semvec/pypi-packages huggingface dataset:
GetOptions 1.0.3
Gets options from the console command
Requires Python: >=3.5, <4
Keywords: GET options Optional parameters GUN Unix Command Line

*************************************
# Get options

> Gets options from the console command, Format the options and return a dict.  This is similar to the way non-GNU Unix systems work.

------------------------------
## Install:

```bash
pip install GetOptions
```

------------------------------
## System requirements

Python >= 3

------------------------------
## Example

```python
import GetOptions
params_config = {
        'host':     {'must': False,  'data': True,    'short': 'H',    'long': 'host',  'default': 'localhost'},
        'port':     {'must': False,  'data': True,    'short': 'O',    'long': 'port',  'default': 3306},
        'user':     {'must': True,   'data': True,    'short': 'U',    'long': 'user'},
        'passwd':   {'must': True,   'data': True,    'short': 'P',    'long': 'passwd'},
        'db':       {'must': True,   'data': True,    'short': 'D',    'long': 'db'},
        'init':     {'must': True,   'data': False,   'short': 'I',    'long': 'init'},
    }
print(GetOptions.get(params_config))
```
+ Shell command: 
    - `python3 test.py -H localhost -U root -P abc123 -D thinkvue -I abc 123`
+ Print result:
    - `{'data': {'host': 'localhost', 'port': 3306, 'user': 'root', 'passwd': 'abc123', 'db': 'thinkvue' , 'init': True}, 'args': ["abc", "123"]}`

## Parameter Description

`GetOptions.get(params_config, params=None, is_show_help=True)`
- `params_config`:<Required>{type:`dict`} A dict used to describe parameters, each primary key has 5 fields:
  + `key`:{type:`string`} The primary key in the result returned
  + `must`:{type:`bool`} This is a required option?
  + `data`:{type:`bool`} Does it have member data?
  + `short`:{type:`string`} The short parameter, example:`-s`
  + `long`:{type:`string`} The long parameter, example:`--longParam`
  + `default`:{type:`string`} Default value
- `params`:[optional]{type:`list`} Default `sys.argv`
- `is_show_help`:[optional]{type:`bool`} Show help?

## LICENSE

[MIT](./LICENSE)
buildbot-matrix 0.0.5
buildbot plugin for using matrix for notifications
Requires Python: >=3.6

# Buildbot Matrix Plugin
[![PyPI version](https://badge.fury.io/py/buildbot-matrix.svg)](https://badge.fury.io/py/buildbot-matrix)

This Plugin for buildbot adds a reporter which sends notifications to a specified matrix room.

Inspired by [buildbot-gitea](https://github.com/lab132/buildbot-gitea) by lab132.

# Installation
```
pip install buildbot-matrix
```

This installs itself into buildbot, no extra imports required.

# Setup

## Matrix Bot
* Create a new Matrix user which will be used to send notifications.
* Set a meaningful display name for the user. For Example `Buildbot Notifications`
* Get an access token for this user.
* Add the user to the room you want the notifications to be in.

## Buildbot
* Add this to the buildbot configuration:

```py

c['services'] = [
	reporters.MatrixStatusPush(
		'https://homeserver.example.com',
		'ROOM_ID:example.com',
		'BOT_ACCESS_TOKEN',
		verbose=True
	)
]
```

* Change the URL of the homeserver to the homeserver you want to use.
* Replace `BOT_ACCESS_TOKEN` with the acces token of the matrix user, this can be a buildbot secret.
* Replace `ROOM_ID` with the id of the matrix room the bot should send the notifications to. Replace the `!` at the beginning with `%21`. 

# Personalizing

You can modify the notifications by changing the following parameters:

| Parameter | Description | Default |
| --- | --- | --- |
| `startDescription` | Short description when a build started | `Build started.` |
| `endDescription` | Short description when a build ended | `Build done.` |
| `context` | Identifier to give messages a context | `Interpolate('buildbot/%(prop:buildername)s')` |
| `context_pr` | Identifier to give messages a context, used on pull requests | `Interpolate('buildbot/pull_request/%(prop:buildername)s')` |
| `warningAsSuccess` | Treat warnings as build success | `False` |
| `onlyEndState` | Only send a message if a build has ended | `False` |
wasserplan 0.1.3
1-Wasserstein distances between districting plans

Wasserplan
==========

``wasserplan`` is a library for computing 1-Wasserstein distances
between `GerryChain`_ partitions.

