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-a8923ac0-1741034129
+  tag: prod-cc4c9655-1741087107
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/auth-service/values-stage.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 2
 image:
   repository: eu.gcr.io/airqo-250220/airqo-stage-auth-api
-  tag: stage-6410a447-1741021914
+  tag: stage-ef828b9a-1741087033
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/data-mgt/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 2
 image:
   repository: eu.gcr.io/airqo-250220/airqo-data-mgt-api
-  tag: prod-a8923ac0-1741034129
+  tag: prod-cc4c9655-1741087107
 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-a8923ac0-1741034129
+  tag: prod-cc4c9655-1741087107
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

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-a8923ac0-1741034129
+  tag: prod-cc4c9655-1741087107
 api:
   name: airqo-prediction-api
   label: prediction-api

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-a8923ac0-1741034129
+  tag: prod-cc4c9655-1741087107
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/workflows/airqo_etl_utils/airnow_utils.py

@@ -8,7 +8,7 @@
 from .data_validator import DataValidationUtils
 from .date import str_to_date, date_to_str
 from .utils import Utils
-
+from .datautils import DataUtils
 from .config import configuration
 import logging
 
@@ -68,8 +68,8 @@ def extract_bam_data(start_date_time: str, end_date_time: str) -> pd.DataFrame:
         Raises:
             ValueError: If no devices are found for the BAM network or if no data is returned for the specified date range.
         """
-        devices = AirQoApi().get_devices_by_network(
-            device_network=DeviceNetwork.METONE, device_category=DeviceCategory.BAM
+        devices, _ = DataUtils.get_devices(
+            device_category=DeviceCategory.BAM, device_network=DeviceNetwork.METONE
         )
         bam_data = pd.DataFrame()
 

src/workflows/airqo_etl_utils/airqo_utils.py

@@ -262,7 +262,7 @@ def merge_aggregated_mobile_devices_data_and_weather_data(
     @staticmethod
     def restructure_airqo_mobile_data_for_bigquery(data: pd.DataFrame) -> pd.DataFrame:
         data["timestamp"] = pd.to_datetime(data["timestamp"])
-        data["network"] = "airqo"
+        data["network"] = DeviceNetwork.AIRQO.str
         big_query_api = BigQueryApi()
         cols = big_query_api.get_columns(
             table=big_query_api.airqo_mobile_measurements_table
@@ -468,7 +468,7 @@ def extract_devices_deployment_logs() -> pd.DataFrame:
         for _, device in devices.iterrows():
             try:
                 maintenance_logs = airqo_api.get_maintenance_logs(
-                    network=device.get("network", "airqo"),
+                    network=device.get("network", DeviceNetwork.AIRQO.str),
                     device=device.get("name", None),
                     activity_type="deployment",
                 )
@@ -570,6 +570,58 @@ def map_site_ids_to_historical_data(
 
     @staticmethod
     def calibrate_data(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
+        """
+        Merges calibrated data back into the original dataset and computes raw PM values after calibration.
+
+        Args:
+            data (pd.DataFrame): The raw sensor data.
+            groupby (str): The column to group by for model selection.
+
+        Returns:
+            pd.DataFrame: The original dataset with calibrated PM2.5 and PM10 values.
+        """
+
+        data["timestamp"] = pd.to_datetime(data["timestamp"])
+
+        to_calibrate = data["network"] == DeviceNetwork.AIRQO.str
+
+        calibrated_data = AirQoDataUtils._airqo_calibrate(
+            data.loc[to_calibrate], groupby
+        )
+
+        data.loc[
+            to_calibrate, ["pm2_5_calibrated_value", "pm10_calibrated_value"]
+        ] = calibrated_data
+
+        data["pm2_5_raw_value"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1)
+        data["pm10_raw_value"] = data[["s1_pm10", "s2_pm10"]].mean(axis=1)
+
+        data = data.assign(
+            pm2_5_calibrated_value=data.get("pm2_5_calibrated_value", np.nan),
+            pm10_calibrated_value=data.get("pm10_calibrated_value", np.nan),
+        )
+
+        data.loc[to_calibrate, "pm2_5"] = data.loc[
+            to_calibrate, "pm2_5_calibrated_value"
+        ].fillna(data.loc[to_calibrate, "pm2_5_raw_value"])
+        data.loc[to_calibrate, "pm10"] = data.loc[
+            to_calibrate, "pm10_calibrated_value"
+        ].fillna(data.loc[to_calibrate, "pm10_raw_value"])
+
+        return data.drop(
+            columns=[
+                "avg_pm2_5",
+                "avg_pm10",
+                "error_pm2_5",
+                "error_pm10",
+                "pm2_5_pm10",
+                "pm2_5_pm10_mod",
+                "hour",
+            ],
+            errors="ignore",
+        )
+
+    def _airqo_calibrate(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
         """
         Calibrates air quality sensor data by applying machine learning models to adjust sensor readings.
 
