Visualization and data loading refactoring

DataModel.py

@@ -12,7 +12,7 @@ class DataModel:
         self.crs_ll = 4326
         self.sleep = 5
 
-    @st.cache_data
+    @st.cache_data(show_spinner='Daten werden geladen ...')
     def get_data(_self, osm_id, radius=25):
         """
         Method for getting and extracting data from backend

Visualization.py

@@ -188,7 +188,13 @@ class Visualization:
         )
 
         return fig
-        
+    
+    def int2str(self, x):
+        return f"{int(x):_}".replace('_', '.')
+    
+    def float2str(self, x):
+        return f"{float(x):_.2f}".replace('.', ',').replace('_', '.')
+    
     def helper_plot_regions(self,):
         return 'Rot markierte Bereiche zeigen Zeitbereiche an, deren Fahrtenanzahl zu gering ist um \
                 eine gute Einschätzung der gewählten Metrik abzubilden.'

app.py

@@ -16,12 +16,12 @@ def main(edge_id):
     # head metrics of the shapes
     cols = st.columns([4,1])
     metric_cols = cols[0].columns(5)
-    metric_cols[0].container(border=True).metric('Gesamtzahl Fahrten:', data['ride_id'].sum())
+    metric_cols[0].container(border=True).metric('Gesamtzahl Fahrten:', visu.int2str(data['ride_id'].sum()))
     metric_cols[0].markdown(' ')
-    metric_cols[1].container(border=True).metric('Geschwindigkeit:', f"{data['speed'].mean():.2f}km/h")
-    metric_cols[2].container(border=True).metric('Erreichte Wunshgeschwindigkeit:', f"{100*data['norm_speed'].mean():.2f}%")
-    metric_cols[3].container(border=True).metric('Zeitverlust:', f"{data['time_loss'].mean():.2f}s")
-    metric_cols[4].container(border=True).metric('Wartezeit:', f"{100*data['waiting'].mean():.2f}s")
+    metric_cols[1].container(border=True).metric('Geschwindigkeit:', f"{visu.float2str(data['speed'].mean())}km/h")
+    metric_cols[2].container(border=True).metric('Erreichte Wunshgeschwindigkeit:', f"{visu.float2str(100*data['norm_speed'].mean())}%")
+    metric_cols[3].container(border=True).metric('Zeitverlust:', f"{visu.float2str(data['time_loss'].mean())}s")
+    metric_cols[4].container(border=True).metric('Wartezeit:', f"{visu.float2str(100*data['waiting'].mean())}s")
     # show shape on map
     with cols[1]:
         st_folium(visu.create_map(geom), returned_objects=[],  key=f'Map',  width=500, height=300,)