Upload interface.py

interface.py

@@ -0,0 +1,1197 @@
+import streamlit as st
+import warnings
+import plotly.express as px
+import pandas as pd
+from datetime import datetime, timedelta
+import plotly.graph_objects as go
+from streamlit_option_menu import option_menu
+from plotly.subplots import make_subplots
+from functions import create_combined_time_series
+from functions import (create_area_chart, create_combined_time_series, create_area_mixte, aggregation_menu,
+                       create_column_mapping, group_by_src, sum_columns_with_suffix, aggregate_by_country,
+                       process_data_by_month, create_pivot_table, create_heatmap, bar_group_consumption,
+
+                       bar_group_ghg,process_ghg_data_by_month, bar_consumption, bar_ghg,download_data_as_csv)
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+# Configuration de la page Streamlit
+st.set_page_config(page_title="Ecodynelec", page_icon=":bar_chart:", layout="wide")
+
+# Create three columns, and place the logo in the rightmost column
+logocol1, logocol2, logocol3 = st.columns([1, 8, 1])  # You can adjust these ratios to fit your needs
+
+# Place the logo in the right column (col3)
+with logocol1:
+    st.image('data/Logo_colored_variant.png', use_container_width=False,width=300)  # Set the logo to fit the column width
+with logocol3:
+    st.image('data/IE_HEIG-VD_logotype_rouge_rvb.svg', use_container_width=True)
+
+
+
+
+# Sidebar Menu with Main Options
+with st.sidebar:
+    main_option = option_menu(
+        menu_title="Main Menu",  # Main menu title
+        options=["Mix data", "Applications", "Methodology"],  # Main menu options
+        icons=["database", "layers", "gear", "info-circle"],  # Optional icons
+        menu_icon="cast",  # Main menu icon
+        default_index=0,  # Default active index
+    )
+
+    if main_option == "Applications":
+        applications_option = option_menu(
+            menu_title="Applications",  # Applications submenu
+            options=["Bâtiment", "PAC"],  # Submenu options
+            icons=["building", "plug"],  # Submenu icons
+            menu_icon="apps",  # Submenu icon
+            default_index=0,
+            orientation="vertical"
+        )
+    else:
+        applications_option = None
+
+
+
+# Données flows
+flows_FR = pd.read_parquet("./data/flows/flows_FR.parquet.gz")
+flows_DE = pd.read_parquet("./data/flows/flows_DE.parquet.gz")
+flows_AT = pd.read_parquet("./data/flows/flows_AT.parquet.gz")
+flows_CH = pd.read_parquet("./data/flows/flows_CH.parquet.gz")
+flows_IT = pd.read_parquet("./data/flows/flows_IT.parquet.gz")
+
+# Données de consommation totale
+tot_consumption_FR = pd.read_parquet("./data/consumptions/tot_consumption_FR.parquet.gz")
+tot_consumption_DE = pd.read_parquet("./data/consumptions/tot_consumption_DE.parquet.gz")
+tot_consumption_AT = pd.read_parquet("./data/consumptions/tot_consumption_AT.parquet.gz")
+tot_consumption_CH = pd.read_parquet("./data/consumptions/tot_consumption_CH.parquet.gz")
+tot_consumption_IT = pd.read_parquet("./data/consumptions/tot_consumption_IT.parquet.gz")
+
+# Données de consommation by src
+raw_consumption_by_src_FR = pd.read_parquet("./data/consumptions/raw_consumption_by_src_FR.parquet.gz")
+raw_consumption_by_src_DE = pd.read_parquet("./data/consumptions/raw_consumption_by_src_DE.parquet.gz")
+raw_consumption_by_src_AT = pd.read_parquet("./data/consumptions/raw_consumption_by_src_AT.parquet.gz")
+raw_consumption_by_src_CH = pd.read_parquet("./data/consumptions/raw_consumption_by_src_CH.parquet.gz")
+raw_consumption_by_src_IT = pd.read_parquet("./data/consumptions/raw_consumption_by_src_IT.parquet.gz")
+
+# electricity_mixs
+tot_electricity_mix_CH = pd.read_parquet("./data/electricity_mixs/electricity_mix_CH.parquet.gz")
+tot_electricity_mix_AT = pd.read_parquet("./data/electricity_mixs/electricity_mix_AT.parquet.gz")
+tot_electricity_mix_DE = pd.read_parquet("./data/electricity_mixs/electricity_mix_DE.parquet.gz")
+tot_electricity_mix_FR = pd.read_parquet("./data/electricity_mixs/electricity_mix_FR.parquet.gz")
+tot_electricity_mix_IT = pd.read_parquet("./data/electricity_mixs/electricity_mix_IT.parquet.gz")
+
+# electricity_impacts
+tot_electricity_impact_CH = pd.read_parquet("./data/electricity_impacts/electricity_impact_CH.parquet.gz")
+tot_electricity_impact_AT = pd.read_parquet("./data/electricity_impacts/electricity_impact_AT.parquet.gz")
+tot_electricity_impact_DE = pd.read_parquet("./data/electricity_impacts/electricity_impact_DE.parquet.gz")
+tot_electricity_impact_FR = pd.read_parquet("./data/electricity_impacts/electricity_impact_FR.parquet.gz")
+tot_electricity_impact_IT = pd.read_parquet("./data/electricity_impacts/electricity_impact_IT.parquet.gz")
+
+# electricity_impacts by source
+electricity_impact_by_src_CH = pd.read_parquet("./data/electricity_impacts/electricity_impact_by_src_CH.parquet.gz")
+electricity_impact_by_src_AT = pd.read_parquet("./data/electricity_impacts/electricity_impact_by_src_AT.parquet.gz")
+electricity_impact_by_src_DE = pd.read_parquet("./data/electricity_impacts/electricity_impact_by_src_DE.parquet.gz")
+electricity_impact_by_src_FR = pd.read_parquet("./data/electricity_impacts/electricity_impact_by_src_FR.parquet.gz")
+electricity_impact_by_src_IT = pd.read_parquet("./data/electricity_impacts/electricity_impact_by_src_IT.parquet.gz")
+
+# Technologies
+Techno_FR = pd.read_parquet("./data/technologies/technologies_FR.parquet.gz")
+Techno_AT = pd.read_parquet("./data/technologies/technologies_AT.parquet.gz")
+Techno_DE = pd.read_parquet("./data/technologies/technologies_DE.parquet.gz")
+Techno_CH = pd.read_parquet("./data/technologies/technologies_CH.parquet.gz")
+Techno_IT = pd.read_parquet("./data/technologies/technologies_IT.parquet.gz")
+
+# Technologies impact
+Techno_impact_FR = pd.read_parquet("./data/technologies/Techno_impact_FR.parquet.gz")
+Techno_impact_AT = pd.read_parquet("./data/technologies/Techno_impact_AT.parquet.gz")
+Techno_impact_DE = pd.read_parquet("./data/technologies/Techno_impact_DE.parquet.gz")