@@ -587,29 +639,26 @@ def calibrate_data(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
             data (pd.DataFrame): The raw air quality sensor data.
 
         Returns:
-            pd.DataFrame: The calibrated dataset with additional processed fields.
+            pd.DataFrame: A DataFrame with calibrated PM2.5 and PM10 values.
         """
+        bucket = Config.FORECAST_MODELS_BUCKET
+        project_id = Config.GOOGLE_CLOUD_PROJECT_ID
+        calibrate_by: Dict[str, Union[CityModels, CountryModels]] = {
+            "city": CityModels,
+            "country": CountryModels,
+        }
+
+        models: Union[CityModels, CountryModels] = calibrate_by.get(
+            groupby, CountryModels
+        )
 
         sites = DataUtils.get_sites()
         if sites.empty:
             raise RuntimeError("Failed to fetch sites data from the cache/API")
 
-        bucket = Config.FORECAST_MODELS_BUCKET
-        project_id = Config.GOOGLE_CLOUD_PROJECT_ID
-
-        data["timestamp"] = pd.to_datetime(data["timestamp"])
         sites = sites[["site_id", groupby]]
         data = pd.merge(data, sites, on="site_id", how="left")
-        data.dropna(subset=["device_id", "timestamp"], inplace=True)
 
-        columns_to_fill = [
-            "s1_pm2_5",
-            "s1_pm10",
-            "s2_pm2_5",
-            "s2_pm10",
-            "temperature",
-            "humidity",
-        ]
         input_variables = [
             "avg_pm2_5",
             "avg_pm10",
@@ -622,78 +671,34 @@ def calibrate_data(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
             "pm2_5_pm10_mod",
         ]
 
-        # TODO: Need to opt for a different approach eg forward fill, can't do here as df only has data of last 1 hour. Perhaps use raw data only?
-        # Fill nas for the specified fields.
-        if "airqo" not in pd.unique(data.network):
-            data = data.reindex(
-                columns=data.columns.union(columns_to_fill, sort=False),
-                fill_value=np.nan,
-            )
-
-        to_calibrate = data["network"] == "airqo"
-        data_to_calibrate = data.loc[to_calibrate]
-        data_to_calibrate[columns_to_fill] = data_to_calibrate[columns_to_fill].fillna(
-            0
-        )
-
         # additional input columns for calibration
-        data_to_calibrate["avg_pm2_5"] = (
-            data_to_calibrate[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1).round(2)
-        )
-        data_to_calibrate["avg_pm10"] = (
-            data_to_calibrate[["s1_pm10", "s2_pm10"]].mean(axis=1).round(2)
-        )
-        data_to_calibrate["error_pm2_5"] = np.abs(
-            data_to_calibrate["s1_pm2_5"] - data_to_calibrate["s2_pm2_5"]
-        )
-        data_to_calibrate["error_pm10"] = np.abs(
-            data_to_calibrate["s1_pm10"] - data_to_calibrate["s2_pm10"]
-        )
-        data_to_calibrate["pm2_5_pm10"] = (
-            data_to_calibrate["avg_pm2_5"] - data_to_calibrate["avg_pm10"]
-        )
-        data_to_calibrate["pm2_5_pm10_mod"] = (
-            data_to_calibrate["avg_pm2_5"] / data_to_calibrate["avg_pm10"]
-        )
-        data_to_calibrate["hour"] = data_to_calibrate["timestamp"].dt.hour
-
-        data_to_calibrate[input_variables] = data_to_calibrate[input_variables].replace(
-            [np.inf, -np.inf], 0
+        data["avg_pm2_5"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1).round(2)
+        data["avg_pm10"] = data[["s1_pm10", "s2_pm10"]].mean(axis=1).round(2)
+        data["error_pm2_5"] = np.abs(data["s1_pm2_5"] - data["s2_pm2_5"])
+        data["error_pm10"] = np.abs(data["s1_pm10"] - data["s2_pm10"])
+        data["pm2_5_pm10"] = data["avg_pm2_5"] - data["avg_pm10"]
+        data["pm2_5_pm10_mod"] = data["avg_pm2_5"] / data["avg_pm10"]
+        data["hour"] = data["timestamp"].dt.hour
+
+        data[input_variables] = data[input_variables].replace([np.inf, -np.inf], 0)
+
+        default_rf_model = GCSUtils.get_trained_model_from_gcs(
+            project_name=project_id,
+            bucket_name=bucket,
+            source_blob_name=Utils.get_calibration_model_path(models.DEFAULT, "pm2_5"),
         )
-
-        calibrate_by: Dict[str, Union[CityModels, CountryModels]] = {
-            "city": CityModels,
-            "country": CountryModels,
-        }
-
-        models: Union[CityModels, CountryModels] = calibrate_by.get(
-            groupby, CountryModels
+        default_lasso_model = GCSUtils.get_trained_model_from_gcs(
+            project_name=project_id,
+            bucket_name=bucket,
+            source_blob_name=Utils.get_calibration_model_path(models.DEFAULT, "pm10"),
         )
-
-        # Explicitly filter data to calibrate. At the moment, only calibrating on AirQo data.
-
-        data_to_calibrate.dropna(subset=input_variables, inplace=True)
-        grouped_df = data_to_calibrate.groupby(groupby, dropna=False)
-        if not data_to_calibrate.empty:
-            default_rf_model = GCSUtils.get_trained_model_from_gcs(
-                project_name=project_id,
-                bucket_name=bucket,
-                source_blob_name=Utils.get_calibration_model_path(
-                    models.DEFAULT, "pm2_5"
-                ),
-            )
-            default_lasso_model = GCSUtils.get_trained_model_from_gcs(
-                project_name=project_id,
-                bucket_name=bucket,
-                source_blob_name=Utils.get_calibration_model_path(
-                    models.DEFAULT, "pm10"
-                ),
-            )
-
         available_models = [c.value for c in models]
 
