added stress hydrique as 4th plot

visualize/visualize.py

@@ -7,7 +7,64 @@ from data_pipelines.historical_weather_data import (
     aggregate_hourly_weather_data,
 )
 import os
-from forecast import get_forecast_datasets, get_forecast_data
+from forecast import get_forecast_data
+from compute_et0_adjusted import compute_et0
+
+
+def water_deficit(df, latitude, longitude, shading_coef=0, historic=True):
+    preprocessed_data = df.copy()
+    preprocessed_data["irradiance"] = preprocessed_data[
+        "Surface Downwelling Shortwave Radiation (W/m²)"
+    ] * (1 - shading_coef)
+    preprocessed_data["air_temperature_min"] = preprocessed_data[
+        "Daily Minimum Near Surface Air Temperature (°C)"
+    ]
+    preprocessed_data["air_temperature_max"] = preprocessed_data[
+        "Daily Maximum Near Surface Air Temperature (°C)"
+    ]
+    if historic == True:
+        preprocessed_data["relative_humidity_min"] = preprocessed_data[
+            "Relative Humidity_min"
+        ]
+        preprocessed_data["relative_humidity_max"] = preprocessed_data[
+            "Relative Humidity_max"
+        ]
+    else:
+        preprocessed_data["relative_humidity_min"] = preprocessed_data[
+            "Relative Humidity (%)"
+        ]
+        preprocessed_data["relative_humidity_max"] = preprocessed_data[
+            "Relative Humidity (%)"
+        ]
+    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
 
 
 def concatenate_historic_forecast(
@@ -31,57 +88,74 @@ def visualize_climate(
         "Precipitation (mm)",
         "Near Surface Air Temperature (°C)",
         "Surface Downwelling Shortwave Radiation (W/m²)",
+        "Water Deficit (mm/day)",
         "year",
         "period",
     ],
 ):
-    concatenated_moderate = concatenate_historic_forecast(historic, moderate, cols_to_keep)
+    concatenated_moderate = concatenate_historic_forecast(
+        historic, moderate, cols_to_keep
+    )
     concatenated_moderate = concatenated_moderate.sort_values(by=x_axis)  # Ensure order
 
     fig = go.Figure()
 
