estimated time to swap calculation

Utilities/DataVis.py

@@ -2,7 +2,7 @@ import pandas as pd
 
 
 def format_dataframe(
-    bess_soc_for_cycle, bess_data, load_profiles_since_start, swap_time
+    bess_soc_for_cycle, bess_data, load_profiles_since_start, swap_time, current_time
 ):
     """Formats the DataFrame for display in the dashboard."""
     # Create a DataFrame for sites
@@ -15,6 +15,7 @@ def format_dataframe(
             "Current Load (kW)",
             "SoC (%)",
             "Predicted Swap Time",
+            "Estimated Time To Swap",
             "Cycle Discharge Profile",
             "Load Profile Since Start",
         ]
@@ -26,6 +27,12 @@ def format_dataframe(
         current_load = bess_data["units"][index]["current_load_kW"]
         unit_name = bess_data["units"][index]["name"]
         predicted_swap_time = swap_time.get(unit_name, "N/A")
+        # calculate estimated time to swap
+        if isinstance(predicted_swap_time, float):
+            estimated_time_to_swap = predicted_swap_time - current_time
+            estimated_time_to_swap = pd.to_timedelta(estimated_time_to_swap, unit="s")
+        else:
+            estimated_time_to_swap = "N/A"
         # convert predicted_swap_time to a readable format
         if isinstance(predicted_swap_time, float):
             predicted_swap_time = pd.to_datetime(
@@ -43,6 +50,7 @@ def format_dataframe(
                             "Current Load (kW)": current_load,
                             "SoC (%)": soc * 100,  # Convert to percentage
                             "Predicted Swap Time": predicted_swap_time,
+                            "Estimated Time To Swap": estimated_time_to_swap,
                             "Cycle Discharge Profile": bess_soc_for_cycle[unit_name][
                                 "SoC"
                             ].tolist(),

dashboard.py

@@ -91,6 +91,13 @@ if st.session_state.running:
             "%Y-%m-%d %H:%M:%S"
         ),
     )
+    st.metric(
+        "Current Time",
+        value=pd.to_datetime(
+            main.c["sim_start_time"] + main.sim_i * main.dt, unit="s"
+        ).strftime("%Y-%m-%d %H:%M:%S"),
+    )
+
     st.metric(
         "Time Elapsed in DD:HH:MM:SS",
         value=str(pd.to_timedelta(main.sim_i * main.dt, unit="s")),

main.py

@@ -122,11 +122,15 @@ def simulation_loop():
 
             # format data for display
             status_df = format_dataframe(
-                bess_soc_for_cycle, bess_data, load_profiles_since_start, swap_times
+                bess_soc_for_cycle,
+                bess_data,
+                load_profiles_since_start,
+                swap_times,
+                timestamps[i],
             )
 
             # small sleep to allow dashboard to refresh / release GIL
-            time.sleep(0.1)
+            time.sleep(0.01)
 
 
 ### <<< CONTROL ADDED >>> Control functions