myems /
myems-api
| @@ 11-622 (lines=612) @@ | ||
| 8 | import excelexporters.shopfloorstatistics |
|
| 9 | ||
| 10 | ||
| 11 | class Reporting: |
|
| 12 | @staticmethod |
|
| 13 | def __init__(): |
|
| 14 | pass |
|
| 15 | ||
| 16 | @staticmethod |
|
| 17 | def on_options(req, resp): |
|
| 18 | resp.status = falcon.HTTP_200 |
|
| 19 | ||
| 20 | #################################################################################################################### |
|
| 21 | # PROCEDURES |
|
| 22 | # Step 1: valid parameters |
|
| 23 | # Step 2: query the shopfloor |
|
| 24 | # Step 3: query energy categories |
|
| 25 | # Step 4: query associated sensors |
|
| 26 | # Step 5: query associated points |
|
| 27 | # Step 6: query base period energy input |
|
| 28 | # Step 7: query reporting period energy input |
|
| 29 | # Step 8: query tariff data |
|
| 30 | # Step 9: query associated sensors and points data |
|
| 31 | # Step 10: construct the report |
|
| 32 | #################################################################################################################### |
|
| 33 | @staticmethod |
|
| 34 | def on_get(req, resp): |
|
| 35 | print(req.params) |
|
| 36 | shopfloor_id = req.params.get('shopfloorid') |
|
| 37 | period_type = req.params.get('periodtype') |
|
| 38 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
|
| 39 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
|
| 40 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
|
| 41 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
|
| 42 | ||
| 43 | ################################################################################################################ |
|
| 44 | # Step 1: valid parameters |
|
| 45 | ################################################################################################################ |
|
| 46 | if shopfloor_id is None: |
|
| 47 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SHOPFLOOR_ID') |
|
| 48 | else: |
|
| 49 | shopfloor_id = str.strip(shopfloor_id) |
|
| 50 | if not shopfloor_id.isdigit() or int(shopfloor_id) <= 0: |
|
| 51 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SHOPFLOOR_ID') |
|
| 52 | ||
| 53 | if period_type is None: |
|
| 54 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 55 | else: |
|
| 56 | period_type = str.strip(period_type) |
|
| 57 | if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
|
| 58 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 59 | ||
| 60 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
|
| 61 | if config.utc_offset[0] == '-': |
|
| 62 | timezone_offset = -timezone_offset |
|
| 63 | ||
| 64 | base_start_datetime_utc = None |
|
| 65 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
|
| 66 | base_start_datetime_local = str.strip(base_start_datetime_local) |
|
| 67 | try: |
|
| 68 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
|
| 69 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 70 | timedelta(minutes=timezone_offset) |
|
| 71 | except ValueError: |
|
| 72 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 73 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
|
| 74 | ||
| 75 | base_end_datetime_utc = None |
|
| 76 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
|
| 77 | base_end_datetime_local = str.strip(base_end_datetime_local) |
|
| 78 | try: |
|
| 79 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
|
| 80 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 81 | timedelta(minutes=timezone_offset) |
|
| 82 | except ValueError: |
|
| 83 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 84 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
|
| 85 | ||
| 86 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
|
| 87 | base_start_datetime_utc >= base_end_datetime_utc: |
|
| 88 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 89 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
|
| 90 | ||
| 91 | if reporting_start_datetime_local is None: |
|
| 92 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 93 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 94 | else: |
|
| 95 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
|
| 96 | try: |
|
| 97 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
|
| 98 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 99 | timedelta(minutes=timezone_offset) |
|
| 100 | except ValueError: |
|
| 101 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 102 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 103 | ||
| 104 | if reporting_end_datetime_local is None: |
|
| 105 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 106 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 107 | else: |
|
| 108 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
|
| 109 | try: |
|
| 110 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
|
| 111 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 112 | timedelta(minutes=timezone_offset) |
|
| 113 | except ValueError: |
|
| 114 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 115 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 116 | ||
| 117 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
|
| 118 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 119 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
|
| 120 | ||
| 121 | ################################################################################################################ |
|
| 122 | # Step 2: query the shopfloor |
|
| 123 | ################################################################################################################ |
|
| 124 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
| 125 | cursor_system = cnx_system.cursor() |
|
| 126 | ||
| 127 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
|
| 128 | cursor_energy = cnx_energy.cursor() |
|
| 129 | ||
| 130 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
| 131 | cursor_historical = cnx_historical.cursor() |
|
| 132 | ||
| 133 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
|
| 134 | " FROM tbl_shopfloors " |
|
| 135 | " WHERE id = %s ", (shopfloor_id,)) |
|
| 136 | row_shopfloor = cursor_system.fetchone() |
|
| 137 | if row_shopfloor is None: |
|
| 138 | if cursor_system: |
|
| 139 | cursor_system.close() |
|
| 140 | if cnx_system: |
|
| 141 | cnx_system.disconnect() |
|
| 142 | ||
| 143 | if cursor_energy: |
|
| 144 | cursor_energy.close() |
|
| 145 | if cnx_energy: |
|
| 146 | cnx_energy.disconnect() |
|
| 147 | ||
| 148 | if cnx_historical: |
|
| 149 | cnx_historical.close() |
|
| 150 | if cursor_historical: |
|
| 151 | cursor_historical.disconnect() |
|
| 152 | raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.SHOPFLOOR_NOT_FOUND') |
|
| 153 | ||
| 154 | shopfloor = dict() |
|
| 155 | shopfloor['id'] = row_shopfloor[0] |
|
| 156 | shopfloor['name'] = row_shopfloor[1] |
|
| 157 | shopfloor['area'] = row_shopfloor[2] |
|
| 158 | shopfloor['cost_center_id'] = row_shopfloor[3] |
|
| 159 | ||
| 160 | ################################################################################################################ |
|
| 161 | # Step 3: query energy categories |
|
| 162 | ################################################################################################################ |
|
| 163 | energy_category_set = set() |
|
| 164 | # query energy categories in base period |
|
| 165 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 166 | " FROM tbl_shopfloor_input_category_hourly " |
|
| 167 | " WHERE shopfloor_id = %s " |
|
| 168 | " AND start_datetime_utc >= %s " |
|
| 169 | " AND start_datetime_utc < %s ", |
|
| 170 | (shopfloor['id'], base_start_datetime_utc, base_end_datetime_utc)) |
|
| 171 | rows_energy_categories = cursor_energy.fetchall() |
|
| 172 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 173 | for row_energy_category in rows_energy_categories: |
|
| 174 | energy_category_set.add(row_energy_category[0]) |
|
| 175 | ||
| 176 | # query energy categories in reporting period |
|
| 177 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 178 | " FROM tbl_shopfloor_input_category_hourly " |
|
| 179 | " WHERE shopfloor_id = %s " |
|
| 180 | " AND start_datetime_utc >= %s " |
|
| 181 | " AND start_datetime_utc < %s ", |
|
| 182 | (shopfloor['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
|
| 183 | rows_energy_categories = cursor_energy.fetchall() |
|
| 184 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 185 | for row_energy_category in rows_energy_categories: |
|
| 186 | energy_category_set.add(row_energy_category[0]) |
|
| 187 | ||
| 188 | # query all energy categories in base period and reporting period |
|
| 189 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
|
| 190 | " FROM tbl_energy_categories " |
|
| 191 | " ORDER BY id ", ) |
|
| 192 | rows_energy_categories = cursor_system.fetchall() |
|
| 193 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
|
| 194 | if cursor_system: |
|
| 195 | cursor_system.close() |
|
| 196 | if cnx_system: |
|
| 197 | cnx_system.disconnect() |
|
| 198 | ||
| 199 | if cursor_energy: |
|
| 200 | cursor_energy.close() |
|
| 201 | if cnx_energy: |
|
| 202 | cnx_energy.disconnect() |
|
| 203 | ||
| 204 | if cnx_historical: |
|
| 205 | cnx_historical.close() |
|
| 206 | if cursor_historical: |
|
| 207 | cursor_historical.disconnect() |
|
| 208 | raise falcon.HTTPError(falcon.HTTP_404, |
|
| 209 | title='API.NOT_FOUND', |
|
| 210 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
|
| 211 | energy_category_dict = dict() |
|
| 212 | for row_energy_category in rows_energy_categories: |
|
| 213 | if row_energy_category[0] in energy_category_set: |
|
| 214 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
|
| 215 | "unit_of_measure": row_energy_category[2], |
|
| 216 | "kgce": row_energy_category[3], |
|
| 217 | "kgco2e": row_energy_category[4]} |
|
| 218 | ||
| 219 | ################################################################################################################ |
|
| 220 | # Step 4: query associated sensors |
|
| 221 | ################################################################################################################ |
|
| 222 | point_list = list() |
|
| 223 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 224 | " FROM tbl_shopfloors st, tbl_sensors se, tbl_shopfloors_sensors ss, " |
|
| 225 | " tbl_points p, tbl_sensors_points sp " |
|
| 226 | " WHERE st.id = %s AND st.id = ss.shopfloor_id AND ss.sensor_id = se.id " |
|
| 227 | " AND se.id = sp.sensor_id AND sp.point_id = p.id " |
|
| 228 | " ORDER BY p.id ", (shopfloor['id'],)) |
|
| 229 | rows_points = cursor_system.fetchall() |
|
| 230 | if rows_points is not None and len(rows_points) > 0: |
|
| 231 | for row in rows_points: |
|
| 232 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 233 | ||
| 234 | ################################################################################################################ |
|
| 235 | # Step 5: query associated points |
|
| 236 | ################################################################################################################ |
|
| 237 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 238 | " FROM tbl_shopfloors s, tbl_shopfloors_points sp, tbl_points p " |
|
| 239 | " WHERE s.id = %s AND s.id = sp.shopfloor_id AND sp.point_id = p.id " |
|
| 240 | " ORDER BY p.id ", (shopfloor['id'],)) |
|
| 241 | rows_points = cursor_system.fetchall() |
|
| 242 | if rows_points is not None and len(rows_points) > 0: |
|
| 243 | for row in rows_points: |
|
| 244 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 245 | ||
| 246 | ################################################################################################################ |
|
| 247 | # Step 6: query base period energy input |
|
| 248 | ################################################################################################################ |
|
| 249 | base = dict() |
|
| 250 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 251 | for energy_category_id in energy_category_set: |
|
| 252 | base[energy_category_id] = dict() |
|
| 253 | base[energy_category_id]['timestamps'] = list() |
|
| 254 | base[energy_category_id]['values'] = list() |
|
| 255 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 256 | base[energy_category_id]['mean'] = None |
|
| 257 | base[energy_category_id]['median'] = None |
|
| 258 | base[energy_category_id]['minimum'] = None |
|
| 259 | base[energy_category_id]['maximum'] = None |
|
| 260 | base[energy_category_id]['stdev'] = None |
|
| 261 | base[energy_category_id]['variance'] = None |
|
| 262 | ||
| 263 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 264 | " FROM tbl_shopfloor_input_category_hourly " |
|
| 265 | " WHERE shopfloor_id = %s " |
|
| 266 | " AND energy_category_id = %s " |
|
| 267 | " AND start_datetime_utc >= %s " |
|
| 268 | " AND start_datetime_utc < %s " |
|
| 269 | " ORDER BY start_datetime_utc ", |
|
| 270 | (shopfloor['id'], |
|
| 271 | energy_category_id, |
|
| 272 | base_start_datetime_utc, |
|
| 273 | base_end_datetime_utc)) |
|
| 274 | rows_shopfloor_hourly = cursor_energy.fetchall() |
|
| 275 | ||
| 276 | rows_shopfloor_periodically, \ |
|
| 277 | base[energy_category_id]['mean'], \ |
|
| 278 | base[energy_category_id]['median'], \ |
|
| 279 | base[energy_category_id]['minimum'], \ |
|
| 280 | base[energy_category_id]['maximum'], \ |
|
| 281 | base[energy_category_id]['stdev'], \ |
|
| 282 | base[energy_category_id]['variance'] = \ |
|
| 283 | utilities.statistics_hourly_data_by_period(rows_shopfloor_hourly, |
|
| 284 | base_start_datetime_utc, |
|
| 285 | base_end_datetime_utc, |
|
| 286 | period_type) |
|
| 287 | ||
| 288 | for row_shopfloor_periodically in rows_shopfloor_periodically: |
|
| 289 | current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 290 | timedelta(minutes=timezone_offset) |
|
| 291 | if period_type == 'hourly': |
|
| 292 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 293 | elif period_type == 'daily': |
|
| 294 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 295 | elif period_type == 'monthly': |
|
| 296 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 297 | elif period_type == 'yearly': |
|
| 298 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 299 | ||
| 300 | actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ |
|
| 301 | else row_shopfloor_periodically[1] |
|
| 302 | base[energy_category_id]['timestamps'].append(current_datetime) |
|
| 303 | base[energy_category_id]['values'].append(actual_value) |
|
| 304 | base[energy_category_id]['subtotal'] += actual_value |
|
| 305 | ||
| 306 | ################################################################################################################ |
|
| 307 | # Step 7: query reporting period energy input |
|
| 308 | ################################################################################################################ |
|
| 309 | reporting = dict() |
|
| 310 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 311 | for energy_category_id in energy_category_set: |
|
| 312 | reporting[energy_category_id] = dict() |
|
| 313 | reporting[energy_category_id]['timestamps'] = list() |
|
| 314 | reporting[energy_category_id]['values'] = list() |
|
| 315 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 316 | reporting[energy_category_id]['mean'] = None |
|
| 317 | reporting[energy_category_id]['median'] = None |
|
| 318 | reporting[energy_category_id]['minimum'] = None |
|
| 319 | reporting[energy_category_id]['maximum'] = None |
|
| 320 | reporting[energy_category_id]['stdev'] = None |
|
| 321 | reporting[energy_category_id]['variance'] = None |
|
| 322 | ||
| 323 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 324 | " FROM tbl_shopfloor_input_category_hourly " |
