move to production

k8s/auth-service/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-auth-api
-  tag: prod-da0db8f0-1733248410
+  tag: prod-1ea1da62-1733249735
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/device-registry/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-device-registry-api
-  tag: prod-da0db8f0-1733248410
+  tag: prod-03a25cf1-1733249950
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/exceedance/values-prod-airqo.yaml

@@ -4,6 +4,6 @@ app:
   configmap: env-exceedance-production
 image:
   repository: eu.gcr.io/airqo-250220/airqo-exceedance-job
-  tag: prod-1ea1da62-1733249735
+  tag: prod-03a25cf1-1733249950
 nameOverride: ''
 fullnameOverride: ''

k8s/exceedance/values-prod-kcca.yaml

@@ -4,6 +4,6 @@ app:
   configmap: env-exceedance-production
 image:
   repository: eu.gcr.io/airqo-250220/kcca-exceedance-job
-  tag: prod-1ea1da62-1733249735
+  tag: prod-03a25cf1-1733249950
 nameOverride: ''
 fullnameOverride: ''

k8s/predict/values-prod.yaml

@@ -7,7 +7,7 @@ images:
     predictJob: eu.gcr.io/airqo-250220/airqo-predict-job
     trainJob: eu.gcr.io/airqo-250220/airqo-train-job
     predictPlaces: eu.gcr.io/airqo-250220/airqo-predict-places-air-quality
-  tag: prod-da0db8f0-1733248410
+  tag: prod-1ea1da62-1733249735
 api:
   name: airqo-prediction-api
   label: prediction-api

k8s/spatial/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-spatial-api
-  tag: prod-da0db8f0-1733248410
+  tag: prod-1ea1da62-1733249735
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/workflows/values-prod.yaml

@@ -10,7 +10,7 @@ images:
     initContainer: eu.gcr.io/airqo-250220/airqo-workflows-xcom
     redisContainer: eu.gcr.io/airqo-250220/airqo-redis
     containers: eu.gcr.io/airqo-250220/airqo-workflows
-  tag: prod-da0db8f0-1733248410
+  tag: prod-03a25cf1-1733249950
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/workflows/values-stage.yaml

@@ -10,7 +10,7 @@ images:
     initContainer: eu.gcr.io/airqo-250220/airqo-stage-workflows-xcom
     redisContainer: eu.gcr.io/airqo-250220/airqo-stage-redis
     containers: eu.gcr.io/airqo-250220/airqo-stage-workflows
-  tag: stage-7f60b036-1733224934
+  tag: stage-f43cad79-1733305404
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/workflows/airqo_etl_utils/airqo_api.py

@@ -135,7 +135,7 @@ def get_devices(
         device_category: DeviceCategory = DeviceCategory.NONE,
     ) -> List[Dict[str, Any]]:
         """
-        Retrieve devices given a tenant and device category.
+        Retrieve devices given a network and device category.
 
         Args:
             - network (str): An Enum that represents site ownership.
@@ -198,7 +198,6 @@ def get_devices(
                 "device_category": str(
                     DeviceCategory.from_str(device.pop("category", None))
                 ),
-                "network": device.get("network"),
                 "device_manufacturer": device.get("network", "airqo"),
                 **device,
             }

src/workflows/airqo_etl_utils/airqo_utils.py

@@ -59,7 +59,7 @@ def extract_uncalibrated_data(start_date_time, end_date_time) -> pd.DataFrame:
             null_cols=["pm2_5_calibrated_value"],
             start_date_time=start_date_time,
             end_date_time=end_date_time,
-            tenant=Tenant.AIRQO,
+            network=str(Tenant.AIRQO),
         )
 
         return DataValidationUtils.remove_outliers(hourly_uncalibrated_data)
@@ -79,7 +79,7 @@ def extract_data_from_bigquery(
             table=table,
             start_date_time=start_date_time,
             end_date_time=end_date_time,
-            tenant=Tenant.AIRQO,
+            network=str(Tenant.AIRQO),
         )
 
         return DataValidationUtils.remove_outliers(raw_data)
@@ -117,7 +117,10 @@ def remove_duplicates(data: pd.DataFrame) -> pd.DataFrame:
 
