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Added the time scale of weekly in statistics_hourly_data_by_period

pull/77/head
Caozhenhui 2021-11-04 16:24:23 +08:00
parent f2bee0bb2c
commit caa65ef136
1 changed files with 45 additions and 1 deletions
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46
myems-api/core/utilities.py
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@ -701,7 +701,7 @@ def statistics_hourly_data_by_period(rows_hourly, start_datetime_utc, end_dateti
if start_datetime_utc is None or \
end_datetime_utc is None or \
start_datetime_utc >= end_datetime_utc or \
period_type not in ('hourly', 'daily', 'monthly', 'yearly'):
period_type not in ('hourly', 'daily', 'weekly', 'monthly', 'yearly'):
return list(), None, None, None, None, None, None
start_datetime_utc = start_datetime_utc.replace(tzinfo=None)
@ -795,6 +795,50 @@ def statistics_hourly_data_by_period(rows_hourly, start_datetime_utc, end_dateti
return result_rows_daily, mean, median, minimum, maximum, stdev, variance
elif period_type == "weekly":
result_rows_daily = list()
sample_data = list()
# todo: add config.working_day_start_time_local
# todo: add config.minutes_to_count
counter = 0
mean = None
median = None
minimum = None
maximum = None
stdev = None
variance = None
# calculate the start datetime in utc of the monday in the first week in local
start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3]))
current_datetime_utc = start_datetime_local.replace(hour=0) - timedelta(hours=int(config.utc_offset[1:3]))
while current_datetime_utc <= end_datetime_utc:
sub_total = Decimal(0.0)
for row in rows_hourly:
if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=7):
sub_total += row[1]
result_rows_daily.append((current_datetime_utc, sub_total))
sample_data.append(sub_total)
counter += 1
if minimum is None:
minimum = sub_total
elif minimum > sub_total:
minimum = sub_total
if maximum is None:
maximum = sub_total
elif maximum < sub_total:
maximum = sub_total
current_datetime_utc += timedelta(days=7)
if len(sample_data) > 1:
mean = statistics.mean(sample_data)
median = statistics.median(sample_data)
stdev = statistics.stdev(sample_data)
variance = statistics.variance(sample_data)
return result_rows_daily, mean, median, minimum, maximum, stdev, variance
elif period_type == "monthly":
result_rows_monthly = list()
sample_data = list()