# crop growth dates/times and coefficients crop = 'corn' plant = datetime.datetime(2000, 4, 15) emergence = 30 growth = 50 full = 60 late = 40 Ki = 0.30 Km = 1.15 Kl = 0.40 # add the information to the etcalculator and calculate the PET time series calculator.add_crop(crop, plant, emergence, growth, full, late, Ki, Km, Kl) calculator.hourly_PET(crop, start, end) # crop growth dates/times and coefficients crop = 'soybeans' plant = datetime.datetime(2000, 5, 15) emergence = 20 growth = 30 full = 60 late = 30 Ki = 0.40 Km = 1.15 Kl = 0.55 # add the information to the etcalculator and calculate the PET time series
calculator.add_crop(crop, plant, emergence, growth, full, late, Ki, Km, Kl) # calculate the daily crop coefficient time series from the start to the end calculator.calculate_daily_crop(crop, start, end) # the calculator stores all daily crop coefficient time series in a dictionary # structure with keys by crop name and values as start datetime and data start, Kc = calculator.dailyKcs[crop] # the calculator can extend the reference ET timeseries and the crop coefficient # to estimate crop-specific PET time series calculator.hourly_PET(crop, start, end) # the calculator stores all the hourly crop coefficient time series in a # dictionary structure with crop name as keys and start and data as values start, PET = calculator.hourlyPETs[crop] # aggregate the hourly to daily values PET = [sum(PET[i : i + 24]) for i in range(0, len(PET), 24)] from matplotlib import pyplot, dates, ticker fig = pyplot.figure() sub = pyplot.subplot2grid((5, 1), (0, 0))