+Techno_impact_CH = pd.read_parquet("./data/technologies/Techno_impact_CH.parquet.gz")
+Techno_impact_IT = pd.read_parquet("./data/technologies/Techno_impact_IT.parquet.gz")
+
+for df in [flows_FR, flows_DE, flows_AT, flows_CH, tot_consumption_FR, tot_consumption_DE, tot_consumption_AT, tot_consumption_CH,
+           tot_electricity_mix_CH,tot_electricity_mix_AT,tot_electricity_mix_DE,tot_electricity_mix_FR,tot_electricity_impact_CH,
+           tot_electricity_impact_AT,tot_electricity_impact_DE,tot_electricity_impact_FR,raw_consumption_by_src_FR,raw_consumption_by_src_CH,
+           raw_consumption_by_src_DE,raw_consumption_by_src_AT,electricity_impact_by_src_CH,electricity_impact_by_src_AT,
+           electricity_impact_by_src_DE,electricity_impact_by_src_FR,Techno_FR,Techno_AT,Techno_DE,Techno_CH,Techno_impact_FR,Techno_impact_AT,
+            Techno_impact_DE,Techno_impact_CH,flows_IT,tot_consumption_IT,raw_consumption_by_src_IT,tot_electricity_mix_IT,
+           tot_electricity_impact_IT,electricity_impact_by_src_IT,Techno_IT,Techno_impact_IT]:
+
+        df.rename(columns={'Unnamed: 0': 'Date'}, inplace=True)
+        df['Date'] = pd.to_datetime(df['Date'])
+        df.set_index('Date', inplace=True)
+
+for df in [flows_FR, flows_DE, flows_AT, flows_CH,flows_IT]:
+    df['total_consumption']=df['production']+df['imports']-df['exports']
+
+# Utilisation de colonnes pour une mise en page personnalisée
+col1, col2, col3, col4, col5 = st.columns(5)  # Créer 5 colonnes
+years = list(range(2016, 2023))
+months = ["January", "February", "March", "April", "May", "June",
+          "July", "August", "September", "October", "November", "December"]
+
+Countries = {'Switzerland': 'CH', 'France': 'FR', 'Germany': 'DE', 'Austria': 'AT','Italy':'IT'}
+
+ordered_countries = ['CH', 'DE', 'FR', 'AT', 'IT', 'Other']
+ordered_colors = ['blue','green', 'red', 'purple',  'orange',  'yellow' ]
+month_dict = {1: "January", 2: "February", 3: "March", 4: "April",5: "May", 6: "June", 7: "July", 8: "August",9: "September", 10: "October", 11: "November", 12: "December"}
+
+
+
+# Dictionary of dataframes with Italy added
+dataframes_flows = {'Switzerland': flows_CH,
+    'France': flows_FR,
+    'Germany': flows_DE,
+    'Austria': flows_AT,
+    'Italy': flows_IT
+}
+
+dataframes_tot_consumption = {
+    'Switzerland': tot_consumption_CH,
+    'France': tot_consumption_FR,
+    'Germany': tot_consumption_DE,
+    'Austria': tot_consumption_AT,
+    'Italy': tot_consumption_IT
+}
+
+dataframes_raw_consumption_by_src = {
+    'Switzerland': raw_consumption_by_src_CH,
+    'France': raw_consumption_by_src_FR,
+    'Germany': raw_consumption_by_src_DE,
+    'Austria': raw_consumption_by_src_AT,
+    'Italy': raw_consumption_by_src_IT
+}
+
+dataframes_tot_electricity_mix = {
+    'Switzerland': tot_electricity_mix_CH,
+    'France': tot_electricity_mix_FR,
+    'Germany': tot_electricity_mix_DE,
+    'Austria': tot_electricity_mix_AT,
+    'Italy': tot_electricity_mix_IT
+}
+
+dataframes_tot_electricity_impact = {
+    'Switzerland': tot_electricity_impact_CH,
+    'France': tot_electricity_impact_FR,
+    'Germany': tot_electricity_impact_DE,
+    'Austria': tot_electricity_impact_AT,
+    'Italy': tot_electricity_impact_IT
+}
+
+dataframes_electricity_impact_by_src = {
+    'Switzerland': electricity_impact_by_src_CH,
+    'France': electricity_impact_by_src_FR,
+    'Germany': electricity_impact_by_src_DE,
+    'Austria': electricity_impact_by_src_AT,
+    'Italy': electricity_impact_by_src_IT
+}
+
+dataframes_techno = {
+    'Switzerland': Techno_CH,
+    'France': Techno_FR,
+    'Germany': Techno_DE,
+    'Austria': Techno_AT,
+    'Italy': Techno_IT
+}
+
+dataframes_techno_impact = {
+    'Switzerland': Techno_impact_CH,
+    'France': Techno_impact_FR,
+    'Germany': Techno_impact_DE,
+    'Austria': Techno_impact_AT,
+    'Italy': Techno_impact_IT
+}
+
+
+
+
+
+
+
+with col1:
+    selected_country_name = st.selectbox('Choose a country:', list(Countries.keys()))
+
+with col2:
+    resolution = st.selectbox('Resolution:', ['Annual', 'Monthly','Daily','Hourly'])
+
+
+# Récupération du DataFrame basé sur le pays sélectionné
+tot_consumption_selected_df = dataframes_tot_consumption [selected_country_name]
+raw_consumption_by_src_selected_df = dataframes_raw_consumption_by_src [selected_country_name]
+flows_selected_df =dataframes_flows[selected_country_name]
+tot_electricity_mix_selected_df = dataframes_tot_electricity_mix [selected_country_name]
+tot_electricity_impact_selected_df = dataframes_tot_electricity_impact [selected_country_name]
+electricity_impact_by_src_selected_df = dataframes_electricity_impact_by_src [selected_country_name]
+techno_selected_df=dataframes_techno [selected_country_name]
+techno_impact_selected_df=dataframes_techno_impact [selected_country_name]
+
+
+
+
+if main_option == "Mix data":
+    if resolution == 'Annual':
+        flows_selected_df['exports'] = -flows_selected_df['exports']
+        flows_annual_df = flows_selected_df.resample('Y').sum() / 1000
+        flows_annual_df.index = flows_annual_df.index.year
+        tot_consumption_annual_df = tot_consumption_selected_df['sum'].resample('Y').sum() / 1000
+
+        col1, col2 = st.columns(2)
+        with col1:
+
+            bar_group_consumption(flows_annual_df, title=f'Yearly Time Series of Production, Imports, and Exports in {selected_country_name}   ', text="GWh",
+                                  y_cols=['total_consumption', 'production', 'imports', 'exports'], barmode='group')
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(flows_annual_df, f"Yearly_Time_Series_of_Production_Imports_and_Exports_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                            **Chart Description:**  
+                                            This bar chart represents the yearly time series of production, imports, exports, and total electricity consumption in **{selected_country_name}**,
+                                             measured in gigawatt-hours (GWh), over a period from 2016 to 2022.
