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.daily_PET(crop, start, end) # the calculator stores the daily PET timeseries in a dictionary structure with # keys as the crop names and values of the start datetime and data start, PET = calculator.dailyPETs[crop] # plot the output from matplotlib import pyplot, dates, ticker fig = pyplot.figure(figsize=(8, 8)) sub = pyplot.subplot2grid((5, 1), (0, 0)) # make a 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.daily_PET(crop, start, end) # the calculator stores the daily PET timeseries in a dictionary structure with # keys as the crop names and values of the start datetime and data start, PET = calculator.dailyPETs[crop] # plot the output from matplotlib import pyplot, dates, ticker fig = pyplot.figure(figsize = (8,8)) sub = pyplot.subplot2grid((5,1), (0,0)) # make a time series
