import pandas as pd
import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')

station_info = pd.read_csv('../assets/station_info.csv')
station_info.head()

scatter = hv.Scatter(station_info, 'services', 'ridership')
scatter

print(scatter)

layout = scatter + hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened'])
layout

print(layout)

taxi_dropoffs = {hour:arr for hour, arr in np.load('../assets/hourly_taxi_data.npz').items()}
print('Hours: {hours}'.format(hours=', '.join(taxi_dropoffs.keys())))
print('Taxi data contains {num} arrays (one per hour).\nDescription of the first array:\n'.format(num=len(taxi_dropoffs)))
np.info(taxi_dropoffs['0'])

bounds = (-74.05, 40.70, -73.90, 40.80)
image = hv.Image(taxi_dropoffs['0'], ['lon','lat'], bounds=bounds)

points = hv.Points(station_info, ['lon','lat']).opts(color="red")
image + image * points

dictionary = {int(hour):hv.Image(arr, ['lon','lat'], bounds=bounds) 
              for hour, arr in taxi_dropoffs.items()}
hv.HoloMap(dictionary, kdims='Hour')

holomap = hv.HoloMap(dictionary, kdims='Hour')
print(holomap)

holomap.select(Hour={3,6,9}).layout()

hotspot = points.select(lon=(-73.99, -73.96), lat=(40.75,40.765))
composition = holomap * hotspot

composition.opts(
    opts.Image(xrotation=90),
    opts.Points(color='red', marker='v', size=6))

composition.select(Hour=7)

hotspot.data

hv.save(composition, 'holomap.html')

hv.save(composition.last, 'image.html')