MyEMS /
myems
| Conditions | 114 |
| Total Lines | 538 |
| Code Lines | 403 |
| Lines | 538 |
| 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.shopfloorcarbon.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 re |
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| 35 | @staticmethod |
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| 36 | def on_get(req, resp): |
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| 37 | print(req.params) |
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| 38 | shopfloor_id = req.params.get('shopfloorid') |
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| 39 | shopfloor_uuid = req.params.get('shopflooruuid') |
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| 40 | period_type = req.params.get('periodtype') |
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| 41 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
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| 42 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
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| 43 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
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| 44 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
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| 45 | |||
| 46 | ################################################################################################################ |
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| 47 | # Step 1: valid parameters |
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| 48 | ################################################################################################################ |
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| 49 | if shopfloor_id is None and shopfloor_uuid is None: |
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| 50 | raise falcon.HTTPError(falcon.HTTP_400, |
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| 51 | title='API.BAD_REQUEST', |
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| 52 | description='API.INVALID_SHOPFLOOR_ID') |
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| 53 | |||
| 54 | if shopfloor_id is not None: |
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| 55 | shopfloor_id = str.strip(shopfloor_id) |
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| 56 | if not shopfloor_id.isdigit() or int(shopfloor_id) <= 0: |
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| 57 | raise falcon.HTTPError(falcon.HTTP_400, |
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| 58 | title='API.BAD_REQUEST', |
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| 59 | description='API.INVALID_SHOPFLOOR_ID') |
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| 60 | |||
| 61 | if shopfloor_uuid is not None: |
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| 62 | regex = re.compile('^[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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| 63 | match = regex.match(str.strip(shopfloor_uuid)) |
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| 64 | if not bool(match): |
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| 65 | raise falcon.HTTPError(falcon.HTTP_400, |
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| 66 | title='API.BAD_REQUEST', |
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| 67 | description='API.INVALID_SHOPFLOOR_UUID') |
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| 68 | |||
| 69 | if period_type is None: |
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| 70 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
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| 71 | else: |
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| 72 | period_type = str.strip(period_type) |
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| 73 | if period_type not in ['hourly', 'daily', 'weekly', 'monthly', 'yearly']: |
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| 74 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
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| 75 | |||
| 76 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 77 | if config.utc_offset[0] == '-': |
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| 78 | timezone_offset = -timezone_offset |
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| 79 | |||
| 80 | base_start_datetime_utc = None |
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| 81 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
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| 82 | base_start_datetime_local = str.strip(base_start_datetime_local) |
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| 83 | try: |
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| 84 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
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| 85 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 86 | timedelta(minutes=timezone_offset) |
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| 87 | except ValueError: |
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| 88 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 89 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
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| 90 | |||
| 91 | base_end_datetime_utc = None |
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| 92 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
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| 93 | base_end_datetime_local = str.strip(base_end_datetime_local) |
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| 94 | try: |
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| 95 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
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| 96 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 97 | timedelta(minutes=timezone_offset) |
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| 98 | except ValueError: |
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| 99 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 100 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
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| 101 | |||
| 102 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
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| 103 | base_start_datetime_utc >= base_end_datetime_utc: |
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| 104 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 105 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
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| 106 | |||
| 107 | if reporting_start_datetime_local is None: |
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| 108 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 109 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 110 | else: |
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| 111 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
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| 112 | try: |
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| 113 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
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| 114 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 115 | timedelta(minutes=timezone_offset) |
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| 116 | except ValueError: |
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| 117 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 118 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 119 | |||
