mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 22:08:38 +08:00
Lindsey Heagy
289 lines
11 KiB
Python
289 lines
11 KiB
Python
import unittest
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from SimPEG import *
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from SimPEG.Tests import OrderTest, checkDerivative
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from scipy.sparse.linalg import dsolve
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from SimPEG.FLOW import Richards
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try:
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from pymatsolver import MumpsSolver
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Solver = MumpsSolver
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except Exception, e:
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pass
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TOL = 1E-8
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np.random.seed(0)
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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)], tolRootFinder=1e-6, debug=False,
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boundaryConditions=bc, initialConditions=h,
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doNewton=False, method='mixed')
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prob.Solver = Solver
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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,f=Hs))
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zJv = z.dot(self.prob.Jtvec(self.Ks,v,f=Hs))
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tol = TOL*(10**int(np.log10(np.abs(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 FULL'
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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(unittest.TestCase):
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def setUp(self):
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M = Mesh.TensorMesh([np.ones(8),np.ones(30)])
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M.setCellGradBC(['neumann','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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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, timeSteps=[(40,3),(60,3)], boundaryConditions=bc, initialConditions=h, doNewton=False, method='mixed', tolRootFinder=1e-6, debug=False)
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prob.Solver = Solver
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locs = Utils.ndgrid(np.array([5,7.]),np.array([5,15,25.]))
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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,f=Hs))
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zJv = z.dot(self.prob.Jtvec(self.Ks,v,f=Hs))
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tol = TOL*(10**int(np.log10(np.abs(zJv))))
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passed = np.abs(vJz - zJv) < tol
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print '2D: 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 '2D: Testing Richards Derivative'
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passed = checkDerivative(derChk, mTrue, num=3, 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 '2D: Testing Richards Derivative FULL'
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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 RichardsTests3D(unittest.TestCase):
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def setUp(self):
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M = Mesh.TensorMesh([np.ones(8),np.ones(20),np.ones(10)])
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M.setCellGradBC(['neumann','neumann','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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bc = np.r_[np.zeros(M.nCy*M.nCz*2),np.zeros(M.nCx*M.nCz*2),np.ones(M.nCx*M.nCy)*bc[0],np.ones(M.nCx*M.nCy)*bc[1]]
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h = np.zeros(M.nC) + bc[0]
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prob = Richards.RichardsProblem(M,E, timeSteps=[(40,3),(60,3)], boundaryConditions=bc, initialConditions=h, doNewton=False, method='mixed', tolRootFinder=1e-6, debug=False)
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prob.Solver = Solver
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locs = Utils.ndgrid(np.r_[5,7.],np.r_[5,15.],np.r_[6,8.])
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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,f=Hs))
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zJv = z.dot(self.prob.Jtvec(self.Ks,v,f=Hs))
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tol = TOL*(10**int(np.log10(np.abs(zJv))))
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passed = np.abs(vJz - zJv) < tol
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print '3D: 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 '3D: 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 '3D: Testing Richards Derivative FULL'
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# passed = checkDerivative(derChk, mTrue, num=4, 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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