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adjoint of fields deriv now returns a tuple (so you don't call derivs wrt _u, _m independently

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This commit is contained in:
Lindsey Heagy
2016-02-14 12:28:25 -08:00
parent 64d89cfb6c
commit 8b152f3890
2 changed files with 57 additions and 22 deletions
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SimPEG/EM/FDEM/FieldsFDEM.py
+49
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@@ -89,26 +89,75 @@ class Fields(SimPEG.Problem.Fields):
return self._jPrimary(solution, srcList) + self._jSecondary(solution, srcList)
def _eDeriv(self, src, du_dm, v, adjoint = False):
"""
Total derivative of e with respect to the inversion model. Returns :math:`d\mathbf{e}/d\mathbf{m}` for forward and (:math:`d\mathbf{e}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param numpy.ndarray du_dm: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
:return: derivative times a vector (or tuple for adjoint)
"""
if getattr(self, '_eDeriv_u', None) is None or getattr(self, '_eDeriv_m', None) is None:
raise NotImplementedError ('Getting eDerivs from %s is not implemented' %self.knownFields.keys()[0])
if adjoint:
return self._eDeriv_u(src, v, adjoint), self._eDeriv_m(src, v, adjoint)
return self._eDeriv_u(src, du_dm, adjoint) + self._eDeriv_m(src, v, adjoint)
def _bDeriv(self, src, du_dm, v, adjoint = False):
"""
Total derivative of b with respect to the inversion model. Returns :math:`d\mathbf{b}/d\mathbf{m}` for forward and (:math:`d\mathbf{b}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param numpy.ndarray du_dm: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
:return: derivative times a vector (or tuple for adjoint)
"""
if getattr(self, '_bDeriv_u', None) is None or getattr(self, '_bDeriv_m', None) is None:
raise NotImplementedError ('Getting bDerivs from %s is not implemented' %self.knownFields.keys()[0])
if adjoint:
return self._bDeriv_u(src, v, adjoint), self._bDeriv_m(src, v, adjoint)
return self._bDeriv_u(src, du_dm, adjoint) + self._bDeriv_m(src, v, adjoint)
def _hDeriv(self, src, du_dm, v, adjoint = False):
"""
Total derivative of h with respect to the inversion model. Returns :math:`d\mathbf{h}/d\mathbf{m}` for forward and (:math:`d\mathbf{h}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param numpy.ndarray du_dm: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
:return: derivative times a vector (or tuple for adjoint)
"""
if getattr(self, '_hDeriv_u', None) is None or getattr(self, '_hDeriv_m', None) is None:
raise NotImplementedError ('Getting hDerivs from %s is not implemented' %self.knownFields.keys()[0])
if adjoint:
return self._hDeriv_u(src, v, adjoint), self._hDeriv_m(src, v, adjoint)
return self._hDeriv_u(src, du_dm, adjoint) + self._hDeriv_m(src, v, adjoint)
def _jDeriv(self, src, du_dm, v, adjoint = False):
"""
Total derivative of j with respect to the inversion model. Returns :math:`d\mathbf{j}/d\mathbf{m}` for forward and (:math:`d\mathbf{j}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param numpy.ndarray du_dm: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
:return: derivative times a vector (or tuple for adjoint)
"""
if getattr(self, '_jDeriv_u', None) is None or getattr(self, '_jDeriv_m', None) is None:
raise NotImplementedError ('Getting jDerivs from %s is not implemented' %self.knownFields.keys()[0])
if adjoint:
return self._jDeriv_u(src, v, adjoint), self._jDeriv_m(src, v, adjoint)
return self._jDeriv_u(src, du_dm, adjoint) + self._jDeriv_m(src, v, adjoint)