myems /
myems-api
| Conditions | 95 |
| Total Lines | 496 |
| Code Lines | 374 |
| Lines | 496 |
| Ratio | 100 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like reports.combinedequipmentload.Reporting.on_get() 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 | import falcon |
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| 31 | @staticmethod |
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| 32 | def on_get(req, resp): |
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| 33 | print(req.params) |
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| 34 | combined_equipment_id = req.params.get('combinedequipmentid') |
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| 35 | period_type = req.params.get('periodtype') |
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| 36 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
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| 37 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
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| 38 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
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| 39 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
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| 40 | |||
| 41 | ################################################################################################################ |
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| 42 | # Step 1: valid parameters |
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| 43 | ################################################################################################################ |
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| 44 | if combined_equipment_id is None: |
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| 45 | raise falcon.HTTPError(falcon.HTTP_400, |
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| 46 | title='API.BAD_REQUEST', |
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| 47 | description='API.INVALID_COMBINED_EQUIPMENT_ID') |
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| 48 | else: |
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| 49 | combined_equipment_id = str.strip(combined_equipment_id) |
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| 50 | if not combined_equipment_id.isdigit() or int(combined_equipment_id) <= 0: |
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| 51 | raise falcon.HTTPError(falcon.HTTP_400, |
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| 52 | title='API.BAD_REQUEST', |
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| 53 | description='API.INVALID_COMBINED_EQUIPMENT_ID') |
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| 54 | |||
| 55 | if period_type is None: |
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| 56 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
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| 57 | else: |
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| 58 | period_type = str.strip(period_type) |
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| 59 | if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
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| 60 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
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| 61 | |||
| 62 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 63 | if config.utc_offset[0] == '-': |
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| 64 | timezone_offset = -timezone_offset |
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| 65 | |||
| 66 | base_start_datetime_utc = None |
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| 67 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
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| 68 | base_start_datetime_local = str.strip(base_start_datetime_local) |
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| 69 | try: |
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| 70 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
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| 71 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 72 | timedelta(minutes=timezone_offset) |
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| 73 | except ValueError: |
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| 74 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 75 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
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| 76 | |||
| 77 | base_end_datetime_utc = None |
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| 78 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
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| 79 | base_end_datetime_local = str.strip(base_end_datetime_local) |
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| 80 | try: |
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| 81 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
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| 82 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 83 | timedelta(minutes=timezone_offset) |
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| 84 | except ValueError: |
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| 85 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 86 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
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| 87 | |||
| 88 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
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| 89 | base_start_datetime_utc >= base_end_datetime_utc: |
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| 90 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 91 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
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| 92 | |||
| 93 | if reporting_start_datetime_local is None: |
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| 94 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 95 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 96 | else: |
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| 97 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
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| 98 | try: |
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| 99 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
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| 100 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 101 | timedelta(minutes=timezone_offset) |
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| 102 | except ValueError: |
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| 103 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 104 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 105 | |||
| 106 | if reporting_end_datetime_local is None: |
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| 107 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 108 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 109 | else: |
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| 110 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
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| 111 | try: |
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| 112 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