|
| 325 | " WHERE shopfloor_id = %s " |
|
| 326 | " AND energy_category_id = %s " |
|
| 327 | " AND start_datetime_utc >= %s " |
|
| 328 | " AND start_datetime_utc < %s " |
|
| 329 | " ORDER BY start_datetime_utc ", |
|
| 330 | (shopfloor['id'], |
|
| 331 | energy_category_id, |
|
| 332 | reporting_start_datetime_utc, |
|
| 333 | reporting_end_datetime_utc)) |
|
| 334 | rows_shopfloor_hourly = cursor_energy.fetchall() |
|
| 335 | ||
| 336 | rows_shopfloor_periodically, \ |
|
| 337 | reporting[energy_category_id]['mean'], \ |
|
| 338 | reporting[energy_category_id]['median'], \ |
|
| 339 | reporting[energy_category_id]['minimum'], \ |
|
| 340 | reporting[energy_category_id]['maximum'], \ |
|
| 341 | reporting[energy_category_id]['stdev'], \ |
|
| 342 | reporting[energy_category_id]['variance'] = \ |
|
| 343 | utilities.statistics_hourly_data_by_period(rows_shopfloor_hourly, |
|
| 344 | reporting_start_datetime_utc, |
|
| 345 | reporting_end_datetime_utc, |
|
| 346 | period_type) |
|
| 347 | ||
| 348 | for row_shopfloor_periodically in rows_shopfloor_periodically: |
|
| 349 | current_datetime_local = row_shopfloor_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 350 | timedelta(minutes=timezone_offset) |
|
| 351 | if period_type == 'hourly': |
|
| 352 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 353 | elif period_type == 'daily': |
|
| 354 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 355 | elif period_type == 'monthly': |
|
| 356 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 357 | elif period_type == 'yearly': |
|
| 358 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 359 | ||
| 360 | actual_value = Decimal(0.0) if row_shopfloor_periodically[1] is None \ |
|
| 361 | else row_shopfloor_periodically[1] |
|
| 362 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
| 363 | reporting[energy_category_id]['values'].append(actual_value) |
|
| 364 | reporting[energy_category_id]['subtotal'] += actual_value |
|
| 365 | ||
| 366 | ################################################################################################################ |
|
| 367 | # Step 8: query tariff data |
|
| 368 | ################################################################################################################ |
|
| 369 | parameters_data = dict() |
|
| 370 | parameters_data['names'] = list() |
|
| 371 | parameters_data['timestamps'] = list() |
|
| 372 | parameters_data['values'] = list() |
|
| 373 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 374 | for energy_category_id in energy_category_set: |
|
| 375 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(shopfloor['cost_center_id'], |
|
| 376 | energy_category_id, |
|
| 377 | reporting_start_datetime_utc, |
|
| 378 | reporting_end_datetime_utc) |
|
| 379 | tariff_timestamp_list = list() |
|
| 380 | tariff_value_list = list() |
|
| 381 | for k, v in energy_category_tariff_dict.items(): |
|
| 382 | # convert k from utc to local |
|
| 383 | k = k + timedelta(minutes=timezone_offset) |
|
| 384 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
| 385 | tariff_value_list.append(v) |
|
| 386 | ||
| 387 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
| 388 | parameters_data['timestamps'].append(tariff_timestamp_list) |
|
| 389 | parameters_data['values'].append(tariff_value_list) |
|
| 390 | ||
| 391 | ################################################################################################################ |
|
| 392 | # Step 9: query associated sensors and points data |
|
| 393 | ################################################################################################################ |
|
| 394 | for point in point_list: |
|
| 395 | point_values = [] |
|
| 396 | point_timestamps = [] |
|
| 397 | if point['object_type'] == 'ANALOG_VALUE': |
|
| 398 | query = (" SELECT utc_date_time, actual_value " |
|
| 399 | " FROM tbl_analog_value " |
|
| 400 | " WHERE point_id = %s " |
|
| 401 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 402 | " ORDER BY utc_date_time ") |
|
| 403 | cursor_historical.execute(query, (point['id'], |
|
| 404 | reporting_start_datetime_utc, |
|
| 405 | reporting_end_datetime_utc)) |
|
| 406 | rows = cursor_historical.fetchall() |
|
| 407 | ||
| 408 | if rows is not None and len(rows) > 0: |
|
| 409 | for row in rows: |
|
| 410 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 411 | timedelta(minutes=timezone_offset) |
|
| 412 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 413 | point_timestamps.append(current_datetime) |
|
| 414 | point_values.append(row[1]) |
|
| 415 | ||
| 416 | elif point['object_type'] == 'ENERGY_VALUE': |
|
| 417 | query = (" SELECT utc_date_time, actual_value " |
|
| 418 | " FROM tbl_energy_value " |
|
| 419 | " WHERE point_id = %s " |
|
| 420 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 421 | " ORDER BY utc_date_time ") |
|
| 422 | cursor_historical.execute(query, (point['id'], |
|
| 423 | reporting_start_datetime_utc, |
|
| 424 | reporting_end_datetime_utc)) |
|
| 425 | rows = cursor_historical.fetchall() |
|
| 426 | ||
| 427 | if rows is not None and len(rows) > 0: |
|
| 428 | for row in rows: |
|
| 429 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 430 | timedelta(minutes=timezone_offset) |
|
| 431 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 432 | point_timestamps.append(current_datetime) |
|
| 433 | point_values.append(row[1]) |
|
| 434 | elif point['object_type'] == 'DIGITAL_VALUE': |
|
| 435 | query = (" SELECT utc_date_time, actual_value " |
|
| 436 | " FROM tbl_digital_value " |
|
| 437 | " WHERE point_id = %s " |
|
| 438 | " AND utc_date_time BETWEEN %s AND %s ") |
|
| 439 | cursor_historical.execute(query, (point['id'], |
|
| 440 | reporting_start_datetime_utc, |
|
| 441 | reporting_end_datetime_utc)) |
|
| 442 | rows = cursor_historical.fetchall() |
|
| 443 | ||
| 444 | if rows is not None and len(rows) > 0: |
|
| 445 | for row in rows: |
|
| 446 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 447 | timedelta(minutes=timezone_offset) |
|
| 448 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 449 | point_timestamps.append(current_datetime) |
|
| 450 | point_values.append(row[1]) |
|
| 451 | ||
| 452 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
| 453 | parameters_data['timestamps'].append(point_timestamps) |
|
| 454 | parameters_data['values'].append(point_values) |
|
| 455 | ||
| 456 | ################################################################################################################ |
|
| 457 | # Step 10: construct the report |
|
| 458 | ################################################################################################################ |
|
| 459 | if cursor_system: |
|
| 460 | cursor_system.close() |
|
| 461 | if cnx_system: |
|
| 462 | cnx_system.disconnect() |
|
| 463 | ||
| 464 | if cursor_energy: |
|
| 465 | cursor_energy.close() |
|
| 466 | if cnx_energy: |
|
| 467 | cnx_energy.disconnect() |
|
| 468 | ||
| 469 | result = dict() |
|
| 470 | ||
| 471 | result['shopfloor'] = dict() |
|
| 472 | result['shopfloor']['name'] = shopfloor['name'] |
|
| 473 | result['shopfloor']['area'] = shopfloor['area'] |
|
| 474 | ||
| 475 | result['base_period'] = dict() |
|
| 476 | result['base_period']['names'] = list() |
|
| 477 | result['base_period']['units'] = list() |
|
| 478 | result['base_period']['timestamps'] = list() |
|
| 479 | result['base_period']['values'] = list() |
|
| 480 | result['base_period']['subtotals'] = list() |
|
| 481 | result['base_period']['means'] = list() |
|
| 482 | result['base_period']['medians'] = list() |
|
| 483 | result['base_period']['minimums'] = list() |
|
| 484 | result['base_period']['maximums'] = list() |
|
| 485 | result['base_period']['stdevs'] = list() |
|
| 486 | result['base_period']['variances'] = list() |
|
| 487 | ||
| 488 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 489 | for energy_category_id in energy_category_set: |
|
| 490 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 491 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 492 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
| 493 | result['base_period']['values'].append(base[energy_category_id]['values']) |
|
| 494 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
|
| 495 | result['base_period']['means'].append(base[energy_category_id]['mean']) |
|
| 496 | result['base_period']['medians'].append(base[energy_category_id]['median']) |
|
| 497 | result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
|
| 498 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
| 499 | result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
|
| 500 | result['base_period']['variances'].append(base[energy_category_id]['variance']) |
|
| 501 | ||
| 502 | result['reporting_period'] = dict() |
|
| 503 | result['reporting_period']['names'] = list() |
|
| 504 | result['reporting_period']['energy_category_ids'] = list() |
|
| 505 | result['reporting_period']['units'] = list() |
|
| 506 | result['reporting_period']['timestamps'] = list() |
|
| 507 | result['reporting_period']['values'] = list() |
|
| 508 | result['reporting_period']['subtotals'] = list() |
|
| 509 | result['reporting_period']['means'] = list() |
|
| 510 | result['reporting_period']['means_per_unit_area'] = list() |
|
| 511 | result['reporting_period']['means_increment_rate'] = list() |
|
| 512 | result['reporting_period']['medians'] = list() |
|
| 513 | result['reporting_period']['medians_per_unit_area'] = list() |
|
| 514 | result['reporting_period']['medians_increment_rate'] = list() |
|
| 515 | result['reporting_period']['minimums'] = list() |
|
| 516 | result['reporting_period']['minimums_per_unit_area'] = list() |
|
| 517 | result['reporting_period']['minimums_increment_rate'] = list() |
|
| 518 | result['reporting_period']['maximums'] = list() |
|
| 519 | result['reporting_period']['maximums_per_unit_area'] = list() |
|
| 520 | result['reporting_period']['maximums_increment_rate'] = list() |
|
| 521 | result['reporting_period']['stdevs'] = list() |
|
| 522 | result['reporting_period']['stdevs_per_unit_area'] = list() |
|
| 523 | result['reporting_period']['stdevs_increment_rate'] = list() |
|
| 524 | result['reporting_period']['variances'] = list() |
|
| 525 | result['reporting_period']['variances_per_unit_area'] = list() |
|
| 526 | result['reporting_period']['variances_increment_rate'] = list() |
|
| 527 | ||
| 528 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 529 | for energy_category_id in energy_category_set: |
|
| 530 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 531 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
| 532 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 533 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
| 534 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
|
| 535 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
|
| 536 | result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
|
| 537 | result['reporting_period']['means_per_unit_area'].append( |
|
| 538 | reporting[energy_category_id]['mean'] / shopfloor['area'] |
|
| 539 | if reporting[energy_category_id]['mean'] is not None and |
|
| 540 | shopfloor['area'] is not None and |
|
| 541 | shopfloor['area'] > Decimal(0.0) |
|
| 542 | else None) |
|
| 543 | result['reporting_period']['means_increment_rate'].append( |
|
| 544 | (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
|
| 545 | base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
|
| 546 | base[energy_category_id]['mean'] > Decimal(0.0)) |
|
| 547 | else None) |
|
| 548 | result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
|
| 549 | result['reporting_period']['medians_per_unit_area'].append( |
|
| 550 | reporting[energy_category_id]['median'] / shopfloor['area'] |
|
| 551 | if reporting[energy_category_id]['median'] is not None and |
|
| 552 | shopfloor['area'] is not None and |
|
| 553 | shopfloor['area'] > Decimal(0.0) |
|
| 554 | else None) |
|
| 555 | result['reporting_period']['medians_increment_rate'].append( |
|
| 556 | (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
|
| 557 | base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
|
| 558 | base[energy_category_id]['median'] > Decimal(0.0)) |
|
| 559 | else None) |
|
| 560 | result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
|
| 561 | result['reporting_period']['minimums_per_unit_area'].append( |
|
| 562 | reporting[energy_category_id]['minimum'] / shopfloor['area'] |
|
| 563 | if reporting[energy_category_id]['minimum'] is not None and |
|
| 564 | shopfloor['area'] is not None and |
|
| 565 | shopfloor['area'] > Decimal(0.0) |
|
| 566 | else None) |
|
| 567 | result['reporting_period']['minimums_increment_rate'].append( |
|
| 568 | (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
|
| 569 | base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
|
| 570 | base[energy_category_id]['minimum'] > Decimal(0.0)) |
|
| 571 | else None) |
|
| 572 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
| 573 | result['reporting_period']['maximums_per_unit_area'].append( |
|
| 574 | reporting[energy_category_id]['maximum'] / shopfloor['area'] |
|
| 575 | if reporting[energy_category_id]['maximum'] is not None and |
|
| 576 | shopfloor['area'] is not None and |
|
| 577 | shopfloor['area'] > Decimal(0.0) |
|
| 578 | else None) |
|
| 579 | result['reporting_period']['maximums_increment_rate'].append( |
|
| 580 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
| 581 | base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
|
| 582 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
| 583 | else None) |
|
| 584 | result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
|
| 585 | result['reporting_period']['stdevs_per_unit_area'].append( |
|
| 586 | reporting[energy_category_id]['stdev'] / shopfloor['area'] |
|
| 587 | if reporting[energy_category_id]['stdev'] is not None and |
|
| 588 | shopfloor['area'] is not None and |
|
| 589 | shopfloor['area'] > Decimal(0.0) |
|
| 590 | else None) |
|
| 591 | result['reporting_period']['stdevs_increment_rate'].append( |
|
| 592 | (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
|
| 593 | base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
|
| 594 | base[energy_category_id]['stdev'] > Decimal(0.0)) |
|
| 595 | else None) |
|
| 596 | result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
|
| 597 | result['reporting_period']['variances_per_unit_area'].append( |
|
| 598 | reporting[energy_category_id]['variance'] / shopfloor['area'] |
|
| 599 | if reporting[energy_category_id]['variance'] is not None and |
|
| 600 | shopfloor['area'] is not None and |
|
| 601 | shopfloor['area'] > Decimal(0.0) |
|
| 602 | else None) |
|
| 603 | result['reporting_period']['variances_increment_rate'].append( |
|
| 604 | (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
|
| 605 | base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
|
| 606 | base[energy_category_id]['variance'] > Decimal(0.0)) |
|
| 607 | else None) |
|
| 608 | ||
| 609 | result['parameters'] = { |
|
| 610 | "names": parameters_data['names'], |
|
| 611 | "timestamps": parameters_data['timestamps'], |
|
| 612 | "values": parameters_data['values'] |
|
| 613 | } |
|
| 614 | ||
| 615 | # export result to Excel file and then encode the file to base64 string |
|
| 616 | result['excel_bytes_base64'] = excelexporters.shopfloorstatistics.export(result, |
|
| 617 | shopfloor['name'], |
|
| 618 | reporting_start_datetime_local, |
|
| 619 | reporting_end_datetime_local, |
|
| 620 | period_type) |
|
| 621 | ||
| 622 | resp.body = json.dumps(result) |
|
| 623 | ||
| @@ 12-620 (lines=609) @@ | ||
| 9 | from decimal import Decimal |
|
| 10 | ||
| 11 | ||
| 12 | class Reporting: |
|
| 13 | @staticmethod |
|
| 14 | def __init__(): |
|
| 15 | pass |
|
| 16 | ||
| 17 | @staticmethod |
|
| 18 | def on_options(req, resp): |
|
| 19 | resp.status = falcon.HTTP_200 |
|
| 20 | ||
| 21 | #################################################################################################################### |
|
| 22 | # PROCEDURES |
|
| 23 | # Step 1: valid parameters |
|
| 24 | # Step 2: query the store |
|
| 25 | # Step 3: query energy categories |
|
| 26 | # Step 4: query associated sensors |
|
| 27 | # Step 5: query associated points |
|
| 28 | # Step 6: query base period energy input |
|
| 29 | # Step 7: query reporting period energy input |
|
| 30 | # Step 8: query tariff data |
|
| 31 | # Step 9: query associated sensors and points data |
|
| 32 | # Step 10: construct the report |
|
| 33 | #################################################################################################################### |
|
| 34 | @staticmethod |
|
| 35 | def on_get(req, resp): |
|
| 36 | print(req.params) |
|
| 37 | store_id = req.params.get('storeid') |
|
| 38 | period_type = req.params.get('periodtype') |
|
| 39 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
|
| 40 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
|
| 41 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
|
| 42 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
|
| 43 | ||
| 44 | ################################################################################################################ |
|
| 45 | # Step 1: valid parameters |
|
| 46 | ################################################################################################################ |
|
| 47 | if store_id is None: |
|
| 48 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') |
|
| 49 | else: |
|
| 50 | store_id = str.strip(store_id) |
|
| 51 | if not store_id.isdigit() or int(store_id) <= 0: |
|
| 52 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') |
|
| 53 | ||
| 54 | if period_type is None: |
|
| 55 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 56 | else: |
|
| 57 | period_type = str.strip(period_type) |
|
| 58 | if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
|
| 59 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 60 | ||
| 61 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
|
| 62 | if config.utc_offset[0] == '-': |
|
| 63 | timezone_offset = -timezone_offset |
|
| 64 | ||
| 65 | base_start_datetime_utc = None |
|
| 66 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
|
| 67 | base_start_datetime_local = str.strip(base_start_datetime_local) |
|
| 68 | try: |
|
| 69 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
|
| 70 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 71 | timedelta(minutes=timezone_offset) |
|
| 72 | except ValueError: |
|
| 73 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 74 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
|
| 75 | ||
| 76 | base_end_datetime_utc = None |
|
| 77 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
|
| 78 | base_end_datetime_local = str.strip(base_end_datetime_local) |
|
| 79 | try: |
|
| 80 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
|
| 81 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 82 | timedelta(minutes=timezone_offset) |
|
| 83 | except ValueError: |
|
| 84 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 85 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
|
| 86 | ||
| 87 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
|
| 88 | base_start_datetime_utc >= base_end_datetime_utc: |
|
| 89 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 90 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
|
| 91 | ||
| 92 | if reporting_start_datetime_local is None: |
|
| 93 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 94 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 95 | else: |
|
| 96 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
|
| 97 | try: |
|
| 98 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
|
| 99 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 100 | timedelta(minutes=timezone_offset) |
|
| 101 | except ValueError: |
|
| 102 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 103 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 104 | ||
| 105 | if reporting_end_datetime_local is None: |
|
| 106 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 107 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 108 | else: |
|
| 109 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
|
| 110 | try: |
|
| 111 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
|
| 112 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 113 | timedelta(minutes=timezone_offset) |
|
| 114 | except ValueError: |
|
| 115 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 116 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 117 | ||
| 118 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
|
| 119 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 120 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
|
| 121 | ||
| 122 | ################################################################################################################ |
|
| 123 | # Step 2: query the store |
|
| 124 | ################################################################################################################ |
|
| 125 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
| 126 | cursor_system = cnx_system.cursor() |
|
| 127 | ||
| 128 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
|
| 129 | cursor_energy = cnx_energy.cursor() |
|
| 130 | ||
| 131 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
| 132 | cursor_historical = cnx_historical.cursor() |
|
| 133 | ||
| 134 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
|
| 135 | " FROM tbl_stores " |
|
| 136 | " WHERE id = %s ", (store_id,)) |
|
| 137 | row_store = cursor_system.fetchone() |
|
| 138 | if row_store is None: |
|
| 139 | if cursor_system: |
|
| 140 | cursor_system.close() |
|
| 141 | if cnx_system: |
|
| 142 | cnx_system.disconnect() |
|
| 143 | ||
| 144 | if cursor_energy: |
|
| 145 | cursor_energy.close() |
|
| 146 | if cnx_energy: |
|
| 147 | cnx_energy.disconnect() |
|
| 148 | ||
| 149 | if cnx_historical: |
|
| 150 | cnx_historical.close() |
|
| 151 | if cursor_historical: |
|
| 152 | cursor_historical.disconnect() |
|
| 153 | raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.STORE_NOT_FOUND') |
|
| 154 | ||
| 155 | store = dict() |
|
| 156 | store['id'] = row_store[0] |
|
| 157 | store['name'] = row_store[1] |
|
| 158 | store['area'] = row_store[2] |
|
| 159 | store['cost_center_id'] = row_store[3] |
|
| 160 | ||
| 161 | ################################################################################################################ |
|
| 162 | # Step 3: query energy categories |
|
| 163 | ################################################################################################################ |
|
| 164 | energy_category_set = set() |
|
| 165 | # query energy categories in base period |
|
| 166 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 167 | " FROM tbl_store_input_category_hourly " |
|
| 168 | " WHERE store_id = %s " |
|
| 169 | " AND start_datetime_utc >= %s " |
|
| 170 | " AND start_datetime_utc < %s ", |
|
| 171 | (store['id'], base_start_datetime_utc, base_end_datetime_utc)) |
|
| 172 | rows_energy_categories = cursor_energy.fetchall() |
|
| 173 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 174 | for row_energy_category in rows_energy_categories: |
|
| 175 | energy_category_set.add(row_energy_category[0]) |
|
| 176 | ||
| 177 | # query energy categories in reporting period |
|
| 178 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 179 | " FROM tbl_store_input_category_hourly " |
|
| 180 | " WHERE store_id = %s " |
|
| 181 | " AND start_datetime_utc >= %s " |
|
| 182 | " AND start_datetime_utc < %s ", |
|
| 183 | (store['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
|
| 184 | rows_energy_categories = cursor_energy.fetchall() |
|
| 185 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 186 | for row_energy_category in rows_energy_categories: |
|
| 187 | energy_category_set.add(row_energy_category[0]) |
|
| 188 | ||
| 189 | # query all energy categories in base period and reporting period |
|
| 190 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
|
| 191 | " FROM tbl_energy_categories " |
|
| 192 | " ORDER BY id ", ) |
|
| 193 | rows_energy_categories = cursor_system.fetchall() |
|
| 194 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
|
| 195 | if cursor_system: |
|
| 196 | cursor_system.close() |
|
| 197 | if cnx_system: |
|
| 198 | cnx_system.disconnect() |
|
| 199 | ||
| 200 | if cursor_energy: |
|
| 201 | cursor_energy.close() |
|
| 202 | if cnx_energy: |
|
| 203 | cnx_energy.disconnect() |
|
| 204 | ||
| 205 | if cnx_historical: |
|
| 206 | cnx_historical.close() |
|
| 207 | if cursor_historical: |
|
| 208 | cursor_historical.disconnect() |
|
| 209 | raise falcon.HTTPError(falcon.HTTP_404, |
|
| 210 | title='API.NOT_FOUND', |
|
| 211 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
|
| 212 | energy_category_dict = dict() |
|
| 213 | for row_energy_category in rows_energy_categories: |
|
| 214 | if row_energy_category[0] in energy_category_set: |
|
| 215 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
|
| 216 | "unit_of_measure": row_energy_category[2], |
|
| 217 | "kgce": row_energy_category[3], |
|
| 218 | "kgco2e": row_energy_category[4]} |
|
| 219 | ||
| 220 | ################################################################################################################ |
|
| 221 | # Step 4: query associated sensors |
|
| 222 | ################################################################################################################ |
|
| 223 | point_list = list() |
|
| 224 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 225 | " FROM tbl_stores st, tbl_sensors se, tbl_stores_sensors ss, " |
|
| 226 | " tbl_points p, tbl_sensors_points sp " |
|
| 227 | " WHERE st.id = %s AND st.id = ss.store_id AND ss.sensor_id = se.id " |
|
| 228 | " AND se.id = sp.sensor_id AND sp.point_id = p.id " |
|
| 229 | " ORDER BY p.id ", (store['id'],)) |
|
| 230 | rows_points = cursor_system.fetchall() |
|
| 231 | if rows_points is not None and len(rows_points) > 0: |
|
| 232 | for row in rows_points: |
|
| 233 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 234 | ||
| 235 | ################################################################################################################ |
|
| 236 | # Step 5: query associated points |
|
| 237 | ################################################################################################################ |
|
| 238 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 239 | " FROM tbl_stores s, tbl_stores_points sp, tbl_points p " |
|
| 240 | " WHERE s.id = %s AND s.id = sp.store_id AND sp.point_id = p.id " |
|
| 241 | " ORDER BY p.id ", (store['id'],)) |
|
| 242 | rows_points = cursor_system.fetchall() |
|
| 243 | if rows_points is not None and len(rows_points) > 0: |
|
| 244 | for row in rows_points: |
|
| 245 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 246 | ||
| 247 | ################################################################################################################ |
|
| 248 | # Step 6: query base period energy input |
|
| 249 | ################################################################################################################ |
|
| 250 | base = dict() |
|
| 251 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 252 | for energy_category_id in energy_category_set: |
|
| 253 | base[energy_category_id] = dict() |
|
| 254 | base[energy_category_id]['timestamps'] = list() |
|
| 255 | base[energy_category_id]['values'] = list() |
|
| 256 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 257 | base[energy_category_id]['mean'] = None |
|
| 258 | base[energy_category_id]['median'] = None |
|
| 259 | base[energy_category_id]['minimum'] = None |
|
| 260 | base[energy_category_id]['maximum'] = None |
|
| 261 | base[energy_category_id]['stdev'] = None |
|
| 262 | base[energy_category_id]['variance'] = None |
|
| 263 | ||
| 264 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 265 | " FROM tbl_store_input_category_hourly " |
|
| 266 | " WHERE store_id = %s " |
|
| 267 | " AND energy_category_id = %s " |
|
| 268 | " AND start_datetime_utc >= %s " |
|
| 269 | " AND start_datetime_utc < %s " |
|
| 270 | " ORDER BY start_datetime_utc ", |
|
| 271 | (store['id'], |
|
| 272 | energy_category_id, |
|
| 273 | base_start_datetime_utc, |
|
| 274 | base_end_datetime_utc)) |
|
| 275 | rows_store_hourly = cursor_energy.fetchall() |
|
| 276 | ||
| 277 | rows_store_periodically, \ |
|
| 278 | base[energy_category_id]['mean'], \ |
|
| 279 | base[energy_category_id]['median'], \ |
|
| 280 | base[energy_category_id]['minimum'], \ |
|
| 281 | base[energy_category_id]['maximum'], \ |
|
| 282 | base[energy_category_id]['stdev'], \ |
|
| 283 | base[energy_category_id]['variance'] = \ |
|
| 284 | utilities.statistics_hourly_data_by_period(rows_store_hourly, |
|
| 285 | base_start_datetime_utc, |
|
| 286 | base_end_datetime_utc, |
|
| 287 | period_type) |
|
| 288 | ||
| 289 | for row_store_periodically in rows_store_periodically: |
|
| 290 | current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 291 | timedelta(minutes=timezone_offset) |
|
| 292 | if period_type == 'hourly': |
|
| 293 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 294 | elif period_type == 'daily': |
|
| 295 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 296 | elif period_type == 'monthly': |
|
| 297 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 298 | elif period_type == 'yearly': |
|
| 299 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 300 | ||
| 301 | actual_value = Decimal(0.0) if row_store_periodically[1] is None else row_store_periodically[1] |
|
| 302 | base[energy_category_id]['timestamps'].append(current_datetime) |
|
| 303 | base[energy_category_id]['values'].append(actual_value) |
|
| 304 | base[energy_category_id]['subtotal'] += actual_value |
|
| 305 | ||
| 306 | ################################################################################################################ |
|
| 307 | # Step 7: query reporting period energy input |
|
| 308 | ################################################################################################################ |
|
| 309 | reporting = dict() |
|
| 310 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 311 | for energy_category_id in energy_category_set: |
|
| 312 | reporting[energy_category_id] = dict() |
|
| 313 | reporting[energy_category_id]['timestamps'] = list() |
|
| 314 | reporting[energy_category_id]['values'] = list() |
|
| 315 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 316 | reporting[energy_category_id]['mean'] = None |
|
| 317 | reporting[energy_category_id]['median'] = None |
|
| 318 | reporting[energy_category_id]['minimum'] = None |
|
| 319 | reporting[energy_category_id]['maximum'] = None |
|
| 320 | reporting[energy_category_id]['stdev'] = None |
|
| 321 | reporting[energy_category_id]['variance'] = None |
|
| 322 | ||
| 323 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 324 | " FROM tbl_store_input_category_hourly " |
|
| 325 | " WHERE store_id = %s " |
|
| 326 | " AND energy_category_id = %s " |
|
| 327 | " AND start_datetime_utc >= %s " |
|
| 328 | " AND start_datetime_utc < %s " |
|
| 329 | " ORDER BY start_datetime_utc ", |
