MyEMS /
myems
| Total Complexity | 58 |
| Total Lines | 576 |
| Duplicated Lines | 5.21 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like clean_energy_value often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | import mysql.connector |
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| 2 | import config |
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| 3 | import time |
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| 4 | from datetime import datetime, timedelta |
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| 5 | |||
| 6 | |||
| 7 | ######################################################################################################################## |
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| 8 | # This procedure will find and tag the bad energy values. |
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| 9 | # |
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| 10 | # Step 1: get the time slot to clean. |
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| 11 | # Step 2: check bad case class 1 with high limits and low limits. |
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| 12 | # Step 3: check bad case class 2 which is in concave shape model. |
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| 13 | # Step 4: tag the is_bad property of energy values. |
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| 14 | ######################################################################################################################## |
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| 15 | |||
| 16 | def process(logger): |
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| 17 | |||
| 18 | while True: |
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| 19 | # the outermost loop to reconnect server if there is a connection error |
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| 20 | cnx_historical = None |
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| 21 | cursor_historical = None |
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| 22 | try: |
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| 23 | cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
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| 24 | cursor_historical = cnx_historical.cursor() |
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| 25 | except Exception as e: |
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| 26 | logger.error("Error at the begin of clean_energy_value.process " + str(e)) |
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| 27 | if cursor_historical: |
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| 28 | cursor_historical.close() |
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| 29 | if cnx_historical: |
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| 30 | cnx_historical.disconnect() |
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| 31 | time.sleep(60) |
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| 32 | continue |
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| 33 | |||
| 34 | # Note: |
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| 35 | # the default value of unchecked values' is_bad property is NULL |
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| 36 | # if a value is checked and the result is bad then is_bad would be set to TRUE |
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| 37 | # else if a value is checked and the result is good then is_bad would be set to FALSE |
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| 38 | |||
| 39 | ################################################################################################################ |
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| 40 | # Step 1: get the time slot to clean. |
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| 41 | ################################################################################################################ |
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| 42 | |||
| 43 | min_datetime = None |
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| 44 | max_datetime = None |
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| 45 | try: |
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| 46 | query = (" SELECT MIN(utc_date_time), MAX(utc_date_time) " |
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| 47 | " FROM tbl_energy_value " |
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| 48 | " WHERE is_bad IS NULL ") |
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| 49 | cursor_historical.execute(query, ()) |
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| 50 | row_datetime = cursor_historical.fetchone() |
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| 51 | if row_datetime is not None and len(row_datetime) == 2 and \ |
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| 52 | isinstance(row_datetime[0], datetime) and isinstance(row_datetime[1], datetime): |
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| 53 | # NOTE: To avoid omission mistakes, we start one hour early |
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| 54 | min_datetime = row_datetime[0] - timedelta(hours=1) |
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| 55 | max_datetime = row_datetime[1] |
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| 56 | |||
| 57 | except Exception as e: |
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| 58 | print("Error in Step 1 of clean_energy_value.process " + str(e)) |
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| 59 | logger.error("Error in Step 1 of clean_energy_value.process " + str(e)) |
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| 60 | if cursor_historical: |
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| 61 | cursor_historical.close() |
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| 62 | if cnx_historical: |
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| 63 | cnx_historical.disconnect() |