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| 113 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 114 | timedelta(minutes=timezone_offset) |
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| 115 | except ValueError: |
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| 116 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 117 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 118 | |||
| 119 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
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| 120 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 121 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
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| 122 | |||
| 123 | ################################################################################################################ |
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| 124 | # Step 2: query the combined equipment |
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| 125 | ################################################################################################################ |
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| 126 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
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| 127 | cursor_system = cnx_system.cursor() |
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| 128 | |||
| 129 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
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| 130 | cursor_energy = cnx_energy.cursor() |
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| 131 | |||
| 132 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
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| 133 | cursor_historical = cnx_historical.cursor() |
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| 134 | |||
| 135 | cursor_system.execute(" SELECT id, name, cost_center_id " |
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| 136 | " FROM tbl_combined_equipments " |
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| 137 | " WHERE id = %s ", (combined_equipment_id,)) |
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| 138 | row_combined_equipment = cursor_system.fetchone() |
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| 139 | if row_combined_equipment is None: |
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| 140 | if cursor_system: |
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| 141 | cursor_system.close() |
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| 142 | if cnx_system: |
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| 143 | cnx_system.disconnect() |
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| 144 | |||
| 145 | if cursor_energy: |
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| 146 | cursor_energy.close() |
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| 147 | if cnx_energy: |
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| 148 | cnx_energy.disconnect() |
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| 149 | |||
| 150 | if cnx_historical: |
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| 151 | cnx_historical.close() |
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| 152 | if cursor_historical: |
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| 153 | cursor_historical.disconnect() |
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| 154 | raise falcon.HTTPError(falcon.HTTP_404, |
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| 155 | title='API.NOT_FOUND', |
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| 156 | description='API.COMBINED_EQUIPMENT_NOT_FOUND') |
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| 157 | |||
| 158 | combined_equipment = dict() |
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| 159 | combined_equipment['id'] = row_combined_equipment[0] |
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| 160 | combined_equipment['name'] = row_combined_equipment[1] |
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| 161 | combined_equipment['cost_center_id'] = row_combined_equipment[2] |
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| 162 | |||
| 163 | ################################################################################################################ |
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| 164 | # Step 3: query energy categories |
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| 165 | ################################################################################################################ |
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| 166 | energy_category_set = set() |
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| 167 | # query energy categories in base period |
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| 168 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
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| 169 | " FROM tbl_combined_equipment_input_category_hourly " |
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| 170 | " WHERE combined_equipment_id = %s " |
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| 171 | " AND start_datetime_utc >= %s " |
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| 172 | " AND start_datetime_utc < %s ", |
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| 173 | (combined_equipment['id'], base_start_datetime_utc, base_end_datetime_utc)) |
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| 174 | rows_energy_categories = cursor_energy.fetchall() |
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| 175 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
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| 176 | for row_energy_category in rows_energy_categories: |
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| 177 | energy_category_set.add(row_energy_category[0]) |
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| 178 | |||
| 179 | # query energy categories in reporting period |
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| 180 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
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| 181 | " FROM tbl_combined_equipment_input_category_hourly " |
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| 182 | " WHERE combined_equipment_id = %s " |
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| 183 | " AND start_datetime_utc >= %s " |
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| 184 | " AND start_datetime_utc < %s ", |
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| 185 | (combined_equipment['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
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| 186 | rows_energy_categories = cursor_energy.fetchall() |
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| 187 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
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| 188 | for row_energy_category in rows_energy_categories: |
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| 189 | energy_category_set.add(row_energy_category[0]) |
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| 190 | |||
| 191 | # query all energy categories in base period and reporting period |