|
| 330 | (store['id'], |
|
| 331 | energy_category_id, |
|
| 332 | reporting_start_datetime_utc, |
|
| 333 | reporting_end_datetime_utc)) |
|
| 334 | rows_store_hourly = cursor_energy.fetchall() |
|
| 335 | ||
| 336 | rows_store_periodically, \ |
|
| 337 | reporting[energy_category_id]['mean'], \ |
|
| 338 | reporting[energy_category_id]['median'], \ |
|
| 339 | reporting[energy_category_id]['minimum'], \ |
|
| 340 | reporting[energy_category_id]['maximum'], \ |
|
| 341 | reporting[energy_category_id]['stdev'], \ |
|
| 342 | reporting[energy_category_id]['variance'] = \ |
|
| 343 | utilities.statistics_hourly_data_by_period(rows_store_hourly, |
|
| 344 | reporting_start_datetime_utc, |
|
| 345 | reporting_end_datetime_utc, |
|
| 346 | period_type) |
|
| 347 | ||
| 348 | for row_store_periodically in rows_store_periodically: |
|
| 349 | current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 350 | timedelta(minutes=timezone_offset) |
|
| 351 | if period_type == 'hourly': |
|
| 352 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 353 | elif period_type == 'daily': |
|
| 354 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 355 | elif period_type == 'monthly': |
|
| 356 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 357 | elif period_type == 'yearly': |
|
| 358 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 359 | ||
| 360 | actual_value = Decimal(0.0) if row_store_periodically[1] is None else row_store_periodically[1] |
|
| 361 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
| 362 | reporting[energy_category_id]['values'].append(actual_value) |
|
| 363 | reporting[energy_category_id]['subtotal'] += actual_value |
|
| 364 | ||
| 365 | ################################################################################################################ |
|
| 366 | # Step 8: query tariff data |
|
| 367 | ################################################################################################################ |
|
| 368 | parameters_data = dict() |
|
| 369 | parameters_data['names'] = list() |
|
| 370 | parameters_data['timestamps'] = list() |
|
| 371 | parameters_data['values'] = list() |
|
| 372 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 373 | for energy_category_id in energy_category_set: |
|
| 374 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(store['cost_center_id'], |
|
| 375 | energy_category_id, |
|
| 376 | reporting_start_datetime_utc, |
|
| 377 | reporting_end_datetime_utc) |
|
| 378 | tariff_timestamp_list = list() |
|
| 379 | tariff_value_list = list() |
|
| 380 | for k, v in energy_category_tariff_dict.items(): |
|
| 381 | # convert k from utc to local |
|
| 382 | k = k + timedelta(minutes=timezone_offset) |
|
| 383 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
| 384 | tariff_value_list.append(v) |
|
| 385 | ||
| 386 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
| 387 | parameters_data['timestamps'].append(tariff_timestamp_list) |
|
| 388 | parameters_data['values'].append(tariff_value_list) |
|
| 389 | ||
| 390 | ################################################################################################################ |
|
| 391 | # Step 9: query associated sensors and points data |
|
| 392 | ################################################################################################################ |
|
| 393 | for point in point_list: |
|
| 394 | point_values = [] |
|
| 395 | point_timestamps = [] |
|
| 396 | if point['object_type'] == 'ANALOG_VALUE': |
|
| 397 | query = (" SELECT utc_date_time, actual_value " |
|
| 398 | " FROM tbl_analog_value " |
|
| 399 | " WHERE point_id = %s " |
|
| 400 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 401 | " ORDER BY utc_date_time ") |
|
| 402 | cursor_historical.execute(query, (point['id'], |
|
| 403 | reporting_start_datetime_utc, |
|
| 404 | reporting_end_datetime_utc)) |
|
| 405 | rows = cursor_historical.fetchall() |
|
| 406 | ||
| 407 | if rows is not None and len(rows) > 0: |
|
| 408 | for row in rows: |
|
| 409 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 410 | timedelta(minutes=timezone_offset) |
|
| 411 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 412 | point_timestamps.append(current_datetime) |
|
| 413 | point_values.append(row[1]) |
|
| 414 | ||
| 415 | elif point['object_type'] == 'ENERGY_VALUE': |
|
| 416 | query = (" SELECT utc_date_time, actual_value " |
|
| 417 | " FROM tbl_energy_value " |
|
| 418 | " WHERE point_id = %s " |
|
| 419 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 420 | " ORDER BY utc_date_time ") |
|
| 421 | cursor_historical.execute(query, (point['id'], |
|
| 422 | reporting_start_datetime_utc, |
|
| 423 | reporting_end_datetime_utc)) |
|
| 424 | rows = cursor_historical.fetchall() |
|
| 425 | ||
| 426 | if rows is not None and len(rows) > 0: |
|
| 427 | for row in rows: |
|
| 428 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 429 | timedelta(minutes=timezone_offset) |
|
| 430 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 431 | point_timestamps.append(current_datetime) |
|
| 432 | point_values.append(row[1]) |
|
| 433 | elif point['object_type'] == 'DIGITAL_VALUE': |
|
| 434 | query = (" SELECT utc_date_time, actual_value " |
|
| 435 | " FROM tbl_digital_value " |
|
| 436 | " WHERE point_id = %s " |
|
| 437 | " AND utc_date_time BETWEEN %s AND %s ") |
|
| 438 | cursor_historical.execute(query, (point['id'], |
|
| 439 | reporting_start_datetime_utc, |
|
| 440 | reporting_end_datetime_utc)) |
|
| 441 | rows = cursor_historical.fetchall() |
|
| 442 | ||
| 443 | if rows is not None and len(rows) > 0: |
|
| 444 | for row in rows: |
|
| 445 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 446 | timedelta(minutes=timezone_offset) |
|
| 447 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 448 | point_timestamps.append(current_datetime) |
|
| 449 | point_values.append(row[1]) |
|
| 450 | ||
| 451 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
| 452 | parameters_data['timestamps'].append(point_timestamps) |
|
| 453 | parameters_data['values'].append(point_values) |
|
| 454 | ||
| 455 | ################################################################################################################ |
|
| 456 | # Step 10: construct the report |
|
| 457 | ################################################################################################################ |
|
| 458 | if cursor_system: |
|
| 459 | cursor_system.close() |
|
| 460 | if cnx_system: |
|
| 461 | cnx_system.disconnect() |
|
| 462 | ||
| 463 | if cursor_energy: |
|
| 464 | cursor_energy.close() |
|
| 465 | if cnx_energy: |
|
| 466 | cnx_energy.disconnect() |
|
| 467 | ||
| 468 | result = dict() |
|
| 469 | ||
| 470 | result['store'] = dict() |
|
| 471 | result['store']['name'] = store['name'] |
|
| 472 | result['store']['area'] = store['area'] |
|
| 473 | ||
| 474 | result['base_period'] = dict() |
|
| 475 | result['base_period']['names'] = list() |
|
| 476 | result['base_period']['units'] = list() |
|
| 477 | result['base_period']['timestamps'] = list() |
|
| 478 | result['base_period']['values'] = list() |
|
| 479 | result['base_period']['subtotals'] = list() |
|
| 480 | result['base_period']['means'] = list() |
|
| 481 | result['base_period']['medians'] = list() |
|
| 482 | result['base_period']['minimums'] = list() |
|
| 483 | result['base_period']['maximums'] = list() |
|
| 484 | result['base_period']['stdevs'] = list() |
|
| 485 | result['base_period']['variances'] = list() |
|
| 486 | ||
| 487 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 488 | for energy_category_id in energy_category_set: |
|
| 489 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 490 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 491 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
| 492 | result['base_period']['values'].append(base[energy_category_id]['values']) |
|
| 493 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
|
| 494 | result['base_period']['means'].append(base[energy_category_id]['mean']) |
|
| 495 | result['base_period']['medians'].append(base[energy_category_id]['median']) |
|
| 496 | result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
|
| 497 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
| 498 | result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
|
| 499 | result['base_period']['variances'].append(base[energy_category_id]['variance']) |
|
| 500 | ||
| 501 | result['reporting_period'] = dict() |
|
| 502 | result['reporting_period']['names'] = list() |
|
| 503 | result['reporting_period']['energy_category_ids'] = list() |
|
| 504 | result['reporting_period']['units'] = list() |
|
| 505 | result['reporting_period']['timestamps'] = list() |
|
| 506 | result['reporting_period']['values'] = list() |
|
| 507 | result['reporting_period']['subtotals'] = list() |
|
| 508 | result['reporting_period']['means'] = list() |
|
| 509 | result['reporting_period']['means_per_unit_area'] = list() |
|
| 510 | result['reporting_period']['means_increment_rate'] = list() |
|
| 511 | result['reporting_period']['medians'] = list() |
|
| 512 | result['reporting_period']['medians_per_unit_area'] = list() |
|
| 513 | result['reporting_period']['medians_increment_rate'] = list() |
|
| 514 | result['reporting_period']['minimums'] = list() |
|
| 515 | result['reporting_period']['minimums_per_unit_area'] = list() |
|
| 516 | result['reporting_period']['minimums_increment_rate'] = list() |
|
| 517 | result['reporting_period']['maximums'] = list() |
|
| 518 | result['reporting_period']['maximums_per_unit_area'] = list() |
|
| 519 | result['reporting_period']['maximums_increment_rate'] = list() |
|
| 520 | result['reporting_period']['stdevs'] = list() |
|
| 521 | result['reporting_period']['stdevs_per_unit_area'] = list() |
|
| 522 | result['reporting_period']['stdevs_increment_rate'] = list() |
|
| 523 | result['reporting_period']['variances'] = list() |
|
| 524 | result['reporting_period']['variances_per_unit_area'] = list() |
|
| 525 | result['reporting_period']['variances_increment_rate'] = list() |
|
| 526 | ||
| 527 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 528 | for energy_category_id in energy_category_set: |
|
| 529 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 530 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
| 531 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 532 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
| 533 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
|
| 534 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
|
| 535 | result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
|
| 536 | result['reporting_period']['means_per_unit_area'].append( |
|
| 537 | reporting[energy_category_id]['mean'] / store['area'] |
|
| 538 | if reporting[energy_category_id]['mean'] is not None and |
|
| 539 | store['area'] is not None and |
|
| 540 | store['area'] > Decimal(0.0) |
|
| 541 | else None) |
|
| 542 | result['reporting_period']['means_increment_rate'].append( |
|
| 543 | (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
|
| 544 | base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
|
| 545 | base[energy_category_id]['mean'] > Decimal(0.0)) |
|
| 546 | else None) |
|
| 547 | result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
|
| 548 | result['reporting_period']['medians_per_unit_area'].append( |
|
| 549 | reporting[energy_category_id]['median'] / store['area'] |
|
| 550 | if reporting[energy_category_id]['median'] is not None and |
|
| 551 | store['area'] is not None and |
|
| 552 | store['area'] > Decimal(0.0) |
|
| 553 | else None) |
|
| 554 | result['reporting_period']['medians_increment_rate'].append( |
|
| 555 | (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
|
| 556 | base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
|
| 557 | base[energy_category_id]['median'] > Decimal(0.0)) |
|
| 558 | else None) |
|
| 559 | result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
|
| 560 | result['reporting_period']['minimums_per_unit_area'].append( |
|
| 561 | reporting[energy_category_id]['minimum'] / store['area'] |
|
| 562 | if reporting[energy_category_id]['minimum'] is not None and |
|
| 563 | store['area'] is not None and |
|
| 564 | store['area'] > Decimal(0.0) |
|
| 565 | else None) |
|
| 566 | result['reporting_period']['minimums_increment_rate'].append( |
|
| 567 | (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
|
| 568 | base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
|
| 569 | base[energy_category_id]['minimum'] > Decimal(0.0)) |
|
| 570 | else None) |
|
| 571 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
| 572 | result['reporting_period']['maximums_per_unit_area'].append( |
|
| 573 | reporting[energy_category_id]['maximum'] / store['area'] |
|
| 574 | if reporting[energy_category_id]['maximum'] is not None and |
|
| 575 | store['area'] is not None and |
|
| 576 | store['area'] > Decimal(0.0) |
|
| 577 | else None) |
|
| 578 | result['reporting_period']['maximums_increment_rate'].append( |
|
| 579 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
| 580 | base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
|
| 581 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
| 582 | else None) |
|
| 583 | result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
|
| 584 | result['reporting_period']['stdevs_per_unit_area'].append( |
|
| 585 | reporting[energy_category_id]['stdev'] / store['area'] |
|
| 586 | if reporting[energy_category_id]['stdev'] is not None and |
|
| 587 | store['area'] is not None and |
|
| 588 | store['area'] > Decimal(0.0) |
|
| 589 | else None) |
|
| 590 | result['reporting_period']['stdevs_increment_rate'].append( |
|
| 591 | (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
|
| 592 | base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
|
| 593 | base[energy_category_id]['stdev'] > Decimal(0.0)) |
|
| 594 | else None) |
|
| 595 | result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
|
| 596 | result['reporting_period']['variances_per_unit_area'].append( |
|
| 597 | reporting[energy_category_id]['variance'] / store['area'] |
|
| 598 | if reporting[energy_category_id]['variance'] is not None and |
|
| 599 | store['area'] is not None and |
|
| 600 | store['area'] > Decimal(0.0) |
|
| 601 | else None) |
|
| 602 | result['reporting_period']['variances_increment_rate'].append( |
|
| 603 | (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
|
| 604 | base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
|
| 605 | base[energy_category_id]['variance'] > Decimal(0.0)) |
|
| 606 | else None) |
|
| 607 | ||
| 608 | result['parameters'] = { |
|
| 609 | "names": parameters_data['names'], |
|
| 610 | "timestamps": parameters_data['timestamps'], |
|
| 611 | "values": parameters_data['values'] |
|
| 612 | } |
|
| 613 | ||
| 614 | # export result to Excel file and then encode the file to base64 string |
|
| 615 | result['excel_bytes_base64'] = excelexporters.storestatistics.export(result, |
|
| 616 | store['name'], |
|
| 617 | reporting_start_datetime_local, |
|
| 618 | reporting_end_datetime_local, |
|
| 619 | period_type) |
|
| 620 | resp.body = json.dumps(result) |
|
| 621 | ||
| @@ 11-619 (lines=609) @@ | ||
| 8 | import excelexporters.tenantstatistics |
|
| 9 | ||
| 10 | ||
| 11 | class Reporting: |
|
| 12 | @staticmethod |
|
| 13 | def __init__(): |
|
| 14 | pass |
|
| 15 | ||
| 16 | @staticmethod |
|
| 17 | def on_options(req, resp): |
|
| 18 | resp.status = falcon.HTTP_200 |
|
| 19 | ||
| 20 | #################################################################################################################### |