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| 64 | time.sleep(60) |
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| 65 | continue |
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| 66 | |||
| 67 | if min_datetime is None or max_datetime is None: |
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| 68 | print("min_datetime or max_datetime is None") |
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| 69 | if cursor_historical: |
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| 70 | cursor_historical.close() |
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| 71 | if cnx_historical: |
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| 72 | cnx_historical.disconnect() |
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| 73 | time.sleep(60) |
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| 74 | continue |
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| 75 | else: |
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| 76 | print("min_datetime: " + min_datetime.isoformat()[0:19]) |
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| 77 | print("max_datetime: " + max_datetime.isoformat()[0:19]) |
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| 78 | |||
| 79 | ################################################################################################################ |
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| 80 | # Step 2: check bad case class 1 with high limits and low limits. |
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| 81 | ################################################################################################################ |
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| 82 | |||
| 83 | ################################################################################################################ |
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| 84 | # bad case 1.1 |
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| 85 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 86 | # 104814811 3333 2018-01-31 16:45:04 115603.0078125 good |
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| 87 | # 104814588 3333 2018-01-31 16:44:00 115603.0078125 good |
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| 88 | # 104815007 3333 2018-01-31 16:46:09 1.832278249396618e21 bad |
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| 89 | # 104815226 3333 2018-01-31 16:47:13 1.832278249396618e21 bad |
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| 90 | # 104815423 3333 2018-01-31 16:48:17 1.832278249396618e21 bad |
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| 91 | # 104815643 3333 2018-01-31 16:49:22 1.832278249396618e21 bad |
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| 92 | # 104815820 3333 2018-01-31 16:50:26 1.832278249396618e21 bad |
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| 93 | # 104816012 3333 2018-01-31 16:51:30 1.832278249396618e21 bad |
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| 94 | # 104816252 3333 2018-01-31 16:52:34 1.832278249396618e21 bad |
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| 95 | # 104816446 3333 2018-01-31 16:53:38 1.832278249396618e21 bad |
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| 96 | # 104816667 3333 2018-01-31 16:54:43 1.832278249396618e21 bad |
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| 97 | # 104816860 3333 2018-01-31 16:55:47 1.832278249396618e21 bad |
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| 98 | # 104817065 3333 2018-01-31 16:56:51 1.832278249396618e21 bad |
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| 99 | # 104817284 3333 2018-01-31 16:57:55 1.832278249396618e21 bad |
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| 100 | # 104817482 3333 2018-01-31 16:58:59 1.832278249396618e21 bad |
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| 101 | # 104817723 3333 2018-01-31 17:00:04 1.832278249396618e21 bad |
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| 102 | # 104817940 3333 2018-01-31 17:01:08 115749.0078125 good |
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| 103 | # 104818142 3333 2018-01-31 17:02:11 115749.0078125 good |
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| 104 | # 104818380 3333 2018-01-31 17:03:16 115749.0078125 good |
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| 105 | # 104818596 3333 2018-01-31 17:04:20 115749.0078125 good |
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| 106 | ################################################################################################################ |
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| 107 | |||
| 108 | ################################################################################################################ |
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| 109 | # bad case 1.2: |
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| 110 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 111 | # 3333 2018-01-31 17:27:53 115823.0078125 good |
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| 112 | # 3333 2018-01-31 17:28:57 115823.0078125 good |
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| 113 | # 3333 2018-01-31 17:30:02 115823.0078125 good |
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| 114 | # 3333 2018-01-31 17:31:06 115823.0078125 good |
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| 115 | # 3333 2018-01-31 17:32:11 0 bad |
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| 116 | # 3333 2018-01-31 17:33:15 0 bad |
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| 117 | # 3333 2018-01-31 17:34:19 0 bad |
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| 118 | # 3333 2018-01-31 17:35:24 0 bad |
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| 119 | # 3333 2018-01-31 17:36:28 0 bad |
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| 120 | # 3333 2018-01-31 17:37:32 0 bad |
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| 121 | # 3333 2018-01-31 17:38:36 0 bad |