+
+                                            **Data Source:** EcoDynElec
+                                        """)
+
+        # pour les impacts
+        consumer_impact_annual = tot_electricity_impact_selected_df['sum'].resample('Y').mean()
+        consumer_impact_annual.index = consumer_impact_annual.index.year
+        with col2:
+            bar_group_ghg(consumer_impact_annual, f'Yearly average of GHG emissions  in {selected_country_name}')
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(consumer_impact_annual, f"Yearly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                        **Chart Description:**  
+                                                        This bar chart represents the yearly average of greenhouse gas (GHG) emissions in **{selected_country_name}**,
+                                                         measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh), from 2016 to 2022.
+
+                                                        **Data Source:** EcoDynElec
+                                                    """)
+
+
+
+
+        selection = aggregation_menu()
+        if selection == "Mixed":
+            raw_consumption_by_src_annual_df = raw_consumption_by_src_selected_df.resample('Y').sum() / 1000
+            raw_consumption_by_src_annual_df = aggregate_by_country(selected_country_name,raw_consumption_by_src_annual_df)
+            raw_consumption_by_src_annual_df.index = raw_consumption_by_src_annual_df.index.year
+            col1, col2 = st.columns(2)
+            with col1:
+
+                bar_consumption(raw_consumption_by_src_annual_df,title=f'Yearly consumption by source in {selected_country_name}')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(raw_consumption_by_src_annual_df, f"Yearly_consumption_by_source_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                    **Chart Description:**  
+                                                                                                    This stacked bar chart represents the yearly electricity consumption by source in **{selected_country_name}** from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                                                     Each bar is divided into segments that correspond to different energy sources contributing to the overall electricity consumption.
+
+                                                                                                    **Data Source:** EcoDynElec
+                                                                                                """)
+
+
+            electricity_impact_by_src_annual_df = electricity_impact_by_src_selected_df.resample('Y').mean()
+            electricity_impact_by_src_annual_df = aggregate_by_country(selected_country_name,electricity_impact_by_src_annual_df)
+            electricity_impact_by_src_annual_df.index = electricity_impact_by_src_annual_df.index.year
+
+            with col2:
+
+                bar_ghg(electricity_impact_by_src_annual_df, f'Yearly average of GHG emissions  in {selected_country_name} by source')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(electricity_impact_by_src_annual_df,  f"Yearly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_by_source.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                                    **Chart Description:**  
+                                                                                                                    This stacked bar chart illustrates the yearly average of greenhouse gas (GHG) emissions in **{selected_country_name}** by energy source, measured in grams
+                                                                                                                     of CO2 equivalent per kilowatt-hour (gCO2eq/kWh), from 2016 to 2022.
+                                                                                                                     Each bar is segmented to show the GHG emissions contribution from various energy sources used in **{selected_country_name}**'s electricity consumption.
+
+                                                                                                                    **Data Source:** EcoDynElec
+                                                                                                                """)
+
+        if selection == "By Technology":
+            col1, col2 = st.columns(2)
+
+            techno_annual_df = techno_selected_df.resample('Y').sum() / 1000
+            techno_annual_df.index = techno_annual_df.index.year
+            col1, col2 = st.columns(2)
+            with col1:
+                #bar_consumption(techno_annual_df,title=f'Yearly consumption of {selected_technologie} in {selected_country_name}')
+                bar_group_consumption(techno_annual_df,
+                                      title=f'Yearly consumption by technology  in {selected_country_name}',
+                                      text="GWh",
+                                      y_cols=techno_annual_df.columns,barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(techno_annual_df, f"Yearly_consumption_by_technology_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                        **Chart Description:**  
+                                                                        This stacked bar chart represents the yearly electricity consumption by technology in **{selected_country_name}** from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                         Each bar is divided into segments that correspond to the contributions of various energy technologies to the total electricity consumption.
+
+                                                                        **Data Source:** EcoDynElec
+                                                                    """)
+
+
+
+            techno_impact_annual_df=techno_impact_selected_df.resample('Y').mean()
+            techno_impact_annual_df.index = techno_impact_annual_df.index.year
+
+            with col2:
+
+                bar_group_consumption(techno_impact_annual_df,
+                                      title=f'Yearly average of GHG emissions by technology in {selected_country_name}',
+                                      text="gCO2eq/kWh",
+                                      y_cols=techno_impact_annual_df.columns, barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(techno_impact_annual_df, f"Yearly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_by_technology.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                        **Chart Description:**  
+                                                                                        This stacked bar chart represents the yearly average of greenhouse gas (GHG) emissions by technology in **{selected_country_name}** from 2016 to 2022, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                         Each bar is divided into segments representing the GHG emissions contributions from various energy technologies.
+
+                                                                                        **Data Source:** EcoDynElec
+                                                                                    """)
+
+
+
+        if selection == "Country of origin":
+            mix_import_annual = tot_electricity_mix_selected_df.drop(['sum'], axis=1)
+            mix_import_annual = mix_import_annual.multiply(tot_consumption_selected_df['sum'], axis='index').resample('Y').sum() / 1000
+            mix_import_annual.index = mix_import_annual.index.year
+
+
+
+            col1, col2 = st.columns(2)
+            with col1:
+
+                bar_group_consumption(mix_import_annual, title=f"Origins of yearly Swiss consumer mix in {selected_country_name}",
+                                      text="GWh",
+                                      y_cols=ordered_countries, barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(mix_import_annual, f"Origins_of_yearly_Swiss_consumer_mix_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                        **Chart Description:**  
+                                                                                                        This stacked bar chart illustrates the origins of the yearly Swiss consumer electricity mix from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                                                         Each bar is divided into segments representing the contributions of electricity sourced from **{selected_country_name}** and its neighboring countries.