| 120 | if reporting_end_datetime_local is None: |
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| 121 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 122 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 123 | else: |
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| 124 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
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| 125 | try: |
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| 126 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
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| 127 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 128 | timedelta(minutes=timezone_offset) |
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| 129 | except ValueError: |
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| 130 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 131 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 132 | |||
| 133 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
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| 134 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 135 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
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| 136 | |||
| 137 | ################################################################################################################ |
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| 138 | # Step 2: query the shopfloor |
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| 139 | ################################################################################################################ |
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| 140 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
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| 141 | cursor_system = cnx_system.cursor() |
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| 142 | |||
| 143 | cnx_carbon = mysql.connector.connect(**config.myems_carbon_db) |
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| 144 | cursor_carbon = cnx_carbon.cursor() |
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| 145 | |||
| 146 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
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| 147 | cursor_historical = cnx_historical.cursor() |
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| 148 | |||
| 149 | if shopfloor_id is not None: |
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| 150 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
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| 151 | " FROM tbl_shopfloors " |
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| 152 | " WHERE id = %s ", (shopfloor_id,)) |
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| 153 | row_shopfloor = cursor_system.fetchone() |
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| 154 | elif shopfloor_uuid is not None: |
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| 155 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
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| 156 | " FROM tbl_shopfloors " |
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| 157 | " WHERE uuid = %s ", (shopfloor_uuid,)) |
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| 158 | row_shopfloor = cursor_system.fetchone() |
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| 159 | |||
| 160 | if row_shopfloor is None: |
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| 161 | if cursor_system: |
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| 162 | cursor_system.close() |
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| 163 | if cnx_system: |
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| 164 | cnx_system.close() |
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| 165 | |||
| 166 | if cursor_carbon: |
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| 167 | cursor_carbon.close() |
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| 168 | if cnx_carbon: |
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| 169 | cnx_carbon.close() |
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| 170 | |||
| 171 | if cursor_historical: |
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| 172 | cursor_historical.close() |
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| 173 | if cnx_historical: |
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| 174 | cnx_historical.close() |
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| 175 | raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.SHOPFLOOR_NOT_FOUND') |
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| 176 | |||
| 177 | shopfloor = dict() |
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| 178 | shopfloor['id'] = row_shopfloor[0] |
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| 179 | shopfloor['name'] = row_shopfloor[1] |
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| 180 | shopfloor['area'] = row_shopfloor[2] |
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| 181 | shopfloor['cost_center_id'] = row_shopfloor[3] |
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| 182 | |||
| 183 | ################################################################################################################ |
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| 184 | # Step 3: query energy categories |
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| 185 | ################################################################################################################ |
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| 186 | energy_category_set = set() |
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| 187 | # query energy categories in base period |
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| 188 | cursor_carbon.execute(" SELECT DISTINCT(energy_category_id) " |
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| 189 | " FROM tbl_shopfloor_input_category_hourly " |
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| 190 | " WHERE shopfloor_id = %s " |
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| 191 | " AND start_datetime_utc >= %s " |
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| 192 | " AND start_datetime_utc < %s ", |
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| 193 | (shopfloor['id'], base_start_datetime_utc, base_end_datetime_utc)) |
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| 194 | rows_energy_categories = cursor_carbon.fetchall() |
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| 195 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
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| 196 | for row_energy_category in rows_energy_categories: |
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| 197 | energy_category_set.add(row_energy_category[0]) |
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| 198 | |||
| 199 | # query energy categories in reporting period |
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| 200 | cursor_carbon.execute(" SELECT DISTINCT(energy_category_id) " |
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| 201 | " FROM tbl_shopfloor_input_category_hourly " |
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| 202 | " WHERE shopfloor_id = %s " |
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| 203 | " AND start_datetime_utc >= %s " |
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| 204 | " AND start_datetime_utc < %s ", |
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| 205 | (shopfloor['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