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| 192 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
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| 193 | " FROM tbl_energy_categories " |
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| 194 | " ORDER BY id ", ) |
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| 195 | rows_energy_categories = cursor_system.fetchall() |
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| 196 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
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| 197 | if cursor_system: |
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| 198 | cursor_system.close() |
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| 199 | if cnx_system: |
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| 200 | cnx_system.disconnect() |
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| 201 | |||
| 202 | if cursor_energy: |
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| 203 | cursor_energy.close() |
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| 204 | if cnx_energy: |
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| 205 | cnx_energy.disconnect() |
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| 206 | |||
| 207 | if cnx_historical: |
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| 208 | cnx_historical.close() |
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| 209 | if cursor_historical: |
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| 210 | cursor_historical.disconnect() |
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| 211 | raise falcon.HTTPError(falcon.HTTP_404, |
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| 212 | title='API.NOT_FOUND', |
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| 213 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
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| 214 | energy_category_dict = dict() |
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| 215 | for row_energy_category in rows_energy_categories: |
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| 216 | if row_energy_category[0] in energy_category_set: |
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| 217 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
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| 218 | "unit_of_measure": row_energy_category[2], |
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| 219 | "kgce": row_energy_category[3], |
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| 220 | "kgco2e": row_energy_category[4]} |
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| 221 | |||
| 222 | ################################################################################################################ |
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| 223 | # Step 4: query associated points |
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| 224 | ################################################################################################################ |
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| 225 | point_list = list() |
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| 226 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
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| 227 | " FROM tbl_combined_equipments e, tbl_combined_equipments_parameters ep, tbl_points p " |
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| 228 | " WHERE e.id = %s AND e.id = ep.combined_equipment_id AND ep.parameter_type = 'point' " |
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| 229 | " AND ep.point_id = p.id " |
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| 230 | " ORDER BY p.id ", (combined_equipment['id'],)) |
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| 231 | rows_points = cursor_system.fetchall() |
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| 232 | if rows_points is not None and len(rows_points) > 0: |
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| 233 | for row in rows_points: |
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| 234 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
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| 235 | |||
| 236 | ################################################################################################################ |
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| 237 | # Step 6: query base period energy input |
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| 238 | ################################################################################################################ |
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| 239 | base = dict() |
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| 240 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 241 | for energy_category_id in energy_category_set: |
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| 242 | base[energy_category_id] = dict() |
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| 243 | base[energy_category_id]['timestamps'] = list() |
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| 244 | base[energy_category_id]['sub_averages'] = list() |
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| 245 | base[energy_category_id]['sub_maximums'] = list() |
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| 246 | base[energy_category_id]['average'] = None |
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| 247 | base[energy_category_id]['maximum'] = None |
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| 248 | base[energy_category_id]['factor'] = None |
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| 249 | |||
| 250 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
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| 251 | " FROM tbl_combined_equipment_input_category_hourly " |
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| 252 | " WHERE combined_equipment_id = %s " |
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| 253 | " AND energy_category_id = %s " |
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| 254 | " AND start_datetime_utc >= %s " |
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| 255 | " AND start_datetime_utc < %s " |
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| 256 | " ORDER BY start_datetime_utc ", |
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| 257 | (combined_equipment['id'], |
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| 258 | energy_category_id, |
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| 259 | base_start_datetime_utc, |
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| 260 | base_end_datetime_utc)) |
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| 261 | rows_combined_equipment_hourly = cursor_energy.fetchall() |
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| 262 | |||
| 263 | rows_combined_equipment_periodically, \ |
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| 264 | base[energy_category_id]['average'], \ |
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| 265 | base[energy_category_id]['maximum'] = \ |
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| 266 | utilities.averaging_hourly_data_by_period(rows_combined_equipment_hourly, |
