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.shopfloorstatistics 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 shopfloor # 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) shopfloor_id = req.params.get('shopfloorid') 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 shopfloor_id is None: raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SHOPFLOOR_ID') else: shopfloor_id = str.strip(shopfloor_id) if not shopfloor_id.isdigit() or int(shopfloor_id) <= 0: raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SHOPFLOOR_ID') 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', '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 shopfloor ################################################################################################################ 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() cursor_system.execute(" SELECT id, name, area, cost_center_id " " FROM tbl_shopfloors " " WHERE id = %s ", (shopfloor_id,)) row_shopfloor = cursor_system.fetchone() if row_shopfloor 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.SHOPFLOOR_NOT_FOUND') shopfloor = dict() shopfloor['id'] = row_shopfloor[0] shopfloor['name'] = row_shopfloor[1] shopfloor['area'] = row_shopfloor[2] shopfloor['cost_center_id'] = row_shopfloor[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_shopfloor_input_category_hourly " " WHERE shopfloor_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s ", (shopfloor['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_shopfloor_input_category_hourly " " WHERE shopfloor_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s ", (shopfloor['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_shopfloors st, tbl_sensors se, tbl_shopfloors_sensors ss, " " tbl_points p, tbl_sensors_points sp " " WHERE st.id = %s AND st.id = ss.shopfloor_id AND ss.sensor_id = se.id " " AND se.id = sp.sensor_id AND sp.point_id = p.id " " ORDER BY p.id ", (shopfloor['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_shopfloors s, tbl_shopfloors_points sp, tbl_points p " " WHERE s.id = %s AND s.id = sp.shopfloor_id AND sp.point_id = p.id " " ORDER BY p.id ", (shopfloor['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]['values'] = list() base[energy_category_id]['subtotal'] = Decimal(0.0) base[energy_category_id]['mean'] = None base[energy_category_id]['median'] = None base[energy_category_id]['minimum'] = None base[energy_category_id]['maximum'] = None base[energy_category_id]['stdev'] = None base[energy_category_id]['variance'] = None cursor_energy.execute(" SELECT start_datetime_utc, actual_value " " FROM tbl_shopfloor_input_category_hourly " " WHERE shopfloor_id = %s " " AND energy_category_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s " " ORDER BY start_datetime_utc ", (shopfloor['id'], energy_category_id, base_start_datetime_utc, base_end_datetime_utc)) rows_shopfloor_hourly = cursor_energy.fetchall() rows_shopfloor_periodically, \ base[energy_category_id]['mean'], \ base[energy_category_id]['median'], \ base[energy_category_id]['minimum'], \ base[energy_category_id]['maximum'], \ base[energy_category_id]['stdev'], \ base[energy_category_id]['variance'] = \ utilities.statistics_hourly_data_by_period(rows_shopfloor_hourly, base_start_datetime_utc, base_end_datetime_utc, period_type) for row_shopfloor_periodically in rows_shopfloor_periodically: current_datetime_local = row_shopfloor_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 == 'monthly': current_datetime = current_datetime_local.strftime('%Y-%m') elif period_type == 'yearly': current_datetime = current_datetime_local.strftime('%Y') actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ else row_shopfloor_periodically[1] base[energy_category_id]['timestamps'].append(current_datetime) base[energy_category_id]['values'].append(actual_value) base[energy_category_id]['subtotal'] += actual_value ################################################################################################################ # 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]['values'] = list() reporting[energy_category_id]['subtotal'] = Decimal(0.0) reporting[energy_category_id]['mean'] = None reporting[energy_category_id]['median'] = None reporting[energy_category_id]['minimum'] = None reporting[energy_category_id]['maximum'] = None reporting[energy_category_id]['stdev'] = None reporting[energy_category_id]['variance'] = None cursor_energy.execute(" SELECT start_datetime_utc, actual_value " " FROM tbl_shopfloor_input_category_hourly " " WHERE shopfloor_id = %s " " AND energy_category_id = %s " " AND start_datetime_utc >= %s " " AND start_datetime_utc < %s " " ORDER BY start_datetime_utc ", (shopfloor['id'], energy_category_id, reporting_start_datetime_utc, reporting_end_datetime_utc)) rows_shopfloor_hourly = cursor_energy.fetchall() rows_shopfloor_periodically, \ reporting[energy_category_id]['mean'], \ reporting[energy_category_id]['median'], \ reporting[energy_category_id]['minimum'], \ reporting[energy_category_id]['maximum'], \ reporting[energy_category_id]['stdev'], \ reporting[energy_category_id]['variance'] = \ utilities.statistics_hourly_data_by_period(rows_shopfloor_hourly, reporting_start_datetime_utc, reporting_end_datetime_utc, period_type) for row_shopfloor_periodically in rows_shopfloor_periodically: current_datetime_local = row_shopfloor_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 == 'monthly': current_datetime = current_datetime_local.strftime('%Y-%m') elif period_type == 'yearly': current_datetime = current_datetime_local.strftime('%Y') actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ else row_shopfloor_periodically[1] reporting[energy_category_id]['timestamps'].append(current_datetime) reporting[energy_category_id]['values'].append(actual_value) reporting[energy_category_id]['subtotal'] += actual_value ################################################################################################################ # 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(shopfloor['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['shopfloor'] = dict() result['shopfloor']['name'] = shopfloor['name'] result['shopfloor']['area'] = shopfloor['area'] result['base_period'] = dict() result['base_period']['names'] = list() result['base_period']['units'] = list() result['base_period']['timestamps'] = list() result['base_period']['values'] = list() result['base_period']['subtotals'] = list() result['base_period']['means'] = list() result['base_period']['medians'] = list() result['base_period']['minimums'] = list() result['base_period']['maximums'] = list() result['base_period']['stdevs'] = list() result['base_period']['variances'] = 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']['values'].append(base[energy_category_id]['values']) result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) result['base_period']['means'].append(base[energy_category_id]['mean']) result['base_period']['medians'].append(base[energy_category_id]['median']) result['base_period']['minimums'].append(base[energy_category_id]['minimum']) result['base_period']['maximums'].append(base[energy_category_id]['maximum']) result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) result['base_period']['variances'].append(base[energy_category_id]['variance']) 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']['values'] = list() result['reporting_period']['subtotals'] = list() result['reporting_period']['means'] = list() result['reporting_period']['means_per_unit_area'] = list() result['reporting_period']['means_increment_rate'] = list() result['reporting_period']['medians'] = list() result['reporting_period']['medians_per_unit_area'] = list() result['reporting_period']['medians_increment_rate'] = list() result['reporting_period']['minimums'] = list() result['reporting_period']['minimums_per_unit_area'] = list() result['reporting_period']['minimums_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']['stdevs'] = list() result['reporting_period']['stdevs_per_unit_area'] = list() result['reporting_period']['stdevs_increment_rate'] = list() result['reporting_period']['variances'] = list() result['reporting_period']['variances_per_unit_area'] = list() result['reporting_period']['variances_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']['values'].append(reporting[energy_category_id]['values']) result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) result['reporting_period']['means_per_unit_area'].append( reporting[energy_category_id]['mean'] / shopfloor['area'] if reporting[energy_category_id]['mean'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['means_increment_rate'].append( (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and base[energy_category_id]['mean'] > Decimal(0.0)) else None) result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) result['reporting_period']['medians_per_unit_area'].append( reporting[energy_category_id]['median'] / shopfloor['area'] if reporting[energy_category_id]['median'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['medians_increment_rate'].append( (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and base[energy_category_id]['median'] > Decimal(0.0)) else None) result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) result['reporting_period']['minimums_per_unit_area'].append( reporting[energy_category_id]['minimum'] / shopfloor['area'] if reporting[energy_category_id]['minimum'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['minimums_increment_rate'].append( (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and base[energy_category_id]['minimum'] > Decimal(0.0)) else None) result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) result['reporting_period']['maximums_per_unit_area'].append( reporting[energy_category_id]['maximum'] / shopfloor['area'] if reporting[energy_category_id]['maximum'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['maximums_increment_rate'].append( (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and base[energy_category_id]['maximum'] > Decimal(0.0)) else None) result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) result['reporting_period']['stdevs_per_unit_area'].append( reporting[energy_category_id]['stdev'] / shopfloor['area'] if reporting[energy_category_id]['stdev'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['stdevs_increment_rate'].append( (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and base[energy_category_id]['stdev'] > Decimal(0.0)) else None) result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) result['reporting_period']['variances_per_unit_area'].append( reporting[energy_category_id]['variance'] / shopfloor['area'] if reporting[energy_category_id]['variance'] is not None and shopfloor['area'] is not None and shopfloor['area'] > Decimal(0.0) else None) result['reporting_period']['variances_increment_rate'].append( (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and base[energy_category_id]['variance'] > 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.shopfloorstatistics.export(result, shopfloor['name'], reporting_start_datetime_local, reporting_end_datetime_local, period_type) resp.body = json.dumps(result)