semvec/pypi-packages huggingface dataset:
kubens 1.2.5
Terminal kubectl namespace selector
License: MIT

kubens
======

``kubens`` is a command line tool to interactively select and set
Kubernetes namespaces.

.. code:: bash

   ~$ kubens
   Current namespace: my-namespace
   Pick your namespace:

   => default
      kube-system
      my-namespace
      another-namespace
      exit

Install
-------

.. code:: bash

   pip install kubens

Clone the repo:

.. code:: bash

   $ git clone https://github.com/roubles/kubens
   $ cd kubens
   $ pip install .

Copy Code

Usage
-----

.. code:: bash

   usage: kubens [-h] [--list] [substring]


   interactively pick a k8s namespace


   positional arguments:
   substring substring to filter namespaces (must match from the beginning)


   optional arguments:
   -h, --help show this help message and exit
   -l, --list list namespaces instead of launching interactive menu

Examples
~~~~~~~~

Selecting namespaces
^^^^^^^^^^^^^^^^^^^^

To run ``kubens`` and select a namespace interactively:

.. code:: bash

   $ kubens

To run ``kubens`` and set the namespace directly if there is an exact
(or partial) match:

::

   $ kubens my-namespace

If there are multiple matches, it will show a menu to select from.

Listing namespaces
^^^^^^^^^^^^^^^^^^

To list namespaces:

.. code:: bash

   $ kubens --list

To list namespaces with a filter:

.. code:: bash

   $ kubens --list ku

This will list namespaces that start with “ku”.

Features
~~~~~~~~

-  Interactively select a Kubernetes namespace from the terminal.
-  Displays the current namespace.
-  Sets the selected namespace as the current context.
-  Lists namespaces with the ``-l`` or ``--list`` argument.
-  Filters namespaces based on a substring that must match from the
   beginning.

Requirements
~~~~~~~~~~~~

-  Python 2.7 or later
-  ``kubectl`` installed and configured
-  ``pick`` library (installed automatically with
   ``pip install kubens``)

License
~~~~~~~

This project is licensed under the MIT license.
periodicity 1.0b6
Useful tools for periodicity analysis in time series data.
License: MIT

# Periodicity

Useful tools for periodicity analysis in time series data.

[![](https://github.com/dioph/periodicity/workflows/CI/badge.svg)](https://github.com/dioph/periodicity/actions?query=branch%3Amaster)
[![PyPI version](https://badge.fury.io/py/periodicity.svg)](https://badge.fury.io/py/periodicity)
[![Downloads](https://pepy.tech/badge/periodicity)](https://pepy.tech/project/periodicity)

__Documentation: https://periodicity.readthedocs.io__

Currently includes:
* Auto-Correlation Function (and other general timeseries utilities!)
* Spectral methods:
    * Lomb-Scargle periodogram
    * Bayesian Lomb-Scargle with linear Trend (soon™)
* Time-frequency methods:
    * Wavelet Transform
    * Hilbert-Huang Transform
    * Composite Spectrum
* Phase-folding methods:
    * String Length
    * Phase Dispersion Minimization
    * Analysis of Variance (soon™)
* Decomposition methods:
    * Empirical Mode Decomposition
    * Local Mean Decomposition
    * Variational Mode Decomposition (soon™)
* Gaussian Processes:
    * `george` implementation
    * `celerite2` implementation
    * `celerite2.theano` implementation

## Installation

The latest version is available to download via PyPI: __`pip install periodicity`__.

Alternatively, you can build the current development version from source by cloning this repo (__`git clone https://github.com/dioph/periodicity.git`__) and running __`pip install ./periodicity`__.

## Development

If you're interested in contributing to periodicity, install __pipenv__ and you can setup everything you need with __`pipenv install --dev`__.

To automatically test the project (and also check formatting, coverage, etc.), simply run __`tox`__ within the project's directory.