fixed dropdown

app.py

@@ -183,6 +183,13 @@ with gr.Blocks() as demo:
                     strong {
                         color:black !important;
                     }
+                    .options.svelte-y6qw75 {
+                        background-color: #FFFFFF;
+                        border-color: #000000;
+                    }
+                    .item.svelte-y6qw75:hover,.active.svelte-y6qw75 {
+                        background: #c0c0c0;
+                    }
 
                 </style>
                 """

summary_test.py

@@ -3,7 +3,10 @@ import pandas as pd
 import pandas as pd
 import numpy as np
 from forecast import get_forecast_datasets, get_forecast_data
-from data_pipelines.historical_weather_data import download_historical_weather_data, aggregate_hourly_weather_data
+from data_pipelines.historical_weather_data import (
+    download_historical_weather_data,
+    aggregate_hourly_weather_data,
+)
 from utils.soil_utils import find_nearest_point_to_coordinates
 from utils.summary import get_meterological_summary, get_agricultural_yield_comparison
 
@@ -11,97 +14,175 @@ from utils.summary import get_meterological_summary, get_agricultural_yield_comp
 def get_meterological_past_data():
     download_historical_weather_data(latitude, longitude, start_year, end_year)
 
-def pre_process_data(scenario: str, lat: float = 47.0, lon:float = 5.0):
+
+def pre_process_data(scenario: str, lat: float = 47.0, lon: float = 5.0):
     start_year, end_year = 2010, 2025
 
-    historical_df = aggregate_hourly_weather_data(download_historical_weather_data(latitude=lat, longitude=lon, start_year=start_year, end_year= end_year))
-    forecast_df = get_forecast_data(scenario=scenario, longitude=lon, latitude=lat, shading_coef=0)
+    historical_df = aggregate_hourly_weather_data(
+        download_historical_weather_data(
+            latitude=lat, longitude=lon, start_year=start_year, end_year=end_year
+        )
+    )
+    forecast_df = get_forecast_data(
+        scenario=scenario, longitude=lon, latitude=lat, shading_coef=0
+    )
 
     forecast_df["time"] = pd.to_datetime(forecast_df["time"])
-    forecast_df['year'] = forecast_df["time"].dt.year
-    new_forecast_df = forecast_df.groupby(by="year", as_index=False).mean().reset_index()
+    forecast_df["year"] = forecast_df["time"].dt.year
+    new_forecast_df = (
+        forecast_df.groupby(by="year", as_index=False).mean().reset_index()
+    )
     # new_forecast_df = new_forecast_df[new_forecast_df["year"] > 2025]
 
-    historical_df = historical_df.reset_index().rename(columns={"index": "time"}).sort_values(by="time")
+    historical_df = (
+        historical_df.reset_index()
+        .rename(columns={"index": "time"})
+        .sort_values(by="time")
+    )
     historical_df["year"] = historical_df["time"].dt.year
-    historical_df["precipitation"] = historical_df["precipitation"] / 3600  # to transform the data to kg m2 per s
+    historical_df["precipitation"] = (
+        historical_df["precipitation"] / 3600
+    )  # to transform the data to kg m2 per s
 
-    new_historical_df = historical_df.groupby(by="year", as_index=False).mean().reset_index()
+    new_historical_df = (
+        historical_df.groupby(by="year", as_index=False).mean().reset_index()
+    )
     new_historical_df = new_historical_df[new_historical_df["year"] < 2024]
 
     return new_historical_df, new_forecast_df
 
-def process_all_data_for_meterological_summary(historical_data: pd.DataFrame, forecast_data: pd.DataFrame):
-    
-    
-    temperature_df = pd.concat([historical_data[["year", "air_temperature_mean"]].rename(columns={"air_temperature_mean": "Near Surface Air Temperature (°C)"}), 
-                                forecast_data[["year", "Near Surface Air Temperature (°C)"]]], axis=0)
 
