def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
for attr, val in own_state.items():
setattr(self, attr, val)
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 __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
fill_value, sp_index = own_state[:2]
self.sp_index = sp_index
self.fill_value = fill_value
def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
fill_value, sp_index = own_state[:2]
self.sp_index = sp_index
self.fill_value = fill_value
def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
index, fill_value, sp_index = own_state[:3]
name = None
if len(own_state) > 3:
name = own_state[3]
self.sp_index = sp_index
self.fill_value = fill_value
self.index = index
self.name = name
def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
index, fill_value, sp_index = own_state[:3]
name = None
if len(own_state) > 3:
name = own_state[3]
self.sp_index = sp_index
self.fill_value = fill_value
self.index = index
self.name = name
def __setstate__(self, state):
"""
Restores the internal state of the TimeSeries, for pickling purposes.
`state` is typically the output of the ``__getstate__`` output, and is a 5-tuple:
- class name
- a tuple giving the shape of the data
- a typecode for the data
- a binary string for the data
- a binary string for the mask.
"""
(ver, shp, typ, isf, raw, frq) = state
ndarray.__setstate__(self, (shp, typ, isf, raw))
self.freq = frq
def __setstate__(self, state):
"""
Restores the internal state of the TimeSeries, for pickling purposes.
`state` is typically the output of the ``__getstate__`` output, and is a 5-tuple:
- class name
- a tuple giving the shape of the data
- a typecode for the data
- a binary string for the data
- a binary string for the mask.
"""
(ver, shp, typ, isf, raw, frq) = state
ndarray.__setstate__(self, (shp, typ, isf, raw))
self._unit = frq
def __setstate__(self, state):
"""Restore the internal state of the masked array, for pickling purposes.
``state`` is typically the output of the ``__getstate__`` output, and is a
5-tuple:
- class name
- a tuple giving the shape of the data
- a typecode for the data
- a binary string for the data
- a binary string for the mask.
"""
(ver, shp, typ, isf, raw, msk, flv) = state
ndarray.__setstate__(self, (shp, typ, isf, raw))
mdtype = dtype([(k, bool_) for (k, _) in self.dtype.descr])
self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))
self.fill_value = flv
def __setstate__(self, state):
"""Restore the internal state of the masked array, for pickling purposes.
``state`` is typically the output of the ``__getstate__`` output, and is a
5-tuple:
- class name
- a tuple giving the shape of the data
- a typecode for the data
- a binary string for the data
- a binary string for the mask.
"""
(ver, shp, typ, isf, raw, msk, flv) = state
ndarray.__setstate__(self, (shp, typ, isf, raw))
mdtype = dtype([(k, bool_) for (k, _) in self.dtype.descr])
self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))
self.fill_value = flv
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 __setstate__(self, state):
(version, shp, typ, isf, raw) = state
ndarray.__setstate__(self, (shp, typ, isf, raw))
def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
index, = own_state
self.index = index
def __setstate__(self, state):
"""Necessary for making this object picklable"""
nd_state, own_state = state
ndarray.__setstate__(self, nd_state)
index, = own_state
self.index = index
def __setstate__(self, p):
ndarray.__setstate__(self, p[0])
self.units = p[1]
self.symbol = p[2]