|
| 21 | # PROCEDURES |
|
| 22 | # Step 1: valid parameters |
|
| 23 | # Step 2: query the tenant |
|
| 24 | # Step 3: query energy categories |
|
| 25 | # Step 4: query associated sensors |
|
| 26 | # Step 5: query associated points |
|
| 27 | # Step 6: query base period energy input |
|
| 28 | # Step 7: query reporting period energy input |
|
| 29 | # Step 8: query tariff data |
|
| 30 | # Step 9: query associated sensors and points data |
|
| 31 | # Step 10: construct the report |
|
| 32 | #################################################################################################################### |
|
| 33 | @staticmethod |
|
| 34 | def on_get(req, resp): |
|
| 35 | print(req.params) |
|
| 36 | tenant_id = req.params.get('tenantid') |
|
| 37 | period_type = req.params.get('periodtype') |
|
| 38 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
|
| 39 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
|
| 40 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
|
| 41 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
|
| 42 | ||
| 43 | ################################################################################################################ |
|
| 44 | # Step 1: valid parameters |
|
| 45 | ################################################################################################################ |
|
| 46 | if tenant_id is None: |
|
| 47 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_TENANT_ID') |
|
| 48 | else: |
|
| 49 | tenant_id = str.strip(tenant_id) |
|
| 50 | if not tenant_id.isdigit() or int(tenant_id) <= 0: |
|
| 51 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_TENANT_ID') |
|
| 52 | ||
| 53 | if period_type is None: |
|
| 54 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 55 | else: |
|
| 56 | period_type = str.strip(period_type) |
|
| 57 | if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
|
| 58 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 59 | ||
| 60 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
|
| 61 | if config.utc_offset[0] == '-': |
|
| 62 | timezone_offset = -timezone_offset |
|
| 63 | ||
| 64 | base_start_datetime_utc = None |
|
| 65 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
|
| 66 | base_start_datetime_local = str.strip(base_start_datetime_local) |
|
| 67 | try: |
|
| 68 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
|
| 69 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 70 | timedelta(minutes=timezone_offset) |
|
| 71 | except ValueError: |
|
| 72 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 73 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
|
| 74 | ||
| 75 | base_end_datetime_utc = None |
|
| 76 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
|
| 77 | base_end_datetime_local = str.strip(base_end_datetime_local) |
|
| 78 | try: |
|
| 79 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
|
| 80 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 81 | timedelta(minutes=timezone_offset) |
|
| 82 | except ValueError: |
|
| 83 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 84 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
|
| 85 | ||
| 86 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
|
| 87 | base_start_datetime_utc >= base_end_datetime_utc: |
|
| 88 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 89 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
|
| 90 | ||
| 91 | if reporting_start_datetime_local is None: |
|
| 92 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 93 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 94 | else: |
|
| 95 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
|
| 96 | try: |
|
| 97 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
|
| 98 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 99 | timedelta(minutes=timezone_offset) |
|
| 100 | except ValueError: |
|
| 101 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 102 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 103 | ||
| 104 | if reporting_end_datetime_local is None: |
|
| 105 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 106 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 107 | else: |
|
| 108 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
|
| 109 | try: |
|
| 110 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
|
| 111 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 112 | timedelta(minutes=timezone_offset) |
|
| 113 | except ValueError: |
|
| 114 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 115 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 116 | ||
| 117 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
|
| 118 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 119 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
|
| 120 | ||
| 121 | ################################################################################################################ |
|
| 122 | # Step 2: query the tenant |
|
| 123 | ################################################################################################################ |
|
| 124 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
| 125 | cursor_system = cnx_system.cursor() |
|
| 126 | ||
| 127 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
|
| 128 | cursor_energy = cnx_energy.cursor() |
|
| 129 | ||
| 130 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
| 131 | cursor_historical = cnx_historical.cursor() |
|
| 132 | ||
| 133 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
|
| 134 | " FROM tbl_tenants " |
|
| 135 | " WHERE id = %s ", (tenant_id,)) |
|
| 136 | row_tenant = cursor_system.fetchone() |
|
| 137 | if row_tenant is None: |
|
| 138 | if cursor_system: |
|
| 139 | cursor_system.close() |
|
| 140 | if cnx_system: |
|
| 141 | cnx_system.disconnect() |
|
| 142 | ||
| 143 | if cursor_energy: |
|
| 144 | cursor_energy.close() |
|
| 145 | if cnx_energy: |
|
| 146 | cnx_energy.disconnect() |
|
| 147 | ||
| 148 | if cnx_historical: |
|
| 149 | cnx_historical.close() |
|
| 150 | if cursor_historical: |
|
| 151 | cursor_historical.disconnect() |
|
| 152 | raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.TENANT_NOT_FOUND') |
|
| 153 | ||
| 154 | tenant = dict() |
|
| 155 | tenant['id'] = row_tenant[0] |
|
| 156 | tenant['name'] = row_tenant[1] |
|
| 157 | tenant['area'] = row_tenant[2] |
|
| 158 | tenant['cost_center_id'] = row_tenant[3] |
|
| 159 | ||
| 160 | ################################################################################################################ |
|
| 161 | # Step 3: query energy categories |
|
| 162 | ################################################################################################################ |
|
| 163 | energy_category_set = set() |
|
| 164 | # query energy categories in base period |
|
| 165 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 166 | " FROM tbl_tenant_input_category_hourly " |
|
| 167 | " WHERE tenant_id = %s " |
|
| 168 | " AND start_datetime_utc >= %s " |
|
| 169 | " AND start_datetime_utc < %s ", |
|
| 170 | (tenant['id'], base_start_datetime_utc, base_end_datetime_utc)) |
|
| 171 | rows_energy_categories = cursor_energy.fetchall() |
|
| 172 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 173 | for row_energy_category in rows_energy_categories: |
|
| 174 | energy_category_set.add(row_energy_category[0]) |
|
| 175 | ||
| 176 | # query energy categories in reporting period |
|
| 177 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 178 | " FROM tbl_tenant_input_category_hourly " |
|
| 179 | " WHERE tenant_id = %s " |
|
| 180 | " AND start_datetime_utc >= %s " |
|
| 181 | " AND start_datetime_utc < %s ", |
|
| 182 | (tenant['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
|
| 183 | rows_energy_categories = cursor_energy.fetchall() |
|
| 184 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 185 | for row_energy_category in rows_energy_categories: |
|
| 186 | energy_category_set.add(row_energy_category[0]) |
|
| 187 | ||
| 188 | # query all energy categories in base period and reporting period |
|
| 189 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
|
| 190 | " FROM tbl_energy_categories " |
|
| 191 | " ORDER BY id ", ) |
|
| 192 | rows_energy_categories = cursor_system.fetchall() |
|
| 193 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
|
| 194 | if cursor_system: |
|
| 195 | cursor_system.close() |
|
| 196 | if cnx_system: |
|
| 197 | cnx_system.disconnect() |
|
| 198 | ||
| 199 | if cursor_energy: |
|
| 200 | cursor_energy.close() |
|
| 201 | if cnx_energy: |
|
| 202 | cnx_energy.disconnect() |
|
| 203 | ||
| 204 | if cnx_historical: |
|
| 205 | cnx_historical.close() |
|
| 206 | if cursor_historical: |
|
| 207 | cursor_historical.disconnect() |
|
| 208 | raise falcon.HTTPError(falcon.HTTP_404, |
|
| 209 | title='API.NOT_FOUND', |
|
| 210 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
|
| 211 | energy_category_dict = dict() |
|
| 212 | for row_energy_category in rows_energy_categories: |
|
| 213 | if row_energy_category[0] in energy_category_set: |
|
| 214 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
|
| 215 | "unit_of_measure": row_energy_category[2], |
|
| 216 | "kgce": row_energy_category[3], |
|
| 217 | "kgco2e": row_energy_category[4]} |
|
| 218 | ||
| 219 | ################################################################################################################ |
|
| 220 | # Step 4: query associated sensors |
|
| 221 | ################################################################################################################ |
|
| 222 | point_list = list() |
|
| 223 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 224 | " FROM tbl_tenants t, tbl_sensors s, tbl_tenants_sensors ts, " |
|
| 225 | " tbl_points p, tbl_sensors_points sp " |
|
| 226 | " WHERE t.id = %s AND t.id = ts.tenant_id AND ts.sensor_id = s.id " |
|
| 227 | " AND s.id = sp.sensor_id AND sp.point_id = p.id " |
|
| 228 | " ORDER BY p.id ", (tenant['id'],)) |
|
| 229 | rows_points = cursor_system.fetchall() |
|
| 230 | if rows_points is not None and len(rows_points) > 0: |
|
| 231 | for row in rows_points: |
|
| 232 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 233 | ||
| 234 | ################################################################################################################ |
|
| 235 | # Step 5: query associated points |
|
| 236 | ################################################################################################################ |
|
| 237 | cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
| 238 | " FROM tbl_tenants t, tbl_tenants_points tp, tbl_points p " |
|
| 239 | " WHERE t.id = %s AND t.id = tp.tenant_id AND tp.point_id = p.id " |
|
| 240 | " ORDER BY p.id ", (tenant['id'],)) |
|
| 241 | rows_points = cursor_system.fetchall() |
|
| 242 | if rows_points is not None and len(rows_points) > 0: |
|
| 243 | for row in rows_points: |
|
| 244 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 245 | ||
| 246 | ################################################################################################################ |
|
| 247 | # Step 6: query base period energy input |
|
| 248 | ################################################################################################################ |
|
| 249 | base = dict() |
|
| 250 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 251 | for energy_category_id in energy_category_set: |
|
| 252 | base[energy_category_id] = dict() |
|
| 253 | base[energy_category_id]['timestamps'] = list() |
|
| 254 | base[energy_category_id]['values'] = list() |
|
| 255 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 256 | base[energy_category_id]['mean'] = None |
|
| 257 | base[energy_category_id]['median'] = None |
|
| 258 | base[energy_category_id]['minimum'] = None |
|
| 259 | base[energy_category_id]['maximum'] = None |
|
| 260 | base[energy_category_id]['stdev'] = None |
|
| 261 | base[energy_category_id]['variance'] = None |
|
| 262 | ||
| 263 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 264 | " FROM tbl_tenant_input_category_hourly " |
|
| 265 | " WHERE tenant_id = %s " |
|
| 266 | " AND energy_category_id = %s " |
|
| 267 | " AND start_datetime_utc >= %s " |
|
| 268 | " AND start_datetime_utc < %s " |
|
| 269 | " ORDER BY start_datetime_utc ", |
|
| 270 | (tenant['id'], |
|
| 271 | energy_category_id, |
|
| 272 | base_start_datetime_utc, |
|
| 273 | base_end_datetime_utc)) |
|
| 274 | rows_tenant_hourly = cursor_energy.fetchall() |
|
| 275 | ||
| 276 | rows_tenant_periodically, \ |
|
| 277 | base[energy_category_id]['mean'], \ |
|
| 278 | base[energy_category_id]['median'], \ |
|
| 279 | base[energy_category_id]['minimum'], \ |
|
| 280 | base[energy_category_id]['maximum'], \ |
|
| 281 | base[energy_category_id]['stdev'], \ |
|
| 282 | base[energy_category_id]['variance'] = \ |
|
| 283 | utilities.statistics_hourly_data_by_period(rows_tenant_hourly, |
|
| 284 | base_start_datetime_utc, |
|
| 285 | base_end_datetime_utc, |
|
| 286 | period_type) |
|
| 287 | ||
| 288 | for row_tenant_periodically in rows_tenant_periodically: |
|
| 289 | current_datetime_local = row_tenant_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 290 | timedelta(minutes=timezone_offset) |
|
| 291 | if period_type == 'hourly': |
|
| 292 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 293 | elif period_type == 'daily': |
|
| 294 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 295 | elif period_type == 'monthly': |
|
| 296 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 297 | elif period_type == 'yearly': |
|
| 298 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 299 | ||
| 300 | actual_value = Decimal(0.0) if row_tenant_periodically[1] is None else row_tenant_periodically[1] |
|
| 301 | base[energy_category_id]['timestamps'].append(current_datetime) |
|
| 302 | base[energy_category_id]['values'].append(actual_value) |
|
| 303 | base[energy_category_id]['subtotal'] += actual_value |
|
| 304 | ||
| 305 | ################################################################################################################ |
|
| 306 | # Step 7: query reporting period energy input |
|
| 307 | ################################################################################################################ |
|
| 308 | reporting = dict() |
|
| 309 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 310 | for energy_category_id in energy_category_set: |
|
| 311 | reporting[energy_category_id] = dict() |
|
| 312 | reporting[energy_category_id]['timestamps'] = list() |
|
| 313 | reporting[energy_category_id]['values'] = list() |
|
| 314 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 315 | reporting[energy_category_id]['mean'] = None |
|
| 316 | reporting[energy_category_id]['median'] = None |
|
| 317 | reporting[energy_category_id]['minimum'] = None |
|
| 318 | reporting[energy_category_id]['maximum'] = None |
|
| 319 | reporting[energy_category_id]['stdev'] = None |
|
| 320 | reporting[energy_category_id]['variance'] = None |
|
| 321 | ||
| 322 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 323 | " FROM tbl_tenant_input_category_hourly " |
|
| 324 | " WHERE tenant_id = %s " |
|
| 325 | " AND energy_category_id = %s " |
|
| 326 | " AND start_datetime_utc >= %s " |
|
| 327 | " AND start_datetime_utc < %s " |
|
| 328 | " ORDER BY start_datetime_utc ", |
|
| 329 | (tenant['id'], |
|
| 330 | energy_category_id, |
|
| 331 | reporting_start_datetime_utc, |
|
| 332 | reporting_end_datetime_utc)) |
|
| 333 | rows_tenant_hourly = cursor_energy.fetchall() |
|
| 334 | ||
| 335 | rows_tenant_periodically, \ |
|
| 336 | reporting[energy_category_id]['mean'], \ |
|
