semvec/pypi-packages huggingface dataset:
dckrctrrunlist 0.1.0
Docker API to create and list container/s

**********************************************************************************
Run container via API and get running containers
**********************************************************************************

.. image:: https://img.shields.io/codecov/c/github/codecov/example-python.svg

* **createJson.py** - Script create Json output(**docker ps** execute outout) from running containers.
* **echoTest.py** - Simple API to learn.
* **dockerApi.py** - This script must be executed in the Web server or Flask default web server.


=====
Usage
=====

Requirements:
    Python2.7 or Python3.4 with ``flask`` must be installed:


Syntax:

.. code-block:: bash

    # pip install flask
    # git clone https://github.com/jamalshahverdiev/python-general-codes.git
    # cd python-general-codes/simple-docker-flask-api
    # ./dockerApi.py
..


* Get contaner status with the name **web**:

.. code-block:: bash

    # curl http://10.1.42.201/getcontainer?name=web
..

* Get status of all running containers:

.. code-block:: bash

    # curl http://10.1.42.201/getRunningContainers
..


* Create new container with the payload data from **jsondata.json** file. In the JSON file we must define values of the needed fields.
* **imagename** - in the value must be defined image name from https://hub.docker.com 
* **containername** - in the value must be defined container name which will be executed in the server
* **exposedPort** - in the value must be defined the port number which will be exposed to the Host OS

.. code-block:: bash

    # curl -v -s -XPOST -H "Content-type: application/json" http://10.1.42.201/createcontainer -d @jsondata.json
..
spycial 0.2
Special functions written in Python and accelerated by Numba

# Spycial

[![CircleCI](https://circleci.com/gh/person142/spycial.svg?style=shield)](https://circleci.com/gh/person142/spycial) [![Documentation Status](https://readthedocs.org/projects/spycial/badge/?version=latest)](https://spycial.readthedocs.io/en/latest/?badge=latest)

Spycial is a collection of special functions written in pure Python
and accelerated by Numba. The goals are to:

- make functionality comparable to SciPy's special module accessible
  inside Numba jitted functions
- have simpler internals by keeping all implementations written in
  Python

For detailed information on the functions available, check the
[documentation](https://spycial.readthedocs.io/en/latest/).
readability-metrics 1.0.0
Calculates multiple readability metrics for large documents.

# Readability Metrics
This project is based extremely heavily on [mmautner's package](https://github.com/mmautner/readability). Modifications were made to support the analysis of large documents.

## Inspiration
When using mmautner's package, the entire document needed to be passed in at once:
```python
large_str = "...."
rd = Readability(large_str)
print('ARI: ', rd.ARI())
```

However, while doing an analysis of Supreme Court transcripts since 1956 across various metrics, my personal computer was not able to load and the needed documents all at once. In order to account for this, I created this package, which allows for pieces of documents to be passed in. Furthermore, the text is not stored, only the resulting calculations. Lastly, all metrics are calculated and returned each time, so individual calculations don't need to be performed.

## Installation
Readability metrics can be installed from PyPi:
```
$ pip3 install readability-metrics
```

## Usage
Readability metrics can be used as follows:

```python
import metrics # import package

rdm = Readability()
rdm.analyze_text("This is a sentence.")
rdm.analyze_text("This is part of the same document.")
rdm.analyze_text("This is also part of the same document.")
rdm.get_results()

# can further modify
rdm.analyze_text("This is also part of the same document.")
rdm.get_results()

# can clear and start new analysis
rdm.clear()
rdm.analyze_text("This is also part of the same document.")
rdm.get_results()
```

You can also calculate readability metrics across multiple categories. For instance, if you had a transcript, you could calculate metrics for all speakers at once:

```python
import metrics
from collections import defaultdict

let transcript = [
    ('John George', 'Words said by John George'),
    ('Apple Dunkin', 'Words said by Apple Dunkin'),
    # ...
]

readability_per_speaker = defaultdict(lambda: Readability())

# Calculate readability metrics
for dialogue in transcript:
    readability_per_speaker[dialogue[0]].analyze_text(dialogue[1])

# Calculate results
for speaker in readability_per_speaker:
    dic[key] = dic[key].get_results()

# readability_per_speaker now in form:
{
    "SPEAKER NAME": {
        {
            'ARI': 12.163787878787879,
            'FleschReadingEase': 58.2319, 'FleschKincaidGradeLevel': 11.2857,
            'GunningFogIndex': 14.5465,
            'SMOGIndex': 12.287087810503355,
            'ColemanLiauIndex': 9.5226,
            'LIX': 46.467171717171716,
            'RIX': 5.375
        }
    },
    # more speakers ...
}
```

## Contribution
Contributions are welcome. Please create a pull request or email me at ericwiener3@gmail.com. Also feel free to create an issue if you need help with something.

## Testing
Testing can be run with pytest. Simple navigate to the directory and run `pytest`.
pywalrus 0.0.2
A Python Package for Email Automation.

PyWalrus
========

A Python Package for Email Automation.

[![Build Status](https://travis-ci.org/silverbackhq/pywalrus.svg?branch=master)](https://travis-ci.org/silverbackhq/pywalrus)
[![PyPI version](https://badge.fury.io/py/pywalrus.svg)](https://badge.fury.io/py/pywalrus)

Installation
------------
To install Walrus run this command:
```
$ pip3 install pywalrus
```

Usage
-----
After installing the library, Read the following usage criteria:

```python
#
```

Misc
====

Changelog
---------

Version 0.0.2:
```
Initial Release.
```

Acknowledgements
----------------

© 2019, Silverback. Released under [MIT License](https://opensource.org/licenses/mit-license.php).

**Walrus** is authored and maintained by [@silverbackhq](http://github.com/silverbackhq).