI](https://fastapi.tiangolo.com/) for the server side.
imgaugmentation 0.0a0
This package is for Data augmentation of image dataset
Keywords: python,augmentation,image,data augmentation,image dataset

It is a model for augmentation of image dataset. Augmentation of image is carried out by the process of resize, scale, rotate, translation, transpose, blurring, and by adding noise to the image. Dataset(X_train) can be augmented to 10x of its original size while saving y_train data for each image.
tambot 1.0
For developers
Keywords: tambot


<h1 align="center">ttbot</h1>

<p align="center">It's a beautiful project that helps developers program.</p>



![](https://img.shields.io/badge/ttbot-orange?style=for-the-badge&logo=python.svg) 

<p align="center">

<a href="#"><img title="Made in Jo" src="https://img.shields.io/badge/MADE%20IN-Jo-red.svg?style=for-the-badge&logo=github"></a>



</p>

<p align="center">

<img alt="Nova-Tools' Github Stats" src="https://github-readme-stats.vercel.app/api?username=NovaTools4&show_icons=true&include_all_commits=true&hide_border=true" />



</p>

<p align="center">

<a href="#"><img title="telegram Num" src="https://img.shields.io/badge/telegram%20Num-The Jordan Ghost-red.svg?style=for-the-badge&logo=telegram"></a>

</p>

<p align="center">

<a href="https://github.com/NovaTools4/followers"><img title="Followers" src="https://img.shields.io/github/followers/NovaTools4?color=blue&style=flat-square"></a>

</p>



## Installation :

```

pip install ttbot

```

### Logan Usage



``` python

from ttbot import *



## Follow us on social media

[![Github](https://img.shields.io/badge/Github-NovaTools-orange?style=for-the-badge&logo=github)](https://github.com/NovaTools4/)

[![Telegram](https://img.shields.io/badge/Telegram-NovaTools-orange?style=for-the-badge&logo=Telegram)](https://t.me/VZX_TEAM)
rongo 0.0.3
This is not a python web framework, it's a complement to such mini frameworks like fastapi sanic. Mainly refer to Thinkphp Laravel Django Masonite.

# Rongo
Rongo provides Service Container, Service Providers, Facades, Caching, Queues and Jobs, Broadcasting, Mail, Configuration management, 
Env file management..., combined with modern asynchronous micro-frameworks (fastapi, sanic), 
to become a very good and easy-to-use full-scale stack web framework.

Rongo提供容器、服务提供者、Facade、缓存、队列、广播、邮件、配置管理、env文件管理...,与现代异步微框架(fastapi、sanic)结合，
成为一个性能很好的非常好用的全栈 Web 框架。

### 简介
1、Rongo，与 Thinkphp Laravel 相同的架构，使用注册树模式构造了一个容器，程序启动时，把所有功能模块按需加载进入容器;

2、全部基于异步协程 asyncio 开发，性能很好;

3、代码的注释清晰、完整。

### 快速入门
创建并激活一个虚拟环境，支持 Python 3.7+ 
```
# 安装
pip install rongo

# 启动 -- 在你的应用入口文件里 (config_path是配置文件路径，具体模板可以直接去开发文档里抄)
import rongo.foundation
rongo.foundation.Application(config_path='config').register()

