Refactor RTU pipeline for improved scalability and maintainability

dashboard.py

@@ -0,0 +1,368 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import mqtt_client
+import time
+from src.rtu.RTUPipeline import RTUPipeline
+from src.rtu.RTUAnomalizer1 import RTUAnomalizer1
+import plotly.express as px
+
+rtu_data_pipeline = RTUPipeline(
+    scaler1_path="src/rtu/models/scaler_rtu_1_2.pkl",
+    scaler2_path="src/rtu/models/scaler_rtu_3_4.pkl",
+)
+rtu_anomalizer1 = RTUAnomalizer1(
+    prediction_model_path="src/rtu/models/lstm_2rtu_smooth_04.keras",
+    clustering_model_paths=[
+        "src/rtu/models/kmeans_rtu_1.pkl",
+        "src/rtu/models/kmeans_rtu_2.pkl",
+    ],
+    pca_model_paths=[
+        "src/rtu/models/pca_rtu_1.pkl",
+        "src/rtu/models/pca_rtu_2.pkl",
+    ],
+    num_inputs=rtu_data_pipeline.num_inputs,
+    num_outputs=rtu_data_pipeline.num_outputs,
+)
+
+print("Kmeans models:")
+print(rtu_anomalizer1.kmeans_models)
+
+# Set the layout of the page to 'wide'
+st.set_page_config(layout="wide")
+
+
+# Energy data generating used in Energy Usage Over Time plot ---- REPLACE WITH ACTUAL DATA ----
+def generate_energy_data():
+    times = pd.date_range("2021-01-01", periods=200, freq="1min")
+    energy = np.random.randn(200).cumsum()
+    return pd.DataFrame({"Time": times, "Energy": energy})
+
+
+# Create three columns for the header
+header_row1_col1, header_row1_col2, header_row1_col3 = st.columns([0.8, 3, 1])
+
+# Add logo to the first column of the header
+with header_row1_col1:
+    st.image("logo.png")
+
+# Add title to the second column of the header
+with header_row1_col2:
+    st.markdown(
+        "<h1 style='text-align: center;'>Building 59 - HVAC Dashboard</h1>",
+        unsafe_allow_html=True,
+    )
+
+# Add Time and Date to the third column of the header
+mqtt_client.start_mqtt_client()
+placeholder_header_time = header_row1_col3.empty()
+
+# Create three columns for the first row
+row1_col1, row1_col2, row1_col3 = st.columns([1.1, 1, 0.75])
+
+
+# Use a container for RTU Status
+rtu_status_container = row1_col1.container()
+rtu_status_container.markdown(
+    """
+    <div style="background-color:#E2F0D9;padding:1px;border-radius:5px;margin-bottom:20px">
+    <h3 style="color:black;text-align:center;">RTU Status</h3>
+    </div>""",
+    unsafe_allow_html=True,
+)
+
+rtu_placeholders = []
+rtu_columns = rtu_status_container.columns(4)
+
+# Initial placeholder, does not update with streaming
+for i in range(4):
+    with rtu_columns[i]:
+        placeholder = {"box": st.empty(), "sa_temp": st.empty(), "ra_temp": st.empty()}
+        rtu_placeholders.append(placeholder)
+        placeholder["box"].markdown(
+            f"""
+        <div style='background-color:#447F80;padding:3px;border-radius:5px;margin-bottom:10px'>
+            <h4 style='color:black;text-align:center;'>RTU{i+1}</h4>
+        </div>
+        """,
+            unsafe_allow_html=True,
+        )
+        placeholder["sa_temp"].markdown("**SA temp:**  --  °C")
+        placeholder["ra_temp"].markdown("**RA temp:**  --  °C")
+
+all_data = []
+
+
+# Temperatures streaming and updates
+def update_status_boxes(df):
+    for i in range(4):
+        sa_temp = df[f"rtu_00{i+1}_sa_temp"].iloc[-1]
+        ra_temp = df[f"rtu_00{i+1}_ra_temp"].iloc[-1]
+        rtu_placeholders[i]["sa_temp"].markdown(f"**SA temp:**  {sa_temp} °C")
+        rtu_placeholders[i]["ra_temp"].markdown(f"**RA temp:**  {ra_temp} °C")
+
+
+# Zones
+with row1_col2:
+    st.markdown(
+        """
+    <div style="background-color:#E2F0D9;padding:1px;border-radius:5px;margin-bottom:20px">
+    <h3 style="color:black;text-align:center;">Zones</h3>
+    </div>""",
+        unsafe_allow_html=True,
+    )
+
+    tab1, tab2, tab3, tab4 = st.tabs(["RTU 1", "RTU 2", "RTU 3", "RTU 4"])
+
+    with tab1:
+
+        zones_ = {36, 37, 38, 39, 40, 41, 42, 64, 65, 66, 67, 68, 69, 70}
+
+        num_cols = 7
+        rows = 2
+
+        for i in range(rows):
+            cols = st.columns(num_cols)
+            if i == 0:
+                for j in range(num_cols):
+                    zone_number = (i + 1) * (j + 1) + 35
+                    if zone_number in zones_:
+                        button_html = f'<button style="width:100%; height:50px; border:none; color:black; background-color:#FFFFFF">{zone_number}</button>'
+                        with cols[j]:
+                            st.markdown(button_html, unsafe_allow_html=True)
+                    else:
+                        with cols[j]:
+                            st.write("")
+            else:
+                for j in range(num_cols):
+                    zone_number = (i + 1) * 30 + j + 4
+                    if zone_number in zones_:
+                        button_html = f'<button style="width:100%; height:50px; border:none; color:black; background-color:#FFFFFF">{zone_number}</button>'
+                        with cols[j]:
+                            st.markdown(button_html, unsafe_allow_html=True)
+                    else:
+                        with cols[j]:
+                            st.write("")
+
+    with tab2:
+        zones_ = [
+            19,
+            20,
+            27,
+            28,
+            29,
+            30,
+            31,
+            32,
+            33,
+            34,
+            35,
+            43,
+            44,
+            49,
+            50,
+            57,
+            58,
+            59,
+            60,
+            62,
+            63,
+            71,
+            72,
+        ]
+        zones_list = list(zones_)
+        num_cols = 7
+        rows = 4
+        zones_list_rows = [
+            zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows)
+        ]
+
+        for row in zones_list_rows:
+            cols = st.columns(num_cols)
+            for col, zone_number in zip(cols, row):
+                button_html = f'<button style="width:100%; height:50px; border:none; color:black; background-color:#FFFFFF">{zone_number}</button>'
+                with col:
+                    st.markdown(button_html, unsafe_allow_html=True)
+
+    with tab3:
+        zones_ = [18, 25, 26, 45, 48, 55, 56, 61]
+        zones_list = sorted(zones_)
+        num_cols = 7
+        rows = 2
+        zones_list_rows = [
+            zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows)
+        ]
+
+        for row in zones_list_rows:
+            cols = st.columns(num_cols)
+            for col, zone_number in zip(cols, row):
+                button_html = f'<button style="width:100%; height:50px; border:none; color:black; background-color:#FFFFFF">{zone_number}</button>'
+                with col:
+                    st.markdown(button_html, unsafe_allow_html=True)
+
+    with tab4:
+        zones_ = [16, 17, 21, 22, 23, 24, 46, 47, 51, 52, 53, 54]
+        zones_list = sorted(zones_)
+        num_cols = 7
+        rows = 2
+        zones_list_rows = [
+            zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows)
+        ]
+
+        for row in zones_list_rows:
+            cols = st.columns(num_cols)
+            for col, zone_number in zip(cols, row):
+                button_html = f'<button style="width:100%; height:50px; border:none; color:black; background-color:#FFFFFF">{zone_number}</button>'
+                with col:
+                    st.markdown(button_html, unsafe_allow_html=True)
+
+# Faults
+with row1_col3:
+    st.markdown(
+        """
+    <div style="background-color:#E2F0D9;padding:1px;border-radius:5px;margin-bottom:20px">
+    <h3 style="color:black;text-align:center;">Faults</h3>
+    </div>""",
+        unsafe_allow_html=True,
+    )
+
+    fault_data = {  # ---- REPLACE WITH ACTUAL DATA ----
+        "Start Time": ["28/12/17 10:00", "29/12/17 11:00"],
+        "End Time": ["28/12/17 12:00", "29/12/17 15:00"],
+        "Issue": ["RTU 3 – Damper stuck", "Zone 6 – Fan failure"],
+    }
+    df_faults = pd.DataFrame(fault_data)
+    st.dataframe(df_faults)
+
+
+# Details
+with st.container():
+    st.markdown(
+        """
+    <div style="background-color:#E2F0D9;padding:1px;border-radius:5px;margin-bottom:20px">
+    <h3 style="color:black;text-align:center;">Details</h3>
+    </div>""",
+        unsafe_allow_html=True,
+    )
+
+    # Create three columns
+    row2_row1_col1, row2_row1_col2 = st.columns([0.9, 1.5])
+
+    # Floor Plan
+    with row2_row1_col1:
+        st.subheader("Floor Map")
+        st.image("floor_plan.jpg", use_column_width=True)
+
+    # Energy Comsumption Plots
+    with row2_row1_col2:
+
+        # Create two rows and two columns
+        row2_row2_col1, row2_row2_col2 = st.columns(2)
+        # cols = st.columns(2)
+
+        with row2_row2_col1:
+            st.subheader("Energy Usage - North Wing")
+            df_energy = generate_energy_data()  # ---- REPLACE WITH ACTUAL DATA ----
+            fig, ax = plt.subplots(figsize=(5, 1.5))
+            ax.plot(df_energy["Time"], df_energy["Energy"])
+            ax.set_xlabel("Time")
+            ax.set_ylabel("Energy (kWh)")
+            st.pyplot(fig)
+
+            # with row2_row2_col2:
+            st.subheader("Energy Usage - South Wing")
+            df_energy = generate_energy_data()  # ---- REPLACE WITH ACTUAL DATA ----
+            fig, ax = plt.subplots(figsize=(5, 1.5))
+            ax.plot(df_energy["Time"], df_energy["Energy"])
+            ax.set_xlabel("Time")
+            ax.set_ylabel("Energy (kWh)")
+            st.pyplot(fig)
+
+            # Energy Comsumption Statistics
+            with row2_row2_col2:
+                st.subheader("Energy Usage Statistics")
+                st.text(
+                    f"Average: 475 kWh\nHighest: 600 kWh"
+                )  # ---- REPLACE WITH ACTUAL DATA ----
+
+
+distance_placeholder = st.empty()
+resid_placeholder = st.empty()
+
+distances = []
+while True:
+
+    if mqtt_client.data_list:
+        all_data.extend(mqtt_client.data_list)
+        df = pd.DataFrame(all_data)
+
+        df_time = df["date"].iloc[-1]  # Obtain the latest datetime of data
+
+        with placeholder_header_time:
+            placeholder_header_time.markdown(
+                f"""
+            <h2 style='text-align: center;'> 🕒 {df_time}</h2>
+            """,
+                unsafe_allow_html=True,
+            )
+
+        # Loop to update
+        update_status_boxes(df)
+
+        dist = None
+        resid_pca_list = None
+        df_new1, df_trans1, df_new2, df_trans2 = rtu_data_pipeline.fit(df)
+        if (
+            not df_new1 is None
+            and not df_trans1 is None
+            and not df_new2 is None
+            and not df_trans2 is None
+        ):
+            actual_list, pred_list, resid_list, resid_pca_list, dist = (
+                rtu_anomalizer1.pipeline(df_new1, df_trans1, rtu_data_pipeline.scaler1)
+            )
+
+        if resid_pca_list is not None:
+            resid_pca_list = np.array(resid_pca_list)
+
+        # plot the distances in a scatter chart in streamlit plotly express
+        with distance_placeholder:
+            if dist is not None:
+                distances.append(float(dist[0][0]))
+                fig = px.line(
+                    x=range(len(distances)),
+                    y=distances,
+                    labels={"x": "Time", "y": "Distance"},
+                    title="Distance from Cluster Center",
+                )
+                st.plotly_chart(fig)
+
+        with resid_placeholder:
+            if resid_pca_list is not None:
+                fig = px.scatter(
+                    x=resid_pca_list[:, 0],
+                    y=resid_pca_list[:, 1],
+                    labels={"x": "Time", "y": "Residual"},
+                    title="Residuals",
+                    width=800,
+                    height=800,
+                )
+                fig.update_layout(
+                    xaxis_range=[-1, 1],
+                    yaxis_range=[-1, 1],
+                    xaxis=dict(showgrid=True, gridwidth=1, gridcolor="lightgray"),
+                    yaxis=dict(showgrid=True, gridwidth=1, gridcolor="lightgray"),
+                    margin=dict(l=20, r=20, t=20, b=20),
+                    hovermode="closest",
+                    showlegend=False,
+                    autosize=False,
+                    hoverlabel=dict(bgcolor="white", font_size=12),
+                    hoverlabel_align="left",
+                    hoverlabel_font_color="black",
+                    hoverlabel_bordercolor="lightgray",
+                )
+                fig.update_traces(marker=dict(size=5, color="blue"))
+                st.plotly_chart(fig)
+
+        mqtt_client.data_list.clear()

mqtt_client.py

@@ -0,0 +1,34 @@
+# mqtt_client.py
+import paho.mqtt.client as mqtt
+import json
+
+broker = "localhost"
+port = 1883
+topic = "sensor_data"
+
+data_list = []
+
+
+def on_connect(client, userdata, flags, rc):
+    print(f"Connected with result code {rc}")
+    client.subscribe(topic)
+
+
+def on_message(client, userdata, msg):
+    global data_list
+    data = json.loads(msg.payload)
+    data_list.append(data)
+
+
+client = mqtt.Client()
+client.on_connect = on_connect
+client.on_message = on_message
+
+
+def start_mqtt_client():
+    client.connect(broker, port)
+    client.loop_start()
+
+
+if __name__ == "__main__":
+    start_mqtt_client()

mqttclient.ipynb

@@ -1,1100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jerin\\AppData\\Local\\Temp\\ipykernel_3260\\368714138.py:22: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
-      "  client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'date': '2018-05-02 00:09:00', 'hp_hws_temp': 96.3, 'rtu_003_sa_temp': 64.2, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.5, 'rtu_003_oa_temp': 61.6, 'rtu_003_ma_temp': 63.5, 'rtu_003_sf_vfd_spd_fbk_tn': 77.8, 'rtu_003_rf_vfd_spd_fbk_tn': 54.3, 'rtu_004_sa_temp': 68.9, 'rtu_004_oadmpr_pct': 54.2, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 67.4, 'rtu_004_ma_temp': 67.6, 'rtu_004_sf_vfd_spd_fbk_tn': 75.7, 'rtu_004_rf_vfd_spd_fbk_tn': 78.3, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:10:00', 'hp_hws_temp': 97.0, 'rtu_003_sa_temp': 64.1, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 61.9, 'rtu_003_ma_temp': 63.3, 'rtu_003_sf_vfd_spd_fbk_tn': 77.2, 'rtu_003_rf_vfd_spd_fbk_tn': 52.5, 'rtu_004_sa_temp': 68.8, 'rtu_004_oadmpr_pct': 80.6, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 68.2, 'rtu_004_ma_temp': 66.1, 'rtu_004_sf_vfd_spd_fbk_tn': 75.6, 'rtu_004_rf_vfd_spd_fbk_tn': 67.3, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:11:00', 'hp_hws_temp': 97.6, 'rtu_003_sa_temp': 64.1, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.0, 'rtu_003_ma_temp': 63.8, 'rtu_003_sf_vfd_spd_fbk_tn': 78.2, 'rtu_003_rf_vfd_spd_fbk_tn': 56.7, 'rtu_004_sa_temp': 67.3, 'rtu_004_oadmpr_pct': 76.6, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.6, 'rtu_004_ma_temp': 64.3, 'rtu_004_sf_vfd_spd_fbk_tn': 78.6, 'rtu_004_rf_vfd_spd_fbk_tn': 77.5, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:12:00', 'hp_hws_temp': 98.3, 'rtu_003_sa_temp': 64.2, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.1, 'rtu_003_ma_temp': 64.1, 'rtu_003_sf_vfd_spd_fbk_tn': 76.4, 'rtu_003_rf_vfd_spd_fbk_tn': 52.9, 'rtu_004_sa_temp': 66.7, 'rtu_004_oadmpr_pct': 51.4, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.9, 'rtu_004_ma_temp': 65.1, 'rtu_004_sf_vfd_spd_fbk_tn': 79.6, 'rtu_004_rf_vfd_spd_fbk_tn': 82.5, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:13:00', 'hp_hws_temp': 98.9, 'rtu_003_sa_temp': 64.3, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.3, 'rtu_003_oa_temp': 62.2, 'rtu_003_ma_temp': 63.8, 'rtu_003_sf_vfd_spd_fbk_tn': 78.5, 'rtu_003_rf_vfd_spd_fbk_tn': 57.0, 'rtu_004_sa_temp': 67.8, 'rtu_004_oadmpr_pct': 52.4, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.9, 'rtu_004_ma_temp': 67.8, 'rtu_004_sf_vfd_spd_fbk_tn': 78.3, 'rtu_004_rf_vfd_spd_fbk_tn': 73.9, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:14:00', 'hp_hws_temp': 99.3, 'rtu_003_sa_temp': 64.4, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.5, 'rtu_003_ma_temp': 63.9, 'rtu_003_sf_vfd_spd_fbk_tn': 75.9, 'rtu_003_rf_vfd_spd_fbk_tn': 49.8, 'rtu_004_sa_temp': 68.8, 'rtu_004_oadmpr_pct': 52.4, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 68.1, 'rtu_004_ma_temp': 68.3, 'rtu_004_sf_vfd_spd_fbk_tn': 76.5, 'rtu_004_rf_vfd_spd_fbk_tn': 74.7, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:15:00', 'hp_hws_temp': 99.0, 'rtu_003_sa_temp': 63.9, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.3, 'rtu_003_oa_temp': 62.6, 'rtu_003_ma_temp': 63.6, 'rtu_003_sf_vfd_spd_fbk_tn': 74.3, 'rtu_003_rf_vfd_spd_fbk_tn': 53.5, 'rtu_004_sa_temp': 69.1, 'rtu_004_oadmpr_pct': 79.0, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 67.2, 'rtu_004_ma_temp': 66.6, 'rtu_004_sf_vfd_spd_fbk_tn': 75.3, 'rtu_004_rf_vfd_spd_fbk_tn': 69.0, 'air_temp_set_1': 16.0, 'air_temp_set_2': 15.48, 'dew_point_temperature_set_1d': 6.63, 'relative_humidity_set_1': 54.0, 'solar_radiation_set_1': 426.3}\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[6], line 26\u001b[0m\n\u001b[0;32m     24\u001b[0m client\u001b[38;5;241m.\u001b[39mconnect(broker_address, broker_port)\n\u001b[0;32m     25\u001b[0m client\u001b[38;5;241m.\u001b[39msubscribe(topic)\n\u001b[1;32m---> 26\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\paho\\mqtt\\client.py:2291\u001b[0m, in \u001b[0;36mClient.loop_forever\u001b[1;34m(self, timeout, retry_first_connection)\u001b[0m\n\u001b[0;32m   2289\u001b[0m rc \u001b[38;5;241m=\u001b[39m MQTTErrorCode\u001b[38;5;241m.\u001b[39mMQTT_ERR_SUCCESS\n\u001b[0;32m   2290\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m rc \u001b[38;5;241m==\u001b[39m MQTTErrorCode\u001b[38;5;241m.\u001b[39mMQTT_ERR_SUCCESS:\n\u001b[1;32m-> 2291\u001b[0m     rc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_loop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2292\u001b[0m     \u001b[38;5;66;03m# We don't need to worry about locking here, because we've\u001b[39;00m\n\u001b[0;32m   2293\u001b[0m     \u001b[38;5;66;03m# either called loop_forever() when in single threaded mode, or\u001b[39;00m\n\u001b[0;32m   2294\u001b[0m     \u001b[38;5;66;03m# in multi threaded mode when loop_stop() has been called and\u001b[39;00m\n\u001b[0;32m   2295\u001b[0m     \u001b[38;5;66;03m# so no other threads can access _out_packet or _messages.\u001b[39;00m\n\u001b[0;32m   2296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_thread_terminate \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   2297\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_out_packet) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   2298\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_out_messages) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m):\n",
