mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-29 21:43:37 +08:00
Dave Marchant
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
import unittest
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from SimPEG import *
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import simpegEM as EM
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from scipy.constants import mu_0
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from simpegEM.Utils.Ana import hzAnalyticDipoleT
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class TDEM_bTests(unittest.TestCase):
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def setUp(self):
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cs = 5.
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ncx = 20
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ncy = 6
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npad = 20
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hx = Utils.meshTensors(((0,cs), (ncx,cs), (npad,cs)))
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hy = Utils.meshTensors(((npad,cs), (ncy,cs), (npad,cs)))
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mesh = Mesh.Cyl1DMesh([hx,hy], -hy.sum()/2)
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model = Model.Vertical1DModel(mesh)
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opts = {'txLoc':0.,
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'txType':'VMD_MVP',
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'rxLoc':np.r_[150., 0.],
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'rxType':'bz',
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'timeCh':np.logspace(-4,-2,20),
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}
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self.dat = EM.TDEM.DataTDEM1D(**opts)
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self.prb = EM.TDEM.ProblemTDEM_b(mesh, model)
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self.prb.setTimes([1e-5, 5e-5, 2.5e-4], [150, 150, 150])
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self.sigma = np.ones(mesh.nCz)*1e-8
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self.sigma[mesh.vectorCCz<0] = 0.1
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self.prb.pair(self.dat)
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def test_analitic_b(self):
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bz_calc = self.dat.dpred(self.sigma)
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bz_ana = mu_0*hzAnalyticDipoleT(self.dat.rxLoc[0], self.prb.times, self.sigma[0])
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diff = np.linalg.norm(bz_calc.flatten() - bz_ana.flatten())/np.linalg.norm(bz_ana.flatten())
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self.assertTrue(diff<0.05)
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class TDEM_bDerivTests(unittest.TestCase):
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def setUp(self):
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cs = 5.
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ncx = 20
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ncy = 6
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npad = 20
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hx = Utils.meshTensors(((0,cs), (ncx,cs), (npad,cs)))
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hy = Utils.meshTensors(((npad,cs), (ncy,cs), (npad,cs)))
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mesh = Mesh.Cyl1DMesh([hx,hy], -hy.sum()/2)
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model = Model.Vertical1DModel(mesh)
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opts = {'txLoc':0.,
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'txType':'VMD_MVP',
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'rxLoc':np.r_[150., 0.],
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'rxType':'bz',
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'timeCh':np.logspace(-4,-2,20),
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}
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self.dat = EM.TDEM.DataTDEM1D(**opts)
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self.prb = EM.TDEM.ProblemTDEM_b(mesh, model)
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self.prb.setTimes([1e-5, 5e-5, 2.5e-4], [10, 10, 10])
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self.sigma = np.ones(mesh.nCz)*1e-8
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self.sigma[mesh.vectorCCz<0] = 0.1
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self.prb.pair(self.dat)
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def test_AhVec(self):
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"""
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Test that fields and AhVec produce consistent results
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"""
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sigma = np.ones(self.prb.mesh.nCz)*1e-8
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sigma[self.prb.mesh.vectorCCz<0] = 0.1
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u = self.prb.fields(sigma)
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Ahu = self.prb.AhVec(sigma, u)
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self.assertTrue(np.linalg.norm(Ahu.get_b(0)-1/self.prb.getDt(0)*u.get_b(-1))/np.linalg.norm(u.get_b(0)) < 1.e-2)
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self.assertTrue(np.linalg.norm(Ahu.get_e(0))/np.linalg.norm(u.get_e(0)) < 1.e-2)
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for i in range(1,u.nTimes):
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self.assertTrue(np.linalg.norm(Ahu.get_b(i))/np.linalg.norm(u.get_b(i)) < 1.e-2)
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self.assertTrue(np.linalg.norm(Ahu.get_e(i))/np.linalg.norm(u.get_e(i)) < 1.e-2)
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def test_DerivG(self):
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"""
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Test the derivative of c with respect to sigma
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"""
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# Random model and perturbation
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sigma = np.random.rand(self.prb.mesh.nCz)
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f = self.prb.fields(sigma)
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dm = np.random.rand(self.prb.mesh.nCz)
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h = 1.
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a = np.linalg.norm(self.prb.AhVec(sigma+h*dm, f).fieldVec() - self.prb.AhVec(sigma, f).fieldVec())
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b = np.linalg.norm(self.prb.AhVec(sigma+h*dm, f).fieldVec() - self.prb.AhVec(sigma, f).fieldVec() - h*self.prb.G(sigma, dm, u=f).fieldVec())
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# Assuming that the gradient is exact to machine precision
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self.assertTrue(b<1e-16)
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if __name__ == '__main__':
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unittest.main()
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