fix yield computation

compute_yield.py

@@ -1,4 +1,5 @@
 import pandas as pd
+from matplotlib import pyplot as plt
 
 from forecast import get_forecast_data
 from retrieve_coefs_max_yield import get_coefs_Kc_Ky_and_max_yield
@@ -19,29 +20,27 @@ def calculate_ETx(Kc, ETo):
     ETx = Kc * ETo
     return ETx
 
-def calculate_ETa(ETx, soil_moisture, field_capacity, wilting_point, water_deficit):
+
+def calculate_ETa(ETx, soil_moisture, field_capacity, wilting_point, water_deficit, ETo):
     """
     Calculate the actual evapotranspiration (ETa) using the maximum evapotranspiration (ETx), soil moisture, field capacity, and wilting point.
 
     Parameters:
     ETx (float): Maximum evapotranspiration (mm)
-    soil_moisture (float): Current soil moisture content (%)
+    soil_moisture (Series): Current soil moisture content (%)
     field_capacity (float): Field capacity of the soil (%)
     wilting_point (float): Wilting point of the soil (%)
 
     Returns:
     float: Actual evapotranspiration (ETa) in mm
     """
-    if soil_moisture > field_capacity:
-        ETa = ETx
-    elif soil_moisture < wilting_point:
-        ETa = 0
-    else:
-        WA = (field_capacity−wilting_point) * root_depth # wter availaible in the root zone
-        root_depth = 0.3
-        Ks = ( WA - water_deficit)/ WA # coef de stress hydrique
-        ETa = ETx * Ks
-    
+    Ks = 1 - (water_deficit / (2 * ETo)) # coef de stress hydrique = precipitation / et0
+    Ks = Ks.clip(lower=0, upper=1)
+    ETa = ETx * Ks
+
+    ETa.loc[soil_moisture > field_capacity] = ETx.loc[soil_moisture > field_capacity]
+    ETa.loc[soil_moisture < wilting_point] = 0
+
     return ETa
 
 
@@ -93,15 +92,17 @@ def compute_yield_forecast(
 
     soil_properties = get_soil_properties(latitude, longitude)
 
-    ETo = monthly_forecast["Evaporation (including sublimation and transpiration) (kg m-2 s-1)"]
+    ETo = monthly_forecast["Evaporation (mm/day)"]
 
     ETx = calculate_ETx(Kc, ETo)
 
     ETa = calculate_ETa(
         ETx,
-        soil_properties["soil_moisture"],
+        monthly_forecast["Moisture in Upper Portion of Soil Column (kg m-2)"],
         soil_properties["field_capacity"],
         soil_properties["wilting_point"],
+        water_deficit=monthly_forecast["Water Deficit (mm/day)"],
+        ETo=ETo,
     )
 
     projected_yield = calculate_yield_projection(
@@ -124,14 +125,34 @@ def get_annual_yield(monthly_forecast: pd.DataFrame) -> pd.Series:
 
 
 if __name__ == '__main__':
+    culture = "Colza d'hiver"
+    scenario = "pessimist"
+    shading_coef = 0.2
     monthly_forecast = compute_yield_forecast(
         latitude=47,
         longitude=5,
-        culture="Colza d'hiver",
-        scenario="pessimist",
+        culture=culture,
+        scenario=scenario,
         shading_coef=0.,
     )
-    print(monthly_forecast.head())
+    # print(monthly_forecast.head())
 
     yield_forecast = get_annual_yield(monthly_forecast)
-    print(yield_forecast)
+    # print(yield_forecast)
+
+    monthly_forecast_with_shading = compute_yield_forecast(
+        latitude=47,
+        longitude=5,
+        culture=culture,
+        scenario=scenario,
+        shading_coef=shading_coef,
+    )
+    # print(monthly_forecast_with_shading.head())
+
+    yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
+    # print(yield_forecast)
+
+    plt.plot(yield_forecast.rolling(5).mean(), label="No shading")
+    plt.plot(yield_forecast_with_shading.rolling(5).mean(), label="20% Shading")
+    plt.legend()
+    plt.show()

forecast.py

@@ -133,15 +133,15 @@ def preprocess_forectast_data(df: pd.DataFrame, latitude, longitude, shading_coe
 
     # Compute ET0
     et0 = compute_et0(preprocessed_data, latitude, longitude)
-    preprocessed_data['Evaporation (mm/day)'] = et0
+    preprocessed_data['Evaporation (mm/day)'] = et0.clip(lower=0)
 
     # Convert Precipitation from kg/m²/s to mm/day
     preprocessed_data['Precipitation (mm/day)'] = 86400 * preprocessed_data['Precipitation (kg m-2 s-1)']
 
     # Calculate Water Deficit: Water Deficit = ET0 - P + M
     preprocessed_data['Water Deficit (mm/day)'] = (
-        (preprocessed_data['Evaporation (mm/day)'] - (preprocessed_data['Precipitation (mm/day)']) +
-         preprocessed_data['Moisture in Upper Portion of Soil Column (kg m-2)'])
+        preprocessed_data['Evaporation (mm/day)'] - preprocessed_data['Precipitation (mm/day)']
+        # + preprocessed_data['Moisture in Upper Portion of Soil Column (kg m-2)'])
     )
 
     return preprocessed_data