semvec/pypi-packages huggingface dataset:
opengraph-writer 0.4.0
Lightweight OpenGraph support for writing and validating objects.
License: MIT
Requires Python: >=3.6
Keywords: facebook opengraph open graph web pyramid

opengraph_writer
================

![Python package](https://github.com/jvanasco/opengraph_writer/workflows/Python%20package/badge.svg)

About
=====

This library was created to both manage writing OpenGraph data, and perform
lightweight verifications for offline debugging.

This library does the following:

- CREATE an object to stash OGP data
- VALIDATE the data, offering debug information on the object
- RENDER the object as html, and optionally include debug data on incorrect elements

This is currently aimed at "single object page data". It is not necessarily
with multi-value items yet (such as multiple profiles or tracks on a single page).

Most of the validation routines were derived from regexing the `https://ogp.me`
site into a Python dict, and researching what they are supposed to do.
The validation routes themselves are super trivial.

At the time of initially writing this library, and several checks ever since, the
specification on  the `https://ogp.me` seems out of date. 

In practice, Facebook's "premier" partners seem to all be using URLs which point 
to certain data , however there aren't any guidelines for this within the
public OGP spec.

An example would be on the Spotify integration: when looking at a `SONG`, the
`ARTIST` and `ALBUM` refer to URLs which contain the relevant data.

Documentation
=============

The Python file contains extensive inline documentation.


Contributions Welcome!
======================

If anyone wants to fork and tackle the multiple value problem, please do.


Framework Support: Pyramid
==========================

This package offers an includeme for Pyramid to set up an `opengraph_item`
as a `@reify` request method.

Simply update your application:

	config.include("opengraph_writer.pyramid_helpers")

And your `Request` objects will be extended as such:

    request.opengraph_item
files-handler 0.2.1
Files Handler Library
License: MIT

## Instalação da biblioteca

- Para instalar a biblioteca basta chamar no console "pip install files-handler"

## Inicialização da biblioteca

- Para iniciar a biblioteca, basta importar as duas classes, ou apenas a de interesse com o "from files_handler import s3_handler, folders_handler"
- O próximo passo é instanciar a classe respectiva:

  s3_handler_class = s3_handler(bucket, path_ref)
  Para essa classe é preciso passar no construtor, o bucket do S3 e também um diretorio de referência local para obter e salvar os arquivos

  folders_handler_class = folders_handler(path_ref)
  Para essa classe é preciso passar no construtor o diretório de referência que contém as pastas a serem manipuladas.

- Após isso, é só chamar as funções de cada classe, um exemplo:
  folders_handler_class.verify_and_create_folder('input')
  Dessa forma, a função verifica a pasta no diretório 'path_ref + input', e caso não exista, ela será criada

## Descrição das classes da biblioteca

A biblioteca possui duas classes, uma para lidar com a conexão com o S3 e outra para lidar com o gerenciamento das pastas para as análises

O s3_handler possui três metódos, sendo eles:

- get_image_from_s3_bucket
  Responsável por obter uma imagem do S3

- upload_image_to_s3_bucket
  Responsável por upar a imagem resultando no S3

- check_if_file_already_exists
  Verificar se já existe algum arquivo ou imagem no S3

O folders_handler possui dois métodos, sendo eles:

- clear_folder
  Responsável por limpar a pasta que foi passada como parâmetro

- verify_and_create_folder
  - Responsável por verificar se a pasta já existe, se não, ele cria a pasta que foi passada como parâmetro

## Testar a biblioteca

Para testar a biblioteca é preciso instalar os pacotes do requirements.txt, configurar as variáveis de ambiente no tests/s3_handler_tests.py e rodar os comandos na raiz da biblioteca:

python -m unittest tests/s3_handler_tests.py
python -m unittest tests/folders_handler_tests.py

## Subir uma nova versão da biblioteca

Para subir uma nova versão da biblioteca no Pypi é preciso ter uma conta no [PyPi](https://pypi.org/), ter instalado o [twine](https://pypi.org/project/twine/) na máquina, modificar o número da versão na linha 18 do setup.py, e rodar os seguintes comandos na raiz da biblioteca:

python setup.py sdist bdist_wheel
python -m twine upload dist/\* --verbose --skip-existing

