Fixed Example Problems.

SimPEG/Examples/DC.py

@@ -208,7 +208,7 @@ if __name__ == '__main__':
     q, Q, rxmidloc = genTxRxmat(nelec, spacelec, surfloc, elecini, M)
     P = Q.T
 
-    model = Model.LogModel()
+    model = Model.LogModel(M)
     prob = DCProblem(M, model)
 
     # Create some data
@@ -224,18 +224,17 @@ if __name__ == '__main__':
     # prob.std = dobs*0 + 0.05
     m0 = M.gridCC[:,0]*0+sig2
 
-    opt = Inverse.InexactGaussNewton(maxIterLS=20, maxIter=3, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
-    reg = Inverse.Regularization(M)
-    inv = Inverse.Inversion(prob, reg, opt, data, beta0=1e4)
+    reg = Regularization.Tikhonov(model)
+    objFunc = ObjFunction.BaseObjFunction(data, reg)
+    opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=3, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
+    inv = Inversion.BaseInversion(objFunc, opt)
 
     # Check Derivative
-    derChk = lambda m: [inv.dataObj(m), inv.dataObjDeriv(m)]
+    derChk = lambda m: [objFunc.dataObj(m), objFunc.dataObjDeriv(m)]
     # Tests.checkDerivative(derChk, mSynth)
 
-
-
-    print inv.dataObj(m0)
-    print inv.dataObj(mSynth)
+    print objFunc.dataObj(m0)
+    print objFunc.dataObj(mSynth)
 
     m = inv.run(m0)
 

SimPEG/Examples/Linear.py

@@ -1,14 +1,15 @@
-from SimPEG import Mesh, Model, Problem, Data, Inversion, np
+from SimPEG import *
 import matplotlib.pyplot as plt
 
 
 class LinearProblem(Problem.BaseProblem):
     """docstring for LinearProblem"""
 
-    def __init__(self, *args, **kwargs):
-        problem.BaseProblem.__init__(self, *args, **kwargs)
+    def __init__(self, mesh, model, G, **kwargs):
+        Problem.BaseProblem.__init__(self, mesh, model, **kwargs)
+        self.G = G
 
-    def dpred(self, m, u=None):
+    def field(self, m, u=None):
         return self.G.dot(m)
 
     def J(self, m, v, u=None):
@@ -20,7 +21,7 @@ class LinearProblem(Problem.BaseProblem):
 
 def example(N):
     h = np.ones(N)/N
-    M = mesh.TensorMesh([h])
+    M = Mesh.TensorMesh([h])
 
     nk = 20
     jk = np.linspace(1.,20.,nk)
@@ -41,21 +42,22 @@ def example(N):
 
 
 
-    prob = LinearProblem(M, None)
-    prob.G = G
+    model = Model.BaseModel(M)
+    prob = LinearProblem(M, model, G)
     data = prob.createSyntheticData(mtrue, std=0.01)
 
-    return prob, data
+    return prob, data, model
 
 
 if __name__ == '__main__':
 
-    prob, data = example(100)
+    prob, data, model = example(100)
     M = prob.mesh
 
-    reg = inverse.Regularization(M)
-    opt = inverse.InexactGaussNewton(maxIter=20)
-    inv = inverse.Inversion(prob,reg,opt,data)
+    reg = Regularization.Tikhonov(model)
+    objFunc = ObjFunction.BaseObjFunction(data, reg)
+    opt = Optimization.InexactGaussNewton(maxIter=20)
+    inv = Inversion.BaseInversion(objFunc, opt)
     m0 = np.zeros_like(data.mtrue)
 
     mrec = inv.run(m0)