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| 206 | rows_energy_categories = cursor_carbon.fetchall() |
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| 207 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
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| 208 | for row_energy_category in rows_energy_categories: |
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| 209 | energy_category_set.add(row_energy_category[0]) |
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| 210 | |||
| 211 | # query all energy categories in base period and reporting period |
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| 212 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
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| 213 | " FROM tbl_energy_categories " |
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| 214 | " ORDER BY id ", ) |
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| 215 | rows_energy_categories = cursor_system.fetchall() |
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| 216 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
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| 217 | if cursor_system: |
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| 218 | cursor_system.close() |
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| 219 | if cnx_system: |
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| 220 | cnx_system.close() |
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| 221 | |||
| 222 | if cursor_carbon: |
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| 223 | cursor_carbon.close() |
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| 224 | if cnx_carbon: |
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| 225 | cnx_carbon.close() |
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| 226 | |||
| 227 | if cursor_historical: |
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| 228 | cursor_historical.close() |
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| 229 | if cnx_historical: |
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| 230 | cnx_historical.close() |
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| 231 | raise falcon.HTTPError(falcon.HTTP_404, |
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| 232 | title='API.NOT_FOUND', |
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| 233 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
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| 234 | energy_category_dict = dict() |
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| 235 | for row_energy_category in rows_energy_categories: |
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| 236 | if row_energy_category[0] in energy_category_set: |
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| 237 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
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| 238 | "unit_of_measure": row_energy_category[2], |
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| 239 | "kgce": row_energy_category[3], |
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| 240 | "kgco2e": row_energy_category[4]} |
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| 241 | |||
| 242 | ################################################################################################################ |
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| 243 | # Step 4: query associated sensors |
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| 244 | ################################################################################################################ |
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| 245 | point_list = list() |
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| 246 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
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| 247 | " FROM tbl_shopfloors st, tbl_sensors se, tbl_shopfloors_sensors ss, " |
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| 248 | " tbl_points p, tbl_sensors_points sp " |
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| 249 | " WHERE st.id = %s AND st.id = ss.shopfloor_id AND ss.sensor_id = se.id " |
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| 250 | " AND se.id = sp.sensor_id AND sp.point_id = p.id " |
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| 251 | " ORDER BY p.id ", (shopfloor['id'], )) |
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| 252 | rows_points = cursor_system.fetchall() |
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| 253 | if rows_points is not None and len(rows_points) > 0: |
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| 254 | for row in rows_points: |
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| 255 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
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| 256 | |||
| 257 | ################################################################################################################ |
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| 258 | # Step 5: query associated points |
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| 259 | ################################################################################################################ |
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| 260 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
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| 261 | " FROM tbl_shopfloors s, tbl_shopfloors_points sp, tbl_points p " |
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| 262 | " WHERE s.id = %s AND s.id = sp.shopfloor_id AND sp.point_id = p.id " |
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| 263 | " ORDER BY p.id ", (shopfloor['id'], )) |
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| 264 | rows_points = cursor_system.fetchall() |
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| 265 | if rows_points is not None and len(rows_points) > 0: |
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| 266 | for row in rows_points: |
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| 267 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
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| 268 | |||
| 269 | ################################################################################################################ |
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| 270 | # Step 6: query base period energy carbon dioxide emissions |
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| 271 | ################################################################################################################ |
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| 272 | base = dict() |
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| 273 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 274 | for energy_category_id in energy_category_set: |
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| 275 | base[energy_category_id] = dict() |
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| 276 | base[energy_category_id]['timestamps'] = list() |
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| 277 | base[energy_category_id]['values'] = list() |
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| 278 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
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| 279 | |||
| 280 | cursor_carbon.execute(" SELECT start_datetime_utc, actual_value " |
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| 281 | " FROM tbl_shopfloor_input_category_hourly " |
