wassname/simpeg
1
0
Fork 0
mirror of https://github.com/wassname/simpeg.git synced 2026-09-10 12:37:30 +08:00
Code Issues Packages Projects Releases Wiki Activity

updates to 2D testing

Browse Source
This commit is contained in:
Rowan Cockett
2015-01-29 14:02:26 -08:00
parent 9067590c65
commit b2d57e8892
2 changed files with 61 additions and 68 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.gitignore
+1
View File
@@ -0,0 +1 @@
*.pyc
simpegFLOW/Tests/test_Richards.py
+60 -68
View File
@@ -109,7 +109,7 @@ class RichardsTests1D(unittest.TestCase):
Hs = self.prob.fields(self.Ks) Hs = self.prob.fields(self.Ks)
vJz = v.dot(self.prob.Jvec(self.Ks,z,u=Hs)) vJz = v.dot(self.prob.Jvec(self.Ks,z,u=Hs))
zJv = z.dot(self.prob.Jtvec(self.Ks,v,u=Hs)) zJv = z.dot(self.prob.Jtvec(self.Ks,v,u=Hs))
tol = TOL*(10**int(np.log10(zJv))) tol = TOL*(10**int(np.log10(np.abs(zJv))))
passed = np.abs(vJz - zJv) < tol passed = np.abs(vJz - zJv) < tol
print 'Richards Adjoint Test - PressureHead' print 'Richards Adjoint Test - PressureHead'
print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol) print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
@@ -127,88 +127,80 @@ class RichardsTests1D(unittest.TestCase):
mTrue = self.Ks*np.ones(self.M.nC) mTrue = self.Ks*np.ones(self.M.nC)
J = self.prob.Jfull(mTrue) J = self.prob.Jfull(mTrue)
derChk = lambda m: [self.survey.dpred(m), J] derChk = lambda m: [self.survey.dpred(m), J]
print 'Testing Richards Derivative' print 'Testing Richards Derivative FULL'
passed = checkDerivative(derChk, mTrue, num=4, plotIt=False) passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
self.assertTrue(passed,True) self.assertTrue(passed,True)
# class RichardsTests2D(object): class RichardsTests2D(unittest.TestCase):
# def setUp(self): def setUp(self):
# M = mesh.TensorMesh([np.ones(8),np.ones(30)]) M = Mesh.TensorMesh([np.ones(8),np.ones(30)])
# Ks = 9.4400e-03
# 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)
# bc = np.array([-61.5,-20.7]) M.setCellGradBC(['neumann','dirichlet'])
# bc = np.r_[np.zeros(M.nCy*2),np.ones(M.nCx)*bc[0],np.ones(M.nCx)*bc[1]]
# h = np.zeros(M.nC) + bc[0]
# prob = Richards.RichardsProblem(M,E, timeStep=60, timeEnd=180, boundaryConditions=bc, initialConditions=h, doNewton=False, method='mixed')
params = Richards.Empirical.HaverkampParams().celia1990
params['Ks'] = np.log(params['Ks'])
E = Richards.Empirical.Haverkamp(M, **params)
# XY = utils.ndgrid(np.array([5,7.]),np.array([5,15,25.])) bc = np.array([-61.5,-20.7])
# q = M.getInterpolationMat(XY,'CC') bc = np.r_[np.zeros(M.nCy*2),np.ones(M.nCx)*bc[0],np.ones(M.nCx)*bc[1]]
# P = sp.kron(sp.identity(prob.numIts),q) h = np.zeros(M.nC) + bc[0]
# prob.P = P prob = Richards.RichardsProblem(M,E, timeSteps=[(40,3),(60,3)], boundaryConditions=bc, initialConditions=h, doNewton=False, method='mixed')
