MyEMS /
myems
| Total Complexity | 226 |
| Total Lines | 1036 |
| Duplicated Lines | 43.53 % |
| 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 core.utilities 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 collections |
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| 2 | import statistics |
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| 3 | from datetime import datetime, timedelta |
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| 4 | from decimal import Decimal |
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| 5 | import mysql.connector |
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| 6 | import config |
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| 7 | import gettext |
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| 8 | |||
| 9 | |||
| 10 | ######################################################################################################################## |
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| 11 | # Aggregate hourly data by period |
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| 12 | # |
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| 13 | # This function aggregates hourly energy data into different time periods (hourly, daily, weekly, monthly, yearly). |
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| 14 | # It processes raw hourly data and groups it according to the specified period type for reporting and analysis. |
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| 15 | # |
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| 16 | # Args: |
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| 17 | # rows_hourly: List of tuples containing (start_datetime_utc, actual_value) for hourly data |
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| 18 | # Should belong to one energy_category_id |
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| 19 | # start_datetime_utc: Start datetime in UTC for the aggregation period |
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| 20 | # end_datetime_utc: End datetime in UTC for the aggregation period |
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| 21 | # period_type: Period type for aggregation - 'hourly', 'daily', 'weekly', 'monthly', or 'yearly' |
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| 22 | # |
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| 23 | # Returns: |
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| 24 | # List of tuples containing (datetime_utc, aggregated_value) for the specified period type |
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| 25 | # |
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| 26 | # Note: This procedure doesn't work with multiple energy categories |
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| 27 | ######################################################################################################################## |
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| 28 | def aggregate_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetime_utc, period_type): |
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| 29 | # Validate input parameters |
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| 30 | if start_datetime_utc is None or \ |
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| 31 | end_datetime_utc is None or \ |
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| 32 | start_datetime_utc >= end_datetime_utc or \ |
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| 33 | period_type not in ('hourly', 'daily', 'weekly', 'monthly', 'yearly'): |
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| 34 | return list() |
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| 35 | |||
| 36 | # Remove timezone info for consistent processing |
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| 37 | start_datetime_utc = start_datetime_utc.replace(tzinfo=None) |
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| 38 | end_datetime_utc = end_datetime_utc.replace(tzinfo=None) |
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| 39 | |||
| 40 | # Process hourly aggregation |
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| 41 | if period_type == "hourly": |
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| 42 | result_rows_hourly = list() |
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| 43 | # TODO: add config.working_day_start_time_local |
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| 44 | # TODO: add config.minutes_to_count |
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| 45 | current_datetime_utc = start_datetime_utc.replace(minute=0, second=0, microsecond=0, tzinfo=None) |
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| 46 | while current_datetime_utc <= end_datetime_utc: |
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| 47 | subtotal = None |
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| 48 | # Sum values within the current hour period |
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| 49 | for row in rows_hourly: |
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| 50 | if current_datetime_utc <= row[0] < current_datetime_utc + \ |
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| 51 | timedelta(minutes=config.minutes_to_count): |
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| 52 | if row[1] is not None: |
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| 53 | if subtotal is None: |
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| 54 | subtotal = row[1] |
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| 55 | else: |
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| 56 | subtotal += row[1] |
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| 57 | result_rows_hourly.append((current_datetime_utc, subtotal)) |
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| 58 | current_datetime_utc += timedelta(minutes=config.minutes_to_count) |
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| 59 | |||
| 60 | return result_rows_hourly |
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| 61 | |||
| 62 | # Process daily aggregation |
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| 63 | elif period_type == "daily": |
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| 64 | result_rows_daily = list() |
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| 65 | # TODO: add config.working_day_start_time_local |
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| 66 | # TODO: add config.minutes_to_count |
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| 67 | # Calculate the start datetime in UTC of the first day in local timezone |
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| 68 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
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| 69 | current_datetime_utc = start_datetime_local.replace(hour=0) - timedelta(hours=int(config.utc_offset[1:3])) |
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| 70 | while current_datetime_utc <= end_datetime_utc: |
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| 71 | subtotal = None |
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| 72 | for row in rows_hourly: |
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| 73 | if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=1): |
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| 74 | if row[1] is not None: |
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| 75 | if subtotal is None: |
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| 76 | subtotal = row[1] |
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| 77 | else: |
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| 78 | subtotal += row[1] |
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| 79 | result_rows_daily.append((current_datetime_utc, subtotal)) |
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| 80 | current_datetime_utc += timedelta(days=1) |
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| 81 | |||
