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



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



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# add the sccript path to opengrid to sys.path
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append(os.path.join(script_dir, os.pardir, os.pardir))

from opengrid.library.config import Config

Get Forecast.io API key from config file



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config = Config()

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

Import API wrapper module



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from opengrid.library import forecastwrapper

Get weather data in daily resolution

Daily resolution has the option of adding degree days. By default, 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 heatingDegreeDays or coolingDegreeDays and supplying heatingBaseTemps and/or coolingBaseTemps as a list (you can add multiple base temperatures, or just a list of 1 element)



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



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WD = forecastwrapper.Weather_Days(api_key=api_key, location='Ukkel', start=start, end=end)



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WD.df



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WD2 = forecastwrapper.Weather_Days(api_key=api_key, location='Ukkel', start=start, end=end, heatingDegreeDays=True, heatingBaseTemps=[15,18], coolingDegreeDays=True, coolingBaseTemps=[18,24])



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WD2.df

Get weather data in hourly resolution



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WHGent = forecastwrapper.Weather_Hours(api_key=api_key, location='Gent', 
                                   start=pd.Timestamp('20150916'), end=pd.Timestamp('20150918'))
WHBoutersem = forecastwrapper.Weather_Hours(api_key=api_key, location='Kapelstraat 1, 3370 Boutersem', 
                                   start=pd.Timestamp('20150916'), end=pd.Timestamp('20150918'))



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df_combined = pd.merge(WHGent.df, WHBoutersem.df, suffixes=('_Gent', '_Boutersem'), 
                           left_index=True, right_index=True)



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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 the dataframe localized to a specified timezone.



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



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WDLondon = forecastwrapper.Weather_Days(api_key=api_key, location='London', start=start, end=end, tz='Asia/Singapore')
WDBrussels = forecastwrapper.Weather_Days(api_key=api_key, location='Brussels', start=start, end=end, tz='Asia/Singapore')



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WDLondon.df



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WDBrussels.df



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