-    irradiance_df = pd.concat([historical_data[["year", "irradiance"]].rename(columns={"irradiance": "Surface Downwelling Shortwave Radiation (W/m²)"}), 
-                                forecast_data[["year", "Surface Downwelling Shortwave Radiation (W/m²)"]]], axis=0)
-    
-    rain_df = pd.concat([historical_data[["year", "precipitation"]].rename(columns={"precipitation": "Precipitation (kg m-2 s-1)"}), 
-                                forecast_data[["year", "Precipitation (kg m-2 s-1)"]]], axis=0)
+def process_all_data_for_meterological_summary(
+    historical_data: pd.DataFrame, forecast_data: pd.DataFrame
+):
+
+    temperature_df = pd.concat(
+        [
+            historical_data[["year", "air_temperature_mean"]].rename(
+                columns={"air_temperature_mean": "Near Surface Air Temperature (°C)"}
+            ),
+            forecast_data[["year", "Near Surface Air Temperature (°C)"]],
+        ],
+        axis=0,
+    )
+
+    irradiance_df = pd.concat(
+        [
+            historical_data[["year", "irradiance"]].rename(
+                columns={"irradiance": "Surface Downwelling Shortwave Radiation (W/m²)"}
+            ),
+            forecast_data[["year", "Surface Downwelling Shortwave Radiation (W/m²)"]],
+        ],
+        axis=0,
+    )
+
+    rain_df = pd.concat(
+        [
+            historical_data[["year", "precipitation"]].rename(
+                columns={"precipitation": "Precipitation (kg m-2 s-1)"}
+            ),
+            forecast_data[["year", "Precipitation (kg m-2 s-1)"]],
+        ],
+        axis=0,
+    )
 
     return temperature_df, rain_df, irradiance_df
 
-def get_yield_data(region: str = "Bourgogne-Franche-Comté", culture: str ="Blé tendre d'hiver"):
+
+def get_yield_data(
+    region: str = "Bourgogne-Franche-Comté", culture: str = "Blé tendre d'hiver"
+):
 
     yield_past_data = pd.read_csv("data/data_yield/data_rendement.csv")
     # yield_forecast_data = pd.read_csv("data/data_yield/data_rendement.csv")
-    yield_past_data = yield_past_data[(yield_past_data["LIB_REG2"]==region) & (yield_past_data["LIB_SAA"].str.contains(culture)) ]
-    yield_past_data = yield_past_data[["LIB_REG2", "LIB_SAA"]+ [col for col in yield_past_data.columns if 'REND' in col ]]
+    yield_past_data = yield_past_data[
+        (yield_past_data["LIB_REG2"] == region)
+        & (yield_past_data["LIB_SAA"].str.contains("Colza grain d'hiver"))
+    ]
+    yield_past_data = yield_past_data[
+        ["LIB_REG2", "LIB_SAA"]
+        + [col for col in yield_past_data.columns if "REND" in col]
+    ]
     # Transformation
-    yield_past_data = yield_past_data.melt(id_vars=["LIB_REG2", "LIB_SAA"], var_name="year", value_name="past_yield")
+    yield_past_data = yield_past_data.melt(
+        id_vars=["LIB_REG2", "LIB_SAA"], var_name="year", value_name="past_yield"
+    )
 
     # Nettoyer la colonne "temps" pour enlever "REND_"
-    yield_past_data["year"] = yield_past_data["year"].str.replace("REND_", "").astype(int)
+    yield_past_data["year"] = (
+        yield_past_data["year"].str.replace("REND_", "").astype(int)
+    )
 
     yield_forecast_data = pd.read_csv("data/data_yield/rendement_forecast.csv")
-    yield_forecast_data = yield_forecast_data[yield_forecast_data["culture"].str.contains(culture)]
-    return yield_past_data[["year", "past_yield"]], yield_forecast_data[["year", "yield_simple_forecast", "yield_with_shading_forecast"]]
+    yield_forecast_data = yield_forecast_data[
+        yield_forecast_data["culture"].str.contains(culture)
+    ]
+    return (
+        yield_past_data[["year", "past_yield"]],
+        yield_forecast_data[
+            ["year", "yield_simple_forecast", "yield_with_shading_forecast"]
+        ],
+    )
 
 
 def get_summaries():
     scenario = "pessimist"
     lat, lon = 47.0, 5.0
-    culture = "Blé tendre d'hiver"
+    culture = "Colza d'hiver"
     region = "Bourgogne-Franche-Comté"
 
     historical_df, forecast_df = pre_process_data(scenario, lat, lon)
 
