semvec/pypi-packages huggingface dataset:
PyShbak 0.3
SHAWPACK NLP processing package
Keywords: NLP,text-data,AI,DS,Shahrour,arabic-py,arabic-nlp

# PyShbak
 ![image](https://github.com/AbdelrahmanShahrour/PyShbak/blob/main/logo/SHAWPACK-LOGO.png?raw=true) 

###  Now with PyShbak, dealing with Arabic texts has become easy, and in the future it will become easier with PyShbak 

## Installation

```python
pip3 install PyShbak
```
---
## Structure 

|> PyShbak

| ------> | `Normalization`

|---------| ------> | `Normalization_ar`

|---------|---------| ------> | alef

|---------|---------| ------> | lamalef

|---------|---------| ------> | hamza

|---------|---------| ------> | tah

|---------|---------| ------> | madah

|---------|---------| ------> | normalization_all

| ------> `Processor`

|---------| ------> | `Arabic_Processor`

|---------|---------| ------> | remove_stopword

|---------|---------| ------> | remove_other_lang

|---------|---------| ------> | remove_diacritics

|---------|---------| ------> | remove_arabic_punctuations

|---------|---------| ------> | arabic_only

|---------|---------| ------> | with_out_num

|---------| ------> | `English_Processor`

|---------|---------| ------> | remove_stopword

|---------|---------| ------> | english_only

|---------|---------| ------> | remove_english_punctuations

|---------|---------| ------> | with_out_num

|---------| ------> | `General_Processor`

|---------|---------| ------> | remove_mentions

|---------|---------| ------> | remove_hasgtag

|---------|---------| ------> | remove_links

|---------|---------| ------> | remove_punctation

|---------|---------| ------> | keep_text

|---------|---------| ------> | remove_emojis

|---------|---------| ------> | remove_whitespace

|---------| ------> | `cleaningtext`

|---------|---------| ------> | delete_repeated_characters

|---------|---------| ------> | replace_letters

|---------|---------| ------> | clean_text

|---------|---------| ------> | remove_vowelization

|---------|---------| ------> | remove_stopword

|---------|---------| ------> | stem_text

|---------|---------| ------> | text_prepare

---

## Example:

```python
pip3 install PyShbak
```

```python
from PyShbak.TweetsCleaner import Clean_tweet
```



```python
text = 'akhhh !تَوقَعْت إذا جات داريا بشوفهم كاملين بس لي للحين احس فيه احد ناقْصهم 💔 #Avlu https://www.messenger.com/ @PyShbak '

Clean_tweet.ar(text)
```

output:
`' توقعت اذا جات داريا بشوفهم كاملين بس لي للحين احس فيه احد ناقصهم '
`
tlaunch 0.0.2
tlaunch
Requires Python: >=3.6
Keywords: multi-agent reinforcement learning algorithms pytorch

# TLaunch_dev
## Introduction
## 安装
```
virtualenv tmarl_env -p python3
# under tmarl_env, go into this repo and execute:
pip install -r requirements_dev.txt
pip install .
```
## Quick Start
### 1.使用SSH登录TPod
```TPod```是一款面向分布式场景，为```TLaunch```准备的用户及资源管理工具。当管理员使用```TPod```创建用户后，可以自定义的为其指定分配的系统资源（包括CPU、GPU、Memory、Storage），并为该用户创建一个已经预装好```TLaunch```框架的```TPod```开发机，用户可以直接通过SSH登录该机器访问集群，以快速进入开发流程。
#### TPod的存储结构
在```TPod```中，我们会在```/TData```目录下创建你的个人文件夹，该文件夹会通过挂载的文件系统与远端同步。```/TData```内会预先创建以下内容：

  - ```code```:用于存放训练代码
    - ```setup.sh```:用于指定训练环境及代码的安装方法
  - ```data```:用于存放训练所需的数据
  - ```cache```:用于存放分布式计算过程中产生的缓存文件
  - ```models```:用于存放模型数据
### 2.存放算法代码并指定安装方法
在多数分布式场景中，少量的代码往往会经常改动。若每次改动代码都重新构建镜像，会浪费大量的时间。因此，我们可以将这部分代码存放在```/TData/code```文件夹中。在训练中，每当一个```pods```被创建时，都将先按照```code```文件夹中的```setup.sh```脚本更新环境
### 3.调用TLaunch创建任务
refs:https://github.com/TARTRL/TMARL/blob/master/docs/tlaunch/README.md
### 4.管理任务
当任务创建完成后，我们可以调用```kubectl```来查看任务状态及日志，其中常用的几条指令包括：
  - 查看正在运行中的任务：```kubectl get lpjobs```
  - 查看正在运行中的pods:```kubectl get pods```
  - 查看pod节点日志:```kubectl logs ${pod name}```
  - 查看pod节点详细信息:```kubectl describe pods ${pod name}```
  - 删除任务:```kubectl delete lpjobs ${lpjob name}```
flask-dataview 0.3.3
Flask Data Visualization Helpers

# flask-dataview

[![Build Status](https://travis-ci.org/wuttem/flask-dataview.svg?branch=master)](https://travis-ci.org/wuttem/flask-dataview)

Dynamic data visualizations for flask.
This Extension makes it easy to show large data in a flask application.
At the moment it only supports timeseries data and shows it in a line graph that can be dynamically zoomed and created.
Performance is really good because only the data that the user has selected is send and rendered.

Warning: This is a very (limited) premature version!

Used Libs:
- jquery
- bootstrap (for styling)
- echarts

## Installation

