def createDefaultStartModel(self):
"""
"""
res = pb.getComplexData(self.data())
parCount = self.regionManager().parameterCount()
re = pg.RVector(parCount, pg.mean(pg.real(res)))
im = pg.RVector(parCount, -pg.mean(pg.imag(res)))
return pg.cat(re, im)
def createDefaultStartModel(self):
"""
"""
res = pb.getComplexData(self.data())
parCount = self.regionManager().parameterCount()
re = pg.RVector(parCount, pg.mean(pg.real(res)))
im = pg.RVector(parCount, -pg.mean(pg.imag(res)))
return pg.cat(re, im)
def createJacobian(self, model):
print('=' * 100)
if self.complex():
modelRe = model[0:int(len(model) / 2)]
modelIm = model[int(len(model) / 2):len(model)]
modelC = pg.toComplex(modelRe, modelIm)
print("Real", min(modelRe), max(modelRe))
print("Imag", min(modelIm), max(modelIm))
u = self.prepareJacobian_(modelC)
if self._J.rows() == 0:
#re(data)/re(mod) = im(data)/im(mod)
# we need a local copy until we have a gimli internal reference counter FIXTHIS
M1 = pg.RMatrix()
M2 = pg.RMatrix()
self.matrixHeap.append(M1)
self.matrixHeap.append(M2)
JRe = self._J.addMatrix(M1)
JIm = self._J.addMatrix(M2)
self._J.addMatrixEntry(JRe, 0, 0)
self._J.addMatrixEntry(JIm, 0, len(modelRe), -1.0)
self._J.addMatrixEntry(JIm, self.data().size(), 0, 1.0)
self._J.addMatrixEntry(JRe, self.data().size(), len(modelRe))
else:
self._J.clean()
k = pg.RVector(self.data()('k'))
self.data().set('k', k * 0.0 + 1.0)
dMapResponse = pb.DataMap()
dMapResponse.collect(self.electrodes(), self.solution())
respRe = dMapResponse.data(self.data(), False, False)
respIm = dMapResponse.data(self.data(), False, True)
#CVector resp(toComplex(respRe, respIm));
#RVector am(abs(resp) * dataContainer_->get("k"));
#RVector ph(-phase(resp));
print("respRe", pg.median(respRe), min(respRe), max(respRe))
print("respIm", pg.median(respIm), min(respIm), max(respIm))
JC = pg.CMatrix()
self.createJacobian_(modelC, u, JC)
for i in range(JC.rows()):
#JC[i] *= 1.0/(modelC*modelC) * k[i]
JC[i] /= (modelC * modelC) / k[i]
self._J.mat(0).copy(pg.real(JC))
self._J.mat(1).copy(pg.imag(JC))
#self.createJacobian_(modelRe*0.0+1.0, pg.real(u), self._J.mat(1))
#self.createJacobian_(modelRe*0.0+1.0, pg.imag(u), self._J.mat(2))
#self.createJacobian_(modelRe*0.0+1.0, pg.imag(u), self._J.mat(3))
sumsens0 = pg.RVector(self._J.mat(0).rows())
sumsens1 = pg.RVector(self._J.mat(0).rows())
sumsens2 = pg.RVector(self._J.mat(0).rows())
for i in range(self._J.mat(0).rows()):
#self._J.mat(0)[i] *= 1./modelRe / respRe[i]
#self._J.mat(1)[i] *= 1./modelIm / respRe[i]
#self._J.mat(2)[i] *= 1./modelRe / respIm[i]
#self._J.mat(3)[i] *= 1./modelIm / respIm[i]
#self._J.mat(0)[i] *= 1./(modelRe * modelRe) * k[i]
#self._J.mat(1)[i] *= 1./(modelRe * modelIm) * k[i]
#self._J.mat(2)[i] *= 1./(modelIm * modelRe) * k[i]
#self._J.mat(3)[i] *= 1./(modelIm * modelIm) * k[i]
sumsens0[i] = sum(self._J.mat(0)[i])
sumsens1[i] = sum(self._J.mat(1)[i])
sumsens2[i] = abs(sum(JC[i]))
print(pg.median(sumsens0), min(sumsens0), max(sumsens0))
print(pg.median(sumsens1), min(sumsens1), max(sumsens1))
print(pg.median(sumsens2), min(sumsens2), max(sumsens2))
self.data().set('k', k)
self._J.recalcMatrixSize()
else:
# self.setVerbose(True)
u = self.prepareJacobian_(model)
