semvec/pypi-packages huggingface dataset:
mattak 0.1.9
Mattak is (will eventually be) a multilingual package containing readers and helpers for RNO-G in C++, Python (via both uproot and PyROOT) and maybe even JS (via rootjs).

# Mattak 

Mattak is a Greenlandic meal consisting of whale skin and blubber. Blubber is a
store of energy. That's close enough to data storage... 

Mattak is (will eventually be) a multilingual package containing readers and
helpers for RNO-G in C++, Python (via both uproot and PyROOT) and maybe even JS (via
rootjs). Writers are provided in C++ ( and Python via PyROOT)
These are all in the same package so that they can be kept in concert with each other. 

# Quickstart
The Python interface for mattak can be installed by running:

    pip install git+https://github.com/RNO-G/mattak.git

In order to use the (faster) PyROOT backend, you need a working installation of [ROOT](https://root.cern/install/). If no ROOT installation can be found, only the uproot backend will be installed.

# Data directory hierarchy 

All data is assumed to reside in a directory named ${RNO_G_DATA}, which in the C++ and Python readers is often accessed via an environmental variable. This may be a mounted directory, or could be an http directory (assuming the HTTP server it resides on supports byte ranges). 


The general schema for ``run-associated" data (i.e., data taken synchronously with a run)  is: 

    ${RNO_G_DATA}/station${STATION_NUMBER}/run${RUN_NUMER}/${PACKET_TYPE}.root 

Config files for each run live in: 

    ${RNO_G_DATA}/station${STATION_NUMBER}/run${RUN_NUMBER}/cfg 

And any auxilliary files we might generate will be under: 

    ${RNO_G_DATA}/station${STATION_NUMBER}/run${RUNNUMBER}/aux 

Note that this might result in a lot of files in a directory. 


# Run-associated Data packets

## Header

Run-associated. This is all metadata associated with a single event, not including waveforms. This is kept separated because some operations need only the metadata and also because we can transfer all metadata but only a portion of waveforms 

## Waveforms

These are all waveforms associated with each event.

## DAQStatus

Asynchronous DAQ-related data (e.g. thresholds, scalers, etc.) recorded every once in a while asynchronous to events but still as part of a run. 

# Non-run associated data packets  (TODO) 

Eventually we might have sensor data, calibration data, GNSS data and who knows what else in the same file tree. That will be defined later...
mlmorph 1.4.0
Malayalam morphology analyser
Requires Python: >=3.8
Keywords: Malayalam, morphology, FST, analyser, generator, spellchecker

Python Interface for Malayalam morphology analyser
==================================================
.. image:: https://img.shields.io/pypi/v/mlmorph.svg
    :target: https://pypi.python.org/pypi/mlmorph
    :alt: PyPI Version

This is python interface for the `Malayalam morphology analyser - mlmorph`_.

Installation
------------

Python 3 is required. Using with `venv`_ is recommended

  .. code-block:: console

    $ pip install mlmorph

Usage
-----

Morphological analysis example
-----------------------------------

  .. code-block:: python

    from mlmorph import Analyser
    analyser = Analyser()
    analyser.analyse("കേരളത്തിന്റെ")


Gives

  .. code-block:: python

    [('കേരളം<np><genitive>', 179)]


The second item in this result is the weight. Sometimes a single word can have multiple analysis. The analysis with less weight is the preferred analysis.

Morphological generator example
--------------------------------

  .. code-block:: python

    from mlmorph import Generator
    generator = Generator()
    generator.generate("കേരളം<np><genitive>")


Gives

  .. code-block:: python

    (('കേരളത്തിന്റെ', 0.0),)


Command line interface
--------------------------

  .. code-block:: console

    $ python -m mlmorph --help
    usage: __main__.py [-h] [-i INFILE] [-a] [-g] [-v]

    optional arguments:
      -h, --help            show this help message and exit
      -i INFILE, --input INFILE
                            source of analysis data
      -a, --analyse         Analyse the input file strings
      -g, --generate        Generate the input file strings
      -v, --verbose         print verbosely while processing


Accepts strings from stdin too. For example:

  .. code-block:: console

    $ python -m mlmorph -a
    നിറങ്ങൾ
    നിറങ്ങൾ   നിറം<n><pl>

.. _`Malayalam morphology analyser - mlmorph`: https://gitlab.com/smc/mlmorph
.. _`venv`: https://docs.python.org/3/library/venv.html
xtesting 1.0.0
OPNFV reference testing framework
Requires Python: >=3.9

Welcome!
========

Xtesting have leveraged on Functest efforts to provide a reference testing
framework:

  * Requirements Management: https://wiki.opnfv.org/display/functest/Requirements+management
  * Docker Slicing: http://testresults.opnfv.org/functest/dockerslicing/
  * Functest Framework: http://testresults.opnfv.org/functest/framework/

Xtesting aims at allowing a smooth integration of new Functest Kubernetes
testcases.

But, more generally, it eases building any CI/CD toolchain for other
domains than testing Virtualized Infrastructure Managers (VIM) such as
`OpenStack <https://www.openstack.org/>`_.

To learn more about Xtesting:

  * Documentation: http://xtesting.readthedocs.io/en/latest/
  * Gerrit: https://gerrit.opnfv.org/gerrit/#/q/project:functest-xtesting

Get in touch via `email <mailto:opnfv-tech-discuss@lists.opnfv.org>`_.