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| 122 | # 3333 2018-01-31 17:39:41 0 bad |
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| 123 | # 3333 2018-01-31 17:40:44 0 bad |
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| 124 | # 3333 2018-01-31 17:41:49 0 bad |
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| 125 | # 3333 2018-01-31 17:43:57 0 bad |
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| 126 | # 3333 2018-01-31 17:42:53 0 bad |
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| 127 | # 3333 2018-01-31 17:45:01 0 bad |
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| 128 | # 3333 2018-01-31 17:46:06 0 bad |
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| 129 | # 3333 2018-01-31 17:47:10 0 bad |
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| 130 | # 3333 2018-01-31 17:48:14 115969.0078125 good |
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| 131 | # 3333 2018-01-31 17:49:18 115969.0078125 good |
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| 132 | # 3333 2018-01-31 17:50:22 115969.0078125 good |
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| 133 | ################################################################################################################ |
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| 134 | |||
| 135 | ################################################################################################################ |
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| 136 | # bad case 1.3: |
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| 137 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 138 | # 3333 2018-02-04 07:00:38 139968 good |
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| 139 | # 3333 2018-02-04 07:01:42 139968 good |
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| 140 | # 3333 2018-02-04 07:03:54 -7.068193740872921e-3 bad |
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| 141 | # 3333 2018-02-04 07:04:58 -7.068193740872921e-3 bad |
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| 142 | # 3333 2018-02-04 07:06:03 -7.068193740872921e-3 bad |
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| 143 | # 3333 2018-02-04 07:07:06 -7.068193740872921e-3 bad |
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| 144 | # 3333 2018-02-04 07:08:10 -7.068193740872921e-3 bad |
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| 145 | # 3333 2018-02-04 07:09:13 -7.068193740872921e-3 bad |
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| 146 | # 3333 2018-02-04 07:10:17 -7.068193740872921e-3 bad |
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| 147 | # 3333 2018-02-04 07:11:21 -7.068193740872921e-3 bad |
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| 148 | # 3333 2018-02-04 07:12:25 -7.068193740872921e-3 bad |
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| 149 | # 3333 2018-02-04 07:13:29 -7.068193740872921e-3 bad |
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| 150 | # 3333 2018-02-04 07:14:33 -7.068193740872921e-3 bad |
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| 151 | # 3333 2018-02-04 07:15:37 -7.068193740872921e-3 bad |
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| 152 | # 3333 2018-02-04 07:16:41 -7.068193740872921e-3 bad |
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| 153 | # 3333 2018-02-04 07:17:45 140114 good |
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| 154 | # 3333 2018-02-04 07:18:49 140114 good |
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| 155 | # 3333 2018-02-04 07:19:53 140114 good |
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| 156 | ################################################################################################################ |
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| 157 | |||
| 158 | ################################################################################################################ |
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| 159 | # bad case 1.4: |
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| 160 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 161 | # 3333 2018-02-08 01:16:38 165746.015625 good |
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| 162 | # 3333 2018-02-08 01:15:34 165746.015625 good |
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| 163 | # 3333 2018-02-08 01:14:30 165746.015625 good |
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| 164 | # 3333 2018-02-08 01:13:27 0.00303281145170331 bad |
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| 165 | # 3333 2018-02-08 01:12:22 0.00303281145170331 bad |
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| 166 | # 3333 2018-02-08 01:11:19 0.00303281145170331 bad |
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| 167 | # 3333 2018-02-08 01:10:15 0.00303281145170331 bad |
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| 168 | # 3333 2018-02-08 01:09:11 0.00303281145170331 bad |
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| 169 | # 3333 2018-02-08 01:08:06 0.00303281145170331 bad |
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| 170 | # 3333 2018-02-08 01:07:02 0.00303281145170331 bad |
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| 171 | # 3333 2018-02-08 01:05:58 0.00303281145170331 bad |
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| 172 | # 3333 2018-02-08 01:04:54 0.00303281145170331 bad |
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| 173 | # 3333 2018-02-08 01:03:50 0.00303281145170331 bad |
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| 174 | # 3333 2018-02-08 01:02:46 0.00303281145170331 bad |
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| 175 | # 3333 2018-02-08 01:01:42 0.00303281145170331 bad |
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| 176 | # 3333 2018-02-08 01:00:39 0.00303281145170331 bad |
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| 177 | # 3333 2018-02-08 00:59:34 0.00303281145170331 bad |