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| 282 | " WHERE shopfloor_id = %s " |
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| 283 | " AND energy_category_id = %s " |
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| 284 | " AND start_datetime_utc >= %s " |
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| 285 | " AND start_datetime_utc < %s " |
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| 286 | " ORDER BY start_datetime_utc ", |
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| 287 | (shopfloor['id'], |
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| 288 | energy_category_id, |
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| 289 | base_start_datetime_utc, |
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| 290 | base_end_datetime_utc)) |
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| 291 | rows_shopfloor_hourly = cursor_carbon.fetchall() |
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| 292 | |||
| 293 | rows_shopfloor_periodically = utilities.aggregate_hourly_data_by_period(rows_shopfloor_hourly, |
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| 294 | base_start_datetime_utc, |
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| 295 | base_end_datetime_utc, |
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| 296 | period_type) |
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| 297 | for row_shopfloor_periodically in rows_shopfloor_periodically: |
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| 298 | current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 299 | timedelta(minutes=timezone_offset) |
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| 300 | if period_type == 'hourly': |
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| 301 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 302 | elif period_type == 'daily': |
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| 303 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 304 | elif period_type == 'weekly': |
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| 305 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 306 | elif period_type == 'monthly': |
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| 307 | current_datetime = current_datetime_local.strftime('%Y-%m') |
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| 308 | elif period_type == 'yearly': |
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| 309 | current_datetime = current_datetime_local.strftime('%Y') |
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| 310 | |||
| 311 | actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ |
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| 312 | else row_shopfloor_periodically[1] |
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| 313 | base[energy_category_id]['timestamps'].append(current_datetime) |
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| 314 | base[energy_category_id]['values'].append(actual_value) |
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| 315 | base[energy_category_id]['subtotal'] += actual_value |
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| 316 | |||
| 317 | ################################################################################################################ |
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| 318 | # Step 7: query reporting period energy carbon dioxide emissions |
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| 319 | ################################################################################################################ |
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| 320 | reporting = dict() |
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| 321 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 322 | for energy_category_id in energy_category_set: |
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| 323 | reporting[energy_category_id] = dict() |
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| 324 | reporting[energy_category_id]['timestamps'] = list() |
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| 325 | reporting[energy_category_id]['values'] = list() |
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| 326 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
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| 327 | reporting[energy_category_id]['toppeak'] = Decimal(0.0) |
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| 328 | reporting[energy_category_id]['onpeak'] = Decimal(0.0) |
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| 329 | reporting[energy_category_id]['midpeak'] = Decimal(0.0) |
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| 330 | reporting[energy_category_id]['offpeak'] = Decimal(0.0) |
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| 331 | |||
| 332 | cursor_carbon.execute(" SELECT start_datetime_utc, actual_value " |
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| 333 | " FROM tbl_shopfloor_input_category_hourly " |
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| 334 | " WHERE shopfloor_id = %s " |
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| 335 | " AND energy_category_id = %s " |
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| 336 | " AND start_datetime_utc >= %s " |
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| 337 | " AND start_datetime_utc < %s " |
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| 338 | " ORDER BY start_datetime_utc ", |
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| 339 | (shopfloor['id'], |
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| 340 | energy_category_id, |
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| 341 | reporting_start_datetime_utc, |
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| 342 | reporting_end_datetime_utc)) |
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| 343 | rows_shopfloor_hourly = cursor_carbon.fetchall() |
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| 344 | |||
| 345 | rows_shopfloor_periodically = utilities.aggregate_hourly_data_by_period(rows_shopfloor_hourly, |
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| 346 | reporting_start_datetime_utc, |
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| 347 | reporting_end_datetime_utc, |
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| 348 | period_type) |
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| 349 | for row_shopfloor_periodically in rows_shopfloor_periodically: |
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| 350 | current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 351 | timedelta(minutes=timezone_offset) |
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| 352 | if period_type == 'hourly': |
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| 353 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 354 | elif period_type == 'daily': |
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| 355 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 356 | elif period_type == 'weekly': |
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| 357 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
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| 358 | elif period_type == 'monthly': |
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| 359 | current_datetime = current_datetime_local.strftime('%Y-%m') |
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| 360 | elif period_type == 'yearly': |
