semvec/pypi-packages huggingface dataset:
dingding-webhook 0.0.3
A simple api for dingding robot
Requires Python: >=2.7

# dingding webhook

This is a simple python api package to send your own message by dingding robot.




#Installation

```bash
pip install dingding_webhook
```



#Usage


send msg

```python
import dingding_webhook.webhook as dingding_webhook


webhook_url="xxx"  # your dingding robot webhook

content = "test text msg"

dingding_webhook.send_text(webhook_url,
                            content, 
                            atMobiles=[], 
                            isAtAll=False)

```


###Examples


- text类型
```python
# text类型
# 参数     参数类型   必须    说明
# msgtype   String   是     消息类型，此时固定为：text
# content   String   是     消息内容
# atMobiles Array    否    被@人的手机号(在content里添加@人的手机号)
# isAtAll   bool     否    @所有人时：true，否则为：false
```
send_text(post_url, content, atMobiles=[], isAtAll=False)


- link类型

```python
# 参数     参数类型   必须    说明
# msgtype   String    是    消息类型，此时固定为：link
# title     String    是    消息标题
# text      String    是    消息内容。如果太长只会部分展示
# messageUrl String   是    点击消息跳转的URL
# picUrl    String    否 图片URL
```
send_link(post_url, title, text, picUrl, msgUrl)

- markdown类型
```python
# 参数     参数类型   必须    说明
# msgtype   String   是     消息类型，此时固定为：markdown
# title     String   是     首屏会话透出的展示内容
# text      String   是     markdown格式的消息
# atMobiles Array    否    被@人的手机号(在content里添加@人的手机号)
# isAtAll   bool     否    @所有人时：true，否则为：false
```
send_markdown(post_url, title, text, atMobiles=[], isAtAll=False)

- 整体跳转ActionCard类型
```python
# 参数      类型      必选      说明
# msgtype   string    true    此消息类型为固定actionCard
# title     string    true    首屏会话透出的展示内容
# text      string    true    markdown格式的消息
# btns.btnOrientation  string  false  "0"-按钮竖直排列，"1"-按钮横向排列
# btns.buttons    [{               "title": "内容不错",         "actionURL": "https://www.dingtalk.com/"            },
#           {       "title": "不感兴趣",                "actionURL": "https://www.dingtalk.com/"         }      ]
# hideAvatar      string  false  "0"-正常发消息者头像，"1"-隐藏发消息者头像
```
send_actionCard(post_url, title, text, btns, hideAvatar="0")


- FeedCard类型
```python
# FeedCard类型
# 参数      类型      必选      说明
# msgtype string      true    此消息类型为固定feedCard
# cardList array      true      
# 
# cardList[0].title   string      true    单条信息文本
# cardList[0].messageURL  string  true    点击单条信息到跳转链接
# cardList[0].picURL  string      true    单条信息后面图片的URL
```
send_feedCard(post_url, cardList)

###Documentation

more docs see

[dingding doc](https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq)
DPD 0.0.3
Dendrogram Prototypical Discourse generator

# Dendrogram Prototypical Discourse Analysis
According to [Harris, 1954] and [Rubenstein and Goodenough, 1965], words in natural languages are structured within linguistic environments (e.g.,sentences, paragraphs), and in this context, words having similar meanings,  tend  to  share  similar  contexts.   This  assumption,  known  as  the Distributional  Hypothesis, suggests that a corpus is often constituted bys everal  discursive  contexts;  each  one  being  a  set  of  extended  linguistic environments,  conveying  similar/related  concepts  and  topics.   Although this  theory  emerged  in  linguistics  in  1954,  it  received  recently  an  in-creasing attention in many other fields such as in cognitive sciences (e.g.,[McDonald and Ramscar, 2001]),  and  natural  language  processing  (e.g.,[Mikolov et al., 2013a]).  This hypothesis is the founding principle of our approach. Our method aims at modeling a large corpus, as a set of so-called DP-discourses, and then studying them as prototypical speeches.  To do so, the core step,  consists in building clusters of words sharing similar dis-cursive contexts.  This was achieved using word-embedding and subspace clustering, but other data-mining techniques could be used.  Then, intra-cluster  words  were  represented  asDendrogram  Prototypical  Discourses(DP-discourses), using a hierarchical clustering algorithm.  Finally, DP-discourses revealed to be comprehensible enough, to be studied using Charaudeau’s methodology, and they could possibly be analyzed using other discourse analysis approaches.

