updated commments in aggregate_hourly_data_by_period of API
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@ -12,7 +12,7 @@ import statistics
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# rows_hourly: list of (start_datetime_utc, actual_value), should belong to one energy_category_id
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# start_datetime_utc: start datetime in utc
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# end_datetime_utc: end datetime in utc
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# period_type: one of the following period types, 'hourly', 'daily', 'monthly' and 'yearly'
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# period_type: one of the following period types, 'hourly', 'daily', 'weekly', 'monthly' and 'yearly'
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# Note: this procedure doesn't work with multiple energy categories
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########################################################################################################################
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def aggregate_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetime_utc, period_type):
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@ -63,7 +63,7 @@ def aggregate_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetim
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result_rows_weekly = list()
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# todo: add config.working_day_start_time_local
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# todo: add config.minutes_to_count
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# calculate the start datetime in utc of the first day in the first month in local
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# calculate the start datetime in utc of the monday in the first week in local
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start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3]))
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weekday = start_datetime_local.weekday()
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current_datetime_utc = \
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@ -166,7 +166,7 @@ def aggregate_hourly_data_by_period(rows_hourly, start_datetime_utc, end_datetim
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result_rows_yearly = list()
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# todo: add config.working_day_start_time_local
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# todo: add config.minutes_to_count
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# calculate the start datetime in utc of the first day in the first month in local
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# calculate the start datetime in utc of the first day in the first year in local
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start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3]))
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current_datetime_utc = start_datetime_local.replace(month=1, day=1, hour=0) - timedelta(
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hours=int(config.utc_offset[1:3]))
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