vgoklani

import asyncio
import aiohttp

# ================================================
# for first run only
class first_async_scraper:
    def __init__(self):
        pass

    async def _fetch(self, symbol, url, session, headers):

vgoklani

import pandas as pd
import numpy as np
import re

# ================================================
class option_parser:
    def __init__(self, symbol, response):
        self.symbol = symbol
        self.response = response
    # ------------------------------------------------

vgoklani

import os
import sys
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import time
import asyncio
from fake_useragent import UserAgent
'''set path variables'''
project_dir = "YOUR/PROJECT/DIR"

vgoklani

import time.sleep
import zmq
context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://127.0.0.1:2000')

# Allow clients to connect before sending data
sleep(10)
socket.send_pyobj({1:[1,2,3]})

vgoklani

# -*- coding: utf-8 -*-

import asyncio
import uvloop
from aiohttp.web import Application, MsgType, WebSocketResponse


def add_socket(app, socket, user_id):
    if user_id in app['connections']:
        pass

vgoklani

import aiohttp
import asyncio

async def get_body(url):
    response = await aiohttp.request('GET', url)
    raw_html = await response.read()
    return raw_html

async def main():
    # run them sequentially (but the loop can do other stuff in the meanwhile)

vgoklani

from time import sleep
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, asynchronous, RequestHandler
from multiprocessing.pool import ThreadPool

_workers = ThreadPool(10)

def run_background(func, callback, args=(), kwds={}):
    def _callback(result):

vgoklani

import seaborn as sns
from scipy.optimize import curve_fit

# Function for linear fit
def func(x, a, b):
    return a + b * x

# Seaborn conveniently provides the data for
# Anscombe's quartet.
df = sns.load_dataset("anscombe")

vgoklani

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

vgoklani

import numpy as np
import pylab as pl

x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)

pl.scatter(x, y, s=1, label="log(x) with noise")
pl.plot(np.arange(1, 100), np.log(np.arange(1, 100)), c="b", label="log(x) true function")
pl.xlabel("x")
pl.ylabel("f(x) = log(x)")