-        for groupedby, group in grouped_df:
-            # If the below condition fails, the rf_model and lasso_model default to the previously ones used and the ones set as "default" outside the forloop.
+        calibrated_data = pd.DataFrame(index=data.index)
+
+        for groupedby, group in data.groupby(groupby, dropna=False):
+            current_rf_model = default_rf_model
+            current_lasso_model = default_lasso_model
             if (
                 groupedby
                 and not pd.isna(groupedby)
@@ -720,49 +725,15 @@ def calibrate_data(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
                     )
                     current_rf_model = default_rf_model
                     current_lasso_model = default_lasso_model
-            else:
-                current_rf_model = default_rf_model
-                current_lasso_model = default_lasso_model
-
-            group["pm2_5_calibrated_value"] = current_rf_model.predict(
-                group[input_variables]
-            )
-            group["pm10_calibrated_value"] = current_lasso_model.predict(
-                group[input_variables]
-            )
-            data.loc[
-                group.index, ["pm2_5_calibrated_value", "pm10_calibrated_value"]
-            ] = group[["pm2_5_calibrated_value", "pm10_calibrated_value"]]
-
-        # Compute raw pm2_5 and pm10 values.
-        data["pm2_5_raw_value"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1)
-        data["pm10_raw_value"] = data[["s1_pm10", "s2_pm10"]].mean(axis=1)
 
-        # Create calibrated columns if they don't exist
-        data["pm2_5_calibrated_value"] = data.get("pm2_5_calibrated_value", np.nan)
-        data["pm10_calibrated_value"] = data.get("pm10_calibrated_value", np.nan)
-
-        # Assign calibrated values, falling back to raw values when missing
-        data.loc[to_calibrate, "pm2_5"] = data.loc[
-            to_calibrate, "pm2_5_calibrated_value"
-        ].fillna(data.loc[to_calibrate, "pm2_5_raw_value"])
-        data.loc[to_calibrate, "pm10"] = data.loc[
-            to_calibrate, "pm10_calibrated_value"
-        ].fillna(data.loc[to_calibrate, "pm10_raw_value"])
+            calibrated_data.loc[
+                group.index, "pm2_5_calibrated_value"
+            ] = current_rf_model.predict(group[input_variables])
+            calibrated_data.loc[
+                group.index, "pm10_calibrated_value"
+            ] = current_lasso_model.predict(group[input_variables])
 
-        return data.drop(
-            columns=[
-                "avg_pm2_5",
-                "avg_pm10",
-                "error_pm2_5",
-                "error_pm10",
-                "pm2_5_pm10",
-                "pm2_5_pm10_mod",
-                "hour",
-                "city",
-            ],
-            errors="ignore",
-        )
+        return calibrated_data
 
     @staticmethod
     def extract_devices_with_uncalibrated_data(
@@ -774,7 +745,7 @@ def extract_devices_with_uncalibrated_data(
         Args:
             start_date (str or datetime): The date for which to check missing uncalibrated data.
             table (str, optional): The name of the BigQuery table. Defaults to None, in which case the appropriate table is determined dynamically.
-            network (DeviceNetwork, optional): The device network to filter by. Defaults to DeviceNetwork.xxxx.
+            network (DeviceNetwork, optional): The device network to filter by. Defaults to DeviceNetwork.AIRQO.
 
         Returns:
             pd.DataFrame: A DataFrame containing the devices with missing uncalibrated data.