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https://github.com/wassname/simpeg.git
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rowanc1
67b067d938
Added Parameter to Utils, which hints at where we are going with functions as parameters.
73 lines
1.5 KiB
Python
73 lines
1.5 KiB
Python
from SimPEG import Mesh, Model, Problem, Data, Inversion, np
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import matplotlib.pyplot as plt
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class LinearProblem(Problem.BaseProblem):
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"""docstring for LinearProblem"""
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def __init__(self, *args, **kwargs):
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problem.BaseProblem.__init__(self, *args, **kwargs)
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def dpred(self, m, u=None):
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return self.G.dot(m)
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def J(self, m, v, u=None):
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return self.G.dot(v)
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def Jt(self, m, v, u=None):
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return self.G.T.dot(v)
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def example(N):
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h = np.ones(N)/N
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M = mesh.TensorMesh([h])
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nk = 20
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jk = np.linspace(1.,20.,nk)
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p = -0.25
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q = 0.25
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g = lambda k: np.exp(p*jk[k]*M.vectorCCx)*np.cos(2*np.pi*q*jk[k]*M.vectorCCx)
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G = np.empty((nk, M.nC))
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for i in range(nk):
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G[i,:] = g(i)
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mtrue = np.zeros(M.nC)
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mtrue[M.vectorCCx > 0.3] = 1.
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mtrue[M.vectorCCx > 0.45] = -0.5
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mtrue[M.vectorCCx > 0.6] = 0
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prob = LinearProblem(M, None)
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prob.G = G
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data = prob.createSyntheticData(mtrue, std=0.01)
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return prob, data
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if __name__ == '__main__':
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prob, data = example(100)
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M = prob.mesh
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reg = inverse.Regularization(M)
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opt = inverse.InexactGaussNewton(maxIter=20)
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inv = inverse.Inversion(prob,reg,opt,data)
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m0 = np.zeros_like(data.mtrue)
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mrec = inv.run(m0)
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plt.figure(1)
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for i in range(prob.G.shape[0]):
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plt.plot(prob.G[i,:])
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plt.figure(2)
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plt.plot(M.vectorCCx, data.mtrue, 'b-')
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plt.plot(M.vectorCCx, mrec, 'r-')
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plt.show()
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