| 82 | return result_rows_daily |
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| 83 | |||
| 84 | elif period_type == 'weekly': |
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| 85 | result_rows_weekly = list() |
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| 86 | # todo: add config.working_day_start_time_local |
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| 87 | # todo: add config.minutes_to_count |
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| 88 | # calculate the start datetime in utc of the monday in the first week in local |
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| 89 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
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| 90 | weekday = start_datetime_local.weekday() |
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| 91 | current_datetime_utc = \ |
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| 92 | start_datetime_local.replace(hour=0) - timedelta(days=weekday, hours=int(config.utc_offset[1:3])) |
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| 93 | while current_datetime_utc <= end_datetime_utc: |
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| 94 | |||
| 95 | next_datetime_utc = current_datetime_utc + timedelta(days=7) |
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| 96 | subtotal = None |
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| 97 | for row in rows_hourly: |
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| 98 | if current_datetime_utc <= row[0] < next_datetime_utc: |
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| 99 | if row[1] is not None: |
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| 100 | if subtotal is None: |
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| 101 | subtotal = row[1] |
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| 102 | else: |
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| 103 | subtotal += row[1] |
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| 104 | result_rows_weekly.append((current_datetime_utc, subtotal)) |
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| 105 | current_datetime_utc = next_datetime_utc |
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| 106 | |||
| 107 | return result_rows_weekly |
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| 108 | |||
| 109 | elif period_type == "monthly": |
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| 110 | result_rows_monthly = list() |
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| 111 | # todo: add config.working_day_start_time_local |
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| 112 | # todo: add config.minutes_to_count |
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| 113 | # calculate the start datetime the first day in the first month in local |
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| 114 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
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| 115 | current_datetime_local = start_datetime_local.replace(day=1, hour=0, minute=0, |
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| 116 | second=0, microsecond=0) |
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| 117 | end_datetime_local = end_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
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| 118 | while current_datetime_local <= end_datetime_local: |
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| 119 | # calculate the next datetime in local |
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| 120 | if current_datetime_local.month < 12: |
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| 121 | next_datetime_local = datetime(year=current_datetime_local.year, |
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| 122 | month=current_datetime_local.month + 1, |
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| 123 | day=1, hour=0, minute=0, second=0, microsecond=0, tzinfo=None) |
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| 124 | elif current_datetime_local.month == 12: |
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| 125 | next_datetime_local = datetime(year=current_datetime_local.year + 1, |
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| 126 | month=1, |
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| 127 | day=1, hour=0, minute=0, second=0, microsecond=0, tzinfo=None) |
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| 128 | current_datetime_utc = current_datetime_local - timedelta(hours=int(config.utc_offset[1:3])) |
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| 129 | next_datetime_utc = next_datetime_local - timedelta(hours=int(config.utc_offset[1:3])) |
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|
introduced
by
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| 130 | subtotal = None |
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| 131 | for row in rows_hourly: |
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| 132 | if current_datetime_utc <= row[0] < next_datetime_utc: |
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| 133 | if row[1] is not None: |
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| 134 | if subtotal is None: |
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| 135 | subtotal = row[1] |
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| 136 | else: |
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| 137 | subtotal += row[1] |
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| 138 | |||
| 139 | result_rows_monthly.append((current_datetime_utc, subtotal)) |
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| 140 | current_datetime_local = next_datetime_local |
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| 141 | |||
| 142 | return result_rows_monthly |
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| 143 | |||
| 144 | elif period_type == "yearly": |
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| 145 | result_rows_yearly = list() |
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| 146 | # todo: add config.working_day_start_time_local |
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| 147 | # todo: add config.minutes_to_count |
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| 148 | # calculate the start datetime in utc of the first day in the first year in local |
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| 149 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
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| 150 | current_datetime_utc = start_datetime_local.replace(month=1, day=1, hour=0) - timedelta( |
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| 151 | hours=int(config.utc_offset[1:3])) |
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| 152 | |||
| 153 | while current_datetime_utc <= end_datetime_utc: |
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| 154 | # calculate the next datetime in utc |
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| 155 | # todo: timedelta of year |
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| 156 | next_datetime_utc = datetime(year=current_datetime_utc.year + 2, |
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| 157 | month=1, |
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| 158 | day=1, |
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| 159 | hour=current_datetime_utc.hour, |
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| 160 | minute=current_datetime_utc.minute, |
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| 161 | second=current_datetime_utc.second, |
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| 162 | microsecond=current_datetime_utc.microsecond, |
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| 163 | tzinfo=current_datetime_utc.tzinfo) - timedelta(days=1) |
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| 164 | subtotal = None |
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| 165 | for row in rows_hourly: |
