MyEMS /
myems
| Total Complexity | 137 |
| Total Lines | 734 |
| Duplicated Lines | 93.87 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like reports.tenantstatistics often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | """ |
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| 2 | Tenant Statistics Report API |
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| 3 | |||
| 4 | This module provides REST API endpoints for generating tenant statistics reports. |
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| 5 | It analyzes statistical data and performance metrics for tenants to provide |
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| 6 | comprehensive insights into tenant performance and operational patterns. |
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| 7 | |||
| 8 | Key Features: |
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| 9 | - Tenant statistical analysis |
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| 10 | - Performance metrics calculation |
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| 11 | - Statistical trend analysis |
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| 12 | - Performance benchmarking |
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| 13 | - Excel export functionality |
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| 14 | - Statistical insights and patterns |
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| 15 | |||
| 16 | Report Components: |
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| 17 | - Tenant statistics summary |
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| 18 | - Performance metrics and KPIs |
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| 19 | - Statistical trend analysis |
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| 20 | - Benchmarking data |
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| 21 | - Performance indicators |
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| 22 | - Statistical patterns and insights |
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| 23 | |||
| 24 | The module uses Falcon framework for REST API and includes: |
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| 25 | - Database queries for statistical data |
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| 26 | - Statistical calculation algorithms |
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| 27 | - Performance analysis tools |
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| 28 | - Excel export via excelexporters |
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| 29 | - Multi-language support |
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| 30 | - User authentication and authorization |
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| 31 | """ |
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| 32 | |||
| 33 | import re |
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| 34 | from datetime import datetime, timedelta, timezone |
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| 35 | from decimal import Decimal |
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| 36 | import falcon |
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| 37 | import mysql.connector |
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| 38 | import simplejson as json |
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| 39 | import config |
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| 40 | import excelexporters.tenantstatistics |
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| 41 | from core import utilities |
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| 42 | from core.useractivity import access_control, api_key_control |
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| 43 | |||
| 44 | |||
| 45 | View Code Duplication | class Reporting: |
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|
Duplication
introduced
by
|
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| 46 | def __init__(self): |
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| 47 | """"Initializes Reporting""" |
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| 48 | pass |
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| 49 | |||
| 50 | @staticmethod |
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| 51 | def on_options(req, resp): |
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| 52 | _ = req |
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| 53 | resp.status = falcon.HTTP_200 |
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| 54 | |||
| 55 | #################################################################################################################### |
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| 56 | # PROCEDURES |
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| 57 | # Step 1: valid parameters |
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| 58 | # Step 2: query the tenant |
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| 59 | # Step 3: query energy categories |
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| 60 | # Step 4: query associated sensors |
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| 61 | # Step 5: query associated points |
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| 62 | # Step 6: query base period energy input |
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| 63 | # Step 7: query reporting period energy input |
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| 64 | # Step 8: query tariff data |
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| 65 | # Step 9: query associated sensors and points data |
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| 66 | # Step 10: construct the report |
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| 67 | #################################################################################################################### |
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| 68 | @staticmethod |
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| 69 | def on_get(req, resp): |
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| 70 | if 'API-KEY' not in req.headers or \ |
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| 71 | not isinstance(req.headers['API-KEY'], str) or \ |
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| 72 | len(str.strip(req.headers['API-KEY'])) == 0: |
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| 73 | access_control(req) |
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| 74 | else: |
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| 75 | api_key_control(req) |
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| 76 | print(req.params) |
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| 77 | tenant_id = req.params.get('tenantid') |
