added ombrage

visualize/visualize.py

@@ -9,7 +9,7 @@ from data_pipelines.historical_weather_data import (
 import os
 from forecast import get_forecast_data
 from compute_et0_adjusted import compute_et0
-
+import copy
 
 def water_deficit(df, latitude, longitude, shading_coef=0, historic=True):
     preprocessed_data = df.copy()
@@ -38,32 +38,26 @@ def water_deficit(df, latitude, longitude, shading_coef=0, historic=True):
         ]
     preprocessed_data["wind_speed"] = preprocessed_data["Near Surface Wind Speed (m/s)"]
 
-    # 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
     et0 = compute_et0(preprocessed_data, latitude, longitude)
     preprocessed_data["Evaporation (mm/day)"] = et0
     preprocessed_data["Evaporation (mm/day)"] = preprocessed_data[
         "Evaporation (mm/day)"
     ].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)"]
-        + 4.5
     )
-
     return preprocessed_data
 
 
@@ -151,7 +145,6 @@ def visualize_climate(
         )
 
     else:
-        # For other columns, continue with the line plot as before
         for condition_value in concatenated_moderate["period"].unique():
             segment = concatenated_moderate[
                 concatenated_moderate["period"] == condition_value
@@ -285,6 +278,7 @@ def generate_plots(
     pessimist: pd.DataFrame,
     x_axes: List[str],
     cols_to_plot: List[str],
+    is_shaded: str = "",
 ):
     plots = []
     for i, col in enumerate(cols_to_plot):
@@ -297,7 +291,7 @@ def get_plots():
         "Precipitation (mm)",
         "Near Surface Air Temperature (°C)",
         "Surface Downwelling Shortwave Radiation (W/m²)",
-        'Water Deficit (mm/day)'
+        "Water Deficit (mm/day)",
     ]
     cols_to_keep: List[str] = [
         "Precipitation (mm)",
@@ -315,31 +309,37 @@ def get_plots():
 
     df = download_historical_weather_data(latitude, longitude, start_year, end_year)
     historic = aggregate_hourly_weather_data(df)
-    historic= historic.reset_index()
+    historic = historic.reset_index()
     historic = historic.rename(
         columns={
             "precipitation": "Precipitation (mm)",
             "air_temperature_mean": "Near Surface Air Temperature (°C)",
             "irradiance": "Surface Downwelling Shortwave Radiation (W/m²)",
-            'index': 'time'
+            "index": "time",
         }
     )
     historic["time"] = pd.to_datetime(historic["time"])
-    historic = historic.sort_values('time')
-    historic = historic[historic["time"]<"2025-01-01"]
-    historic = historic.rename(columns={"air_temperature_min":"Daily Minimum Near Surface Air Temperature (°C)",
-                                        "air_temperature_max":"Daily Maximum Near Surface Air Temperature (°C)",
-                                        "relative_humidity_min": 'Relative Humidity_min',
-                                        "relative_humidity_max": 'Relative Humidity_max',
-                                        "wind_speed":"Near Surface Wind Speed (m/s)",
-                                        'Precipitation (mm)':'Precipitation (kg m-2 s-1)'
-                                        })
-    historic["Precipitation (kg m-2 s-1)"] = historic["Precipitation (kg m-2 s-1)"]/3600
+    historic = historic.sort_values("time")
+    historic = historic[historic["time"] < "2025-01-01"]
+    historic = historic.rename(
+        columns={
+            "air_temperature_min": "Daily Minimum Near Surface Air Temperature (°C)",
+            "air_temperature_max": "Daily Maximum Near Surface Air Temperature (°C)",
+            "relative_humidity_min": "Relative Humidity_min",
+            "relative_humidity_max": "Relative Humidity_max",
+            "wind_speed": "Near Surface Wind Speed (m/s)",
+            "Precipitation (mm)": "Precipitation (kg m-2 s-1)",
+        }
+    )
+    historic["Precipitation (kg m-2 s-1)"] = (
+        historic["Precipitation (kg m-2 s-1)"] / 3600
+    )
 
-    historic = water_deficit(historic,latitude,longitude)
-    historic = historic.rename(columns={'Precipitation (kg m-2 s-1)':'Precipitation (mm)'
-                                        })
-    historic['Precipitation (mm)'] = historic['Precipitation (mm)']*3600
+    historic = water_deficit(historic, latitude, longitude)
+    historic = historic.rename(
+        columns={"Precipitation (kg m-2 s-1)": "Precipitation (mm)"}
+    )
+    historic["Precipitation (mm)"] = historic["Precipitation (mm)"] * 3600
 
     moderate = get_forecast_data(latitude, longitude, "moderate")
     pessimist = get_forecast_data(latitude, longitude, "pessimist")
@@ -365,4 +365,23 @@ def get_plots():
         historic = aggregate_yearly(historic, col)
         pessimist = aggregate_yearly(pessimist, col)
     plots = generate_plots(moderate, historic, pessimist, x_axes, cols_to_plot)
-    return plots
+    moderate = get_forecast_data(latitude, longitude, "moderate", shading_coef=0.2)
+    pessimist = get_forecast_data(latitude, longitude, "pessimist", shading_coef=0.2)
+    pessimist["year"] = pessimist["time"].dt.year
+    pessimist = pessimist[["year", "Water Deficit (mm/day)"]]
+    pessimist = aggregate_yearly(pessimist, 'Water Deficit (mm/day)')
+    plot_ombrage = copy.deepcopy(plots[-1])
+    plot_ombrage.add_trace(
+        go.Scatter(
+            x=pessimist["year"],
+            y=pessimist['Water Deficit (mm/day)'],
+            mode="lines",
+            name="forecast scénario pessimisste ombrage de 20%",
+            line=dict(
+                color="green",
+                dash="dot",
+            ),
+        )
+    )
+    plots.append(plot_ombrage)
+    return plots, pessimist