## Installation

The easiest way to install the generator is using `pip` the package installer for Python.
Typing the command:

`pip install DPD`

## Tutorial

Check the jupyter notebook tutorial `tutorials/tutorial1.ipynb` for a basic usage illustration

## License

This project is under the GNU GENERAL PUBLIC LICENSE (Version 3, 29 June 2007)
cnsp 0.0.3
Search predictions in mainland China
Requires Python: >=3.6

# cnsp
[![Python package](https://github.com/wangxinhe2006/cnsp/workflows/Python%20package/badge.svg)](https://github.com/wangxinhe2006/cnsp/actions?query=workflow%3A%22Python+package%22)
[![Upload Python Package](https://github.com/wangxinhe2006/cnsp/workflows/Upload%20Python%20Package/badge.svg)](https://github.com/wangxinhe2006/cnsp/actions?query=workflow%3A%22Upload+Python+Package%22)
[![Build Status](https://www.travis-ci.org/wangxinhe2006/cnsp.svg)](https://www.travis-ci.org/wangxinhe2006/cnsp)
[![Python Version](https://img.shields.io/pypi/pyversions/cnsp.svg)](https://www.python.org/downloads/)
[![GitHub pre-release](https://img.shields.io/github/release-pre/wangxinhe2006/cnsp.svg)](https://github.com/wangxinhe2006/cnsp/releases)
[![PyPI](https://img.shields.io/pypi/v/cnsp.svg)](https://pypi.org/project/cnsp/#history)
[![Wheel](https://img.shields.io/pypi/wheel/cnsp.svg)](https://pypi.org/project/cnsp/#files)
[![License](https://img.shields.io/github/license/wangxinhe2006/cnsp.svg)](LICENSE)

�� **����English** | [����大陆简体](README.zh_Hans_CN.md) | [����香港繁體](README.zh_Hant_HK.md) | [����臺灣正體](README.zh_Hant_TW.md)

Search predictions in mainland China

## Installation
```sh
pip install cnsp
```

## Usage
```python
>>> from cnsp import google, baidu
>>> google('Google')
['google', 'google play', 'google翻译', 'google translate', 'google scholar', 'google map', 'google drive', 'google earth', 'google voice', 'google play下载']
>>> baidu('baidu')
['百度翻译', '百度', '百度识图', '百度地图', '百度网盘', 'baidu.com', '百度翻译在线翻译', '百度贴吧', '百度网盘破解版开发者', '百度推荐为什么关不了']
>>>
```
For more search engines, see [§Supported search engines](#supported-search-engines).

## Supported search engines
Function | Search engine | URL | API domain
-------- | ------------- | --- | ----------
`baidu` | 百度一下，你就知道 | https://www.baidu.com | `www.baidu.com`
`sm` | 神马搜索 | https://m.sm.cn | `sugs.m.sm.cn`
`sogou` | 搜狗搜索引擎 - 上网从搜狗开始 | https://www.sogou.com | `www.sogou.com`
`so` | 360搜索，SO靠谱 | https://www.so.com | `sug.so.360.cn`
`google` | Google | https://www.google.com.hk | `www.google.cn`
`yam` | yam蕃薯藤-搜尋 | https://search.yam.com | `search.yam.com`
twitter-activity-api-dummy 0.0.3
This library is to help you work with web-hook, Classify twitter account activities and save these activities
Requires Python: >=3.6

# twitter_activity_api_dummy

You can use this library in order to :

  - Register, get, and delete the webhook url 
  - Make a user subscribe his account for the twitter activity API 
  - Classify twitter account activities and save these activities in a local folder or inside an AWS S3 Bucket

*In order to install, open the command prompt and type:*
```
pip install twitter_activity_api_dummy
```

It  has two main module:

  - webhook_manager
  - activity_manager

In order to use this library  you should have a verified twitter application and you have to save your credential  inside a json file as following:
 ```
 {
"consumer_key": " your one", 
"consumer_secret": "your one", 
"access_token": "your one", 
"access_token_secret": "your one",
"env_name": "your one"
}
```

 The following directory [Example](https://github.com/Samer92/twitter_activity_api/tree/master/Example) shows a multiple example about how to use webhook_manager modules
mysb-scraping 5.0
Remaining data notifications with LINE Notify
Keywords: softbank,scraping

