MyEMS /
myems
| Conditions | 117 |
| Total Lines | 553 |
| Code Lines | 422 |
| Lines | 0 |
| Ratio | 0 % |
| 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.spaceprediction.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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| 33 | @staticmethod |
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| 34 | def on_get(req, resp): |
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| 35 | if 'API-KEY' not in req.headers or \ |
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| 36 | not isinstance(req.headers['API-KEY'], str) or \ |
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| 37 | len(str.strip(req.headers['API-KEY'])) == 0: |
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| 38 | access_control(req) |
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| 39 | else: |
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| 40 | api_key_control(req) |
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| 41 | print(req.params) |
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| 42 | space_id = req.params.get('spaceid') |
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| 43 | space_uuid = req.params.get('spaceuuid') |
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| 44 | period_type = req.params.get('periodtype') |
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| 45 | base_period_start_datetime_local = req.params.get('baseperiodstartdatetime') |
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| 46 | base_period_end_datetime_local = req.params.get('baseperiodenddatetime') |
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| 47 | reporting_period_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
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| 48 | reporting_period_end_datetime_local = req.params.get('reportingperiodenddatetime') |
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| 49 | language = req.params.get('language') |
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| 50 | quick_mode = req.params.get('quickmode') |
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| 51 | |||
| 52 | ################################################################################################################ |
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| 53 | # Step 1: valid parameters |
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| 54 | ################################################################################################################ |
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| 55 | if space_id is None and space_uuid is None: |
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| 56 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 57 | title='API.BAD_REQUEST', |
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| 58 | description='API.INVALID_SPACE_ID') |
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| 59 | |||
| 60 | if space_id is not None: |
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| 61 | space_id = str.strip(space_id) |
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| 62 | if not space_id.isdigit() or int(space_id) <= 0: |
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| 63 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 64 | title='API.BAD_REQUEST', |
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| 65 | description='API.INVALID_SPACE_ID') |
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| 66 | |||
| 67 | if space_uuid is not None: |
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| 68 | regex = re.compile(r'^[a-f0-9]{8}-?[a-f0-9]{4}-?4[a-f0-9]{3}-?[89ab][a-f0-9]{3}-?[a-f0-9]{12}\Z', re.I) |
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| 69 | match = regex.match(str.strip(space_uuid)) |
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| 70 | if not bool(match): |
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| 71 | raise falcon.HTTPError(status=falcon.HTTP_400, |
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| 72 | title='API.BAD_REQUEST', |
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| 73 | description='API.INVALID_SPACE_UUID') |
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| 74 | |||
| 75 | if period_type is None: |
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| 76 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 77 | description='API.INVALID_PERIOD_TYPE') |
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| 78 | else: |
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| 79 | period_type = str.strip(period_type) |
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| 80 | if period_type not in ['hourly', 'daily', 'weekly', 'monthly', 'yearly']: |
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| 81 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 82 | description='API.INVALID_PERIOD_TYPE') |
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| 83 | |||
| 84 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 85 | if config.utc_offset[0] == '-': |
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| 86 | timezone_offset = -timezone_offset |
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| 87 | |||
| 88 | base_start_datetime_utc = None |
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| 89 | if base_period_start_datetime_local is not None and len(str.strip(base_period_start_datetime_local)) > 0: |
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| 90 | base_period_start_datetime_local = str.strip(base_period_start_datetime_local) |
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| 91 | try: |
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| 92 | base_start_datetime_utc = datetime.strptime(base_period_start_datetime_local, '%Y-%m-%dT%H:%M:%S') |
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| 93 | |||
| 94 | except ValueError: |
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| 95 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 96 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
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| 97 | base_start_datetime_utc = \ |
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| 98 | base_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 99 | # nomalize the start datetime |
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| 100 | if config.minutes_to_count == 30 and base_start_datetime_utc.minute >= 30: |
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| 101 | base_start_datetime_utc = base_start_datetime_utc.replace(minute=30, second=0, microsecond=0) |
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| 102 | else: |
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| 103 | base_start_datetime_utc = base_start_datetime_utc.replace(minute=0, second=0, microsecond=0) |
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| 104 | |||
| 105 | base_end_datetime_utc = None |
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| 106 | if base_period_end_datetime_local is not None and len(str.strip(base_period_end_datetime_local)) > 0: |
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| 107 | base_period_end_datetime_local = str.strip(base_period_end_datetime_local) |
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| 108 | try: |
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| 109 | base_end_datetime_utc = datetime.strptime(base_period_end_datetime_local, '%Y-%m-%dT%H:%M:%S') |
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| 110 | |||
| 111 | except ValueError: |
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| 112 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 113 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
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| 114 | base_end_datetime_utc = \ |
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| 115 | base_end_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 116 | |||
| 117 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
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| 118 | base_start_datetime_utc >= base_end_datetime_utc: |
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| 119 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 120 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
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| 121 | |||
| 122 | if reporting_period_start_datetime_local is None: |
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| 123 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 124 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 125 | else: |
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| 126 | reporting_period_start_datetime_local = str.strip(reporting_period_start_datetime_local) |
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| 127 | try: |
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| 128 | reporting_start_datetime_utc = datetime.strptime(reporting_period_start_datetime_local, |
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| 129 | '%Y-%m-%dT%H:%M:%S') |
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| 130 | |||
| 131 | except ValueError: |
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| 132 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 133 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
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| 134 | reporting_start_datetime_utc = \ |
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| 135 | reporting_start_datetime_utc.replace(tzinfo=timezone.utc) - timedelta(minutes=timezone_offset) |
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| 136 | # nomalize the start datetime |
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| 137 | if config.minutes_to_count == 30 and reporting_start_datetime_utc.minute >= 30: |
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| 138 | reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=30, second=0, microsecond=0) |
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| 139 | else: |
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| 140 | reporting_start_datetime_utc = reporting_start_datetime_utc.replace(minute=0, second=0, microsecond=0) |
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| 141 | |||
| 142 | if reporting_period_end_datetime_local is None: |
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| 143 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 144 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 145 | else: |
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| 146 | reporting_period_end_datetime_local = str.strip(reporting_period_end_datetime_local) |
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| 147 | try: |
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| 148 | reporting_end_datetime_utc = datetime.strptime(reporting_period_end_datetime_local, |
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| 149 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
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| 150 | timedelta(minutes=timezone_offset) |
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| 151 | except ValueError: |
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| 152 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 153 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
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| 154 | |||
| 155 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
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| 156 | raise falcon.HTTPError(status=falcon.HTTP_400, title='API.BAD_REQUEST', |
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| 157 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
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| 158 | |||
| 159 | # if turn quick mode on, do not return parameters data and excel file |
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| 160 | is_quick_mode = False |
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| 161 | if quick_mode is not None and \ |
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| 162 | len(str.strip(quick_mode)) > 0 and \ |
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| 163 | str.lower(str.strip(quick_mode)) in ('true', 't', 'on', 'yes', 'y'): |
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| 164 | is_quick_mode = True |
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| 165 | |||
| 166 | trans = utilities.get_translation(language) |
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| 167 | trans.install() |
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| 168 | _ = trans.gettext |
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| 169 | |||
| 170 | ################################################################################################################ |
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| 171 | # Step 2: query the space |
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| 172 | ################################################################################################################ |
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| 173 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
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| 174 | cursor_system = cnx_system.cursor() |
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| 175 | |||
| 176 | cnx_energy_prediction = mysql.connector.connect(**config.myems_energy_prediction_db) |
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| 177 | cursor_energy_prediction = cnx_energy_prediction.cursor() |
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| 178 | |||
| 179 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
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| 180 | cursor_historical = cnx_historical.cursor() |
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| 181 | |||
| 182 | if space_id is not None: |
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| 183 | cursor_system.execute(" SELECT id, name, area, number_of_occupants, cost_center_id " |
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| 184 | " FROM tbl_spaces " |
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| 185 | " WHERE id = %s ", (space_id,)) |
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| 186 | row_space = cursor_system.fetchone() |
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| 187 | elif space_uuid is not None: |
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| 188 | cursor_system.execute(" SELECT id, name, area, number_of_occupants, cost_center_id " |
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| 189 | " FROM tbl_spaces " |
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| 190 | " WHERE uuid = %s ", (space_uuid,)) |
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| 191 | row_space = cursor_system.fetchone() |
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| 192 | |||
| 193 | if row_space is None: |
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|
introduced
by
|
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| 194 | if cursor_system: |
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| 195 | cursor_system.close() |
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| 196 | if cnx_system: |
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| 197 | cnx_system.close() |
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| 198 | |||
| 199 | if cursor_energy_prediction: |
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| 200 | cursor_energy_prediction.close() |
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| 201 | if cnx_energy_prediction: |
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| 202 | cnx_energy_prediction.close() |
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| 203 | |||
| 204 | if cursor_historical: |
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| 205 | cursor_historical.close() |
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| 206 | if cnx_historical: |
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| 207 | cnx_historical.close() |
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| 208 | raise falcon.HTTPError(status=falcon.HTTP_404, title='API.NOT_FOUND', description='API.SPACE_NOT_FOUND') |
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| 209 | |||
| 210 | space = dict() |
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| 211 | space['id'] = row_space[0] |
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| 212 | space['name'] = row_space[1] |
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| 213 | space['area'] = row_space[2] |
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| 214 | space['number_of_occupants'] = row_space[3] |
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| 215 | space['cost_center_id'] = row_space[4] |
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| 216 | |||
| 217 | ################################################################################################################ |
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| 218 | # Step 3: query energy categories |
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| 219 | ################################################################################################################ |
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| 220 | energy_category_set = set() |
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| 221 | # query energy categories in base period |
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| 222 | cursor_energy_prediction.execute(" SELECT DISTINCT(energy_category_id) " |
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| 223 | " FROM tbl_space_input_category_hourly " |
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| 224 | " WHERE space_id = %s " |
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| 225 | " AND start_datetime_utc >= %s " |
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| 226 | " AND start_datetime_utc < %s ", |
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| 227 | (space['id'], base_start_datetime_utc, base_end_datetime_utc)) |
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| 228 | rows_energy_categories = cursor_energy_prediction.fetchall() |
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| 229 | if rows_energy_categories is not None and len(rows_energy_categories) > 0: |
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| 230 | for row_energy_category in rows_energy_categories: |
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| 231 | energy_category_set.add(row_energy_category[0]) |
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| 232 | |||
| 233 | # query energy categories in reporting period |
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| 234 | cursor_energy_prediction.execute(" SELECT DISTINCT(energy_category_id) " |
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| 235 | " FROM tbl_space_input_category_hourly " |
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| 236 | " WHERE space_id = %s " |
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| 237 | " AND start_datetime_utc >= %s " |
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| 238 | " AND start_datetime_utc < %s ", |
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| 239 | (space['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
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| 240 | rows_energy_categories = cursor_energy_prediction.fetchall() |
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| 241 | if rows_energy_categories is not None and len(rows_energy_categories) > 0: |
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| 242 | for row_energy_category in rows_energy_categories: |
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| 243 | energy_category_set.add(row_energy_category[0]) |
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| 244 | |||
| 245 | # query all energy categories in base period and reporting period |
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| 246 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
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| 247 | " FROM tbl_energy_categories " |
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| 248 | " ORDER BY id ", ) |
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| 249 | rows_energy_categories = cursor_system.fetchall() |
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| 250 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
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| 251 | if cursor_system: |
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| 252 | cursor_system.close() |
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| 253 | if cnx_system: |
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| 254 | cnx_system.close() |
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| 255 | |||
| 256 | if cursor_energy_prediction: |
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| 257 | cursor_energy_prediction.close() |
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| 258 | if cnx_energy_prediction: |
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| 259 | cnx_energy_prediction.close() |
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| 260 | |||
| 261 | if cursor_historical: |
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| 262 | cursor_historical.close() |
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| 263 | if cnx_historical: |
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| 264 | cnx_historical.close() |
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| 265 | raise falcon.HTTPError(status=falcon.HTTP_404, |
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| 266 | title='API.NOT_FOUND', |
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| 267 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
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| 268 | energy_category_dict = dict() |
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| 269 | for row_energy_category in rows_energy_categories: |
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| 270 | if row_energy_category[0] in energy_category_set: |
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| 271 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
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| 272 | "unit_of_measure": row_energy_category[2], |
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| 273 | "kgce": row_energy_category[3], |
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| 274 | "kgco2e": row_energy_category[4]} |
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| 275 | |||
| 276 | ################################################################################################################ |
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| 277 | # Step 4: query base period energy prediction |
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| 278 | ################################################################################################################ |
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| 279 | base = dict() |
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| 280 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 281 | for energy_category_id in energy_category_set: |
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| 282 | kgce = energy_category_dict[energy_category_id]['kgce'] |
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| 283 | kgco2e = energy_category_dict[energy_category_id]['kgco2e'] |
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| 284 | |||
| 285 | base[energy_category_id] = dict() |
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| 286 | base[energy_category_id]['timestamps'] = list() |
