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# l2 norm of a vector
#  = sqrt(a1^2 + a2^2 + a3^2)
from numpy import array
from numpy.linalg import norm
a = array([1, 2, 3])
print(a)
l2 = norm(a)
print(l2)  # 3.7416573867

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"""
	DISTANCE BETWEEN COORDINATES
"""

import math

lat1 = 40.813078
lat2 = 55.340000
lon1 = -73.046388
lon2 = -131.640000

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from datetime import datetime

from sqlalchemy import (MetaData, Table, Column, Integer, Numeric, String,
                        DateTime, ForeignKey, create_engine)
metadata = MetaData()

cookies = Table('cookies', metadata,
    Column('cookie_id', Integer(), primary_key=True),
    Column('cookie_name', String(50), index=True),

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# AR example
from statsmodels.tsa.ar_model import AR
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = AR(data)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))

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# MA example
from statsmodels.tsa.arima_model import ARMA
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = ARMA(data, order=(0, 1))
model_fit = model.fit(disp=False)
# make prediction
yhat = model_fit.predict(len(data), len(data))

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# ARMA example
from statsmodels.tsa.arima_model import ARMA
from random import random
# contrived dataset
data = [random() for x in range(1, 100)]
# fit model
model = ARMA(data, order=(2, 1))
model_fit = model.fit(disp=False)
# make prediction
yhat = model_fit.predict(len(data), len(data))

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# SES example
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = SimpleExpSmoothing(data)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))

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# HWES example
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = ExponentialSmoothing(data)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))

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import glob
import os
import cv2
import concurrent.futures


def load_and_resize(image_filename):
  ### Read in the image data
  img = cv2.imread(image_filename)
  

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from geopy import GoogleV3
place = "221b Baker Street, London"
location = GoogleV3().geocode(place)
print(location.address)
print(location.location)