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Academic Torrents Python APIs

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Copyright (c) 2018 academictorrents

Permission is hereby granted, free of charge, to any person obtaining a copy
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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
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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/python-r-api)

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 cPickle, gzip
import sys, os, time

mnist = gzip.open(filename, 'rb')
train_set, validation_set, test_set = cPickle.load(mnist)
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

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