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| 178 | # 3333 2018-02-08 00:58:31 0.00303281145170331 bad |
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| 179 | # 3333 2018-02-08 00:57:27 165599.015625 good |
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| 180 | # 3333 2018-02-08 00:56:23 165599.015625 good |
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| 181 | # 3333 2018-02-08 00:55:20 165599.015625 good |
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| 182 | # 3333 2018-02-08 00:54:16 165599.015625 good |
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| 183 | ################################################################################################################ |
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| 184 | print("Step 2: Processing bad case 1.x") |
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| 185 | cnx_system = None |
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| 186 | cursor_system = None |
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| 187 | try: |
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| 188 | cnx_system = mysql.connector.connect(**config.myems_system_db) |
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| 189 | cursor_system = cnx_system.cursor(dictionary=True) |
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| 190 | |||
| 191 | query = (" SELECT id, high_limit, low_limit " |
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| 192 | " FROM tbl_points " |
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| 193 | " WHERE object_type='ENERGY_VALUE'") |
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| 194 | cursor_system.execute(query) |
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| 195 | rows_points = cursor_system.fetchall() |
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| 196 | |||
| 197 | point_dict = dict() |
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| 198 | if rows_points is not None and len(rows_points) > 0: |
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| 199 | for row in rows_points: |
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| 200 | point_dict[row['id']] = {"high_limit": row['high_limit'], |
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| 201 | "low_limit": row['low_limit']} |
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| 202 | except Exception as e: |
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| 203 | logger.error("Error in step 2.1 of clean_energy_value.process " + str(e)) |
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| 204 | time.sleep(60) |
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| 205 | continue |
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| 206 | finally: |
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| 207 | if cursor_system: |
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| 208 | cursor_system.close() |
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| 209 | if cnx_system: |
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| 210 | cnx_system.disconnect() |
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| 211 | |||
| 212 | try: |
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| 213 | query = (" SELECT id, point_id, actual_value " |
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| 214 | " FROM tbl_energy_value " |
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| 215 | " WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE ") |
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| 216 | cursor_historical.execute(query, (min_datetime, max_datetime,)) |
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| 217 | rows_energy_values = cursor_historical.fetchall() |
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| 218 | except Exception as e: |
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| 219 | logger.error("Error in step 2.2 of clean_energy_value.process " + str(e)) |
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| 220 | if cursor_historical: |
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| 221 | cursor_historical.close() |
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| 222 | if cnx_historical: |
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| 223 | cnx_historical.disconnect() |
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| 224 | time.sleep(60) |
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| 225 | continue |
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| 226 | |||
| 227 | # initialize bad list |
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| 228 | bad_list = list() |
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| 229 | |||
| 230 | if rows_energy_values is not None and len(rows_energy_values) > 0: |
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| 231 | for row_energy_value in rows_energy_values: |
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| 232 | point_id = row_energy_value[1] |
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| 233 | actual_value = row_energy_value[2] |
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| 234 | point = point_dict.get(point_id, None) |
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|
introduced
by
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| 235 | if point is None or actual_value > point['high_limit'] or actual_value < point['low_limit']: |
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| 236 | bad_list.append(row_energy_value[0]) |
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| 237 | |||
| 238 | print('bad list: ' + str(bad_list)) |
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| 239 | View Code Duplication | if len(bad_list) > 0: |
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| 240 | try: |
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| 241 | update = (" UPDATE tbl_energy_value " |
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| 242 | " SET is_bad = TRUE " |
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| 243 | " WHERE id IN (" + ', '.join(map(str, bad_list)) + ")") |
