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)
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]['sub_averages'] = list()
base[energy_category_id]['sub_maximums'] = list()
base[energy_category_id]['average'] = None
base[energy_category_id]['maximum'] = None
base[energy_category_id]['factor'] = None
cursor_energy.execute(
" SELECT start_datetime_utc, actual_value "
" FROM tbl_store_input_category_hourly "
" WHERE store_id = %s "
" AND energy_category_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s "
" ORDER BY start_datetime_utc ",
(store['id'], energy_category_id, base_start_datetime_utc,
base_end_datetime_utc))
rows_store_hourly = cursor_energy.fetchall()
rows_store_periodically, \
base[energy_category_id]['average'], \
base[energy_category_id]['maximum'] = \
utilities.averaging_hourly_data_by_period(rows_store_hourly,
base_start_datetime_utc,
base_end_datetime_utc,
period_type)
base[energy_category_id]['factor'] = \
(base[energy_category_id]['average'] / base[energy_category_id]['maximum']
if (base[energy_category_id]['average'] is not None and
base[energy_category_id]['maximum'] is not None and
base[energy_category_id]['maximum'] > Decimal(0.0))
else None)
for row_store_periodically in rows_store_periodically:
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
if period_type == 'hourly':
current_datetime = current_datetime_local.strftime(
'%Y-%m-%dT%H:%M:%S')
elif period_type == 'daily':
current_datetime = current_datetime_local.strftime(
'%Y-%m-%d')
elif period_type == 'monthly':
current_datetime = current_datetime_local.strftime(
'%Y-%m')
elif period_type == 'yearly':
current_datetime = current_datetime_local.strftime(
'%Y')
base[energy_category_id]['timestamps'].append(
current_datetime)
base[energy_category_id]['sub_averages'].append(
row_store_periodically[1])
base[energy_category_id]['sub_maximums'].append(
row_store_periodically[2])
################################################################################################################
# Step 7: query reporting period energy input
################################################################################################################
reporting = dict()
if energy_category_set is not None and len(energy_category_set) > 0:
for energy_category_id in energy_category_set:
reporting[energy_category_id] = dict()
reporting[energy_category_id]['timestamps'] = list()
reporting[energy_category_id]['sub_averages'] = list()
reporting[energy_category_id]['sub_maximums'] = list()
reporting[energy_category_id]['average'] = None
reporting[energy_category_id]['maximum'] = None
reporting[energy_category_id]['factor'] = None
cursor_energy.execute(
" SELECT start_datetime_utc, actual_value "
" FROM tbl_store_input_category_hourly "
" WHERE store_id = %s "
" AND energy_category_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s "
" ORDER BY start_datetime_utc ",
(store['id'], energy_category_id,
reporting_start_datetime_utc, reporting_end_datetime_utc))
rows_store_hourly = cursor_energy.fetchall()
rows_store_periodically, \
reporting[energy_category_id]['average'], \
reporting[energy_category_id]['maximum'] = \
utilities.averaging_hourly_data_by_period(rows_store_hourly,
reporting_start_datetime_utc,
reporting_end_datetime_utc,
period_type)
reporting[energy_category_id]['factor'] = \
(reporting[energy_category_id]['average'] / reporting[energy_category_id]['maximum']
if (reporting[energy_category_id]['average'] is not None and
reporting[energy_category_id]['maximum'] is not None and
reporting[energy_category_id]['maximum'] > Decimal(0.0))
else None)
for row_store_periodically in rows_store_periodically:
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
if period_type == 'hourly':
current_datetime = current_datetime_local.strftime(
'%Y-%m-%dT%H:%M:%S')
elif period_type == 'daily':
current_datetime = current_datetime_local.strftime(
'%Y-%m-%d')
elif period_type == 'monthly':
current_datetime = current_datetime_local.strftime(
'%Y-%m')
elif period_type == 'yearly':
current_datetime = current_datetime_local.strftime(
'%Y')
reporting[energy_category_id]['timestamps'].append(
current_datetime)
reporting[energy_category_id]['sub_averages'].append(
row_store_periodically[1])
reporting[energy_category_id]['sub_maximums'].append(
row_store_periodically[2])
################################################################################################################
# Step 8: query tariff data
################################################################################################################
parameters_data = dict()
parameters_data['names'] = list()
parameters_data['timestamps'] = list()
parameters_data['values'] = list()
if energy_category_set is not None and len(energy_category_set) > 0:
for energy_category_id in energy_category_set:
energy_category_tariff_dict = utilities.get_energy_category_tariffs(
store['cost_center_id'], energy_category_id,
reporting_start_datetime_utc, reporting_end_datetime_utc)
tariff_timestamp_list = list()
tariff_value_list = list()
for k, v in energy_category_tariff_dict.items():
# convert k from utc to local
k = k + timedelta(minutes=timezone_offset)
tariff_timestamp_list.append(k.isoformat()[0:19][0:19])
tariff_value_list.append(v)
parameters_data['names'].append(
'TARIFF-' +
energy_category_dict[energy_category_id]['name'])
parameters_data['timestamps'].append(tariff_timestamp_list)
parameters_data['values'].append(tariff_value_list)
################################################################################################################
