Major Reorganization (Things are likely still broken...)

Added Parameter to Utils, which hints at where we are going with functions as parameters.

SimPEG/Tests/test_optimizers.py

@@ -4,7 +4,7 @@ from SimPEG.Mesh import TensorMesh
 from SimPEG.Utils import sdiag
 import numpy as np
 import scipy.sparse as sp
-from SimPEG import Inverse
+from SimPEG import Optimization
 from SimPEG.Tests import getQuadratic, Rosenbrock
 
 TOL = 1e-2
@@ -16,7 +16,7 @@ class TestOptimizers(unittest.TestCase):
         self.b = np.array([-5,-5])
 
     def test_GN_Rosenbrock(self):
-        GN = Inverse.GaussNewton()
+        GN = Optimization.GaussNewton()
         xopt = GN.minimize(Rosenbrock,np.array([0,0]))
         x_true = np.array([1.,1.])
         print 'xopt: ', xopt
@@ -24,7 +24,7 @@ class TestOptimizers(unittest.TestCase):
         self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
 
     def test_GN_quadratic(self):
-        GN = Inverse.GaussNewton()
+        GN = Optimization.GaussNewton()
         xopt = GN.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
         x_true = np.array([5.,5.])
         print 'xopt: ', xopt
@@ -32,7 +32,7 @@ class TestOptimizers(unittest.TestCase):
         self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
 
     def test_ProjGradient_quadraticBounded(self):
-        PG = Inverse.ProjectedGradient(debug=True)
+        PG = Optimization.ProjectedGradient(debug=True)
         PG.lower, PG.upper = -2, 2
         xopt = PG.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
         x_true = np.array([2.,2.])
@@ -42,7 +42,7 @@ class TestOptimizers(unittest.TestCase):
 
     def test_ProjGradient_quadratic1Bound(self):
         myB = np.array([-5,1])
-        PG = Inverse.ProjectedGradient()
+        PG = Optimization.ProjectedGradient()
         PG.lower, PG.upper = -2, 2
         xopt = PG.minimize(getQuadratic(self.A,myB),np.array([0,0]))
         x_true = np.array([2.,-1.])
@@ -53,7 +53,7 @@ class TestOptimizers(unittest.TestCase):
     def test_NewtonRoot(self):
         fun = lambda x, return_g=True: np.sin(x) if not return_g else ( np.sin(x), sdiag( np.cos(x) ) )
         x = np.array([np.pi-0.3, np.pi+0.1, 0])
-        xopt = Inverse.NewtonRoot(comments=False).root(fun,x)
+        xopt = Optimization.NewtonRoot(comments=False).root(fun,x)
         x_true = np.array([np.pi,np.pi,0])
         print 'Newton Root Finding'
         print 'xopt: ', xopt