| 337 | reporting[energy_category_id]['median'], \ |
|
| 338 | reporting[energy_category_id]['minimum'], \ |
|
| 339 | reporting[energy_category_id]['maximum'], \ |
|
| 340 | reporting[energy_category_id]['stdev'], \ |
|
| 341 | reporting[energy_category_id]['variance'] = \ |
|
| 342 | utilities.statistics_hourly_data_by_period(rows_tenant_hourly, |
|
| 343 | reporting_start_datetime_utc, |
|
| 344 | reporting_end_datetime_utc, |
|
| 345 | period_type) |
|
| 346 | ||
| 347 | for row_tenant_periodically in rows_tenant_periodically: |
|
| 348 | current_datetime_local = row_tenant_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 349 | timedelta(minutes=timezone_offset) |
|
| 350 | if period_type == 'hourly': |
|
| 351 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 352 | elif period_type == 'daily': |
|
| 353 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 354 | elif period_type == 'monthly': |
|
| 355 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 356 | elif period_type == 'yearly': |
|
| 357 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 358 | ||
| 359 | actual_value = Decimal(0.0) if row_tenant_periodically[1] is None else row_tenant_periodically[1] |
|
| 360 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
| 361 | reporting[energy_category_id]['values'].append(actual_value) |
|
| 362 | reporting[energy_category_id]['subtotal'] += actual_value |
|
| 363 | ||
| 364 | ################################################################################################################ |
|
| 365 | # Step 8: query tariff data |
|
| 366 | ################################################################################################################ |
|
| 367 | parameters_data = dict() |
|
| 368 | parameters_data['names'] = list() |
|
| 369 | parameters_data['timestamps'] = list() |
|
| 370 | parameters_data['values'] = list() |
|
| 371 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 372 | for energy_category_id in energy_category_set: |
|
| 373 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(tenant['cost_center_id'], |
|
| 374 | energy_category_id, |
|
| 375 | reporting_start_datetime_utc, |
|
| 376 | reporting_end_datetime_utc) |
|
| 377 | tariff_timestamp_list = list() |
|
| 378 | tariff_value_list = list() |
|
| 379 | for k, v in energy_category_tariff_dict.items(): |
|
| 380 | # convert k from utc to local |
|
| 381 | k = k + timedelta(minutes=timezone_offset) |
|
| 382 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
| 383 | tariff_value_list.append(v) |
|
| 384 | ||
| 385 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
| 386 | parameters_data['timestamps'].append(tariff_timestamp_list) |
|
| 387 | parameters_data['values'].append(tariff_value_list) |
|
| 388 | ||
| 389 | ################################################################################################################ |
|
| 390 | # Step 9: query associated sensors and points data |
|
| 391 | ################################################################################################################ |
|
| 392 | for point in point_list: |
|
| 393 | point_values = [] |
|
| 394 | point_timestamps = [] |
|
| 395 | if point['object_type'] == 'ANALOG_VALUE': |
|
| 396 | query = (" SELECT utc_date_time, actual_value " |
|
| 397 | " FROM tbl_analog_value " |
|
| 398 | " WHERE point_id = %s " |
|
| 399 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 400 | " ORDER BY utc_date_time ") |
|
| 401 | cursor_historical.execute(query, (point['id'], |
|
| 402 | reporting_start_datetime_utc, |
|
| 403 | reporting_end_datetime_utc)) |
|
| 404 | rows = cursor_historical.fetchall() |
|
| 405 | ||
| 406 | if rows is not None and len(rows) > 0: |
|
| 407 | for row in rows: |
|
| 408 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 409 | timedelta(minutes=timezone_offset) |
|
| 410 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 411 | point_timestamps.append(current_datetime) |
|
| 412 | point_values.append(row[1]) |
|
| 413 | ||
| 414 | elif point['object_type'] == 'ENERGY_VALUE': |
|
| 415 | query = (" SELECT utc_date_time, actual_value " |
|
| 416 | " FROM tbl_energy_value " |
|
| 417 | " WHERE point_id = %s " |
|
| 418 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 419 | " ORDER BY utc_date_time ") |
|
| 420 | cursor_historical.execute(query, (point['id'], |
|
| 421 | reporting_start_datetime_utc, |
|
| 422 | reporting_end_datetime_utc)) |
|
| 423 | rows = cursor_historical.fetchall() |
|
| 424 | ||
| 425 | if rows is not None and len(rows) > 0: |
|
| 426 | for row in rows: |
|
| 427 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 428 | timedelta(minutes=timezone_offset) |
|
| 429 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 430 | point_timestamps.append(current_datetime) |
|
| 431 | point_values.append(row[1]) |
|
| 432 | elif point['object_type'] == 'DIGITAL_VALUE': |
|
| 433 | query = (" SELECT utc_date_time, actual_value " |
|
| 434 | " FROM tbl_digital_value " |
|
| 435 | " WHERE point_id = %s " |
|
| 436 | " AND utc_date_time BETWEEN %s AND %s ") |
|
| 437 | cursor_historical.execute(query, (point['id'], |
|
| 438 | reporting_start_datetime_utc, |
|
| 439 | reporting_end_datetime_utc)) |
|
| 440 | rows = cursor_historical.fetchall() |
|
| 441 | ||
| 442 | if rows is not None and len(rows) > 0: |
|
| 443 | for row in rows: |
|
| 444 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 445 | timedelta(minutes=timezone_offset) |
|
| 446 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 447 | point_timestamps.append(current_datetime) |
|
| 448 | point_values.append(row[1]) |
|
| 449 | ||
| 450 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
| 451 | parameters_data['timestamps'].append(point_timestamps) |
|
| 452 | parameters_data['values'].append(point_values) |
|
| 453 | ||
| 454 | ################################################################################################################ |
|
| 455 | # Step 10: construct the report |
|
| 456 | ################################################################################################################ |
|
| 457 | if cursor_system: |
|
| 458 | cursor_system.close() |
|
| 459 | if cnx_system: |
|
| 460 | cnx_system.disconnect() |
|
| 461 | ||
| 462 | if cursor_energy: |
|
| 463 | cursor_energy.close() |
|
| 464 | if cnx_energy: |
|
| 465 | cnx_energy.disconnect() |
|
| 466 | ||
| 467 | result = dict() |
|
| 468 | ||
| 469 | result['tenant'] = dict() |
|
| 470 | result['tenant']['name'] = tenant['name'] |
|
| 471 | result['tenant']['area'] = tenant['area'] |
|
| 472 | ||
| 473 | result['base_period'] = dict() |
|
| 474 | result['base_period']['names'] = list() |
|
| 475 | result['base_period']['units'] = list() |
|
| 476 | result['base_period']['timestamps'] = list() |
|
| 477 | result['base_period']['values'] = list() |
|
| 478 | result['base_period']['subtotals'] = list() |
|
| 479 | result['base_period']['means'] = list() |
|
| 480 | result['base_period']['medians'] = list() |
|
| 481 | result['base_period']['minimums'] = list() |
|
| 482 | result['base_period']['maximums'] = list() |
|
| 483 | result['base_period']['stdevs'] = list() |
|
| 484 | result['base_period']['variances'] = list() |
|
| 485 | ||
| 486 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 487 | for energy_category_id in energy_category_set: |
|
| 488 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 489 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 490 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
| 491 | result['base_period']['values'].append(base[energy_category_id]['values']) |
|
| 492 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
|
| 493 | result['base_period']['means'].append(base[energy_category_id]['mean']) |
|
| 494 | result['base_period']['medians'].append(base[energy_category_id]['median']) |
|
| 495 | result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
|
| 496 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
| 497 | result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
|
| 498 | result['base_period']['variances'].append(base[energy_category_id]['variance']) |
|
| 499 | ||
| 500 | result['reporting_period'] = dict() |
|
| 501 | result['reporting_period']['names'] = list() |
|
| 502 | result['reporting_period']['energy_category_ids'] = list() |
|
| 503 | result['reporting_period']['units'] = list() |
|
| 504 | result['reporting_period']['timestamps'] = list() |
|
| 505 | result['reporting_period']['values'] = list() |
|
| 506 | result['reporting_period']['subtotals'] = list() |
|
| 507 | result['reporting_period']['means'] = list() |
|
| 508 | result['reporting_period']['means_per_unit_area'] = list() |
|
| 509 | result['reporting_period']['means_increment_rate'] = list() |
|
| 510 | result['reporting_period']['medians'] = list() |
|
| 511 | result['reporting_period']['medians_per_unit_area'] = list() |
|
| 512 | result['reporting_period']['medians_increment_rate'] = list() |
|
| 513 | result['reporting_period']['minimums'] = list() |
|
| 514 | result['reporting_period']['minimums_per_unit_area'] = list() |
|
| 515 | result['reporting_period']['minimums_increment_rate'] = list() |
|
| 516 | result['reporting_period']['maximums'] = list() |
|
| 517 | result['reporting_period']['maximums_per_unit_area'] = list() |
|
| 518 | result['reporting_period']['maximums_increment_rate'] = list() |
|
| 519 | result['reporting_period']['stdevs'] = list() |
|
| 520 | result['reporting_period']['stdevs_per_unit_area'] = list() |
|
| 521 | result['reporting_period']['stdevs_increment_rate'] = list() |
|
| 522 | result['reporting_period']['variances'] = list() |
|
| 523 | result['reporting_period']['variances_per_unit_area'] = list() |
|
| 524 | result['reporting_period']['variances_increment_rate'] = list() |
|
| 525 | ||
| 526 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 527 | for energy_category_id in energy_category_set: |
|
| 528 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 529 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
| 530 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 531 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
| 532 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
|
| 533 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
|
| 534 | result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
|
| 535 | result['reporting_period']['means_per_unit_area'].append( |
|
| 536 | reporting[energy_category_id]['mean'] / tenant['area'] |
|
| 537 | if reporting[energy_category_id]['mean'] is not None and |
|
| 538 | tenant['area'] is not None and |
|
| 539 | tenant['area'] > Decimal(0.0) |
|
| 540 | else None) |
|
| 541 | result['reporting_period']['means_increment_rate'].append( |
|
| 542 | (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
|
| 543 | base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
|
| 544 | base[energy_category_id]['mean'] > Decimal(0.0)) |
|
| 545 | else None) |
|
| 546 | result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
|
| 547 | result['reporting_period']['medians_per_unit_area'].append( |
|
| 548 | reporting[energy_category_id]['median'] / tenant['area'] |
|
| 549 | if reporting[energy_category_id]['median'] is not None and |
|
| 550 | tenant['area'] is not None and |
|
| 551 | tenant['area'] > Decimal(0.0) |
|
| 552 | else None) |
|
| 553 | result['reporting_period']['medians_increment_rate'].append( |
|
| 554 | (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
|
| 555 | base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
|
| 556 | base[energy_category_id]['median'] > Decimal(0.0)) |
|
| 557 | else None) |
|
| 558 | result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
|
| 559 | result['reporting_period']['minimums_per_unit_area'].append( |
|
| 560 | reporting[energy_category_id]['minimum'] / tenant['area'] |
|
| 561 | if reporting[energy_category_id]['minimum'] is not None and |
|
| 562 | tenant['area'] is not None and |
|
| 563 | tenant['area'] > Decimal(0.0) |
|
| 564 | else None) |
|
| 565 | result['reporting_period']['minimums_increment_rate'].append( |
|
| 566 | (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
|
| 567 | base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
|
| 568 | base[energy_category_id]['minimum'] > Decimal(0.0)) |
|
| 569 | else None) |
|
| 570 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
| 571 | result['reporting_period']['maximums_per_unit_area'].append( |
|
| 572 | reporting[energy_category_id]['maximum'] / tenant['area'] |
|
| 573 | if reporting[energy_category_id]['maximum'] is not None and |
|
| 574 | tenant['area'] is not None and |
|
| 575 | tenant['area'] > Decimal(0.0) |
|
| 576 | else None) |
|
| 577 | result['reporting_period']['maximums_increment_rate'].append( |
|
| 578 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
| 579 | base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
|
| 580 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
| 581 | else None) |
|
| 582 | result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
|
| 583 | result['reporting_period']['stdevs_per_unit_area'].append( |
|
| 584 | reporting[energy_category_id]['stdev'] / tenant['area'] |
|
| 585 | if reporting[energy_category_id]['stdev'] is not None and |
|
| 586 | tenant['area'] is not None and |
|
| 587 | tenant['area'] > Decimal(0.0) |
|
| 588 | else None) |
|
| 589 | result['reporting_period']['stdevs_increment_rate'].append( |
|
| 590 | (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
|
| 591 | base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
|
| 592 | base[energy_category_id]['stdev'] > Decimal(0.0)) |
|
| 593 | else None) |
|
| 594 | result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
|
| 595 | result['reporting_period']['variances_per_unit_area'].append( |
|
| 596 | reporting[energy_category_id]['variance'] / tenant['area'] |
|
| 597 | if reporting[energy_category_id]['variance'] is not None and |
|
| 598 | tenant['area'] is not None and |
|
| 599 | tenant['area'] > Decimal(0.0) |
|
| 600 | else None) |
|
| 601 | result['reporting_period']['variances_increment_rate'].append( |
|
| 602 | (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
|
| 603 | base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
|
| 604 | base[energy_category_id]['variance'] > Decimal(0.0)) |
|
| 605 | else None) |
|
| 606 | ||
| 607 | result['parameters'] = { |
|
| 608 | "names": parameters_data['names'], |
|
| 609 | "timestamps": parameters_data['timestamps'], |
|
| 610 | "values": parameters_data['values'] |
|
| 611 | } |
|
| 612 | # export result to Excel file and then encode the file to base64 string |
|
| 613 | result['excel_bytes_base64'] = excelexporters.tenantstatistics.export(result, |
|
| 614 | tenant['name'], |
|
| 615 | reporting_start_datetime_local, |
|
| 616 | reporting_end_datetime_local, |
|
| 617 | period_type) |
|
| 618 | ||
| 619 | resp.body = json.dumps(result) |
|
| 620 | ||
| @@ 11-619 (lines=609) @@ | ||
| 8 | import excelexporters.spacestatistics |
|
| 9 | ||
| 10 | ||
| 11 | class Reporting: |
|
| 12 | @staticmethod |
|
| 13 | def __init__(): |
|
| 14 | pass |
|
| 15 | ||
| 16 | @staticmethod |
|
| 17 | def on_options(req, resp): |
|
| 18 | resp.status = falcon.HTTP_200 |
|
| 19 | ||
| 20 | #################################################################################################################### |
|
| 21 | # PROCEDURES |
|
| 22 | # Step 1: valid parameters |
|
| 23 | # Step 2: query the space |
|
| 24 | # Step 3: query energy categories |
|
| 25 | # Step 4: query associated sensors |
|
| 26 | # Step 5: query associated points |
|
| 27 | # Step 6: query base period energy input |
