What's new in the Forecastwrapper

• No more seperate objects Weather_Days and Weather_Hours, just Weather

• The Weather object has methods days() and hours() which return a dataframe in the desired resolution. (We chose this approach because now you only need 1 call to forecast.io to get both resolutions, in V1 you needed 2 calls if you wanted both)

• More verbose and pythonian named arguments. Eg. the flag heatingDegreeDays is now called include_heating_degree_days

• The temperature equivalent is now by default included in the daily dataframe. It can be excluded by using the flag include_temperature_equivalent = False

Demo of the forecast.io wrapper to get past and future weather data

Important: you need to register for an apikey here: https://developer.forecast.io/ Put the key you obtain in the opengrid.cfg file as follows:

[Forecast.io]
apikey: your_key



In [ ]:

    
import os
import sys
import inspect
import pandas as pd
import charts

from opengrid import config

Get Forecast.io API key from config file



In [ ]:

    
config = config.Config()

#get Forecast.io API Key
api_key = config.get('Forecast.io', 'apikey')

Import API wrapper module



In [ ]:

    
from opengrid.library import forecastwrapper

Get weather data in daily and hourly resolution

To get started, create a Weather object for a certain location and a period



In [ ]:

    
start = pd.Timestamp('20150813')
end = pd.Timestamp('20150816')



In [ ]:

    
Weather_Ukkel = forecastwrapper.Weather(api_key=api_key, location='Ukkel', start=start, end=end)

You can use the methods days() and hours() to get a dataframe in daily or hourly resolution



In [ ]:

    
Weather_Ukkel.days().info()



In [ ]:

    
Weather_Ukkel.hours().info()

Degree Days

Daily resolution has the option of adding degree days. By default, the temperature equivalent and heating degree days with a base temperature of 16.5°C are added.

Heating degree days are calculated as follows:

$$heatingDegreeDays = max(0 , baseTemp - 0.6 * T_{today} + 0.3 * T_{today-1} + 0.1 * T_{today-2} )$$

Cooling degree days are calculated in an analog way:

$$coolingDegreeDays = max(0, 0.6 * T_{today} + 0.3 * T_{today-1} + 0.1 * T_{today-2} - baseTemp )$$

Add degree days by setting the flag include_heating_degree_days or include_cooling_degree_days and supplying heating_base_temperatures and/or cooling_base_temperatures as a list (you can add multiple base temperatures, or just a list of 1 element)

Get some more degree days



In [ ]:

    
Weather_Ukkel.days(include_heating_degree_days = True,
                   heating_base_temperatures = [15,18],
                   include_cooling_degree_days = True,
                   cooling_base_temperatures = [18,24]).filter(like='DegreeDays')

Daytime Averages

Daily resolution has the option of adding average values during the daytime only (between sunrise and sunset). Implemented are daytimeCloudCover and daytimeTemperature, and are included by default. They can be excluded by setting include_daytime_cloud_cover = False or include_daytime_temperature = False.

By default, daily weather data is extended with daytimeCloudCover and daytimeTemperature. These are the averaged cloud cover and temperature between sunrise and sunset.



In [ ]:

    
Weather_Ukkel.days().filter(like='daytime')

Hourly resolution example

Location can also be coördinates



In [ ]:

    
start = pd.Timestamp('20150916')
end = pd.Timestamp('20150918')
Weather_Brussel = forecastwrapper.Weather(api_key=api_key, location=[50.8503396, 4.3517103], start=start, end=end)
Weather_Boutersem = forecastwrapper.Weather(api_key=api_key, location='Kapelstraat 1, 3370 Boutersem', start=start, end=end)



In [ ]:

    
df_combined = pd.merge(Weather_Brussel.hours(), Weather_Boutersem.hours(), suffixes=('_Brussel', '_Boutersem'), 
                       left_index=True, right_index=True)



In [ ]:

    
charts.plot(df_combined.filter(like='cloud'), stock=True, show='inline')

Deal with different timezones

By default, all dataframes are localized to the timezone of the specified location. If you want, you can specify the tz argument to get all dataframes localized to a specified timezone.



In [ ]:

    
start = pd.Timestamp('20150815')
end = pd.Timestamp('20150816')



In [ ]:

    
Weather_London = forecastwrapper.Weather(api_key=api_key, location='London', start=start, end=end, tz='Asia/Singapore')
Weather_Brussels = forecastwrapper.Weather(api_key=api_key, location='Brussels', start=start, end=end, tz='Asia/Singapore')



In [ ]:

    
Weather_London.days()



In [ ]:

    
Weather_Brussels.days()



In [ ]: