Merge pull request #36 from starschema/main

Move to production 230627

.gitignore

@@ -142,3 +142,5 @@ deployment/*/Pulumi.prod*.yaml
 
 tmp_downloads
 
+containers/hvr/*.gz
+containers/hvr/*.lic

containers/predictive_maintenance/Dockerfile

@@ -0,0 +1,14 @@
+FROM python:3.10
+
+WORKDIR /app_pred
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# load or predict
+# ENV LOAD_MODE="load"
+
+ENTRYPOINT ["./entrypoint.sh"]

containers/predictive_maintenance/config.py

@@ -0,0 +1,18 @@
+import os
+
+# influx
+token = os.environ.get("INFLUX_TOKEN")
+org = os.environ.get("INFLUX_ORG") or "Starschema"
+url_base = os.environ.get("INFLUX_URL") or "http://influxdb2"
+url_port = int(os.environ.get("INFLUX_PORT") or 80)
+url = url_base + ":" + str(url_port)
+
+bucket = os.environ.get("INFLUX_BUCKET") or "default"
+
+# mqtt
+broker_address = os.environ.get("MQTT_HOST")
+broker_port = int(os.environ.get("MQTT_PORT") or 1883)
+wait_time_on_publish = 6
+
+raw_data_topic = "/engines/raw"
+predictions_topic = "/engines/predictions"

containers/predictive_maintenance/data_help_funcs.py

@@ -0,0 +1,56 @@
+import pandas as pd
+
+
+def get_test_data():
+    TEST_DATA_PATH = "test_data_looped.csv"
+    test_set = pd.read_csv(TEST_DATA_PATH).sort_values("time_in_cycles")
+    test_set = test_set.sort_values(by=["new_cycle", "engine_no"])
+    test_set = test_set.drop("new_cycle", axis=1)
+    return test_set
+
+
+def rename_sensors(row, pred_feautures, anomaly_features):
+    sensor_names = [
+        "Total temperature at fan inlet",
+        "Total temperature at LPC outlet",
+        "Total temperature at HPC outlet",
+        "Total Temperature LPT outlet",
+        "Pressure at fan inlet",
+        "Total pressure in bypass-duct",
+        "Total pressure at HPC outlet",
+        "Physical fan speed",
+        "Physical core speed",
+        "Engine pressure ratio (P50/P2)",
+        "Static pressure at HPC outlet",
+        "Ratio of fuel flow to Ps",
+        "Corrected fan speed",
+        "Corrected core speed",
+        "Bypass Ratio",
+        "Burner fuel-air ratio",
+        "Bleed enthalpy",
+        "Demanded fan speed",
+        "Demanded corrected fan speed",
+        "HPT coolant bleed",
+        "LPT coolant bleed",
+    ]
+
+    sid_to_fname = {f"sensor_{i+1}": name for i, name in enumerate(sensor_names)}
+
+    col_rename_dict = {
+        "engine_no": "Engine ID",
+        "time_in_cycles": "Completed cycles",
+        "op_setting_1": "Altitude",
+        "op_setting_2": "Mach number",
+        **sid_to_fname,
+    }
+
+    row.index = [col_rename_dict[attr_name] for attr_name in row.index]
+    top_predicitive_features = {
+        f"top_{ind+1}_predictive_feature": col_rename_dict[f.replace("_avg", "")]
+        for ind, f in enumerate(pred_feautures)
+    }
+    top_anomalous_features = {
+        f"anomaly_feature_{ind+1}": col_rename_dict[f]
+        for ind, f in enumerate(anomaly_features)
+    }
+    return row, top_predicitive_features, top_anomalous_features

containers/predictive_maintenance/entrypoint.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# The first argument is assigned to the variable 'input'
+input=${LOAD_MODEL:-"predict"}
+
+# Check the input value and execute the corresponding script
+if [[ $input == "predict" ]]; then
+    echo "Executing main_predict.py"
+    python main_predict.py
+elif [[ $input == "load" ]]; then
+    echo "Executing main_data_load.py"
+    python main_data_load.py
+else
+    echo "Invalid input. Please enter either 'load' or 'predict'."
+fi

containers/predictive_maintenance/first_9_records_test.csv

@@ -0,0 +1,91 @@
+engine_no,time_in_cycles,op_setting_1,op_setting_2,sensor_2,sensor_3,sensor_4,sensor_6,sensor_7,sensor_8,sensor_9,sensor_11,sensor_12,sensor_13,sensor_15,sensor_17,sensor_20,sensor_21
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+10,9,0.0027,0.0004,642.22,1588.95,1393.89,21.61,554.81,2388.03,9071.66,47.15,522.75,2388.0,8.3943,392,38.94,23.4024