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| 287 | base[energy_category_id]['values'] = list() |
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| 288 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
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| 289 | base[energy_category_id]['subtotal_in_kgce'] = Decimal(0.0) |
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| 290 | base[energy_category_id]['subtotal_in_kgco2e'] = Decimal(0.0) |
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| 291 | |||
| 292 | cursor_energy_prediction.execute(" SELECT start_datetime_utc, actual_value " |
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| 293 | " FROM tbl_space_input_category_hourly " |
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| 294 | " WHERE space_id = %s " |
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| 295 | " AND energy_category_id = %s " |
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| 296 | " AND start_datetime_utc >= %s " |
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| 297 | " AND start_datetime_utc < %s " |
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| 298 | " ORDER BY start_datetime_utc ", |
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| 299 | (space['id'], |
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| 300 | energy_category_id, |
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| 301 | base_start_datetime_utc, |
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| 302 | base_end_datetime_utc)) |
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| 303 | rows_space_hourly = cursor_energy_prediction.fetchall() |
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| 304 | |||
| 305 | rows_space_periodically = utilities.aggregate_hourly_data_by_period(rows_space_hourly, |
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| 306 | base_start_datetime_utc, |
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| 307 | base_end_datetime_utc, |
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| 308 | period_type) |
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| 309 | for row_space_periodically in rows_space_periodically: |
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| 310 | current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 311 | timedelta(minutes=timezone_offset) |
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| 312 | if period_type == 'hourly': |
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| 313 | current_datetime = current_datetime_local.isoformat()[0:19] |
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| 314 | elif period_type == 'daily': |
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| 315 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 316 | elif period_type == 'weekly': |
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| 317 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 318 | elif period_type == 'monthly': |
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| 319 | current_datetime = current_datetime_local.isoformat()[0:7] |
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| 320 | elif period_type == 'yearly': |
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| 321 | current_datetime = current_datetime_local.isoformat()[0:4] |
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| 322 | |||
| 323 | actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1] |
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| 324 | base[energy_category_id]['timestamps'].append(current_datetime) |
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| 325 | base[energy_category_id]['values'].append(actual_value) |
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| 326 | base[energy_category_id]['subtotal'] += actual_value |
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| 327 | base[energy_category_id]['subtotal_in_kgce'] += actual_value * kgce |
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| 328 | base[energy_category_id]['subtotal_in_kgco2e'] += actual_value * kgco2e |
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| 329 | |||
| 330 | ################################################################################################################ |
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| 331 | # Step 5: query reporting period energy prediction |
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| 332 | ################################################################################################################ |
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| 333 | reporting = dict() |
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| 334 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 335 | for energy_category_id in energy_category_set: |
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| 336 | kgce = energy_category_dict[energy_category_id]['kgce'] |
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| 337 | kgco2e = energy_category_dict[energy_category_id]['kgco2e'] |
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| 338 | |||
| 339 | reporting[energy_category_id] = dict() |
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| 340 | reporting[energy_category_id]['timestamps'] = list() |
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| 341 | reporting[energy_category_id]['values'] = list() |
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| 342 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
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| 343 | reporting[energy_category_id]['subtotal_in_kgce'] = Decimal(0.0) |
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| 344 | reporting[energy_category_id]['subtotal_in_kgco2e'] = Decimal(0.0) |
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| 345 | reporting[energy_category_id]['toppeak'] = Decimal(0.0) |
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| 346 | reporting[energy_category_id]['onpeak'] = Decimal(0.0) |