+
+                                                                                                        **Data Source:** EcoDynElec
+                                                                                                    """)
+
+
+
+            mix_impact_annual = tot_electricity_impact_selected_df.drop(['sum'], axis=1).resample('Y').mean()
+            mix_impact_annual.index=mix_impact_annual.index.year
+            with col2:
+
+                bar_group_consumption(mix_impact_annual,title=f'Yearly average of GHG emissions  in {selected_country_name} by country',
+                                      text="gCO2eq/kWh",y_cols=ordered_countries, barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(mix_impact_annual, f"Yearly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_by_country.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                                        **Chart Description:**  
+                                                                                                                        This stacked bar chart represents the yearly average of greenhouse gas (GHG) emissions in **{selected_country_name}** by country from 2016 to 2022, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                                                         The stacked bars show the contributions of GHG emissions from domestic electricity production and imports from neighboring countries.
+
+                                                                                                                        **Data Source:** EcoDynElec
+                                                                                                                    """)
+
+
+
+
+    elif resolution == 'Monthly':
+
+        with col3:
+            # Utilisation d'un slider pour choisir une année
+            selected_year = st.slider('Choose a year:', min_value=min(years), max_value=max(years), value=min(years))
+
+        #flows
+        flows_selected_df['exports'] = -flows_selected_df['exports']
+        flows_monthly_df = flows_selected_df[(flows_selected_df.index.year == selected_year)]
+        flows_monthly_df = flows_monthly_df.resample('M').sum() / 1000
+        flows_monthly_df.index = flows_monthly_df.index.month.map(lambda x: month_dict[x])
+        #tot_consumption
+        tot_consumption_monthly_df = tot_consumption_selected_df[(tot_consumption_selected_df.index.year == selected_year)]
+        tot_consumption_monthly_df = tot_consumption_monthly_df['sum'].resample('M').sum() / 1000
+
+        col1, col2 = st.columns(2)
+        with col1:
+
+            bar_group_consumption(flows_monthly_df, title=f'Monthly Time Series of Production, Imports, and Exports in {selected_country_name} in {selected_year} ',
+                                  text="GWh",
+                                  y_cols=['total_consumption', 'production', 'imports', 'exports'], barmode='group')
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(flows_monthly_df, f"Monthly_Time_Series_of_Production_Imports_and_Exports_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                        **Chart Description:**  
+                                                        This bar chart represents the monthly time series of production, imports, exports, and total electricity consumption in **{selected_country_name}**,
+                                                         measured in gigawatt-hours (GWh), over a period from 2016 to 2022.
+
+                                                        **Data Source:** EcoDynElec
+                                                    """)
+
+
+
+
+            # pour les impacts
+        tot_electricity_impact_monthly_df=tot_electricity_impact_selected_df[(tot_electricity_impact_selected_df.index.year == selected_year)]
+        monthly_consumer_impact = tot_electricity_impact_monthly_df['sum'].resample('M').mean()
+        monthly_consumer_impact.index = monthly_consumer_impact.index.month.map(lambda x: month_dict[x])
+        with col2:
+
+            bar_group_ghg(monthly_consumer_impact, f'Monthly average of GHG emissions  in {selected_country_name} in {selected_year} ')
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(monthly_consumer_impact, f"Monthly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                    **Chart Description:**  
+                                                                    This bar chart represents the monthly average of greenhouse gas (GHG) emissions in **{selected_country_name}**,
+                                                                     measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh), from 2016 to 2022.
+
+                                                                    **Data Source:** EcoDynElec
+                                                                """)
+
+
+        selection = aggregation_menu()
+        if selection == "Mixed":
+
+            raw_consumption_by_src_monthly_df = process_data_by_month(raw_consumption_by_src_selected_df, selected_year, selected_country_name, month_dict,
+                aggregate_by_country)
+
+            electricity_impact_by_src_monthly_df = process_ghg_data_by_month(electricity_impact_by_src_selected_df, selected_year, selected_country_name, month_dict,
+                aggregate_by_country)
+
+            col1, col2 = st.columns(2)
+            with col1:
+                bar_consumption(raw_consumption_by_src_monthly_df,title=f'Monthly consumption by source in {selected_country_name} in {selected_year}')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(raw_consumption_by_src_monthly_df, f"Monthly_consumption_by_source_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                    **Chart Description:**  
+                                                                                    This stacked bar chart represents the monthly electricity consumption by source in **{selected_country_name}** from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                                     Each bar is divided into segments that correspond to different energy sources contributing to the overall electricity consumption.
+
+                                                                                    **Data Source:** EcoDynElec
+                                                                                """)
+
+            with col2:
+                bar_ghg(electricity_impact_by_src_monthly_df,f'Monthly average of GHG emissions  in {selected_country_name} in {selected_year} by source')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(electricity_impact_by_src_monthly_df, f"Monthly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}_by_source.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                                                    **Chart Description:**  
+                                                                                                                                    This stacked bar chart illustrates the monthly average of greenhouse gas (GHG) emissions in **{selected_country_name}** by energy source, measured in grams
+                                                                                                                                     of CO2 equivalent per kilowatt-hour (gCO2eq/kWh), from 2016 to 2022.
+                                                                                                                                     Each bar is segmented to show the GHG emissions contribution from various energy sources used in **{selected_country_name}**'s electricity consumption.
+
+                                                                                                                                    **Data Source:** EcoDynElec
+                                                                                                                                """)
+
+        if selection == "By Technology":
+
+            techno_monthly_df = techno_selected_df.loc[techno_selected_df.index.year == selected_year]
+            techno_monthly_df = techno_monthly_df.resample('M').sum() / 1000
+            techno_monthly_df.index = techno_monthly_df.index.month.map(lambda x: month_dict[x])
+            col1, col2 = st.columns(2)
+            with col1:
+                bar_consumption(techno_monthly_df,title=f'Monthly consumption by technology in {selected_country_name} in {selected_year}')
+
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(techno_monthly_df, f"Monthly_consumption_by_technology_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                        **Chart Description:**  
+                                                                                        This stacked bar chart represents the monthly electricity consumption by technology in **{selected_country_name}** from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                                         Each bar is divided into segments that correspond to the contributions of various energy technologies to the total electricity consumption.