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| 267 | base_start_datetime_utc, |
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| 268 | base_end_datetime_utc, |
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| 269 | period_type) |
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| 270 | base[energy_category_id]['factor'] = \ |
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| 271 | (base[energy_category_id]['average'] / base[energy_category_id]['maximum'] |
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| 272 | if (base[energy_category_id]['average'] is not None and |
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| 273 | base[energy_category_id]['maximum'] is not None and |
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| 274 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
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| 275 | else None) |
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| 276 | |||
| 277 | for row_combined_equipment_periodically in rows_combined_equipment_periodically: |
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| 278 | current_datetime_local = row_combined_equipment_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 279 | timedelta(minutes=timezone_offset) |
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| 280 | if period_type == 'hourly': |
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| 281 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 282 | elif period_type == 'daily': |
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| 283 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 284 | elif period_type == 'monthly': |
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| 285 | current_datetime = current_datetime_local.strftime('%Y-%m') |
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| 286 | elif period_type == 'yearly': |
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| 287 | current_datetime = current_datetime_local.strftime('%Y') |
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| 288 | |||
| 289 | base[energy_category_id]['timestamps'].append(current_datetime) |
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| 290 | base[energy_category_id]['sub_averages'].append(row_combined_equipment_periodically[1]) |
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| 291 | base[energy_category_id]['sub_maximums'].append(row_combined_equipment_periodically[2]) |
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| 292 | |||
| 293 | ################################################################################################################ |
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| 294 | # Step 7: query reporting period energy input |
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| 295 | ################################################################################################################ |
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| 296 | reporting = dict() |
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| 297 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 298 | for energy_category_id in energy_category_set: |
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| 299 | reporting[energy_category_id] = dict() |
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| 300 | reporting[energy_category_id]['timestamps'] = list() |
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| 301 | reporting[energy_category_id]['sub_averages'] = list() |
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| 302 | reporting[energy_category_id]['sub_maximums'] = list() |
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| 303 | reporting[energy_category_id]['average'] = None |
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| 304 | reporting[energy_category_id]['maximum'] = None |
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| 305 | reporting[energy_category_id]['factor'] = None |
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| 306 | |||
| 307 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
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| 308 | " FROM tbl_combined_equipment_input_category_hourly " |
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| 309 | " WHERE combined_equipment_id = %s " |
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| 310 | " AND energy_category_id = %s " |
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| 311 | " AND start_datetime_utc >= %s " |
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| 312 | " AND start_datetime_utc < %s " |
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| 313 | " ORDER BY start_datetime_utc ", |
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| 314 | (combined_equipment['id'], |
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| 315 | energy_category_id, |
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| 316 | reporting_start_datetime_utc, |
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| 317 | reporting_end_datetime_utc)) |
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| 318 | rows_combined_equipment_hourly = cursor_energy.fetchall() |
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| 319 | |||
| 320 | rows_combined_equipment_periodically, \ |
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| 321 | reporting[energy_category_id]['average'], \ |
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| 322 | reporting[energy_category_id]['maximum'] = \ |
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| 323 | utilities.averaging_hourly_data_by_period(rows_combined_equipment_hourly, |
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| 324 | reporting_start_datetime_utc, |
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| 325 | reporting_end_datetime_utc, |
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| 326 | period_type) |
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| 327 | reporting[energy_category_id]['factor'] = \ |
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| 328 | (reporting[energy_category_id]['average'] / reporting[energy_category_id]['maximum'] |
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| 329 | if (reporting[energy_category_id]['average'] is not None and |
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| 330 | reporting[energy_category_id]['maximum'] is not None and |
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| 331 | reporting[energy_category_id]['maximum'] > Decimal(0.0)) |
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| 332 | else None) |
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| 333 | |||
| 334 | for row_combined_equipment_periodically in rows_combined_equipment_periodically: |
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| 335 | current_datetime_local = row_combined_equipment_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 336 | timedelta(minutes=timezone_offset) |
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| 337 | if period_type == 'hourly': |
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| 338 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 339 | elif period_type == 'daily': |
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| 340 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 341 | elif period_type == 'monthly': |
