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