+
+                                                                                        **Data Source:** EcoDynElec
+                                                                                    """)
+
+            techno_impact_monthly_df = techno_impact_selected_df.loc[techno_impact_selected_df.index.year == selected_year]
+            techno_impact_monthly_df = techno_impact_monthly_df.resample('M').mean()
+            techno_impact_monthly_df.index = techno_impact_monthly_df.index.month.map(lambda x: month_dict[x])
+
+            with col2:
+                bar_ghg(techno_impact_monthly_df,f'Monthly average  of GHG emissions by technology in {selected_country_name} in {selected_year}')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(techno_impact_monthly_df, f"Monthly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}_by_technology.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                        **Chart Description:**  
+                                                                                                        This stacked bar chart represents the monthly average of greenhouse gas (GHG) emissions by technology in **{selected_country_name}** from 2016 to 2022, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                                         Each bar is divided into segments representing the GHG emissions contributions from various energy technologies.
+
+                                                                                                        **Data Source:** EcoDynElec
+                                                                                                    """)
+
+
+
+
+        if selection == "Country of origin":
+            tot_electricity_mix_monthly_df = tot_electricity_mix_selected_df[(tot_electricity_mix_selected_df.index.year == selected_year)]
+            monthly_mix_import = tot_electricity_mix_monthly_df.drop(['sum'], axis=1)
+            monthly_mix_import = monthly_mix_import.multiply(tot_consumption_monthly_df, axis='index').resample('M').sum() / 1000
+            monthly_mix_import.index = monthly_mix_import.index.month.map(lambda x: month_dict[x])
+
+
+            col1, col2 = st.columns(2)
+            with col1:
+                bar_group_consumption(monthly_mix_import,
+                                      title=f"Origins of monthly consumer mix by country in {selected_country_name} in {selected_year}",
+                                      text="GWh",
+                                      y_cols=ordered_countries, barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(monthly_mix_import, f"Monthly_consumption_by_country_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                                        **Chart Description:**  
+                                                                                                                        This stacked bar chart illustrates the origins of the monthly Swiss consumer electricity mix from 2016 to 2022, measured in gigawatt-hours (GWh).
+                                                                                                                         Each bar is divided into segments representing the contributions of electricity sourced from **{selected_country_name}** and its neighboring countries.
+
+                                                                                                                        **Data Source:** EcoDynElec
+                                                                                                                    """)
+
+
+            tot_electricity_impact_monthly_df = tot_electricity_impact_selected_df[(tot_electricity_impact_selected_df.index.year == selected_year)]
+            monthly_mix_impact = tot_electricity_impact_monthly_df.drop(['sum'], axis=1).resample('M').mean()
+            monthly_mix_impact.index = monthly_mix_impact.index.month.map(lambda x: month_dict[x])
+            with col2:
+
+                bar_group_consumption(monthly_mix_impact,
+                                      title=f'Monthly average of GHG emissions by country  in {selected_country_name} in {selected_year}',
+                                      text="gCO2eq/kWh",
+                                      y_cols=ordered_countries, barmode='stack')
+                download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+                # Bouton pour télécharger le CSV dans download_col (gauche)
+                with download_col:
+                    download_data_as_csv(monthly_mix_impact, f"Monthly_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}_in_{selected_year}_by_country.csv")
+
+                    # Use an expander to display the general description in info_col (right)
+                    with info_col:
+                        with st.expander("ℹ️ Chart's Information"):
+                            st.write(f"""
+                                                                                                                                        **Chart Description:**  
+                                                                                                                                        This stacked bar chart represents the monthly average of greenhouse gas (GHG) emissions in **{selected_country_name}** by country from 2016 to 2022, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                                                                         The stacked bars show the contributions of GHG emissions from domestic electricity production and imports from neighboring countries.
+
+                                                                                                                                        **Data Source:** EcoDynElec
+                                                                                                                                    """)
+
+
+
+    elif resolution == 'Daily':
+
+        # Supposons que flows_selected_df est déjà défini et correctement configuré
+        min_date = flows_selected_df.index.min()
+        max_date = flows_selected_df.index.max()
+
+        # Positionnement des widgets dans les colonnes si déjà définies
+        with col3:  # Exemple de placement dans la colonne
+            start_date, end_date = st.date_input(
+                "Select a date range:",
+                [min_date, max_date],  # Utilisez les extrêmes de l'index comme valeur par défaut
+                min_value=min_date,  # Date minimale extraite de l'index
+                max_value=max_date,  # Date maximale extraite de l'index
+                help="You can select a range within the available dates in the data."
+            )
+            # Ajustez end_date pour inclure toute la journée
+            start_date = pd.Timestamp(start_date)
+            end_date = pd.Timestamp(end_date) + pd.Timedelta(days=1, seconds=-1)
+
+
+
+
+
+        # Filtrer le DataFrame selon la plage sélectionnée
+        flows_daily= flows_selected_df.loc[(flows_selected_df.index >= start_date) & (flows_selected_df.index <= end_date)].resample('D').sum() / 1000
+        tot_consumption_daily= tot_consumption_selected_df.loc[(tot_consumption_selected_df.index >= start_date) & (tot_consumption_selected_df.index <= end_date)].resample('D').sum()
+        tot_consumption_daily=tot_consumption_daily['sum'].resample('D').sum() / 1000
+        col1, col2 = st.columns(2)
+        with col1:
+            create_combined_time_series(flows_daily, tot_consumption_daily, title=f'Daily Time Series of Production, Imports, and Exports in {selected_country_name} ')
+            # Ajouter des colonnes pour les boutons à l'intérieur de col1
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(flows_daily,
+                                     f"Daily_Time_Series_of_Production_Imports_and_Exports_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                **Chart Description:**  
+                                This chart presents the daily evolution of energy flows in **{selected_country_name}**, 
+                                including production, imports, exports, and total electricity consumption.