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| 78 | tenant_uuid = req.params.get('tenantuuid') |
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| 79 | period_type = req.params.get('periodtype') |
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| 80 | base_period_start_datetime_local = req.params.get('baseperiodstartdatetime') |
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| 81 | base_period_end_datetime_local = req.params.get('baseperiodenddatetime') |
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| 82 | reporting_period_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
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| 83 | reporting_period_end_datetime_local = req.params.get('reportingperiodenddatetime') |
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| 84 | language = req.params.get('language') |
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| 85 | quick_mode = req.params.get('quickmode') |
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| 86 | |||
| 87 | ################################################################################################################ |
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| 88 | # Step 1: valid parameters |
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| 89 | ################################################################################################################ |
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| 90 | if tenant_id is None and tenant_uuid is None: |
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| 91 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 92 | title='API.BAD_REQUEST', |
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| 93 | description='API.INVALID_TENANT_ID') |
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| 94 | |||
| 95 | if tenant_id is not None: |
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| 96 | tenant_id = str.strip(tenant_id) |
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| 97 | if not tenant_id.isdigit() or int(tenant_id) <= 0: |
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| 98 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 99 | title='API.BAD_REQUEST', |
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| 100 | description='API.INVALID_TENANT_ID') |
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| 101 | |||
| 102 | if tenant_uuid is not None: |
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| 103 | regex = re.compile(r'^[a-f0-9]{8}-?[a-f0-9]{4}-?4[a-f0-9]{3}-?[89ab][a-f0-9]{3}-?[a-f0-9]{12}\Z', re.I) |
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| 104 | match = regex.match(str.strip(tenant_uuid)) |
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| 105 | if not bool(match): |
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| 106 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 107 | title='API.BAD_REQUEST', |
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| 108 | description='API.INVALID_TENANT_UUID') |
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| 109 | |||
| 110 | if period_type is None: |
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| 111 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 112 | description='API.INVALID_PERIOD_TYPE') |
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| 113 | else: |
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| 114 | period_type = str.strip(period_type) |
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| 115 | if period_type not in ['hourly', 'daily', 'weekly', 'monthly', 'yearly']: |
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| 116 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 117 | description='API.INVALID_PERIOD_TYPE') |
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| 118 | |||
| 119 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 120 | if config.utc_offset[0] == '-': |
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| 121 | timezone_offset = -timezone_offset |
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| 122 | |||
| 123 | base_start_datetime_utc = None |
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| 124 | if base_period_start_datetime_local is not None and len(str.strip(base_period_start_datetime_local)) > 0: |
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| 125 | base_period_start_datetime_local = str.strip(base_period_start_datetime_local) |
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| 126 | try: |
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| 127 | base_start_datetime_utc = datetime.strptime(base_period_start_datetime_local, '%Y-%m-%dT%H:%M:%S') |
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| 128 | except ValueError: |
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| 129 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 130 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
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| 131 | base_start_datetime_utc = \ |
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| 132 | base_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 133 | # nomalize the start datetime |
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| 134 | if config.minutes_to_count == 30 and base_start_datetime_utc.minute >= 30: |
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| 135 | base_start_datetime_utc = base_start_datetime_utc.replace(minute=30, second=0, microsecond=0) |
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| 136 | else: |
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| 137 | base_start_datetime_utc = base_start_datetime_utc.replace(minute=0, second=0, microsecond=0) |
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| 138 | |||
| 139 | base_end_datetime_utc = None |
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| 140 | if base_period_end_datetime_local is not None and len(str.strip(base_period_end_datetime_local)) > 0: |
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| 141 | base_period_end_datetime_local = str.strip(base_period_end_datetime_local) |
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| 142 | try: |
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| 143 | base_end_datetime_utc = datetime.strptime(base_period_end_datetime_local, '%Y-%m-%dT%H:%M:%S') |
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| 144 | except ValueError: |
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| 145 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 146 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
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| 147 | base_end_datetime_utc = \ |
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| 148 | base_end_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 149 | |||
| 150 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