     @staticmethod
     def extract_aggregated_raw_data(
-        start_date_time: str, end_date_time: str, dynamic_query: bool = False
+        start_date_time: str,
+        end_date_time: str,
+        network: str = None,
+        dynamic_query: bool = False,
     ) -> pd.DataFrame:
         """
         Retrieves raw pm2.5 sensor data from bigquery and computes averages for the numeric columns grouped by device_number, device_id and site_id
@@ -128,9 +131,7 @@ def extract_aggregated_raw_data(
             start_date_time=start_date_time,
             end_date_time=end_date_time,
             table=bigquery_api.raw_measurements_table,
-            network=str(
-                Tenant.AIRQO
-            ),  # TODO Replace tenant implementation with network implementation
+            network=network,
             dynamic_query=dynamic_query,
         )
 
@@ -757,19 +758,26 @@ def clean_low_cost_sensor_data(
                     AirQoGxExpectations.from_pandas().pm2_5_low_cost_sensor_raw_data(
                         data
                     )
-
+        else:
+            data["timestamp"] = pd.to_datetime(data["timestamp"])
         data.dropna(subset=["timestamp"], inplace=True)
-        data["timestamp"] = pd.to_datetime(data["timestamp"])
+
         data.drop_duplicates(
             subset=["timestamp", "device_id"], keep="first", inplace=True
         )
         # TODO Find an appropriate place to put this
         if device_category == DeviceCategory.LOW_COST:
-            data["pm2_5_raw_value"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1)
-            data["pm2_5"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1)
-            data["pm10_raw_value"] = data[["s1_pm10", "s2_pm10"]].mean(axis=1)
-            data["pm10"] = data[["s1_pm10", "s2_pm10"]].mean(axis=1)
+            is_airqo_network = data["network"] == "airqo"
 
+            pm2_5_mean = data.loc[is_airqo_network, ["s1_pm2_5", "s2_pm2_5"]].mean(
+                axis=1
+            )
+            pm10_mean = data.loc[is_airqo_network, ["s1_pm10", "s2_pm10"]].mean(axis=1)
+
+            data.loc[is_airqo_network, "pm2_5_raw_value"] = pm2_5_mean
+            data.loc[is_airqo_network, "pm2_5"] = pm2_5_mean
+            data.loc[is_airqo_network, "pm10_raw_value"] = pm10_mean
+            data.loc[is_airqo_network, "pm10"] = pm10_mean
         return data
 
     @staticmethod
@@ -1032,7 +1040,7 @@ def merge_aggregated_weather_data(
     @staticmethod
     def extract_devices_deployment_logs() -> pd.DataFrame:
         airqo_api = AirQoApi()
-        devices = airqo_api.get_devices(tenant=Tenant.AIRQO)
+        devices = airqo_api.get_devices(network=str(Tenant.AIRQO))
         devices_history = pd.DataFrame()
         for device in devices:
             try:

src/workflows/airqo_etl_utils/bigquery_api.py

@@ -509,7 +509,7 @@ def compose_query(
         table: str,
         start_date_time: str,
         end_date_time: str,
-        network: str,
+        network: str = "all",
         where_fields: dict = None,
         null_cols: list = None,
         columns: list = None,
@@ -536,17 +536,15 @@ def compose_query(
             Exception: If an invalid column is provided in `where_fields` or `null_cols`,
                       or if the `query_type` is not supported.
         """
-        tenant = "airqo"
 
         null_cols = [] if null_cols is None else null_cols
         where_fields = {} if where_fields is None else where_fields
 
         columns = ", ".join(map(str, columns)) if columns else " * "
-        where_clause = (
-            f" timestamp >= '{start_date_time}' and timestamp <= '{end_date_time}' "
-        )
-        if tenant != Tenant.ALL:
-            where_clause = f" {where_clause} and tenant = '{str(tenant)}' or network = '{str(network)}' "
+        where_clause = f" timestamp between '{start_date_time}' and '{end_date_time}' "
+
+        if network:
+            where_clause += f"AND network = '{network}' "
 
         valid_cols = self.get_columns(table=table)
 
@@ -613,7 +611,7 @@ def query_data(
         start_date_time: str,
         end_date_time: str,
         table: str,
-        network: str,
+        network: str = None,
         dynamic_query: bool = False,
         columns: list = None,
         where_fields: dict = None,
@@ -649,7 +647,11 @@ def query_data(
             )
         else:
             query = self.dynamic_averaging_query(
-                table, start_date_time, end_date_time, time_granularity=time_granularity
+                table,
+                start_date_time,
+                end_date_time,
+                network=network,
+                time_granularity=time_granularity,
             )
 
         dataframe = self.client.query(query=query).result().to_dataframe()
@@ -669,6 +671,7 @@ def dynamic_averaging_query(
         end_date_time: str,
         exclude_columns: list = None,
         group_by: list = None,
+        network: str = "all",
         time_granularity: str = "HOUR",
     ) -> str:
         """
@@ -728,11 +731,18 @@ def dynamic_averaging_query(
             ]
         )
 
