Fixed directive to save iteration dictionary.

Fixing MT namespace to NSEM.

.travis.yml

@@ -35,7 +35,7 @@ before_install:
 
 # Install packages
 install:
-  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk
+  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython ipywidgets nose vtk
   - pip install nose-cov python-coveralls
 
   - git clone https://github.com/rowanc1/pymatsolver.git

README.rst

@@ -25,6 +25,10 @@ SimPEG
     :target: https://coveralls.io/r/simpeg/simpeg?branch=master
     :alt: Coverage status
 
+.. image:: http://img.shields.io/badge/GITTER-JOIN_CHAT-brightgreen.svg?style=flat-square
+    :alt: gitter chat room at https://gitter.im/simpeg/simpeg
+    :target: https://gitter.im/simpeg/simpeg
+    
 Simulation and Parameter Estimation in Geophysics  -  A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
 
 The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:

SimPEG/DCIP/DCIPUtils.py

@@ -1,12 +1,16 @@
-from SimPEG import np
+from SimPEG import np, Utils
 import BaseDC as DC
 import BaseDC as IP
+import warnings
 
 def getActiveindfromTopo(mesh, topo):
 # def genActiveindfromTopo(mesh, topo):
     """
         Get active indices from topography
     """
+    warnings.warn(
+            "`getActiveindfromTopo` is deprecated and will be removed in future versions. Use `SimPEG.Utils.surface2ind_topo` instead",
+            FutureWarning)
     from scipy.interpolate import NearestNDInterpolator
     if mesh.dim==3:
         nCxy = mesh.nCx*mesh.nCy
@@ -28,6 +32,9 @@ def gettopoCC(mesh, airind):
     """
         Get topography from active indices of mesh.
     """
+    warnings.warn(
+            "`gettopoCC` is deprecated and will be removed in future versions. Use `SimPEG.Utils.surface2ind_topo` instead",
+            FutureWarning)
     mesh2D = Mesh.TensorMesh([mesh.hx, mesh.hy], mesh.x0[:2])
     zc = mesh.gridCC[:,2]
     AIRIND = airind.reshape((mesh.vnC[0]*mesh.vnC[1],mesh.vnC[2]), order='F')
@@ -118,34 +125,27 @@ def readUBC_DC3Dobstopo(filename,mesh,topo,probType="CC"):
 
 def readUBC_DC2DModel(fileName):
     """
-        Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
+    Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
 
-        Input:
-        :param fileName, path to the UBC GIF 2D model file
-
-        Output:
-        :param SimPEG TensorMesh 2D object
-        :return
-
-        Created on Thu Nov 12 13:14:10 2015
-
-        @author: dominiquef
+    :param string fileName: path to the UBC GIF 2D model file
+    :rtype: TensorMesh
+    :return: SimPEG TensorMesh 2D object
 
     """
     from SimPEG import np, mkvc
 
     # Open fileand skip header... assume that we know the mesh already
-    obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
+    obsfile = np.genfromtxt(fileName, delimiter=' \n', dtype=np.str, comments='!')
 
-    dim = np.array(obsfile[0].split(),dtype=float)
+    dim = np.array(obsfile[0].split(), dtype=float)
 
-    temp = np.array(obsfile[1].split(),dtype=float)
+    temp = np.array(obsfile[1].split(), dtype=float)
 
     if len(temp) > 1:
         model = np.zeros(dim)
 
         for ii in range(len(obsfile)-1):
-            mm = np.array(obsfile[ii+1].split(),dtype=float)
+            mm = np.array(obsfile[ii+1].split(), dtype=float)
             model[:,ii] = mm
 
         model = model[:,::-1]
@@ -153,10 +153,10 @@ def readUBC_DC2DModel(fileName):
     else:
 
         if len(obsfile[1:])==1:
-            mm = np.array(obsfile[1:].split(),dtype=float)
+            mm = np.array(obsfile[1:].split(), dtype=float)
 
         else:
-            mm = np.array(obsfile[1:],dtype=float)
+            mm = np.array(obsfile[1:], dtype=float)
 
         # Permute the second dimension to flip the order
         model = mm.reshape(dim[1],dim[0])
@@ -169,32 +169,25 @@ def readUBC_DC2DModel(fileName):
 
     return model
 
-def plot_pseudoSection(DCsurvey, axs, stype='dpdp', dtype="appc", clim=None):
+
+def plot_pseudoSection(DCsurvey, axs, surveyType='dipole-dipole', unitType='volt', clim=None, cblabel=True, axlabel = True, colorbar = True, contour = None):
     """
-        Read list of 2D tx-rx location and plot a speudo-section of apparent
-        resistivity.
+    Read list of 2D tx-rx location and plot a speudo-section of apparent
+    resistivity.
 
-        Assumes flat topo for now...
+    Assumes flat topo for now...
 
-        Input:
-        :param d2D, z0
-        :switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
-        :switch dtype=-> Either 'appr' (app. res) | 'appc' (app. con) | 'volt' (potential)
-        Output:
-        :figure scatter plot overlayed on image
-
-        Edited Feb 17th, 2016
-
-        @author: dominiquef
+    :param SurveyDC DCsurvey:
+    :param string surveyType: Either 'pole-dipole'  | 'dipole-dipole'
+    :param string unitType: Either 'appResistivity' | 'appConductivity'  | 'volt'
+    :rtype: matplotlib.plt
+    :return: figure scatter plot overlayed on image
 
     """
     from SimPEG import np
     from scipy.interpolate import griddata
     import pylab as plt
 
-    # Set depth to 0 for now
-    z0 = 0.
-
     # Pre-allocate
     midx = []
     midz = []
@@ -221,76 +214,92 @@ def plot_pseudoSection(DCsurvey, axs, stype='dpdp', dtype="appc", clim=None):
         Cmid = (Tx[0][0] + Tx[1][0])/2
         Pmid = (Rx[0][:,0] + Rx[1][:,0])/2
 
-        # Change output for dtype
-        if dtype == 'volt':
+        # Change output for unitType
+        if unitType == 'volt':
 
             rho = np.hstack([rho,data])
 
         else:
 
             # Compute pant leg of apparent rho
-            if stype == 'pdp':
+            if surveyType == 'pole-dipole':
 
                 leg =  data * 2*np.pi  * MA * ( MA + MN ) / MN
 
-            elif stype == 'dpdp':
+            elif surveyType == 'dipole-dipole':
 
                 leg = data * 2*np.pi / ( 1/MA - 1/MB - 1/NB + 1/NA )
 
             else:
-                print """dtype must be 'pdp'(pole-dipole) | 'dpdp' (dipole-dipole) """
+                print """unitType must be 'pole-dipole' | 'dipole-dipole' """
                 break
 
 
-            if dtype == 'appc':
+            if unitType == 'appConductivity':
 
                 leg = np.log10(abs(1./leg))
                 rho = np.hstack([rho,leg])
 
-            elif dtype == 'appr':
+            elif unitType == 'appResistivity':
 
                 leg = np.log10(abs(leg))
                 rho = np.hstack([rho,leg])
 
             else:
-                print """dtype must be 'appr' | 'appc' | 'volt' """
+                print """unitType must be 'appResistivity' | 'appConductivity' | 'volt' """
                 break
 
         midx = np.hstack([midx, ( Cmid + Pmid )/2 ])
-        midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + z0 ])
-
-    ax = axs
+        midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + (Tx[0][2] + Tx[1][2])/2 ])
 
     # Grid points
     grid_x, grid_z = np.mgrid[np.min(midx):np.max(midx), np.min(midz):np.max(midz)]
     grid_rho = griddata(np.c_[midx,midz], rho.T, (grid_x, grid_z), method='linear')
 
+    # Scale the color scheme
     if clim == None:
         vmin, vmax = rho.min(), rho.max()
     else:
         vmin, vmax = clim[0], clim[1]
 
+    # Plot data
     grid_rho = np.ma.masked_where(np.isnan(grid_rho), grid_rho)
-    ph = plt.pcolormesh(grid_x[:,0],grid_z[0,:],grid_rho.T, clim=(vmin, vmax))
-    cbar = plt.colorbar(format="$10^{%.1f}$",fraction=0.04,orientation="horizontal")
 
-    cmin,cmax = cbar.get_clim()
-    ticks = np.linspace(cmin,cmax,3)
-    cbar.set_ticks(ticks)
-    cbar.ax.tick_params(labelsize=10)
-    
-    if dtype == 'appc':
-        cbar.set_label("App.Cond",size=12)
-    elif dtype == 'appr':
-        cbar.set_label("App.Res.",size=12)
-    elif dtype == 'volt':
-        cbar.set_label("Potential (V)",size=12)
 
-    # Plot apparent resistivity
-    ax.scatter(midx,midz,s=10,c=rho.T, vmin =vmin, vmax = vmax, clim=(vmin, vmax))
 
-    #ax.set_xticklabels([])
-    #ax.set_yticklabels([])
+    ph = plt.pcolormesh(grid_x[:,0],grid_z[0,:],grid_rho.T, vmin = vmin, vmax = vmax)
+    plt.gca().tick_params(axis='both', which='major', labelsize=8)
+
+    if contour is not None:
+        plt.contour(grid_x,grid_z,grid_rho,levels = contour,colors = 'r', vmin = vmin, vmax = vmax)
+
+    # Add scatter points
+    axs.scatter(midx,midz,s=10,c=rho.T, vmin = vmin, vmax = vmax)
+
+    if colorbar:
+
+        if unitType == 'volt':
+            cbar = plt.colorbar(ph, ax = axs, format="%4.1f",fraction=0.04,orientation="horizontal")
+
+        else:
+            cbar = plt.colorbar(ph, ax = axs, format="$10^{%.1f}$",fraction=0.04,orientation="horizontal")
+
+            cmin,cmax = cbar.get_clim()
+            ticks = np.linspace(cmin,cmax,3)
+            cbar.set_ticks(ticks)
+            cbar.ax.tick_params(labelsize=10)
+
+            if unitType == 'appConductivity':
+                cbar.set_label("App.Cond",size=12)
+            elif unitType == 'appResistivity':
+                cbar.set_label("App.Res.",size=12)
+            elif unitType == 'volt':
+                cbar.set_label("Potential (V)",size=12)
+
+
+    if not axlabel:
+        axs.set_xticklabels([])
+        axs.set_yticklabels([])
 
     plt.gca().set_aspect('equal', adjustable='box')
 
@@ -298,27 +307,24 @@ def plot_pseudoSection(DCsurvey, axs, stype='dpdp', dtype="appc", clim=None):
 
     return ph
 
-def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
+def gen_DCIPsurvey(endl, mesh, surveyType, AM_sep, MN_sep, nrx):
     """
-        Load in endpoints and survey specifications to generate Tx, Rx location
-        stations.
+    Load in endpoints and survey specifications to generate Tx, Rx location
+    stations.
 
-        Assumes flat topo for now...
+    Assumes flat topo for now...
 
-        Input:
-        :param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
-        :object mesh -> SimPEG mesh object
-        :switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole) | 'gradient'
-        : param a, n -> pole seperation, number of rx dipoles per tx
+    :param numpy.array endl: input endpoints [[x1, y1] , [x2, y2]]
+    :param Mesh mesh: SimPEG mesh object
+    :param string surveyType: 'dipole-dipole' | 'pole-dipole' | 'gradient'
+    :param float AM_sep: transmitter (A) - receiver (M) seperation
+    :param float b: receiver dipole seperation
+    :param float nrx: pole seperation, number of rx dipoles per tx
 
-        Output:
-        :param Tx, Rx -> List objects for each tx location
-            Lines: P1x, P1y, P1z, P2x, P2y, P2z
+    :rtype: DC.Survey, Src, Rx
+    :returns: DC survey, Source
 
-        Created on Wed December 9th, 2015
-
-        @author: dominiquef
-        !! Require clean up to deal with DCsurvey
+    !! Require clean up to deal with DCsurvey
     """
 
     from SimPEG import np
@@ -334,17 +340,17 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
     dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
     dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
 
-    nstn = np.floor( dl_len / a )
+    nstn = np.floor( dl_len / AM_sep )
 
     # Compute discrete pole location along line
-    stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
-    stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
+    stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*AM_sep
+    stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*AM_sep
 
     # Create line of P1 locations
     M = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
 
     # Create line of P2 locations
-    N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
+    N = np.c_[stn_x+AM_sep*dl_x, stn_y+AM_sep*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
 
     ## Build list of Tx-Rx locations depending on survey type
     # Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
@@ -354,14 +360,14 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
     SrcList = []
 
 
-    if stype != 'gradient':
+    if surveyType != 'gradient':
 
         for ii in range(0, int(nstn)-1):
 
 
-            if stype == 'dpdp':
+            if surveyType == 'dipole-dipole':
                 tx = np.c_[M[ii,:],N[ii,:]]
-            elif stype == 'pdp':
+            elif surveyType == 'pole-dipole':
                 tx = np.c_[M[ii,:],M[ii,:]]
 
             # Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
@@ -370,33 +376,33 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
             AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
 
             # Number of receivers to fit
-            nstn = np.min([np.floor( (AB - b) / a ) , n])
+            nstn = np.min([np.floor( (AB - MN_sep) / AM_sep ) , nrx])
 
             # Check if there is enough space, else break the loop
             if nstn <= 0:
                 continue
 
             # Compute discrete pole location along line
-            stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
-            stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
+            stn_x = N[ii,0] + dl_x*MN_sep + np.array(range(int(nstn)))*dl_x*AM_sep
+            stn_y = N[ii,1] + dl_y*MN_sep + np.array(range(int(nstn)))*dl_y*AM_sep
 
             # Create receiver poles
             # Create line of P1 locations
             P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
 
             # Create line of P2 locations
-            P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
+            P2 = np.c_[stn_x+AM_sep*dl_x, stn_y+AM_sep*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
 
             Rx.append(np.c_[P1,P2])
             rxClass = DC.RxDipole(P1, P2)
             Tx.append(tx)
-            if stype == 'dpdp':
+            if surveyType == 'dipole-dipole':
                 srcClass = DC.SrcDipole([rxClass], M[ii,:],N[ii,:])
-            elif stype == 'pdp':
+            elif surveyType == 'pole-dipole':
                 srcClass = DC.SrcDipole([rxClass], M[ii,:],M[ii,:])
             SrcList.append(srcClass)
 
-    elif stype == 'gradient':
+    elif surveyType == 'gradient':
 
         # Gradient survey only requires Tx at end of line and creates a square
         # grid of receivers at in the middle at a pre-set minimum distance
@@ -404,23 +410,23 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
         Tx.append(np.c_[M[0,:],N[-1,:]])
 
         # Get the edge limit of survey area
-        min_x = endl[0,0] + dl_x * b
-        min_y = endl[0,1] + dl_y * b
+        min_x = endl[0,0] + dl_x * MN_sep
+        min_y = endl[0,1] + dl_y * MN_sep
 
-        max_x = endl[1,0] - dl_x * b
-        max_y = endl[1,1] - dl_y * b
+        max_x = endl[1,0] - dl_x * MN_sep
+        max_y = endl[1,1] - dl_y * MN_sep
 
         box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
         box_w = box_l/2.
 
-        nstn = np.floor( box_l / a )
+        nstn = np.floor( box_l / AM_sep )
 
         # Compute discrete pole location along line
-        stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
-        stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
+        stn_x = min_x + np.array(range(int(nstn)))*dl_x*AM_sep
+        stn_y = min_y + np.array(range(int(nstn)))*dl_y*AM_sep
 
         # Define number of cross lines
-        nlin = int(np.floor( box_w / a ))
+        nlin = int(np.floor( box_w / AM_sep ))
         lind = range(-nlin,nlin+1)
 
         ngrad = nstn * len(lind)
@@ -429,12 +435,12 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
         for ii in range( len(lind) ):
 
             # Move line in perpendicular direction by dipole spacing
-            lxx = stn_x - lind[ii]*a*dl_y
-            lyy = stn_y + lind[ii]*a*dl_x
+            lxx = stn_x - lind[ii]*AM_sep*dl_y
+            lyy = stn_y + lind[ii]*AM_sep*dl_x
 
 
             M = np.c_[ lxx, lyy , np.ones(nstn).T*mesh.vectorNz[-1]]
-            N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
+            N = np.c_[ lxx+AM_sep*dl_x, lyy+AM_sep*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
 
             rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
 
@@ -443,37 +449,37 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
         srcClass = DC.SrcDipole([rxClass], M[0,:], N[-1,:])
         SrcList.append(srcClass)
     else:
-        print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
+        print """surveyType must be either 'pole-dipole', 'dipole-dipole' or 'gradient'. """
 
     survey = DC.SurveyDC(SrcList)
     return survey, Tx, Rx
 
-def writeUBC_DCobs(fileName, DCsurvey, dtype, stype):
+
+def writeUBC_DCobs(fileName, DCsurvey, dim, surveyType, iptype = 0):
     """
         Write UBC GIF DCIP 2D or 3D observation file
 
-        Input:
-        :string fileName -> including path where the file is written out
-        :DCsurvey -> DC survey class object
-        :string dtype ->  either '2D' | '3D'
-        :string  stype ->  either 'SURFACE' | 'GENERAL'
-
-        Output:
-        :param UBC2D-Data file
-        :return
-
-        Last edit: February 16th, 2016
-
-        @author: dominiquef
-
+        :param string fileName: including path where the file is written out
+        :param Survey DCsurvey: DC survey class object
+        :param string dim:  either '2D' | '3D'
+        :param string surveyType:  either 'SURFACE' | 'GENERAL'
+        :rtype: file
+        :return: UBC2D-Data file
     """
+
     from SimPEG import mkvc
 
-    assert (dtype=='2D') | (dtype=='3D'), "Data must be either '2D' | '3D'"
-    assert (stype=='SURFACE') | (stype=='GENERAL') | (stype=='SIMPLE'), "Data must be either 'SURFACE' | 'GENERAL' | 'SIMPLE'"
+    assert (dim=='2D') | (dim=='3D'), "Data must be either '2D' | '3D'"
+    assert (surveyType=='SURFACE') | (surveyType=='GENERAL') | (surveyType=='SIMPLE'), "Data must be either 'SURFACE' | 'GENERAL' | 'SIMPLE'"
 
     fid = open(fileName,'w')
-    fid.write('! ' + stype + ' FORMAT\n')
+    fid.write('! ' + surveyType + ' FORMAT\n')
+
+    if iptype!=0:
+        fid.write('IPTYPE=%i\n'%iptype)
+
+    else:
+        fid.write('! ' + stype + ' FORMAT\n')
 
     count = 0
 
@@ -488,10 +494,10 @@ def writeUBC_DCobs(fileName, DCsurvey, dtype, stype):
         M = rx[0]
         N = rx[1]
 
-        # Adapt source-receiver location for dtype and stype
-        if dtype=='2D':
+        # Adapt source-receiver location for dim and surveyType
+        if dim=='2D':
 
-            if stype == 'SIMPLE':
+            if surveyType == 'SIMPLE':
 
                 #fid.writelines("%e " % ii for ii in mkvc(tx[0,:]))
                 A = np.repeat(tx[0,0],M.shape[0],axis=0)
@@ -504,41 +510,49 @@ def writeUBC_DCobs(fileName, DCsurvey, dtype, stype):
 
             else:
 
-                if stype == 'SURFACE':
+                if surveyType == 'SURFACE':
 
-                    fid.writelines("%e " % ii for ii in mkvc(tx[0,:]))
+                    fid.writelines("%f " % ii for ii in mkvc(tx[0,:]))
                     M = M[:,0]
                     N = N[:,0]
 
-                if stype == 'GENERAL':
+                if surveyType == 'GENERAL':
+
+                    # Flip sign for z-elevation to depth
+                    tx[2::2,:] = -tx[2::2,:]
 
                     fid.writelines("%e " % ii for ii in mkvc(tx[::2,:]))
                     M = M[:,0::2]
                     N = N[:,0::2]
 
+                    # Flip sign for z-elevation to depth
+                    M[:,1::2] = -M[:,1::2]
+                    N[:,1::2] = -N[:,1::2]
+
                 fid.write('%i\n'% nD)
-                np.savetxt(fid, np.c_[ M, N , DCsurvey.dobs[count:count+nD], DCsurvey.std[count:count+nD] ], fmt='%e',delimiter=' ',newline='\n')
+                np.savetxt(fid, np.c_[ M, N , DCsurvey.dobs[count:count+nD], DCsurvey.std[count:count+nD] ], fmt='%f',delimiter=' ',newline='\n')
 
-        if dtype=='3D':
+        if dim=='3D':
 
-            if stype == 'SURFACE':
+            if surveyType == 'SURFACE':
 
                 fid.writelines("%e " % ii for ii in mkvc(tx[0:2,:]))
                 M = M[:,0:2]
                 N = N[:,0:2]
 
-            if stype == 'GENERAL':
+            if surveyType == 'GENERAL':
 
-                fid.writelines("%e " % ii for ii in mkvc(tx))
+                fid.writelines("%e " % ii for ii in mkvc(tx[0:3,:]))
 
             fid.write('%i\n'% nD)
             np.savetxt(fid, np.c_[ M, N , DCsurvey.dobs[count:count+nD], DCsurvey.std[count:count+nD] ], fmt='%e',delimiter=' ',newline='\n')
+            fid.write('\n')
 
         count += nD
 
     fid.close()
 
-def convertObs_DC3D_to_2D(DCsurvey,lineID, flag = 'local'):
+def convertObs_DC3D_to_2D(DCsurvey, lineID, flag='local'):
     """
         Read DC survey and projects the coordinate system
         according to the flag = 'Xloc' | 'Yloc' | 'local' (default)
@@ -547,15 +561,9 @@ def convertObs_DC3D_to_2D(DCsurvey,lineID, flag = 'local'):
 
         The Z value is preserved, but Y coordinates zeroed.
 
-        Input:
-        :param survey3D
-
-        Output:
-        :figure survey2D
-
-        Edited April 6th, 2016
-
-        @author: dominiquef
+        :param DC.Survey survey3D: 3D simpeg DC survey
+        :rtype: DC.Survey
+        :return: survey2D
 
     """
     from SimPEG import np
@@ -641,50 +649,53 @@ def convertObs_DC3D_to_2D(DCsurvey,lineID, flag = 'local'):
 
     return DCsurvey2D
 
-def readUBC_DC3Dobs(fileName):
+def readUBC_DC3Dobs(fileName, rtype = 'DC'):
     """
-        Read UBC GIF DCIP 3D observation file and generate survey
+        Read UBC GIF IP 3D observation file and generate survey
 
-        Input:
-        :param fileName, path to the UBC GIF 3D obs file
-
-        Output:
-        :param DCIPsurvey
-        :return
-
-        Created on Mon April 6th, 2015
-
-        @author: dominiquef
+        :param string fileName:, path to the UBC GIF 3D obs file
+        :rtype: Survey
+        :return: DCIPsurvey
 
     """
+    zflag = True # Flag for z value provided
 
     # Load file
-    obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
+    if rtype == 'IP':
+        obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='IPTYPE')
+
+    elif rtype == 'DC':
+        obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
+
+    else:
+        print "rtype must be 'DC'(default) | 'IP'"
 
     # Pre-allocate
     srcLists = []
     Rx = []
     d = []
     wd = []
-    zflag = True # Flag for z value provided
+
 
     # Countdown for number of obs/tx
     count = 0
     for ii in range(obsfile.shape[0]):
 
+        # Skip if blank line
         if not obsfile[ii]:
             continue
 
-        # First line is transmitter with number of receivers
+        # First line or end of a transmitter block, read transmitter info
         if count==0:
-
-            temp = (np.fromstring(obsfile[ii], dtype=float,sep=' ').T)
+            # Read the line
+            temp = (np.fromstring(obsfile[ii], dtype=float, sep=' ').T)
             count = int(temp[-1])
 
             # Check if z value is provided, if False -> nan
             if len(temp)==5:
-                tx = np.r_[temp[0:2],np.nan,temp[0:2],np.nan]
-                zflag = False
+                tx = np.r_[temp[0:2],np.nan,temp[2:4],np.nan]
+
+                zflag = False # Pass on the flag to the receiver loc
 
             else:
                 tx = temp[:-1]
@@ -692,8 +703,16 @@ def readUBC_DC3Dobs(fileName):
             rx = []
             continue
 
-        temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
+        temp = np.fromstring(obsfile[ii], dtype=float,sep=' ') # Get the string
 
+        # Filter out negative IP
+#        if temp[-2] < 0:
+#            count = count -1
+#            print "Negative!"
+#
+#        else:
+
+        # If the Z-location is provided, otherwise put nan
         if zflag:
 
             rx.append(temp[:-2])
@@ -703,7 +722,7 @@ def readUBC_DC3Dobs(fileName):
                 wd.append(temp[-1])
 
         else:
-            rx.append(np.r_[temp[0:2],np.nan,temp[0:2],np.nan] )
+            rx.append(np.r_[temp[0:2],np.nan,temp[2:4],np.nan] )
             # Check if there is data with the location
             if len(temp)==6:
                 d.append(temp[-2])
@@ -711,7 +730,7 @@ def readUBC_DC3Dobs(fileName):
 
         count = count -1
 
-        # Reach the end of transmitter block
+        # Reach the end of transmitter block, append the src, rx and continue
         if count == 0:
             rx = np.asarray(rx)
             Rx = DC.RxDipole(rx[:,:3],rx[:,3:])
@@ -730,17 +749,9 @@ def readUBC_DC2Dobs(fileName):
         ------- NEEDS TO BE UPDATED ------
         Read UBC GIF 2D observation file and generate arrays for tx-rx location
 
-        Input:
-        :param fileName, path to the UBC GIF 2D model file
-
-        Output:
-        :param rx, tx
-        :return
-
-        Created on Thu Nov 12 13:14:10 2015
-
-        @author: dominiquef
-
+        :param string fileName: path to the UBC GIF 2D model file
+        :rtype: (DC.Src, DC.Rx, ??, ??)
+        :return: source_locs, rx_locs, ??, ??
     """
 
     from SimPEG import np
@@ -780,11 +791,9 @@ def readUBC_DC2Dpre(fileName):
         Read UBC GIF DCIP 2D observation file and generate arrays for tx-rx location
 
         Input:
-        :param fileName, path to the UBC GIF 3D obs file
-
-        Output:
-        DCsurvey
-        :return
+        :param string fileName: path to the UBC GIF 3D obs file
+        :rtype: DC.Survey
+        :return: DCsurvey
 
         Created on Mon March 9th, 2016 << Doug's 70th Birthday !! >>
 
@@ -846,12 +855,9 @@ def readUBC_DC2DMesh(fileName):
     """
         Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
 
-        Input:
-        :param fileName, path to the UBC GIF mesh file
-
-        Output:
-        :param SimPEG TensorMesh 2D object
-        :return
+        :param string fileName: path to the UBC GIF mesh file
+        :rtype: Mesh.TensorMesh
+        :return: SimPEG TensorMesh 2D object
 
         Created on Thu Nov 12 13:14:10 2015
 
@@ -917,12 +923,9 @@ def xy_2_lineID(DCsurvey):
         they were collected. May need to generalize for random
         point locations, but will be more expensive
 
-        Input:
-        :param DCdict Vectors of station location
-
-        Output:
-        :param LineID Vector of integers
-        :return
+        :param numpy.array DCdict: Vectors of station location
+        :rtype: numpy.array
+        :return: LineID Vector of integers
 
         Created on Thu Feb 11, 2015
 

SimPEG/Directives.py

@@ -144,12 +144,18 @@ class BetaSchedule(InversionDirective):
             if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
             self.invProb.beta /= self.coolingFactor
 
+
 class TargetMisfit(InversionDirective):
 
+    chifact = 1.
+    phi_d_star = None
+
     @property
     def target(self):
         if getattr(self, '_target', None) is None:
-            self._target = self.survey.nD*0.5
+            if self.phi_d_star is None:
+                self.phi_d_star = 0.5 * self.survey.nD
+            self._target = self.chifact * self.phi_d_star # the factor of 0.5 is because we do phid = 0.5*|| dpred - dobs||^2
         return self._target
     @target.setter
     def target(self, val):
@@ -207,138 +213,218 @@ class SaveOutputEveryIteration(_SaveEveryIteration):
         f.close()
 
 class SaveOutputDictEveryIteration(_SaveEveryIteration):
-    """SaveOutputDictEveryIteration"""
+    """
+    Saves inversion parameters at every iteraion.
+
+
+    """
 
     def initialize(self):
         print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-%s.npz'"%self.fileName
 
     def endIter(self):
-        # Save the data.
-        ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
-        phi_ms = 0.5*ms.dot(ms)
-        if self.reg.mrefInSmooth == True:
-            mref = self.reg.mref
-        else:
-            mref = 0
-        mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
-        phi_mx = 0.5 * mx.dot(mx)
-        if self.prob.mesh.dim==2:
-            my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_my = 0.5 * my.dot(my)
-        else:
-            phi_my = 'NaN'
-        if self.prob.mesh.dim==3:
-            mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_mz = 0.5 * mz.dot(mz)
-        else:
-            phi_mz = 'NaN'
 
+        # Initialize the output dict
+        outDict = {}
+        # Save the data.
+        outDict['iter'] = self.opt.iter
+        outDict['beta'] = self.invProb.beta
+        outDict['phi_d'] = self.invProb.phi_d
+        outDict['phi_ms'] = self.reg._evalSmall(self.invProb.curModel)
+        outDict['phi_mx'] = self.reg._evalSmoothx(self.invProb.curModel)
+        outDict['phi_my'] = self.reg._evalSmoothy(self.invProb.curModel) if self.prob.mesh.dim >= 2 else 'NaN'
+        outDict['phi_mz'] = self.reg._evalSmoothz(self.invProb.curModel) if self.prob.mesh.dim==3 else 'NaN'
+        outDict['f'] = self.opt.f
+        outDict['m'] = self.invProb.curModel
+        outDict['dpred'] = self.invProb.dpred
 
         # Save the file as a npz
-        np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
+        np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), outDict)
 
-class SaveOutputDictEveryIteration(_SaveEveryIteration):
-    """SaveOutputDictEveryIteration
-    A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
-    """
-
-    def initialize(self):
-        print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
-
-    def endIter(self):
-        # Save the data.
-        ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
-        phi_ms = 0.5*ms.dot(ms)
-        if self.reg.mrefInSmooth == True:
-            mref = self.reg.mref
-        else:
-            mref = 0
-        mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
-        phi_mx = 0.5 * mx.dot(mx)
-        if self.prob.mesh.dim==2:
-            my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_my = 0.5 * my.dot(my)
-        else:
-            phi_my = 'NaN'
-        if self.prob.mesh.dim==3 and 'CYL' not in self.prob.mesh._meshType:
-            mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_mz = 0.5 * mz.dot(mz)
-        else:
-            phi_mz = 'NaN'
-
-
-        # Save the file as a npz
-        np.savez('{:s}-{:03d}'.format(self.fileName,self.opt.iter), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
-
-
-# class UpdateReferenceModel(Parameter):
-
-#     mref0 = None
-
-#     def nextIter(self):
-#         mref = getattr(self, 'm_prev', None)
-#         if mref is None:
-#             if self.debug: print 'UpdateReferenceModel is using mref0'
-#             mref = self.mref0
-#         self.m_prev = self.invProb.m_current
-#         return mref
-
-class update_IRLS(InversionDirective):
+class Update_IRLS(InversionDirective):
 
     eps_min = None
+    eps_p = None
+    eps_q = None
+    norms = [2.,2.,2.,2.]
     factor = None
     gamma = None
     phi_m_last = None
     phi_d_last = None
-    
+    f_old = None
+    f_min_change = 1e-2
+    beta_tol = 5e-2
+
+    # Solving parameter for IRLS (mode:2)
+    IRLSiter   = 0
+    minGNiter = 5
+    maxIRLSiter = 10
+    iterStart = 0
+
+    # Beta schedule
+    coolingFactor = 2.
+    coolingRate = 1
+
+    mode = 1
+
+    @property
+    def target(self):
+        if getattr(self, '_target', None) is None:
+            self._target = self.survey.nD*0.5
+        return self._target
+    @target.setter
+    def target(self, val):
+        self._target = val
+
     def initialize(self):
 
-        # Scale the regularization for changes in norm
-        if getattr(self, 'phi_m_last', None) is not None:
-            self.reg.gamma = 1.
-            phim_new = self.reg.eval(self.invProb.curModel)
-            self.gamma = self.phi_m_last / phim_new
+        if self.mode == 1:
+            self.reg.norms = [2., 2., 2., 2.]
 
-        self.reg.curModel = self.invProb.curModel
-        self.reg.gamma = self.gamma
-        
-        if getattr(self, 'phi_d_last', None) is None:
+    def endIter(self):
+
+        # After reaching target misfit with l2-norm, switch to IRLS (mode:2)
+        if self.invProb.phi_d < self.target and self.mode == 1:
+            print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
+
+            self.mode = 2
+            print self.eps_p, self.eps_q, self.norms
+            self.reg.eps_p = self.eps_p
+            self.reg.eps_q = self.eps_q
+            self.reg.norms = self.norms
+            self.coolingFactor = 1.
+            self.coolingRate = 1
+            self.iterStart = self.opt.iter
             self.phi_d_last = self.invProb.phi_d
-            
+            self.phi_m_last = self.invProb.phi_m_last
+
+            self.reg.l2model = self.invProb.curModel
+            self.reg.curModel = self.invProb.curModel
+
+            if getattr(self, 'f_old', None) is None:
+                self.f_old = self.reg.eval(self.invProb.curModel)#self.invProb.evalFunction(self.invProb.curModel, return_g=False, return_H=False)
+
+        # Beta Schedule
+        if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
+            if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
+            self.invProb.beta /= self.coolingFactor
+
+
+        # Only update after GN iterations
+        if (self.opt.iter-self.iterStart) % self.minGNiter == 0 and self.mode==2:
+
+            self.IRLSiter += 1
+
+            phim_new = self.reg.eval(self.invProb.curModel)
+            self.f_change = np.abs(self.f_old - phim_new) / self.f_old
+
+            print "Regularization decrease: %6.3e" % (self.f_change)
+
+            # Check for maximum number of IRLS cycles
+            if self.IRLSiter == self.maxIRLSiter:
+                print "Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter
+                self.opt.stopNextIteration = True
+                return
+
+            # Check if the function has changed enough
+            if self.f_change < self.f_min_change and self.IRLSiter > 1:
+                print "Minimum decrease in regularization. End of IRLS"
+                self.opt.stopNextIteration = True
+                return
+            else:
+                self.f_old = phim_new
+
+            # Cool the threshold parameter if required
+            if getattr(self, 'factor', None) is not None:
+                eps = self.reg.eps / self.factor
+
+                if getattr(self, 'eps_min', None) is not None:
+                    self.reg.eps = np.max([self.eps_min,eps])
+                else:
+                    self.reg.eps = eps
+
+            # Get phi_m at the end of current iteration
+            self.phi_m_last = self.invProb.phi_m_last
+
+            # Reset the regularization matrices so that it is
+            # recalculated for current model
+            self.reg._Wsmall = None
+            self.reg._Wx = None
+            self.reg._Wy = None
+            self.reg._Wz = None
+
+             # Update the model used for the IRLS weights
+            self.reg.curModel = self.invProb.curModel
+
+            # Temporarely set gamma to 1. to get raw phi_m
+            self.reg.gamma = 1.
+
+            # Compute new model objective function value
+            phim_new = self.reg.eval(self.invProb.curModel)
+
+            # Update gamma to scale the regularization between IRLS iterations
+            self.reg.gamma = self.phi_m_last / phim_new
+
+            # Reset the regularization matrices again for new gamma
+            self.reg._Wsmall = None
+            self.reg._Wx = None
+            self.reg._Wy = None
+            self.reg._Wz = None
+
+            # Check if misfit is within the tolerance, otherwise scale beta
+            val = self.invProb.phi_d / (self.survey.nD*0.5)
+
+            if np.abs(1.-val) > self.beta_tol:
+                self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
+
+class Update_lin_PreCond(InversionDirective):
+    """
+    Create a Jacobi preconditioner for the linear problem
+    """
+    onlyOnStart=False
+
+    def initialize(self):
+
+        if getattr(self.opt, 'approxHinv', None) is None:
+            # Update the pre-conditioner
+            diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() #* (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
+            PC     = Utils.sdiag((self.prob.mapping.deriv(None).T *diagA)**-1.)
+            self.opt.approxHinv = PC
+
     def endIter(self):
         # Cool the threshold parameter
-        if getattr(self, 'factor', None) is not None:
-            eps = self.reg.eps / self.factor
+        if self.onlyOnStart==True:
+            return
 
-            if getattr(self, 'eps_min', None) is not None:
-                self.reg.eps = np.max([self.eps_min,eps])
-            else:
-                self.reg.eps = eps
+        if getattr(self.opt, 'approxHinv', None) is not None:
+            # Update the pre-conditioner
+            diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() #* (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
+            PC     = Utils.sdiag((self.prob.mapping.deriv(None).T *diagA)**-1.)
+            self.opt.approxHinv = PC
 
-        # Get phi_m at the end of current iteration
-        self.phi_m_last = self.invProb.phi_m_last
-        
-         # Update the model used for the IRLS weights
-        self.reg.curModel = self.invProb.curModel
 
-        # Update the pre-conditioner
-        diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() * (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
-        PC     = Utils.sdiag(diagA**-1.)
-        self.opt.approxHinv = PC
-         
-        # Temporarely set gamma to 1.
-        self.reg.gamma = 1.
-        
-        # Compute change in model objective function and update scaling
-        phim_new = self.reg.eval(self.invProb.curModel)
-        self.reg.gamma = self.phi_m_last / phim_new
-        
-        # TO DO: Re-scale beta if too much change in misfit
-        self.invProb.beta = self.invProb.beta * self.phi_d_last / self.invProb.phi_d
-        
-#==============================================================================
-#          import pylab as plt
-#          plt.figure()
-#          ax = plt.subplot(221)
-#          self.prob.mesh.plotSlice(self.invProb.curModel, ax = ax, normal = 'Z', ind=-5, clim = (0, 0.005))
-#==============================================================================
+class Update_Wj(InversionDirective):
+    """
+        Create approx-sensitivity base weighting using the probing method
+    """
+    k = None # Number of probing cycles
+    itr = None # Iteration number to update Wj, or always update if None
+
+    def endIter(self):
+
+        if self.itr is None or self.itr == self.opt.iter:
+
+            m = self.invProb.curModel
+            if self.k is None:
+                self.k = int(self.survey.nD/10)
+
+            def JtJv(v):
+
+                Jv = self.prob.Jvec(m, v)
+
+                return self.prob.Jtvec(m,Jv)
+
+            JtJdiag = Utils.diagEst(JtJv,len(m),k=self.k)
+            JtJdiag = JtJdiag / max(JtJdiag)
+
+            self.reg.wght = JtJdiag

