change visualization of yield

compute_yield.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 
@@ -34,7 +35,7 @@ def calculate_ETa(ETx, soil_moisture, field_capacity, wilting_point, water_defic
     Returns:
     float: Actual evapotranspiration (ETa) in mm
     """
-    Ks = 1 - (water_deficit / (2 * ETo)) # coef de stress hydrique = precipitation / et0
+    Ks = 1 - (water_deficit / ETo) # coef de stress hydrique = precipitation / et0
     Ks = Ks.clip(lower=0, upper=1)
     ETa = ETx * Ks
 
@@ -152,7 +153,22 @@ if __name__ == '__main__':
     yield_forecast_with_shading = get_annual_yield(monthly_forecast_with_shading)
     # print(yield_forecast)
 
-    plt.plot(yield_forecast.rolling(5).mean(), label="No shading")
-    plt.plot(yield_forecast_with_shading.rolling(5).mean(), label="20% Shading")
+    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")
+
+    width = 3  # the width of the bars
+    fig, ax = plt.subplots(layout='constrained')
+
+    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()