change yield plot function for plots with no shading

compute_yield.py

@@ -131,7 +131,7 @@ def plot_yield(
         culture: str = "Colza d'hiver",
         region: str = "Bourgogne-Franche-Comté",
         scenario: str = "pessimist",
-        shading_coef: float = 0.,
+        shading_coef: float = 0.2,
 ) -> plt.Figure:
     monthly_forecast = compute_yield_forecast(
         latitude=latitude,
@@ -141,33 +141,38 @@ def plot_yield(
         shading_coef=0.,
     )
 
-    monthly_forecast_with_shading = compute_yield_forecast(
-        latitude=latitude,
-        longitude=longitude,
-        culture=culture,
-        scenario=scenario,
-        shading_coef=shading_coef,
-    )
-
     yield_forecast = get_annual_yield(monthly_forecast)
-    yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
-
     n_years = 10
-    years = 2025 + np.arange(len(yield_forecast_with_shading))
+    years = 2025 + np.arange(len(yield_forecast))
+    aggregated_years = years[years % n_years == 0]
     aggregated_forecasts = yield_forecast.rolling(n_years).sum()[years % n_years == 0]
-    aggregated_forecasts_with_shading = yield_forecast_with_shading.rolling(n_years).sum()[years % n_years == 0]
 
     width = 3  # the width of the bars
     fig, ax = plt.subplots(layout='constrained')
-    aggregated_years = years[years % n_years == 0]
     _ = ax.bar(aggregated_years, aggregated_forecasts, width, label="No shading")
-    _ = ax.bar(aggregated_years + width, aggregated_forecasts_with_shading, width, label="20% shading")
+
+    if shading_coef > 0:
+        monthly_forecast_with_shading = compute_yield_forecast(
+            latitude=latitude,
+            longitude=longitude,
+            culture=culture,
+            scenario=scenario,
+            shading_coef=shading_coef,
+        )
+        yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
+        aggregated_forecasts_with_shading = yield_forecast_with_shading.rolling(n_years).sum()[years % n_years == 0]
+        _ = ax.bar(aggregated_years + width, aggregated_forecasts_with_shading, width, label="20% shading")
+
     ax.legend()
+    ax.set_xlabel("Année")
+    ax.set_ylabel(f"Production agricole de {culture} estimée (quintal / ha)")
     ax.set_ylim(150)
 
     return fig
 
 if __name__ == '__main__':
+    latitude = 47
+    longitude = 5
     cultures = ["Colza d'hiver", "Blé tendre d'hiver", "Orge d'hiver"]
     dfs = []
     for culture in cultures:
@@ -180,14 +185,6 @@ if __name__ == '__main__':
             scenario=scenario,
             shading_coef=0.,
         )
-        # print(monthly_forecast.head())
-
-        yield_forecast = get_annual_yield(monthly_forecast)
-        yield_forecast_df = yield_forecast.reset_index()
-        yield_forecast_df.columns = ["time", "yield_simple_forecast"]
-        yield_forecast_df["year"] = yield_forecast_df["time"].dt.year
-
-        print(yield_forecast_df.head())
 
         monthly_forecast_with_shading = compute_yield_forecast(
             latitude=47,
@@ -196,43 +193,22 @@ if __name__ == '__main__':
             scenario=scenario,
             shading_coef=shading_coef,
         )
-        # print(monthly_forecast_with_shading.head())
 
-<<<<<<< Updated upstream
-    n_years = 10
-    years = 2025 + np.arange(len(yield_forecast_with_shading))
-    aggregated_forecasts = yield_forecast.rolling(n_years).sum()[years % n_years == 0]
-    aggregated_forecasts_with_shading = yield_forecast_with_shading.rolling(n_years).sum()[years % n_years == 0]
-    # plt.plot(yield_forecast.rolling(n_years).sum(), label="No shading")
-    # plt.plot(yield_forecast_with_shading.rolling(n_years).sum(), label="20% Shading")
-    # plt.bar(years[years % n_years == 0], aggregated_forecasts, label="No shading")
-    # plt.bar(years[years % n_years == 0], aggregated_forecasts_with_shading, label="20% Shading")
+        fig = plot_yield(latitude, longitude, culture, scenario="pessimist", shading_coef=shading_coef)
+        plt.show()
 
-    width = 3  # the width of the bars
-    fig, ax = plt.subplots(layout='constrained')
+        # yield_forecast = get_annual_yield(monthly_forecast)
+        # yield_forecast_df = yield_forecast.reset_index()
+        # yield_forecast_df.columns = ["time", "yield_simple_forecast"]
+        # yield_forecast_df["year"] = yield_forecast_df["time"].dt.year
+        # yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
+        # yield_forecast_with_shading_df = yield_forecast_with_shading.reset_index()
+        # yield_forecast_with_shading_df.columns = ["time", "yield_with_shading_forecast"]
+        # yield_forecast_with_shading_df["year"] = yield_forecast_with_shading_df["time"].dt.year
+        # final_df = pd.merge(yield_forecast_df[["year", "yield_simple_forecast"]], yield_forecast_with_shading_df[["year", "yield_with_shading_forecast"]], on="year")
+        # final_df["culture"] = culture
+        # dfs.append(final_df)
 
-    aggregated_years = years[years % n_years == 0]
-    rects = ax.bar(aggregated_years, aggregated_forecasts, width, label="No shading")
-    rects2 = ax.bar(aggregated_years + width, aggregated_forecasts_with_shading, width, label="20% shading")
 
-    plt.legend()
-    plt.ylim(150)
-    plt.show()
-=======
-        yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
-        yield_forecast_with_shading_df = yield_forecast_with_shading.reset_index()
-        yield_forecast_with_shading_df.columns = ["time", "yield_with_shading_forecast"]
-        yield_forecast_with_shading_df["year"] = yield_forecast_with_shading_df["time"].dt.year
-        final_df = pd.merge(yield_forecast_df[["year", "yield_simple_forecast"]], yield_forecast_with_shading_df[["year", "yield_with_shading_forecast"]], on="year")
-        final_df["culture"] = culture
-        dfs.append(final_df)
-        
-
-    result = pd.concat(dfs, axis=0)
-    result.to_csv("data/data_yield/rendement_forecast.csv", index=False)
-
-    # plt.plot(yield_forecast.rolling(5).mean(), label="No shading")
-    # plt.plot(yield_forecast_with_shading.rolling(5).mean(), label="20% Shading")
-    # plt.legend()
-    # plt.show()
->>>>>>> Stashed changes
+    # result = pd.concat(dfs, axis=0)
+    # result.to_csv("data/data_yield/rendement_forecast.csv", index=False)