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| 166 | if current_datetime_utc <= row[0] < next_datetime_utc: |
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| 167 | if row[1] is not None: |
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| 168 | if subtotal is None: |
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| 169 | subtotal = row[1] |
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| 170 | else: |
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| 171 | subtotal += row[1] |
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| 172 | |||
| 173 | result_rows_yearly.append((current_datetime_utc, subtotal)) |
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| 174 | current_datetime_utc = next_datetime_utc |
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| 175 | return result_rows_yearly |
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| 176 | else: |
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| 177 | return list() |
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| 178 | |||
| 179 | |||
| 180 | ######################################################################################################################## |
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| 181 | # Get tariffs by energy category |
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| 182 | ######################################################################################################################## |
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| 183 | View Code Duplication | def get_energy_category_tariffs(cost_center_id, energy_category_id, start_datetime_utc, end_datetime_utc): |
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| 184 | # todo: validate parameters |
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| 185 | if cost_center_id is None: |
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| 186 | return dict() |
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| 187 | |||
| 188 | start_datetime_utc = start_datetime_utc.replace(tzinfo=None) |
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| 189 | end_datetime_utc = end_datetime_utc.replace(tzinfo=None) |
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| 190 | |||
| 191 | # get timezone offset in minutes, this value will be returned to client |
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| 192 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 193 | if config.utc_offset[0] == '-': |
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| 194 | timezone_offset = -timezone_offset |
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| 195 | |||
| 196 | tariff_dict = collections.OrderedDict() |
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| 197 | |||
| 198 | cnx = None |
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| 199 | cursor = None |
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| 200 | try: |
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| 201 | cnx = mysql.connector.connect(**config.myems_system_db) |
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| 202 | cursor = cnx.cursor() |
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| 203 | query_tariffs = (" SELECT t.id, t.valid_from_datetime_utc, t.valid_through_datetime_utc " |
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| 204 | " FROM tbl_tariffs t, tbl_cost_centers_tariffs cct " |
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| 205 | " WHERE t.energy_category_id = %s AND " |
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| 206 | " t.id = cct.tariff_id AND " |
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| 207 | " cct.cost_center_id = %s AND " |
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| 208 | " t.valid_through_datetime_utc >= %s AND " |
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| 209 | " t.valid_from_datetime_utc <= %s " |
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| 210 | " ORDER BY t.valid_from_datetime_utc ") |
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| 211 | cursor.execute(query_tariffs, (energy_category_id, cost_center_id, start_datetime_utc, end_datetime_utc,)) |
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| 212 | rows_tariffs = cursor.fetchall() |
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| 213 | except Exception as e: |
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| 214 | print(str(e)) |
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| 215 | if cnx: |
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| 216 | cnx.close() |
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| 217 | if cursor: |
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| 218 | cursor.close() |
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| 219 | return dict() |
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| 220 | |||
| 221 | if rows_tariffs is None or len(rows_tariffs) == 0: |
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| 222 | if cursor: |
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| 223 | cursor.close() |
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| 224 | if cnx: |
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| 225 | cnx.close() |
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| 226 | return dict() |
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| 227 | |||
| 228 | for row in rows_tariffs: |
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| 229 | tariff_dict[row[0]] = {'valid_from_datetime_utc': row[1], |
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| 230 | 'valid_through_datetime_utc': row[2], |
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| 231 | 'rates': list()} |
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| 232 | |||
| 233 | try: |
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| 234 | query_timeofuse_tariffs = (" SELECT tariff_id, start_time_of_day, end_time_of_day, price " |
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| 235 | " FROM tbl_tariffs_timeofuses " |
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| 236 | " WHERE tariff_id IN ( " + ', '.join(map(str, tariff_dict.keys())) + ")" |
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| 237 | " ORDER BY tariff_id, start_time_of_day ") |
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| 238 | cursor.execute(query_timeofuse_tariffs, ) |
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| 239 | rows_timeofuse_tariffs = cursor.fetchall() |
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| 240 | except Exception as e: |
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| 241 | print(str(e)) |
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| 242 | if cnx: |
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| 243 | cnx.close() |
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| 244 | if cursor: |
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| 245 | cursor.close() |
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| 246 | return dict() |
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| 247 | |||
| 248 | if cursor: |
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| 249 | cursor.close() |
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| 250 | if cnx: |
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| 251 | cnx.close() |
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| 252 | |||
| 253 | if rows_timeofuse_tariffs is None or len(rows_timeofuse_tariffs) == 0: |
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| 254 | return dict() |
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| 255 | |||
| 256 | for row in rows_timeofuse_tariffs: |
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| 257 | tariff_dict[row[0]]['rates'].append({'start_time_of_day': row[1], |
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| 258 | 'end_time_of_day': row[2], |
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| 259 | 'price': row[3]}) |
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| 260 | |||
