semvec/pypi-packages huggingface dataset:
steelconnection 1.1.8
Simplify access to the Riverbed SteelConnect CX REST API.
License: MIT
Keywords: SteelConnect CX,REST,API,Riverbed,Grelleum

Welcome to SteelConnection!
===========================

.. image:: https://img.shields.io/pypi/v/steelconnection.svg
        :target: https://pypi.python.org/pypi/steelconnection

.. image:: https://readthedocs.org/projects/steelconnection/badge/?version=latest
        :target: https://steelconnection.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://img.shields.io/travis/grelleum/steelconnection.svg
        :target: https://travis-ci.com/grelleum/steelconnection

.. image:: https://codecov.io/gh/grelleum/steelconnection/branch/develop/graph/badge.svg
        :target: https://codecov.io/gh/grelleum/steelconnection


Simplify access to the Riverbed SteelConnect CX REST API.

.. code::

          ______          __
         / __/ /____ ___ / /
     ____\ \/ __/ -_) -_) /      __  _
    / _____/\__/\__/\__/_/_ ____/ /_(_)__  ___
   / /__/ _ \/ _ \/ _ \/ -_) __/ __/ / _ \/ _ \
   \___/\___/_//_/_//_/\__/\__/\__/_/\___/_//_/

   version = "1.1.8"
   pip install steelconnection

-  Always crafts a correct URL based on the resource provided.
-  Accepts and returns native Python data: no need to convert to/from JSON.
-  Provides convinience methods for object lookup and image download.
-  Reuses TCP connection for subsequent API requests.

^^^^^^^

| **Supports:**
| Python 2.7, 3.4, 3.5, 3.6, 3.7, 3.8

^^^^^^^

**With** SteelConnection, a request to get a list of all organizations
in the realm would look like this:

.. code:: python

   orgs = sc.get('orgs')

**Without** SteelConnection, the same request would look like this:

.. code:: python

   response = requests.get(
       'https://REALM.riverbed.cc/api/scm.config/1.0/orgs',
       auth=(username, password)
   )
   orgs = response.json()['items']

^^^^^^^

Documentation
-------------

| Full Documentation is available on *Read the Docs*.
| https://steelconnection.readthedocs.io/
flixr 0.0.2
A command line utility for television show information.
License: MIT
Requires Python: >=3.6

# Flixr

<p align=center>

  <img src="https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/logo.png" height="200px"/>

  <br>
  <span>A command line utility for television show information.</span>
  <br>
  <a target="_blank" href="https://www.python.org/downloads/" title="Python version"><img src="https://img.shields.io/badge/python-%3E=_3.6-green.svg"></a>
  <a target="_blank" href="LICENSE" title="License: MIT"><img src="https://img.shields.io/badge/License-MIT-blue.svg"></a>
  <a target="_blank" href="https://pypi.python.org/pypi/flixr/"><img alt="pypi package" src="https://badge.fury.io/py/flixr.svg"></a>
</p>

<p align="center">
  <a href="#installation">Installation</a>
  &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
  <a href="#usage">Usage</a>
  &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
  <a href="#examples">Examples</a>
</p>

## Installation

```bash
pip install flixr
```

## Usage

```plain
usage: flixr [-h] show

A command line utility for television show information

positional arguments:
  show        TV show name

optional arguments:
  -h, --help  show this help message and exit
```

## Examples

- ```bash
  flixr "Breaking Bad"
  ```

  ![Breaking Bad](https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/examples/01-breaking-bad.png)

- ```bash
  flixr "Friends"
  ```

  ![Friends](https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/examples/02-friends.png)

- ```bash
  flixr "Rick & Morty"
  ```

  ![Rick & Morty](https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/examples/03-rick-and-morty.png)

- ```bash
  flixr "Dark"
  ```

  ![Dark](https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/examples/04-dark.png)

- ```bash
  flixr "Community"
  ```

  ![Community](https://raw.githubusercontent.com/kelvindecosta/flixr/master/assets/examples/05-community.png)

> Note that colors are completely random.
azure-databricks-api 0.6.2
A wrapper for the Azure Databricks REST API
License: MIT
Keywords: azure,databricks

# Azure Databricks API Wrapper
A Python, object-oriented wrapper for the [Azure Databricks REST API 2.0](https://docs.azuredatabricks.net/api/latest/index.html)