|
| 28 | # Step 7: query reporting period energy input |
|
| 29 | # Step 8: query tariff data |
|
| 30 | # Step 9: query associated sensors and points data |
|
| 31 | # Step 10: construct the report |
|
| 32 | #################################################################################################################### |
|
| 33 | @staticmethod |
|
| 34 | def on_get(req, resp): |
|
| 35 | print(req.params) |
|
| 36 | space_id = req.params.get('spaceid') |
|
| 37 | period_type = req.params.get('periodtype') |
|
| 38 | base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
|
| 39 | base_end_datetime_local = req.params.get('baseperiodenddatetime') |
|
| 40 | reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
|
| 41 | reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
|
| 42 | ||
| 43 | ################################################################################################################ |
|
| 44 | # Step 1: valid parameters |
|
| 45 | ################################################################################################################ |
|
| 46 | if space_id is None: |
|
| 47 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SPACE_ID') |
|
| 48 | else: |
|
| 49 | space_id = str.strip(space_id) |
|
| 50 | if not space_id.isdigit() or int(space_id) <= 0: |
|
| 51 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_SPACE_ID') |
|
| 52 | ||
| 53 | if period_type is None: |
|
| 54 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 55 | else: |
|
| 56 | period_type = str.strip(period_type) |
|
| 57 | if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
|
| 58 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
| 59 | ||
| 60 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
|
| 61 | if config.utc_offset[0] == '-': |
|
| 62 | timezone_offset = -timezone_offset |
|
| 63 | ||
| 64 | base_start_datetime_utc = None |
|
| 65 | if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
|
| 66 | base_start_datetime_local = str.strip(base_start_datetime_local) |
|
| 67 | try: |
|
| 68 | base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
|
| 69 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 70 | timedelta(minutes=timezone_offset) |
|
| 71 | except ValueError: |
|
| 72 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 73 | description="API.INVALID_BASE_PERIOD_START_DATETIME") |
|
| 74 | ||
| 75 | base_end_datetime_utc = None |
|
| 76 | if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
|
| 77 | base_end_datetime_local = str.strip(base_end_datetime_local) |
|
| 78 | try: |
|
| 79 | base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
|
| 80 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 81 | timedelta(minutes=timezone_offset) |
|
| 82 | except ValueError: |
|
| 83 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 84 | description="API.INVALID_BASE_PERIOD_END_DATETIME") |
|
| 85 | ||
| 86 | if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
|
| 87 | base_start_datetime_utc >= base_end_datetime_utc: |
|
| 88 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 89 | description='API.INVALID_BASE_PERIOD_END_DATETIME') |
|
| 90 | ||
| 91 | if reporting_start_datetime_local is None: |
|
| 92 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 93 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 94 | else: |
|
| 95 | reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
|
| 96 | try: |
|
| 97 | reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
|
| 98 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 99 | timedelta(minutes=timezone_offset) |
|
| 100 | except ValueError: |
|
| 101 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 102 | description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
| 103 | ||
| 104 | if reporting_end_datetime_local is None: |
|
| 105 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 106 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 107 | else: |
|
| 108 | reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
|
| 109 | try: |
|
| 110 | reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
|
| 111 | '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
| 112 | timedelta(minutes=timezone_offset) |
|
| 113 | except ValueError: |
|
| 114 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 115 | description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
| 116 | ||
| 117 | if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
|
| 118 | raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
| 119 | description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
|
| 120 | ||
| 121 | ################################################################################################################ |
|
| 122 | # Step 2: query the space |
|
| 123 | ################################################################################################################ |
|
| 124 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
| 125 | cursor_system = cnx_system.cursor() |
|
| 126 | ||
| 127 | cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
|
| 128 | cursor_energy = cnx_energy.cursor() |
|
| 129 | ||
| 130 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
| 131 | cursor_historical = cnx_historical.cursor() |
|
| 132 | ||
| 133 | cursor_system.execute(" SELECT id, name, area, cost_center_id " |
|
| 134 | " FROM tbl_spaces " |
|
| 135 | " WHERE id = %s ", (space_id,)) |
|
| 136 | row_space = cursor_system.fetchone() |
|
| 137 | if row_space is None: |
|
| 138 | if cursor_system: |
|
| 139 | cursor_system.close() |
|
| 140 | if cnx_system: |
|
| 141 | cnx_system.disconnect() |
|
| 142 | ||
| 143 | if cursor_energy: |
|
| 144 | cursor_energy.close() |
|
| 145 | if cnx_energy: |
|
| 146 | cnx_energy.disconnect() |
|
| 147 | ||
| 148 | if cnx_historical: |
|
| 149 | cnx_historical.close() |
|
| 150 | if cursor_historical: |
|
| 151 | cursor_historical.disconnect() |
|
| 152 | raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.SPACE_NOT_FOUND') |
|
| 153 | ||
| 154 | space = dict() |
|
| 155 | space['id'] = row_space[0] |
|
| 156 | space['name'] = row_space[1] |
|
| 157 | space['area'] = row_space[2] |
|
| 158 | space['cost_center_id'] = row_space[3] |
|
| 159 | ||
| 160 | ################################################################################################################ |
|
| 161 | # Step 3: query energy categories |
|
| 162 | ################################################################################################################ |
|
| 163 | energy_category_set = set() |
|
| 164 | # query energy categories in base period |
|
| 165 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 166 | " FROM tbl_space_input_category_hourly " |
|
| 167 | " WHERE space_id = %s " |
|
| 168 | " AND start_datetime_utc >= %s " |
|
| 169 | " AND start_datetime_utc < %s ", |
|
| 170 | (space['id'], base_start_datetime_utc, base_end_datetime_utc)) |
|
| 171 | rows_energy_categories = cursor_energy.fetchall() |
|
| 172 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 173 | for row_energy_category in rows_energy_categories: |
|
| 174 | energy_category_set.add(row_energy_category[0]) |
|
| 175 | ||
| 176 | # query energy categories in reporting period |
|
| 177 | cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
| 178 | " FROM tbl_space_input_category_hourly " |
|
| 179 | " WHERE space_id = %s " |
|
| 180 | " AND start_datetime_utc >= %s " |
|
| 181 | " AND start_datetime_utc < %s ", |
|
| 182 | (space['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
|
| 183 | rows_energy_categories = cursor_energy.fetchall() |
|
| 184 | if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
| 185 | for row_energy_category in rows_energy_categories: |
|
| 186 | energy_category_set.add(row_energy_category[0]) |
|
| 187 | ||
| 188 | # query all energy categories in base period and reporting period |
|
| 189 | cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
|
| 190 | " FROM tbl_energy_categories " |
|
| 191 | " ORDER BY id ", ) |
|
| 192 | rows_energy_categories = cursor_system.fetchall() |
|
| 193 | if rows_energy_categories is None or len(rows_energy_categories) == 0: |
|
| 194 | if cursor_system: |
|
| 195 | cursor_system.close() |
|
| 196 | if cnx_system: |
|
| 197 | cnx_system.disconnect() |
|
| 198 | ||
| 199 | if cursor_energy: |
|
| 200 | cursor_energy.close() |
|
| 201 | if cnx_energy: |
|
| 202 | cnx_energy.disconnect() |
|
| 203 | ||
| 204 | if cnx_historical: |
|
| 205 | cnx_historical.close() |
|
| 206 | if cursor_historical: |
|
| 207 | cursor_historical.disconnect() |
|
| 208 | raise falcon.HTTPError(falcon.HTTP_404, |
|
| 209 | title='API.NOT_FOUND', |
|
| 210 | description='API.ENERGY_CATEGORY_NOT_FOUND') |
|
| 211 | energy_category_dict = dict() |
|
| 212 | for row_energy_category in rows_energy_categories: |
|
| 213 | if row_energy_category[0] in energy_category_set: |
|
| 214 | energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
|
| 215 | "unit_of_measure": row_energy_category[2], |
|
| 216 | "kgce": row_energy_category[3], |
|
| 217 | "kgco2e": row_energy_category[4]} |
|
| 218 | ||
| 219 | ################################################################################################################ |
|
| 220 | # Step 4: query associated sensors |
|
| 221 | ################################################################################################################ |
|
| 222 | point_list = list() |
|
| 223 | cursor_system.execute(" SELECT po.id, po.name, po.units, po.object_type " |
|
| 224 | " FROM tbl_spaces sp, tbl_sensors se, tbl_spaces_sensors spse, " |
|
| 225 | " tbl_points po, tbl_sensors_points sepo " |
|
| 226 | " WHERE sp.id = %s AND sp.id = spse.space_id AND spse.sensor_id = se.id " |
|
| 227 | " AND se.id = sepo.sensor_id AND sepo.point_id = po.id " |
|
| 228 | " ORDER BY po.id ", (space['id'], )) |
|
| 229 | rows_points = cursor_system.fetchall() |
|
| 230 | if rows_points is not None and len(rows_points) > 0: |
|
| 231 | for row in rows_points: |
|
| 232 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 233 | ||
| 234 | ################################################################################################################ |
|
| 235 | # Step 5: query associated points |
|
| 236 | ################################################################################################################ |
|
| 237 | cursor_system.execute(" SELECT po.id, po.name, po.units, po.object_type " |
|
| 238 | " FROM tbl_spaces sp, tbl_spaces_points sppo, tbl_points po " |
|
| 239 | " WHERE sp.id = %s AND sp.id = sppo.space_id AND sppo.point_id = po.id " |
|
| 240 | " ORDER BY po.id ", (space['id'], )) |
|
| 241 | rows_points = cursor_system.fetchall() |
|
| 242 | if rows_points is not None and len(rows_points) > 0: |
|
| 243 | for row in rows_points: |
|
| 244 | point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
| 245 | ||
| 246 | ################################################################################################################ |
|
| 247 | # Step 6: query base period energy input |
|
| 248 | ################################################################################################################ |
|
| 249 | base = dict() |
|
| 250 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 251 | for energy_category_id in energy_category_set: |
|
| 252 | base[energy_category_id] = dict() |
|
| 253 | base[energy_category_id]['timestamps'] = list() |
|
| 254 | base[energy_category_id]['values'] = list() |
|
| 255 | base[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 256 | base[energy_category_id]['mean'] = None |
|
| 257 | base[energy_category_id]['median'] = None |
|
| 258 | base[energy_category_id]['minimum'] = None |
|
| 259 | base[energy_category_id]['maximum'] = None |
|
| 260 | base[energy_category_id]['stdev'] = None |
|
| 261 | base[energy_category_id]['variance'] = None |
|
| 262 | ||
| 263 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 264 | " FROM tbl_space_input_category_hourly " |
|
| 265 | " WHERE space_id = %s " |
|
| 266 | " AND energy_category_id = %s " |
|
| 267 | " AND start_datetime_utc >= %s " |
|
| 268 | " AND start_datetime_utc < %s " |
|
| 269 | " ORDER BY start_datetime_utc ", |
|
| 270 | (space['id'], |
|
| 271 | energy_category_id, |
|
| 272 | base_start_datetime_utc, |
|
| 273 | base_end_datetime_utc)) |
|
| 274 | rows_space_hourly = cursor_energy.fetchall() |
|
| 275 | ||
| 276 | rows_space_periodically, \ |
|
| 277 | base[energy_category_id]['mean'], \ |
|
| 278 | base[energy_category_id]['median'], \ |
|
| 279 | base[energy_category_id]['minimum'], \ |
|
| 280 | base[energy_category_id]['maximum'], \ |
|
| 281 | base[energy_category_id]['stdev'], \ |
|
| 282 | base[energy_category_id]['variance'] = \ |
|
| 283 | utilities.statistics_hourly_data_by_period(rows_space_hourly, |
|
| 284 | base_start_datetime_utc, |
|
| 285 | base_end_datetime_utc, |
|
| 286 | period_type) |
|
| 287 | ||
| 288 | for row_space_periodically in rows_space_periodically: |
|
| 289 | current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 290 | timedelta(minutes=timezone_offset) |
|
| 291 | if period_type == 'hourly': |
|
| 292 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 293 | elif period_type == 'daily': |
|
| 294 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 295 | elif period_type == 'monthly': |
|
| 296 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 297 | elif period_type == 'yearly': |
|
| 298 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 299 | ||
| 300 | actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1] |
|
| 301 | base[energy_category_id]['timestamps'].append(current_datetime) |
|
| 302 | base[energy_category_id]['values'].append(actual_value) |
|
| 303 | base[energy_category_id]['subtotal'] += actual_value |
|
| 304 | ||
| 305 | ################################################################################################################ |
|
| 306 | # Step 7: query reporting period energy input |
|
| 307 | ################################################################################################################ |
|
| 308 | reporting = dict() |
|
| 309 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 310 | for energy_category_id in energy_category_set: |
|
| 311 | reporting[energy_category_id] = dict() |
|
| 312 | reporting[energy_category_id]['timestamps'] = list() |
|
| 313 | reporting[energy_category_id]['values'] = list() |
|
| 314 | reporting[energy_category_id]['subtotal'] = Decimal(0.0) |
|
| 315 | reporting[energy_category_id]['mean'] = None |
|
| 316 | reporting[energy_category_id]['median'] = None |
|
| 317 | reporting[energy_category_id]['minimum'] = None |
|
| 318 | reporting[energy_category_id]['maximum'] = None |
|
| 319 | reporting[energy_category_id]['stdev'] = None |
|
| 320 | reporting[energy_category_id]['variance'] = None |
|
| 321 | ||
| 322 | cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
| 323 | " FROM tbl_space_input_category_hourly " |
|
| 324 | " WHERE space_id = %s " |
|
| 325 | " AND energy_category_id = %s " |
|
| 326 | " AND start_datetime_utc >= %s " |
|
| 327 | " AND start_datetime_utc < %s " |
|
| 328 | " ORDER BY start_datetime_utc ", |
|
| 329 | (space['id'], |
|
| 330 | energy_category_id, |
|
| 331 | reporting_start_datetime_utc, |
|
| 332 | reporting_end_datetime_utc)) |
|
| 333 | rows_space_hourly = cursor_energy.fetchall() |
|
| 334 | ||
| 335 | rows_space_periodically, \ |
|
| 336 | reporting[energy_category_id]['mean'], \ |
|
| 337 | reporting[energy_category_id]['median'], \ |
|
| 338 | reporting[energy_category_id]['minimum'], \ |