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| 347 | reporting[energy_category_id]['midpeak'] = Decimal(0.0) |
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| 348 | reporting[energy_category_id]['offpeak'] = Decimal(0.0) |
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| 349 | reporting[energy_category_id]['deep'] = Decimal(0.0) |
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| 350 | |||
| 351 | cursor_energy_prediction.execute(" SELECT start_datetime_utc, actual_value " |
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| 352 | " FROM tbl_space_input_category_hourly " |
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| 353 | " WHERE space_id = %s " |
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| 354 | " AND energy_category_id = %s " |
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| 355 | " AND start_datetime_utc >= %s " |
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| 356 | " AND start_datetime_utc < %s " |
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| 357 | " ORDER BY start_datetime_utc ", |
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| 358 | (space['id'], |
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| 359 | energy_category_id, |
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| 360 | reporting_start_datetime_utc, |
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| 361 | reporting_end_datetime_utc)) |
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| 362 | rows_space_hourly = cursor_energy_prediction.fetchall() |
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| 363 | |||
| 364 | rows_space_periodically = utilities.aggregate_hourly_data_by_period(rows_space_hourly, |
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| 365 | reporting_start_datetime_utc, |
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| 366 | reporting_end_datetime_utc, |
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| 367 | period_type) |
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| 368 | for row_space_periodically in rows_space_periodically: |
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| 369 | current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \ |
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| 370 | timedelta(minutes=timezone_offset) |
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| 371 | if period_type == 'hourly': |
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| 372 | current_datetime = current_datetime_local.isoformat()[0:19] |
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| 373 | elif period_type == 'daily': |
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| 374 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 375 | elif period_type == 'weekly': |
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| 376 | current_datetime = current_datetime_local.isoformat()[0:10] |
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| 377 | elif period_type == 'monthly': |
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| 378 | current_datetime = current_datetime_local.isoformat()[0:7] |
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| 379 | elif period_type == 'yearly': |
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| 380 | current_datetime = current_datetime_local.isoformat()[0:4] |
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| 381 | |||
| 382 | actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1] |
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| 383 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
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| 384 | reporting[energy_category_id]['values'].append(actual_value) |
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| 385 | reporting[energy_category_id]['subtotal'] += actual_value |
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| 386 | reporting[energy_category_id]['subtotal_in_kgce'] += actual_value * kgce |
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| 387 | reporting[energy_category_id]['subtotal_in_kgco2e'] += actual_value * kgco2e |
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| 388 | |||
| 389 | energy_category_tariff_dict = utilities.get_energy_category_peak_types(space['cost_center_id'], |
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| 390 | energy_category_id, |
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| 391 | reporting_start_datetime_utc, |
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| 392 | reporting_end_datetime_utc) |
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| 393 | for row in rows_space_hourly: |
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| 394 | peak_type = energy_category_tariff_dict.get(row[0], None) |
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| 395 | if peak_type == 'toppeak': |
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| 396 | reporting[energy_category_id]['toppeak'] += row[1] |
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| 397 | elif peak_type == 'onpeak': |
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| 398 | reporting[energy_category_id]['onpeak'] += row[1] |
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| 399 | elif peak_type == 'midpeak': |
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| 400 | reporting[energy_category_id]['midpeak'] += row[1] |
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| 401 | elif peak_type == 'offpeak': |
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| 402 | reporting[energy_category_id]['offpeak'] += row[1] |
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| 403 | elif peak_type == 'deep': |
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| 404 | reporting[energy_category_id]['deep'] += row[1] |
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| 405 | ################################################################################################################ |