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| 151 | base_start_datetime_utc >= base_end_datetime_utc: |
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| 152 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 153 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
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| 154 | |||
| 155 | if reporting_period_start_datetime_local is None: |
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| 156 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 157 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 158 | else: |
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| 159 | reporting_period_start_datetime_local = str.strip(reporting_period_start_datetime_local) |
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| 160 | try: |
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| 161 | reporting_start_datetime_utc = datetime.strptime(reporting_period_start_datetime_local, |
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| 162 | '%Y-%m-%dT%H:%M:%S') |
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| 163 | except ValueError: |
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| 164 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 165 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 166 | reporting_start_datetime_utc = \ |
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| 167 | reporting_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 168 | # nomalize the start datetime |
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| 169 | if config.minutes_to_count == 30 and reporting_start_datetime_utc.minute >= 30: |
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| 170 | reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=30, second=0, microsecond=0) |
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| 171 | else: |
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| 172 | reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=0, second=0, microsecond=0) |
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| 173 | |||
| 174 | if reporting_period_end_datetime_local is None: |
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| 175 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 176 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 177 | else: |
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| 178 | reporting_period_end_datetime_local = str.strip(reporting_period_end_datetime_local) |
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| 179 | try: |
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| 180 | reporting_end_datetime_utc = datetime.strptime(reporting_period_end_datetime_local, |
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| 181 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 182 | timedelta(minutes=timezone_offset) |
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| 183 | except ValueError: |
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| 184 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 185 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 186 | |||
| 187 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
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| 188 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 189 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
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| 190 | |||
| 191 | # if turn quick mode on, do not return parameters data and excel file |
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| 192 | is_quick_mode = False |
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| 193 | if quick_mode is not None and \ |
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| 194 | len(str.strip(quick_mode)) > 0 and \ |
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| 195 | str.lower(str.strip(quick_mode)) in ('true', 't', 'on', 'yes', 'y'): |
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| 196 | is_quick_mode = True |
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| 197 | |||
| 198 | trans = utilities.get_translation(language) |
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| 199 | trans.install() |
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| 200 | _ = trans.gettext |
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| 201 | |||
| 202 | ################################################################################################################ |
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| 203 | # Step 2: query the tenant |
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| 204 | ################################################################################################################ |
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| 205 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
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| 206 | cursor_system = cnx_system.cursor() |
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| 207 | |||
| 208 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
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| 209 | cursor_energy = cnx_energy.cursor() |
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| 210 | |||
| 211 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
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| 212 | cursor_historical = cnx_historical.cursor() |
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| 213 | |||
| 214 | if tenant_id is not None: |
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| 215 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
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| 216 | " FROM tbl_tenants " |
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| 217 | " WHERE id = %s ", (tenant_id,)) |
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| 218 | row_tenant = cursor_system.fetchone() |
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| 219 | elif tenant_uuid is not None: |
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| 220 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
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| 221 | " FROM tbl_tenants " |
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| 222 | " WHERE uuid = %s ", (tenant_uuid,)) |
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| 223 | row_tenant = cursor_system.fetchone() |
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| 224 | |||
| 225 | if row_tenant is None: |
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| 226 | if cursor_system: |
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| 227 | cursor_system.close() |
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| 228 | if cnx_system: |
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| 229 | cnx_system.close() |
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| 230 | |||
| 231 | if cursor_energy: |
