semvec/pypi-packages huggingface dataset:
qtrunner 0.0.6
快速命令启动器
License: MIT
Requires Python: >=3.6
Keywords: qt,launcher

runner
======

-  `中文说明 <README_CN.md>`__

introduction
------------

``runner`` is a ``configurable`` quick launcher for
``starting (running)`` commonly used ``commands (programs)``. It comes
with a ``log`` output interface, which is convenient for users to
``view (color)`` and ``save`` log. And the log output in a special
format can also drive the program to draw a ``progress bar``.

.. figure:: https://i.ibb.co/CtZ55GP/main.png
   :alt: main-ui

   main-ui

install
-------

pip install -r requirements.txt

only tested on the ``windwos`` platform, recommended to use the
``python 3.7`` version.

start up
--------

-  python runner.py - run from codebase
-  runner - run after install

configure
---------

main configuration file
~~~~~~~~~~~~~~~~~~~~~~~

config.json

.. code:: json

   {
       "maxLogLines": 1000,        // max lines to view
       "maxStdout": 40960,         // max block of stdout 
       "defaultEncoding": "gbk",   // default stdout encodding
       "configs": [                // sub configuration item
           {
               "file": "runner_common.json",   // sub configuration file
               "title": "通用(测试)"            // title in ui
           }
       ]
   }

sub configuration file
~~~~~~~~~~~~~~~~~~~~~~

runner_xxxx.json

.. code:: js

   [
       {
           "title": "change codepage to gbk(use with caution)",  // title in ui
           "cmd": "cmd /c  chcp 936",          // command ling
           "encoding": "gbk",                  // output encoding
           "qss": "color: rgb(150, 0, 0);",    // ui styles in qss format
           "cwd": "",                          // current working directory
           "env": {                            // environment variables 
                "GP_LANGUAGE": "zh_CN"
            }
        }
   ]
yargy 0.16.0
Rule-based facts extraction for Russian language
License: MIT
Keywords: natural language processing,russian morphology,glr,parser

<img src="https://github.com/natasha/natasha-logos/blob/master/yargy.svg">

![CI](https://github.com/natasha/yargy/actions/workflows/test.yml/badge.svg)

Yargy uses rules and dictionaries to extract structured information from Russian texts. Yargy is similar to <a href="https://yandex.ru/dev/tomita">Tomita parser</a>.

## Install

Yargy supports Python 3.7+, PyPy 3, depends only on <a href="http://github.com/pymorphy2/pymorphy2">Pymorphy2</a>.

```bash
$ pip install yargy
```

## Usage

```python
from yargy import Parser, rule, and_, not_
from yargy.interpretation import fact
from yargy.predicates import gram
from yargy.relations import gnc_relation
from yargy.pipelines import morph_pipeline


Name = fact(
    'Name',
    ['first', 'last'],
)
Person = fact(
    'Person',
    ['position', 'name']
)

LAST = and_(
    gram('Surn'),
    not_(gram('Abbr')),
)
FIRST = and_(
    gram('Name'),
    not_(gram('Abbr')),
)

POSITION = morph_pipeline([
    'управляющий директор',
    'вице-мэр'
])

gnc = gnc_relation()
NAME = rule(
    FIRST.interpretation(
        Name.first
    ).match(gnc),
    LAST.interpretation(
        Name.last
    ).match(gnc)
).interpretation(
    Name
)

PERSON = rule(
    POSITION.interpretation(
        Person.position
    ).match(gnc),
    NAME.interpretation(
        Person.name
    )
).interpretation(
    Person
)

parser = Parser(PERSON)

match = parser.match('управляющий директор Иван Ульянов')
print(match)

Person(
    position='управляющий директор',
    name=Name(
        first='Иван',
        last='Ульянов'
    )
)

```

## Documentation

All materials are in Russian:

* <a href="https://habr.com/ru/post/349864/">Overview</a>
* <a href="https://www.youtube.com/watch?v=NQxzx0qYgK8">Video from workshop</a>
* <a href="https://nbviewer.jupyter.org/github/natasha/yargy/blob/master/docs/index.ipynb">Getting started</a>
* <a href="https://nbviewer.jupyter.org/github/natasha/yargy/blob/master/docs/ref.ipynb">Reference</a>
* <a href="https://nbviewer.jupyter.org/github/natasha/yargy/blob/master/docs/cookbook.ipynb">Cookbook</a>
* <a href="https://github.com/natasha/yargy-examples">Examples</a>
* <a href="https://github.com/natasha/natasha-usage#yargy">Code snippets</a>

## Support

- Chat — https://t.me/natural_language_processing
- Issues — https://github.com/natasha/yargy/issues
- Commercial support — https://lab.alexkuk.ru

## Development

Dev env

```bash
brew install graphviz

python -m venv ~/.venvs/natasha-yargy
source ~/.venvs/natasha-yargy/bin/activate

pip install -r requirements/dev.txt
pip install -e .

python -m ipykernel install --user --name natasha-yargy
```

Test + lint

```bash
make test
```

Update docs

```bash
make exec-docs

# Manually check git diff docs/, commit
```

Release

```bash
# Update setup.py version

git commit -am 'Up version'
git tag v0.16.0

git push
git push --tags

# Github Action builds dist and publishes to PyPi
```