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| 406 | # Step 6: query tariff data |
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| 407 | ################################################################################################################ |
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| 408 | parameters_data = dict() |
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| 409 | parameters_data['names'] = list() |
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| 410 | parameters_data['timestamps'] = list() |
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| 411 | parameters_data['values'] = list() |
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| 412 | if config.is_tariff_appended and not is_quick_mode: |
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| 413 | if energy_category_set is not None and len(energy_category_set) > 0: |
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| 414 | for energy_category_id in energy_category_set: |
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| 415 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(space['cost_center_id'], |
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| 416 | energy_category_id, |
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| 417 | reporting_start_datetime_utc, |
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| 418 | reporting_end_datetime_utc) |
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| 419 | tariff_timestamp_list = list() |
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| 420 | tariff_value_list = list() |
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| 421 | for k, v in energy_category_tariff_dict.items(): |
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| 422 | # convert k from utc to local |
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| 423 | k = k + timedelta(minutes=timezone_offset) |
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| 424 | tariff_timestamp_list.append(k.isoformat()[0:19]) |
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| 425 | tariff_value_list.append(v) |
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| 426 | |||
| 427 | parameters_data['names'].append(_('Tariff') + '-' |
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| 428 | + energy_category_dict[energy_category_id]['name']) |
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| 429 | parameters_data['timestamps'].append(tariff_timestamp_list) |
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| 430 | parameters_data['values'].append(tariff_value_list) |
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| 431 | |||
| 432 | ################################################################################################################ |
||
| 433 | # Step 7: construct the report |
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| 434 | ################################################################################################################ |
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| 435 | if cursor_system: |
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| 436 | cursor_system.close() |
||
| 437 | if cnx_system: |
||
| 438 | cnx_system.close() |
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| 439 | |||
| 440 | if cursor_energy_prediction: |
||
| 441 | cursor_energy_prediction.close() |
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| 442 | if cnx_energy_prediction: |
||
| 443 | cnx_energy_prediction.close() |
||
| 444 | |||
| 445 | if cursor_historical: |
||
| 446 | cursor_historical.close() |
||
| 447 | if cnx_historical: |
||
| 448 | cnx_historical.close() |
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| 449 | |||
| 450 | result = dict() |
||
| 451 | |||
| 452 | result['space'] = dict() |
||
| 453 | result['space']['id'] = space['id'] |
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| 454 | result['space']['name'] = space['name'] |
||
| 455 | result['space']['area'] = space['area'] |
||
| 456 | result['space']['number_of_occupants'] = space['number_of_occupants'] |
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| 457 | |||
| 458 | result['base_period'] = dict() |
||
| 459 | result['base_period']['names'] = list() |
||
| 460 | result['base_period']['units'] = list() |
||
| 461 | result['base_period']['timestamps'] = list() |
||
| 462 | result['base_period']['values'] = list() |
||
| 463 | result['base_period']['subtotals'] = list() |
||
| 464 | result['base_period']['subtotals_in_kgce'] = list() |
||
| 465 | result['base_period']['subtotals_in_kgco2e'] = list() |
||
| 466 | result['base_period']['total_in_kgce'] = Decimal(0.0) |
||
| 467 | result['base_period']['total_in_kgco2e'] = Decimal(0.0) |
||
| 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']['values'].append(base[energy_category_id]['values']) |
||
| 474 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
||
| 475 | result['base_period']['subtotals_in_kgce'].append(base[energy_category_id]['subtotal_in_kgce']) |
||
| 476 | result['base_period']['subtotals_in_kgco2e'].append(base[energy_category_id]['subtotal_in_kgco2e']) |
||
| 477 | result['base_period']['total_in_kgce'] += base[energy_category_id]['subtotal_in_kgce'] |
||
| 478 | result['base_period']['total_in_kgco2e'] += base[energy_category_id]['subtotal_in_kgco2e'] |
||
| 479 | |||
| 480 | result['reporting_period'] = dict() |
||
| 481 | result['reporting_period']['names'] = list() |
||
| 482 | result['reporting_period']['energy_category_ids'] = list() |
||
| 483 | result['reporting_period']['units'] = list() |
||
| 484 | result['reporting_period']['timestamps'] = list() |
||
| 485 | result['reporting_period']['values'] = list() |
||
| 486 | result['reporting_period']['rates'] = list() |
||
| 487 | result['reporting_period']['subtotals'] = list() |
||
| 488 | result['reporting_period']['subtotals_in_kgce'] = list() |
||
| 489 | result['reporting_period']['subtotals_in_kgco2e'] = list() |
||