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| 361 | current_datetime = current_datetime_local.strftime('%Y') |
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| 362 | |||
| 363 | actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ |
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| 364 | else row_shopfloor_periodically[1] |
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| 365 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
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| 366 | reporting[energy_category_id]['values'].append(actual_value) |
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| 367 | reporting[energy_category_id]['subtotal'] += actual_value |
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| 368 | |||
| 369 | energy_category_tariff_dict = utilities.get_energy_category_peak_types(shopfloor['cost_center_id'], |
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| 370 | energy_category_id, |
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| 371 | reporting_start_datetime_utc, |
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| 372 | reporting_end_datetime_utc) |
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| 373 | for row in rows_shopfloor_hourly: |
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| 374 | peak_type = energy_category_tariff_dict.get(row[0], None) |
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| 375 | if peak_type == 'toppeak': |
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| 376 | reporting[energy_category_id]['toppeak'] += row[1] |
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| 377 | elif peak_type == 'onpeak': |
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| 378 | reporting[energy_category_id]['onpeak'] += row[1] |
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| 379 | elif peak_type == 'midpeak': |
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| 380 | reporting[energy_category_id]['midpeak'] += row[1] |
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| 381 | elif peak_type == 'offpeak': |
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| 382 | reporting[energy_category_id]['offpeak'] += row[1] |
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| 383 | |||
| 384 | ################################################################################################################ |
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| 385 | # Step 8: query tariff data |
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| 386 | ################################################################################################################ |
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| 387 | parameters_data = dict() |
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| 388 | parameters_data['names'] = list() |
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| 389 | parameters_data['timestamps'] = list() |
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| 390 | parameters_data['values'] = list() |
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| 391 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 392 | for energy_category_id in energy_category_set: |
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| 393 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(shopfloor['cost_center_id'], |
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| 394 | energy_category_id, |
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| 395 | reporting_start_datetime_utc, |
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| 396 | reporting_end_datetime_utc) |
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| 397 | tariff_timestamp_list = list() |
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| 398 | tariff_value_list = list() |
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| 399 | for k, v in energy_category_tariff_dict.items(): |
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| 400 | # convert k from utc to local |
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| 401 | k = k + timedelta(minutes=timezone_offset) |
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| 402 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
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| 403 | tariff_value_list.append(v) |
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| 404 | |||
| 405 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
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| 406 | parameters_data['timestamps'].append(tariff_timestamp_list) |
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| 407 | parameters_data['values'].append(tariff_value_list) |
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| 408 | |||
| 409 | ################################################################################################################ |
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| 410 | # Step 9: query associated sensors and points data |
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| 411 | ################################################################################################################ |
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| 412 | for point in point_list: |
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| 413 | point_values = [] |
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| 414 | point_timestamps = [] |
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| 415 | if point['object_type'] == 'ANALOG_VALUE': |
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| 416 | query = (" SELECT utc_date_time, actual_value " |
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| 417 | " FROM tbl_analog_value " |
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| 418 | " WHERE point_id = %s " |
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| 419 | " AND utc_date_time BETWEEN %s AND %s " |
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| 420 | " ORDER BY utc_date_time ") |
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| 421 | cursor_historical.execute(query, (point['id'], |
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| 422 | reporting_start_datetime_utc, |
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| 423 | reporting_end_datetime_utc)) |
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| 424 | rows = cursor_historical.fetchall() |
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| 425 | |||
| 426 | if rows is not None and len(rows) > 0: |
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| 427 | for row in rows: |
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| 428 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
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| 429 | timedelta(minutes=timezone_offset) |
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| 430 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 431 | point_timestamps.append(current_datetime) |
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| 432 | point_values.append(row[1]) |
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| 433 | |||
| 434 | elif point['object_type'] == 'ENERGY_VALUE': |
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| 435 | query = (" SELECT utc_date_time, actual_value " |
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| 436 | " FROM tbl_energy_value " |
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| 437 | " WHERE point_id = %s " |
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| 438 | " AND utc_date_time BETWEEN %s AND %s " |
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| 439 | " ORDER BY utc_date_time ") |
