Metadata-Version: 1.1
Name: academictorrents
Version: 2.1.15
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

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in the Software without restriction, including without limitation the rights
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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.

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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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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/at-python)
        
        This repository is an implementation of the BitTorrent protocol written in Python and downloadable as a `pip` module. You can download datasets from AcademicTorrents.com in two lines of code:
        ```
        import academictorrents as at
        path_of_giant_dataset = at.get("323a0048d87ca79b68f12a6350a57776b6a3b7fb") # Download massive dataset
        ```
        
        # For people who want to download datasets
        we're compatible with Python versions: 2.7, 3.4, 3.5, 3.6
        
        To install:
        `pip install academictorrents`
        
        This package works with the academictorrents tracker. You can add a hash from [academictorrents.com](academictorrents.com) for your torrent, and download datasets into your project.
        
        Here's a little example (it's implemented in `examples/basic_test.py` in case you want to play with our source code):
        ```
        # Import the library
        import academictorrents as at
        
        # Download the data (or verify existing data)
        filename = at.get("323a0048d87ca79b68f12a6350a57776b6a3b7fb")
        
        # Then work with the data
        import pickle, gzip
        import sys, os, time
        
        mnist = gzip.open(filename, 'rb')
        train_set, valid_set, test_set = pickle.load(mnist, encoding='latin1')
        mnist.close()
        ```
        
        
        # For Contributors to the AcademicTorrents Python client
        ## Introduction
        We use Github issues and pull requests to manage development of this repository. The folllowing is a guide for setting up the codebase to contribute PRs or try to debug issues.
        
        ## Installation
        Getting setup to work on this client is pretty easy. First, install the source code, then install the dependencies with `pip`:
        
        ```
        git clone https://github.com/AcademicTorrents/at-python.git
        cd at-python
        pip install -r requirements.txt
        ```
        Done!
        
        ## Testing
        We've got a test suite that you can run with `pytest -s tests/`. These tests also run on travis after every push to github. Some of our tests are empty -- usually in parts of the codebase that have been changing quickly -- but we should continue increasing our coverage. If you want to just run one little download, use `python examples/basic_test.py` (example code above).
        
        ## Architecture
        The `academictorrents` module only has one "public" function, `.get`. This function checks for the torrent data on the filesystem. If it's not present, then it initiates a `Client` to download the data for us.
        
        `Client` is our main thread, it manages the lifecycle of our other threads including `Tracker`, `PeerManager`, `PieceManager`, and many `WebSeedManager` threads. The `Client` thread uses the `PieceManager` class (not thread) to keep track of all the pieces of data. The main thread makes socket requests to `Peer`s, and enqueues jobs to request data from `HttpPeer`s. Socket responses are handled by `PeerManager`, whereas a fleet of `WebSeedManagers` threads handles the `HttpPeer`s.
        
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