# 使用
from rongo.facades import Cache
await Cache.set('age', 18)
```

### 什么时候可以用于生产环境？
现在还处于初步开发阶段，等版本号到0.1.0的时候，就可以用于生产环境了。
版本0.1.0之前，会把基本功能写完；并把单元测试写完，测试覆盖率至少90%以上；以及一份完整的开发手册。

### 为什么有Rongo?
它很像框架，但还称不上框架。 本人特别喜欢 Thinkphp Laravel 这两个 PHP 框架，成熟稳定，功能齐全，但又做到了轻量，按需加载。 
python 语言方面的框架，成熟、稳定、性能好、又好用、又全面的框架，很少，几乎没有。 

我最早发现的是 Masonite， 跟我喜欢的两个 PHP 框架很像，功能全面， 架构方面也都是在框架启动时通过注册树模式把所有服务加载到内存中。 开发文档写得很好，很清晰。
我研究了它的功能，还参与翻译了开发手册，也看了底层代码。发现有些基本功能是缺失的，比如 ORM 模块无法定义联合索引； 还发现些常用的功能，有BUG， 我参与修复了其中一个BUG；
它底层代码，有点不太会用设计模式但又用得模有样的感觉；性能不好，比如它的 Cache 模块，循环 get 1万次1.5秒，我重写后 Rongo 是1秒。

后来我发现了 Django，它的热度非常高，据说成熟稳定，于是我决定好好研究它， 想着以后写 Python 方面的程序就用它。
但我经过整整一两个月的阅读文档、实践，依然学不会，不知是框架太难用，还是我太笨。个人感觉，Django 体系太杂，古老又杂乱无章。

后来我迷上了异步微框架，FastAPI，感觉发现了新大陆。性能特别好，代码特别清晰，类型提示特别完整，入参检测非常严谨又方便好用，深度自动化API文档。
学习成本也小，我用了一晚上把开发文档看了一遍，第二天写了会儿代码，基本就上手了。但在我心目中缺点是，它只是个微框架，只有 web 框架最基本的验证、请求和响应相关。

所以，有了Rongo

### 许可 License
Rongo 是在 MIT 许可下获得许可的开源软件。

The Rongo is open-sourced software licensed under the MIT license.
disconv 0.0.3
basic value datatype conversion module in datastructures

# Data Structures (datatype conversion) Conversion package

<sup> current data-structure supported: List, Dictionary, Tuple, Set  </sup>

##Installation
Run the following to install:
'''python
pip install disconv'''


##Usage
'''python
import disconv as foo

#Conversion to int
foo.tonum(data)

#Conversion to float
foo.tofloat(data)

#Conversion to int
foo.tostr(data)

#data refers to a variable of any of the supported datastructure types 
'''




##Methods:
### tonum() : converts values in supported datastructures to int type
	conversions: source_type:output_type => int:int, float:int, str: list of ASCII values if len(str)>1; else, returns ASCII value of str , bool: {True:1,False:0}

### tostr() : converts values in supported datastructures to str type

### tofloat() : converts values in supported datastructures to float type
	conversions: source_type:output_type => int:float, float:float, str: list of float of ASCII values if len(str)>1; else, returns float of ASCII value of str , bool: {True:1.0,False:0.0}

Note_1: Conversion of dict type data-structures convert the datatype of the values of the dictionary

# Developing disconv

To install disconv, along with the tools you need to develop and run tests, run the following in your virtualenv:
'''bash
$ pip install -e .[dev]
'''

Note_2: This is a work in progress. Do feel free to contact me with any suggestions, improvements or bugs you find at you have at: leejohnsdecarmel@gmail.com
Distribution-1-3 0.2.1
Probability Distributions

## Title

Stat Package 101

## Description

This project was created to showcase my OOP knowledge and also to
practice how to deploy a python package. The package contains helps you 
in calculation relating to stsatistical distributions. Four distributions were
 chosen for this project which includes; 
* Normal Distribution
* Bernoulli Distribution
* Binomial Distribution
* Poisson Dsistribution.

## Prerequisites

Before you continue, ensure you have met the following requirements:

* You have a basic understanding of statistics.

## Installation:
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Distribution_1_2
```bash
pip install distribution_1_3
```
## Usage:

```python
from Distribution.binomial import Binomial

b = Binomial(3, 0.2)
print(b)

b = b.binomial_distribution_formula(2)
print(b)
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
go to my GitHub page [here](https://github.com/oluwatomsin)
apache-airflow-providers-y42-demo 1.0.7
Y42 provider
Requires Python: ~=3.7


Package ``apache-airflow-providers-y42_demo``

Release: ``1.0.7``


Provider package
----------------

This is a provider package for ``y42`` provider. All classes for this provider package
are in ``airflow.providers.y42`` python package.

Installation
------------

You can install this package on top of an existing Airflow 2 installation (see ``Requirements`` below
for the minimum Airflow version supported) via
``pip install apache-airflow-providers-y42_demo``

The package supports the following python versions: 3.7,3.8,3.9,3.10


1.0.2
.....

Initial version of the provider.