     if column == "Precipitation (mm)":
         for condition_value in concatenated_moderate["period"].unique():
-            segment = concatenated_moderate[concatenated_moderate["period"] == condition_value]
-            avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index()  
-            
+            segment = concatenated_moderate[
+                concatenated_moderate["period"] == condition_value
+            ]
+            avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index()
+
             fig.add_trace(
                 go.Bar(
-                    x=avg_precipitation[x_axis],  
-                    y=avg_precipitation[column],  
+                    x=avg_precipitation[x_axis],
+                    y=avg_precipitation[column],
                     name=f"{condition_value}",
-                    marker=dict(color="blue" if condition_value == "historique" else "purple"),
+                    marker=dict(
+                        color="blue" if condition_value == "historique" else "purple"
+                    ),
                 )
             )
-        
-        concatenated_pessimist = concatenate_historic_forecast(historic, pessimist, cols_to_keep, "forecast scénario pessimiste")
+
+        concatenated_pessimist = concatenate_historic_forecast(
+            historic, pessimist, cols_to_keep, "forecast scénario pessimiste"
+        )
         concatenated_pessimist = concatenated_pessimist.sort_values(by=x_axis)
-        concatenated_pessimist = concatenated_pessimist[concatenated_pessimist["period"]!="historique"]
+        concatenated_pessimist = concatenated_pessimist[
+            concatenated_pessimist["period"] != "historique"
+        ]
         for condition_value in concatenated_pessimist["period"].unique():
-            segment = concatenated_pessimist[concatenated_pessimist["period"] == condition_value]
-            avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index()  
-            
+            segment = concatenated_pessimist[
+                concatenated_pessimist["period"] == condition_value
+            ]
+            avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index()
+
             fig.add_trace(
                 go.Bar(
-                    x=avg_precipitation[x_axis],  
-                    y=avg_precipitation[column],  
+                    x=avg_precipitation[x_axis],
+                    y=avg_precipitation[column],
                     name=f"{condition_value}",
-                    marker=dict(color="orange" if condition_value != "historique" else "blue"),
+                    marker=dict(
+                        color="orange" if condition_value != "historique" else "blue"
+                    ),
                 )
             )
-        
+
         # Update layout for bar chart
         fig.update_layout(
             title=f"Moyenne de {column} par année",
             xaxis_title="Année",  # Set the x-axis title to Year
             yaxis_title="Précipitation (mm)",  # Set the y-axis title to Precipitation
-            barmode='group'  # Group bars for different conditions
+            barmode="group",  # Group bars for different conditions
         )
-    
+
     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]
+            segment = concatenated_moderate[
+                concatenated_moderate["period"] == condition_value
+            ]
             if condition_value == "historique":
                 fig.add_trace(
                     go.Scatter(
@@ -89,10 +163,13 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
-                        legendgroup='group1',
+                        legendgroup="group1",
                         showlegend=False,
-                        line=dict(color="blue" if condition_value == "historique" else "purple"),
-
+                        line=dict(
+                            color=(
+                                "blue" if condition_value == "historique" else "purple"
+                            )
+                        ),
                     )
                 )
             else:
@@ -102,17 +179,26 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
-                        legendgroup='group2',
+                        legendgroup="group2",
                         showlegend=False,
-                        line=dict(color="blue" if condition_value == "historique" else "purple",  dash='dot'),
+                        line=dict(
+                            color=(
+                                "blue" if condition_value == "historique" else "purple"
+                            ),
+                            dash="dot",
+                        ),
                     )
                 )
-            
+
         # Continue with pessimistic data as in the original function...
-        concatenated_pessimist = concatenate_historic_forecast(historic, pessimist, cols_to_keep, "forecast scénario pessimiste")
+        concatenated_pessimist = concatenate_historic_forecast(
+            historic, pessimist, cols_to_keep, "forecast scénario pessimiste"
+        )
         concatenated_pessimist = concatenated_pessimist.sort_values(by=x_axis)
         for condition_value in concatenated_pessimist["period"].unique():
-            segment = concatenated_pessimist[concatenated_pessimist["period"] == condition_value]
+            segment = concatenated_pessimist[
+                concatenated_pessimist["period"] == condition_value
+            ]
             if condition_value == "historique":
                 fig.add_trace(
                     go.Scatter(
@@ -120,8 +206,13 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
-                        legendgroup='group1',
-                        line=dict(color="blue" if condition_value == "historique" else "orange",  dash='dot' if condition_value != "historique" else None),
+                        legendgroup="group1",
+                        line=dict(
+                            color=(
+                                "blue" if condition_value == "historique" else "orange"
+                            ),
+                            dash="dot" if condition_value != "historique" else None,
+                        ),
                     )
                 )
             else:
@@ -131,37 +222,47 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
-                        legendgroup='group3',
-                        line=dict(color="blue" if condition_value == "historique" else "orange",  dash='dot' if condition_value != "historique" else None),
+                        legendgroup="group3",
+                        line=dict(
+                            color=(
+                                "blue" if condition_value == "historique" else "orange"
+                            ),
+                            dash="dot" if condition_value != "historique" else None,
+                        ),
                     )
                 )
         # Interpolation for the pessimistic scenario...
-        interpolation_pessimist = concatenated_pessimist[concatenated_pessimist[x_axis] > 2023]
-        interpolation_pessimist = interpolation_pessimist[interpolation_pessimist[x_axis] <= 2025]
+        interpolation_pessimist = concatenated_pessimist[
+            concatenated_pessimist[x_axis] > 2023
+        ]
+        interpolation_pessimist = interpolation_pessimist[
+            interpolation_pessimist[x_axis] <= 2025
+        ]
         fig.add_trace(
             go.Scatter(
                 x=interpolation_pessimist[x_axis],
                 y=interpolation_pessimist[column].interpolate(),
                 mode="lines",
-                name = "forecast scénario pessimiste",
-                legendgroup='group3',
+                name="forecast scénario pessimiste",
+                legendgroup="group3",
                 showlegend=False,
-                line=dict(color="orange", dash='dot'),
-
+                line=dict(color="orange", dash="dot"),
             ),
         )
-        interpolation_moderate = concatenated_moderate[concatenated_moderate[x_axis] > 2023]
-        interpolation_moderate = interpolation_moderate[interpolation_moderate[x_axis] <= 2025]
+        interpolation_moderate = concatenated_moderate[
+            concatenated_moderate[x_axis] > 2023
+        ]
+        interpolation_moderate = interpolation_moderate[
+            interpolation_moderate[x_axis] <= 2025
+        ]
         fig.add_trace(
             go.Scatter(
                 x=interpolation_moderate[x_axis],
                 y=interpolation_moderate[column].interpolate(),
                 mode="lines",
-                name = "forecast scénario modéré",
-                legendgroup='group2',
-                line=dict(color="purple", dash='dot'),
-
-                
+                name="forecast scénario modéré",
+                legendgroup="group2",
+                line=dict(color="purple", dash="dot"),
             ),
         )
         fig.update_layout(
@@ -196,11 +297,13 @@ def get_plots():
         "Precipitation (mm)",
         "Near Surface Air Temperature (°C)",
         "Surface Downwelling Shortwave Radiation (W/m²)",
+        'Water Deficit (mm/day)'
     ]
-    cols_to_keep = [
+    cols_to_keep: List[str] = [
         "Precipitation (mm)",
         "Near Surface Air Temperature (°C)",
         "Surface Downwelling Shortwave Radiation (W/m²)",
+        "Water Deficit (mm/day)",
         "year",
         "period",
     ]
@@ -212,15 +315,31 @@ 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 = 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")