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| 232 | cursor_energy.close() |
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| 233 | if cnx_energy: |
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| 234 | cnx_energy.close() |
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| 235 | |||
| 236 | if cursor_historical: |
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| 237 | cursor_historical.close() |
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| 238 | if cnx_historical: |
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| 239 | cnx_historical.close() |
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| 240 | raise falcon.HTTPError(status=falcon.HTTP_404, title='API.NOT_FOUND', description='API.TENANT_NOT_FOUND') |
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| 241 | |||
| 242 | tenant = dict() |
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| 243 | tenant['id'] = row_tenant[0] |
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| 244 | tenant['name'] = row_tenant[1] |
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| 245 | tenant['area'] = row_tenant[2] |
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| 246 | tenant['cost_center_id'] = row_tenant[3] |
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| 247 | |||
| 248 | ################################################################################################################ |
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| 249 | # Step 3: query energy categories |
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| 250 | ################################################################################################################ |
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| 251 | energy_category_set = set() |
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| 252 | # query energy categories in base period |
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| 253 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
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| 254 | " FROM tbl_tenant_input_category_hourly " |
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| 255 | " WHERE tenant_id = %s " |
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| 256 | " AND start_datetime_utc >= %s " |
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| 257 | " AND start_datetime_utc < %s ", |
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| 258 | (tenant['id'], base_start_datetime_utc, base_end_datetime_utc)) |
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| 259 | rows_energy_categories = cursor_energy.fetchall() |
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| 260 | if rows_energy_categories is not None and len(rows_energy_categories) > 0: |
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| 261 | for row_energy_category in rows_energy_categories: |
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| 262 | energy_category_set.add(row_energy_category[0]) |
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| 263 | |||
| 264 | # query energy categories in reporting period |
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| 265 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
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| 266 | " FROM tbl_tenant_input_category_hourly " |
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| 267 | " WHERE tenant_id = %s " |
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| 268 | " AND start_datetime_utc >= %s " |
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| 269 | " AND start_datetime_utc < %s ", |
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| 270 | (tenant['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
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| 271 | rows_energy_categories = cursor_energy.fetchall() |
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| 272 | if rows_energy_categories is not None and len(rows_energy_categories) > 0: |
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| 273 | for row_energy_category in rows_energy_categories: |
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| 274 | energy_category_set.add(row_energy_category[0]) |
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| 275 | |||
| 276 | # query all energy categories in base period and reporting period |
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| 277 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
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| 278 | " FROM tbl_energy_categories " |
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| 279 | " ORDER BY id ", ) |
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| 280 | rows_energy_categories = cursor_system.fetchall() |
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| 281 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
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| 282 | if cursor_system: |
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| 283 | cursor_system.close() |
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| 284 | if cnx_system: |
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| 285 | cnx_system.close() |
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| 286 | |||
| 287 | if cursor_energy: |
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| 288 | cursor_energy.close() |
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| 289 | if cnx_energy: |
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| 290 | cnx_energy.close() |
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| 291 | |||
| 292 | if cursor_historical: |
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| 293 | cursor_historical.close() |
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| 294 | if cnx_historical: |
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| 295 | cnx_historical.close() |
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| 296 | raise falcon.HTTPError(status=falcon.HTTP_404, |
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| 297 | title='API.NOT_FOUND', |
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| 298 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
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| 299 | energy_category_dict = dict() |
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| 300 | for row_energy_category in rows_energy_categories: |
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| 301 | if row_energy_category[0] in energy_category_set: |
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| 302 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
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| 303 | "unit_of_measure": row_energy_category[2], |
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| 304 | "kgce": row_energy_category[3], |
