625 lines
37 KiB
Python
625 lines
37 KiB
Python
import falcon
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import simplejson as json
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import mysql.connector
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import config
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from datetime import datetime, timedelta, timezone
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from core import utilities
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from decimal import Decimal
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import excelexporters.spacestatistics
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class Reporting:
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@staticmethod
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def __init__():
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pass
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@staticmethod
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def on_options(req, resp):
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resp.status = falcon.HTTP_200
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####################################################################################################################
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# PROCEDURES
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# Step 1: valid parameters
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# Step 2: query the space
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# Step 3: query energy categories
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# Step 4: query associated sensors
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# Step 5: query associated points
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# Step 6: query base period energy input
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# Step 7: query reporting period energy input
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# Step 8: query tariff data
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# Step 9: query associated sensors and points data
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# Step 10: construct the report
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####################################################################################################################
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@staticmethod
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def on_get(req, resp):
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print(req.params)
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space_id = req.params.get('spaceid')
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period_type = req.params.get('periodtype')
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base_start_datetime_local = req.params.get('baseperiodstartdatetime')
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base_end_datetime_local = req.params.get('baseperiodenddatetime')
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reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime')
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reporting_end_datetime_local = req.params.get('reportingperiodenddatetime')
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################################################################################################################
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# Step 1: valid parameters
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################################################################################################################
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if space_id is None:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SPACE_ID')
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else:
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space_id = str.strip(space_id)
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if not space_id.isdigit() or int(space_id) <= 0:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SPACE_ID')
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if period_type is None:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE')
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else:
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period_type = str.strip(period_type)
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if period_type not in ['hourly', 'daily', 'monthly', 'yearly']:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE')
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timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6])
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if config.utc_offset[0] == '-':
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timezone_offset = -timezone_offset
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base_start_datetime_utc = None
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if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0:
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base_start_datetime_local = str.strip(base_start_datetime_local)
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try:
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base_start_datetime_utc = datetime.strptime(base_start_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_BASE_PERIOD_START_DATETIME")
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base_end_datetime_utc = None
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if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0:
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base_end_datetime_local = str.strip(base_end_datetime_local)
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try:
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base_end_datetime_utc = datetime.strptime(base_end_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_BASE_PERIOD_END_DATETIME")
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if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \
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base_start_datetime_utc >= base_end_datetime_utc:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description='API.INVALID_BASE_PERIOD_END_DATETIME')
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if reporting_start_datetime_local is None:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
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else:
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reporting_start_datetime_local = str.strip(reporting_start_datetime_local)
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try:
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reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
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if reporting_end_datetime_local is None:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
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else:
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reporting_end_datetime_local = str.strip(reporting_end_datetime_local)
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try:
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reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
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if reporting_start_datetime_utc >= reporting_end_datetime_utc:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description='API.INVALID_REPORTING_PERIOD_END_DATETIME')
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################################################################################################################
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# Step 2: query the space
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################################################################################################################
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cnx_system = mysql.connector.connect(**config.myems_system_db)
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cursor_system = cnx_system.cursor()
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cnx_energy = mysql.connector.connect(**config.myems_energy_db)
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cursor_energy = cnx_energy.cursor()
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cnx_historical = mysql.connector.connect(**config.myems_historical_db)
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cursor_historical = cnx_historical.cursor()
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cursor_system.execute(" SELECT id, name, area, cost_center_id "
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" FROM tbl_spaces "
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" WHERE id = %s ", (space_id,))
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row_space = cursor_system.fetchone()
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if row_space is None:
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if cursor_system:
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cursor_system.close()
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if cnx_system:
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cnx_system.disconnect()
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if cursor_energy:
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cursor_energy.close()
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if cnx_energy:
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cnx_energy.disconnect()
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if cnx_historical:
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cnx_historical.close()
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if cursor_historical:
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cursor_historical.disconnect()
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raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.SPACE_NOT_FOUND')
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space = dict()
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space['id'] = row_space[0]
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space['name'] = row_space[1]
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space['area'] = row_space[2]
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space['cost_center_id'] = row_space[3]
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################################################################################################################
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# Step 3: query energy categories
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################################################################################################################
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energy_category_set = set()
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# query energy categories in base period
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cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
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" FROM tbl_space_input_category_hourly "
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" WHERE space_id = %s "
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" AND start_datetime_utc >= %s "
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" AND start_datetime_utc < %s ",
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(space['id'], base_start_datetime_utc, base_end_datetime_utc))
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rows_energy_categories = cursor_energy.fetchall()
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if rows_energy_categories is not None or len(rows_energy_categories) > 0:
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for row_energy_category in rows_energy_categories:
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energy_category_set.add(row_energy_category[0])
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# query energy categories in reporting period
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cursor_energy.execute(" SELECT DISTINCT(energy_category_id) "
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" FROM tbl_space_input_category_hourly "
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" WHERE space_id = %s "
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" AND start_datetime_utc >= %s "
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" AND start_datetime_utc < %s ",
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(space['id'], reporting_start_datetime_utc, reporting_end_datetime_utc))
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rows_energy_categories = cursor_energy.fetchall()
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if rows_energy_categories is not None or len(rows_energy_categories) > 0:
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for row_energy_category in rows_energy_categories:
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energy_category_set.add(row_energy_category[0])
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# query all energy categories in base period and reporting period
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cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e "
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" FROM tbl_energy_categories "
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" ORDER BY id ", )
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rows_energy_categories = cursor_system.fetchall()
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if rows_energy_categories is None or len(rows_energy_categories) == 0:
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if cursor_system:
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cursor_system.close()
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if cnx_system:
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cnx_system.disconnect()
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if cursor_energy:
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cursor_energy.close()
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if cnx_energy:
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cnx_energy.disconnect()
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if cnx_historical:
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cnx_historical.close()
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if cursor_historical:
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cursor_historical.disconnect()
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raise falcon.HTTPError(falcon.HTTP_404,
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title='API.NOT_FOUND',
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description='API.ENERGY_CATEGORY_NOT_FOUND')
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energy_category_dict = dict()
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for row_energy_category in rows_energy_categories:
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if row_energy_category[0] in energy_category_set:
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energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1],
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"unit_of_measure": row_energy_category[2],
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"kgce": row_energy_category[3],
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"kgco2e": row_energy_category[4]}
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################################################################################################################
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# Step 4: query associated sensors
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################################################################################################################
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point_list = list()
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cursor_system.execute(" SELECT po.id, po.name, po.units, po.object_type "
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" FROM tbl_spaces sp, tbl_sensors se, tbl_spaces_sensors spse, "
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" tbl_points po, tbl_sensors_points sepo "
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" WHERE sp.id = %s AND sp.id = spse.space_id AND spse.sensor_id = se.id "
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" AND se.id = sepo.sensor_id AND sepo.point_id = po.id "
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" ORDER BY po.id ", (space['id'], ))
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rows_points = cursor_system.fetchall()
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if rows_points is not None and len(rows_points) > 0:
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for row in rows_points:
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point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
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################################################################################################################
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# Step 5: query associated points
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################################################################################################################
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cursor_system.execute(" SELECT po.id, po.name, po.units, po.object_type "
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" FROM tbl_spaces sp, tbl_spaces_points sppo, tbl_points po "
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" WHERE sp.id = %s AND sp.id = sppo.space_id AND sppo.point_id = po.id "
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" ORDER BY po.id ", (space['id'], ))
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rows_points = cursor_system.fetchall()
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if rows_points is not None and len(rows_points) > 0:
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for row in rows_points:
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point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
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################################################################################################################
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# Step 6: query base period energy input
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################################################################################################################
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base = dict()
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if energy_category_set is not None and len(energy_category_set) > 0:
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for energy_category_id in energy_category_set:
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base[energy_category_id] = dict()
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base[energy_category_id]['timestamps'] = list()
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base[energy_category_id]['values'] = list()
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base[energy_category_id]['subtotal'] = Decimal(0.0)
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base[energy_category_id]['mean'] = None
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base[energy_category_id]['median'] = None
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base[energy_category_id]['minimum'] = None
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base[energy_category_id]['maximum'] = None
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base[energy_category_id]['stdev'] = None
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base[energy_category_id]['variance'] = None
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cursor_energy.execute(" SELECT start_datetime_utc, actual_value "
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" FROM tbl_space_input_category_hourly "
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" WHERE space_id = %s "
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" AND energy_category_id = %s "
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" AND start_datetime_utc >= %s "
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" AND start_datetime_utc < %s "
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" ORDER BY start_datetime_utc ",
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(space['id'],
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energy_category_id,
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base_start_datetime_utc,
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base_end_datetime_utc))
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rows_space_hourly = cursor_energy.fetchall()
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rows_space_periodically, \
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base[energy_category_id]['mean'], \
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base[energy_category_id]['median'], \
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base[energy_category_id]['minimum'], \
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base[energy_category_id]['maximum'], \
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base[energy_category_id]['stdev'], \
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base[energy_category_id]['variance'] = \
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utilities.statistics_hourly_data_by_period(rows_space_hourly,
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base_start_datetime_utc,
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base_end_datetime_utc,
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period_type)
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for row_space_periodically in rows_space_periodically:
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current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \
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timedelta(minutes=timezone_offset)
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if period_type == 'hourly':
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current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
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elif period_type == 'daily':
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current_datetime = current_datetime_local.strftime('%Y-%m-%d')
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elif period_type == 'monthly':
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current_datetime = current_datetime_local.strftime('%Y-%m')
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elif period_type == 'yearly':
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current_datetime = current_datetime_local.strftime('%Y')
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actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1]
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base[energy_category_id]['timestamps'].append(current_datetime)
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base[energy_category_id]['values'].append(actual_value)
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base[energy_category_id]['subtotal'] += actual_value
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################################################################################################################
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# Step 7: query reporting period energy input
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################################################################################################################
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reporting = dict()
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if energy_category_set is not None and len(energy_category_set) > 0:
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for energy_category_id in energy_category_set:
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reporting[energy_category_id] = dict()
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reporting[energy_category_id]['timestamps'] = list()
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reporting[energy_category_id]['values'] = list()
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reporting[energy_category_id]['subtotal'] = Decimal(0.0)
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reporting[energy_category_id]['mean'] = None
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reporting[energy_category_id]['median'] = None
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reporting[energy_category_id]['minimum'] = None
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reporting[energy_category_id]['maximum'] = None
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reporting[energy_category_id]['stdev'] = None
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reporting[energy_category_id]['variance'] = None
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cursor_energy.execute(" SELECT start_datetime_utc, actual_value "
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" FROM tbl_space_input_category_hourly "
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" WHERE space_id = %s "
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" AND energy_category_id = %s "
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" AND start_datetime_utc >= %s "
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" AND start_datetime_utc < %s "
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" ORDER BY start_datetime_utc ",
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(space['id'],
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energy_category_id,
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reporting_start_datetime_utc,
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reporting_end_datetime_utc))
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rows_space_hourly = cursor_energy.fetchall()
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rows_space_periodically, \
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reporting[energy_category_id]['mean'], \
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reporting[energy_category_id]['median'], \
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reporting[energy_category_id]['minimum'], \
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reporting[energy_category_id]['maximum'], \
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reporting[energy_category_id]['stdev'], \
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reporting[energy_category_id]['variance'] = \
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utilities.statistics_hourly_data_by_period(rows_space_hourly,
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reporting_start_datetime_utc,
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reporting_end_datetime_utc,
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period_type)