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| 244 | cursor_historical.execute(update, ) |
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| 245 | cnx_historical.commit() |
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| 246 | except Exception as e: |
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| 247 | logger.error("Error in step 2.3 of clean_energy_value.process " + str(e)) |
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| 248 | if cursor_historical: |
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| 249 | cursor_historical.close() |
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| 250 | if cnx_historical: |
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| 251 | cnx_historical.disconnect() |
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| 252 | time.sleep(60) |
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| 253 | continue |
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| 254 | |||
| 255 | ################################################################################################################ |
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| 256 | # Step 3: check bad case class 2 which is in concave shape model. |
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| 257 | ################################################################################################################ |
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| 258 | print("Step 3: Processing bad case 2.x") |
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| 259 | ################################################################################################################ |
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| 260 | # bad case 2.1 |
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| 261 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 262 | # 3333 2018-02-05 04:55:45 146129.015 good |
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| 263 | # 3333 2018-02-05 04:56:49 146129.015 good |
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| 264 | # 3333 2018-02-05 04:57:54 146129.015 good |
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| 265 | # 3333 2018-02-05 05:22:52 145693.015 bad |
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| 266 | # 3333 2018-02-05 05:25:01 146274 good |
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| 267 | # 3333 2018-02-05 05:26:03 146274 good |
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| 268 | # 3333 2018-02-05 05:27:05 146274 good |
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| 269 | # 3333 2018-02-05 05:29:30 146274 good |
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| 270 | ################################################################################################################ |
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| 271 | |||
| 272 | ################################################################################################################ |
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| 273 | # bad case 2.2 |
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| 274 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 275 | # 3321 2018-05-15 15:09:54 33934040 good |
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| 276 | # 3321 2018-05-15 15:08:51 33934040 good |
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| 277 | # 3321 2018-05-15 15:07:47 33934040 good |
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| 278 | # 3321 2018-05-15 15:06:44 33934040 good |
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| 279 | # 3321 2018-05-15 15:05:40 33934040 good |
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| 280 | # 3321 2018-05-15 15:04:36 33934040 good |
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| 281 | # 3321 2018-05-15 09:09:00 33928880 bad |
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| 282 | # 3321 2018-05-15 09:05:23 33933568 good |
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| 283 | # 3321 2018-05-15 09:04:20 33933568 good |
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| 284 | # 3321 2018-05-15 09:03:16 33933568 good |
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| 285 | # 3321 2018-05-15 09:02:13 33933560 good |
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| 286 | # 3321 2018-05-15 09:01:09 33933560 good |
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| 287 | # 3321 2018-05-15 09:00:04 33933560 good |
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| 288 | ################################################################################################################ |
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| 289 | |||
| 290 | ################################################################################################################ |
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| 291 | # bad case 2.3 |
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| 292 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 293 | # 554 2018-05-19 15:32:52 24001 good |
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| 294 | # 554 2018-05-19 15:30:45 24001 good |
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| 295 | # 554 2018-05-19 15:28:39 24001 good |
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| 296 | # 554 2018-05-19 15:26:32 24001 good |
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| 297 | # 554 2018-05-19 15:24:25 24001 good |
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| 298 | # 554 2018-05-19 15:22:18 24001 good |
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| 299 | # 554 2018-05-19 15:20:10 24001 good |
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| 300 | # 554 2018-05-19 15:18:04 24001 good |
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| 301 | # 554 2018-05-19 15:15:58 24001 good |
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| 302 | # 554 2018-05-19 15:13:51 24001 good |