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| 305 | "kgco2e": row_energy_category[4]} |
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| 306 | |||
| 307 | ################################################################################################################ |
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| 308 | # Step 4: query associated sensors |
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| 309 | ################################################################################################################ |
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| 310 | point_list = list() |
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| 311 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
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| 312 | " FROM tbl_tenants t, tbl_sensors s, tbl_tenants_sensors ts, " |
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| 313 | " tbl_points p, tbl_sensors_points sp " |
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| 314 | " WHERE t.id = %s AND t.id = ts.tenant_id AND ts.sensor_id = s.id " |
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| 315 | " AND s.id = sp.sensor_id AND sp.point_id = p.id " |
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| 316 | " ORDER BY p.id ", (tenant['id'],)) |
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| 317 | rows_points = cursor_system.fetchall() |
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| 318 | if rows_points is not None and len(rows_points) > 0: |
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| 319 | for row in rows_points: |
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| 320 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
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| 321 | |||
| 322 | ################################################################################################################ |
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| 323 | # Step 5: query associated points |
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| 324 | ################################################################################################################ |
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| 325 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
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| 326 | " FROM tbl_tenants t, tbl_tenants_points tp, tbl_points p " |
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| 327 | " WHERE t.id = %s AND t.id = tp.tenant_id AND tp.point_id = p.id " |
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| 328 | " ORDER BY p.id ", (tenant['id'],)) |
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| 329 | rows_points = cursor_system.fetchall() |
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| 330 | if rows_points is not None and len(rows_points) > 0: |
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| 331 | for row in rows_points: |
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| 332 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
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| 333 | |||
| 334 | ################################################################################################################ |
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| 335 | # Step 6: query base period energy input |
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| 336 | ################################################################################################################ |
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| 337 | base = dict() |
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| 338 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 339 | for energy_category_id in energy_category_set: |
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| 340 | base[energy_category_id] = dict() |
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| 341 | base[energy_category_id]['timestamps'] = list() |
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| 342 | base[energy_category_id]['values'] = list() |
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| 343 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
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| 344 | base[energy_category_id]['mean'] = None |
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| 345 | base[energy_category_id]['median'] = None |
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| 346 | base[energy_category_id]['minimum'] = None |
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| 347 | base[energy_category_id]['maximum'] = None |
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| 348 | base[energy_category_id]['stdev'] = None |
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| 349 | base[energy_category_id]['variance'] = None |
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| 350 | |||
| 351 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
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| 352 | " FROM tbl_tenant_input_category_hourly " |
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| 353 | " WHERE tenant_id = %s " |
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| 354 | " AND energy_category_id = %s " |
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| 355 | " AND start_datetime_utc >= %s " |
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| 356 | " AND start_datetime_utc < %s " |
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| 357 | " ORDER BY start_datetime_utc ", |
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| 358 | (tenant['id'], |
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| 359 | energy_category_id, |
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| 360 | base_start_datetime_utc, |
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| 361 | base_end_datetime_utc)) |
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| 362 | rows_tenant_hourly = cursor_energy.fetchall() |
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| 363 | |||
| 364 | rows_tenant_periodically, \ |
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| 365 | base[energy_category_id]['mean'], \ |
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| 366 | base[energy_category_id]['median'], \ |
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| 367 | base[energy_category_id]['minimum'], \ |
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| 368 | base[energy_category_id]['maximum'], \ |
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| 369 | base[energy_category_id]['stdev'], \ |
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| 370 | base[energy_category_id]['variance'] = \ |
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| 371 | utilities.statistics_hourly_data_by_period(rows_tenant_hourly, |
