def setUp(self):
aSpacing=2.5
nElecs=5
surveySize = nElecs*aSpacing - aSpacing
cs = surveySize/nElecs/4
mesh = Mesh.TensorMesh([
[(cs,10, -1.3),(cs,surveySize/cs),(cs,10, 1.3)],
[(cs,3, -1.3),(cs,3,1.3)],
# [(cs,5, -1.3),(cs,10)]
],'CN')
srcList = DC.Utils.WennerSrcList(nElecs, aSpacing, in2D=True)
survey = IP.Survey(srcList)
sigma = np.ones(mesh.nC)
problem = IP.Problem3D_N(mesh, sigma=sigma)
problem.pair(survey)
mSynth = np.ones(mesh.nC)*0.1
survey.makeSyntheticData(mSynth)
# Now set up the problem to do some minimization
dmis = DataMisfit.l2_DataMisfit(survey)
reg = Regularization.Tikhonov(mesh)
opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e4)
inv = Inversion.BaseInversion(invProb)
self.inv = inv
self.reg = reg
self.p = problem
self.mesh = mesh
self.m0 = mSynth
self.survey = survey
self.dmis = dmis
def test_Problem3D_N(self):
problemDC = DC.Problem3D_N(self.mesh)
problemDC.Solver = self.Solver
problemDC.pair(self.surveyDC)
data0 = self.surveyDC.dpred(self.sigma0)
finf = problemDC.fields(self.sigmaInf)
datainf = self.surveyDC.dpred(self.sigmaInf, f=finf)
problemIP = IP.Problem3D_N(self.mesh,
sigma=self.sigmaInf,
Ainv=problemDC.Ainv,
f=finf)
problemIP.Solver = self.Solver
surveyIP = IP.Survey([self.src])
problemIP.pair(surveyIP)
data_full = data0 - datainf
data = surveyIP.dpred(self.eta)
err = np.linalg.norm(
(data - data_full) / data_full)**2 / data_full.size
if err < 0.05:
passed = True
print ">> IP forward test for Problem3D_N is passed"
else:
passed = False
print ">> IP forward test for Problem3D_N is failed"
self.assertTrue(passed)