SimPEG/EM/Analytics/DC.py

@@ -0,0 +1,118 @@
+import numpy as np
+from scipy.constants import mu_0, pi
+from scipy import special
+
+def DCAnalyticHalf(txloc, rxlocs, sigma, earth_type="wholespace"):
+    """
+        Analytic solution for electric potential from a postive pole
+
+        :param array txloc: a xyz location of A (+) electrode (np.r_[xa, ya, za])
+        :param list rxlocs: xyz locations of M (+) and N (-) electrodes [M, N]
+
+            e.g.
+            rxlocs = [M, N]
+            M: xyz locations of M (+) electrode (np.c_[xmlocs, ymlocs, zmlocs])
+            N: xyz locations of N (-) electrode (np.c_[xnlocs, ynlocs, znlocs])
+
+        :param float or complex sigma: values of conductivity
+        :param string earth_type: values of conductivity ("wholsespace" or "halfspace")
+
+    """
+    M = rxlocs[0]
+    N = rxlocs[1]
+
+    rM = np.sqrt( (M[:,0]-txloc[0])**2 + (M[:,1]-txloc[1])**2 + (M[:,2]-txloc[1])**2 )
+    rN = np.sqrt( (N[:,0]-txloc[0])**2 + (N[:,1]-txloc[1])**2 + (N[:,2]-txloc[1])**2 )
+
+    phiM = 1./(4*np.pi*rM*sigma)
+    phiN = 1./(4*np.pi*rN*sigma)
+    phi = phiM - phiN
+
+    if earth_type == "halfspace":
+        phi *= 2
+
+    return phi
+
+deg2rad  = lambda deg: deg/180.*np.pi
+rad2deg  = lambda rad: rad*180./np.pi
+
+def DCAnalyticSphere(txloc, rxloc, xc, radius, sigma, sigma1, \
+                 field_type = "secondary", order=12, halfspace=False):
+# def DCSpherePointCurrent(txloc, rxloc, xc, radius, rho, rho1, \
+#                  field_type = "secondary", order=12):
+    """
+
+        Parameters:
+
+            :param array txloc: A (+) current electrode location (x,y,z)
+            :param array xc: x center of depressed sphere
+            :param array rxloc: M(+) electrode locations / (Nx3 array, # of electrodes)
+
+            :param float radius: radius (float): radius of the sphere (m)
+            :param float rho: resistivity of the background (ohm-m)
+            :param float rho1: resistivity of the sphere
+            :param string field_type: : "secondary", "total", "primary"
+                  (default="secondary")
+                  "secondary": secondary potential only due to sphere
+                  "primary": primary potential from the point source
+                  "total": "secondary"+"primary"
+            :param float order: maximum order of Legendre polynomial (default=12)
+
+        Written by Seogi Kang (skang@eos.ubc.ca)
+        Ph.D. Candidate of University of British Columbia, Canada
+
+    """
+
+    Pleg = []
+    # Compute Legendre Polynomial
+    for i in range(order):
+        Pleg.append(special.legendre(i, monic=0))
+
+
+    rho = 1./sigma
+    rho1 = 1./sigma1
+
+    # Center of the sphere should be aligned in txloc in y-direction
+    yc = txloc[1]
+    xyz = np.c_[rxloc[:,0]-xc, rxloc[:,1]-yc, rxloc[:,2]]
+    r = np.sqrt( (xyz**2).sum(axis=1) )
+
+    x0 = abs(txloc[0]-xc)
+
+    costheta = xyz[:,0]/r * (txloc[0]-xc)/x0
+    phi = np.zeros_like(r)
+    R = (r**2+x0**2.-2.*r*x0*costheta)**0.5
+    # primary potential in a whole space
+    prim = rho*1./(4*np.pi*R)
+
+    if field_type =="primary":
+        return prim
+
+    sphind = r < radius
+    out = np.zeros_like(r)
+    for n in range(order):
+        An, Bn = AnBnfun(n, radius, x0, rho, rho1)
+        dumout = An*r[~sphind]**(-n-1.)*Pleg[n](costheta[~sphind])
+        out[~sphind] += dumout
+        dumin = Bn*r[sphind]**(n)*Pleg[n](costheta[sphind])
+        out[sphind] += dumin
+
+    out[~sphind] += prim[~sphind]
+
+    if halfspace:
+        scale = 2
+    else:
+        scale = 1
+
+    if field_type == "secondary":
+        return scale*(out-prim)
+    elif field_type == "total":
+        return scale*out
+
+def AnBnfun(n, radius, x0, rho, rho1, I=1.):
+    const = I*rho/(4*np.pi)
+    bunmo = n*rho + (n+1)*rho1
+    An = const * radius**(2*n+1) / x0 ** (n+1.) * n * \
+        (rho1-rho) / bunmo
+    Bn = const * 1. / x0 ** (n+1.) * (2*n+1) * (rho1) / bunmo
+    return An, Bn

SimPEG/EM/Analytics/__init__.py

@@ -1,3 +1,4 @@
 from TDEM import hzAnalyticDipoleT
 from FDEM import hzAnalyticDipoleF
 from FDEMcasing import *
+from DC import DCAnalyticHalf, DCAnalyticSphere

SimPEG/EM/Base.py

@@ -1,6 +1,7 @@
 from SimPEG import Survey, Problem, Utils, Models, Maps, PropMaps, np, sp, Solver as SimpegSolver
 from scipy.constants import mu_0
 
+
 class EMPropMap(Maps.PropMap):
     """
         Property Map for EM Problems. The electrical conductivity (\\(\\sigma\\)) is the default inversion property, and the default value of the magnetic permeability is that of free space (\\(\\mu = 4\\pi\\times 10^{-7} \\) H/m)
@@ -61,6 +62,15 @@ class BaseEMProblem(Problem.BaseProblem):
             self._Me = self.mesh.getEdgeInnerProduct()
         return self._Me
 
+    @property
+    def MeI(self):
+        """
+            Edge inner product matrix
+        """
+        if getattr(self, '_MeI', None) is None:
+            self._MeI = self.mesh.getEdgeInnerProduct(invMat=True)
+        return self._MeI
+
     @property
     def Mf(self):
         """
@@ -70,6 +80,20 @@ class BaseEMProblem(Problem.BaseProblem):
             self._Mf = self.mesh.getFaceInnerProduct()
         return self._Mf
 
+    @property
+    def MfI(self):
+        """
+            Face inner product matrix
+        """
+        if getattr(self, '_MfI', None) is None:
+            self._MfI = self.mesh.getFaceInnerProduct(invMat=True)
+        return self._MfI
+
+    @property
+    def Vol(self):
+        if getattr(self, '_Vol', None) is None:
+            self._Vol = Utils.sdiag(self.mesh.vol)
+        return self._Vol
 
     # ----- Magnetic Permeability ----- #
     @property
@@ -127,7 +151,6 @@ class BaseEMProblem(Problem.BaseProblem):
         """
         return self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma)(u) * self.curModel.sigmaDeriv
 
-
     @property
     def MeSigmaI(self):
         """
@@ -146,10 +169,7 @@ class BaseEMProblem(Problem.BaseProblem):
 
         dMeSigmaI_dI = -self.MeSigmaI**2
         dMe_dsig = self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma)(u)
-        dsig_dm = self.curModel.sigmaDeriv
-        return dMeSigmaI_dI * ( dMe_dsig * ( dsig_dm))
-        # return self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma, invMat=True)(u)
-
+        return dMeSigmaI_dI * ( dMe_dsig * self.curModel.sigmaDeriv )
 
     @property
     def MfRho(self):
@@ -165,8 +185,7 @@ class BaseEMProblem(Problem.BaseProblem):
         """
             Derivative of :code:`MfRho` with respect to the model.
         """
-        return self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u) * (-Utils.sdiag(self.curModel.rho**2) * self.curModel.sigmaDeriv)
-        # self.curModel.rhoDeriv
+        return self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u) * self.curModel.rhoDeriv
 
     @property
     def MfRhoI(self):
@@ -183,7 +202,10 @@ class BaseEMProblem(Problem.BaseProblem):
         """
             Derivative of :code:`MfRhoI` with respect to the model.
         """
-        return self.mesh.getFaceInnerProductDeriv(self.curModel.rho, invMat=True)(u) * self.curModel.rhoDeriv
+
+        dMfRhoI_dI = -self.MfRhoI**2
+        dMf_drho = self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u)
+        return dMfRhoI_dI * ( dMf_drho * self.curModel.rhoDeriv )
 
 class BaseEMSurvey(Survey.BaseSurvey):
 
@@ -192,7 +214,7 @@ class BaseEMSurvey(Survey.BaseSurvey):
         self.srcList = srcList
         Survey.BaseSurvey.__init__(self, **kwargs)
 
-    def eval(self, u):
+    def eval(self, f):
         """
         Project fields to receiver locations
         :param Fields u: fields object
@@ -202,8 +224,8 @@ class BaseEMSurvey(Survey.BaseSurvey):
         data = Survey.Data(self)
         for src in self.srcList:
             for rx in src.rxList:
-                data[src, rx] = rx.eval(src, self.mesh, u)
+                data[src, rx] = rx.eval(src, self.mesh, f)
         return data
 
-    def evalDeriv(self, u):
+    def evalDeriv(self, f):
         raise Exception('Use Receivers to project fields deriv.')

SimPEG/EM/FDEM/FieldsFDEM.py

@@ -160,9 +160,9 @@ class Fields(SimPEG.Problem.Fields):
             return self._jDeriv_u(src, v, adjoint), self._jDeriv_m(src, v, adjoint)
         return np.array(self._jDeriv_u(src, du_dm_v, adjoint) + self._jDeriv_m(src, v, adjoint), dtype = complex)
 
-class Fields_e(Fields):
+class Fields3D_e(Fields):
     """
-    Fields object for Problem_e.
+    Fields object for Problem3D_e.
 
     :param Mesh mesh: mesh
     :param Survey survey: survey
@@ -181,7 +181,7 @@ class Fields_e(Fields):
                   }
 
     def __init__(self, mesh, survey, **kwargs):
-        Fields.__init__(self,mesh,survey,**kwargs)
+        Fields.__init__(self, mesh, survey, **kwargs)
 
     def startup(self):
         self.prob = self.survey.prob
@@ -426,9 +426,9 @@ class Fields_e(Fields):
 
 
 
-class Fields_b(Fields):
+class Fields3D_b(Fields):
     """
-    Fields object for Problem_b.
+    Fields object for Problem3D_b.
 
     :param Mesh mesh: mesh
     :param Survey survey: survey
@@ -693,9 +693,9 @@ class Fields_b(Fields):
         return Zero()
 
 
-class Fields_j(Fields):
+class Fields3D_j(Fields):
     """
-    Fields object for Problem_j.
+    Fields object for Problem3D_j.
 
     :param Mesh mesh: mesh
     :param Survey survey: survey
@@ -988,9 +988,9 @@ class Fields_j(Fields):
         return 1./(1j * omega(src.freq)) * VI * (self._aveE2CCV * ( s_mDeriv(v) - self._edgeCurl.T * ( self._MfRhoDeriv(jSolution) * v ) ) )
 
 
-class Fields_h(Fields):
+class Fields3D_h(Fields):
     """
-    Fields object for Problem_h.
+    Fields object for Problem3D_h.
 
     :param Mesh mesh: mesh
     :param Survey survey: survey

SimPEG/EM/FDEM/ProblemFDEM.py

@@ -1,7 +1,7 @@
 from SimPEG import Problem, Utils, np, sp, Solver as SimpegSolver
 from scipy.constants import mu_0
 from SurveyFDEM import Survey as SurveyFDEM
-from FieldsFDEM import Fields, Fields_e, Fields_b, Fields_h, Fields_j
+from FieldsFDEM import Fields, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
 from SimPEG.EM.Base import BaseEMProblem
 from SimPEG.EM.Utils import omega
 
@@ -17,8 +17,8 @@ class BaseFDEMProblem(BaseEMProblem):
             \mathbf{C} \mathbf{e} + i \omega \mathbf{b} = \mathbf{s_m} \\\\
             {\mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{M_{\sigma}^e} \mathbf{e} = \mathbf{s_e}}
 
-        if using the E-B formulation (:code:`Problem_e`
-        or :code:`Problem_b`). Note that in this case, :math:`\mathbf{s_e}` is an integrated quantity.
+        if using the E-B formulation (:code:`Problem3D_e`
+        or :code:`Problem3D_b`). Note that in this case, :math:`\mathbf{s_e}` is an integrated quantity.
 
         If we write Maxwell's equations in terms of
         \\\(\\\mathbf{h}\\\) and current density \\\(\\\mathbf{j}\\\)
@@ -28,7 +28,7 @@ class BaseFDEMProblem(BaseEMProblem):
             \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{j} + i \omega \mathbf{M_{\mu}^e} \mathbf{h} = \mathbf{s_m} \\\\
             \mathbf{C} \mathbf{h} - \mathbf{j} = \mathbf{s_e}
 
-        if using the H-J formulation (:code:`Problem_j` or :code:`Problem_h`). Note that here, :math:`\mathbf{s_m}` is an integrated quantity.
+        if using the H-J formulation (:code:`Problem3D_j` or :code:`Problem3D_h`). Note that here, :math:`\mathbf{s_m}` is an integrated quantity.
 
         The problem performs the elimination so that we are solving the system for \\\(\\\mathbf{e},\\\mathbf{b},\\\mathbf{j} \\\) or \\\(\\\mathbf{h}\\\)
     """
@@ -87,7 +87,7 @@ class BaseFDEMProblem(BaseEMProblem):
                 du_dm_v = Ainv * ( - dA_dm_v + dRHS_dm_v )
 
                 for rx in src.rxList:
-                    df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                    df_dmFun = getattr(f, '_{0}Deriv'.format(rx.projField), None)
                     df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
                     Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
             Ainv.clean()
@@ -125,7 +125,7 @@ class BaseFDEMProblem(BaseEMProblem):
                 for rx in src.rxList:
                     PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
 
-                    df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                    df_duTFun = getattr(f, '_{0}Deriv'.format(rx.projField), None)
                     df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
 
                     ATinvdf_duT = ATinv * df_duT
@@ -137,10 +137,9 @@ class BaseFDEMProblem(BaseEMProblem):
                     df_dmT = df_dmT + du_dmT
 
                     # TODO: this should be taken care of by the reciever?
-                    real_or_imag = rx.projComp
-                    if real_or_imag is 'real':
+                    if rx.component is 'real':
                         Jtv +=   np.array(df_dmT, dtype=complex).real
-                    elif real_or_imag is 'imag':
+                    elif rx.component is 'imag':
                         Jtv += - np.array(df_dmT, dtype=complex).real
                     else:
                         raise Exception('Must be real or imag')
@@ -167,6 +166,7 @@ class BaseFDEMProblem(BaseEMProblem):
 
         for i, src in enumerate(Srcs):
             smi, sei = src.eval(self)
+            #Why are you adding?
             s_m[:,i] = s_m[:,i] + smi
             s_e[:,i] = s_e[:,i] + sei
 
@@ -177,7 +177,7 @@ class BaseFDEMProblem(BaseEMProblem):
 ################################ E-B Formulation #########################################
 ##########################################################################################
 
-class Problem_e(BaseFDEMProblem):
+class Problem3D_e(BaseFDEMProblem):
     """
     By eliminating the magnetic flux density using
 
@@ -199,7 +199,7 @@ class Problem_e(BaseFDEMProblem):
 
     _solutionType = 'eSolution'
     _formulation  = 'EB'
-    fieldsPair    = Fields_e
+    fieldsPair    = Fields3D_e
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -288,7 +288,7 @@ class Problem_e(BaseFDEMProblem):
             return C.T * (MfMui * s_mDeriv(v)) -1j * omega(freq) * s_eDeriv(v)
 
 
-class Problem_b(BaseFDEMProblem):
+class Problem3D_b(BaseFDEMProblem):
     """
     We eliminate :math:`\mathbf{e}` using
 
@@ -310,7 +310,7 @@ class Problem_b(BaseFDEMProblem):
 
     _solutionType = 'bSolution'
     _formulation  = 'EB'
-    fieldsPair    = Fields_b
+    fieldsPair    = Fields3D_b
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -436,7 +436,7 @@ class Problem_b(BaseFDEMProblem):
 ##########################################################################################
 
 
-class Problem_j(BaseFDEMProblem):
+class Problem3D_j(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{h}\\\) using
 
@@ -458,7 +458,7 @@ class Problem_j(BaseFDEMProblem):
 
     _solutionType = 'jSolution'
     _formulation  = 'HJ'
-    fieldsPair    = Fields_j
+    fieldsPair    = Fields3D_j
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -577,7 +577,7 @@ class Problem_j(BaseFDEMProblem):
 
 
 
-class Problem_h(BaseFDEMProblem):
+class Problem3D_h(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{j}\\\) using
 
@@ -596,7 +596,7 @@ class Problem_h(BaseFDEMProblem):
 
     _solutionType = 'hSolution'
     _formulation  = 'HJ'
-    fieldsPair    = Fields_h
+    fieldsPair    = Fields3D_h
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)

SimPEG/EM/FDEM/RxFDEM.py

@@ -0,0 +1,126 @@
+import SimPEG
+from SimPEG import sp
+
+class BaseRx(SimPEG.Survey.BaseRx):
+    """
+    Frequency domain receiver base class
+
+    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
+    :param string orientation: receiver orientation 'x', 'y' or 'z'
+    :param string component: real or imaginary component 'real' or 'imag'
+    """
+
+    def __init__(self, locs, orientation=None, component=None):
+        assert(orientation in ['x','y','z']), "Orientation %s not known. Orientation must be in 'x', 'y', 'z'. Arbitrary orientations have not yet been implemented."%orientation
+        assert(component in ['real', 'imag']), "'component' must be 'real' or 'imag', not %s"%component
+
+        self.projComp = orientation
+        self.component = component
+
+        SimPEG.Survey.BaseRx.__init__(self, locs, rxType=None) #TODO: remove rxType from baseRx
+
+    def projGLoc(self, u):
+        """Grid Location projection (e.g. Ex Fy ...)"""
+        return u._GLoc(self.projField) + self.projComp
+
+    def eval(self, src, mesh, f):
+        """
+        Project fields to recievers to get data.
+
+        :param Source src: FDEM source
+        :param Mesh mesh: mesh used
+        :param Fields f: fields object
+        :rtype: numpy.ndarray
+        :return: fields projected to recievers
+        """
+
+        P = self.getP(mesh, self.projGLoc(f))
+        f_part_complex = f[src, self.projField]
+        f_part = getattr(f_part_complex, self.component) # get the real or imag component
+
+        return P*f_part
+
+    def evalDeriv(self, src, mesh, f, v, adjoint=False):
+        """
+        Derivative of projected fields with respect to the inversion model times a vector.
+
+        :param Source src: FDEM source
+        :param Mesh mesh: mesh used
+        :param Fields f: fields object
+        :param numpy.ndarray v: vector to multiply
+        :rtype: numpy.ndarray
+        :return: fields projected to recievers
+        """
+
+        P = self.getP(mesh, self.projGLoc(f))
+
+        if not adjoint:
+            Pv_complex = P * v
+            Pv = getattr(Pv_complex, self.component)
+        elif adjoint:
+            Pv_real = P.T * v
+
+            if self.component == 'imag':
+                Pv = 1j*Pv_real
+            elif self.component == 'real':
+                Pv = Pv_real.astype(complex)
+            else:
+                raise NotImplementedError('must be real or imag')
+
+        return Pv
+
+
+class Point_e(BaseRx):
+    """
+    Electric field FDEM receiver
+
+    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
+    :param string orientation: receiver orientation 'x', 'y' or 'z'
+    :param string component: real or imaginary component 'real' or 'imag'
+    """
+
+    def __init__(self, locs, orientation=None, component=None):
+        self.projField = 'e'
+        super(Point_e, self).__init__(locs, orientation, component)
+
+
+class Point_b(BaseRx):
+    """
+    Magnetic flux FDEM receiver
+
+    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
+    :param string orientation: receiver orientation 'x', 'y' or 'z'
+    :param string component: real or imaginary component 'real' or 'imag'
+    """
+
+    def __init__(self, locs, orientation=None, component=None):
+        self.projField = 'b'
+        super(Point_b, self).__init__(locs, orientation, component)
+
+
+class Point_h(BaseRx):
+    """
+    Magnetic field FDEM receiver
+
+    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
+    :param string orientation: receiver orientation 'x', 'y' or 'z'
+    :param string component: real or imaginary component 'real' or 'imag'
+    """
+
+    def __init__(self, locs, orientation=None, component=None):
+        self.projField = 'h'
+        super(Point_h, self).__init__(locs, orientation, component)
+
+
+class Point_j(BaseRx):
+    """
+    Current density FDEM receiver
+
+    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
+    :param string orientation: receiver orientation 'x', 'y' or 'z'
+    :param string component: real or imaginary component 'real' or 'imag'
+    """
+
+    def __init__(self, locs, orientation=None, component=None):
+        self.projField = 'j'
+        super(Point_j, self).__init__(locs, orientation, component)

SimPEG/EM/FDEM/SrcFDEM.py

@@ -9,12 +9,17 @@ class BaseSrc(Survey.BaseSrc):
     """
 
     freq = None
-    # rxPair = RxFDEM
-    integrate = True
+    integrate = False
+    _ePrimary = None
+    _bPrimary = None
+    _hPrimary = None
+    _jPrimary = None
+
+    def __init__(self, rxList, **kwargs):
+        Survey.BaseSrc.__init__(self, rxList, **kwargs)
 
     def eval(self, prob):
         """
-        Evaluate the source terms.
         - :math:`s_m` : magnetic source term
         - :math:`s_e` : electric source term
 
@@ -51,7 +56,9 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary magnetic flux density
         """
-        return Zero()
+        if self._bPrimary is None:
+            return Zero()
+        return self._bPrimary
 
     def hPrimary(self, prob):
         """
@@ -61,7 +68,9 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
-        return Zero()
+        if self._hPrimary is None:
+            return Zero()
+        return self._hPrimary
 
     def ePrimary(self, prob):
         """
@@ -71,7 +80,9 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary electric field
         """
-        return Zero()
+        if self._ePrimary is None:
+            return Zero()
+        return self._ePrimary
 
     def jPrimary(self, prob):
         """
@@ -81,7 +92,9 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary current density
         """
-        return Zero()
+        if self._jPrimary is None:
+            return Zero()
+        return self._jPrimary
 
     def s_m(self, prob):
         """
@@ -136,15 +149,14 @@ class RawVec_e(BaseSrc):
     :param list rxList: receiver list
     :param float freq: frequency
     :param numpy.array s_e: electric source term
-    :param bool integrate: Integrate the source term (multiply by Me) [True]
+    :param bool integrate: Integrate the source term (multiply by Me) [False]
     """
 
-    def __init__(self, rxList, freq, s_e, integrate=True): #, ePrimary=None, bPrimary=None, hPrimary=None, jPrimary=None):
+    def __init__(self, rxList, freq, s_e, **kwargs):
         self._s_e = np.array(s_e, dtype=complex)
         self.freq = float(freq)
-        self.integrate = integrate
 
-        BaseSrc.__init__(self, rxList)
+        BaseSrc.__init__(self, rxList, **kwargs)
 
     def s_e(self, prob):
         """
@@ -166,15 +178,14 @@ class RawVec_m(BaseSrc):
     :param float freq: frequency
     :param rxList: receiver list
     :param numpy.array s_m: magnetic source term
-    :param bool integrate: Integrate the source term (multiply by Me) [True]
+    :param bool integrate: Integrate the source term (multiply by Me) [False]
     """
 
-    def __init__(self, rxList, freq, s_m, integrate=True):  #ePrimary=Zero(), bPrimary=Zero(), hPrimary=Zero(), jPrimary=Zero()):
+    def __init__(self, rxList, freq, s_m, **kwargs):  #ePrimary=Zero(), bPrimary=Zero(), hPrimary=Zero(), jPrimary=Zero()):
         self._s_m = np.array(s_m, dtype=complex)
         self.freq = float(freq)
-        self.integrate = integrate
 
-        BaseSrc.__init__(self, rxList)
+        BaseSrc.__init__(self, rxList, **kwargs)
 
     def s_m(self, prob):
         """
@@ -197,14 +208,13 @@ class RawVec(BaseSrc):
     :param float freq: frequency
     :param numpy.array s_m: magnetic source term
     :param numpy.array s_e: electric source term
-    :param bool integrate: Integrate the source term (multiply by Me) [True]
+    :param bool integrate: Integrate the source term (multiply by Me) [False]
     """
-    def __init__(self, rxList, freq, s_m, s_e, integrate=True):
+    def __init__(self, rxList, freq, s_m, s_e, **kwargs):
         self._s_m = np.array(s_m, dtype=complex)
         self._s_e = np.array(s_e, dtype=complex)
         self.freq = float(freq)
-        self.integrate = integrate
-        BaseSrc.__init__(self, rxList)
+        BaseSrc.__init__(self, rxList, **kwargs)
 
     def s_m(self, prob):
         """
@@ -278,14 +288,13 @@ class MagDipole(BaseSrc):
     :param float mu: background magnetic permeability
     """
 
-    def __init__(self, rxList, freq, loc, orientation='Z', moment=1., mu=mu_0):
+    def __init__(self, rxList, freq, loc, orientation='Z', moment=1., mu=mu_0, **kwargs):
         self.freq = float(freq)
         self.loc = loc
         self.orientation = orientation
         assert orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
         self.moment = moment
         self.mu = mu
-        self.integrate = False
         BaseSrc.__init__(self, rxList)
 
     def bPrimary(self, prob):
@@ -543,7 +552,7 @@ class CircularLoop(BaseSrc):
             if not prob.mesh.isSymmetric:
                 # TODO ?
                 raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            a = MagneticDipoleVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
+            a = MagneticLoopVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
 
         else:
             srcfct = MagneticDipoleVectorPotential

SimPEG/EM/FDEM/SurveyFDEM.py

@@ -4,126 +4,9 @@ from SimPEG.EM.Base import BaseEMSurvey
 from scipy.constants import mu_0
 from SimPEG.Utils import Zero, Identity
 import SrcFDEM as Src
+import RxFDEM as Rx
 from SimPEG import sp
 
-
-####################################################
-# Receivers
-####################################################
-
-class Rx(SimPEG.Survey.BaseRx):
-    """
-    Frequency domain receivers
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string rxType: reciever type from knownRxTypes
-    """
-
-    knownRxTypes = {
-                    'exr':['e', 'x', 'real'],
-                    'eyr':['e', 'y', 'real'],
-                    'ezr':['e', 'z', 'real'],
-                    'exi':['e', 'x', 'imag'],
-                    'eyi':['e', 'y', 'imag'],
-                    'ezi':['e', 'z', 'imag'],
-
-                    'bxr':['b', 'x', 'real'],
-                    'byr':['b', 'y', 'real'],
-                    'bzr':['b', 'z', 'real'],
-                    'bxi':['b', 'x', 'imag'],
-                    'byi':['b', 'y', 'imag'],
-                    'bzi':['b', 'z', 'imag'],
-
-                    'jxr':['j', 'x', 'real'],
-                    'jyr':['j', 'y', 'real'],
-                    'jzr':['j', 'z', 'real'],
-                    'jxi':['j', 'x', 'imag'],
-                    'jyi':['j', 'y', 'imag'],
-                    'jzi':['j', 'z', 'imag'],
-
-                    'hxr':['h', 'x', 'real'],
-                    'hyr':['h', 'y', 'real'],
-                    'hzr':['h', 'z', 'real'],
-                    'hxi':['h', 'x', 'imag'],
-                    'hyi':['h', 'y', 'imag'],
-                    'hzi':['h', 'z', 'imag'],
-                   }
-    radius = None
-
-    def __init__(self, locs, rxType):
-        SimPEG.Survey.BaseRx.__init__(self, locs, rxType)
-
-    @property
-    def projField(self):
-        """Field Type projection (e.g. e b ...)"""
-        return self.knownRxTypes[self.rxType][0]
-
-    @property
-    def projComp(self):
-        """Component projection (real/imag)"""
-        return self.knownRxTypes[self.rxType][2]
-
-    def projGLoc(self, u):
-        """Grid Location projection (e.g. Ex Fy ...)"""
-        return u._GLoc(self.rxType[0]) + self.knownRxTypes[self.rxType][1]
-
-    def eval(self, src, mesh, f):
-        """
-        Project fields to recievers to get data.
-
-        :param Source src: FDEM source
-        :param Mesh mesh: mesh used
-        :param Fields f: fields object
-        :rtype: numpy.ndarray
-        :return: fields projected to recievers
-        """
-        # projGLoc = u._GLoc(self.knownRxTypes[self.rxType][0])
-        # projGLoc += self.knownRxTypes[self.rxType][1]
-
-        P = self.getP(mesh, self.projGLoc(f))
-        f_part_complex = f[src, self.projField]
-        # get the real or imag component
-        real_or_imag = self.projComp
-        f_part = getattr(f_part_complex, real_or_imag)
-
-        return P*f_part
-
-    def evalDeriv(self, src, mesh, f, v, adjoint=False):
-        """
-        Derivative of projected fields with respect to the inversion model times a vector.
-
-        :param Source src: FDEM source
-        :param Mesh mesh: mesh used
-        :param Fields f: fields object
-        :param numpy.ndarray v: vector to multiply
-        :rtype: numpy.ndarray
-        :return: fields projected to recievers
-        """
-
-        P = self.getP(mesh, self.projGLoc(f))
-
-        if not adjoint:
-            Pv_complex = P * v
-            real_or_imag = self.projComp
-            Pv = getattr(Pv_complex, real_or_imag)
-        elif adjoint:
-            Pv_real = P.T * v
-
-            real_or_imag = self.projComp
-            if real_or_imag == 'imag':
-                Pv = 1j*Pv_real
-            elif real_or_imag == 'real':
-                Pv = Pv_real.astype(complex)
-            else:
-                raise NotImplementedError('must be real or imag')
-
-        return Pv
-
-
-####################################################
-# Survey
-####################################################
-
 class Survey(BaseEMSurvey):
     """
     Frequency domain electromagnetic survey
@@ -132,7 +15,7 @@ class Survey(BaseEMSurvey):
     """
 
     srcPair = Src.BaseSrc
-    rxPair = Rx
+    rxPair = Rx.BaseRx
 
     def __init__(self, srcList, **kwargs):
         # Sort these by frequency

SimPEG/EM/FDEM/__init__.py

@@ -1,3 +1,5 @@
-from SurveyFDEM import Rx, Src, Survey
-from FDEM import BaseFDEMProblem, Problem_e, Problem_b, Problem_j, Problem_h
-from FieldsFDEM import *
+from SurveyFDEM import Survey
+import SrcFDEM as Src
+import RxFDEM as Rx
+from ProblemFDEM import Problem3D_e, Problem3D_b, Problem3D_j, Problem3D_h
+from FieldsFDEM import Fields3D_e, Fields3D_b, Fields3D_j, Fields3D_h

SimPEG/EM/Static/DC/BoundaryUtils.py

@@ -0,0 +1,160 @@
+import numpy as np
+
+def getxBCyBC_CC(mesh, alpha, beta, gamma):
+# def getxBCyBC(mesh, alpha, beta, gamma):
+    """
+    This is a subfunction generating mixed-boundary condition:
+
+    .. math::
+
+        \nabla \cdot \vec{j} = -\nabla \cdot \vec{j}_s = q
+
+        \rho \vec{j} = -\nabla \phi \phi
+
+        \alpha \phi + \beta \frac{\partial \phi}{\partial r} = \gamma \ at \ r = \partial \Omega
+
+        xBC = f_1(\alpha, \beta, \gamma)
+        yBC = f(\alpha, \beta, \gamma)
+
+    Computes xBC and yBC for cell-centered discretizations
+    """
+    if mesh.dim == 1: #1D
+        if (len(alpha) != 2 or len(beta) != 2 or len(gamma) != 2):
+            raise Exception("Lenght of list, alpha should be 2")
+        fCCxm,fCCxp = mesh.cellBoundaryInd
+        nBC = fCCxm.sum()+fCCxp.sum()
+        h_xm, h_xp = mesh.gridCC[fCCxm], mesh.gridCC[fCCxp]
+
+        alpha_xm, beta_xm, gamma_xm = alpha[0], beta[0], gamma[0]
+        alpha_xp, beta_xp, gamma_xp = alpha[1], beta[1], gamma[1]
+
+        # h_xm, h_xp = mesh.gridCC[fCCxm], mesh.gridCC[fCCxp]
+        h_xm, h_xp = mesh.hx[0], mesh.hx[-1]
+
+        a_xm = gamma_xm/(0.5*alpha_xm-beta_xm/h_xm)
+        b_xm = (0.5*alpha_xm+beta_xm/h_xm)/(0.5*alpha_xm-beta_xm/h_xm)
+        a_xp = gamma_xp/(0.5*alpha_xp-beta_xp/h_xp)
+        b_xp = (0.5*alpha_xp+beta_xp/h_xp)/(0.5*alpha_xp-beta_xp/h_xp)
+
+        xBC_xm = 0.5*a_xm
+        xBC_xp = 0.5*a_xp/b_xp
+        yBC_xm = 0.5*(1.-b_xm)
+        yBC_xp = 0.5*(1.-1./b_xp)
+
+        xBC = np.r_[xBC_xm, xBC_xp]
+        yBC = np.r_[yBC_xm, yBC_xp]
+
+    elif mesh.dim == 2: #2D
+        if (len(alpha) != 4 or len(beta) != 4 or len(gamma) != 4):
+            raise Exception("Lenght of list, alpha should be 4")
+
+        fxm,fxp,fym,fyp = mesh.faceBoundaryInd
+        nBC = fxm.sum()+fxp.sum()+fxm.sum()+fxp.sum()
+
+        alpha_xm, beta_xm, gamma_xm = alpha[0], beta[0], gamma[0]
+        alpha_xp, beta_xp, gamma_xp = alpha[1], beta[1], gamma[1]
+        alpha_ym, beta_ym, gamma_ym = alpha[2], beta[2], gamma[2]
+        alpha_yp, beta_yp, gamma_yp = alpha[3], beta[3], gamma[3]
+
+        # h_xm, h_xp = mesh.gridCC[fCCxm,0], mesh.gridCC[fCCxp,0]
+        # h_ym, h_yp = mesh.gridCC[fCCym,1], mesh.gridCC[fCCyp,1]
+
+        h_xm, h_xp = mesh.hx[0]*np.ones_like(alpha_xm), mesh.hx[-1]*np.ones_like(alpha_xp)
+        h_ym, h_yp = mesh.hy[0]*np.ones_like(alpha_ym), mesh.hy[-1]*np.ones_like(alpha_yp)
+
+        a_xm = gamma_xm/(0.5*alpha_xm-beta_xm/h_xm)
+        b_xm = (0.5*alpha_xm+beta_xm/h_xm)/(0.5*alpha_xm-beta_xm/h_xm)
+        a_xp = gamma_xp/(0.5*alpha_xp-beta_xp/h_xp)
+        b_xp = (0.5*alpha_xp+beta_xp/h_xp)/(0.5*alpha_xp-beta_xp/h_xp)
+
+        a_ym = gamma_ym/(0.5*alpha_ym-beta_ym/h_ym)
+        b_ym = (0.5*alpha_ym+beta_ym/h_ym)/(0.5*alpha_ym-beta_ym/h_ym)
+        a_yp = gamma_yp/(0.5*alpha_yp-beta_yp/h_yp)
+        b_yp = (0.5*alpha_yp+beta_yp/h_yp)/(0.5*alpha_yp-beta_yp/h_yp)
+
+        xBC_xm = 0.5*a_xm
+        xBC_xp = 0.5*a_xp/b_xp
+        yBC_xm = 0.5*(1.-b_xm)
+        yBC_xp = 0.5*(1.-1./b_xp)
+        xBC_ym = 0.5*a_ym
+        xBC_yp = 0.5*a_yp/b_yp
+        yBC_ym = 0.5*(1.-b_ym)
+        yBC_yp = 0.5*(1.-1./b_yp)
+
+        sortindsfx = np.argsort(np.r_[np.arange(mesh.nFx)[fxm], np.arange(mesh.nFx)[fxp]])
+        sortindsfy = np.argsort(np.r_[np.arange(mesh.nFy)[fym], np.arange(mesh.nFy)[fyp]])
+
+        xBC_x = np.r_[xBC_xm, xBC_xp][sortindsfx]
+        xBC_y = np.r_[xBC_ym, xBC_yp][sortindsfy]
+        yBC_x = np.r_[yBC_xm, yBC_xp][sortindsfx]
+        yBC_y = np.r_[yBC_ym, yBC_yp][sortindsfy]
+
+        xBC = np.r_[xBC_x, xBC_y]
+        yBC = np.r_[yBC_x, yBC_y]
+
+    elif mesh.dim == 3: #3D
+        if (len(alpha) != 6 or len(beta) != 6 or len(gamma) != 6):
+            raise Exception("Lenght of list, alpha should be 6")
+        # fCCxm,fCCxp,fCCym,fCCyp,fCCzm,fCCzp = mesh.cellBoundaryInd
+        fxm,fxp,fym,fyp,fzm,fzp = mesh.faceBoundaryInd
+        nBC = fxm.sum()+fxp.sum()+fxm.sum()+fxp.sum()
+
+        alpha_xm, beta_xm, gamma_xm = alpha[0], beta[0], gamma[0]
+        alpha_xp, beta_xp, gamma_xp = alpha[1], beta[1], gamma[1]
+        alpha_ym, beta_ym, gamma_ym = alpha[2], beta[2], gamma[2]
+        alpha_yp, beta_yp, gamma_yp = alpha[3], beta[3], gamma[3]
+        alpha_zm, beta_zm, gamma_zm = alpha[4], beta[4], gamma[4]
+        alpha_zp, beta_zp, gamma_zp = alpha[5], beta[5], gamma[5]
+
+        # h_xm, h_xp = mesh.gridCC[fCCxm,0], mesh.gridCC[fCCxp,0]
+        # h_ym, h_yp = mesh.gridCC[fCCym,1], mesh.gridCC[fCCyp,1]
+        # h_zm, h_zp = mesh.gridCC[fCCzm,2], mesh.gridCC[fCCzp,2]
+
+        h_xm, h_xp = mesh.hx[0]*np.ones_like(alpha_xm), mesh.hx[-1]*np.ones_like(alpha_xp)
+        h_ym, h_yp = mesh.hy[0]*np.ones_like(alpha_ym), mesh.hy[-1]*np.ones_like(alpha_yp)
+        h_zm, h_zp = mesh.hz[0]*np.ones_like(alpha_zm), mesh.hz[-1]*np.ones_like(alpha_zp)
+
+        a_xm = gamma_xm/(0.5*alpha_xm-beta_xm/h_xm)
+        b_xm = (0.5*alpha_xm+beta_xm/h_xm)/(0.5*alpha_xm-beta_xm/h_xm)
+        a_xp = gamma_xp/(0.5*alpha_xp-beta_xp/h_xp)
+        b_xp = (0.5*alpha_xp+beta_xp/h_xp)/(0.5*alpha_xp-beta_xp/h_xp)
+
+        a_ym = gamma_ym/(0.5*alpha_ym-beta_ym/h_ym)
+        b_ym = (0.5*alpha_ym+beta_ym/h_ym)/(0.5*alpha_ym-beta_ym/h_ym)
+        a_yp = gamma_yp/(0.5*alpha_yp-beta_yp/h_yp)
+        b_yp = (0.5*alpha_yp+beta_yp/h_yp)/(0.5*alpha_yp-beta_yp/h_yp)
+
+        a_zm = gamma_zm/(0.5*alpha_zm-beta_zm/h_zm)
+        b_zm = (0.5*alpha_zm+beta_zm/h_zm)/(0.5*alpha_zm-beta_zm/h_zm)
+        a_zp = gamma_zp/(0.5*alpha_zp-beta_zp/h_zp)
+        b_zp = (0.5*alpha_zp+beta_zp/h_zp)/(0.5*alpha_zp-beta_zp/h_zp)
+
+        xBC_xm = 0.5*a_xm
+        xBC_xp = 0.5*a_xp/b_xp
+        yBC_xm = 0.5*(1.-b_xm)
+        yBC_xp = 0.5*(1.-1./b_xp)
+        xBC_ym = 0.5*a_ym
+        xBC_yp = 0.5*a_yp/b_yp
+        yBC_ym = 0.5*(1.-b_ym)
+        yBC_yp = 0.5*(1.-1./b_yp)
+        xBC_zm = 0.5*a_zm
+        xBC_zp = 0.5*a_zp/b_zp
+        yBC_zm = 0.5*(1.-b_zm)
+        yBC_zp = 0.5*(1.-1./b_zp)
+
+        sortindsfx = np.argsort(np.r_[np.arange(mesh.nFx)[fxm], np.arange(mesh.nFx)[fxp]])
+        sortindsfy = np.argsort(np.r_[np.arange(mesh.nFy)[fym], np.arange(mesh.nFy)[fyp]])
+        sortindsfz = np.argsort(np.r_[np.arange(mesh.nFz)[fzm], np.arange(mesh.nFz)[fzp]])
+
+        xBC_x = np.r_[xBC_xm, xBC_xp][sortindsfx]
+        xBC_y = np.r_[xBC_ym, xBC_yp][sortindsfy]
+        xBC_z = np.r_[xBC_zm, xBC_zp][sortindsfz]
+
+        yBC_x = np.r_[yBC_xm, yBC_xp][sortindsfx]
+        yBC_y = np.r_[yBC_ym, yBC_yp][sortindsfy]
+        yBC_z = np.r_[yBC_zm, yBC_zp][sortindsfz]
+
+        xBC = np.r_[xBC_x, xBC_y, xBC_z]
+        yBC = np.r_[yBC_x, yBC_y, yBC_z]
+
+    return xBC, yBC