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| 372 | base_start_datetime_utc, |
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| 373 | base_end_datetime_utc, |
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| 374 | period_type) |
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| 375 | |||
| 376 | for row_tenant_periodically in rows_tenant_periodically: |
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| 377 | current_datetime_local = row_tenant_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 378 | timedelta(minutes=timezone_offset) |
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| 379 | if period_type == 'hourly': |
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| 380 | current_datetime = current_datetime_local.isoformat()[0:19] |
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| 381 | elif period_type == 'daily': |
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| 382 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 383 | elif period_type == 'weekly': |
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| 384 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 385 | elif period_type == 'monthly': |
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| 386 | current_datetime = current_datetime_local.isoformat()[0:7] |
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| 387 | elif period_type == 'yearly': |
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| 388 | current_datetime = current_datetime_local.isoformat()[0:4] |
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| 389 | |||
| 390 | actual_value = Decimal(0.0) if row_tenant_periodically[1] is None else row_tenant_periodically[1] |
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| 391 | base[energy_category_id]['timestamps'].append(current_datetime) |
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| 392 | base[energy_category_id]['values'].append(actual_value) |
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| 393 | base[energy_category_id]['subtotal'] += actual_value |
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| 394 | |||
| 395 | ################################################################################################################ |
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| 396 | # Step 7: query reporting period energy input |
||
| 397 | ################################################################################################################ |
||
| 398 | reporting = dict() |
||
| 399 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 400 | for energy_category_id in energy_category_set: |
||
| 401 | reporting[energy_category_id] = dict() |
||
| 402 | reporting[energy_category_id]['timestamps'] = list() |
||
| 403 | reporting[energy_category_id]['values'] = list() |
||
| 404 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
||
| 405 | reporting[energy_category_id]['mean'] = None |
||
| 406 | reporting[energy_category_id]['median'] = None |
||
| 407 | reporting[energy_category_id]['minimum'] = None |
||
| 408 | reporting[energy_category_id]['maximum'] = None |
||
| 409 | reporting[energy_category_id]['stdev'] = None |
||
| 410 | reporting[energy_category_id]['variance'] = None |
||
| 411 | |||
| 412 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
||
| 413 | " FROM tbl_tenant_input_category_hourly " |
||
| 414 | " WHERE tenant_id = %s " |
||
| 415 | " AND energy_category_id = %s " |
||
| 416 | " AND start_datetime_utc >= %s " |
||
| 417 | " AND start_datetime_utc < %s " |
||
| 418 | " ORDER BY start_datetime_utc ", |
||
| 419 | (tenant['id'], |
||
| 420 | energy_category_id, |
||
| 421 | reporting_start_datetime_utc, |
||
| 422 | reporting_end_datetime_utc)) |
||
| 423 | rows_tenant_hourly = cursor_energy.fetchall() |
||
| 424 | |||
| 425 | rows_tenant_periodically, \ |
||
| 426 | reporting[energy_category_id]['mean'], \ |
||
| 427 | reporting[energy_category_id]['median'], \ |
||
| 428 | reporting[energy_category_id]['minimum'], \ |
||
| 429 | reporting[energy_category_id]['maximum'], \ |
||
| 430 | reporting[energy_category_id]['stdev'], \ |
||
| 431 | reporting[energy_category_id]['variance'] = \ |
||
| 432 | utilities.statistics_hourly_data_by_period(rows_tenant_hourly, |
||
| 433 | reporting_start_datetime_utc, |
||
| 434 | reporting_end_datetime_utc, |
||
| 435 | period_type) |
||
| 436 | |||
| 437 | for row_tenant_periodically in rows_tenant_periodically: |
||
| 438 | current_datetime_local = row_tenant_periodically[0].replace(tzinfo=timezone.utc) + \ |
||
| 439 | timedelta(minutes=timezone_offset) |
||
| 440 | if period_type == 'hourly': |
||
| 441 | current_datetime = current_datetime_local.isoformat()[0:19] |
||
| 442 | elif period_type == 'daily': |
||
| 443 | current_datetime = current_datetime_local.isoformat()[0:10] |
||
| 444 | elif period_type == 'weekly': |
||
| 445 | current_datetime = current_datetime_local.isoformat()[0:10] |
||
| 446 | elif period_type == 'monthly': |
||
| 447 | current_datetime = current_datetime_local.isoformat()[0:7] |
||
| 448 | elif period_type == 'yearly': |
||
| 449 | current_datetime = current_datetime_local.isoformat()[0:4] |
||
| 450 | |||
| 451 | actual_value = Decimal(0.0) if row_tenant_periodically[1] is None else row_tenant_periodically[1] |
||
| 452 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
||
| 453 | reporting[energy_category_id]['values'].append(actual_value) |
||
| 454 | reporting[energy_category_id]['subtotal'] += actual_value |
||
| 455 | |||
| 456 | ################################################################################################################ |
||
| 457 | # Step 8: query tariff data |
||
| 458 | ################################################################################################################ |
||
| 459 | parameters_data = dict() |
||
| 460 | parameters_data['names'] = list() |
||
| 461 | parameters_data['timestamps'] = list() |
||
| 462 | parameters_data['values'] = list() |
||
| 463 | if config.is_tariff_appended and energy_category_set is not None and len(energy_category_set) > 0 \ |
||
| 464 | and not is_quick_mode: |
||
| 465 | for energy_category_id in energy_category_set: |
||
| 466 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(tenant['cost_center_id'], |
||
| 467 | energy_category_id, |
||
| 468 | reporting_start_datetime_utc, |
||
| 469 | reporting_end_datetime_utc) |
||
| 470 | tariff_timestamp_list = list() |
||
| 471 | tariff_value_list = list() |
||
| 472 | for k, v in energy_category_tariff_dict.items(): |
||
| 473 | # convert k from utc to local |
||
| 474 | k = k + timedelta(minutes=timezone_offset) |
||
| 475 | tariff_timestamp_list.append(k.isoformat()[0:19]) |
||
| 476 | tariff_value_list.append(v) |
||
| 477 | |||
| 478 | parameters_data['names'].append(_('Tariff') + '-' + energy_category_dict[energy_category_id]['name']) |