| 261 | result = dict() |
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| 262 | for tariff_id, tariff_value in tariff_dict.items(): |
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| 263 | current_datetime_utc = tariff_value['valid_from_datetime_utc'] |
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| 264 | while current_datetime_utc < tariff_value['valid_through_datetime_utc']: |
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| 265 | for rate in tariff_value['rates']: |
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| 266 | current_datetime_local = current_datetime_utc + timedelta(minutes=timezone_offset) |
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| 267 | seconds_since_midnight = (current_datetime_local - |
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| 268 | current_datetime_local.replace(hour=0, |
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| 269 | second=0, |
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| 270 | microsecond=0, |
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| 271 | tzinfo=None)).total_seconds() |
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| 272 | if rate['start_time_of_day'].total_seconds() <= \ |
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| 273 | seconds_since_midnight < rate['end_time_of_day'].total_seconds(): |
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| 274 | result[current_datetime_utc] = rate['price'] |
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| 275 | break |
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| 276 | |||
| 277 | # start from the next time slot |
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| 278 | current_datetime_utc += timedelta(minutes=config.minutes_to_count) |
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| 279 | |||
| 280 | return {k: v for k, v in result.items() if start_datetime_utc <= k <= end_datetime_utc} |
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| 281 | |||
| 282 | |||
| 283 | ######################################################################################################################## |
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| 284 | # Get peak types of tariff by energy category |
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| 285 | # peak types: toppeak, onpeak, midpeak, offpeak, deep |
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| 286 | ######################################################################################################################## |
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| 287 | View Code Duplication | def get_energy_category_peak_types(cost_center_id, energy_category_id, start_datetime_utc, end_datetime_utc): |
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| 288 | # todo: validate parameters |
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| 289 | if cost_center_id is None: |
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| 290 | return dict() |
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| 291 | |||
| 292 | start_datetime_utc = start_datetime_utc.replace(tzinfo=None) |
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| 293 | end_datetime_utc = end_datetime_utc.replace(tzinfo=None) |
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| 294 | |||
| 295 | # get timezone offset in minutes, this value will be returned to client |
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| 296 | timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
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| 297 | if config.utc_offset[0] == '-': |
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| 298 | timezone_offset = -timezone_offset |
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| 299 | |||
| 300 | tariff_dict = collections.OrderedDict() |
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| 301 | |||
| 302 | cnx = None |
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| 303 | cursor = None |
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| 304 | try: |
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| 305 | cnx = mysql.connector.connect(**config.myems_system_db) |
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| 306 | cursor = cnx.cursor() |
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| 307 | query_tariffs = (" SELECT t.id, t.valid_from_datetime_utc, t.valid_through_datetime_utc " |
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| 308 | " FROM tbl_tariffs t, tbl_cost_centers_tariffs cct " |
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| 309 | " WHERE t.energy_category_id = %s AND " |
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| 310 | " t.id = cct.tariff_id AND " |
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| 311 | " cct.cost_center_id = %s AND " |
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| 312 | " t.valid_through_datetime_utc >= %s AND " |
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| 313 | " t.valid_from_datetime_utc <= %s " |
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| 314 | " ORDER BY t.valid_from_datetime_utc ") |
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| 315 | cursor.execute(query_tariffs, (energy_category_id, cost_center_id, start_datetime_utc, end_datetime_utc,)) |
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| 316 | rows_tariffs = cursor.fetchall() |
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| 317 | except Exception as e: |
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| 318 | print(str(e)) |
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| 319 | if cnx: |
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| 320 | cnx.close() |
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| 321 | if cursor: |
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| 322 | cursor.close() |
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| 323 | return dict() |
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| 324 | |||
| 325 | if rows_tariffs is None or len(rows_tariffs) == 0: |
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| 326 | if cursor: |
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| 327 | cursor.close() |
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| 328 | if cnx: |
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| 329 | cnx.close() |
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| 330 | return dict() |
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| 331 | |||
| 332 | for row in rows_tariffs: |
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| 333 | tariff_dict[row[0]] = {'valid_from_datetime_utc': row[1], |
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| 334 | 'valid_through_datetime_utc': row[2], |
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| 335 | 'rates': list()} |
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| 336 | |||
| 337 | try: |
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| 338 | query_timeofuse_tariffs = (" SELECT tariff_id, start_time_of_day, end_time_of_day, peak_type " |
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| 339 | " FROM tbl_tariffs_timeofuses " |
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| 340 | " WHERE tariff_id IN ( " + ', '.join(map(str, tariff_dict.keys())) + ")" |
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| 341 | " ORDER BY tariff_id, start_time_of_day ") |
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| 342 | cursor.execute(query_timeofuse_tariffs, ) |
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| 343 | rows_timeofuse_tariffs = cursor.fetchall() |
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| 344 | except Exception as e: |
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| 345 | print(str(e)) |
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| 346 | if cnx: |
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| 347 | cnx.close() |
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| 348 | if cursor: |
