text: import re
  import falcon
  import simplejson as json
  import mysql.connector
  import config
  from datetime import datetime, timedelta, timezone
  from core import utilities
  from decimal import Decimal
  import excelexporters.storeload
  
  
  class Reporting:
      @staticmethod
      def __init__():
          """"Initializes Reporting"""
          pass
  
      @staticmethod
      def on_options(req, resp):
          resp.status = falcon.HTTP_200
  
      ####################################################################################################################
      # PROCEDURES
      # Step 1: valid parameters
      # Step 2: query the store
      # Step 3: query energy categories
      # Step 4: query associated sensors
      # Step 5: query associated points
      # Step 6: query base period energy input
      # Step 7: query reporting period energy input
      # Step 8: query tariff data
      # Step 9: query associated sensors and points data
      # Step 10: construct the report
      ####################################################################################################################
      @staticmethod
      def on_get(req, resp):
          print(req.params)
          store_id = req.params.get('storeid')
          store_uuid = req.params.get('storeuuid')
          period_type = req.params.get('periodtype')
          base_start_datetime_local = req.params.get('baseperiodstartdatetime')
          base_end_datetime_local = req.params.get('baseperiodenddatetime')
          reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime')
          reporting_end_datetime_local = req.params.get('reportingperiodenddatetime')
  
          ################################################################################################################
          # Step 1: valid parameters
          ################################################################################################################
          if store_id is None and store_uuid is None:
              raise falcon.HTTPError(falcon.HTTP_400,
                                     title='API.BAD_REQUEST',
                                     description='API.INVALID_STORE_ID')
  
          if store_id is not None:
              store_id = str.strip(store_id)
              if not store_id.isdigit() or int(store_id) <= 0:
                  raise falcon.HTTPError(falcon.HTTP_400,
                                         title='API.BAD_REQUEST',
                                         description='API.INVALID_STORE_ID')
  
          if store_uuid is not None:
              regex = re.compile('^[a-f0-9]{8}-?[a-f0-9]{4}-?4[a-f0-9]{3}-?[89ab][a-f0-9]{3}-?[a-f0-9]{12}\Z', re.I)
              match = regex.match(str.strip(store_uuid))
              if not bool(match):
                  raise falcon.HTTPError(falcon.HTTP_400,
                                         title='API.BAD_REQUEST',
                                         description='API.INVALID_STORE_UUID')
  
          if period_type is None:
              raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE')
          else:
              period_type = str.strip(period_type)
              if period_type not in ['hourly', 'daily', 'weekly', 'monthly', 'yearly']:
                  raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE')
  
          timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6])
          if config.utc_offset[0] == '-':
              timezone_offset = -timezone_offset
  
          base_start_datetime_utc = None
          if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0:
              base_start_datetime_local = str.strip(base_start_datetime_local)
              try:
                  base_start_datetime_utc = datetime.strptime(base_start_datetime_local,
                                                              '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
                      timedelta(minutes=timezone_offset)
              except ValueError:
                  raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                         description="API.INVALID_BASE_PERIOD_START_DATETIME")
  
          base_end_datetime_utc = None
          if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0:
              base_end_datetime_local = str.strip(base_end_datetime_local)
              try:
                  base_end_datetime_utc = datetime.strptime(base_end_datetime_local,
                                                            '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
                      timedelta(minutes=timezone_offset)
              except ValueError:
                  raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                         description="API.INVALID_BASE_PERIOD_END_DATETIME")
  
          if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \
                  base_start_datetime_utc >= base_end_datetime_utc:
              raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                     description='API.INVALID_BASE_PERIOD_END_DATETIME')
  
          if reporting_start_datetime_local is None:
              raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                     description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
          else:
              reporting_start_datetime_local = str.strip(reporting_start_datetime_local)
              try:
                  reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local,
                                                                   '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
                      timedelta(minutes=timezone_offset)
              except ValueError:
                  raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                         description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
  
          if reporting_end_datetime_local is None:
              raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                     description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
          else:
              reporting_end_datetime_local = str.strip(reporting_end_datetime_local)
              try:
                  reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local,
                                                                 '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
                      timedelta(minutes=timezone_offset)
              except ValueError:
                  raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                         description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
  
          if reporting_start_datetime_utc >= reporting_end_datetime_utc:
              raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
                                     description='API.INVALID_REPORTING_PERIOD_END_DATETIME')
  
          ################################################################################################################
          # Step 2: query the store
          ################################################################################################################
          cnx_system = mysql.connector.connect(**config.myems_system_db)
          cursor_system = cnx_system.cursor()
  
          cnx_energy = mysql.connector.connect(**config.myems_energy_db)
          cursor_energy = cnx_energy.cursor()
  
          cnx_historical = mysql.connector.connect(**config.myems_historical_db)
          cursor_historical = cnx_historical.cursor()
  
          if store_id is not None:
              cursor_system.execute(" SELECT id, name, area, cost_center_id "
                                    " FROM tbl_stores "
                                    " WHERE id = %s ", (store_id,))
              row_store = cursor_system.fetchone()
          elif store_uuid is not None:
              cursor_system.execute(" SELECT id, name, area, cost_center_id "
                                    " FROM tbl_stores "
                                    " WHERE uuid = %s ", (store_uuid,))
              row_store = cursor_system.fetchone()
  
          if row_store is None:
              if cursor_system:
                  cursor_system.close()
              if cnx_system:
                  cnx_system.disconnect()
  
              if cursor_energy:
                  cursor_energy.close()
              if cnx_energy:
                  cnx_energy.disconnect()
  
              if cursor_historical:
                  cursor_historical.close()
              if cnx_historical:
                  cnx_historical.disconnect()
              raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.STORE_NOT_FOUND')
  
          store = dict()
          store['id'] = row_store[0]
          store['name'] = row_store[1]
          store['area'] = row_store[2]
          store['cost_center_id'] = row_store[3]
  
          ################################################################################################################
          # Step 3: query energy categories
          ################################################################################################################
          energy_category_set = set()
          # query energy categories in base period
          cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
                                " FROM tbl_store_input_category_hourly "
                                " WHERE store_id = %s "
                                "     AND start_datetime_utc >= %s "
                                "     AND start_datetime_utc < %s ",
                                (store['id'], base_start_datetime_utc, base_end_datetime_utc))
          rows_energy_categories = cursor_energy.fetchall()
          if rows_energy_categories is not None or len(rows_energy_categories) > 0:
              for row_energy_category in rows_energy_categories:
                  energy_category_set.add(row_energy_category[0])
  
          # query energy categories in reporting period
          cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
                                " FROM tbl_store_input_category_hourly "
                                " WHERE store_id = %s "
                                "     AND start_datetime_utc >= %s "
                                "     AND start_datetime_utc < %s ",
                                (store['id'], reporting_start_datetime_utc, reporting_end_datetime_utc))
          rows_energy_categories = cursor_energy.fetchall()
          if rows_energy_categories is not None or len(rows_energy_categories) > 0:
              for row_energy_category in rows_energy_categories:
                  energy_category_set.add(row_energy_category[0])
  
          # query all energy categories in base period and reporting period
          cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e "
                                " FROM tbl_energy_categories "
                                " ORDER BY id ", )
          rows_energy_categories = cursor_system.fetchall()
          if rows_energy_categories is None or len(rows_energy_categories) == 0:
              if cursor_system:
                  cursor_system.close()
              if cnx_system:
                  cnx_system.disconnect()
  
              if cursor_energy:
                  cursor_energy.close()
              if cnx_energy:
                  cnx_energy.disconnect()
  
              if cursor_historical:
                  cursor_historical.close()
              if cnx_historical:
                  cnx_historical.disconnect()
              raise falcon.HTTPError(falcon.HTTP_404,
                                     title='API.NOT_FOUND',
                                     description='API.ENERGY_CATEGORY_NOT_FOUND')
          energy_category_dict = dict()
          for row_energy_category in rows_energy_categories:
              if row_energy_category[0] in energy_category_set:
                  energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1],
                                                                  "unit_of_measure": row_energy_category[2],
                                                                  "kgce": row_energy_category[3],
                                                                  "kgco2e": row_energy_category[4]}
  
          ################################################################################################################
          # Step 4: query associated sensors
          ################################################################################################################
          point_list = list()
          cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type  "
                                " FROM tbl_stores st, tbl_sensors se, tbl_stores_sensors ss, "
                                "      tbl_points p, tbl_sensors_points sp "
                                " WHERE st.id = %s AND st.id = ss.store_id AND ss.sensor_id = se.id "
                                "       AND se.id = sp.sensor_id AND sp.point_id = p.id "
                                " ORDER BY p.id ", (store['id'],))
          rows_points = cursor_system.fetchall()
          if rows_points is not None and len(rows_points) > 0:
              for row in rows_points:
                  point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
  
          ################################################################################################################
          # Step 5: query associated points
          ################################################################################################################
          cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type  "
                                " FROM tbl_stores s, tbl_stores_points sp, tbl_points p "
                                " WHERE s.id = %s AND s.id = sp.store_id AND sp.point_id = p.id "
                                " ORDER BY p.id ", (store['id'],))
          rows_points = cursor_system.fetchall()
          if rows_points is not None and len(rows_points) > 0:
              for row in rows_points:
                  point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
  
          ################################################################################################################
          # Step 6: query base period energy input
          ################################################################################################################
          base = dict()
          if energy_category_set is not None and len(energy_category_set) > 0:
              for energy_category_id in energy_category_set:
                  base[energy_category_id] = dict()
                  base[energy_category_id]['timestamps'] = list()
                  base[energy_category_id]['sub_averages'] = list()
                  base[energy_category_id]['sub_maximums'] = list()
                  base[energy_category_id]['average'] = None
                  base[energy_category_id]['maximum'] = None
                  base[energy_category_id]['factor'] = None
  
                  cursor_energy.execute(" SELECT start_datetime_utc, actual_value "
                                        " FROM tbl_store_input_category_hourly "
                                        " WHERE store_id = %s "
                                        "     AND energy_category_id = %s "
                                        "     AND start_datetime_utc >= %s "
                                        "     AND start_datetime_utc < %s "
                                        " ORDER BY start_datetime_utc ",
                                        (store['id'],
                                         energy_category_id,
                                         base_start_datetime_utc,
                                         base_end_datetime_utc))
                  rows_store_hourly = cursor_energy.fetchall()
  
                  rows_store_periodically, \
                      base[energy_category_id]['average'], \
                      base[energy_category_id]['maximum'] = \
                      utilities.averaging_hourly_data_by_period(rows_store_hourly,
                                                                base_start_datetime_utc,
                                                                base_end_datetime_utc,
                                                                period_type)
                  base[energy_category_id]['factor'] = \
                      (base[energy_category_id]['average'] / base[energy_category_id]['maximum']
                          if (base[energy_category_id]['average'] is not None and
                              base[energy_category_id]['maximum'] is not None and
                              base[energy_category_id]['maximum'] > Decimal(0.0))
                          else None)
  
                  for row_store_periodically in rows_store_periodically:
                      current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \
                                               timedelta(minutes=timezone_offset)
                      if period_type == 'hourly':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
                      elif period_type == 'daily':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%d')
                      elif period_type == 'weekly':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%d')
                      elif period_type == 'monthly':
                          current_datetime = current_datetime_local.strftime('%Y-%m')
                      elif period_type == 'yearly':
                          current_datetime = current_datetime_local.strftime('%Y')
  
                      base[energy_category_id]['timestamps'].append(current_datetime)
                      base[energy_category_id]['sub_averages'].append(row_store_periodically[1])
                      base[energy_category_id]['sub_maximums'].append(row_store_periodically[2])
  
          ################################################################################################################
          # Step 7: query reporting period energy input
          ################################################################################################################
          reporting = dict()
          if energy_category_set is not None and len(energy_category_set) > 0:
              for energy_category_id in energy_category_set:
                  reporting[energy_category_id] = dict()
                  reporting[energy_category_id]['timestamps'] = list()
                  reporting[energy_category_id]['sub_averages'] = list()
                  reporting[energy_category_id]['sub_maximums'] = list()
                  reporting[energy_category_id]['average'] = None
                  reporting[energy_category_id]['maximum'] = None
                  reporting[energy_category_id]['factor'] = None
  
                  cursor_energy.execute(" SELECT start_datetime_utc, actual_value "
                                        " FROM tbl_store_input_category_hourly "
                                        " WHERE store_id = %s "
                                        "     AND energy_category_id = %s "
                                        "     AND start_datetime_utc >= %s "
                                        "     AND start_datetime_utc < %s "
                                        " ORDER BY start_datetime_utc ",
                                        (store['id'],
                                         energy_category_id,
                                         reporting_start_datetime_utc,
                                         reporting_end_datetime_utc))
                  rows_store_hourly = cursor_energy.fetchall()
  
                  rows_store_periodically, \
                      reporting[energy_category_id]['average'], \
                      reporting[energy_category_id]['maximum'] = \
                      utilities.averaging_hourly_data_by_period(rows_store_hourly,
                                                                reporting_start_datetime_utc,
                                                                reporting_end_datetime_utc,
                                                                period_type)
                  reporting[energy_category_id]['factor'] = \
                      (reporting[energy_category_id]['average'] / reporting[energy_category_id]['maximum']
                       if (reporting[energy_category_id]['average'] is not None and
                           reporting[energy_category_id]['maximum'] is not None and
                           reporting[energy_category_id]['maximum'] > Decimal(0.0))
                       else None)
  
                  for row_store_periodically in rows_store_periodically:
                      current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \
                                               timedelta(minutes=timezone_offset)
                      if period_type == 'hourly':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
                      elif period_type == 'daily':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%d')
                      elif period_type == 'weekly':
                          current_datetime = current_datetime_local.strftime('%Y-%m-%d')
                      elif period_type == 'monthly':
                          current_datetime = current_datetime_local.strftime('%Y-%m')
                      elif period_type == 'yearly':
                          current_datetime = current_datetime_local.strftime('%Y')
  
                      reporting[energy_category_id]['timestamps'].append(current_datetime)
                      reporting[energy_category_id]['sub_averages'].append(row_store_periodically[1])
                      reporting[energy_category_id]['sub_maximums'].append(row_store_periodically[2])
  
          ################################################################################################################
          # Step 8: query tariff data
          ################################################################################################################
          parameters_data = dict()
          parameters_data['names'] = list()
          parameters_data['timestamps'] = list()
          parameters_data['values'] = list()
          if energy_category_set is not None and len(energy_category_set) > 0:
              for energy_category_id in energy_category_set:
                  energy_category_tariff_dict = utilities.get_energy_category_tariffs(store['cost_center_id'],
                                                                                      energy_category_id,
                                                                                      reporting_start_datetime_utc,
                                                                                      reporting_end_datetime_utc)
                  tariff_timestamp_list = list()
                  tariff_value_list = list()
                  for k, v in energy_category_tariff_dict.items():
                      # convert k from utc to local
                      k = k + timedelta(minutes=timezone_offset)
                      tariff_timestamp_list.append(k.isoformat()[0:19][0:19])
                      tariff_value_list.append(v)
  
                  parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name'])
                  parameters_data['timestamps'].append(tariff_timestamp_list)
                  parameters_data['values'].append(tariff_value_list)
  
          ################################################################################################################
          # Step 9: query associated sensors and points data
          ################################################################################################################
          for point in point_list:
              point_values = []
              point_timestamps = []
              if point['object_type'] == 'ANALOG_VALUE':
                  query = (" SELECT utc_date_time, actual_value "
                           " FROM tbl_analog_value "
                           " WHERE point_id = %s "
                           "       AND utc_date_time BETWEEN %s AND %s "
                           " ORDER BY utc_date_time ")
                  cursor_historical.execute(query, (point['id'],
                                                    reporting_start_datetime_utc,
                                                    reporting_end_datetime_utc))
                  rows = cursor_historical.fetchall()
  
                  if rows is not None and len(rows) > 0:
                      for row in rows:
                          current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
                                                   timedelta(minutes=timezone_offset)
                          current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
                          point_timestamps.append(current_datetime)
                          point_values.append(row[1])
  
              elif point['object_type'] == 'ENERGY_VALUE':
                  query = (" SELECT utc_date_time, actual_value "
                           " FROM tbl_energy_value "
                           " WHERE point_id = %s "
                           "       AND utc_date_time BETWEEN %s AND %s "
                           " ORDER BY utc_date_time ")
                  cursor_historical.execute(query, (point['id'],
                                                    reporting_start_datetime_utc,
                                                    reporting_end_datetime_utc))
                  rows = cursor_historical.fetchall()
  
                  if rows is not None and len(rows) > 0:
                      for row in rows:
                          current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
                                                   timedelta(minutes=timezone_offset)
                          current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
                          point_timestamps.append(current_datetime)
                          point_values.append(row[1])
              elif point['object_type'] == 'DIGITAL_VALUE':
                  query = (" SELECT utc_date_time, actual_value "
                           " FROM tbl_digital_value "
                           " WHERE point_id = %s "
                           "       AND utc_date_time BETWEEN %s AND %s "
                           " ORDER BY utc_date_time ")
                  cursor_historical.execute(query, (point['id'],
                                                    reporting_start_datetime_utc,
                                                    reporting_end_datetime_utc))
                  rows = cursor_historical.fetchall()
  
                  if rows is not None and len(rows) > 0:
                      for row in rows:
                          current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
                                                   timedelta(minutes=timezone_offset)
                          current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
                          point_timestamps.append(current_datetime)
                          point_values.append(row[1])
  
              parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')')
              parameters_data['timestamps'].append(point_timestamps)
              parameters_data['values'].append(point_values)
  
          ################################################################################################################
          # Step 10: construct the report
          ################################################################################################################
          if cursor_system:
              cursor_system.close()
          if cnx_system:
              cnx_system.disconnect()
  
          if cursor_energy:
              cursor_energy.close()
          if cnx_energy:
              cnx_energy.disconnect()
  
          if cursor_historical:
              cursor_historical.close()
          if cnx_historical:
              cnx_historical.disconnect()
  
          result = dict()
  
          result['store'] = dict()
          result['store']['name'] = store['name']
          result['store']['area'] = store['area']
  
          result['base_period'] = dict()
          result['base_period']['names'] = list()
          result['base_period']['units'] = list()
          result['base_period']['timestamps'] = list()
          result['base_period']['sub_averages'] = list()
          result['base_period']['sub_maximums'] = list()
          result['base_period']['averages'] = list()
          result['base_period']['maximums'] = list()
          result['base_period']['factors'] = list()
          if energy_category_set is not None and len(energy_category_set) > 0:
              for energy_category_id in energy_category_set:
                  result['base_period']['names'].append(energy_category_dict[energy_category_id]['name'])
                  result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure'])
                  result['base_period']['timestamps'].append(base[energy_category_id]['timestamps'])
                  result['base_period']['sub_averages'].append(base[energy_category_id]['sub_averages'])
                  result['base_period']['sub_maximums'].append(base[energy_category_id]['sub_maximums'])
                  result['base_period']['averages'].append(base[energy_category_id]['average'])
                  result['base_period']['maximums'].append(base[energy_category_id]['maximum'])
                  result['base_period']['factors'].append(base[energy_category_id]['factor'])
  
          result['reporting_period'] = dict()
          result['reporting_period']['names'] = list()
          result['reporting_period']['energy_category_ids'] = list()
          result['reporting_period']['units'] = list()
          result['reporting_period']['timestamps'] = list()
          result['reporting_period']['sub_averages'] = list()
          result['reporting_period']['sub_maximums'] = list()
          result['reporting_period']['averages'] = list()
          result['reporting_period']['averages_per_unit_area'] = list()
          result['reporting_period']['averages_increment_rate'] = list()
          result['reporting_period']['maximums'] = list()
          result['reporting_period']['maximums_per_unit_area'] = list()
          result['reporting_period']['maximums_increment_rate'] = list()
          result['reporting_period']['factors'] = list()
          result['reporting_period']['factors_increment_rate'] = list()
  
          if energy_category_set is not None and len(energy_category_set) > 0:
              for energy_category_id in energy_category_set:
                  result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name'])
                  result['reporting_period']['energy_category_ids'].append(energy_category_id)
                  result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure'])
                  result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps'])
                  result['reporting_period']['sub_averages'].append(reporting[energy_category_id]['sub_averages'])
                  result['reporting_period']['sub_maximums'].append(reporting[energy_category_id]['sub_maximums'])
                  result['reporting_period']['averages'].append(reporting[energy_category_id]['average'])
                  result['reporting_period']['averages_per_unit_area'].append(
                          reporting[energy_category_id]['average'] / store['area']
                          if reporting[energy_category_id]['average'] is not None and
                          store['area'] is not None and
                          store['area'] > Decimal(0.0)
                          else None)
                  result['reporting_period']['averages_increment_rate'].append(
                      (reporting[energy_category_id]['average'] - base[energy_category_id]['average']) /
                      base[energy_category_id]['average'] if (reporting[energy_category_id]['average'] is not None and
                                                              base[energy_category_id]['average'] is not None and
                                                              base[energy_category_id]['average'] > Decimal(0.0))
                      else None)
                  result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum'])
                  result['reporting_period']['maximums_increment_rate'].append(
                      (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) /
                      base[energy_category_id]['maximum'] if (reporting[energy_category_id]['maximum'] is not None and
                                                              base[energy_category_id]['maximum'] is not None and
                                                              base[energy_category_id]['maximum'] > Decimal(0.0))
                      else None)
                  result['reporting_period']['maximums_per_unit_area'].append(
                      reporting[energy_category_id]['maximum'] / store['area']
                      if reporting[energy_category_id]['maximum'] is not None and
                      store['area'] is not None and
                      store['area'] > Decimal(0.0)
                      else None)
                  result['reporting_period']['factors'].append(reporting[energy_category_id]['factor'])
                  result['reporting_period']['factors_increment_rate'].append(
                      (reporting[energy_category_id]['factor'] - base[energy_category_id]['factor']) /
                      base[energy_category_id]['factor'] if (reporting[energy_category_id]['factor'] is not None and
                                                             base[energy_category_id]['factor'] is not None and
                                                             base[energy_category_id]['factor'] > Decimal(0.0))
                      else None)
  
          result['parameters'] = {
              "names": parameters_data['names'],
              "timestamps": parameters_data['timestamps'],
              "values": parameters_data['values']
          }
  
          # export result to Excel file and then encode the file to base64 string
          result['excel_bytes_base64'] = excelexporters.storeload.export(result,
                                                                         store['name'],
                                                                         reporting_start_datetime_local,
                                                                         reporting_end_datetime_local,
                                                                         period_type)
  
          resp.text = json.dumps(result)
  