```bash
pip install flask-dataview
```

## Example Charts

App:

```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import pendulum
import random

from flask import Flask, render_template
from flask_dataview import FlaskDataViews, RangeTimeSeries


e = FlaskDataViews()
app = Flask(__name__, template_folder=".")
e.init_app(app)


class MySeries(RangeTimeSeries):
    def get_range(self):
        d1 = pendulum.now("utc").subtract(days=300)
        d2 = pendulum.now("utc")
        return (d1, d2)

    def get_data_range(self, dt_from, dt_to):
        out = []
        cur = dt_from.replace(microsecond=0)
        while cur < dt_to:
            val = ((cur.int_timestamp / 3600) % 24) + random.random() * 10
            out.append((cur.isoformat(), val))
            cur = cur.add(minutes=10)
        return out

@app.route('/', methods=['POST', 'GET'])
def home():
    data = [MySeries("temp"), MySeries("act"), MySeries("ph", active=False)]

    mychart = e.basechart("myid1", "My Chart", series=data)
    if mychart.is_post_request():
        return mychart.data()
    return render_template("template.html", chart=mychart)
```

Template HTML

```html
{{ jquery_cdn }}
{{ echarts_cdn }}
{{ dataview_javascript }}
{{ bootstrap_cdn }}

<h1>Demo Charts</h1>

<div id="mydivid" style="height: 400px;"></div>
{{ chart.render("mydivid") }}

```
The subclass of `RangeTimeSeries` should implement `get_range` to return the full range of the data (minimum timestamp to maximum timestamp).
The method `get_data_range` should return the data in the selected timerange in tuples of isoformat timestamps and float values.


## Development Build / Upload
```
python setup.py sdist
twine upload dist/*
```
openscreen 1.3.1
Openscreen's SDK for QR Engage and Tracking
Requires Python: >=3.10

# Openscreen Python SDK
[Openscreen](www.openscreen.com) is a developer-first platform that enables innovative customer engagement and commerce solutions using QR Codes. 
Additional information on using the Openscreen python SDK can be found in the developer [documentation](https://docs.openscreen.com/overview). 
## Requirements
 - [python](https://www.python.org/downloads/release/python-3100/) 3.10.0 or above
 
You will need your Openscreen account key and secret to get started. If you do not have an Openscreen account, you can sign-up for free [here](https://app.openscreen.com/signup). 
## Installation
```
pip3 install openscreen
```
## Getting Started
Openscreen authenticates your API requests using your account's API key and API secret key. You will also need the ID of the project you wish to work with. Your API key, secret key and project ID can all be found in your by logging into your Openscreen [dashboard](https://app.openscreen.com/). The following examples demonstrates how to use these values in the SDK to generate your first QR code.
```py
from openscreen import Openscreen, save_qr_image_data_to_file
#Obtain your access_key and secret by logging into your Openscreen dashboard
os  =  Openscreen(access_key='YOUR_ACCESS_KEY', access_secret='YOUR_SECRET_KEY')
#You must create a project and obtain project_id from your Openscreen dashboard
my_asset  =  os.project('YOUR_PROJECT_ID').assets().create({
    'name': 'My First QR Code',
    'description': 'Dynamic QR Code',
    'qr_codes': [
        {
            'intent': 'https://www.openscreen.com',
            'intent_type': 'DYNAMIC_REDIRECT',
            'dynamic_redirect_type': 'NO_SCAN_ID',
            'locator_key_type': 'SHORT_URL',
            'status': 'ACTIVE'
        }
    ]
})
qr_code  =  my_asset.asset.qr_codes[0]
save_qr_image_data_to_file(qr_code.image_data, 'qr_code.png')
```
## Links 
- [Openscreen Website](https://www.openscreen.com/)
- [Developer Documentation](https://docs.openscreen.com/overview)
- [Solutions and Use-cases](https://docs.openscreen.com/tutorials/brand-protection)