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215 lines
8.3 KiB
Python
215 lines
8.3 KiB
Python
import unittest
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from SimPEG import *
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from SimPEG.Tests.TestUtils import OrderTest, checkDerivative
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from scipy.sparse.linalg import dsolve
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from simpegFLOW import Richards
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TOL = 1E-8
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class TestModels(unittest.TestCase):
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def test_BaseHaverkamp_Theta(self):
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mesh = Mesh.TensorMesh([50])
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hav = Richards.Empirical._haverkamp_theta(mesh)
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m = np.random.randn(50)
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def wrapper(u):
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return hav.transform(u, m), hav.transformDerivU(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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def test_vangenuchten_theta(self):
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mesh = Mesh.TensorMesh([50])
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hav = Richards.Empirical._vangenuchten_theta(mesh)
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m = np.random.randn(50)
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def wrapper(u):
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return hav.transform(u, m), hav.transformDerivU(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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def test_BaseHaverkamp_k(self):
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mesh = Mesh.TensorMesh([50])
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hav = Richards.Empirical._haverkamp_k(mesh)
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m = np.random.randn(50)
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def wrapper(u):
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return hav.transform(u, m), hav.transformDerivU(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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hav = Richards.Empirical._haverkamp_k(mesh)
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u = np.random.randn(50)
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def wrapper(m):
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return hav.transform(u, m), hav.transformDerivM(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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def test_vangenuchten_k(self):
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mesh = Mesh.TensorMesh([50])
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hav = Richards.Empirical._vangenuchten_k(mesh)
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m = np.random.randn(50)
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def wrapper(u):
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return hav.transform(u, m), hav.transformDerivU(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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hav = Richards.Empirical._vangenuchten_k(mesh)
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u = np.random.randn(50)
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def wrapper(m):
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return hav.transform(u, m), hav.transformDerivM(u, m)
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passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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self.assertTrue(passed,True)
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class RichardsTests1D(unittest.TestCase):
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def setUp(self):
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M = Mesh.TensorMesh([np.ones(20)])
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M.setCellGradBC('dirichlet')
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params = Richards.Empirical.HaverkampParams().celia1990
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params['Ks'] = np.log(params['Ks'])
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E = Richards.Empirical.Haverkamp(M, **params)
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bc = np.array([-61.5,-20.7])
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h = np.zeros(M.nC) + bc[0]
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prob = Richards.RichardsProblem(M, mapping=E, timeSteps=[(40,3),(60,3)],
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boundaryConditions=bc, initialConditions=h,
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doNewton=False, method='mixed')
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locs = np.r_[5.,10,15]
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times = prob.times[3:5]
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rxSat = Richards.RichardsRx(locs, times, 'saturation')
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rxPre = Richards.RichardsRx(locs, times, 'pressureHead')
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survey = Richards.RichardsSurvey([rxSat, rxPre])
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prob.pair(survey)
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self.h0 = h
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self.M = M
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self.Ks = params['Ks']
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self.prob = prob
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self.survey = survey
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def test_Richards_getResidual_Newton(self):
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self.prob.doNewton = True
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m = self.Ks
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passed = checkDerivative(lambda hn1: self.prob.getResidual(m, self.h0, hn1, self.prob.timeSteps[0], self.prob.boundaryConditions), self.h0, plotIt=False)
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self.assertTrue(passed,True)
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def test_Richards_getResidual_Picard(self):
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self.prob.doNewton = False
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m = self.Ks
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passed = checkDerivative(lambda hn1: self.prob.getResidual(m, self.h0, hn1, self.prob.timeSteps[0], self.prob.boundaryConditions), self.h0, plotIt=False, expectedOrder=1)
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self.assertTrue(passed,True)
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def test_Adjoint(self):
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v = np.random.rand(self.survey.nD)
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z = np.random.rand(self.M.nC)
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Hs = self.prob.fields(self.Ks)
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vJz = v.dot(self.prob.Jvec(self.Ks,z,u=Hs))
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zJv = z.dot(self.prob.Jtvec(self.Ks,v,u=Hs))
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tol = TOL*(10**int(np.log10(zJv)))
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passed = np.abs(vJz - zJv) < tol
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print 'Richards Adjoint Test - PressureHead'
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print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