SimPEG/EM/Static/DC/FieldsDC.py

@@ -0,0 +1,148 @@
+import SimPEG
+from SimPEG.Utils import Identity, Zero
+import numpy as np
+from scipy.constants import epsilon_0
+
+class Fields(SimPEG.Problem.Fields):
+    knownFields = {}
+    dtype = float
+
+    def _phiDeriv(self, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_phiDeriv_u', None) is None or getattr(self, '_phiDeriv_m', None) is None:
+            raise NotImplementedError ('Getting phiDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._phiDeriv_u(src, v, adjoint=adjoint), self._phiDeriv_m(src, v, adjoint=adjoint)
+
+        return np.array(self._phiDeriv_u(src, du_dm_v, adjoint) + self._phiDeriv_m(src, v, adjoint), dtype = float)
+
+    def _eDeriv(self, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_eDeriv_u', None) is None or getattr(self, '_eDeriv_m', None) is None:
+            raise NotImplementedError ('Getting eDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._eDeriv_u(src, v, adjoint), self._eDeriv_m(src, v, adjoint)
+        return np.array(self._eDeriv_u(src, du_dm_v, adjoint) + self._eDeriv_m(src, v, adjoint), dtype = float)
+
+    def _jDeriv(self, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_jDeriv_u', None) is None or getattr(self, '_jDeriv_m', None) is None:
+            raise NotImplementedError ('Getting jDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._jDeriv_u(src, v, adjoint), self._jDeriv_m(src, v, adjoint)
+        return np.array(self._jDeriv_u(src, du_dm_v, adjoint) + self._jDeriv_m(src, v, adjoint), dtype = float)
+
+
+class Fields_CC(Fields):
+    knownFields = {'phiSolution':'CC'}
+    aliasFields = {
+                    'phi': ['phiSolution','CC','_phi'],
+                    'j' : ['phiSolution','F','_j'],
+                    'e' : ['phiSolution','F','_e'],
+                    'charge' : ['phiSolution','CC','_charge'],
+                  }
+                  # primary - secondary
+                  # CC variables
+
+    def __init__(self, mesh, survey, **kwargs):
+        Fields.__init__(self, mesh, survey, **kwargs)
+        mesh.setCellGradBC("neumann")
+        cellGrad = mesh.cellGrad
+    def startup(self):
+        self.prob = self.survey.prob
+
+    def _GLoc(self, fieldType):
+        if fieldType == 'phi':
+            return 'CC'
+        elif fieldType == 'e' or fieldType == 'j':
+            return 'F'
+        else:
+            raise Exception('Field type must be phi, e, j')
+
+    def _phi(self, phiSolution, srcList):
+        return phiSolution
+
+    def _phiDeriv_u(self, src, v, adjoint = False):
+        return Identity()*v
+
+    def _phiDeriv_m(self, src, v, adjoint = False):
+        return Zero()
+
+    def _j(self, phiSolution, srcList):
+        """
+            .. math::
+                \mathbf{j} = \mathbf{M}^{f \ -1}_{\rho} \mathbf{G} \phi
+        """
+        return self.prob.MfRhoI*self.prob.Grad*phiSolution
+
+    def _e(self, phiSolution, srcList):
+        """
+            In HJ formulation e is not well-defined!!
+            .. math::
+                \vec{e} = -\nabla \phi
+        """
+        return -self.mesh.cellGrad*phiSolution
+
+    def _charge(self, phiSolution, srcList):
+        """
+            .. math::
+                \int \nabla \codt \vec{e} =  \int \frac{\rho_v }{\epsillon_0}
+        """
+        return epsilon_0*self.prob.Vol*(self.mesh.faceDiv*self._e(phiSolution, srcList))
+
+class Fields_N(Fields):
+    knownFields = {'phiSolution':'N'}
+    aliasFields = {
+                    'phi': ['phiSolution','N','_phi'],
+                    'j' : ['phiSolution','E','_j'],
+                    'e' : ['phiSolution','E','_e'],
+                    'charge' : ['phiSolution','N','_charge'],
+                  }
+                  # primary - secondary
+                  # N variables
+
+    def __init__(self, mesh, survey, **kwargs):
+        Fields.__init__(self, mesh, survey, **kwargs)
+
+    def startup(self):
+        self.prob = self.survey.prob
+
+    def _GLoc(self, fieldType):
+        if fieldType == 'phi':
+            return 'N'
+        elif fieldType == 'e' or fieldType == 'j':
+            return 'E'
+        else:
+            raise Exception('Field type must be phi, e, j')
+
+    def _phi(self, phiSolution, srcList):
+        return phiSolution
+
+    def _phiDeriv_u(self, src, v, adjoint = False):
+        return Identity()*v
+
+    def _phiDeriv_m(self, src, v, adjoint = False):
+        return Zero()
+
+    def _j(self, phiSolution, srcList):
+        """
+            In EB formulation j is not well-defined!!
+            .. math::
+                \mathbf{j} = - \mathbf{M}^{e}_{\sigma} \mathbf{G} \phi
+        """
+        return self.prob.MeSigma * self._e(phiSolution, srcList)
+
+    def _e(self, phiSolution, srcList):
+        """
+            In HJ formulation e is not well-defined!!
+            .. math::
+                \vec{e} = -\nabla \phi
+        """
+        return -self.mesh.nodalGrad * phiSolution
+
+    def _charge(self, phiSolution, srcList):
+        """
+            .. math::
+                \int \nabla \codt \vec{e} =  \int \frac{\rho_v }{\epsillon_0}
+        """
+        return - epsilon_0*(self.mesh.nodalGrad.T*self.mesh.getEdgeInnerProduct()*self._e(phiSolution, srcList))

SimPEG/EM/Static/DC/FieldsDC_2D.py

@@ -0,0 +1,146 @@
+import SimPEG
+from SimPEG.Utils import Identity, Zero
+import numpy as np
+
+class Fields_ky(SimPEG.Problem.TimeFields):
+
+    """
+
+    Fancy Field Storage for a 2.5D code.
+
+    u[:,'phi', kyInd] = phi
+    print u[src0,'phi']
+
+    Only one field type is stored for
+    each problem, the rest are computed. The fields obejct acts like an array and is indexed by
+    .. code-block:: python
+        f = problem.fields(m)
+        e = f[srcList,'e']
+        j = f[srcList,'j']
+
+    If accessing all sources for a given field, use the :code:`:`
+    .. code-block:: python
+        f = problem.fields(m)
+        phi = f[:,'phi']
+        e = f[:,'e']
+        b = f[:,'b']
+    The array returned will be size (nE or nF, nSrcs :math:`\\times` nFrequencies)
+    """
+
+    knownFields = {}
+    dtype = float
+
+    def _phiDeriv(self,kyInd, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_phiDeriv_u', None) is None or getattr(self, '_phiDeriv_m', None) is None:
+            raise NotImplementedError ('Getting phiDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._phiDeriv_u(kyInd, src, v, adjoint=adjoint), self._phiDeriv_m(kyInd, src, v, adjoint=adjoint)
+
+        return np.array(self._phiDeriv_u(kyInd, src, du_dm_v, adjoint) + self._phiDeriv_m(kyInd, src, v, adjoint), dtype = float)
+
+    def _eDeriv(self,kyInd, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_eDeriv_u', None) is None or getattr(self, '_eDeriv_m', None) is None:
+            raise NotImplementedError ('Getting eDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._eDeriv_u(kyInd, src, v, adjoint), self._eDeriv_m(kyInd, src, v, adjoint)
+        return np.array(self._eDeriv_u(kyInd, src, du_dm_v, adjoint) + self._eDeriv_m(kyInd, src, v, adjoint), dtype = float)
+
+    def _jDeriv(self,kyInd, src, du_dm_v, v, adjoint=False):
+        if getattr(self, '_jDeriv_u', None) is None or getattr(self, '_jDeriv_m', None) is None:
+            raise NotImplementedError ('Getting jDerivs from %s is not implemented' %self.knownFields.keys()[0])
+
+        if adjoint:
+            return self._jDeriv_u(kyInd, src, v, adjoint), self._jDeriv_m(kyInd, src, v, adjoint)
+        return np.array(self._jDeriv_u(kyInd, src, du_dm_v, adjoint) + self._jDeriv_m(kyInd, src, v, adjoint), dtype = float)
+
+
+    # def _eDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+    #     if adjoint is True:
+    #         return self._eDeriv_u(tInd, src, v, adjoint), self._eDeriv_m(tInd, src, v, adjoint)
+    #     return self._eDeriv_u(tInd, src, dun_dm_v) + self._eDeriv_m(tInd, src, v)
+
+    # def _bDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+    #     if adjoint is True:
+    #         return self._bDeriv_u(tInd, src, v, adjoint), self._bDeriv_m(tInd, src, v, adjoint)
+    #     return self._bDeriv_u(tInd, src, dun_dm_v) + self._bDeriv_m(tInd, src, v)
+
+
+class Fields_ky_CC(Fields_ky):
+    knownFields = {'phiSolution':'CC'}
+    aliasFields = {
+                    'phi': ['phiSolution','CC','_phi'],
+                    'j' : ['phiSolution','F','_j'],
+                    'e' : ['phiSolution','F','_e'],
+                  }
+                  # primary - secondary
+                  # CC variables
+
+    def __init__(self, mesh, survey, **kwargs):
+        Fields_ky.__init__(self, mesh, survey, **kwargs)
+
+    def startup(self):
+        self.prob = self.survey.prob
+
+    def _GLoc(self, fieldType):
+        if fieldType == 'phi':
+            return 'CC'
+        elif fieldType == 'e' or fieldType == 'j':
+            return 'F'
+        else:
+            raise Exception('Field type must be phi, e, j')
+
+    def _phi(self, phiSolution, src, kyInd):
+        return phiSolution
+
+    def _phiDeriv_u(self, kyInd, src, v, adjoint = False):
+        return Identity()*v
+
+    def _phiDeriv_m(self, kyInd, src, v, adjoint = False):
+        return Zero()
+
+    def _j(self, phiSolution, srcList):
+        raise NotImplementedError
+
+    def _e(self, phiSolution, srcList):
+        raise NotImplementedError
+
+class Fields_ky_N(Fields_ky):
+    knownFields = {'phiSolution':'N'}
+    aliasFields = {
+                    'phi': ['phiSolution','N','_phi'],
+                    'j' : ['phiSolution','E','_j'],
+                    'e' : ['phiSolution','E','_e'],
+                  }
+                  # primary - secondary
+                  # CC variables
+
+    def __init__(self, mesh, survey, **kwargs):
+        Fields_ky.__init__(self, mesh, survey, **kwargs)
+
+    def startup(self):
+        self.prob = self.survey.prob
+
+    def _GLoc(self, fieldType):
+        if fieldType == 'phi':
+            return 'N'
+        elif fieldType == 'e' or fieldType == 'j':
+            return 'E'
+        else:
+            raise Exception('Field type must be phi, e, j')
+
+    def _phi(self, phiSolution, src, kyInd):
+        return phiSolution
+
+    def _phiDeriv_u(self, kyInd, src, v, adjoint = False):
+        return Identity()*v
+
+    def _phiDeriv_m(self, kyInd, src, v, adjoint = False):
+        return Zero()
+
+    def _j(self, phiSolution, srcList):
+        raise NotImplementedError
+
+    def _e(self, phiSolution, srcList):
+        raise NotImplementedError

SimPEG/EM/Static/DC/ProblemDC.py

@@ -0,0 +1,296 @@
+from SimPEG import Problem, Utils
+from SimPEG.EM.Base import BaseEMProblem
+from SurveyDC import Survey
+from FieldsDC import Fields, Fields_CC, Fields_N
+from SimPEG.Utils import sdiag
+import numpy as np
+from SimPEG.Utils import Zero
+from BoundaryUtils import getxBCyBC_CC
+
+class BaseDCProblem(BaseEMProblem):
+
+    surveyPair = Survey
+    fieldsPair = Fields
+    Ainv = None
+
+    def fields(self, m):
+        self.curModel = m
+
+        if not self.Ainv == None:
+            self.Ainv.clean()
+
+        f = self.fieldsPair(self.mesh, self.survey)
+        A = self.getA()
+        self.Ainv = self.Solver(A, **self.solverOpts)
+        RHS = self.getRHS()
+        u = self.Ainv * RHS
+        Srcs = self.survey.srcList
+        f[Srcs, self._solutionType] = u
+        return f
+
+    def Jvec(self, m, v, f=None):
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        Jv = self.dataPair(self.survey) #same size as the data
+
+        A = self.getA()
+
+        for src in self.survey.srcList:
+            u_src = f[src, self._solutionType] # solution vector
+            dA_dm_v = self.getADeriv(u_src, v)
+            dRHS_dm_v = self.getRHSDeriv(src, v)
+            du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
+
+            for rx in src.rxList:
+                df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
+                Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
+        return Utils.mkvc(Jv)
+
+    def Jtvec(self, m, v, f=None):
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv = np.zeros(m.size)
+        AT = self.getA()
+
+
+        for src in self.survey.srcList:
+            u_src = f[src, self._solutionType]
+            for rx in src.rxList:
+                PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
+                df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
+
+                ATinvdf_duT = self.Ainv * df_duT
+
+                dA_dmT = self.getADeriv(u_src, ATinvdf_duT, adjoint=True)
+                dRHS_dmT = self.getRHSDeriv(src, ATinvdf_duT, adjoint=True)
+                du_dmT = -dA_dmT + dRHS_dmT
+                Jtv += (df_dmT + du_dmT).astype(float)
+
+        return Utils.mkvc(Jtv)
+
+    def getSourceTerm(self):
+        """
+        takes concept of source and turns it into a matrix
+        """
+        """
+        Evaluates the sources, and puts them in matrix form
+
+        :rtype: (numpy.ndarray, numpy.ndarray)
+        :return: q (nC or nN, nSrc)
+        """
+
+        Srcs = self.survey.srcList
+
+        if self._formulation is 'EB':
+            n = self.mesh.nN
+            # return NotImplementedError
+
+        elif self._formulation is 'HJ':
+            n = self.mesh.nC
+
+        q = np.zeros((n, len(Srcs)))
+
+        for i, src in enumerate(Srcs):
+            q[:,i] = src.eval(self)
+        return q
+
+class Problem3D_CC(BaseDCProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'HJ' # CC potentials means J is on faces
+    fieldsPair    = Fields_CC
+
+    def __init__(self, mesh, **kwargs):
+        BaseDCProblem.__init__(self, mesh, **kwargs)
+        self.setBC()
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = D MfRhoI G
+
+        """
+
+        D = self.Div
+        G = self.Grad
+        MfRhoI = self.MfRhoI
+        A = D * MfRhoI * G
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return V.T * A
+        return A
+
+    def getADeriv(self, u, v, adjoint= False):
+
+        D = self.Div
+        G = self.Grad
+        MfRhoIDeriv = self.MfRhoIDeriv
+
+        if adjoint:
+            return(MfRhoIDeriv( G * u ).T) * ( D.T * v)
+
+        return D * (MfRhoIDeriv( G * u ) * v)
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+    def setBC(self):
+        if self.mesh.dim==3:
+            fxm,fxp,fym,fyp,fzm,fzp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+            gBFzm = self.mesh.gridFz[fzm,:]
+            gBFzp = self.mesh.gridFz[fzp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+            temp_zm, temp_zp = np.ones_like(gBFzm[:,2]), np.ones_like(gBFzp[:,2])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+            alpha_zm, alpha_zp = temp_zm*0., temp_zp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+            beta_zm, beta_zp = temp_zm, temp_zp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+            gamma_zm, gamma_zp = temp_zm*0., temp_zp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp, alpha_zm, alpha_zp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp, beta_zm, beta_zp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp, gamma_zm, gamma_zp]
+
+        elif self.mesh.dim==2:
+
+            fxm,fxp,fym,fyp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp]
+
+        x_BC, y_BC = getxBCyBC_CC(self.mesh, alpha, beta, gamma)
+        V = self.Vol
+        self.Div = V * self.mesh.faceDiv
+        P_BC, B = self.mesh.getBCProjWF_simple()
+        M = B*self.mesh.aveCC2F
+        self.Grad = self.Div.T - P_BC*Utils.sdiag(y_BC)*M
+
+
+class Problem3D_N(BaseDCProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'EB' # N potentials means B is on faces
+    fieldsPair    = Fields_N
+
+    def __init__(self, mesh, **kwargs):
+        BaseDCProblem.__init__(self, mesh, **kwargs)
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = G.T MeSigma G
+
+        """
+
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        A = Grad.T * MeSigma * Grad
+
+        # Handling Null space of A
+        A[0,0] = A[0,0] + 1.
+
+        return A
+
+    def getADeriv(self, u, v, adjoint=False):
+        """
+
+        Product of the derivative of our system matrix with respect to the model and a vector
+
+        """
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        if not adjoint:
+            return Grad.T*(self.MeSigmaDeriv(Grad*u)*v)
+        elif adjoint:
+            return self.MeSigmaDeriv(Grad*u).T * (Grad*v)
+
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+
+
+

SimPEG/EM/Static/DC/ProblemDC_2D.py

@@ -0,0 +1,349 @@
+from SimPEG import Problem, Utils
+from SimPEG.EM.Base import BaseEMProblem
+from SurveyDC import Survey, Survey_ky
+from FieldsDC_2D import Fields_ky, Fields_ky_CC, Fields_ky_N
+from SimPEG.Utils import sdiag
+import numpy as np
+from SimPEG.Utils import Zero
+from BoundaryUtils import getxBCyBC_CC
+
+class BaseDCProblem_2D(BaseEMProblem):
+
+    surveyPair = Survey_ky
+    fieldsPair = Fields_ky
+    nky = 15
+    kys = np.logspace(-4, 1, nky)
+    Ainv = [None for i in range(nky)]
+    nT = nky # Only for using TimeFields
+
+    def fields(self, m):
+        self.curModel = m
+
+        if not self.Ainv[0] == None:
+            for i in range(self.nky):
+                self.Ainv[i].clean()
+
+        f = self.fieldsPair(self.mesh, self.survey)
+        Srcs = self.survey.srcList
+        for iky in range(self.nky):
+            ky = self.kys[iky]
+            A = self.getA(ky)
+            self.Ainv[iky] = self.Solver(A, **self.solverOpts)
+            RHS = self.getRHS(ky)
+            u = self.Ainv[iky] * RHS
+            f[Srcs, self._solutionType, iky] = u
+        return f
+
+    def Jvec(self, m, v, f=None):
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        Jv = self.dataPair(self.survey) #same size as the data
+        Jv0 = self.dataPair(self.survey)
+
+        # Assume y=0.
+        # This needs some thoughts to implement in general when src is dipole
+        dky = np.diff(self.kys)
+        dky = np.r_[dky[0], dky]
+        y = 0.
+
+        #TODO: this loop is pretty slow .. (Parellize)
+        for iky in range(self.nky):
+            ky = self.kys[iky]
+            A = self.getA(ky)
+            for src in self.survey.srcList:
+                u_src = f[src, self._solutionType, iky] # solution vector
+                dA_dm_v = self.getADeriv(ky, u_src, v)
+                dRHS_dm_v = self.getRHSDeriv(ky, src, v)
+                du_dm_v = self.Ainv[iky] * ( - dA_dm_v + dRHS_dm_v )
+                for rx in src.rxList:
+                    df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                    df_dm_v = df_dmFun(iky, src, du_dm_v, v, adjoint=False)
+                    # Trapezoidal intergration
+                    Jv1_temp = 1./np.pi*rx.evalDeriv(ky, src, self.mesh, f, df_dm_v)
+                    if iky==0:
+                        #First assigment
+                        Jv[src, rx] = Jv1_temp*dky[iky]*np.cos(ky*y)
+                    else:
+                        Jv[src, rx] += Jv1_temp*dky[iky]    /2.*np.cos(ky*y)
+                        Jv[src, rx] += Jv0[src, rx]*dky[iky]/2.*np.cos(ky*y)
+                    Jv0[src, rx] = Jv1_temp.copy()
+        return Utils.mkvc(Jv)
+
+    def Jtvec(self, m, v, f=None):
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv = np.zeros(m.size, dtype=float)
+
+        # Assume y=0.
+        # This needs some thoughts to implement in general when src is dipole
+        dky = np.diff(self.kys)
+        dky = np.r_[dky[0], dky]
+        y = 0.
+
+        for src in self.survey.srcList:
+            for rx in src.rxList:
+                Jtv_temp1 = np.zeros(m.size, dtype=float)
+                Jtv_temp0 = np.zeros(m.size, dtype=float)
+                #TODO: this loop is pretty slow .. (Parellize)
+                for iky in range(self.nky):
+                    u_src = f[src, self._solutionType, iky]
+                    ky = self.kys[iky]
+                    AT = self.getA(ky)
+                    PTv = rx.evalDeriv(ky, src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
+                    df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                    df_duT, df_dmT = df_duTFun(iky, src, None, PTv, adjoint=True)
+
+                    ATinvdf_duT = self.Ainv[iky] * df_duT
+
+                    dA_dmT = self.getADeriv(ky, u_src, ATinvdf_duT, adjoint=True)
+                    dRHS_dmT = self.getRHSDeriv(ky, src, ATinvdf_duT, adjoint=True)
+                    du_dmT = -dA_dmT + dRHS_dmT
+                    Jtv_temp1 = 1./np.pi*(df_dmT + du_dmT).astype(float)
+                    # Trapezoidal intergration
+                    if iky==0:
+                        #First assigment
+                        Jtv += Jtv_temp1*dky[iky]*np.cos(ky*y)
+                    else:
+                        Jtv += Jtv_temp1*dky[iky]/2.*np.cos(ky*y)
+                        Jtv += Jtv_temp0*dky[iky]/2.*np.cos(ky*y)
+                    Jtv_temp0 = Jtv_temp1.copy()
+        return Utils.mkvc(Jtv)
+
+    def getSourceTerm(self, ky):
+        """
+        takes concept of source and turns it into a matrix
+        """
+        """
+        Evaluates the sources, and puts them in matrix form
+
+        :rtype: (numpy.ndarray, numpy.ndarray)
+        :return: q (nC or nN, nSrc)
+        """
+
+        Srcs = self.survey.srcList
+
+        if self._formulation is 'EB':
+            n = self.mesh.nN
+            # return NotImplementedError
+
+        elif self._formulation is 'HJ':
+            n = self.mesh.nC
+
+        q = np.zeros((n, len(Srcs)))
+
+        for i, src in enumerate(Srcs):
+            q[:,i] = src.eval(self)
+        return q
+
+class Problem2D_CC(BaseDCProblem_2D):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'HJ' # CC potentials means J is on faces
+    fieldsPair    = Fields_ky_CC
+
+    def __init__(self, mesh, **kwargs):
+        BaseDCProblem_2D.__init__(self, mesh, **kwargs)
+        self.setBC()
+
+    def getA(self, ky):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = D MfRhoI G
+
+        """
+
+        D = self.Div
+        G = self.Grad
+        vol = self.mesh.vol
+        MfRhoI = self.MfRhoI
+        # Get resistivity rho
+        rho = self.curModel.rho
+        A = D * MfRhoI * G + Utils.sdiag(ky**2*vol/rho)
+        return A
+
+    def getADeriv(self, ky, u, v, adjoint= False):
+
+        D = self.Div
+        G = self.Grad
+        vol = self.mesh.vol
+        MfRhoIDeriv = self.MfRhoIDeriv
+        rho = self.curModel.rho
+        if adjoint:
+            return(MfRhoIDeriv( G * u ).T) * ( D.T * v) + ky**2*Utils.sdiag(u.flatten()*vol*(-1./rho**2))*v
+        return D * ((MfRhoIDeriv( G * u )) * v) + ky**2*Utils.sdiag(u.flatten()*vol*(-1./rho**2))*v
+
+    def getRHS(self, ky):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm(ky)
+        return RHS
+
+    def getRHSDeriv(self, ky, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, ky, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+    def setBC(self):
+        if self.mesh.dim==3:
+            fxm,fxp,fym,fyp,fzm,fzp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+            gBFzm = self.mesh.gridFz[fzm,:]
+            gBFzp = self.mesh.gridFz[fzp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+            temp_zm, temp_zp = np.ones_like(gBFzm[:,2]), np.ones_like(gBFzp[:,2])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+            alpha_zm, alpha_zp = temp_zm*0., temp_zp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+            beta_zm, beta_zp = temp_zm, temp_zp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+            gamma_zm, gamma_zp = temp_zm*0., temp_zp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp, alpha_zm, alpha_zp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp, beta_zm, beta_zp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp, gamma_zm, gamma_zp]
+
+        elif self.mesh.dim==2:
+
+            fxm,fxp,fym,fyp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp]
+
+        x_BC, y_BC = getxBCyBC_CC(self.mesh, alpha, beta, gamma)
+        V = self.Vol
+        self.Div = V * self.mesh.faceDiv
+        P_BC, B = self.mesh.getBCProjWF_simple()
+        M = B*self.mesh.aveCC2F
+        self.Grad = self.Div.T - P_BC*Utils.sdiag(y_BC)*M
+
+class Problem2D_N(BaseDCProblem_2D):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'EB' # CC potentials means J is on faces
+    fieldsPair    = Fields_ky_N
+
+    def __init__(self, mesh, **kwargs):
+        BaseDCProblem_2D.__init__(self, mesh, **kwargs)
+        # self.setBC()
+
+    @property
+    def MnSigma(self):
+        """
+            Node inner product matrix for \\(\\sigma\\). Used in the E-B formulation
+        """
+        # TODO: only works isotropic sigma
+        sigma = self.curModel.sigma
+        vol = self.mesh.vol
+        MnSigma = Utils.sdiag(self.mesh.aveN2CC.T*(Utils.sdiag(vol)*sigma))
+
+        return MnSigma
+
+    def MnSigmaDeriv(self, u):
+        """
+            Derivative of MnSigma with respect to the model
+        """
+        sigma = self.curModel.sigma
+        sigmaderiv = self.curModel.sigmaDeriv
+        vol = self.mesh.vol
+        return Utils.sdiag(u)*self.mesh.aveN2CC.T*Utils.sdiag(vol) * self.curModel.sigmaDeriv
+
+    def getA(self, ky):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = D MfRhoI G
+
+        """
+
+        MeSigma = self.MeSigma
+        MnSigma = self.MnSigma
+        Grad = self.mesh.nodalGrad
+        # Get conductivity sigma
+        sigma = self.curModel.sigma
+        A = Grad.T * MeSigma * Grad + ky**2*MnSigma
+
+        # Handling Null space of A
+        A[0,0] = A[0,0] + 1.
+        return A
+
+    def getADeriv(self, ky, u, v, adjoint= False):
+
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        sigma = self.curModel.sigma
+        vol = self.mesh.vol
+
+        if adjoint:
+            return self.MeSigmaDeriv(Grad*u).T * (Grad*v) + ky**2*self.MnSigmaDeriv(u).T*v
+        return Grad.T*(self.MeSigmaDeriv(Grad*u)*v) + ky**2*self.MnSigmaDeriv(u)*v
+
+    def getRHS(self, ky):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm(ky)
+        return RHS
+
+    def getRHSDeriv(self, ky, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, ky, adjoint=adjoint)
+        # return qDeriv
+        return Zero()

SimPEG/EM/Static/DC/RxDC.py

@@ -0,0 +1,129 @@
+import SimPEG
+import numpy as np
+from SimPEG.Utils import Zero, closestPoints
+
+class BaseRx(SimPEG.Survey.BaseRx):
+    locs = None
+    rxType = None
+
+    knownRxTypes = {
+                    'phi':['phi',None],
+                    'ex':['e','x'],
+                    'ey':['e','y'],
+                    'ez':['e','z'],
+                    'jx':['j','x'],
+                    'jy':['j','y'],
+                    'jz':['j','z'],
+                    }
+
+    def __init__(self, locs, rxType, **kwargs):
+        SimPEG.Survey.BaseRx.__init__(self, locs, rxType, **kwargs)
+
+
+    @property
+    def projField(self):
+        """Field Type projection (e.g. e b ...)"""
+        return self.knownRxTypes[self.rxType][0]
+
+    def projGLoc(self, f):
+        """Grid Location projection (e.g. Ex Fy ...)"""
+        comp = self.knownRxTypes[self.rxType][1]
+        if comp is not None:
+            return f._GLoc(self.rxType) + comp
+        return f._GLoc(self.rxType)
+
+    def eval(self, src, mesh, f):
+        P = self.getP(mesh, self.projGLoc(f))
+        return P*f[src, self.projField]
+
+    def evalDeriv(self, src, mesh, f, v, adjoint=False):
+        P = self.getP(mesh, self.projGLoc(f))
+        if not adjoint:
+            return P*v
+        elif adjoint:
+            return P.T*v
+
+# DC.Rx.Dipole(locs)
+class Dipole(BaseRx):
+
+    def __init__(self, locsM, locsN, rxType = 'phi', **kwargs):
+        assert locsM.shape == locsN.shape, 'locsM and locsN need to be the same size'
+        locs = [locsM, locsN]
+        # We may not need this ...
+        BaseRx.__init__(self, locs, rxType)
+
+    @property
+    def nD(self):
+        """Number of data in the receiver."""
+        return self.locs[0].shape[0]
+
+        # Not sure why ...
+        # return int(self.locs[0].size / 2)
+
+
+    def getP(self, mesh, Gloc):
+        if mesh in self._Ps:
+            return self._Ps[mesh]
+
+        P0 = mesh.getInterpolationMat(self.locs[0], Gloc)
+        P1 = mesh.getInterpolationMat(self.locs[1], Gloc)
+        P = P0 - P1
+
+        if self.storeProjections:
+            self._Ps[mesh] = P
+
+        return P
+
+
+class Dipole_ky(BaseRx):
+
+    def __init__(self, locsM, locsN, rxType = 'phi', **kwargs):
+        assert locsM.shape == locsN.shape, 'locsM and locsN need to be the same size'
+        locs = [locsM, locsN]
+        # We may not need this ...
+        BaseRx.__init__(self, locs, rxType)
+
+    @property
+    def nD(self):
+        """Number of data in the receiver."""
+        return self.locs[0].shape[0]
+
+        # Not sure why ...
+        # return int(self.locs[0].size / 2)
+
+    def getP(self, mesh, Gloc):
+        if mesh in self._Ps:
+            return self._Ps[mesh]
+
+        P0 = mesh.getInterpolationMat(self.locs[0], Gloc)
+        P1 = mesh.getInterpolationMat(self.locs[1], Gloc)
+        P = P0 - P1
+        if self.storeProjections:
+            self._Ps[mesh] = P
+        return P
+
+    def eval(self, kys, src, mesh, f):
+        P = self.getP(mesh, self.projGLoc(f))
+        Pf = P*f[src, self.projField,:]
+        return self.IntTrapezoidal(kys, Pf, y=0.)
+
+    def evalDeriv(self, ky, src, mesh, f, v, adjoint=False):
+        P = self.getP(mesh, self.projGLoc(f))
+        if not adjoint:
+            return P*v
+        elif adjoint:
+            return P.T*v
+
+    def IntTrapezoidal(self, kys, Pf, y=0.):
+        phi = np.zeros(Pf.shape[0])
+        nky = kys.size
+        dky = np.diff(kys)
+        dky = np.r_[dky[0], dky]
+        phi0 = 1./np.pi*Pf[:,0]
+        for iky in range(nky):
+            phi1 = 1./np.pi*Pf[:,iky]
+            phi += phi1*dky[iky]/2.*np.cos(kys[iky]*y)
+            phi += phi0*dky[iky]/2.*np.cos(kys[iky]*y)
+            phi0 = phi1.copy()
+        return phi
+

SimPEG/EM/Static/DC/SrcDC.py

@@ -0,0 +1,86 @@
+import SimPEG
+# from SimPEG.EM.Base import BaseEMSurvey
+from SimPEG.Utils import Zero, closestPoints, mkvc
+import numpy as np
+
+class BaseSrc(SimPEG.Survey.BaseSrc):
+
+    current = 1.0
+    loc = None
+
+    def __init__(self, rxList, **kwargs):
+        SimPEG.Survey.BaseSrc.__init__(self, rxList, **kwargs)
+
+    def eval(self, prob):
+        raise NotImplementedError
+
+    def evalDeriv(self, prob):
+        return Zero()
+
+
+class Dipole(BaseSrc):
+
+    def __init__(self, rxList, locA, locB, **kwargs):
+        assert locA.shape == locB.shape, 'Shape of locA and locB should be the same'
+        self.loc = [locA, locB]
+        BaseSrc.__init__(self, rxList, **kwargs)
+
+    def eval(self, prob):
+        if prob._formulation == 'HJ':
+            inds = closestPoints(prob.mesh, self.loc, gridLoc='CC')
+            q = np.zeros(prob.mesh.nC)
+            q[inds] = self.current * np.r_[1., -1.]
+        elif prob._formulation == 'EB':
+            qa = prob.mesh.getInterpolationMat(self.loc[0], locType='N').todense()
+            qb = -prob.mesh.getInterpolationMat(self.loc[1], locType='N').todense()
+            q = self.current * mkvc(qa+qb)
+        return q
+
+class Pole(BaseSrc):
+
+    def __init__(self, rxList, loc, **kwargs):
+        BaseSrc.__init__(self, rxList, loc=loc, **kwargs)
+
+    def eval(self, prob):
+        if prob._formulation == 'HJ':
+            inds = closestPoints(prob.mesh, self.loc)
+            q = np.zeros(prob.mesh.nC)
+            q[inds] = self.current * np.r_[1.]
+        elif prob._formulation == 'EB':
+            q = prob.mesh.getInterpolationMat(self.loc, locType='N').todense()
+            q = self.current * mkvc(q)
+        return q
+
+
+# class Dipole_ky(BaseSrc):
+
+#     def __init__(self, rxList, locA, locB, **kwargs):
+#         assert locA.shape == locB.shape, 'Shape of locA and locB should be the same'
+#         self.loc = [locA[[0,2]], locB[[0,2]]]
+#         BaseSrc.__init__(self, rxList, **kwargs)
+
+#     def eval(self, prob):
+#         if prob._formulation == 'HJ':
+#             inds = closestPoints(prob.mesh, self.loc, gridLoc='CC')
+#             q = np.zeros(prob.mesh.nC)
+#             q[inds] = self.current * np.r_[1., -1.]
+#         elif prob._formulation == 'EB':
+#             qa = prob.mesh.getInterpolationMat(self.loc[0], locType='N').todense()
+#             qb = -prob.mesh.getInterpolationMat(self.loc[1], locType='N').todense()
+#             q = self.current * mkvc(qa+qb)
+#         return q
+
+# class Pole_ky(BaseSrc):
+
+#     def __init__(self, rxList, loc, **kwargs):
+#         BaseSrc.__init__(self, rxList, loc=loc, **kwargs)
+
+#     def eval(self, prob):
+#         if prob._formulation == 'HJ':
+#             inds = closestPoints(prob.mesh, self.loc[[0,2]])
+#             q = np.zeros(prob.mesh.nC)
+#             q[inds] = self.current * np.r_[1.]
+#         elif prob._formulation == 'EB':
+#             q = prob.mesh.getInterpolationMat(self.loc[[0,2]], locType='N').todense()
+#             q = self.current * mkvc(q)
+#         return q

SimPEG/EM/Static/DC/SurveyDC.py

@@ -0,0 +1,38 @@
+import SimPEG
+from SimPEG.EM.Base import BaseEMSurvey
+from SimPEG import sp, Survey
+from SimPEG.Utils import Zero, Identity
+from RxDC import BaseRx
+from SrcDC import BaseSrc
+
+class Survey(BaseEMSurvey):
+    rxPair  = BaseRx
+    srcPair = BaseSrc
+
+    def __init__(self, srcList, **kwargs):
+        self.srcList = srcList
+        BaseEMSurvey.__init__(self, srcList, **kwargs)
+
+class Survey_ky(BaseEMSurvey):
+    rxPair  = BaseRx
+    srcPair = BaseSrc
+
+    def __init__(self, srcList, **kwargs):
+        self.srcList = srcList
+        BaseEMSurvey.__init__(self, srcList, **kwargs)
+
+    def eval(self, f):
+        """
+        Project fields to receiver locations
+        :param Fields u: fields object
+        :rtype: numpy.ndarray
+        :return: data
+        """
+        data = SimPEG.Survey.Data(self)
+        kys = self.prob.kys
+        for src in self.srcList:
+            for rx in src.rxList:
+                data[src, rx] = rx.eval(kys, src, self.mesh, f)
+        return data
+
+

SimPEG/EM/Static/DC/Utils.py

@@ -0,0 +1,38 @@
+import numpy as np
+
+def WennerSrcList(nElecs, aSpacing, in2D=False, plotIt=False):
+
+    import SimPEG.EM.Static.DC as DC
+
+    elocs = np.arange(0,aSpacing*nElecs,aSpacing)
+    elocs -= (nElecs*aSpacing - aSpacing)/2
+    space = 1
+    WENNER = np.zeros((0,),dtype=int)
+    for ii in range(nElecs):
+        for jj in range(nElecs):
+            test = np.r_[jj,jj+space,jj+space*2,jj+space*3]
+            if np.any(test >= nElecs):
+                break
+            WENNER = np.r_[WENNER, test]
+        space += 1
+    WENNER = WENNER.reshape((-1,4))
+
+
+    if plotIt:
+        for i, s in enumerate('rbkg'):
+            plt.plot(elocs[WENNER[:,i]],s+'.')
+        plt.show()
+
+    # Create sources and receivers
+    i = 0
+    if in2D:
+        getLoc = lambda ii, abmn: np.r_[elocs[WENNER[ii,abmn]],0]
+    else:
+        getLoc = lambda ii, abmn: np.r_[elocs[WENNER[ii,abmn]],0, 0]
+    srcList = []
+    for i in range(WENNER.shape[0]):
+        rx = DC.Rx.Dipole(getLoc(i,1).reshape([1,-1]),getLoc(i,2).reshape([1,-1]))
+        src = DC.Src.Dipole([rx], getLoc(i,0),getLoc(i,3))
+        srcList += [src]
+
+    return srcList

SimPEG/EM/Static/DC/__init__.py

@@ -0,0 +1,8 @@
+from ProblemDC import Problem3D_CC, Problem3D_N
+from ProblemDC_2D import Problem2D_CC, Problem2D_N
+from SurveyDC import Survey, Survey_ky
+import SrcDC as Src #Pole
+import RxDC as Rx
+from FieldsDC import Fields_CC
+from BoundaryUtils import getxBCyBC_CC
+import Utils