containers/predictive_maintenance/main_data_load.py

@@ -0,0 +1,29 @@
+import time
+import traceback
+import json
+import config
+import pandas as pd
+from publish import publish_msg
+from data_help_funcs import get_test_data
+import warnings
+
+warnings.filterwarnings("ignore")
+
+test_set = get_test_data()
+
+
+def generate():
+    while True:
+        for idx, row in test_set.iterrows():
+            try:
+                formated_msg = {**row}
+                formated_msg = json.dumps(formated_msg)
+                print(formated_msg)
+                publish_msg(formated_msg, config.raw_data_topic)
+            except Exception as exc:
+                print(traceback.format_exc())
+                print(f"issue {exc}")
+
+
+if __name__ == "__main__":
+    generate()

containers/predictive_maintenance/main_predict.py

@@ -0,0 +1,115 @@
+import time
+import traceback
+import json
+import threading
+import paho.mqtt.client as mqtt
+import pandas as pd
+import influxdb_client, os, time
+from influxdb_client import InfluxDBClient, Point, WritePrecision
+from influxdb_client.client.write_api import SYNCHRONOUS
+from influxdb_client.client.write_api import WriteApi
+import config
+from pred_maintenance_and_anomaly import get_model_dict
+from data_help_funcs import rename_sensors
+from publish import publish_msg
+import warnings
+
+warnings.filterwarnings("ignore")
+
+try:
+    write_client = influxdb_client.InfluxDBClient(
+        url=config.url, token=config.token, org=config.org
+    )
+except:
+    print("Failed to init influx conn...")
+
+model_dict = get_model_dict()
+
+
+def predict_on_msg(client, userdata, message):
+    print(f"Received message: {message.payload.decode('utf-8')}")
+    row = json.loads(message.payload.decode("utf-8"))
+    for k, v in row.items():
+        row[k] = float(v)
+    row["engine_no"] = int(row["engine_no"])
+    row = pd.Series(row)
+
+    engine_no = row["engine_no"]
+    data = model_dict[engine_no].get_scores(row)
+    row, top_predicitive_features, top_anomalous_features = rename_sensors(
+        row, data[1], data[3]
+    )
+
+    formated_msg_orig = {
+        "Probability of failure within 30 cycles": data[0],
+        **top_predicitive_features,
+        "Anomaly score": data[2],
+        **top_anomalous_features,
+        **row,
+    }
+    formated_msg = json.dumps(formated_msg_orig)
+    publish_msg(formated_msg, config.predictions_topic)
+
+    print(f"Sent msg: {formated_msg}")
+
+    try:
+        influx_send_msg(formated_msg_orig, engine_no)
+    except Exception as exc:
+        print(f"Failed to send data to influx due to: {exc}")
+
+
+def influx_send_msg(formated_msg_orig, engine_no):
+    write_api = write_client.write_api(write_options=SYNCHRONOUS)
+
+    for key, value in formated_msg_orig.items():
+        point = Point("measurement").tag("engine_no", int(engine_no)).field(key, value)
+        point2 = Point("measurement2").field(key, value)
+        write_api.write(bucket=config.bucket, org=config.org, record=point)
+        write_api.write(bucket=config.bucket, org=config.org, record=point2)
+        # time.sleep(0.1) # separate points by 1 second
+
+    print("shared")
+
+
+def listen_to_mqtt_topic():
+    client = mqtt.Client()
+    client.connect(config.broker_address, config.broker_port)
+    client.subscribe(config.raw_data_topic)
+
+    client.on_message = predict_on_msg
+
+    client.loop_start()
+    time.sleep(30)
+    client.loop_stop()
+    return True
+
+
+class MQTTWatcher(threading.Thread):
+    def __init__(self):
+        super().__init__()
+        self._stop_event = threading.Event()
+
+    def run(self):
+        while not self._stop_event.is_set():
+            listen_to_mqtt_topic()
+
+    def stop(self):
+        self._stop_event.set()
+
+
+def main():
+    while True:
+        mqtt_watcher = MQTTWatcher()
+        mqtt_watcher.start()
+
+        mqtt_watcher.join(timeout=36)
+
+        if mqtt_watcher.is_alive():
+            print("Thread is still running, terminating...")
+            mqtt_watcher.stop()
+        else:
+            print("Thread has finished")
+
+
+if __name__ == "__main__":
+    main()