| 490 | result['reporting_period']['subtotals_per_unit_area'] = list() |
||
| 491 | result['reporting_period']['subtotals_per_capita'] = list() |
||
| 492 | result['reporting_period']['toppeaks'] = list() |
||
| 493 | result['reporting_period']['onpeaks'] = list() |
||
| 494 | result['reporting_period']['midpeaks'] = list() |
||
| 495 | result['reporting_period']['offpeaks'] = list() |
||
| 496 | result['reporting_period']['deeps'] = list() |
||
| 497 | result['reporting_period']['increment_rates'] = list() |
||
| 498 | result['reporting_period']['total_in_kgce'] = Decimal(0.0) |
||
| 499 | result['reporting_period']['total_in_kgco2e'] = Decimal(0.0) |
||
| 500 | result['reporting_period']['increment_rate_in_kgce'] = Decimal(0.0) |
||
| 501 | result['reporting_period']['increment_rate_in_kgco2e'] = Decimal(0.0) |
||
| 502 | |||
| 503 | if energy_category_set is not None and len(energy_category_set) > 0: |
||
| 504 | for energy_category_id in energy_category_set: |
||
| 505 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
||
| 506 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
||
| 507 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
||
| 508 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
||
| 509 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
||
| 510 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
||
| 511 | result['reporting_period']['subtotals_in_kgce'].append( |
||
| 512 | reporting[energy_category_id]['subtotal_in_kgce']) |
||
| 513 | result['reporting_period']['subtotals_in_kgco2e'].append( |
||
| 514 | reporting[energy_category_id]['subtotal_in_kgco2e']) |
||
| 515 | result['reporting_period']['subtotals_per_unit_area'].append( |
||
| 516 | reporting[energy_category_id]['subtotal'] / space['area'] if space['area'] > 0.0 else None) |
||
| 517 | result['reporting_period']['subtotals_per_capita'].append( |
||
| 518 | reporting[energy_category_id]['subtotal'] / space['number_of_occupants'] |
||
| 519 | if space['number_of_occupants'] > 0.0 else None) |
||
| 520 | result['reporting_period']['toppeaks'].append(reporting[energy_category_id]['toppeak']) |
||
| 521 | result['reporting_period']['onpeaks'].append(reporting[energy_category_id]['onpeak']) |
||
| 522 | result['reporting_period']['midpeaks'].append(reporting[energy_category_id]['midpeak']) |
||
| 523 | result['reporting_period']['offpeaks'].append(reporting[energy_category_id]['offpeak']) |
||
| 524 | result['reporting_period']['deeps'].append(reporting[energy_category_id]['deep']) |
||
| 525 | result['reporting_period']['increment_rates'].append( |
||
| 526 | (reporting[energy_category_id]['subtotal'] - base[energy_category_id]['subtotal']) / |
||
| 527 | base[energy_category_id]['subtotal'] |
||
| 528 | if base[energy_category_id]['subtotal'] > 0.0 else None) |
||
| 529 | result['reporting_period']['total_in_kgce'] += reporting[energy_category_id]['subtotal_in_kgce'] |
||
| 530 | result['reporting_period']['total_in_kgco2e'] += reporting[energy_category_id]['subtotal_in_kgco2e'] |
||
| 531 | |||
| 532 | rate = list() |
||
| 533 | for index, value in enumerate(reporting[energy_category_id]['values']): |
||
| 534 | if index < len(base[energy_category_id]['values']) \ |
||
| 535 | and base[energy_category_id]['values'][index] != 0 and value != 0: |
||
| 536 | rate.append((value - base[energy_category_id]['values'][index]) |
||
| 537 | / base[energy_category_id]['values'][index]) |
||
| 538 | else: |
||
| 539 | rate.append(None) |
||
| 540 | result['reporting_period']['rates'].append(rate) |
||
| 541 | |||
| 542 | result['reporting_period']['total_in_kgco2e_per_unit_area'] = \ |
||
| 543 | result['reporting_period']['total_in_kgce'] / space['area'] if space['area'] > 0.0 else None |
||
| 544 | |||
| 545 | result['reporting_period']['total_in_kgco2e_per_capita'] = \ |
||
| 546 | result['reporting_period']['total_in_kgce'] / space['number_of_occupants'] \ |
||
| 547 | if space['number_of_occupants'] > 0.0 else None |
||
| 548 | |||
| 549 | result['reporting_period']['increment_rate_in_kgce'] = \ |
||
| 550 | (result['reporting_period']['total_in_kgce'] - result['base_period']['total_in_kgce']) / \ |
||
| 551 | result['base_period']['total_in_kgce'] \ |
||
| 552 | if result['base_period']['total_in_kgce'] > Decimal(0.0) else None |
||
| 553 | |||
| 554 | result['reporting_period']['total_in_kgce_per_unit_area'] = \ |
||
| 555 | result['reporting_period']['total_in_kgco2e'] / space['area'] if space['area'] > 0.0 else None |
||
| 556 | |||
| 557 | result['reporting_period']['total_in_kgce_per_capita'] = \ |
||
| 558 | result['reporting_period']['total_in_kgco2e'] / space['number_of_occupants'] \ |
||
| 559 | if space['number_of_occupants'] > 0.0 else None |
||
| 560 | |||
| 561 | result['reporting_period']['increment_rate_in_kgco2e'] = \ |
||
| 562 | (result['reporting_period']['total_in_kgco2e'] - result['base_period']['total_in_kgco2e']) / \ |
||
| 563 | result['base_period']['total_in_kgco2e'] \ |
||
| 564 | if result['base_period']['total_in_kgco2e'] > Decimal(0.0) else None |
||
| 565 | |||
| 566 | result['parameters'] = { |
||
| 567 | "names": parameters_data['names'], |
||
| 568 | "timestamps": parameters_data['timestamps'], |
||
| 569 | "values": parameters_data['values'] |
||
| 570 | } |
||
| 571 | |||
| 572 | # export result to Excel file and then encode the file to base64 string |
||
| 573 | # TODO |
||
| 574 | # if not is_quick_mode: |
||
| 575 | # result['excel_bytes_base64'] = \ |
||
| 576 | # excelexporters.spaceenergyprediction.export(result, |
||
| 577 | # space['name'], |
||
| 578 | # base_period_start_datetime_local, |
||
| 579 | # base_period_end_datetime_local, |
||
| 580 | # reporting_period_start_datetime_local, |
||
| 581 | # reporting_period_end_datetime_local, |
||
| 582 | # period_type, |
||
| 583 | # language) |
||
| 584 | |||
| 585 | resp.text = json.dumps(result) |
||
| 586 |
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