diff --git a/rrompy/hfengines/base/matrix_engine_base.py b/rrompy/hfengines/base/matrix_engine_base.py
index b5cac5d..625a211 100644
--- a/rrompy/hfengines/base/matrix_engine_base.py
+++ b/rrompy/hfengines/base/matrix_engine_base.py
@@ -1,519 +1,519 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from abc import abstractmethod
 import numpy as np
 import scipy.sparse as scsp
 from numbers import Number
 from matplotlib import pyplot as plt
 from copy import deepcopy as copy, copy as softcopy
 from rrompy.utilities.base.types import (Np1D, Np2D, ScOp, strLst, TupleAny,
                                          List, ListAny, DictAny, paramVal,
                                          paramList, sampList)
 from rrompy.utilities.base import purgeList, getNewFilename
 from rrompy.utilities.expression import (expressionEvaluator, createMonomial,
                                          createMonomialList)
 from rrompy.utilities.numerical import (hashDerivativeToIdx as hashD,
                                         solve as tsolve, dot, customPInv)
 from rrompy.utilities.exception_manager import RROMPyException, RROMPyAssert
 from rrompy.parameter import checkParameter, checkParameterList
 from rrompy.sampling import sampleList, emptySampleList
 from rrompy.solver import setupSolver
 
 __all__ = ['MatrixEngineBase']
     
 class MatrixEngineBase:
     """
     Generic solver for parametric matrix problems.
 
     Attributes:
         verbosity: Verbosity level.
         As: Scipy sparse array representation (in CSC format) of As.
         bs: Numpy array representation of bs.
         cs: Numpy array representation of cs.
         energyNormMatrix: Scipy sparse matrix representing inner product.
         energyNormDualMatrix: Scipy sparse matrix representing dual inner
             product.
         energyNormPartialDualMatrix: Scipy sparse matrix representing dual
             inner product without duality.
     """
 
     def __init__(self, verbosity : int = 10, timestamp : bool = True):
         self.verbosity = verbosity
         self.timestamp = timestamp
         self._affinePoly = True
         self.nAs, self.nbs = 1, 1
         self._C = None
         self.setSolver("SPSOLVE", {"use_umfpack" : False})
         self.npar = 0
         self.outputNormMatrix = 1.
     
     def name(self) -> str:
         return self.__class__.__name__
         
     def __str__(self) -> str:
         return self.name()
 
     def __repr__(self) -> str:
         return self.__str__() + " at " + hex(id(self))
 
     def __dir_base__(self):
         return [x for x in self.__dir__() if x[:2] != "__"]
 
     def __deepcopy__(self, memo):
         return softcopy(self)
 
     @property
     def npar(self):
         """Value of npar."""
         return self._npar
     @npar.setter
     def npar(self, npar):
         nparOld = self._npar if hasattr(self, "_npar") else -1
         if npar != nparOld:
             self.rescalingExp = [1.] * npar
             self._npar = npar
 
     @property
     def nAs(self):
         """Value of nAs."""
         return self._nAs
     @nAs.setter
     def nAs(self, nAs):
         self._nAs = nAs
         self.resetAs()
 
     @property
     def nbs(self):
         """Value of nbs."""
         return self._nbs
     @nbs.setter
     def nbs(self, nbs):
         self._nbs = nbs
         self.resetbs()
 
     @property
     def C(self):
         """Value of C."""
         if self._C is None: self.buildC()
         return self._C
 
     @property
     def isCEye(self):
         return isinstance(self.C, Number)
     
     @property
     def affinePoly(self):
         return self._affinePoly
 
     @property
     def spacedim(self):
         return self.bs[0].shape[0]
 
     def checkParameter(self, mu:paramVal):
         return checkParameter(mu, self.npar)
 
     def checkParameterList(self, mu:paramList):
         return checkParameterList(mu, self.npar)
 
     def buildEnergyNormForm(self):
         """
         Build sparse matrix (in CSR format) representative of scalar product.
         """
         self.energyNormMatrix = 1.
 
     def buildEnergyNormDualForm(self):
         """
         Build sparse matrix (in CSR format) representative of dual scalar
             product.
         """
         self.energyNormDualMatrix = 1.
 
     def buildEnergyNormPartialDualForm(self):
         """
         Build sparse matrix (in CSR format) representative of dual scalar
             product without duality.
         """
         self.energyNormPartialDualMatrix = 1.
 
     def innerProduct(self, u:Np2D, v:Np2D, onlyDiag : bool = False,
                      dual : bool = False, is_state : bool = True) -> Np2D:
         """Scalar product."""
         if is_state or self.isCEye:
             if dual:
                 if not hasattr(self, "energyNormPartialDualMatrix"):
                     self.buildEnergyNormPartialDualForm()
                 energyMat = self.energyNormPartialDualMatrix
             else:
                 if not hasattr(self, "energyNormMatrix"):
                     self.buildEnergyNormForm()
                 energyMat = self.energyNormMatrix
         else:
             energyMat = self.outputNormMatrix
         if not isinstance(u, (np.ndarray,)): u = u.data
         if not isinstance(v, (np.ndarray,)): v = v.data
         if onlyDiag:
             return np.sum(dot(energyMat, u) * v.conj(), axis = 0)
         return dot(dot(energyMat, u).T, v.conj()).T
 
     def norm(self, u:Np2D, dual : bool = False,
              is_state : bool = True) -> Np1D:
         return np.abs(self.innerProduct(u, u, onlyDiag = True, dual = dual,
                                         is_state = is_state)) ** .5
 
     def checkAInBounds(self, derI : int = 0):
         """Check if derivative index is oob for operator of linear system."""
         if derI < 0:
             d = self.spacedim
             return scsp.csr_matrix((np.zeros(0), np.zeros(0), np.zeros(d + 1)),
                                    shape = (d, d), dtype = np.complex)
 
     def checkbInBounds(self, derI : int = 0):
         """Check if derivative index is oob for RHS of linear system."""
         if derI < 0:
             return np.zeros(self.spacedim, dtype = np.complex)
 
     def resetAs(self):
         """Reset (derivatives of) operator of linear system."""
         self.setAs([None] * self.nAs)
         self.setthAs([None] * self.nAs)
 
     def resetbs(self):
         """Reset (derivatives of) RHS of linear system."""
         self.setbs([None] * self.nbs)
         self.setthbs([None] * self.nbs)
 
     def getMonomialSingleWeight(self, deg:List[int]):
         return createMonomial(deg, True)
 
     def getMonomialWeights(self, n:int):
         return createMonomialList(n, self.npar, True)
 
     def setAs(self, As:List[Np2D]):
         """Assign terms of operator of linear system."""
         if len(As) != self.nAs:
             raise RROMPyException(("Expected number {} of terms of As not "
                              "matching given list length {}.").format(self.nAs,
                                                                       len(As)))
         self.As = [copy(A) for A in As]
 
     def setthAs(self, thAs:List[List[TupleAny]]):
         """Assign terms of operator of linear system."""
         if len(thAs) != self.nAs:
             raise RROMPyException(("Expected number {} of terms of thAs not "
                            "matching given list length {}.").format(self.nAs,
                                                                     len(thAs)))
         self.thAs = copy(thAs)
 
     def setbs(self, bs:List[Np1D]):
         """Assign terms of RHS of linear system."""
         if len(bs) != self.nbs:
             raise RROMPyException(("Expected number {} of terms of bs not "
                              "matching given list length {}.").format(self.nbs,
                                                                       len(bs)))
         self.bs = [copy(b) for b in bs]
 
     def setthbs(self, thbs:List[List[TupleAny]]):
         """Assign terms of RHS of linear system."""
         if len(thbs) != self.nbs:
             raise RROMPyException(("Expected number {} of terms of thbs not "
                            "matching given list length {}.").format(self.nbs,
                                                                     len(thbs)))
         self.thbs = copy(thbs)
 
     def _assembleObject(self, mu:paramVal, objs:ListAny, th:ListAny,
                         derI:int) -> ScOp:
         """Assemble (derivative of) object from list of derivatives."""
         mu = self.checkParameter(mu)
         rExp = self.rescalingExp
         muE = mu ** rExp
         obj = None
         for j in range(len(objs)):
             if len(th[j]) <= derI and th[j][-1] is not None:
                 raise RROMPyException(("Cannot assemble operator. Non enough "
                                        "derivatives of theta provided."))
             if len(th[j]) > derI and th[j][derI] is not None:
                 expr = expressionEvaluator(th[j][derI], muE)
                 if hasattr(expr, "__len__"):
                     if len(expr) > 1:
                         raise RROMPyException(("Size mismatch in value of "
                                                "theta function. Only scalars "
                                                "allowed."))
                     expr = expr[0]
                 if obj is None:
                     obj = expr * objs[j]
                 else:
                     obj = obj + expr * objs[j]
         return obj
 
     @abstractmethod
     def buildA(self):
         """Build terms of operator of linear system."""
         if self.thAs[0] is None: self.thAs = self.getMonomialWeights(self.nAs)
         if self.As[0] is None:
             self.As[0] = scsp.eye(self.spacedim, format = "csr")
         for j in range(self.nAs):
             if self.As[j] is None: self.As[j] = self.checkAInBounds(-1)
 
     def A(self, mu : paramVal = [], der : List[int] = 0) -> ScOp:
         """
         Assemble terms of operator of linear system and return it (or its
             derivative) at a given parameter.
         """
         derI = hashD(der) if hasattr(der, "__len__") else der
         Anull = self.checkAInBounds(derI)
         if Anull is not None: return Anull
         self.buildA()
         assembledA = self._assembleObject(mu, self.As, self.thAs, derI)
         if assembledA is None: return self.checkAInBounds(-1)
         return assembledA
 
     @abstractmethod
     def buildb(self):
         """Build terms of RHS of linear system."""
         if self.thbs[0] is None: self.thbs = self.getMonomialWeights(self.nbs)
         for j in range(self.nbs):
             if self.bs[j] is None: self.bs[j] = self.checkbInBounds(-1)
 
     def b(self, mu : paramVal = [], der : List[int] = 0) -> Np1D:
         """
         Assemble terms of RHS of linear system and return it (or its
             derivative) at a given parameter.
         """
         derI = hashD(der) if hasattr(der, "__len__") else der
         bnull = self.checkbInBounds(derI)
         if bnull is not None: return bnull
         self.buildb()
         assembledb = self._assembleObject(mu, self.bs, self.thbs, derI)
         if assembledb is None: return self.checkbInBounds(-1)
         return assembledb
 
     def buildC(self):
         """Build terms of LHS of linear system."""
         if self._C is None: self._C = 1.
 
     def applyC(self, u:sampList):
         """Apply LHS of linear system."""
         return dot(self.C, u)
 
     def applyCpInv(self, u:sampList):
         """Apply pseudoinverse of LHS of linear system."""
         return dot(customPInv(self.C), u)
 
     _isStateShiftZero = True
     def stateShift(self, mu : paramVal = []) -> Np1D:
         return np.zeros((self.spacedim, len(mu)))
 
     _isOutputShiftZero = True
     def outputShift(self, mu : paramVal = []) -> Np1D:
         return self.applyC(self.stateShift(mu))
 
     def setSolver(self, solverType:str, solverArgs : DictAny = {}):
         """Choose solver type and parameters."""
         self._solver, self._solverArgs = setupSolver(solverType, solverArgs)
 
     def solve(self, mu : paramList = [], RHS : sampList = None,
-              force_state : bool = False, verbose : bool = False) -> sampList:
+              return_state : bool = False, verbose : bool = False) -> sampList:
         """
         Find solution of linear system.
 
         Args:
             mu: parameter value.
             RHS: RHS of linear system. If None, defaults to that of parametric
                 system. Defaults to None.
-            force_state: whether to return state before multiplication by c.
+            return_state: whether to return state before multiplication by c.
                 Defaults to False.
             verbose: whether to notify for each solution computed. Defaults to
                 False.
         """
         if mu == []: mu = self.mu0
         mu = self.checkParameterList(mu)[0]
         if self.npar == 0: mu.reset((1, self.npar), mu.dtype)
         if len(mu) == 0: return emptySampleList()
         if RHS is None: RHS = [self.b(m) for m in mu]
         RHS = sampleList(RHS)
         mult = 0 if len(RHS) == 1 else 1
         RROMPyAssert(mult * (len(mu) - 1) + 1, len(RHS), "Sample size")
         for j in range(len(mu)):
             u = tsolve(self.A(mu[j]), RHS[mult * j], self._solver,
                        self._solverArgs)
-            if force_state:
+            if return_state:
                 if j == 0:
                     sol = emptySampleList()
                     sol.reset((len(u), len(mu)), dtype = u.dtype)
                 sol[j] = u
             else:
                 if j == 0: sol = np.empty((len(u), len(mu)), dtype = u.dtype)
                 sol[:, j] = u
             if verbose:
                 print("." * (j % 5 != 4) + "*" * (j % 5 == 4), end = "")
-        if not force_state:
+        if not return_state:
             sol = sampleList(self.applyC(sol) - self.outputShift(mu)) #FIXME
         else:
             sol = sampleList(sol - self.stateShift(mu)) #FIXME
         if verbose: print()
         return sol
 
     def residual(self, mu : paramList = [], u : sampList = None,
                  post_c : bool = True) -> sampList:
         """
         Find residual of linear system for given approximate solution.
 
         Args:
             mu: parameter value.
             u: numpy complex array with function dofs. If None, set to 0.
             post_c: whether to post-process using c. Defaults to True.
         """
         if mu == []: mu = self.mu0
         mu = self.checkParameterList(mu)[0]
         if self.npar == 0: mu.reset((1, self.npar), mu.dtype)
         if len(mu) == 0: return emptySampleList()
         v = sampleList(self.stateShift(mu))
         if u is not None: v = v + sampleList(u) #FIXME
         for j in range(len(mu)):
             r = self.b(mu[j]) - dot(self.A(mu[j]), v[j])
             if post_c:
                 if j == 0: res = np.empty((len(r), len(mu)), dtype = r.dtype)
                 res[:, j] = r
             else:
                 if j == 0:
                     res = emptySampleList()
                     res.reset((len(r), len(mu)), dtype = r.dtype)
                 res[j] = r
         if post_c: res = sampleList(self.applyC(res))
         return res
 
     def _rayleighQuotient(self, A:Np2D, v0:Np1D, M:Np2D, sigma : float = 0.,
                           nIterP : int = 10, nIterR : int = 10) -> float:
         nIterP = min(nIterP, len(v0) // 2)
         nIterR = min(nIterR, (len(v0) + 1) // 2)
         v0 /= dot(dot(M, v0).T, v0.conj()) ** .5
         for j in range(nIterP):
             v0 = tsolve(A - sigma * M, dot(M, v0), self._solver,
                         self._solverArgs)
             v0 /= dot(dot(M, v0).T, v0.conj()) ** .5
             l0 = dot(A.dot(v0).T, v0.conj())
         for j in range(nIterR):
             v0 = tsolve(A - l0 * M, dot(M, v0), self._solver, self._solverArgs)
             v0 /= dot(dot(M, v0).T, v0.conj()) ** .5
             l0 = dot(A.dot(v0).T, v0.conj())
         if np.isnan(l0): l0 = np.finfo(float).eps
         return np.abs(l0)
 
     def stabilityFactor(self, mu : paramList = [], u : sampList = None,
                         nIterP : int = 10, nIterR : int = 10) -> sampList:
         """
         Find stability factor of matrix of linear system using iterative
             inverse power iteration- and Rayleigh quotient-based procedure.
 
         Args:
             mu: parameter values.
             u: numpy complex arrays with function dofs.
             nIterP: number of iterations of power method.
             nIterR: number of iterations of Rayleigh quotient method.
         """
         if mu == []: mu = self.mu0
         mu = self.checkParameterList(mu)[0]
         if self.npar == 0: mu.reset((1, self.npar), mu.dtype)
         u = sampleList(u)
         solShift = self.stateShift(mu)
         if len(u) == len(mu):
             u = u + solShift #FIXME
         else:
             u = sampleList(solShift) + np.tile(u.data, (1, len(mu))) #FIXME
         stabFact = np.empty(len(mu), dtype = float)
         if not hasattr(self, "energyNormMatrix"):
             self.buildEnergyNormForm()
         for j in range(len(mu)):
             stabFact[j] = self._rayleighQuotient(self.A(mu[j]), u[j],
                                                  self.energyNormMatrix,
                                                  0., nIterP, nIterR)
         return stabFact
 
     def plot(self, u:Np1D, warping : List[callable] = None, name : str = "u",
              save : str = None, what : strLst = 'all',
              saveFormat : str = "eps", saveDPI : int = 100, show : bool = True,
              pyplotArgs : dict = {}, **figspecs) -> str:
         """
         Do some nice plots of the complex-valued function with given dofs.
 
         Args:
             u: numpy complex array with function dofs.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             pyplotArgs(optional): Optional arguments for pyplot.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
 
         Returns:
             Output filename.
         """
         if isinstance(what, (str,)):
             if what.upper() == 'ALL':
                 what = ['ABS', 'PHASE', 'REAL', 'IMAG']
             else:
                 what = [what]
         what = purgeList(what, ['ABS', 'PHASE', 'REAL', 'IMAG'],
                          listname = self.name() + ".what", baselevel = 1)
         if len(what) == 0: return
         if 'figsize' not in figspecs.keys():
             figspecs['figsize'] = (13. * len(what) / 4, 3)
         subplotcode = 100 + len(what) * 10
 
         idxs = np.arange(self.spacedim)
         if warping is not None:
             idxs = warping[0](np.arange(self.spacedim))
         plt.figure(**figspecs)
         plt.jet()
         if 'ABS' in what:
             subplotcode = subplotcode + 1
             plt.subplot(subplotcode)
             plt.plot(idxs, np.abs(u).flatten(), **pyplotArgs)
             plt.title("|{0}|".format(name))
         if 'PHASE' in what:
             subplotcode = subplotcode + 1
             plt.subplot(subplotcode)
             plt.plot(idxs, np.angle(u).flatten(), **pyplotArgs)
             plt.title("phase({0})".format(name))
         if 'REAL' in what:
             subplotcode = subplotcode + 1
             plt.subplot(subplotcode)
             plt.plot(idxs, np.real(u).flatten(), **pyplotArgs)
             plt.title("Re({0})".format(name))
         if 'IMAG' in what:
             subplotcode = subplotcode + 1
             plt.subplot(subplotcode)
             plt.plot(idxs, np.imag(u).flatten(), **pyplotArgs)
             plt.title("Im({0})".format(name))
         if save is not None:
             save = save.strip()
             fileOut = getNewFilename("{}_fig_".format(save), saveFormat)
             plt.savefig(fileOut, format = saveFormat, dpi = saveDPI)
         else: fileOut = None
         if show:
             plt.show()
         plt.close()
         return fileOut
diff --git a/rrompy/reduction_methods/base/generic_approximant.py b/rrompy/reduction_methods/base/generic_approximant.py
index 5bca9bc..34e15c5 100644
--- a/rrompy/reduction_methods/base/generic_approximant.py
+++ b/rrompy/reduction_methods/base/generic_approximant.py
@@ -1,906 +1,907 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from abc import abstractmethod
 import numpy as np
 from itertools import product as iterprod
 from copy import deepcopy as copy
 from os import remove as osrm
 from rrompy.sampling.standard import (SamplingEngineStandard,
                                       SamplingEngineStandardPOD)
 from rrompy.utilities.base.types import (Np1D, DictAny, HFEng, List, Tuple,
                                          ListAny, strLst, paramVal, paramList,
                                          sampList)
 from rrompy.utilities.base import (purgeDict, verbosityManager as vbMng,
                                    getNewFilename)
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPy_READY, RROMPy_FRAGILE)
 from rrompy.utilities.base import pickleDump, pickleLoad
 from rrompy.parameter import (emptyParameterList, checkParameter,
                               checkParameterList)
 from rrompy.sampling import sampleList, emptySampleList
 
 __all__ = ['GenericApproximant']
 
 def addNormFieldToClass(self, fieldName):
     def objFunc(self, mu:paramList, *args, **kwargs) -> Np1D:
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         kwargs["is_state"] = False
         val = self.HFEngine.norm(uV, *args, **kwargs)
         return val
     setattr(self.__class__, "norm" + fieldName, objFunc)
 
 def addNormDualFieldToClass(self, fieldName):
     def objFunc(self, mu:paramList, *args, **kwargs) -> Np1D:
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         kwargs["is_state"] = True
         if "dual" not in kwargs.keys(): kwargs["dual"] = True
         val = self.HFEngine.norm(uV, *args, **kwargs)
         return val
     setattr(self.__class__, "norm" + fieldName, objFunc)
 
 def addPlotFieldToClass(self, fieldName):
     def objFunc(self, mu:paramList, *args, **kwargs):
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         kwargsCopy = copy(kwargs)
         filesOut = []
         for j, u in enumerate(uV):
             if "name" in kwargs.keys():
                 kwargsCopy["name"] = kwargs["name"] + str(j)
             filesOut += [self.HFEngine.plot(u, *args, **kwargsCopy)]
         if filesOut[0] is None: return None
         return filesOut
     setattr(self.__class__, "plot" + fieldName, objFunc)
 
 def addPlotDualFieldToClass(self, fieldName):
     def objFunc(self, mu:paramList, *args, **kwargs):
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         kwargsCopy = copy(kwargs)
         filesOut = []
         for j, u in enumerate(uV):
             if "name" in kwargs.keys():
                 kwargsCopy["name"] = kwargs["name"] + str(j)
             filesOut += [self.HFEngine.plot(u, *args, **kwargsCopy)]
         if filesOut[0] is None: return None
         return filesOut
     setattr(self.__class__, "plot" + fieldName, objFunc)
 
 def addOutParaviewFieldToClass(self, fieldName):
     def objFunc(self, mu:paramVal, *args, **kwargs):
         if not hasattr(self.HFEngine, "outParaview"):
             raise RROMPyException(("High fidelity engine cannot output to "
                                    "Paraview."))
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         kwargsCopy = copy(kwargs)
         filesOut = []
         for j, u in enumerate(uV):
             if "name" in kwargs.keys():
                 kwargsCopy["name"] = kwargs["name"] + str(j)
             filesOut += [self.HFEngine.outParaview(u, *args, **kwargsCopy)]
         if filesOut[0] is None: return None
         return filesOut
     setattr(self.__class__, "outParaview" + fieldName, objFunc)
 
 def addOutParaviewTimeDomainFieldToClass(self, fieldName):
     def objFunc(self, mu:paramVal, *args, **kwargs):
         if not hasattr(self.HFEngine, "outParaviewTimeDomain"):
             raise RROMPyException(("High fidelity engine cannot output to "
                                    "Paraview."))
         uV = getattr(self.__class__, "get" + fieldName)(self, mu)
         omega = args.pop(0) if len(args) > 0 else np.real(mu)
         kwargsCopy = copy(kwargs)
         filesOut = []
         for j, u in enumerate(uV):
             if "name" in kwargs.keys():
                 kwargsCopy["name"] = kwargs["name"] + str(j)
             filesOut += [self.HFEngine.outParaviewTimeDomain(u, omega, *args,
                                                              **kwargsCopy)]
         if filesOut[0] is None: return None
         return filesOut
     setattr(self.__class__, "outParaviewTimeDomain" + fieldName, objFunc)
 
 def getTrainedModelClass(name):
     from importlib import import_module as im
     try:
         return getattr(im("rrompy.reduction_methods.trained_model"), name)
     except:
         raise RROMPyException("Trained model name not recognized.")
 
 class GenericApproximant:
     """
     ABSTRACT
     ROM approximant computation for parametric problems.
     
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': total number of samples current approximant relies upon.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
 
     Attributes:
         HFEngine: HF problem solver.
         trainedModel: Trained model evaluator.
         mu0: Default parameter.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList{Soft,Critical}.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         S: Number of solution snapshots over which current approximant is
             based upon.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
     """
 
     __all__ += [ftype + dtype for ftype, dtype in iterprod(
                       ["norm", "plot", "outParaview", "outParaviewTimeDomain"],
                       ["HF", "RHS", "Approx", "Res", "Err"])]
 
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         self._mode = RROMPy_READY
-        self.force_state = force_state
+        self.approx_state = approx_state
         self.verbosity = verbosity
         self.timestamp = timestamp
         vbMng(self, "INIT",
               "Initializing engine of type {}.".format(self.name()), 10)
         self._HFEngine = HFEngine
         self.trainedModel = None
         self.lastSolvedHF = emptyParameterList()
         self.uHF = emptySampleList()
         self._addParametersToList(["POD"], [True], ["S"], [1])
         if mu0 is None:
             if hasattr(self.HFEngine, "mu0"):
                 self.mu0 = checkParameter(self.HFEngine.mu0)
             else:
                 raise RROMPyException(("Center of approximation cannot be "
                                        "inferred from HF engine. Parameter "
                                        "required"))
         else:
             self.mu0 = checkParameter(mu0, self.HFEngine.npar)
         self.resetSamples()
         self.approxParameters = approxParameters
         self._postInit()
 
         ### add norm{HF,Err} methods
         """
         Compute norm of * at arbitrary parameter.
         Args:
             mu: Target parameter.
         Returns:
             Target norm of *.
         """
         for objName in ["HF", "Err"]:
             addNormFieldToClass(self, objName)
 
         ### add norm{RHS,Res} methods
         """
         Compute norm of * at arbitrary parameter.
         Args:
             mu: Target parameter.
         Returns:
             Target norm of *.
         """
         for objName in ["RHS", "Res"]:
             addNormDualFieldToClass(self, objName)
 
         ### add plot{HF,Approx,Err} methods
         """
         Do some nice plots of * at arbitrary parameter.
 
         Args:
             mu: Target parameter.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
         """
         for objName in ["HF", "Approx", "Err"]:
             addPlotFieldToClass(self, objName)
 
         ### add plot{RHS,Res} methods
         """
         Do some nice plots of * at arbitrary parameter.
 
         Args:
             mu: Target parameter.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
         """
         for objName in ["RHS", "Res"]:
             addPlotDualFieldToClass(self, objName)
 
         ### add outParaview{HF,RHS,Approx,Res,Err} methods
         """
         Output * to ParaView file.
 
         Args:
             mu: Target parameter.
             name(optional): Base name to be used for data output.
             filename(optional): Name of output file.
             time(optional): Timestamp.
             what(optional): Which plots to do. If list, can contain 'MESH',
                 'ABS', 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard
                 'ALL'. Defaults to 'ALL'.
             forceNewFile(optional): Whether to create new output file.
             filePW(optional): Fenics File entity (for time series).
         """
         for objName in ["HF", "RHS", "Approx", "Res", "Err"]:
             addOutParaviewFieldToClass(self, objName)
 
         ### add outParaviewTimeDomain{HF,RHS,Approx,Res,Err} methods
         """
         Output * to ParaView file, converted to time domain.
 
