move to production

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-4fb630c8-1741195697
+  tag: prod-35663377-1741203957
 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-4fb630c8-1741195697
+  tag: prod-35663377-1741203957
 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-158790b4-1741195433
+  tag: prod-35663377-1741203957
 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-4fb630c8-1741195697
+  tag: prod-35663377-1741203957
 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-b3cf9be4-1741179758
+  tag: stage-bc9720b8-1741203916
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/workflows/airqo_etl_utils/airnow_utils.py

@@ -2,42 +2,46 @@
 import pandas as pd
 
 from .airnow_api import AirNowApi
-from .airqo_api import AirQoApi
 from .constants import DataSource, DeviceCategory, Frequency, DeviceNetwork
-from .data_validator import DataValidationUtils
 from .date import str_to_date, date_to_str
 from .utils import Utils
+from .data_validator import DataValidationUtils
 from .datautils import DataUtils
-from .config import configuration
+from .config import configuration as Config
 import logging
 
 logger = logging.getLogger(__name__)
 
 
 class AirnowDataUtils:
-    @staticmethod
-    def parameter_column_name(parameter: str) -> str:
-        parameter = parameter.lower()
-        if parameter == "pm2.5":
-            return "pm2_5"
-        elif parameter == "pm10":
-            return "pm10"
-        elif parameter == "no2":
-            return "no2"
-        else:
-            raise Exception(f"Unknown parameter {parameter}")
-
     @staticmethod
     def query_bam_data(
         api_key: str, start_date_time: str, end_date_time: str
     ) -> pd.DataFrame:
+        """
+        Queries BAM (Beta Attenuation Monitor) data from the AirNow API within the given date range.
+
+        This function converts the input date strings into the required format for the API,
+        retrieves air quality data, and returns it as a Pandas DataFrame.
+
+        Args:
+            api_key(str): The API key required for authentication with AirNow.
+            start_date_time(str): The start datetime in string format (expected format: "YYYY-MM-DD HH:MM").
+            end_date_time(str): The end datetime in string format (expected format: "YYYY-MM-DD HH:MM").
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the air quality data retrieved from the AirNow API.
+
+        Example:
+            >>> df = query_bam_data("your_api_key", "2024-03-01 00:00", "2024-03-02 23:59")
+            >>> print(df.head())
+        """
         airnow_api = AirNowApi()
+        date_format = "%Y-%m-%dT%H:%M"
         start_date_time = date_to_str(
-            str_to_date(start_date_time), str_format="%Y-%m-%dT%H:%M"
-        )
-        end_date_time = date_to_str(
-            str_to_date(end_date_time), str_format="%Y-%m-%dT%H:%M"
+            str_to_date(start_date_time), str_format=date_format
         )
+        end_date_time = date_to_str(str_to_date(end_date_time), str_format=date_format)
 
         data = airnow_api.get_data(
             api_key=api_key,
@@ -46,7 +50,7 @@ def query_bam_data(
             end_date_time=end_date_time,
         )
 
-        return pd.DataFrame(data)
+        return pd.DataFrame(data) if data else pd.DataFrame()
 
     @staticmethod
     def extract_bam_data(start_date_time: str, end_date_time: str) -> pd.DataFrame:
@@ -78,7 +82,7 @@ def extract_bam_data(start_date_time: str, end_date_time: str) -> pd.DataFrame:
         if not dates:
             raise ValueError("Invalid or empty date range provided.")
 
-        api_key = configuration.US_EMBASSY_API_KEY
+        api_key = Config.US_EMBASSY_API_KEY
 
         all_device_data = []
         device_data = []
@@ -141,10 +145,11 @@ def process_bam_data(data: pd.DataFrame) -> pd.DataFrame:
                     logger.exception(f"Device with ID {device_id_} not found")
                     continue
 
-                parameter_col_name = AirnowDataUtils.parameter_column_name(
-                    row["Parameter"]
-                )
-                if parameter_col_name in pollutant_value:
+                parameter_col_name = Config.device_config_mapping.get(
+                    DeviceCategory.BAM.str, {}
+                ).get(row["Parameter"].lower(), None)
+
+                if parameter_col_name and parameter_col_name in pollutant_value:
                     pollutant_value[parameter_col_name] = row["Value"]
 
                 if row["network"] != device_details.get("network"):

src/workflows/airqo_etl_utils/airqo_utils.py

@@ -2,7 +2,7 @@
 import ast
 import numpy as np
 import pandas as pd
-from typing import List, Dict, Any, Union, Generator
+from typing import List, Dict, Any, Union, Generator, Optional
 
 from .airqo_api import AirQoApi
 from .bigquery_api import BigQueryApi
@@ -738,15 +738,17 @@ def _airqo_calibrate(data: pd.DataFrame, groupby: str) -> pd.DataFrame:
 
