%feature("docstring") OT::InverseTrendTransform
"Inverse Trend transformation.

Parameters
----------
myInverseTrendFunc : :class:`~openturns.Function`
    The  inverse trend function :math:`f_{trend}^{-1}`.


Notes
-----
A multivariate stochastic  process :math:`X: \Omega \times\cD \rightarrow \Rset^d` of dimension *d* where :math:`\cD \in \Rset^n` may write as the sum of a trend function :math:`f_{trend}: \Rset^n \rightarrow \Rset^d` and a stationary multivariate stochastic process :math:`X_{stat}: \Omega \times\cD \rightarrow \Rset^d` of dimension *d* as follows:

.. math::

    X(\omega,\vect{t}) = X_{stat}(\omega,\vect{t}) + f_{trend}(\vect{t})
    

We note :math:`(\vect{x}_0, \dots, \vect{x}_{N-1})` the values of one field of the process *X*, associated to the mesh :math:`\cM = (\vect{t}_0, \dots, \vect{t}_{N-1})` of :math:`\cD`. We note :math:`(\vect{x}^{stat}_0, \dots, \vect{x}^{stat}_{N-1})` the values of the resulting stationary field. Then we have:

.. math::

    \vect{x}^{stat}_i = \vect{x}_i - f_{trend}(\vect{t}_i)

The inverse trend transformation enables to get the :math:`X_{stat}` process or to get the :math:`(\vect{x}^{stat}_0, \dots, \vect{x}^{stat}_{N-1})` field.


Examples
--------
Create a trend function: :math:`f_{trend} : \Rset \mapsto \Rset` where :math:`f_{trend}(t,s)=-(1+2t+t^2)`:

>>> import openturns as ot
>>> h = ot.SymbolicFunction(['t'], ['-(1+2*t+t^2)'])
>>> fTrendInv = ot.InverseTrendTransform(h)

"

// ---------------------------------------------------------------------
%feature("docstring") OT::InverseTrendTransform::getInverse
"Accessor to the  trend function.

Returns
-------
myTrendTransform : :class:`~openturns.TrendTransform`
    The :math:`f_{trend}` function.

"