+        where_clause: str = (
+            f"timestamp BETWEEN '{start_date_time}' AND '{end_date_time}' "
+        )
+
+        if network:
+            where_clause += f"AND network = '{network}' "
+
         # Include time granularity in both SELECT and GROUP BY
         timestamp_trunc = f"TIMESTAMP_TRUNC(timestamp, {time_granularity.upper()}) AS {time_granularity.lower()}"
         group_by_clause = ", ".join(group_by + [time_granularity.lower()])
 
-        query = f"""SELECT {", ".join(group_by)}, {timestamp_trunc}, {avg_columns} FROM `{table}` WHERE timestamp BETWEEN '{start_date_time}' AND '{end_date_time}' GROUP BY {group_by_clause} ORDER BY {time_granularity.lower()};"""
+        query = f"""SELECT {", ".join(group_by)}, {timestamp_trunc}, {avg_columns} FROM `{table}` WHERE {where_clause} GROUP BY {group_by_clause} ORDER BY {time_granularity.lower()};"""
 
         return query
 

src/workflows/airqo_etl_utils/daily_data_utils.py

@@ -44,7 +44,6 @@ def query_hourly_data(start_date_time, end_date_time) -> pd.DataFrame:
             table=bigquery_api.hourly_measurements_table,
             start_date_time=start_date_time,
             end_date_time=end_date_time,
-            tenant=Tenant.ALL,
         )
 
         return DataValidationUtils.remove_outliers(raw_data)
@@ -57,7 +56,6 @@ def query_daily_data(start_date_time, end_date_time) -> pd.DataFrame:
             table=bigquery_api.daily_measurements_table,
             start_date_time=start_date_time,
             end_date_time=end_date_time,
-            tenant=Tenant.ALL,
         )
 
         return DataValidationUtils.remove_outliers(raw_data)

src/workflows/airqo_etl_utils/data_validator.py

@@ -72,10 +72,10 @@ def format_data_types(
                 data[col] = (
                     data[col]
                     .astype(str)
-                    .str.replace(r"[^\w\s\.\-:]", "", regex=True)
+                    .str.replace(r"[^\w\s\.\-+:]", "", regex=True)
                     .str.replace(r"(?<!\.\d{3})Z$", ".000Z", regex=True)
                 )  # Negative lookbehind to add missing milliseconds if needed
-                data[col] = pd.to_datetime(data[col], errors="coerce")
+                data[col] = pd.to_datetime(data[col], errors="coerce", utc=True)
 
         if integers:
             for col in integers:
@@ -142,7 +142,6 @@ def remove_outliers(data: pd.DataFrame) -> pd.DataFrame:
             dtype: list(set(columns) & set(data.columns))
             for dtype, columns in column_types.items()
         }
-
         data = DataValidationUtils.format_data_types(
             data=data,
             floats=filtered_columns[ColumnDataType.FLOAT],
@@ -151,7 +150,6 @@ def remove_outliers(data: pd.DataFrame) -> pd.DataFrame:
         )
 
         validated_columns = list(chain.from_iterable(filtered_columns.values()))
-
         for col in validated_columns:
             is_airqo_network = data["network"] == "airqo"
             mapped_name = configuration.AIRQO_DATA_COLUMN_NAME_MAPPING.get(col, None)

src/workflows/airqo_etl_utils/data_warehouse_utils.py

@@ -33,7 +33,6 @@ def extract_hourly_bam_data(
             start_date_time=start_date_time,
             end_date_time=end_date_time,
             table=biq_query_api.bam_measurements_table,
-            tenant=Tenant.ALL,
         )
 
         if data.empty:
@@ -59,7 +58,6 @@ def extract_data_from_big_query(
             start_date_time=start_date_time,
             end_date_time=end_date_time,
             table=biq_query_api.consolidated_data_table,
-            tenant=Tenant.ALL,
         )
 
     @staticmethod
@@ -83,7 +81,6 @@ def extract_hourly_low_cost_data(
             start_date_time=start_date_time,
             end_date_time=end_date_time,
             table=biq_query_api.hourly_measurements_table,
-            tenant=Tenant.ALL,
         )
 
         if data.empty: