myems-api/reports/shopfloorload.py
import re
from datetime import datetime, timedelta, timezone
from decimal import Decimal
import falcon
import mysql.connector
import simplejson as json
import config
import excelexporters.shopfloorload
from core import utilities
from core.useractivity import access_control, api_key_control
class Reporting:
def __init__(self):
""""Initializes Reporting"""
pass
@staticmethod
def on_options(req, resp):
resp.status = falcon.HTTP_200
####################################################################################################################
# PROCEDURES
# Step 1: valid parameters
# Step 2: query the shopfloor
# Step 3: query energy categories
# Step 4: query associated sensors
# Step 5: query associated points
# Step 6: query base period energy input
# Step 7: query reporting period energy input
# Step 8: query tariff data
# Step 9: query associated sensors and points data
# Step 10: construct the report
####################################################################################################################
@staticmethod
def on_get(req, resp):
if 'API-KEY' not in req.headers or \
not isinstance(req.headers['API-KEY'], str) or \
len(str.strip(req.headers['API-KEY'])) == 0:
access_control(req)
else:
api_key_control(req)
print(req.params)
shopfloor_id = req.params.get('shopfloorid')
shopfloor_uuid = req.params.get('shopflooruuid')
period_type = req.params.get('periodtype')
base_period_start_datetime_local = req.params.get('baseperiodstartdatetime')
base_period_end_datetime_local = req.params.get('baseperiodenddatetime')
reporting_period_start_datetime_local = req.params.get('reportingperiodstartdatetime')
reporting_period_end_datetime_local = req.params.get('reportingperiodenddatetime')
language = req.params.get('language')
quick_mode = req.params.get('quickmode')
################################################################################################################
# Step 1: valid parameters
################################################################################################################
if shopfloor_id is None and shopfloor_uuid is None:
raise falcon.HTTPError(status=falcon.HTTP_400,
title='API.BAD_REQUEST',
description='API.INVALID_SHOPFLOOR_ID')
if shopfloor_id is not None:
shopfloor_id = str.strip(shopfloor_id)
if not shopfloor_id.isdigit() or int(shopfloor_id) <= 0:
raise falcon.HTTPError(status=falcon.HTTP_400,
title='API.BAD_REQUEST',
description='API.INVALID_SHOPFLOOR_ID')
if shopfloor_uuid is not None:
regex = re.compile(r'^[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(shopfloor_uuid))
if not bool(match):
raise falcon.HTTPError(status=falcon.HTTP_400,
title='API.BAD_REQUEST',
description='API.INVALID_SHOPFLOOR_UUID')
if period_type is None:
raise falcon.HTTPError(status=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(status=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_period_start_datetime_local is not None and len(str.strip(base_period_start_datetime_local)) > 0:
base_period_start_datetime_local = str.strip(base_period_start_datetime_local)
try:
base_start_datetime_utc = datetime.strptime(base_period_start_datetime_local, '%Y-%m-%dT%H:%M:%S')
except ValueError:
raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description="API.INVALID_BASE_PERIOD_START_DATETIME")
base_start_datetime_utc = \
base_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset)
# nomalize the start datetime
if config.minutes_to_count == 30 and base_start_datetime_utc.minute >= 30:
base_start_datetime_utc = base_start_datetime_utc.replace(minute=30, second=0, microsecond=0)
else:
base_start_datetime_utc = base_start_datetime_utc.replace(minute=0, second=0, microsecond=0)
base_end_datetime_utc = None
if base_period_end_datetime_local is not None and len(str.strip(base_period_end_datetime_local)) > 0:
base_period_end_datetime_local = str.strip(base_period_end_datetime_local)
try:
base_end_datetime_utc = datetime.strptime(base_period_end_datetime_local, '%Y-%m-%dT%H:%M:%S')
except ValueError:
raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description="API.INVALID_BASE_PERIOD_END_DATETIME")
base_end_datetime_utc = \
base_end_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset)
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(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description='API.INVALID_BASE_PERIOD_END_DATETIME')
if reporting_period_start_datetime_local is None:
raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
else:
reporting_period_start_datetime_local = str.strip(reporting_period_start_datetime_local)
try:
reporting_start_datetime_utc = datetime.strptime(reporting_period_start_datetime_local,
'%Y-%m-%dT%H:%M:%S')
except ValueError:
raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
reporting_start_datetime_utc = \
reporting_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset)
# nomalize the start datetime
if config.minutes_to_count == 30 and reporting_start_datetime_utc.minute >= 30:
reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=30, second=0, microsecond=0)
else:
reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=0, second=0, microsecond=0)
if reporting_period_end_datetime_local is None:
raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
else:
reporting_period_end_datetime_local = str.strip(reporting_period_end_datetime_local)
try:
reporting_end_datetime_utc = datetime.strptime(reporting_period_end_datetime_local,
'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
timedelta(minutes=timezone_offset)
except ValueError:
raise falcon.HTTPError(status=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(status=falcon.HTTP_400, title='API.BAD_REQUEST',
description='API.INVALID_REPORTING_PERIOD_END_DATETIME')
# if turn quick mode on, do not return parameters data and excel file
is_quick_mode = False
if quick_mode is not None and \
len(str.strip(quick_mode)) > 0 and \
str.lower(str.strip(quick_mode)) in ('true', 't', 'on', 'yes', 'y'):
is_quick_mode = True
trans = utilities.get_translation(language)
trans.install()
_ = trans.gettext
################################################################################################################
# Step 2: query the shopfloor
################################################################################################################
cnx_system = mysql.connector.connect(**config.myems_system_db)
cursor_system = cnx_system.cursor()
cnx_energy = mysql.connector.connect(**config.myems_energy_db)
cursor_energy = cnx_energy.cursor()
cnx_historical = mysql.connector.connect(**config.myems_historical_db)
cursor_historical = cnx_historical.cursor()
if shopfloor_id is not None:
cursor_system.execute(" SELECT id, name, area, cost_center_id "
" FROM tbl_shopfloors "
" WHERE id = %s ", (shopfloor_id,))
row_shopfloor = cursor_system.fetchone()
elif shopfloor_uuid is not None:
cursor_system.execute(" SELECT id, name, area, cost_center_id "
" FROM tbl_shopfloors "
" WHERE uuid = %s ", (shopfloor_uuid,))
row_shopfloor = cursor_system.fetchone()
if row_shopfloor 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(status=falcon.HTTP_404, title='API.NOT_FOUND', description='API.SHOPFLOOR_NOT_FOUND')
shopfloor = dict()
shopfloor['id'] = row_shopfloor[0]
shopfloor['name'] = row_shopfloor[1]
shopfloor['area'] = row_shopfloor[2]
shopfloor['cost_center_id'] = row_shopfloor[3]
################################################################################################################
# Step 3: query energy categories
################################################################################################################
energy_category_set = set()
# query energy categories in base period
cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
" FROM tbl_shopfloor_input_category_hourly "
" WHERE shopfloor_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s ",
(shopfloor['id'], base_start_datetime_utc, base_end_datetime_utc))
rows_energy_categories = cursor_energy.fetchall()
if rows_energy_categories is not None and len(rows_energy_categories) > 0:
for row_energy_category in rows_energy_categories:
energy_category_set.add(row_energy_category[0])
# query energy categories in reporting period
cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
" FROM tbl_shopfloor_input_category_hourly "
" WHERE shopfloor_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s ",
(shopfloor['id'], reporting_start_datetime_utc, reporting_end_datetime_utc))
rows_energy_categories = cursor_energy.fetchall()
if rows_energy_categories is not None and 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(status=falcon.HTTP_404,
title='API.NOT_FOUND',
description='API.ENERGY_CATEGORY_NOT_FOUND')
energy_category_dict = dict()
for row_energy_category in rows_energy_categories:
if row_energy_category[0] in energy_category_set:
energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1],
"unit_of_measure": row_energy_category[2],
"kgce": row_energy_category[3],
"kgco2e": row_energy_category[4]}
################################################################################################################
# Step 4: query associated sensors
################################################################################################################
point_list = list()
cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type "
" FROM tbl_shopfloors st, tbl_sensors se, tbl_shopfloors_sensors ss, "
" tbl_points p, tbl_sensors_points sp "
" WHERE st.id = %s AND st.id = ss.shopfloor_id AND ss.sensor_id = se.id "
" AND se.id = sp.sensor_id AND sp.point_id = p.id "
" ORDER BY p.id ", (shopfloor['id'],))
rows_points = cursor_system.fetchall()
if rows_points is not None and len(rows_points) > 0:
for row in rows_points:
point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
################################################################################################################
# Step 5: query associated points
################################################################################################################
cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type "
" FROM tbl_shopfloors s, tbl_shopfloors_points sp, tbl_points p "
" WHERE s.id = %s AND s.id = sp.shopfloor_id AND sp.point_id = p.id "
" ORDER BY p.id ", (shopfloor['id'],))
rows_points = cursor_system.fetchall()
if rows_points is not None and len(rows_points) > 0:
for row in rows_points:
point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
################################################################################################################
# Step 6: query base period energy input
################################################################################################################
base = dict()
if energy_category_set is not None and len(energy_category_set) > 0:
for energy_category_id in energy_category_set:
base[energy_category_id] = dict()
base[energy_category_id]['timestamps'] = list()
base[energy_category_id]['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_shopfloor_input_category_hourly "
" WHERE shopfloor_id = %s "
" AND energy_category_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s "
" ORDER BY start_datetime_utc ",
(shopfloor['id'],
energy_category_id,
base_start_datetime_utc,
base_end_datetime_utc))
rows_shopfloor_hourly = cursor_energy.fetchall()
rows_shopfloor_periodically, \
base[energy_category_id]['average'], \
base[energy_category_id]['maximum'] = \
utilities.averaging_hourly_data_by_period(rows_shopfloor_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_shopfloor_periodically in rows_shopfloor_periodically:
current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
if period_type == 'hourly':
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
elif period_type == 'daily':
current_datetime = current_datetime_local.strftime('%Y-%m-%d')
elif period_type == '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_shopfloor_periodically[1])
base[energy_category_id]['sub_maximums'].append(row_shopfloor_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_shopfloor_input_category_hourly "
" WHERE shopfloor_id = %s "
" AND energy_category_id = %s "
" AND start_datetime_utc >= %s "
" AND start_datetime_utc < %s "
" ORDER BY start_datetime_utc ",
(shopfloor['id'],
energy_category_id,
reporting_start_datetime_utc,
reporting_end_datetime_utc))
rows_shopfloor_hourly = cursor_energy.fetchall()
rows_shopfloor_periodically, \
reporting[energy_category_id]['average'], \
reporting[energy_category_id]['maximum'] = \
utilities.averaging_hourly_data_by_period(rows_shopfloor_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_shopfloor_periodically in rows_shopfloor_periodically:
current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
if period_type == 'hourly':
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
elif period_type == 'daily':
current_datetime = current_datetime_local.strftime('%Y-%m-%d')
elif period_type == '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_shopfloor_periodically[1])
reporting[energy_category_id]['sub_maximums'].append(row_shopfloor_periodically[2])
################################################################################################################
# Step 8: query tariff data
################################################################################################################
parameters_data = dict()
parameters_data['names'] = list()
parameters_data['timestamps'] = list()
parameters_data['values'] = list()
if config.is_tariff_appended and energy_category_set is not None and len(energy_category_set) > 0 \
and not is_quick_mode:
for energy_category_id in energy_category_set:
energy_category_tariff_dict = utilities.get_energy_category_tariffs(shopfloor['cost_center_id'],
energy_category_id,
reporting_start_datetime_utc,
reporting_end_datetime_utc)
tariff_timestamp_list = list()
tariff_value_list = list()
for k, v in energy_category_tariff_dict.items():
# convert k from utc to local
k = k + timedelta(minutes=timezone_offset)
tariff_timestamp_list.append(k.isoformat()[0:19][0:19])
tariff_value_list.append(v)
parameters_data['names'].append(_('Tariff') + '-' + energy_category_dict[energy_category_id]['name'])
parameters_data['timestamps'].append(tariff_timestamp_list)
parameters_data['values'].append(tariff_value_list)
################################################################################################################
# Step 9: query associated sensors and points data
################################################################################################################
if not is_quick_mode:
for point in point_list:
point_values = []
point_timestamps = []
if 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'] == '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'] == 'DIGITAL_VALUE':
query = (" SELECT utc_date_time, actual_value "
" FROM tbl_digital_value "
" WHERE point_id = %s "
" AND utc_date_time BETWEEN %s AND %s "
" ORDER BY utc_date_time ")
cursor_historical.execute(query, (point['id'],
reporting_start_datetime_utc,
reporting_end_datetime_utc))
rows = cursor_historical.fetchall()
if rows is not None and len(rows) > 0:
for row in rows:
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
timedelta(minutes=timezone_offset)
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
point_timestamps.append(current_datetime)
point_values.append(row[1])
parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')')
parameters_data['timestamps'].append(point_timestamps)
parameters_data['values'].append(point_values)
################################################################################################################
# Step 10: construct the report
################################################################################################################
if cursor_system:
cursor_system.close()
if cnx_system:
cnx_system.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['shopfloor'] = dict()
result['shopfloor']['name'] = shopfloor['name']
result['shopfloor']['area'] = shopfloor['area']
result['base_period'] = dict()
result['base_period']['names'] = list()
result['base_period']['units'] = list()
result['base_period']['timestamps'] = list()
result['base_period']['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']['rates_of_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'] / shopfloor['area']
if reporting[energy_category_id]['average'] is not None and
shopfloor['area'] is not None and
shopfloor['area'] > Decimal(0.0)
else None)
result['reporting_period']['averages_increment_rate'].append(
(reporting[energy_category_id]['average'] - base[energy_category_id]['average']) /
base[energy_category_id]['average'] if (reporting[energy_category_id]['average'] is not None and
base[energy_category_id]['average'] is not None and
base[energy_category_id]['average'] > Decimal(0.0))
else None)
result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum'])
result['reporting_period']['maximums_increment_rate'].append(
(reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) /
base[energy_category_id]['maximum'] if (reporting[energy_category_id]['maximum'] is not None and
base[energy_category_id]['maximum'] is not None and
base[energy_category_id]['maximum'] > Decimal(0.0))
else None)
result['reporting_period']['maximums_per_unit_area'].append(
reporting[energy_category_id]['maximum'] / shopfloor['area']
if reporting[energy_category_id]['maximum'] is not None and
shopfloor['area'] is not None and
shopfloor['area'] > Decimal(0.0)
else None)
result['reporting_period']['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)
rate = list()
for index, value in enumerate(reporting[energy_category_id]['sub_maximums']):
if index < len(base[energy_category_id]['sub_maximums']) \
and base[energy_category_id]['sub_maximums'][index] != 0 and value != 0\
and base[energy_category_id]['sub_maximums'][index] is not None and value is not None:
rate.append((value - base[energy_category_id]['sub_maximums'][index])
/ base[energy_category_id]['sub_maximums'][index])
else:
rate.append(None)
result['reporting_period']['rates_of_sub_maximums'].append(rate)
result['parameters'] = {
"names": parameters_data['names'],
"timestamps": parameters_data['timestamps'],
"values": parameters_data['values']
}
# export result to Excel file and then encode the file to base64 string
result['excel_bytes_base64'] = None
if not is_quick_mode:
result['excel_bytes_base64'] = excelexporters.shopfloorload.export(result,
shopfloor['name'],
base_period_start_datetime_local,
base_period_end_datetime_local,
reporting_period_start_datetime_local,
reporting_period_end_datetime_local,
period_type,
language)
resp.text = json.dumps(result)