semvec/pypi-packages huggingface dataset:
genie.parsergen 19.12.1b0
Genie Library for parsing both tabular and non-tabular device output
License: Apache 2.0
Requires Python: >=3.4
Keywords: genie parser pyats cisco

Genie ParserGen Component
=========================

Genie Parsergen package provides a one-step parsing mechanism that is
capable of parsing dynamic tabular and non-tabular device outputs in a
noticeably less lines of code compared to the standard parsing mechanisms.

pyATS is an end-to-end testing ecosystem, specializing in data-driven and
reusable testing, and engineered to be suitable for Agile, rapid development
iterations. Extensible by design, pyATS enables developers start with small,
simple and linear test cases, and scale towards large, complex and asynchronous
test suites.

Genie was initially developed internally in Cisco, and is now available to the
general public starting early 2018 through `Cisco DevNet`_. Visit the Genie
home page at

    https://developer.cisco.com/site/pyats/

.. _Cisco DevNet: https://developer.cisco.com/


Parsergen Package
-----------------

Genie Parsergen package provides a one-step parsing mechanism that is
capable of parsing dynamic tabular and non-tabular device outputs in a
noticeably less lines of code compared to the standard parsing mechanisms.

Requirements
------------

Genie currently supports Python 3.4+ on Linux & Mac systems. Windows platforms
are not yet supported.

Quick Start
-----------

.. code-block:: console

    # install genie as a whole
    $ pip install genie.parsergen

    # to upgrade this package manually
    $ pip install --upgrade genie.parsergen

    # to install alpha/beta versions, add --pre
    $ pip install --pre genie.parsergen


For more information on setting up your Python development environment,
such as creating virtual environment and installing ``pip`` on your system, 
please refer to `Virtual Environment and Packages`_ in Python tutorials.

.. _Virtual Environment and Packages: https://docs.python.org/3/tutorial/venv.html