     @staticmethod
     def extract_devices_with_uncalibrated_data(
-        start_date, table: str = None, network: DeviceNetwork = DeviceNetwork.AIRQO
+        start_date: str,
+        table: Optional[str] = None,
+        network: Optional[DeviceNetwork] = DeviceNetwork.AIRQO,
     ) -> pd.DataFrame:
         """
         Extracts devices with uncalibrated data for a given start date from BigQuery.
 
         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.AIRQO.
+            start_date(datetime like string): 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.AIRQO.
 
         Returns:
             pd.DataFrame: A DataFrame containing the devices with missing uncalibrated data.
@@ -788,9 +790,8 @@ def extract_aggregate_calibrate_raw_data(
 
         # TODO Might have to change approach to group by device_id depending on performance.
         for _, row in devices.iterrows():
-            end_date_time = datetime.strptime(row.timestamp, "%Y-%m-%d %H:%M:%S%z")
             end_date_time = DateUtils.format_datetime_by_unit_str(
-                end_date_time, "hours_end"
+                row.timestamp, "hours_end"
             )
             raw_device_data = DataUtils.extract_data_from_bigquery(
                 DataType.RAW,

src/workflows/airqo_etl_utils/bigquery_api.py

@@ -1048,7 +1048,10 @@ def fetch_satellite_readings(
             logger.info(f"Error fetching data from bigquery", {e})
 
     def generate_missing_data_query(
-        self, date: str, table: str, network: DeviceNetwork
+        self,
+        date: str,
+        table: str,
+        network: Optional[DeviceNetwork] = DeviceNetwork.AIRQO,
     ) -> str:
         """
         Generates a BigQuery SQL query to find missing hourly air quality data for devices.
@@ -1071,7 +1074,11 @@ def generate_missing_data_query(
             SELECT device_id, TIMESTAMP_TRUNC(timestamp, HOUR) AS timestamp
             FROM `{table}` 
             WHERE 
-            DATE(timestamp) = '{date}'
+            TIMESTAMP_TRUNC(timestamp, DAY) = '{date}'
+            AND s1_pm2_5 IS NOT NULL
+            AND s2_pm2_5 IS NOT NULL
+            AND s1_pm10 IS NOT NULL
+            AND s2_pm10 IS NOT NULL
             AND pm2_5_calibrated_value IS NULL
             AND network = '{network.str}'
             )

src/workflows/airqo_etl_utils/config.py

@@ -305,6 +305,8 @@ class Config:
         "ts": "timestamp",
     }
 
+    AIRBEAM_BAM_FIELD_MAPPING = {"pm2.5": "pm2_5", "pm10": "pm10", "no2": "no2"}
+
     DATA_RESOLUTION_MAPPING = {
         "iqair": {"hourly": "instant", "raw": "instant", "current": "current"}
     }
@@ -363,7 +365,10 @@ class Config:
     device_config_mapping = {
         "bam": {
             "field_8_cols": list(AIRQO_BAM_MAPPING_NEW.get("field8", {}).values()),
-            "mapping": {"airqo": AIRQO_BAM_MAPPING_NEW},
+            "mapping": {
+                "airqo": AIRQO_BAM_MAPPING_NEW,
+                "airbeam": AIRBEAM_BAM_FIELD_MAPPING,
+            },
             "other_fields_cols": [],
         },
         "gas": {