#J = pg.RMatrix()
if self._J.rows() == 0:
print('#' * 100)
M1 = pg.RMatrix()
Jid = self._J.addMatrix(M1)
self._J.addMatrixEntry(Jid, 0, 0)
else:
self._J.clean()
self.createJacobian_(model, u, self._J.mat(0))
self._J.recalcMatrixSize()
def createJacobian(self, model):
print('=' * 100)
if self.complex():
modelRe = model[0:int(len(model)/2)]
modelIm = model[int(len(model)/2):len(model)]
modelC = pg.toComplex(modelRe, modelIm)
print("Real", min(modelRe), max(modelRe))
print("Imag", min(modelIm), max(modelIm))
u = self.prepareJacobian_(modelC)
if self._J.rows() == 0:
#re(data)/re(mod) = im(data)/im(mod)
# we need a local copy until we have a gimli internal reference counter FIXTHIS
M1 = pg.RMatrix()
M2 = pg.RMatrix()
self.matrixHeap.append(M1)
self.matrixHeap.append(M2)
JRe = self._J.addMatrix(M1)
JIm = self._J.addMatrix(M2)
self._J.addMatrixEntry(JRe, 0, 0)
self._J.addMatrixEntry(JIm, 0, len(modelRe), -1.0)
self._J.addMatrixEntry(JIm, self.data().size(), 0, 1.0)
self._J.addMatrixEntry(JRe, self.data().size(), len(modelRe))
else:
self._J.clean()
k = pg.RVector(self.data()('k'))
self.data().set('k', k*0.0 + 1.0)
dMapResponse = pb.DataMap()
dMapResponse.collect(self.electrodes(), self.solution())
respRe = dMapResponse.data(self.data(), False, False)
respIm = dMapResponse.data(self.data(), False, True)
#CVector resp(toComplex(respRe, respIm));
#RVector am(abs(resp) * dataContainer_->get("k"));
#RVector ph(-phase(resp));
print("respRe", pg.median(respRe), min(respRe), max(respRe))
print("respIm", pg.median(respIm), min(respIm), max(respIm))
JC = pg.CMatrix()
self.createJacobian_(modelC, u, JC)
for i in range(JC.rows()):
#JC[i] *= 1.0/(modelC*modelC) * k[i]
JC[i] /= (modelC * modelC) / k[i]
self._J.mat(0).copy(pg.real(JC))
self._J.mat(1).copy(pg.imag(JC))
#self.createJacobian_(modelRe*0.0+1.0, pg.real(u), self._J.mat(1))
#self.createJacobian_(modelRe*0.0+1.0, pg.imag(u), self._J.mat(2))
#self.createJacobian_(modelRe*0.0+1.0, pg.imag(u), self._J.mat(3))
sumsens0 = pg.RVector(self._J.mat(0).rows())
sumsens1 = pg.RVector(self._J.mat(0).rows())
sumsens2 = pg.RVector(self._J.mat(0).rows())
for i in range(self._J.mat(0).rows()):
#self._J.mat(0)[i] *= 1./modelRe / respRe[i]
#self._J.mat(1)[i] *= 1./modelIm / respRe[i]
#self._J.mat(2)[i] *= 1./modelRe / respIm[i]
#self._J.mat(3)[i] *= 1./modelIm / respIm[i]
#self._J.mat(0)[i] *= 1./(modelRe * modelRe) * k[i]
#self._J.mat(1)[i] *= 1./(modelRe * modelIm) * k[i]
#self._J.mat(2)[i] *= 1./(modelIm * modelRe) * k[i]
#self._J.mat(3)[i] *= 1./(modelIm * modelIm) * k[i]
sumsens0[i] = sum(self._J.mat(0)[i])
sumsens1[i] = sum(self._J.mat(1)[i])
sumsens2[i] = abs(sum(JC[i]))
print(pg.median(sumsens0), min(sumsens0), max(sumsens0))
print(pg.median(sumsens1), min(sumsens1), max(sumsens1))
print(pg.median(sumsens2), min(sumsens2), max(sumsens2))
self.data().set('k', k)
self._J.recalcMatrixSize()
else:
# self.setVerbose(True)
u = self.prepareJacobian_(model)
#J = pg.RMatrix()
if self._J.rows() == 0:
print('#' * 100)
M1 = pg.RMatrix()
Jid = self._J.addMatrix(M1)
self._J.addMatrixEntry(Jid, 0, 0)
else:
self._J.clean()
self.createJacobian_(model, u, self._J.mat(0))
self._J.recalcMatrixSize()