Dependencies
------------

``wasserplan`` depends on GerryChain and `CVXPY`_. We recommend
installing these dependencies with Anaconda (see the `official
GerryChain installation instructions`_).

Using
-----

We principally use this library to compute pairwise transport distances
which we (approximately) project into 2D Euclidean space. A simple
demonstration of this use case using Virginia congressional districts is
available `as a notebook`_ `(open in Colab)`_.

Performance
-----------

By default, ``wasserplan`` uses the open-source, lightweight `ECOS
solver`_, which is included with CVXPY. However, this solver may exhibit
performance issues for large graphs (thousands of nodes). Performance
can be improved by using a commercial solver such as `MOSEK`_.

License
-------

This library is available under the MIT License.

.. _GerryChain: https://github.com/mggg/gerrychain
.. _CVXPY: https://www.cvxpy.org/
.. _official GerryChain installation instructions: https://github.com/mggg/gerrychain#installation
.. _as a notebook: https://github.com/vrdi/geometry-of-graph-partitions/blob/master/wasserplan/VA%20ReCom%20demo.ipynb
.. _(open in Colab): https://colab.research.google.com/github/vrdi/geometry-of-graph-partitions/blob/master/wasserplan/VA%20ReCom%20demo.ipynb
.. _ECOS solver: https://github.com/embotech/ecos
.. _MOSEK: https://www.mosek.com/
punits 0.2
A simple utility to convert values between different units
Requires Python: >=3.6

# punits

<a href="https://pypi.org/ju-sh/punits"><img alt="PyPI" src="https://img.shields.io/pypi/v/punits"></a>
<a href="https://travis-ci.com/ju-sh/punits"><img alt="Build Status" src="https://api.travis-ci.com/ju-sh/punits.svg?branch=master"></a>
<a href="https://github.com/ju-sh/punits/blob/master/LICENSE.md"><img alt="License: MIT" src="https://img.shields.io/pypi/l/punits"></a>

A simple utility to convert values between different units.

<h2>Installation</h2>

You need Python>=3.6 to use punits.

It can be installed from PyPI with pip using

    pip install punits

<h2>Command line usage</h2>

    python3 -m punits <choice> <source-unit> <target-unit> <value-list>

where choice is the kind of units. Allowed values for <choice> are:
 - mass
 - length
 - volume
 - data
 - temperature.

For example, you can use

    python3 -m punits mass lb kg 23 46.2 12.46

to get

    10.43 20.96 5.65

Use `python3 -m punits --help` for more information.

<h2>Usage as module</h2>

The `punits()` function can be used.

Its signature is:

    def punits(measure: str,
               src_unit: str,
               target_unit: str,
               values: List[float],
               params: Optional[dict] = None) -> List[float]

where the `params` argument can be used to pass additional parameters that may be needed (like `dpi` value when conversion is from or to pixesl).

For example,

    >>> punits.punits('temperature', 'K', 'C', [235, 123.2])
    [-38.14999999999998, -149.95]

or

    >>> punits.punits('length', 'px', 'mm', [235, 123.2], {'dpi': 200})
    [29.844999999999995, 15.646400000000002]
django-payments-portmone 0.1.1
A django-payments backend for the Portmone payment gateway
Requires Python: >=3.6

# django-payments-portmone

A [django-payments](https://github.com/mirumee/django-payments) backend for the [Portmone](https://www.portmone.com.ua/) payment gateway ([Reference documentation](https://docs.portmone.com.ua/docs/en/PaymentGatewayEng/))