|
| 339 | reporting[energy_category_id]['maximum'], \ |
|
| 340 | reporting[energy_category_id]['stdev'], \ |
|
| 341 | reporting[energy_category_id]['variance'] = \ |
|
| 342 | utilities.statistics_hourly_data_by_period(rows_space_hourly, |
|
| 343 | reporting_start_datetime_utc, |
|
| 344 | reporting_end_datetime_utc, |
|
| 345 | period_type) |
|
| 346 | ||
| 347 | for row_space_periodically in rows_space_periodically: |
|
| 348 | current_datetime_local = row_space_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
| 349 | timedelta(minutes=timezone_offset) |
|
| 350 | if period_type == 'hourly': |
|
| 351 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 352 | elif period_type == 'daily': |
|
| 353 | current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
| 354 | elif period_type == 'monthly': |
|
| 355 | current_datetime = current_datetime_local.strftime('%Y-%m') |
|
| 356 | elif period_type == 'yearly': |
|
| 357 | current_datetime = current_datetime_local.strftime('%Y') |
|
| 358 | ||
| 359 | actual_value = Decimal(0.0) if row_space_periodically[1] is None else row_space_periodically[1] |
|
| 360 | reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
| 361 | reporting[energy_category_id]['values'].append(actual_value) |
|
| 362 | reporting[energy_category_id]['subtotal'] += actual_value |
|
| 363 | ||
| 364 | ################################################################################################################ |
|
| 365 | # Step 8: query tariff data |
|
| 366 | ################################################################################################################ |
|
| 367 | parameters_data = dict() |
|
| 368 | parameters_data['names'] = list() |
|
| 369 | parameters_data['timestamps'] = list() |
|
| 370 | parameters_data['values'] = list() |
|
| 371 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 372 | for energy_category_id in energy_category_set: |
|
| 373 | energy_category_tariff_dict = utilities.get_energy_category_tariffs(space['cost_center_id'], |
|
| 374 | energy_category_id, |
|
| 375 | reporting_start_datetime_utc, |
|
| 376 | reporting_end_datetime_utc) |
|
| 377 | tariff_timestamp_list = list() |
|
| 378 | tariff_value_list = list() |
|
| 379 | for k, v in energy_category_tariff_dict.items(): |
|
| 380 | # convert k from utc to local |
|
| 381 | k = k + timedelta(minutes=timezone_offset) |
|
| 382 | tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
| 383 | tariff_value_list.append(v) |
|
| 384 | ||
| 385 | parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
| 386 | parameters_data['timestamps'].append(tariff_timestamp_list) |
|
| 387 | parameters_data['values'].append(tariff_value_list) |
|
| 388 | ||
| 389 | ################################################################################################################ |
|
| 390 | # Step 9: query associated sensors and points data |
|
| 391 | ################################################################################################################ |
|
| 392 | for point in point_list: |
|
| 393 | point_values = [] |
|
| 394 | point_timestamps = [] |
|
| 395 | if point['object_type'] == 'ANALOG_VALUE': |
|
| 396 | query = (" SELECT utc_date_time, actual_value " |
|
| 397 | " FROM tbl_analog_value " |
|
| 398 | " WHERE point_id = %s " |
|
| 399 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 400 | " ORDER BY utc_date_time ") |
|
| 401 | cursor_historical.execute(query, (point['id'], |
|
| 402 | reporting_start_datetime_utc, |
|
| 403 | reporting_end_datetime_utc)) |
|
| 404 | rows = cursor_historical.fetchall() |
|
| 405 | ||
| 406 | if rows is not None and len(rows) > 0: |
|
| 407 | for row in rows: |
|
| 408 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 409 | timedelta(minutes=timezone_offset) |
|
| 410 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 411 | point_timestamps.append(current_datetime) |
|
| 412 | point_values.append(row[1]) |
|
| 413 | ||
| 414 | elif point['object_type'] == 'ENERGY_VALUE': |
|
| 415 | query = (" SELECT utc_date_time, actual_value " |
|
| 416 | " FROM tbl_energy_value " |
|
| 417 | " WHERE point_id = %s " |
|
| 418 | " AND utc_date_time BETWEEN %s AND %s " |
|
| 419 | " ORDER BY utc_date_time ") |
|
| 420 | cursor_historical.execute(query, (point['id'], |
|
| 421 | reporting_start_datetime_utc, |
|
| 422 | reporting_end_datetime_utc)) |
|
| 423 | rows = cursor_historical.fetchall() |
|
| 424 | ||
| 425 | if rows is not None and len(rows) > 0: |
|
| 426 | for row in rows: |
|
| 427 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 428 | timedelta(minutes=timezone_offset) |
|
| 429 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 430 | point_timestamps.append(current_datetime) |
|
| 431 | point_values.append(row[1]) |
|
| 432 | elif point['object_type'] == 'DIGITAL_VALUE': |
|
| 433 | query = (" SELECT utc_date_time, actual_value " |
|
| 434 | " FROM tbl_digital_value " |
|
| 435 | " WHERE point_id = %s " |
|
| 436 | " AND utc_date_time BETWEEN %s AND %s ") |
|
| 437 | cursor_historical.execute(query, (point['id'], |
|
| 438 | reporting_start_datetime_utc, |
|
| 439 | reporting_end_datetime_utc)) |
|
| 440 | rows = cursor_historical.fetchall() |
|
| 441 | ||
| 442 | if rows is not None and len(rows) > 0: |
|
| 443 | for row in rows: |
|
| 444 | current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
| 445 | timedelta(minutes=timezone_offset) |
|
| 446 | current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
| 447 | point_timestamps.append(current_datetime) |
|
| 448 | point_values.append(row[1]) |
|
| 449 | ||
| 450 | parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
| 451 | parameters_data['timestamps'].append(point_timestamps) |
|
| 452 | parameters_data['values'].append(point_values) |
|
| 453 | ||
| 454 | ################################################################################################################ |
|
| 455 | # Step 10: construct the report |
|
| 456 | ################################################################################################################ |
|
| 457 | if cursor_system: |
|
| 458 | cursor_system.close() |
|
| 459 | if cnx_system: |
|
| 460 | cnx_system.disconnect() |
|
| 461 | ||
| 462 | if cursor_energy: |
|
| 463 | cursor_energy.close() |
|
| 464 | if cnx_energy: |
|
| 465 | cnx_energy.disconnect() |
|
| 466 | ||
| 467 | result = dict() |
|
| 468 | ||
| 469 | result['space'] = dict() |
|
| 470 | result['space']['name'] = space['name'] |
|
| 471 | result['space']['area'] = space['area'] |
|
| 472 | ||
| 473 | result['base_period'] = dict() |
|
| 474 | result['base_period']['names'] = list() |
|
| 475 | result['base_period']['units'] = list() |
|
| 476 | result['base_period']['timestamps'] = list() |
|
| 477 | result['base_period']['values'] = list() |
|
| 478 | result['base_period']['subtotals'] = list() |
|
| 479 | result['base_period']['means'] = list() |
|
| 480 | result['base_period']['medians'] = list() |
|
| 481 | result['base_period']['minimums'] = list() |
|
| 482 | result['base_period']['maximums'] = list() |
|
| 483 | result['base_period']['stdevs'] = list() |
|
| 484 | result['base_period']['variances'] = list() |
|
| 485 | ||
| 486 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 487 | for energy_category_id in energy_category_set: |
|
| 488 | result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 489 | result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 490 | result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
| 491 | result['base_period']['values'].append(base[energy_category_id]['values']) |
|
| 492 | result['base_period']['subtotals'].append(base[energy_category_id]['subtotal']) |
|
| 493 | result['base_period']['means'].append(base[energy_category_id]['mean']) |
|
| 494 | result['base_period']['medians'].append(base[energy_category_id]['median']) |
|
| 495 | result['base_period']['minimums'].append(base[energy_category_id]['minimum']) |
|
| 496 | result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
| 497 | result['base_period']['stdevs'].append(base[energy_category_id]['stdev']) |
|
| 498 | result['base_period']['variances'].append(base[energy_category_id]['variance']) |
|
| 499 | ||
| 500 | result['reporting_period'] = dict() |
|
| 501 | result['reporting_period']['names'] = list() |
|
| 502 | result['reporting_period']['energy_category_ids'] = list() |
|
| 503 | result['reporting_period']['units'] = list() |
|
| 504 | result['reporting_period']['timestamps'] = list() |
|
| 505 | result['reporting_period']['values'] = list() |
|
| 506 | result['reporting_period']['subtotals'] = list() |
|
| 507 | result['reporting_period']['means'] = list() |
|
| 508 | result['reporting_period']['means_per_unit_area'] = list() |
|
| 509 | result['reporting_period']['means_increment_rate'] = list() |
|
| 510 | result['reporting_period']['medians'] = list() |
|
| 511 | result['reporting_period']['medians_per_unit_area'] = list() |
|
| 512 | result['reporting_period']['medians_increment_rate'] = list() |
|
| 513 | result['reporting_period']['minimums'] = list() |
|
| 514 | result['reporting_period']['minimums_per_unit_area'] = list() |
|
| 515 | result['reporting_period']['minimums_increment_rate'] = list() |
|
| 516 | result['reporting_period']['maximums'] = list() |
|
| 517 | result['reporting_period']['maximums_per_unit_area'] = list() |
|
| 518 | result['reporting_period']['maximums_increment_rate'] = list() |
|
| 519 | result['reporting_period']['stdevs'] = list() |
|
| 520 | result['reporting_period']['stdevs_per_unit_area'] = list() |
|
| 521 | result['reporting_period']['stdevs_increment_rate'] = list() |
|
| 522 | result['reporting_period']['variances'] = list() |
|
| 523 | result['reporting_period']['variances_per_unit_area'] = list() |
|
| 524 | result['reporting_period']['variances_increment_rate'] = list() |
|
| 525 | ||
| 526 | if energy_category_set is not None and len(energy_category_set) > 0: |
|
| 527 | for energy_category_id in energy_category_set: |
|
| 528 | result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
| 529 | result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
| 530 | result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
| 531 | result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
| 532 | result['reporting_period']['values'].append(reporting[energy_category_id]['values']) |
|
| 533 | result['reporting_period']['subtotals'].append(reporting[energy_category_id]['subtotal']) |
|
| 534 | result['reporting_period']['means'].append(reporting[energy_category_id]['mean']) |
|
| 535 | result['reporting_period']['means_per_unit_area'].append( |
|
| 536 | reporting[energy_category_id]['mean'] / space['area'] |
|
| 537 | if reporting[energy_category_id]['mean'] is not None and |
|
| 538 | space['area'] is not None and |
|
| 539 | space['area'] > Decimal(0.0) |
|
| 540 | else None) |
|
| 541 | result['reporting_period']['means_increment_rate'].append( |
|
| 542 | (reporting[energy_category_id]['mean'] - base[energy_category_id]['mean']) / |
|
| 543 | base[energy_category_id]['mean'] if (base[energy_category_id]['mean'] is not None and |
|
| 544 | base[energy_category_id]['mean'] > Decimal(0.0)) |
|
| 545 | else None) |
|
| 546 | result['reporting_period']['medians'].append(reporting[energy_category_id]['median']) |
|
| 547 | result['reporting_period']['medians_per_unit_area'].append( |
|
| 548 | reporting[energy_category_id]['median'] / space['area'] |
|
| 549 | if reporting[energy_category_id]['median'] is not None and |
|
| 550 | space['area'] is not None and |
|
| 551 | space['area'] > Decimal(0.0) |
|
| 552 | else None) |
|
| 553 | result['reporting_period']['medians_increment_rate'].append( |
|
| 554 | (reporting[energy_category_id]['median'] - base[energy_category_id]['median']) / |
|
| 555 | base[energy_category_id]['median'] if (base[energy_category_id]['median'] is not None and |
|
| 556 | base[energy_category_id]['median'] > Decimal(0.0)) |
|
| 557 | else None) |
|
| 558 | result['reporting_period']['minimums'].append(reporting[energy_category_id]['minimum']) |
|
| 559 | result['reporting_period']['minimums_per_unit_area'].append( |
|
| 560 | reporting[energy_category_id]['minimum'] / space['area'] |
|
| 561 | if reporting[energy_category_id]['minimum'] is not None and |
|
| 562 | space['area'] is not None and space['area'] > Decimal(0.0) |
|
| 563 | else None) |
|
| 564 | result['reporting_period']['minimums_increment_rate'].append( |
|
| 565 | (reporting[energy_category_id]['minimum'] - base[energy_category_id]['minimum']) / |
|
| 566 | base[energy_category_id]['minimum'] if (base[energy_category_id]['minimum'] is not None and |
|
| 567 | base[energy_category_id]['minimum'] > Decimal(0.0)) |
|
| 568 | else None) |
|
| 569 | result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
| 570 | result['reporting_period']['maximums_per_unit_area'].append( |
|
| 571 | reporting[energy_category_id]['maximum'] / space['area'] |
|
| 572 | if reporting[energy_category_id]['maximum'] is not None and |
|
| 573 | space['area'] is not None and |
|
| 574 | space['area'] > Decimal(0.0) |
|
| 575 | else None) |
|
| 576 | result['reporting_period']['maximums_increment_rate'].append( |
|
| 577 | (reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
| 578 | base[energy_category_id]['maximum'] |
|
| 579 | if (base[energy_category_id]['maximum'] is not None and |
|
| 580 | base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
| 581 | else None) |
|
| 582 | result['reporting_period']['stdevs'].append(reporting[energy_category_id]['stdev']) |
|
| 583 | result['reporting_period']['stdevs_per_unit_area'].append( |
|
| 584 | reporting[energy_category_id]['stdev'] / space['area'] |
|
| 585 | if reporting[energy_category_id]['stdev'] is not None and |
|
| 586 | space['area'] is not None and |
|
| 587 | space['area'] > Decimal(0.0) |
|
| 588 | else None) |
|
| 589 | result['reporting_period']['stdevs_increment_rate'].append( |
|
| 590 | (reporting[energy_category_id]['stdev'] - base[energy_category_id]['stdev']) / |
|
| 591 | base[energy_category_id]['stdev'] if (base[energy_category_id]['stdev'] is not None and |
|
| 592 | base[energy_category_id]['stdev'] > Decimal(0.0)) |
|
| 593 | else None) |
|
| 594 | result['reporting_period']['variances'].append(reporting[energy_category_id]['variance']) |
|
| 595 | result['reporting_period']['variances_per_unit_area'].append( |
|
| 596 | reporting[energy_category_id]['variance'] / space['area'] |
|
| 597 | if reporting[energy_category_id]['variance'] is not None and |
|
| 598 | space['area'] is not None and |
|
| 599 | space['area'] > Decimal(0.0) |
|
| 600 | else None) |
|
| 601 | result['reporting_period']['variances_increment_rate'].append( |
|
| 602 | (reporting[energy_category_id]['variance'] - base[energy_category_id]['variance']) / |
|
| 603 | base[energy_category_id]['variance'] if (base[energy_category_id]['variance'] is not None and |
|
| 604 | base[energy_category_id]['variance'] > Decimal(0.0)) |
|
| 605 | else None) |
|
| 606 | ||
| 607 | result['parameters'] = { |
|
| 608 | "names": parameters_data['names'], |
|
| 609 | "timestamps": parameters_data['timestamps'], |
|
| 610 | "values": parameters_data['values'] |
|
| 611 | } |
|
| 612 | # export result to Excel file and then encode the file to base64 string |
|
| 613 | result['excel_bytes_base64'] = excelexporters.spacestatistics.export(result, |
|
| 614 | space['name'], |
|
| 615 | reporting_start_datetime_local, |
|
| 616 | reporting_end_datetime_local, |
|
| 617 | period_type) |
|
| 618 | ||
| 619 | resp.body = json.dumps(result) |
|
| 620 | ||
