pipeline to retrieve soil properties

compute_yield.py

@@ -1,27 +1,5 @@
-import json
-
-
-def calcul_metriques_sol(clay, silt, sand, soc):
-    """
-    Calcule le point de flétrissement (WP), l'humidité actuelle du sol (SM) et la capacité au champ (FC).
-
-    :param clay: Pourcentage d'argil dans le sol (%)
-    :param silt: Pourcentage de silt dans le sol (%)
-    :param sand: Pourcentage de sand dans le sol (%)
-    :param soc: Carbone organique du sol (%)
-    :return: WP, SM et FC (%)
-    """
-    # Calcul de la capacité au champ (FC)
-    FC = 0.2576 * silt + 0.2484 * clay + 0.3664 * soc + 20.83
-
-    # Calcul du point de flétrissement (WP)
-    WP = 0.026 * clay + 0.5 * soc + 3.0
-
-    # Calcul de l'humidité actuelle du sol (SM) comme 50% de la plage entre FC et WP
-    SM = WP + 0.5 * (FC - WP)
-
-    # Retourne les résultats sous forme de dictionnaire
-    return round(SM, 2), round(FC, 2), round(WP, 2)
+from forecast import get_forecast_data
+from utils.soil_utils import get_soil_properties
 
 
 def calculate_ETx(Kc, ETo):
@@ -78,17 +56,26 @@ def calculate_yield_projection(Yx, ETx, ETa, Ky):
     return round(Ya, 2)
 
 
+def get_yield_forecast(latitude: float, longitude: float, 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()
 
+    soil_properties = get_soil_properties(latitude, longitude)
+
+    ETo = monthly_forecast["et0"]
 
-if __name__ =="__main__":
-    # Exemple d'utilisation
-    Yx = 10000  # Rendement maximum en quintal/ha
-    Kc = 1.2  # Coefficient de culture
-    Ky = 1.25  # Facteur de réponse du rendement pour le maïs
-    soil_moisture, field_capacity, wilting_point = calcul_metriques_sol(clay=20, silt=40, sand=40, soc=1.5)
-    ETo = 5.0  # Evapotranspiration de référence en mm/jour
     ETx = calculate_ETx(Kc, ETo)
-    ETa = calculate_ETa(ETx, soil_moisture, field_capacity, wilting_point)
+
+    ETa = calculate_ETa(
+        ETx,
+        soil_properties["soil_moisture"],
+        soil_properties["field_capacity"],
+        soil_properties["wilting_point"],
+    )
 
     projected_yield = calculate_yield_projection(Yx, ETx, ETa, Ky)
-    print(f"Le rendement projeté est de {projected_yield} quintal/ha")
+
+    return projected_yield

utils/soil_utils.py

@@ -1,4 +1,3 @@
-import json
 import geopandas as gpd
 from dotenv import load_dotenv 
 from geopy.geocoders import Nominatim
@@ -6,7 +5,7 @@ from shapely.geometry import Point
 
 load_dotenv()
 
-file_rmqs ='data/soil_data/raw_data/rmqs.geojson'
+file_rmqs ='../data/soil_data/raw_data/rmqs.geojson'
 df = gpd.read_file(file_rmqs)
 
 def get_city_coordinates(city_name):
@@ -17,7 +16,7 @@ def get_city_coordinates(city_name):
         return (location.longitude, location.latitude)
     return None
 
-def find_nearest_point(city_name):
+def find_nearest_point_to_city(city_name):
     """Find the closest GPS point in the dataset to the given city."""
     city_coords = get_city_coordinates(city_name)
     print("city coords", city_coords)
@@ -32,8 +31,68 @@ def find_nearest_point(city_name):
     return closest_point  # Returns the closest feature
 
 
+def find_nearest_point_to_coordinates(latitude, longitude):
+    """Find the closest GPS point in the dataset to the given GPS coordinates."""
+    # Iterate through GeoJSON features
+    df["distance"] = df["geometry"].distance(Point(latitude, longitude))
+    closest_point = df.loc[df["distance"].idxmin()]
+
+    return closest_point  # Returns the closest feature
+
+
+def get_soil_properties(latitude, longitude):
+    closest_feature = find_nearest_point_to_coordinates(latitude, longitude)
+    clay = closest_feature.clay
+    silt = closest_feature.silt
+    sand = closest_feature.sand
+    soc = closest_feature.soc
+    soil_mosture, field_capacity, wilting_point = calcul_metriques_sol(clay, silt, sand, soc)
+
+    soil_properties = {
+        "clay": clay,
+        "silt": silt,
+        "sand": sand,
+        "soc": soc,
+        "soil_mosture": soil_mosture,
+        "field_capacity": field_capacity,
+        "wilting_point": wilting_point,
+    }
+
+    return soil_properties
+
+
+def calcul_metriques_sol(clay, silt, sand, soc):
+    """
+    Calcule le point de flétrissement (WP), l'humidité actuelle du sol (SM) et la capacité au champ (FC).
+
+    :param clay: Pourcentage d'argil dans le sol (%)
+    :param silt: Pourcentage de silt dans le sol (%)
+    :param sand: Pourcentage de sand dans le sol (%)
+    :param soc: Carbone organique du sol (%)
+    :return: WP, SM et FC (%)
+    """
+    # Calcul de la capacité au champ (FC)
+    FC = 0.2576 * silt + 0.2484 * clay + 0.3664 * soc + 20.83
+
+    # Calcul du point de flétrissement (WP)
+    WP = 0.026 * clay + 0.5 * soc + 3.0
+
+    # Calcul de l'humidité actuelle du sol (SM) comme 50% de la plage entre FC et WP
+    SM = WP + 0.5 * (FC - WP)
+
+    # Retourne les résultats sous forme de dictionnaire
+    return round(SM, 2), round(FC, 2), round(WP, 2)
+
+
 if __name__ == "__main__":
     # Example usage
     city = "Paris"
-    closest_feature = find_nearest_point(city)
+    closest_feature = find_nearest_point_to_city(city)
+    print(closest_feature)
+
+    latitude, longitude = 47, 5
+    closest_feature = find_nearest_point_to_coordinates(latitude, longitude)
     print(closest_feature)
+
+    soil_properties = get_soil_properties(latitude, longitude)
+    print(soil_properties)