[![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/)
[![wemake-python-styleguide](https://img.shields.io/badge/style-wemake-000000.svg)](https://github.com/wemake-services/wemake-python-styleguide)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![wemake.services](https://img.shields.io/badge/%20-wemake.services-green.svg?label=%20&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAMAAAAoLQ9TAAAABGdBTUEAALGPC%2FxhBQAAAAFzUkdCAK7OHOkAAAAbUExURQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP%2F%2F%2F5TvxDIAAAAIdFJOUwAjRA8xXANAL%2Bv0SAAAADNJREFUGNNjYCAIOJjRBdBFWMkVQeGzcHAwksJnAPPZGOGAASzPzAEHEGVsLExQwE7YswCb7AFZSF3bbAAAAABJRU5ErkJggg%3D%3D)](https://wemake.services)


## Install

    pip install django-payments-portmone

## Parameters

* payee_id (required): ID of the payee company.
* login (required): Your company login in the Portmone.com system.
* password (required): Your company password in the Portmone.com system.
* currency (default:'UAH'): possible values - UAH, USD, EUR, GBP, BYN, KZT, RUB
* endpoint (default:'https://www.portmone.com.ua/gateway/'): desired endpoint.
* prefix (default:''): Prefix of Portmone order number "{prefix}{payment.pk}".

settings.py
----------

```python
PAYMENT_VARIANTS = {
    'portmone': ('payments_portmone.PortmoneProvider', {
        'payee_id': '1185',
        'login': 'WDISHOP',
        'password': 'wdi451',
        'endpoint': 'https://www.portmone.com.ua/gateway/',
        'prefix': 'P000-'}
    ),
}
```

## Requirements

- `python3.6`
- `django` with version `1.11`
- `django-payments` with version `0.13`


## Development

When developing locally, we use:

- [`editorconfig`](http://editorconfig.org/) plugin (**required**)
- [`pipenv`](https://github.com/pypa/pipenv) (**required**)
- `pycharm 2020+`


Copyright (C) 2020 Onufrienko Vyacheslav
localpdb 0.1.0
Store, manage and query the local copy of the PDB (Protein Data Bank) resources.
Requires Python: >=3.6.1

# localpdb
![localpdb](https://github.com/labstructbioinf/localpdb/workflows/localpdb/badge.svg) 
[![codecov](https://codecov.io/gh/labstructbioinf/localpdb/branch/master/graph/badge.svg)](https://codecov.io/gh/labstructbioinf/localpdb) 
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

- **Store a local copy of the [PDB](https://www.rcsb.org/) data and [related](#additional-resources) 
protein resources,**
- **Access and query the data convinently through pandas `DataFrame` 
structures,**
- **Update with weekly releases of a new data with full history versioning,**
- **Customize to your needs or add other data sources with simple [plugin](#plugins) system.**

### Quick start
- Install **localpdb** with pip and run setup script to download PDB data and protein structures in the 
PDB format:
```sh
pip3 install localpdb
localpdb_setup.py -db_path /path/to/your/localpdb --fetch_pdb
```
* Simple pipeline that selects for further analysis a representative set of protein structures:
    * solved with X-ray crystallography,
    * with resolution better than 2.5 angstroms, 
    * deposited in 2010 or later, 
    * with redundancy removed at the sequence level.
```python
from localpdb import PDB
import gzip

lpdb = PDB(db_path='/path/to/your/localpdb')

# Select protein structures solved with X-ray diffraction (resolution above 2.5 A)
lpdb.entries = lpdb.entries.query('type == "prot"')
lpdb.entries = lpdb.entries.query('method == "diffraction"')
lpdb.entries = lpdb.entries.query('resolution <= 2.5')
lpdb.entries = lpdb.entries.query('deposition_date.dt.year >= 2010')

# Remove redundancy (select only representative structure from each sequence cluster)
lpdb.load_clustering_data(cutoff=90)
lpdb.chains = lpdb.chains[lpdb.chains['clust-90'].notnull()]

representative = lpdb.chains.groupby(by='clust-90')['resolution'].idxmin()
lpdb.chains = lpdb.chains.loc[representative]

for pdb_fn in lpdb.chains.pdb_fn:
    # your analysis here


```
### Additional resources
**(In development)**
### Plugins
**(In development)**