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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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import excelexporters.storestatistics |
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from core import utilities |
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from decimal import Decimal |
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View Code Duplication |
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 store |
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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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store_id = req.params.get('storeid') |
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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 store_id is None: |
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') |
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else: |
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store_id = str.strip(store_id) |
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if not store_id.isdigit() or int(store_id) <= 0: |
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_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 store |
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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_stores " |
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" WHERE id = %s ", (store_id,)) |
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row_store = cursor_system.fetchone() |
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if row_store 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.STORE_NOT_FOUND') |
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store = dict() |
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store['id'] = row_store[0] |
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store['name'] = row_store[1] |
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store['area'] = row_store[2] |
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store['cost_center_id'] = row_store[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_store_input_category_hourly " |
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" WHERE store_id = %s " |
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" AND start_datetime_utc >= %s " |
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" AND start_datetime_utc < %s ", |
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(store['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_store_input_category_hourly " |
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" WHERE store_id = %s " |
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" AND start_datetime_utc >= %s " |
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" AND start_datetime_utc < %s ", |
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(store['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 p.id, p.name, p.units, p.object_type " |
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" FROM tbl_stores st, tbl_sensors se, tbl_stores_sensors ss, " |
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" tbl_points p, tbl_sensors_points sp " |
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" WHERE st.id = %s AND st.id = ss.store_id AND ss.sensor_id = se.id " |
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" AND se.id = sp.sensor_id AND sp.point_id = p.id " |
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" ORDER BY p.id ", (store['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 p.id, p.name, p.units, p.object_type " |
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" FROM tbl_stores s, tbl_stores_points sp, tbl_points p " |
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" WHERE s.id = %s AND s.id = sp.store_id AND sp.point_id = p.id " |
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" ORDER BY p.id ", (store['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_store_input_category_hourly " |
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" WHERE store_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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(store['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_store_hourly = cursor_energy.fetchall() |
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rows_store_periodically, \ |
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base[energy_category_id]['mean'], \ |
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279
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base[energy_category_id]['median'], \ |
|
280
|
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|
base[energy_category_id]['minimum'], \ |
