- Ausbesserungen Gschliefgraben Glasfaser

gschliefgraben_glasfaser/update_daily_cron.py

@@ -5,23 +5,28 @@ Sqlalchemy version: 1.2.15
 Python version: 3.10
 '''
 
-import os, json
+import os
+import json
 import uuid
+from datetime import datetime
+from dotenv import load_dotenv, find_dotenv
 from sqlalchemy.orm import session
 from sqlalchemy import func, asc, desc
 # from db.pg_models import Platform
-from gschliefgraben_glasfaser.models import ObservationSchema, Observation, create_pg_session, Dataset, Procedure, Phenomenon, Platform
+from gschliefgraben_glasfaser.models import (
+    ObservationSchema, Observation, create_pg_session,
+    Dataset, Procedure, Phenomenon, Platform)
 from gschliefgraben_glasfaser.my_api import MyApi
-from datetime import datetime
+
 
 def main():
     ''' main method '''
     pg_session: session = create_pg_session()
-    platform_sta_identifier = "gschliefgraben_glasfaser"  
+    platform_sta_identifier = "gschliefgraben_glasfaser"
     # sensor_list = ["inclino1_14", "inclino1_02"]
     #sensor_list = os.environ.get("GLASFASER_GSCHLIEFGRABEN_SENSORS")
     sensor_list = json.loads(os.environ['GLASFASER_GSCHLIEFGRABEN_SENSORS'])
-    
+
     # this will print elements along with their index value
     for sensor in sensor_list:
         pg_query = pg_session.query(Dataset) \
@@ -32,7 +37,8 @@ def main():
             Phenomenon.sta_identifier == "Slope").first()
         if not slope_dataset:
             print("Sensor " + sensor + " ist noch nicht angelegt!")
-            exit()
+            # exit()
+            continue
         if not slope_dataset.is_published:
             slope_dataset.is_published = 1
             slope_dataset.is_hidden = 0
@@ -40,18 +46,18 @@ def main():
             slope_dataset.observation_type = "simple"
             slope_dataset.value_type = "quantity"
             pg_session.commit()
-            
+
         platform_exists: bool = pg_session.query(Platform.id).filter_by(
-            sta_identifier = platform_sta_identifier).scalar() is not None
+            sta_identifier=platform_sta_identifier).scalar() is not None
         if platform_exists:
             sensor_platform = pg_session.query(Platform.id) \
                 .filter(Platform.sta_identifier == platform_sta_identifier) \
                 .first()
             slope_dataset.fk_platform_id = sensor_platform.id
-            
+
         # create all the observation for the given sensor names
         create_observations(sensor, slope_dataset)
-        
+
         first_slope_observation = pg_session.query(Observation) \
             .filter(Observation.fk_dataset_id == slope_dataset.id) \
             .order_by(asc('sampling_time_start')) \
@@ -68,27 +74,29 @@ def main():
             slope_dataset.last_time = last_slope_observation.sampling_time_start
             slope_dataset.last_value = last_slope_observation.value_quantity
             slope_dataset.fk_last_observation_id = last_slope_observation.id
-            
+
         pg_session.commit()
         pg_session.close()
 
-    
-def create_observations(sensor: str, slope_dataset: Dataset):
-    ''' create_observations method for given sensor '''    
 
-    pg_session: session = create_pg_session() 
-       
+def create_observations(sensor: str, slope_dataset: Dataset):
+    ''' create_observations method for given sensor '''
+
+    pg_session: session = create_pg_session()
+
    # create access token
     token_api = os.environ.get("TOKEN_API")
     test_api = MyApi(token_api)
-    
+
     # The size of each step in days
     # consider the start date as 2021-february 1 st
     start_date = datetime.today()
     query_date = start_date.strftime('%Y-%m-%d')
-    create_db_observations(sensor, query_date, test_api, pg_session, slope_dataset)  
+    create_db_observations(sensor, query_date, test_api,
+                           pg_session, slope_dataset)
     pg_session.commit()
 
+
 def create_db_observations(sensor: str, query_date, test_api, pg_session, dataset: Dataset):
     ''' to do '''
     query_date_obj = datetime.strptime(query_date, "%Y-%m-%d")
@@ -113,7 +121,8 @@ def create_db_observations(sensor: str, query_date, test_api, pg_session, datase
         max_id = create_observation(
             observation_json, pg_session, max_id, dataset)
     # pg_session.commit()
-    print("observations for date " + query_date + " succesfully imported \n")
+    print("observations for date " + query_date + " and sensor " + sensor +
+          " succesfully imported \n")
 
 
 def create_observation(observation_json: ObservationSchema, db_session, max_id, dataset: Dataset):
@@ -142,8 +151,8 @@ def create_observation(observation_json: ObservationSchema, db_session, max_id,
         new_observation: Observation = schema.load(observation_json)
         new_observation.id = max_id
         new_observation.sta_identifier = str(uuid.uuid4())
-        new_observation.sampling_time_start=new_observation.result_time
-        new_observation.sampling_time_end=new_observation.result_time
+        new_observation.sampling_time_start = new_observation.result_time
+        new_observation.sampling_time_end = new_observation.result_time
         new_observation.fk_dataset_id = dataset.id
 
         # Add the person to the database
@@ -159,6 +168,10 @@ def create_observation(observation_json: ObservationSchema, db_session, max_id,
     else:
         print(409, f'Observation {ob_id} exists already')
         return max_id
-    
+
+
 if __name__ == "__main__":
+    load_dotenv(find_dotenv())
+    print('sensors:  {}'.format(os.environ.get(
+        'GLASFASER_GSCHLIEFGRABEN_SENSORS', [])))
     main()