semvec/pypi-packages huggingface dataset:
pyvscode 2.2.1
Package for interacting with IDE called Visual Studio Code.
Requires Python: >=3.7

<h1 align="center">pyvscode</h1>

<p align="center"><i>Take the control over Visual Studio Code with python code!</i></p>

```shell
pip install pyvscode
```

---

Python package for interacting with the most popular IDE called Visual Studio Code.

## Abilities

- Open files and workspaces
- Open files comparison (difference) tabs
- Change language of opening workspace
- Get version of Visual Studio Code installed
- Install or uninstall extensions
- List all extensions and filter them up by categories
dewloosh.plotly 0.0.4
Domain spceific data visualization with plotly.
Requires Python: >=3.7, <3.11

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dewloosh/dewloosh-plotly/main?labpath=notebooks%5Cbasic.ipynb)
[![CircleCI](https://circleci.com/gh/dewloosh/dewloosh-plotly.svg?style=shield)](https://circleci.com/gh/dewloosh/dewloosh-plotly) 
[![Documentation Status](https://readthedocs.org/projects/dewloosh-plotly/badge/?version=latest)](https://dewloosh-plotly.readthedocs.io/en/latest/?badge=latest) 
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI](https://badge.fury.io/py/dewloosh-plotly.svg)](https://pypi.org/project/dewloosh-plotly) 

# **dewloosh.plotly** - Domain specific data visualization with Plotly

This a lightweight library serving as a place to gather code snippets for
working with Plotly. The uses cases are usually domain specific, likely to be related
to other dewloosh projects.

## **Documentation**

Click [here](https://dewloosh-plotly.readthedocs.io/en/latest/) to read the documentation.

## **Installation**
This is optional, but we suggest you to create a dedicated virtual enviroment at all times to avoid conflicts with your other projects. Create a folder, open a command shell in that folder and use the following command

```console
>>> python -m venv venv_name
```

Once the enviroment is created, activate it via typing

```console
>>> .\venv_name\Scripts\activate
```

`latexdocs` can be installed (either in a virtual enviroment or globally) from PyPI using `pip` on Python >= 3.7:

```console
>>> pip install dewloosh.plotly
```

## **License**

Copyright 2022 Bence Balogh, under [the MIT license](https://github.com/dewloosh/latexdocs/blob/master/LICENSE).
dewloosh.mpl 0.0.6
Domain spceific data visualization with matplotlib.
Requires Python: >=3.7, <3.11

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dewloosh/dewloosh-mpl/main?labpath=notebooks%5Cbasic.ipynb)
[![CircleCI](https://circleci.com/gh/dewloosh/dewloosh-mpl.svg?style=shield)](https://circleci.com/gh/dewloosh/dewloosh-mpl) 
[![Documentation Status](https://readthedocs.org/projects/dewloosh-mpl/badge/?version=latest)](https://dewloosh-mpl.readthedocs.io/en/latest/?badge=latest) 
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI](https://badge.fury.io/py/dewloosh-mpl.svg)](https://pypi.org/project/dewloosh-mpl) 

# **dewloosh.mpl** - Domain specific data visualization with Matplotlib

This a lightweight library serving as a place to gather code snippets for
working with matplotlib. The uses cases are usually domain specific, likely to be related
to other dewloosh projects.

## **Documentation**

Click [here](https://dewloosh-mpl.readthedocs.io/en/latest/) to read the documentation.

## **Installation**
This is optional, but we suggest you to create a dedicated virtual enviroment at all times to avoid conflicts with your other projects. Create a folder, open a command shell in that folder and use the following command

```console
>>> python -m venv venv_name
```

Once the enviroment is created, activate it via typing

```console
>>> .\venv_name\Scripts\activate
```

`latexdocs` can be installed (either in a virtual enviroment or globally) from PyPI using `pip` on Python >= 3.7:

```console
>>> pip install dewloosh.mpl
```

## **License**

Copyright 2022 Bence Balogh, under [the MIT license](https://github.com/dewloosh/latexdocs/blob/master/LICENSE).
social-b-matching 0.0.1
Optimal b-matching for scored meeting assignments.
Requires Python: >=3.8

This module implements an algorithm designed for optimal matchings encouraging
informal social connections between team members in a workgroup.

While our company's WFH policy was in place during the COVID-19 pandemic, my
team sought ideas to strengthen social ties and help new hires feel welcomed.
One very successful program involved randomly assigning weekly 15-minute
meetings between team members for a quick chat. However, many participants felt
these random assignments weren't ideal: Newcomers wanted more meetings early on,
to meet more people. Other team members found themselves meeting with their own
close teammates, or with the same person multiple times in just a few weeks!

To address this, we explored an optimal matching algorithm.  The ideal approach
would assign scores to each potential pairing, assigning
higher scores to pairs of participants in different cities, or different
subteams, and lower scores to pairs that had already been assigned
recently. New hires would be assigned multiple pairings
for new hires in their first few months.

