semvec/pypi-packages huggingface dataset:
pydomywork 1.4
Do you homework with this library. This library converts computer characters to handwritten characters.
License: MIT
Keywords: convert_text,img_to_handtxt,download,getimg

# pydomywork

Have to write too many assignments by hand but not have enough time or too lazy to do it, try this library of Python.

## Installation

```bash
pip install pydomywork
```

## Usage

```python
import pydomywork as py

py.convert_text(string,[r,g,b])#Will convert the string to handwritten characters, the characters' color will be in that RGB combination.

py.img_to_handtxt(path[r,g,b])#Will extract the texts from the image in the given path and convert it to handwritten characters.

py.feedback()#Developer's contact information.

```

## Source code
https://github.com/Ankit404butfound/HomeworkMachine

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)
nevada 1.0.9
For those who want to use Naver Search Ad easily.
License: MIT
Requires Python: >=3.4
Keywords: python_nevada,naver,search,ad

# nevada

##### Its English Documentation is [here](https://github.com/taegyumin/python_nevada/blob/master/README(english).md).

[<img src="https://img.shields.io/pypi/v/nevada">](https://pypi.org/project/nevada/)
[<img src="https://img.shields.io/pypi/pyversions/nevada">](https://pypi.org/project/nevada/)
[<img src="https://img.shields.io/github/license/taegyumin/python_nevada">](https://github.com/taegyumin/python_nevada/blob/master/LICENSE)

## nevada는?
nevada는 [네이버의 검색광고 API](https://github.com/naver/searchad-apidoc)를 좀 더 쉽게 사용할 수 있도록 해주는 Python 라이브러리입니다.

## 준비하기 (nevada 설치 전)
- [Python](https://www.python.org/) <br>
- [Git client](https://git-scm.com/downloads) <br>

## 설치하기
- `pip3 install nevada` <br>
- `git clone https://github.com/taegyumin/python_nevada.git` <br>

## 네이버에서 API Licnese 발급하기
nevada를 이용하기 위해서는 네이버 검색광고시스템에서 API_KEY를 발급 받아야 합니다. <br>

1. [네이버 검색광고 시스템 메인 탭](http://searchad.naver.com)에서 회원가입을 합니다.
2. [네이버 검색광고 관리 탭](http://manage.searchad.naver.com) 으로 이동합니다.
3. 사이트 내에서 도구 > API 관리 로 이동합니다.
4. API license를 발급합니다.


## 잘 설치 됐는지 확인하기
Python 파일을 만들고, 아래 코드를 실행하십시오. <br>
현재 시간이 출력되면 코드가 정상적으로 실행된 겁니다. <br>
이때, 코드에서 `api_key`, `secret_key`, `customer_id`의 값은 네이버 검색광고 시스템에서 발급 받은 값으로 변경하십시오.

	from nevada.Common.Connector import *

	base_url = 'https://api.naver.com'
	api_key = "Naver-search-AD_ACCESS_LICCENSE"
	secret_key = "Naver-search-AD_SECRET_KEY"
	customer_id = "Naver-search-AD_CUSTOMER_ID"

	conn = Connector(base_url, api_key, secret_key, customer_id)
	print(conn.get_datetime())

### 자세한 사용 방법은 [docs](https://github.com/taegyumin/python_nevada/tree/master/docs)의 설명서를 참고해주시기 바랍니다.
pyrenko 0.1
Renko calculation and chart
License: MIT

# pyrenko

This is a class for calculating and visualizing a renko chart.

## Author

Credit to the [author](https://github.com/lamres/renko_trend_following_strategy_catalyst) for source code of pyrenko.

The only contribution from [dennislwm](https://github.com/dennislwm/pyrenko) is made a package and upload it to PyPi.

## Installation

     $ pip install pyrenko

## Example

     >>> import pyrenko

## Getting Data

     >>> import yfinance
     >>> data = yfinance.download('SPY', start="2019-01-01", end="2019-12-31")

Note: Variable **data** is of type DataFrame that consists of columns ['Open', 'High', 'Low', 'Close'].

### Get optimal brick size based

     >>> optimal_brick = pyrenko.renko().set_brick_size(auto = True, HLC_history = data[["High", "Low", "Close"]])

### Build Renko  

     >>> objRenko = pyrenko.renko()
     >>> print('Set brick size: ', objRenko.set_brick_size(auto = False, brick_size = optimal_brick))
     >>> objRenko.build_history(prices = data.Close)

### Renko data

     >>> print('Renko length:' , len(objRenko.get_renko_prices()))
     >>> print('Renko bar directions: ', objRenko.get_renko_directions())
     >>> print('Renko bar evaluation: ', objRenko.evaluate())

### Plot chart

     >>> objRenko.plot_renko()