|
281
|
|
|
base[energy_category_id]['maximum'], \ |
|
282
|
|
|
base[energy_category_id]['stdev'], \ |
|
283
|
|
|
base[energy_category_id]['variance'] = \ |
|
284
|
|
|
utilities.statistics_hourly_data_by_period(rows_store_hourly, |
|
285
|
|
|
base_start_datetime_utc, |
|
286
|
|
|
base_end_datetime_utc, |
|
287
|
|
|
period_type) |
|
288
|
|
|
|
|
289
|
|
|
for row_store_periodically in rows_store_periodically: |
|
290
|
|
|
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
291
|
|
|
timedelta(minutes=timezone_offset) |
|
292
|
|
|
if period_type == 'hourly': |
|
293
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
294
|
|
|
elif period_type == 'daily': |
|
295
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
296
|
|
|
elif period_type == 'monthly': |
|
297
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m') |
|
298
|
|
|
elif period_type == 'yearly': |
|
299
|
|
|
current_datetime = current_datetime_local.strftime('%Y') |
|
300
|
|
|
|
|
301
|
|
|
actual_value = Decimal(0.0) if row_store_periodically[1] is None else row_store_periodically[1] |
|
302
|
|
|
base[energy_category_id]['timestamps'].append(current_datetime) |
|
|
|
|
|
|
303
|
|
|
base[energy_category_id]['values'].append(actual_value) |
|
304
|
|
|
base[energy_category_id]['subtotal'] += actual_value |
|
305
|
|
|
|
|
306
|
|
|
################################################################################################################ |
|
307
|
|
|
# Step 7: query reporting period energy input |
|
308
|
|
|
################################################################################################################ |
|
309
|
|
|
reporting = dict() |
|
310
|
|
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if energy_category_set is not None and len(energy_category_set) > 0: |
|
311
|
|
|
for energy_category_id in energy_category_set: |
|
312
|
|
|
reporting[energy_category_id] = dict() |
|
313
|
|
|
reporting[energy_category_id]['timestamps'] = list() |
|
314
|
|
|
reporting[energy_category_id]['values'] = list() |
|
315
|
|
|
reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
|
316
|
|
|
reporting[energy_category_id]['mean'] = None |
|
317
|
|
|
reporting[energy_category_id]['median'] = None |
|
318
|
|
|
reporting[energy_category_id]['minimum'] = None |
|
319
|
|
|
reporting[energy_category_id]['maximum'] = None |
|
320
|
|
|
reporting[energy_category_id]['stdev'] = None |
|
321
|
|
|
reporting[energy_category_id]['variance'] = None |
|
322
|
|
|
|
|
323
|
|
|
cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
324
|
|
|
" FROM tbl_store_input_category_hourly " |
|
325
|
|
|
" WHERE store_id = %s " |
|
326
|
|
|
" AND energy_category_id = %s " |
|
327
|
|
|
" AND start_datetime_utc >= %s " |
|
328
|
|
|
" AND start_datetime_utc < %s " |
|
329
|
|
|
" ORDER BY start_datetime_utc ", |
|
330
|
|
|
(store['id'], |
|
331
|
|
|
energy_category_id, |
|
332
|
|
|
reporting_start_datetime_utc, |
|
333
|
|
|
reporting_end_datetime_utc)) |
|
334
|
|
|
rows_store_hourly = cursor_energy.fetchall() |
|
335
|
|
|
|
|
336
|
|
|
rows_store_periodically, \ |
|
337
|
|
|
reporting[energy_category_id]['mean'], \ |
|
338
|
|
|
reporting[energy_category_id]['median'], \ |
|
339
|
|
|
reporting[energy_category_id]['minimum'], \ |
|
340
|
|
|
reporting[energy_category_id]['maximum'], \ |
|
341
|
|
|
reporting[energy_category_id]['stdev'], \ |
|
342
|
|
|
reporting[energy_category_id]['variance'] = \ |
|
343
|
|
|
utilities.statistics_hourly_data_by_period(rows_store_hourly, |
|
344
|
|
|
reporting_start_datetime_utc, |
|
345
|
|
|
reporting_end_datetime_utc, |
|
346
|
|
|
period_type) |
|
347
|
|
|
|
|
348
|
|
|
for row_store_periodically in rows_store_periodically: |
|
349
|
|
|
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
350
|
|
|
timedelta(minutes=timezone_offset) |
|
351
|
|
|
if period_type == 'hourly': |
|
352
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
353
|
|
|
elif period_type == 'daily': |
|
354
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
355
|
|
|
elif period_type == 'monthly': |
|
356
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m') |
|
357
|
|
|
elif period_type == 'yearly': |
|
358
|
|
|
current_datetime = current_datetime_local.strftime('%Y') |
|
359
|
|
|
|
|
360
|
|
|
actual_value = Decimal(0.0) if row_store_periodically[1] is None else row_store_periodically[1] |
|
361
|
|
|
reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
362
|
|
|
reporting[energy_category_id]['values'].append(actual_value) |
|
363
|
|
|
reporting[energy_category_id]['subtotal'] += actual_value |
|
364
|
|
|
|
|
365
|
|
|
################################################################################################################ |
