semvec/pypi-packages huggingface dataset:
django-bestzhlocale 0.1.1
The best Chinese locale.
License: MIT
Keywords: django-bestzhlocale

=============================
The Best Chinese Locale
=============================

Collect the best chinese translation.

.. image:: https://badge.fury.io/py/django-bestzhlocale.svg
    :target: https://badge.fury.io/py/django-bestzhlocale

.. image:: https://travis-ci.org/daimon99/django-bestzhlocale.svg?branch=master
    :target: https://travis-ci.org/daimon99/django-bestzhlocale

.. image:: https://codecov.io/gh/daimon99/django-bestzhlocale/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/daimon99/django-bestzhlocale

The best Chinese locale.

Documentation
-------------

The full documentation is at https://django-bestzhlocale.readthedocs.io.

Quickstart
----------

Install The Best Chinese Locale::

    pip install django-bestzhlocale

Add it to your `INSTALLED_APPS`:

.. code-block:: python

    INSTALLED_APPS = (
        ...
        'django_bestzhlocale',
        ...
    )

Add The Best Chinese Locale's URL patterns:

.. code-block:: python

    from django_bestzhlocale import urls as django_bestzhlocale_urls


    urlpatterns = [
        ...
        url(r'^', include(django_bestzhlocale_urls)),
        ...
    ]

Features
--------

* TODO

Running Tests
-------------

Does the code actually work?

::

    source <YOURVIRTUALENV>/bin/activate
    (myenv) $ pip install tox
    (myenv) $ tox

Credits
-------

Tools used in rendering this package:

*  Cookiecutter_
*  `cookiecutter-djangopackage`_

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`cookiecutter-djangopackage`: https://github.com/pydanny/cookiecutter-djangopackage




History
-------

0.1.0 (2020-01-17)
++++++++++++++++++

* First release on PyPI.
aws-toolkit 1.0.2
An AWS Provisioning Tool
License: MIT

# AWS Provisioning Tool

This project this target to automate the AWS provisioning process for ec2 and route53

### Prerequisites

This code is tested with python 2.7, addtional modules (IPy, prettytable, boto3, paramiko) are required which will be installed automaticlly.

Need to configure following at home directory.

#### For Linux and Mac.
```
~/.aws/config
~/.aws/credentials
```

#### For Windows.
```
"%UserProfile%"/.aws/config
"%UserProfile%"/.aws/credentials
```

#### config file details
```
[default]
region=ap-southeast-1
output=json
```
#### credentials file details
```
[account1]
aws_access_key_id=AKIAIOSFODNN7EXAMPLE
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

[account2]
aws_access_key_id=AKIAIOSFODNN7TEST
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYTESTKEY
```

### Installing

* I have tested working on Mac and Linux

* sudo pip install aws_toolkit==1.0.2

## How to use it

```
from awspackage import aws

myaws = aws.AWS(DOMAIN="abc.com #Hosted at Route53",
        	PEM_LOCATION="/path/to/pem",
                Module_Path="/path/to/ansible/playbook",
                USERNAME="EC2_Username")

myaws.main()
```

## Parameters to take note

* "Domain" refer to the domain name that registered to the AWS under the same account, eg. cjaiwenwen.com (Put dummy data if you dont have route53 service)
* "PEM_LOCATION" refer to the local pem key path eg, /Users/cjaiwenwen/Desktop/chenjun.pem 
* "Module_Path" is exact location where the ansible yaml file is located, and the server is under [servers]
* "USERNAME" is the username to access the remote server 

## What can the library could achieve

* Could select the account to provision based on the section by select the account1 and account2
* Create instance on any region if the VPC has been already created
* Control the number of the VMs could provisioned
* Choose the AMI image
* Choose the subnets
* Choose the security group
* Modify the security group rules if need to be
* Choose the size of the VM
* Assign CNAME for the provisioned VM
* Continue ping the provisioned host
* SSH to the host to confirm accessible (need to add ssh incoming rule)
* Deploy

## Authors

* **Chen Jun** - *Initial work* - [CJAIWENWEN](https://github.com/cjaiwenwen)

## Connect with me on Linkedin

[Chen Jun](https://www.linkedin.com/in/cjaiwenwen/) 

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
vishwakarma 0.1.3
Python visualization library for Probabilistic Graphical Models, Discrete & Continuous Distributions, and a lot more!
License: MIT
Keywords: probability-distribution,discrete-distribution,continuous-distribution,pdf,pdfplot,pmf,pmfplot,PGM,pgmpy,visualization,probabilistic-graphical-model

# vishwakarma
Python visualization library for Probabilistic Graphical Models, Discrete & Continuous Distributions, and a lot more!
www.diagram.ai
tesseract2dict 1.3
Solution to extract the text from image and get wordlevel output as dataframe and also to extract text from given boundingBox
License: MIT

# TESSERACT2DICT
This class contains two main funtions:

1. **tess2dict**: Input an image and get the extracted text as a dataframe which gives the content, coordinates (x,y,w,h) and confidence of each word. Essentially, it is a wrapper on pytesseract to output a dataframe.
2. **word2text**: Once you obtain the dataframe, you can pass it through this function along with a bounding box to get the text inside the given box with proper formatting.
### Prerequisites

- beautifulsoup4
- [MakeTreeDir](https://github.com/Sreekiranar/MakeTreeDir.git)
- numpy
- opencv-python
- pandas
- pytesseract

### Tesseract Installation
(currently solution works on Tesseract 5.0.0 only)

[What is Tesseract?](https://github.com/tesseract-ocr/tesseract)
#### For Windows
- [installation link](https://github.com/UB-Mannheim/tesseract/wiki)
##### adding path to path variable (for Tesseract)
- [windows 10](https://helpdeskgeek.com/windows-10/add-windows-path-environment-variable/)
- [windows 7](https://docs.alfresco.com/4.2/tasks/fot-addpath.html)

#### For Linux
- `sudo apt install tesseract-ocr`
- `sudo apt install libtesseract-dev`


### Installation

`pip install tesseract2dict`


### Usage
A sample usage of our solution is shown below. Input an image as numpy.ndarray and the extracted
dataframe at word level is returned.
You can also get the text as plain of a given bounding box with proper formatting using the second function
eg:
```python
import cv2
from tesseract2dict import TessToDict
td=TessToDict()
inputImage=cv2.imread('path/to/image.jpg')
### function 1
word_dict=td.tess2dict(inputImage,'out','outfolder')

### function 2
text_plain=td.word2text(word_dict,(0,0,inputImage.shape[1],inputImage.shape[0]))

```

## Authors

* **Sreekiran A R** - *Analytics Consultant, AI Labs, Bridgei2i Analytics Solutions* -
 [Github](https://github.com/Sreekiranar) ,
[Stackoverflow](https://stackoverflow.com/users/9605907/sreekiran)

* **Anil Prasad M N** - *Project Manager, AI Labs, Bridgei2i Analytics Solutions* -
 [Github](https://github.com/anilprasadmn)


## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

NOTE: This software depends on other packages that may be licensed under different open source licenses.

### Useful links
1. http://gwang-cv.github.io/2017/08/25/ubuntu16.04+Tesseract4.0/