# self.h0 = h locs = Utils.ndgrid(np.array([5,7.]),np.array([5,15,25.]))
# self.M = M times = prob.times[3:5]
# self.Ks = Ks rxSat = Richards.RichardsRx(locs, times, 'saturation')
# self.prob = prob rxPre = Richards.RichardsRx(locs, times, 'pressureHead')
survey = Richards.RichardsSurvey([rxSat, rxPre])
# def test_Richards_getResidual_Newton(self): prob.pair(survey)
# self.prob.doNewton = True
# passed = checkDerivative(lambda hn1: self.prob.getResidual(self.h0,hn1), self.h0, plotIt=False)
# self.assertTrue(passed,True)
# def test_Richards_getResidual_Picard(self): self.h0 = h
# self.prob.doNewton = False self.M = M
# passed = checkDerivative(lambda hn1: self.prob.getResidual(self.h0,hn1), self.h0, plotIt=False, expectedOrder=1) self.Ks = params['Ks']
# self.assertTrue(passed,True) self.prob = prob
self.survey = survey
# def test_Adjoint_PressureHead(self): def test_Richards_getResidual_Newton(self):
# self.prob.dataType = 'pressureHead' self.prob.doNewton = True
# Ks = self.Ks m = self.Ks
# v = np.random.rand(self.prob.P.shape[0]) passed = checkDerivative(lambda hn1: self.prob.getResidual(m, self.h0, hn1, self.prob.timeSteps[0], self.prob.boundaryConditions), self.h0, plotIt=False)
# z = np.random.rand(self.M.nC) self.assertTrue(passed,True)
# Hs = self.prob.field(np.log(Ks))
# vJz = v.dot(self.prob.J(np.log(Ks),z,u=Hs))
# zJv = z.dot(self.prob.Jt(np.log(Ks),v,u=Hs))
# tol = TOL*(10**int(np.log10(zJv)))
# passed = np.abs(vJz - zJv) < tol
# print 'Richards Adjoint Test - PressureHead'
# print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
# self.assertTrue(passed,True)
def test_Richards_getResidual_Picard(self):
self.prob.doNewton = False
m = self.Ks
passed = checkDerivative(lambda hn1: self.prob.getResidual(m, self.h0, hn1, self.prob.timeSteps[0], self.prob.boundaryConditions), self.h0, plotIt=False, expectedOrder=1)
self.assertTrue(passed,True)
# def test_Adjoint_Saturation(self): def test_Adjoint(self):
# self.prob.dataType = 'saturation' v = np.random.rand(self.survey.nD)
# Ks = self.Ks z = np.random.rand(self.M.nC)
# v = np.random.rand(self.prob.P.shape[0]) Hs = self.prob.fields(self.Ks)
# z = np.random.rand(self.M.nC) vJz = v.dot(self.prob.Jvec(self.Ks,z,u=Hs))
# Hs = self.prob.field(np.log(Ks)) zJv = z.dot(self.prob.Jtvec(self.Ks,v,u=Hs))
# vJz = v.dot(self.prob.J(np.log(Ks),z,u=Hs)) tol = TOL*(10**int(np.log10(np.abs(zJv))))
# zJv = z.dot(self.prob.Jt(np.log(Ks),v,u=Hs)) passed = np.abs(vJz - zJv) < tol
# tol = TOL #*(10**int(np.log10(zJv))) print '2D: Richards Adjoint Test - PressureHead'
# passed = np.abs(vJz - zJv) < tol print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
# print 'Richards Adjoint Test - Saturation' self.assertTrue(passed,True)
# print '%4.4e === %4.4e, diff=%4.4e < %4.e'%(vJz, zJv,np.abs(vJz - zJv),tol)
# self.assertTrue(passed,True) def test_Sensitivity(self):
mTrue = self.Ks*np.ones(self.M.nC)
derChk = lambda m: [self.survey.dpred(m), lambda v: self.prob.Jvec(m, v)]
print '2D: Testing Richards Derivative'
passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
self.assertTrue(passed,True)
def test_Sensitivity_full(self):
mTrue = self.Ks*np.ones(self.M.nC)
J = self.prob.Jfull(mTrue)
derChk = lambda m: [self.survey.dpred(m), J]
print '2D: Testing Richards Derivative FULL'
passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
self.assertTrue(passed,True)
# def test_Sensitivity(self):
# self.prob.dataType = 'pressureHead'
# mTrue = np.ones(self.M.nC)*self.Ks
# stdev = 0.01 # The standard deviation for the noise
# dobs = self.prob.createSyntheticSurvey(mTrue,std=stdev)[0]
# self.prob.dobs = dobs
# self.prob.std = dobs*0 + stdev
# Hs = self.prob.field(mTrue)
# opt = inverse.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
# reg = regularization.Regularization(self.M)
# inv = inverse.Inversion(self.prob, reg, opt, beta0=1e4)
# derChk = lambda m: [inv.dataObj(m), inv.dataObjDeriv(m)]
# print 'Testing Richards Derivative'
# passed = checkDerivative(derChk, mTrue, num=5, plotIt=False)
# self.assertTrue(passed,True)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()