|
366
|
|
|
# Step 8: query tariff data |
|
367
|
|
|
################################################################################################################ |
|
368
|
|
|
parameters_data = dict() |
|
369
|
|
|
parameters_data['names'] = list() |
|
370
|
|
|
parameters_data['timestamps'] = list() |
|
371
|
|
|
parameters_data['values'] = list() |
|
372
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
373
|
|
|
for energy_category_id in energy_category_set: |
|
374
|
|
|
energy_category_tariff_dict = utilities.get_energy_category_tariffs(store['cost_center_id'], |
|
375
|
|
|
energy_category_id, |
|
376
|
|
|
reporting_start_datetime_utc, |
|
377
|
|
|
reporting_end_datetime_utc) |
|
378
|
|
|
tariff_timestamp_list = list() |
|
379
|
|
|
tariff_value_list = list() |
|
380
|
|
|
for k, v in energy_category_tariff_dict.items(): |
|
381
|
|
|
# convert k from utc to local |
|
382
|
|
|
k = k + timedelta(minutes=timezone_offset) |
|
383
|
|
|
tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
384
|
|
|
tariff_value_list.append(v) |
|
385
|
|
|
|
|
386
|
|
|
parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
387
|
|
|
parameters_data['timestamps'].append(tariff_timestamp_list) |
|
388
|
|
|
parameters_data['values'].append(tariff_value_list) |
|
389
|
|
|
|
|
390
|
|
|
################################################################################################################ |
|
391
|
|
|
# Step 9: query associated sensors and points data |
|
392
|
|
|
################################################################################################################ |
|
393
|
|
|
for point in point_list: |
|
394
|
|
|
point_values = [] |
|
395
|
|
|
point_timestamps = [] |
|
396
|
|
|
if point['object_type'] == 'ANALOG_VALUE': |
|
397
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
398
|
|
|
" FROM tbl_analog_value " |
|
399
|
|
|
" WHERE point_id = %s " |
|
400
|
|
|
" AND utc_date_time BETWEEN %s AND %s " |
|
401
|
|
|
" ORDER BY utc_date_time ") |
|
402
|
|
|
cursor_historical.execute(query, (point['id'], |
|
403
|
|
|
reporting_start_datetime_utc, |
|
404
|
|
|
reporting_end_datetime_utc)) |
|
405
|
|
|
rows = cursor_historical.fetchall() |
|
406
|
|
|
|
|
407
|
|
|
if rows is not None and len(rows) > 0: |
|
408
|
|
|
for row in rows: |
|
409
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
410
|
|
|
timedelta(minutes=timezone_offset) |
|
411
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
412
|
|
|
point_timestamps.append(current_datetime) |
|
413
|
|
|
point_values.append(row[1]) |
|
414
|
|
|
|
|
415
|
|
|
elif point['object_type'] == 'ENERGY_VALUE': |
|
416
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
417
|
|
|
" FROM tbl_energy_value " |
|
418
|
|
|
" WHERE point_id = %s " |
|
419
|
|
|
" AND utc_date_time BETWEEN %s AND %s " |
|
420
|
|
|
" ORDER BY utc_date_time ") |
|
421
|
|
|
cursor_historical.execute(query, (point['id'], |
|
422
|
|
|
reporting_start_datetime_utc, |
|
423
|
|
|
reporting_end_datetime_utc)) |
|
424
|
|
|
rows = cursor_historical.fetchall() |
|
425
|
|
|
|
|
426
|
|
|
if rows is not None and len(rows) > 0: |
|
427
|
|
|
for row in rows: |
|
428
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
429
|
|
|
timedelta(minutes=timezone_offset) |
|
430
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
431
|
|
|
point_timestamps.append(current_datetime) |
|
432
|
|
|
point_values.append(row[1]) |
|
433
|
|
|
elif point['object_type'] == 'DIGITAL_VALUE': |
|
434
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
435
|
|
|
" FROM tbl_digital_value " |
|
436
|
|
|
" WHERE point_id = %s " |
|
437
|
|
|
" AND utc_date_time BETWEEN %s AND %s ") |
|
438
|
|
|
cursor_historical.execute(query, (point['id'], |
|
439
|
|
|
reporting_start_datetime_utc, |
|
440
|
|
|
reporting_end_datetime_utc)) |
|
441
|
|
|
rows = cursor_historical.fetchall() |
|
442
|
|
|
|
|
443
|
|
|
if rows is not None and len(rows) > 0: |
|
444
|
|
|
for row in rows: |
|
445
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
446
|
|
|
timedelta(minutes=timezone_offset) |
|
447
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
448
|
|
|
point_timestamps.append(current_datetime) |
|
449
|
|
|
point_values.append(row[1]) |
|
450
|
|
|
|
|
451
|
|
|
parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
452
|
|
|
parameters_data['timestamps'].append(point_timestamps) |
|
453
|
|
|
parameters_data['values'].append(point_values) |
|
454
|
|
|
|
|
455
|
|
|
################################################################################################################ |
|
456
|
|
|
# Step 10: construct the report |
|
457
|
|
|
