# now add the hourly time series to the calculator
calculator.add_timeseries('temperature', 'hourly', start, hourlytemps)
calculator.add_timeseries('dewpoint', 'hourly', start, hdewt)
calculator.add_timeseries('wind', 'hourly', start, hwind)
# the solar radiation data from NSRDB are already hourly
calculator.add_timeseries('solar', 'hourly', start, solar)
# calculate the reference evapotranspiration (RET) time series from the hourly
# and daily Penman-Monteith Equation
calculator.penman_hourly(start, end)
calculator.penman_daily(start, end)
# create pointers to the time series in the etcalculator's dictionaries
start, RET = calculator.hourly['RET']
start, dRET = calculator.daily['RET']
# aggregate the hourly back to daily
hRET = [sum(RET[i:i + 24]) for i in range(0, len(RET), 24)]
# plot up the results (note that there are no observations over the winter)
from matplotlib import pyplot, dates, ticker
fig = pyplot.figure(figsize=(8, 10))
# get the areal-weighted averages lon = sum([a * x for a, x in zip(areas, xs)]) / sum(areas) lat = sum([a * y for a, y in zip(areas, ys)]) / sum(areas) elev = sum([a * z for a, z in zip(areas, zs)]) / sum(areas) # add the information to the calculator calculator.add_location(lon, lat, elev) # it is pretty trivial to get the corresponding reference evapotranspiration # (RET) time series from the Daily Penman-Monteith Equation if the necessary # data are available by calling the public "penman_daily" method calculator.penman_daily(start, end) # the RET estimates are stored in the calculator's daily timeseries dictionary # with the start date and data (the timestep is 1440 minutes = 1 day) start, RET = calculator.daily['RET'] # calculate the linear regression between the Penman-Monteith model and the # observed pan evaporation using scipy from scipy import stats # plot up the results (note that there are no observations over the winter) from matplotlib import pyplot, dates, ticker