+
+                                **Data Source:** EcoDynElec
+                            """)
+
+
+
+
+
+        # for impacts
+        tot_electricity_impact_daily_df = tot_electricity_impact_selected_df.loc[
+            (tot_electricity_impact_selected_df.index >= start_date) &
+            (tot_electricity_impact_selected_df.index <= end_date)
+            ]
+
+
+
+
+
+        # disp the chart based on user's choice
+        with col2:
+
+                # Create a line plot (assuming you want to use Plotly)
+                fig = px.line(tot_electricity_impact_daily_df.resample('D').mean(),
+                              x=tot_electricity_impact_daily_df.resample('D').mean().index, y='sum',
+                              title=f'Line Plot of GHG Emissions in {selected_country_name}')
+                fig.update_layout(legend_title_text='')
+
+                fig.update_traces(hovertemplate='%{y:.0f} gCO2eq/KWh<br>Date: %{x|%a %d %b %Y}<extra></extra>')
+                fig.add_annotation(text="© Ecodynelec-HEIG-VD", xref="paper", yref="paper", x=0, y=-0.2,
+                                   showarrow=False, font=dict(size=12, color="gray"), xanchor='left', yanchor='bottom')
+                # Add annotation for unit
+                fig.add_annotation(text="gCO2eq/KWh", xref="paper", yref="paper", x=-0.05, y=1.1, showarrow=False,
+                                   font=dict(size=12))
+                fig.update_xaxes(title_text='', tickformat='%a %d %b %y')
+                fig.update_yaxes(title_text='')
+                st.plotly_chart(fig)
+
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(tot_electricity_impact_daily_df, f"Daily_Line_Plot_of_GHG_Emissions_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                **Chart's Description:** 
+                                This line plot visualizes the temporal evolution of greenhouse gas (GHG) emissions in **{selected_country_name}**,
+                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                 The x-axis represents the time series data over several days, while the y-axis indicates the magnitude of GHG emissions
+
+                                **Data Source:** EcoDynElec
+                            """)
+
+
+
+
+
+
+        selection = aggregation_menu()
+        if selection == "Mixed":
+            raw_consumption_by_src_daily_df = raw_consumption_by_src_selected_df.loc[(raw_consumption_by_src_selected_df.index >= start_date) & (
+                            raw_consumption_by_src_selected_df.index <= end_date)].resample('D').sum()
+            raw_consumption_by_src_daily_df = raw_consumption_by_src_daily_df.resample('D').sum() / 1000
+            raw_consumption_by_src_daily_df = aggregate_by_country(selected_country_name,
+                                                                     raw_consumption_by_src_daily_df)
+            col1, col2 = st.columns(2)
+            with col1:
+                create_area_mixte(raw_consumption_by_src_daily_df,
+                                  title=f'Daily consumption by source in {selected_country_name}',text='GWh')
+
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(raw_consumption_by_src_daily_df, f"Daily_consumption_by_source_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                **Chart Description:**  
+                                
+                                This stacked area chart illustrates the daily electricity consumption by energy source in **{selected_country_name}** over the selected time period.
+                                 Each area in the plot corresponds to a different energy source contributing to the total electricity consumption.
+
+                                **Data Source:** EcoDynElec
+                            """)
+
+
+            electricity_impact_by_src_daily_df = electricity_impact_by_src_selected_df.loc[(electricity_impact_by_src_selected_df.index >= start_date) & (
+                        electricity_impact_by_src_selected_df.index <= end_date)]
+            electricity_impact_by_src_daily_df = electricity_impact_by_src_daily_df.resample('D').mean()
+            electricity_impact_by_src_daily_df = aggregate_by_country(selected_country_name,
+                                                                     electricity_impact_by_src_daily_df)
+            with col2:
+                create_area_mixte(electricity_impact_by_src_daily_df,title=f'Daily average of GHG emissions by source in {selected_country_name}',text='gCO2/KWh')
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(electricity_impact_by_src_daily_df,  f"Daily_average_of_GHG_emissions_by_source_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart Description:**  
+                                                This stacked area chart visualizes the daily average of greenhouse gas (GHG) emissions by energy source in **{selected_country_name}** over the selected period.
+                                                 Each color band represents the contribution of a different energy source to the total GHG emissions,
+                                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh)
+
+                                                **Data Source:** EcoDynElec
+                                            """)
+
+        if selection == "By Technology":
+
+            col1, col2 = st.columns(2)  # Crée deux colonnes pour les graphiques
+            with col1:
+                techno_daily_df=(techno_selected_df.loc[(techno_selected_df.index >= start_date) & (techno_selected_df.index <= end_date)]
+                             .resample('D').sum()) / 1000
+                #create_time_series(techno_daily_df,title=f'Daily average of GHG emissions by source in {selected_country_name}')
+                create_area_mixte(techno_daily_df,
+                                              title=f'Daily consumption by technology in {selected_country_name}',text='GWh')
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(techno_daily_df, f"Daily_consumption_by_technology_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart Description:**  
+
+                                                This stacked area chart illustrates the daily electricity consumption by technology in **{selected_country_name}** over the selected time period.
+                                                 Each area in the plot corresponds to a different energy source contributing to the total electricity consumption.
+
+                                                **Data Source:** EcoDynElec
+                                            """)
+
+
+            techno_impact_daily_df = techno_impact_selected_df.loc[(techno_impact_selected_df.index >= start_date) &
+                                                                                           (techno_impact_selected_df.index <= end_date)].resample('D').mean()
+
+
+
+            with col2:
+                create_area_mixte(techno_impact_daily_df,
+                                              title=f'Daily average of GHG emissions by technology in {selected_country_name}', text='gCO2eq/KWh')
+
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(techno_impact_daily_df,  f"Daily_average_of_GHG_emissions_by_technology_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                **Chart Description:**  
+                                This stacked area chart visualizes the daily average of greenhouse gas (GHG) emissions by technology in **{selected_country_name}** over the selected period.
+                                                 Each color band represents the contribution of a different energy source to the total GHG emissions,
+                                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh)
+
+                                                                
+
+                                **Data Source:** EcoDynElec
+                                                            """)
+
+
+
+
+        if selection == "Country of origin":
+            tot_electricity_mix_daily_df = tot_electricity_mix_selected_df.loc[
+                (tot_electricity_mix_selected_df.index >= start_date) & (tot_electricity_mix_selected_df.index <= end_date)].resample('D').sum()
+            daily_mix_import = tot_electricity_mix_daily_df.drop(['sum'], axis=1)
+            daily_mix_import = daily_mix_import.multiply(tot_consumption_daily, axis='index').resample('D').sum() / 1000
+
+
+            col1, col2 = st.columns(2)
+            with col1:
+                create_area_chart(daily_mix_import,title=f"Origins of daily consumer mix by country in {selected_country_name}")
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(daily_mix_import, f"Daily_consumption_by_country_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                **Chart Description:**  
+
+                                                                This stacked area chart displays the origins of the daily consumer electricity mix by country in Switzerland over the selected time period.
+                                                                 Each color band represents the contribution of electricity imports from different countries to the total electricity mix consumed in **{selected_country_name}**.