-      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\paho\\mqtt\\client.py:1657\u001b[0m, in \u001b[0;36mClient._loop\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m   1654\u001b[0m     rlist \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sockpairR]\n\u001b[0;32m   1656\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1657\u001b[0m     socklist \u001b[38;5;241m=\u001b[39m select\u001b[38;5;241m.\u001b[39mselect(rlist, wlist, [], timeout)\n\u001b[0;32m   1658\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;66;03m# Socket isn't correct type, in likelihood connection is lost\u001b[39;00m\n\u001b[0;32m   1660\u001b[0m     \u001b[38;5;66;03m# ... or we called disconnect(). In that case the socket will\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1663\u001b[0m     \u001b[38;5;66;03m# rc != MQTT_ERR_SUCCESS and we don't want state to change from\u001b[39;00m\n\u001b[0;32m   1664\u001b[0m     \u001b[38;5;66;03m# mqtt_cs_disconnecting.\u001b[39;00m\n\u001b[0;32m   1665\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (_ConnectionState\u001b[38;5;241m.\u001b[39mMQTT_CS_DISCONNECTING, _ConnectionState\u001b[38;5;241m.\u001b[39mMQTT_CS_DISCONNECTED):\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "import paho.mqtt.client as mqtt\n",
-    "import json\n",
-    "import pandas as pd\n",
-    "\n",
-    "broker_address = \"localhost\"\n",
-    "broker_port = 1883\n",
-    "topic = \"sensor_data\"\n",
-    "\n",
-    "df = pd.DataFrame(columns=[\"sa_temp\", \"ma_temp\"])\n",
-    "\n",
-    "def on_message(client, userdata, message):\n",
-    "    global df\n",
-    "    payload = json.loads(message.payload.decode())\n",
-    "    # sa_temp = payload[\"sa_temp\"]\n",
-    "    # ma_temp = payload[\"ma_temp\"]\n",
-    "    \n",
-    "    print(payload)\n",
-    "    # df.loc[len(df)] = {\"sa_temp\": sa_temp, \"ma_temp\": ma_temp}\n",
-    " \n",
-    "\n",
-    "\n",
-    "client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)\n",
-    "client.on_message = on_message\n",
-    "client.connect(broker_address, broker_port)\n",
-    "client.subscribe(topic)\n",
-    "client.loop_forever()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th></th>\n",
-       "      <th>sa_temp</th>\n",
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-       "      <td>68.6</td>\n",
-       "      <td>70.0</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>68.6</td>\n",
-       "      <td>70.0</td>\n",
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-       "   sa_temp  ma_temp\n",
-       "0     68.6     70.0\n",
-       "1     68.6     70.0\n",
-       "2     68.6     70.0\n",
-       "3     68.6     70.0"
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-     "execution_count": 25,
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-     "output_type": "execute_result"
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-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
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-      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\plotly\\io\\_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    390\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    391\u001b[0m         )\n\u001b[0;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 394\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    395\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    396\u001b[0m         )\n\u001b[0;32m    398\u001b[0m     ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n",
-      "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
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                       {\"responsive\": true}                    )                };                            </script>        </div>"
-      ],
-      "text/plain": [
-       "Figure({\n",
-       "    'data': [{'hovertemplate': 'index=%{x}<br>sa_temp=%{y}<extra></extra>',\n",
-       "              'legendgroup': '',\n",
-       "              'line': {'color': '#636efa', 'dash': 'solid'},\n",
-       "              'marker': {'symbol': 'circle'},\n",
-       "              'mode': 'lines',\n",
-       "              'name': '',\n",
-       "              'orientation': 'v',\n",
-       "              'showlegend': False,\n",
-       "              'type': 'scatter',\n",
-       "              'x': array([], dtype=int64),\n",
-       "              'xaxis': 'x',\n",
-       "              'y': array([], dtype=object),\n",
-       "              'yaxis': 'y'}],\n",
-       "    'layout': {'legend': {'tracegroupgap': 0},\n",
-       "               'margin': {'t': 60},\n",
-       "               'template': '...',\n",
-       "               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'index'}},\n",
-       "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'sa_temp'}}}\n",
-       "})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "smartbuilding",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

