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.combinedequipmentload class Reporting: @staticmethod def __init__(): """Initializes Class""" pass @staticmethod def on_options(req, resp): resp.status = falcon.HTTP_200 #################################################################################################################### # PROCEDURES # Step 1: valid parameters # Step 2: query the combined equipment # Step 3: query energy categories # Step 4: query associated points # Step 5: query associated equipments # Step 6: query base period energy input # Step 7: query reporting period energy input # Step 8: query tariff data # Step 9: query associated points data # Step 10: query associated equipments energy input # Step 11: construct the report #################################################################################################################### @staticmethod def on_get(req, resp): print(req.params) combined_equipment_id = req.params.get('combinedequipmentid') combined_equipment_uuid = req.params.get('combinedequipmentuuid') 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 combined_equipment_id is None and combined_equipment_uuid is None: raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_COMBINED_EQUIPMENT_ID') if combined_equipment_id is not None: combined_equipment_id = str.strip(combined_equipment_id) if not combined_equipment_id.isdigit() or int(combined_equipment_id) <= 0: raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_COMBINED_EQUIPMENT_ID') if combined_equipment_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(combined_equipment_uuid)) if not bool(match): raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_COMBINED_EQUIPMENT_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 combined equipment ################################################################################################################ 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 combined_equipment_id is not None: cursor_system.execute(" SELECT id, name, cost_center_id " " FROM tbl_combined_equipments " " WHERE id = %s ", (combined_equipment_id,)) row_combined_equipment = cursor_system.fetchone() elif combined_equipment_uuid is not None: cursor_system.execute(" SELECT id, name, cost_center_id " " FROM tbl_combined_equipments " " WHERE uuid = %s ", (combined_equipment_uuid,)) row_combined_equipment = cursor_system.fetchone() if row_combined_equipment is None: if cursor_system: cursor_system.close() if cnx_system: cnx_system.close() if cursor_energy: cursor_energy.close() if cnx_energy: cnx_energy.close() if cursor_historical: cursor_historical.close() if cnx_historical: cnx_historical.close() raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.COMBINED_EQUIPMENT_NOT_FOUND') combined_equipment = dict() combined_equipment['id'] = row_combined_equipment[0] combined_equipment['name'] = row_combined_equipment[1] combined_equipment['cost_center_id'] = row_combined_equipment[2] ################################################################################################################ # 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_combined_equipment_input_category_hourly " " WHERE combined_equipment_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s ", (combined_equipment['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_combined_equipment_input_category_hourly " " WHERE combined_equipment_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s ", (combined_equipment['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.close() if cursor_energy: cursor_energy.close() if cnx_energy: cnx_energy.close() if cursor_historical: cursor_historical.close() if cnx_historical: cnx_historical.close() 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 points ################################################################################################################ point_list = list() cursor_system.execute(" SELECT p.id, ep.name, p.units, p.object_type " " FROM tbl_combined_equipments e, tbl_combined_equipments_parameters ep, tbl_points p " " WHERE e.id = %s AND e.id = ep.combined_equipment_id AND ep.parameter_type = 'point' " " AND ep.point_id = p.id " " ORDER BY p.id ", (combined_equipment['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 equipments ################################################################################################################ associated_equipment_list = list() cursor_system.execute(" SELECT e.id, e.name " " FROM tbl_equipments e,tbl_combined_equipments_equipments ee" " WHERE ee.combined_equipment_id = %s AND e.id = ee.equipment_id" " ORDER BY id ", (combined_equipment['id'],)) rows_associated_equipments = cursor_system.fetchall() if rows_associated_equipments is not None and len(rows_associated_equipments) > 0: for row in rows_associated_equipments: associated_equipment_list.append({"id": row[0], "name": row[1]}) ################################################################################################################ # 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_combined_equipment_input_category_hourly " " WHERE combined_equipment_id = %s " " AND energy_category_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s " " ORDER BY start_datetime_utc ", (combined_equipment['id'], energy_category_id, base_start_datetime_utc, base_end_datetime_utc)) rows_combined_equipment_hourly = cursor_energy.fetchall() rows_combined_equipment_periodically, \ base[energy_category_id]['average'], \ base[energy_category_id]['maximum'] = \ utilities.averaging_hourly_data_by_period(rows_combined_equipment_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_combined_equipment_periodically in rows_combined_equipment_periodically: current_datetime_local = row_combined_equipment_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_combined_equipment_periodically[1]) base[energy_category_id]['sub_maximums'].append(row_combined_equipment_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_combined_equipment_input_category_hourly " " WHERE combined_equipment_id = %s " " AND energy_category_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s " " ORDER BY start_datetime_utc ", (combined_equipment['id'], energy_category_id, reporting_start_datetime_utc, reporting_end_datetime_utc)) rows_combined_equipment_hourly = cursor_energy.fetchall() rows_combined_equipment_periodically, \ reporting[energy_category_id]['average'], \ reporting[energy_category_id]['maximum'] = \ utilities.averaging_hourly_data_by_period(rows_combined_equipment_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_combined_equipment_periodically in rows_combined_equipment_periodically: current_datetime_local = row_combined_equipment_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_combined_equipment_periodically[1]) reporting[energy_category_id]['sub_maximums'].append(row_combined_equipment_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(combined_equipment['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 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: query associated equipments energy input ################################################################################################################ associated_equipment_data = dict() if energy_category_set is not None and len(energy_category_set) > 0: for energy_category_id in energy_category_set: associated_equipment_data[energy_category_id] = dict() associated_equipment_data[energy_category_id]['associated_equipment_names'] = list() associated_equipment_data[energy_category_id]['average'] = list() associated_equipment_data[energy_category_id]['maximum'] = list() associated_equipment_data[energy_category_id]['sub_averages'] = list() associated_equipment_data[energy_category_id]['sub_maximums'] = list() for associated_equipment in associated_equipment_list: associated_equipment_data[energy_category_id]['associated_equipment_names'].append( associated_equipment['name']) cursor_energy.execute(" SELECT start_datetime_utc, actual_value " " FROM tbl_equipment_input_category_hourly " " WHERE equipment_id = %s " " AND energy_category_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s " " ORDER BY start_datetime_utc ", (associated_equipment['id'], energy_category_id, reporting_start_datetime_utc, reporting_end_datetime_utc)) rows_associated_equipments_hourly = cursor_energy.fetchall() rows_associated_equipment_periodically, \ associated_equipment_data[energy_category_id]['average'], \ associated_equipment_data[energy_category_id]['maximum'] = \ utilities.averaging_hourly_data_by_period(rows_associated_equipments_hourly, reporting_start_datetime_utc, reporting_end_datetime_utc, period_type) associated_equipment_data[energy_category_id]['sub_averages'].append( associated_equipment_data[energy_category_id]['average']) associated_equipment_data[energy_category_id]['sub_maximums'].append( associated_equipment_data[energy_category_id]['maximum']) ################################################################################################################ # Step 11: construct the report ################################################################################################################ if cursor_system: cursor_system.close() if cnx_system: cnx_system.close() if cursor_energy: cursor_energy.close() if cnx_energy: cnx_energy.close() if cursor_historical: cursor_historical.close() if cnx_historical: cnx_historical.close() result = dict() result['combined_equipment'] = dict() result['combined_equipment']['name'] = combined_equipment['name'] 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_increment_rate'] = list() result['reporting_period']['maximums'] = 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_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']['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'] } result['associated_equipment'] = dict() result['associated_equipment']['energy_category_names'] = list() result['associated_equipment']['units'] = list() result['associated_equipment']['associated_equipment_names_array'] = list() result['associated_equipment']['sub_averages_array'] = list() result['associated_equipment']['sub_maximums_array'] = list() if energy_category_set is not None and len(energy_category_set) > 0: for energy_category_id in energy_category_set: result['associated_equipment']['energy_category_names'].append( energy_category_dict[energy_category_id]['name']) result['associated_equipment']['units'].append( energy_category_dict[energy_category_id]['unit_of_measure']) result['associated_equipment']['associated_equipment_names_array'].append( associated_equipment_data[energy_category_id]['associated_equipment_names']) result['associated_equipment']['sub_averages_array'].append( associated_equipment_data[energy_category_id]['sub_averages']) result['associated_equipment']['sub_maximums_array'].append( associated_equipment_data[energy_category_id]['sub_maximums']) # export result to Excel file and then encode the file to base64 string result['excel_bytes_base64'] = excelexporters.combinedequipmentload.export(result, combined_equipment['name'], reporting_start_datetime_local, reporting_end_datetime_local, period_type) resp.text = json.dumps(result)