         Args:
             mu: Target parameter.
             omega(optional): frequency.
             timeFinal(optional): final time of simulation.
             periodResolution(optional): number of time steps per period.
             name(optional): Base name to be used for data output.
             filename(optional): Name of output file.
             forceNewFile(optional): Whether to create new output file.
         """
         for objName in ["HF", "RHS", "Approx", "Res", "Err"]:
             addOutParaviewTimeDomainFieldToClass(self, objName)
 
     def _preInit(self):
         if not hasattr(self, "depth"): self.depth = 0
         else: self.depth += 1
 
     @property
     def tModelType(self):
         raise RROMPyException("No trainedModel type assigned.")
 
     def initializeModelData(self, datadict):
         from rrompy.reduction_methods.trained_model import TrainedModelData
         return (TrainedModelData(datadict["mu0"], datadict.pop("projMat"),
                                  datadict["scaleFactor"],
                                  datadict.pop("rescalingExp")),
                 ["mu0", "scaleFactor", "mus"])
 
     @property
     def parameterList(self):
         """Value of parameterListSoft + parameterListCritical."""
         return self.parameterListSoft + self.parameterListCritical
 
     def _addParametersToList(self, whatSoft:strLst, defaultSoft:ListAny,
                              whatCritical : strLst = [],
                              defaultCritical : ListAny = [],
                              toBeExcluded : strLst = []):
         if not hasattr(self, "parameterToBeExcluded"):
             self.parameterToBeExcluded = []
         self.parameterToBeExcluded = toBeExcluded + self.parameterToBeExcluded
         if not hasattr(self, "parameterListSoft"):
             self.parameterListSoft = []
         if not hasattr(self, "parameterDefaultSoft"):
             self.parameterDefaultSoft = {}
         if not hasattr(self, "parameterListCritical"):
             self.parameterListCritical = []
         if not hasattr(self, "parameterDefaultCritical"):
             self.parameterDefaultCritical = {}
         for j, what in enumerate(whatSoft):
             if what not in self.parameterToBeExcluded:
                 self.parameterListSoft = [what] + self.parameterListSoft
                 self.parameterDefaultSoft[what] = defaultSoft[j]
         for j, what in enumerate(whatCritical):
             if what not in self.parameterToBeExcluded:
                 self.parameterListCritical = ([what]
                                             + self.parameterListCritical)
                 self.parameterDefaultCritical[what] = defaultCritical[j]
 
     def _postInit(self):
         if self.depth == 0:
             vbMng(self, "DEL", "Done initializing.", 10)
             del self.depth
         else: self.depth -= 1
 
     def name(self) -> str:
         return self.__class__.__name__
         
     def __str__(self) -> str:
         return self.name()
 
     def __repr__(self) -> str:
         return self.__str__() + " at " + hex(id(self))
 
     def setupSampling(self):
         """Setup sampling engine."""
         RROMPyAssert(self._mode, message = "Cannot setup sampling engine.")
         if not hasattr(self, "_POD") or self._POD is None: return
         if self.POD:
             SamplingEngine = SamplingEngineStandardPOD
         else:
             SamplingEngine = SamplingEngineStandard
         self.samplingEngine = SamplingEngine(self.HFEngine,
-                                             force_state = self.force_state,
+                                             sample_state = self.approx_state,
                                              verbosity = self.verbosity)
 
     @property
     def HFEngine(self):
         """Value of HFEngine."""
         return self._HFEngine
     @HFEngine.setter
     def HFEngine(self, HFEngine):
         raise RROMPyException("Cannot change HFEngine.")
 
     @property
     def mu0(self):
         """Value of mu0."""
         return self._mu0
     @mu0.setter
     def mu0(self, mu0):
         mu0 = checkParameter(mu0)
         if not hasattr(self, "_mu0") or mu0 != self.mu0:
             self.resetSamples()
         self._mu0 = mu0
 
     @property
     def npar(self):
         """Number of parameters."""
         return self.mu0.shape[1]
 
     @property
     def approxParameters(self):
         """Value of approximant parameters."""
         return self._approxParameters
     @approxParameters.setter
     def approxParameters(self, approxParams):
         if not hasattr(self, "approxParameters"):
             self._approxParameters = {}
         approxParameters = purgeDict(approxParams, self.parameterList,
                                   dictname = self.name() + ".approxParameters",
                                   baselevel = 1)
         keyList = list(approxParameters.keys())
         for key in self.parameterListCritical:
             if key in keyList:
                 setattr(self, "_" + key, self.parameterDefaultCritical[key])
         for key in self.parameterListSoft:
             if key in keyList:
                 setattr(self, "_" + key, self.parameterDefaultSoft[key])
         fragile = False
         for key in self.parameterListCritical:
             if key in keyList:
                 val = approxParameters[key]
             else:
                 val = getattr(self, "_" + key, None)
                 if val is None:
                     val = self.parameterDefaultCritical[key]
             getattr(self.__class__, key, None).fset(self, val)
             fragile = fragile or val is None
         for key in self.parameterListSoft:
             if key in keyList:
                 val = approxParameters[key]
             else:
                 val = getattr(self, "_" + key, None)
                 if val is None:
                     val = self.parameterDefaultSoft[key]
             getattr(self.__class__, key, None).fset(self, val)
         if fragile:
             self._mode = RROMPy_FRAGILE
 
     @property
     def POD(self):
         """Value of POD."""
         return self._POD
     @POD.setter
     def POD(self, POD):
         if hasattr(self, "_POD"): PODold = self.POD
         else: PODold = -1
         self._POD = POD
         self._approxParameters["POD"] = self.POD
         if PODold != self.POD:
             self.samplingEngine = None
             self.resetSamples()
 
     @property
-    def force_state(self):
-        """Value of force_state."""
-        return self._force_state
-    @force_state.setter
-    def force_state(self, force_state):
-        if hasattr(self, "_force_state"): force_stateold = self.force_state
-        else: force_stateold = -1
-        self._force_state = force_state
-        if force_stateold != self.force_state:
+    def approx_state(self):
+        """Value of approx_state."""
+        return self._approx_state
+    @approx_state.setter
+    def approx_state(self, approx_state):
+        if hasattr(self, "_approx_state"): approx_stateold = self.approx_state
+        else: approx_stateold = -1
+        self._approx_state = approx_state
+        if approx_stateold != self.approx_state:
             self.samplingEngine = None
             self.resetSamples()
 
     @property
     def S(self):
         """Value of S."""
         return self._S
     @S.setter
     def S(self, S):
         if S <= 0: raise RROMPyException("S must be positive.")
         if hasattr(self, "_S") and self._S is not None: Sold = self.S
         else: Sold = -1
         self._S = S
         self._approxParameters["S"] = self.S
         if Sold != self.S: self.resetSamples()
 
     @property
     def trainedModel(self):
         """Value of trainedModel."""
         return self._trainedModel
     @trainedModel.setter
     def trainedModel(self, trainedModel):
         self._trainedModel = trainedModel
         if self._trainedModel is not None:
             self._trainedModel.lastSolvedApproxReduced = emptyParameterList()
             self._trainedModel.lastSolvedApprox = emptyParameterList()
         self.lastSolvedApproxReduced = emptyParameterList()
         self.lastSolvedApprox = emptyParameterList()
         self.uApproxReduced = emptySampleList()
         self.uApprox = emptySampleList()
 
     def resetSamples(self):
         if hasattr(self, "samplingEngine") and self.samplingEngine is not None:
             self.samplingEngine.resetHistory()
         else:
             self.setupSampling()
         self._mode = RROMPy_READY
 
     def plotSamples(self, warping : List[callable] = None, name : str = "u",
                     save : str = None, what : strLst = 'all',
                     saveFormat : str = "eps", saveDPI : int = 100,
                     show : bool = True, plotArgs : dict = {},
                     **figspecs) -> List[str]:
         """
         Do some nice plots of the samples.
 
         Args:
             warping(optional): Domain warping functions.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             plotArgs(optional): Optional arguments for fen/pyplot.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
 
         Returns:
             Output filenames.
         """
         RROMPyAssert(self._mode, message = "Cannot plot samples.")
         return self.samplingEngine.plotSamples(warping, name, save, what,
                                                saveFormat, saveDPI, show,
                                                plotArgs, **figspecs)
         
     def outParaviewSamples(self, name : str = "u", filename : str = "out",
                            times : Np1D = None, what : strLst = 'all',
                            forceNewFile : bool = True, folders : bool = False,
                            filePW = None) -> List[str]:
         """
         Output samples to ParaView file.
 
         Args:
             name(optional): Base name to be used for data output.
             filename(optional): Name of output file.
             times(optional): Timestamps.
             what(optional): Which plots to do. If list, can contain 'MESH',
                 'ABS', 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard
                 'ALL'. Defaults to 'ALL'.
             forceNewFile(optional): Whether to create new output file.
             folders(optional): Whether to split output in folders.
             filePW(optional): Fenics File entity (for time series).
 
         Returns:
             Output filenames.
         """
         RROMPyAssert(self._mode, message = "Cannot output samples.")
         return self.samplingEngine.outParaviewSamples(name, folders, filename,
                                                       times, what,
                                                       forceNewFile, filePW)
         
     def outParaviewTimeDomainSamples(self, omegas : Np1D = None,
                                      timeFinal : Np1D = None, 
                                      periodResolution : int = 20,
                                      name : str = "u",
                                      filename : str = "out",
                                      forceNewFile : bool = True,
                                      folders : bool = False) -> List[str]:
         """
         Output samples to ParaView file, converted to time domain.
 
         Args:
             omegas(optional): frequencies.
             timeFinal(optional): final time of simulation.
             periodResolution(optional): number of time steps per period.
             name(optional): Base name to be used for data output.
             filename(optional): Name of output file.
             forceNewFile(optional): Whether to create new output file.
             folders(optional): Whether to split output in folders.
 
         Returns:
             Output filenames.
         """
         RROMPyAssert(self._mode, message = "Cannot output samples.")
         return self.samplingEngine.outParaviewTimeDomainSamples(
                                                        omegas, timeFinal,
                                                        periodResolution,
                                                        name, folders, filename,
                                                        forceNewFile)
     
     def setSamples(self, samplingEngine):
         """Copy samplingEngine and samples."""
         vbMng(self, "INIT", "Transfering samples.", 10)
         self.samplingEngine = copy(samplingEngine)
         vbMng(self, "DEL", "Done transfering samples.", 10)
 
     def setTrainedModel(self, model):
         """Deepcopy approximation from trained model."""
         if hasattr(model, "storeTrainedModel"):
             verb = model.verbosity
             model.verbosity = 0
             fileOut = model.storeTrainedModel()
             model.verbosity = verb
         else:
             try:
                 fileOut = getNewFilename("trained_model", "pkl")
                 pickleDump(model.data.__dict__, fileOut)
             except:
                 raise RROMPyException(("Failed to store model data. Parameter "
                                        "model must have either "
                                        "storeTrainedModel or "
                                        "data.__dict__ properties."))
         self.loadTrainedModel(fileOut)
         osrm(fileOut)
 
     @abstractmethod
     def setupApprox(self):
         """
         Setup approximant. (ABSTRACT)
 
         Any specialization should include something like
             if self.checkComputedApprox():
                 return
             RROMPyAssert(self._mode, message = "Cannot setup approximant.")
             ...
             self.trainedModel = ...
             self.trainedModel.data = ...
             self.trainedModel.data.approxParameters = copy(
                                                          self.approxParameters)
         """
         pass
 
     def checkComputedApprox(self) -> bool:
         """
         Check if setup of new approximant is not needed.
 
         Returns:
             True if new setup is not needed. False otherwise.
         """
         return self._mode == RROMPy_FRAGILE or (self.trainedModel is not None
           and self.trainedModel.data.approxParameters == self.approxParameters)
 
     def _pruneBeforeEval(self, mu:paramList, field:str, append:bool,
                          prune:bool) -> Tuple[paramList, Np1D, Np1D, bool]:
         mu = checkParameterList(mu, self.npar)[0]
         idx = np.empty(len(mu), dtype = np.int)
         if prune:
             jExtra = np.zeros(len(mu), dtype = bool)
             muExtra = emptyParameterList()
             lastSolvedMus = getattr(self, "lastSolved" + field)
             if (len(mu) > 0 and len(mu) == len(lastSolvedMus)
             and mu == lastSolvedMus):
                 idx = np.arange(len(mu), dtype = np.int)
                 return muExtra, jExtra, idx, True
             muKeep = copy(muExtra)
             for j in range(len(mu)):
                 jPos = lastSolvedMus.find(mu[j])
                 if jPos is not None:
                     idx[j] = jPos
                     muKeep.append(mu[j])
                 else:
                     jExtra[j] = True
                     muExtra.append(mu[j])
             if len(muKeep) > 0 and not append:
                 lastSolvedu = getattr(self, "u" + field)
                 idx[~jExtra] = getattr(self.__class__, "set" + field)(self,
                                      muKeep, lastSolvedu[idx[~jExtra]], append)
                 append = True
         else:
             jExtra = np.ones(len(mu), dtype = bool)
             muExtra = mu
         return muExtra, jExtra, idx, append
 
     def _setObject(self, mu:paramList, field:str, object:sampList,
                    append:bool) -> List[int]:
         newMus = checkParameterList(mu, self.npar)[0]
         newObj = sampleList(object)
         if append:
             getattr(self, "lastSolved" + field).append(newMus)
             getattr(self, "u" + field).append(newObj)
             Ltot = len(getattr(self, "u" + field))
             return list(range(Ltot - len(newObj), Ltot))
         setattr(self, "lastSolved" + field,  copy(newMus))
         setattr(self, "u" + field, copy(newObj))
         return list(range(len(getattr(self, "u" + field))))
 
     def setHF(self, muHF:paramList, uHF:sampleList,
               append : bool = False) -> List[int]:
         """Assign high fidelity solution."""
         return self._setObject(muHF, "HF", uHF, append)
 
     def evalHF(self, mu:paramList, append : bool = False,
                prune : bool = True) -> List[int]:
         """
         Find high fidelity solution with original parameters and arbitrary
             parameter.
 
         Args:
             mu: Target parameter.
             append(optional): Whether to append new HF solutions to old ones.
             prune(optional): Whether to remove duplicates of already appearing
                 HF solutions.
         """
         muExtra, jExtra, idx, append = self._pruneBeforeEval(mu, "HF", append,
                                                              prune)
         if len(muExtra) > 0:
             vbMng(self, "INIT", "Solving HF model for mu = {}.".format(mu),
                   15)
             newuHFs = self.HFEngine.solve(muExtra)
             vbMng(self, "DEL", "Done solving HF model.", 15)
             idx[jExtra] = self.setHF(muExtra, newuHFs, append)
         return list(idx)
 
     def setApproxReduced(self, muApproxR:paramList, uApproxR:sampleList,
                          append : bool = False) -> List[int]:
         """Assign high fidelity solution."""
         return self._setObject(muApproxR, "ApproxReduced", uApproxR, append)
 
     def evalApproxReduced(self, mu:paramList, append : bool = False,
                           prune : bool = True) -> List[int]:
         """
         Evaluate reduced representation of approximant at arbitrary parameter.
 
         Args:
             mu: Target parameter.
             append(optional): Whether to append new HF solutions to old ones.
             prune(optional): Whether to remove duplicates of already appearing
                 HF solutions.
         """
         self.setupApprox()
         muExtra, jExtra, idx, append = self._pruneBeforeEval(mu,
                                                              "ApproxReduced",
                                                              append, prune)
         if len(muExtra) > 0:
             newuApproxs = self.trainedModel.getApproxReduced(muExtra)
             idx[jExtra] = self.setApproxReduced(muExtra, newuApproxs, append)
         return list(idx)
 
     def setApprox(self, muApprox:paramList, uApprox:sampleList, 
                   append : bool = False) -> List[int]:
         """Assign high fidelity solution."""
         return self._setObject(muApprox, "Approx", uApprox, append)
 
     def evalApprox(self, mu:paramList, append : bool = False,
                    prune : bool = True) -> List[int]:
         """
         Evaluate approximant at arbitrary parameter.
 
         Args:
             mu: Target parameter.
             append(optional): Whether to append new HF solutions to old ones.
             prune(optional): Whether to remove duplicates of already appearing
                 HF solutions.
         """
         self.setupApprox()
         muExtra, jExtra, idx, append = self._pruneBeforeEval(mu, "Approx",
                                                              append, prune)
         if len(muExtra) > 0:
             newuApproxs = self.trainedModel.getApprox(muExtra)
             idx[jExtra] = self.setApprox(muExtra, newuApproxs, append)
         return list(idx)
 
     def getHF(self, mu:paramList, append : bool = False,
               prune : bool = True) -> sampList:
         """
         Get HF solution at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             HFsolution.
         """
         mu = checkParameterList(mu, self.npar)[0]
         idx = self.evalHF(mu, append = append, prune = prune)
         return self.uHF(idx)
 
     def getRHS(self, mu:paramList) -> sampList:
         """
         Get linear system RHS at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Linear system RHS.
         """
         return self.HFEngine.residual(mu, None)
 
     def getApproxReduced(self, mu:paramList, append : bool = False,
                          prune : bool = True) -> sampList:
         """
         Get approximant at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Reduced approximant.
         """
         mu = checkParameterList(mu, self.npar)[0]
         idx = self.evalApproxReduced(mu, append = append, prune = prune)
         return self.uApproxReduced(idx)
 
     def getApprox(self, mu:paramList, append : bool = False,
                   prune : bool = True) -> sampList:
         """
         Get approximant at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Approximant.
         """
         mu = checkParameterList(mu, self.npar)[0]
         idx = self.evalApprox(mu, append = append, prune = prune)
         return self.uApprox(idx)
 
     def getRes(self, mu:paramList) -> sampList:
         """
         Get residual at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Approximant residual.
         """
-        if not (self.force_state or self.HFEngine.isCEye):
+        if not (self.approx_state or self.HFEngine.isCEye):
             raise RROMPyException(("Residual of solution with non-scalar C "
                                    "not computable."))
         return self.HFEngine.residual(mu, self.getApprox(mu) / self.HFEngine.C)
 
     def getErr(self, mu:paramList, append : bool = False,
                prune : bool = True) -> sampList:
         """
         Get error at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Approximant error.
         """
         return (self.getApprox(mu, append = append, prune =prune)
               - self.getHF(mu, append = append, prune = prune))
 
     def normApprox(self, mu:paramList) -> float:
         """
         Compute norm of approximant at arbitrary parameter.
 
         Args:
             mu: Target parameter.
 
         Returns:
             Target norm of approximant.
         """
         if not (self.POD and self.HFEngine.isCEye):
             return self.HFEngine.norm(self.getApprox(mu), is_state = False)
         return np.linalg.norm(self.C * self.getApproxReduced(mu).data,
                               axis = 0)
 
     def getPoles(self, *args, **kwargs) -> Np1D:
         """
         Obtain approximant poles.
 
         Returns:
             Numpy complex vector of poles.
         """
         self.setupApprox()
         vbMng(self, "INIT", "Computing poles of model.", 20)
         poles = self.trainedModel.getPoles(*args, **kwargs)
         vbMng(self, "DEL", "Done computing poles.", 20)
         return poles
 
     def storeTrainedModel(self, filenameBase : str = "trained_model",
                           forceNewFile : bool = True) -> str:
         """Store trained reduced model to file."""
         self.setupApprox()
         vbMng(self, "INIT", "Storing trained model to file.", 20)
         if forceNewFile:
             filename = getNewFilename(filenameBase, "pkl")
         else:
             filename = "{}.pkl".format(filenameBase)
         pickleDump(self.trainedModel.data.__dict__, filename)
         vbMng(self, "DEL", "Done storing trained model.", 20)
         return filename
 
     def loadTrainedModel(self, filename:str):
         """Load trained reduced model from file."""
         vbMng(self, "INIT", "Loading pre-trained model from file.", 20)
         datadict = pickleLoad(filename)
         self.mu0 = datadict["mu0"]
         self.scaleFactor = datadict["scaleFactor"]
         self.mus = datadict["mus"]
         trainedModel = self.tModelType()
         trainedModel.verbosity = self.verbosity
         trainedModel.timestamp = self.timestamp
         data, selfkeys = self.initializeModelData(datadict)
         for key in selfkeys: setattr(self, key, datadict.pop(key))
         approxParameters = datadict.pop("approxParameters")
         data.approxParameters = copy(approxParameters)
         for apkey in data.approxParameters.keys():
             self._approxParameters[apkey] = approxParameters.pop(apkey)
         for key in datadict: setattr(data, key, datadict[key])
         trainedModel.data = data
         self.trainedModel = trainedModel
         self._mode = RROMPy_FRAGILE
         vbMng(self, "DEL", "Done loading pre-trained model.", 20)
diff --git a/rrompy/reduction_methods/greedy/generic_greedy_approximant.py b/rrompy/reduction_methods/greedy/generic_greedy_approximant.py
index ea97708..d8a1891 100644
--- a/rrompy/reduction_methods/greedy/generic_greedy_approximant.py
+++ b/rrompy/reduction_methods/greedy/generic_greedy_approximant.py
@@ -1,677 +1,678 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from rrompy.reduction_methods.standard.generic_standard_approximant \
                                               import GenericStandardApproximant
 from rrompy.utilities.base.types import (Np1D, Np2D, DictAny, HFEng, Tuple,
                                          List, normEng, paramVal, paramList,
                                          sampList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import dot
 from rrompy.utilities.expression import expressionEvaluator
 from rrompy.solver import normEngine
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPyWarning)
 from rrompy.parameter import checkParameterList, emptyParameterList
 
 __all__ = ['GenericGreedyApproximant']
 
 def localL2Distance(mus:Np2D, badmus:Np2D) -> Np2D:
     return np.linalg.norm(np.tile(mus[..., np.newaxis], [1, 1, len(badmus)])
                         - badmus[..., np.newaxis].T, axis = 1)
 
 def pruneSamples(mus:paramList, badmus:paramList,
                  tol : float = 1e-8) -> Np1D:
     """Remove from mus all the elements which are too close to badmus."""
     if len(badmus) == 0: return mus
     proximity = np.min(localL2Distance(mus.data, badmus.data), axis = 1)
     return np.arange(len(mus))[proximity <= tol]
 
 class GenericGreedyApproximant(GenericStandardApproximant):
     """
     ROM greedy interpolant computation for parametric problems
         (ABSTRACT).
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': number of starting training points;
             - 'sampler': sample point generator;
             - 'greedyTol': uniform error tolerance for greedy algorithm;
                 defaults to 1e-2;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
                 defaults to 0.;
             - 'interactive': whether to interactively terminate greedy
                 algorithm; defaults to False;
             - 'maxIter': maximum number of greedy steps; defaults to 1e2;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement; defaults to 0.2;
             - 'nTestPoints': number of test points; defaults to 5e2;
             - 'trainSetGenerator': training sample points generator; defaults
                 to sampler.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
 
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
             - 'greedyTol': uniform error tolerance for greedy algorithm;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
             - 'interactive': whether to interactively terminate greedy
                 algorithm;
             - 'maxIter': maximum number of greedy steps;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement;
             - 'nTestPoints': number of test points;
             - 'trainSetGenerator': training sample points generator.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: whether to compute POD of snapshots.
         S: number of test points.
         sampler: Sample point generator.
         greedyTol: Uniform error tolerance for greedy algorithm.
         collinearityTol: Collinearity tolerance for greedy algorithm.
         interactive: whether to interactively terminate greedy algorithm.
         maxIter: maximum number of greedy steps.
         refinementRatio: ratio of training points to be exhausted before
             training set refinement.
         nTestPoints: number of starting training points.
         trainSetGenerator: training sample points generator.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         estimatorNormEngine: Engine for estimator norm computation.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
     """
     
     TOL_INSTABILITY = 1e-6
 
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         self._addParametersToList(["greedyTol", "collinearityTol",
                                    "interactive", "maxIter", "refinementRatio",
                                    "nTestPoints"],
                                    [1e-2, 0., False, 1e2, .2, 5e2],
                                    ["trainSetGenerator"], ["AUTO"])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def greedyTol(self):
         """Value of greedyTol."""
         return self._greedyTol
     @greedyTol.setter
     def greedyTol(self, greedyTol):
         if greedyTol < 0:
             raise RROMPyException("greedyTol must be non-negative.")
         if hasattr(self, "_greedyTol") and self.greedyTol is not None:
             greedyTolold = self.greedyTol
         else:
             greedyTolold = -1
         self._greedyTol = greedyTol
         self._approxParameters["greedyTol"] = self.greedyTol
         if greedyTolold != self.greedyTol:
             self.resetSamples()
 
     @property
     def collinearityTol(self):
         """Value of collinearityTol."""
         return self._collinearityTol
     @collinearityTol.setter
     def collinearityTol(self, collinearityTol):
         if collinearityTol < 0:
             raise RROMPyException("collinearityTol must be non-negative.")
         if (hasattr(self, "_collinearityTol")
         and self.collinearityTol is not None):
             collinearityTolold = self.collinearityTol
         else:
             collinearityTolold = -1
         self._collinearityTol = collinearityTol
         self._approxParameters["collinearityTol"] = self.collinearityTol
         if collinearityTolold != self.collinearityTol:
             self.resetSamples()
 
     @property
     def interactive(self):
         """Value of interactive."""
         return self._interactive
     @interactive.setter
     def interactive(self, interactive):
         self._interactive = interactive
 
     @property
     def maxIter(self):
         """Value of maxIter."""
         return self._maxIter
     @maxIter.setter
     def maxIter(self, maxIter):
         if maxIter <= 0: raise RROMPyException("maxIter must be positive.")
         if hasattr(self, "_maxIter") and self.maxIter is not None:
             maxIterold = self.maxIter
         else:
             maxIterold = -1
         self._maxIter = maxIter
         self._approxParameters["maxIter"] = self.maxIter
         if maxIterold != self.maxIter:
             self.resetSamples()
 
     @property
     def refinementRatio(self):
         """Value of refinementRatio."""
         return self._refinementRatio
     @refinementRatio.setter
     def refinementRatio(self, refinementRatio):
         if refinementRatio <= 0. or refinementRatio > 1.:
             raise RROMPyException(("refinementRatio must be between 0 "
                                    "(excluded) and 1."))
         if (hasattr(self, "_refinementRatio")
         and self.refinementRatio is not None):
             refinementRatioold = self.refinementRatio
         else:
             refinementRatioold = -1
         self._refinementRatio = refinementRatio
         self._approxParameters["refinementRatio"] = self.refinementRatio
         if refinementRatioold != self.refinementRatio:
             self.resetSamples()
 
     @property
     def nTestPoints(self):
         """Value of nTestPoints."""
         return self._nTestPoints
     @nTestPoints.setter
     def nTestPoints(self, nTestPoints):
         if nTestPoints <= 0:
             raise RROMPyException("nTestPoints must be positive.")
         if not np.isclose(nTestPoints, np.int(nTestPoints)):
             raise RROMPyException("nTestPoints must be an integer.")
         nTestPoints = np.int(nTestPoints)
         if hasattr(self, "_nTestPoints") and self.nTestPoints is not None:
             nTestPointsold = self.nTestPoints
         else:
             nTestPointsold = -1
         self._nTestPoints = nTestPoints
         self._approxParameters["nTestPoints"] = self.nTestPoints
         if nTestPointsold != self.nTestPoints:
             self.resetSamples()
 
     @property
     def trainSetGenerator(self):
         """Value of trainSetGenerator."""
         return self._trainSetGenerator
     @trainSetGenerator.setter
     def trainSetGenerator(self, trainSetGenerator):
         if (isinstance(trainSetGenerator, (str,))
         and trainSetGenerator.upper() == "AUTO"):
             trainSetGenerator = self.sampler
         if 'generatePoints' not in dir(trainSetGenerator):
             raise RROMPyException("trainSetGenerator type not recognized.")
         if (hasattr(self, '_trainSetGenerator')
         and self.trainSetGenerator not in [None, "AUTO"]):
             trainSetGeneratorOld = self.trainSetGenerator
         self._trainSetGenerator = trainSetGenerator
         self._approxParameters["trainSetGenerator"] = self.trainSetGenerator
         if (not 'trainSetGeneratorOld' in locals()
          or trainSetGeneratorOld != self.trainSetGenerator):
             self.resetSamples()
 
     def resetSamples(self):
         """Reset samples."""
         super().resetSamples()
         self._mus = emptyParameterList()
 
     def initEstimatorNormEngine(self, normEngn : normEng = None):
         """Initialize estimator norm engine."""
         if (normEngn is not None or not hasattr(self, "estimatorNormEngine")
                                  or self.estimatorNormEngine is None):
             if normEngn is None:
                 if not hasattr(self.HFEngine, "energyNormPartialDualMatrix"):
                     self.HFEngine.buildEnergyNormPartialDualForm()
                 estimatorEnergyMatrix = (
                                      self.HFEngine.energyNormPartialDualMatrix)
             else:
                 if hasattr(normEngn, "buildEnergyNormPartialDualForm"):
                     if not hasattr(normEngn, "energyNormPartialDualMatrix"):
                         normEngn.buildEnergyNormPartialDualForm()
                     estimatorEnergyMatrix = (
                                           normEngn.energyNormPartialDualMatrix)
                 else:
                     estimatorEnergyMatrix = normEngn
             self.estimatorNormEngine = normEngine(estimatorEnergyMatrix)
 
     def _affineResidualMatricesContraction(self, rb:Np2D, rA : Np2D = None) \
                                                     -> Tuple[Np1D, Np1D, Np1D]:
         self.assembleReducedResidualBlocks(full = True)
         # 'ij,jk,ik->k', resbb, radiusb, radiusb.conj()
         ff = np.sum(self.trainedModel.data.resbb.dot(rb) * rb.conj(), axis = 0)
         if rA is None: return ff
         # 'ijk,jkl,il->l', resAb, radiusA, radiusb.conj()
         Lf = np.sum(np.tensordot(self.trainedModel.data.resAb, rA, 2)
                   * rb.conj(), axis = 0)
         # 'ijkl,klt,ijt->t', resAA, radiusA, radiusA.conj()
         LL = np.sum(np.tensordot(self.trainedModel.data.resAA, rA, 2)
                   * rA.conj(), axis = (0, 1))
         return ff, Lf, LL
 
     def errorEstimator(self, mus:Np1D) -> Np1D:
         """Standard residual-based error estimator."""
         self.setupApprox()
         mus = checkParameterList(mus, self.npar)[0]
         vbMng(self.trainedModel, "INIT",
               "Evaluating error estimator at mu = {}.".format(mus), 10)
         verb = self.trainedModel.verbosity
         self.trainedModel.verbosity = 0
         uApproxRs = self.getApproxReduced(mus)
         muTestEff = mus ** self.HFEngine.rescalingExp
         radiusA = np.empty((len(self.HFEngine.thAs), len(mus)),
                            dtype = np.complex)
         radiusb = np.empty((len(self.HFEngine.thbs), len(mus)),
                            dtype = np.complex)
         for j, thA in enumerate(self.HFEngine.thAs):
             radiusA[j] = expressionEvaluator(thA[0], muTestEff)
         for j, thb in enumerate(self.HFEngine.thbs):
             radiusb[j] = expressionEvaluator(thb[0], muTestEff)
         radiusA = np.expand_dims(uApproxRs.data, 1) * radiusA
         ff, Lf, LL = self._affineResidualMatricesContraction(radiusb, radiusA)
         err = np.abs((LL - 2. * np.real(Lf) + ff) / ff) ** .5
         self.trainedModel.verbosity = verb
         vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
         return err
 
     def getMaxErrorEstimator(self, mus:paramList) -> Tuple[Np1D, int, float]:
         """
         Compute maximum of (and index of maximum of) error estimator over given
             parameters.
         """
         errorEstTest = self.errorEstimator(mus)
         idxMaxEst = [np.argmax(errorEstTest)]
         return errorEstTest, idxMaxEst, errorEstTest[idxMaxEst]
 
     def _isLastSampleCollinear(self) -> bool:
         """Check collinearity of last sample."""
         if self.collinearityTol <= 0.: return False
         if self.POD:
             reff = self.samplingEngine.RPOD[:, -1]
         else:
             RROMPyWarning(("Repeated orthogonalization of the samples for "
                            "collinearity check. Consider setting POD to "
                            "True."))
             if not hasattr(self, "_PODEngine"):
                 from rrompy.sampling.base.pod_engine import PODEngine
                 self._PODEngine = PODEngine(self.HFEngine)
             reff = self._PODEngine.generalizedQR(self.samplingEngine.samples,
                                                  only_R = True,
                                                  is_state = True)[:, -1]
         cLevel = np.abs(reff[-1]) / np.linalg.norm(reff)
         vbMng(self, "MAIN", "Collinearity indicator {:.4e}.".format(cLevel), 5)
         return cLevel < self.collinearityTol
 
     def greedyNextSample(self, muidx:int, plotEst : bool = False)\
                                           -> Tuple[Np1D, int, float, paramVal]:
         """Compute next greedy snapshot of solution map."""
         RROMPyAssert(self._mode, message = "Cannot add greedy sample.")
         mus = copy(self.muTest[muidx])
         self.muTest.pop(muidx)
         for mu in mus:
             vbMng(self, "MAIN",
                   ("Adding sample point no. {} at {} to training "
                    "set.").format(self.samplingEngine.nsamples + 1, mu), 2)
             self.mus.append(mu)
             self.samplingEngine.nextSample(mu)
             if self._isLastSampleCollinear():
                 RROMPyWarning("Collinearity above tolerance detected.")
                 errorEstTest = np.empty(len(self.muTest))
                 errorEstTest[:] = np.nan
                 return errorEstTest, [-1], np.nan, np.nan
         errorEstTest, muidx, maxErrorEst = self.getMaxErrorEstimator(
                                                                    self.muTest)
         if (plotEst and not np.any(np.isnan(errorEstTest))
                     and not np.any(np.isinf(errorEstTest))):
             musre = copy(self.muTest.re.data)
             from matplotlib import pyplot as plt
             plt.figure()
             errCP = copy(errorEstTest)
             while len(musre) > 0:
                 if self.npar == 1:
                     currIdx = np.arange(len(musre))
                 else:
                     currIdx = np.where(np.isclose(np.sum(
                              np.abs(musre[:, 1 :] - musre[0, 1 :]), 1), 0.))[0]
                 plt.semilogy(musre[currIdx, 0], errCP[currIdx], 'k',
                              linewidth = 1)
                 musre = np.delete(musre, currIdx, 0)
                 errCP = np.delete(errCP, currIdx)
             plt.semilogy([self.muTest.re(0, 0), self.muTest.re(-1, 0)],
                          [self.greedyTol] * 2, 'r--')
             plt.semilogy(self.mus.re(0),
                          2. * self.greedyTol * np.ones(len(self.mus)), '*m')
             plt.semilogy(self.muTest.re(muidx, 0), maxErrorEst, 'xr')
             plt.grid()
             plt.show()
             plt.close()
         return errorEstTest, muidx, maxErrorEst, self.muTest[muidx]
 
     def _preliminaryTraining(self):
         """Initialize starting snapshots of solution map."""
         RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
         if self.samplingEngine.nsamples > 0:
             return
         self.computeScaleFactor()
         self.resetSamples()
         self.mus = self.trainSetGenerator.generatePoints(self.S)[
                                                            list(range(self.S))]
         muTestBase = self.sampler.generatePoints(self.nTestPoints)
         idxPop = pruneSamples(muTestBase ** self.HFEngine.rescalingExp,
                               self.mus ** self.HFEngine.rescalingExp,
                               1e-10 * self.scaleFactor[0])
         muTestBase.pop(idxPop)
         muTestBase = muTestBase.sort()
         muLast = copy(self.mus[-1])
         self.mus.pop()
         if len(self.mus) > 0:
             vbMng(self, "MAIN", 
                   ("Adding first {} sample point{} at {} to training "
                    "set.").format(self.S - 1, "" + "s" * (self.S > 2),
                                   self.mus), 2)
             self.samplingEngine.iterSample(self.mus)
         self.muTest = emptyParameterList()
         self.muTest.reset((len(muTestBase) + 1, self.mus.shape[1]))
         self.muTest[: -1] = muTestBase.data
         self.muTest[-1] = muLast.data
 
     def _enrichTestSet(self, nTest:int):
         """Add extra elements to test set."""
         RROMPyAssert(self._mode, message = "Cannot enrich test set.")
         muTestExtra = self.sampler.generatePoints(2 * nTest)
         muTotal = copy(self.mus)
         muTotal.append(self.muTest)
         idxPop = pruneSamples(muTestExtra ** self.HFEngine.rescalingExp,
                               muTotal ** self.HFEngine.rescalingExp,
                               1e-10 * self.scaleFactor[0])
         muTestExtra.pop(idxPop)
         muTestNew = np.empty((len(self.muTest) + len(muTestExtra),
                               self.muTest.shape[1]), dtype = np.complex)
         muTestNew[: len(self.muTest)] = self.muTest.data
         muTestNew[len(self.muTest) :] = muTestExtra.data
         self.muTest = checkParameterList(muTestNew, self.npar)[0].sort()
         vbMng(self, "MAIN",
               "Enriching test set by {} elements.".format(len(muTestExtra)), 5)
 
     def greedy(self, plotEst : bool = False):
         """Compute greedy snapshots of solution map."""
         RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
         if self.samplingEngine.nsamples > 0:
             return
         vbMng(self, "INIT", "Starting computation of snapshots.", 2)
         self._preliminaryTraining()
         nTest = self.nTestPoints
         muT0 = copy(self.muTest[-1])
         errorEstTest, muidx, maxErrorEst, mu = self.greedyNextSample(
                                                [len(self.muTest) - 1], plotEst)
         if np.any(np.isnan(maxErrorEst)):
             RROMPyWarning(("Instability in a posteriori estimator. "
                            "Starting preemptive greedy loop termination."))
             self.muTest.append(muT0)
             self.mus.pop(-1)
             self.samplingEngine.popSample()
             self.setupApprox()
         else:
             vbMng(self, "MAIN", ("Uniform testing error estimate "
                                  "{:.4e}.").format(np.max(maxErrorEst)), 2)
             trainedModelOld = copy(self.trainedModel)
             while (self.samplingEngine.nsamples < self.maxIter
                and np.max(maxErrorEst) > self.greedyTol):
                 if (1. - self.refinementRatio) * nTest > len(self.muTest):
                     self._enrichTestSet(nTest)
                     nTest = len(self.muTest)
                 muTestOld, maxErrorEstOld = self.muTest, np.max(maxErrorEst)
                 errorEstTest, muidx, maxErrorEst, mu = self.greedyNextSample(
                                                                 muidx, plotEst)
                 vbMng(self, "MAIN", ("Uniform testing error estimate "
                                      "{:.4e}.").format(np.max(maxErrorEst)), 2)
                 if (np.any(np.isnan(maxErrorEst))
                  or np.any(np.isinf(maxErrorEst))
                  or maxErrorEstOld < (np.max(maxErrorEst)
                                     * self.TOL_INSTABILITY)):
                     RROMPyWarning(("Instability in a posteriori estimator. "
                                    "Starting preemptive greedy loop "
                                    "termination."))
                     self.muTest = muTestOld
                     self.mus.pop(-1)
                     self.samplingEngine.popSample()
                     self.trainedModel.data = copy(trainedModelOld.data)
                     break
                 trainedModelOld.data = copy(self.trainedModel.data)
                 if (self.interactive and np.max(maxErrorEst) <= self.greedyTol):
                     vbMng(self, "MAIN",
                           ("Required tolerance {} achieved. Want to decrease "
                            "greedyTol and continue? "
                            "Y/N").format(self.greedyTol), 0, end = "")
                     increasemaxIter = input()
                     if increasemaxIter.upper() == "Y":
                         vbMng(self, "MAIN", "Reducing value of greedyTol...",
                               0)
                         while np.max(maxErrorEst) <= self._greedyTol:
                             self._greedyTol *= .5
                 if (self.interactive
                 and self.samplingEngine.nsamples >= self.maxIter):
                     vbMng(self, "MAIN",
                           ("Maximum number of iterations {} reached. Want to "
                            "increase maxIter and continue? "
                            "Y/N").format(self.maxIter), 0, end = "")
                     increasemaxIter = input()
                     if increasemaxIter.upper() == "Y":
                         vbMng(self, "MAIN", "Doubling value of maxIter...", 0)
                         self._maxIter *= 2
         vbMng(self, "DEL", 
               ("Done computing snapshots (final snapshot count: "
                "{}).").format(self.samplingEngine.nsamples), 2)
 
     def checkComputedApprox(self) -> bool:
         """
         Check if setup of new approximant is not needed.
 
         Returns:
             True if new setup is not needed. False otherwise.
         """
         return (super().checkComputedApprox()
             and len(self.mus) == self.trainedModel.data.projMat.shape[1])
 
     def assembleReducedResidualGramian(self, pMat:sampList):
         """
         Build residual gramian of reduced linear system through projections.
         """
         self.initEstimatorNormEngine()
         if (not hasattr(self.trainedModel.data, "gramian")
          or self.trainedModel.data.gramian is None):
             gramian = self.estimatorNormEngine.innerProduct(pMat, pMat)
         else:
             Sold = self.trainedModel.data.gramian.shape[0]
             S = len(self.mus)
             if Sold > S:
                 gramian = self.trainedModel.data.gramian[: S, : S]
             else:
                 idxOld = list(range(Sold))
                 idxNew = list(range(Sold, S))
                 gramian = np.empty((S, S), dtype = np.complex)
                 gramian[: Sold, : Sold] = self.trainedModel.data.gramian
                 gramian[: Sold, Sold :] = (
                            self.estimatorNormEngine.innerProduct(pMat(idxNew),
                                                                  pMat(idxOld)))
                 gramian[Sold :, : Sold] = gramian[: Sold, Sold :].T.conj()
                 gramian[Sold :, Sold :] = (
                            self.estimatorNormEngine.innerProduct(pMat(idxNew),
                                                                  pMat(idxNew)))
         self.trainedModel.data.gramian = gramian
 
     def assembleReducedResidualBlocksbb(self, bs:List[Np1D]):
         """
         Build blocks (of type bb) of reduced linear system through projections.
         """
         self.initEstimatorNormEngine()
         nbs = len(bs)
         if (not hasattr(self.trainedModel.data, "resbb")
          or self.trainedModel.data.resbb is None):
             resbb = np.empty((nbs, nbs), dtype = np.complex)
             for i in range(nbs):
                 Mbi = bs[i]
                 resbb[i, i] = self.estimatorNormEngine.innerProduct(Mbi, Mbi)
                 for j in range(i):
                     Mbj = bs[j]
                     resbb[i, j] = self.estimatorNormEngine.innerProduct(Mbj,
                                                                         Mbi)
             for i in range(nbs):
                 for j in range(i + 1, nbs):
                     resbb[i, j] = resbb[j, i].conj()
             self.trainedModel.data.resbb = resbb
 
     def assembleReducedResidualBlocksAb(self, As:List[Np2D], bs:List[Np1D],
                                         pMat:sampList):
         """
         Build blocks (of type Ab) of reduced linear system through projections.
         """
         self.initEstimatorNormEngine()
         nAs = len(As)
         nbs = len(bs)
         S = len(self.mus)
         if (not hasattr(self.trainedModel.data, "resAb")
          or self.trainedModel.data.resAb is None):
             if not isinstance(pMat, (np.ndarray,)): pMat = pMat.data
             resAb = np.empty((nbs, S, nAs), dtype = np.complex)
             for j in range(nAs):
                 MAj = dot(As[j], pMat)
                 for i in range(nbs):
                     Mbi = bs[i]
                     resAb[i, :, j] = self.estimatorNormEngine.innerProduct(MAj,
                                                                            Mbi)
         else:
             Sold = self.trainedModel.data.resAb.shape[1]
             if Sold == S: return
             if Sold > S:
                 resAb = self.trainedModel.data.resAb[:, : S, :]
             else:
                 if not isinstance(pMat, (np.ndarray,)): pMat = pMat.data
                 resAb = np.empty((nbs, S, nAs), dtype = np.complex)
                 resAb[:, : Sold, :] = self.trainedModel.data.resAb
                 for j in range(nAs):
                     MAj = dot(As[j], pMat[:, Sold :])
                     for i in range(nbs):
                         Mbi = bs[i]
                         resAb[i, Sold :, j] = (
                                self.estimatorNormEngine.innerProduct(MAj, Mbi))
         self.trainedModel.data.resAb = resAb
 
     def assembleReducedResidualBlocksAA(self, As:List[Np2D], pMat:sampList):
         """
         Build blocks (of type AA) of reduced linear system through projections.
         """
         self.initEstimatorNormEngine()
         nAs = len(As)
         S = len(self.mus)
         if (not hasattr(self.trainedModel.data, "resAA")
          or self.trainedModel.data.resAA is None):
             if not isinstance(pMat, (np.ndarray,)): pMat = pMat.data
             resAA = np.empty((S, nAs, S, nAs), dtype = np.complex)
             for i in range(nAs):
                 MAi = dot(As[i], pMat)
                 resAA[:, i, :, i] = (
                                self.estimatorNormEngine.innerProduct(MAi, MAi))
                 for j in range(i):
                     MAj = dot(As[j], pMat)
                     resAA[:, i, :, j] = (
                                self.estimatorNormEngine.innerProduct(MAj, MAi))
             for i in range(nAs):
                 for j in range(i + 1, nAs):
                     resAA[:, i, :, j] = resAA[:, j, :, i].T.conj()
         else:
             Sold = self.trainedModel.data.resAA.shape[0]
             if Sold == S: return
             if Sold > S:
                 resAA = self.trainedModel.data.resAA[: S, :, : S, :]
             else:
                 if not isinstance(pMat, (np.ndarray,)): pMat = pMat.data
                 resAA = np.empty((S, nAs, S, nAs), dtype = np.complex)
                 resAA[: Sold, :, : Sold, :] = self.trainedModel.data.resAA
                 for i in range(nAs):
                     MAi = dot(As[i], pMat)
                     resAA[: Sold, i, Sold :, i] = (
                          self.estimatorNormEngine.innerProduct(MAi[:, Sold :],
                                                                MAi[:, : Sold]))
                     resAA[Sold :, i, : Sold, i] = resAA[: Sold, i,
                                                         Sold :, i].T.conj()
                     resAA[Sold :, i, Sold :, i] = (
                          self.estimatorNormEngine.innerProduct(MAi[:, Sold :],
                                                                MAi[:, Sold :]))
                     for j in range(i):
                         MAj = dot(As[j], pMat)
                         resAA[: Sold, i, Sold :, j] = (
                          self.estimatorNormEngine.innerProduct(MAj[:, Sold :],
                                                                MAi[:, : Sold]))
                         resAA[Sold :, i, : Sold, j] = (
                          self.estimatorNormEngine.innerProduct(MAj[:, : Sold],
                                                                MAi[:, Sold :]))
                         resAA[Sold :, i, Sold :, j] = (
                          self.estimatorNormEngine.innerProduct(MAj[:, Sold :],
                                                                MAi[:, Sold :]))
                 for i in range(nAs):
                     for j in range(i + 1, nAs):
                         resAA[: Sold, i, Sold :, j] = (
                                           resAA[Sold :, j, : Sold, i].T.conj())
                         resAA[Sold :, i, : Sold, j] = (
                                           resAA[: Sold, j, Sold :, i].T.conj())
                         resAA[Sold :, i, Sold :, j] = (
                                           resAA[Sold :, j, Sold :, i].T.conj())
         self.trainedModel.data.resAA = resAA
 
     def assembleReducedResidualBlocks(self, full : bool = False):
         """Build affine blocks of affine decomposition of residual."""
         self.assembleReducedResidualBlocksbb(self.HFEngine.bs)
         if full:
             pMat = self.samplingEngine.samples
             self.assembleReducedResidualBlocksAb(self.HFEngine.As,
                                                  self.HFEngine.bs, pMat)
             self.assembleReducedResidualBlocksAA(self.HFEngine.As, pMat)
diff --git a/rrompy/reduction_methods/greedy/rational_interpolant_greedy.py b/rrompy/reduction_methods/greedy/rational_interpolant_greedy.py
index 92de7cb..c52dcfd 100644
--- a/rrompy/reduction_methods/greedy/rational_interpolant_greedy.py
+++ b/rrompy/reduction_methods/greedy/rational_interpolant_greedy.py
@@ -1,497 +1,497 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from .generic_greedy_approximant import (GenericGreedyApproximant,
                                          localL2Distance as lL2D)
 from rrompy.utilities.poly_fitting.polynomial import (polybases, polydomcoeff,
                                                   PolynomialInterpolator as PI,
                                                   polyvanderTotal as pvT)
 from rrompy.utilities.numerical import totalDegreeN, dot
 from rrompy.utilities.expression import expressionEvaluator
 from rrompy.reduction_methods.standard import RationalInterpolant
 from rrompy.utilities.base.types import (Np1D, Tuple, DictAny, HFEng, paramVal,
                                          paramList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.poly_fitting import customFit
 from rrompy.utilities.exception_manager import (RROMPyWarning, RROMPyException,
                                                 RROMPyAssert)
 from rrompy.parameter import checkParameterList
 
 __all__ = ['RationalInterpolantGreedy']
 
 class RationalInterpolantGreedy(GenericGreedyApproximant, RationalInterpolant):
     """
     ROM greedy rational interpolant computation for parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': number of starting training points;
             - 'sampler': sample point generator;
             - 'greedyTol': uniform error tolerance for greedy algorithm;
                 defaults to 1e-2;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
                 defaults to 0.;
             - 'interactive': whether to interactively terminate greedy
                 algorithm; defaults to False;
             - 'maxIter': maximum number of greedy steps; defaults to 1e2;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement; defaults to 0.2;
             - 'nTestPoints': number of test points; defaults to 5e2;
             - 'trainSetGenerator': training sample points generator; defaults
                 to sampler;
             - 'polybasis': type of basis for interpolation; defaults to
                 'MONOMIAL';
             - 'errorEstimatorKind': kind of error estimator; available values
                 include 'AFFINE', 'DISCREPANCY', 'INTERPOLATORY',
                 'EIM_INTERPOLATORY', and 'EIM_DIAGONAL'; defaults to 'AFFINE';
             - 'interpRcond': tolerance for interpolation; defaults to None;
             - 'robustTol': tolerance for robust rational denominator
                 management; defaults to 0.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults and must
+        approx_state(optional): Whether to approximate state. Defaults and must
             be True.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
             - 'greedyTol': uniform error tolerance for greedy algorithm;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
             - 'interactive': whether to interactively terminate greedy
                 algorithm;
             - 'maxIter': maximum number of greedy steps;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement;
             - 'nTestPoints': number of test points;
             - 'trainSetGenerator': training sample points generator;
             - 'errorEstimatorKind': kind of error estimator;
             - 'interpRcond': tolerance for interpolation;
             - 'robustTol': tolerance for robust rational denominator
                 management.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: whether to compute POD of snapshots.
         S: number of test points.
         sampler: Sample point generator.
         greedyTol: uniform error tolerance for greedy algorithm.
         collinearityTol: Collinearity tolerance for greedy algorithm.
         interactive: whether to interactively terminate greedy algorithm.
         maxIter: maximum number of greedy steps.
         refinementRatio: ratio of training points to be exhausted before
             training set refinement.
         nTestPoints: number of starting training points.
         trainSetGenerator: training sample points generator.
         robustTol: tolerance for robust rational denominator management.
         errorEstimatorKind: kind of error estimator.
         interpRcond: tolerance for interpolation.
         robustTol: tolerance for robust rational denominator management.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         estimatorNormEngine: Engine for estimator norm computation.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
     """
     
     _allowedEstimatorKinds = ["AFFINE", "DISCREPANCY", "INTERPOLATORY",
                               "EIM_INTERPOLATORY", "EIM_DIAGONAL"]
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = True,
+                 approxParameters : DictAny = {}, approx_state : bool = True,
                  verbosity : int = 10, timestamp : bool = True):
-        if not force_state: RROMPyWarning("Overriding force_state to True.")
+        if not approx_state: RROMPyWarning("Overriding approx_state to True.")
         self._preInit()
         self._addParametersToList(["errorEstimatorKind"], ["AFFINE"],
                                   toBeExcluded = ["M", "N", "polydegreetype", 
                                                   "radialDirectionalWeights",
                                                   "nNearestNeighbor"])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = True, verbosity = verbosity,
+                         approx_state = True, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def E(self):
         """Value of E."""
         self._E = self.sampleBatchIdx - 1
         return self._E
     @E.setter
     def E(self, E):
         RROMPyWarning(("E is used just to simplify inheritance, and its value "
                        "cannot be changed from that of sampleBatchIdx - 1."))
         
     @property
     def polydegreetype(self):
         """Value of polydegreetype."""
         return "TOTAL"
     @polydegreetype.setter
     def polydegreetype(self, polydegreetype):
         RROMPyWarning(("polydegreetype is used just to simplify inheritance, "
                        "and its value cannot be changed from 'TOTAL'."))
         
     @property
     def polybasis(self):
         """Value of polybasis."""
         return self._polybasis
     @polybasis.setter
     def polybasis(self, polybasis):
         try:
             polybasis = polybasis.upper().strip().replace(" ","")
             if polybasis not in polybases: 
                 raise RROMPyException("Sample type not recognized.")
             self._polybasis = polybasis
         except:
             RROMPyWarning(("Prescribed polybasis not recognized. Overriding "
                            "to 'MONOMIAL'."))
             self._polybasis = "MONOMIAL"
         self._approxParameters["polybasis"] = self.polybasis
 
     @property
     def errorEstimatorKind(self):
         """Value of errorEstimatorKind."""
         return self._errorEstimatorKind
     @errorEstimatorKind.setter
     def errorEstimatorKind(self, errorEstimatorKind):
         errorEstimatorKind = errorEstimatorKind.upper()
         if errorEstimatorKind not in self._allowedEstimatorKinds:
             RROMPyWarning(("Error estimator kind not recognized. Overriding "
                            "to 'AFFINE'."))
             errorEstimatorKind = "AFFINE"
         self._errorEstimatorKind = errorEstimatorKind
         self._approxParameters["errorEstimatorKind"] = self.errorEstimatorKind
 
     def errorEstimator(self, mus:Np1D) -> Np1D:
         """Standard residual-based error estimator."""
         if self.errorEstimatorKind == "AFFINE":
             return super().errorEstimator(mus)
         setupOK = self.setupApprox()
         if not setupOK:
             err = np.empty(len(mus))
             err[:] = np.nan
             return err
         if self.errorEstimatorKind == "DIAGONAL":
             return self.errorEstimatorEIM(mus)
         mus = checkParameterList(mus, self.npar)[0]
         muCTest = self.trainedModel.centerNormalize(mus)
         vbMng(self.trainedModel, "INIT",
               "Evaluating error estimator at mu = {}.".format(mus), 10)
         verb = self.trainedModel.verbosity
         self.trainedModel.verbosity = 0
         QTest = self.trainedModel.getQVal(mus)
         if self.errorEstimatorKind == "DISCREPANCY":
             nAs, nbs = len(self.HFEngine.thAs), len(self.HFEngine.thbs)
             muTrainEff = self.mus ** self.HFEngine.rescalingExp
             muTestEff = mus ** self.HFEngine.rescalingExp
             PTrain = self.trainedModel.getPVal(self.mus).data.T
             QTrain = self.trainedModel.getQVal(self.mus)
             PTest = self.trainedModel.getPVal(mus).data
             radiusAbTrain = np.empty((self.S, nAs * self.S + nbs),
                                      dtype = np.complex)
             radiusA = np.empty((self.S, nAs, len(mus)), dtype = np.complex)
             radiusb = np.empty((nbs, len(mus)), dtype = np.complex)
             for j, thA in enumerate(self.HFEngine.thAs):
                 idxs = j * self.S + np.arange(self.S)
                 radiusAbTrain[:, idxs] = expressionEvaluator(thA[0],
                                               muTrainEff, (self.S, 1)) * PTrain
                 radiusA[:, j] = PTest * expressionEvaluator(thA[0],
                                                         muTestEff, (len(mus),))
             for j, thb in enumerate(self.HFEngine.thbs):
                 idx = nAs * self.S + j
                 radiusAbTrain[:, idx] = QTrain * expressionEvaluator(thb[0],
                                                          muTrainEff, (self.S,))
                 radiusb[j] = QTest * expressionEvaluator(thb[0], muTestEff,
                                                          (len(mus),))
             QRHSNorm2 = self._affineResidualMatricesContraction(radiusb)
             vanTrain, _, vanTrainIdxs = pvT(self._musUniqueCN, self.N,
                                             self.polybasis0, self._derIdxs,
                                             self._reorder)
             interpPQ = customFit(vanTrain[:, vanTrainIdxs], radiusAbTrain,
                                  rcond = self.interpRcond)
             vanTest, _, vanTestIdxs = pvT(muCTest, self.N, self.polybasis0)
             DradiusAb = vanTest[:, vanTestIdxs].dot(interpPQ)
             radiusA = (radiusA
                      - DradiusAb[:, : - nbs].reshape(len(mus), -1, self.S).T)
             radiusb = radiusb - DradiusAb[:, - nbs :].T
             ff, Lf, LL = self._affineResidualMatricesContraction(radiusb,
                                                                  radiusA)
             err = np.abs((LL - 2. * np.real(Lf) + ff) / QRHSNorm2) ** .5
         else: #if self.errorEstimatorKind == "INTERPOLATORY":
             muCTrain = self.trainedModel.centerNormalize(self.mus)
             samplingRatio = np.prod(lL2D(muCTest.data, muCTrain.data),
                                     axis = 1) / np.abs(QTest)
             self.initEstimatorNormEngine()
             QTest = np.abs(QTest)
             sampRCP = copy(samplingRatio)
             idx_muTestSample = np.empty(self.sampleBatchSize,
                                         dtype = int)
             for j in range(self.sampleBatchSize):
                 k = np.argmax(sampRCP)
                 idx_muTestSample[j] = k
                 if j + 1 < self.sampleBatchSize:
                     musZero = self.trainedModel.centerNormalize(mus, mus[k])
                     sampRCP *= np.linalg.norm(musZero.data, axis = 1)
             mu_muTestSample = mus[idx_muTestSample]
             app_muTestSample = self.getApproxReduced(mu_muTestSample)
             app_muTestSample = dot(self.samplingEngine.samples,
                                    app_muTestSample)
             resmu = self.HFEngine.residual(mu_muTestSample, app_muTestSample,
                                            post_c = False)
             RHSmu = self.HFEngine.residual(mu_muTestSample, None,
                                            post_c = False)
             ressamples = (self.estimatorNormEngine.norm(resmu)
                         / self.estimatorNormEngine.norm(RHSmu))
             musT = copy(self.mus)
             musT.append(mu_muTestSample)
             musT = self.trainedModel.centerNormalize(musT)
             musC = self.trainedModel.centerNormalize(mus)
             resT = np.zeros(len(musT), dtype = np.complex)
             err = np.zeros(len(mus))
             for l in range(len(mu_muTestSample)):
                 resT[len(self.mus) + l] = (ressamples[l]
                                          * QTest[idx_muTestSample[l]])
                 p = PI()
                 wellCond, msg = p.setupByInterpolation(musT, resT, self.M + 1,
                                          self.polybasis, self.verbosity >= 15,
                                          True, {}, {"rcond": self.interpRcond})
                 err += np.abs(p(musC))
                 resT[len(self.mus) + l] = 0.
             err /= QTest
             vbMng(self, "MAIN", msg, 15)
         self.trainedModel.verbosity = verb
         vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
         return err
 
     def errorEstimatorEIM(self, mus:Np1D,
                           return_max_idxs : bool = False) -> Np1D:
         """EIM-like interpolation error estimator."""
         setupOK = self.setupApprox()
         if not setupOK:
             err = np.empty(len(mus))
             err[:] = np.nan
             return err
         mus = checkParameterList(mus, self.npar)[0]
         vbMng(self.trainedModel, "INIT",
               "Evaluating error estimator at mu = {}.".format(mus), 10)
         verb = self.trainedModel.verbosity
         self.trainedModel.verbosity = 0
         QTest = self.trainedModel.getQVal(mus)
         muCTest = self.trainedModel.centerNormalize(mus)
         muCTrain = self.trainedModel.centerNormalize(self.mus)
         vanderTest, _, vanderTestR = pvT(muCTest, self.E, self.polybasis)
         vanderTest = vanderTest[:, vanderTestR]
         vanderTestNext, _, vanderTestNextR = pvT(muCTest, self.E + 1,
                                                  self.polybasis)
         vanderTestNext = vanderTestNext[:, vanderTestNextR[
                                                         vanderTest.shape[1] :]]
         idxsTest = np.arange(vanderTestNext.shape[1])
         basis = np.zeros((len(idxsTest), 0), dtype = float)
         idxMaxEst = []
         err = None
         while len(idxsTest) > 0:
             vanderTrial, _, vanderTrialR = pvT(muCTrain, self.E,
                                                self.polybasis)
             vanderTrial = vanderTrial[:, vanderTrialR]
             vanderTrialNext, _, vanderTrialNextR = pvT(muCTrain, self.E + 1,
                                                        self.polybasis)
             vanderTrialNext = vanderTrialNext[:, vanderTrialNextR[
                                                        vanderTrial.shape[1] :]]
             vanderTrial = np.hstack((vanderTrial,
                                      vanderTrialNext.dot(basis).reshape(
                                                           len(vanderTrialNext),
                                                           basis.shape[1])))
             valuesTrial = vanderTrialNext[:, idxsTest]
             vanderTestEff = np.hstack((vanderTest,
                                        vanderTestNext.dot(basis).reshape(
                                                            len(vanderTestNext),
                                                            basis.shape[1])))
             vanderTestNextEff = vanderTestNext[:, idxsTest]
             coeffTest = np.linalg.solve(vanderTrial, valuesTrial)
             errTest = np.abs((vanderTestNextEff - vanderTestEff.dot(coeffTest))
                            / np.expand_dims(QTest, 1))
             idxMaxErr = np.unravel_index(np.argmax(errTest), errTest.shape)
             idxMaxEst += [idxMaxErr[0]]
             if err is None: err = np.max(errTest, axis = 1)
             if not return_max_idxs: break
             muCTrain.append(muCTest[idxMaxErr[0]])
             basis = np.pad(basis, [(0, 0), (0, 1)], "constant")
             basis[idxsTest[idxMaxErr[1]], -1] = 1.
             idxsTest = np.delete(idxsTest, idxMaxErr[1])
         if self.errorEstimatorKind == "EIM_DIAGONAL":
             self.assembleReducedResidualBlocks(full = False)
             muTestEff = mus ** self.HFEngine.rescalingExp
             radiusb = np.empty((len(self.HFEngine.thbs), len(mus)),
                                dtype = np.complex)
             for j, thb in enumerate(self.HFEngine.thbs):
                 radiusb[j] = expressionEvaluator(thb[0], muTestEff)
             bresb = self._affineResidualMatricesContraction(radiusb)
             self.assembleReducedResidualGramian(self.trainedModel.data.projMat)
             pDom = (polydomcoeff(self.E, self.polybasis)
                   * self.trainedModel.data.P[(-1,) + (0,) * (self.npar - 1)])
             LL = pDom.conj().dot(self.trainedModel.data.gramian.dot(pDom))
             if not hasattr(self, "Anorm2Approx"):
                 if self.HFEngine.nAs > 1:
                     Ader = self.HFEngine.A(self.mu0,
                                            [1] + [0] * (self.npar - 1))
                     try:
                         Adiag = self.scaleFactor[0] * Ader.diagonal()
                     except:
                         Adiag = self.scaleFactor[0] * np.diagonal(Ader)
                     self.Anorm2Approx = np.mean(np.abs(Adiag) ** 2.)
                 if (np.isclose(self.Anorm2Approx, 0.)
                  or self.HFEngine.nAs <= 1):
                     self.Anorm2Approx = 1
             jOpt = np.abs(self.Anorm2Approx * LL / bresb) ** .5
             err = jOpt * err
         else: #if self.errorEstimatorKind == "EIM_INTERPOLATORY":
             self.initEstimatorNormEngine()
             mu_muTestSample = mus[idxMaxEst[0]]
             app_muTestSample = self.getApproxReduced(mu_muTestSample)
             app_muTestSample = dot(self.samplingEngine.samples,
                                    app_muTestSample)
             resmu = self.HFEngine.residual(mu_muTestSample, app_muTestSample,
                                            post_c = False)
             RHSmu = self.HFEngine.residual(mu_muTestSample, None,
                                            post_c = False)
             jOpt = np.abs(self.estimatorNormEngine.norm(resmu)[0]
                         / err[idxMaxEst[0]]
                         / self.estimatorNormEngine.norm(RHSmu)[0])
             err = jOpt * err
         self.trainedModel.verbosity = verb
         vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
         if return_max_idxs: return err, idxMaxEst
         return err
 
     def getMaxErrorEstimator(self, mus:paramList) -> Tuple[Np1D, int, float]:
         """
         Compute maximum of (and index of maximum of) error estimator over given
             parameters.
         """
         if self.errorEstimatorKind[: 4] == "EIM_":
             errorEstTest, idxMaxEst = self.errorEstimatorEIM(mus, True)
         else:
             errorEstTest = self.errorEstimator(mus)
             idxMaxEst = np.empty(self.sampleBatchSize, dtype = int)
             errCP = copy(errorEstTest)
             for j in range(self.sampleBatchSize):
                 k = np.argmax(errCP)
                 idxMaxEst[j] = k
                 if j + 1 < self.sampleBatchSize:
                     musZero = self.trainedModel.centerNormalize(mus, mus[k])
                     errCP *= np.linalg.norm(musZero.data, axis = 1)
         maxEst = errorEstTest[idxMaxEst]
         return errorEstTest, idxMaxEst, maxEst
 
     def greedyNextSample(self, muidx:int, plotEst : bool = False)\
                                           -> Tuple[Np1D, int, float, paramVal]:
         """Compute next greedy snapshot of solution map."""
         RROMPyAssert(self._mode, message = "Cannot add greedy sample.")
         self.sampleBatchIdx += 1
         self.sampleBatchSize = totalDegreeN(self.npar - 1, self.sampleBatchIdx)
         return super().greedyNextSample(muidx, plotEst)
 
     def _preliminaryTraining(self):
         """Initialize starting snapshots of solution map."""
         RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
         if self.samplingEngine.nsamples > 0:
             return
         S = self.S
         self.sampleBatchIdx, self.sampleBatchSize, self._S = -1, 0, 0
         nextBatchSize = 1
         while self._S + nextBatchSize <= S:
             self.sampleBatchIdx += 1
             self.sampleBatchSize = nextBatchSize
             self._S += self.sampleBatchSize
             nextBatchSize = totalDegreeN(self.npar - 1,
                                          self.sampleBatchIdx + 1)
         super()._preliminaryTraining()
 
     def setupApprox(self, plotEst : bool = False):
         """
         Compute rational interpolant.
         SVD-based robust eigenvalue management.
         """
         if self.checkComputedApprox():
             return True
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.".format(self.name()), 5)
         self.greedy(plotEst)
         self._S = len(self.mus)
         self._N, self._M = [self.E] * 2
         pMat = self.samplingEngine.samples.data
-        pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
+        pMatEff = dot(self.HFEngine.C, pMat)
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp}
             self.trainedModel.data = self.initializeModelData(datadict)[0]
         else:
             self.trainedModel = self.trainedModel
             self.trainedModel.data.projMat = copy(pMatEff)
             self.trainedModel.data.mus = copy(self.mus)
         self.trainedModel.data.mus = copy(self.mus)
         self.catchInstability = True
         if self.N > 0:
             try:
                 Q = self._setupDenominator()[0]
             except RROMPyException as RE:
                 RROMPyWarning(RE)
                 vbMng(self, "DEL", "Done setting up approximant.", 5)
                 return False
         else:
             Q = PI()
             Q.coeffs = np.ones(1, dtype = np.complex)
             Q.npar = 1
             Q.polybasis = self.polybasis
         self.trainedModel.data.Q = copy(Q)
         try:
             self.trainedModel.data.P = copy(self._setupNumerator())
         except RROMPyException as RE:
             RROMPyWarning(RE)
             vbMng(self, "DEL", "Done setting up approximant.", 5)
             return False
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
         return True
diff --git a/rrompy/reduction_methods/greedy/reduced_basis_greedy.py b/rrompy/reduction_methods/greedy/reduced_basis_greedy.py
index ca2c0bb..82b22ff 100644
--- a/rrompy/reduction_methods/greedy/reduced_basis_greedy.py
+++ b/rrompy/reduction_methods/greedy/reduced_basis_greedy.py
@@ -1,183 +1,183 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 from .generic_greedy_approximant import GenericGreedyApproximant
 from rrompy.reduction_methods.standard import ReducedBasis
 from rrompy.utilities.base.types import DictAny, HFEng, paramVal
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import dot
 from rrompy.utilities.exception_manager import RROMPyWarning, RROMPyAssert
 
 __all__ = ['ReducedBasisGreedy']
 
 class ReducedBasisGreedy(GenericGreedyApproximant, ReducedBasis):
     """
     ROM greedy RB approximant computation for parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': number of starting training points;
             - 'sampler': sample point generator;
             - 'greedyTol': uniform error tolerance for greedy algorithm;
                 defaults to 1e-2;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
                 defaults to 0.;
             - 'interactive': whether to interactively terminate greedy
                 algorithm; defaults to False;
             - 'maxIter': maximum number of greedy steps; defaults to 1e2;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement; defaults to 0.2;
             - 'nTestPoints': number of test points; defaults to 5e2;
             - 'trainSetGenerator': training sample points generator; defaults
                 to sampler.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults and must
+        approx_state(optional): Whether to approximate state. Defaults and must
             be True.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
             - 'greedyTol': uniform error tolerance for greedy algorithm;
             - 'collinearityTol': collinearity tolerance for greedy algorithm;
             - 'interactive': whether to interactively terminate greedy
                 algorithm;
             - 'maxIter': maximum number of greedy steps;
             - 'refinementRatio': ratio of test points to be exhausted before
                 test set refinement;
             - 'nTestPoints': number of test points;
             - 'trainSetGenerator': training sample points generator.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: whether to compute POD of snapshots.
         S: number of test points.
         sampler: Sample point generator.
         greedyTol: uniform error tolerance for greedy algorithm.
         collinearityTol: Collinearity tolerance for greedy algorithm.
         interactive: whether to interactively terminate greedy algorithm.
         maxIter: maximum number of greedy steps.
         refinementRatio: ratio of training points to be exhausted before
             training set refinement.
         nTestPoints: number of starting training points.
         trainSetGenerator: training sample points generator.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         estimatorNormEngine: Engine for estimator norm computation.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
         As: List of sparse matrices (in CSC format) representing coefficients
             of linear system matrix.
         bs: List of numpy vectors representing coefficients of linear system
            RHS.
         ARBs: List of sparse matrices (in CSC format) representing coefficients
             of compressed linear system matrix.
         bRBs: List of numpy vectors representing coefficients of compressed 
             linear system RHS.
     """
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = True,
+                 approxParameters : DictAny = {}, approx_state : bool = True,
                  verbosity : int = 10, timestamp : bool = True):
-        if not force_state: RROMPyWarning("Overriding force_state to True.")
+        if not approx_state: RROMPyWarning("Overriding approx_state to True.")
         self._preInit()
         self._addParametersToList([], [], toBeExcluded = ["R", "PODTolerance"])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = True, verbosity = verbosity,
+                         approx_state = True, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def R(self):
         """Value of R."""
         self._R = self._S
         return self._R
     @R.setter
     def R(self, R):
         RROMPyWarning(("R is used just to simplify inheritance, and its value "
                        "cannot be changed from that of S."))
         
     @property
     def PODTolerance(self):
         """Value of PODTolerance."""
         self._PODTolerance = -1
         return self._PODTolerance
     @PODTolerance.setter
     def PODTolerance(self, PODTolerance):
         RROMPyWarning(("PODTolerance is used just to simplify inheritance, "
                        "and its value cannot be changed from -1."))
         
     def setupApprox(self, plotEst : bool = False):
         """Compute RB projection matrix."""
         if self.checkComputedApprox():
             return
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
         self.greedy(plotEst)
         vbMng(self, "INIT", "Computing projection matrix.", 7)
         pMat = self.samplingEngine.samples.data
-        pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
+        pMatEff = dot(self.HFEngine.C, pMat)
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp}
             data = self.initializeModelData(datadict)[0]
             ARBs, bRBs = self.assembleReducedSystem(pMat)
             data.affinePoly = self.HFEngine.affinePoly
             data.thAs, data.thbs = self.HFEngine.thAs, self.HFEngine.thbs
             self.trainedModel.data = data
         else:
             self.trainedModel = self.trainedModel
             Sold = self.trainedModel.data.projMat.shape[1]
             ARBs, bRBs = self.assembleReducedSystem(pMat[:, Sold :],
                                                     pMat[:, : Sold])
             self.trainedModel.data.projMat = copy(pMatEff)
         self.trainedModel.data.mus = copy(self.mus)
         self.trainedModel.data.ARBs = ARBs
         self.trainedModel.data.bRBs = bRBs
         vbMng(self, "DEL", "Done computing projection matrix.", 7)
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
 
diff --git a/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py b/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py
index 0e4811a..06c56f4 100644
--- a/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py
+++ b/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py
@@ -1,538 +1,539 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from copy import deepcopy as copy
 from rrompy.reduction_methods.base.generic_approximant import (
                                                             GenericApproximant)
 from rrompy.utilities.poly_fitting.polynomial import (polybases as ppb,
                                                   PolynomialInterpolator as PI)
 from rrompy.utilities.poly_fitting.radial_basis import (polybases as rbpb,
                                                 RadialBasisInterpolator as RBI)
 from rrompy.utilities.poly_fitting.moving_least_squares import (
                                         polybases as mlspb,
                                         MovingLeastSquaresInterpolator as MLSI)
 from rrompy.sampling.pivoted import (SamplingEnginePivoted,
                                      SamplingEnginePivotedPOD)
 from rrompy.utilities.base.types import ListAny, DictAny, HFEng, paramVal
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import (fullDegreeN, totalDegreeN,
                                         nextDerivativeIndices)
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPyWarning)
 from rrompy.parameter import emptyParameterList
 
 __all__ = ['GenericPivotedApproximant']
 
 class GenericPivotedApproximant(GenericApproximant):
     """
     ROM pivoted approximant (with pole matching) computation for parametric
         problems (ABSTRACT).
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         directionPivot(optional): Pivot components. Defaults to [0].
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'matchingWeight': weight for pole matching optimization; defaults
                 to 1;
             - 'cutOffTolerance': tolerance for ignoring parasitic poles;
                 defaults to np.inf;
             - 'cutOffType': rule for tolerance computation for parasitic poles;
                 defaults to 'MAGNITUDE';
             - 'S': total number of pivot samples current approximant relies
                 upon;
             - 'samplerPivot': pivot sample point generator;
             - 'SMarginal': total number of marginal samples current approximant
                 relies upon;
             - 'samplerMarginal': marginal sample point generator;
             - 'polybasisMarginal': type of polynomial basis for marginal
                 interpolation; allowed values include 'MONOMIAL', 'CHEBYSHEV'
                 and 'LEGENDRE'; defaults to 'MONOMIAL';
             - 'MMarginal': degree of marginal interpolant; defaults to 0;
             - 'polydegreetypeMarginal': type of polynomial degree for marginal;
                 defaults to 'TOTAL';
             - 'radialDirectionalWeightsMarginal': radial basis weights for
                 marginal interpolant; defaults to 0, i.e. identity;
             - 'nNearestNeighborMarginal': number of marginal nearest neighbors
                 considered if polybasisMarginal allows; defaults to -1;
             - 'interpRcondMarginal': tolerance for marginal interpolation;
                 defaults to None.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
 
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         directionPivot: Pivot components.
         mus: Array of snapshot parameters.
         musPivot: Array of pivot snapshot parameters.
         musMarginal: Array of marginal snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots;
             - 'matchingWeight': weight for pole matching optimization;
             - 'cutOffTolerance': tolerance for ignoring parasitic poles;
             - 'cutOffType': rule for tolerance computation for parasitic poles;
             - 'polybasisMarginal': type of polynomial basis for marginal
                 interpolation;
             - 'MMarginal': degree of marginal interpolant;
             - 'polydegreetypeMarginal': type of polynomial degree for marginal;
             - 'radialDirectionalWeightsMarginal': radial basis weights for
                 marginal interpolant;
             - 'nNearestNeighborMarginal': number of marginal nearest neighbors
                 considered if polybasisMarginal allows;
             - 'interpRcondMarginal': tolerance for marginal interpolation.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of pivot samples current approximant relies
                 upon;
             - 'samplerPivot': pivot sample point generator;
             - 'SMarginal': total number of marginal samples current approximant
                 relies upon;
             - 'samplerMarginal': marginal sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         matchingWeight: Weight for pole matching optimization.
         cutOffTolerance: Tolerance for ignoring parasitic poles.
         cutOffType: Rule for tolerance computation for parasitic poles.
         S: Total number of pivot samples current approximant relies upon.
         samplerPivot: Pivot sample point generator.
         SMarginal: Total number of marginal samples current approximant relies
             upon.
         samplerMarginal: Marginal sample point generator.
         polybasisMarginal: Type of polynomial basis for marginal interpolation.
         MMarginal: Degree of marginal interpolant.
         polydegreetypeMarginal: Type of polynomial degree for marginal.
         radialDirectionalWeightsMarginal: Radial basis weights for marginal
             interpolant.
         nNearestNeighborMarginal: Number of marginal nearest neighbors
             considered if polybasisMarginal allows.
         interpRcondMarginal: Tolerance for marginal interpolation.
         muBoundsPivot: list of bounds for pivot parameter values.
         muBoundsMarginal: list of bounds for marginal parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
     """
 
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
                  directionPivot : ListAny = [0],
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         if len(directionPivot) > 1:
             raise RROMPyException(("Exactly 1 pivot parameter allowed in pole "
                                    "matching."))
         from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
         QSBase = QS([[0], [1]], "UNIFORM")
         self._addParametersToList(["matchingWeight", "cutOffTolerance",
                                    "cutOffType", "polybasisMarginal",
                                    "MMarginal", "polydegreetypeMarginal",
                                    "radialDirectionalWeightsMarginal",
                                    "nNearestNeighborMarginal",
                                    "interpRcondMarginal"],
                                   [1, np.inf, "MAGNITUDE", "MONOMIAL", 0,
                                    "TOTAL", 1, -1, -1],
                                   ["samplerPivot", "SMarginal",
                                    "samplerMarginal"], [QSBase, [1], QSBase])
         del QS
         self._directionPivot = directionPivot
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     def setupSampling(self):
         """Setup sampling engine."""
         RROMPyAssert(self._mode, message = "Cannot setup sampling engine.")
         if not hasattr(self, "_POD") or self._POD is None: return
         if self.POD:
             SamplingEngine = SamplingEnginePivotedPOD
         else:
             SamplingEngine = SamplingEnginePivoted
         self.samplingEngine = SamplingEngine(self.HFEngine,
                                              self.directionPivot,
-                                             force_state = self.force_state,
+                                             sample_state = self.approx_state,
                                              verbosity = self.verbosity)
 
     @property
     def matchingWeight(self):
         """Value of matchingWeight."""
         return self._matchingWeight
     @matchingWeight.setter
     def matchingWeight(self, matchingWeight):
         self._matchingWeight = matchingWeight
         self._approxParameters["matchingWeight"] = self.matchingWeight
 
     @property
     def cutOffTolerance(self):
         """Value of cutOffTolerance."""
         return self._cutOffTolerance
     @cutOffTolerance.setter
     def cutOffTolerance(self, cutOffTolerance):
         self._cutOffTolerance = cutOffTolerance
         self._approxParameters["cutOffTolerance"] = self.cutOffTolerance
 
     @property
     def cutOffType(self):
         """Value of cutOffType."""
         return self._cutOffType
     @cutOffType.setter
     def cutOffType(self, cutOffType):
         try:
             cutOffType = cutOffType.upper().strip().replace(" ","")
             if cutOffType not in ["MAGNITUDE", "POTENTIAL"]: 
                 raise RROMPyException("Prescribed cutOffType not recognized.")
             self._cutOffType = cutOffType
         except:
             RROMPyWarning(("Prescribed cutOffType not recognized. Overriding "
                            "to 'MAGNITUDE'."))
             self._cutOffType = "MAGNITUDE"
         self._approxParameters["cutOffType"] = self.cutOffType
 
     @property
     def SMarginal(self):
         """Value of SMarginal."""
         return self._SMarginal
     @SMarginal.setter
     def SMarginal(self, SMarginal):
         if SMarginal <= 0:
             raise RROMPyException("SMarginal must be positive.")
         if hasattr(self, "_SMarginal") and self._SMarginal is not None:
             Sold = self.SMarginal
         else: Sold = -1
         self._SMarginal = SMarginal
         self._approxParameters["SMarginal"] = self.SMarginal
         if Sold != self.SMarginal: self.resetSamples()
 
     @property
     def polybasisMarginal(self):
         """Value of polybasisMarginal."""
         return self._polybasisMarginal
     @polybasisMarginal.setter
     def polybasisMarginal(self, polybasisMarginal):
         try:
             polybasisMarginal = polybasisMarginal.upper().strip().replace(" ",
                                                                           "")
             if polybasisMarginal not in ppb + rbpb + mlspb: 
                 raise RROMPyException(
                                "Prescribed marginal polybasis not recognized.")
             self._polybasisMarginal = polybasisMarginal
         except:
             RROMPyWarning(("Prescribed marginal polybasis not recognized. "
                            "Overriding to 'MONOMIAL'."))
             self._polybasisMarginal = "MONOMIAL"
         self._approxParameters["polybasisMarginal"] = self.polybasisMarginal
 
     @property
     def MMarginal(self):
         """Value of MMarginal."""
         return self._MMarginal
     @MMarginal.setter
     def MMarginal(self, MMarginal):
         if MMarginal < 0:
             raise RROMPyException("MMarginal must be non-negative.")
         self._MMarginal = MMarginal
         self._approxParameters["MMarginal"] = self.MMarginal
 
     @property
     def polydegreetypeMarginal(self):
         """Value of polydegreetypeMarginal."""
         return self._polydegreetypeMarginal
     @polydegreetypeMarginal.setter
     def polydegreetypeMarginal(self, polydegreetypeM):
         try:
             polydegreetypeM = polydegreetypeM.upper().strip().replace(" ","")
             if polydegreetypeM not in ["TOTAL", "FULL"]: 
                 raise RROMPyException(("Prescribed polydegreetypeMarginal not "
                                        "recognized."))
             self._polydegreetypeMarginal = polydegreetypeM
         except:
             RROMPyWarning(("Prescribed polydegreetypeMarginal not recognized. "
                            "Overriding to 'TOTAL'."))
             self._polydegreetypeMarginal = "TOTAL"
         self._approxParameters["polydegreetypeMarginal"] = (
                                                    self.polydegreetypeMarginal)
 
     @property
     def radialDirectionalWeightsMarginal(self):
         """Value of radialDirectionalWeightsMarginal."""
         return self._radialDirectionalWeightsMarginal
     @radialDirectionalWeightsMarginal.setter
     def radialDirectionalWeightsMarginal(self, radialDirWeightsMarginal):
         self._radialDirectionalWeightsMarginal = radialDirWeightsMarginal
         self._approxParameters["radialDirectionalWeightsMarginal"] = (
                                          self.radialDirectionalWeightsMarginal)
 
     @property
     def nNearestNeighborMarginal(self):
         """Value of nNearestNeighborMarginal."""
         return self._nNearestNeighborMarginal
     @nNearestNeighborMarginal.setter
     def nNearestNeighborMarginal(self, nNearestNeighborMarginal):
         self._nNearestNeighborMarginal = nNearestNeighborMarginal
         self._approxParameters["nNearestNeighborMarginal"] = (
                                                  self.nNearestNeighborMarginal)
 
     @property
     def interpRcondMarginal(self):
         """Value of interpRcondMarginal."""
         return self._interpRcondMarginal
     @interpRcondMarginal.setter
     def interpRcondMarginal(self, interpRcondMarginal):
         self._interpRcondMarginal = interpRcondMarginal
         self._approxParameters["interpRcondMarginal"] = (
                                                       self.interpRcondMarginal)
 
     @property
     def directionPivot(self):
         """Value of directionPivot. Its assignment may reset snapshots."""
         return self._directionPivot
     @directionPivot.setter
     def directionPivot(self, directionPivot):
         if hasattr(self, '_directionPivot'):
             directionPivotOld = copy(self.directionPivot)
         else:
             directionPivotOld = None
         if (directionPivotOld is None
          or len(directionPivot) != len(directionPivotOld)
          or not directionPivot == directionPivotOld):
             self.resetSamples()
             self._directionPivot = directionPivot
 
     @property
     def directionMarginal(self):
         return [x for x in range(self.HFEngine.npar) \
                                                if x not in self.directionPivot]
 
     @property
     def nparPivot(self):
         return len(self.directionPivot)
 
     @property
     def nparMarginal(self):
         return self.npar - self.nparPivot
 
     @property
     def rescalingExpPivot(self):
         return [self.HFEngine.rescalingExp[x] for x in self.directionPivot]
 
     @property
     def rescalingExpMarginal(self):
         return [self.HFEngine.rescalingExp[x] for x in self.directionMarginal]
 
     @property
     def muBoundsPivot(self):
         """Value of muBoundsPivot."""
         return self.samplerPivot.lims
 
     @property
     def muBoundsMarginal(self):
         """Value of muBoundsMarginal."""
         return self.samplerMarginal.lims
 
     @property
     def samplerPivot(self):
         """Value of samplerPivot."""
         return self._samplerPivot
     @samplerPivot.setter
     def samplerPivot(self, samplerPivot):
         if 'generatePoints' not in dir(samplerPivot):
             raise RROMPyException("Pivot sampler type not recognized.")
         if hasattr(self, '_samplerPivot') and self._samplerPivot is not None:
             samplerOld = self.samplerPivot
         self._samplerPivot = samplerPivot
         self._approxParameters["samplerPivot"] = self.samplerPivot.__str__()
         if not 'samplerOld' in locals() or samplerOld != self.samplerPivot:
             self.resetSamples()
     
     @property
     def samplerMarginal(self):
         """Value of samplerMarginal."""
         return self._samplerMarginal
     @samplerMarginal.setter
     def samplerMarginal(self, samplerMarginal):
         if 'generatePoints' not in dir(samplerMarginal):
             raise RROMPyException("Marginal sampler type not recognized.")
         if (hasattr(self, '_samplerMarginal')
         and self._samplerMarginal is not None):
             samplerOld = self.samplerMarginal
         self._samplerMarginal = samplerMarginal
         self._approxParameters["samplerMarginal"] = (
                                                 self.samplerMarginal.__str__())
         if not 'samplerOld' in locals() or samplerOld != self.samplerMarginal:
             self.resetSamples()
     
     def resetSamples(self):
         """Reset samples."""
         super().resetSamples()
         self._musMUniqueCN = None
         self._derMIdxs = None
         self._reorderM = None
 
     def setSamples(self, samplingEngine):
         """Copy samplingEngine and samples."""
         super().setSamples(samplingEngine)
         self.mus = copy(self.samplingEngine[0].mus)
         for sEj in self.samplingEngine[1:]:
             self.mus.append(sEj.mus)
 
     def computeSnapshots(self):
         """Compute snapshots of solution map."""
         RROMPyAssert(self._mode,
                      message = "Cannot start snapshot computation.")
         if self.samplingEngine.nsamplesTot != self.S * self.SMarginal:
             self.computeScaleFactor()
             self.resetSamples()
             vbMng(self, "INIT", "Starting computation of snapshots.", 5)
             self.HFEngine.buildA()
             self.HFEngine.buildb()
             self.musPivot = self.samplerPivot.generatePoints(self.S)
             self.musMarginal = self.samplerMarginal.generatePoints(
                                                                 self.SMarginal)
             self.mus = emptyParameterList()
             self.mus.reset((self.S * self.SMarginal, self.HFEngine.npar))
             self.samplingEngine.resetHistory(self.SMarginal)
             for j, muMarg in enumerate(self.musMarginal):
                 for k in range(j * self.S, (j + 1) * self.S):
                     self.mus.data[k, self.directionPivot] = (
                                             self.musPivot[k - j * self.S].data)
                     self.mus.data[k, self.directionMarginal] = muMarg.data
             self.samplingEngine.iterSample(self.musPivot, self.musMarginal)
             if self.POD:
                 self.samplingEngine.coalesceSamples(self.interpRcondMarginal)
             else:
                 self.samplingEngine.coalesceSamples()
             vbMng(self, "DEL", "Done computing snapshots.", 5)
 
     def _setupMarginalInterpolationIndices(self):
         """Setup parameters for polyvander."""
         RROMPyAssert(self._mode,
                      message = "Cannot setup interpolation indices.")
         if (self._musMUniqueCN is None
          or len(self._reorderM) != len(self.musMarginal)):
             self._musMUniqueCN, musMIdxsTo, musMIdxs, musMCount = (
                    self.trainedModel.centerNormalizeMarginal(self.musMarginal)\
                             .unique(return_index = True, return_inverse = True,
                                     return_counts = True))
             self._musMUnique = self.musMarginal[musMIdxsTo]
             self._derMIdxs = [None] * len(self._musMUniqueCN)
             self._reorderM = np.empty(len(musMIdxs), dtype = int)
             filled = 0
             for j, cnt in enumerate(musMCount):
                 self._derMIdxs[j] = nextDerivativeIndices([],
                                                         self.nparMarginal, cnt)
                 jIdx = np.nonzero(musMIdxs == j)[0]
                 self._reorderM[jIdx] = np.arange(filled, filled + cnt)
                 filled += cnt
 
     def _setupMarginalInterp(self):
         """Compute marginal interpolator."""
         RROMPyAssert(self._mode, message = "Cannot setup numerator.")
         vbMng(self, "INIT", "Starting computation of marginal interpolator.",
               7)
         self._setupMarginalInterpolationIndices()
         if self.polydegreetypeMarginal == "TOTAL":
             cfun = totalDegreeN
         else:
             cfun = fullDegreeN
         MM = copy(self.MMarginal)
         while len(self.musMarginal) < cfun(MM, self.nparMarginal): MM -= 1
         if MM < self.MMarginal:
             RROMPyWarning(
                       ("MMarginal too large compared to SMarginal. "
                        "Reducing MMarginal by {}").format(self.MMarginal - MM))
             self.MMarginal = MM
         mI = []
         for j in range(len(self.musMarginal)):
             canonicalj = 1. * (np.arange(len(self.musMarginal)) == j)
             self._MMarginal = MM
             while self.MMarginal >= 0:
                 if self.polybasisMarginal in ppb:
                     p = PI()
                     wellCond, msg = p.setupByInterpolation(
                               self._musMUniqueCN, canonicalj, self.MMarginal, 
                               self.polybasisMarginal, self.verbosity >= 5,
                               self.polydegreetypeMarginal == "TOTAL",
                               {"derIdxs": self._derMIdxs,
                                "reorder": self._reorderM,
                                "scl": np.power(self.scaleFactorMarginal, -1.)},
                               {"rcond": self.interpRcondMarginal})
                 elif self.polybasisMarginal in rbpb:
                     p = RBI()
                     wellCond, msg = p.setupByInterpolation(
                               self._musMUniqueCN, canonicalj, self.MMarginal,
                               self.polybasisMarginal,
                               self.radialDirectionalWeightsMarginal,
                               self.verbosity >= 5,
                               self.polydegreetypeMarginal == "TOTAL",
                               {"derIdxs": self._derMIdxs,
                            "reorder": self._reorderM,
                            "scl": np.power(self.scaleFactorMarginal, -1.),
                            "nNearestNeighbor" : self.nNearestNeighborMarginal},
                               {"rcond": self.interpRcondMarginal})
                 else:# if self.polybasisMarginal in mlspb:
                     p = MLSI()
                     wellCond, msg = p.setupByInterpolation(
                               self._musMUniqueCN, canonicalj, self.MMarginal,
                               self.polybasisMarginal,
                               self.radialDirectionalWeightsMarginal,
                               self.verbosity >= 5,
                               self.polydegreetypeMarginal == "TOTAL",
                               {"derIdxs": self._derMIdxs,
                            "reorder": self._reorderM,
                            "scl": np.power(self.scaleFactorMarginal, -1.),
                            "nNearestNeighbor" : self.nNearestNeighborMarginal})
                 vbMng(self, "MAIN", msg, 5)
                 if wellCond: break
                 RROMPyWarning(("Polyfit is poorly conditioned. Reducing "
                                "MMarginal by 1."))
                 self.MMarginal = self.MMarginal - 1
             mI = mI + [copy(p)]
         vbMng(self, "DEL", "Done computing marginal interpolator.", 7)
         return mI
 
     def computeScaleFactor(self):
         """Compute parameter rescaling factor."""
         RROMPyAssert(self._mode, message = "Cannot compute rescaling factor.")
         self.scaleFactorPivot = .5 * np.abs(
                                self.muBoundsPivot[0] ** self.rescalingExpPivot
                              - self.muBoundsPivot[1] ** self.rescalingExpPivot)
         self.scaleFactorMarginal = .5 * np.abs(
                          self.muBoundsMarginal[0] ** self.rescalingExpMarginal
                        - self.muBoundsMarginal[1] ** self.rescalingExpMarginal)
         self.scaleFactor = np.empty(self.npar)
         self.scaleFactor[self.directionPivot] = self.scaleFactorPivot
         self.scaleFactor[self.directionMarginal] = self.scaleFactorMarginal
diff --git a/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py b/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py
index 40aad79..9b3c244 100644
--- a/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py
+++ b/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py
@@ -1,643 +1,644 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from rrompy.reduction_methods.base import checkRobustTolerance
 from .generic_pivoted_approximant import GenericPivotedApproximant
 from rrompy.reduction_methods.standard.rational_interpolant import (
                                                      RationalInterpolant as RI)
 from rrompy.utilities.poly_fitting.polynomial import (
                                     polybases as ppb, polyfitname,
                                     polyvander as pvP, polyvanderTotal as pvTP,
                                     PolynomialInterpolator as PI)
 from rrompy.utilities.poly_fitting.radial_basis import (polybases as rbpb,
                                                 RadialBasisInterpolator as RBI)
 from rrompy.utilities.poly_fitting.moving_least_squares import (
                                         polybases as mlspb,
                                         MovingLeastSquaresInterpolator as MLSI)
 from rrompy.utilities.base.types import (Np1D, Np2D, HFEng, DictAny, Tuple,
                                          List, ListAny, paramVal)
 from rrompy.utilities.base import verbosityManager as vbMng, freepar as fp
 from rrompy.utilities.numerical import (multifactorial, customPInv, dot,
                                         fullDegreeN, totalDegreeN, 
                                         degreeTotalToFull, fullDegreeMaxMask,
                                         totalDegreeMaxMask,
                                         nextDerivativeIndices,
                                         hashDerivativeToIdx as hashD,
                                         hashIdxToDerivative as hashI)
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPyWarning)
 from rrompy.parameter import checkParameter
 
 __all__ = ['RationalInterpolantPivoted']
 
 class RationalInterpolantPivoted(GenericPivotedApproximant):
     """
     ROM pivoted rational interpolant (with pole matching) computation for
         parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         directionPivot(optional): Pivot components. Defaults to [0].
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'matchingWeight': weight for pole matching optimization; defaults
                 to 1;
             - 'cutOffTolerance': tolerance for ignoring parasitic poles;
                 defaults to np.inf;
             - 'cutOffType': rule for tolerance computation for parasitic poles;
                 defaults to 'MAGNITUDE';
             - 'S': total number of pivot samples current approximant relies
                 upon;
             - 'samplerPivot': pivot sample point generator;
             - 'SMarginal': total number of marginal samples current approximant
                 relies upon;
             - 'samplerMarginal': marginal sample point generator;
             - 'polybasisPivot': type of polynomial basis for pivot
                 interpolation; defaults to 'MONOMIAL';
             - 'polybasisMarginal': type of polynomial basis for marginal
                 interpolation; allowed values include 'MONOMIAL', 'CHEBYSHEV'
                 and 'LEGENDRE'; defaults to 'MONOMIAL';
             - 'M': degree of rational interpolant numerator; defaults to 0;
             - 'N': degree of rational interpolant denominator; defaults to 0;
             - 'polydegreetype': type of polynomial degree; defaults to 'TOTAL';
             - 'MMarginal': degree of marginal interpolant; defaults to 0;
             - 'polydegreetypeMarginal': type of polynomial degree for marginal;
                 defaults to 'TOTAL';
             - 'radialDirectionalWeightsPivot': radial basis weights for pivot
                 numerator; defaults to 0, i.e. identity;
             - 'radialDirectionalWeightsMarginal': radial basis weights for
                 marginal interpolant; defaults to 0, i.e. identity;
             - 'nNearestNeighborPivot': number of pivot nearest neighbors
                 considered if polybasisPivot allows; defaults to -1;
             - 'nNearestNeighborMarginal': number of marginal nearest neighbors
                 considered if polybasisMarginal allows; defaults to -1;
             - 'interpRcondPivot': tolerance for pivot interpolation; defaults
                 to None;
             - 'interpRcondMarginal': tolerance for marginal interpolation;
                 defaults to None;
             - 'robustTol': tolerance for robust rational denominator
                 management; defaults to 0.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         directionPivot: Pivot components.
         mus: Array of snapshot parameters.
         musPivot: Array of pivot snapshot parameters.
         musMarginal: Array of marginal snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots;
             - 'matchingWeight': weight for pole matching optimization;
             - 'cutOffTolerance': tolerance for ignoring parasitic poles;
             - 'cutOffType': rule for tolerance computation for parasitic poles;
             - 'polybasisPivot': type of polynomial basis for pivot
                 interpolation;
             - 'polybasisMarginal': type of polynomial basis for marginal
                 interpolation;
             - 'M': degree of rational interpolant numerator;
             - 'N': degree of rational interpolant denominator;
             - 'polydegreetype': type of polynomial degree;
             - 'MMarginal': degree of marginal interpolant;
             - 'polydegreetypeMarginal': type of polynomial degree for marginal;
             - 'radialDirectionalWeightsPivot': radial basis weights for pivot
                 numerator;
             - 'radialDirectionalWeightsMarginal': radial basis weights for
                 marginal interpolant;
             - 'nNearestNeighborPivot': number of pivot nearest neighbors
                 considered if polybasisPivot allows;
             - 'nNearestNeighborMarginal': number of marginal nearest neighbors
                 considered if polybasisMarginal allows;
             - 'interpRcondPivot': tolerance for pivot interpolation;
             - 'interpRcondMarginal': tolerance for marginal interpolation;
             - 'robustTol': tolerance for robust rational denominator
                 management.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of pivot samples current approximant relies
                 upon;
             - 'samplerPivot': pivot sample point generator;
             - 'SMarginal': total number of marginal samples current approximant
                 relies upon;
             - 'samplerMarginal': marginal sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         matchingWeight: Weight for pole matching optimization.
         cutOffTolerance: Tolerance for ignoring parasitic poles.
         cutOffType: Rule for tolerance computation for parasitic poles.
         S: Total number of pivot samples current approximant relies upon.
         sampler: Pivot sample point generator.
         polybasisPivot: Type of polynomial basis for pivot interpolation.
         polybasisMarginal: Type of polynomial basis for marginal interpolation.
         M: Numerator degree of approximant.
         N: Denominator degree of approximant.
         polydegreetype: Type of polynomial degree.
         MMarginal: Degree of marginal interpolant.
         polydegreetypeMarginal: Type of polynomial degree for marginal.
         radialDirectionalWeightsPivot: Radial basis weights for pivot
             numerator.
         radialDirectionalWeightsMarginal: Radial basis weights for marginal
             interpolant.
         nNearestNeighborPivot: Number of pivot nearest neighbors considered if
             polybasisPivot allows.
         nNearestNeighborMarginal: Number of marginal nearest neighbors
             considered if polybasisMarginal allows.
         interpRcondPivot: Tolerance for pivot interpolation.
         interpRcondMarginal: Tolerance for marginal interpolation.
         robustTol: Tolerance for robust rational denominator management.
         muBoundsPivot: list of bounds for pivot parameter values.
         muBoundsMarginal: list of bounds for marginal parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
         Q: Numpy 1D vector containing complex coefficients of approximant
             denominator.
         P: Numpy 2D vector whose columns are FE dofs of coefficients of
             approximant numerator.
     """
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
                  directionPivot : ListAny = [0],
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         self._addParametersToList(["polybasisPivot", "M", "N",
                                    "polydegreetype",
                                    "radialDirectionalWeightsPivot",
                                    "nNearestNeighborPivot",
                                    "interpRcondPivot", "robustTol"],
                                   ["MONOMIAL", 0, 0, "TOTAL", 1, -1, -1, 0])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          directionPivot = directionPivot,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def tModelType(self):
         from rrompy.reduction_methods.trained_model import \
                                                     TrainedModelPivotedRational
         return TrainedModelPivotedRational
 
     def initializeModelData(self, datadict):
         from rrompy.reduction_methods.trained_model import \
                                                         TrainedModelPivotedData
         return (TrainedModelPivotedData(datadict["mu0"],
                                         datadict.pop("projMat"),
                                         datadict["scaleFactor"],
                                         datadict.pop("rescalingExp"),
                                         datadict["directionPivot"]),
                 ["mu0", "scaleFactor", "directionPivot", "mus"])
 
     @property
     def polybasisPivot(self):
         """Value of polybasisPivot."""
         return self._polybasisPivot
     @polybasisPivot.setter
     def polybasisPivot(self, polybasisPivot):
         try:
             polybasisPivot = polybasisPivot.upper().strip().replace(" ","")
             if polybasisPivot not in ppb + rbpb + mlspb: 
                 raise RROMPyException(
                                   "Prescribed pivot polybasis not recognized.")
             self._polybasisPivot = polybasisPivot
         except:
             RROMPyWarning(("Prescribed pivot polybasis not recognized. "
                            "Overriding to 'MONOMIAL'."))
             self._polybasisPivot = "MONOMIAL"
         self._approxParameters["polybasisPivot"] = self.polybasisPivot
 
     @property
     def polybasisPivot0(self):
         if "_" in self.polybasisPivot:
             return self.polybasisPivot.split("_")[0]
         return self.polybasisPivot
 
     @property
     def radialDirectionalWeightsPivot(self):
         """Value of radialDirectionalWeightsPivot."""
         return self._radialDirectionalWeightsPivot
     @radialDirectionalWeightsPivot.setter
     def radialDirectionalWeightsPivot(self, radialDirectionalWeightsPivot):
         self._radialDirectionalWeightsPivot = radialDirectionalWeightsPivot
         self._approxParameters["radialDirectionalWeightsPivot"] = (
                                             self.radialDirectionalWeightsPivot)
 
     @property
     def nNearestNeighborPivot(self):
         """Value of nNearestNeighborPivot."""
         return self._nNearestNeighborPivot
     @nNearestNeighborPivot.setter
     def nNearestNeighborPivot(self, nNearestNeighborPivot):
         self._nNearestNeighborPivot = nNearestNeighborPivot
         self._approxParameters["nNearestNeighborPivot"] = (
                                                     self.nNearestNeighborPivot)
 
     @property
     def interpRcondPivot(self):
         """Value of interpRcondPivot."""
         return self._interpRcondPivot
     @interpRcondPivot.setter
     def interpRcondPivot(self, interpRcondPivot):
         self._interpRcondPivot = interpRcondPivot
         self._approxParameters["interpRcondPivot"] = self.interpRcondPivot
 
     @property
     def M(self):
         """Value of M."""
         return self._M
     @M.setter
     def M(self, M):
         if M < 0: raise RROMPyException("M must be non-negative.")
         self._M = M
         self._approxParameters["M"] = self.M
 
     @property
     def N(self):
         """Value of N."""
         return self._N
     @N.setter
     def N(self, N):
         if N < 0: raise RROMPyException("N must be non-negative.")
         self._N = N
         self._approxParameters["N"] = self.N
 
     @property
     def polydegreetype(self):
         """Value of polydegreetype."""
         return self._polydegreetype
     @polydegreetype.setter
     def polydegreetype(self, polydegreetype):
         try:
             polydegreetype = polydegreetype.upper().strip().replace(" ","")
             if polydegreetype not in ["TOTAL", "FULL"]: 
                 raise RROMPyException(("Prescribed polydegreetype not "
                                        "recognized."))
             self._polydegreetype = polydegreetype
         except:
             RROMPyWarning(("Prescribed polydegreetype not recognized. "
                            "Overriding to 'TOTAL'."))
             self._polydegreetype = "TOTAL"
         self._approxParameters["polydegreetype"] = self.polydegreetype
 
     @property
     def robustTol(self):
         """Value of tolerance for robust rational denominator management."""
         return self._robustTol
     @robustTol.setter
     def robustTol(self, robustTol):
         if robustTol < 0.:
             RROMPyWarning(("Overriding prescribed negative robustness "
                            "tolerance to 0."))
             robustTol = 0.
         self._robustTol = robustTol
         self._approxParameters["robustTol"] = self.robustTol
 
     def resetSamples(self):
         """Reset samples."""
         super().resetSamples()
         self._musPUniqueCN = None
         self._derPIdxs = None
         self._reorderP = None
 
     def _setupPivotInterpolationIndices(self):
         """Setup parameters for polyvander."""
         RROMPyAssert(self._mode,
                      message = "Cannot setup interpolation indices.")
         if (self._musPUniqueCN is None
          or len(self._reorderP) != len(self.musPivot)):
             self._musPUniqueCN, musPIdxsTo, musPIdxs, musPCount = (
                   self.trainedModel.centerNormalizePivot(self.musPivot).unique(
                                     return_index = True, return_inverse = True,
                                     return_counts = True))
             self._musPUnique = self.mus[musPIdxsTo]
             self._derPIdxs = [None] * len(self._musPUniqueCN)
             self._reorderP = np.empty(len(musPIdxs), dtype = int)
             filled = 0
             for j, cnt in enumerate(musPCount):
                 self._derPIdxs[j] = nextDerivativeIndices([],
                                                           self.nparPivot, cnt)
                 jIdx = np.nonzero(musPIdxs == j)[0]
                 self._reorderP[jIdx] = np.arange(filled, filled + cnt)
                 filled += cnt
 
     def _setupDenominator(self):
         """Compute rational denominator."""
         RROMPyAssert(self._mode, message = "Cannot setup denominator.")
         vbMng(self, "INIT", "Starting computation of denominator.", 7)
         NinvD = None
         N0 = copy(self.N)
         qs = []
         self.verbosity -= 10
         for j in range(len(self.musMarginal)):
             self._N = N0
             while self.N > 0:
                 if NinvD != self.N:
                     invD, fitinvP = self._computeInterpolantInverseBlocks()
                     NinvD = self.N
                 if self.POD:
                     ev, eV = RI.findeveVGQR(self, self.samplingEngine.RPOD[j],
                                             invD)
                 else:
                     ev, eV = RI.findeveVGExplicit(self,
                                           self.samplingEngine.samples[j], invD)
                 nevBad = checkRobustTolerance(ev, self.robustTol)
                 if nevBad <= 1: break
                 if self.catchInstability:
                     raise RROMPyException(("Instability in denominator "
                                            "computation: eigenproblem is "
                                            "poorly conditioned."))
                 RROMPyWarning(("Smallest {} eigenvalues below tolerance. "
                                "Reducing N by 1.").format(nevBad))
                 self.N = self.N - 1
             if self.N <= 0:
                 self._N = 0
                 eV = np.ones((1, 1))
             q = PI()
             q.npar = self.nparPivot
             q.polybasis = self.polybasisPivot0
             if self.polydegreetype == "TOTAL":
                 q.coeffs = degreeTotalToFull(tuple([self.N + 1] * q.npar),
                                              q.npar, eV[:, 0])
             else:
                 q.coeffs = eV[:, 0].reshape([self.N + 1] * q.npar)
             qs = qs + [copy(q)]
         self.verbosity += 10
         vbMng(self, "DEL", "Done computing denominator.", 7)
         return qs, fitinvP
 
     def _setupNumerator(self):
         """Compute rational numerator."""
         RROMPyAssert(self._mode, message = "Cannot setup numerator.")
         vbMng(self, "INIT", "Starting computation of numerator.", 7)
         Qevaldiag = np.zeros((len(self.musPivot), len(self.musPivot)),
                              dtype = np.complex)
         verb = self.trainedModel.verbosity
         self.trainedModel.verbosity = 0
         self._setupPivotInterpolationIndices()
         cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
         M = copy(self.M)
         while len(self.musPivot) < cfun(M, self.nparPivot): M -= 1
         if M < self.M:
             RROMPyWarning(("M too large compared to S. Reducing M by "
                            "{}").format(self.M - M))
             self.M = M
         tensor_idx = 0
         ps = []
         for k, muM in enumerate(self.musMarginal):
             self._M = M
             idxGlob = 0
             for j, derIdxs in enumerate(self._derPIdxs):
                 mujEff = [fp] * self.npar
                 for jj, kk in enumerate(self.directionPivot):
                     mujEff[kk] = self._musPUnique[j, jj]
                 for jj, kk in enumerate(self.directionMarginal):
                     mujEff[kk] = muM(0, jj)
                 mujEff = checkParameter(mujEff, self.npar)
                 nder = len(derIdxs)
                 idxLoc = np.arange(len(self.musPivot))[
                                              (self._reorderP >= idxGlob)
                                            * (self._reorderP < idxGlob + nder)]
                 idxGlob += nder
                 Qval = [0] * nder
                 for der in range(nder):
                     derIdx = hashI(der, self.nparPivot)
                     derIdxEff = [0] * self.npar
                     sclEff = [0] * self.npar
                     for jj, kk in enumerate(self.directionPivot):
                         derIdxEff[kk] = derIdx[jj]
                         sclEff[kk] = self.scaleFactorPivot[jj] ** -1.
                     Qval[der] = (self.trainedModel.getQVal(mujEff, derIdxEff,
                                                            scl = sclEff)
                                / multifactorial(derIdx))
                 for derU, derUIdx in enumerate(derIdxs):
                     for derQ, derQIdx in enumerate(derIdxs):
                         diffIdx = [x - y for (x, y) in zip(derUIdx, derQIdx)]
                         if all([x >= 0 for x in diffIdx]):
                             diffj = hashD(diffIdx)
                             Qevaldiag[idxLoc[derU], idxLoc[derQ]] = Qval[diffj]
             while self.M >= 0:
                 if self.polybasisPivot in ppb:
                     p = PI()
                     wellCond, msg = p.setupByInterpolation(
                                  self._musPUniqueCN, Qevaldiag, self.M, 
                                  self.polybasisPivot, self.verbosity >= 5,
                                  self.polydegreetype == "TOTAL",
                                  {"derIdxs": self._derPIdxs,
                                   "reorder": self._reorderP,
                                   "scl": np.power(self.scaleFactorPivot, -1.)},
                                  {"rcond": self.interpRcondPivot})
                 elif self.polybasisPivot in rbpb:
                     p = RBI()
                     wellCond, msg = p.setupByInterpolation(
                                  self._musPUniqueCN, Qevaldiag, self.M,
                                  self.polybasisPivot,
                                  self.radialDirectionalWeightsPivot,
                                  self.verbosity >= 5,
                                  self.polydegreetype == "TOTAL",
                                  {"derIdxs": self._derPIdxs,
                               "reorder": self._reorderP,
                               "scl": np.power(self.scaleFactorPivot, -1.),
                               "nNearestNeighbor" : self.nNearestNeighborPivot},
                                  {"rcond": self.interpRcondPivot})
                 else:# if self.polybasisPivot in mlspb:
                     p = MLSI()
                     wellCond, msg = p.setupByInterpolation(
                                  self._musPUniqueCN, Qevaldiag, self.M,
                                  self.polybasisPivot,
                                  self.radialDirectionalWeightsPivot,
                                  self.verbosity >= 5,
                                  self.polydegreetype == "TOTAL",
                                  {"derIdxs": self._derPIdxs,
                               "reorder": self._reorderP,
                               "scl": np.power(self.scaleFactorPivot, -1.),
                               "nNearestNeighbor" : self.nNearestNeighborPivot})
                 vbMng(self, "MAIN", msg, 5)
                 if wellCond: break
                 if self.catchInstability:
                     raise RROMPyException(("Instability in numerator "
                                            "computation: polyfit is "
                                            "poorly conditioned."))
                 RROMPyWarning(("Polyfit is poorly conditioned. "
                                "Reducing M by 1."))
                 self.M = self.M - 1
             tensor_idx_new = tensor_idx + Qevaldiag.shape[1]
             if self.POD:
                 p.postmultiplyTensorize(self.samplingEngine.RPODCoalesced.T[
                                                tensor_idx : tensor_idx_new, :])
             else:
                 p.pad(tensor_idx, len(self.mus) - tensor_idx_new)
             tensor_idx = tensor_idx_new
             ps = ps + [copy(p)]
         self.trainedModel.verbosity = verb
         vbMng(self, "DEL", "Done computing numerator.", 7)
         return ps
 