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| 349 | cursor.close() |
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| 350 | return dict() |
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| 351 | |||
| 352 | if cursor: |
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| 353 | cursor.close() |
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| 354 | if cnx: |
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| 355 | cnx.close() |
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| 356 | |||
| 357 | if rows_timeofuse_tariffs is None or len(rows_timeofuse_tariffs) == 0: |
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| 358 | return dict() |
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| 359 | |||
| 360 | for row in rows_timeofuse_tariffs: |
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| 361 | tariff_dict[row[0]]['rates'].append({'start_time_of_day': row[1], |
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| 362 | 'end_time_of_day': row[2], |
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| 363 | 'peak_type': row[3]}) |
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| 364 | |||
| 365 | result = dict() |
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| 366 | for tariff_id, tariff_value in tariff_dict.items(): |
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| 367 | current_datetime_utc = tariff_value['valid_from_datetime_utc'] |
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| 368 | while current_datetime_utc < tariff_value['valid_through_datetime_utc']: |
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| 369 | for rate in tariff_value['rates']: |
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| 370 | current_datetime_local = current_datetime_utc + timedelta(minutes=timezone_offset) |
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| 371 | seconds_since_midnight = (current_datetime_local - |
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| 372 | current_datetime_local.replace(hour=0, |
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| 373 | second=0, |
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| 374 | microsecond=0, |
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| 375 | tzinfo=None)).total_seconds() |
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| 376 | if rate['start_time_of_day'].total_seconds() <= \ |
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| 377 | seconds_since_midnight < rate['end_time_of_day'].total_seconds(): |
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| 378 | result[current_datetime_utc] = rate['peak_type'] |
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| 379 | break |
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| 380 | |||
| 381 | # start from the next time slot |
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| 382 | current_datetime_utc += timedelta(minutes=config.minutes_to_count) |
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| 383 | |||
| 384 | return {k: v for k, v in result.items() if start_datetime_utc <= k <= end_datetime_utc} |
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| 385 | |||
| 386 | |||
| 387 | ######################################################################################################################## |
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| 388 | # Averaging calculator of hourly data by period |
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| 389 | # rows_hourly: list of (start_datetime_utc, actual_value), should belong to one energy_category_id |
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| 390 | # start_datetime_utc: start datetime in utc |
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| 391 | # end_datetime_utc: end datetime in utc |
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| 392 | # period_type: use one of the period types, 'hourly', 'daily', 'weekly', 'monthly' and 'yearly' |
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| 393 | # Returns: periodically data of average and maximum |
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| 394 | # Note: this procedure doesn't work with multiple energy categories |
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| 395 | ######################################################################################################################## |
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| 396 | def averaging_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetime_utc, period_type): |
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| 397 | # todo: validate parameters |
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| 398 | if start_datetime_utc is None or \ |
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| 399 | end_datetime_utc is None or \ |
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| 400 | start_datetime_utc >= end_datetime_utc or \ |
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| 401 | period_type not in ('hourly', 'daily', 'weekly', 'monthly', 'yearly'): |
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| 402 | return list(), None, None |
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| 403 | |||
| 404 | start_datetime_utc = start_datetime_utc.replace(tzinfo=None) |
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| 405 | end_datetime_utc = end_datetime_utc.replace(tzinfo=None) |
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| 406 | |||
| 407 | if period_type == "hourly": |
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| 408 | result_rows_hourly = list() |
||
| 409 | # todo: add config.working_day_start_time_local |
||
| 410 | # todo: add config.minutes_to_count |
||
| 411 | total = Decimal(0.0) |
||
| 412 | maximum = None |
||
| 413 | counter = 0 |
||
| 414 | current_datetime_utc = start_datetime_utc.replace(minute=0, second=0, microsecond=0, tzinfo=None) |
||
| 415 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 416 | sub_total = Decimal(0.0) |
||
| 417 | sub_maximum = None |
||
| 418 | sub_counter = 0 |
||
| 419 | for row in rows_hourly: |
||
| 420 | if current_datetime_utc <= row[0] < current_datetime_utc + \ |
||
| 421 | timedelta(minutes=config.minutes_to_count): |
||
| 422 | sub_total += row[1] |
||
| 423 | if sub_maximum is None: |
||
| 424 | sub_maximum = row[1] |
||
| 425 | elif sub_maximum < row[1]: |
||
| 426 | sub_maximum = row[1] |
||
| 427 | sub_counter += 1 |
||
| 428 | |||
| 429 | sub_average = (sub_total / sub_counter) if sub_counter > 0 else None |
||
| 430 | result_rows_hourly.append((current_datetime_utc, sub_average, sub_maximum)) |
||
| 431 | |||
| 432 | total += sub_total |
||
| 433 | counter += sub_counter |
||
| 434 | if sub_maximum is None: |
||
| 435 | pass |
||
| 436 | elif maximum is None: |
||
| 437 | maximum = sub_maximum |
||
| 438 | elif maximum < sub_maximum: |
||
| 439 | maximum = sub_maximum |
||
| 440 | |||
| 441 | current_datetime_utc += timedelta(minutes=config.minutes_to_count) |
||
| 442 | |||
| 443 | average = total / counter if counter > 0 else None |
||
| 444 | return result_rows_hourly, average, maximum |
||
| 445 | |||
| 446 | elif period_type == "daily": |
||
| 447 | result_rows_daily = list() |
||
| 448 | # todo: add config.working_day_start_time_local |
||
| 449 | # todo: add config.minutes_to_count |
||
| 450 | total = Decimal(0.0) |
||
| 451 | maximum = None |
||
| 452 | counter = 0 |
||
| 453 | # calculate the start datetime in utc of the first day in local |
||
| 454 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 455 | current_datetime_utc = start_datetime_local.replace(hour=0) - timedelta(hours=int(config.utc_offset[1:3])) |
||
| 456 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 457 | sub_total = Decimal(0.0) |
||
| 458 | sub_maximum = None |
||
| 459 | sub_counter = 0 |
||
| 460 | for row in rows_hourly: |
||
| 461 | if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=1): |
||
| 462 | sub_total += row[1] |
||
| 463 | if sub_maximum is None: |
||