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| 342 | current_datetime = current_datetime_local.strftime('%Y-%m') |
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| 343 | elif period_type == 'yearly': |
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| 344 | current_datetime = current_datetime_local.strftime('%Y') |
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| 345 | |||
| 346 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
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| 347 | reporting[energy_category_id]['sub_averages'].append(row_combined_equipment_periodically[1]) |
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| 348 | reporting[energy_category_id]['sub_maximums'].append(row_combined_equipment_periodically[2]) |
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| 349 | |||
| 350 | ################################################################################################################ |
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| 351 | # Step 8: query tariff data |
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| 352 | ################################################################################################################ |
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| 353 | parameters_data = dict() |
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| 354 | parameters_data['names'] = list() |
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| 355 | parameters_data['timestamps'] = list() |
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| 356 | parameters_data['values'] = list() |
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| 357 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 358 | for energy_category_id in energy_category_set: |
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| 359 | energy_category_tariff_dict = \ |
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| 360 | utilities.get_energy_category_tariffs(combined_equipment['cost_center_id'], |
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| 361 | energy_category_id, |
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| 362 | reporting_start_datetime_utc, |
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| 363 | reporting_end_datetime_utc) |
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| 364 | tariff_timestamp_list = list() |
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| 365 | tariff_value_list = list() |
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| 366 | for k, v in energy_category_tariff_dict.items(): |
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| 367 | # convert k from utc to local |
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| 368 | k = k + timedelta(minutes=timezone_offset) |
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| 369 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
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| 370 | tariff_value_list.append(v) |
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| 371 | |||
| 372 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
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| 373 | parameters_data['timestamps'].append(tariff_timestamp_list) |
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| 374 | parameters_data['values'].append(tariff_value_list) |
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| 375 | |||
| 376 | ################################################################################################################ |
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| 377 | # Step 9: query associated points data |
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| 378 | ################################################################################################################ |
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| 379 | for point in point_list: |
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| 380 | point_values = [] |
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| 381 | point_timestamps = [] |
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| 382 | if point['object_type'] == 'ANALOG_VALUE': |
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| 383 | query = (" SELECT utc_date_time, actual_value " |
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| 384 | " FROM tbl_analog_value " |
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| 385 | " WHERE point_id = %s " |
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| 386 | " AND utc_date_time BETWEEN %s AND %s " |
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| 387 | " ORDER BY utc_date_time ") |
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| 388 | cursor_historical.execute(query, (point['id'], |
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| 389 | reporting_start_datetime_utc, |
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| 390 | reporting_end_datetime_utc)) |
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| 391 | rows = cursor_historical.fetchall() |
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| 392 | |||
| 393 | if rows is not None and len(rows) > 0: |
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| 394 | for row in rows: |
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| 395 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
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| 396 | timedelta(minutes=timezone_offset) |
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| 397 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 398 | point_timestamps.append(current_datetime) |
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| 399 | point_values.append(row[1]) |
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| 400 | |||
| 401 | elif point['object_type'] == 'ENERGY_VALUE': |
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| 402 | query = (" SELECT utc_date_time, actual_value " |
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| 403 | " FROM tbl_energy_value " |
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| 404 | " WHERE point_id = %s " |
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| 405 | " AND utc_date_time BETWEEN %s AND %s " |
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| 406 | " ORDER BY utc_date_time ") |
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| 407 | cursor_historical.execute(query, (point['id'], |
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| 408 | reporting_start_datetime_utc, |
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| 409 | reporting_end_datetime_utc)) |
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| 410 | rows = cursor_historical.fetchall() |
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| 411 | |||
| 412 | if rows is not None and len(rows) > 0: |
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| 413 | for row in rows: |
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| 414 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
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| 415 | timedelta(minutes=timezone_offset) |
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| 416 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 417 | point_timestamps.append(current_datetime) |