     def setupApprox(self):
         """
         Compute rational interpolant.
         SVD-based robust eigenvalue management.
         """
         if self.checkComputedApprox():
             return
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
         self.computeSnapshots()
         pMat = self.samplingEngine.samplesCoalesced.data
-        pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
+        pMatEff = dot(self.HFEngine.C, pMat) if self.approx_state else pMat
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp,
                         "directionPivot": self.directionPivot}
             self.trainedModel.data = self.initializeModelData(datadict)[0]
         else:
             self.trainedModel = self.trainedModel
             self.trainedModel.data.projMat = copy(pMatEff)
         self.trainedModel.data.musPivot = copy(self.musPivot)
         self.trainedModel.data.musMarginal = copy(self.musMarginal)
         self.trainedModel.data.marginalInterp = self._setupMarginalInterp()
         if self.N > 0:
             Qs = self._setupDenominator()[0]
         else:
             Q = PI()
             Q.npar = self.nparPivot
             Q.coeffs = np.ones(tuple([1] * Q.npar),
                                dtype = self.musMarginal.dtype)
             Q.polybasis = self.polybasisPivot0
             Qs = [Q for _ in range(len(self.musMarginal))]
         self.trainedModel.data._temporary = 1
         self.trainedModel.data.Qs = Qs
         self.trainedModel.data.Ps = self._setupNumerator()
         vbMng(self, "INIT", "Matching poles.", 10)
         self.trainedModel.initializeFromRational(self.HFEngine,
                                                  self.matchingWeight, self.POD,
-                                                 self.force_state)
+                                                 self.approx_state)
         del self.trainedModel.data._temporary
         vbMng(self, "DEL", "Done matching poles.", 10)
         if not np.isinf(self.cutOffTolerance):
             vbMng(self, "INIT", "Recompressing by cut-off.", 10)
             msg = self.trainedModel.recompressByCutOff([-1., 1.],
                                                        self.cutOffTolerance,
                                                        self.cutOffType)
             vbMng(self, "DEL", "Done recompressing." + msg, 10)
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
 
     def _computeInterpolantInverseBlocks(self) -> Tuple[List[Np2D], Np2D]:
         """
         Compute inverse factors for minimal interpolant target functional.
         """
         RROMPyAssert(self._mode, message = "Cannot solve eigenvalue problem.")
         self._setupPivotInterpolationIndices()
         cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
         N = copy(self.N)
         while len(self.musPivot) < cfun(N, self.nparPivot): N -= 1
         if N < self.N:
             RROMPyWarning(("N too large compared to S. Reducing N by "
                            "{}").format(self.N - N))
             self.N = N
         while self.N >= 0:
             if self.polydegreetype == "TOTAL":
                 TE, _, argIdxs = pvTP(self._musPUniqueCN, self.N,
                                     self.polybasisPivot0, self._derPIdxs,
                                     self._reorderP,
                                     scl = np.power(self.scaleFactorPivot, -1.))
                 TE = TE[:, argIdxs]
                 idxsB = totalDegreeMaxMask(self.N, self.nparPivot)
             else: #if self.polydegreetype == "FULL":
                 TE = pvP(self._musPUniqueCN, [self.N] * self.nparPivot,
                          self.polybasisPivot0, self._derPIdxs, self._reorderP,
                          scl = np.power(self.scaleFactorPivot, -1.))
                 idxsB = fullDegreeMaxMask(self.N, self.nparPivot)
             fitOut = customPInv(TE, rcond = self.interpRcondPivot,
                                 full = True)
             vbMng(self, "MAIN",
                   ("Fitting {} samples with degree {} through {}... "
                    "Conditioning of pseudoinverse system: {:.4e}.").format(
                                            TE.shape[0], self.N,
                                            polyfitname(self.polybasisPivot0),
                                            fitOut[1][1][0] / fitOut[1][1][-1]),
                   5)
             if fitOut[1][0] == TE.shape[1]:
                 fitinvP = fitOut[0][idxsB, :]
                 break
             RROMPyWarning("Polyfit is poorly conditioned. Reducing N by 1.")
             self.N -= 1
         if self.N < 0:
             raise RROMPyException(("Instability in computation of "
                                    "denominator. Aborting."))
         TN, _, argIdxs = pvTP(self._musPUniqueCN, self.N, self.polybasisPivot0,
                               self._derPIdxs, self._reorderP,
                               scl = np.power(self.scaleFactorPivot, -1.))
         TN = TN[:, argIdxs]
         invD = [None] * (len(idxsB))
         for k in range(len(idxsB)):
             pseudoInv = np.diag(fitinvP[k, :])
             idxGlob = 0
             for j, derIdxs in enumerate(self._derPIdxs):
                 nder = len(derIdxs)
                 idxGlob += nder
                 if nder > 1:
                     idxLoc = np.arange(len(self.musPivot))[
                                              (self._reorderP >= idxGlob - nder)
                                            * (self._reorderP < idxGlob)]
                     invLoc = fitinvP[k, idxLoc]
                     pseudoInv[np.ix_(idxLoc, idxLoc)] = 0.
                     for diffj, diffjIdx in enumerate(derIdxs):
                         for derQ, derQIdx in enumerate(derIdxs):
                             derUIdx = [x - y for (x, y) in 
                                                     zip(diffjIdx, derQIdx)]
                             if all([x >= 0 for x in derUIdx]):
                                 derU = hashD(derUIdx)
                                 pseudoInv[idxLoc[derU], idxLoc[derQ]] = (
                                                              invLoc[diffj])
             invD[k] = dot(pseudoInv, TN)
         return invD, fitinvP
 
     def getResidues(self, *args, **kwargs) -> Np1D:
         """
         Obtain approximant residues.
 
         Returns:
             Matrix with residues as columns.
         """
         return self.trainedModel.getResidues(*args, **kwargs)
diff --git a/rrompy/reduction_methods/standard/generic_standard_approximant.py b/rrompy/reduction_methods/standard/generic_standard_approximant.py
index 01056da..a403e4c 100644
--- a/rrompy/reduction_methods/standard/generic_standard_approximant.py
+++ b/rrompy/reduction_methods/standard/generic_standard_approximant.py
@@ -1,147 +1,148 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from copy import deepcopy as copy
 from rrompy.reduction_methods.base.generic_approximant import (
                                                             GenericApproximant)
 from rrompy.utilities.base.types import DictAny, HFEng, paramVal
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.exception_manager import RROMPyException, RROMPyAssert
 from rrompy.parameter import checkParameterList
 
 __all__ = ['GenericStandardApproximant']
 
 class GenericStandardApproximant(GenericApproximant):
     """
     ROM interpolant computation for parametric problems (ABSTRACT).
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
 
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         S: Number of solution snapshots over which current approximant is
             based upon.
         sampler: Sample point generator.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
     """
 
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
         self._addParametersToList([], [], ["sampler"],
                                   [QS([[0], [1]], "UNIFORM")])
         del QS
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def mus(self):
         """Value of mus. Its assignment may reset snapshots."""
         return self._mus
     @mus.setter
     def mus(self, mus):
         mus = checkParameterList(mus, self.npar)[0]
         musOld =  copy(self.mus) if hasattr(self, '_mus') else None
         if (musOld is None or len(mus) != len(musOld) or not mus == musOld):
             self.resetSamples()
             self._mus = mus
 
     @property
     def muBounds(self):
         """Value of muBounds."""
         return self.sampler.lims
 
     @property
     def sampler(self):
         """Value of sampler."""
         return self._sampler
     @sampler.setter
     def sampler(self, sampler):
         if 'generatePoints' not in dir(sampler):
             raise RROMPyException("Sampler type not recognized.")
         if hasattr(self, '_sampler') and self._sampler is not None:
             samplerOld = self.sampler
         self._sampler = sampler
         self._approxParameters["sampler"] = self.sampler.__str__()
         if not 'samplerOld' in locals() or samplerOld != self.sampler:
             self.resetSamples()
     
     def setSamples(self, samplingEngine):
         """Copy samplingEngine and samples."""
         super().setSamples(samplingEngine)
         self.mus = copy(self.samplingEngine.mus)
 
     def computeSnapshots(self):
         """Compute snapshots of solution map."""
         RROMPyAssert(self._mode,
                      message = "Cannot start snapshot computation.")
         if self.samplingEngine.nsamples != self.S:
             self.computeScaleFactor()
             vbMng(self, "INIT", "Starting computation of snapshots.", 5)
             self.mus = self.sampler.generatePoints(self.S)
             self.samplingEngine.iterSample(self.mus)
             vbMng(self, "DEL", "Done computing snapshots.", 5)
 
     def computeScaleFactor(self):
         """Compute parameter rescaling factor."""
         RROMPyAssert(self._mode, message = "Cannot compute rescaling factor.")
         self.scaleFactor = .5 * np.abs(
                                 self.muBounds[0] ** self.HFEngine.rescalingExp
                               - self.muBounds[1] ** self.HFEngine.rescalingExp)
diff --git a/rrompy/reduction_methods/standard/rational_interpolant.py b/rrompy/reduction_methods/standard/rational_interpolant.py
index c515f92..a5f9743 100644
--- a/rrompy/reduction_methods/standard/rational_interpolant.py
+++ b/rrompy/reduction_methods/standard/rational_interpolant.py
@@ -1,568 +1,569 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from rrompy.reduction_methods.base import checkRobustTolerance
 from .generic_standard_approximant import GenericStandardApproximant
 from rrompy.utilities.poly_fitting.polynomial import (
                                     polybases as ppb, polyfitname,
                                     polyvander as pvP, polyvanderTotal as pvTP,
                                     PolynomialInterpolator as PI)
 from rrompy.utilities.poly_fitting.radial_basis import (polybases as rbpb,
                                                 RadialBasisInterpolator as RBI)
 from rrompy.utilities.poly_fitting.moving_least_squares import (
                                         polybases as mlspb,
                                         MovingLeastSquaresInterpolator as MLSI)
 from rrompy.utilities.base.types import (Np1D, Np2D, HFEng, DictAny, Tuple,
                                          List, paramVal, sampList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import (multifactorial, customPInv, dot,
                                         fullDegreeN, totalDegreeN,
                                         degreeTotalToFull, fullDegreeMaxMask,
                                         totalDegreeMaxMask,
                                         nextDerivativeIndices,
                                         hashDerivativeToIdx as hashD,
                                         hashIdxToDerivative as hashI)
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPyWarning)
 
 __all__ = ['RationalInterpolant']
 
 class RationalInterpolant(GenericStandardApproximant):
     """
     ROM rational interpolant computation for parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator;
             - 'polybasis': type of polynomial basis for interpolation; defaults
                 to 'MONOMIAL';
             - 'M': degree of rational interpolant numerator; defaults to 0;
             - 'N': degree of rational interpolant denominator; defaults to 0;
             - 'polydegreetype': type of polynomial degree; defaults to 'TOTAL';
             - 'radialDirectionalWeights': radial basis weights for interpolant
                 numerator; defaults to 0, i.e. identity;
             - 'nNearestNeighbor': mumber of nearest neighbors considered in
                 numerator if polybasis allows; defaults to -1;
             - 'interpRcond': tolerance for interpolation; defaults to None;
             - 'robustTol': tolerance for robust rational denominator
                 management; defaults to 0.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots;
             - 'polybasis': type of polynomial basis for interpolation;
             - 'M': degree of rational interpolant numerator;
             - 'N': degree of rational interpolant denominator;
             - 'polydegreetype': type of polynomial degree;
             - 'radialDirectionalWeights': radial basis weights for interpolant
                 numerator;
             - 'nNearestNeighbor': mumber of nearest neighbors considered in
                 numerator if polybasis allows;
             - 'interpRcond': tolerance for interpolation via numpy.polyfit;
             - 'robustTol': tolerance for robust rational denominator
                 management.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         S: Number of solution snapshots over which current approximant is
             based upon.
         sampler: Sample point generator.
         polybasis: type of polynomial basis for interpolation.
         M: Numerator degree of approximant.
         N: Denominator degree of approximant.
         polydegreetype: Type of polynomial degree.
         radialDirectionalWeights: Radial basis weights for interpolant
             numerator.
         nNearestNeighbor: Number of nearest neighbors considered in numerator
             if polybasis allows.
         interpRcond: Tolerance for interpolation via numpy.polyfit.
         robustTol: Tolerance for robust rational denominator management.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
         Q: Numpy 1D vector containing complex coefficients of approximant
             denominator.
         P: Numpy 2D vector whose columns are FE dofs of coefficients of
             approximant numerator.
     """
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         self._addParametersToList(["polybasis", "M", "N", "polydegreetype", 
                                    "radialDirectionalWeights",
                                    "nNearestNeighbor", "interpRcond",
                                    "robustTol"],
                                   ["MONOMIAL", 0, 0, "TOTAL", 1, -1, -1, 0])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self.catchInstability = False
         self._postInit()
 
     @property
     def tModelType(self):
         from rrompy.reduction_methods.trained_model import TrainedModelRational
         return TrainedModelRational
 
     @property
     def polybasis(self):
         """Value of polybasis."""
         return self._polybasis
     @polybasis.setter
     def polybasis(self, polybasis):
         try:
             polybasis = polybasis.upper().strip().replace(" ","")
             if polybasis not in ppb + rbpb + mlspb: 
                 raise RROMPyException("Prescribed polybasis not recognized.")
             self._polybasis = polybasis
         except:
             RROMPyWarning(("Prescribed polybasis not recognized. Overriding "
                            "to 'MONOMIAL'."))
             self._polybasis = "MONOMIAL"
         self._approxParameters["polybasis"] = self.polybasis
 
     @property
     def polybasis0(self):
         if "_" in self.polybasis:
             return self.polybasis.split("_")[0]
         return self.polybasis
 
     @property
     def interpRcond(self):
         """Value of interpRcond."""
         return self._interpRcond
     @interpRcond.setter
     def interpRcond(self, interpRcond):
         self._interpRcond = interpRcond
         self._approxParameters["interpRcond"] = self.interpRcond
 
     @property
     def radialDirectionalWeights(self):
         """Value of radialDirectionalWeights."""
         return self._radialDirectionalWeights
     @radialDirectionalWeights.setter
     def radialDirectionalWeights(self, radialDirectionalWeights):
         self._radialDirectionalWeights = radialDirectionalWeights
         self._approxParameters["radialDirectionalWeights"] = (
                                                  self.radialDirectionalWeights)
 
     @property
     def nNearestNeighbor(self):
         """Value of nNearestNeighbor."""
         return self._nNearestNeighbor
     @nNearestNeighbor.setter
     def nNearestNeighbor(self, nNearestNeighbor):
         self._nNearestNeighbor = nNearestNeighbor
         self._approxParameters["nNearestNeighbor"] = self.nNearestNeighbor
 
     @property
     def M(self):
         """Value of M."""
         return self._M
     @M.setter
     def M(self, M):
         if M < 0: raise RROMPyException("M must be non-negative.")
         self._M = M
         self._approxParameters["M"] = self.M
 
     @property
     def N(self):
         """Value of N."""
         return self._N
     @N.setter
     def N(self, N):
         if N < 0: raise RROMPyException("N must be non-negative.")
         self._N = N
         self._approxParameters["N"] = self.N
 
     @property
     def polydegreetype(self):
         """Value of polydegreetype."""
         return self._polydegreetype
     @polydegreetype.setter
     def polydegreetype(self, polydegreetype):
         try:
             polydegreetype = polydegreetype.upper().strip().replace(" ","")
             if polydegreetype not in ["TOTAL", "FULL"]: 
                 raise RROMPyException(("Prescribed polydegreetype not "
                                        "recognized."))
             self._polydegreetype = polydegreetype
         except:
             RROMPyWarning(("Prescribed polydegreetype not recognized. "
                            "Overriding to 'TOTAL'."))
             self._polydegreetype = "TOTAL"
         self._approxParameters["polydegreetype"] = self.polydegreetype
 
     @property
     def robustTol(self):
         """Value of tolerance for robust rational denominator management."""
         return self._robustTol
     @robustTol.setter
     def robustTol(self, robustTol):
         if robustTol < 0.:
             RROMPyWarning(("Overriding prescribed negative robustness "
                            "tolerance to 0."))
             robustTol = 0.
         self._robustTol = robustTol
         self._approxParameters["robustTol"] = self.robustTol
 
     def resetSamples(self):
         """Reset samples."""
         super().resetSamples()
         self._musUniqueCN = None
         self._derIdxs = None
         self._reorder = None
 
     def _setupInterpolationIndices(self):
         """Setup parameters for polyvander."""
         RROMPyAssert(self._mode,
                      message = "Cannot setup interpolation indices.")
         if self._musUniqueCN is None or len(self._reorder) != len(self.mus):
             self._musUniqueCN, musIdxsTo, musIdxs, musCount = (
                             self.trainedModel.centerNormalize(self.mus).unique(
                                     return_index = True, return_inverse = True,
                                     return_counts = True))
             self._musUnique = self.mus[musIdxsTo]
             self._derIdxs = [None] * len(self._musUniqueCN)
             self._reorder = np.empty(len(musIdxs), dtype = int)
             filled = 0
             for j, cnt in enumerate(musCount):
                 self._derIdxs[j] = nextDerivativeIndices([], self.mus.shape[1],
                                                          cnt)
                 jIdx = np.nonzero(musIdxs == j)[0]
                 self._reorder[jIdx] = np.arange(filled, filled + cnt)
                 filled += cnt
 
     def _setupDenominator(self):
         """Compute rational denominator."""
         RROMPyAssert(self._mode, message = "Cannot setup denominator.")
         vbMng(self, "INIT", "Starting computation of denominator.", 7)
         while self.N > 0:
             invD, fitinv = self._computeInterpolantInverseBlocks()
             if self.POD:
                 ev, eV = self.findeveVGQR(self.samplingEngine.RPOD, invD)
             else:
                 ev, eV = self.findeveVGExplicit(self.samplingEngine.samples,
                                                 invD)
             nevBad = checkRobustTolerance(ev, self.robustTol)
             if nevBad <= 1: break
             if self.catchInstability:
                 raise RROMPyException(("Instability in denominator "
                                        "computation: eigenproblem is poorly "
                                        "conditioned."))
             RROMPyWarning(("Smallest {} eigenvalues below tolerance. Reducing "
                            "N by 1.").format(nevBad))
             self.N = self.N - 1
         if self.N <= 0:
             self._N = 0
             eV = np.ones((1, 1))
         q = PI()
         q.npar = self.npar
         q.polybasis = self.polybasis0
         if self.polydegreetype == "TOTAL":
             q.coeffs = degreeTotalToFull(tuple([self.N + 1] * self.npar),
                                          self.npar, eV[:, 0])
         else:
             q.coeffs = eV[:, 0].reshape([self.N + 1] * self.npar)
         vbMng(self, "DEL", "Done computing denominator.", 7)
         return q, fitinv
 
     def _setupNumerator(self):
         """Compute rational numerator."""
         RROMPyAssert(self._mode, message = "Cannot setup numerator.")
         vbMng(self, "INIT", "Starting computation of numerator.", 7)
         Qevaldiag = np.zeros((len(self.mus), len(self.mus)),
                              dtype = np.complex)
         verb = self.trainedModel.verbosity
         self.trainedModel.verbosity = 0
         self._setupInterpolationIndices()
         idxGlob = 0
         for j, derIdxs in enumerate(self._derIdxs):
             nder = len(derIdxs)
             idxLoc = np.arange(len(self.mus))[(self._reorder >= idxGlob)
                                             * (self._reorder < idxGlob + nder)]
             idxGlob += nder
             Qval = [0] * nder
             for der in range(nder):
                 derIdx = hashI(der, self.npar)
                 Qval[der] = (self.trainedModel.getQVal(
                                          self._musUnique[j], derIdx,
                                          scl = np.power(self.scaleFactor, -1.))
                            / multifactorial(derIdx))
             for derU, derUIdx in enumerate(derIdxs):
                 for derQ, derQIdx in enumerate(derIdxs):
                     diffIdx = [x - y for (x, y) in zip(derUIdx, derQIdx)]
                     if all([x >= 0 for x in diffIdx]):
                         diffj = hashD(diffIdx)
                         Qevaldiag[idxLoc[derU], idxLoc[derQ]] = Qval[diffj]
         if self.POD:
             Qevaldiag = Qevaldiag.dot(self.samplingEngine.RPOD.T)
         self.trainedModel.verbosity = verb
         cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
         M = copy(self.M)
         while len(self.mus) < cfun(M, self.npar): M -= 1
         if M < self.M:
             RROMPyWarning(("M too large compared to S. Reducing M by "
                            "{}").format(self.M - M))
             self.M = M
         while self.M >= 0:
             if self.polybasis in ppb:
                 p = PI()
                 wellCond, msg = p.setupByInterpolation(
                                       self._musUniqueCN, Qevaldiag, self.M, 
                                       self.polybasis, self.verbosity >= 5,
                                       self.polydegreetype == "TOTAL",
                                       {"derIdxs": self._derIdxs,
                                        "reorder": self._reorder,
                                        "scl": np.power(self.scaleFactor, -1.)},
                                       {"rcond": self.interpRcond})
             elif self.polybasis in rbpb:
                 p = RBI()
                 wellCond, msg = p.setupByInterpolation(
                           self._musUniqueCN, Qevaldiag, self.M, self.polybasis,
                           self.radialDirectionalWeights, self.verbosity >= 5,
                           self.polydegreetype == "TOTAL",
                           {"derIdxs": self._derIdxs, "reorder": self._reorder,
                            "scl": np.power(self.scaleFactor, -1.),
                            "nNearestNeighbor": self.nNearestNeighbor},
                           {"rcond": self.interpRcond})
             else:# if self.polybasis in mlspb:
                 p = MLSI()
                 wellCond, msg = p.setupByInterpolation(
                           self._musUniqueCN, Qevaldiag, self.M, self.polybasis,
                           self.radialDirectionalWeights, self.verbosity >= 5,
                           self.polydegreetype == "TOTAL",
                           {"derIdxs": self._derIdxs, "reorder": self._reorder,
                            "scl": np.power(self.scaleFactor, -1.),
                            "nNearestNeighbor": self.nNearestNeighbor})
             vbMng(self, "MAIN", msg, 5)
             if wellCond: break
             if self.catchInstability:
                 raise RROMPyException(("Instability in numerator computation: "
                                        "polyfit is poorly conditioned."))
             RROMPyWarning("Polyfit is poorly conditioned. Reducing M by 1.")
             self.M = self.M - 1
         if self.M < 0:
             raise RROMPyException(("Instability in computation of numerator. "
                                    "Aborting."))
         vbMng(self, "DEL", "Done computing numerator.", 7)
         return p
 
     def setupApprox(self):
         """
         Compute rational interpolant.
         SVD-based robust eigenvalue management.
         """
         if self.checkComputedApprox():
             return
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
         self.computeSnapshots()
         pMat = self.samplingEngine.samples.data
-        pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
+        pMatEff = dot(self.HFEngine.C, pMat) if self.approx_state else pMat
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp}
             self.trainedModel.data = self.initializeModelData(datadict)[0]
         else:
             self.trainedModel = self.trainedModel
             self.trainedModel.data.projMat = copy(pMatEff)
         if self.N > 0:
             Q = self._setupDenominator()[0]
         else:
             Q = PI()
             Q.coeffs = np.ones(tuple([1] * self.npar), dtype = np.complex)
             Q.npar = self.npar
             Q.polybasis = self.polybasis
         self.trainedModel.data.mus = copy(self.mus)
         self.trainedModel.data.Q = Q
         self.trainedModel.data.P = self._setupNumerator()
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
 
     def _computeInterpolantInverseBlocks(self) -> Tuple[List[Np2D], Np2D]:
         """
         Compute inverse factors for minimal interpolant target functional.
         """
         RROMPyAssert(self._mode, message = "Cannot solve eigenvalue problem.")
         self._setupInterpolationIndices()
         cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
         N = copy(self.N)
         while len(self.mus) < cfun(N, self.npar): N -= 1
         if N < self.N:
             RROMPyWarning(("N too large compared to S. Reducing N by "
                            "{}").format(self.N - N))
             self.N = N
         while self.N >= 0:
             if self.polydegreetype == "TOTAL":
                 TE, _, argIdxs = pvTP(self._musUniqueCN, self.N,
                                       self.polybasis0, self._derIdxs,
                                       self._reorder,
                                       scl = np.power(self.scaleFactor, -1.))
                 TE = TE[:, argIdxs]
                 idxsB = totalDegreeMaxMask(self.N, self.npar)
             else: #if self.polydegreetype == "FULL":
                 TE = pvP(self._musUniqueCN, [self.N] * self.npar,
                          self.polybasis0, self._derIdxs, self._reorder,
                          scl = np.power(self.scaleFactor, -1.))
                 idxsB = fullDegreeMaxMask(self.N, self.npar)
             fitOut = customPInv(TE, rcond = self.interpRcond, full = True)
             vbMng(self, "MAIN",
                   ("Fitting {} samples with degree {} through {}... "
                    "Conditioning of pseudoinverse system: {:.4e}.").format(
                                            TE.shape[0], self.N,
                                            polyfitname(self.polybasis0),
                                            fitOut[1][1][0] / fitOut[1][1][-1]),
                   5)
             if fitOut[1][0] == TE.shape[1]:
                 fitinv = fitOut[0][idxsB, :]
                 break
             if self.catchInstability:
                 raise RROMPyException(("Instability in denominator "
                                        "computation: polyfit is poorly "
                                        "conditioned."))
             RROMPyWarning("Polyfit is poorly conditioned. Reducing N by 1.")
             self.N = self.N - 1
         if self.polydegreetype == "TOTAL":
             TN, _, argIdxs = pvTP(self._musUniqueCN, self.N, self.polybasis0,
                                   self._derIdxs, self._reorder, 
                                   scl = np.power(self.scaleFactor, -1.))
             TN = TN[:, argIdxs]
         else: #if self.polydegreetype == "FULL":
             TN = pvP(self._musUniqueCN, [self.N] * self.npar,
                      self.polybasis0, self._derIdxs, self._reorder,
                      scl = np.power(self.scaleFactor, -1.))
         invD = [None] * (len(idxsB))
         for k in range(len(idxsB)):
             pseudoInv = np.diag(fitinv[k, :])
             idxGlob = 0
             for j, derIdxs in enumerate(self._derIdxs):
                 nder = len(derIdxs)
                 idxGlob += nder
                 if nder > 1:
                     idxLoc = np.arange(len(self.mus))[
                                               (self._reorder >= idxGlob - nder)
                                             * (self._reorder < idxGlob)]
                     invLoc = fitinv[k, idxLoc]
                     pseudoInv[np.ix_(idxLoc, idxLoc)] = 0.
                     for diffj, diffjIdx in enumerate(derIdxs):
                         for derQ, derQIdx in enumerate(derIdxs):
                             derUIdx = [x - y for (x, y) in 
                                                         zip(diffjIdx, derQIdx)]
                             if all([x >= 0 for x in derUIdx]):
                                 derU = hashD(derUIdx)
                                 pseudoInv[idxLoc[derU], idxLoc[derQ]] = (
                                                                  invLoc[diffj])
             invD[k] = dot(pseudoInv, TN)
         return invD, fitinv
 
     def findeveVGExplicit(self, sampleE:sampList,
                           invD:List[Np2D]) -> Tuple[Np1D, Np2D]:
         """
         Compute explicitly eigenvalues and eigenvectors of rational denominator
             matrix.
         """
         RROMPyAssert(self._mode, message = "Cannot solve eigenvalue problem.")
         nEnd = invD[0].shape[1]
         eWidth = len(invD)
         vbMng(self, "INIT", "Building gramian matrix.", 10)
         gramian = self.HFEngine.innerProduct(sampleE, sampleE,
-                                             is_state = self.force_state)
+                                             is_state = self.approx_state)
         G = np.zeros((nEnd, nEnd), dtype = np.complex)
         for k in range(eWidth):
              G += dot(dot(gramian, invD[k]).T, invD[k].conj()).T
         vbMng(self, "DEL", "Done building gramian.", 10)
         vbMng(self, "INIT", "Solving eigenvalue problem for gramian matrix.",
               7)
         ev, eV = np.linalg.eigh(G)
         vbMng(self, "MAIN",
               ("Solved eigenvalue problem of size {} with condition number "
                "{:.4e}.").format(nEnd, ev[-1] / ev[0]), 5)
         vbMng(self, "DEL", "Done solving eigenvalue problem.", 7)
         return ev, eV
 
     def findeveVGQR(self, RPODE:Np2D, invD:List[Np2D]) -> Tuple[Np1D, Np2D]:
         """
         Compute eigenvalues and eigenvectors of rational denominator matrix
             through SVD of R factor.
         """
         RROMPyAssert(self._mode, message = "Cannot solve eigenvalue problem.")
         nEnd = invD[0].shape[1]
         S = RPODE.shape[0]
         eWidth = len(invD)
         vbMng(self, "INIT", "Building half-gramian matrix stack.", 10)
         Rstack = np.zeros((S * eWidth, nEnd), dtype = np.complex)
         for k in range(eWidth):
             Rstack[k * S : (k + 1) * S, :] = dot(RPODE, invD[k])
         vbMng(self, "DEL", "Done building half-gramian.", 10)
         vbMng(self, "INIT", "Solving svd for square root of gramian matrix.",
               7)
         _, s, eV = np.linalg.svd(Rstack, full_matrices = False)
         ev = s[::-1]
         eV = eV[::-1, :].T.conj()
         vbMng(self, "MAIN", 
               ("Solved svd problem of size {} x {} with condition number "
                "{:.4e}.").format(*Rstack.shape, s[0] / s[-1]), 5)
         vbMng(self, "DEL", "Done solving svd.", 7)
         return ev, eV
 
     def getResidues(self, *args, **kwargs) -> Np1D:
         """
         Obtain approximant residues.
 
         Returns:
             Matrix with residues as columns.
         """
         return self.trainedModel.getResidues(*args, **kwargs)
diff --git a/rrompy/reduction_methods/standard/rational_moving_least_squares.py b/rrompy/reduction_methods/standard/rational_moving_least_squares.py
index 4093a9b..4fc7629 100644
--- a/rrompy/reduction_methods/standard/rational_moving_least_squares.py
+++ b/rrompy/reduction_methods/standard/rational_moving_least_squares.py
@@ -1,311 +1,312 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from .rational_interpolant import RationalInterpolant
 from rrompy.utilities.poly_fitting.polynomial import (polybases as ppb,
                                                       polyvander as pvP,
                                                       polyvanderTotal as pvTP)
 from rrompy.utilities.base.types import Np2D, HFEng, DictAny, paramVal
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import (fullDegreeMaxMask, totalDegreeMaxMask,
                                         dot)
 from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
                                                 RROMPyWarning)
 
 __all__ = ['RationalMovingLeastSquares']
 
 class RationalMovingLeastSquares(RationalInterpolant):
     """
     ROM rational moving LS interpolant computation for parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator;
             - 'polybasis': type of polynomial basis for interpolation; defaults
                 to 'MONOMIAL';
             - 'M': degree of rational interpolant numerator; defaults to 0;
             - 'N': degree of rational interpolant denominator; defaults to 0;
             - 'polydegreetype': type of polynomial degree; defaults to 'TOTAL';
             - 'radialBasis': numerator radial basis type; defaults to
                 'GAUSSIAN';
             - 'radialDirectionalWeights': radial basis weights for interpolant
                 numerator; defaults to 0, i.e. identity;
             - 'nNearestNeighbor': number of nearest neighbors considered in 
                 numerator if radialBasis allows; defaults to -1;
             - 'radialBasisDen': denominator radial basis type; defaults to
                 'GAUSSIAN';
             - 'radialDirectionalWeightsDen': radial basis weights for
                 interpolant denominator; defaults to 0, i.e. identity;
             - 'nNearestNeighborDen': number of nearest neighbors considered in
                 denominator if radialBasisDen allows; defaults to -1;
             - 'interpRcond': tolerance for interpolation; defaults to None;
             - 'robustTol': tolerance for robust rational denominator
                 management; defaults to 0.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults to False.
+        approx_state(optional): Whether to approximate state. Defaults to
+            False.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots;
             - 'polybasis': type of polynomial basis for interpolation;
             - 'M': degree of rational interpolant numerator;
             - 'N': degree of rational interpolant denominator;
             - 'polydegreetype': type of polynomial degree;
             - 'radialBasis': numerator radial basis type;
             - 'radialDirectionalWeights': radial basis weights for interpolant
                 numerator;
             - 'nNearestNeighbor': number of nearest neighbors considered in
                 numerator if radialBasis allows;
             - 'radialBasisDen': denominator radial basis type;
             - 'radialDirectionalWeightsDen': radial basis weights for
                 interpolant denominator;
             - 'nNearestNeighborDen': number of nearest neighbors considered in
                 denominator if radialBasisDen allows;
             - 'interpRcond': tolerance for interpolation via numpy.polyfit;
             - 'robustTol': tolerance for robust rational denominator
                 management.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator.
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         S: Number of solution snapshots over which current approximant is
             based upon.
         sampler: Sample point generator.
         polybasis: type of polynomial basis for interpolation.
         M: Numerator degree of approximant.
         N: Denominator degree of approximant.
         polydegreetype: Type of polynomial degree.
         radialBasis: Numerator radial basis type.
         radialDirectionalWeights: Radial basis weights for interpolant
             numerator.
         nNearestNeighbor: Number of nearest neighbors considered in numerator
             if radialBasis allows.
         radialBasisDen: Denominator radial basis type.
         radialDirectionalWeightsDen: Radial basis weights for interpolant
             denominator.
         nNearestNeighborDen: Number of nearest neighbors considered in
             denominator if radialBasisDen allows.
         interpRcond: Tolerance for interpolation via numpy.polyfit.
         robustTol: Tolerance for robust rational denominator management.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
         Q: Numpy 1D vector containing complex coefficients of approximant
             denominator.
         P: Numpy 2D vector whose columns are FE dofs of coefficients of
             approximant numerator.
     """
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = False,
+                 approxParameters : DictAny = {}, approx_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
         self._preInit()
         self._addParametersToList(["radialBasis", "radialBasisDen",
                                    "radialDirectionalWeightsDen",
                                    "nNearestNeighborDen"],
                                   ["GAUSSIAN", "GAUSSIAN", 1, -1])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = force_state, verbosity = verbosity,
+                         approx_state = approx_state, verbosity = verbosity,
                          timestamp = timestamp)
         self.catchInstability = False
         self._postInit()
 
     @property
     def tModelType(self):
         from rrompy.reduction_methods.trained_model import \
                                                         TrainedModelRationalMLS
         return TrainedModelRationalMLS
 
     @property
     def polybasis(self):
         """Value of polybasis."""
         return self._polybasis
     @polybasis.setter
     def polybasis(self, polybasis):
         try:
             polybasis = polybasis.upper().strip().replace(" ","")
             if polybasis not in ppb:
                 raise RROMPyException("Prescribed polybasis not recognized.")
             self._polybasis = polybasis
         except:
             RROMPyWarning(("Prescribed polybasis not recognized. Overriding "
                            "to 'MONOMIAL'."))
             self._polybasis = "MONOMIAL"
         self._approxParameters["polybasis"] = self.polybasis
 
     @property
     def radialBasis(self):
         """Value of radialBasis."""
         return self._radialBasis
     @radialBasis.setter
     def radialBasis(self, radialBasis):
         self._radialBasis = radialBasis
         self._approxParameters["radialBasis"] = self.radialBasis
 
     @property
     def radialBasisDen(self):
         """Value of radialBasisDen."""
         return self._radialBasisDen
     @radialBasisDen.setter
     def radialBasisDen(self, radialBasisDen):
         self._radialBasisDen = radialBasisDen
         self._approxParameters["radialBasisDen"] = self.radialBasisDen
 
     @property
     def radialDirectionalWeightsDen(self):
         """Value of radialDirectionalWeightsDen."""
         return self._radialDirectionalWeightsDen
     @radialDirectionalWeightsDen.setter
     def radialDirectionalWeightsDen(self, radialDirectionalWeightsDen):
         self._radialDirectionalWeightsDen = radialDirectionalWeightsDen
         self._approxParameters["radialDirectionalWeightsDen"] = (
                                               self.radialDirectionalWeightsDen)
 
     @property
     def nNearestNeighborDen(self):
         """Value of nNearestNeighborDen."""
         return self._nNearestNeighborDen
     @nNearestNeighborDen.setter
     def nNearestNeighborDen(self, nNearestNeighborDen):
         self._nNearestNeighborDen = nNearestNeighborDen
         self._approxParameters["nNearestNeighborDen"] = (
                                                       self.nNearestNeighborDen)
 
     def _setupDenominator(self) -> Np2D:
         """Compute rational denominator."""
         RROMPyAssert(self._mode, message = "Cannot setup denominator.")
         vbMng(self, "INIT",
               "Starting computation of denominator-related blocks.", 7)
         self._setupInterpolationIndices()
         if self.polydegreetype == "TOTAL":
             TN, _, argIdxs = pvTP(self._musUniqueCN, self.N, self.polybasis0,
                                   self._derIdxs, self._reorder, 
                                   scl = np.power(self.scaleFactor, -1.))
             TN = TN[:, argIdxs]
         else: #if self.polydegreetype == "FULL":
             TN = pvP(self._musUniqueCN, [self.N] * self.npar,
                      self.polybasis0, self._derIdxs, self._reorder,
                      scl = np.power(self.scaleFactor, -1.))
         TNTen = np.zeros((self.S, self.S, TN.shape[1]), dtype = TN.dtype)
         TNTen[np.arange(self.S), np.arange(self.S)] = TN
         if self.POD: TNTen = dot(self.samplingEngine.RPOD, TNTen)
         vbMng(self, "DEL", "Done computing denominator-related blocks.", 7)
         return TN, TNTen
 
     def _setupNumerator(self) -> Np2D:
         """Compute rational numerator."""
         RROMPyAssert(self._mode, message = "Cannot setup numerator.")
         vbMng(self, "INIT",
               "Starting computation of denominator-related blocks.", 7)
         self._setupInterpolationIndices()
         if self.polydegreetype == "TOTAL":
             TM, _, argIdxs = pvTP(self._musUniqueCN, self.M, self.polybasis0,
                                   self._derIdxs, self._reorder, 
                                   scl = np.power(self.scaleFactor, -1.))
             TM = TM[:, argIdxs]
         else: #if self.polydegreetype == "FULL":
             TM = pvP(self._musUniqueCN, [self.M] * self.npar,
                      self.polybasis0, self._derIdxs, self._reorder,
                      scl = np.power(self.scaleFactor, -1.))
         vbMng(self, "DEL", "Done computing denominator-related blocks.", 7)
         return TM
 
     def setupApprox(self):
         """
         Compute rational interpolant.
         SVD-based robust eigenvalue management.
         """
         if self.checkComputedApprox():
             return
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
         self.computeSnapshots()
         pMat = self.samplingEngine.samples.data
-        pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
+        pMatEff = dot(self.HFEngine.C, pMat) if self.approx_state else pMat
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp}
             data = self.initializeModelData(datadict)[0]
             data.POD = self.POD
             data.polybasis = self.polybasis
             data.polydegreetype = self.polydegreetype
             data.radialBasis = self.radialBasis
             data.radialWeights = self.radialDirectionalWeights
             data.nNearestNeighbor = self.nNearestNeighbor
             data.radialBasisDen = self.radialBasisDen
             data.radialWeightsDen = self.radialDirectionalWeightsDen
             data.nNearestNeighborDen = self.nNearestNeighborDen
             data.interpRcond = self.interpRcond
             self.trainedModel.data = data
         else:
             self.trainedModel = self.trainedModel
             self.trainedModel.data.projMat = copy(pMatEff)
         if not self.POD:
             self.trainedModel.data.gramian = self.HFEngine.innerProduct(
-                                                   self.samplingEngine.samples,
-                                                   self.samplingEngine.samples,
-                                                   is_state = self.force_state)
+                                                  self.samplingEngine.samples,
+                                                  self.samplingEngine.samples,
+                                                  is_state = self.approx_state)
         self.trainedModel.data.mus = copy(self.mus)
         self.trainedModel.data.M = self.M
         self.trainedModel.data.N = self.N
         QVan, self.trainedModel.data.QBlocks = self._setupDenominator()
         self.trainedModel.data.PVan = self._setupNumerator()
         if self.polydegreetype == "TOTAL":
             degreeMaxMask = totalDegreeMaxMask
         else: #if self.polydegreetype == "FULL":
             degreeMaxMask = fullDegreeMaxMask
         if self.N > self.M:
             self.trainedModel.data.QVan = QVan
             self.trainedModel.data.domQIdxs = degreeMaxMask(self.N, self.npar)
         else:
             self.trainedModel.data.domQIdxs = degreeMaxMask(self.M, self.npar)
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
 
diff --git a/rrompy/reduction_methods/standard/reduced_basis.py b/rrompy/reduction_methods/standard/reduced_basis.py
index 8df607e..ec07db6 100644
--- a/rrompy/reduction_methods/standard/reduced_basis.py
+++ b/rrompy/reduction_methods/standard/reduced_basis.py
@@ -1,214 +1,214 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from .generic_standard_approximant import GenericStandardApproximant
 from rrompy.reduction_methods.base.reduced_basis_utils import \
                                                      projectAffineDecomposition
 from rrompy.utilities.base.types import (Np1D, Np2D, List, Tuple, DictAny,
                                          HFEng, paramVal, sampList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.numerical import dot
 from rrompy.utilities.exception_manager import (RROMPyWarning, RROMPyException,
                                                 RROMPyAssert)
 
 __all__ = ['ReducedBasis']
 
 class ReducedBasis(GenericStandardApproximant):
     """
     ROM RB approximant computation for parametric problems.
 
     Args:
         HFEngine: HF problem solver.
         mu0(optional): Default parameter. Defaults to 0.
         approxParameters(optional): Dictionary containing values for main
             parameters of approximant. Recognized keys are:
             - 'POD': whether to compute POD of snapshots; defaults to True;
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator;
             - 'R': rank for Galerkin projection; defaults to S;
             - 'PODTolerance': tolerance for snapshots POD; defaults to -1.
             Defaults to empty dict.
-        force_state(optional): Whether to approximate state. Defaults and must
+        approx_state(optional): Whether to approximate state. Defaults and must
             be True.
         verbosity(optional): Verbosity level. Defaults to 10.
             
     Attributes:
         HFEngine: HF problem solver.
         mu0: Default parameter.
         mus: Array of snapshot parameters.
         approxRadius: Dummy radius of approximant (i.e. distance from mu0 to
             farthest sample point).
         approxParameters: Dictionary containing values for main parameters of
             approximant. Recognized keys are in parameterList.
         parameterListSoft: Recognized keys of soft approximant parameters:
             - 'POD': whether to compute POD of snapshots.
             - 'R': rank for Galerkin projection;
             - 'PODTolerance': tolerance for snapshots POD.
         parameterListCritical: Recognized keys of critical approximant
             parameters:
             - 'S': total number of samples current approximant relies upon;
             - 'sampler': sample point generator;
-        force_state: Whether to approximate state.
+        approx_state: Whether to approximate state.
         verbosity: Verbosity level.
         POD: Whether to compute POD of snapshots.
         S: Number of solution snapshots over which current approximant is
             based upon.
         sampler: Sample point generator.
         R: Rank for Galerkin projection.
         muBounds: list of bounds for parameter values.
         samplingEngine: Sampling engine.
         uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
             sampleList.
         lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
             solution(s) as parameterList.
         uApproxReduced: Reduced approximate solution(s) with parameter(s)
             lastSolvedApprox as sampleList.
         lastSolvedApproxReduced: Parameter(s) corresponding to last computed
             reduced approximate solution(s) as parameterList.
         uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
             sampleList.
         lastSolvedApprox: Parameter(s) corresponding to last computed
             approximate solution(s) as parameterList.
         As: List of sparse matrices (in CSC format) representing coefficients
             of linear system matrix.
         bs: List of numpy vectors representing coefficients of linear system
            RHS.
         ARBs: List of sparse matrices (in CSC format) representing coefficients
             of compressed linear system matrix.
         bRBs: List of numpy vectors representing coefficients of compressed 
             linear system RHS.
     """
     
     def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
-                 approxParameters : DictAny = {}, force_state : bool = True,
+                 approxParameters : DictAny = {}, approx_state : bool = True,
                  verbosity : int = 10, timestamp : bool = True):
-        if not force_state: RROMPyWarning("Overriding force_state to True.")
+        if not approx_state: RROMPyWarning("Overriding approx_state to True.")
         self._preInit()
         self._addParametersToList(["R", "PODTolerance"], ["AUTO", -1])
         super().__init__(HFEngine = HFEngine, mu0 = mu0,
                          approxParameters = approxParameters,
-                         force_state = True, verbosity = verbosity,
+                         approx_state = True, verbosity = verbosity,
                          timestamp = timestamp)
         self._postInit()
 
     @property
     def tModelType(self):
         from rrompy.reduction_methods.trained_model import \
                                                        TrainedModelReducedBasis
         return TrainedModelReducedBasis
 
     @property
     def R(self):
         """Value of R. Its assignment may change S."""
         return self._R
     @R.setter
     def R(self, R):
         if R == "AUTO":
             if not hasattr(self, "_S"):
                 raise RROMPyException(("Cannot assign R automatically without "
                                        "S."))
             R = self.S
         if R < 0: raise RROMPyException("R must be non-negative.")
         self._R = R
         self._approxParameters["R"] = self.R
 
     @property
     def PODTolerance(self):
         """Value of PODTolerance."""
         return self._PODTolerance
     @PODTolerance.setter
     def PODTolerance(self, PODTolerance):
         self._PODTolerance = PODTolerance
         self._approxParameters["PODTolerance"] = self.PODTolerance
 
     def _setupProjectionMatrix(self):
         """Compute projection matrix."""
         RROMPyAssert(self._mode, message = "Cannot setup numerator.")
         vbMng(self, "INIT", "Starting computation of projection matrix.", 7)
         nsamples = self.samplingEngine.nsamples
         if self.R > nsamples:
             RROMPyWarning(("R too large compared to S. Reducing R by "
                            "{}").format(self.R - nsamples))
             self.R = nsamples
         if self.POD:
             U, s, _ = np.linalg.svd(self.samplingEngine.RPOD)
             s = s ** 2.
         else:
             Gramian = self.HFEngine.innerProduct(self.samplingEngine.samples,
                                                  self.samplingEngine.samples,
                                                  is_state = True)
             U, s, _ = np.linalg.svd(Gramian)
         snorm = np.cumsum(s[::-1]) / np.sum(s)
         nPODTrunc = min(nsamples - np.argmax(snorm > self.PODTolerance),
                         self.R)
         pMat = dot(self.samplingEngine.samples, U[:, : nPODTrunc])
         vbMng(self, "MAIN",
               ("Assembling {}x{} projection matrix from {} "
                "samples.").format(*(pMat.shape), nsamples), 5)
         vbMng(self, "DEL", "Done computing projection matrix.", 7)
         return pMat
     
     def setupApprox(self):
         """Compute RB projection matrix."""
         if self.checkComputedApprox():
             return
         RROMPyAssert(self._mode, message = "Cannot setup approximant.")
         vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
         self.computeSnapshots()
         pMat = self._setupProjectionMatrix().data
         pMatEff = dot(self.HFEngine.C, pMat)
         if self.trainedModel is None:
             self.trainedModel = self.tModelType()
             self.trainedModel.verbosity = self.verbosity
             self.trainedModel.timestamp = self.timestamp
             datadict = {"mu0": self.mu0, "projMat": pMatEff,
                         "scaleFactor": self.scaleFactor,
                         "rescalingExp": self.HFEngine.rescalingExp}
             data = self.initializeModelData(datadict)[0]
             data.affinePoly = self.HFEngine.affinePoly
             data.thAs, data.thbs = self.HFEngine.thAs, self.HFEngine.thbs
             self.trainedModel.data = data
         else:
             self.trainedModel = self.trainedModel
             self.trainedModel.data.projMat = copy(pMatEff)
         self.trainedModel.data.mus = copy(self.mus)
         ARBs, bRBs = self.assembleReducedSystem(pMat)
         self.trainedModel.data.ARBs = ARBs
         self.trainedModel.data.bRBs = bRBs
         self.trainedModel.data.approxParameters = copy(self.approxParameters)
         vbMng(self, "DEL", "Done setting up approximant.", 5)
 
     def assembleReducedSystem(self, pMat : sampList = None,
                               pMatOld : sampList = None)\
                                               -> Tuple[List[Np2D], List[Np1D]]:
         """Build affine blocks of RB linear system through projections."""
         if pMat is None:
             self.setupApprox()
             ARBs = self.trainedModel.data.ARBs
             bRBs = self.trainedModel.data.bRBs
         else:
             vbMng(self, "INIT", "Projecting affine terms of HF model.", 10)
             ARBsOld = None if pMatOld is None else self.trainedModel.data.ARBs
             bRBsOld = None if pMatOld is None else self.trainedModel.data.bRBs
             ARBs, bRBs = projectAffineDecomposition(self.HFEngine.As,
                                                     self.HFEngine.bs, pMat,
                                                     ARBsOld, bRBsOld, pMatOld)
             vbMng(self, "DEL", "Done projecting affine terms.", 10)
         return ARBs, bRBs
diff --git a/rrompy/sampling/base/sampling_engine_base.py b/rrompy/sampling/base/sampling_engine_base.py
index 63a25fb..d34c933 100644
--- a/rrompy/sampling/base/sampling_engine_base.py
+++ b/rrompy/sampling/base/sampling_engine_base.py
@@ -1,206 +1,206 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from rrompy.utilities.base.types import (Np1D, HFEng, List, strLst, paramVal,
                                          paramList, sampList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.exception_manager import RROMPyWarning
 from rrompy.parameter import (emptyParameterList, checkParameter,
                               checkParameterList)
 from rrompy.sampling import emptySampleList
 
 __all__ = ['SamplingEngineBase']
 
 class SamplingEngineBase:
     """HERE"""
 
-    def __init__(self, HFEngine:HFEng, force_state : bool = False,
+    def __init__(self, HFEngine:HFEng, sample_state : bool = False,
                  verbosity : int = 10, timestamp : bool = True):
-        self.force_state = force_state
+        self.sample_state = sample_state
         self.verbosity = verbosity
         self.timestamp = timestamp
         vbMng(self, "INIT",
               "Initializing sampling engine of type {}.".format(self.name()),
               10)
         self.HFEngine = HFEngine
         vbMng(self, "DEL", "Done initializing sampling engine.", 10)
             
     def name(self) -> str:
         return self.__class__.__name__
         
     def __str__(self) -> str:
         return self.name()
 
     def __repr__(self) -> str:
         return self.__str__() + " at " + hex(id(self))
 
     def resetHistory(self):
         self.samples = emptySampleList()
         self.nsamples = 0
         self.mus = emptyParameterList()
         self._derIdxs = []
         
     def popSample(self):
         if hasattr(self, "nsamples") and self.nsamples > 1:
             if self.samples.shape[1] > self.nsamples:
                 RROMPyWarning(("More than 'nsamples' memory allocated for "
                                "samples. Popping empty sample column."))
                 self.nsamples += 1
             self.nsamples -= 1
             self.samples.pop()
             self.mus.pop()
         else:
             self.resetHistory()
         
     def preallocateSamples(self, u:sampList, mu:paramVal, n:int):
         self.samples.reset((u.shape[0], n), u.dtype)
         self.samples[0] = u
         mu = checkParameter(mu, self.HFEngine.npar)
         self.mus.reset((n, self.HFEngine.npar))
         self.mus[0] = mu[0]
 
     @property
     def HFEngine(self):
         """Value of HFEngine. Its assignment resets history."""
         return self._HFEngine
     @HFEngine.setter
     def HFEngine(self, HFEngine):
         self._HFEngine = HFEngine
         self.resetHistory()
     
     def solveLS(self, mu : paramList = [], RHS : sampList = None) -> sampList:
         """
         Solve linear system.
 
         Args:
             mu: Parameter value.
         
         Returns:
             Solution of system.
         """
         mu = checkParameterList(mu, self.HFEngine.npar)[0]
         vbMng(self, "INIT", "Solving HF model for mu = {}.".format(mu), 15)
-        u = self.HFEngine.solve(mu, RHS, force_state = self.force_state,
+        u = self.HFEngine.solve(mu, RHS, return_state = self.sample_state,
                                 verbose = (self.verbosity >= 20))
         vbMng(self, "DEL", "Done solving HF model.", 15)
         return u
 
     def plotSamples(self, warping : List[callable] = None, name : str = "u",
                     save : str = None, what : strLst = 'all',
                     saveFormat : str = "eps", saveDPI : int = 100,
                     show : bool = True, plotArgs : dict = {},
                     **figspecs) -> List[str]:
         """
         Do some nice plots of the samples.
 
         Args:
             warping(optional): Domain warping functions.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             plotArgs(optional): Optional arguments for fen/pyplot.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
 
         Returns:
             Output filenames.
         """
         filesOut = [None] * self.nsamples
         for j in range(self.nsamples):
             filesOut[j] = self.HFEngine.plot(self.samples[j], warping, False,
                                              "{}_{}".format(name, j), save,
                                              what, saveFormat, saveDPI, show,
                                              plotArgs, **figspecs)
         if filesOut[0] is None: return None
         return filesOut
 
     def outParaviewSamples(self, name : str = "u", folders : bool = True,
                            filename : str = "out", times : Np1D = None,
                            what : strLst = 'all', forceNewFile : bool = True,
                            filePW = None) -> List[str]:
         """
         Output samples to ParaView file.
 
         Args:
             name(optional): Base name to be used for data output.
             folders(optional): Whether to split output in folders.
             filename(optional): Name of output file.
             times(optional): Timestamps.
             what(optional): Which plots to do. If list, can contain 'MESH',
                 'ABS', 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard
                 'ALL'. Defaults to 'ALL'.
             forceNewFile(optional): Whether to create new output file.
             filePW(optional): Fenics File entity (for time series).
 
         Returns:
             Output filenames.
         """
         if times is None: times = [0.] * self.nsamples
         filesOut = [None] * self.nsamples
         for j in range(self.nsamples):
             filesOut[j] = self.HFEngine.outParaview(self.samples[j], None,
                                                 False, "{}_{}".format(name, j),
                                                 "{}_{}".format(filename, j),
                                                 times[j], what, forceNewFile,
                                                 folders, filePW)
         if filesOut[0] is None: return None
         return filesOut
 
     def outParaviewTimeDomainSamples(self, omegas : Np1D = None,
                                      timeFinal : Np1D = None, 
                                      periodResolution : int = 20,
                                      name : str = "u", folders : bool = True,
                                      filename : str = "out",
                                      forceNewFile : bool = True) -> List[str]:
         """
         Output samples to ParaView file, converted to time domain.
 