################################################################################################################ |
|
458
|
|
|
if cursor_system: |
|
459
|
|
|
cursor_system.close() |
|
460
|
|
|
if cnx_system: |
|
461
|
|
|
cnx_system.disconnect() |
|
462
|
|
|
|
|
463
|
|
|
if cursor_energy: |
|
464
|
|
|
cursor_energy.close() |
|
465
|
|
|
if cnx_energy: |
|
466
|
|
|
cnx_energy.disconnect() |
|
467
|
|
|
|
|
468
|
|
|
result = dict() |
|
469
|
|
|
|
|
470
|
|
|
result['store'] = dict() |
|
471
|
|
|
result['store']['name'] = store['name'] |
|
472
|
|
|
result['store']['area'] = store['area'] |
|
473
|
|
|
|
|
474
|
|
|
result['base_period'] = dict() |
|
475
|
|
|
result['base_period']['names'] = list() |
|
476
|
|
|
result['base_period']['units'] = list() |
|
477
|
|
|
result['base_period']['timestamps'] = list() |
|
478
|
|
|
result['base_period']['values'] = list() |
|
479
|
|
|
result['base_period']['subtotals'] = list() |
|
480
|
|
|
result['base_period']['means'] = list() |
|
481
|
|
|
result['base_period']['medians'] = list() |
|
482
|
|
|
result['base_period']['minimums'] = list() |
|
483
|
|
|
result['base_period']['maximums'] = list() |
|
484
|
|
|
result['base_period']['stdevs'] = list() |
|
485
|
|
|
result['base_period']['variances'] = list() |
|
486
|
|
|
|
|
487
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
488
|
|
|
for energy_category_id in energy_category_set: |
|
489
|
|
|
result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
490
|
|
|
result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
491
|
|
|
result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
492
|
|
|
result['base_period']['values'].append(base[energy_category_id]['values']) |
|
493
|
|
|
result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
|
494
|
|
|
result['base_period']['means'].append(base[energy_category_id]['mean']) |
|
495
|
|
|
result['base_period']['medians'].append(base[energy_category_id]['median']) |
|
496
|
|
|
result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
|
497
|
|
|
result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
498
|
|
|
result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
|
499
|
|
|
result['base_period']['variances'].append(base[energy_category_id]['variance']) |
|
500
|
|
|
|
|
501
|
|
|
result['reporting_period'] = dict() |
|
502
|
|
|
result['reporting_period']['names'] = list() |
|
503
|
|
|
result['reporting_period']['energy_category_ids'] = list() |
|
504
|
|
|
result['reporting_period']['units'] = list() |
|
505
|
|
|
result['reporting_period']['timestamps'] = list() |
|
506
|
|
|
result['reporting_period']['values'] = list() |
|
507
|
|
|
result['reporting_period']['subtotals'] = list() |
|
508
|
|
|
result['reporting_period']['means'] = list() |
|
509
|
|
|
result['reporting_period']['means_per_unit_area'] = list() |
|
510
|
|
|
result['reporting_period']['means_increment_rate'] = list() |
|
511
|
|
|
result['reporting_period']['medians'] = list() |
|
512
|
|
|
result['reporting_period']['medians_per_unit_area'] = list() |
|
513
|
|
|
result['reporting_period']['medians_increment_rate'] = list() |
|
514
|
|
|
result['reporting_period']['minimums'] = list() |
|
515
|
|
|
result['reporting_period']['minimums_per_unit_area'] = list() |
|
516
|
|
|
result['reporting_period']['minimums_increment_rate'] = list() |
|
517
|
|
|
result['reporting_period']['maximums'] = list() |
|
518
|
|
|
result['reporting_period']['maximums_per_unit_area'] = list() |
|
519
|
|
|
result['reporting_period']['maximums_increment_rate'] = list() |
|
520
|
|
|
result['reporting_period']['stdevs'] = list() |
|
521
|
|
|
result['reporting_period']['stdevs_per_unit_area'] = list() |
|
522
|
|
|
result['reporting_period']['stdevs_increment_rate'] = list() |
|
523
|
|
|
result['reporting_period']['variances'] = list() |
|
524
|
|
|
result['reporting_period']['variances_per_unit_area'] = list() |
|
525
|
|
|
result['reporting_period']['variances_increment_rate'] = list() |
|
526
|
|
|
|
|
527
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
528
|
|
|
for energy_category_id in energy_category_set: |
|
529
|
|
|
result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
530
|
|
|
result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
531
|
|
|
result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
532
|
|
|
result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
533
|
|
|
result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
|
534
|
|
|
result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
|
535
|
|
|
result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
|
536
|
|
|
result['reporting_period']['means_per_unit_area'].append( |
|
537
|
|
|
reporting[energy_category_id]['mean'] / store['area'] |