SimPEG/EM/Static/IP/ProblemIP.py

@@ -0,0 +1,372 @@
+from SimPEG import Problem, Utils, Maps, Mesh
+from SimPEG.EM.Base import BaseEMProblem
+from SimPEG.EM.Static.DC.FieldsDC import Fields, Fields_CC, Fields_N
+from SimPEG.Utils import sdiag
+import numpy as np
+from SimPEG.Utils import Zero
+from SimPEG.EM.Static.DC import getxBCyBC_CC
+from SurveyIP import Survey
+
+class IPPropMap(Maps.PropMap):
+    """
+        Property Map for IP Problems. The electrical chargeability,
+        (\\(\\eta\\)) is the default inversion property
+    """
+    eta = Maps.Property("Electrical Chargeability", defaultInvProp = True)
+
+class BaseIPProblem(BaseEMProblem):
+
+    surveyPair = Survey
+    fieldsPair = Fields
+    PropMap = IPPropMap
+    Ainv = None
+    sigma = None
+    rho = None
+    f = None
+    Ainv = None
+
+    def fields(self, m):
+        self.curModel = m
+        if self.f is None:
+            self.f = self.fieldsPair(self.mesh, self.survey)
+            if self.Ainv == None:
+                A = self.getA()
+                self.Ainv = self.Solver(A, **self.solverOpts)
+            RHS = self.getRHS()
+            u = self.Ainv * RHS
+            Srcs = self.survey.srcList
+            self.f[Srcs, self._solutionType] = u
+        return self.f
+
+    def Jvec(self, m, v, f=None):
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        Jv = self.dataPair(self.survey) #same size as the data
+
+        A = self.getA()
+
+        for src in self.survey.srcList:
+            u_src = f[src, self._solutionType] # solution vector
+            dA_dm_v = self.getADeriv(u_src, v)
+            dRHS_dm_v = self.getRHSDeriv(src, v)
+            du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
+
+            for rx in src.rxList:
+                df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
+                Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
+        # Conductivity (d u / d log sigma)
+        if self._formulation is 'EB':
+            return -Utils.mkvc(Jv)
+        # Conductivity (d u / d log rho)
+        if self._formulation is 'HJ':
+            return Utils.mkvc(Jv)
+
+    def Jtvec(self, m, v, f=None):
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv = np.zeros(m.size)
+        AT = self.getA()
+
+        for src in self.survey.srcList:
+            u_src = f[src, self._solutionType]
+            for rx in src.rxList:
+                PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
+                df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
+                ATinvdf_duT = self.Ainv * df_duT
+                dA_dmT = self.getADeriv(u_src, ATinvdf_duT, adjoint=True)
+                dRHS_dmT = self.getRHSDeriv(src, ATinvdf_duT, adjoint=True)
+                du_dmT = -dA_dmT + dRHS_dmT
+                Jtv += (df_dmT + du_dmT).astype(float)
+        # Conductivity ((d u / d log sigma).T)
+        if self._formulation is 'EB':
+            return -Utils.mkvc(Jtv)
+        # Conductivity ((d u / d log rho).T)
+        if self._formulation is 'HJ':
+            return Utils.mkvc(Jtv)
+
+    def getSourceTerm(self):
+        """
+        takes concept of source and turns it into a matrix
+        """
+        """
+        Evaluates the sources, and puts them in matrix form
+
+        :rtype: (numpy.ndarray, numpy.ndarray)
+        :return: q (nC or nN, nSrc)
+        """
+
+        Srcs = self.survey.srcList
+
+        if self._formulation is 'EB':
+            n = self.mesh.nN
+            # return NotImplementedError
+
+        elif self._formulation is 'HJ':
+            n = self.mesh.nC
+
+        q = np.zeros((n, len(Srcs)))
+
+        for i, src in enumerate(Srcs):
+            q[:,i] = src.eval(self)
+        return q
+
+    @property
+    def deleteTheseOnModelUpdate(self):
+        toDelete = []
+        return toDelete
+
+    # assume log rho or log cond
+    @property
+    def MeSigma(self):
+        """
+            Edge inner product matrix for \\(\\sigma\\). Used in the E-B formulation
+        """
+        if getattr(self, '_MeSigma', None) is None:
+            self._MeSigma = self.mesh.getEdgeInnerProduct(self.sigma)
+        return self._MeSigma
+
+    @property
+    def MfRhoI(self):
+        """
+            Inverse of :code:`MfRho`
+        """
+        if getattr(self, '_MfRhoI', None) is None:
+            self._MfRhoI = self.mesh.getFaceInnerProduct(self.rho, invMat=True)
+        return self._MfRhoI
+
+    def MfRhoIDeriv(self,u):
+        """
+            Derivative of :code:`MfRhoI` with respect to the model.
+        """
+
+        dMfRhoI_dI = -self.MfRhoI**2
+        dMf_drho = self.mesh.getFaceInnerProductDeriv(self.rho)(u)
+        drho_dlogrho = Utils.sdiag(self.rho)*self.curModel.etaDeriv
+        return dMfRhoI_dI * ( dMf_drho * ( drho_dlogrho))
+
+    # TODO: This should take a vector
+    def MeSigmaDeriv(self, u):
+        """
+            Derivative of MeSigma with respect to the model
+        """
+        dsigma_dlogsigma = Utils.sdiag(self.sigma)*self.curModel.etaDeriv
+        return self.mesh.getEdgeInnerProductDeriv(self.sigma)(u) * dsigma_dlogsigma
+
+class Problem3D_CC(BaseIPProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'HJ' # CC potentials means J is on faces
+    fieldsPair    = Fields_CC
+
+    def __init__(self, mesh, **kwargs):
+        BaseIPProblem.__init__(self, mesh, **kwargs)
+        self.setBC()
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = D MfRhoI G
+
+        """
+
+        D = self.Div
+        G = self.Grad
+        MfRhoI = self.MfRhoI
+        A = D * MfRhoI * G
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return V.T * A
+        return A
+
+    def getADeriv(self, u, v, adjoint= False):
+
+        D = self.Div
+        G = self.Grad
+        MfRhoIDeriv = self.MfRhoIDeriv
+
+        if adjoint:
+            # if self._makeASymmetric is True:
+            #     v = V * v
+            return(MfRhoIDeriv( G * u ).T) * ( D.T * v)
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return V.T * ( D * ( MfRhoIDeriv( D.T * ( V * u ) ) * v ) )
+        return D * (MfRhoIDeriv( G * u ) * v)
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return self.Vol.T * RHS
+
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+    def setBC(self):
+        if self.mesh.dim==3:
+            fxm,fxp,fym,fyp,fzm,fzp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+            gBFzm = self.mesh.gridFz[fzm,:]
+            gBFzp = self.mesh.gridFz[fzp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+            temp_zm, temp_zp = np.ones_like(gBFzm[:,2]), np.ones_like(gBFzp[:,2])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+            alpha_zm, alpha_zp = temp_zm*0., temp_zp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+            beta_zm, beta_zp = temp_zm, temp_zp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+            gamma_zm, gamma_zp = temp_zm*0., temp_zp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp, alpha_zm, alpha_zp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp, beta_zm, beta_zp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp, gamma_zm, gamma_zp]
+
+        elif self.mesh.dim==2:
+
+            fxm,fxp,fym,fyp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp]
+
+        x_BC, y_BC = getxBCyBC_CC(self.mesh, alpha, beta, gamma)
+        V = self.Vol
+        self.Div = V * self.mesh.faceDiv
+        P_BC, B = self.mesh.getBCProjWF_simple()
+        M = B*self.mesh.aveCC2F
+        self.Grad = self.Div.T - P_BC*Utils.sdiag(y_BC)*M
+
+
+class Problem3D_N(BaseIPProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'EB' # N potentials means B is on faces
+    fieldsPair    = Fields_N
+
+    def __init__(self, mesh, **kwargs):
+        BaseIPProblem.__init__(self, mesh, **kwargs)
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = G.T MeSigma G
+
+        """
+
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        A = Grad.T * MeSigma * Grad
+
+        # Handling Null space of A
+        A[0,0] = A[0,0] + 1.
+
+        return A
+
+    def getADeriv(self, u, v, adjoint=False):
+        """
+
+        Product of the derivative of our system matrix with respect to the model and a vector
+
+        """
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        if not adjoint:
+            return Grad.T*(self.MeSigmaDeriv(Grad*u)*v)
+        elif adjoint:
+            return self.MeSigmaDeriv(Grad*u).T * (Grad*v)
+
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+if __name__ == '__main__':
+
+
+    cs = 12.5
+    hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+    hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+    hz = [(cs,7, -1.3),(cs,20)]
+    mesh = Mesh.TensorMesh([hx, hy, hz],x0="CCN")
+    sigma = np.ones(mesh.nC)
+    prob = BaseIPProblem(mesh, sigma=sigma)
+
+

SimPEG/EM/Static/IP/SurveyIP.py

@@ -0,0 +1,23 @@
+import SimPEG
+from SimPEG.EM.Base import BaseEMSurvey
+from SimPEG import sp, Survey
+from SimPEG.Utils import Zero, Identity
+from SimPEG.EM.Static.DC.SrcDC import BaseSrc
+from SimPEG.EM.Static.DC.RxDC import BaseRx
+
+class Survey(BaseEMSurvey):
+    rxPair  = BaseRx
+    srcPair = BaseSrc
+
+    def __init__(self, srcList, **kwargs):
+        self.srcList = srcList
+        BaseEMSurvey.__init__(self, srcList, **kwargs)
+
+    def dpred(self, m, f=None):
+        """
+            Predicted data.
+
+            .. math::
+                d_\\text{pred} = Pf(m)
+        """
+        return self.prob.Jvec(m, m, f=f)

SimPEG/EM/Static/IP/__init__.py

@@ -0,0 +1,2 @@
+from ProblemIP import Problem3D_CC, Problem3D_N
+from SurveyIP import Survey

SimPEG/EM/Static/SIP/ProblemSIP.py

@@ -0,0 +1,445 @@
+from SimPEG import Problem, Utils, Maps, Mesh
+from SimPEG.EM.Base import BaseEMProblem
+from SimPEG.EM.Static.DC.FieldsDC import Fields, Fields_CC, Fields_N
+from SimPEG.Utils import sdiag
+import numpy as np
+from SimPEG.Utils import Zero
+from SimPEG.EM.Static.DC import getxBCyBC_CC
+from SurveySIP import Survey, Data
+
+class ColeColePropMap(Maps.PropMap):
+    """
+        Property Map for EM Problems. The electrical conductivity (\\(\\sigma\\)) is the default inversion property, and the default value of the magnetic permeability is that of free space (\\(\\mu = 4\\pi\\times 10^{-7} \\) H/m)
+    """
+
+    eta = Maps.Property("Electrical Conductivity", defaultInvProp=True)
+    tau = Maps.Property("Electrical Conductivity", defaultVal=0.1, propertyLink=('taui', Maps.ReciprocalMap))
+    taui   = Maps.Property("Electrical Conductivity", defaultVal=1., propertyLink=('tau', Maps.ReciprocalMap))
+    c   = Maps.Property("Electrical Conductivity", defaultVal=1.)
+
+
+class BaseSIPProblem(BaseEMProblem):
+
+    surveyPair = Survey
+    fieldsPair = Fields
+    dataPair = Data
+    PropMap = ColeColePropMap
+    Ainv = None
+    sigma = None
+    rho = None
+    f = None
+    Ainv = None
+
+    def DebyeTime(self, t):
+        peta = self.curModel.eta*np.exp(-self.curModel.taui*t)
+        return peta
+
+    def EtaDeriv(self, t, v, adjoint=False):
+        v = np.array(v, dtype=float)
+        if adjoint:
+            return self.curModel.etaDeriv.T * (np.exp(-self.curModel.taui*t)*v)
+        else:
+            return np.exp(-self.curModel.taui*t) * (self.curModel.etaDeriv*v)
+
+
+    def TauiDeriv(self, t, v, adjoint=False):
+        v = np.array(v, dtype=float)
+        if adjoint:
+            return -self.curModel.tauiDeriv.T * (self.curModel.eta*t*np.exp(-self.curModel.taui*t)*v)
+        else:
+            return -self.curModel.eta*t*np.exp(-self.curModel.taui*t) * (self.curModel.tauiDeriv*v)
+
+    def fields(self, m):
+        self.curModel = m
+        if self.f is None:
+            self.f = self.fieldsPair(self.mesh, self.survey)
+            if self.Ainv == None:
+                A = self.getA()
+                self.Ainv = self.Solver(A, **self.solverOpts)
+            RHS = self.getRHS()
+            u = self.Ainv * RHS
+            Srcs = self.survey.srcList
+            self.f[Srcs, self._solutionType] = u
+        return self.f
+
+    def forward(self, m, f=None):
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+        Jv = self.dataPair(self.survey) #same size as the data
+        # A = self.getA()
+        JvAll = []
+        for tind in range(len(self.survey.times)):
+            #Pseudo-chareability
+            t = self.survey.times[tind]
+            v = self.DebyeTime(t)
+            for src in self.survey.srcList:
+                u_src = f[src, self._solutionType] # solution vector
+                dA_dm_v = self.getADeriv(u_src, v)
+                dRHS_dm_v = self.getRHSDeriv(src, v)
+                du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
+                for rx in src.rxList:
+                    timeindex = rx.getTimeP(self.survey.times)
+                    if timeindex[tind]:
+                        df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                        df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
+                        Jv[src, rx, t] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
+
+        # Conductivity (d u / d log sigma)
+        if self._formulation is 'EB':
+            return -Utils.mkvc(Jv)
+        # Resistivity (d u / d log rho)
+        if self._formulation is 'HJ':
+            return Utils.mkvc(Jv)
+
+    def Jvec(self, m, v, f=None):
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+        Jv = self.dataPair(self.survey) #same size as the data
+        # A = self.getA()
+        JvAll = []
+        #Assume only eta and tau (eta first then tau)
+        # v = [2*Mx1]
+        v = v.reshape((int(v.size/2), 2), order='F')
+
+        for tind in range(len(self.survey.times)):
+            t = self.survey.times[tind]
+            v0 = self.EtaDeriv(t, v[:,0])
+            v1 = self.TauiDeriv(t, v[:,1])
+            for src in self.survey.srcList:
+                u_src = f[src, self._solutionType] # solution vector
+                dA_dm_v0 = self.getADeriv(u_src, v0)
+                dRHS_dm_v0 = self.getRHSDeriv(src, v0)
+                du_dm_v0 = self.Ainv * ( - dA_dm_v0 + dRHS_dm_v0 )
+                dA_dm_v1 = self.getADeriv(u_src, v1)
+                dRHS_dm_v1 = self.getRHSDeriv(src, v1)
+                du_dm_v1 = self.Ainv * ( - dA_dm_v1 + dRHS_dm_v1 )
+                for rx in src.rxList:
+                    timeindex = rx.getTimeP(self.survey.times)
+                    if timeindex[tind]:
+                        df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                        df_dm_v0 = df_dmFun(src, du_dm_v0, v0, adjoint=False)
+                        df_dm_v1 = df_dmFun(src, du_dm_v1, v1, adjoint=False)
+                        Jv[src, rx, t] = rx.evalDeriv(src, self.mesh, f, df_dm_v0)
+                        Jv[src, rx, t] += rx.evalDeriv(src, self.mesh, f, df_dm_v1)
+        # Conductivity (d u / d log sigma)
+        if self._formulation is 'EB':
+            return -Jv.tovec()
+        # Resistivity (d u / d log rho)
+        if self._formulation is 'HJ':
+            return Jv.tovec()
+
+    def Jtvec(self, m, v, f=None):
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv= np.zeros(m.size)
+        for tind in range(len(self.survey.times)):
+            t = self.survey.times[tind]
+            for src in self.survey.srcList:
+                u_src = f[src, self._solutionType]
+                for rx in src.rxList:
+                    timeindex = rx.getTimeP(self.survey.times)
+                    if timeindex[tind]:
+                        PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx, t], adjoint=True) # wrt f, need possibility wrt m
+                        df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                        df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
+                        ATinvdf_duT = self.Ainv * df_duT
+                        dA_dmT = self.getADeriv(u_src, ATinvdf_duT, adjoint=True)
+                        dRHS_dmT = self.getRHSDeriv(src, ATinvdf_duT, adjoint=True)
+                        du_dmT = -dA_dmT + dRHS_dmT
+                        Jtv += np.r_[self.EtaDeriv(self.survey.times[tind], du_dmT, adjoint=True), self.TauiDeriv(self.survey.times[tind], du_dmT, adjoint=True)]
+
+        # Conductivity ((d u / d log sigma).T)
+        if self._formulation is 'EB':
+            return -Jtv
+        # Conductivity ((d u / d log rho).T)
+        if self._formulation is 'HJ':
+            return Jtv
+
+    def getSourceTerm(self):
+        """
+        takes concept of source and turns it into a matrix
+        """
+        """
+        Evaluates the sources, and puts them in matrix form
+
+        :rtype: (numpy.ndarray, numpy.ndarray)
+        :return: q (nC or nN, nSrc)
+        """
+
+        Srcs = self.survey.srcList
+
+        if self._formulation is 'EB':
+            n = self.mesh.nN
+            # return NotImplementedError
+
+        elif self._formulation is 'HJ':
+            n = self.mesh.nC
+
+        q = np.zeros((n, len(Srcs)))
+
+        for i, src in enumerate(Srcs):
+            q[:,i] = src.eval(self)
+        return q
+
+    @property
+    def deleteTheseOnModelUpdate(self):
+        toDelete = []
+        return toDelete
+
+    # assume log rho or log cond
+    @property
+    def MeSigma(self):
+        """
+            Edge inner product matrix for \\(\\sigma\\). Used in the E-B formulation
+        """
+        if getattr(self, '_MeSigma', None) is None:
+            self._MeSigma = self.mesh.getEdgeInnerProduct(self.sigma)
+        return self._MeSigma
+
+    @property
+    def MfRhoI(self):
+        """
+            Inverse of :code:`MfRho`
+        """
+        if getattr(self, '_MfRhoI', None) is None:
+            self._MfRhoI = self.mesh.getFaceInnerProduct(self.rho, invMat=True)
+        return self._MfRhoI
+
+    def MfRhoIDeriv(self,u):
+        """
+            Derivative of :code:`MfRhoI` with respect to the model.
+        """
+
+        dMfRhoI_dI = -self.MfRhoI**2
+        dMf_drho = self.mesh.getFaceInnerProductDeriv(self.rho)(u)
+        drho_dlogrho = Utils.sdiag(self.rho)
+        return dMfRhoI_dI * ( dMf_drho * ( drho_dlogrho))
+
+    # TODO: This should take a vector
+    def MeSigmaDeriv(self, u):
+        """
+            Derivative of MeSigma with respect to the model
+        """
+        dsigma_dlogsigma = Utils.sdiag(self.sigma)
+        return self.mesh.getEdgeInnerProductDeriv(self.sigma)(u) * dsigma_dlogsigma
+
+class Problem3D_CC(BaseSIPProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'HJ' # CC potentials means J is on faces
+    fieldsPair    = Fields_CC
+
+    def __init__(self, mesh, **kwargs):
+        BaseSIPProblem.__init__(self, mesh, **kwargs)
+        self.setBC()
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = D MfRhoI G
+
+        """
+
+        D = self.Div
+        G = self.Grad
+        # TODO: this won't work for full anisotropy
+        MfRhoI = self.MfRhoI
+        A = D * MfRhoI * G
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return V.T * A
+        return A
+
+    def getADeriv(self, u, v, adjoint= False):
+
+        D = self.Div
+        G = self.Grad
+        MfRhoIDeriv = self.MfRhoIDeriv
+
+        if adjoint:
+            # if self._makeASymmetric is True:
+            #     v = V * v
+            return(MfRhoIDeriv( G * u ).T) * ( D.T * v)
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return V.T * ( D * ( MfRhoIDeriv( D.T * ( V * u ) ) * v ) )
+        return D * (MfRhoIDeriv( G * u ) * v)
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+
+        # I think we should deprecate this for DC problem.
+        # if self._makeASymmetric is True:
+        #     return self.Vol.T * RHS
+
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+    def setBC(self):
+        if self.mesh.dim==3:
+            fxm,fxp,fym,fyp,fzm,fzp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+            gBFzm = self.mesh.gridFz[fzm,:]
+            gBFzp = self.mesh.gridFz[fzp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+            temp_zm, temp_zp = np.ones_like(gBFzm[:,2]), np.ones_like(gBFzp[:,2])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+            alpha_zm, alpha_zp = temp_zm*0., temp_zp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+            beta_zm, beta_zp = temp_zm, temp_zp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+            gamma_zm, gamma_zp = temp_zm*0., temp_zp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp, alpha_zm, alpha_zp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp, beta_zm, beta_zp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp, gamma_zm, gamma_zp]
+
+        elif self.mesh.dim==2:
+
+            fxm,fxp,fym,fyp = self.mesh.faceBoundaryInd
+            gBFxm = self.mesh.gridFx[fxm,:]
+            gBFxp = self.mesh.gridFx[fxp,:]
+            gBFym = self.mesh.gridFy[fym,:]
+            gBFyp = self.mesh.gridFy[fyp,:]
+
+            # Setup Mixed B.C (alpha, beta, gamma)
+            temp_xm, temp_xp = np.ones_like(gBFxm[:,0]), np.ones_like(gBFxp[:,0])
+            temp_ym, temp_yp = np.ones_like(gBFym[:,1]), np.ones_like(gBFyp[:,1])
+
+            alpha_xm, alpha_xp = temp_xm*0., temp_xp*0.
+            alpha_ym, alpha_yp = temp_ym*0., temp_yp*0.
+
+            beta_xm, beta_xp = temp_xm, temp_xp
+            beta_ym, beta_yp = temp_ym, temp_yp
+
+            gamma_xm, gamma_xp = temp_xm*0., temp_xp*0.
+            gamma_ym, gamma_yp = temp_ym*0., temp_yp*0.
+
+            alpha = [alpha_xm, alpha_xp, alpha_ym, alpha_yp]
+            beta =  [beta_xm, beta_xp, beta_ym, beta_yp]
+            gamma = [gamma_xm, gamma_xp, gamma_ym, gamma_yp]
+
+        x_BC, y_BC = getxBCyBC_CC(self.mesh, alpha, beta, gamma)
+        V = self.Vol
+        self.Div = V * self.mesh.faceDiv
+        P_BC, B = self.mesh.getBCProjWF_simple()
+        M = B*self.mesh.aveCC2F
+        self.Grad = self.Div.T - P_BC*Utils.sdiag(y_BC)*M
+
+
+class Problem3D_N(BaseSIPProblem):
+
+    _solutionType = 'phiSolution'
+    _formulation  = 'EB' # N potentials means B is on faces
+    fieldsPair    = Fields_N
+
+    def __init__(self, mesh, **kwargs):
+        BaseSIPProblem.__init__(self, mesh, **kwargs)
+
+    def getA(self):
+        """
+
+        Make the A matrix for the cell centered DC resistivity problem
+
+        A = G.T MeSigma G
+
+        """
+
+        # TODO: this won't work for full anisotropy
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        A = Grad.T * MeSigma * Grad
+
+        # Handling Null space of A
+        A[0,0] = A[0,0] + 1.
+
+        return A
+
+    def getADeriv(self, u, v, adjoint=False):
+        """
+
+        Product of the derivative of our system matrix with respect to the model and a vector
+
+        """
+        MeSigma = self.MeSigma
+        Grad = self.mesh.nodalGrad
+        if not adjoint:
+            return Grad.T*(self.MeSigmaDeriv(Grad*u)*v)
+        elif adjoint:
+            return self.MeSigmaDeriv(Grad*u).T * (Grad*v)
+
+
+    def getRHS(self):
+        """
+        RHS for the DC problem
+
+        q
+        """
+
+        RHS = self.getSourceTerm()
+        return RHS
+
+    def getRHSDeriv(self, src, v, adjoint=False):
+        """
+        Derivative of the right hand side with respect to the model
+        """
+        # TODO: add qDeriv for RHS depending on m
+        # qDeriv = src.evalDeriv(self, adjoint=adjoint)
+        # return qDeriv
+        return Zero()
+
+if __name__ == '__main__':
+
+
+    cs = 12.5
+    hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+    hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+    hz = [(cs,7, -1.3),(cs,20)]
+    mesh = Mesh.TensorMesh([hx, hy, hz],x0="CCN")
+    sigma = np.ones(mesh.nC)
+    prob = BaseSIPProblem(mesh, sigma=sigma)
+
+

SimPEG/EM/Static/SIP/Regularization.py

@@ -0,0 +1,204 @@
+from SimPEG import Utils, Maps, Mesh, sp, np
+from SimPEG.Regularization import BaseRegularization, Simple
+
+class MultiRegularization(Simple):
+    """
+    **MultiRegularization Class**
+
+    This is used to regularize the model space
+    having multiple models [m1, m2, m3, ...] ::
+
+        reg = Regularization(mesh)
+
+    """
+    nModels = None # Number of models
+    ratios = None  # Ratio for different models
+    crossgrad = False # Use cross gradient or not
+    betacross = 1.
+    wx = []
+    wy = []
+    wz = []
+
+    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
+        BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+        if self.nModels == None:
+            raise Exception("Put nModels as a initial input!")
+        if self.ratios == None:
+            self.ratios = [1. for imodel in range(self.nModels)]
+
+    @property
+    def Wsmall(self):
+        """Regularization matrix Wsmall"""
+        if getattr(self,'_Wsmall', None) is None:
+            vecs = []
+            for imodel in range(self.nModels):
+                vecs.append((self.regmesh.vol*self.alpha_s*self.wght*self.ratios[imodel])**0.5)
+            self._Wsmall = Utils.sdiag(np.hstack(vecs))
+        return self._Wsmall
+
+    @property
+    def Wx(self):
+        """Regularization matrix Wx"""
+        if getattr(self, '_Wx', None) is None:
+            mats = []
+            for imodel in range(self.nModels):
+                self.wx.append(Utils.sdiag((self.regmesh.aveCC2Fx * self.regmesh.vol*self.alpha_x*self.ratios[imodel]*(self.regmesh.aveCC2Fx*self.wght))**0.5))
+                mats.append(self.wx[imodel]*self.regmesh.cellDiffxStencil)
+            self._Wx = sp.block_diag(mats)
+        return self._Wx
+
+    @property
+    def Wy(self):
+        """Regularization matrix Wy"""
+        if getattr(self, '_Wy', None) is None:
+            mats = []
+            for imodel in range(self.nModels):
+                self.wy.append(Utils.sdiag((self.regmesh.aveCC2Fy * self.regmesh.vol*self.alpha_y*self.ratios[imodel]*(self.regmesh.aveCC2Fy*self.wght))**0.5))
+                mats.append(self.wy[imodel]*self.regmesh.cellDiffyStencil)
+            self._Wy = sp.block_diag(mats)
+        return self._Wy
+
+    @property
+    def Wz(self):
+        """Regularization matrix Wz"""
+        if getattr(self, '_Wz', None) is None:
+            mats = []
+            for imodel in range(self.nModels):
+                self.wz.append(Utils.sdiag((self.regmesh.aveCC2Fz * self.regmesh.vol*self.alpha_z*self.ratios[imodel]*(self.regmesh.aveCC2Fz*self.wght))**0.5))
+                mats.append(self.wz[imodel]*self.regmesh.cellDiffzStencil)
+            self._Wz = sp.block_diag(mats)
+        return self._Wz
+
+    @property
+    def Wsmooth(self):
+        """Full smoothness regularization matrix W"""
+        if getattr(self, '_Wsmooth', None) is None:
+            wlist = (self.Wx,)
+            if self.regmesh.dim > 1:
+                wlist += (self.Wy,)
+            if self.regmesh.dim > 2:
+                wlist += (self.Wz,)
+            self._Wsmooth = sp.vstack(wlist)
+        return self._Wsmooth
+
+    @property
+    def W(self):
+        """Full regularization matrix W"""
+        if getattr(self, '_W', None) is None:
+            wlist = (self.Wsmall, self.Wsmooth)
+            self._W = sp.vstack(wlist)
+        return self._W
+
+
+    @Utils.timeIt
+    def eval(self, m):
+        return self._evalSmall(m) + self._evalSmooth(m)
+
+    @Utils.timeIt
+    def _evalSmall(self, m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmooth(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wsmooth * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wsmooth * ( self.mapping * m)
+        return 0.5 * r.dot(r)
+
+    def cross(a,b):
+        ax, ay, az = a[0], a[1], a[2]
+        bx, by, bz = b[0], b[1], b[2]
+        cx = ay*bz - az*by
+        cy = az*bx - ax*bz
+        cz = ax*by - ay*bx
+        return [cx, cy, cz]
+
+     # TODO: Implement Cross Gradients..
+    @Utils.timeIt
+    def _evalCross(self, m):
+        if self.crossgrad == False:
+            return 0.
+        elif self.crossgrad == True:
+            M = (self.mapping * m).reshape((self.regmesh.nC, self.nModels), order="F")
+
+            ax = self.regmesh.aveFx2CC*self.regmesh.wx[0]*M[:,0]
+            ay = self.regmesh.aveFy2CC*self.regmesh.wy[0]*M[:,0]
+            az = self.regmesh.aveFz2CC*self.regmesh.wz[0]*M[:,0]
+            bx = self.regmesh.aveFx2CC*self.regmesh.wx[1]*M[:,1]
+            by = self.regmesh.aveFy2CC*self.regmesh.wy[1]*M[:,1]
+            bz = self.regmesh.aveFz2CC*self.regmesh.wz[1]*M[:,1]
+            #ab
+            out_ab = cross([ax, ay, az], [bx, by, bz])
+            r = np.r_[out_ab[0], out_ab[1], out_ab[2]]*np.sqrt(self.betacross)
+
+            if self.nModels == 3:
+                cx = self.regmesh.aveFx2CC*self.regmesh.wx[1]*M[:,1]
+                cy = self.regmesh.aveFy2CC*self.regmesh.wy[1]*M[:,1]
+                cz = self.regmesh.aveFz2CC*self.regmesh.wz[1]*M[:,1]
+                #ac
+                out_ac = cross([ax, ay, az], [cx, cy, cz])
+                #bc
+                out_bc = cross([bx, by, bz], [cx, cy, cz])
+                r = np.r_[r, np.hstack(out_ac)*np.sqrt(self.betacross), np.hstack(out_bc)*np.sqrt(self.betacross)]
+
+            return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def evalDeriv(self, m):
+        """
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
+
+        """
+        deriv = self._evalSmallDeriv(m) + self._evalSmoothDeriv(m)
+        if self.crossgrad==True:
+            deriv += self._evalCrossDeriv(m)
+        return deriv
+
+    @Utils.timeIt
+    def _evalCrossDeriv(self,m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return r.T * ( self.Wsmall * self.mapping.deriv(m - self.mref) )
+
+    @Utils.timeIt
+    def eval2Deriv(self, m, v=None):
+        """
+        Second derivative
+
+        :param numpy.array m: geophysical model
+        :param numpy.array v: vector to multiply
+        :rtype: scipy.sparse.csr_matrix or numpy.ndarray
+        :return: WtW or WtW*v
+
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the second derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W}
+
+        """
+        mD = self.mapping.deriv(m - self.mref)
+        if v is None:
+            return mD.T * self.W.T * self.W * mD
+
+        return mD.T * ( self.W.T * ( self.W * ( mD * v) ) )
+
+
+

SimPEG/EM/Static/SIP/RxSIP.py

@@ -0,0 +1,88 @@
+import SimPEG
+import numpy as np
+from SimPEG.Utils import Zero, closestPoints
+
+class BaseRx(SimPEG.Survey.BaseTimeRx):
+    locs = None
+    rxType = None
+
+    knownRxTypes = {
+                    'phi':['phi',None],
+                    'ex':['e','x'],
+                    'ey':['e','y'],
+                    'ez':['e','z'],
+                    'jx':['j','x'],
+                    'jy':['j','y'],
+                    'jz':['j','z'],
+                    }
+
+    def __init__(self, locs, times, rxType, **kwargs):
+        SimPEG.Survey.BaseTimeRx.__init__(self, locs, times, rxType, **kwargs)
+
+    @property
+    def projField(self):
+        """Field Type projection (e.g. e b ...)"""
+        return self.knownRxTypes[self.rxType][0]
+
+    def projGLoc(self, f):
+        """Grid Location projection (e.g. Ex Fy ...)"""
+        comp = self.knownRxTypes[self.rxType][1]
+        if comp is not None:
+            return f._GLoc(self.rxType) + comp
+        return f._GLoc(self.rxType)
+
+    def getTimeP(self, timesall):
+        """
+            Returns the time projection matrix.
+
+            .. note::
+
+                This is not stored in memory, but is created on demand.
+        """
+        time_inds = np.in1d(timesall, self.times)
+        return time_inds
+
+    def evalDeriv(self, src, mesh, f, v, adjoint=False):
+        P = self.getP(mesh, self.projGLoc(f))
+        if not adjoint:
+            return P*v
+        elif adjoint:
+            return P.T*v
+
+
+# DC.Rx.Dipole(locs)
+class Dipole(BaseRx):
+
+    def __init__(self, locsM, locsN, times, rxType = 'phi', **kwargs):
+        assert locsM.shape == locsN.shape, 'locsM and locsN need to be the same size'
+        locs = [locsM, locsN]
+        # We may not need this ...
+        BaseRx.__init__(self, locs, times, rxType)
+
+    @property
+    def nD(self):
+        """Number of data in the receiver."""
+        # return self.locs[0].shape[0] * len(self.times)
+        return self.locs[0].shape[0]
+
+    @property
+    def nRx(self):
+        """Number of data in the receiver."""
+        return self.locs[0].shape[0]
+
+        # Not sure why ...
+        # return int(self.locs[0].size / 2)
+
+
+    def getP(self, mesh, Gloc):
+        if mesh in self._Ps:
+            return self._Ps[mesh]
+
+        P0 = mesh.getInterpolationMat(self.locs[0], Gloc)
+        P1 = mesh.getInterpolationMat(self.locs[1], Gloc)
+        P = P0 - P1
+
+        if self.storeProjections:
+            self._Ps[mesh] = P
+
+        return P

SimPEG/EM/Static/SIP/SrcSIP.py

@@ -0,0 +1,64 @@
+import SimPEG
+# from SimPEG.EM.Base import BaseEMSurvey
+from SimPEG.Utils import Zero, closestPoints, mkvc
+import numpy as np
+
+class BaseSrc(SimPEG.Survey.BaseSrc):
+
+    current = 1.0
+    loc = None
+
+    def __init__(self, rxList, **kwargs):
+        SimPEG.Survey.BaseSrc.__init__(self, rxList, **kwargs)
+
+    def eval(self, prob):
+        raise NotImplementedError
+
+    def evalDeriv(self, prob):
+        return Zero()
+
+    @property
+    def nD(self):
+        """Number of data"""
+        return self.vnD.sum()
+
+    @property
+    def vnD(self):
+        """Vector number of data"""
+        return np.array([rx.nD*len(rx.times) for rx in self.rxList])
+
+
+
+class Dipole(BaseSrc):
+
+    def __init__(self, rxList, locA, locB, **kwargs):
+        assert locA.shape == locB.shape, 'Shape of locA and locB should be the same'
+        self.loc = [locA, locB]
+        BaseSrc.__init__(self, rxList, **kwargs)
+
+    def eval(self, prob):
+        if prob._formulation == 'HJ':
+            inds = closestPoints(prob.mesh, self.loc, gridLoc='CC')
+            q = np.zeros(prob.mesh.nC)
+            q[inds] = self.current * np.r_[1., -1.]
+        elif prob._formulation == 'EB':
+            qa = prob.mesh.getInterpolationMat(self.loc[0], locType='N').todense()
+            qb = -prob.mesh.getInterpolationMat(self.loc[1], locType='N').todense()
+            q = self.current * mkvc(qa+qb)
+        return q
+
+class Pole(BaseSrc):
+
+    def __init__(self, rxList, loc, **kwargs):
+        BaseSrc.__init__(self, rxList, loc=loc, **kwargs)
+
+    def eval(self, prob):
+        if prob._formulation == 'HJ':
+            inds = closestPoints(prob.mesh, self.loc)
+            q = np.zeros(prob.mesh.nC)
+            q[inds] = self.current * np.r_[1.]
+        elif prob._formulation == 'EB':
+            q = prob.mesh.getInterpolationMat(self.loc, locType='N').todense()
+            q = self.current * mkvc(q)
+        return q
+

SimPEG/EM/Static/SIP/SurveySIP.py

@@ -0,0 +1,102 @@
+import SimPEG
+from SimPEG.EM.Base import BaseEMSurvey
+from SimPEG import np, sp, Survey, Utils
+from SimPEG.Utils import Zero, Identity
+from SimPEG.EM.Static.SIP.SrcSIP import BaseSrc
+from SimPEG.EM.Static.SIP.RxSIP import BaseRx
+import uuid
+
+
+class Survey(BaseEMSurvey):
+    rxPair  = BaseRx
+    srcPair = BaseSrc
+    times = None
+
+    def __init__(self, srcList, **kwargs):
+        self.srcList = srcList
+        BaseEMSurvey.__init__(self, srcList, **kwargs)
+        self.getUniqueTimes()
+
+    def getUniqueTimes(self):
+        time_rx = []
+        for src in self.srcList:
+            for rx in src.rxList:
+                time_rx.append(rx.times)
+        self.times = np.unique(np.hstack(time_rx))
+
+    def dpred(self, m, f=None):
+        """
+            Predicted data.
+
+            .. math::
+                d_\\text{pred} = Pf(m)
+        """
+        return self.prob.forward(m, f=f)
+
+
+class Data(SimPEG.Survey.Data):
+    """Fancy data storage by Src and Rx"""
+
+    def __init__(self, survey, v=None):
+        self.uid = str(uuid.uuid4())
+        self.survey = survey
+        self._dataDict = {}
+        for src in self.survey.srcList:
+            self._dataDict[src] = {}
+            for rx in src.rxList:
+                self._dataDict[src][rx] = {}
+
+        if v is not None:
+            self.fromvec(v)
+
+    def _ensureCorrectKey(self, key):
+        if type(key) is tuple:
+            if len(key) is not 3:
+                raise KeyError('Key must be [Src, Rx, tInd]')
+            if key[0] not in self.survey.srcList:
+                raise KeyError('Src Key must be a source in the survey.')
+            if key[1] not in key[0].rxList:
+                raise KeyError('Rx Key must be a receiver for the source.')
+            return key
+        elif isinstance(key, self.survey.srcPair):
+            if key not in self.survey.srcList:
+                raise KeyError('Key must be a source in the survey.')
+            return key, None, None
+        else:
+            raise KeyError('Key must be [Src] or [Src,Rx] or [Src, Rx, tInd]')
+
+    def __setitem__(self, key, value):
+        src, rx, t = self._ensureCorrectKey(key)
+        assert rx is not None, 'set data using [Src, Rx]'
+        assert isinstance(value, np.ndarray), 'value must by ndarray'
+        assert value.size == rx.nD, "value must have the same number of data as the source."
+        self._dataDict[src][rx][t] = Utils.mkvc(value)
+
+    def __getitem__(self, key):
+        src, rx, t = self._ensureCorrectKey(key)
+        if rx is not None:
+            if rx not in self._dataDict[src]:
+                raise Exception('Data for receiver has not yet been set.')
+            return self._dataDict[src][rx][t]
+
+        return np.concatenate([self[src,rx, t] for rx in src.rxList])
+
+    def tovec(self):
+        val = []
+        for src in self.survey.srcList:
+            for rx in src.rxList:
+                for t in rx.times:
+                    val.append(self[src, rx, t])
+        return np.concatenate(val)
+
+
+    def fromvec(self, v):
+        v = Utils.mkvc(v)
+        assert v.size == self.survey.nD, 'v must have the correct number of data.'
+        indBot, indTop = 0, 0
+        for src in self.survey.srcList:
+            for rx in src.rxList:
+                for t in rx.times:
+                    indTop += rx.nRx
+                    self[src, rx, t] = v[indBot:indTop]
+                    indBot += rx.nRx

SimPEG/EM/Static/SIP/__init__.py

@@ -0,0 +1,5 @@
+from ProblemSIP import Problem3D_CC, Problem3D_N
+from SurveySIP import Survey, Data
+import SrcSIP as Src #Pole
+import RxSIP as Rx
+from Regularization import MultiRegularization