         Args:
             omegas(optional): frequencies.
             timeFinal(optional): final time of simulation.
             periodResolution(optional): number of time steps per period.
             name(optional): Base name to be used for data output.
             folders(optional): Whether to split output in folders.
             filename(optional): Name of output file.
             forceNewFile(optional): Whether to create new output file.
 
         Returns:
             Output filename.
         """
         if omegas is None: omegas = np.real(self.mus)
         if not isinstance(timeFinal, (list, tuple,)):
             timeFinal = [timeFinal] * self.nsamples
         filesOut = [None] * self.nsamples
         for j in range(self.nsamples):
             filesOut[j] = self.HFEngine.outParaviewTimeDomain(self.samples[j],
                                                    omegas[j], None, False,
                                                    timeFinal[j],
                                                    periodResolution,
                                                    "{}_{}".format(name, j),
                                                    "{}_{}".format(filename, j),
                                                    forceNewFile, folders)
         if filesOut[0] is None: return None
         return filesOut
diff --git a/rrompy/sampling/base/sampling_engine_base_pivoted.py b/rrompy/sampling/base/sampling_engine_base_pivoted.py
index b5358b5..2df5180 100644
--- a/rrompy/sampling/base/sampling_engine_base_pivoted.py
+++ b/rrompy/sampling/base/sampling_engine_base_pivoted.py
@@ -1,233 +1,233 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from rrompy.utilities.base.types import (Np1D, HFEng, List, ListAny, strLst,
                                          paramVal, paramList, sampList)
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.exception_manager import RROMPyWarning
 from rrompy.parameter import (emptyParameterList, checkParameter,
                               checkParameterList)
 from rrompy.sampling import emptySampleList
 from .sampling_engine_base import SamplingEngineBase
 
 __all__ = ['SamplingEngineBasePivoted']
 
 class SamplingEngineBasePivoted(SamplingEngineBase):
     """HERE"""
 
     def __init__(self, HFEngine:HFEng, directionPivot:ListAny,
-                 force_state : bool = False, verbosity : int = 10,
+                 sample_state : bool = False, verbosity : int = 10,
                  timestamp : bool = True):
-        super().__init__(HFEngine, force_state, verbosity, timestamp)
+        super().__init__(HFEngine, sample_state, verbosity, timestamp)
         self.directionPivot = directionPivot
         self.HFEngineMarginalized = None
         self.resetHistory()
             
     @property
     def directionMarginal(self):
         return tuple([x for x in range(self.HFEngine.npar) \
                                               if x not in self.directionPivot])
 
     @property
     def nPivot(self):
         return len(self.directionPivot)
     
     @property
     def nMarginal(self):
         return len(self.directionMarginal)
     
     @property
     def nsamplesTot(self):
         return np.sum(self.nsamples)
     
     def resetHistory(self, j : int = 1):
         self.samples = [emptySampleList() for _ in range(j)]
         self.nsamples = [0] * j
         self.mus = [emptyParameterList() for _ in range(j)]
         self._derIdxs = [[] for _ in range(j)]
         
     def popSample(self, j:int):
         if hasattr(self, "nsamples") and self.nsamples[j] > 1:
             if self.samples[j].shape[1] > self.nsamples[j]:
                 RROMPyWarning(("More than 'nsamples' memory allocated for "
                                "samples. Popping empty sample column."))
                 self.nsamples[j] += 1
             self.nsamples[j] -= 1
             self.samples[j].pop()
             self.mus[j].pop()
         else:
             self.resetHistory()
         
     def preallocateSamples(self, u:sampList, mu:paramVal, n:int, j:int):
         self.samples[j].reset((u.shape[0], n), u.dtype)
         self.samples[j][0] = u
         mu = checkParameter(mu, self.nPivot)
         self.mus[j].reset((n, self.nPivot))
         self.mus[j][0] = mu[0]
 
     def coalesceSamples(self):
         self.samplesCoalesced = emptySampleList()
         self.samplesCoalesced.reset((self.samples[0].shape[0],
                                      np.sum([samp.shape[1] \
                                                    for samp in self.samples])),
                                     self.samples[0].dtype)
         run_idx = 0
         for samp in self.samples:
             slen = samp.shape[1]
             self.samplesCoalesced.data[:, run_idx : run_idx + slen] = samp.data
             run_idx += slen
 
     def solveLS(self, mu : paramList = [], RHS : sampList = None) -> sampList:
         """
         Solve linear system.
 
         Args:
             mu: Parameter value.
         
         Returns:
             Solution of system.
         """
         mu = checkParameterList(mu, self.nPivot)[0]
         vbMng(self, "INIT",
               ("Solving HF model for muPivot = {} and muMarginal = "
                "{}.").format(mu, self.HFEngineMarginalized.muFixed), 15)
         u = self.HFEngineMarginalized.solve(mu, RHS,
-                                            force_state = self.force_state,
+                                            return_state = self.sample_state,
                                             verbose = (self.verbosity >= 20))
         vbMng(self, "DEL", "Done solving HF model.", 15)
         return u
 
     def plotSamples(self, warping : List[callable] = None, name : str = "u",
                     save : str = None, what : strLst = 'all',
                     saveFormat : str = "eps", saveDPI : int = 100,
                     show : bool = True, plotArgs : dict = {},
                     **figspecs) -> List[str]:
         """
         Do some nice plots of the samples.
 
         Args:
             warping(optional): Domain warping functions.
             name(optional): Name to be shown as title of the plots. Defaults to
                 'u'.
             save(optional): Where to save plot(s). Defaults to None, i.e. no
                 saving.
             what(optional): Which plots to do. If list, can contain 'ABS',
                 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
                 Defaults to 'ALL'.
             saveFormat(optional): Format for saved plot(s). Defaults to "eps".
             saveDPI(optional): DPI for saved plot(s). Defaults to 100.
             show(optional): Whether to show figure. Defaults to True.
             plotArgs(optional): Optional arguments for fen/pyplot.
             figspecs(optional key args): Optional arguments for matplotlib
                 figure creation.
 
         Returns:
             Output filenames.
         """
         filesOut = []
         for i in range(len(self.nsamples)):
             filesOuti = [None] * self.nsamples[i]
             for j in range(self.nsamples[i]):
                 filesOuti[j] = self.HFEngine.plot(self.samples[i][j],
                                                  warping, False,
                                                  "{}_{}_{}".format(name, i, j),
                                                  save, what, saveFormat,
                                                  saveDPI, show, plotArgs,
                                                  **figspecs)
             filesOut += [filesOuti]
         if filesOut[0][0] is None: return None
         return filesOut
 
     def outParaviewSamples(self, name : str = "u", folders : bool = True,
                            filename : str = "out", times : Np1D = None,
                            what : strLst = 'all', forceNewFile : bool = True,
                            filePW = None) -> List[str]:
         """
         Output samples to ParaView file.
 
         Args:
             name(optional): Base name to be used for data output.
             folders(optional): Whether to split output in folders.
             filename(optional): Name of output file.
             times(optional): Timestamps.
             what(optional): Which plots to do. If list, can contain 'MESH',
                 'ABS', 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard
                 'ALL'. Defaults to 'ALL'.
             forceNewFile(optional): Whether to create new output file.
             filePW(optional): Fenics File entity (for time series).
 
         Returns:
             Output filenames.
         """
         if times is None: times = [[0.] * self.nsamples[i] \
                                             for i in range(len(self.nsamples))]
         filesOut = []
         for i in range(len(self.nsamples)):
             filesOuti = [None] * self.nsamples[i]
             for j in range(self.nsamples[i]):
                 filesOuti[j] = self.HFEngine.outParaview(
                                              self.samples[i][j], None, False,
                                              "{}_{}_{}".format(name, i, j),
                                              "{}_{}_{}".format(filename, i, j),
                                              times[i][j], what, forceNewFile,
                                              folders, filePW)
             filesOut += [filesOuti]
         if filesOut[0][0] is None: return None
         return filesOut
 
     def outParaviewTimeDomainSamples(self, omegas : Np1D = None,
                                      timeFinal : Np1D = None, 
                                      periodResolution : int = 20,
                                      name : str = "u", folders : bool = True,
                                      filename : str = "out",
                                      forceNewFile : bool = True) -> List[str]:
         """
         Output samples to ParaView file, converted to time domain.
 
         Args:
             omegas(optional): frequencies.
             timeFinal(optional): final time of simulation.
             periodResolution(optional): number of time steps per period.
             name(optional): Base name to be used for data output.
             folders(optional): Whether to split output in folders.
             filename(optional): Name of output file.
             forceNewFile(optional): Whether to create new output file.
 
         Returns:
             Output filenames.
         """
         if omegas is None: omegas = [[np.real(self.mus[i])] \
                                             for i in range(len(self.nsamples))]
         if not isinstance(timeFinal, (list, tuple,)):
             timeFinal = [[timeFinal] * self.nsamples[i] \
                                             for i in range(len(self.nsamples))]
         filesOut = []
         for i in range(len(self.nsamples)):
             filesOuti = [None] * self.nsamples[i]
             for j in range(self.nsamples[i]):
                 filesOuti[j] = self.HFEngine.outParaviewTimeDomain(
                                              self.samples[i][j], omegas[i][j],
                                              None, False, timeFinal[i][j],
                                              periodResolution,
                                              "{}_{}_{}".format(name, i, j),
                                              "{}_{}_{}".format(filename, i, j),
                                              forceNewFile, folders)
             filesOut += [filesOuti]
         if filesOut[0][0] is None: return None
         return filesOut
diff --git a/rrompy/sampling/pivoted/sampling_engine_pivoted.py b/rrompy/sampling/pivoted/sampling_engine_pivoted.py
index bc2012c..343fa3c 100644
--- a/rrompy/sampling/pivoted/sampling_engine_pivoted.py
+++ b/rrompy/sampling/pivoted/sampling_engine_pivoted.py
@@ -1,131 +1,131 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from rrompy.sampling.base.sampling_engine_base_pivoted import (
                                                      SamplingEngineBasePivoted)
 from rrompy.hfengines.base import MarginalProxyEngine
 from rrompy.utilities.base.types import Np1D, paramVal, paramList, sampList
 from rrompy.utilities.base import verbosityManager as vbMng, freepar as fp
 from rrompy.utilities.exception_manager import RROMPyException
 from rrompy.utilities.numerical import nextDerivativeIndices, dot
 from rrompy.parameter import checkParameter, checkParameterList
 from rrompy.sampling import sampleList
 
 __all__ = ['SamplingEnginePivoted']
 
 class SamplingEnginePivoted(SamplingEngineBasePivoted):
     """HERE"""
 
     def preprocesssamples(self, idxs:Np1D, j:int) -> sampList:
         if self.samples[j] is None or len(self.samples[j]) == 0: return
         return self.samples[j](idxs)
 
     def setsample(self, u:sampList, j:int, overwrite : bool = False) -> Np1D:
         if overwrite:
             self.samples[j][self.nsamples[j]] = u
         else:
             if self.nsamples[j] == 0:
                 self.samples[j] = sampleList(u)
             else:
                 self.samples[j].append(u)
 
     def postprocessu(self, u:sampList, j:int, overwrite : bool = False):
         self.setsample(u, j, overwrite)
 
     def postprocessuBulk(self, j:int):
         pass
 
     def _getSampleConcurrence(self, mu:paramVal, j:int,
                               previous:Np1D) -> sampList:
-        if not (self.force_state or self.HFEngine.isCEye):
+        if not (self.sample_state or self.HFEngine.isCEye):
             raise RROMPyException(("Derivatives of solution with non-scalar "
                                    "C not computable."))
         if not self.HFEngine._isStateShiftZero:
             raise RROMPyException(("Derivatives of solution with non-zero "
                                    "solution shift not computable."))
         if len(previous) >= len(self._derIdxs[j]):
             self._derIdxs[j] += nextDerivativeIndices(
                                      self._derIdxs[j], self.nPivot,
                                      len(previous) + 1 - len(self._derIdxs[j]))
         derIdx = self._derIdxs[j][len(previous)]
         mu = checkParameter(mu, self.nPivot)
         samplesOld = self.preprocesssamples(previous, j)
         RHS = self.HFEngineMarginalized.b(mu, derIdx)
         for j, derP in enumerate(self._derIdxs[j][: len(previous)]):
             diffP = [x - y for (x, y) in zip(derIdx, derP)]
             if np.all([x >= 0 for x in diffP]):
                 RHS -= dot(self.HFEngineMarginalized.A(mu, diffP),
                            samplesOld[j])
         return self.solveLS(mu, RHS = RHS)
 
     def nextSample(self, mu:paramVal, j:int, overwrite : bool = False,
                    postprocess : bool = True) -> Np1D:
         mu = checkParameter(mu, self.nPivot)
         muidxs = self.mus[j].findall(mu[0])
         if len(muidxs) > 0:
             u = self._getSampleConcurrence(mu, j, np.sort(muidxs))
         else:
             u = self.solveLS(mu)
         if postprocess:
             self.postprocessu(u, j, overwrite = overwrite)
         else:
             self.setsample(u, j, overwrite)
         if overwrite:
             self.mus[j][self.nsamples[j]] = mu[0]
         else:
             self.mus[j].append(mu)
         self.nsamples[j] += 1
         return self.samples[j][self.nsamples[j] - 1]
 
     def iterSample(self, mus:paramList, musM:paramList) -> sampList:
         mus = checkParameterList(mus, self.nPivot)[0]
         musM = checkParameterList(musM, self.nMarginal)[0]
         vbMng(self, "INIT", "Starting sampling iterations.", 5)
         n = len(mus)
         m = len(musM)
         if n <= 0:
             raise RROMPyException("Number of samples must be positive.")
         if m <= 0:
             raise RROMPyException(("Number of marginal samples must be "
                                    "positive."))
         repeatedSamples = len(mus.unique()) != n
         for j in range(m):
             muMEff = [fp] * self.HFEngine.npar
             for k, x in enumerate(self.directionMarginal):
                 muMEff[x] = musM(j, k)
             self.HFEngineMarginalized = MarginalProxyEngine(self.HFEngine,
                                                             list(muMEff))
             if repeatedSamples:
                 for k in range(n):
                     vbMng(self, "MAIN",
                           "Computing sample {} / {} for marginal {} / {}."\
                                                    .format(k + 1, n, j, m), 10)
                     self.nextSample(mus[k], j, overwrite = (k > 0),
                                     postprocess = False)
                     if n > 1 and k == 0:
                         self.preallocateSamples(self.samples[j][0], mus[0],
                                                 n, j)
             else:
                 self.samples[j] = self.postprocessuBulk(self.solveLS(mus), j)
                 self.mus[j] = copy(mus)
                 self.nsamples[j] = n
             self.postprocessuBulk(j)
         vbMng(self, "DEL", "Finished sampling iterations.", 5)
         return self.samples[j]
diff --git a/rrompy/sampling/pivoted/sampling_engine_pivoted_pod.py b/rrompy/sampling/pivoted/sampling_engine_pivoted_pod.py
index 674c987..815b033 100644
--- a/rrompy/sampling/pivoted/sampling_engine_pivoted_pod.py
+++ b/rrompy/sampling/pivoted/sampling_engine_pivoted_pod.py
@@ -1,124 +1,124 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from rrompy.sampling.base.pod_engine import PODEngine
 from .sampling_engine_pivoted import SamplingEnginePivoted
 from rrompy.utilities.base.types import Np1D, paramVal, sampList
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.sampling import sampleList, emptySampleList
 
 __all__ = ['SamplingEnginePivotedPOD']
 
 class SamplingEnginePivotedPOD(SamplingEnginePivoted):
     """HERE"""
 
     def resetHistory(self, j : int = 1):
         super().resetHistory(j)
         self.samples_full = [emptySampleList() for _ in range(j)]
         self.RPOD = [np.zeros((0, 0), dtype = np.complex) for _ in range(j)]
 
     def popSample(self, j:int):
         if hasattr(self, "nsamples") and self.nsamples[j] > 1:
             self.RPOD[j] = self.RPOD[j][: -1, : -1]
             self.samples_full[j].pop()
         super().popSample(j)
 
     def coalesceSamples(self, tol : float = 1e-12):
         super().coalesceSamples()
         self.samplesCoalesced, RPODC = self.PODEngine.generalizedQR(
-                                                   self.samplesCoalesced,
-                                                   is_state = self.force_state)
+                                                  self.samplesCoalesced,
+                                                  is_state = self.sample_state)
         self.RPODCoalesced = np.zeros((self.samplesCoalesced.shape[1],
                                        self.samplesCoalesced.shape[1]),
                                       dtype = self.RPOD[0].dtype)
         self.samples_fullCoalesced = emptySampleList()
         self.samples_fullCoalesced.reset((self.samples_full[0].shape[0],
                                           self.samplesCoalesced.shape[1]),
                                          self.samples_full[0].dtype)
         ci = 0
         for j, (Rloc, samp) in enumerate(zip(self.RPOD, self.samples_full)):
             ri = 0
             Rheg = Rloc.shape[1]
             for k, Rloc2 in enumerate(self.RPOD[: j + 1]):
                 Rlen = Rloc2.shape[1]
                 self.RPODCoalesced[ri : ri + Rlen, ci : ci + Rheg] = (
                                RPODC[ri : ri + Rlen, ci : ci + Rheg].dot(Rloc))
                 ri += Rlen
             self.samples_fullCoalesced.data[:, ci : ci + Rheg] = samp.data
             ci += Rheg
         RCdiag = np.abs(np.diag(self.RPODCoalesced))
         RCdiag /= RCdiag[0]
         ntrunc = np.nonzero(RCdiag < tol)[0]
         if len(ntrunc) == 0: return
         self.samplesCoalesced.data = self.samplesCoalesced.data[:, : ntrunc[0]]
         self.RPODCoalesced = self.RPODCoalesced[: ntrunc[0], :]
 
     @property
     def HFEngine(self):
         """Value of HFEngine. Its assignment resets history."""
         return self._HFEngine
     @HFEngine.setter
     def HFEngine(self, HFEngine):
         self._HFEngine = HFEngine
         self.resetHistory()
         self.PODEngine = PODEngine(self._HFEngine)
 
     def preprocesssamples(self, idxs:Np1D, j:int) -> sampList:
         if self.samples_full[j] is None or len(self.samples_full[j]) == 0:
             return
         return self.samples_full[j](idxs)
 
     def setsample(self, u:sampList, j:int, overwrite : bool = False):
         super().setsample(u, j, overwrite)
         if overwrite:
             self.samples_full[j][self.nsamples[j]] = u
         else:
             if self.nsamples[j] == 0:
                 self.samples_full[j] = sampleList(u)
             else:
                 self.samples_full[j].append(u)
 
     def postprocessu(self, u:sampList, j:int, overwrite : bool = False):
         if overwrite:
             self.samples_full[j][self.nsamples[j]] = u
         else:
             if self.nsamples[j] == 0:
                 self.samples_full[j] = sampleList(u)
             else:
                 self.samples_full[j].append(u)
         vbMng(self, "INIT", "Starting orthogonalization.", 20)
         u, r, _ = self.PODEngine.GS(u, self.samples[j],
-                                    is_state = self.force_state)
+                                    is_state = self.sample_state)
         self.RPOD[j] = np.pad(self.RPOD[j], ((0, 1), (0, 1)), 'constant')
         self.RPOD[j][:, -1] = r
         vbMng(self, "DEL", "Done orthogonalizing.", 20)
         super().setsample(u, j, overwrite)
 
     def postprocessuBulk(self, j:int):
         vbMng(self, "INIT",
               "Starting orthogonalization for marginal no {}.".format(j), 40)
         u, self.RPOD[j] = self.PODEngine.generalizedQR(self.samples_full[j],
-                                                   is_state = self.force_state)
+                                                  is_state = self.sample_state)
         vbMng(self, "DEL", "Done orthogonalizing.", 40)
         self.samples[j] = sampleList(u)
       
     def preallocateSamples(self, u:Np1D, mu:paramVal, n:int, j:int):
         super().preallocateSamples(u, mu, n, j)
         self.samples_full[j].reset((u.shape[0], n), u.dtype)
         self.samples_full[j][0] = u
diff --git a/rrompy/sampling/standard/sampling_engine_standard.py b/rrompy/sampling/standard/sampling_engine_standard.py
index d6c0c83..82a08f1 100644
--- a/rrompy/sampling/standard/sampling_engine_standard.py
+++ b/rrompy/sampling/standard/sampling_engine_standard.py
@@ -1,115 +1,115 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 from copy import deepcopy as copy
 import numpy as np
 from rrompy.sampling.base.sampling_engine_base import SamplingEngineBase
 from rrompy.utilities.base.types import Np1D, paramVal, paramList, sampList
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.utilities.exception_manager import RROMPyException
 from rrompy.utilities.numerical import nextDerivativeIndices, dot
 from rrompy.parameter import checkParameter, checkParameterList
 from rrompy.sampling import sampleList
 
 __all__ = ['SamplingEngineStandard']
 
 class SamplingEngineStandard(SamplingEngineBase):
     """HERE"""
 
     def preprocesssamples(self, idxs:Np1D) -> sampList:
         if self.samples is None or len(self.samples) == 0: return
         return self.samples(idxs)
 
     def setsample(self, u:sampList, overwrite : bool = False):
         if overwrite:
             self.samples[self.nsamples] = u
         else:
             if self.nsamples == 0:
                 self.samples = sampleList(u)
             else:
                 self.samples.append(u)
 
     def postprocessu(self, u:sampList, overwrite : bool = False):
         self.setsample(u, overwrite)
 
     def postprocessuBulk(self):
         pass
 
     def _getSampleConcurrence(self, mu:paramVal, previous:Np1D) -> sampList:
-        if not (self.force_state or self.HFEngine.isCEye):
+        if not (self.sample_state or self.HFEngine.isCEye):
             raise RROMPyException(("Derivatives of solution with non-scalar "
                                    "C not computable."))
         if not self.HFEngine._isStateShiftZero:
             raise RROMPyException(("Derivatives of solution with non-zero "
                                    "solution shift not computable."))
         if len(previous) >= len(self._derIdxs):
             self._derIdxs += nextDerivativeIndices(self._derIdxs,
                                         self.HFEngine.npar,
                                         len(previous) + 1 - len(self._derIdxs))
         derIdx = self._derIdxs[len(previous)]
         mu = checkParameter(mu, self.HFEngine.npar)
         samplesOld = self.preprocesssamples(previous)
         RHS = self.HFEngine.b(mu, derIdx)
         for j, derP in enumerate(self._derIdxs[: len(previous)]):
             diffP = [x - y for (x, y) in zip(derIdx, derP)]
             if np.all([x >= 0 for x in diffP]):
                 RHS -= dot(self.HFEngine.A(mu, diffP), samplesOld[j])
         return self.solveLS(mu, RHS = RHS)
 
     def nextSample(self, mu : paramVal = [], overwrite : bool = False,
                    postprocess : bool = True) -> Np1D:
         mu = checkParameter(mu, self.HFEngine.npar)
         muidxs = self.mus.findall(mu[0])
         if len(muidxs) > 0:
             u = self._getSampleConcurrence(mu, np.sort(muidxs))
         else:
             u = self.solveLS(mu)
         if postprocess:
             self.postprocessu(u, overwrite = overwrite)
         else:
             self.setsample(u, overwrite)
         if overwrite:
             self.mus[self.nsamples] = mu[0]
         else:
             self.mus.append(mu)
         self.nsamples += 1
         return self.samples[self.nsamples - 1]
 
     def iterSample(self, mus:paramList) -> sampList:
         mus = checkParameterList(mus, self.HFEngine.npar)[0]
         vbMng(self, "INIT", "Starting sampling iterations.", 5)
         n = len(mus)
         if n <= 0:
             raise RROMPyException(("Number of samples must be positive."))
         self.resetHistory()
         if len(mus.unique()) != n:
             for j in range(n):
                 vbMng(self, "MAIN",
                       "Computing sample {} / {}.".format(j + 1, n), 7)
                 self.nextSample(mus[j], overwrite = (j > 0),
                                 postprocess = False)
                 if n > 1 and j == 0:
                     self.preallocateSamples(self.samples[0], mus[0], n)
         else:
             self.setsample(self.solveLS(mus), overwrite = False)
             self.mus = copy(mus)
             self.nsamples = n
         self.postprocessuBulk()
         vbMng(self, "DEL", "Finished sampling iterations.", 5)
         return self.samples
 
diff --git a/rrompy/sampling/standard/sampling_engine_standard_pod.py b/rrompy/sampling/standard/sampling_engine_standard_pod.py
index 3297fce..3c9a3b9 100644
--- a/rrompy/sampling/standard/sampling_engine_standard_pod.py
+++ b/rrompy/sampling/standard/sampling_engine_standard_pod.py
@@ -1,91 +1,91 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from rrompy.sampling.base.pod_engine import PODEngine
 from .sampling_engine_standard import SamplingEngineStandard
 from rrompy.utilities.base.types import Np1D, paramVal, sampList
 from rrompy.utilities.base import verbosityManager as vbMng
 from rrompy.sampling import sampleList, emptySampleList
 
 __all__ = ['SamplingEngineStandardPOD']
 
 class SamplingEngineStandardPOD(SamplingEngineStandard):
     """HERE"""
 
     def resetHistory(self):
         super().resetHistory()
         self.samples_full = emptySampleList()
         self.RPOD = np.zeros((0, 0), dtype = np.complex)
 
     def popSample(self):
         if hasattr(self, "nsamples") and self.nsamples > 1:
             self.RPOD = self.RPOD[: -1, : -1]
             self.samples_full.pop()
         super().popSample()
 
     @property
     def HFEngine(self):
         """Value of HFEngine. Its assignment resets history."""
         return self._HFEngine
     @HFEngine.setter
     def HFEngine(self, HFEngine):
         SamplingEngineStandard.HFEngine.fset(self, HFEngine)
         self.PODEngine = PODEngine(self._HFEngine)
 
     def preprocesssamples(self, idxs:Np1D) -> sampList:
         if self.samples_full is None or len(self.samples_full) == 0: return
         return self.samples_full(idxs)
 
     def setsample(self, u:sampList, overwrite : bool = False):
         super().setsample(u, overwrite)
         if overwrite:
             self.samples_full[self.nsamples] = u
         else:
             if self.nsamples == 0:
                 self.samples_full = sampleList(u)
             else:
                 self.samples_full.append(u)
 
     def postprocessu(self, u:sampList, overwrite : bool = False):
         if overwrite:
             self.samples_full[self.nsamples] = u
         else:
             if self.nsamples == 0:
                 self.samples_full = sampleList(u)
             else:
                 self.samples_full.append(u)
         vbMng(self, "INIT", "Starting orthogonalization.", 20)
         u, r, _ = self.PODEngine.GS(u, self.samples,
-                                    is_state = self.force_state)
+                                    is_state = self.sample_state)
         self.RPOD = np.pad(self.RPOD, ((0, 1), (0, 1)), 'constant')
         self.RPOD[:, -1] = r
         vbMng(self, "DEL", "Done orthogonalizing.", 20)
         super().setsample(u, overwrite)
         
     def postprocessuBulk(self):
         vbMng(self, "INIT", "Starting orthogonalization.", 10)
         u, self.RPOD = self.PODEngine.generalizedQR(self.samples_full,
-                                                   is_state = self.force_state)
+                                                  is_state = self.sample_state)
         vbMng(self, "DEL", "Done orthogonalizing.", 10)
         self.samples = sampleList(u)
       
     def preallocateSamples(self, u:Np1D, mu:paramVal, n:int):
         super().preallocateSamples(u, mu, n)
         self.samples_full.reset((u.shape[0], n), u.dtype)
         self.samples_full[0] = u