move to production

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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/device-registry/values-stage.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 2
 image:
   repository: eu.gcr.io/airqo-250220/airqo-stage-device-registry-api
-  tag: stage-dfe6eb16-1733832983
+  tag: stage-c1e369be-1733844028
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 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-ee15b958-1733833086
+  tag: prod-ae2e15a3-1733844050
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/analytics/api/models/events.py

@@ -413,6 +413,7 @@ def download_from_bigquery(
             frequency (str): Data frequency (e.g., 'raw', 'daily', 'hourly').
             pollutants (list): List of pollutants to include in the data.
             data_type (str): Type of data ('raw' or 'aggregated').
+            filter_columns(list)
             weather_fields (list): List of weather fields to retrieve.
 
         Returns:
@@ -438,14 +439,11 @@ def download_from_bigquery(
         weather_columns = []
         for pollutant in pollutants:
 
-            if pollutant == "raw":
-                key = pollutant
-            else:
-                key = f"{pollutant}_{data_type}"
-
+            key = f"{pollutant}_{data_type}"
             pollutant_mapping = BIGQUERY_FREQUENCY_MAPPER.get(frequency, {}).get(
                 key, []
             )
+
             pollutant_columns.extend(
                 cls.get_columns(
                     cls,
@@ -459,6 +457,10 @@ def download_from_bigquery(
 
             # TODO Clean up by use using `get_columns` helper method
             if pollutant in {"pm2_5", "pm10", "no2"}:
+                if data_type == "raw":
+                    # Add dummy column to fix union column number missmatch.
+                    bam_pollutant_columns.append("-1 as pm2_5")
+
                 if frequency in ["weekly", "monthly", "yearly"]:
                     bam_pollutant_columns.extend(
                         [f"ROUND(AVG({pollutant}), {decimal_places}) AS {key}_value"]
@@ -467,6 +469,7 @@ def download_from_bigquery(
                     bam_pollutant_columns.extend(
                         [f"ROUND({pollutant}, {decimal_places}) AS {key}_value"]
                     )
+
         # TODO Fix query when weather data is included. Currently failing
         if weather_fields:
             for field in weather_fields:
@@ -536,12 +539,55 @@ def download_from_bigquery(
             drop_columns.append("datetime")
             sorting_cols.append("datetime")
 
+        if data_type == "raw":
+            cls.simple_data_cleaning(dataframe)
+
         dataframe.drop_duplicates(subset=drop_columns, inplace=True, keep="first")
         dataframe.sort_values(sorting_cols, ascending=True, inplace=True)
         dataframe["frequency"] = frequency
         dataframe = dataframe.replace(np.nan, None)
         return dataframe
 
+    @classmethod
+    def simple_data_cleaning(cls, data: pd.DataFrame) -> pd.DataFrame:
+        """
+         Perform data cleaning on a pandas DataFrame to handle specific conditions
+         related to "pm2_5" and "pm2_5_raw_value" columns.
+
+         The cleaning process includes:
+         1. Ensuring correct numeric data types for "pm2_5" and "pm2_5_raw_value".
+         2. Removing "pm2_5" values where "pm2_5_raw_value" has data.
+         3. Dropping the "pm2_5_raw_value" column if it has no data at all.
+         4. Retaining "pm2_5" values where "pm2_5_raw_value" has no data, and removing
+         "pm2_5" values where "pm2_5_raw_value" has data.
+         5. Dropping any column (including "pm2_5" and "pm2_5_raw_value") if it is
+        entirely empty.
+
+         Args:
+             cls: Class reference (used in classmethods).
+             data (pd.DataFrame): Input pandas DataFrame with "pm2_5" and
+                                 "pm2_5_raw_value" columns.
+
+         Returns:
+             pd.DataFrame: Cleaned DataFrame with updates applied in place.
+
+        """
+        data["pm2_5_raw_value"] = pd.to_numeric(
+            data["pm2_5_raw_value"], errors="coerce"
+        )
+        data["pm2_5"] = pd.to_numeric(data["pm2_5"], errors="coerce")
+
+        data.loc[~data["pm2_5_raw_value"].isna(), "pm2_5"] = np.nan
+
+        if data["pm2_5_raw_value"].isna().all():
+            data.drop(columns=["pm2_5_raw_value"], inplace=True)
+
+        data["pm2_5"] = data["pm2_5"].where(data["pm2_5_raw_value"].isna(), np.nan)
+
+        data.dropna(how="all", axis=1, inplace=True)
+
+        return data
+
     @classmethod
     def data_export_query(
         cls,
@@ -1273,9 +1319,7 @@ def get_d3_chart_events(self, sites, start_date, end_date, pollutant, frequency)
         )
 