| 464 | sub_maximum = row[1] |
||
| 465 | elif sub_maximum < row[1]: |
||
| 466 | sub_maximum = row[1] |
||
| 467 | sub_counter += 1 |
||
| 468 | |||
| 469 | sub_average = (sub_total / sub_counter) if sub_counter > 0 else None |
||
| 470 | result_rows_daily.append((current_datetime_utc, sub_average, sub_maximum)) |
||
| 471 | total += sub_total |
||
| 472 | counter += sub_counter |
||
| 473 | if sub_maximum is None: |
||
| 474 | pass |
||
| 475 | elif maximum is None: |
||
| 476 | maximum = sub_maximum |
||
| 477 | elif maximum < sub_maximum: |
||
| 478 | maximum = sub_maximum |
||
| 479 | current_datetime_utc += timedelta(days=1) |
||
| 480 | |||
| 481 | average = total / counter if counter > 0 else None |
||
| 482 | return result_rows_daily, average, maximum |
||
| 483 | |||
| 484 | elif period_type == 'weekly': |
||
| 485 | result_rows_weekly = list() |
||
| 486 | # todo: add config.working_day_start_time_local |
||
| 487 | # todo: add config.minutes_to_count |
||
| 488 | total = Decimal(0.0) |
||
| 489 | maximum = None |
||
| 490 | counter = 0 |
||
| 491 | # calculate the start datetime in utc of the monday in the first week in local |
||
| 492 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 493 | weekday = start_datetime_local.weekday() |
||
| 494 | current_datetime_utc = \ |
||
| 495 | start_datetime_local.replace(hour=0) - timedelta(days=weekday, hours=int(config.utc_offset[1:3])) |
||
| 496 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 497 | sub_total = Decimal(0.0) |
||
| 498 | sub_maximum = None |
||
| 499 | sub_counter = 0 |
||
| 500 | for row in rows_hourly: |
||
| 501 | if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=7): |
||
| 502 | sub_total += row[1] |
||
| 503 | if sub_maximum is None: |
||
| 504 | sub_maximum = row[1] |
||
| 505 | elif sub_maximum < row[1]: |
||
| 506 | sub_maximum = row[1] |
||
| 507 | sub_counter += 1 |
||
| 508 | |||
| 509 | sub_average = (sub_total / sub_counter) if sub_counter > 0 else None |
||
| 510 | result_rows_weekly.append((current_datetime_utc, sub_average, sub_maximum)) |
||
| 511 | total += sub_total |
||
| 512 | counter += sub_counter |
||
| 513 | if sub_maximum is None: |
||
| 514 | pass |
||
| 515 | elif maximum is None: |
||
| 516 | maximum = sub_maximum |
||
| 517 | elif maximum < sub_maximum: |
||
| 518 | maximum = sub_maximum |
||
| 519 | current_datetime_utc += timedelta(days=7) |
||
| 520 | |||
| 521 | average = total / counter if counter > 0 else None |
||
| 522 | return result_rows_weekly, average, maximum |
||
| 523 | |||
| 524 | elif period_type == "monthly": |
||
| 525 | result_rows_monthly = list() |
||
| 526 | # todo: add config.working_day_start_time_local |
||
| 527 | # todo: add config.minutes_to_count |
||
| 528 | total = Decimal(0.0) |
||
| 529 | maximum = None |
||
| 530 | counter = 0 |
||
| 531 | # calculate the start datetime in utc of the first day in the first month in local |
||
| 532 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 533 | current_datetime_utc = \ |
||
| 534 | start_datetime_local.replace(day=1, hour=0) - timedelta(hours=int(config.utc_offset[1:3])) |
||
| 535 | |||
| 536 | while current_datetime_utc <= end_datetime_utc: |
||
| 537 | # calculate the next datetime in utc |
||
| 538 | View Code Duplication | if current_datetime_utc.month == 1: |
|
| 539 | temp_day = 28 |
||
| 540 | ny = current_datetime_utc.year |
||
| 541 | if (ny % 100 != 0 and ny % 4 == 0) or (ny % 100 == 0 and ny % 400 == 0): |
||
| 542 | temp_day = 29 |
||
| 543 | |||
| 544 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 545 | month=current_datetime_utc.month + 1, |
||
| 546 | day=temp_day, |
||
| 547 | hour=current_datetime_utc.hour, |
||
| 548 | minute=current_datetime_utc.minute, |
||
| 549 | second=0, |
||
| 550 | microsecond=0, |
||
| 551 | tzinfo=None) |
||
| 552 | elif current_datetime_utc.month == 2: |
||
| 553 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 554 | month=current_datetime_utc.month + 1, |
||
| 555 | day=31, |
||
| 556 | hour=current_datetime_utc.hour, |
||
| 557 | minute=current_datetime_utc.minute, |
||
| 558 | second=0, |
||
| 559 | microsecond=0, |
||
| 560 | tzinfo=None) |
||
| 561 | elif current_datetime_utc.month in [3, 5, 8, 10]: |
||
| 562 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 563 | month=current_datetime_utc.month + 1, |
||
| 564 | day=30, |
||
| 565 | hour=current_datetime_utc.hour, |
||
| 566 | minute=current_datetime_utc.minute, |
||
| 567 | second=0, |
||
| 568 | microsecond=0, |
||
| 569 | tzinfo=None) |
||
| 570 | elif current_datetime_utc.month == 7: |
||
| 571 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 572 | month=current_datetime_utc.month + 1, |
||
| 573 | day=31, |
||
| 574 | hour=current_datetime_utc.hour, |
||
| 575 | minute=current_datetime_utc.minute, |
||
| 576 | second=0, |
||
| 577 | microsecond=0, |
||
| 578 | tzinfo=None) |
||
| 579 | elif current_datetime_utc.month in [4, 6, 9, 11]: |
||
| 580 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 581 | month=current_datetime_utc.month + 1, |
||
| 582 | day=31, |
||
| 583 | hour=current_datetime_utc.hour, |
||
| 584 | minute=current_datetime_utc.minute, |
||
| 585 | second=0, |
||
| 586 | microsecond=0, |
||
| 587 | tzinfo=None) |
||
| 588 | elif current_datetime_utc.month == 12: |
||
| 589 | next_datetime_utc = datetime(year=current_datetime_utc.year + 1, |
||
| 590 | month=1, |
||
| 591 | day=31, |
||
| 592 | hour=current_datetime_utc.hour, |
||
| 593 | minute=current_datetime_utc.minute, |
||
| 594 | second=0, |
||
| 595 | microsecond=0, |
||
| 596 | tzinfo=None) |
||
| 597 | |||
| 598 | sub_total = Decimal(0.0) |
||
| 599 | sub_maximum = None |
||
| 600 | sub_counter = 0 |
||
| 601 | for row in rows_hourly: |
||
| 602 | if current_datetime_utc <= row[0] < next_datetime_utc: |
||
| 603 | sub_total += row[1] |
||
| 604 | if sub_maximum is None: |
||
| 605 | sub_maximum = row[1] |
||
| 606 | elif sub_maximum < row[1]: |
||
| 607 | sub_maximum = row[1] |
||
| 608 | sub_counter += 1 |
||
| 609 | |||
| 610 | sub_average = (sub_total / sub_counter) if sub_counter > 0 else None |
||
| 611 | result_rows_monthly.append((current_datetime_utc, sub_average, sub_maximum)) |
||
| 612 | total += sub_total |
||
| 613 | counter += sub_counter |
||
| 614 | if sub_maximum is None: |
||
| 615 | pass |
||
| 616 | elif maximum is None: |
||
| 617 | maximum = sub_maximum |
||
| 618 | elif maximum < sub_maximum: |
||
| 619 | maximum = sub_maximum |
||
| 620 | current_datetime_utc = next_datetime_utc |
||
| 621 | |||
| 622 | average = total / counter if counter > 0 else None |
||
| 623 | return result_rows_monthly, average, maximum |
||
| 624 | |||
| 625 | elif period_type == "yearly": |
||
| 626 | result_rows_yearly = list() |
||
| 627 | # todo: add config.working_day_start_time_local |
||
| 628 | # todo: add config.minutes_to_count |
||
| 629 | total = Decimal(0.0) |
||
| 630 | maximum = None |
||
| 631 | counter = 0 |
||
| 632 | # calculate the start datetime in utc of the first day in the first month in local |
||
| 633 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 634 | current_datetime_utc = start_datetime_local.replace(month=1, day=1, hour=0) - timedelta( |
||
| 635 | hours=int(config.utc_offset[1:3])) |
||
| 636 | |||
| 637 | while current_datetime_utc <= end_datetime_utc: |
||
| 638 | # calculate the next datetime in utc |
||
| 639 | # todo: timedelta of year |
||
| 640 | next_datetime_utc = datetime(year=current_datetime_utc.year + 2, |
||
| 641 | month=1, |
||
| 642 | day=1, |
||
| 643 | hour=current_datetime_utc.hour, |
||
| 644 | minute=current_datetime_utc.minute, |
||
| 645 | second=current_datetime_utc.second, |
||
| 646 | microsecond=current_datetime_utc.microsecond, |
||
| 647 | tzinfo=current_datetime_utc.tzinfo) - timedelta(days=1) |
||
| 648 | sub_total = Decimal(0.0) |
||
| 649 | sub_maximum = None |
||
| 650 | sub_counter = 0 |
||
| 651 | for row in rows_hourly: |
||
| 652 | if current_datetime_utc <= row[0] < next_datetime_utc: |
||
| 653 | sub_total += row[1] |
