def test_vangenuchten_k(self):
mesh = Mesh.TensorMesh([50])
hav = Richards.Empirical._vangenuchten_k(mesh)
m = np.random.randn(50)
def wrapper(u):
return hav.transform(u, m), hav.transformDerivU(u, m)
passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
self.assertTrue(passed,True)
hav = Richards.Empirical._vangenuchten_k(mesh)
u = np.random.randn(50)
def wrapper(m):
return hav.transform(u, m), hav.transformDerivM(u, m)
passed = checkDerivative(wrapper, np.random.randn(50), 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_BaseHaverkamp_Theta(self):
mesh = Mesh.TensorMesh([50])
hav = Richards.Empirical._haverkamp_theta(mesh)
m = np.random.randn(50)
def wrapper(u):
return hav.transform(u, m), hav.transformDerivU(u, m)
passed = checkDerivative(wrapper, np.random.randn(50), plotIt=False)
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 '3D: Testing Richards Derivative'
passed = checkDerivative(derChk, mTrue, num=4, plotIt=False)
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)
