import streamlit as st
from streamlit_folium import st_folium
from DataModel import DataModel
from Visualization import Visualization
  
def app_run(node_id=0):
    # page configs
    st.set_page_config(page_title='Zeitverluste an Knotenpunkten', layout='wide')
    # handle empty query parameter
    node = st.experimental_get_query_params()
    if 'node_id' in node:
        node_id = node['node_id'][0]
    else:
        st.error('No NodeID given !!')
        st.stop()
    # load data model and visualization tools
    dm = DataModel()
    visu = Visualization()
    # Top Page Layout with Input controll,node metrics and map
    metric_cols = st.columns([1,1,1,1,3])
    # select node by node id
    st.session_state['node_id'] = node_id
    st.session_state['extension_radius'] = 30#[m]
    
    # get data from backend
    directs, feature, _, gdf = dm.get_data_of_node(st.session_state['node_id'],st.session_state['extension_radius'])
    # create images for direction and 
    with st.spinner('Grafiken werden erstellt ...'):
        folium_map = visu.create_folium_map(gdf)
        figures, results, worst_relevant_loss = visu.get_diretion_results(directs, feature)

    usable_rides = int(sum([df.tail(1)['Fahrten'] for df in results]))
    mean_time_loss = float(sum([df.tail(1)['Fahrten']*df.tail(1)['Zeitverlust'] for df in results])/usable_rides)
    mean_waiting_time = float(sum([df.tail(1)['Fahrten']*df.tail(1)['Standzeit'] for df in results])/usable_rides)
    # dsipaly time quality plots, time quality dataframes and rush hour data frames for each travel direction in a different tab
    input_directions = [f"Richtung {directs[d]['name']}" for d in directs]
    #
    elem0 = metric_cols[0].container()#border=True
    elem0.markdown('Auswertbare Fahrten im Knoten')
    elem0.header(visu.int2str(usable_rides))
    #
    elem1 = metric_cols[1].container()#border=True
    elem1.markdown('Gesamter Zeitverlust')
    color = visu.get_metric_color(mean_time_loss)
    elem1.header(f":{color}[{visu.float2str(mean_time_loss)}s]")
    #
    elem2 = metric_cols[2].container()#border=True
    elem2.markdown('Gesamte Standzeit')
    color = visu.get_metric_color(mean_waiting_time)
    elem2.header(f":{color}[{visu.float2str(mean_waiting_time)}s]")
    #
    elem3 = metric_cols[3].container()#border=True
    elem3.markdown('Gesamtbewertung')
    color = visu.get_metric_color(worst_relevant_loss['Zeitverlust'])
    final_class = visu.get_final_result(worst_relevant_loss['Zeitverlust'])
    indirection = input_directions[worst_relevant_loss['InDirection']]
    elem3.header(f":{color}[{final_class}, {visu.float2str(worst_relevant_loss['Zeitverlust'])}s]")
    elem3.text(f" Einfahrtsrichtung: {indirection[9:]} \n Ausfahrtsrichtung: {worst_relevant_loss['Richtung']}")
    # define column layout
    cols = st.columns([2,4])
    # display node geometry on map
    with cols[0]:
        st_folium(folium_map, returned_objects=[], width=500, height=300, key=f'Map')
    selection = cols[1].radio(':compass: Wähle Einfahrtsrichtung:', options=input_directions, horizontal=True)
    idx = input_directions.index(selection)
    # tooltip
    cols[1].text('', help=get_plot_helper_text())
    # display time quality plot for travel direction
    cols[1].pyplot(figures[idx], use_container_width=True)
    # helper text for table
    cols[0].text('', help=get_table_helper_text())
    # display time quality dataframe of travel direction
    cols[0].dataframe(results[idx], hide_index=True, use_container_width=True)
        
def get_plot_helper_text():
    return '''Darstellung der Stand- und Verlustzeiten an den Ein- und Ausfahrtswegen im Knotenpunkt. 
            Zeitliche Verluste werden entsprechend den Qualitätsklassen farblich visualisiert. 
            Zusätzlich erfolgt die Darstellung der Anzahl der Fahrten pro Fahrtrichtung durch die Breite der Linien. '''
    
def get_table_helper_text():
    return '''Liste der durchschnittlichen Stand- und Verlustzeiten pro Fahrtrichtung, 
            wobei die Richtungen den in der Abbildung dargestellten Fahrtrichtungen entsprechen.'''

if __name__ == '__main__':
    app_run()