Metadata-Version: 1.1
Name: academictorrents
Version: 2.0.34
Summary: Academic Torrents Python APIs
Home-page: https://github.com/AcademicTorrents/python-r-api
Author: Martin Weiss, Alexis Gallepe, Jonathan Nogueira
Author-email: contact@academictorrents.com
License: MIT License

Copyright (c) 2018 academictorrents

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Description: # Academic Torrents Python API
        
        [![Build Status](https://travis-ci.org/AcademicTorrents/at-python.svg?branch=master)](https://travis-ci.org/AcademicTorrents/at-python)
        [![codecov](https://codecov.io/gh/AcademicTorrents/at-python/branch/master/graph/badge.svg)](https://codecov.io/gh/AcademicTorrents/python-r-api)
        
        The idea of this API is to allow scripts to download datasets easily. The library should be available on every system and allow a simple interface to download small and large files.
        
        We are currently out on pip! Install AT with this command:
        ```pip install academictorrents```
        
        
        ```
        import academictorrents as at
        
        # Download the data (or verify existing data)
        filename = at.get("323a0048d87ca79b68f12a6350a57776b6a3b7fb")
        
        # Then work with the data
        import cPickle, gzip
        import sys, os, time
        import numpy as np
        
        mnist = gzip.open(filename, 'rb')
        train_set, validation_set, test_set = cPickle.load(mnist)
        mnist.close()
        ```
        More documentation will be released soon!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.6Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
