def _genTimeSeries(reduce_args, state):
import scikits.timeseries.tseries as ts
from numpy import ndarray
from numpy.ma import MaskedArray
time_series = ts._tsreconstruct(*reduce_args)
#from setstate modified
(ver, shp, typ, isf, raw, msk, flv, dsh, dtm, dtyp, frq, infodict) = state
#print 'regenerating %s' % dtyp
MaskedArray.__setstate__(time_series, (ver, shp, typ, isf, raw, msk, flv))
_dates = time_series._dates
#_dates.__setstate__((ver, dsh, typ, isf, dtm, frq)) #use remote typ
ndarray.__setstate__(_dates, (dsh, dtyp, isf, dtm))
_dates.freq = frq
_dates._cachedinfo.update(
dict(full=None,
hasdups=None,
steps=None,
toobj=None,
toord=None,
tostr=None))
# Update the _optinfo dictionary
time_series._optinfo.update(infodict)
return time_series
def _genTimeSeries(reduce_args, state):
import scikits.timeseries.tseries as ts
from numpy import ndarray
from numpy.ma import MaskedArray
time_series = ts._tsreconstruct(*reduce_args)
# from setstate modified
(ver, shp, typ, isf, raw, msk, flv, dsh, dtm, dtyp, frq, infodict) = state
# print 'regenerating %s' % dtyp
MaskedArray.__setstate__(time_series, (ver, shp, typ, isf, raw, msk, flv))
_dates = time_series._dates
# _dates.__setstate__((ver, dsh, typ, isf, dtm, frq)) #use remote typ
ndarray.__setstate__(_dates, (dsh, dtyp, isf, dtm))
_dates.freq = frq
_dates._cachedinfo.update(dict(full=None, hasdups=None, steps=None, toobj=None, toord=None, tostr=None))
# Update the _optinfo dictionary
time_series._optinfo.update(infodict)
return time_series