src/main.py

import json
from rtu.RTUAnomalizer1 import RTUAnomalizer1
from rtu.RTUAnomalizer2 import RTUAnomalizer2
from rtu.RTUPipeline import RTUPipeline
from vav.VAVPipeline import VAVPipeline
from vav.VAVAnomalizer import VAVAnomalizer
from energy_prediction.EnergyPredictionNorth import EnergyPredictionNorth
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_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,
    )
    #RTU - 3,4
    rtu_anomalizer2 = RTUAnomalizer2(
        prediction_model_path="src/rtu/models/lstm_2rtu_smooth_03.keras",
        clustering_model_paths=[
            "src/rtu/models/kmeans_rtu_3.pkl",
            "src/rtu/models/kmeans_rtu_4.pkl",
        ],
        pca_model_paths=[
            "src/rtu/models/pca_rtu_3.pkl",
            "src/rtu/models/pca_rtu_4.pkl",
        ],
        num_inputs=rtu_data_pipeline.num_inputs,
        num_outputs=rtu_data_pipeline.num_outputs,
    )

    vav_pipeline = VAVPipeline(rtu_id=1, scaler_path="src/vav/models/scaler_vav_1.pkl")

    vav_anomalizer = VAVAnomalizer(
        rtu_id=1,
        prediction_model_path="src/vav/models/lstm_vav_01.keras",
        clustering_model_path="src/vav/models/kmeans_vav_1.pkl",
        num_inputs=vav_pipeline.num_inputs,
        num_outputs=vav_pipeline.num_outputs,
    )
    # print(vav_pipeline.input_col_names)

    # 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
        vav_anomalizer.num_outputs = vav_pipeline.num_outputs
        if not df_new_vav is None and not df_trans_vav is None:
            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)

    broker_address = "localhost"
    broker_port = 1883
    topic = "sensor_data"
    client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)
    print("Connecting to broker")
    client.on_message = on_message
    client.connect(broker_address, broker_port)
    client.subscribe(topic)
    client.loop_forever()


if __name__ == "__main__":
    main()