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import streamlit as st |
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from DataModel import DataModel |
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from Visualization import Visualization |
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import pandas as pd |
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from streamlit_folium import st_folium |
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def main(edge_id): |
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""" |
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Streamlit Dashbaord for Quality Metric Data as time series from BIQE Monitor |
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:edge_id: biqe monitor edge id |
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""" |
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st.set_page_config(page_title='Straßenabschnitt Analyse', layout='wide') |
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data, geom = DataModel().get_data(edge_id, 30) |
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visu = Visualization(data) |
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cols = st.columns([4,1]) |
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metric_cols = cols[0].columns(5) |
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metric_cols[0].container(border=True).metric('Durchfahrten:', visu.int2str(data['ride_id'].sum())) |
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metric_cols[0].markdown(' ') |
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metric_cols[1].container(border=True).metric('Geschwindigkeit [km/h]', f"{visu.float2str(data['speed'].mean())}") |
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metric_cols[2].container(border=True).metric('Wunschgeschwindigkeit [%]', f"{visu.float2str(100*data['norm_speed'].mean())}") |
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metric_cols[3].container(border=True).metric('Zeitverlust [Sekunden]', f"{visu.float2str(data['time_loss'].mean())}") |
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metric_cols[4].container(border=True).metric('Standzeit [Sekunden]', f"{visu.float2str(100*data['waiting'].mean())}") |
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with cols[1]: |
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st_folium(visu.create_map(geom), returned_objects=[], |
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key=f'Map', use_container_width=True) |
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visu.filter_component(cols[0]) |
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visu.aggregate_data() |
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cols[0].markdown(f"{visu.selected_metric} pro {visu.selected_time}", help=visu.helper_plot_regions()) |
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cols[0].plotly_chart(visu.timeline_plot_plotly(), use_container_width=True) |
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if __name__ == '__main__': |
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if 'edge_id' not in st.query_params: |
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edge_id = [] |
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else: |
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edge_id = st.query_params['edge_id'].split('_')[0] |
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main(edge_id) |
