def __detect_anomalies__()

in lab/03-Package-Deploy/greengrass-v2/artifacts/aws.samples.windturbine.detector/1.0.0/inference/windturbine.py [0:0]

• 17 lines of code
• 3 McCabe index (conditional complexity)

    def __detect_anomalies__(self, buffer):     
        """
        Process the data received from the turbine and reports the 
        anomalies detected via MQTT
        """
        
        start_time = time.time()

        # create a copy & prep the data
        data = self.__data_prep__(np.array(buffer))
            
        if not self.edge_agent.is_model_loaded(self.model_meta['model_name']):
            model_label_data = {"model_label_status" : "Model not loaded"}
            self.msg_client.publish_model_status(model_label_data)
            return

        x = self.__preprocess_data__(data)
        # run the model                    
        p = self.edge_agent.predict(self.model_meta['model_name'], x)
        
        if p is not None:
            values, anomalies = self.__calculate_anomalies__(x, p)
            anomaly_result = {"values" : values.tolist(), "anomalies" : anomalies.tolist()} 
            self.msg_client.publish_anomalies(message=anomaly_result)   
        else:
            logging.info(f"No anomalies detected")

        elapsed_time = time.time() - start_time
        time.sleep(0.5-elapsed_time)