refactor: move schedule to fleet

rssched/app/pages/03_Depots.py → rssched/app/pages/01_Depots.py

@@ -2,22 +2,35 @@
 
 from rssched.app.utils.io import get_uploaded_data
 from rssched.app.utils.plot import plot_depots_bar_chart
-from rssched.app.utils.transform import flatten_depots
+from rssched.app.utils.transform import flatten_depots, get_vehicle_summary
 from rssched.visualization.depot_loads import plot_depot_vehicle_loads
 
 st.title("Depots")
 
 request, response, instance_name = get_uploaded_data()
 
-tabs = st.tabs(["Overview", "Details"])
+tabs = st.tabs(["Overview", "Details", "Data"])
 
 df_depots = flatten_depots(request, response)
 
 
 with tabs[0]:
-    st.plotly_chart(plot_depots_bar_chart(df_depots))
+    st.plotly_chart(plot_depots_bar_chart(df_depots, instance_name))
+    st.markdown("### Vehicle Demand")
+    st.dataframe(get_vehicle_summary(response), hide_index=True)
 
-    show_aggregated = st.checkbox("Aggregate", value=True)
+with tabs[1]:
+    selected_depot: str = st.selectbox(
+        "Choose depot to see detailed statistics",
+        df_depots[df_depots["vehicles"] > 0]["depot_id"].unique(),
+    )
+    st.plotly_chart(
+        plot_depot_vehicle_loads(request, response, instance_name, selected_depot)
+    )
+    st.dataframe(df_depots[df_depots["depot_id"] == selected_depot], hide_index=True)
+
+with tabs[2]:
+    show_aggregated = st.checkbox("Aggregate", value=False)
 
     if show_aggregated:
         df_grouped = (
@@ -29,14 +42,3 @@
         st.dataframe(df_grouped, hide_index=True)
     else:
         st.dataframe(df_depots, hide_index=True)
-
-
-with tabs[1]:
-    selected_depot: str = st.selectbox(
-        "Choose depot to see detailed statistics",
-        df_depots[df_depots["vehicles"] > 0]["depot_id"].unique(),
-    )
-    st.plotly_chart(
-        plot_depot_vehicle_loads(request, response, instance_name, selected_depot)
-    )
-    st.dataframe(df_depots[df_depots["depot_id"] == selected_depot], hide_index=True)

rssched/app/pages/02_Fleet.py

@@ -1,23 +1,33 @@
+from typing import Any
+
 import streamlit as st
 
 from rssched.app.utils.io import get_uploaded_data
 from rssched.visualization.active_events import plot_active_events_over_time
 from rssched.visualization.fleet_efficiency import plot_fleet_efficiency
+from rssched.visualization.vehicle_type_gantt import plot_gantt_per_vehicle_type
 from rssched.visualization.vehicle_utilization import plot_vehicle_utilization
 
 st.title("Fleet")
 
 request, response, instance_name = get_uploaded_data()
 
 
-tabs = st.tabs(["Active Events", "Vehicle Utilization", "Efficiency"])
+tabs = st.tabs(
+    ["Vehicle Circuits", "Active Events", "Vehicle Utilization", "Efficiency"]
+)
 
 with tabs[0]:
-    st.plotly_chart(plot_active_events_over_time(response, instance_name))
+    plots = plot_gantt_per_vehicle_type(response, instance_name)
+    selected_vehicle_type: str = st.selectbox("Choose vehicle type", plots.keys())
+    st.plotly_chart(plots[selected_vehicle_type])
 
 with tabs[1]:
-    st.plotly_chart(plot_vehicle_utilization(response, instance_name))
+    st.plotly_chart(plot_active_events_over_time(response, instance_name))
 
 with tabs[2]:
+    st.plotly_chart(plot_vehicle_utilization(response, instance_name))
+
+with tabs[3]:
     for fig in plot_fleet_efficiency(response, instance_name):
         st.plotly_chart(fig)

rssched/app/utils/plot.py

@@ -3,7 +3,7 @@
 from plotly.graph_objs import Figure
 
 
-def plot_depots_bar_chart(df_depots: pd.DataFrame) -> Figure:
+def plot_depots_bar_chart(df_depots: pd.DataFrame, instance_name: str) -> Figure:
     # Group by depot_id to calculate the total capacity and sort by the number of vehicles
     df_depots_grouped = (
         df_depots[df_depots["vehicles"] > 0]
@@ -38,7 +38,7 @@ def plot_depots_bar_chart(df_depots: pd.DataFrame) -> Figure:
     # Update layout of the bar chart
     fig.update_layout(
         barmode="group",  # Bars grouped side by side
-        title="Depot Vehicles and Capacity",
+        title=f"Depot Usage (Instance: {instance_name})",
         xaxis_title="Depot ID",
         # yaxis_title="Count",
         legend_title="Legend",

rssched/app/utils/transform.py

@@ -34,6 +34,27 @@ def get_response_summary(response: Response) -> pd.DataFrame:
     )
 
 
+def get_vehicle_summary(response: Response):
+    data = []
+
+    for depot_load in response.schedule.depot_loads:
+        depot = depot_load.depot
+        for load in depot_load.load:
+            vehicle_type = load.vehicle_type
+            spawn_count = load.spawn_count
+            data.append(
+                {"location": depot, "vehicle_type": vehicle_type, "count": spawn_count}
+            )
+
+    return (
+        pd.DataFrame(data)
+        .groupby("vehicle_type")["count"]
+        .sum()
+        .sort_values()
+        .reset_index()
+    )
+
+
 def flatten_depots(request: Request, response: Response) -> pd.DataFrame:
     df_request = _flatten_request_depots(request.depots)
     df_response = _flatten_response_depot_loads(response.schedule.depot_loads)

rssched/visualization/vehicle_type_gantt.py

@@ -1,10 +1,13 @@
 import plotly.figure_factory as ff
+import plotly.graph_objects as go
 
 from rssched.model.response import Response
 from rssched.visualization.colors import EVENT_TYPES
 
 
-def plot_gantt_per_vehicle_type(response: Response, instance_name: str) -> dict:
+def plot_gantt_per_vehicle_type(
+    response: Response, instance_name: str
+) -> dict[str, go.Figure]:
     figures = {}
 
     for vehicle_type in response.schedule.fleet:
@@ -39,7 +42,7 @@ def plot_gantt_per_vehicle_type(response: Response, instance_name: str) -> dict:
                 )
         figures[vehicle_type.vehicle_type] = ff.create_gantt(
             vis_data,
-            title=f"Rolling stock schedule: {vehicle_type.vehicle_type} (instance: {instance_name})",
+            title=f"Vehicle Circuits '{vehicle_type.vehicle_type}' (instance: {instance_name})",
             colors=EVENT_TYPES,
             index_col="Type",
             show_colorbar=True,