+
+                                                                **Data Source:** EcoDynElec
+                                                            """)
+
+
+            tot_electricity_impact_daily_df = tot_electricity_impact_selected_df[(tot_electricity_impact_selected_df.index >= start_date) &
+                                                                                 (tot_electricity_impact_selected_df.index <= end_date)]
+
+            daily_mix_impact = tot_electricity_impact_daily_df.drop(['sum'], axis=1).resample('D').mean()
+            with col2:
+                create_area_chart(daily_mix_impact, title=f'Daily average of GHG emissions by country (gCO2eq/kWh) in {selected_country_name}', unit="gCO2eq/kWh")
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+
+                    download_data_as_csv(daily_mix_impact,  f"Daily_average_of_GHG_emissions_by_country_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                                                                        **Chart Description:**  
+
+                                                                                                                        This stacked area chart shows the daily average of greenhouse gas (GHG) emissions by country in **{selected_country_name}**, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                                                         Each color band represents the GHG emissions associated with electricity produced or imported from different countries.
+
+                                                                                                                        **Data Source:** EcoDynElec
+                                                                                                                    """)
+
+    elif resolution == 'Hourly':
+
+        # Supposons que flows_selected_df est déjà défini et correctement configuré
+        min_date = flows_selected_df.index.min()
+        max_date = flows_selected_df.index.max()
+
+        # Positionnement des widgets dans les colonnes si déjà définies
+        with col3:  # Exemple de placement dans la colonne
+            start_date, end_date = st.date_input(
+                "Select a date range:",
+                [min_date, max_date],  # Utilisez les extrêmes de l'index comme valeur par défaut
+                min_value=min_date,  # Date minimale extraite de l'index
+                max_value=max_date,  # Date maximale extraite de l'index
+                help="You can select a range within the available dates in the data."
+            )
+            # Ajustez end_date pour inclure toute la journée
+            start_date = pd.Timestamp(start_date)
+            end_date = pd.Timestamp(end_date) + pd.Timedelta(days=1, seconds=-1)
+
+
+
+        # Filtrer le DataFrame selon la plage sélectionnée
+        flows_hourly = flows_selected_df.loc[(flows_selected_df.index >= start_date) & (flows_selected_df.index <= end_date)]/ 1000
+        tot_consumption_hourly = tot_consumption_selected_df.loc[
+            (tot_consumption_selected_df.index >= start_date) & (tot_consumption_selected_df.index <= end_date)]
+        tot_consumption_hourly=tot_consumption_hourly['sum'] / 1000
+        col1,col2= st.columns(2)
+        with col1:
+            create_combined_time_series(flows_hourly, tot_consumption_hourly, title=f'Hourly Time Series of Production, Imports, and Exports in {selected_country_name}',resolution='hourly')
+            download_col, info_col = st.columns([0.7, 0.3])  # Adjust the ratios as needed
+
+            # Bouton pour télécharger le CSV dans download_col (gauche)
+            with download_col:
+                download_data_as_csv(flows_hourly, f"Hourly_Time_Series_of_Production_Imports_and_Exports_in_{selected_country_name.replace(' ', '_')}.csv")
+
+                # Use an expander to display the general description in info_col (right)
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                            **Chart Description:**  
+                                            This chart presents the hourly evolution of energy flows in **{selected_country_name}**, 
+                                            including production, imports, exports, and total electricity consumption.
+
+                                            **Data Source:** EcoDynElec
+                                        """)
+
+
+
+        # pour les impacts
+        tot_electricity_impact_hourly_df = tot_electricity_impact_selected_df.loc[(tot_electricity_impact_selected_df.index >= start_date) & (
+                    tot_electricity_impact_selected_df.index <= end_date)]
+
+
+        pivot_table = create_pivot_table(tot_electricity_impact_hourly_df['sum'].resample('H').mean())
+
+
+        # Display the chart based on user's choice
+        with col4:
+            # Add a selectbox to allow the user to choose between heatmap and line plot
+            chart_type = st.selectbox("Choose chart type:", ["Heatmap", "Line Plot"])
+        with col2:
+            if chart_type == "Line Plot":
+                # Create a line plot (assuming you want to use Plotly)
+                fig = px.line(tot_electricity_impact_hourly_df.resample('H').mean(),
+                              x=tot_electricity_impact_hourly_df.index, y='sum',
+                              title=f'Line Plot of GHG Emissions in {selected_country_name}')
+                fig.update_layout(legend_title_text='')
+                fig.update_traces(hovertemplate='%{y:.0f} gCO2eq/KWh<br>Date: %{x| %a %d %b %Y %H %M}<extra></extra>')
+                fig.update_xaxes(title_text='', tickformat=' %a %d %b %y %H %M')
+                fig.update_yaxes(title_text='')
+                fig.add_annotation(text="© Ecodynelec-HEIG-VD", xref="paper", yref="paper", x=0, y=-0.5,
+                                   showarrow=False, font=dict(size=12, color="gray"), xanchor='left', yanchor='bottom')
+                fig.add_annotation(text="gCO2eq/KWh", xref="paper", yref="paper", x=-0.05, y=1.1, showarrow=False,
+                                   font=dict(size=12))
+                st.plotly_chart(fig)
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(tot_electricity_impact_hourly_df, f"Hourly_Line_Plot_of_GHG_Emissions_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart's Description:** 
+                                                This line plot visualizes the temporal evolution of greenhouse gas (GHG) emissions in **{selected_country_name}**,
+                                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                 The x-axis represents the time series data over time (hourly), while the y-axis indicates the magnitude of GHG emissions
+    
+                                                **Data Source:** EcoDynElec
+                                            """)
+
+            elif chart_type == "Heatmap":
+                create_heatmap(pivot_table, f'Heatmap of the average of GHG emissions in {selected_country_name}')
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(pivot_table, f"Heatmap_of_the_average_of_GHG_emissions_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart Description:**  
+                                                This heatmap displays the average greenhouse gas (GHG) emissions in **{selected_country_name}** over time.
+                                                 The horizontal axis represents the dates, while the vertical axis represents the hours of the day.
