# I had to use this directive to show the matplot lib plots within Jupyter.
%matplotlib inline 
import certifi
from datetime import datetime,timedelta
import json
import matplotlib.pyplot as plt
import numpy as np
import urllib3

# Use the darksky weather service to get today's forecast.  
# The 24 hour clock is used.  We pull out the probability of rain, the intensity of
# rain that will fall if it does rain, the cloud cover, and the temperature into a prediction_list.
def get_weather_forecast():
    _NUMBER_OF_HOURS = 15
    '''
    Use the darksky weather service to get today's forecast.  
    The 24 hour clock is used.  We pull out the probability of rain, the intensity of
    rain that will fall if it does rain, the cloud cover, and the temperature into a prediction_list.
    '''
    http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED',ca_certs=certifi.where())
    url = "https://api.darksky.net/forecast/<your info here>"
    request = http.request('GET',url)
    # Try a five times in case there is a failure to connect
    for i in range(5):
        try:
            weather_prediction = json.loads(request.data.decode('utf8'))
        except HTTPError as e:
            weather_prediction = None
        else:
            prediction_data = weather_prediction['hourly']['data']
            first_hour = prediction_data[0]['time']
            # Just predict for _NUMBER_OF_HOURS
            last_hour = datetime.fromtimestamp(first_hour) + timedelta(hours=_NUMBER_OF_HOURS)
            prediction_list = []
            for hour_data in prediction_data:
                hour_as_datetime = datetime.fromtimestamp(hour_data['time'])
                int_hour = int(hour_as_datetime.strftime('%H'))
                if hour_as_datetime >= last_hour or int_hour == 0:
                    return prediction_list
                prediction_list.append([int_hour,hour_data['precipIntensity'],hour_data['precipProbability'],
                                       hour_data['cloudCover'],hour_data['temperature']])
           
    

def plot_weather(prediction_list):
    '''
    Make a four plots: hour of the day (x-axis) vs probability of rain, intensity of rain,
    cloud cover, and temperature.  Each reading will be in it's own graph.
    
    store in PNG file.
    '''
    # Set up the numpy array.
    array = np.array(prediction_list)
    # Set up the x-axis. The array's columns = hour, intensity, probability, cloud_cover, temperature.
    # The first hour is at location [0,0] and the last hour with readings is at the end of the first
    # column at [-1,0]
    start_hour = array[0,0].astype(int)
    end_hour = array[-1,0].astype(int)
    x = np.linspace(start_hour,end_hour,num=len(array))
    # set up the x-axis and y-axis data.
    hour = array[:,0].astype('int')
    intensity = array[:,1]
    probability = array[:,2]
    cloud_cover = array[:,3]
    temperature = array[:,4]
    # make room for 4 subplots.  We picked a size that looks good when the plot is saved as a png.
    fig, (ax1,ax2,ax3,ax4) = plt.subplots(nrows=4,figsize=(6,7))
    # The first plot will be the probability of rain.
    ax1.set_ylim([0,1])
    ax1.plot(hour,probability)
    ax1.set_ylabel('probability')
    ax1.set_title('Probability of rain',fontsize = 14)
    # The second plot will be the expected intensity of rain (assuming it rains at all).
    ax2.set_ylim([0,1])
    ax2.set_title('Rain Intensity',fontsize = 14)
    ax2.set_ylabel('intensity')
    ax2.plot(hour,intensity)
    # Plot the cloud cover
    ax3.set_ylim([0,1])
    ax3.set_title('Cloud Cover',fontsize = 14)
    ax3.set_ylabel('cloud cover')
    ax3.plot(hour,cloud_cover)
    # Plot the temperature
    ax4.set_xlabel('Hour (24 hour clock)',fontsize = 16)
    ax4.set_title('Temperature',fontsize = 14)
    ax4.set_ylabel('temperature')
    # Set the y - axis to fit the data within 10's.
    y_min = np.amin(temperature)
    rem = y_min % 10
    y_min -= rem
    y_max = np.amax(temperature)
    rem = y_max % 10
    y_max += (10-rem)
    ax4.set_ylim(y_min,y_max)
    ax4.plot(hour,temperature)
    # plt.tight_layout()
    # Adjust the layout
    fig.subplots_adjust(hspace=1)
    # Save as a png
    fig.savefig('/<your path>/weather.png')
    # Fun to see...just checking...
    plt.show()
    plt.close(fig)

# Make a plot of the weather and save into png.
prediction_list = get_weather_forecast()
plot_weather(prediction_list)

# Evenly spaced ticks on x axis labeled 0 to 24 for 24 hour clock.
x = np.linspace(0,24)
# Chance of precipitation for each hour.
# We have the intensity and the probability
y = np.cumsum(np.random.randn(50))+6

fig, (ax,ax2) = plt.subplots(nrows=2, sharex=True)

extent = [x[0]-(x[1]-x[0])/2., x[-1]+(x[1]-x[0])/2.,0,1]
ax.imshow(y[np.newaxis,:], cmap="plasma", aspect="auto", extent=extent)
ax.set_yticks([])
ax.set_xlim(extent[0], extent[1])

ax2.plot(x,y)

plt.tight_layout()
plt.show()

import numpy as np; np.random.seed(1)
a = np.array([[1,2,3], [4,5,6]])
print(a)
print(np.cumsum(a))
random = np.random.randn(50)
print(random)
y = np.cumsum(random) + 6
print(y)