SimPEG/EM/Static/Utils/StaticUtils.py

@@ -0,0 +1,317 @@
+from SimPEG import np
+from SimPEG.EM.Static import DC, IP
+
+def plot_pseudoSection(DCsurvey, axs, stype='dpdp', dtype="appc", clim=None):
+    """
+        Read list of 2D tx-rx location and plot a speudo-section of apparent
+        resistivity.
+
+        Assumes flat topo for now...
+
+        Input:
+        :param d2D, z0
+        :switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
+        :switch dtype=-> Either 'appr' (app. res) | 'appc' (app. con) | 'volt' (potential)
+        Output:
+        :figure scatter plot overlayed on image
+
+        Edited Feb 17th, 2016
+
+        @author: dominiquef
+
+    """
+    from SimPEG import np
+    from scipy.interpolate import griddata
+    import pylab as plt
+
+    # Set depth to 0 for now
+    z0 = 0.
+
+    # Pre-allocate
+    midx = []
+    midz = []
+    rho = []
+    LEG = []
+    count = 0 # Counter for data
+    for ii in range(DCsurvey.nSrc):
+
+        Tx = DCsurvey.srcList[ii].loc
+        Rx = DCsurvey.srcList[ii].rxList[0].locs
+
+        nD = DCsurvey.srcList[ii].rxList[0].nD
+
+        data = DCsurvey.dobs[count:count+nD]
+        count += nD
+
+        # Get distances between each poles A-B-M-N
+        if stype == 'pdp':
+            MA = np.abs(Tx[0] - Rx[0][:,0])
+            NA = np.abs(Tx[0] - Rx[1][:,0])
+            MN = np.abs(Rx[1][:,0] - Rx[0][:,0])
+
+            # Create mid-point location
+            Cmid = Tx[0]
+            Pmid = (Rx[0][:,0] + Rx[1][:,0])/2
+            if DCsurvey.mesh.dim == 2:
+                zsrc = Tx[1]
+            elif DCsurvey.mesh.dim ==3:
+                zsrc = Tx[2]
+
+        elif stype == 'dpdp':
+            MA = np.abs(Tx[0][0] - Rx[0][:,0])
+            MB = np.abs(Tx[1][0] - Rx[0][:,0])
+            NA = np.abs(Tx[0][0] - Rx[1][:,0])
+            NB = np.abs(Tx[1][0] - Rx[1][:,0])
+
+            # Create mid-point location
+            Cmid = (Tx[0][0] + Tx[1][0])/2
+            Pmid = (Rx[0][:,0] + Rx[1][:,0])/2
+            if DCsurvey.mesh.dim == 2:
+                zsrc = (Tx[0][1] + Tx[1][1])/2
+            elif DCsurvey.mesh.dim ==3:
+                zsrc = (Tx[0][2] + Tx[1][2])/2
+
+        # Change output for dtype
+        if dtype == 'volt':
+
+            rho = np.hstack([rho,data])
+
+        else:
+
+            # Compute pant leg of apparent rho
+            if stype == 'pdp':
+
+                leg =  data * 2*np.pi  * MA * ( MA + MN ) / MN
+
+            elif stype == 'dpdp':
+
+                leg = data * 2*np.pi / ( 1/MA - 1/MB + 1/NB - 1/NA )
+                LEG.append(1./(2*np.pi) *( 1/MA - 1/MB + 1/NB - 1/NA ))
+            else:
+                print """dtype must be 'pdp'(pole-dipole) | 'dpdp' (dipole-dipole) """
+                break
+
+
+            if dtype == 'appc':
+
+                leg = np.log10(abs(1./leg))
+                rho = np.hstack([rho,leg])
+
+            elif dtype == 'appr':
+
+                leg = np.log10(abs(leg))
+                rho = np.hstack([rho,leg])
+
+            else:
+                print """dtype must be 'appr' | 'appc' | 'volt' """
+                break
+
+
+        midx = np.hstack([midx, ( Cmid + Pmid )/2 ])
+        if DCsurvey.mesh.dim==3:
+            midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + zsrc ])
+        elif DCsurvey.mesh.dim==2:
+            midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + zsrc ])
+    ax = axs
+
+    # Grid points
+    grid_x, grid_z = np.mgrid[np.min(midx):np.max(midx), np.min(midz):np.max(midz)]
+    grid_rho = griddata(np.c_[midx,midz], rho.T, (grid_x, grid_z), method='linear')
+
+    if clim == None:
+        vmin, vmax = rho.min(), rho.max()
+    else:
+        vmin, vmax = clim[0], clim[1]
+
+    grid_rho = np.ma.masked_where(np.isnan(grid_rho), grid_rho)
+    ph = plt.pcolormesh(grid_x[:,0],grid_z[0,:],grid_rho.T, clim=(vmin, vmax), vmin=vmin, vmax=vmax)
+    cbar = plt.colorbar(format="$10^{%.1f}$",fraction=0.04,orientation="horizontal")
+
+    cmin,cmax = cbar.get_clim()
+    ticks = np.linspace(cmin,cmax,3)
+    cbar.set_ticks(ticks)
+    cbar.ax.tick_params(labelsize=10)
+
+    if dtype == 'appc':
+        cbar.set_label("App.Cond",size=12)
+    elif dtype == 'appr':
+        cbar.set_label("App.Res.",size=12)
+    elif dtype == 'volt':
+        cbar.set_label("Potential (V)",size=12)
+
+    # Plot apparent resistivity
+    ax.scatter(midx,midz,s=10,c=rho.T, vmin =vmin, vmax = vmax, clim=(vmin, vmax))
+
+    #ax.set_xticklabels([])
+    #ax.set_yticklabels([])
+
+    plt.gca().set_aspect('equal', adjustable='box')
+
+
+
+    return ph, LEG
+
+def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
+    """
+        Load in endpoints and survey specifications to generate Tx, Rx location
+        stations.
+
+        Assumes flat topo for now...
+
+        Input:
+        :param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
+        :object mesh -> SimPEG mesh object
+        :switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole) | 'gradient'
+        : param a, n -> pole seperation, number of rx dipoles per tx
+
+        Output:
+        :param Tx, Rx -> List objects for each tx location
+            Lines: P1x, P1y, P1z, P2x, P2y, P2z
+
+        Created on Wed December 9th, 2015
+
+        @author: dominiquef
+        !! Require clean up to deal with DCsurvey
+    """
+
+    from SimPEG import np
+
+    def xy_2_r(x1,x2,y1,y2):
+        r = np.sqrt( np.sum((x2 - x1)**2 + (y2 - y1)**2) )
+        return r
+
+    ## Evenly distribute electrodes and put on surface
+    # Mesure survey length and direction
+    dl_len = xy_2_r(endl[0,0],endl[1,0],endl[0,1],endl[1,1])
+
+    dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
+    dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
+
+    nstn = np.floor( dl_len / a )
+
+    # Compute discrete pole location along line
+    stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
+    stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
+
+    if mesh.dim==2:
+        ztop = mesh.vectorNy[-1]
+        # Create line of P1 locations
+        M = np.c_[stn_x, np.ones(nstn).T*ztop]
+        # Create line of P2 locations
+        N = np.c_[stn_x+a*dl_x, np.ones(nstn).T*ztop]
+
+    elif mesh.dim==3:
+        ztop = mesh.vectorNz[-1]
+        # Create line of P1 locations
+        M = np.c_[stn_x, stn_y, np.ones(nstn).T*ztop]
+        # Create line of P2 locations
+        N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*ztop]
+
+
+    ## Build list of Tx-Rx locations depending on survey type
+    # Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
+    # Pole-dipole: Moving pole on one end -> [A a MN1 a MN2 ... MNn a B]
+    SrcList = []
+
+
+    if stype != 'gradient':
+
+        for ii in range(0, int(nstn)-1):
+
+
+            if stype == 'dpdp':
+                tx = np.c_[M[ii,:],N[ii,:]]
+            elif stype == 'pdp':
+                tx = np.c_[M[ii,:],M[ii,:]]
+
+            # Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
+
+            # Current elctrode seperation
+            AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
+
+            # Number of receivers to fit
+            nstn = np.min([np.floor( (AB - b) / a ) , n])
+
+            # Check if there is enough space, else break the loop
+            if nstn <= 0:
+                continue
+
+            # Compute discrete pole location along line
+            stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
+            stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
+
+            # Create receiver poles
+
+            if mesh.dim==3:
+                # Create line of P1 locations
+                P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*ztop]
+                # Create line of P2 locations
+                P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*ztop]
+                rxClass = DC.Rx.Dipole(P1, P2)
+
+            elif mesh.dim==2:
+                # Create line of P1 locations
+                P1 = np.c_[stn_x, np.ones(nstn).T*ztop]
+                # Create line of P2 locations
+                P2 = np.c_[stn_x+a*dl_x, np.ones(nstn).T*ztop]
+                rxClass = DC.Rx.Dipole_ky(P1, P2)
+
+            if stype == 'dpdp':
+                    srcClass = DC.Src.Dipole([rxClass], M[ii,:],N[ii,:])
+            elif stype == 'pdp':
+                    srcClass = DC.Src.Pole([rxClass], M[ii,:])
+            SrcList.append(srcClass)
+
+    elif stype == 'gradient':
+
+        # Gradient survey only requires Tx at end of line and creates a square
+        # grid of receivers at in the middle at a pre-set minimum distance
+
+        # Get the edge limit of survey area
+        min_x = endl[0,0] + dl_x * b
+        min_y = endl[0,1] + dl_y * b
+
+        max_x = endl[1,0] - dl_x * b
+        max_y = endl[1,1] - dl_y * b
+
+        box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
+        box_w = box_l/2.
+
+        nstn = np.floor( box_l / a )
+
+        # Compute discrete pole location along line
+        stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
+        stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
+
+        # Define number of cross lines
+        nlin = int(np.floor( box_w / a ))
+        lind = range(-nlin,nlin+1)
+
+        ngrad = nstn * len(lind)
+
+        rx = np.zeros([ngrad,6])
+        for ii in range( len(lind) ):
+
+            # Move line in perpendicular direction by dipole spacing
+            lxx = stn_x - lind[ii]*a*dl_y
+            lyy = stn_y + lind[ii]*a*dl_x
+
+
+            M = np.c_[ lxx, lyy , np.ones(nstn).T*ztop]
+            N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*ztop]
+            rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
+
+            if mesh.dim==3:
+                rxClass = DC.Rx.Dipole(rx[:,:3], rx[:,3:])
+            elif mesh.dim==2:
+                M = M[:,[0,2]]
+                N = N[:,[0,2]]
+                rxClass = DC.Rx.Dipole_ky(rx[:,[0,2]], rx[:,[3,5]])
+            srcClass = DC.Src.Dipole([rxClass], M[0,:], N[-1,:])
+        SrcList.append(srcClass)
+    else:
+        print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
+
+
+    return SrcList
+

SimPEG/EM/Static/Utils/__init__.py

@@ -0,0 +1 @@
+from StaticUtils import *

SimPEG/EM/Static/__init__.py

@@ -0,0 +1,3 @@
+import DC
+import IP
+import SIP

SimPEG/EM/TDEM/SurveyTDEM.py

@@ -87,7 +87,7 @@ class SrcTDEM_VMD_MVP(SrcTDEM):
     def getInitialFields(self, mesh):
         """Vertical magnetic dipole, magnetic vector potential"""
         if self.waveformType == "STEPOFF":
-            print ">> Step waveform: Non-zero initial condition"        
+            print ">> Step waveform: Non-zero initial condition"
             if mesh._meshType is 'CYL':
                 if mesh.isSymmetric:
                     MVP = MagneticDipoleVectorPotential(self.loc, mesh, 'Ey')
@@ -96,8 +96,8 @@ class SrcTDEM_VMD_MVP(SrcTDEM):
             elif mesh._meshType is 'TENSOR':
                 MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
             else:
-                raise Exception('Unknown mesh for VMD') 
-            return {"b": mesh.edgeCurl*MVP}               
+                raise Exception('Unknown mesh for VMD')
+            return {"b": mesh.edgeCurl*MVP}
         elif self.waveformType == "GENERAL":
             print ">> General waveform: Zero initial condition"
             return {"b": np.zeros(mesh.nF)}
@@ -113,7 +113,7 @@ class SrcTDEM_VMD_MVP(SrcTDEM):
         elif mesh._meshType is 'TENSOR':
             MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
         else:
-            raise Exception('Unknown mesh for VMD')            
+            raise Exception('Unknown mesh for VMD')
         return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
 
 
@@ -122,7 +122,7 @@ class SrcTDEM_CircularLoop_MVP(SrcTDEM):
         self.loc = loc
         self.radius = radius
         self.waveformType = waveformType
-        SrcTDEM.__init__(self,rxList)        
+        SrcTDEM.__init__(self,rxList)
 
     def getInitialFields(self, mesh):
         """Circular Loop, magnetic vector potential"""
@@ -153,7 +153,7 @@ class SrcTDEM_CircularLoop_MVP(SrcTDEM):
         elif mesh._meshType is 'TENSOR':
             MVP = MagneticLoopVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'], self.radius)
         else:
-            raise Exception('Unknown mesh for CircularLoop')            
+            raise Exception('Unknown mesh for CircularLoop')
         return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
 
 

SimPEG/EM/Utils/testingUtils.py

@@ -20,56 +20,61 @@ def getFDEMProblem(fdemType, comp, SrcList, freq, useMu=False, verbose=False):
     mesh = Mesh.TensorMesh([hx,hy,hz],['C','C','C'])
 
     if useMu is True:
-        mapping = [('sigma', Maps.ExpMap(mesh)), ('mu', Maps.IdentityMap(mesh))] 
+        mapping = [('sigma', Maps.ExpMap(mesh)), ('mu', Maps.IdentityMap(mesh))]
     else:
         mapping = Maps.ExpMap(mesh)
 
     x = np.array([np.linspace(-5.*cs,-2.*cs,3),np.linspace(5.*cs,2.*cs,3)]) + cs/4. #don't sample right by the source, slightly off alignment from either staggered grid
     XYZ = Utils.ndgrid(x,x,np.linspace(-2.*cs,2.*cs,5))
-    Rx0 = EM.FDEM.Rx(XYZ, comp)
+    Rx0 = getattr(EM.FDEM.Rx, 'Point_' + comp[0])
+    if comp[2] == 'r':
+        real_or_imag = 'real'
+    elif comp[2] == 'i':
+        real_or_imag = 'imag'
+    rx0 = Rx0(XYZ, comp[1], 'imag')
 
     Src = []
 
     for SrcType in SrcList:
         if SrcType is 'MagDipole':
-            Src.append(EM.FDEM.Src.MagDipole([Rx0], freq=freq, loc=np.r_[0.,0.,0.]))
+            Src.append(EM.FDEM.Src.MagDipole([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
         elif SrcType is 'MagDipole_Bfield':
-            Src.append(EM.FDEM.Src.MagDipole_Bfield([Rx0], freq=freq, loc=np.r_[0.,0.,0.]))
+            Src.append(EM.FDEM.Src.MagDipole_Bfield([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
         elif SrcType is 'CircularLoop':
-            Src.append(EM.FDEM.Src.CircularLoop([Rx0], freq=freq, loc=np.r_[0.,0.,0.]))
+            Src.append(EM.FDEM.Src.CircularLoop([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
         elif SrcType is 'RawVec':
             if fdemType is 'e' or fdemType is 'b':
                 S_m = np.zeros(mesh.nF)
                 S_e = np.zeros(mesh.nE)
                 S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
                 S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
-                Src.append(EM.FDEM.Src.RawVec([Rx0], freq, S_m, S_e))
+                Src.append(EM.FDEM.Src.RawVec([rx0], freq, S_m, mesh.getEdgeInnerProduct()*S_e))
 
             elif fdemType is 'h' or fdemType is 'j':
                 S_m = np.zeros(mesh.nE)
                 S_e = np.zeros(mesh.nF)
                 S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
                 S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
-                Src.append(EM.FDEM.Src.RawVec([Rx0], freq, S_m, S_e))
+                Src.append(EM.FDEM.Src.RawVec([rx0], freq, mesh.getEdgeInnerProduct()*S_m, S_e))
 
     if verbose:
         print '  Fetching %s problem' % (fdemType)
 
     if fdemType == 'e':
         survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem_e(mesh, mapping=mapping)
+        prb = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
 
     elif fdemType == 'b':
         survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem_b(mesh, mapping=mapping)
+        prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
 
     elif fdemType == 'j':
         survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem_j(mesh, mapping=mapping)
+        prb = EM.FDEM.Problem3D_j(mesh, mapping=mapping)
 
     elif fdemType == 'h':
         survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem_h(mesh, mapping=mapping)
+        prb = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
 
     else:
         raise NotImplementedError()
@@ -90,7 +95,7 @@ def crossCheckTest(SrcList, fdemType1, fdemType2, comp, addrandoms = False, useM
     prb1 = getFDEMProblem(fdemType1, comp, SrcList, freq, useMu, verbose)
     mesh = prb1.mesh
     print 'Cross Checking Forward: %s, %s formulations - %s' % (fdemType1, fdemType2, comp)
-    
+
     logsig = np.log(np.ones(mesh.nC)*CONDUCTIVITY)
     mu = np.ones(mesh.nC)*MU
 

SimPEG/EM/__init__.py

@@ -1,5 +1,6 @@
 import TDEM
 import FDEM
+import Static
 import Base
 import Analytics
 import Utils

SimPEG/Examples/DC_Forward_PseudoSection.py

@@ -2,27 +2,27 @@ from SimPEG import Mesh, Utils, np, sp
 import SimPEG.DCIP as DC
 import time
 
-def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', plotIt=True):
+def run(loc=None, sig=None, radi=None, param=None, surveyType='dipole-dipole', unitType='appConductivity', plotIt=True):
     """
         DC Forward Simulation
         =====================
 
-        Forward model two conductive spheres in a half-space and plot a 
+        Forward model two conductive spheres in a half-space and plot a
         pseudo-section. Assumes an infinite line source and measures along the
         center of the spheres.
-        
+
         INPUT:
         loc     = Location of spheres [[x1,y1,z1],[x2,y2,z2]]
         radi    = Radius of spheres [r1,r2]
         param   = Conductivity of background and two spheres [m0,m1,m2]
-        stype   = survey type "pdp" (pole dipole) or "dpdp" (dipole dipole)
-        dtype   = Data type "appr" (app res) | "appc" (app cond) | "volt" (potential)
-        Created by @fourndo on Mon Feb 01 19:28:06 2016
+        surveyType = survey type 'pole-dipole' or 'dipole-dipole'
+        unitType = Data type "appResistivity" | "appConductivity"  | "volt"
+        Created by @fourndo
 
     """
 
-    assert stype in ['pdp', 'dpdp'], "Source type (stype) must be pdp or dpdp (pole dipole or dipole dipole)"
-    assert dtype in ['appr', 'appc', 'volt'], "Data type (dtype) must be appr (app res) or appc (app cond) or volt (potential)"
+    assert surveyType in ['pole-dipole', 'dipole-dipole'], "Source type (surveyType) must be pdp or dpdp (pole dipole or dipole dipole)"
+    assert unitType in ['appResistivity', 'appConductivity', 'volt'], "Unit type (unitType) must be appResistivity or appConductivity or volt (potential)"
 
     if loc is None:
         loc = np.c_[[-50.,0.,-50.],[50.,0.,-50.]]
@@ -73,8 +73,8 @@ def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', p
     locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]
 
     # We will handle the geometry of the survey for you and create all the combination of tx-rx along line
-    # [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
-    survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
+    # [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, surveyType, param[0], param[1], param[2])
+    survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, surveyType, param[0], param[1], param[2])
 
     # Define some global geometry
     dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) )
@@ -118,8 +118,8 @@ def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', p
         rxloc_N = np.asarray(Rx[ii][:,3:])
 
 
-        # For usual cases "dpdp" or "gradient"
-        if stype == 'pdp':
+        # For usual cases 'dipole-dipole' or "gradient"
+        if surveyType == 'pole-dipole':
             # Create an "inifinity" pole
             tx =  np.squeeze(Tx[ii][:,0:1])
             tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
@@ -157,12 +157,12 @@ def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', p
         fig = plt.figure(figsize=(7,7))
         ax = plt.subplot(2,1,1, aspect='equal')
         # Plot the location of the spheres for reference
-        circle1=plt.Circle((loc[0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3)
-        circle2=plt.Circle((loc[0,1],loc[2,1]),radi[1],color='k',fill=False, lw=3)
+        circle1=plt.Circle((loc[0,0], loc[2,0]), radi[0], color='w', fill=False, lw=3)
+        circle2=plt.Circle((loc[0,1], loc[2,1]), radi[1], color='k', fill=False, lw=3)
         ax.add_artist(circle1)
         ax.add_artist(circle2)
 
-        dat = mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y',
+        dat = mesh.plotSlice(np.log10(model), ax = ax, normal = 'Y',
                              ind = indy,grid=True, clim = np.log10([sig.min(),sig.max()]))
 
         ax.set_title('3-D model')
@@ -188,15 +188,13 @@ def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', p
         ax2 = plt.subplot(2,1,2, aspect='equal')
 
         # Plot the location of the spheres for reference
-        circle1=plt.Circle((loc[0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3)
-        circle2=plt.Circle((loc[0,1],loc[2,1]),radi[1],color='k',fill=False, lw=3)
+        circle1=plt.Circle((loc[0,0], loc[2,0]), radi[0], color='w', fill=False, lw=3)
+        circle2=plt.Circle((loc[0,1], loc[2,1]), radi[1], color='k', fill=False, lw=3)
         ax2.add_artist(circle1)
         ax2.add_artist(circle2)
 
         # Add the speudo section
-        dat = DC.plot_pseudoSection(survey2D,ax2,stype=stype, dtype = dtype)
-
-        # plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
+        dat = DC.plot_pseudoSection(survey2D, ax2, surveyType=surveyType, unitType=unitType)        # plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
         # plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
         # plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
         ax2.set_title('Apparent Conductivity data')

SimPEG/Examples/EM_FDEM_1D_Inversion.py

@@ -42,8 +42,8 @@ def run(plotIt=True):
         ax.grid(color='k', alpha=0.5, linestyle='dashed', linewidth=0.5)
 
 
-    rxOffset=10. 
-    bzi = EM.FDEM.Rx(np.array([[rxOffset, 0., 1e-3]]), 'bzi')
+    rxOffset=10.
+    bzi = EM.FDEM.Rx.Point_b(np.array([[rxOffset, 0., 1e-3]]), orientation='z', component='imag')
 
     freqs = np.logspace(1,3,10)
     srcLoc = np.array([0., 0., 10.])
@@ -51,7 +51,7 @@ def run(plotIt=True):
     srcList = [EM.FDEM.Src.MagDipole([bzi],freq, srcLoc,orientation='Z') for freq in freqs]
 
     survey = EM.FDEM.Survey(srcList)
-    prb = EM.FDEM.Problem_b(mesh, mapping=mapping)
+    prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
 
     try:
         from pymatsolver import MumpsSolver

SimPEG/Examples/EM_Schenkel_Morrison_Casing.py

@@ -215,7 +215,7 @@ def run(plotIt=True):
     # ------------ Problem and Survey ---------------
     survey = FDEM.Survey(sg_p + dg_p)
     mapping = [('sigma', Maps.IdentityMap(mesh))]
-    problem = FDEM.Problem_h(mesh, mapping=mapping)
+    problem = FDEM.Problem3D_h(mesh, mapping=mapping)
     problem.pair(survey)
 
     # ------------- Solve ---------------------------

SimPEG/Examples/Inversion_IRLS.py

@@ -0,0 +1,124 @@
+from SimPEG import *
+
+
+def run(N=100, plotIt=True):
+    """
+        Inversion: Linear Problem
+        =========================
+
+        Here we go over the basics of creating a linear problem and inversion.
+
+    """
+
+
+    np.random.seed(1)
+
+    std_noise = 1e-2
+
+    mesh = Mesh.TensorMesh([N])
+
+    m0 = np.ones(mesh.nC) * 1e-4
+    mref = np.zeros(mesh.nC)
+
+    nk = 10
+    jk = np.linspace(1.,nk,nk)
+    p = -2.
+    q = 1.
+
+    g = lambda k: np.exp(p*jk[k]*mesh.vectorCCx)*np.cos(np.pi*q*jk[k]*mesh.vectorCCx)
+
+    G = np.empty((nk, mesh.nC))
+
+    for i in range(nk):
+        G[i,:] = g(i)
+
+    mtrue = np.zeros(mesh.nC)
+    mtrue[mesh.vectorCCx > 0.3] = 1.
+    mtrue[mesh.vectorCCx > 0.45] = -0.5
+    mtrue[mesh.vectorCCx > 0.6] = 0
+
+
+    prob = Problem.LinearProblem(mesh, G)
+    survey = Survey.LinearSurvey()
+    survey.pair(prob)
+    survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk)
+    #survey.makeSyntheticData(mtrue, std=std_noise)
+
+    wd = np.ones(nk) * std_noise
+
+    #print survey.std[0]
+    #M = prob.mesh
+    # Distance weighting
+    wr = np.sum(prob.G**2.,axis=0)**0.5
+    wr = ( wr/np.max(wr) )
+
+#    reg = Regularization.Simple(mesh)
+#    reg.mref = mref
+#    reg.cell_weights = wr
+#
+    dmis = DataMisfit.l2_DataMisfit(survey)
+    dmis.Wd = 1./wd
+#
+#    opt = Optimization.ProjectedGNCG(maxIter=20,lower=-2.,upper=2., maxIterCG= 10, tolCG = 1e-4)
+#    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
+#    invProb.curModel = m0
+#
+#    beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
+#    target = Directives.TargetMisfit()
+#
+    betaest = Directives.BetaEstimate_ByEig()
+#    inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])
+#
+#
+#    mrec = inv.run(m0)
+#    ml2 = mrec
+#    print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
+#
+#    # Switch regularization to sparse
+#    phim = invProb.phi_m_last
+#    phid =  invProb.phi_d
+
+    reg = Regularization.Sparse(mesh)
+    reg.mref = mref
+    reg.cell_weights = wr
+
+    reg.mref = np.zeros(mesh.nC)
+    eps_p = 5e-2
+    eps_q = 5e-2
+    norms   = [0., 0., 2., 2.]
+
+    opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
+    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
+    update_Jacobi = Directives.Update_lin_PreCond()
+    IRLS = Directives.Update_IRLS( norms=norms,  eps_p=eps_p, eps_q=eps_q)
+
+    inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
+
+    # Run inversion
+    mrec = inv.run(m0)
+
+    print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
+
+
+    if plotIt:
+        import matplotlib.pyplot as plt
+
+        fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
+        for i in range(prob.G.shape[0]):
+            axes[0].plot(prob.G[i,:])
+        axes[0].set_title('Columns of matrix G')
+
+        axes[1].plot(mesh.vectorCCx, mtrue, 'b-')
+        axes[1].plot(mesh.vectorCCx, reg.l2model, 'r-')
+        #axes[1].legend(('True Model', 'Recovered Model'))
+        axes[1].set_ylim(-1.0,1.25)
+
+        axes[1].plot(mesh.vectorCCx, mrec, 'k-',lw = 2)
+        axes[1].legend(('True Model', 'Smooth l2-l2',
+        'Sparse lp:' + str(reg.norms[0]) + ', lqx:' + str(reg.norms[1]) ), fontsize = 12)
+        plt.show()
+
+    return prob, survey, mesh, mrec
+
+if __name__ == '__main__':
+    run()

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
@@ -17,13 +19,13 @@ def run(plotIt=True):
     ## Setup the forward modeling
     # Setting up 1D mesh and conductivity models to forward model data.
     # Frequency
-    nFreq = 31
-    freqs = np.logspace(3,-3,nFreq)
+    nFreq = 26
+    freqs = np.logspace(2,-3,nFreq)
     # Set mesh parameters
-    ct = 20
-    air = simpeg.Utils.meshTensor([(ct,16,1.4)])
+    ct = 10
+    air = simpeg.Utils.meshTensor([(ct,25,1.4)])
     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],25,-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)
@@ -33,7 +35,7 @@ def run(plotIt=True):
     layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)
     layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)
     # Set the conductivity values
-    sig_half = 2e-3
+    sig_half = 1e-2
     sig_air = 1e-8
     sig_layer1 = .2
     sig_layer2 = .2
@@ -50,38 +52,38 @@ def run(plotIt=True):
     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([-0.5]),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
@@ -90,30 +92,33 @@ def run(plotIt=True):
     # Define a counter
     C =  simpeg.Utils.Counter()
     # Set the optimization
-    opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)
+    opt = simpeg.Optimization.ProjectedGNCG(maxIter = 25)
     opt.counter = C
-    opt.LSshorten = 0.5
+    opt.lower = np.log(1e-4)
+    opt.upper = np.log(5)
+    opt.LSshorten = 0.1
     opt.remember('xc')
     # Data misfit
     dmis = simpeg.DataMisfit.l2_DataMisfit(survey)
     dmis.Wd = Wd
     # Regularization - with a regularization mesh
-    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)
+    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[active]],m1d.x0)
     reg = simpeg.Regularization.Tikhonov(regMesh)
     reg.mrefInSmooth = True
-    reg.alpha_s = 1e-7
+    reg.alpha_s = 1e-1
     reg.alpha_x = 1.
+
     # Inversion problem
     invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)
     invProb.counter = C
     # Beta cooling
     beta = simpeg.Directives.BetaSchedule()
-    beta.coolingRate = 4
-    betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)
+    beta.coolingRate = 4.
+    beta.coolingFactor = 4.
+    betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=1.)
+    betaest.beta0 = 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])
 
@@ -121,8 +126,9 @@ 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')
+        fig.axes[0].set_ylim([-10000,500])
         plt.show()
 
 if __name__ == '__main__':

SimPEG/Examples/MT_1D_analytic_nlayer_Earth.py

@@ -0,0 +1,428 @@
+from scipy.constants import epsilon_0, mu_0
+import matplotlib.pyplot as plt
+import numpy as np
+from ipywidgets import *
+from SimPEG.EM.Utils import k, omega
+
+"""
+MT1D: n layered earth problem
+*****************************
+
+Author: Thibaut Astic
+Contact: thast@eos.ubc.ca
+Date: January 2016
+
+This code compute the analytic response of a n-layered Earth to a plane wave (Magneto-Tellurics).
+
+We start by looking at Maxwell's equations in the electric
+field \\\(\\\mathbf{E}\\) and the magnetic flux
+\\\(\\\mathbf{H}\\) to write the wave equations
+\\(\\ \nabla ^2  \mathbf{E_x} + k^2 \mathbf{E_x} = 0 \\) &
+\\(\\ \nabla ^2  \mathbf{H_y} + k^2 \mathbf{H_y} = 0 \\)
+
+Then solving the equations in each layer "j" between z_{j-1} and z_j in the form of
+\\(\\ E_{x,j} (z) = U_j e^{i k (z-z_{j-1})} + D_j e^{-i k (z-z_{j-1})} \\)
+\\(\\ H_{y,j} (z) = \frac{1}{Z_j} (D_j e^{-i k (z-z_{j-1})} - U_j e^{i k (z-z_{j-1})}) \\)
+
+With U and D the Up and Down components of the E-field.
+
+The iteration from one layer to another is ensure by:
+
+\\(\\  \left(\begin{matrix} E_{x,j} \\ H_{y,j} \end{matrix} \right) =
+    P_j T_j P^{-1}_J \left(\begin{matrix} E_{x,j+1} \\ H_{y,j+1} \end{matrix} \right) \\)
+
+And the Boundary Condition is set for the E-field in the last layer, with no Up component (=0)
+and only a down component (=1 then normalized by the highest amplitude to ensure numeric stability)
+
+The layer 0 is assumed to be the air layer.
+
+"""
+
+#Define a frquency range for a survey
+frange = lambda minfreq, maxfreq, step: np.logspace(minfreq,maxfreq,num = step, base = 10.)
+
+#Functions to create random physical Perties for a n-layered earth
+thick = lambda minthick, maxthick, nlayer: np.append(np.array([1.2*10.**5]),
+                                                     np.ndarray.round(minthick + (maxthick-minthick)* np.random.rand(nlayer-1,1)
+                                                            ,decimals =1))
+
+sig = lambda minsig, maxsig, nlayer: np.append(np.array([0.]),
+                                               np.ndarray.round(10.**minsig + (10.**maxsig-10.**minsig)* np.random.rand(nlayer,1)
+                                                      ,decimals=3))
+
+mu  = lambda minmu, maxmu, nlayer: np.append(np.array([1.]),
+                                             np.ndarray.round(minmu + (maxmu-minmu)* np.random.rand(nlayer,1)
+                                                    ,decimals=1))
+
+eps = lambda mineps, maxeps, nlayer: np.append(np.array([1.]),
+                                               np.ndarray.round(mineps + (maxeps-mineps)* np.random.rand(nlayer,1)
+                                                                ,decimals=1))
+
+#Evaluate Impedance Z of a layer
+ImpZ = lambda f, mu, k: omega(f)*mu*mu_0/k
+
+#Complex Cole-Cole Conductivity - EM utils
+PCC= lambda siginf,m,t,c,f: siginf*(1.-(m/(1.+(1j*omega(f)*t)**c)))
+
+#Converted thickness array into top of layer array
+top = lambda thick: np.cumsum(thick)
+
+#Propagation Matrix and theirs inverses
+
+#matrix T for transition of Up and Down components accross a layer
+T = lambda h,k: np.matrix([[np.exp(1j*k*h),0.],[0.,np.exp(-1j*k*h)]],dtype='complex_')
+
+Tinv = lambda h,k: np.matrix([[np.exp(-1j*k*h),0.],[0.,np.exp(1j*k*h)]],dtype='complex_')
+
+#transition of Up and Down components accross a layer
+UD_Z = lambda UD,z,zj,k : T((z-zj),k)*UD
+
+
+#matrix P relating Up and Down components with E and H fields
+P = lambda z: np.matrix([[1.,1,],[-1./z,1./z]],dtype='complex_')
+
+Pinv = lambda z: np.matrix([[1.,-z],[1.,z]],dtype='complex_')/2.
+
+
+#Time Variation of E and H
+E_ZT = lambda U,D,f,t : np.exp(1j*omega(f)*t)*(U+D)
+H_ZT = lambda U,D,Z,f,t : (1./Z)*np.exp(1j*omega(f)*t)*(D-U)
+
+#Plot the configuration of the problem
+def PlotConfiguration(thick,sig,eps,mu,ax,widthg,z):
+
+    topn = top(thick)
+    widthn = np.arange(-widthg,widthg+widthg/10.,widthg/10.)
+
+    ax.set_ylim([z.min(),z.max()])
+    ax.set_xlim([-widthg,widthg])
+
+    ax.set_ylabel("Depth (m)", fontsize=16.)
+    ax.yaxis.tick_right()
+    ax.yaxis.set_label_position("right")
+
+    #define filling for the different layers
+    hatches=['/'  , '+', 'x', '|' , '\\', '-'  , 'o' , 'O' , '.' , '*' ]
+
+    #Write the physical properties of air
+    ax.annotate(("Air, $\sigma$ =%1.0f mS/m")%(sig[0]*10**(3)),
+            xy=(-widthg/2., -np.abs(z.max())/2.), xycoords='data',
+            xytext=(-widthg/2., -np.abs(z.max())/2.), textcoords='data',
+            fontsize=14.)
+
+    ax.annotate(("$\epsilon_r$= %1i")%(eps[0]),
+            xy=(-widthg/2., -np.abs(z.max())/3.), xycoords='data',
+            xytext=(-widthg/2., -np.abs(z.max())/3.), textcoords='data',
+            fontsize=14.)
+
+    ax.annotate(("$\mu_r$= %1i")%(mu[0]),
+            xy=(-widthg/2., -np.abs(z.max())/3.), xycoords='data',
+            xytext=(0, -np.abs(z.max())/3.), textcoords='data',
+            fontsize=14.)
+
+    #Write the physical properties of the differents layers up to the (n-1)-th and fill it with pattern
+    for i in range(1,len(topn)-1,1):
+        if topn[i] == topn[i+1]:
+            pass
+        else:
+            ax.annotate(("$\sigma$ =%3.3f mS/m")%(sig[i]*10**(3)),
+                xy=(0., (2.*topn[i]+topn[i+1])/3), xycoords='data',
+                xytext=(0., (2.*topn[i]+topn[i+1])/3), textcoords='data',
+                fontsize=14.)
+
+            ax.annotate(("$\epsilon_r$= %1i")%(eps[i]),
+                xy=(-widthg/1.1, (2.*topn[i]+topn[i+1])/3), xycoords='data',
+                xytext=(-widthg/1.1, (2.*topn[i]+topn[i+1])/3), textcoords='data',
+                fontsize=14.)
+
+            ax.annotate(("$\mu_r$= %1.2f")%(mu[i]),
+                xy=(-widthg/2., (2.*topn[i]+topn[i+1])/3), xycoords='data',
+                xytext=(-widthg/2., (2.*topn[i]+topn[i+1])/3), textcoords='data',
+                fontsize=14.)
+
+            ax.plot(widthn,topn[i]*np.ones_like(widthn),color='black')
+            ax.fill_between(widthn,topn[i],topn[i+1],alpha=0.3,color="none",edgecolor='black', hatch=hatches[(i-1)%10])
+
+    #Write the physical properties of the n-th layer and fill it with pattern
+    ax.plot(widthn,topn[-1]*np.ones_like(widthn),color='black')
+    ax.fill_between(widthn,topn[-1],z.max(),alpha=0.3,color="none",edgecolor='black', hatch=hatches[(len(topn)-2)%10])
+
+    ax.annotate(("$\sigma$ =%3.3f mS/m")%(sig[-1]*10**(3)),
+            xy=(0., (2.*topn[-1]+z.max())/3), xycoords='data',
+            xytext=(0., (2.*topn[-1]+z.max())/3), textcoords='data',
+            fontsize=14.)
+
+    ax.annotate(("$\epsilon_r$= %1i")%(eps[-1]),
+            xy=(-widthg/1.1, (2.*topn[-1]+z.max())/3), xycoords='data',
+            xytext=(-widthg/1.1, (2.*topn[-1]+z.max())/3), textcoords='data',
+            fontsize=14.)
+
+    ax.annotate(("$\mu_r$= %1.2f")%(mu[-1]),
+            xy=(-widthg/2., (2.*topn[-1]+z.max())/3), xycoords='data',
+            xytext=(-widthg/2., (2.*topn[-1]+z.max())/3), textcoords='data',
+            fontsize=14.)
+
+    #plot Trees!
+    ax.annotate("",
+            xy=(widthg/2., -1.*z.max()/5.), xycoords='data',
+            xytext=(widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(widthg/2., -3./4.*z.max()/5.), xycoords='data',
+            xytext=(widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(widthg/2., -1./2.*z.max()/5.), xycoords='data',
+            xytext=(widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.2*widthg/2., -1.*z.max()/5.), xycoords='data',
+            xytext=(1.2*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.2*widthg/2., -3./4.*z.max()/5.), xycoords='data',
+            xytext=(1.2*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.2*widthg/2., -1./2.*z.max()/5.), xycoords='data',
+            xytext=(1.2*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.5*widthg/2., -1.*z.max()/5.), xycoords='data',
+            xytext=(1.5*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.5*widthg/2., -3./4.*z.max()/5.), xycoords='data',
+            xytext=(1.5*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.)
+            )
+
+    ax.annotate("",
+            xy=(1.5*widthg/2., -1./2.*z.max()/5.), xycoords='data',
+            xytext=(1.5*widthg/2., 0.), textcoords='data',
+            arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.)
+            )
+
+
+    ax.invert_yaxis()
+
+    return ax
+
+#Propagate Up and Down component for a certain frequency & evaluate E and H field
+
+def Propagate(f,H,sig,chg,taux,c,mu,eps,n):
+
+    sigcm = np.zeros_like(sig,dtype='complex_')
+
+    for j in range(1,len(sig)):
+        sigcm[j]=PCC(sig[j],chg[j],taux[j],c[j],f)
+
+    K = k(f, sigcm, mu, eps)
+    Z = ImpZ(f,mu,K)
+
+    EH = np.matrix(np.zeros((2,n+1),dtype = 'complex_'),dtype = 'complex_')
+    UD = np.matrix(np.zeros((2,n+1),dtype = 'complex_'),dtype = 'complex_')
+
+    UD[1,-1] = 1.
+
+    for i in range(-2,-(n+2),-1):
+
+        UD[:,i] = Tinv(H[i+1],K[i])*Pinv(Z[i])*P(Z[i+1])*UD[:,i+1]
+        UD = UD/((np.abs(UD[0,:]+UD[1,:])).max())
+
+    for j in range(0,n+1):
+        EH[:,j] = np.matrix([[1.,1,],[-1./Z[j],1./Z[j]]])*UD[:,j]
+
+    return UD, EH, Z ,K
+
+
+#Evaluate the apparent resistivity and phase for a frequency range
+def appres(F,H,sig,chg,taux,c,mu,eps,n):
+
+    Res = np.zeros_like(F)
+    Phase = np.zeros_like(F)
+    App_ImpZ= np.zeros_like(F,dtype='complex_')
+
+    for i in range(0,len(F)):
+
+        UD,EH,Z ,K = Propagate(F[i],H,sig,chg,taux,c,mu,eps,n)
+
+        App_ImpZ[i] = EH[0,1]/EH[1,1]
+
+        Res[i] = np.abs(App_ImpZ[i])**2./(mu_0*omega(F[i]))
+        Phase[i] = np.angle(App_ImpZ[i], deg = True)
+
+    return Res,Phase
+
+#Evaluate Up, Down components, E and H field, for a frequency range,
+#a discretized depth range and a time range (use to calculate envelope)
+def calculateEHzt(F,H,sig,chg,taux,c,mu,eps,n,zsample,tsample):
+
+    topc = top(H)
+
+    layer = np.zeros(len(zsample),dtype=np.int)-1
+
+    Exzt = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_')
+    Hyzt = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_')
+    Uz = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_')
+    Dz = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_')
+    UDaux = np.matrix(np.zeros((2,len(zsample)),dtype = 'complex_'),dtype = 'complex_')
+
+    for i in range(0,n+1,1):
+        layer = layer+(zsample>=topc[i])*1
+
+    for j in range(0,len(F)):
+
+        UD,EH,Z ,K = Propagate(F[j],H,sig,chg,taux,c,mu,eps,n)
+
+        for p in range(0,len(zsample)):
+
+            UDaux[:,p] = UD_Z(UD[:,layer[p]],zsample[p],topc[layer[p]],K[layer[p]])
+
+            for q in range(0,len(tsample)):
+
+                Exzt[p,q]  = Exzt[p,q] + E_ZT(UDaux[0,p],UDaux[1,p],F[j],tsample[q])/len(F)
+                Hyzt[p,q] = Hyzt[p,q] + H_ZT(UDaux[0,p],UDaux[1,p],Z[layer[p]],F[j],tsample[q])/len(F)
+                Uz[p,q] = Uz[p,q] + UDaux[0,p]*np.exp(1j*omega(F[j])*tsample[q])/len(F)
+                Dz[p,q] = Dz[p,q] + UDaux[1,p]*np.exp(1j*omega(F[j])*tsample[q])/len(F)
+
+    return  Exzt,Hyzt,Uz,Dz,UDaux,layer
+
+
+#Function to Plot Apparent Resistivity and Phase
+def PlotAppRes(F,H,sig,chg,taux,c,mu,eps,n,fenvelope,PlotEnvelope):
+
+    Res, Phase = appres(F,H,sig,chg,taux,c,mu,eps,n)
+
+    fig,ax = plt.subplots(1,2,figsize=(16,10))
+
+    ax[0].scatter(Res,F,color='black')
+    ax[0].set_xscale('Log')
+    ax[0].set_yscale('Log')
+    ax[0].set_xlim([10.**(np.log10(Res.min())-1.),10.**(np.log10(Res.max())+1.)])
+    ax[0].set_ylim([F.min(),F.max()])
+    ax[0].set_xlabel('Apparent Resistivity (Ohm*m)',fontsize=16.,color="black")
+    ax[0].set_ylabel('Frequency (Hz)',fontsize=16.)
+    ax[0].grid(which='major')
+
+    ax0 = ax[0].twiny()
+
+    ax0.set_xlim([0.,90.])
+    ax0.set_ylim([F.min(),F.max()])
+    ax0.scatter(Phase,F,color='purple')
+    ax0.set_xlabel('Phase (Degrees)',fontsize=16.,color="purple")
+
+    zc=np.arange(-(H[1:].max()+10)*n,(H[1:].max()+10)*n,10.)
+
+    ax[0].tick_params(labelsize=16)
+    ax[1].tick_params(labelsize=16)
+    ax0.tick_params(labelsize=16)
+
+    if PlotEnvelope:
+
+        widthn=np.logspace(np.log10(Res.min())-1., np.log10(Res.max())+1., num=100, endpoint=True, base=10.0)
+        fenvelope1n=np.ones(100)*fenvelope
+        ax[0].plot(widthn,fenvelope1n,linestyle='dashed',color='black')
+
+        tc=np.arange(0.,1./fenvelope,0.01/(fenvelope))
+        Exzt,Hyzt,Uz,Dz,UDaux,layer = calculateEHzt(np.array([fenvelope]),H,sig,chg,taux,c,mu,eps,n,zc,tc)
+
+        ax1=ax[1].twiny()
+
+        ax[1].tick_params(labelsize=16)
+        ax1.tick_params(labelsize=16)
+
+        ax[1].set_xlabel('Amplitude Electric Field E (V/m)',color='blue',fontsize=16)
+
+        ax1.set_xlabel('Amplitude Magnetic Field H (A/m)',color='red',fontsize=16)
+
+        ax[1].fill_betweenx(zc,np.squeeze(np.asarray(np.real(Exzt.min(axis=1)))),
+                      np.squeeze(np.asarray(np.real(Exzt.max(axis=1)))),
+                      color='blue', alpha=0.1)
+
+        ax1.fill_betweenx(zc,np.squeeze(np.asarray(np.real(Hyzt.min(axis=1)))),
+                      np.squeeze(np.asarray(np.real(Hyzt.max(axis=1)))),
+                      color='red', alpha=0.1)
+
+        ax[1] = PlotConfiguration(H,sig,eps,mu,ax[1],(1.5*np.abs(Exzt).max()),zc)
+        ax1.set_xlim([-1.5*np.abs(Hyzt).max(),1.5*np.abs(Hyzt).max()])
+        ax1.set_xlim([-1.5*np.abs(Hyzt).max(),1.5*np.abs(Hyzt).max()])
+    else:
+        print 'No envelop (if True, might be slow)'
+        ax[1] = PlotConfiguration(H,sig,eps,mu,ax[1],1.,zc)
+        ax[1].get_xaxis().set_ticks([])
+
+    plt.show()
+
+#Interactive MT for Notebook
+def PlotAppRes3LayersInteract(h1,h2,sigl1,sigl2,sigl3,mul1,mul2,mul3,epsl1,epsl2,epsl3,PlotEnvelope,F_Envelope):
+
+    frangn=frange(-5,5,100.)
+    sig3= np.array([0.,0.001,0.1, 0.001])
+    thick3 = np.array([120000.,50.,50.])
+    eps3=np.array([1.,1.,1.,1])
+    mu3=np.array([1.,1.,1.,1])
+    chg3=np.array([0.,0.1,0.,0.2])
+    chg3_0=np.array([0.,0.1,0.,0.])
+    taux3=np.array([0.,0.1,0.,0.1])
+    c3=np.array([1.,1.,1.,1.])
+
+    sig3[1]=sigl1
+    sig3[1]=10.**sig3[1]
+    sig3[2]=sigl2
+    sig3[2]=10.**sig3[2]
+    sig3[3]=sigl3
+    sig3[3]=10.**sig3[3]
+    mu3[1]=mul1
+    mu3[2]=mul2
+    mu3[3]=mul3
+    eps3[1]=epsl1
+    eps3[2]=epsl2
+    eps3[3]=epsl3
+    thick3[1]=h1
+    thick3[2]=h2
+
+    PlotAppRes(frangn,thick3,sig3,chg3_0,taux3,c3,mu3,eps3,3,F_Envelope,PlotEnvelope)
+
+
+def run(n=3,plotIt=True):
+    # something to make a plot
+
+    F = frange(-5.,5.,20)
+    H = thick(50.,100.,n)
+    sign = sig(-5.,0.,n)
+    mun = mu(1.,2.,n)
+    epsn = eps(1.,9.,n)
+    chg = np.zeros_like(sign)
+    taux = np.zeros_like(sign)
+    c = np.zeros_like(sign)
+
+    Res, Phase = appres(F,H,sign,chg,taux,c,mun,epsn,n)
+
+    if plotIt:
+
+        PlotAppRes(F, H, sign, chg, taux, c, mun, epsn, n, fenvelope=1000., PlotEnvelope=True)
+
+    return Res, Phase
+
+if __name__ == '__main__':
+    run()
+
+
+
+
+