+                                                  Each colored cell in the heatmap corresponds to the intensity of GHG emissions, measured in grams of
+                                                   CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+    
+                                                **Data Source:** EcoDynElec
+                                            """)
+
+        selection = aggregation_menu()
+        if selection == "Mixed":
+            raw_consumption_by_src_hourly_df = raw_consumption_by_src_selected_df.loc[(raw_consumption_by_src_selected_df.index >= start_date) & (
+                        raw_consumption_by_src_selected_df.index <= end_date)].resample('H').sum()
+            raw_consumption_by_src_hourly_df = raw_consumption_by_src_hourly_df.resample('H').sum() / 1000
+            raw_consumption_by_src_hourly_df = aggregate_by_country(selected_country_name,raw_consumption_by_src_hourly_df)
+            col1, col2 = st.columns(2)
+            with col1:
+                create_area_mixte(raw_consumption_by_src_hourly_df,
+                                              title=f'Hourly consumption by source in {selected_country_name}',text='GWh',resolution='hourly')
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(raw_consumption_by_src_hourly_df, f"Hourly_consumption_by_source_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart Description:**  
+
+                                                This stacked area chart illustrates the hourly electricity consumption by energy source in **{selected_country_name}** over the selected time period.
+                                                 Each area in the plot corresponds to a different energy source contributing to the total electricity consumption.
+
+                                                **Data Source:** EcoDynElec
+                                            """)
+
+
+            electricity_impact_by_src_hourly_df = electricity_impact_by_src_selected_df.loc[(electricity_impact_by_src_selected_df.index >= start_date) & (
+                        electricity_impact_by_src_selected_df.index <= end_date)].resample('H').sum()
+            electricity_impact_by_src_hourly_df = electricity_impact_by_src_hourly_df.resample('H').mean()
+            electricity_impact_by_src_hourly_df = aggregate_by_country(selected_country_name,
+                                                                      electricity_impact_by_src_hourly_df)
+            with col2:
+                create_area_mixte(electricity_impact_by_src_hourly_df,
+                                      title=f'Hourly average of GHG emissions by source in {selected_country_name}',text='gCO2/KWh',resolution='hourly')
+
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(electricity_impact_by_src_hourly_df,  f"Hourly_average_of_GHG_emissions_by_source_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                **Chart Description:**  
+                                                                This stacked area chart visualizes the hourly average of greenhouse gas (GHG) emissions by energy source in **{selected_country_name}** over the selected period.
+                                                                 Each color band represents the contribution of a different energy source to the total GHG emissions,
+                                                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh)
+
+                                                                **Data Source:** EcoDynElec
+                                                            """)
+
+
+
+        if selection == "By Technology":
+
+            col1, col2 = st.columns(2)  # Crée deux colonnes pour les graphiques
+            with col1:
+                techno_hourly_df = (techno_selected_df.loc[
+                                    (techno_selected_df.index >= start_date) &
+                                    (techno_selected_df.index <= end_date)]
+                                .resample('H').sum()) / 1000
+
+                create_area_mixte(techno_hourly_df,
+                               title=f'Daily average of GHG emissions by technology in {selected_country_name}',text='GWh',resolution='hourly')
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(techno_hourly_df,  f"Hourly_average_of_GHG_emissions_by_technology_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                **Chart Description:**  
+
+                                                                This stacked area chart illustrates the hourly electricity consumption by technology in **{selected_country_name}** over the selected time period.
+                                                                 Each area in the plot corresponds to a different energy source contributing to the total electricity consumption.
+
+                                                                **Data Source:** EcoDynElec
+                                                            """)
+
+
+            with col2:
+                techno_impact_hourly_df = techno_impact_selected_df.loc[
+                                              (techno_impact_selected_df.index >= start_date) &
+                                              (techno_impact_selected_df.index <= end_date)].resample('D').mean()
+
+                create_area_mixte(techno_impact_hourly_df,
+                                   title=f'Hourly average of GHG emissions by technology in {selected_country_name}',text='gCO2/KWh',resolution="hourly")
+
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(techno_impact_hourly_df, f"Hourly_consumption_by_technology_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                **Chart Description:**  
+                                                This stacked area chart visualizes the hourly average of greenhouse gas (GHG) emissions by technology in **{selected_country_name}** over the selected period.
+                                                                 Each color band represents the contribution of a different energy source to the total GHG emissions,
+                                                                 measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh)
+
+
+
+                                                **Data Source:** EcoDynElec
+                                                                            """)
+
+
+        if selection == "Country of origin":
+            tot_electricity_mix_hourly_df = tot_electricity_mix_selected_df.loc[
+                (tot_electricity_mix_selected_df.index >= start_date) & (tot_electricity_mix_selected_df.index <= end_date)].resample('H').sum()
+            hourly_mix_import = tot_electricity_mix_hourly_df.drop(['sum'], axis=1)
+            hourly_mix_import = hourly_mix_import.multiply(tot_consumption_hourly, axis='index').resample('H').sum() / 1000
+
+
+            col1, col2 = st.columns(2)
+            with col1:
+                create_area_chart(hourly_mix_import,title=f"Origins of hourly consumer mix by country in {selected_country_name}",resolution="hourly")
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(hourly_mix_import, f"Hourly_consumption_by_country_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                                **Chart Description:**  
+
+                                                                                This stacked area chart displays the origins of the hourly consumer electricity mix by country in Switzerland over the selected time period.
+                                                                                 Each color band represents the contribution of electricity imports from different countries to the total electricity mix consumed in **{selected_country_name}**.
+
+                                                                                **Data Source:** EcoDynElec
+                                                                            """)
+
+
+            tot_electricity_impact_hourly_df = tot_electricity_impact_selected_df[(tot_electricity_impact_selected_df.index >= start_date) &
+                                                                                 (tot_electricity_impact_selected_df.index <= end_date)]
+
+            hourly_mix_impact = tot_electricity_impact_hourly_df.drop(['sum'], axis=1).resample('H').mean()
+            with col2:
+                create_area_chart(hourly_mix_impact, title=f'Hourly average of GHG emissions by country in {selected_country_name}',resolution="hourly",unit="gCO2eq/KWh")
+                download_col, info_col = st.columns([0.7, 0.3])
+                with download_col:
+                    download_data_as_csv(hourly_mix_impact,  f"Hourly_average_of_GHG_emissions_by_country_in_{selected_country_name.replace(' ', '_')}.csv")
+                with info_col:
+                    with st.expander("ℹ️ Chart's Information"):
+                        st.write(f"""
+                                                                                                **Chart Description:**  
+
+                                                                                                This stacked area chart shows the hourly average of greenhouse gas (GHG) emissions by country in **{selected_country_name}**, measured in grams of CO2 equivalent per kilowatt-hour (gCO2eq/kWh).
+                                                                                                 Each color band represents the GHG emissions associated with electricity produced or imported from different countries.
+
+                                                                                                **Data Source:** EcoDynElec
+                                                                                            """)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+