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| 303 | # 554 2018-05-19 15:11:43 24001 good |
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| 304 | # 554 2018-05-19 15:09:37 24001 good |
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| 305 | # 554 2018-05-19 15:07:29 24000 good |
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| 306 | # 554 2018-05-19 15:05:22 23000 bad |
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| 307 | # 554 2018-05-19 15:03:14 23999 good |
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| 308 | # 554 2018-05-19 15:01:06 23999 good |
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| 309 | # 554 2018-05-19 14:58:59 23999 good |
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| 310 | # 554 2018-05-19 14:56:52 23998 good |
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| 311 | # 554 2018-05-19 14:54:45 23998 good |
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| 312 | # 554 2018-05-19 14:52:39 23998 good |
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| 313 | ################################################################################################################ |
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| 314 | # todo bad case 2.3.1 |
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| 315 | # "id", "point_id", "utc_date_time", "actual_value", "is_bad" (actual) |
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| 316 | # 68504700, 2, "2021-01-09 03:40:12.0", 40454414.063, 0 |
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| 317 | # 68507243, 2, "2021-01-09 03:43:12.0", 40454476.563, 0 |
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| 318 | # 68510030, 2, "2021-01-09 03:47:17.0", 40428074.219, 0 ? |
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| 319 | # 68512573, 2, "2021-01-09 03:50:18.0", 40454621.094, 0 |
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| 320 | # 68515421, 2, "2021-01-09 03:54:23.0", 40454703.125, 0 |
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| 321 | # 68517964, 2, "2021-01-09 03:57:23.0", 40454761.719, 0 |
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| 322 | |||
| 323 | ################################################################################################################ |
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| 324 | # bad case 2.4 |
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| 325 | # id point_id utc_date_time actual_value is_bad (expected) |
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| 326 | # 104373141 3336 2018-01-30 03:04:12 216463.015625 good |
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| 327 | # 104373337 3336 2018-01-30 03:05:15 216463.015625 good |
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| 328 | # 104373555 3336 2018-01-30 03:06:20 216463.015625 good |
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| 329 | # 104373750 3336 2018-01-30 03:07:25 192368.015625 bad |
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| 330 | # 104373957 3336 2018-01-30 03:08:29 192368.015625 bad |
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| 331 | # 104374175 3336 2018-01-30 03:09:33 192368.015625 bad |
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| 332 | # 104374382 3336 2018-01-30 03:10:38 192368.015625 bad |
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| 333 | # 104374604 3336 2018-01-30 03:11:42 192368.015625 bad |
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| 334 | # 104374792 3336 2018-01-30 03:12:47 192368.015625 bad |
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| 335 | # 104375010 3336 2018-01-30 03:13:51 192368.015625 bad |
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| 336 | # 104375200 3336 2018-01-30 03:14:55 192368.015625 bad |
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| 337 | # 104375418 3336 2018-01-30 03:16:00 192368.015625 bad |
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| 338 | # 104375617 3336 2018-01-30 03:17:04 192368.015625 bad |
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| 339 | # 104375837 3336 2018-01-30 03:18:08 192368.015625 bad |
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| 340 | # 104376023 3336 2018-01-30 03:19:12 192368.015625 bad |
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| 341 | # 104376216 3336 2018-01-30 03:20:16 192368.015625 bad |
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| 342 | # 104376435 3336 2018-01-30 03:21:21 192368.015625 bad |
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| 343 | # 104376634 3336 2018-01-30 03:22:25 192368.015625 bad |
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| 344 | # 104376853 3336 2018-01-30 03:23:30 192368.015625 bad |
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| 345 | # 104377071 3336 2018-01-30 03:24:34 192368.015625 bad |
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| 346 | # 104377274 3336 2018-01-30 03:25:38 192368.015625 bad |
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| 347 | # 104377501 3336 2018-01-30 03:26:42 216574.015625 good |
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| 348 | # 104377714 3336 2018-01-30 03:27:47 216574.015625 good |
||
| 349 | ################################################################################################################ |
||
| 350 | |||
| 351 | ################################################################################################################ |
||
| 352 | # bad case 2.5 |
||
| 353 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 354 | # 104370839 3334 2018-01-30 02:52:23 844966.0625 good |
||
| 355 | # 104371064 3334 2018-01-30 02:53:27 844966.0625 good |
||
| 356 | # 104371261 3334 2018-01-30 02:54:32 844966.0625 good |
||
| 357 | # 104371479 3334 2018-01-30 02:55:36 826142.0625 bad |
||
| 358 | # 104371672 3334 2018-01-30 02:56:41 826142.0625 bad |
||
| 359 | # 104371884 3334 2018-01-30 02:57:45 826142.0625 bad |
||
| 360 | # 104372110 3334 2018-01-30 02:58:49 826142.0625 bad |
||
