Documentation updates.

SimPEG/forward/LinearProblem.py

@@ -5,47 +5,6 @@ from SimPEG.regularization import Regularization
 from SimPEG.inverse import *
 import matplotlib.pyplot as plt
 
-N = 100
-h = np.ones(N)/N
-M = 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)
-
-
-
-plt.figure(1)
-for i in range(nk):
-    plt.plot(G[i,:])
-
-
-m_true = np.zeros(M.nC)
-m_true[M.vectorCCx > 0.3] = 1.
-m_true[M.vectorCCx > 0.45] = -0.5
-m_true[M.vectorCCx > 0.6] = 0
-
-
-d_true = G.dot(m_true)
-noise = 0.1 * np.random.rand(d_true.size)
-
-d_obs = d_true + noise
-
-# plt.figure(3)
-# plt.plot(d_true,'-o')
-# plt.plot(d_obs,'r-o')
-
-
 
 class LinearProblem(Problem):
     """docstring for LinearProblem"""
@@ -59,27 +18,72 @@ class LinearProblem(Problem):
     def Jt(self, m, v, u=None):
         return G.T.dot(v)
 
-prob = LinearProblem(M)
-prob.G = G
-prob.dobs = d_obs
-prob.std = np.ones_like(d_obs)*0.1
+if __name__ == '__main__':
+    N = 100
+    h = np.ones(N)/N
+    M = TensorMesh([h])
 
-reg = Regularization(M)
-
-opt = InexactGaussNewton(maxIter=20)
-
-inv = Inversion(prob,reg,opt,beta0=1e-4)
-
-m0 = np.zeros_like(m_true)
-
-mrec = inv.run(m0)
-
-
-plt.figure(2)
-
-plt.plot(M.vectorCCx, m_true, 'b-')
-plt.plot(M.vectorCCx, mrec, 'r-')
+    nk = 20
+    jk = np.linspace(1.,20.,nk)
+    p = -0.25
+    q = 0.25
 
 
 
-plt.show()
+    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)
+
+
+
+    plt.figure(1)
+    for i in range(nk):
+        plt.plot(G[i,:])
+
+
+    m_true = np.zeros(M.nC)
+    m_true[M.vectorCCx > 0.3] = 1.
+    m_true[M.vectorCCx > 0.45] = -0.5
+    m_true[M.vectorCCx > 0.6] = 0
+
+
+    d_true = G.dot(m_true)
+    noise = 0.1 * np.random.rand(d_true.size)
+
+    d_obs = d_true + noise
+
+    # plt.figure(3)
+    # plt.plot(d_true,'-o')
+    # plt.plot(d_obs,'r-o')
+
+
+
+
+
+    prob = LinearProblem(M)
+    prob.G = G
+    prob.dobs = d_obs
+    prob.std = np.ones_like(d_obs)*0.1
+
+    reg = Regularization(M)
+
+    opt = InexactGaussNewton(maxIter=20)
+
+    inv = Inversion(prob,reg,opt,beta0=1e-4)
+
+    m0 = np.zeros_like(m_true)
+
+    mrec = inv.run(m0)
+
+
+    plt.figure(2)
+
+    plt.plot(M.vectorCCx, m_true, 'b-')
+    plt.plot(M.vectorCCx, mrec, 'r-')
+
+
+
+    plt.show()

SimPEG/forward/__init__.py

@@ -1,3 +1,4 @@
 from Problem import *
 from DCProblem import DCProblem
+from LinearProblem import LinearProblem
 import ModelTransforms

SimPEG/inverse/BetaSchedule.py

@@ -1,7 +1,7 @@
 
 
 class Cooling(object):
-    """Simple Beta Schedual"""
+    """Simple Beta Schedule"""
 
     beta0 = 1.e6
     beta_coolingFactor = 5.

SimPEG/inverse/__init__.py

@@ -1,3 +1,3 @@
 from Optimize import *
 from Inversion import *
-import BetaSchedual
+import BetaSchedule

docs/api_Optimize.rst

@@ -6,3 +6,18 @@ Optimize
 .. automodule:: SimPEG.inverse.Optimize
     :members:
     :undoc-members:
+
+
+Inversion
+*********
+
+.. automodule:: SimPEG.inverse.Inversion
+    :members:
+    :undoc-members:
+
+Beta Schedule
+*************
+
+.. automodule:: SimPEG.inverse.BetaSchedule
+    :members:
+    :undoc-members:

docs/api_Problem.rst

@@ -13,14 +13,16 @@ Problem
 DCProblem
 *********
 
-.. automodule:: SimPEG.forward.DCProblem.DCProblem
+.. automodule:: SimPEG.forward.DCProblem
     :members:
     :undoc-members:
 
 
-DCutils
-*******
 
-.. automodule:: SimPEG.forward.DCProblem.DCutils
+Linear Problem
+**************
+
+.. automodule:: SimPEG.forward.LinearProblem
     :members:
     :undoc-members:
+