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for row_space_periodically in rows_space_periodically:
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current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \
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timedelta(minutes=timezone_offset)
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if period_type == 'hourly':
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current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
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elif period_type == 'daily':
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current_datetime = current_datetime_local.strftime('%Y-%m-%d')
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elif period_type == 'monthly':
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current_datetime = current_datetime_local.strftime('%Y-%m')
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elif period_type == 'yearly':
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current_datetime = current_datetime_local.strftime('%Y')
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actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1]
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reporting[energy_category_id]['timestamps'].append(current_datetime)
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reporting[energy_category_id]['values'].append(actual_value)
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reporting[energy_category_id]['subtotal'] += actual_value
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################################################################################################################
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# Step 8: query tariff data
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################################################################################################################
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parameters_data = dict()
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parameters_data['names'] = list()
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parameters_data['timestamps'] = list()
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parameters_data['values'] = list()
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if energy_category_set is not None and len(energy_category_set) > 0:
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for energy_category_id in energy_category_set:
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energy_category_tariff_dict = utilities.get_energy_category_tariffs(space['cost_center_id'],
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energy_category_id,
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reporting_start_datetime_utc,
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reporting_end_datetime_utc)
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tariff_timestamp_list = list()
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tariff_value_list = list()
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for k, v in energy_category_tariff_dict.items():
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# convert k from utc to local
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k = k + timedelta(minutes=timezone_offset)
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tariff_timestamp_list.append(k.isoformat()[0:19][0:19])
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tariff_value_list.append(v)
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parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name'])
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parameters_data['timestamps'].append(tariff_timestamp_list)
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parameters_data['values'].append(tariff_value_list)
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################################################################################################################
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# Step 9: query associated sensors and points data
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################################################################################################################
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for point in point_list:
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point_values = []
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point_timestamps = []
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if point['object_type'] == 'ANALOG_VALUE':
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query = (" SELECT utc_date_time, actual_value "
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" FROM tbl_analog_value "
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" WHERE point_id = %s "
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" AND utc_date_time BETWEEN %s AND %s "
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" ORDER BY utc_date_time ")
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cursor_historical.execute(query, (point['id'],
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reporting_start_datetime_utc,
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reporting_end_datetime_utc))
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rows = cursor_historical.fetchall()
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if rows is not None and len(rows) > 0:
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for row in rows:
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current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \
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timedelta(minutes=timezone_offset)
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current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S')
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point_timestamps.append(current_datetime)
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point_values.append(row[1])
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elif point['object_type'] == 'ENERGY_VALUE':
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query = (" SELECT utc_date_time, actual_value "
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" FROM tbl_energy_value "
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" WHERE point_id = %s "
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" AND utc_date_time BETWEEN %s AND %s "
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" ORDER BY utc_date_time ")
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cursor_historical.execute(query, (point['id'],
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reporting_start_datetime_utc,
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reporting_end_datetime_utc))
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rows = cursor_historical.fetchall()
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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()
|
|
|
|
if cnx_historical:
|
|
cnx_historical.close()
|
|
if cursor_historical:
|
|
cursor_historical.disconnect()
|
|
|
|
result = dict()
|
|
|
|
result['space'] = dict()
|
|
result['space']['name'] = space['name']
|
|
result['space']['area'] = space['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'] / space['area']
|
|
if reporting[energy_category_id]['mean'] is not None and
|
|
space['area'] is not None and
|
|
space['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'] / space['area']
|
|
if reporting[energy_category_id]['median'] is not None and
|
|
space['area'] is not None and
|
|
space['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'] / space['area']
|
|
if reporting[energy_category_id]['minimum'] is not None and
|
|
space['area'] is not None and space['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'] / space['area']
|
|
if reporting[energy_category_id]['maximum'] is not None and
|
|
space['area'] is not None and
|
|
space['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'] / space['area']
|
|
if reporting[energy_category_id]['stdev'] is not None and
|
|
space['area'] is not None and
|
|
space['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'] / space['area']
|
|
if reporting[energy_category_id]['variance'] is not None and
|
|
space['area'] is not None and
|
|
space['area'] > Decimal(0.0)
|
|
else None)
|
|
result['reporting_period']['variances_increment_rate'].append(
|
|
(reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) /
|
|
base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and
|
|
base[energy_category_id]['variance'] > Decimal(0.0))
|
|
else None)
|
|
|
|
result['parameters'] = {
|
|
"names": parameters_data['names'],
|
|
"timestamps": parameters_data['timestamps'],
|
|
"values": parameters_data['values']
|
|
}
|
|
# export result to Excel file and then encode the file to base64 string
|
|
result['excel_bytes_base64'] = excelexporters.spacestatistics.export(result,
|
|
space['name'],
|
|
reporting_start_datetime_local,
|
|
reporting_end_datetime_local,
|
|
period_type)
|
|
|
|
resp.body = json.dumps(result)
|