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

SimPEG/Examples/Mesh_Basic_ForwardDC.py

@@ -1,22 +1,25 @@
 from SimPEG import Mesh, Utils, np, SolverLU
 
-## 2D DC forward modeling example with Tensor and Curvilinear Meshes
-
 def run(plotIt=True):
+
+    """
+        Mesh: Basic Forward 2D DC Resistivity
+        =====================================
+
+        2D DC forward modeling example with Tensor and Curvilinear Meshes
+    """
+
     # Step1: Generate Tensor and Curvilinear Mesh
     sz = [40,40]
-    # Tensor Mesh
     tM = Mesh.TensorMesh(sz)
-    # Curvilinear Mesh
     rM = Mesh.CurvilinearMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))
+
     # Step2: Direct Current (DC) operator
     def DCfun(mesh, pts):
         D = mesh.faceDiv
-        G = D.T
         sigma = 1e-2*np.ones(mesh.nC)
-        Msigi = mesh.getFaceInnerProduct(1./sigma)
-        MsigI = Utils.sdInv(Msigi)
-        A = D*MsigI*G
+        MsigI = mesh.getFaceInnerProduct(sigma, invProp=True, invMat=True)
+        A = -D*MsigI*D.T
         A[-1,-1] /= mesh.vol[-1] # Remove null space
         rhs = np.zeros(mesh.nC)
         txind = Utils.meshutils.closestPoints(mesh, pts)
@@ -37,39 +40,17 @@ def run(plotIt=True):
     if not plotIt: return
 
     import matplotlib.pyplot as plt
-    import matplotlib
-    from matplotlib.mlab import griddata
 
     #Step4: Making Figure
     fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
-    label = ["(a)", "(b)"]
-    opts = {}
     vmin, vmax = phitM.min(), phitM.max()
     dat = tM.plotImage(phitM, ax=axes[0], clim=(vmin, vmax), grid=True)
-
-    #TODO: At the moment Curvilinear Mesh do not have plotimage
-
-    Xi = tM.gridCC[:,0].reshape(sz[0], sz[1], order='F')
-    Yi = tM.gridCC[:,1].reshape(sz[0], sz[1], order='F')
-    PHIrM = griddata(rM.gridCC[:,0], rM.gridCC[:,1], phirM, Xi, Yi, interp='linear')
-    axes[1].contourf(Xi, Yi, PHIrM, 100, vmin=vmin, vmax=vmax)
-
+    dat = rM.plotImage(phirM, ax=axes[1], clim=(vmin, vmax), grid=True)
     cb = plt.colorbar(dat[0], ax=axes[0]); cb.set_label("Voltage (V)")
     cb = plt.colorbar(dat[0], ax=axes[1]); cb.set_label("Voltage (V)")
 
-    tM.plotGrid(ax=axes[0], **opts)
     axes[0].set_title('TensorMesh')
-    rM.plotGrid(ax=axes[1], **opts)
     axes[1].set_title('CurvilinearMesh')
-    for i in range(2):
-        axes[i].set_xlim(0.025, 0.975)
-        axes[i].set_ylim(0.025, 0.975)
-        axes[i].text(0., 1.0, label[i], fontsize=20)
-        if i==0:
-            axes[i].set_ylabel("y")
-        else:
-            axes[i].set_ylabel(" ")
-        axes[i].set_xlabel("x")
     plt.show()
 
 

SimPEG/Examples/Utils_surface2ind_topo.py

@@ -0,0 +1,41 @@
+from SimPEG import *
+from SimPEG.Utils import surface2ind_topo
+
+
+def run(plotIt=False, nx = 5, ny = 5):
+    """
+        Here we show how to use :code:`Utils.surface2ind_topo` to identify cells below
+        a topographic surface.
+
+    """
+
+    mesh = Mesh.TensorMesh([nx,ny], x0='CC') # 2D mesh
+    xtopo = np.linspace(mesh.gridN[:,0].min(), mesh.gridN[:,0].max())
+    topo = 0.4*np.sin(xtopo*5) # define a topographic surface
+
+    Topo = np.hstack([Utils.mkvc(xtopo,2),Utils.mkvc(topo,2)]) #make it an array
+
+    indcc = surface2ind_topo(mesh, Topo,'CC')
+
+    if plotIt:
+        from matplotlib.pylab import plt
+        from scipy.interpolate import interp1d
+        fig, ax = plt.subplots(1,1,figsize=(6,6))
+        mesh.plotGrid(ax=ax, nodes=True, centers=True)
+        ax.plot(xtopo,topo,'k',linewidth=1)
+        # ax.plot(mesh.vectorNx, interp1d(xtopo,topo)(mesh.vectorNx),'--k',linewidth=3)
+        ax.plot(mesh.vectorCCx, interp1d(xtopo,topo)(mesh.vectorCCx),'--k',linewidth=3)
+
+
+        aveN2CC = Utils.sdiag(mesh.aveN2CC.T.sum(1))*mesh.aveN2CC.T
+        a = aveN2CC * indcc
+        a[a > 0] = 1.
+        a[a < 0.25] = np.nan
+        a = a.reshape(mesh.vnN, order='F')
+        masked_array = np.ma.array(a, mask=np.isnan(a))
+        ax.pcolor(mesh.vectorNx,mesh.vectorNy,masked_array.T, cmap = plt.cm.gray,alpha=0.2)
+        plt.show()
+
+
+if __name__ == '__main__':
+    run(plotIt=True)

SimPEG/Examples/__init__.py

@@ -1,26 +1,29 @@
 # Run this file to add imports.
 
 ##### AUTOIMPORTS #####
-import DC_Analytic_Dipole
-import DC_Forward_PseudoSection
 import EM_FDEM_1D_Inversion
-import EM_FDEM_Analytic_MagDipoleWholespace
-import EM_Schenkel_Morrison_Casing
-import EM_TDEM_1D_Inversion
-import FLOW_Richards_1D_Celia1990
-import Forward_BasicDirectCurrent
-import Inversion_Linear
-import Mesh_Basic_PlotImage
-import Mesh_Basic_Types
-import Mesh_Operators_CahnHilliard
 import Mesh_QuadTree_Creation
+import EM_TDEM_1D_Inversion
 import Mesh_QuadTree_FaceDiv
-import Mesh_QuadTree_HangingNodes
 import Mesh_Tensor_Creation
-import MT_1D_ForwardAndInversion
+import FLOW_Richards_1D_Celia1990
+import DC_Forward_PseudoSection
+import Mesh_Operators_CahnHilliard
+import Mesh_Basic_Types
+import Inversion_IRLS
+import Inversion_Linear
+import EM_Schenkel_Morrison_Casing
 import MT_3D_Foward
+import Mesh_Basic_ForwardDC
+import MT_1D_ForwardAndInversion
+import Utils_surface2ind_topo
+import MT_1D_analytic_nlayer_Earth
+import EM_FDEM_Analytic_MagDipoleWholespace
+import Mesh_Basic_PlotImage
+import DC_Analytic_Dipole
+import Mesh_QuadTree_HangingNodes
 
-__examples__ = ["DC_Analytic_Dipole", "DC_Forward_PseudoSection", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_Schenkel_Morrison_Casing", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "Forward_BasicDirectCurrent", "Inversion_Linear", "Mesh_Basic_PlotImage", "Mesh_Basic_Types", "Mesh_Operators_CahnHilliard", "Mesh_QuadTree_Creation", "Mesh_QuadTree_FaceDiv", "Mesh_QuadTree_HangingNodes", "Mesh_Tensor_Creation", "MT_1D_ForwardAndInversion", "MT_3D_Foward"]
+__examples__ = ["EM_FDEM_1D_Inversion", "Mesh_QuadTree_Creation", "EM_TDEM_1D_Inversion", "Mesh_QuadTree_FaceDiv", "Mesh_Tensor_Creation", "FLOW_Richards_1D_Celia1990", "DC_Forward_PseudoSection", "Mesh_Operators_CahnHilliard", "Mesh_Basic_Types", "Inversion_IRLS", "Inversion_Linear", "EM_Schenkel_Morrison_Casing", "MT_3D_Foward", "Mesh_Basic_ForwardDC", "MT_1D_ForwardAndInversion", "Utils_surface2ind_topo", "MT_1D_analytic_nlayer_Earth", "EM_FDEM_Analytic_MagDipoleWholespace", "Mesh_Basic_PlotImage", "DC_Analytic_Dipole", "Mesh_QuadTree_HangingNodes"]
 
 ##### AUTOIMPORTS #####
 

SimPEG/Inversion.py

@@ -33,7 +33,9 @@ class BaseInversion(object):
         self._directiveList = value
         self._directiveList.inversion = self
 
-    def __init__(self, invProb, directiveList=[], **kwargs):
+    def __init__(self, invProb, directiveList=None, **kwargs):
+        if directiveList is None:
+            directiveList = []
         self.directiveList = directiveList
         Utils.setKwargs(self, **kwargs)
 

SimPEG/MT/BaseMT.py

@@ -1,132 +0,0 @@
-from SimPEG import SolverLU as SimpegSolver, PropMaps, Utils, mkvc, sp, np
-from SimPEG.EM.FDEM.FDEM import BaseFDEMProblem
-from SurveyMT import Survey, Data
-from FieldsMT import BaseMTFields
-
-
-class BaseMTProblem(BaseFDEMProblem):
-    """
-        Base class for all Natural source problems.
-    """
-
-    def __init__(self, mesh, **kwargs):
-        BaseFDEMProblem.__init__(self, mesh, **kwargs)
-        Utils.setKwargs(self, **kwargs)
-    # Set the default pairs of the problem
-    surveyPair = Survey
-    dataPair = Data
-    fieldsPair = BaseMTFields
-
-    # Set the solver
-    Solver = SimpegSolver
-    solverOpts = {}
-
-    verbose = False
-    # Notes:
-    # Use the forward and devs from BaseFDEMProblem
-    # Might need to add more stuff here.
-
-    ## NEED to clean up the Jvec and Jtvec to use Zero and Identities for None components.
-    def Jvec(self, m, v, f=None):
-        """
-        Function to calculate the data sensitivities dD/dm times a vector.
-
-            :param numpy.ndarray m (nC, 1) - conductive model
-            :param numpy.ndarray v (nC, 1) - random vector
-            :param MTfields object (optional) - MT fields object, if not given it is calculated
-            :rtype: MTdata object
-            :return: Data sensitivities wrt m
-        """
-
-        # Calculate the fields
-        if f is None:
-           f= self.fields(m)
-        # Set current model
-        self.curModel = m
-        # Initiate the Jv object
-        Jv = self.dataPair(self.survey)
-
-        # Loop all the frequenies
-        for freq in self.survey.freqs:
-            dA_du = self.getA(freq) #
-
-            dA_duI = self.Solver(dA_du, **self.solverOpts)
-
-            for src in self.survey.getSrcByFreq(freq):
-                # We need fDeriv_m = df/du*du/dm + df/dm
-                # Construct du/dm, it requires a solve
-                # NOTE: need to account for the 2 polarizations in the derivatives.
-                f_src = f[src,:]
-                # dA_dm and dRHS_dm should be of size nE,2, so that we can multiply by dA_duI. The 2 columns are each of the polarizations.
-                dA_dm = self.getADeriv_m(freq, f_src, v) # Size: nE,2 (u_px,u_py) in the columns.
-                dRHS_dm = self.getRHSDeriv_m(freq, v) # Size: nE,2 (u_px,u_py) in the columns.
-                if dRHS_dm is None:
-                    du_dm = dA_duI * ( -dA_dm )
-                else:
-                    du_dm = dA_duI * ( -dA_dm + dRHS_dm )
-                # Calculate the projection derivatives
-                for rx in src.rxList:
-                    # Get the projection derivative
-                    # v should be of size 2*nE (for 2 polarizations)
-                    PDeriv_u = lambda t: rx.evalDeriv(src, self.mesh, f, t) # wrt u, we don't have have PDeriv wrt m
-                    Jv[src, rx] = PDeriv_u(mkvc(du_dm))
-            dA_duI.clean()
-        # Return the vectorized sensitivities
-        return mkvc(Jv)
-
-    def Jtvec(self, m, v, f=None):
-        """
-        Function to calculate the transpose of the data sensitivities (dD/dm)^T times a vector.
-
-            :param numpy.ndarray m (nC, 1) - conductive model
-            :param numpy.ndarray v (nD, 1) - vector
-            :param MTfields object u (optional) - MT fields object, if not given it is calculated
-            :rtype: MTdata object
-            :return: Data sensitivities wrt m
-        """
-
-        if f is None:
-            f = self.fields(m)
-
-        self.curModel = m
-
-        # Ensure v is a data object.
-        if not isinstance(v, self.dataPair):
-            v = self.dataPair(self.survey, v)
-
-        Jtv = np.zeros(m.size)
-
-        for freq in self.survey.freqs:
-            AT = self.getA(freq).T
-
-            ATinv = self.Solver(AT, **self.solverOpts)
-
-            for src in self.survey.getSrcByFreq(freq):
-                ftype = self._fieldType + 'Solution'
-                f_src = f[src, :]
-
-                for rx in src.rxList:
-                    # Get the adjoint evalDeriv
-                    # PTv needs to be nE,
-                    PTv = rx.evalDeriv(src, self.mesh, f, mkvc(v[src, rx],2), adjoint=True) # wrt u, need possibility wrt m
-                    # Get the
-                    dA_duIT = ATinv * PTv
-                    dA_dmT = self.getADeriv_m(freq, f_src, mkvc(dA_duIT), adjoint=True)
-                    dRHS_dmT = self.getRHSDeriv_m(freq, mkvc(dA_duIT), adjoint=True)
-                    # Make du_dmT
-                    if dRHS_dmT is None:
-                        du_dmT = -dA_dmT
-                    else:
-                        du_dmT = -dA_dmT + dRHS_dmT
-                    # Select the correct component
-                    # du_dmT needs to be of size nC,
-                    real_or_imag = rx.projComp
-                    if real_or_imag == 'real':
-                        Jtv +=  du_dmT.real
-                    elif real_or_imag == 'imag':
-                        Jtv +=  -du_dmT.real
-                    else:
-                        raise Exception('Must be real or imag')
-            # Clean the factorization, clear memory.
-            ATinv.clean()
-        return Jtv

SimPEG/MT/Problem1D/Probs.py

@@ -1,291 +0,0 @@
-from SimPEG.EM.Utils import omega
-from SimPEG import mkvc
-from scipy.constants import mu_0
-from SimPEG.MT.BaseMT import BaseMTProblem
-from SimPEG.MT.SurveyMT import Survey, Data
-from SimPEG.MT.FieldsMT import Fields1D_e
-from SimPEG.MT.Utils.MT1Danalytic import getEHfields
-import numpy as np
-import multiprocessing, sys, time
-
-
-class eForm_psField(BaseMTProblem):
-    """
-    A MT problem soving a e formulation and primary/secondary fields decomposion.
-
-    By eliminating the magnetic flux density using
-
-        .. math ::
-
-            \mathbf{b} = \\frac{1}{i \omega}\\left(-\mathbf{C} \mathbf{e} \\right)
-
-
-    we can write Maxwell's equations as a second order system in \\\(\\\mathbf{e}\\\) only:
-
-    .. math ::
-        \\left(\mathbf{C}^T \mathbf{M^e_{\mu^{-1}}} \mathbf{C} + i \omega \mathbf{M^f_\sigma}] \mathbf{e}_{s} =& i \omega \mathbf{M^f_{\delta \sigma}} \mathbf{e}_{p}
-    which we solve for \\\(\\\mathbf{e_s}\\\). The total field \\\mathbf{e}\\ = \\\mathbf{e_p}\\ + \\\mathbf{e_s}\\.
-
-    The primary field is estimated from a background model (commonly half space ).
-
-
-    """
-    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
-    _fieldType = 'e_1d'
-    _eqLocs    = 'EF'
-    _sigmaPrimary = None
-
-
-    def __init__(self, mesh, **kwargs):
-        BaseMTProblem.__init__(self, mesh, **kwargs)
-        self.fieldsPair = Fields1D_e
-        # self._sigmaPrimary = sigmaPrimary
-    @property
-    def MeMui(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_MeMui', None) is None:
-            self._MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
-        return self._MeMui
-
-    @property
-    def MfSigma(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_MfSigma', None) is None:
-            self._MfSigma = self.mesh.getFaceInnerProduct(self.curModel.sigma)
-        return self._MfSigma
-
-    @property
-    def sigmaPrimary(self):
-        """
-        A background model, use for the calculation of the primary fields.
-
-        """
-        return self._sigmaPrimary
-
-    @sigmaPrimary.setter
-    def sigmaPrimary(self, val):
-        # Note: TODO add logic for val, make sure it is the correct size.
-        self._sigmaPrimary = val
-
-    def getA(self, freq):
-        """
-            Function to get the A matrix.
-
-            :param float freq: Frequency
-            :rtype: scipy.sparse.csr_matrix
-            :return: A
-        """
-
-        # Note: need to use the code above since in the 1D problem I want
-        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
-        # Possible that _fieldType and _eqLocs can fix this
-        MeMui = self.MeMui
-        MfSigma = self.MfSigma
-        C = self.mesh.nodalGrad
-        # Make A
-        A = C.T*MeMui*C + 1j*omega(freq)*MfSigma
-        # Either return full or only the inner part of A
-        return A
-
-    def getADeriv_m(self, freq, u, v, adjoint=False):
-        """
-        The derivative of A wrt sigma
-        """
-
-        dsig_dm = self.curModel.sigmaDeriv
-        MeMui = self.MeMui
-        #
-        u_src = u['e_1dSolution']
-        dMfSigma_dm = self.mesh.getFaceInnerProductDeriv(self.curModel.sigma)(u_src) * self.curModel.sigmaDeriv
-        if adjoint:
-            return 1j * omega(freq) * (  dMfSigma_dm.T * v )
-        # Note: output has to be nN/nF, not nC/nE.
-        # v should be nC
-        return 1j * omega(freq) * ( dMfSigma_dm * v )
-
-    def getRHS(self, freq):
-        """
-            Function to return the right hand side for the system.
-            :param float freq: Frequency
-            :rtype: numpy.ndarray (nF, 1), numpy.ndarray (nF, 1)
-            :return: RHS for 1 polarizations, primary fields
-        """
-
-        # Get sources for the frequncy(polarizations)
-        Src = self.survey.getSrcByFreq(freq)[0]
-        S_e = Src.S_e(self)
-        return -1j * omega(freq) * S_e
-
-    def getRHSDeriv_m(self, freq, v, adjoint=False):
-        """
-        The derivative of the RHS wrt sigma
-        """
-
-        Src = self.survey.getSrcByFreq(freq)[0]
-        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
-        return -1j * omega(freq) * S_eDeriv
-
-    def fields(self, m):
-        '''
-        Function to calculate all the fields for the model m.
-
-        :param np.ndarray (nC,) m: Conductivity model
-        '''
-        # Set the current model
-        self.curModel = m
-
-        F = Fields1D_e(self.mesh, self.survey)
-        for freq in self.survey.freqs:
-            if self.verbose:
-                startTime = time.time()
-                print 'Starting work for {:.3e}'.format(freq)
-                sys.stdout.flush()
-            A = self.getA(freq)
-            rhs  = self.getRHS(freq)
-            Ainv = self.Solver(A, **self.solverOpts)
-            e_s = Ainv * rhs
-
-            # Store the fields
-            Src = self.survey.getSrcByFreq(freq)[0]
-            # NOTE: only store the e_solution(secondary), all other components calculated in the fields object
-            F[Src, 'e_1dSolution'] = e_s[:,-1] # Only storing the yx polarization as 1d
-
-            # Note curl e = -iwb so b = -curl e /iw
-            # b = -( self.mesh.nodalGrad * e )/( 1j*omega(freq) )
-            # F[Src, 'b_1d'] = b[:,1]
-            if self.verbose:
-                print 'Ran for {:f} seconds'.format(time.time()-startTime)
-                sys.stdout.flush()
-        return F
-
-# Note this is not fully functional.
-# Missing:
-# Fields class corresponding to the fields
-# Update Jvec and Jtvec to include all the derivatives components
-# Other things ...
-class eForm_TotalField(BaseMTProblem):
-    """
-    A MT problem solving a e formulation and a Total bondary domain decompostion.
-
-    Solves the equation:
-
-    Math:
-
-
-    """
-
-    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
-    _fieldType = 'e'
-    _eqLocs    = 'EF'
-
-
-    def __init__(self, mesh, **kwargs):
-        BaseMTProblem.__init__(self, mesh, **kwargs)
-    @property
-    def MeMui(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_MeMui', None) is None:
-            self._MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
-        return self._MeMui
-
-    @property
-    def MfSigma(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_MfSigma', None) is None:
-            self._MfSigma = self.mesh.getFaceInnerProduct(self.curModel.sigma)
-        return self._MfSigma
-
-    def getA(self, freq, full=False):
-        """
-            Function to get the A matrix.
-
-            :param float freq: Frequency
-            :param logic full: Return full A or the inner part
-            :rtype: scipy.sparse.csr_matrix
-            :return: A
-        """
-
-        MeMui = self.MeMui
-        MfSigma = self.MfSigma
-        # Note: need to use the code above since in the 1D problem I want
-        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
-        # Possible that _fieldType and _eqLocs can fix this
-        # MeMui = self.MfMui
-        # MfSigma = self.MfSigma
-        C = self.mesh.nodalGrad
-        # Make A
-        A = C.T*MeMui*C + 1j*omega(freq)*MfSigma
-        # Either return full or only the inner part of A
-        if full:
-            return A
-        else:
-            return A[1:-1,1:-1]
-
-    def getADeriv_m(self, freq, u, v, adjoint=False):
-        raise NotImplementedError('getADeriv is not implemented')
-
-    def getRHS(self, freq):
-        """
-            Function to return the right hand side for the system.
-            :param float freq: Frequency
-            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
-            :return: RHS for both polarizations, primary fields
-        """
-        # Get sources for the frequency
-        # NOTE: Need to use the source information, doesn't really apply in 1D
-        src = self.survey.getSrcByFreq(freq)
-        # Get the full A
-        A = self.getA(freq,full=True)
-        # Define the outer part of the solution matrix
-        Aio = A[1:-1,[0,-1]]
-        Ed, Eu, Hd, Hu = getEHfields(self.mesh,self.curModel.sigma,freq,self.mesh.vectorNx)
-        Etot = (Ed + Eu)
-        sourceAmp = 1.0
-        Etot = ((Etot/Etot[-1])*sourceAmp) # Scale the fields to be equal to sourceAmp at the top
-        ## Note: The analytic solution is derived with e^iwt
-        eBC = np.r_[Etot[0],Etot[-1]]
-        # The right hand side
-
-        return -Aio*eBC, eBC
-
-    def getRHSderiv_m(self, freq, backSigma, u, v, adjoint=False):
-        raise NotImplementedError('getRHSDeriv not implemented yet')
-        return None
-
-    def fields(self, m):
-        '''
-        Function to calculate all the fields for the model m.
-
-        :param np.ndarray (nC,) m: Conductivity model
-        :param np.ndarray (nC,) m_back: Background conductivity model
-        '''
-        self.curModel = m
-        # RHS, CalcFields = self.getRHS(freq,m_back), self.calcFields
-
-        F = Fields1D_e(self.mesh, self.survey)
-        for freq in self.survey.freqs:
-            if self.verbose:
-                startTime = time.time()
-                print 'Starting work for {:.3e}'.format(freq)
-                sys.stdout.flush()
-            A = self.getA(freq)
-            rhs, e_o = self.getRHS(freq)
-            Ainv = self.Solver(A, **self.solverOpts)
-            e_i = Ainv * rhs
-            e = mkvc(np.r_[e_o[0], e_i, e_o[1]],2)
-            # Store the fields
-            Src = self.survey.getSrcByFreq(freq)
-            # NOTE: only store e fields
-            F[Src, 'e_1dSolution'] = e[:,0]
-            if self.verbose:
-                print 'Ran for {:f} seconds'.format(time.time()-startTime)
-                sys.stdout.flush()
-        return F

SimPEG/MT/Problem1D/__init__.py

@@ -1 +0,0 @@
-from Probs import eForm_TotalField, eForm_psField

SimPEG/MT/Problem2D/__init__.py

@@ -1 +0,0 @@
-pass

SimPEG/MT/Problem3D/Probs.py

@@ -1,138 +0,0 @@
-from SimPEG import Survey, Problem, Utils, Models, np, sp, mkvc, SolverLU as SimpegSolver
-from SimPEG.EM.Utils import omega
-from scipy.constants import mu_0
-from SimPEG.MT.BaseMT import BaseMTProblem
-from SimPEG.MT.SurveyMT import Survey, Data
-from SimPEG.MT.FieldsMT import Fields3D_e
-import multiprocessing, sys, time
-
-
-
-class eForm_ps(BaseMTProblem):
-    """
-    A MT problem solving a e formulation and a primary/secondary fields decompostion.
-
-    By eliminating the magnetic flux density using
-
-        .. math ::
-
-            \mathbf{b} = \\frac{1}{i \omega}\\left(-\mathbf{C} \mathbf{e} \\right)
-
-
-    we can write Maxwell's equations as a second order system in \\\(\\\mathbf{e}\\\) only:
-
-    .. math ::
-        \\left(\mathbf{C}^T \mathbf{M^f_{\mu^{-1}}} \mathbf{C} + i \omega \mathbf{M^e_\sigma}] \mathbf{e}_{s} =& i \omega \mathbf{M^e_{\delta \sigma}} \mathbf{e}_{p}
-    which we solve for \\\(\\\mathbf{e_s}\\\). The total field \\\mathbf{e}\\ = \\\mathbf{e_p}\\ + \\\mathbf{e_s}\\.
-
-    The primary field is estimated from a background model (commonly as a 1D model).
-
-    """
-
-    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
-    _fieldType = 'e'
-    _eqLocs    = 'FE'
-    fieldsPair = Fields3D_e
-    _sigmaPrimary = None
-
-    def __init__(self, mesh, **kwargs):
-        BaseMTProblem.__init__(self, mesh, **kwargs)
-
-    @property
-    def sigmaPrimary(self):
-        """
-        A background model, use for the calculation of the primary fields.
-
-        """
-        return self._sigmaPrimary
-    @sigmaPrimary.setter
-    def sigmaPrimary(self, val):
-        # Note: TODO add logic for val, make sure it is the correct size.
-        self._sigmaPrimary = val
-
-    def getA(self, freq):
-        """
-            Function to get the A system.
-
-            :param float freq: Frequency
-            :rtype: scipy.sparse.csr_matrix
-            :return: A
-        """
-        Mmui = self.MfMui
-        Msig = self.MeSigma
-        C = self.mesh.edgeCurl
-
-        return C.T*Mmui*C + 1j*omega(freq)*Msig
-
-    def getADeriv_m(self, freq, u, v, adjoint=False):
-        """
-        Calculate the derivative of A wrt m.
-
-        """
-
-        # This considers both polarizations and returns a nE,2 matrix for each polarization
-        if adjoint:
-            dMe_dsigV = sp.hstack(( self.MeSigmaDeriv( u['e_pxSolution'] ).T, self.MeSigmaDeriv(u['e_pySolution'] ).T ))*v
-        else:
-            # Need a nE,2 matrix to be returned
-            dMe_dsigV = np.hstack(( mkvc(self.MeSigmaDeriv( u['e_pxSolution'] )*v,2), mkvc( self.MeSigmaDeriv(u['e_pySolution'] )*v,2) ))
-        return 1j * omega(freq) * dMe_dsigV
-
-
-    def getRHS(self, freq):
-        """
-        Function to return the right hand side for the system.
-
-            :param float freq: Frequency
-            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
-            :return: RHS for both polarizations, primary fields
-        """
-
-        # Get sources for the frequncy(polarizations)
-        Src = self.survey.getSrcByFreq(freq)[0]
-        S_e = Src.S_e(self)
-        return -1j * omega(freq) * S_e
-
-    def getRHSDeriv_m(self, freq, v, adjoint=False):
-        """
-        The derivative of the RHS with respect to sigma
-        """
-
-        Src = self.survey.getSrcByFreq(freq)[0]
-        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
-        return -1j * omega(freq) * S_eDeriv
-
-    def fields(self, m):
-        '''
-        Function to calculate all the fields for the model m.
-
-        :param np.ndarray (nC,) m: Conductivity model
-        '''
-        # Set the current model
-        self.curModel = m
-
-        F = Fields3D_e(self.mesh, self.survey)
-        for freq in self.survey.freqs:
-            if self.verbose:
-                startTime = time.time()
-                print 'Starting work for {:.3e}'.format(freq)
-                sys.stdout.flush()
-            A = self.getA(freq)
-            rhs  = self.getRHS(freq)
-            # Solve the system
-            Ainv = self.Solver(A, **self.solverOpts)
-            e_s = Ainv * rhs
-
-            # Store the fields
-            Src = self.survey.getSrcByFreq(freq)[0]
-            # Store the fieldss
-            F[Src, 'e_pxSolution'] = e_s[:,0]
-            F[Src, 'e_pySolution'] = e_s[:,1]
-            # Note curl e = -iwb so b = -curl/iw
-
-            if self.verbose:
-                print 'Ran for {:f} seconds'.format(time.time()-startTime)
-                sys.stdout.flush()
-            Ainv.clean()
-        return F
-

SimPEG/MT/Problem3D/__init__.py

@@ -1 +0,0 @@
-from Probs import eForm_ps

SimPEG/MT/Utils/__init__.py

@@ -1,4 +0,0 @@
-from MT1Dsolutions import *  # Add the names of the functions
-from MT1Danalytic import *
-from dataUtils import *
-from ediFilesUtils import *

SimPEG/MT/Utils/srcUtils.py

@@ -1,46 +0,0 @@
-import SimPEG as simpeg, numpy as np
-
-def homo1DModelSource(mesh,freq,m_back):
-    '''
-        Function that calculates and return background fields for a 3D mesh and model.
-        The calculuations use 1D field solution for a vertical slice throught model (south-western most column),
-        which is assigned at the fields everywhere for the respective polarizations.2
-
-        :param Simpeg mesh object mesh: Holds information on the discretization
-        :param float freq: The frequency to solve at
-        :param np.array m_back: Background model of conductivity to base the calculations on.
-        :rtype: numpy.ndarray (mesh.nE,2)
-        :return: eBG_bp, E fields for the background model at both polarizations.
-
-    '''
-
-    # import
-    from SimPEG.MT.Utils import get1DEfields
-    # Get a 1d solution for a halfspace background
-    mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
-    # Note: Everything is using e^iwt
-    e0_1d = get1DEfields(mesh1d,mesh.r(m_back,'CC','CC','M')[0,0,:],freq)
-    # Setup x (east) polarization (_x)
-    ex_px = np.zeros(mesh.vnEx,dtype=complex)
-    ey_px = np.zeros((mesh.nEy,1),dtype=complex)
-    ez_px = np.zeros((mesh.nEz,1),dtype=complex)
-    # Assign the source to ex_x
-    for i in np.arange(mesh.vnEx[0]):
-        for j in np.arange(mesh.vnEx[1]):
-            ex_px[i,j,:] = -e0_1d
-    eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px,ez_px))
-    # Setup y (north) polarization (_py)
-    ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
-    ey_py = np.zeros(mesh.vnEy, dtype='complex128')
-    ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
-    # Assign the source to ey_py
-
-    for i in np.arange(mesh.vnEy[0]):
-        for j in np.arange(mesh.vnEy[1]):
-            ey_py[i,j,:] = e0_1d
-    # ey_py[1:-1,1:-1,1:-1] = 0
-    eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
-
-    # Return the electric fields
-    eBG_bp = np.hstack((eBG_px,eBG_py))
-    return eBG_bp

SimPEG/MT/__init__.py

@@ -1,5 +0,0 @@
-import Utils
-from SurveyMT import Rx, Survey, Data
-from FieldsMT import Fields1D_e, Fields3D_e
-import Problem1D, Problem2D, Problem3D
-import SrcMT

SimPEG/Maps.py

@@ -533,83 +533,6 @@ class ActiveCells(InjectActiveCells):
             FutureWarning)
         InjectActiveCells.__init__(self, mesh, indActive, valInactive, nC)
 
-class InjectActiveCellsTopo(IdentityMap):
-    """
-        Active model parameters. Extend for cells on topography to air cell (only works for tensor mesh)
-
-    """
-
-    indActive   = None #: Active Cells
-    valInactive = None #: Values of inactive Cells
-    nC          = None #: Number of cells in the full model
-
-    def __init__(self, mesh, indActive, nC=None):
-        self.mesh  = mesh
-
-        self.nC = nC or mesh.nC
-
-        if indActive.dtype is not bool:
-            z = np.zeros(self.nC,dtype=bool)
-            z[indActive] = True
-            indActive = z
-        self.indActive = indActive
-
-        self.indInactive = np.logical_not(indActive)
-        inds = np.nonzero(self.indActive)[0]
-        self.P = sp.csr_matrix((np.ones(inds.size),(inds, range(inds.size))), shape=(self.nC, self.nP))
-
-    @property
-    def shape(self):
-        return (self.nC, self.nP)
-
-    @property
-    def nP(self):
-        """Number of parameters in the model."""
-        return self.indActive.sum()
-
-    def _transform(self, m):
-        val_temp = np.zeros(self.mesh.nC)
-        val_temp[self.indActive] = m
-        valInactive = np.zeros(self.mesh.nC)
-        #1D
-        if self.mesh.dim == 1:
-            z_temp = self.mesh.gridCC
-            val_temp[~self.indActive] = val_temp[np.argmax(z_temp[self.indActive])]
-        #2D
-        elif self.mesh.dim == 2:
-            act_temp = self.indActive.reshape((self.mesh.nCx, self.mesh.nCy), order = 'F')
-            val_temp = val_temp.reshape((self.mesh.nCx, self.mesh.nCy), order = 'F')
-            y_temp = self.mesh.gridCC[:,1].reshape((self.mesh.nCx, self.mesh.nCy), order = 'F')
-            for i in range(self.mesh.nCx):
-                act_tempx = act_temp[i,:] == 1
-                val_temp[i,~act_tempx] = val_temp[i,np.argmax(y_temp[i,act_tempx])]
-            valInactive[~self.indActive] = Utils.mkvc(val_temp)[~self.indActive]
-        #3D
-        elif self.mesh.dim == 3:
-            act_temp = self.indActive.reshape((self.mesh.nCx*self.mesh.nCy, self.mesh.nCz), order = 'F')
-            val_temp = val_temp.reshape((self.mesh.nCx*self.mesh.nCy, self.mesh.nCz), order = 'F')
-            z_temp = self.mesh.gridCC[:,2].reshape((self.mesh.nCx*self.mesh.nCy, self.mesh.nCz), order = 'F')
-            for i in range(self.mesh.nCx*self.mesh.nCy):
-                act_tempxy = act_temp[i,:] == 1
-                val_temp[i,~act_tempxy] = val_temp[i,np.argmax(z_temp[i,act_tempxy])]
-            valInactive[~self.indActive] = Utils.mkvc(val_temp)[~self.indActive]
-
-        self.valInactive = valInactive
-
-        return self.P*m + self.valInactive
-
-    def inverse(self, D):
-        return self.P.T*D
-
-    def deriv(self, m):
-        return self.P
-
-class ActiveCellsTopo(InjectActiveCellsTopo):
-    def __init__(self, mesh, indActive, valInactive, nC=None):
-        warnings.warn(
-            "`ActiveCellsTopo` is deprecated and will be removed in future versions. Use `InjectActiveCellsTopo` instead",
-            FutureWarning)
-        InjectActiveCellsTopo.__init__(self, mesh, indActive, valInactive, nC)
 
 class Weighting(IdentityMap):
     """
@@ -759,15 +682,29 @@ class PolyMap(IdentityMap):
 
             m = [\sigma_1, \sigma_2, c]
 
+        Can take in an actInd vector to account for topography.
+
     """
-    def __init__(self, mesh, order, logSigma=True, normal='X'):
+    def __init__(self, mesh, order, logSigma=True, normal='X', actInd = None):
         IdentityMap.__init__(self, mesh)
         self.logSigma = logSigma
         self.order = order
         self.normal = normal
+        self.actInd = actInd
+
+        if getattr(self, 'actInd', None) is None:
+            self.actInd = range(self.mesh.nC)
+            self.nC = self.mesh.nC
+
+        else:
+            self.nC = len(self.actInd)
 
     slope = 1e4
 
+    @property
+    def shape(self):
+        return (self.nC, self.nP)
+
     @property
     def nP(self):
         if np.isscalar(self.order):
@@ -785,8 +722,8 @@ class PolyMap(IdentityMap):
             sig1, sig2 = np.exp(sig1), np.exp(sig2)
         #2D
         if self.mesh.dim == 2:
-            X = self.mesh.gridCC[:,0]
-            Y = self.mesh.gridCC[:,1]
+            X = self.mesh.gridCC[self.actInd,0]
+            Y = self.mesh.gridCC[self.actInd,1]
             if self.normal =='X':
                 f = polynomial.polyval(Y, c) - X
             elif self.normal =='Y':
@@ -795,9 +732,9 @@ class PolyMap(IdentityMap):
                 raise(Exception("Input for normal = X or Y or Z"))
         #3D
         elif self.mesh.dim == 3:
-            X = self.mesh.gridCC[:,0]
-            Y = self.mesh.gridCC[:,1]
-            Z = self.mesh.gridCC[:,2]
+            X = self.mesh.gridCC[self.actInd,0]
+            Y = self.mesh.gridCC[self.actInd,1]
+            Z = self.mesh.gridCC[self.actInd,2]
             if self.normal =='X':
                 f = polynomial.polyval2d(Y, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - X
             elif self.normal =='Y':
@@ -806,6 +743,7 @@ class PolyMap(IdentityMap):
                 f = polynomial.polyval2d(X, Y, c.reshape((self.order[0]+1,self.order[1]+1))) - Z
             else:
                 raise(Exception("Input for normal = X or Y or Z"))
+
         else:
             raise(Exception("Only supports 2D"))
 
@@ -819,8 +757,8 @@ class PolyMap(IdentityMap):
             sig1, sig2 = np.exp(sig1), np.exp(sig2)
         #2D
         if self.mesh.dim == 2:
-            X = self.mesh.gridCC[:,0]
-            Y = self.mesh.gridCC[:,1]
+            X = self.mesh.gridCC[self.actInd,0]
+            Y = self.mesh.gridCC[self.actInd,1]
 
             if self.normal =='X':
                 f = polynomial.polyval(Y, c) - X
@@ -832,9 +770,9 @@ class PolyMap(IdentityMap):
                 raise(Exception("Input for normal = X or Y or Z"))
         #3D
         elif self.mesh.dim == 3:
-            X = self.mesh.gridCC[:,0]
-            Y = self.mesh.gridCC[:,1]
-            Z = self.mesh.gridCC[:,2]
+            X = self.mesh.gridCC[self.actInd,0]
+            Y = self.mesh.gridCC[self.actInd,1]
+            Z = self.mesh.gridCC[self.actInd,2]
 
             if self.normal =='X':
                 f = polynomial.polyval2d(Y, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - X

SimPEG/Mesh/CurvilinearMesh.py

@@ -2,6 +2,7 @@ from SimPEG import Utils, np
 from BaseMesh import BaseRectangularMesh
 from DiffOperators import DiffOperators
 from InnerProducts import InnerProducts
+from View import CurvView
 
 # Some helper functions.
 length2D = lambda x: (x[:, 0]**2 + x[:, 1]**2)**0.5
@@ -10,7 +11,7 @@ normalize2D = lambda x: x/np.kron(np.ones((1, 2)), Utils.mkvc(length2D(x), 2))
 normalize3D = lambda x: x/np.kron(np.ones((1, 3)), Utils.mkvc(length3D(x), 2))
 
 
-class CurvilinearMesh(BaseRectangularMesh, DiffOperators, InnerProducts):
+class CurvilinearMesh(BaseRectangularMesh, DiffOperators, InnerProducts, CurvView):
     """
     CurvilinearMesh is a mesh class that deals with curvilinear meshes.
 
