diff --git a/SimPEG/Examples/MT_1D_ForwardAndInversion.py b/SimPEG/Examples/MT_1D_ForwardAndInversion.py
index 69cedd98..59a6c87e 100644
--- a/SimPEG/Examples/MT_1D_ForwardAndInversion.py
+++ b/SimPEG/Examples/MT_1D_ForwardAndInversion.py
@@ -1,9 +1,11 @@
 import SimPEG as simpeg
 import numpy as np
-import SimPEG.MT as MT
+from SimPEG import NSEM
 from scipy.constants import mu_0
 import matplotlib.pyplot as plt
 
+np.random.seed(1983)
+
 def run(plotIt=True):
     """
         MT: 1D: Inversion
@@ -23,7 +25,7 @@ def run(plotIt=True):
     ct = 20
     air = simpeg.Utils.meshTensor([(ct,16,1.4)])
     core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )
-    bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])
+    bot = simpeg.Utils.meshTensor([(core[0],12,-1.4)])
     x0 = -np.array([np.sum(np.concatenate((core,bot)))])
     # Make the model
     m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)
@@ -47,41 +49,43 @@ def run(plotIt=True):
     # Make the background model
     sigma_0 = np.ones(m1d.nCx)*sig_air
     sigma_0[active] = sig_half
+    sigma_0[layer1] = sig_layer1
+    sigma_0[layer2] = .002
     m_0 = np.log(sigma_0[active])
 
     # Set the mapping
-    actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
+    actMap = simpeg.Maps.InjectActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
     mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap
 
     ## Setup the layout of the survey, set the sources and the connected receivers
     # Receivers
     rxList = []
     for rxType in ['z1dr','z1di']:
-        rxList.append(MT.Rx(simpeg.mkvc(np.array([0.0]),2).T,rxType))
+        rxList.append(NSEM.Rx(simpeg.mkvc(np.array([0.0]),2).T,rxType))
     # Source list
     srcList =[]
     for freq in freqs:
-            srcList.append(MT.SrcMT.polxy_1Dprimary(rxList,freq))
+            srcList.append(NSEM.SrcNSEM.polxy_1Dprimary(rxList,freq))
     # Make the survey
-    survey = MT.Survey(srcList)
+    survey = NSEM.Survey(srcList)
     survey.mtrue = m_true
 
     ## Set the problem
-    problem = MT.Problem1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)
+    problem = NSEM.Problem1D_ePrimSec(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)
     problem.pair(survey)
 
     ## Forward model data
     # Project the data
     survey.dtrue = survey.dpred(m_true)
-    survey.dobs = survey.dtrue + 0.025*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)
+    survey.dobs = survey.dtrue + 0.01*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)
 
     if plotIt:
-        fig = MT.Utils.dataUtils.plotMT1DModelData(problem)
+        fig = NSEM.Utils.dataUtils.plotMT1DModelData(problem,[])
         fig.suptitle('Target - smooth true')
 
 
     # Assign uncertainties
-    std = 0.05 # 5% std
+    std = 0.025 # 5% std
     survey.std = np.abs(survey.dobs*std)
     # Assign the data weight
     Wd = 1./survey.std
@@ -92,6 +96,7 @@ def run(plotIt=True):
     # Set the optimization
     opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)
     opt.counter = C
+    opt.maxStep = m1d.nC * survey.nD
     opt.LSshorten = 0.5
     opt.remember('xc')
     # Data misfit
@@ -99,7 +104,7 @@ def run(plotIt=True):
     dmis.Wd = Wd
     # Regularization - with a regularization mesh
     regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)
-    reg = simpeg.Regularization.Tikhonov(regMesh)
+    reg = simpeg.Regularization.Tikhonov(m1d,indActive=active)
     reg.mrefInSmooth = True
     reg.alpha_s = 1e-7
     reg.alpha_x = 1.
@@ -109,11 +114,9 @@ def run(plotIt=True):
     # Beta cooling
     beta = simpeg.Directives.BetaSchedule()
     beta.coolingRate = 4
-    betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)
+    betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=1.)
     targmis = simpeg.Directives.TargetMisfit()
     targmis.target = survey.nD
-    saveModel = simpeg.Directives.SaveModelEveryIteration()
-    saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue'
     # Create an inversion object
     inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis])
 
@@ -121,7 +124,7 @@ def run(plotIt=True):
     mopt = inv.run(m_0)
 
     if plotIt:
-        fig = MT.Utils.dataUtils.plotMT1DModelData(problem,[mopt])
+        fig = NSEM.Utils.dataUtils.plotMT1DModelData(problem,[mopt])
         fig.suptitle('Target - smooth true')
         plt.show()
 
diff --git a/SimPEG/Examples/MT_3D_Foward.py b/SimPEG/Examples/MT_3D_Foward.py
index da16eeee..01700afe 100644
--- a/SimPEG/Examples/MT_3D_Foward.py
+++ b/SimPEG/Examples/MT_3D_Foward.py
@@ -2,7 +2,7 @@
 
 # Import
 import SimPEG as simpeg
-from SimPEG import MT
+from SimPEG import NSEM
 import numpy as np
 try:
     from pymatsolver import MumpsSolver as Solver
@@ -37,16 +37,16 @@ def run(plotIt=True, nFreq=1):
     for loc in rx_loc:
         # NOTE: loc has to be a (1,3) np.ndarray otherwise errors accure
         for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
-            rxList.append(MT.Rx(simpeg.mkvc(loc,2).T,rxType))
+            rxList.append(NSEM.Rx(simpeg.mkvc(loc,2).T,rxType))
     # Source list
     srcList =[]
     for freq in np.logspace(3,-3,nFreq):
-        srcList.append(MT.SrcMT.polxy_1Dprimary(rxList,freq))
+        srcList.append(NSEM.SrcNSEM.polxy_1Dprimary(rxList,freq))
     # Survey MT
-    survey = MT.Survey(srcList)
+    survey = NSEM.Survey(srcList)
 
     ## Setup the problem object
-    problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG)
+    problem = NSEM.Problem3D_ePrimSec(M, sigmaPrimary=sigBG)
     problem.pair(survey)
     problem.Solver = Solver
 
@@ -55,7 +55,7 @@ def run(plotIt=True, nFreq=1):
     dataVec = survey.eval(fields)
 
     # Make the data
-    mtData = MT.Data(survey,dataVec)
+    mtData = NSEM.Data(survey,dataVec)
     # Add plots
     if plotIt:
         pass