# mysb-scraping
[MySoftbank](https://www.softbank.jp/mysoftbank/)をスクレイピングして通信データ残量をLINE通知するやつ

## Install 
```
$ pip3 install mysb-scraping
```

## Initialize
```python
import mysoftbank

telnum = "your_phone_number"
password = "your_MySoftbank_password"
line_access_token = "your_line_notify_access_token"

api = mysoftbank.API(telnum=telnum, password=password, line_access_token=line_access_token)
```

## Feature
```python
# データ（使用量、残量、繰越残量、追加量、追加使用量、追加繰越量、追加繰越使用量）の取得
data = api.get_data()
print(data)

# out
{
    'used_data': 6.34,
    'remain_data': 0.02, 
    'step_remain_data': 0.0,
    'additional_data': 1.0,
    'additional_used_data': 0.98,
    'given_data': 0.36, 
    'given_used_data': 0.36
}
```
```python
# lineに通知
status = api.send_message()
print(status)

# out
200
```

## Demo
[サンプルコード](https://github.com/miya/MySB_dataTraffic/blob/master/sample.py)を実行した！

<img src="https://user-images.githubusercontent.com/34241526/80058598-aafaac00-8564-11ea-849b-b09d9e0d49b5.png" width=50%>
automapping 0.0.1
A utility library to map between dataclass
Requires Python: >=3.7

Python 3.7 helper tool to work with mapping of dataclasses.



It allow automapping a dataclass attributes

```python
from automapping import Mapper, Rename

@dataclass
class InnerA:
    number: int
    list_of_values: List[str]
    untyped_list: list


@dataclass
class A:
    name: str
    id_a: int
    inner: InnerA


@dataclass
class InnerB:
    number: int
    list_of_values: List[str]
    untyped_list: list


@dataclass
class B:
    name: str
    id_b: int
    inner: InnerB

mapper = Mapper()
mapper.add_mapper(AutoMap(A, B, [
    Rename('id_a', 'id_b')
]))
mapper.add_mapper(AutoMap(InnerA, InnerB))


input_a = A(name='Paul', id_a=6, inner=InnerA(
    number=10, list_of_values=['a', 'b'], untyped_list=[1, True]))
result = mapper.map(input_a, B, ObjectDictTypeUpdater)
print(result) # B(name='Paul', id_b=6, inner=InnerB(number=10, list_of_values=['a', 'b'], untyped_list=[1, True]))

```
mobiko 0.3.0
Make images for iOS app icon from a big PNG
Keywords: image

Sample 
----------

    mobiko big-file.png

This will create files in the different formats needed
for iOS project in Xcode with the nomenclature:

    big-file-128@3x.png

If you want to optimize images size use the flag ``--optimize``. Or event better ``--pngquant`` which will create more optimized, but it has an external dependency.
knapsack-pip 0.2
Knapsack problem solver
Requires Python: >=3
Keywords: knapsack backpack

# knapsack-pip: A 0-1 knapsack solver
This is a library for solving knapsack problems.

Use this solver for maximization or minimization of 0-1 knapsack problems a Branch and Bound algorithm.

Non negative weights and profits can also be included.

## Installation
This library can be installed via pip. Use command: `pip install knapsack-pip`

## Usage
So far, there are different knapsack solvers for different algorithms:
- BBKnapsack: a knapsack solver using branch and bound with a stack. To import: `from knapsack01.BBKnapsack import BBKnapsack`
- HSKnapsack: a knapsack solver using the Horowitz-Sahni branch and bound algorithm. To import: `from knapsack01.HSKnapsack import HSKnapsack`

The output includes the maximum or minimum profit and its corresponding solution as a list of the same length as list of items (solution[i] = 1 if ith item is included in the solution and solution[i] = 0 otherwise)


### Example of maximization
```
from knapsack01.BBKnapsack import BBKnapsack

capacity = 50
weights = [31, 10, 20, 20, 5, 3, -6]
profits = [70, 20, 39, 37, 7, 5, 10]

my_knapsack1 = BBKnapsack(capacity, profits, weights)
max_profit, max_solution = my_knapsack1.maximize()
# (126, [1, 0, 1, 0, 1, 0, 1])
```
### Example of minimization
```
from knapsack01.BBKnapsack import BBKnapsack

capacity = 50
weights = [31, 10, 20, 20, 5, 3, -6]
profits = [70, 20, 39, 37, 7, 5, 10]

my_knapsack2 = BBKnapsack(capacity, profits, weights)
min_profit, min_solution = my_knapsack2.minimize()
# (96, [0, 1, 1, 1, 0, 1, 0])
```