本文整理汇总了Python中pthelma.timeseries.Timeseries.sum方法的典型用法代码示例。如果您正苦于以下问题：Python Timeseries.sum方法的具体用法？Python Timeseries.sum怎么用？Python Timeseries.sum使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pthelma.timeseries.Timeseries的用法示例。
在下文中一共展示了Timeseries.sum方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: regularize_raw_series
# 需要导入模块: from pthelma.timeseries import Timeseries [as 别名]
# 或者: from pthelma.timeseries.Timeseries import sum [as 别名]
def regularize_raw_series(raw_series_db, proc_series_db, rs, re, ps, pe ):
"""
This function regularize raw_series_db object from database and
writes a processed proc_series_db in database.
Raw series is a continuously increasing values time series,
aggregating the water consumption. Resulting processed timeseries
contains water consumption for each of its interval. I.e. if the
timeseries is of 15 minutes time step, then each record contains
the water consumption for each record period.
"""
raw_series = TSeries(id=raw_series_db.id)
raw_series.read_from_db(db.connection)
# We keep the last value for x-checking reasons, see last print
# command
test_value = raw_series[raw_series.bounding_dates()[1]]
time_step = ReadTimeStep(proc_series_db.id, proc_series_db)
proc_series = TSeries(id=proc_series_db.id, time_step = time_step)
# The following code can be used in real conditions to append only
# new records to db, in a next version
#if not pe:
# start = proc_series.time_step.down(rs)
#else:
# start = proc_series.time_step.up(pe)
# Instead of the above we use now:
start = proc_series.time_step.down(rs)
end = proc_series.time_step.up(re)
pointer = start
# Pass 1: Initialize proc_series
while pointer<=end:
proc_series[pointer] = float('nan')
pointer = proc_series.time_step.next(pointer)
# Pass 2: Transfer cummulative raw series to differences series:
prev_s = 0
for i in xrange(len(raw_series)):
dat, value = raw_series.items(pos=i)
if not math.isnan(value):
raw_series[dat] = value-prev_s
prev_s = value
# Pass 3: Regularize step: loop over raw series records and distribute
# floating point values to processed series
for i in xrange(len(raw_series)):
dat, value = raw_series.items(pos=i)
if not math.isnan(value):
# find previous, next timestamp of the proc time series
d1 = proc_series.time_step.down(dat)
d2 = proc_series.time_step.up(dat)
if math.isnan(proc_series[d1]): proc_series[d1] = 0
if math.isnan(proc_series[d2]): proc_series[d2] = 0
if d1==d2: # if dat on proc step then d1=d2
proc_series[d1] += value
continue
dif1 = _dif_in_secs(d1, dat)
dif2 = _dif_in_secs(dat, d2)
dif = dif1+dif2
# Distribute value to d1, d2
proc_series[d1] += (dif2/dif)*value
proc_series[d2] += (dif1/dif)*value
# Uncomment the following line in order to show debug information.
# Usually the three following sums are consistent by equality. If
# not equality is satisfied then there is a likelyhood of algorith
# error
print raw_series.sum(), proc_series.sum(), test_value
proc_series.write_to_db(db=db.connection, commit=True) #False)
#return the full timeseries
return proc_series