| 361 | # 104372278 3334 2018-01-30 02:59:54 845019.0625 good |
||
| 362 | # 104372512 3334 2018-01-30 03:00:58 845019.0625 good |
||
| 363 | # 104372704 3334 2018-01-30 03:02:03 845019.0625 good |
||
| 364 | ################################################################################################################ |
||
| 365 | |||
| 366 | ################################################################################################################ |
||
| 367 | # bad case 2.6 |
||
| 368 | # 394084273 1001444 2019-08-22 03:39:44 38969028 good |
||
| 369 | # 394083709 1001444 2019-08-22 03:38:43 38968876 good |
||
| 370 | # 394083145 1001444 2019-08-22 03:37:43 28371884 bad |
||
| 371 | # 394082019 1001444 2019-08-22 03:35:42 28371884 bad |
||
| 372 | # 394081456 1001444 2019-08-22 03:34:42 28371884 bad |
||
| 373 | # 394080892 1001444 2019-08-22 03:33:42 28371884 bad |
||
| 374 | # 394079200 1001444 2019-08-22 03:30:38 28371884 bad |
||
| 375 | # 394077511 1001444 2019-08-22 03:27:37 38968408 good |
||
| 376 | # 394076947 1001444 2019-08-22 03:26:37 38968236 good |
||
| 377 | # 394076384 1001444 2019-08-22 03:25:37 38968060 good |
||
| 378 | ################################################################################################################ |
||
| 379 | |||
| 380 | ################################################################################################################ |
||
| 381 | # bad case 2.7 |
||
| 382 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 383 | # 17303260 11 2020-3-15 05:43:52 33600 good |
||
| 384 | # 17303399 11 2020-3-15 05:44:58 33600 good |
||
| 385 | # 17303538 11 2020-3-15 05:46:04 33600 good |
||
| 386 | # 17303677 11 2020-3-15 05:47:10 33500 bad |
||
| 387 | # 17303816 11 2020-3-15 05:48:15 33500 bad |
||
| 388 | # 17303955 11 2020-3-15 05:49:21 33600 good |
||
| 389 | # 17304094 11 2020-3-15 05:50:27 33600 good |
||
| 390 | # 17304233 11 2020-3-15 05:51:33 33600 good |
||
| 391 | ################################################################################################################ |
||
| 392 | |||
| 393 | try: |
||
| 394 | query = (" SELECT point_id, id, utc_date_time, actual_value " |
||
| 395 | " FROM tbl_energy_value " |
||
| 396 | " WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE " |
||
| 397 | " ORDER BY point_id, utc_date_time ") |
||
| 398 | cursor_historical.execute(query, (min_datetime, max_datetime,)) |
||
| 399 | rows_energy_values = cursor_historical.fetchall() |
||
| 400 | except Exception as e: |
||
| 401 | logger.error("Error in step 3.1 of clean_energy_value.process " + str(e)) |
||
| 402 | if cursor_historical: |
||
| 403 | cursor_historical.close() |
||
| 404 | if cnx_historical: |
||
| 405 | cnx_historical.disconnect() |
||
| 406 | time.sleep(60) |
||
| 407 | continue |
||
| 408 | |||
| 409 | point_value_dict = dict() |
||
| 410 | current_point_value_list = list() |
||
| 411 | current_point_id = 0 |
||
| 412 | |||
| 413 | if rows_energy_values is not None and len(rows_energy_values) > 0: |
||
| 414 | for row_energy_value in rows_energy_values: |
||
| 415 | previous_point_id = current_point_id |
||
| 416 | current_point_id = row_energy_value[0] |
||
| 417 | if current_point_id not in point_value_dict.keys(): |
||
| 418 | # new point id found |
||
| 419 | # save previous point values |
||
| 420 | if len(current_point_value_list) > 0: |
||
| 421 | point_value_dict[previous_point_id] = current_point_value_list |
||
| 422 | current_point_value_list = list() |
||
| 423 | |||
| 424 | current_point_value_list.append({'id': row_energy_value[1], |
||
| 425 | 'actual_value': row_energy_value[3]}) |
||
| 426 | else: |
||
| 427 | current_point_value_list.append({'id': row_energy_value[1], |
||
| 428 | 'actual_value': row_energy_value[3]}) |
||
| 429 | # end of for loop |
||
| 430 | # save rest point values |
||
| 431 | if len(current_point_value_list) > 0: |
||
| 432 | point_value_dict[current_point_id] = current_point_value_list |
||
| 433 | |||
| 434 | # reinitialize bad list |
||
| 435 | bad_list = list() |
||
| 436 | |||
| 437 | for point_id, point_value_list in point_value_dict.items(): |
||
| 438 | if len(point_value_list) <= 1: |
||
| 439 | continue |
||
| 440 | elif len(point_value_list) == 2: |
||
| 441 | if point_value_list[1]['actual_value'] < point_value_list[0]['actual_value']: |
||
| 442 | bad_list.append(point_value_list[1]['id']) |
||
| 443 | continue |
||
| 444 | else: |
||
| 445 | base_point_value = point_value_list[0]['actual_value'] |
||
| 446 | concave_point_value_list = list() |
||
| 447 | for i in range(len(point_value_list)): |
||
| 448 | if point_value_list[i]['actual_value'] < base_point_value: |
||
| 449 | # candidate concave value found |
||
| 450 | concave_point_value_list.append(point_value_list[i]['id']) |
||
| 451 | else: |
||
| 452 | # normal value found |
||
| 453 | if len(concave_point_value_list) > 0: |
||
| 454 | # save confirmed concave value(s) to bad value(s) |
||
| 455 | bad_list.extend(concave_point_value_list) |
||
| 456 | |||
| 457 | # prepare for next candidate concave value list |
||
| 458 | base_point_value = point_value_list[i]['actual_value'] |
||
| 459 | concave_point_value_list.clear() |
||
| 460 | continue |
||
| 461 | |||
| 462 | print('bad list: ' + str(bad_list)) |
||
| 463 | View Code Duplication | if len(bad_list) > 0: |
|
| 464 | try: |
||
| 465 | update = (" UPDATE tbl_energy_value " |
||
| 466 | " SET is_bad = TRUE " |
||
| 467 | " WHERE id IN (" + ', '.join(map(str, bad_list)) + ")") |
||
| 468 | cursor_historical.execute(update, ) |
||
| 469 | cnx_historical.commit() |
||
| 470 | except Exception as e: |
||
| 471 | logger.error("Error in step 3.2 of clean_energy_value.process " + str(e)) |
||
| 472 | if cursor_historical: |
||
| 473 | cursor_historical.close() |
||
| 474 | if cnx_historical: |
||
| 475 | cnx_historical.disconnect() |
||
| 476 | time.sleep(60) |
||
| 477 | continue |
||
| 478 | |||
| 479 | ################################################################################################################ |