-    temperature_df, rain_df, irradiance_df = process_all_data_for_meterological_summary(historical_df, forecast_df)
-    
+    temperature_df, rain_df, irradiance_df = process_all_data_for_meterological_summary(
+        historical_df, forecast_df
+    )
+
     #######@
-    meterological_summary = get_meterological_summary(scenario=scenario, 
-                                                      temperature_df=temperature_df,
-                                                      irradiance_df=irradiance_df,
-                                                      rain_df=rain_df)
+    meterological_summary = get_meterological_summary(
+        scenario=scenario,
+        temperature_df=temperature_df,
+        irradiance_df=irradiance_df,
+        rain_df=rain_df,
+    )
     print(meterological_summary)
 
-    climate_data = temperature_df.merge(rain_df, on='year').merge(irradiance_df, on='year') # meteo ok
-    closest_soil_data = find_nearest_point_to_coordinates(latitude=lat, longitude=lon)  # soil ok
+    climate_data = temperature_df.merge(rain_df, on="year").merge(
+        irradiance_df, on="year"
+    )  # meteo ok
+    closest_soil_data = find_nearest_point_to_coordinates(
+        latitude=lat, longitude=lon
+    )  # soil ok
     water_deficit_data = forecast_df[["year", "Water Deficit (mm/day)"]]
     ############ forecast data PV ############
-    forecast_df_pv = get_forecast_data(scenario=scenario, longitude=lon, latitude=lat, shading_coef=0.2)
+    forecast_df_pv = get_forecast_data(
+        scenario=scenario, longitude=lon, latitude=lat, shading_coef=0.2
+    )
     forecast_df_pv["time"] = pd.to_datetime(forecast_df_pv["time"])
-    forecast_df_pv['year'] = forecast_df_pv["time"].dt.year
-    water_deficit_data_pv = forecast_df_pv.groupby(by="year", as_index=False).mean().reset_index()[["year", "Water Deficit (mm/day)"]]
-    
+    forecast_df_pv["year"] = forecast_df_pv["time"].dt.year
+    water_deficit_data_pv = (
+        forecast_df_pv.groupby(by="year", as_index=False)
+        .mean()
+        .reset_index()[["year", "Water Deficit (mm/day)"]]
+    )
+
     # add a step to transform gps coordinates into french region to be able to filter yield data
-    yield_past_data, yield_forecast_data = get_yield_data(region=region, culture=culture)
+    yield_past_data, yield_forecast_data = get_yield_data(
+        region=region, culture=culture
+    )
     print(yield_forecast_data.tail())
 
     # rendement (avec et sans ombrage)
-    
-    second_summary = get_agricultural_yield_comparison(culture=culture, 
-                                            region="bourgogne franche comté",
-                                            water_df=water_deficit_data,
-                                            water_df_pv = water_deficit_data_pv,
-                                            climate_df=climate_data,
-                                            soil_df=closest_soil_data, 
-                                            forecast_yield_df=yield_forecast_data,
-                                            historical_yield_df=yield_past_data)
+
+    second_summary = get_agricultural_yield_comparison(
+        culture=culture,
+        region="bourgogne franche comté",
+        water_df=water_deficit_data,
+        water_df_pv=water_deficit_data_pv,
+        climate_df=climate_data,
+        soil_df=closest_soil_data,
+        forecast_yield_df=yield_forecast_data,
+        historical_yield_df=yield_past_data,
+    )
 
     print(yield_forecast_data.tail())
     print(second_summary)
@@ -155,11 +236,10 @@ def get_summaries():
     # })
     # water_deficit_data = pd.DataFrame()
     # climate_data = pd.DataFrame()
-    
-    # print(get_agricultural_yield_comparison(culture="orge", 
+
+    # print(get_agricultural_yield_comparison(culture="orge",
     #                                         region="bourgogne franche comté",
-    #                                         water_df=water_deficit_data, 
+    #                                         water_df=water_deficit_data,
     #                                         climate_df=climate_data,
-    #                                         soil_df=closest_soil_features, 
+    #                                         soil_df=closest_soil_features,
     #                                         agri_yield_df=df))
-