Spaces:

worldiety
/

BIQEMonitor_Zeitverlust_an_Knotenpunkten

Running

App Files Files Community

BIQEMonitor_Zeitverlust_an_Knotenpunkten / app.py

ChristophS

reanming fronent filter

9b34089 2 months ago

raw history blame contribute delete

No virus

4.16 kB

	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()