src/workflows/airqo_etl_utils/data_summary_utils.py

@@ -4,6 +4,32 @@
 class DataSummaryUtils:
     @staticmethod
     def compute_devices_summary(data: pd.DataFrame) -> pd.DataFrame:
+        """
+        Computes a summary of device records from the given dataset.
+
+        This function processes a DataFrame containing device readings, grouping data
+        by device and daily timestamps. It calculates the number of hourly records,
+        calibrated and uncalibrated records, and their respective percentages.
+
+        Args:
+            data (pd.DataFrame): A DataFrame containing device data with at least the columns:
+                - timestamp(datetime-like string): The time of the record.
+                - device(str): The device identifier.
+                - site_id(str): The site identifier.
+                - pm2_5_calibrated_value(float): The calibrated PM2.5 value.
+
+        Returns:
+            pd.DataFrame: A summary DataFrame with aggregated daily records for each device,
+            containing the following columns:
+                - timestamp(str): Date (YYYY-MM-DD) representing the aggregation period.
+                - device(str): Device identifier.
+                - site_id(str): Site identifier.
+                - hourly_records(int): Total records for that device on that date.
+                - calibrated_records(int): Count of non-null calibrated PM2.5 values.
+                - uncalibrated_records(int): Count of missing PM2.5 calibrated values.
+                - calibrated_percentage(float): Percentage of calibrated records.
+                - uncalibrated_percentage(float): Percentage of uncalibrated records.
+        """
         devices_summary = pd.DataFrame()
         data["timestamp"] = pd.to_datetime(data["timestamp"])
         data.drop_duplicates(subset=["device", "timestamp"], inplace=True)

src/workflows/airqo_etl_utils/datautils.py

@@ -2,6 +2,7 @@
 import pandas as pd
 from pathlib import Path
 import json
+import ast
 from confluent_kafka import KafkaException
 from typing import List, Dict, Any, Union, Tuple, Optional
 
@@ -1036,32 +1037,26 @@ def process_data_for_message_broker(
     # Clarity
     def _flatten_location_coordinates_clarity(coordinates: str) -> pd.Series:
         """
-        Extracts latitude and longitude from a coordinate string.
-
-        The function expects a string representation of coordinates in the format "[longitude, latitude]". It removes square brackets and spaces, splits
-        the values, and returns them as a Pandas Series.
+        Extracts latitude and longitude from a string representation of coordinates.
 
         Args:
-            coordinates(str): A string containing coordinates in the format "[longitude, latitude]".
+            coordinates(str): A string containing a list or tuple with two numeric values representing latitude and longitude (e.g., "[37.7749, -122.4194]").
 
         Returns:
-            pd.Series: A Pandas Series with 'latitude' and 'longitude' as keys. Returns None for both values if an error occurs.
-
-        Example:
-            >>> _flatten_location_coordinates("[-73.935242, 40.730610]")
-            latitude     40.730610
-            longitude   -73.935242
-            dtype: object
+            pd.Series: A Pandas Series containing two values:
+                    - latitude (float) at index 0
+                    - longitude (float) at index 1
+                    If parsing fails or the format is invalid, returns Series([None, None]).
         """
-
         try:
-            coords = coordinates.strip("[] ").split(",")
-            return pd.Series(
-                {"latitude": coords[1].strip(), "longitude": coords[0].strip()}
-            )
-        except Exception as ex:
-            logger.exception("Error parsing coordinates: %s", ex)
-            return pd.Series({"latitude": None, "longitude": None})
+            coords = ast.literal_eval(coordinates)
+
+            if isinstance(coords, (list, tuple)) and len(coords) == 2:
+                return pd.Series(coords)
+        except (ValueError, SyntaxError):
+            logger.exception("Error occurred while cleaning up coordinates")
+
+        return pd.Series([None, None])
 
     def _transform_clarity_data(data: pd.DataFrame) -> pd.DataFrame:
         """
@@ -1106,7 +1101,7 @@ def _transform_clarity_data(data: pd.DataFrame) -> pd.DataFrame:
         )
 
         data[["latitude", "longitude"]] = data["location.coordinates"].apply(
-            DataUtils._flatten_location_coordinates
+            DataUtils._flatten_location_coordinates_clarity
         )
 
         devices, _ = DataUtils.get_devices()

src/workflows/airqo_etl_utils/weather_data_utils.py

@@ -178,7 +178,9 @@ def fetch_openweathermap_data_for_sites(sites: pd.DataFrame) -> pd.DataFrame:
         for multiple sites in parallel batches.
         """
 
-        def process_batch(batch_of_coordinates: List[Dict[str, Any]]):
+        def process_batch(
+            batch_of_coordinates: List[Dict[str, Any]]
+        ) -> List[Dict[str, Any]]:
             """
             Fetches weather data from OpenWeatherMap API for a given list of sites.
 

src/workflows/dags/data_summary.py

@@ -47,3 +47,4 @@ def save_summary(data: pd.DataFrame):
 
 
 # data_summary()
+# TODO This is not being used. Will be deleted with all it's utilities once a data health analytics dashboard is in place.