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| 418 | point_values.append(row[1]) |
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| 419 | elif point['object_type'] == 'DIGITAL_VALUE': |
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| 420 | query = (" SELECT utc_date_time, actual_value " |
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| 421 | " FROM tbl_digital_value " |
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| 422 | " WHERE point_id = %s " |
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| 423 | " AND utc_date_time BETWEEN %s AND %s ") |
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| 424 | cursor_historical.execute(query, (point['id'], |
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| 425 | reporting_start_datetime_utc, |
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| 426 | reporting_end_datetime_utc)) |
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| 427 | rows = cursor_historical.fetchall() |
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| 428 | |||
| 429 | if rows is not None and len(rows) > 0: |
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| 430 | for row in rows: |
||
| 431 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
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| 432 | timedelta(minutes=timezone_offset) |
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| 433 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 434 | point_timestamps.append(current_datetime) |
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| 435 | point_values.append(row[1]) |
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| 436 | |||
| 437 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
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| 438 | parameters_data['timestamps'].append(point_timestamps) |
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| 439 | parameters_data['values'].append(point_values) |
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| 440 | |||
| 441 | ################################################################################################################ |
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| 442 | # Step 10: construct the report |
||
| 443 | ################################################################################################################ |
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| 444 | if cursor_system: |
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| 445 | cursor_system.close() |
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| 446 | if cnx_system: |
||
| 447 | cnx_system.disconnect() |
||
| 448 | |||
| 449 | if cursor_energy: |
||
| 450 | cursor_energy.close() |
||
| 451 | if cnx_energy: |
||
| 452 | cnx_energy.disconnect() |
||
| 453 | |||
| 454 | result = dict() |
||
| 455 | |||
| 456 | result['combined_equipment'] = dict() |
||
| 457 | result['combined_equipment']['name'] = combined_equipment['name'] |
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| 458 | |||
| 459 | result['base_period'] = dict() |
||
| 460 | result['base_period']['names'] = list() |
||
| 461 | result['base_period']['units'] = list() |
||
| 462 | result['base_period']['timestamps'] = list() |
||
| 463 | result['base_period']['sub_averages'] = list() |
||
| 464 | result['base_period']['sub_maximums'] = list() |
||
| 465 | result['base_period']['averages'] = list() |
||
| 466 | result['base_period']['maximums'] = list() |
||
| 467 | result['base_period']['factors'] = list() |
||
| 468 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 469 | for energy_category_id in energy_category_set: |
||
| 470 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 471 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
||
| 472 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
||
| 473 | result['base_period']['sub_averages'].append(base[energy_category_id]['sub_averages']) |
||
| 474 | result['base_period']['sub_maximums'].append(base[energy_category_id]['sub_maximums']) |
||
| 475 | result['base_period']['averages'].append(base[energy_category_id]['average']) |
||
| 476 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
||
| 477 | result['base_period']['factors'].append(base[energy_category_id]['factor']) |
||
| 478 | |||
| 479 | result['reporting_period'] = dict() |
||
| 480 | result['reporting_period']['names'] = list() |
||
| 481 | result['reporting_period']['energy_category_ids'] = list() |
||
| 482 | result['reporting_period']['units'] = list() |
||
| 483 | result['reporting_period']['timestamps'] = list() |
||
| 484 | result['reporting_period']['sub_averages'] = list() |
||
| 485 | result['reporting_period']['sub_maximums'] = list() |
||
| 486 | result['reporting_period']['averages'] = list() |
||
| 487 | result['reporting_period']['averages_increment_rate'] = list() |
||
| 488 | result['reporting_period']['maximums'] = list() |
||
| 489 | result['reporting_period']['maximums_increment_rate'] = list() |
||
| 490 | result['reporting_period']['factors'] = list() |
||
| 491 | result['reporting_period']['factors_increment_rate'] = list() |
||
| 492 | |||
| 493 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 494 | for energy_category_id in energy_category_set: |
||
| 495 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 496 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
||
| 497 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
||
| 498 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
||
| 499 | result['reporting_period']['sub_averages'].append(reporting[energy_category_id]['sub_averages']) |
||
| 500 | result['reporting_period']['sub_maximums'].append(reporting[energy_category_id]['sub_maximums']) |
||
| 501 | result['reporting_period']['averages'].append(reporting[energy_category_id]['average']) |
||
| 502 | result['reporting_period']['averages_increment_rate'].append( |
||
| 503 | (reporting[energy_category_id]['average'] - base[energy_category_id]['average']) / |
||
| 504 | base[energy_category_id]['average'] if (base[energy_category_id]['average'] is not None and |
||
| 505 | base[energy_category_id]['average'] > Decimal(0.0)) |
||
| 506 | else None) |
||
| 507 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
||
| 508 | result['reporting_period']['maximums_increment_rate'].append( |
||
| 509 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
||
| 510 | base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
||
| 511 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
||
| 512 | else None) |
||
| 513 | result['reporting_period']['factors'].append(reporting[energy_category_id]['factor']) |
||
| 514 | result['reporting_period']['factors_increment_rate'].append( |
||
| 515 | (reporting[energy_category_id]['factor'] - base[energy_category_id]['factor']) / |
||
| 516 | base[energy_category_id]['factor'] if (base[energy_category_id]['factor'] is not None and |
||
| 517 | base[energy_category_id]['factor'] > Decimal(0.0)) |
||
| 518 | else None) |
||
| 519 | |||
| 520 | result['parameters'] = { |
||
| 521 | "names": parameters_data['names'], |
||
| 522 | "timestamps": parameters_data['timestamps'], |
||
| 523 | "values": parameters_data['values'] |
||
| 524 | } |
||
| 525 | |||
| 526 | resp.body = json.dumps(result) |
||
| 527 |
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