||
| 480 | # TODO: bad case 2.8 |
||
| 481 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 482 | # 105752070 3333 2018-02-04 00:27:15 138144 good |
||
| 483 | # 105752305 3333 2018-02-04 00:28:19 138144 good |
||
| 484 | # 105752523 3333 2018-02-04 00:29:22 138144 good |
||
| 485 | # 105752704 3333 2018-02-04 00:30:26 138144 good |
||
| 486 | # 105752924 3333 2018-02-04 00:31:30 138144 good |
||
| 487 | # 105753138 3333 2018-02-04 00:32:34 138144 good |
||
| 488 | # 105753351 3333 2018-02-04 00:33:38 138144 good |
||
| 489 | # 105753577 3333 2018-02-04 00:34:42 52776558592 bad? |
||
| 490 | # 105753794 3333 2018-02-04 00:35:46 52776558592 bad? |
||
| 491 | # 105753999 3333 2018-02-04 00:36:50 52776558592 bad? |
||
| 492 | # 105754231 3333 2018-02-04 00:37:54 52776558592 bad? |
||
| 493 | # 105754443 3333 2018-02-04 00:38:58 52776558592 bad? |
||
| 494 | # 105754655 3333 2018-02-04 00:40:01 52776558592 bad? |
||
| 495 | # 105754878 3333 2018-02-04 00:41:06 52776558592 bad? |
||
| 496 | # 105755092 3333 2018-02-04 00:42:09 52776558592 bad? |
||
| 497 | # 105755273 3333 2018-02-04 00:43:14 52776558592 bad? |
||
| 498 | # 105755495 3333 2018-02-04 00:44:17 52776558592 bad? |
||
| 499 | # 105755655 3333 2018-02-04 00:45:21 52776558592 bad? |
||
| 500 | # 105755854 3333 2018-02-04 00:46:25 52776558592 bad? |
||
| 501 | # 105756073 3333 2018-02-04 00:47:29 52776558592 bad? |
||
| 502 | # 105756272 3333 2018-02-04 00:48:34 52776558592 bad? |
||
| 503 | # 105756489 3333 2018-02-04 00:49:38 52776558592 bad? |
||
| 504 | ################################################################################################################ |
||
| 505 | |||
| 506 | ################################################################################################################ |
||
| 507 | # TODO: bad case 2.10 |
||
| 508 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 509 | # 106363135 3336 2018-02-06 04:45:57 253079.015625 good |
||
| 510 | # 106363776 3336 2018-02-06 04:49:09 253079.015625 good |
||
| 511 | # 106364381 3336 2018-02-06 04:52:21 253079.015625 good |
||
| 512 | # 106364603 3336 2018-02-06 04:53:25 253079.015625 good |
||
| 513 | # 106365213 3336 2018-02-06 04:56:37 253079.015625 good |
||
| 514 | # 106365634 3336 2018-02-06 04:58:45 253079.015625 good |
||
| 515 | # 106366055 3336 2018-02-06 05:00:53 253079.015625 good |
||
| 516 | # 106367097 3336 2018-02-06 05:06:12 259783.015625 bad? |
||
| 517 | # 106367507 3336 2018-02-06 05:08:21 259783.015625 bad? |
||
| 518 | # 106368318 3336 2018-02-06 05:12:37 259783.015625 bad? |
||
| 519 | # 106368732 3336 2018-02-06 05:14:44 259783.015625 bad? |
||
| 520 | # 106368952 3336 2018-02-06 05:15:48 259783.015625 bad? |
||
| 521 | # 106369145 3336 2018-02-06 05:16:52 259783.015625 bad? |
||
| 522 | # 106369353 3336 2018-02-06 05:17:56 259783.015625 bad? |
||
| 523 | ################################################################################################################ |
||
| 524 | |||
| 525 | ################################################################################################################ |
||
| 526 | # TODO: bad case 2.11 |
||
| 527 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 528 | # 14784589 21 2020-03-05 07:22:22 17990 good |
||
| 529 | # 14784450 21 2020-03-05 07:21:17 17990 good |
||
| 530 | # 14784311 21 2020-03-05 07:20:10 17990 good |
||
| 531 | # 14784172 21 2020-03-05 07:19:04 17990 good |
||
| 532 | # 14784033 21 2020-03-05 07:17:58 18990 bad |
||
| 533 | # 14783894 21 2020-03-05 07:16:52 17990 good |
||
| 534 | # 14783755 21 2020-03-05 07:15:46 17990 good |
||
| 535 | # 14783616 21 2020-03-05 07:14:40 17990 good |
||
| 536 | # 14783477 21 2020-03-05 07:13:34 17990 good |
||
| 537 | # 14783338 21 2020-03-05 07:12:28 17990 good |
||
| 538 | # 14783199 21 2020-03-05 07:11:22 17990 good |
||
| 539 | ################################################################################################################ |
||
| 540 | |||
| 541 | ################################################################################################################ |
||
| 542 | # TODO: bad case 2.12 |
||
| 543 | # id point_id utc_date_time actual_value is_bad (expected) |
||
| 544 | # 3337308 21 2020-01-07 09:02:18 7990 good |
||
| 545 | # 3337174 21 2020-01-07 09:01:13 7990 good |
||
| 546 | # 3337040 21 2020-01-07 09:00:08 7990 good |
||
| 547 | # 3336906 21 2020-01-07 08:59:04 7990 good |
||
| 548 | # 3336772 21 2020-01-07 08:57:59 7990 good |
||
| 549 | # 3336638 21 2020-01-07 08:56:54 8990 bad |
||
| 550 | # 3336504 21 2020-01-07 08:55:49 7990 good |
||
| 551 | # 3336370 21 2020-01-07 08:54:44 7990 good |
||
| 552 | # 3336236 21 2020-01-07 08:53:39 7990 good |
||
| 553 | # 3336102 21 2020-01-07 08:52:34 7990 good |
||
| 554 | # 3335968 21 2020-01-07 08:51:30 7990 good |
||
| 555 | ################################################################################################################ |
||
| 556 | # Step 4: tag the is_bad property of energy values. |
||
| 557 | ################################################################################################################ |
||
| 558 | try: |
||
| 559 | update = (" UPDATE tbl_energy_value " |
||
| 560 | " SET is_bad = FALSE " |
||
| 561 | " WHERE utc_date_time >= %s AND utc_date_time < %s AND is_bad IS NULL ") |
||
| 562 | # NOTE: use '<' instead of '<=' in WHERE statement because there may be some new inserted values |
||
| 563 | cursor_historical.execute(update, (min_datetime, max_datetime,)) |
||
| 564 | cnx_historical.commit() |
||
| 565 | except Exception as e: |
||
| 566 | logger.error("Error in step 4 of clean_energy_value.process " + str(e)) |
||
| 567 | time.sleep(60) |
||
| 568 | continue |
||
| 569 | finally: |
||
| 570 | if cursor_historical: |
||
| 571 | cursor_historical.close() |
||
| 572 | if cnx_historical: |
||
| 573 | cnx_historical.disconnect() |
||
| 574 | |||
| 575 | time.sleep(900) |
||
| 576 |
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