||
| 654 | if sub_maximum is None: |
||
| 655 | sub_maximum = row[1] |
||
| 656 | elif sub_maximum < row[1]: |
||
| 657 | sub_maximum = row[1] |
||
| 658 | sub_counter += 1 |
||
| 659 | |||
| 660 | sub_average = (sub_total / sub_counter) if sub_counter > 0 else None |
||
| 661 | result_rows_yearly.append((current_datetime_utc, sub_average, sub_maximum)) |
||
| 662 | total += sub_total |
||
| 663 | counter += sub_counter |
||
| 664 | if sub_maximum is None: |
||
| 665 | pass |
||
| 666 | elif maximum is None: |
||
| 667 | maximum = sub_maximum |
||
| 668 | elif maximum < sub_maximum: |
||
| 669 | maximum = sub_maximum |
||
| 670 | current_datetime_utc = next_datetime_utc |
||
| 671 | |||
| 672 | average = total / counter if counter > 0 else None |
||
| 673 | return result_rows_yearly, average, maximum |
||
| 674 | else: |
||
| 675 | return list(), None, None |
||
| 676 | |||
| 677 | |||
| 678 | ######################################################################################################################## |
||
| 679 | # Statistics calculator of hourly data by period |
||
| 680 | # rows_hourly: list of (start_datetime_utc, actual_value), should belong to one energy_category_id |
||
| 681 | # start_datetime_utc: start datetime in utc |
||
| 682 | # end_datetime_utc: end datetime in utc |
||
| 683 | # period_type: use one of the period types, 'hourly', 'daily', 'weekly', 'monthly' and 'yearly' |
||
| 684 | # Returns: periodically data of values and statistics of mean, median, minimum, maximum, stdev and variance |
||
| 685 | # Note: this procedure doesn't work with multiple energy categories |
||
| 686 | ######################################################################################################################## |
||
| 687 | def statistics_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetime_utc, period_type): |
||
| 688 | # todo: validate parameters |
||
| 689 | if start_datetime_utc is None or \ |
||
| 690 | end_datetime_utc is None or \ |
||
| 691 | start_datetime_utc >= end_datetime_utc or \ |
||
| 692 | period_type not in ('hourly', 'daily', 'weekly', 'monthly', 'yearly'): |
||
| 693 | return list(), None, None, None, None, None, None |
||
| 694 | |||
| 695 | start_datetime_utc = start_datetime_utc.replace(tzinfo=None) |
||
| 696 | end_datetime_utc = end_datetime_utc.replace(tzinfo=None) |
||
| 697 | |||
| 698 | if period_type == "hourly": |
||
| 699 | result_rows_hourly = list() |
||
| 700 | sample_data = list() |
||
| 701 | # todo: add config.working_day_start_time_local |
||
| 702 | # todo: add config.minutes_to_count |
||
| 703 | counter = 0 |
||
| 704 | mean = None |
||
| 705 | median = None |
||
| 706 | minimum = None |
||
| 707 | maximum = None |
||
| 708 | stdev = None |
||
| 709 | variance = None |
||
| 710 | current_datetime_utc = start_datetime_utc.replace(minute=0, second=0, microsecond=0, tzinfo=None) |
||
| 711 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 712 | sub_total = Decimal(0.0) |
||
| 713 | for row in rows_hourly: |
||
| 714 | if current_datetime_utc <= row[0] < current_datetime_utc + \ |
||
| 715 | timedelta(minutes=config.minutes_to_count): |
||
| 716 | sub_total += row[1] |
||
| 717 | |||
| 718 | result_rows_hourly.append((current_datetime_utc, sub_total)) |
||
| 719 | sample_data.append(sub_total) |
||
| 720 | |||
| 721 | counter += 1 |
||
| 722 | if minimum is None: |
||
| 723 | minimum = sub_total |
||
| 724 | elif minimum > sub_total: |
||
| 725 | minimum = sub_total |
||
| 726 | |||
| 727 | if maximum is None: |
||
| 728 | maximum = sub_total |
||
| 729 | elif maximum < sub_total: |
||
| 730 | maximum = sub_total |
||
| 731 | |||
| 732 | current_datetime_utc += timedelta(minutes=config.minutes_to_count) |
||
| 733 | |||
| 734 | if len(sample_data) > 1: |
||
| 735 | mean = statistics.mean(sample_data) |
||
| 736 | median = statistics.median(sample_data) |
||
| 737 | stdev = statistics.stdev(sample_data) |
||
| 738 | variance = statistics.variance(sample_data) |
||
| 739 | |||
| 740 | return result_rows_hourly, mean, median, minimum, maximum, stdev, variance |
||
| 741 | |||
| 742 | elif period_type == "daily": |
||
| 743 | result_rows_daily = list() |
||
| 744 | sample_data = list() |
||
| 745 | # todo: add config.working_day_start_time_local |
||
| 746 | # todo: add config.minutes_to_count |
||
| 747 | counter = 0 |
||
| 748 | mean = None |
||
| 749 | median = None |
||
| 750 | minimum = None |
||
| 751 | maximum = None |
||
| 752 | stdev = None |
||
| 753 | variance = None |
||
| 754 | # calculate the start datetime in utc of the first day in local |
||
| 755 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 756 | current_datetime_utc = start_datetime_local.replace(hour=0) - timedelta(hours=int(config.utc_offset[1:3])) |
||
| 757 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 758 | sub_total = Decimal(0.0) |
||
| 759 | for row in rows_hourly: |
||
| 760 | if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=1): |
||
| 761 | sub_total += row[1] |
||
| 762 | |||
| 763 | result_rows_daily.append((current_datetime_utc, sub_total)) |
||
| 764 | sample_data.append(sub_total) |
||
| 765 | |||
| 766 | counter += 1 |
||
| 767 | if minimum is None: |
||
| 768 | minimum = sub_total |
||
| 769 | elif minimum > sub_total: |
||
| 770 | minimum = sub_total |
||
| 771 | |||
| 772 | if maximum is None: |
||
| 773 | maximum = sub_total |
||
| 774 | elif maximum < sub_total: |
||
| 775 | maximum = sub_total |
||
| 776 | current_datetime_utc += timedelta(days=1) |
||
| 777 | |||
| 778 | if len(sample_data) > 1: |
||
| 779 | mean = statistics.mean(sample_data) |
||
| 780 | median = statistics.median(sample_data) |
||
| 781 | stdev = statistics.stdev(sample_data) |
||
| 782 | variance = statistics.variance(sample_data) |
||
| 783 | |||
| 784 | return result_rows_daily, mean, median, minimum, maximum, stdev, variance |
||
| 785 | |||
| 786 | elif period_type == "weekly": |
||
| 787 | result_rows_weekly = list() |
||
| 788 | sample_data = list() |
||
| 789 | # todo: add config.working_day_start_time_local |
||
| 790 | # todo: add config.minutes_to_count |
||
| 791 | counter = 0 |
||
| 792 | mean = None |
||
| 793 | median = None |
||
| 794 | minimum = None |
||
| 795 | maximum = None |
||
| 796 | stdev = None |
||
| 797 | variance = None |
||
| 798 | # calculate the start datetime in utc of the monday in the first week in local |
||
| 799 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 800 | weekday = start_datetime_local.weekday() |
||
| 801 | current_datetime_utc = \ |
||
| 802 | start_datetime_local.replace(hour=0) - timedelta(days=weekday, hours=int(config.utc_offset[1:3])) |
||
| 803 | View Code Duplication | while current_datetime_utc <= end_datetime_utc: |
|
| 804 | sub_total = Decimal(0.0) |
||
| 805 | for row in rows_hourly: |
||
| 806 | if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=7): |
||
| 807 | sub_total += row[1] |
||
| 808 | |||
| 809 | result_rows_weekly.append((current_datetime_utc, sub_total)) |
||
| 810 | sample_data.append(sub_total) |
||
| 811 | |||
| 812 | counter += 1 |
||
| 813 | if minimum is None: |
||
| 814 | minimum = sub_total |
||
| 815 | elif minimum > sub_total: |
||
| 816 | minimum = sub_total |
||
| 817 | |||
| 818 | if maximum is None: |
||
| 819 | maximum = sub_total |
||
| 820 | elif maximum < sub_total: |
||
| 821 | maximum = sub_total |
||
| 822 | current_datetime_utc += timedelta(days=7) |
||
| 823 | |||
| 824 | if len(sample_data) > 1: |
||
| 825 | mean = statistics.mean(sample_data) |
||
| 826 | median = statistics.median(sample_data) |
||
| 827 | stdev = statistics.stdev(sample_data) |
||
| 828 | variance = statistics.variance(sample_data) |
||
| 829 | |||
| 830 | return result_rows_weekly, mean, median, minimum, maximum, stdev, variance |
||
| 831 | |||
| 832 | elif period_type == "monthly": |
||
| 833 | result_rows_monthly = list() |
||
| 834 | sample_data = list() |
||
| 835 | # todo: add config.working_day_start_time_local |
||
