testing M04 inserted or not into INfluxDB with cronjob

server/cronjob/prediction-cron

@@ -1 +1 @@
-0 2 * * * root /usr/bin/python /usr/src/app/cronjob/prediction.py >> /var/log/cron.log 2>&1
+0 17 * * * root /usr/bin/python /usr/src/app/cronjob/prediction.py >> /var/log/cron.log 2>&1

server/cronjob/prediction.py

@@ -4,6 +4,32 @@
 import numpy as np
 
 from model.autoarima import AutoARIMA
+from datetime import datetime
+
+# Testing outside via VPN
+ENDPOINT = 'http://data-storage.integration/prediction' 
+PROMETHEUS = 'http://prometheus.integration'
+# Inside cluster
+ENDPOINT = 'http://data-storage-service.dkg-engine.svc.cluster.local:8080/prediction',
+PROMETHEUS = 'http://monitoring-stack-prometheus-server.monitoring.svc.cluster.local:80'
+
+TEST_DATA = {
+    "aggregation_interval": 86400,
+    "forecasting_lower_bounds": [5],
+    "forecasting_model": "ARIMA",
+    "forecasting_period": 1,
+    "forecasting_upper_bounds": [5],
+    "forecasting_values": [5],
+    "metricId": "M02",
+    "time": [
+        "2024-07-12",
+        "2024-07-13",
+        "2024-07-14",
+        "2024-07-15",
+        "2024-07-16",
+    ],
+    "timeseries": [0,1,2,3,4]
+}
 
 
 def main():
@@ -15,27 +41,63 @@ def main():
         '--prometheus_query', 
         type=str, 
         help='API endpoint to get the relevant metric', 
-        default='http://prometheus.integration/api/v1/query_range?query=sum(increase(kepler_container_joules_total{job=%27kepler%27}[10m]))&start=2024-07-20T00:00:00.000Z&end=2024-07-23T00:00:00.000Z&step=600s'
+        default=f'{PROMETHEUS}' + '/api/v1/query_range?query=sum(increase(kepler_container_joules_total{job=%27kepler%27}[10m]))&start=2024-08-11T00:00:00.000Z&end=2024-08-13T00:00:00.000Z&step=600s'
     )
     parser.add_argument('--forecasting_method', help='Choose forecasting method [ARIMA, HBNN]', default='ARIMA')
     args = parser.parse_args()
 
     # Get the metrics
     res = requests.get(args.prometheus_query)
+    print(res.content)
     if (res.ok):
         res_json = json.loads(res.content)['data']['result'][0]['values']
         data_dict = {item[0]:item[1] for item in res_json}
         data_dict = dict(sorted(data_dict.items()))
+        times = data_dict.keys()
         vals = data_dict.values()
         vals = np.array(list(vals)).astype(float)
-        print(vals)
+        print('times', times)
+        print('vals', vals)
 
         # Get the precition
         pred = AutoARIMA(vals, evaluate=10)
-        print(pred)
+        print(pred.prediction, pred.upper_bound, pred.lower_bound)
+
+        # Construct JSON format data to be posted
+        data = {
+            "aggregation_interval": 600,
+            "forecasting_lower_bounds": pred.lower_bound if isinstance(pred.lower_bound, list) else [pred.lower_bound],
+            "forecasting_model": "ARIMA",
+            "forecasting_period": 1,
+            "forecasting_upper_bounds": pred.upper_bound if isinstance(pred.upper_bound, list) else [pred.upper_bound],
+            "forecasting_values": pred.prediction if isinstance(pred.prediction, list) else [pred.prediction],
+            "metricId": "M04",
+            "time": [datetime.utcfromtimestamp(t).strftime('%Y-%m-%dT%H:%M:%S.000Z') for t in list(times)],
+            "timeseries": list(vals)
+        }
+
+        store(ENDPOINT, data)
     else:
         print(f'Response: {res}')
 
 
+def store(
+    endpoint: str = 'http://data-storage.integration/prediction', 
+    data: dict = TEST_DATA,
+):
+    """ Post data to REST API endpoint
+
+    Parameters:
+        endpoint: REST API endpoint
+        data: dictionary for posting to data storage endpoint
+    
+    """
+    print(data)
+    res = requests.post(endpoint, json=data)
+    print(res.status_code)
+    return res.status_code
+
+
 if __name__ == '__main__':
     main()
+    #store()