|
538
|
|
|
if reporting[energy_category_id]['mean'] is not None and |
|
539
|
|
|
store['area'] is not None and |
|
540
|
|
|
store['area'] > Decimal(0.0) |
|
541
|
|
|
else None) |
|
542
|
|
|
result['reporting_period']['means_increment_rate'].append( |
|
543
|
|
|
(reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
|
544
|
|
|
base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
|
545
|
|
|
base[energy_category_id]['mean'] > Decimal(0.0)) |
|
546
|
|
|
else None) |
|
547
|
|
|
result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
|
548
|
|
|
result['reporting_period']['medians_per_unit_area'].append( |
|
549
|
|
|
reporting[energy_category_id]['median'] / store['area'] |
|
550
|
|
|
if reporting[energy_category_id]['median'] is not None and |
|
551
|
|
|
store['area'] is not None and |
|
552
|
|
|
store['area'] > Decimal(0.0) |
|
553
|
|
|
else None) |
|
554
|
|
|
result['reporting_period']['medians_increment_rate'].append( |
|
555
|
|
|
(reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
|
556
|
|
|
base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
|
557
|
|
|
base[energy_category_id]['median'] > Decimal(0.0)) |
|
558
|
|
|
else None) |
|
559
|
|
|
result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
|
560
|
|
|
result['reporting_period']['minimums_per_unit_area'].append( |
|
561
|
|
|
reporting[energy_category_id]['minimum'] / store['area'] |
|
562
|
|
|
if reporting[energy_category_id]['minimum'] is not None and |
|
563
|
|
|
store['area'] is not None and |
|
564
|
|
|
store['area'] > Decimal(0.0) |
|
565
|
|
|
else None) |
|
566
|
|
|
result['reporting_period']['minimums_increment_rate'].append( |
|
567
|
|
|
(reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
|
568
|
|
|
base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
|
569
|
|
|
base[energy_category_id]['minimum'] > Decimal(0.0)) |
|
570
|
|
|
else None) |
|
571
|
|
|
result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
572
|
|
|
result['reporting_period']['maximums_per_unit_area'].append( |
|
573
|
|
|
reporting[energy_category_id]['maximum'] / store['area'] |
|
574
|
|
|
if reporting[energy_category_id]['maximum'] is not None and |
|
575
|
|
|
store['area'] is not None and |
|
576
|
|
|
store['area'] > Decimal(0.0) |
|
577
|
|
|
else None) |
|
578
|
|
|
result['reporting_period']['maximums_increment_rate'].append( |
|
579
|
|
|
(reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
580
|
|
|
base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
|
581
|
|
|
base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
582
|
|
|
else None) |
|
583
|
|
|
result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
|
584
|
|
|
result['reporting_period']['stdevs_per_unit_area'].append( |
|
585
|
|
|
reporting[energy_category_id]['stdev'] / store['area'] |
|
586
|
|
|
if reporting[energy_category_id]['stdev'] is not None and |
|
587
|
|
|
store['area'] is not None and |
|
588
|
|
|
store['area'] > Decimal(0.0) |
|
589
|
|
|
else None) |
|
590
|
|
|
result['reporting_period']['stdevs_increment_rate'].append( |
|
591
|
|
|
(reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
|
592
|
|
|
base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
|
593
|
|
|
base[energy_category_id]['stdev'] > Decimal(0.0)) |
|
594
|
|
|
else None) |
|
595
|
|
|
result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
|
596
|
|
|
result['reporting_period']['variances_per_unit_area'].append( |
|
597
|
|
|
reporting[energy_category_id]['variance'] / store['area'] |
|
598
|
|
|
if reporting[energy_category_id]['variance'] is not None and |
|
599
|
|
|
store['area'] is not None and |
|
600
|
|
|
store['area'] > Decimal(0.0) |
|
601
|
|
|
else None) |
|
602
|
|
|
result['reporting_period']['variances_increment_rate'].append( |
|
603
|
|
|
(reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
|
604
|
|
|
base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
|
605
|
|
|
base[energy_category_id]['variance'] > Decimal(0.0)) |
|
606
|
|
|
else None) |
|
607
|
|
|
|
|
608
|
|
|
result['parameters'] = { |
|
609
|
|
|
"names": parameters_data['names'], |
|
610
|
|
|
"timestamps": parameters_data['timestamps'], |
|
611
|
|
|
"values": parameters_data['values'] |
|
612
|
|
|
} |
|
613
|
|
|
|
|
614
|
|
|
# export result to Excel file and then encode the file to base64 string |
|
615
|
|
|
result['excel_bytes_base64'] = excelexporters.storestatistics.export(result, |
|
616
|
|
|
store['name'], |
|
617
|
|
|
reporting_start_datetime_local, |
|
618
|
|
|
reporting_end_datetime_local, |
|
619
|
|
|
period_type) |
|
620
|
|
|
resp.body = json.dumps(result) |
|
621
|
|
|
|
myems /
myems-api
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