     @cache.memoize()
-    def get_d3_chart_events_v2(
-        self, sites, start_date, end_date, pollutant, frequency, tenant
-    ):
+    def get_d3_chart_events_v2(self, sites, start_date, end_date, pollutant, frequency):
         if pollutant not in ["pm2_5", "pm10", "no2", "pm1"]:
             raise Exception("Invalid pollutant")
 
@@ -1293,7 +1337,6 @@ def get_d3_chart_events_v2(
           JOIN {self.BIGQUERY_SITES} ON {self.BIGQUERY_SITES}.id = {self.BIGQUERY_EVENTS}.site_id 
           WHERE  {self.BIGQUERY_EVENTS}.timestamp >= '{start_date}'
           AND {self.BIGQUERY_EVENTS}.timestamp <= '{end_date}'
-          AND {self.BIGQUERY_EVENTS}.tenant = '{tenant}'
           AND `airqo-250220.metadata.sites`.id in UNNEST({sites})
         """
 

src/analytics/api/utils/data_formatters.py

@@ -328,6 +328,31 @@ def filter_non_private_sites(filter_type: str, sites: List[str]) -> Dict[str, An
         logger.exception(f"Error while filtering non private devices {rex}")
 
 
+def validate_network(network_name: str) -> bool:
+    """
+    Validate if a given network name exists in the list of networks.
+
+    Args:
+        network_name (str): The name of the network to validate.
+
+    Returns:
+        bool: True if the network name exists, False otherwise.
+    """
+    if not network_name:
+        return False
+
+    endpoint: str = "/users/networks"
+    airqo_requests = AirQoRequests()
+    response = airqo_requests.request(endpoint=endpoint, method="get")
+
+    if response and "networks" in response:
+        networks = response["networks"]
+        # TODO Could add an active network filter
+        return any(network.get("net_name") == network_name for network in networks)
+
+    return False
+
+
 def filter_non_private_devices(filter_type: str, devices: List[str]) -> Dict[str, Any]:
     """
     FilterS out private device IDs from a provided array of device IDs.

src/analytics/api/utils/pollutants/pm_25.py

@@ -56,19 +56,14 @@
 
 COMMON_POLLUTANT_MAPPING = {
     "pm2_5_calibrated": ["pm2_5_calibrated_value"],
-    "pm2_5_raw": ["pm2_5_raw_value"],
+    "pm2_5_raw": ["pm2_5_raw_value", "pm2_5"],
     "pm10_calibrated": ["pm10_calibrated_value"],
-    "pm10_raw": ["pm10_raw_value"],
+    "pm10_raw": ["pm10_raw_value", "pm10"],
     "no2_calibrated": ["no2_calibrated_value"],
     "no2_raw": ["no2_raw_value"],
 }
 
 BIGQUERY_FREQUENCY_MAPPER = {
-    "raw": {
-        "pm2_5": ["pm2_5", "s1_pm2_5", "s2_pm2_5"],
-        "pm10": ["pm10", "s1_pm10", "s2_pm10"],
-        "no2": ["no2"],
-    },
     "daily": COMMON_POLLUTANT_MAPPING,
     "hourly": COMMON_POLLUTANT_MAPPING,
     "weekly": COMMON_POLLUTANT_MAPPING,

src/analytics/api/views/dashboard.py

@@ -206,7 +206,6 @@ def _get_validated_filter(self, json_data):
         return filter_type, validated_data, error_message
 
     def post(self):
-        tenant = request.args.get("tenant", "airqo")
 
         json_data = request.get_json()
 
@@ -219,20 +218,16 @@ def post(self):
         except Exception as e:
             logger.exception(f"An error has occured; {e}")
 
-        sites = filter_non_private_sites("sites", json_data.get("sites", {})).get(
-            "sites", []
-        )
-
         start_date = json_data["startDate"]
         end_date = json_data["endDate"]
         frequency = json_data["frequency"]
         pollutant = json_data["pollutant"]
         chart_type = json_data["chartType"]
 
-        events_model = EventsModel(tenant)
-        # data = events_model.get_d3_chart_events(sites, start_date, end_date, pollutant, frequency)
+        events_model = EventsModel("airqo")
+
         data = events_model.get_d3_chart_events_v2(
-            filter_value, start_date, end_date, pollutant, frequency, tenant
+            filter_value, start_date, end_date, pollutant, frequency
         )
 
         if chart_type.lower() == "pie":

src/analytics/api/views/data.py

@@ -166,6 +166,7 @@ def post(self):
                 data_frame.drop(
                     columns=[
                         "site_id",
+                        "timestamp",
                     ],
                     inplace=True,
                 )