semvec/pypi-packages huggingface dataset:
moban-velocity 0.0.2
Provide velocity templating capability to moban
License: MIT
Keywords: python

================================================================================
moban-velocity
================================================================================

.. image:: https://api.travis-ci.org/moremoban/moban-velocity.svg
   :target: http://travis-ci.org/moremoban/moban-velocity

.. image:: https://codecov.io/github/moremoban/moban-velocity/coverage.png
   :target: https://codecov.io/github/moremoban/moban-velocity
.. image:: https://badge.fury.io/py/moban-velocity.svg
   :target: https://pypi.org/project/moban-velocity

.. image:: https://pepy.tech/badge/moban-velocity/month
   :target: https://pepy.tech/project/moban-velocity/month

.. image:: https://img.shields.io/github/stars/moremoban/moban-velocity.svg?style=social&maxAge=3600&label=Star
    :target: https://github.com/moremoban/moban-velocity/stargazers


With `airspeed` for Python 3, this library allows moban users to have velocity
template in their next documentation endeavour.

Given the following data.json:

.. code-block::

   {"people":
       [
           {"name": "Bill", "age": 100},
           {"name": "Bob", "age": 90},
           {"name": "Mark", "age": 25}
       ]
   }

And given the following velocity.template:

.. code-block::

   Old people:
   #foreach ($person in $people)
    #if($person.age > 70)
     $person.name
    #end
   #end

   Third person is $people[2].name

**moban** can do the template:

.. code-block:: bash

   $ moban --template-type velocity -c data.json -t velocity.template
   Velocity-templating vo.t to moban.output
   Velocity-templated 1 file.
   $ cat moban.output
   Old people:

   Bill

   Bob


   Third person is Mark




Installation
================================================================================


You can install moban-velocity via pip:

.. code-block:: bash

    $ pip install moban-velocity


or clone it and install it:

.. code-block:: bash

    $ git clone https://github.com/moremoban/moban-velocity.git
    $ cd moban-velocity
    $ python setup.py install

Author
================================================================================

`Ayan Banerjee <https://github.com/ayan-b>`_

Contributors
================================================================================

In alphabetical order:

* `C. W. <https://github.com/chfw>`_
* `John Vandenberg <https://github.com/jayvdb>`_
* `PRAJWAL M <https://github.com/PrajwalM2212>`_

Change log
================================================================================

0.0.2 - 03.06.2020
--------------------------------------------------------------------------------

**Added**

#. Support moban v0.6.0+

0.0.1 - 02.02.2018
--------------------------------------------------------------------------------

**First release**

#. Render .velocity, .vtl files for moban
pecho 0.5.2
Easy way to write things like status bars
License: MIT
Requires Python: >=3.6,<4.0

# pecho
[![Tests](https://github.com/nihaals/pecho/workflows/Tests/badge.svg)](https://github.com/nihaals/pecho/actions?query=workflow%3ATests)
[![codecov](https://codecov.io/gh/nihaals/pecho/branch/master/graph/badge.svg)](https://codecov.io/gh/nihaals/pecho)
[![PyPI](https://img.shields.io/pypi/v/pecho)](https://pypi.org/project/pecho/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pecho)](https://pypi.org/project/pecho/)
[![PyPI - Wheel](https://img.shields.io/pypi/wheel/pecho)](https://pypi.org/project/pecho/)

Pecho makes it easy to write things like status bars.

## Usage
```python
from pecho import echo

echo('1%')  # 1%
echo('2%')  # Replaces with 2%
echo('3%', newline=True)  # Replaces with 3% and appends a newline
echo('4%')  # 3%\n4%
```
mtdlearn 0.2.3
Package for training Mixture Transition Distribution (MTD) models
License: MIT

# mtd-learn: Package for training Mixture Transition Distribution (MTD) models

## Installation

```
pip install mtdlearn
```

## Communication
GitHub [Issues](https://github.com/PiotrekGa/mtd-learn/issues) for bug reports, feature requests and questions.

## Contribution
Any contribution is welcome! Please follow this [branching model](https://nvie.com/posts/a-successful-git-branching-model/).

## License
MIT License (see [LICENSE](https://github.com/PiotrekGa/mtd-learn/blob/master/LICENSE)).

## References
1. Introduction to MTD model [The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series](https://projecteuclid.org/euclid.ss/1042727943) by André Berchtold and Adrian Raftery
2. Paper with estimation algorithm implemented in the package [An EM algorithm for estimation in the Mixture Transition Distribution model
](https://arxiv.org/abs/0803.0525) by Sophie Lèbre and Pierre-Yves Bourguinon.