1+ import numpy as np
2+ import pandas as pd
3+
4+ def prepare_gdd (weather_df , sim_start , sim_end , gdd , crop , sum_fun ):
5+ """
6+ function to read in GDD and crop data to return
7+ mean or median GDD crop growth stages based on all seasons
8+ in the simulation period
9+ Arguments:
10+ weather_df(DataFrame): weather data across entire simulation timespan
11+
12+ sim_start (str): simulation start date
13+
14+ sim_end (str): simulation end date
15+
16+ gdd (numeric vector): daily gdd values across entire simulation time
17+
18+ crop (Crop object): Crop object containing crop parameters
19+
20+ sum_fun (str): 'mean' or 'median', select method of summarising
21+ multiple years of GDD growth stages calculations
22+ Returns:
23+ crop (Crop object): updated crop object with new GDD growth stages
24+ calculated based on entire simulation period
25+ """
26+ # convert str to datetime
27+ sim_start_date = pd .to_datetime (sim_start )
28+ sim_end_date = pd .to_datetime (sim_end )
29+
30+ # add gdd as column
31+ assert len (gdd ) == len (weather_df ), "The length of 'gdd' does not match the number of rows in 'weather_df', check planting date is on or after simulation start date in first year."
32+ weather_df ['gdd' ]= gdd
33+
34+ # Convert mm/dd formatted dates to datetime objects
35+ def parse_mmdd_to_datetime (mmdd_date , year ):
36+ mm , dd = map (int , mmdd_date .split ('/' ))
37+ return pd .to_datetime (f'{ year } { mm :02d} { dd :02d} )
38+
39+ # Initialize the season counter
40+ season_counter = 1
41+
42+ current_year = sim_start_date .year
43+ planting_date = crop .planting_date
44+
45+ # Determine the first planting date
46+ first_planting_date = parse_mmdd_to_datetime (planting_date , current_year )
47+ if first_planting_date < sim_start_date :
48+ current_year += 1
49+ first_planting_date = parse_mmdd_to_datetime (planting_date , current_year )
50+
51+ # Loop through each year to assign seasons
52+ while first_planting_date <= sim_end_date :
53+ # Determine the end date of the season (a day before the next planting date)
54+ next_planting_date = parse_mmdd_to_datetime (planting_date , current_year + 1 )
55+ season_end_date = next_planting_date - pd .Timedelta (days = 1 )
56+
57+ # If the season end date is beyond the simulation end date, truncate it
58+ if season_end_date > sim_end_date :
59+ season_end_date = sim_end_date
60+
61+ # Update the 'season' column for the current season
62+ weather_df .loc [(weather_df ['Date' ] >= first_planting_date ) & (weather_df ['Date' ] <= season_end_date ), 'season' ] = season_counter
63+
64+ # Increment the season counter
65+ season_counter += 1
66+
67+ # Move to the next year
68+ current_year += 1
69+ first_planting_date = next_planting_date
70+
71+ # List of growth stages
72+ growth_stages = [
73+ 'Emergence' , 'Canopy10Pct' , 'MaxRooting' , 'MaxCanopy' , 'CanopyDevEnd' ,
74+ 'Senescence' , 'Maturity' , 'HIstart' , 'HIend' , 'YieldFormation'
75+ ]
76+ if crop .CropType == 3 :
77+ growth_stages .extend (['FloweringEnd' , 'FloweringDuration' ])
78+
79+ # Create dictionary of lists to store yearly GDD values for each growth stage
80+ # i.e. create as many lists as there are elements in 'growth_stages',
81+ # named with the values in 'growth_stages' in a dictionary
82+ gdd_lists = {f'{ stage } : [] for stage in growth_stages }
83+
84+ # get list of season numbers to iterate across
85+ seasons = weather_df ['season' ].unique ()
86+
87+ # iterate across seasons, calculating GDD to each CD growth stage
88+ # per season and storing in the gdd_lists lists
89+ for season in seasons :
90+ # filter to current season
91+ season_data = weather_df [weather_df ['season' ]== season ]
92+
93+ # get cumulative GDD for current season
94+ gdd_cum = np .cumsum (season_data ['gdd' ])
95+
96+ # Find GDD equivalent for each crop calendar day growth stage
97+ gdd_lists ['Emergence' ].append (gdd_cum .iloc [int (crop .EmergenceCD )])
98+ gdd_lists ['Canopy10Pct' ].append (gdd_cum .iloc [int (crop .Canopy10PctCD )])
99+ gdd_lists ['MaxRooting' ].append (gdd_cum .iloc [int (crop .MaxRootingCD )])
100+ gdd_lists ['MaxCanopy' ].append (gdd_cum .iloc [int (crop .MaxCanopyCD )])
101+ gdd_lists ['CanopyDevEnd' ].append (gdd_cum .iloc [int (crop .CanopyDevEndCD )])
102+ gdd_lists ['Senescence' ].append (gdd_cum .iloc [int (crop .SenescenceCD )])
103+ gdd_lists ['Maturity' ].append (gdd_cum .iloc [int (crop .MaturityCD )])
104+ gdd_lists ['HIstart' ].append (gdd_cum .iloc [int (crop .HIstartCD )])
105+ gdd_lists ['HIend' ].append (gdd_cum .iloc [int (crop .HIendCD )])
106+ gdd_lists ['YieldFormation' ].append (crop .HIend - crop .HIstart )
107+
108+ # Duration of flowering (gdd's) - (fruit/grain crops only)
109+ if crop .CropType == 3 :
110+ flowering_end = gdd_cum .iloc [int (crop .FloweringEndCD )]
111+ # gdd's from sowing to end of flowering
112+ gdd_lists ['FloweringEnd' ].append (flowering_end )
113+ # Duration of flowering (gdd's)
114+ gdd_lists ['FloweringDuration' ].append (flowering_end - crop .HIstart )
115+
116+ # calculate mean/median of GDD growth stages using dictionary logic,
117+ # set the attribute to update the crop object
118+ if sum_fun == 'mean' :
119+ for stage , values in gdd_lists .items ():
120+ setattr (crop , stage , np .mean (values ))
121+ elif sum_fun == 'median' :
122+ for stage , values in gdd_lists .items ():
123+ setattr (crop , stage , np .median (values ))
124+
125+ return crop
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