src/main.py

@@ -9,8 +9,11 @@ import paho.mqtt.client as mqtt
 
 
 def main():
-    rtu_data_pipeline = RTUPipeline(scaler1_path="src/rtu/models/scaler_rtu_1_2.pkl",scaler2_path="src/rtu/models/scaler_rtu_3_4.pkl")
-    #RTU - 1, 2
+    rtu_data_pipeline = RTUPipeline(
+        scaler1_path="src/rtu/models/scaler_rtu_1_2.pkl",
+        scaler2_path="src/rtu/models/scaler_rtu_3_4.pkl",
+    )
+    # RTU - 1, 2
     rtu_anomalizer1 = RTUAnomalizer1(
         prediction_model_path="src/rtu/models/lstm_2rtu_smooth_04.keras",
         clustering_model_paths=[
@@ -24,7 +27,8 @@ def main():
         num_inputs=rtu_data_pipeline.num_inputs,
         num_outputs=rtu_data_pipeline.num_outputs,
     )
-    #RTU - 3,4
+    print(rtu_anomalizer1.kmeans_models)
+    # RTU - 3,4
     rtu_anomalizer2 = RTUAnomalizer2(
         prediction_model_path="src/rtu/models/lstm_2rtu_smooth_03.keras",
         clustering_model_paths=[
@@ -52,9 +56,6 @@ def main():
 
     # print(len(vav_pipeline.output_col_names))
 
-
-
-
     def on_message(client, userdata, message):
         df_new_vav, df_trans_vav = vav_pipeline.fit(message)
         vav_anomalizer.num_inputs = vav_pipeline.num_inputs
@@ -63,12 +64,21 @@ def main():
             out_vav = vav_anomalizer.pipeline(
                 df_new_vav, df_trans_vav, vav_pipeline.scaler
             )
-            
-        df_new1, df_trans1, df_new2, df_trans2 = rtu_data_pipeline.fit(message)    
-        if not df_new1 is None and not df_trans1 is None and not df_new2 is None and not df_trans2 is None:
-            out1,out2,out3,out4 = rtu_anomalizer1.pipeline(df_new1, df_trans1, rtu_data_pipeline.scaler1)
-            out5,out6,out7,out8 = rtu_anomalizer2.pipeline(df_new2, df_trans2, rtu_data_pipeline.scaler2)
-            #print(out2)
+
+        df_new1, df_trans1, df_new2, df_trans2 = rtu_data_pipeline.fit(message)
+        if (
+            not df_new1 is None
+            and not df_trans1 is None
+            and not df_new2 is None
+            and not df_trans2 is None
+        ):
+            out1, out2, out3, out4 = rtu_anomalizer1.pipeline(
+                df_new1, df_trans1, rtu_data_pipeline.scaler1
+            )
+            out5, out6, out7, out8 = rtu_anomalizer2.pipeline(
+                df_new2, df_trans2, rtu_data_pipeline.scaler2
+            )
+            # print(out2)
 
