fixed legend, added doted lines for forecast

visualize/visualize.py

@@ -10,7 +10,9 @@ import os
 from forecast import get_forecast_datasets, get_forecast_data
 
 
-def concatenate_historic_forecast(historic, forecast, cols_to_keep, value_period_col = "forecast scénario modéré"):
+def concatenate_historic_forecast(
+    historic, forecast, cols_to_keep, value_period_col="forecast scénario modéré"
+):
     historic["period"] = "historique"
     forecast["period"] = value_period_col
     historic = historic[cols_to_keep]
@@ -87,8 +89,10 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
+                        legendgroup='group1',
+                        showlegend=False,
                         line=dict(color="blue" if condition_value == "historique" else "purple"),
-                        showlegend=False
+
                     )
                 )
             else:
@@ -98,7 +102,9 @@ def visualize_climate(
                         y=segment[column],  # Precipitation values on y-axis
                         mode="lines",
                         name=f"{condition_value}",
-                        line=dict(color="blue" if condition_value == "historique" else "purple"),
+                        legendgroup='group2',
+                        showlegend=False,
+                        line=dict(color="blue" if condition_value == "historique" else "purple",  dash='dot'),
                     )
                 )
             
@@ -107,38 +113,54 @@ def visualize_climate(
         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]
-            fig.add_trace(
-                go.Scatter(
-                    x=segment[x_axis],  # Years on x-axis
-                    y=segment[column],  # Precipitation values on y-axis
-                    mode="lines",
-                    name=f"{condition_value}",
-                    line=dict(color="blue" if condition_value == "historique" else "orange"),
+            if condition_value == "historique":
+                fig.add_trace(
+                    go.Scatter(
+                        x=segment[x_axis],  # Years on x-axis
+                        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),
+                    )
+                )
+            else:
+                fig.add_trace(
+                    go.Scatter(
+                        x=segment[x_axis],  # Years on x-axis
+                        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),
+                    )
                 )
-            )
-        
         # Interpolation for the pessimistic scenario...
         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",
-                line=dict(color="orange"),
-                showlegend=False
-                
+                legendgroup='group3',
+                showlegend=False,
+                line=dict(color="orange", dash='dot'),
+
             ),
         )
         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",
-                line=dict(color="purple"),
-                showlegend=False
+                legendgroup='group2',
+                line=dict(color="purple", dash='dot'),
+
                 
             ),
         )
@@ -150,7 +172,8 @@ def visualize_climate(
 
     return fig
 
-def aggregate_yearly(df, col_to_agg, operation = "mean"):
+
+def aggregate_yearly(df, col_to_agg, operation="mean"):
     df[col_to_agg] = df.groupby("year")[col_to_agg].transform(operation)
     return df
 
@@ -167,12 +190,13 @@ def generate_plots(
         plots.append(visualize_climate(moderate, historic, pessimist, x_axes[i], col))
     return plots
 
+
 def get_plots():
     cols_to_plot = [
-    "Precipitation (mm)",
-    "Near Surface Air Temperature (°C)",
-    "Surface Downwelling Shortwave Radiation (W/m²)",
-]
+        "Precipitation (mm)",
+        "Near Surface Air Temperature (°C)",
+        "Surface Downwelling Shortwave Radiation (W/m²)",
+    ]
     cols_to_keep = [
         "Precipitation (mm)",
         "Near Surface Air Temperature (°C)",
@@ -200,22 +224,26 @@ def get_plots():
 
     moderate = get_forecast_data(latitude, longitude, "moderate")
     pessimist = get_forecast_data(latitude, longitude, "pessimist")
-    moderate = moderate.rename(columns={"Precipitation (kg m-2 s-1)": "Precipitation (mm)"})
+    moderate = moderate.rename(
+        columns={"Precipitation (kg m-2 s-1)": "Precipitation (mm)"}
+    )
     moderate["time"] = pd.to_datetime(moderate["time"])
-    moderate = moderate.sort_values('time')
-    moderate['year'] = moderate["time"].dt.year
-    moderate["Precipitation (mm)"] = moderate["Precipitation (mm)"]*31536000
-    pessimist = pessimist.rename(columns={"Precipitation (kg m-2 s-1)": "Precipitation (mm)"})
+    moderate = moderate.sort_values("time")
+    moderate["year"] = moderate["time"].dt.year
+    moderate["Precipitation (mm)"] = moderate["Precipitation (mm)"] * 31536000
+    pessimist = pessimist.rename(
+        columns={"Precipitation (kg m-2 s-1)": "Precipitation (mm)"}
+    )
     pessimist["time"] = pd.to_datetime(pessimist["time"])
-    pessimist = pessimist.sort_values('time')
-    pessimist['year'] = pessimist["time"].dt.year
-    pessimist["Precipitation (mm)"] = pessimist["Precipitation (mm)"]*31536000
+    pessimist = pessimist.sort_values("time")
+    pessimist["year"] = pessimist["time"].dt.year
+    pessimist["Precipitation (mm)"] = pessimist["Precipitation (mm)"] * 31536000
     pessimist["period"] = "forecast scénario pessimiste"
-    historic['year'] = historic["time"].dt.year
-    historic["Precipitation (mm)"] = historic["Precipitation (mm)"]*8760.0
+    historic["year"] = historic["time"].dt.year
+    historic["Precipitation (mm)"] = historic["Precipitation (mm)"] * 8760.0
     for col in cols_to_plot:
         moderate = aggregate_yearly(moderate, col)
         historic = aggregate_yearly(historic, col)
         pessimist = aggregate_yearly(pessimist, col)
     plots = generate_plots(moderate, historic, pessimist, x_axes, cols_to_plot)
-    return plots
+    return plots