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| 440 | cursor_historical.execute(query, (point['id'], |
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| 441 | reporting_start_datetime_utc, |
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| 442 | reporting_end_datetime_utc)) |
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| 443 | rows = cursor_historical.fetchall() |
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| 444 | |||
| 445 | if rows is not None and len(rows) > 0: |
||
| 446 | for row in rows: |
||
| 447 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
||
| 448 | timedelta(minutes=timezone_offset) |
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| 449 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
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| 450 | point_timestamps.append(current_datetime) |
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| 451 | point_values.append(row[1]) |
||
| 452 | elif point['object_type'] == 'DIGITAL_VALUE': |
||
| 453 | query = (" SELECT utc_date_time, actual_value " |
||
| 454 | " FROM tbl_digital_value " |
||
| 455 | " WHERE point_id = %s " |
||
| 456 | " AND utc_date_time BETWEEN %s AND %s " |
||
| 457 | " ORDER BY utc_date_time ") |
||
| 458 | cursor_historical.execute(query, (point['id'], |
||
| 459 | reporting_start_datetime_utc, |
||
| 460 | reporting_end_datetime_utc)) |
||
| 461 | rows = cursor_historical.fetchall() |
||
| 462 | |||
| 463 | if rows is not None and len(rows) > 0: |
||
| 464 | for row in rows: |
||
| 465 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
||
| 466 | timedelta(minutes=timezone_offset) |
||
| 467 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
||
| 468 | point_timestamps.append(current_datetime) |
||
| 469 | point_values.append(row[1]) |
||
| 470 | |||
| 471 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
||
| 472 | parameters_data['timestamps'].append(point_timestamps) |
||
| 473 | parameters_data['values'].append(point_values) |
||
| 474 | |||
| 475 | ################################################################################################################ |
||
| 476 | # Step 10: construct the report |
||
| 477 | ################################################################################################################ |
||
| 478 | if cursor_system: |
||
| 479 | cursor_system.close() |
||
| 480 | if cnx_system: |
||
| 481 | cnx_system.close() |
||
| 482 | |||
| 483 | if cursor_carbon: |
||
| 484 | cursor_carbon.close() |
||
| 485 | if cnx_carbon: |
||
| 486 | cnx_carbon.close() |
||
| 487 | |||
| 488 | if cursor_historical: |
||
| 489 | cursor_historical.close() |
||
| 490 | if cnx_historical: |
||
| 491 | cnx_historical.close() |
||
| 492 | |||
| 493 | result = dict() |
||
| 494 | |||
| 495 | result['shopfloor'] = dict() |
||
| 496 | result['shopfloor']['name'] = shopfloor['name'] |
||
| 497 | result['shopfloor']['area'] = shopfloor['area'] |
||
| 498 | |||
| 499 | result['base_period'] = dict() |
||
| 500 | result['base_period']['names'] = list() |
||
| 501 | result['base_period']['units'] = list() |
||
| 502 | result['base_period']['timestamps'] = list() |
||
| 503 | result['base_period']['values'] = list() |
||
| 504 | result['base_period']['subtotals'] = list() |
||
| 505 | result['base_period']['total'] = Decimal(0.0) |
||
| 506 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 507 | for energy_category_id in energy_category_set: |
||
| 508 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 509 | result['base_period']['units'].append('KG') |
||
| 510 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
||
| 511 | result['base_period']['values'].append(base[energy_category_id]['values']) |
||
| 512 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
||
| 513 | result['base_period']['total'] += base[energy_category_id]['subtotal'] |
||
| 514 | |||
| 515 | result['reporting_period'] = dict() |
||
| 516 | result['reporting_period']['names'] = list() |
||
| 517 | result['reporting_period']['energy_category_ids'] = list() |
||
| 518 | result['reporting_period']['units'] = list() |
||
| 519 | result['reporting_period']['timestamps'] = list() |
||
| 520 | result['reporting_period']['values'] = list() |
||
| 521 | result['reporting_period']['subtotals'] = list() |
||
| 522 | result['reporting_period']['subtotals_per_unit_area'] = list() |
||
| 523 | result['reporting_period']['toppeaks'] = list() |
||
| 524 | result['reporting_period']['onpeaks'] = list() |
||
| 525 | result['reporting_period']['midpeaks'] = list() |
||
| 526 | result['reporting_period']['offpeaks'] = list() |
||
| 527 | result['reporting_period']['increment_rates'] = list() |
||
| 528 | result['reporting_period']['total'] = Decimal(0.0) |
||
| 529 | result['reporting_period']['total_per_unit_area'] = Decimal(0.0) |
||
| 530 | result['reporting_period']['total_increment_rate'] = Decimal(0.0) |
||
| 531 | result['reporting_period']['total_unit'] = 'KG' |
||
| 532 | |||
| 533 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 534 | for energy_category_id in energy_category_set: |
||
| 535 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 536 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
||
| 537 | result['reporting_period']['units'].append('KG') |
||
| 538 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
||
| 539 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
||
| 540 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
||
| 541 | result['reporting_period']['subtotals_per_unit_area'].append( |
||
| 542 | reporting[energy_category_id]['subtotal'] / shopfloor['area'] if shopfloor['area'] > 0.0 else None) |
||
| 543 | result['reporting_period']['toppeaks'].append(reporting[energy_category_id]['toppeak']) |
||
| 544 | result['reporting_period']['onpeaks'].append(reporting[energy_category_id]['onpeak']) |
||
| 545 | result['reporting_period']['midpeaks'].append(reporting[energy_category_id]['midpeak']) |
||
| 546 | result['reporting_period']['offpeaks'].append(reporting[energy_category_id]['offpeak']) |
||
| 547 | result['reporting_period']['increment_rates'].append( |
||
| 548 | (reporting[energy_category_id]['subtotal'] - base[energy_category_id]['subtotal']) / |
||
| 549 | base[energy_category_id]['subtotal'] |
||
| 550 | if base[energy_category_id]['subtotal'] > 0.0 else None) |
||
| 551 | result['reporting_period']['total'] += reporting[energy_category_id]['subtotal'] |
||
| 552 | |||
| 553 | result['reporting_period']['total_per_unit_area'] = \ |
||
| 554 | result['reporting_period']['total'] / shopfloor['area'] if shopfloor['area'] > 0.0 else None |
||
| 555 | |||
| 556 | result['reporting_period']['total_increment_rate'] = \ |
||
| 557 | (result['reporting_period']['total'] - result['base_period']['total']) / \ |
||
| 558 | result['base_period']['total'] \ |
||
| 559 | if result['base_period']['total'] > Decimal(0.0) else None |
||
| 560 | |||
| 561 | result['parameters'] = { |
||
| 562 | "names": parameters_data['names'], |
||
| 563 | "timestamps": parameters_data['timestamps'], |
||
| 564 | "values": parameters_data['values'] |
||
| 565 | } |
||
| 566 | # export result to Excel file and then encode the file to base64 string |
||
| 567 | result['excel_bytes_base64'] = excelexporters.shopfloorcarbon.export(result, |
||
| 568 | shopfloor['name'], |
||
| 569 | reporting_start_datetime_local, |
||
| 570 | reporting_end_datetime_local, |
||
| 571 | period_type) |
||
| 572 | resp.text = json.dumps(result) |
||
| 573 |
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