font setting for dashboard

.streamlit/config.toml

@@ -0,0 +1,34 @@
+[server]
+enableStaticServing = true
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-VARIABLEFONT_WGHT.TTF"
+style = "normal"
+weight = 400
+
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-BOLD.TTF"
+style = "bold"
+weight = 700
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-ITALIC.TTF"
+style = "italic"
+weight = 400
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-BOLDITALIC.TTF"
+style = "bold italic"
+weight = 7
+
+[theme]
+base="dark"
+primaryColor="#fcd913"
+font="Exo2"
+codeFont="Exo2"
+

dashboard.py

@@ -0,0 +1,33 @@
+# dashboard.py
+import streamlit as st
+import matplotlib.pyplot as plt
+from main import start_sim, stop_sim, reset_sim, bess_soc_since_start
+
+# Header
+st.logo("https://rooftop.my/logo.svg", size="large")
+st.title("MEOS Control Dashboard")
+st.subheader("Mobile Energy Operations Simulation (MEOS)")
+st.text("Run MEOS Simulation and Monitor MBESS Status")
+
+# some instructions
+
+# --- SESSION STATE SETUP ---
+if "running" not in st.session_state:
+    st.session_state.running = False
+if "plot_area" not in st.session_state:
+    st.session_state.plot_area = st.empty()
+
+# --- CONTROL BUTTONS ---
+col1, col2, col3 = st.columns(3)
+with col1:
+    if st.button("Start", use_container_width=True):
+        start_sim()
+        st.session_state.running = True
+with col2:
+    if st.button("Stop", use_container_width=True):
+        stop_sim()
+        st.session_state.running = False
+with col3:
+    if st.button("Reset", use_container_width=True):
+        reset_sim()
+        st.session_state.running = False

main.py

@@ -13,6 +13,8 @@ import matplotlib.pyplot as pl
 import pandas as pd
 from concurrent.futures import ThreadPoolExecutor
 
+import threading  ### <<< CONTROL ADDED >>>
+import time  ### <<< CONTROL ADDED >>>
 
 # read config file
 c = yaml.safe_load(open("YAMLs/config.yml"))
@@ -21,105 +23,125 @@ c = yaml.safe_load(open("YAMLs/config.yml"))
 bess_data = yaml.safe_load(open(c["paths"]["bess"]))
 
 ## simulation time setup
-# get current time
 c["sim_start_time"] = get_start_time()
-# get time step in minutes, then convert to seconds
 dt = c["sim_time"]["time_step_minutes"] * 60
-# compute end time based on duration in days
 duration = c["sim_time"]["duration_days"] * 24 * 60 * 60
 c["sim_end_time"] = c["sim_start_time"] + duration
 timestamps = np.arange(c["sim_start_time"], c["sim_end_time"] + 1, dt)
-
-# batch process hours in seconds
 c["sim_time"]["batch_process_seconds"] = c["sim_time"]["batch_process_hours"] * 60 * 60
 
 # load site info
 c["site_info"] = yaml.safe_load(open(c["paths"]["site_info"]))
 
+### <<< CONTROL ADDED >>> Initialize simulation state globals
+sim_i = 0
+running = False
+is_running_in_async = False
+sim_lock = threading.Lock()
 
-def generate_and_cache_profiles(c, dt):
-    """Generates load profiles for all sites and caches them."""
-    return get_load_profiles(
+
+# initialise BESS
+def _init_state():
+    global bess_data, bess_soc_since_start, bess_soc_for_cycle, cumulative_load_profiles
+    bd = initialise_SoC(bess_data.copy())
+    bd = initial_site_assignment(c, bd)
+    bess_data = bd
+
+    bess_soc_since_start = pd.DataFrame(
+        columns=[unit["name"] for unit in bess_data["units"]]
+    )
+    init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
+    bess_soc_for_cycle = {unit["name"]: init_df for unit in bess_data["units"]}
+
+    cumulative_load_profiles = get_load_profiles(
         c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"]
     )
 
 
-# initialise BESS
-bess_data = initialise_SoC(bess_data)
-bess_data = initial_site_assignment(c, bess_data)
-# bess SoC dataframe
-bess_soc_since_start = pd.DataFrame(
-    columns=[unit["name"] for unit in bess_data["units"]]
-)
-# bess SoC dictionary, meant to track SoC progress over each cycle.
-# resets after each charging cycle. This is for predicting swap times.
-init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
-bess_soc_for_cycle = {unit["name"]: init_df for unit in bess_data["units"]}
+# do initial setup
+_init_state()
 
-# get initial load profiles
-cumulative_load_profiles = get_load_profiles(
-    c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"]
-)
 
