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%matplotlib inline

import datetime
import matplotlib.pyplot as plt
import numpy as np

from IPython.display import display


    

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# Load code to setup authorization for an IPython Notebook. Note that this is a temporary step
# that is required until the Earth Engine Python API is updated to include this logic.
%run 'authorize_earth_engine_in_notebook.ipynb'

# Initialize Earth Engine
# Note that we are calling a function defined in the previously run IPython Notebook, rather than
# the typical call to ee.Initialize()
ee_initialize()


    

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%run 'define_google_maps_interactive_widget.ipynb'


    

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map = GoogleMapsWidget(lat=70, lng=-140.0, zoom=6)
display(map)

image1 = ee.Image('LC81412332013146LGN00')
vis_params1 = {'bands':'B4,B3,B2','min':5000,'max':10000}
map.addLayer(image=image1, vis_params=vis_params1, name='Night')

image2 = ee.Image('LC80660112013132LGN01')
vis_params2 = {'bands':'B4,B3,B2','min':0,'max':40000}
map.addLayer(image=image2, vis_params=vis_params2, name='Day', visible=False)


    

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(map.lat, map.lng, map.zoom, map.bounds)


    

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lng, lat = (-120.6, 40.0)
map2 = GoogleMapsWidget(lat=lat, lng=lng, zoom=6)
display(map2)

# Add an Image Collection of Landsat 8 data to the map.
collection = (ee.ImageCollection('LC8')
                .filterDate('2014-01-01', '2014-06-01')
                .filterMetadata('SUN_ELEVATION', 'greater_than', 0)
             )
vis_params3 = {'bands': 'B4,B3,B2', 'min':5000, 'max':30000, 'gamma': 1.6}
map2.addLayer(image=collection.mosaic(), vis_params=vis_params3, name='Landsat 8 mosaic')
map2.addLayer(image=collection.median(), vis_params=vis_params3, name='Landsat 8 median', visible=False)


    

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# Get the coordinates of the center of the map.
(map2.lng, map2.lat)


    

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point = ee.Geometry.Point(map2.lng, map2.lat);
info = collection.getRegion(point,500).getInfo()


    

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xBand = 'time'
yBandList = ['B4','B5']
# extract the header column names
header = info[0]
# create a Numpy array of the data
data = np.array(info[1:])
# extract the time information
iTime = header.index(xBand)
# convert to Python datetime objects
time = [datetime.datetime.fromtimestamp(i/1000) for i in (data[0:,iTime].astype(int))]
# Extract the data columns we want to plot.
iBands = [header.index(b) for b in yBandList]
yData = data[0:,iBands].astype(np.float)


    

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red = yData[:,0]
nir = yData[:,1]
ndvi = (nir - red) / (nir + red)


    

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fig, ax = plt.subplots(2, sharex=True, figsize=(10,10))
ax[0].plot(time, red, 'o', color="red", label="RED band")
ax[0].plot(time, nir, 'o', color="blue",  label="NIR band")
ax[0].set_ylabel('Band Values')
ax[0].legend(loc='best')
ax[1].plot(time, ndvi, 'o', color="black", label="NDVI")
ax[1].set_ylabel('NDVI')
ax[1].legend(loc='best')


    

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