[![GitHub Workflow Status (branch)](https://img.shields.io/github/workflow/status/ezwiefel/azure-databricks-api/Unit%20Tests/main?style=for-the-badge)](https://github.com/ezwiefel/azure-databricks-api/actions?query=workflow%3A%22Unit+Tests%22)
[![Coveralls github](https://img.shields.io/coveralls/github/ezwiefel/azure-databricks-api?style=for-the-badge)](https://coveralls.io/github/ezwiefel/azure-databricks-api)
[![PyPI](https://img.shields.io/pypi/v/azure-databricks-api?style=for-the-badge)](https://pypi.org/project/azure-databricks-api/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/azure-databricks-api?label=PIP%20DOWNLOADS&style=for-the-badge)](https://pypi.org/project/azure-databricks-api/)
[![GitHub](https://img.shields.io/github/license/ezwiefel/azure-databricks-api?style=for-the-badge)](https://github.com/ezwiefel/azure-databricks-api/blob/main/LICENSE)

### Installation
This package is pip installable.
```bash
pip install azure-databricks-api
```

### Implemented APIs
As of June 25th, 2020 there are 12 different services available in the Azure Databricks API. Currently, the following services are supported by the Azure Databricks API Wrapper.
* [x] Clusters
* [ ] Cluster Policies _(Preview)_
* [x] DBFS
* [x] Groups _(Must be Databricks admin)_
* [ ] Instance Pools
* [ ] Jobs
* [X] Libraries
* [ ] MLflow
* [ ] SCIM _(Preview)_
* [ ] Secrets
* [x] Token
* [x] Workspace


### Client Instantiation
To create the client object, you pass the Azure region your workspace is located in and the [generated Personal Access Token](https://docs.databricks.com/api/latest/authentication.html#generate-a-token)
```python
from azure_databricks_api import AzureDatabricksRESTClient

azure_region = '[INSERT YOUR AZURE REGION HERE]'
token = '[INSERT YOUR PERSONAL ACCESS TOKEN]' 

client = AzureDatabricksRESTClient(region=azure_region, token=token)
```

### Clusters Client Usage
The services above are implemented as children objects of the client. For example, to pin a cluster, you can either pass the `cluster_name` or `cluster_id` to the `pin()` method:
```python
client.clusters.pin(cluster_name='test_cluster_name')
```

The other services are implemented similarly. (e.g. `client.tokens` or `client.groups`)
TpTnOsc 1.2
Tools for analyzing TP, TN and OSC matrices
License: MIT
Requires Python: >=3.8
Keywords: TP TN I-TN oscillatory SEB planar-network

# TpTnOsc
A Python package for analyzing totally positive (TP), totally non-negative (TN), and oscillatory (OSC) matrices.

The tool contains many functions and classes that can perform the following:
* compute matrix minors and multiplicative compound matrices
* compute and display the SEB factorization of an Invertible TN (I-TN) matrix
* test if a matrix is TP, TN, I-TN, or OSC
* compute the number of (cyclic and non-cyclic) sign variations in a vector
* compute the exponent of an oscillatory matrix
* compute and display families of vertex-disjoint paths corresponding to lower-left and upper-right corner minors of oscillatory matrices

See [notebook](https://github.com/yoramzarai/TpTnOsc/blob/master/examples/osc_exp_examples.ipynb) for details and examples.

## Modules
- utils.py contains several functions related to the number of sign variations in a vector, multiplicative compound matrices, SEB factorization, planar networks and other TP/TN/OSC matrices related functions.
- osc_exp.py contains the functions and classes to compute the exponent of an oscillatory matrix and the corresponding families of vertex-disjoint paths.


## Examples
- osc_exp_examples.ipynb contains examples of using the triangle graph (derived from the planar networks of an SEB factorization) to deduce the exponent of an oscillatory matrix ![A](https://render.githubusercontent.com/render/math?math=A), and to compute the families of vertex-disjoint paths of each corner minor of ![A^r](https://render.githubusercontent.com/render/math?math=A%5Er), where ![r](https://render.githubusercontent.com/render/math?math=r) is the exponent of ![A](https://render.githubusercontent.com/render/math?math=A)

- basic_examples.py contains few basic examples

- mat_call_py_example.m is a Matlab example of using Python functions from utils.py

## Install
The simplest way to install TpTnOsc is through pip or conda:

```
pip install TpTnOsc

# or

conda install --channel "yoramzarai" TpTnOsc
```

Alternately, TpTnOsc can be installed from github:
```
git clone https://github.com/yoramzarai/TpTnOsc.git

cd TpTnOsc

python setup.py install
```