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"""
This is Python implementation of Repository pattern for accessing Data model
in an Object Oriented manner, simulating collection interface and abstracting
persistence operations.

The Repository also has Factory method for dealing with different Databases. Another
approach is to add direct engine string ingestion to the Repository __init__ method.
"""

from abc import ABC

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import math
from operator import lt


class Heap:
    """Binary heap, with pluggable comparison function."""

    def __init__(self, cmp=lt):
        self.len = 0
        self.arr = []

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from itertools import groupby
from operator import itemgetter

from utils import outlier_threshold


def filter_view_deviations(data):
    query_sorted_data = sorted(data, key=itemgetter('query'))
    result = []
    for k, group in groupby(query_sorted_data, key=itemgetter('query')):

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import signal
import asyncio

from tornado import gen
from tornado.ioloop import IOLoop
from tornado.iostream import StreamClosedError
from tornado.tcpserver import TCPServer
from tornado.platform.asyncio import AsyncIOMainLoop, to_asyncio_future
import aioredis

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from concurrent.futures import ThreadPoolExecutor

from tornado import gen
from tornado.ioloop import IOLoop
from tornado.iostream import StreamClosedError
from tornado.tcpclient import TCPClient


class Client(TCPClient):
    msg_separator = b'\r\n'

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from tornado import gen
from tornado.ioloop import IOLoop
from tornado.tcpclient import TCPClient


class Client(TCPClient):
    msg_separator = b'\r\n'

    @gen.coroutine
    def run(self, host, port):

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import os
import asyncio
import sys

from asyncio.streams import StreamWriter, FlowControlMixin

reader, writer = None, None

@asyncio.coroutine
def stdio(loop=None):

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# -*- coding: utf-8 -*-
"""
An example flask application showing how to upload a file to S3
while creating a REST API using Flask-Restful.

Note: This method of uploading files is fine for smaller file sizes,
      but uploads should be queued using something like celery for
      larger ones.
"""
from cStringIO import StringIO

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# Data structures

## Polygon
- name : Big polygon (string, required) - name of the polygon
- points : [[[0, 0], [0, 5], [5, 5], [0, 5], [0, 0]], [[1, 1], [4, 1], [4, 4], [1, 4], [1, 1]]] (required) - list of lists of points (x, y) coordinate pairs.
First list is an enclosing polygon. Next ones are cut out areas.