-# async function is running
-is_running_in_async = False
+def simulation_loop():
+    """Runs the loop, stepping through timestamps until stopped or finished."""
+    global sim_i, running, is_running_in_async, cumulative_load_profiles, bess_data
+    with ThreadPoolExecutor() as executor:
+        while True:
+            with sim_lock:
+                if not running or sim_i >= len(timestamps):
+                    break
 
-# loop through
-with ThreadPoolExecutor() as executor:
-    for i in range(0, len(timestamps)):
-        # start generating load profiles 200 seconds before data required
-        if len(cumulative_load_profiles) <= len(timestamps):
-            if is_running_in_async is False:
-                # generate load profiles
-                future = executor.submit(generate_and_cache_profiles, c, dt)
-                is_running_in_async = True
-        else:
-            is_running_in_async = False
+                i = sim_i
+                sim_i += 1
 
-        # check if any BESS units are below threshold (buffer as defined in config)
-
-        # discharge BESS for each site
-        for site in c["site_info"]["sites"]:
-            site_name = site["name"]
-            discharge_power = cumulative_load_profiles[site_name].iloc[i]
-            bess_data = discharge_bess(bess_data, site_name, dt, discharge_power)
-        temp_soc = [unit["SoC"] for unit in bess_data["units"]]
-
-        # append SoC to dataframe
-        bess_soc_since_start = pd.concat(
-            [
-                bess_soc_since_start,
-                pd.DataFrame(
-                    [temp_soc],
-                    columns=bess_soc_since_start.columns,
-                    index=[timestamps[i]],
-                ),
-            ],
-            axis=0,
-        )
-
-        # update cycle SoC
-        # this is for predicting swap times
-        bess_soc_for_cycle = update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamps)
-
-        # predict swap times
-        swap_times = predict_swap_time(bess_soc_for_cycle)
-
-        # add to cumulative load profiles
-        # check if future exists and is done
-        if is_running_in_async:
-            if future.done():
-                load_profiles = future.result()
-                cumulative_load_profiles = pd.concat(
-                    [
-                        cumulative_load_profiles,
-                        load_profiles,
-                    ],
-                    axis=0,
-                )
-                print(len(cumulative_load_profiles), "load profiles generated")
+            # pre-fetch next batch if needed
+            if len(cumulative_load_profiles) <= len(timestamps):
+                if not is_running_in_async:
+                    future = executor.submit(
+                        get_load_profiles,
+                        c,
+                        dt,
+                        c["sim_start_time"],
+                        c["sim_time"]["batch_process_seconds"],
+                    )
+                    is_running_in_async = True
+            else:
                 is_running_in_async = False
 
-pl.plot(cumulative_load_profiles)
-pl.show()
-pl.plot(bess_soc_since_start)
+            # discharge BESS for each site
+            for site in c["site_info"]["sites"]:
+                name = site["name"]
+                p = cumulative_load_profiles[name].iloc[i]
+                bess_data = discharge_bess(bess_data, name, dt, p)
+
+            # record SoC
+            temp_soc = [u["SoC"] for u in bess_data["units"]]
+            bess_soc_since_start.loc[timestamps[i]] = temp_soc
+
+            # update cycle SoC and predict swaps
+            bess_soc_for_cycle = update_cycle_SoC(
+                bess_data, bess_soc_for_cycle, timestamps
+            )
+            swap_times = predict_swap_time(bess_soc_for_cycle)
+
+            # integrate newly fetched profiles
+            if is_running_in_async and future.done():
+                load_profiles = future.result()
+                cumulative_load_profiles = pd.concat(
+                    [cumulative_load_profiles, load_profiles], axis=0
+                )
+                print(len(cumulative_load_profiles), "profiles generated")
+                is_running_in_async = False
+
+            # small sleep to allow dashboard to refresh / release GIL
+            time.sleep(0.01)
+
+    # once loop ends, you can plot or notify completion here
+    pl.plot(cumulative_load_profiles)
+    pl.show()
+    pl.plot(bess_soc_since_start)
+    pl.show()
+
+
+### <<< CONTROL ADDED >>> Control functions
+def start_sim():
+    """Starts the simulation in a background thread."""
+    global running, sim_thread
+    if not running:
+        running = True
+        sim_thread = threading.Thread(target=simulation_loop, daemon=True)
+        sim_thread.start()
+
+
+def stop_sim():
+    """Stops the simulation loop."""
+    global running
+    running = False
+
+
+def reset_sim():
+    """Stops and re-initializes the simulation state."""
+    global running, sim_i
+    running = False
+    sim_i = 0
+    _init_state()