||
| 479 | parameters_data['timestamps'].append(tariff_timestamp_list) |
||
| 480 | parameters_data['values'].append(tariff_value_list) |
||
| 481 | |||
| 482 | ################################################################################################################ |
||
| 483 | # Step 9: query associated sensors and points data |
||
| 484 | ################################################################################################################ |
||
| 485 | if not is_quick_mode: |
||
| 486 | for point in point_list: |
||
| 487 | point_values = [] |
||
| 488 | point_timestamps = [] |
||
| 489 | if point['object_type'] == 'ENERGY_VALUE': |
||
| 490 | query = (" SELECT utc_date_time, actual_value " |
||
| 491 | " FROM tbl_energy_value " |
||
| 492 | " WHERE point_id = %s " |
||
| 493 | " AND utc_date_time BETWEEN %s AND %s " |
||
| 494 | " ORDER BY utc_date_time ") |
||
| 495 | cursor_historical.execute(query, (point['id'], |
||
| 496 | reporting_start_datetime_utc, |
||
| 497 | reporting_end_datetime_utc)) |
||
| 498 | rows = cursor_historical.fetchall() |
||
| 499 | |||
| 500 | if rows is not None and len(rows) > 0: |
||
| 501 | for row in rows: |
||
| 502 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
||
| 503 | timedelta(minutes=timezone_offset) |
||
| 504 | current_datetime = current_datetime_local.isoformat()[0:19] |
||
| 505 | point_timestamps.append(current_datetime) |
||
| 506 | point_values.append(row[1]) |
||
| 507 | elif point['object_type'] == 'ANALOG_VALUE': |
||
| 508 | query = (" SELECT utc_date_time, actual_value " |
||
| 509 | " FROM tbl_analog_value " |
||
| 510 | " WHERE point_id = %s " |
||
| 511 | " AND utc_date_time BETWEEN %s AND %s " |
||
| 512 | " ORDER BY utc_date_time ") |
||
| 513 | cursor_historical.execute(query, (point['id'], |
||
| 514 | reporting_start_datetime_utc, |
||
| 515 | reporting_end_datetime_utc)) |
||
| 516 | rows = cursor_historical.fetchall() |
||
| 517 | |||
| 518 | if rows is not None and len(rows) > 0: |
||
| 519 | for row in rows: |
||
| 520 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
||
| 521 | timedelta(minutes=timezone_offset) |
||
| 522 | current_datetime = current_datetime_local.isoformat()[0:19] |
||
| 523 | point_timestamps.append(current_datetime) |
||
| 524 | point_values.append(row[1]) |
||
| 525 | elif point['object_type'] == 'DIGITAL_VALUE': |
||
| 526 | query = (" SELECT utc_date_time, actual_value " |
||
| 527 | " FROM tbl_digital_value " |
||
| 528 | " WHERE point_id = %s " |
||
| 529 | " AND utc_date_time BETWEEN %s AND %s " |
||
| 530 | " ORDER BY utc_date_time ") |
||
| 531 | cursor_historical.execute(query, (point['id'], |
||
| 532 | reporting_start_datetime_utc, |
||
| 533 | reporting_end_datetime_utc)) |
||
| 534 | rows = cursor_historical.fetchall() |
||
| 535 | |||
| 536 | if rows is not None and len(rows) > 0: |
||
| 537 | for row in rows: |
||
| 538 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
||
| 539 | timedelta(minutes=timezone_offset) |
||
| 540 | current_datetime = current_datetime_local.isoformat()[0:19] |
||
| 541 | point_timestamps.append(current_datetime) |
||
| 542 | point_values.append(row[1]) |
||
| 543 | |||
| 544 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
||
| 545 | parameters_data['timestamps'].append(point_timestamps) |
||
| 546 | parameters_data['values'].append(point_values) |
||
| 547 | |||
| 548 | ################################################################################################################ |
||
| 549 | # Step 10: construct the report |
||
| 550 | ################################################################################################################ |
||
| 551 | if cursor_system: |
||
| 552 | cursor_system.close() |
||
| 553 | if cnx_system: |
||
| 554 | cnx_system.close() |
||
| 555 | |||
| 556 | if cursor_energy: |
||
| 557 | cursor_energy.close() |
||
| 558 | if cnx_energy: |
||
| 559 | cnx_energy.close() |
||
| 560 | |||
| 561 | if cursor_historical: |
||
| 562 | cursor_historical.close() |
||
| 563 | if cnx_historical: |
||
| 564 | cnx_historical.close() |
||
| 565 | |||
| 566 | result = dict() |
||
| 567 | |||
| 568 | result['tenant'] = dict() |
||
| 569 | result['tenant']['name'] = tenant['name'] |
||
| 570 | result['tenant']['area'] = tenant['area'] |
||
| 571 | |||
| 572 | result['base_period'] = dict() |
||
| 573 | result['base_period']['names'] = list() |
||
| 574 | result['base_period']['units'] = list() |
||
| 575 | result['base_period']['timestamps'] = list() |
||
| 576 | result['base_period']['values'] = list() |
||
| 577 | result['base_period']['subtotals'] = list() |
||
| 578 | result['base_period']['means'] = list() |
||
| 579 | result['base_period']['medians'] = list() |
||
| 580 | result['base_period']['minimums'] = list() |
||
| 581 | result['base_period']['maximums'] = list() |
||
| 582 | result['base_period']['stdevs'] = list() |
||
| 583 | result['base_period']['variances'] = list() |
||
| 584 | |||
| 585 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 586 | for energy_category_id in energy_category_set: |
||
| 587 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 588 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
||
| 589 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
||
| 590 | result['base_period']['values'].append(base[energy_category_id]['values']) |
||
| 591 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
||
| 592 | result['base_period']['means'].append(base[energy_category_id]['mean']) |
||
| 593 | result['base_period']['medians'].append(base[energy_category_id]['median']) |
||
| 594 | result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
||
| 595 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
||
| 596 | result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
||
| 597 | result['base_period']['variances'].append(base[energy_category_id]['variance']) |
||
| 598 | |||
| 599 | result['reporting_period'] = dict() |
||
| 600 | result['reporting_period']['names'] = list() |
||
| 601 | result['reporting_period']['energy_category_ids'] = list() |
||
| 602 | result['reporting_period']['units'] = list() |
||
| 603 | result['reporting_period']['timestamps'] = list() |
||
| 604 | result['reporting_period']['values'] = list() |
||
| 605 | result['reporting_period']['rates'] = list() |
||
| 606 | result['reporting_period']['subtotals'] = list() |
||
| 607 | result['reporting_period']['means'] = list() |
||
| 608 | result['reporting_period']['means_per_unit_area'] = list() |
||
| 609 | result['reporting_period']['means_increment_rate'] = list() |
||
| 610 | result['reporting_period']['medians'] = list() |
||
| 611 | result['reporting_period']['medians_per_unit_area'] = list() |
||
| 612 | result['reporting_period']['medians_increment_rate'] = list() |
