Removed Examples dir, and put it up a level (outside SimPEG path) renamed to tutorials for future tutorial development.

Tutorials/Linear.py

@@ -0,0 +1,74 @@
+from SimPEG import *
+import matplotlib.pyplot as plt
+
+
+class LinearProblem(Problem.BaseProblem):
+    """docstring for LinearProblem"""
+
+    def __init__(self, mesh, model, G, **kwargs):
+        Problem.BaseProblem.__init__(self, mesh, model, **kwargs)
+        self.G = G
+
+    def fields(self, m, u=None):
+        return self.G.dot(m)
+
+    def J(self, m, v, u=None):
+        return self.G.dot(v)
+
+    def Jt(self, m, v, u=None):
+        return self.G.T.dot(v)
+
+
+def example(N):
+    h = np.ones(N)/N
+    M = Mesh.TensorMesh([h])
+
+    nk = 20
+    jk = np.linspace(1.,20.,nk)
+    p = -0.25
+    q = 0.25
+
+    g = lambda k: np.exp(p*jk[k]*M.vectorCCx)*np.cos(2*np.pi*q*jk[k]*M.vectorCCx)
+
+    G = np.empty((nk, M.nC))
+
+    for i in range(nk):
+        G[i,:] = g(i)
+
+    mtrue = np.zeros(M.nC)
+    mtrue[M.vectorCCx > 0.3] = 1.
+    mtrue[M.vectorCCx > 0.45] = -0.5
+    mtrue[M.vectorCCx > 0.6] = 0
+
+
+
+    model = Model.BaseModel(M)
+    prob = LinearProblem(M, model, G)
+    data = prob.createSyntheticData(mtrue, std=0.01)
+
+    return prob, data, model
+
+
+if __name__ == '__main__':
+
+    prob, data, model = example(100)
+    M = prob.mesh
+
+    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)
+
+    plt.figure(1)
+    for i in range(prob.G.shape[0]):
+        plt.plot(prob.G[i,:])
+
+    plt.figure(2)
+
+    plt.plot(M.vectorCCx, data.mtrue, 'b-')
+    plt.plot(M.vectorCCx, mrec, 'r-')
+
+    plt.show()