     broker_address = "localhost"
     broker_port = 1883

src/rtu/RTUAnomalizer1.py

@@ -8,10 +8,6 @@ class RTUAnomalizer1:
     Class for performing anomaly detection on RTU (Roof Top Unit) data.
     """
 
-    model = None
-    kmeans_models = []
-    pca_models = []
-
     def __init__(
         self,
         prediction_model_path=None,
@@ -29,12 +25,24 @@ class RTUAnomalizer1:
             num_inputs (int): Number of input features.
             num_outputs (int): Number of output features.
         """
+        self.model = None
+        self.kmeans_models = []
+        self.pca_models = []
+
         self.num_inputs = num_inputs
         self.num_outputs = num_outputs
-        if prediction_model_path is not None and clustering_model_paths is not None and pca_model_paths is not None:
-            self.load_models(prediction_model_path, clustering_model_paths, pca_model_paths)
-            
-        self.actual_list, self.pred_list, self.resid_list = self.initialize_lists()
+        if (
+            prediction_model_path is not None
+            and clustering_model_paths is not None
+            and pca_model_paths is not None
+        ):
+            self.load_models(
+                prediction_model_path, clustering_model_paths, pca_model_paths
+            )
+
+        self.actual_list, self.pred_list, self.resid_list, self.resid_pca_list = (
+            self.initialize_lists()
+        )
 
     def initialize_lists(self, size=30):
         """
@@ -46,10 +54,18 @@ class RTUAnomalizer1:
         Returns:
             tuple: A tuple containing three lists initialized with zeros.
         """
-        initial_values = [[0]*self.num_outputs] * size
-        return initial_values.copy(), initial_values.copy(), initial_values.copy()
+        initial_values = [[0] * self.num_outputs] * size
+        initial_values1 = [[0] * 4] * size
+        return (
+            initial_values.copy(),
+            initial_values.copy(),
+            initial_values.copy(),
+            initial_values1.copy(),
+        )
 
-    def load_models(self, prediction_model_path, clustering_model_paths, pca_model_paths):
+    def load_models(
+        self, prediction_model_path, clustering_model_paths, pca_model_paths
+    ):
         """
         Load the prediction and clustering models.
 
@@ -61,7 +77,7 @@ class RTUAnomalizer1:
 
         for path in clustering_model_paths:
             self.kmeans_models.append(joblib.load(path))
-            
+
         for path in pca_model_paths:
             self.pca_models.append(joblib.load(path))
 
@@ -75,7 +91,7 @@ class RTUAnomalizer1:
         Returns:
             array: Predicted values.
         """
-        return self.model.predict(df_new,verbose=0)
+        return self.model.predict(df_new, verbose=0)
 
     def calculate_residuals(self, df_trans, pred):
         """
@@ -161,14 +177,24 @@ class RTUAnomalizer1:
             array: Array of distances.
         """
         dist = []
+        resid_pcas = []
         for i, model in enumerate(self.kmeans_models):
+            resid_pca = self.pca_models[i].transform(
+                resid[:, (i * 7) + 1 : (i * 7) + 8]
+            )
+
+            resid_pcas = resid_pcas + resid_pca.tolist()
+
             dist.append(
                 np.linalg.norm(
-                    self.pca_models[i].transform(resid[:, (i * 7) + 1 : (i * 7) + 8]) - model.cluster_centers_[0],
+                    resid_pca - model.cluster_centers_[0],
                     ord=2,
                     axis=1,
                 )
             )
+        resid_pcas = np.array(resid_pcas).flatten().tolist()
+        self.resid_pca_list.pop(0)
+        self.resid_pca_list.append(resid_pcas)
 
         return np.array(dist)
 