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self.assertTrue(passed,True)
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def test_Sensitivity(self):
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mTrue = self.Ks*np.ones(self.M.nC)
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derChk = lambda m: [self.survey.dpred(m), lambda v: self.prob.Jvec(m, v)]
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print 'Testing Richards Derivative'
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passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
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self.assertTrue(passed,True)
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def test_Sensitivity_full(self):
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mTrue = self.Ks*np.ones(self.M.nC)
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J = self.prob.Jfull(mTrue)
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derChk = lambda m: [self.survey.dpred(m), J]
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print 'Testing Richards Derivative'
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passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
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self.assertTrue(passed,True)
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# class RichardsTests2D(object):
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# def setUp(self):
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# M = mesh.TensorMesh([np.ones(8),np.ones(30)])
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# Ks = 9.4400e-03
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# E = Richards.Haverkamp(Ks=np.log(Ks), A=1.1750e+06, gamma=4.74, alpha=1.6110e+06, theta_s=0.287, theta_r=0.075, beta=3.96)
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# bc = np.array([-61.5,-20.7])
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# bc = np.r_[np.zeros(M.nCy*2),np.ones(M.nCx)*bc[0],np.ones(M.nCx)*bc[1]]
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# h = np.zeros(M.nC) + bc[0]
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# prob = Richards.RichardsProblem(M,E, timeStep=60, timeEnd=180, boundaryConditions=bc, initialConditions=h, doNewton=False, method='mixed')
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# XY = utils.ndgrid(np.array([5,7.]),np.array([5,15,25.]))
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# q = M.getInterpolationMat(XY,'CC')
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# P = sp.kron(sp.identity(prob.numIts),q)
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# prob.P = P
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# self.h0 = h
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# self.M = M
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# self.Ks = Ks
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# self.prob = prob
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# def test_Richards_getResidual_Newton(self):
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# self.prob.doNewton = True
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# passed = checkDerivative(lambda hn1: self.prob.getResidual(self.h0,hn1), self.h0, plotIt=False)
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# self.assertTrue(passed,True)
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# def test_Richards_getResidual_Picard(self):
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# self.prob.doNewton = False
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# passed = checkDerivative(lambda hn1: self.prob.getResidual(self.h0,hn1), self.h0, plotIt=False, expectedOrder=1)
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# self.assertTrue(passed,True)
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# def test_Adjoint_PressureHead(self):
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# self.prob.dataType = 'pressureHead'
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# Ks = self.Ks
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# v = np.random.rand(self.prob.P.shape[0])
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# z = np.random.rand(self.M.nC)
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# Hs = self.prob.field(np.log(Ks))
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# vJz = v.dot(self.prob.J(np.log(Ks),z,u=Hs))
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# zJv = z.dot(self.prob.Jt(np.log(Ks),v,u=Hs))
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# tol = TOL*(10**int(np.log10(zJv)))
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# passed = np.abs(vJz - zJv) < tol
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# print 'Richards Adjoint Test - PressureHead'
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# print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
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# self.assertTrue(passed,True)
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# def test_Adjoint_Saturation(self):
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# self.prob.dataType = 'saturation'
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# Ks = self.Ks
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# v = np.random.rand(self.prob.P.shape[0])
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# z = np.random.rand(self.M.nC)
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# Hs = self.prob.field(np.log(Ks))
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# vJz = v.dot(self.prob.J(np.log(Ks),z,u=Hs))
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# zJv = z.dot(self.prob.Jt(np.log(Ks),v,u=Hs))
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# tol = TOL #*(10**int(np.log10(zJv)))
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# passed = np.abs(vJz - zJv) < tol
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# print 'Richards Adjoint Test - Saturation'
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# print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
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# self.assertTrue(passed,True)
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# def test_Sensitivity(self):
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# self.prob.dataType = 'pressureHead'
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# mTrue = np.ones(self.M.nC)*self.Ks
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# stdev = 0.01 # The standard deviation for the noise
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# dobs = self.prob.createSyntheticSurvey(mTrue,std=stdev)[0]
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# self.prob.dobs = dobs
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# self.prob.std = dobs*0 + stdev
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# Hs = self.prob.field(mTrue)
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# opt = inverse.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
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# reg = regularization.Regularization(self.M)
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# inv = inverse.Inversion(self.prob, reg, opt, beta0=1e4)
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# derChk = lambda m: [inv.dataObj(m), inv.dataObjDeriv(m)]
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# print 'Testing Richards Derivative'
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# passed = checkDerivative(derChk, mTrue, num=5, plotIt=False)
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# self.assertTrue(passed,True)
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if __name__ == '__main__':
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unittest.main()
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