semvec/pypi-packages huggingface dataset:
caseine_design 0.0.5
A set of decorations used for the VPL Design Plugin on caseine platform.

# Caseine Design Python support

The VPL Design Plugin allows an educator to create moodle VPL that may be prepared and pushed on 
a moodle instance using a command line.

To handle python labs, the VPL Design Plugin make uses of python decorations that have no 
python level semantics, but may be used by the VPL Design Plugin command line to prepare the lab 
before upload.

This plugin has been created mainly for the caseine.org platform, but may be used for other Moddle
instances with the VPL tool.
S3PdfSplitter 1.2.0
Splitter de fichiers pdfs sur S3

# S3PdfSplitter [![Build Status](https://travis-ci.org/Nydareld/S3PdfSplitter.svg?branch=master)](https://travis-ci.org/Nydareld/S3PdfSplitter) [![Coverage Status](https://coveralls.io/repos/github/Nydareld/S3PdfSplitter/badge.svg)](https://coveralls.io/github/Nydareld/S3PdfSplitter) [![PyPI version](https://badge.fury.io/py/S3PdfSplitter.svg)](https://badge.fury.io/py/S3PdfSplitter) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/S3PdfSplitter.svg)

Python aws-s3 pdf spliter

## Usage

basic usage :

```python
from PdfSplitter import Splitter

spliter = Splitter("config.json")
spliter.split(data)
```

exemple config.json :
```json
{
    "aws" : {
        "access_key_id" : "aws-acces-key",
        "secret_access_key" : "aws secret",
    },
    "s3" : {
        "bucket" : "bucket"
    }
}
```
Note that the config is managed with [ConfigEnv](https://pypi.org/project/ConfigEnv/) so you can provide an .ini file or overide the config with environement variable ( AWS_S3_BUCKET, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY )

example data :
```json
{
    "input" : [
        "firstFile.pdf",
        "secondFile.pdf",
        "http://urlFile.pdf"
    ],
    "output": [
        {
            "s3Key": "output1.pdf",
            "pages": [
                { "index": 0, "pages": [0,1] },
                { "index": 1, "pages": [0,1] }
            ]
        },{
            "s3Key": "output2.pdf",
            "pages": [
                { "index": 0, "pages": [0] },
                { "index": 1, "pages": [0] },
                { "index": 0, "pages": [1] },
                { "index": 1, "pages": [1] }
            ]
        }
    ]
}
```

this will produce 2 pdfs in your s3:
 - the first, output1.pdf, with page 0 and 1 from firstFile and page 0 and 1 from secondFile
 - the second, output2.pdf, with page 0 from firstFile, page 0 from secondFile, page 1 from firstFile and page 1 from secondFile


## Developpement guide

### installation

with virtualenv :

    # create virtualenv
    virtualenv -p python3 .venv

    # activate venv
    source .venv/bin/activate

    # install dependancies
    pip install -r requirements.txt
    pip install -r requirements-dev.txt

### testing

with unittest :

    # if your test config is setup :
    python -m unittest

    # if you want to overide your test config :
    S3_BUCKET=<your bucket> AWS_ACCESS_KEY_ID=<your key id> AWS_SECRET_ACCESS_KEY=<your key secret> python -m unittest
datapungi_imf 0.2.0
Gets data from the IMF (IMF) by connecting to its API.

<!--
TODO: add explanation of the request part of the vintage.
-->

[![image](https://img.shields.io/pypi/v/datapungi_imf.svg)](https://pypi.org/project/datapungi_imf/) 
[![build Status](https://travis-ci.com/jjotterson/datapungi_imf.svg?branch=master)](https://travis-ci.com/jjotterson/datapungi_imf)
[![downloads](https://img.shields.io/pypi/dm/datapungi_imf.svg)](https://pypi.org/project/datapungi_imf/)
[![image](https://img.shields.io/pypi/pyversions/datapungi_imf.svg)](https://pypi.org/project/datapungi_imf/)

install code: pip install datapungi_imf 

<h1> datapungi_imf  </h1>

  datapungi_imf is a python package that provides a simplified way to extract data from the API of IMF (IMF).  Overall it:
  - 
  -      
  - can read a saved API key (in json/yaml files or environment variables (default)) to avoid having a copy of it on a script.
  - can automatically test: 
      * the connectivity to all BEA datasets, 
      * the quality of the cleaned up data, and 
      * if the provided requests code snippet returns the correct result. 

## Sections
  -  [Short sample runs](#Sample-runs)
  -  [Short sample runs of all drivers](#Sample-run-of-all-drivers)
  -  [Description of a full return](#Full-request-result) 
  -  [Setting up datapungi_imf](#Setting-up-datapungi_imf)
  -  [Testing the package](#Running-Tests) 

## Sample runs

First, [set the package up](#Setting-up-datapungi_imf) (get an API key from BEA, save it somewhere and let datapungi_imf know its location).  After setting datapungi_imf up, you can run the following:

```python
'''
  Short datapungi_imf sample run
'''

import datapungi_imf as dpi

data = dpi.data() #or data = dpi.data("API Key"), see setting up section   

data.list()
data.params('IFS')
data.data('IFS/A.GB.PMP_IX')
data.meta('IFS/A.GB.PMP_IX')
```