def to_relative(self, cutoff=None):
"""Return relative values
Parameters
----------
cutoff : pd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value is required to convert a relative forecasting
horizon to an absolute one and vice versa.
Returns
-------
fh : ForecastingHorizon
Relative representation of forecasting horizon
"""
if self.is_relative:
return self._new()
else:
self._check_cutoff(cutoff)
values = self.to_pandas() - cutoff
if isinstance(self.to_pandas(),
(pd.PeriodIndex, pd.DatetimeIndex)):
values = _coerce_duration_to_int(values,
unit=_get_unit(cutoff))
return self._new(values, is_relative=True)
def _align_seasonal(self, y):
"""Helper function to align seasonal components with y's time index"""
shift = (-_get_duration(
y.index[0],
self._y_index[0],
coerce_to_int=True,
unit=_get_unit(self._y_index),
) % self.sp)
return np.resize(np.roll(self.seasonal_, shift=shift), y.shape[0])
def test_coerce_duration_to_int(duration):
ret = _coerce_duration_to_int(duration, unit=_get_unit(duration))
# check output type is always integer
assert type(ret) in (pd.Int64Index, np.integer, int)
# check result
if isinstance(duration, pd.Index):
np.testing.assert_array_equal(ret, range(3))
if isinstance(duration, pd.tseries.offsets.BaseOffset):
ret == 3
def to_absolute_int(self, start, cutoff=None):
"""Return absolute values as zero-based integer index
Parameters
----------
start : pd.Period, pd.Timestamp, int
Start value
cutoff : pd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value is required to convert a relative forecasting
horizon to an absolute one and vice versa.
Returns
-------
fh : ForecastingHorizon
Absolute representation of forecasting horizon as zero-based
integer index
"""
self._check_cutoff(start)
absolute = self.to_absolute(cutoff).to_pandas()
values = absolute - start
if isinstance(absolute, (pd.PeriodIndex, pd.DatetimeIndex)):
values = _coerce_duration_to_int(values, unit=_get_unit(cutoff))
return self._new(values, is_relative=False)