@@ -184,12 +210,11 @@ class RTUAnomalizer1:
         Returns:
             tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
         """
-        
+
         pred = self.predict(df_new)
         actual, resid = self.calculate_residuals(df_trans, pred)
         pred = self.resize_prediction(pred, df_trans)
         actual, pred = self.inverse_transform(scaler, pred, df_trans)
-        actual_list, pred_list, resid_list = self.update_lists(
-             actual, pred, resid)
+        actual_list, pred_list, resid_list = self.update_lists(actual, pred, resid)
         dist = self.calculate_distances(resid)
-        return actual_list, pred_list, resid_list, dist
+        return actual_list, pred_list, resid_list, self.resid_pca_list, dist

src/rtu/RTUPipeline.py

@@ -7,10 +7,10 @@ import numpy as np
 
 
 class RTUPipeline:
-    scaler1 = None # RTU 1,2
-    scaler2 = None # RTU 3,4
+    scaler1 = None  # RTU 1,2
+    scaler2 = None  # RTU 3,4
 
-    def __init__(self, rtus=[1, 2, 3, 4], scaler1_path=None,scaler2_path=None):
+    def __init__(self, rtus=[1, 2, 3, 4], scaler1_path=None, scaler2_path=None):
 
         outputs = [
             "sa_temp",
@@ -28,11 +28,11 @@ class RTUPipeline:
         for rtu in rtus:
             for output in outputs:
                 self.output_col_names.append(f"rtu_00{rtu}_{output}")
-                
+
         self.input_col_names = []
-                
+
         for rtu in rtus:
-            self.input_col_names.append(f"rtu_00{rtu}_sat_sp_tn")        
+            self.input_col_names.append(f"rtu_00{rtu}_sat_sp_tn")
 
         self.input_col_names = self.input_col_names + [
             "air_temp_set_1",
@@ -41,53 +41,50 @@ class RTUPipeline:
             "relative_humidity_set_1",
             "solar_radiation_set_1",
         ]
-        self.num_inputs = len(self.input_col_names)-2
-        self.num_outputs = len(self.output_col_names)-14
+        self.num_inputs = len(self.input_col_names) - 2
+        self.num_outputs = len(self.output_col_names) - 14
         self.column_names = self.output_col_names + self.input_col_names
 
         if scaler1_path:
             self.scaler1 = self.get_scaler(scaler1_path)
         if scaler2_path:
             self.scaler2 = self.get_scaler(scaler2_path)
-            
+
         self.df = pd.DataFrame(columns=self.column_names)
 
     def get_scaler(self, scaler_path):
         return joblib.load(scaler_path)
 
-    def get_window(self, df):
-        len_df = len(df)
-        print(len_df)
-        if len_df > 30:
-            df = df.rolling(window=30,min_periods=1).mean()
-            return df[len_df - 31 : len_df].astype("float32")
-        else:
-            return None
-
     def transform_window(self, df_window):
-        columns_scaler1 = [0] + list(range(1,15)) + [29,30] + list(range(33, 38))
-        columns_scaler2 = [0] + list(range(15, 29)) + [31,32] + list(range(33, 38))
-        return self.scaler1.transform(df_window.iloc[:, columns_scaler1]),self.scaler2.transform(df_window.iloc[:, columns_scaler2])
+        columns_scaler1 = [0] + list(range(1, 15)) + [29, 30] + list(range(33, 38))
+        columns_scaler2 = [0] + list(range(15, 29)) + [31, 32] + list(range(33, 38))
+        return self.scaler1.transform(
+            df_window.iloc[:, columns_scaler1].values
+        ), self.scaler2.transform(df_window.iloc[:, columns_scaler2].values)
 
     def prepare_input(self, df_trans):
-        return df_trans[:30, :].reshape((1, 30, len(self.column_names)-16))
-
-    def extract_data_from_message(self, message):
-        payload = json.loads(message.payload.decode())
+        return df_trans[:30, :].reshape((1, 30, len(self.column_names) - 16))
 
+    def extract_data_from_message(self, df: pd.DataFrame):
+        df = df[self.column_names]
         len_df = len(self.df)
+        if len(self.df) != 0:
+            self.df = pd.concat([self.df, df], axis=0)
+        else:
+            self.df = df
+
+        if len_df > 31:
+            self.df = self.df.iloc[len_df - 31 : len_df]
+            self.df.loc[len_df] = self.df.mean()
+            return self.df
+        else:
+            return None
 
-        k = {}
-        for col in self.column_names:
-            k[col] = payload[col]
-        self.df.loc[len_df] = k
-        return self.df
+    def fit(self, df: pd.DataFrame):
 
-    def fit(self, message):
-        df = self.extract_data_from_message(message)
-        df_window = self.get_window(df)
+        df_window = self.extract_data_from_message(df)
         if df_window is not None:
-            df_trans1,df_trans2 = self.transform_window(df_window)
+            df_trans1, df_trans2 = self.transform_window(df_window)
             df_new1 = self.prepare_input(df_trans1)
             df_new2 = self.prepare_input(df_trans2)
         else: