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]['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_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]['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_combined_equipment_hourly, base_start_datetime_utc, base_end_datetime_utc, period_type) 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') actual_value = Decimal(0.0) if row_combined_equipment_periodically[1] is None \ else row_combined_equipment_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_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]['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_combined_equipment_hourly, reporting_start_datetime_utc, reporting_end_datetime_utc, period_type) 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') actual_value = Decimal(0.0) if row_combined_equipment_periodically[1] is None \ else row_combined_equipment_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(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]['subtotals'] = 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 SUM(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)) row_subtotal = cursor_energy.fetchone() subtotal = Decimal(0.0) if (row_subtotal is None or row_subtotal[0] is None) else row_subtotal[0] associated_equipment_data[energy_category_id]['subtotals'].append(subtotal) ################################################################################################################ # 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']['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_increment_rate'] = list() result['reporting_period']['medians'] = list() result['reporting_period']['medians_increment_rate'] = list() result['reporting_period']['minimums'] = list() result['reporting_period']['minimums_increment_rate'] = list() result['reporting_period']['maximums'] = list() result['reporting_period']['maximums_increment_rate'] = list() result['reporting_period']['stdevs'] = list() result['reporting_period']['stdevs_increment_rate'] = list() result['reporting_period']['variances'] = 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_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_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_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_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_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_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'] } 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']['subtotals_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']['subtotals_array'].append( associated_equipment_data[energy_category_id]['subtotals']) # export result to Excel file and then encode the file to base64 string result['excel_bytes_base64'] = excelexporters.combinedequipmentstatistics.export(result, combined_equipment['name'], reporting_start_datetime_local, reporting_end_datetime_local, period_type) resp.text = json.dumps(result)
def on_get(req, resp): print(req.params) store_id = req.params.get('storeid') 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: raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') else: 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 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 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() cursor_system.execute( " SELECT id, name, area, cost_center_id " " FROM tbl_stores " " WHERE id = %s ", (store_id, )) 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 cnx_historical: cnx_historical.close() if cursor_historical: cursor_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 cnx_historical: cnx_historical.close() if cursor_historical: cursor_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]['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_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]['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_store_hourly, base_start_datetime_utc, base_end_datetime_utc, period_type) 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 == '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_store_periodically[ 1] is None else row_store_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_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]['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_store_hourly, reporting_start_datetime_utc, reporting_end_datetime_utc, period_type) 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 == '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_store_periodically[ 1] is None else row_store_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( 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 ") 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() 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']['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'] / store['area'] if reporting[energy_category_id]['mean'] is not None and store['area'] is not None and store['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'] / store['area'] if reporting[energy_category_id]['median'] is not None and store['area'] is not None and store['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'] / store['area'] if reporting[energy_category_id]['minimum'] is not None and store['area'] is not None and store['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'] / 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']['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'] / store['area'] if reporting[energy_category_id]['stdev'] is not None and store['area'] is not None and store['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'] / store['area'] if reporting[energy_category_id]['variance'] is not None and store['area'] is not None and store['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'] } resp.body = json.dumps(result)