@@ -330,102 +331,6 @@ class CurvilinearMesh(BaseRectangularMesh, DiffOperators, InnerProducts):
 
 
 
-    #############################################
-    #            Plotting Functions             #
-    #############################################
-
-    def plotGrid(self, ax=None, nodes=False, faces=False, centers=False, edges=False, lines=True,  showIt=False):
-        """Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions.
-
-
-        .. plot::
-            :include-source:
-
-            from SimPEG import Mesh, Utils
-            X, Y = Utils.exampleLrmGrid([3,3],'rotate')
-            M = Mesh.CurvilinearMesh([X, Y])
-            M.plotGrid(showIt=True)
-
-        """
-        import matplotlib.pyplot as plt
-        import matplotlib
-        from mpl_toolkits.mplot3d import Axes3D
-        mkvc = Utils.mkvc
-
-        axOpts = {'projection':'3d'} if self.dim == 3 else {}
-        if ax is None: ax = plt.subplot(111, **axOpts)
-
-        NN = self.r(self.gridN, 'N', 'N', 'M')
-        if self.dim == 2:
-
-            if lines:
-                X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
-                Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
-
-                X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
-                Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
-
-                X = np.r_[X1, X2]
-                Y = np.r_[Y1, Y2]
-
-                ax.plot(X, Y, 'b-')
-            if centers:
-                ax.plot(self.gridCC[:,0],self.gridCC[:,1],'ro')
-
-            # Nx = self.r(self.normals, 'F', 'Fx', 'V')
-            # Ny = self.r(self.normals, 'F', 'Fy', 'V')
-            # Tx = self.r(self.tangents, 'E', 'Ex', 'V')
-            # Ty = self.r(self.tangents, 'E', 'Ey', 'V')
-
-            # ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
-
-            # nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
-            # nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
-            # ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
-            # ax.plot(nX, nY, 'r-')
-
-            # nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
-            # nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
-            # #ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
-            # ax.plot(nX, nY, 'g-')
-
-            # tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
-            # tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
-            # ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
-            # ax.plot(tX, tY, 'r-')
-
-            # nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
-            # nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
-            # #ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
-            # ax.plot(nX, nY, 'g-')
-
-        elif self.dim == 3:
-            X1 = np.c_[mkvc(NN[0][:-1, :, :]), mkvc(NN[0][1:, :, :]), mkvc(NN[0][:-1, :, :])*np.nan].flatten()
-            Y1 = np.c_[mkvc(NN[1][:-1, :, :]), mkvc(NN[1][1:, :, :]), mkvc(NN[1][:-1, :, :])*np.nan].flatten()
-            Z1 = np.c_[mkvc(NN[2][:-1, :, :]), mkvc(NN[2][1:, :, :]), mkvc(NN[2][:-1, :, :])*np.nan].flatten()
-
-            X2 = np.c_[mkvc(NN[0][:, :-1, :]), mkvc(NN[0][:, 1:, :]), mkvc(NN[0][:, :-1, :])*np.nan].flatten()
-            Y2 = np.c_[mkvc(NN[1][:, :-1, :]), mkvc(NN[1][:, 1:, :]), mkvc(NN[1][:, :-1, :])*np.nan].flatten()
-            Z2 = np.c_[mkvc(NN[2][:, :-1, :]), mkvc(NN[2][:, 1:, :]), mkvc(NN[2][:, :-1, :])*np.nan].flatten()
-
-            X3 = np.c_[mkvc(NN[0][:, :, :-1]), mkvc(NN[0][:, :, 1:]), mkvc(NN[0][:, :, :-1])*np.nan].flatten()
-            Y3 = np.c_[mkvc(NN[1][:, :, :-1]), mkvc(NN[1][:, :, 1:]), mkvc(NN[1][:, :, :-1])*np.nan].flatten()
-            Z3 = np.c_[mkvc(NN[2][:, :, :-1]), mkvc(NN[2][:, :, 1:]), mkvc(NN[2][:, :, :-1])*np.nan].flatten()
-
-            X = np.r_[X1, X2, X3]
-            Y = np.r_[Y1, Y2, Y3]
-            Z = np.r_[Z1, Z2, Z3]
-
-            ax.plot(X, Y, 'b', zs=Z)
-            ax.set_zlabel('x3')
-
-        ax.grid(True)
-        ax.set_xlabel('x1')
-        ax.set_ylabel('x2')
-
-        if showIt: plt.show()
-
-
 if __name__ == '__main__':
     nc = 5
     h1 = np.cumsum(np.r_[0, np.ones(nc)/(nc)])

SimPEG/Mesh/CylMesh.py

@@ -330,7 +330,7 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
                 raise NotImplementedError('wrapping in the averaging is not yet implemented')
         return self._aveF2CCV
 
-    def getInterpolationMatCartMesh(self, Mrect, locType='CC'):
+    def getInterpolationMatCartMesh(self, Mrect, locType='CC', locTypeTo=None):
         """
             Takes a cartesian mesh and returns a projection to translate onto the cartesian grid.
         """
@@ -338,19 +338,22 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
         assert self.isSymmetric, "Currently we have not taken into account other projections for more complicated CylMeshes"
 
 
+        if locTypeTo is None:
+            locTypeTo = locType
+
         if locType == 'F':
             # do this three times for each component
-            X = self.getInterpolationMatCartMesh(Mrect, locType='Fx')
-            Y = self.getInterpolationMatCartMesh(Mrect, locType='Fy')
-            Z = self.getInterpolationMatCartMesh(Mrect, locType='Fz')
+            X = self.getInterpolationMatCartMesh(Mrect, locType='Fx', locTypeTo=locTypeTo+'x')
+            Y = self.getInterpolationMatCartMesh(Mrect, locType='Fy', locTypeTo=locTypeTo+'y')
+            Z = self.getInterpolationMatCartMesh(Mrect, locType='Fz', locTypeTo=locTypeTo+'z')
             return sp.vstack((X,Y,Z))
         if locType == 'E':
-            X = self.getInterpolationMatCartMesh(Mrect, locType='Ex')
-            Y = self.getInterpolationMatCartMesh(Mrect, locType='Ey')
-            Z = spzeros(Mrect.nEz, self.nE)
+            X = self.getInterpolationMatCartMesh(Mrect, locType='Ex', locTypeTo=locTypeTo+'x')
+            Y = self.getInterpolationMatCartMesh(Mrect, locType='Ey', locTypeTo=locTypeTo+'y')
+            Z = spzeros(getattr(Mrect, 'n' + locTypeTo + 'z'), self.nE)
             return sp.vstack((X,Y,Z))
 
-        grid = getattr(Mrect, 'grid' + locType)
+        grid = getattr(Mrect, 'grid' + locTypeTo)
         # This is unit circle stuff, 0 to 2*pi, starting at x-axis, rotating counter clockwise in an x-y slice
         theta = - np.arctan2(grid[:,0] - self.cartesianOrigin[0], grid[:,1] - self.cartesianOrigin[1]) + np.pi/2
         theta[theta < 0] += np.pi*2.0
@@ -366,7 +369,7 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
                         'Ex': Mrect.tangents[:Mrect.nEx,:],
                         'Ey': Mrect.tangents[Mrect.nEx:(Mrect.nEx+Mrect.nEy),:],
                         'Ez': Mrect.tangents[-Mrect.nEz:,:],
-                    }[locType]
+                    }[locTypeTo]
             if 'F' in locType:
                 normals = np.c_[np.cos(theta), np.sin(theta), np.zeros(theta.size)]
                 proj = ( normals * dotMe ).sum(axis=1)

SimPEG/Mesh/MeshIO.py

@@ -24,7 +24,6 @@ class TensorMeshIO(object):
                     re = int(sp[0])*(' ' + sp[1])
                     line = line.replace(st,re.strip())
             return np.array(line.split(),dtype=float)
-
         # Read the file as line strings, remove lines with comment = !
         msh = np.genfromtxt(fileName,delimiter='\n',dtype=np.str,comments='!')
 
@@ -141,7 +140,6 @@ class TensorMeshIO(object):
                 vtkObj.GetCellData().AddArray(vtkDoubleArr)
             # Set the active scalar
             vtkObj.GetCellData().SetActiveScalars(models.keys()[0])
-        # vtkObj.Update()
 
         # Check the extension of the fileName
         ext = os.path.splitext(fileName)[1]
@@ -158,6 +156,50 @@ class TensorMeshIO(object):
         vtrWriteFilter.SetFileName(fileName)
         vtrWriteFilter.Update()
 
+    def _toVTRObj(mesh,models=None):
+        """
+        Makes and saves a VTK rectilinear file (vtr) for a simpeg Tensor mesh and model.
+
+        Input:
+        :param str, path to the output vtk file
+        :param mesh, SimPEG TensorMesh object - mesh to be transfer to VTK
+        :param models, dictionary of numpy.array - Name('s) and array('s). Match number of cells
+
+        """
+        # Import
+        from vtk import vtkRectilinearGrid as rectGrid, VTK_VERSION
+        from vtk.util.numpy_support import numpy_to_vtk
+
+        # Deal with dimensionalities
+        if mesh.dim >= 1:
+            vX = mesh.vectorNx
+            xD = mesh.nNx
+            yD,zD = 1,1
+            vY, vZ = np.array([0,0])
+        if mesh.dim >= 2:
+            vY = mesh.vectorNy
+            yD = mesh.nNy
+        if mesh.dim == 3:
+            vZ = mesh.vectorNz
+            zD = mesh.nNz
+        # Use rectilinear VTK grid.
+        # Assign the spatial information.
+        vtkObj = rectGrid()
+        vtkObj.SetDimensions(xD,yD,zD)
+        vtkObj.SetXCoordinates(numpy_to_vtk(vX,deep=1))
+        vtkObj.SetYCoordinates(numpy_to_vtk(vY,deep=1))
+        vtkObj.SetZCoordinates(numpy_to_vtk(vZ,deep=1))
+
+        # Assign the model('s) to the object
+        if models is not None:
+            for item in models.iteritems():
+                # Convert numpy array
+                vtkDoubleArr = numpy_to_vtk(item[1],deep=1)
+                vtkDoubleArr.SetName(item[0])
+                vtkObj.GetCellData().AddArray(vtkDoubleArr)
+            # Set the active scalar
+            vtkObj.GetCellData().SetActiveScalars(models.keys()[0])
+        return vtkObj
 
     def readModelUBC(mesh, fileName):
         """

SimPEG/Mesh/TreeMesh.py

@@ -2131,10 +2131,16 @@ class TreeMesh(BaseTensorMesh, InnerProducts, TreeMeshIO):
     def plotSlice(self, v, vType='CC',
         normal='Z', ind=None, grid=True, view='real',
         ax=None, clim=None, showIt=False,
-        pcolorOpts={},
-        streamOpts={'color':'k'},
-        gridOpts={'color':'k', 'alpha':0.5}):
+        pcolorOpts=None,
+        streamOpts=None,
+        gridOpts=None):
 
+        if pcolorOpts is None:
+            pcolorOpts = {}
+        if streamOpts is None:
+            streamOpts = {'color':'k'}
+        if gridOpts is None:
+            gridOpts = {'color':'k', 'alpha':0.5}
         assert vType in ['CC','F','E']
         assert self.dim == 3
 

SimPEG/Mesh/View.py

@@ -42,9 +42,9 @@ class TensorView(object):
 
     def plotImage(self, v, vType='CC', grid=False, view='real',
               ax=None, clim=None, showIt=False,
-              pcolorOpts={},
-              streamOpts={'color':'k'},
-              gridOpts={'color':'k'},
+              pcolorOpts=None,
+              streamOpts=None,
+              gridOpts=None,
               numbering=True, annotationColor='w'
               ):
         """
@@ -84,6 +84,12 @@ class TensorView(object):
             M.plotImage(v, annotationColor='k', showIt=True)
 
         """
+        if pcolorOpts is None:
+            pcolorOpts = {}
+        if streamOpts is None:
+            streamOpts = {'color':'k'}
+        if gridOpts is None:
+            gridOpts = {'color':'k'}
 
         if ax is None:
             fig = plt.figure()
@@ -174,9 +180,9 @@ class TensorView(object):
     def plotSlice(self, v, vType='CC',
                   normal='Z', ind=None, grid=False, view='real',
                   ax=None, clim=None, showIt=False,
-                  pcolorOpts={},
-                  streamOpts={'color':'k'},
-                  gridOpts={'color':'k', 'alpha':0.5}
+                  pcolorOpts=None,
+                  streamOpts=None,
+                  gridOpts=None
                   ):
 
         """
@@ -197,6 +203,12 @@ class TensorView(object):
             M.plotSlice(M.cellGrad*b, 'F', view='vec', grid=True, showIt=True, pcolorOpts={'alpha':0.8})
 
         """
+        if pcolorOpts is None:
+            pcolorOpts = {}
+        if streamOpts is None:
+            streamOpts = {'color':'k'}
+        if gridOpts is None:
+            gridOpts = {'color':'k', 'alpha':0.5}
         if type(vType) in [list, tuple]:
             assert ax is None, "cannot specify an axis to plot on with this function."
             fig, axs = plt.subplots(1,len(vType))
@@ -206,7 +218,7 @@ class TensorView(object):
             return out
         viewOpts = ['real','imag','abs','vec']
         normalOpts = ['X', 'Y', 'Z']
-        vTypeOpts = ['CC', 'CCv','F','E','Fx','Fy','Fz','E','Ex','Ey','Ez']
+        vTypeOpts = ['CC', 'CCv','N','F','E','Fx','Fy','Fz','E','Ex','Ey','Ez']
 
         # Some user error checking
         assert vType in vTypeOpts, "vType must be in ['%s']" % "','".join(vTypeOpts)
@@ -289,11 +301,17 @@ class TensorView(object):
 
     def _plotImage2D(self, v, vType='CC', grid=False, view='real',
               ax=None, clim=None, showIt=False,
-              pcolorOpts={},
-              streamOpts={'color':'k'},
-              gridOpts={'color':'k'}
+              pcolorOpts=None,
+              streamOpts=None,
+              gridOpts=None
               ):
 
+        if pcolorOpts is None:
+            pcolorOpts = {}
+        if streamOpts is None:
+            streamOpts = {'color':'k'}
+        if gridOpts is None:
+            gridOpts = {'color':'k'}
         vTypeOptsCC = ['N','CC','Fx','Fy','Ex','Ey']
         vTypeOptsV = ['CCv','F','E']
         vTypeOpts = vTypeOptsCC + vTypeOptsV
@@ -534,7 +552,8 @@ class CurvView(object):
     def __init__(self):
         pass
 
-    def plotGrid(self, length=0.05, showIt=False):
+
+    def plotGrid(self, ax=None, nodes=False, faces=False, centers=False, edges=False, lines=True,  showIt=False):
         """Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions.
 
 
@@ -542,60 +561,63 @@ class CurvView(object):
             :include-source:
 
             from SimPEG import Mesh, Utils
-            X, Y = Utils.exampleCurvGird([3,3],'rotate')
+            X, Y = Utils.exampleLrmGrid([3,3],'rotate')
             M = Mesh.CurvilinearMesh([X, Y])
             M.plotGrid(showIt=True)
 
         """
+        import matplotlib.pyplot as plt
+        import matplotlib
+        from mpl_toolkits.mplot3d import Axes3D
+
+        axOpts = {'projection':'3d'} if self.dim == 3 else {}
+        if ax is None: ax = plt.subplot(111, **axOpts)
+
         NN = self.r(self.gridN, 'N', 'N', 'M')
         if self.dim == 2:
-            fig = plt.figure(2)
-            fig.clf()
-            ax = plt.subplot(111)
-            X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
-            Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
 
-            X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
-            Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
+            if lines:
+                X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
+                Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
 
-            X = np.r_[X1, X2]
-            Y = np.r_[Y1, Y2]
+                X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
+                Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
 
-            plt.plot(X, Y)
+                X = np.r_[X1, X2]
+                Y = np.r_[Y1, Y2]
 
-            plt.hold(True)
-            Nx = self.r(self.normals, 'F', 'Fx', 'V')
-            Ny = self.r(self.normals, 'F', 'Fy', 'V')
-            Tx = self.r(self.tangents, 'E', 'Ex', 'V')
-            Ty = self.r(self.tangents, 'E', 'Ey', 'V')
+                ax.plot(X, Y, 'b-')
+            if centers:
+                ax.plot(self.gridCC[:,0],self.gridCC[:,1],'ro')
 
-            plt.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
+            # Nx = self.r(self.normals, 'F', 'Fx', 'V')
+            # Ny = self.r(self.normals, 'F', 'Fy', 'V')
+            # Tx = self.r(self.tangents, 'E', 'Ex', 'V')
+            # Ty = self.r(self.tangents, 'E', 'Ey', 'V')
 
-            nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
-            plt.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
-            plt.plot(nX, nY, 'r-')
+            # ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
 
-            nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
-            #plt.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
-            plt.plot(nX, nY, 'g-')
+            # nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
+            # ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
+            # ax.plot(nX, nY, 'r-')
 
-            tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
-            tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
-            plt.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
-            plt.plot(tX, tY, 'r-')
+            # nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
+            # #ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
+            # ax.plot(nX, nY, 'g-')
 
-            nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
-            #plt.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
-            plt.plot(nX, nY, 'g-')
-            plt.axis('equal')
+            # tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
+            # tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
+            # ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
+            # ax.plot(tX, tY, 'r-')
+
+            # nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
+            # #ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
+            # ax.plot(nX, nY, 'g-')
 
         elif self.dim == 3:
-            fig = plt.figure(3)
-            fig.clf()
-            ax = fig.add_subplot(111, projection='3d')
             X1 = np.c_[mkvc(NN[0][:-1, :, :]), mkvc(NN[0][1:, :, :]), mkvc(NN[0][:-1, :, :])*np.nan].flatten()
             Y1 = np.c_[mkvc(NN[1][:-1, :, :]), mkvc(NN[1][1:, :, :]), mkvc(NN[1][:-1, :, :])*np.nan].flatten()
             Z1 = np.c_[mkvc(NN[2][:-1, :, :]), mkvc(NN[2][1:, :, :]), mkvc(NN[2][:-1, :, :])*np.nan].flatten()
@@ -612,16 +634,50 @@ class CurvView(object):
             Y = np.r_[Y1, Y2, Y3]
             Z = np.r_[Z1, Z2, Z3]
 
-            plt.plot(X, Y, 'b', zs=Z)
+            ax.plot(X, Y, 'b', zs=Z)
             ax.set_zlabel('x3')
 
         ax.grid(True)
-        ax.hold(False)
         ax.set_xlabel('x1')
         ax.set_ylabel('x2')
 
         if showIt: plt.show()
 
+    def plotImage(self, I, ax=None, showIt=False, grid=False, clim=None):
+        if self.dim == 3: raise NotImplementedError('This is not yet done!')
+
+        import matplotlib.pyplot as plt
+        import matplotlib
+        from mpl_toolkits.mplot3d import Axes3D
+        import matplotlib.colors as colors
+        import matplotlib.cm as cmx
+
+        if ax is None: ax = plt.subplot(111)
+        jet = cm = plt.get_cmap('jet')
+        cNorm  = colors.Normalize(
+            vmin=I.min() if clim is None else clim[0],
+            vmax=I.max() if clim is None else clim[1])
+
+        scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
+        # ax.set_xlim((self.x0[0], self.h[0].sum()))
+        # ax.set_ylim((self.x0[1], self.h[1].sum()))
+
+        Nx = self.r(self.gridN[:,0],'N','N','M')
+        Ny = self.r(self.gridN[:,1],'N','N','M')
+        cell = self.r(I,'CC','CC','M')
+
+        for ii in range(self.nCx):
+            for jj in range(self.nCy):
+                I = [ii,ii+1,ii+1,ii]
+                J = [jj,jj,jj+1,jj+1]
+                ax.add_patch(plt.Polygon(np.c_[Nx[I,J],Ny[I,J]], facecolor=scalarMap.to_rgba(cell[ii,jj]), edgecolor='k' if grid else 'none'))
+
+        scalarMap._A = []  # http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots
+        ax.set_xlabel('x')
+        ax.set_ylabel('y')
+        if showIt: plt.show()
+        return [scalarMap]
+
 
 if __name__ == '__main__':
     from SimPEG import *

SimPEG/NSEM/FieldsNSEM.py

@@ -4,18 +4,21 @@ import sys
 from numpy.lib import recfunctions as recFunc
 from SimPEG.EM.Utils import omega
 
+
 ##############
 ### Fields ###
 ##############
-class BaseMTFields(Problem.Fields):
-    """Field Storage for a MT survey."""
+class BaseNSEMFields(Problem.Fields):
+    """Field Storage for a NSEM survey."""
     knownFields = {}
     dtype = complex
 
-
-class Fields1D_e(BaseMTFields):
+###########
+# 1D Fields
+###########
+class Fields1D_ePrimSec(BaseNSEMFields):
     """
-    Fields storage for the 1D MT solution.
+    Fields storage for the 1D NSEM solution.
     """
     knownFields = {'e_1dSolution':'F'}
     aliasFields = {
@@ -28,7 +31,119 @@ class Fields1D_e(BaseMTFields):
                   }
 
     def __init__(self,mesh,survey,**kwargs):
-        BaseMTFields.__init__(self,mesh,survey,**kwargs)
+        BaseNSEMFields.__init__(self,mesh,survey,**kwargs)
+
+    def _ePrimary(self, eSolution, srcList):
+        ePrimary = np.zeros_like(eSolution)
+        for i, src in enumerate(srcList):
+            ep = src.ePrimary(self.survey.prob)
+            if ep is not None:
+                ePrimary[:,i] = ep[:,-1]
+        return ePrimary
+
+    def _eSecondary(self, eSolution, srcList):
+        return eSolution
+
+    def _e(self, eSolution, srcList):
+        return self._ePrimary(eSolution,srcList) + self._eSecondary(eSolution,srcList)
+
+    def _eDeriv_u(self, src, du_dm_v, adjoint = False):
+
+
+        return Utils.Identity()*du_dm_v
+
+    def _eDeriv_m(self, src, v, adjoint = False):
+        # assuming primary does not depend on the model
+        return Utils.Zero()
+
+    def _bPrimary(self, eSolution, srcList):
+        bPrimary = np.zeros([self.survey.mesh.nE,eSolution.shape[1]], dtype = complex)
+        for i, src in enumerate(srcList):
+            bp = src.bPrimary(self.survey.prob)
+            if bp is not None:
+                bPrimary[:,i] += bp[:,-1]
+        return bPrimary
+
+    def _bSecondary(self, eSolution, srcList):
+        C = self.mesh.nodalGrad
+        b = (C * eSolution)
+        for i, src in enumerate(srcList):
+            b[:,i] *= - 1./(1j*omega(src.freq))
+            # There is no magnetic source in the MT problem
+            # S_m, _ = src.eval(self.survey.prob)
+            # if S_m is not None:
+            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
+        return b
+
+    def _b(self, eSolution, srcList):
+        return self._bPrimary(eSolution, srcList) + self._bSecondary(eSolution, srcList)
+
+    def _bSecondaryDeriv_u(self, src, v, adjoint = False):
+        C = self.mesh.nodalGrad
+        if adjoint:
+            return - 1./(1j*omega(src.freq)) * (C.T * v)
+        return - 1./(1j*omega(src.freq)) * (C * v)
+
+    def _bSecondaryDeriv_m(self, src, v, adjoint = False):
+        # Doesn't depend on m
+        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
+        # S_eDeriv = S_eDeriv(v)
+        # if S_eDeriv is not None:
+        #     return 1./(1j * omega(src.freq)) * S_eDeriv
+        return None
+
+    def _bDeriv_u(self, src, v, adjoint=False):
+        # Primary does not depend on u
+        return self._bSecondaryDeriv_u(src, v, adjoint)
+
+    def _bDeriv_m(self, src, v, adjoint=False):
+        # Assuming the primary does not depend on the model
+        return self._bSecondaryDeriv_m(src, v, adjoint)
+
+    def _fDeriv_u(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt u.
+
+        :param NSEMsrc src: NSEM source
+        :param numpy.ndarray v: random vector of f_sol.size
+        This function stacks the fields derivatives appropriately
+
+        return a vector of size (nreEle+nrbEle)
+        """
+
+        de_du = v #Utils.spdiag(np.ones((self.nF,)))
+        db_du = self._bDeriv_u(src, v, adjoint)
+        # Return the stack
+        # This doesn't work...
+        return np.vstack((de_du,db_du))
+
+    def _fDeriv_m(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt m.
+
+        This function stacks the fields derivatives appropriately
+        """
+        return None
+
+
+class Fields1D_eTotal(BaseNSEMFields):
+    """
+    Fields storage for the 1D NSEM solution solved with for a total domain formulation.
+
+    Used in conjuction with Problem1D_eTotal.
+    """
+    knownFields = {'e_1dSolution':'F'}
+    aliasFields = {
+                    'e_1d' : ['e_1dSolution','F','_e'],
+                    'e_1dPrimary' : ['e_1dSolution','F','_ePrimary'],
+                    'e_1dSecondary' : ['e_1dSolution','F','_eSecondary'],
+                    'b_1d' : ['e_1dSolution','E','_b'],
+                    'b_1dPrimary' : ['e_1dSolution','E','_bPrimary'],
+                    'b_1dSecondary' : ['e_1dSolution','E','_bSecondary']
+                  }
+
+    def __init__(self,mesh,survey,**kwargs):
+        BaseNSEMFields.__init__(self,mesh,survey,**kwargs)
 
     def _ePrimary(self, eSolution, srcList):
         ePrimary = np.zeros_like(eSolution)
@@ -99,7 +214,7 @@ class Fields1D_e(BaseMTFields):
         """
         Derivative of the fields object wrt u.
 
-        :param MTsrc src: MT source
+        :param NSEMsrc src: NSEM source
         :param numpy.ndarray v: random vector of f_sol.size
         This function stacks the fields derivatives appropriately
 
@@ -120,9 +235,18 @@ class Fields1D_e(BaseMTFields):
         """
         return None
 
-class Fields3D_e(BaseMTFields):
+
+###########
+# 2D Fields
+###########
+
+
+###########
+# 3D Fields
+###########
+class Fields3D_ePrimSec(BaseNSEMFields):
     """
-    Fields storage for the 3D MT solution. Labels polarizations by px and py.
+    Fields storage for the 3D NSEM solution. Labels polarizations by px and py.
 
         :param SimPEG object mesh: The solution mesh
         :param SimPEG object survey: A survey object
@@ -147,7 +271,7 @@ class Fields3D_e(BaseMTFields):
                   }
 
     def __init__(self,mesh,survey,**kwargs):
-        BaseMTFields.__init__(self,mesh,survey,**kwargs)
+        BaseNSEMFields.__init__(self,mesh,survey,**kwargs)
 
     def _e_pxPrimary(self, e_pxSolution, srcList):
         e_pxPrimary = np.zeros_like(e_pxSolution)
@@ -228,7 +352,7 @@ class Fields3D_e(BaseMTFields):
         b = (C * e_pxSolution)
         for i, src in enumerate(srcList):
             b[:,i] *= - 1./(1j*omega(src.freq))
-            # There is no magnetic source in the MT problem
+            # There is no magnetic source in the NSEM problem
             # S_m, _ = src.eval(self.survey.prob)
             # if S_m is not None:
             #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
@@ -239,7 +363,7 @@ class Fields3D_e(BaseMTFields):
         b = (C * e_pySolution)
         for i, src in enumerate(srcList):
             b[:,i] *= - 1./(1j*omega(src.freq))
-            # There is no magnetic source in the MT problem
+            # There is no magnetic source in the NSEM problem
             # S_m, _ = src.eval(self.survey.prob)
             # if S_m is not None:
             #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
@@ -302,7 +426,7 @@ class Fields3D_e(BaseMTFields):
         """
         Derivative of the fields object wrt u.
 
-        :param MTsrc src: MT source
+        :param NSEMsrc src: NSEM source
         :param numpy.ndarray v: random vector of f_sol.size
         This function stacks the fields derivatives appropriately
 
@@ -319,7 +443,7 @@ class Fields3D_e(BaseMTFields):
         """
         Derivative of the fields object wrt u.
 
-        :param MTsrc src: MT source
+        :param NSEMsrc src: NSEM source
         :param numpy.ndarray v: random vector of f_sol.size
         This function stacks the fields derivatives appropriately
 

