semvec/pypi-packages huggingface dataset:
libpythonpro-edna 0.2
Módulo para exemplificar construção de projetos Python no curso PyTools

# libpythonpro
MÃ³dulo para exemplificar construÃ§Ã£o de projetos python no curso pytools

suportada versao 3 de python
1comnado: python -V(maisculo)
2comonado para saber de onde vem o python:where python!
3 criando venv manual mente no terminal:
python -m venv .venv
4 no terminal do powershell do windowns executar o seguinte comando
:Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
5 comnado : confirma com 's'
6 como .env nao estava aprencendo  var no power shel do pycharm e eceute o seguinte comando:
.venv\Scripts\activate
 assim o vem  sera ativado

7- apos excluir o .venv -
-py -3 -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

suportada versao 3 de python

para instalar :

''' console
-py -3 -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
'''

para conferir qualidade de codigo:
''' console terminal flake8'''
CloudStackAIO 0.0.8
Very thin Python CloudStack client using asyncio
Keywords: asyncio cloudstack client

[![Build Status](https://github.com/giffels/CloudStackAIO/actions/workflows/unittests.yml/badge.svg)](https://github.com/giffels/CloudStackAIO/actions/workflows/unittests.yml)
[![Verification](https://github.com/giffels/CloudStackAIO/actions/workflows/verification.yml/badge.svg)](https://github.com/giffels/CloudStackAIO/actions/workflows/verification.yml)
[![codecov](https://codecov.io/gh/giffels/CloudStackAIO/branch/master/graph/badge.svg)](https://codecov.io/gh/giffels/CloudStackAIO)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/giffels/CloudStackAIO/blob/master/LICENSE.txt)
[![Documentation Status](https://readthedocs.org/projects/cloudstackaio/badge/?version=latest)](https://cloudstackaio.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/CloudStackAIO.svg)](https://badge.fury.io/py/CloudStackAIO)

# CloudStackAIO
Very thin Python CloudStack Client using asyncio
karton-asciimagic 1.2.0
Various encoders for ascii-encoded executables for Karton framework

# AsciiMagic karton service

Extracts next stages of various ASCII files that contain malware, for example hex, base64, etc.

**Author**: CERT.pl

**Maintainers**: ola, msm, nazywam

**Consumes:**
```json
{
    "type": "sample",
    "stage": "recognized",
    "kind": "ascii"
} 
```

**Produces:**
```json
{
    "type": "sample",
    "kind": "runnable",
    "stage": "recognized",
    "platform": "win32",
    "extension": "exe",
}, {
    "type": "sample",
    "kind": "raw"
}
```


## Usage

First of all, make sure you have setup the core system: https://github.com/CERT-Polska/karton

Then install karton-asciimagic from PyPi:

```shell
$ pip install karton-asciimagic

$ karton-asciimagic
```

![Co-financed by the Connecting Europe Facility by of the European Union](https://www.cert.pl/uploads/2019/02/en_horizontal_cef_logo-e1550495232540.png)
karton-autoit-ripper 1.2.0
AutoIt script ripper for Karton framework

# AutoIt-Ripper karton service

Uses [AutoIt-Ripper](https://github.com/nazywam/AutoIt-Ripper) to automatically extract embedded AutoIt scripts and resources from compiled binaries.

**Author**: CERT.pl

**Maintainers**: nazywam

**Consumes:**
```json
{
    "type": "sample",
    "stage": "recognized",
    "kind": "runnable",
    "platform": "win32"
}, {
    "type": "sample",
    "stage": "recognized",
    "kind": "runnable",
    "platform": "win64"
}
```

**Produces:**
```json
{
    "type": "sample",
    "kind": "raw"
}, {
    "type": "sample",
    "kind": "script",
    "stage": "analyzed",
    "extension": "au3",
}
```

## Usage

First of all, make sure you have setup the core system: https://github.com/CERT-Polska/karton

Then install karton-autoit-ripper from PyPi:

```shell
$ pip install karton-autoit-ripper

$ karton-autoit-ripper
```

![Co-financed by the Connecting Europe Facility by of the European Union](https://www.cert.pl/uploads/2019/02/en_horizontal_cef_logo-e1550495232540.png)
grog 0.93
GeneRic wOrkload Generator
Requires Python: >=3.6

# GROG a GeneRic wOrkload Generator

GROG is a generic workload generator (Cloud and HPC like) to create datasets of tasks for scheduling evaluation.

This workload generator is based on a [dataset](https://research.google/tools/datasets/cluster-workload-traces/) from Google studied in [Modeling, classifying and generating large-scale Google-like workload](https://www.sciencedirect.com/science/article/abs/pii/S2210537917301634) published in the SUSCOM journal ([open access version](https://hal.archives-ouvertes.fr/hal-02640354/document))

In the current version, the format of generated traces is the [JSON Batsim workload format](https://batsim.readthedocs.io/en/latest/input-workload.html), compatible with the Resource and Jobs Management System (RJMS) simulator [Batsim](https://gitlab.inria.fr/batsim/batsim).

## Installation

    pip3 install grog

## Usage

    usage: grog-tool [-h] [--seed SEED] [--offline] [--duration DURATION] [--density DENSITY] [--speed SPEED] output_file

    positional arguments:
      output_file          filename to store the workload

    options:
      -h, --help           show this help message and exit
      --seed SEED          change seed, default: 42
      --offline            to change from online to offline
      --duration DURATION  duration in days, default: 3
      --density DENSITY    hourly density, default:50
      --speed SPEED        server speed in Gflops, default = 14.72 (similar to Taurus a cluster in Grid5000, France)

## Example

Simple example, uses the default values to generate a file called *workload.json*

    grog-tool workload.json

Longer example, uses some flags to generate a longer duration (here one year).	

    grog-tool --offline --seed 128 --duration 365 --density 5 long_workload.json

## Citation

Please cite ([bibtex version](https://hal.archives-ouvertes.fr/hal-02640354v1/bibtex)) the following if you use this tool

    Georges da Costa, Léo Grange, Inès De Courchelle. Modeling, classifying and generating large-scale Google-like workload. Sustainable Computing : Informatics and Systems, Elsevier, 2018, 19, pp.305-314. ff10.1016/j.suscom.2017.12.004ff.
    
    
# Misc

 * This software is under the GPLv3.
 * The current point of contact is Georges.Da-Costa@irit.fr
 * The last version is available here : https://gitlab.irit.fr/sepia-pub/grog
 * The current internal API migh evolve, but command line interface should be stable
 * The time needed for generating dense and/or long duration traces might be long