Yield computation

compute_yield.py

@@ -1,4 +1,7 @@
+import pandas as pd
+
 from forecast import get_forecast_data
+from retrieve_coefs_max_yield import get_coefs_Kc_Ky_and_max_yield
 from utils.soil_utils import get_soil_properties
 
 
@@ -52,20 +55,42 @@ def calculate_yield_projection(Yx, ETx, ETa, Ky):
     Returns:
     float: Projected yield (quintal/ha)
     """
+
     Ya = Yx * (1 - Ky * (1 - ETa / ETx))
+    Ya.loc[ETx == 0] = 0
+
     return round(Ya, 2)
 
 
-def get_yield_forecast(latitude: float, longitude: float, scenario: str = "pessimist", shading_coef: float = 0.):
+def add_cultural_coefs(monthly_forecast: pd.DataFrame, cultural_coefs: pd.DataFrame) -> pd.DataFrame:
+    monthly_forecast["Kc"] = 0
+    monthly_forecast["Ky"] = 0
+    for month in range(1, 13):
+        Kc = cultural_coefs["Kc"][cultural_coefs.Mois == month].iloc[0]
+        Ky = cultural_coefs["Ky"][cultural_coefs.Mois == month].iloc[0]
+        monthly_forecast.loc[(monthly_forecast.month==month).to_numpy(), "Kc"] = Kc
+        monthly_forecast.loc[(monthly_forecast.month==month).to_numpy(), "Ky"] = Ky
+    return monthly_forecast
+
+
+def compute_yield_forecast(
+        latitude: float,
+        longitude: float,
+        culture: str = "Colza d'hiver",
+        region: str = "Bourgogne-Franche-Comté",
+        scenario: str = "pessimist",
+        shading_coef: float = 0.,
+):
     monthly_forecast = get_forecast_data(latitude, longitude, scenario=scenario, shading_coef=shading_coef)
 
-    Kc, Ky = get_cultural_coefficients()
-
-    Yx = get_maximum_theoretical_yield()
+    cultural_coefs, max_yield = get_coefs_Kc_Ky_and_max_yield(culture, region)
+    monthly_forecast = add_cultural_coefs(monthly_forecast, cultural_coefs)
+    Kc = monthly_forecast["Kc"]
+    Ky = monthly_forecast["Ky"]
 
     soil_properties = get_soil_properties(latitude, longitude)
 
-    ETo = monthly_forecast["et0"]
+    ETo = monthly_forecast["Evaporation (including sublimation and transpiration) (kg m-2 s-1)"]
 
     ETx = calculate_ETx(Kc, ETo)
 
@@ -76,6 +101,34 @@ def get_yield_forecast(latitude: float, longitude: float, scenario: str = "pessi
         soil_properties["wilting_point"],
     )
 
-    projected_yield = calculate_yield_projection(Yx, ETx, ETa, Ky)
+    projected_yield = calculate_yield_projection(
+        Yx=max_yield,
+        ETx=ETx,
+        ETa=ETa,
+        Ky=Ky)
+    monthly_forecast["Estimated yield (quintal/ha)"] = projected_yield
+
+    return monthly_forecast
+
+
+def get_annual_yield(monthly_forecast: pd.DataFrame) -> pd.Series:
+    yield_forecast = pd.Series(
+        index=monthly_forecast["time"],
+        data=monthly_forecast["Estimated yield (quintal/ha)"].to_numpy(),
+    )
+    yield_forecast = yield_forecast.resample("1YE").mean()
+    return yield_forecast
+
+
+if __name__ == '__main__':
+    monthly_forecast = compute_yield_forecast(
+        latitude=47,
+        longitude=5,
+        culture="Colza d'hiver",
+        scenario="pessimist",
+        shading_coef=0.,
+    )
+    print(monthly_forecast.head())
 
-    return projected_yield
+    yield_forecast = get_annual_yield(monthly_forecast)
+    print(yield_forecast)

forecast.py

@@ -128,6 +128,7 @@ def preprocess_forectast_data(df: pd.DataFrame, latitude, longitude, shading_coe
 
     # Convert 'time' to datetime and calculate Julian day
     preprocessed_data['time'] = pd.to_datetime(preprocessed_data['time'], errors='coerce')
+    preprocessed_data['month'] = preprocessed_data['time'].dt.month
     preprocessed_data['day_of_year'] = preprocessed_data['time'].dt.dayofyear
 
     # Compute ET0

utils/soil_utils.py

@@ -1,3 +1,6 @@
+import os
+from pathlib import Path
+
 import geopandas as gpd
 from dotenv import load_dotenv 
 from geopy.geocoders import Nominatim
@@ -5,7 +8,8 @@ from shapely.geometry import Point
 
 load_dotenv()
 
-file_rmqs ='data/soil_data/raw_data/rmqs.geojson'
+project_root = Path(__file__).parent.parent
+file_rmqs = os.path.join(project_root, 'data/soil_data/raw_data/rmqs.geojson')
 df = gpd.read_file(file_rmqs)
 
 def get_city_coordinates(city_name):
@@ -53,7 +57,7 @@ def get_soil_properties(latitude, longitude):
         "silt": silt,
         "sand": sand,
         "soc": soc,
-        "soil_mosture": soil_mosture,
+        "soil_moisture": soil_mosture,
         "field_capacity": field_capacity,
         "wilting_point": wilting_point,
     }