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Merge pull request #158 from aquacropos/gdd_switch_type
Gdd switch type
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| def prepare_gdd(weather_df, sim_start, sim_end, gdd, crop, sum_fun): | ||
| """ | ||
| function to read in GDD and crop data to return | ||
| mean or median GDD crop growth stages based on all seasons | ||
| in the simulation period | ||
| Arguments: | ||
| weather_df(DataFrame): weather data across entire simulation timespan | ||
| sim_start (str): simulation start date | ||
| sim_end (str): simulation end date | ||
| gdd (numeric vector): daily gdd values across entire simulation time | ||
| crop (Crop object): Crop object containing crop parameters | ||
| sum_fun (str): 'mean' or 'median', select method of summarising | ||
| multiple years of GDD growth stages calculations | ||
| Returns: | ||
| crop (Crop object): updated crop object with new GDD growth stages | ||
| calculated based on entire simulation period | ||
| """ | ||
| # convert str to datetime | ||
| sim_start_date=pd.to_datetime(sim_start) | ||
| sim_end_date=pd.to_datetime(sim_end) | ||
|
|
||
| # add gdd as column | ||
| 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." | ||
| weather_df['gdd']=gdd | ||
|
|
||
| # Convert mm/dd formatted dates to datetime objects | ||
| def parse_mmdd_to_datetime(mmdd_date, year): | ||
| mm, dd = map(int, mmdd_date.split('/')) | ||
| return pd.to_datetime(f'{year}-{mm:02d}-{dd:02d}') | ||
|
|
||
| # Initialize the season counter | ||
| season_counter = 1 | ||
|
|
||
| current_year = sim_start_date.year | ||
| planting_date=crop.planting_date | ||
|
|
||
| # Determine the first planting date | ||
| first_planting_date = parse_mmdd_to_datetime(planting_date, current_year) | ||
| if first_planting_date < sim_start_date: | ||
| current_year += 1 | ||
| first_planting_date = parse_mmdd_to_datetime(planting_date, current_year) | ||
|
|
||
| # Loop through each year to assign seasons | ||
| while first_planting_date <= sim_end_date: | ||
| # Determine the end date of the season (a day before the next planting date) | ||
| next_planting_date = parse_mmdd_to_datetime(planting_date, current_year + 1) | ||
| season_end_date = next_planting_date - pd.Timedelta(days=1) | ||
|
|
||
| # If the season end date is beyond the simulation end date, truncate it | ||
| if season_end_date > sim_end_date: | ||
| season_end_date = sim_end_date | ||
|
|
||
| # Update the 'season' column for the current season | ||
| weather_df.loc[(weather_df['Date'] >= first_planting_date) & (weather_df['Date'] <= season_end_date), 'season'] = season_counter | ||
|
|
||
| # Increment the season counter | ||
| season_counter += 1 | ||
|
|
||
| # Move to the next year | ||
| current_year += 1 | ||
| first_planting_date = next_planting_date | ||
|
|
||
| # List of growth stages | ||
| growth_stages = [ | ||
| 'Emergence', 'Canopy10Pct', 'MaxRooting', 'MaxCanopy', 'CanopyDevEnd', | ||
| 'Senescence', 'Maturity', 'HIstart', 'HIend', 'YieldFormation' | ||
| ] | ||
| if crop.CropType == 3: | ||
| growth_stages.extend(['FloweringEnd', 'FloweringDuration']) | ||
|
|
||
| # Create dictionary of lists to store yearly GDD values for each growth stage | ||
| # i.e. create as many lists as there are elements in 'growth_stages', | ||
| # named with the values in 'growth_stages' in a dictionary | ||
| gdd_lists = {f'{stage}': [] for stage in growth_stages} | ||
|
|
||
| # get list of season numbers to iterate across | ||
| seasons=weather_df['season'].unique() | ||
|
|
||
| # iterate across seasons, calculating GDD to each CD growth stage | ||
| # per season and storing in the gdd_lists lists | ||
| for season in seasons: | ||
| # filter to current season | ||
| season_data = weather_df[weather_df['season']==season] | ||
|
|
||
| # get cumulative GDD for current season | ||
| gdd_cum=np.cumsum(season_data['gdd']) | ||
|
|
||
| # Find GDD equivalent for each crop calendar day growth stage | ||
| gdd_lists['Emergence'].append(gdd_cum.iloc[int(crop.EmergenceCD)]) | ||
| gdd_lists['Canopy10Pct'].append(gdd_cum.iloc[int(crop.Canopy10PctCD)]) | ||
| gdd_lists['MaxRooting'].append(gdd_cum.iloc[int(crop.MaxRootingCD)]) | ||
| gdd_lists['MaxCanopy'].append(gdd_cum.iloc[int(crop.MaxCanopyCD)]) | ||
| gdd_lists['CanopyDevEnd'].append(gdd_cum.iloc[int(crop.CanopyDevEndCD)]) | ||
| gdd_lists['Senescence'].append(gdd_cum.iloc[int(crop.SenescenceCD)]) | ||
| gdd_lists['Maturity'].append(gdd_cum.iloc[int(crop.MaturityCD)]) | ||
| gdd_lists['HIstart'].append(gdd_cum.iloc[int(crop.HIstartCD)]) | ||
| gdd_lists['HIend'].append(gdd_cum.iloc[int(crop.HIendCD)]) | ||
| gdd_lists['YieldFormation'].append(crop.HIend - crop.HIstart) | ||
|
|
||
| # Duration of flowering (gdd's) - (fruit/grain crops only) | ||
| if crop.CropType == 3: | ||
| flowering_end=gdd_cum.iloc[int(crop.FloweringEndCD)] | ||
| # gdd's from sowing to end of flowering | ||
| gdd_lists['FloweringEnd'].append(flowering_end) | ||
| # Duration of flowering (gdd's) | ||
| gdd_lists['FloweringDuration'].append(flowering_end - crop.HIstart) | ||
|
|
||
| # calculate mean/median of GDD growth stages using dictionary logic, | ||
| # set the attribute to update the crop object | ||
| if sum_fun == 'mean': | ||
| for stage, values in gdd_lists.items(): | ||
| setattr(crop, stage, np.mean(values)) | ||
| elif sum_fun == 'median': | ||
| for stage, values in gdd_lists.items(): | ||
| setattr(crop, stage, np.median(values)) | ||
|
|
||
| return crop |
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