A URL da lib no PyPi é essa: https://pypi.org/project/files-handler/
bringorder 0.1.0
The tool library is aimed at guiding data scientists with their analysis using custom widgets inside Jupyter Notebook.
License: MIT

# bring-order
![GitHub Actions](https://github.com/Order-Team/bring-order/workflows/CI/badge.svg)
[![codecov](https://codecov.io/gh/Order-team/bring-order/branch/main/graph/badge.svg?token=e8bdd46f-46b0-410c-820b-84ffca9ca53c)](https://codecov.io/gh/Order-team/bring-order)
[![GitHub](https://img.shields.io/github/license/Order-Team/bring-order)](LICENSE.md)

The tool is aimed at guiding data scientists with their analysis using custom widgets inside Jupyter Notebook.
User can choose deductive or inductive analysis.
Deductive analysis asks the user to set the hypothesis and null hypothesis, run their analysis, and confirm one of the hypotheses.
Inductive analysis is an option to perform explorative analysis and write notes about it.


## Documentation
* [Product backlog](https://docs.google.com/spreadsheets/d/1xqybqVAUIn4vhW-fBfhInQun7nY-uYH79M6l8oCiAzg/edit#gid=0)

* [Definition of Done](https://github.com/Order-Team/bring-order/blob/main/documentation/DoD.md)

* [Minutes of meetings](https://drive.google.com/drive/folders/1kwXCKbx7egHf8qYDIb4fRffNnad6Qd1t)

## Installation

### Installation from TestPypi
- If you don't have Jupyter Notebook installed, install it with
```bash
    pip install notebook
```

- If you don't have ipywidgets installed, install it with

```bash
    pip install ipywidgets
```

- If you have an old version of ipywidgets installed, upgrade it with

```bash
    pip install --upgrade ipywidgets
```

- Install testbringorder extention:
```bash
    pip install -i https://test.pypi.org/simple/ testbringorder
```

### Usage
- Open Jupyter Notebook with
```bash
    jupyter notebook
```

- In Jupyter Notebook execute

``` 
    from bring_order import BringOrder
    BringOrder()
```

- Make sure that your notebook is in Trusted state. Otherwise the widgets might not work correctly.

## Development

### Dependencies
- Clone the project and install dependencies in the main folder

```bash
    poetry install
```

### Testing
- Navigate to main folder and go to the virtual environment

```bash
    poetry shell
```

#### Run unit tests

```bash
    pytest tests
```

#### Run style check

```bash
    pylint bring_order
```

#### Run robot tests
- Make sure you have chromedriver installed and matching your Chrome version before running

```bash
    ./run_robot_tests.sh
```

- If the script won't run, give it execution rights first and try again
```bash
chmod +x run_robot_tests.sh
```

- If the first run fails, try once again. Sometimes there are some connection issues.
When you are done, you can exit the virtual environment with command

```bash
exit
```

#### Run tests with Invoke

- The previous tests can also be run using Invoke in poetry shell:

```bash
invoke tests
```
to run unit tests

```bash
invoke lint
```
to run pylint

```bash
invoke robottests
```
to run robottests

```bash
invoke alltests
```
to run all of the above
networkxtra 1.0.1
Extra functions for networkx.
License: MIT
Requires Python: >=3.8,<4.0

# networkxtra

[![Tests](https://img.shields.io/github/actions/workflow/status/maxmouchet/networkxtra/tests.yml?logo=github)](https://github.com/maxmouchet/networkxtra/actions/workflows/tests.yml)
[![PyPI](https://img.shields.io/pypi/v/networkxtra?color=blue&logo=pypi&logoColor=white)](https://pypi.org/project/networkxtra/)

Extra functions for [networkx](https://github.com/networkx/networkx):

- Algorithms
  - `largest_connected_component`
- Conversion
  - `from_geojsonl`, `to_geojsonl`
- Drawing
  - `draw_cairo` (much faster than the matplotlib backend)
- Functions
  - `describe(G)`, `remove_leafs(G)`
- Layouts
  - `bfs_layout` (https://github.com/networkx/networkx/pull/5179)
  - `lgl_layout`, `lgl_layout_from_file` (via [minilgl](https://github.com/maxmouchet/minilgl))
- Read/Write
  - `generate_geojsonl`, `parse_geojsonl`, `read_geojsonl`, `write_geojsonl`
  - `generate_lgl`, `parse_lgl`, `read_lgl`, `write_lgl`