move to production

k8s/analytics/values-prod.yaml

@@ -8,7 +8,7 @@ images:
     celeryWorker: eu.gcr.io/airqo-250220/airqo-analytics-celery-worker
     reportJob: eu.gcr.io/airqo-250220/airqo-analytics-report-job
     devicesSummaryJob: eu.gcr.io/airqo-250220/airqo-analytics-devices-summary-job
-  tag: prod-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 api:
   name: airqo-analytics-api
   label: analytics-api

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-ee15b958-1733833086
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/website/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-website-api
-  tag: prod-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 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-75b96595-1734025664
+  tag: prod-85d64e95-1734081670
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/analytics/api/models/events.py

@@ -589,7 +589,7 @@ def simple_data_cleaning(cls, data: pd.DataFrame) -> pd.DataFrame:
 
         data.loc[data["pm2_5_raw_value"] != 0, "pm2_5"] = np.nan
 
-        if (data["pm2_5_raw_value"] == 0).all():
+        if ((data["pm2_5_raw_value"] == 0) | (data["pm2_5_raw_value"].isna())).all():
             data.drop(columns=["pm2_5_raw_value"], inplace=True)
 
         zero_columns = data.loc[:, (data == 0).all()].columns

src/workflows/airqo_etl_utils/bigquery_api.py

@@ -604,22 +604,52 @@ def reload_data(
         self,
         dataframe: pd.DataFrame,
         table: str,
-        tenant: Tenant = Tenant.ALL,
+        network: str = "all",
         start_date_time: str = None,
         end_date_time: str = None,
         where_fields: dict = None,
         null_cols: list = None,
     ) -> None:
+        """
+        Reloads data into a specified table in BigQuery by:
+        1. Deleting existing records in the table based on the provided date range,
+        network, and optional filtering criteria.
+        2. Inserting new records from the provided DataFrame.
+
+        Args:
+            dataframe (pd.DataFrame): The data to be reloaded into the table.
+            table (str): The target table in BigQuery.
+            network (str, optional): The network filter to be applied. Defaults to "all".
+            start_date_time (str, optional): The start of the date range for deletion.
+                                            If None, inferred from the DataFrame's earliest timestamp.
+            end_date_time (str, optional): The end of the date range for deletion.
+                                        If None, inferred from the DataFrame's latest timestamp.
+            where_fields (dict, optional): Additional fields and values for filtering rows to delete.
+            null_cols (list, optional): Columns to filter on `NULL` values during deletion.
+
+        Returns:
+            None: The function performs operations directly on the BigQuery table.
+
+        Raises:
+            ValueError: If `timestamp` column is missing in the DataFrame.
+        """
+
         if start_date_time is None or end_date_time is None:
-            data = dataframe.copy()
+            if "timestamp" not in dataframe.columns:
+                raise ValueError(
+                    "The DataFrame must contain a 'timestamp' column to derive the date range."
+                )
+            data = (
+                dataframe.copy()
+            )  # Not sure why this dataframe is being copied. # Memory wastage?
             data["timestamp"] = pd.to_datetime(data["timestamp"])
             start_date_time = date_to_str(data["timestamp"].min())
             end_date_time = date_to_str(data["timestamp"].max())
 
         query = self.compose_query(
             QueryType.DELETE,
             table=table,
-            tenant=tenant,
+            network=network,
             start_date_time=start_date_time,
             end_date_time=end_date_time,
             where_fields=where_fields,

src/workflows/airqo_etl_utils/daily_data_utils.py

@@ -8,31 +8,47 @@
 class DailyDataUtils:
     @staticmethod
     def average_data(data: pd.DataFrame) -> pd.DataFrame:
-        averaged_data = pd.DataFrame()
-        data["timestamp"] = data["timestamp"].apply(pd.to_datetime)
-
-        for _, by_tenant in data.groupby("tenant"):
-            tenant = by_tenant.iloc[0]["tenant"]
-            del by_tenant["tenant"]
-            for _, by_device in by_tenant.groupby("device_id"):
-                site_id = by_device.iloc[0]["site_id"]
-                device_id = by_device.iloc[0]["device_id"]
-                device_number = by_device.iloc[0]["device_number"]
-
-                del by_device["site_id"]
-                del by_device["device_id"]
-                del by_device["device_number"]
-
-                device_averages = by_device.resample("1D", on="timestamp").mean()
-                device_averages["timestamp"] = device_averages.index
-                device_averages["device_id"] = device_id
-                device_averages["site_id"] = site_id
-                device_averages["device_number"] = device_number
-                device_averages["tenant"] = tenant
-
-                averaged_data = pd.concat(
-                    [averaged_data, device_averages], ignore_index=True
-                )
+        """
+        Averages data in a pandas DataFrame on a daily basis for each device,
+        grouped by network and device ID. The function resamples data
+        to compute daily averages for numerical columns.
+
+        Args:
+            data (pd.DataFrame): A pandas DataFrame containing the following columns:
+                - "timestamp": Timestamps of the data.
+                - "network": The network the data belongs to.
+                - "device_id": Unique identifier for the device.
+                - "site_id": Unique identifier for the site associated with the device.
+                - "device_number": Device number.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing daily averages for each device,
+            including metadata columns such as "tenant", "device_id", "site_id",
+            and "device_number".
+        """
+        data["timestamp"] = pd.to_datetime(data["timestamp"])
+
+        averaged_data_list = []
+
+        for (network, device_id), group in data.groupby(["network", "device_id"]):
+            network = group["network"].iloc[0]
+            site_id = group["site_id"].iloc[0]
+            device_number = group["device_number"].iloc[0]
+
+            device_averages = (
+                group.resample("1D", on="timestamp")
+                .mean(numeric_only=True)
+                .reset_index()
+            )
+
+            device_averages["network"] = network
+            device_averages["device_id"] = device_id
+            device_averages["site_id"] = site_id
+            device_averages["device_number"] = device_number
+
+            averaged_data_list.append(device_averages)
+
+        averaged_data = pd.concat(averaged_data_list, ignore_index=True)
 
         return averaged_data
 
@@ -77,7 +93,7 @@ def cleanup_and_reload(
         )
 
         bigquery_api.reload_data(
-            tenant=Tenant.ALL,
+            network="all",
             table=table,
             dataframe=data,
             start_date_time=start_date_time,