# Step 9: query associated sensors and points data
################################################################################################################
for point in point_list:
point_values = []
point_timestamps = []
if point['object_type'] == 'ANALOG_VALUE':
query = (" SELECT utc_date_time, actual_value "
" FROM tbl_analog_value "
" WHERE point_id = %s "
" AND utc_date_time BETWEEN %s AND %s "
" ORDER BY utc_date_time ")
cursor_historical.execute(
query, (point['id'], reporting_start_datetime_utc,
reporting_end_datetime_utc))
rows = cursor_historical.fetchall()
if rows is not None and len(rows) > 0:
for row in rows:
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
current_datetime = current_datetime_local.strftime(
'%Y-%m-%dT%H:%M:%S')
point_timestamps.append(current_datetime)
point_values.append(row[1])
elif point['object_type'] == 'ENERGY_VALUE':
query = (" SELECT utc_date_time, actual_value "
" FROM tbl_energy_value "
" WHERE point_id = %s "
" AND utc_date_time BETWEEN %s AND %s "
" ORDER BY utc_date_time ")
cursor_historical.execute(
query, (point['id'], reporting_start_datetime_utc,
reporting_end_datetime_utc))
rows = cursor_historical.fetchall()
if rows is not None and len(rows) > 0:
for row in rows:
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
current_datetime = current_datetime_local.strftime(
'%Y-%m-%dT%H:%M:%S')
point_timestamps.append(current_datetime)
point_values.append(row[1])
elif point['object_type'] == 'DIGITAL_VALUE':
query = (" SELECT utc_date_time, actual_value "
" FROM tbl_digital_value "
" WHERE point_id = %s "
" AND utc_date_time BETWEEN %s AND %s ")
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']['sub_averages'] = list()
result['base_period']['sub_maximums'] = list()
result['base_period']['averages'] = list()
result['base_period']['maximums'] = list()
result['base_period']['factors'] = list()
if energy_category_set is not None and len(energy_category_set) > 0:
for energy_category_id in energy_category_set:
result['base_period']['names'].append(
energy_category_dict[energy_category_id]['name'])
result['base_period']['units'].append(
energy_category_dict[energy_category_id]
['unit_of_measure'])
result['base_period']['timestamps'].append(
base[energy_category_id]['timestamps'])
result['base_period']['sub_averages'].append(
base[energy_category_id]['sub_averages'])
result['base_period']['sub_maximums'].append(
base[energy_category_id]['sub_maximums'])
result['base_period']['averages'].append(
base[energy_category_id]['average'])
result['base_period']['maximums'].append(
base[energy_category_id]['maximum'])
result['base_period']['factors'].append(
base[energy_category_id]['factor'])
result['reporting_period'] = dict()
result['reporting_period']['names'] = list()
result['reporting_period']['energy_category_ids'] = list()
result['reporting_period']['units'] = list()
result['reporting_period']['timestamps'] = list()
result['reporting_period']['sub_averages'] = list()
result['reporting_period']['sub_maximums'] = list()
result['reporting_period']['averages'] = list()
result['reporting_period']['averages_per_unit_area'] = list()
result['reporting_period']['averages_increment_rate'] = list()
result['reporting_period']['maximums'] = list()
result['reporting_period']['maximums_per_unit_area'] = list()
result['reporting_period']['maximums_increment_rate'] = list()
result['reporting_period']['factors'] = list()
result['reporting_period']['factors_increment_rate'] = list()
if energy_category_set is not None and len(energy_category_set) > 0:
for energy_category_id in energy_category_set:
result['reporting_period']['names'].append(
energy_category_dict[energy_category_id]['name'])
result['reporting_period']['energy_category_ids'].append(
energy_category_id)
result['reporting_period']['units'].append(
energy_category_dict[energy_category_id]
['unit_of_measure'])
result['reporting_period']['timestamps'].append(
reporting[energy_category_id]['timestamps'])
result['reporting_period']['sub_averages'].append(
reporting[energy_category_id]['sub_averages'])
result['reporting_period']['sub_maximums'].append(
reporting[energy_category_id]['sub_maximums'])
result['reporting_period']['averages'].append(
reporting[energy_category_id]['average'])
result['reporting_period']['averages_per_unit_area'].append(
reporting[energy_category_id]['average'] /
store['area'] if reporting[energy_category_id]['average']
is not None and store['area'] is not None
and store['area'] > Decimal(0.0) else None)
result['reporting_period']['averages_increment_rate'].append(
(reporting[energy_category_id]['average'] -
base[energy_category_id]['average']) /
base[energy_category_id]['average'] if (
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 (
base[energy_category_id]['maximum'] is not None
and base[energy_category_id]['maximum'] > Decimal(0.0)
) else None)
result['reporting_period']['maximums_per_unit_area'].append(
reporting[energy_category_id]['maximum'] /
store['area'] if reporting[energy_category_id]['maximum']
is not None and store['area'] is not None
and store['area'] > Decimal(0.0) else None)
result['reporting_period']['factors'].append(
reporting[energy_category_id]['factor'])
result['reporting_period']['factors_increment_rate'].append(
(reporting[energy_category_id]['factor'] -
base[energy_category_id]['factor']) /
base[energy_category_id]['factor'] if (
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']
}
resp.body = json.dumps(result)