| 836 | # todo: add config.minutes_to_count |
||
| 837 | counter = 0 |
||
| 838 | mean = None |
||
| 839 | median = None |
||
| 840 | minimum = None |
||
| 841 | maximum = None |
||
| 842 | stdev = None |
||
| 843 | variance = None |
||
| 844 | # calculate the start datetime in utc of the first day in the first month in local |
||
| 845 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 846 | current_datetime_utc = \ |
||
| 847 | start_datetime_local.replace(day=1, hour=0) - timedelta(hours=int(config.utc_offset[1:3])) |
||
| 848 | |||
| 849 | while current_datetime_utc <= end_datetime_utc: |
||
| 850 | # calculate the next datetime in utc |
||
| 851 | View Code Duplication | if current_datetime_utc.month == 1: |
|
| 852 | temp_day = 28 |
||
| 853 | ny = current_datetime_utc.year |
||
| 854 | if (ny % 100 != 0 and ny % 4 == 0) or (ny % 100 == 0 and ny % 400 == 0): |
||
| 855 | temp_day = 29 |
||
| 856 | |||
| 857 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 858 | month=current_datetime_utc.month + 1, |
||
| 859 | day=temp_day, |
||
| 860 | hour=current_datetime_utc.hour, |
||
| 861 | minute=current_datetime_utc.minute, |
||
| 862 | second=0, |
||
| 863 | microsecond=0, |
||
| 864 | tzinfo=None) |
||
| 865 | elif current_datetime_utc.month == 2: |
||
| 866 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 867 | month=current_datetime_utc.month + 1, |
||
| 868 | day=31, |
||
| 869 | hour=current_datetime_utc.hour, |
||
| 870 | minute=current_datetime_utc.minute, |
||
| 871 | second=0, |
||
| 872 | microsecond=0, |
||
| 873 | tzinfo=None) |
||
| 874 | elif current_datetime_utc.month in [3, 5, 8, 10]: |
||
| 875 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 876 | month=current_datetime_utc.month + 1, |
||
| 877 | day=30, |
||
| 878 | hour=current_datetime_utc.hour, |
||
| 879 | minute=current_datetime_utc.minute, |
||
| 880 | second=0, |
||
| 881 | microsecond=0, |
||
| 882 | tzinfo=None) |
||
| 883 | elif current_datetime_utc.month == 7: |
||
| 884 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 885 | month=current_datetime_utc.month + 1, |
||
| 886 | day=31, |
||
| 887 | hour=current_datetime_utc.hour, |
||
| 888 | minute=current_datetime_utc.minute, |
||
| 889 | second=0, |
||
| 890 | microsecond=0, |
||
| 891 | tzinfo=None) |
||
| 892 | elif current_datetime_utc.month in [4, 6, 9, 11]: |
||
| 893 | next_datetime_utc = datetime(year=current_datetime_utc.year, |
||
| 894 | month=current_datetime_utc.month + 1, |
||
| 895 | day=31, |
||
| 896 | hour=current_datetime_utc.hour, |
||
| 897 | minute=current_datetime_utc.minute, |
||
| 898 | second=0, |
||
| 899 | microsecond=0, |
||
| 900 | tzinfo=None) |
||
| 901 | elif current_datetime_utc.month == 12: |
||
| 902 | next_datetime_utc = datetime(year=current_datetime_utc.year + 1, |
||
| 903 | month=1, |
||
| 904 | day=31, |
||
| 905 | hour=current_datetime_utc.hour, |
||
| 906 | minute=current_datetime_utc.minute, |
||
| 907 | second=0, |
||
| 908 | microsecond=0, |
||
| 909 | tzinfo=None) |
||
| 910 | |||
| 911 | sub_total = Decimal(0.0) |
||
| 912 | for row in rows_hourly: |
||
| 913 | if current_datetime_utc <= row[0] < next_datetime_utc: |
||
| 914 | sub_total += row[1] |
||
| 915 | |||
| 916 | result_rows_monthly.append((current_datetime_utc, sub_total)) |
||
| 917 | sample_data.append(sub_total) |
||
| 918 | |||
| 919 | counter += 1 |
||
| 920 | if minimum is None: |
||
| 921 | minimum = sub_total |
||
| 922 | elif minimum > sub_total: |
||
| 923 | minimum = sub_total |
||
| 924 | |||
| 925 | if maximum is None: |
||
| 926 | maximum = sub_total |
||
| 927 | elif maximum < sub_total: |
||
| 928 | maximum = sub_total |
||
| 929 | current_datetime_utc = next_datetime_utc |
||
| 930 | |||
| 931 | if len(sample_data) > 1: |
||
| 932 | mean = statistics.mean(sample_data) |
||
| 933 | median = statistics.median(sample_data) |
||
| 934 | stdev = statistics.stdev(sample_data) |
||
| 935 | variance = statistics.variance(sample_data) |
||
| 936 | |||
| 937 | return result_rows_monthly, mean, median, minimum, maximum, stdev, variance |
||
| 938 | |||
| 939 | elif period_type == "yearly": |
||
| 940 | result_rows_yearly = list() |
||
| 941 | sample_data = list() |
||
| 942 | # todo: add config.working_day_start_time_local |
||
| 943 | # todo: add config.minutes_to_count |
||
| 944 | mean = None |
||
| 945 | median = None |
||
| 946 | minimum = None |
||
| 947 | maximum = None |
||
| 948 | stdev = None |
||
| 949 | variance = None |
||
| 950 | # calculate the start datetime in utc of the first day in the first month in local |
||
| 951 | start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3])) |
||
| 952 | current_datetime_utc = start_datetime_local.replace(month=1, day=1, hour=0) - timedelta( |
||
| 953 | hours=int(config.utc_offset[1:3])) |
||
| 954 | |||
| 955 | while current_datetime_utc <= end_datetime_utc: |
||
| 956 | # calculate the next datetime in utc |
||
| 957 | # todo: timedelta of year |
||
| 958 | next_datetime_utc = datetime(year=current_datetime_utc.year + 2, |
||
| 959 | month=1, |
||
| 960 | day=1, |
||
| 961 | hour=current_datetime_utc.hour, |
||
| 962 | minute=current_datetime_utc.minute, |
||
| 963 | second=current_datetime_utc.second, |
||
| 964 | microsecond=current_datetime_utc.microsecond, |
||
| 965 | tzinfo=current_datetime_utc.tzinfo) - timedelta(days=1) |
||
| 966 | sub_total = Decimal(0.0) |
||
| 967 | for row in rows_hourly: |
||
| 968 | if current_datetime_utc <= row[0] < next_datetime_utc: |
||
| 969 | sub_total += row[1] |
||
| 970 | |||
| 971 | result_rows_yearly.append((current_datetime_utc, sub_total)) |
||
| 972 | sample_data.append(sub_total) |
||
| 973 | |||
| 974 | if minimum is None: |
||
| 975 | minimum = sub_total |
||
| 976 | elif minimum > sub_total: |
||
| 977 | minimum = sub_total |
||
| 978 | if maximum is None: |
||
| 979 | maximum = sub_total |
||
| 980 | elif maximum < sub_total: |
||
| 981 | maximum = sub_total |
||
| 982 | |||
| 983 | current_datetime_utc = next_datetime_utc |
||
| 984 | |||
| 985 | if len(sample_data) > 1: |
||
| 986 | mean = statistics.mean(sample_data) |
||
| 987 | median = statistics.median(sample_data) |
||
| 988 | stdev = statistics.stdev(sample_data) |
||
| 989 | variance = statistics.variance(sample_data) |
||
| 990 | |||
| 991 | return result_rows_yearly, mean, median, minimum, maximum, stdev, variance |
||
| 992 | |||
| 993 | else: |
||
| 994 | return list(), None, None, None, None, None, None |
||
| 995 | |||
| 996 | |||
| 997 | def get_translation(language): |
||
| 998 | if language is None or not isinstance(language, str) or len(language) == 0: |
||
| 999 | return gettext.translation('myems', './i18n/', languages=['en']) |
||
| 1000 | |||
| 1001 | if language not in ['zh_CN', 'en', 'de', 'fr', 'es', 'ru', 'ar', 'vi', 'th', 'tr', 'ms', 'id', 'zh_TW', 'pt']: |
||
| 1002 | return gettext.translation('myems', './i18n/', languages=['en']) |
||
| 1003 | else: |
||
| 1004 | language_list = [language] |
||
| 1005 | return gettext.translation('myems', './i18n/', languages=language_list) |
||
| 1006 | |||
| 1007 | |||
| 1008 | def int16_to_hhmm(actual_value): |
||
| 1009 | """Convert int16 to time in HH:mm""" |
||
| 1010 | hh = int(actual_value / 256) |
||
| 1011 | if hh < 10: |
||
| 1012 | hh = '0' + str(hh) |
||
| 1013 | elif hh < 24: |
||
| 1014 | hh = str(hh) |
||
| 1015 | else: |
||
| 1016 | return None |
||
| 1017 | mm = actual_value % 256 |
||
| 1018 | if mm < 10: |
||
| 1019 | mm = '0' + str(mm) |
||
| 1020 | elif mm < 60: |
||
| 1021 | mm = str(mm) |
||
| 1022 | else: |
||
| 1023 | return None |
||
| 1024 | return hh + ':' + mm |
||
| 1025 | |||
| 1026 | |||
| 1027 | def round2(actual_value, precision): |
||
| 1028 | if actual_value is not None: |
||
| 1029 | try: |
||
| 1030 | result = round(actual_value, precision) |
||
| 1031 | except (TypeError, NameError, SyntaxError): |
||
| 1032 | return "-" |
||
| 1033 | return result |
||
| 1034 | else: |
||
| 1035 | return "-" |
||
| 1036 |
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