Make static thresholds for testing purposes.

import logging

from enum import Enum

import pandas as pd

from collections import defaultdict

logger = logging.getLogger(__name__)

            return results

        else:

            return []

def find(data , type , value):

    return next((item for item in data if item[type] == value), None)

def aggregation_handler_total_latency(

        batch_type_name, key, batch, input_kpi_list, output_kpi_list, thresholds

):

    """

      Process a batch of data and calculate aggregated values for each input KPI

      and maps them to the output KPIs. """

    # Group and sum

    # Track sum and count

    sum_dict = defaultdict(int)

    count_dict = defaultdict(int)

    for item in batch:

        kpi_id = item["kpi_id"]

        if kpi_id in input_kpi_list:

            sum_dict[kpi_id] += item["kpi_value"]

            count_dict[kpi_id] += 1

    # Compute average

    avg_dict = {kpi_id: sum_dict[kpi_id] / count_dict[kpi_id] for kpi_id in sum_dict}

    total_kpi_lat = 0

    for kpi_id, total_value in avg_dict.items():

        total_kpi_lat += total_value

    result = {

        "kpi_id": output_kpi_list[0],

        "avg": total_kpi_lat,

        "THRESHOLD_RAISE": bool(total_kpi_lat > 2600),

        "THRESHOLD_FALL": bool(total_kpi_lat < 699)

    }

    results = []

    results.append(result)

    logger.warning(f"result : {result}.")

    return results