# Our own module import.
from mad import median_mad

# Import the data
print('Importing...')
zipped = zipfile.ZipFile('data/taxirides.csv.zip')
file_list = zipped.namelist()
file_in_archive = file_list[0]


with zipped.open(file_list[0]) as f:
    data = pandas.read_csv(f)

print('Calculating...')

med_long, mad_long = median_mad(data.pickup_longitude)
std_long = np.std(data.pickup_longitude)

med_long, mad_long = median_mad(data.pickup_longitude)
long_lo = med_long - 20*mad_long
long_hi = med_long + 20*mad_long

med_lat, mad_lat = median_mad(data.pickup_latitude)
lat_lo = med_lat - 10*mad_lat
lat_hi = med_lat + 10*mad_lat

nwr = [40.69, -74.174]
jfk = [40.641, -73.778]
lga = [40.777, -73.874]

airports = np.array([nwr, jfk, lga])
import matplotlib.pyplot as plt
import zipfile
from numpy import array
import pandas

from mad import median_mad

zipped = zipfile.ZipFile('data/taxirides.csv.zip')

file_list = zipped.namelist()
file_in_archive = file_list[0]

with zipped.open(file_list[0]) as f:
    data = pandas.read_csv(f)

median, mad = median_mad(data.pickup_longitude)

long_lo = median - 20*mad
long_hi = median + 20*mad

median, mad = median_mad(data.pickup_latitude)
lat_lo = median - 10*mad
lat_hi = median + 10*mad

plt.subplots(figsize=(4, 3))
plt.plot(data.dropoff_longitude,
         data.dropoff_latitude, 'k,')
plt.axis('scaled')
plt.xlim(long_lo, long_hi)
plt.ylim(lat_lo, lat_hi)