||
| 613 | result['reporting_period']['minimums'] = list() |
||
| 614 | result['reporting_period']['minimums_per_unit_area'] = list() |
||
| 615 | result['reporting_period']['minimums_increment_rate'] = list() |
||
| 616 | result['reporting_period']['maximums'] = list() |
||
| 617 | result['reporting_period']['maximums_per_unit_area'] = list() |
||
| 618 | result['reporting_period']['maximums_increment_rate'] = list() |
||
| 619 | result['reporting_period']['stdevs'] = list() |
||
| 620 | result['reporting_period']['stdevs_per_unit_area'] = list() |
||
| 621 | result['reporting_period']['stdevs_increment_rate'] = list() |
||
| 622 | result['reporting_period']['variances'] = list() |
||
| 623 | result['reporting_period']['variances_per_unit_area'] = list() |
||
| 624 | result['reporting_period']['variances_increment_rate'] = list() |
||
| 625 | |||
| 626 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 627 | for energy_category_id in energy_category_set: |
||
| 628 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 629 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
||
| 630 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
||
| 631 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
||
| 632 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
||
| 633 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
||
| 634 | result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
||
| 635 | result['reporting_period']['means_per_unit_area'].append( |
||
| 636 | reporting[energy_category_id]['mean'] / tenant['area'] |
||
| 637 | if reporting[energy_category_id]['mean'] is not None and |
||
| 638 | tenant['area'] is not None and |
||
| 639 | tenant['area'] > Decimal(0.0) |
||
| 640 | else None) |
||
| 641 | result['reporting_period']['means_increment_rate'].append( |
||
| 642 | (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
||
| 643 | base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
||
| 644 | base[energy_category_id]['mean'] > Decimal(0.0)) |
||
| 645 | else None) |
||
| 646 | result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
||
| 647 | result['reporting_period']['medians_per_unit_area'].append( |
||
| 648 | reporting[energy_category_id]['median'] / tenant['area'] |
||
| 649 | if reporting[energy_category_id]['median'] is not None and |
||
| 650 | tenant['area'] is not None and |
||
| 651 | tenant['area'] > Decimal(0.0) |
||
| 652 | else None) |
||
| 653 | result['reporting_period']['medians_increment_rate'].append( |
||
| 654 | (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
||
| 655 | base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
||
| 656 | base[energy_category_id]['median'] > Decimal(0.0)) |
||
| 657 | else None) |
||
| 658 | result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
||
| 659 | result['reporting_period']['minimums_per_unit_area'].append( |
||
| 660 | reporting[energy_category_id]['minimum'] / tenant['area'] |
||
| 661 | if reporting[energy_category_id]['minimum'] is not None and |
||
| 662 | tenant['area'] is not None and |
||
| 663 | tenant['area'] > Decimal(0.0) |
||
| 664 | else None) |
||
| 665 | result['reporting_period']['minimums_increment_rate'].append( |
||
| 666 | (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
||
| 667 | base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
||
| 668 | base[energy_category_id]['minimum'] > Decimal(0.0)) |
||
| 669 | else None) |
||
| 670 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
||
| 671 | result['reporting_period']['maximums_per_unit_area'].append( |
||
| 672 | reporting[energy_category_id]['maximum'] / tenant['area'] |
||
| 673 | if reporting[energy_category_id]['maximum'] is not None and |
||
| 674 | tenant['area'] is not None and |
||
| 675 | tenant['area'] > Decimal(0.0) |
||
| 676 | else None) |
||
| 677 | result['reporting_period']['maximums_increment_rate'].append( |
||
| 678 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
||
| 679 | base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
||
| 680 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
||
| 681 | else None) |
||
| 682 | result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
||
| 683 | result['reporting_period']['stdevs_per_unit_area'].append( |
||
| 684 | reporting[energy_category_id]['stdev'] / tenant['area'] |
||
| 685 | if reporting[energy_category_id]['stdev'] is not None and |
||
| 686 | tenant['area'] is not None and |
||
| 687 | tenant['area'] > Decimal(0.0) |
||
| 688 | else None) |
||
| 689 | result['reporting_period']['stdevs_increment_rate'].append( |
||
| 690 | (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
||
| 691 | base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
||
| 692 | base[energy_category_id]['stdev'] > Decimal(0.0)) |
||
| 693 | else None) |
||
| 694 | result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
||
| 695 | result['reporting_period']['variances_per_unit_area'].append( |
||
| 696 | reporting[energy_category_id]['variance'] / tenant['area'] |
||
| 697 | if reporting[energy_category_id]['variance'] is not None and |
||
| 698 | tenant['area'] is not None and |
||
| 699 | tenant['area'] > Decimal(0.0) |
||
| 700 | else None) |
||
| 701 | result['reporting_period']['variances_increment_rate'].append( |
||
| 702 | (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
||
| 703 | base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
||
| 704 | base[energy_category_id]['variance'] > Decimal(0.0)) |
||
| 705 | else None) |
||
| 706 | |||
| 707 | rate = list() |
||
| 708 | for index, value in enumerate(reporting[energy_category_id]['values']): |
||
| 709 | if index < len(base[energy_category_id]['values']) \ |
||
| 710 | and base[energy_category_id]['values'][index] != 0 and value != 0: |
||
| 711 | rate.append((value - base[energy_category_id]['values'][index]) |
||
| 712 | / base[energy_category_id]['values'][index]) |
||
| 713 | else: |
||
| 714 | rate.append(None) |
||
| 715 | result['reporting_period']['rates'].append(rate) |
||
| 716 | |||
| 717 | result['parameters'] = { |
||
| 718 | "names": parameters_data['names'], |
||
| 719 | "timestamps": parameters_data['timestamps'], |
||
| 720 | "values": parameters_data['values'] |
||
| 721 | } |
||
| 722 | # export result to Excel file and then encode the file to base64 string |
||
| 723 | if not is_quick_mode: |
||
| 724 | result['excel_bytes_base64'] = excelexporters.tenantstatistics.export(result, |
||
| 725 | tenant['name'], |
||
| 726 | base_period_start_datetime_local, |
||
| 727 | base_period_end_datetime_local, |
||
| 728 | reporting_period_start_datetime_local, |
||
| 729 | reporting_period_end_datetime_local, |
||
| 730 | period_type, |
||
| 731 | language) |
||
| 732 | |||
| 733 | resp.text = json.dumps(result) |
||
| 734 |
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