After a literature review, I learned this problem is known in the literature as
the **maximum weighted b-matching** problem. Unlike [stable marriage](https://en.wikipedia.org/wiki/Stable_marriage_problem) or [maximum-weighted
matching](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.matching.max_weight_matching.html#networkx.algorithms.matching.max_weight_matching), b-matching allows some nodes to be matched more than once. There is some recent research demonstrating
[polynomial time solutions](https://arxiv.org/abs/1706.07418) for general
b-matching, but I found no easy-to-use open source implementations. We observed
that for the case of small `n`, a solution based on integer programming runs in
only a few seconds on desktop PCs, which makes it well-suited to this
application's requirements.

In addition to the integer programming solver for standard maximum-weighted
b-matching -- `b_matching.maximize_weighted_b_matching` -- this package also
includes a small wrapper `b_matching.inclusive_matching` enforcing the
additional constraint that all nodes must be used in at least one matched pair.
A short proof for the inclusive matching algorithm can be found in
`inclusive_b_matching.md`. The `toy_example` provides a small example
illustrating the use of inclusive matching to assign social meetings across
a tiny but distributed team.

Acknowledgements: Mirkó Visontai provided helpful math research, suggestions,
and code reviews. Bo Zulonas provided the score-based matching idea, original
scoring functions, and useful feedback.

This is not an officially supported Google product.
tempt 0.0.1
tempt is a flexible and powerful Python template engine for dynamic substitution of values in templates.
Keywords: python,template engine,templating,web development,template,dynamic templates,template inheritance,control structures,filters,reusability

tempt is a flexible and powerful Python template engine designed to make it 
easy to substitute placeholders in templates with actual values. 

### Supported features:
* template inheritance. 
* control structures. 
* filters

With a simple syntax, tempt makes it easy to create dynamic and customizable templates for web applications. The 
engine is safe to use and undergoes ongoing development to meet our needs as web developers.

For more information and examples, check out tempt's documentation ( coming soon) and Github 
repository at https://github.com/Dcohen52/tempt.
amqencode 0.2.3
Encoding utility for Anime Music Quiz
Requires Python: >=3.6

# ffmpeg scripts for handicaps

This Python 3 package simplifies 2-pass VP9/Opus encoding for AMQ using `ffmpeg`.
It's basically a wrapper with presets for [ffmpeg-python](https://github.com/kkroening/ffmpeg-python),
with some hacky stuff to get 2-pass encodes working.

You can use it to encode every resolution of a video with a single command.

You can override a lot of the presets.
Passing `norm=True` automatically adjusts volume levels for you.

I'll write up documentation later.

## Install it with `pip`

First, install Python 3 if you haven't already. Then,

```bash
pip install amqencode
```

or maybe this on Windows

```cmd
py -3 -m pip install amqencode
```

Also make sure that `ffmpeg` is on your PATH.

## Usage

`import amqencode` in your python scripts.

See `sample_encode.py` for an example of a script that encodes a video into
mp3 and webms, then muxes those outputs with clean audio.

### CLI

If you just want to encode a file without writing a standalone script,
you can call the package directly from a terminal.

*nix

```bash
python3 -m amqencode
```

Windows

```cmd
py -3 -m amqencode
```

However, this is incapable of muxing clean audio into an encode.
cryptoGR 1.0.0
This project refers to a password encryptor, which also serves to encrypt and decrypt files through a password entered by users.
Requires Python: >=3.8

# CRYPTOGR
==========



This project refers to a password encryptor, which also serves to encrypt and decrypt files through a password entered by users. <br>
[Project Cripto](https://github.com/NaraAndrad3/Cripto/)
to write your content.


<h3><li>1 - Para instalar o pacote use:</li></h3>

``````
pip install cryptoGR
``````


<h3><li>2 - import o modulo usando:</li></h3>

``````
from cryptoGR import *
``````



<h3><li>3 - Formas de usar o pacote:</li></h3>

``````
#Encriptando e decriptando o arquivo

f = FilesDec()



print('Criptografar: ')
arq = 'arquivo.pdf'
f.encryptFile(arq,'1234')


print('Decriptografar: ')
f.decryptFiles(arq,'1234')


#Gerando senhas
senha1 = pwd.password_letters(8)

``````
zca 0.1.1
ZCA whitening

===
zca
===

ZCA whitening in Python with a Scikit-Learn like interface.

Usage
-----

.. code:: python

    from zca import ZCA
    import numpy as np

    X = np.random.random((10000, 15)) # data array
    trf = ZCA().fit(X)
    X_whitened = trf.transform(X)
    X_reconstructed = trf.inverse_transform(X_whitened)
    assert(np.allclose(X, X_reconstructed)) # True


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

.. code:: bash

    pip install -U zca


Licence
-------
GPLv3

Authors
-------

`zca` was written by `Maarten Versteegh <maartenversteegh@gmail.com>`_.