SimPEG/NSEM/ProblemNSEM.py

@@ -0,0 +1,560 @@
+from SimPEG.EM.Utils.EMUtils import omega, mu_0
+from SimPEG import SolverLU as SimpegSolver, PropMaps, Utils, mkvc, sp, np
+from SimPEG.EM.FDEM.ProblemFDEM import BaseFDEMProblem
+from SurveyNSEM import Survey, Data
+from FieldsNSEM import BaseNSEMFields, Fields1D_ePrimSec, Fields3D_ePrimSec
+from SimPEG.NSEM.Utils.MT1Danalytic import getEHfields
+import time, sys
+
+class BaseNSEMProblem(BaseFDEMProblem):
+    """
+        Base class for all Natural source problems.
+    """
+
+    def __init__(self, mesh, **kwargs):
+        BaseFDEMProblem.__init__(self, mesh, **kwargs)
+        Utils.setKwargs(self, **kwargs)
+    # Set the default pairs of the problem
+    surveyPair = Survey
+    dataPair = Data
+    fieldsPair = BaseNSEMFields
+
+    # Set the solver
+    Solver = SimpegSolver
+    solverOpts = {}
+
+    verbose = False
+    # Notes:
+    # Use the forward and devs from BaseFDEMProblem
+    # Might need to add more stuff here.
+
+    ## NEED to clean up the Jvec and Jtvec to use Zero and Identities for None components.
+    def Jvec(self, m, v, f=None):
+        """
+        Function to calculate the data sensitivities dD/dm times a vector.
+
+            :param numpy.ndarray m (nC, 1) - conductive model
+            :param numpy.ndarray v (nC, 1) - random vector
+            :param NSEMfields object (optional) - NSEM fields object, if not given it is calculated
+            :rtype: NSEMdata object
+            :return: Data sensitivities wrt m
+        """
+
+        # Calculate the fields
+        if f is None:
+           f= self.fields(m)
+        # Set current model
+        self.curModel = m
+        # Initiate the Jv object
+        Jv = self.dataPair(self.survey)
+
+        # Loop all the frequenies
+        for freq in self.survey.freqs:
+            dA_du = self.getA(freq) #
+
+            dA_duI = self.Solver(dA_du, **self.solverOpts)
+
+            for src in self.survey.getSrcByFreq(freq):
+                # We need fDeriv_m = df/du*du/dm + df/dm
+                # Construct du/dm, it requires a solve
+                # NOTE: need to account for the 2 polarizations in the derivatives.
+                u_src = f[src,:] # u should be a vector by definition. Need to fix this...
+                # dA_dm and dRHS_dm should be of size nE,2, so that we can multiply by dA_duI. The 2 columns are each of the polarizations.
+                dA_dm = self.getADeriv_m(freq, u_src, v) # Size: nE,2 (u_px,u_py) in the columns.
+                dRHS_dm = self.getRHSDeriv_m(freq, v) # Size: nE,2 (u_px,u_py) in the columns.
+                if dRHS_dm is None:
+                    du_dm = dA_duI * ( -dA_dm )
+                else:
+                    du_dm = dA_duI * ( -dA_dm + dRHS_dm )
+                # Calculate the projection derivatives
+                for rx in src.rxList:
+                    # Get the projection derivative
+                    # v should be of size 2*nE (for 2 polarizations)
+                    PDeriv_u = lambda t: rx.evalDeriv(src, self.mesh, f, t) # wrt u, we don't have have PDeriv wrt m
+                    Jv[src, rx] = PDeriv_u(mkvc(du_dm))
+            dA_duI.clean()
+        # Return the vectorized sensitivities
+        return mkvc(Jv)
+
+    def Jtvec(self, m, v, f=None):
+        """
+        Function to calculate the transpose of the data sensitivities (dD/dm)^T times a vector.
+
+            :param numpy.ndarray m (nC, 1) - conductive model
+            :param numpy.ndarray v (nD, 1) - vector
+            :param NSEMfields object f (optional) - NSEM fields object, if not given it is calculated
+            :rtype: NSEMdata object
+            :return: Data sensitivities wrt m
+        """
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv = np.zeros(m.size)
+
+        for freq in self.survey.freqs:
+            AT = self.getA(freq).T
+
+            ATinv = self.Solver(AT, **self.solverOpts)
+
+            for src in self.survey.getSrcByFreq(freq):
+                ftype = self._solutionType
+                f_src = f[src, :] # Need to fix this...
+
+                for rx in src.rxList:
+                    # Get the adjoint evalDeriv
+                    # PTv needs to be nE,
+                    PTv = rx.evalDeriv(src, self.mesh, f, mkvc(v[src, rx],2), adjoint=True) # wrt u, need possibility wrt m
+                    # Get the
+                    dA_duIT = ATinv * PTv
+                    dA_dmT = self.getADeriv_m(freq, f_src, mkvc(dA_duIT), adjoint=True)
+                    dRHS_dmT = self.getRHSDeriv_m(freq, mkvc(dA_duIT), adjoint=True)
+                    # Make du_dmT
+                    if dRHS_dmT is None:
+                        du_dmT = -dA_dmT
+                    else:
+                        du_dmT = -dA_dmT + dRHS_dmT
+                    # Select the correct component
+                    # du_dmT needs to be of size nC,
+                    real_or_imag = rx.projComp
+                    if real_or_imag == 'real':
+                        Jtv +=  du_dmT.real
+                    elif real_or_imag == 'imag':
+                        Jtv +=  -du_dmT.real
+                    else:
+                        raise Exception('Must be real or imag')
+            # Clean the factorization, clear memory.
+            ATinv.clean()
+        return Jtv
+
+###################################
+## 1D problems
+###################################
+
+class Problem1D_ePrimSec(BaseNSEMProblem):
+    """
+    A NSEM problem soving a e formulation and primary/secondary fields decomposion.
+
+    By eliminating the magnetic flux density using
+
+        .. math ::
+
+            \mathbf{b} = \\frac{1}{i \omega}\\left(-\mathbf{C} \mathbf{e} \\right)
+
+
+    we can write Maxwell's equations as a second order system in \\\(\\\mathbf{e}\\\) only:
+
+    .. math ::
+        \\left(\mathbf{C}^T \mathbf{M^e_{\mu^{-1}}} \mathbf{C} + i \omega \mathbf{M^f_\sigma}] \mathbf{e}_{s} =& i \omega \mathbf{M^f_{\delta \sigma}} \mathbf{e}_{p}
+    which we solve for \\\(\\\mathbf{e_s}\\\). The total field \\\mathbf{e}\\ = \\\mathbf{e_p}\\ + \\\mathbf{e_s}\\.
+
+    The primary field is estimated from a background model (commonly half space ).
+
+
+    """
+
+    # From FDEMproblem: Used to project the fields. Currently not used for NSEMproblem.
+    _solutionType = 'e_1dSolution'
+    _formulation  = 'EF'
+    fieldsPair = Fields1D_ePrimSec
+
+    # Initiate properties
+    _sigmaPrimary = None
+
+
+    def __init__(self, mesh, **kwargs):
+        BaseNSEMProblem.__init__(self, mesh, **kwargs)
+        # self._sigmaPrimary = sigmaPrimary
+    @property
+    def MeMui(self):
+        """
+            Edge inner product matrix
+        """
+        if getattr(self, '_MeMui', None) is None:
+            self._MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
+        return self._MeMui
+
+    @property
+    def MfSigma(self):
+        """
+            Edge inner product matrix
+        """
+        if getattr(self, '_MfSigma', None) is None:
+            self._MfSigma = self.mesh.getFaceInnerProduct(self.curModel.sigma)
+        return self._MfSigma
+
+    @property
+    def sigmaPrimary(self):
+        """
+        A background model, use for the calculation of the primary fields.
+
+        """
+        return self._sigmaPrimary
+
+    @sigmaPrimary.setter
+    def sigmaPrimary(self, val):
+        # Note: TODO add logic for val, make sure it is the correct size.
+        self._sigmaPrimary = val
+
+    def getA(self, freq):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+
+        # Note: need to use the code above since in the 1D problem I want
+        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
+        # Possible that _fieldType and _eqLocs can fix this
+        MeMui = self.MeMui
+        MfSigma = self.MfSigma
+        C = self.mesh.nodalGrad
+        # Make A
+        A = C.T*MeMui*C + 1j*omega(freq)*MfSigma
+        # Either return full or only the inner part of A
+        return A
+
+    def getADeriv_m(self, freq, u, v, adjoint=False):
+        """
+        The derivative of A wrt sigma
+        """
+
+        dsig_dm = self.curModel.sigmaDeriv
+        MeMui = self.MeMui
+        #
+        u_src = u['e_1dSolution']
+        dMfSigma_dm = self.mesh.getFaceInnerProductDeriv(self.curModel.sigma)(u_src) * self.curModel.sigmaDeriv
+        if adjoint:
+            return 1j * omega(freq) * (  dMfSigma_dm.T * v )
+        # Note: output has to be nN/nF, not nC/nE.
+        # v should be nC
+        return 1j * omega(freq) * ( dMfSigma_dm * v )
+
+    def getRHS(self, freq):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nF, 1), numpy.ndarray (nF, 1)
+            :return: RHS for 1 polarizations, primary fields
+        """
+
+        # Get sources for the frequncy(polarizations)
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_e = Src.S_e(self)
+        return -1j * omega(freq) * S_e
+
+    def getRHSDeriv_m(self, freq, v, adjoint=False):
+        """
+        The derivative of the RHS wrt sigma
+        """
+
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
+        return -1j * omega(freq) * S_eDeriv
+
+    def fields(self, m):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        '''
+        # Set the current model
+        self.curModel = m
+        # Make the fields object
+        F = self.fieldsPair(self.mesh, self.survey)
+        # Loop over the frequencies
+        for freq in self.survey.freqs:
+            if self.verbose:
+                startTime = time.time()
+                print 'Starting work for {:.3e}'.format(freq)
+                sys.stdout.flush()
+            A = self.getA(freq)
+            rhs  = self.getRHS(freq)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs
+
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)[0]
+            # NOTE: only store the e_solution(secondary), all other components calculated in the fields object
+            F[Src, 'e_1dSolution'] = e_s[:,-1] # Only storing the yx polarization as 1d
+
+            # Note curl e = -iwb so b = -curl e /iw
+            # b = -( self.mesh.nodalGrad * e )/( 1j*omega(freq) )
+            # F[Src, 'b_1d'] = b[:,1]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+        return F
+
+# Note this is not fully functional.
+# Missing:
+# Fields class corresponding to the fields
+# Update Jvec and Jtvec to include all the derivatives components
+# Other things ...
+class Problem1D_eTotal(BaseNSEMProblem):
+    """
+    A NSEM problem solving a e formulation and a Total bondary domain decompostion.
+
+    Solves the equation:
+
+    Math:
+        Have to do this...
+        Not implement correctly.......
+    """
+
+    # From FDEMproblem: Used to project the fields. Currently not used for NSEMproblem.
+    _solutionType = 'e_1dSolution'
+    _formulation  = 'EF'
+    # fieldsPair = Fields1D_eTotal
+
+    def __init__(self, mesh, **kwargs):
+        BaseNSEMProblem.__init__(self, mesh, **kwargs)
+    @property
+    def MeMui(self):
+        """
+            Edge inner product matrix
+        """
+        if getattr(self, '_MeMui', None) is None:
+            self._MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
+        return self._MeMui
+
+    @property
+    def MfSigma(self):
+        """
+            Edge inner product matrix
+        """
+        if getattr(self, '_MfSigma', None) is None:
+            self._MfSigma = self.mesh.getFaceInnerProduct(self.curModel.sigma)
+        return self._MfSigma
+
+    def getA(self, freq, full=False):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :param logic full: Return full A or the inner part
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+
+        MeMui = self.MeMui
+        MfSigma = self.MfSigma
+        # Note: need to use the code above since in the 1D problem I want
+        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
+        # Possible that _fieldType and _eqLocs can fix this
+        # MeMui = self.MfMui
+        # MfSigma = self.MfSigma
+        C = self.mesh.nodalGrad
+        # Make A
+        A = C.T*MeMui*C + 1j*omega(freq)*MfSigma
+        # Either return full or only the inner part of A
+        if full:
+            return A
+        else:
+            return A[1:-1,1:-1]
+
+    def getADeriv_m(self, freq, u, v, adjoint=False):
+        raise NotImplementedError('getADeriv is not implemented')
+
+    def getRHS(self, freq):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
+            :return: RHS for both polarizations, primary fields
+        """
+        # Get sources for the frequency
+        # NOTE: Need to use the source information, doesn't really apply in 1D
+        src = self.survey.getSrcByFreq(freq)
+        # Get the full A
+        A = self.getA(freq,full=True)
+        # Define the outer part of the solution matrix
+        Aio = A[1:-1,[0,-1]]
+        Ed, Eu, Hd, Hu = getEHfields(self.mesh,self.curModel.sigma,freq,self.mesh.vectorNx)
+        Etot = (Ed + Eu)
+        sourceAmp = 1.0
+        Etot = ((Etot/Etot[-1])*sourceAmp) # Scale the fields to be equal to sourceAmp at the top
+        ## Note: The analytic solution is derived with e^iwt
+        eBC = np.r_[Etot[0],Etot[-1]]
+        # The right hand side
+
+        return -Aio*eBC, eBC
+
+    def getRHSderiv_m(self, freq, backSigma, u, v, adjoint=False):
+        raise NotImplementedError('getRHSDeriv not implemented yet')
+        return None
+
+    def fields(self, m):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        :param np.ndarray (nC,) m_back: Background conductivity model
+        '''
+
+
+        self.curModel = m
+        # RHS, CalcFields = self.getRHS(freq,m_back), self.calcFields
+
+        F = Fields1D_eTotal(self.mesh, self.survey)
+        for freq in self.survey.freqs:
+            if self.verbose:
+                startTime = time.time()
+                print 'Starting work for {:.3e}'.format(freq)
+                sys.stdout.flush()
+            A = self.getA(freq)
+            rhs, e_o = self.getRHS(freq)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_i = Ainv * rhs
+            e = mkvc(np.r_[e_o[0], e_i, e_o[1]],2)
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)
+            # NOTE: only store e fields
+            F[Src, 'e_1dSolution'] = e[:,0]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+        return F
+
+
+###################################
+## 3D problems
+###################################
+class Problem3D_ePrimSec(BaseNSEMProblem):
+    """
+    A NSEM problem solving a e formulation and a primary/secondary fields decompostion.
+
+    By eliminating the magnetic flux density using
+
+        .. math ::
+
+            \mathbf{b} = \\frac{1}{i \omega}\\left(-\mathbf{C} \mathbf{e} \\right)
+
+
+    we can write Maxwell's equations as a second order system in \\\(\\\mathbf{e}\\\) only:
+
+    .. math ::
+        \\left(\mathbf{C}^T \mathbf{M^f_{\mu^{-1}}} \mathbf{C} + i \omega \mathbf{M^e_\sigma}] \mathbf{e}_{s} =& i \omega \mathbf{M^e_{\delta \sigma}} \mathbf{e}_{p}
+    which we solve for \\\(\\\mathbf{e_s}\\\). The total field \\\mathbf{e}\\ = \\\mathbf{e_p}\\ + \\\mathbf{e_s}\\.
+
+    The primary field is estimated from a background model (commonly as a 1D model).
+
+    """
+
+    # From FDEMproblem: Used to project the fields. Currently not used for NSEMproblem.
+    _solutionType = [ 'e_pxSolution', 'e_pySolution']  # Forces order on the object
+    _formulation  = 'EB'
+    fieldsPair = Fields3D_ePrimSec
+
+    # Initiate properties
+    _sigmaPrimary = None
+
+    def __init__(self, mesh, **kwargs):
+        BaseNSEMProblem.__init__(self, mesh, **kwargs)
+
+    @property
+    def sigmaPrimary(self):
+        """
+        A background model, use for the calculation of the primary fields.
+
+        """
+        return self._sigmaPrimary
+    @sigmaPrimary.setter
+    def sigmaPrimary(self, val):
+        # Note: TODO add logic for val, make sure it is the correct size.
+        self._sigmaPrimary = val
+
+    def getA(self, freq):
+        """
+            Function to get the A system.
+
+            :param float freq: Frequency
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+        Mmui = self.MfMui
+        Msig = self.MeSigma
+        C = self.mesh.edgeCurl
+
+        return C.T*Mmui*C + 1j*omega(freq)*Msig
+
+    def getADeriv_m(self, freq, u, v, adjoint=False):
+        """
+        Calculate the derivative of A wrt m.
+
+        """
+        # Fix u to be a matrix nE,2
+        # This considers both polarizations and returns a nE,2 matrix for each polarization
+        if adjoint:
+            dMe_dsigV = sp.hstack(( self.MeSigmaDeriv( u['e_pxSolution'] ).T, self.MeSigmaDeriv(u['e_pySolution'] ).T ))*v
+        else:
+            # Need a nE,2 matrix to be returned
+            dMe_dsigV = np.hstack(( mkvc(self.MeSigmaDeriv( u['e_pxSolution'] )*v,2), mkvc( self.MeSigmaDeriv(u['e_pySolution'] )*v,2) ))
+        return 1j * omega(freq) * dMe_dsigV
+
+
+    def getRHS(self, freq):
+        """
+        Function to return the right hand side for the system.
+
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
+            :return: RHS for both polarizations, primary fields
+        """
+
+        # Get sources for the frequncy(polarizations)
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_e = Src.S_e(self)
+        return -1j * omega(freq) * S_e
+
+    def getRHSDeriv_m(self, freq, v, adjoint=False):
+        """
+        The derivative of the RHS with respect to sigma
+        """
+
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
+        return -1j * omega(freq) * S_eDeriv
+
+    def fields(self, m):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        '''
+        # Set the current model
+        self.curModel = m
+
+        F = self.fieldsPair(self.mesh, self.survey)
+        for freq in self.survey.freqs:
+            if self.verbose:
+                startTime = time.time()
+                print 'Starting work for {:.3e}'.format(freq)
+                sys.stdout.flush()
+            A = self.getA(freq)
+            rhs  = self.getRHS(freq)
+            # Solve the system
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs
+
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)[0]
+            # Store the fields
+            # Use self._solutionType
+            F[Src, 'e_pxSolution'] = e_s[:,0]
+            F[Src, 'e_pySolution'] = e_s[:,1]
+            # Note curl e = -iwb so b = -curl/iw
+
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+            Ainv.clean()
+        return F

SimPEG/NSEM/SrcNSEM.py

@@ -11,9 +11,9 @@ import sys
 ###   Sources ###
 #################
 
-class BaseMTSrc(FDEMBaseSrc):
+class BaseNSEMSrc(FDEMBaseSrc):
     '''
-    Sources for the MT problem.
+    Sources for the NSEM problem.
     Use the SimPEG BaseSrc, since the source fields share properties with the transmitters.
 
     :param float freq: The frequency of the source
@@ -29,28 +29,28 @@ class BaseMTSrc(FDEMBaseSrc):
         FDEMBaseSrc.__init__(self, rxList)
 
 # 1D sources
-class polxy_1DhomotD(BaseMTSrc):
+class polxy_1DhomotD(BaseNSEMSrc):
     """
-    MT source for both polarizations (x and y) for the total Domain.
+    NSEM source for both polarizations (x and y) for the total Domain.
 
     It calculates fields calculated based on conditions on the boundary of the domain.
     """
     def __init__(self, rxList, freq):
-        BaseMTSrc.__init__(self, rxList, freq)
+        BaseNSEMSrc.__init__(self, rxList, freq)
 
 
     # TODO: need to add the  primary fields calc and source terms into the problem.
 
 # Need to implement such that it works for all dims.
-class polxy_1Dprimary(BaseMTSrc):
+class polxy_1Dprimary(BaseNSEMSrc):
     """
-    MT source for both polarizations (x and y) given a 1D primary models.
+    NSEM source for both polarizations (x and y) given a 1D primary models.
     It assigns fields calculated from the 1D model as fields in the full space of the problem.
     """
     def __init__(self, rxList, freq):
         # assert mkvc(self.mesh.hz.shape,1) == mkvc(sigma1d.shape,1),'The number of values in the 1D background model does not match the number of vertical cells (hz).'
         self.sigma1d = None
-        BaseMTSrc.__init__(self, rxList, freq)
+        BaseNSEMSrc.__init__(self, rxList, freq)
         # Hidden property of the ePrimary
         self._ePrimary = None
 
@@ -86,7 +86,7 @@ class polxy_1Dprimary(BaseMTSrc):
         Get the electrical field source
         """
         e_p = self.ePrimary(problem)
-        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
         sigma_p = Map_sigma_p._transform(self.sigma1d)
         # Make mass matrix
         # Note: M(sig) - M(sig_p) = M(sig - sig_p)
@@ -128,15 +128,15 @@ class polxy_1Dprimary(BaseMTSrc):
             # v should be nC size
             return MsigmaDeriv * v
 
-class polxy_3Dprimary(BaseMTSrc):
+class polxy_3Dprimary(BaseNSEMSrc):
     """
-    MT source for both polarizations (x and y) given a 3D primary model. It assigns fields calculated from the 1D model
+    NSEM source for both polarizations (x and y) given a 3D primary model. It assigns fields calculated from the 1D model
     as fields in the full space of the problem.
     """
     def __init__(self, rxList, freq):
         # assert mkvc(self.mesh.hz.shape,1) == mkvc(sigma1d.shape,1),'The number of values in the 1D background model does not match the number of vertical cells (hz).'
         self.sigmaPrimary = None
-        BaseMTSrc.__init__(self, rxList, freq)
+        BaseNSEMSrc.__init__(self, rxList, freq)
         # Hidden property of the ePrimary
         self._ePrimary = None
 
@@ -163,7 +163,7 @@ class polxy_3Dprimary(BaseMTSrc):
         Get the electrical field source
         """
         e_p = self.ePrimary(problem)
-        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
         sigma_p = Map_sigma_p._transform(self.sigma1d)
         # Make mass matrix
         # Note: M(sig) - M(sig_p) = M(sig - sig_p)

SimPEG/NSEM/SurveyNSEM.py

@@ -4,7 +4,7 @@ from SimPEG.EM.Utils import omega
 from scipy.constants import mu_0
 from numpy.lib import recfunctions as recFunc
 from Utils import rec2ndarr
-import SrcMT
+import SrcNSEM
 import sys
 
 #################
@@ -63,9 +63,9 @@ class Rx(SimPEGsurvey.BaseRx):
         '''
         Project the fields to natural source data.
 
-            :param SrcMT src: The source of the fields to project
+            :param SrcNSEM src: The source of the fields to project
             :param SimPEG.Mesh mesh:
-            :param FieldsMT f: Natural source fields object to project
+            :param FieldsNSEM f: Natural source fields object to project
         '''
 
         ## NOTE: Assumes that e is on t
@@ -143,9 +143,9 @@ class Rx(SimPEGsurvey.BaseRx):
         """
         The derivative of the projection wrt u
 
-        :param MTsrc src: MT source
+        :param NSEMsrc src: NSEM source
         :param TensorMesh mesh: Mesh defining the topology of the problem
-        :param MTfields f: MT fields object of the source
+        :param NSEMfields f: NSEM fields object of the source
         :param numpy.ndarray v: Random vector of size
         """
 
@@ -390,12 +390,12 @@ class Rx(SimPEGsurvey.BaseRx):
 #################
 class Survey(SimPEGsurvey.BaseSurvey):
     """
-        Survey class for MT. Contains all the sources associated with the survey.
+        Survey class for NSEM. Contains all the sources associated with the survey.
 
         :param list srcList: List of sources associated with the survey
 
     """
-    srcPair = SrcMT.BaseMTSrc
+    srcPair = SrcNSEM.BaseNSEMSrc
 
     def __init__(self, srcList, **kwargs):
         # Sort these by frequency
@@ -443,7 +443,7 @@ class Survey(SimPEGsurvey.BaseSurvey):
 #################
 class Data(SimPEGsurvey.Data):
     '''
-    Data class for MTdata. Stores the data vector indexed by the survey.
+    Data class for NSEMdata. Stores the data vector indexed by the survey.
 
     :param SimPEG survey object survey:
     :param v vector of the data in order matching of the survey
@@ -461,7 +461,7 @@ class Data(SimPEGsurvey.Data):
 
     def toRecArray(self,returnType='RealImag'):
         '''
-        Function that returns a numpy.recarray for a SimpegMT impedance data object.
+        Function that returns a numpy.recarray for a SimpegNSEM impedance data object.
 
         :param str returnType: Switches between returning a rec array where the impedance is split to real and imaginary ('RealImag') or is a complex ('Complex')
 
@@ -483,7 +483,7 @@ class Data(SimPEGsurvey.Data):
                 locs = np.hstack((np.array([[0.0]]),locs))
             tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],12))),axis=1).view(dtRI)
             # np.array([(src.freq,rx.locs[0,0],rx.locs[0,1],rx.locs[0,2],np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ) for rx in src.rxList],dtype=dtRI)
-            # Get the type and the value for the DataMT object as a list
+            # Get the type and the value for the DataNSEM object as a list
             typeList = [[rx.rxType.replace('z1d','zyx'),self[src,rx]] for rx in src.rxList]
             # Insert the values to the temp array
             for nr,(key,val) in enumerate(typeList):
@@ -517,17 +517,17 @@ class Data(SimPEGsurvey.Data):
     @classmethod
     def fromRecArray(cls, recArray, srcType='primary'):
         """
-        Class method that reads in a numpy record array to MTdata object.
+        Class method that reads in a numpy record array to NSEMdata object.
 
         Only imports the impedance data.
 
         """
         if srcType=='primary':
-            src = SrcMT.polxy_1Dprimary
+            src = SrcNSEM.polxy_1Dprimary
         elif srcType=='total':
-            src = SrcMT.polxy_1DhomotD
+            src = SrcNSEM.polxy_1DhomotD
         else:
-            raise NotImplementedError('{:s} is not a valid source type for MTdata')
+            raise NotImplementedError('{:s} is not a valid source type for NSEMdata')
 
         # Find all the frequencies in recArray
         uniFreq = np.unique(recArray['freq'])

SimPEG/NSEM/Utils/MT1Danalytic.py

@@ -3,7 +3,7 @@
 import numpy as np, SimPEG as simpeg
 from scipy.constants import mu_0, epsilon_0 as eps_0
 
-def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
+def getEHfields(m1d,sigma,freq,zd,scaleUD=True,scaleValue=1):
     '''Analytic solution for MT 1D layered earth. Returns E and H fields.
 
     :param SimPEG.mesh, object m1d: Mesh object with the 1D spatial information.
@@ -12,7 +12,7 @@ def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
     :param numpy array, vector zd: location to calculate EH fields at
     :param bollean, scaleUD: scales the output to be 1 at the top, increases numeracal stability.
 
-    Assumes a halfspace with the same conductive as the last cell below.
+    Assumes a halfspace with the same conductive as the deepest cell.
 
     '''
     # Note add an error check for the mesh and sigma are the same size.
@@ -29,7 +29,7 @@ def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
 
     # Initiate the propagation matrix, in the order down up.
     UDp = np.zeros((2,m1d.nC+1),dtype=complex)
-    UDp[1,0] = 1. # Set the wave amplitude as 1 into the half-space at the bottom of the mesh
+    UDp[1,0] = scaleValue # Set the wave amplitude as 1 into the half-space at the bottom of the mesh
     # Loop over all the layers, starting at the bottom layer
     for lnr, h in enumerate(m1d.hx): # lnr-number of layer, h-thickness of the layer
         # Calculate
@@ -38,9 +38,9 @@ def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
         # Build the propagation matrix
 
         # Convert fields to down/up going components in layer below current layer
-        Pj1 = np.array([[1,1],[yp1,-yp1]])
+        Pj1 = np.array([[1,1],[yp1,-yp1]],dtype=complex)
         # Convert fields to down/up going components in current layer
-        Pjinv = 1./2*np.array([[1,zp],[1,-zp]])
+        Pjinv = 1./2*np.array([[1,zp],[1,-zp]],dtype=complex)
         # Propagate down and up components through the current layer
         elamh = np.array([[np.exp(-1j*k[lnr+1]*h),0],[0,np.exp(1j*k[lnr+1]*h)]])
 
@@ -48,7 +48,14 @@ def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
         UDp[:,lnr+1] = elamh.dot(Pjinv.dot(Pj1)).dot(UDp[:,lnr])
 
         if scaleUD:
-             UDp[:,lnr+1::-1] = UDp[:,lnr+1::-1]/UDp[1,lnr+1]
+            # Scale the values such that 1 at the top
+            scaleVal = UDp[:,lnr+1::-1]/UDp[1,lnr+1]
+            if np.any(np.isnan(scaleVal)):
+                # If there is a nan (thickness very great), rebuild the move up cell
+                scaleVal = np.zeros_like(UDp[:,lnr+1::-1],dtype=complex)
+                scaleVal[1,0] = scaleValue
+
+            UDp[:,lnr+1::-1] = scaleVal
 
     # Calculate the fields
     Ed = np.empty((zd.size,),dtype=complex)

SimPEG/NSEM/Utils/__init__.py

@@ -0,0 +1,5 @@
+from MT1Dsolutions import get1DEfields  # Add the names of the functions
+from MT1Danalytic import getEHfields, getImpedance
+from dataUtils import *
+from ediFilesUtils import *
+from testUtils import *

SimPEG/NSEM/Utils/dataUtils.py

@@ -5,25 +5,25 @@ import numpy.lib.recfunctions as recFunc
 from scipy.constants import mu_0
 from scipy import interpolate as sciint
 
-def getAppRes(MTdata):
+def getAppRes(NSEMdata):
     # Make impedance
     zList = []
-    for src in MTdata.survey.srcList:
+    for src in NSEMdata.survey.srcList:
         zc = [src.freq]
         for rx in src.rxList:
             if 'i' in rx.rxType:
                 m=1j
             else:
                 m = 1
-            zc.append(m*MTdata[src,rx])
+            zc.append(m*NSEMdata[src,rx])
         zList.append(zc)
     return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
 
-def rotateData(MTdata,rotAngle):
+def rotateData(NSEMdata,rotAngle):
     '''
     Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
     '''
-    recData = MTdata.toRecArray('Complex')
+    recData = NSEMdata.toRecArray('Complex')
     impData = rec2ndarr(recData[['zxx','zxy','zyx','zyy']],complex)
     # Make the rotation matrix
     # c,s,zxx,zxy,zyx,zyy = sympy.symbols('c,s,zxx,zxy,zyx,zyy')
@@ -40,8 +40,8 @@ def rotateData(MTdata,rotAngle):
     for nr,comp in enumerate(['zxx','zxy','zyx','zyy']):
         outRec[comp] = rotData[:,nr]
 
-    from SimPEG import MT
-    return MT.Data.fromRecArray(outRec)
+    from SimPEG import NSEM
+    return NSEM.Data.fromRecArray(outRec)
 
 
 def appResPhs(freq,z):
@@ -57,10 +57,10 @@ def rec2ndarr(x,dt=float):
     return x.view((dt, len(x.dtype.names)))
 
 def makeAnalyticSolution(mesh,model,elev,freqs):
-    from SimPEG import MT
+    from SimPEG import NSEM
     data1D = []
     for freq in freqs:
-        anaEd, anaEu, anaHd, anaHu = MT.Utils.MT1Danalytic.getEHfields(mesh,model,freq,elev)
+        anaEd, anaEu, anaHd, anaHu = NSEM.Utils.MT1Danalytic.getEHfields(mesh,model,freq,elev)
         anaE = anaEd+anaEu
         anaH = anaHd+anaHu
 
@@ -71,7 +71,7 @@ def makeAnalyticSolution(mesh,model,elev,freqs):
     return dataRec
 
 def plotMT1DModelData(problem,models,symList=None):
-    from SimPEG import MT
+    from SimPEG import NSEM
     # Setup the figure
     fontSize = 15
 
@@ -79,7 +79,7 @@ def plotMT1DModelData(problem,models,symList=None):
     axM = fig.add_axes([0.075,.1,.25,.875])
     axM.set_xlabel('Resistivity [Ohm*m]',fontsize=fontSize)
     axM.set_xlim(1e-1,1e5)
-    axM.set_ylim(-10000,5000)
+    # axM.set_ylim(-10000,5000)
     axM.set_ylabel('Depth [km]',fontsize=fontSize)
     axR = fig.add_axes([0.42,.575,.5,.4])
     axR.set_xscale('log')
@@ -132,38 +132,94 @@ def plotMT1DModelData(problem,models,symList=None):
     freq = simpeg.mkvc(data1D['freq'],2)
     res, phs = appResPhs(freq,allData)
 
-    stdCol = 'gray'
-    axRtw = axR.twinx()
-    axRtw.set_ylabel('Std of log10',color=stdCol)
-    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
-    axPtw = axP.twinx()
-    axPtw.set_ylabel('Std ',color=stdCol)
-    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
-    axRtw.plot(freq, np.std(np.log10(res),1),'--',color=stdCol)
-    axPtw.plot(freq, np.std(phs,1),'--',color=stdCol)
+    if False:
+        stdCol = 'gray'
+        axRtw = axR.twinx()
+        axRtw.set_ylabel('Std of log10',color=stdCol)
+        [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
+        axPtw = axP.twinx()
+        axPtw.set_ylabel('Std ',color=stdCol)
+        [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
+        axRtw.plot(freq, np.std(np.log10(res),1),'--',color=stdCol)
+        axPtw.plot(freq, np.std(phs,1),'--',color=stdCol)
 
     # Fix labels and ticks
 
-    yMtick = [l/1000 for l in axM.get_yticks().tolist()]
-    axM.set_yticklabels(yMtick)
+    # yMtick = [l/1000 for l in axM.get_yticks().tolist()]
+    # axM.set_yticklabels(yMtick)
     [ l.set_rotation(90) for l in axM.get_yticklabels()]
     [ l.set_rotation(90) for l in axR.get_yticklabels()]
-    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
-    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
+    # [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
+    # [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
     for ax in [axM,axR,axP]:
         ax.xaxis.set_tick_params(labelsize=fontSize)
         ax.yaxis.set_tick_params(labelsize=fontSize)
     return fig
 
+def plotImpAppRes(dataArrays,plotLoc,textStr=[]):
+    ''' Plots amplitude impedance and phase'''
+    # fig = plt.figure(1,(7, 7))
+    import plotDataTypes as pDt
+    # axes = ImageGrid(fig, (0.05,0.05,0.875,0.875),nrows_ncols = (2, 2),axes_pad = 0.25,add_all=True,share_all=True,label_mode = "L")
+    # Make the figure and axes
+    fig,axT=plt.subplots(2,2,sharex=True)
+    axes = axT.ravel()
+    fig.set_size_inches((13.5,7.0))
+    fig.suptitle('{:s}\nStation at: {:.1f}x ; {:.1f}y'.format(textStr,plotLoc[0],plotLoc[1]))
+    # Have to deal with axes
+    # Set log
+    for ax in axes.ravel():
+        ax.set_xscale('log')
+
+    axes[0].invert_xaxis()
+    axes[0].set_yscale('log')
+    axes[2].set_yscale('log')
+    # Set labels
+    axes[2].set_xlabel('Frequency [Hz]')
+    axes[3].set_xlabel('Frequency [Hz]')
+    axes[0].set_ylabel('Apperent resistivity [Ohm m]')
+    axes[1].set_ylabel('Apperent phase [degrees]')
+    axes[1].set_ylim(-180,180)
+    axes[2].set_ylabel('Impedance amplitude [V/A]')
+    axes[3].set_ylim(-180,180)
+    axes[3].set_ylabel('Impedance angle [degrees]')
+
+
+    # Plot the data
+    for nr,dataArray in enumerate(dataArrays):
+        if nr==1:
+            parSym = '*'
+        else:
+            parSym = 's'
+        # app res
+        pDt.plotIsoStaImpedance(axes[0],plotLoc,dataArray,'zxy',par='res',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[0],plotLoc,dataArray,'zyx',par='res',pSym=parSym)
+        # app phs
+        pDt.plotIsoStaImpedance(axes[1],plotLoc,dataArray,'zxy',par='phs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[1],plotLoc,dataArray,'zyx',par='phs',pSym=parSym)
+        # imp abs
+        pDt.plotIsoStaImpedance(axes[2],plotLoc,dataArray,'zxx',par='abs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[2],plotLoc,dataArray,'zxy',par='abs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[2],plotLoc,dataArray,'zyx',par='abs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[2],plotLoc,dataArray,'zyy',par='abs',pSym=parSym)
+            # imp abs
+        pDt.plotIsoStaImpedance(axes[3],plotLoc,dataArray,'zxx',par='phs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[3],plotLoc,dataArray,'zxy',par='phs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[3],plotLoc,dataArray,'zyx',par='phs',pSym=parSym)
+        pDt.plotIsoStaImpedance(axes[3],plotLoc,dataArray,'zyy',par='phs',pSym=parSym)
+
+    return fig,axes
+
+
 def printTime():
     import time
     print time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime())
 
-def convert3Dto1Dobject(MTdata,rxType3D='zyx'):
-    from SimPEG import MT
+def convert3Dto1Dobject(NSEMdata,rxType3D='zyx'):
+    from SimPEG import NSEM
     # Find the unique locations
     # Need to find the locations
-    recDataTemp = MTdata.toRecArray()
+    recDataTemp = NSEMdata.toRecArray()
     # Check if survey.std has been assigned.
     ## NEED TO: write this...
     # Calculte and add the DET of the tensor to the recArray
@@ -185,24 +241,24 @@ def convert3Dto1Dobject(MTdata,rxType3D='zyx'):
         # Make the receiver list
         rx1DList = []
         for rxType in ['z1dr','z1di']:
-            rx1DList.append(MT.Rx(simpeg.mkvc(loc,2).T,rxType))
+            rx1DList.append(NSEM.Rx(simpeg.mkvc(loc,2).T,rxType))
         # Source list
         locrecData = recData[np.sqrt(np.sum( (rec2ndarr(recData[['x','y','z']]).data - loc )**2,axis=1)) < 1e-5]
         dat1DList = []
         src1DList = []
         for freq in locrecData['freq']:
-            src1DList.append(MT.SrcMT.src_polxy_1Dprimary(rx1DList,freq))
+            src1DList.append(NSEM.SrcNSEM.src_polxy_1Dprimary(rx1DList,freq))
             for comp  in ['r','i']:
                 dat1DList.append( corr * locrecData[rxType3D+comp][locrecData['freq']== freq].data )
 
         # Make the survey
-        sur1D = MT.Survey(src1DList)
+        sur1D = NSEM.Survey(src1DList)
 
         # Make the data
         dataVec = np.hstack(dat1DList)
-        dat1D = MT.Data(sur1D,dataVec)
+        dat1D = NSEM.Data(sur1D,dataVec)
         sur1D.dobs = dataVec
-        # Need to take MTdata.survey.std and split it as well.
+        # Need to take NSEMdata.survey.std and split it as well.
         std=0.05
         sur1D.std =  np.abs(sur1D.dobs*std) #+ 0.01*np.linalg.norm(sur1D.dobs)
         mtData1DList.append(dat1D)
@@ -210,29 +266,29 @@ def convert3Dto1Dobject(MTdata,rxType3D='zyx'):
     # Return the the list of data.
     return mtData1DList
 
-def resampleMTdataAtFreq(MTdata,freqs):
+def resampleNSEMdataAtFreq(NSEMdata,freqs):
     """
-    Function to resample MTdata at set of frequencies
+    Function to resample NSEMdata at set of frequencies
 
     """
-    from SimPEG import MT
+    from SimPEG import NSEM
     # Make a rec array
-    MTrec = MTdata.toRecArray().data
+    NSEMrec = NSEMdata.toRecArray().data
 
     # Find unique locations
-    uniLoc = np.unique(MTrec[['x','y','z']])
-    uniFreq = MTdata.survey.freqs
+    uniLoc = np.unique(NSEMrec[['x','y','z']])
+    uniFreq = NSEMdata.survey.freqs
     # Get the comps
-    dNames = MTrec.dtype
+    dNames = NSEMrec.dtype
 
     # Loop over all the locations and interpolate
     for loc in uniLoc:
         # Find the index of the station
-        ind = np.sqrt(np.sum((rec2ndarr(MTrec[['x','y','z']]) - rec2ndarr(loc))**2,axis=1)) < 1. # Find dist of 1 m accuracy
+        ind = np.sqrt(np.sum((rec2ndarr(NSEMrec[['x','y','z']]) - rec2ndarr(loc))**2,axis=1)) < 1. # Find dist of 1 m accuracy
         # Make a temporary recArray and interpolate all the components
         tArrRec = np.concatenate((simpeg.mkvc(freqs,2),np.ones((len(freqs),1))*rec2ndarr(loc),np.nan*np.ones((len(freqs),12))),axis=1).view(dNames)
         for comp in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
-            int1d = sciint.interp1d(MTrec[ind]['freq'],MTrec[ind][comp],bounds_error=False)
+            int1d = sciint.interp1d(NSEMrec[ind]['freq'],NSEMrec[ind][comp],bounds_error=False)
             tArrRec[comp] = simpeg.mkvc(int1d(freqs),2)
 
         # Join together
@@ -241,5 +297,5 @@ def resampleMTdataAtFreq(MTdata,freqs):
         except NameError as e:
             outRecArr = tArrRec
 
-    # Make the MTdata and return
-    return MT.Data.fromRecArray(outRecArr)
+    # Make the NSEMdata and return
+    return NSEM.Data.fromRecArray(outRecArr)

SimPEG/NSEM/Utils/ediFilesUtils.py

@@ -2,22 +2,21 @@
 from SimPEG import mkvc
 from scipy.constants import mu_0
 from numpy.lib import recfunctions as recFunc
-from SimPEG.MT.Utils.dataUtils import rec2ndarr
+from SimPEG.NSEM.Utils.dataUtils import rec2ndarr
 
 # Import modules
 import numpy as np
 import os, sys, re
-try:
-    import osr
-except ImportError as e:
-    print 'Could not import osr, missing the gdal package'
-    pass
+
 
 class EDIimporter:
     """
     A class to import EDIfiles.
 
     """
+
+
+    # Define data converters
     _impUnitEDI2SI = 4*np.pi*1e-4 # Convert Z[mV/km/nT] (as in EDI)to Z[V/A] SI unit
     _impUnitSI2EDI = 1./_impUnitEDI2SI # ConvertZ[V/A] SI unit to Z[mV/km/nT] (as in EDI)
 
@@ -26,8 +25,8 @@ class EDIimporter:
     comps = None
 
     # Hidden properties
-    _outEPSG = None
-    _2out = None
+    _outEPSG = None # Project info
+    _2out = None    # The projection operator
 
 
     def __init__(self, EDIfilesList, compList=None, outEPSG=None):
@@ -113,6 +112,12 @@ class EDIimporter:
     # nOutData=length(obj.data);
     # obj.data(nOutData+1:nOutData+length(TEMP.data),:) = TEMP.data;
     def _transfromPoints(self,longD,latD):
+        # Import the coordinate projections
+        try:
+            import osr
+        except ImportError as e:
+            print 'Could not import osr, missing the gdal package\nCan not project coordinates'
+            raise e
         # Coordinates convertor
         if self._2out is None:
             src = osr.SpatialReference()

SimPEG/NSEM/Utils/sourceUtils.py

@@ -12,7 +12,7 @@ def homo1DModelSource(mesh,freq,sigma_1d):
 
     '''
     # import
-    from SimPEG.MT.Utils import get1DEfields
+    from SimPEG.NSEM.Utils import get1DEfields
     # Get a 1d solution for a halfspace background
     if mesh.dim == 1:
         mesh1d = mesh
@@ -77,7 +77,7 @@ def analytic1DModelSource(mesh,freq,sigma_1d):
 
     '''
     # import
-    from SimPEG.MT.Utils import getEHfields
+    from SimPEG.NSEM.Utils import getEHfields
     # Get a 1d solution for a halfspace background
     if mesh.dim == 1:
         mesh1d = mesh

SimPEG/NSEM/Utils/testUtils.py

@@ -0,0 +1,198 @@
+import unittest
+import sys
+from scipy.constants import mu_0
+import SimPEG as simpeg
+
+from SimPEG.Utils import meshTensor
+import numpy as np
+
+np.random.seed(1100)
+# Define the tolerances
+TOLr = 5e-2
+TOLp = 5e-2
+
+
+def getAppResPhs(NSEMdata):
+    # Make impedance
+    from SimPEG.NSEM.Utils import appResPhs
+    zList = []
+    for src in NSEMdata.survey.srcList:
+        zc = [src.freq]
+        for rx in src.rxList:
+            if 'i' in rx.rxType:
+                m=1j
+            else:
+                m = 1
+            zc.append(m*NSEMdata[src,rx])
+        zList.append(zc)
+    return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
+
+
+def setup1DSurvey(sigmaHalf,tD=True,structure=False):
+    from SimPEG import NSEM
+    # Frequency
+    nFreq = 33
+    freqs = np.logspace(3,-3,nFreq)
+    # Make the mesh
+    ct = 5
+    air = meshTensor([(ct,25,1.3)])
+    # coreT0 = meshTensor([(ct,15,1.2)])
+    # coreT1 = np.kron(meshTensor([(coreT0[-1],15,1.3)]),np.ones((7,)))
+    core = np.concatenate( (  np.kron(meshTensor([(ct,15,-1.2)]),np.ones((10,))) , meshTensor([(ct,20)]) ) )
+    bot = meshTensor([(core[0],20,-1.3)])
+    x0 = -np.array([np.sum(np.concatenate((core,bot)))])
+    m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)
+    # Make the model
+    sigma = np.zeros(m1d.nC) + sigmaHalf
+    sigma[m1d.gridCC > 0 ] = 1e-8
+    sigmaBack = sigma.copy()
+    # Add structure
+    if structure:
+        shallow = (m1d.gridCC < -200) * (m1d.gridCC > -600)
+        deep = (m1d.gridCC < -3000) * (m1d.gridCC > -5000)
+        sigma[shallow] = 1
+        sigma[deep] = 0.1
+
+    rxList = []
+    for rxType in ['z1dr','z1di']:
+        rxList.append(NSEM.Rx(simpeg.mkvc(np.array([0.0]),2).T,rxType))
+    # Source list
+    srcList =[]
+    if tD:
+        for freq in freqs:
+            srcList.append(NSEM.SrcNSEM.polxy_1DhomotD(rxList,freq))
+    else:
+        for freq in freqs:
+            srcList.append(NSEM.SrcNSEM.polxy_1Dprimary(rxList,freq))
+
+    survey = NSEM.Survey(srcList)
+    return survey, sigma, m1d
+
+
+def setupSimpegNSEM_ePrimSec(inputSetup,comp='Imp',singleFreq=False,expMap=True):
+    from SimPEG import NSEM
+
+    M,freqs,sig,sigBG,rx_loc = inputSetup
+    # Make a receiver list
+    rxList = []
+    if comp == 'All':
+        for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
+            rxList.append(NSEM.Rx(rx_loc,rxType))
+    elif comp == 'Imp':
+        for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']:
+            rxList.append(NSEM.Rx(rx_loc,rxType))
+    elif comp == 'Tip':
+        for rxType in ['tzxr','tzxi','tzyr','tzyi']:
+            rxList.append(NSEM.Rx(rx_loc,rxType))
+    else:
+        rxList.append(NSEM.Rx(rx_loc,comp))
+    # Source list
+    srcList =[]
+
+    if singleFreq:
+        srcList.append(NSEM.SrcNSEM.polxy_1Dprimary(rxList,singleFreq))
+    else:
+        for freq in freqs:
+            srcList.append(NSEM.SrcNSEM.polxy_1Dprimary(rxList,freq))
+    # Survey NSEM
+    survey = NSEM.Survey(srcList)
+
+    ## Setup the problem object
+    sigma1d = M.r(sigBG,'CC','CC','M')[0,0,:]
+    if expMap:
+        problem = NSEM.Problem3D_ePrimSec(M,sigmaPrimary= np.log(sigma1d) )
+        problem.mapping = simpeg.Maps.ExpMap(problem.mesh)
+        problem.curModel = np.log(sig)
+    else:
+        problem = NSEM.Problem3D_ePrimSec(M,sigmaPrimary= sigma1d)
+        problem.curModel = sig
+    problem.pair(survey)
+    problem.verbose = False
+    try:
+        from pymatsolver import MumpsSolver
+        problem.Solver = MumpsSolver
+    except:
+        pass
+
+    return (survey, problem)
+
+def getInputs():
+    """
+    Function that returns Mesh, freqs, rx_loc, elev.
+    """
+    # Make a mesh
+    # M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])
+    # M = simpeg.Mesh.TensorMesh([[(1000,6,-1.5),(1000.,6),(1000,6,1.5)],[(1000,6,-1.5),(1000.,2),(1000,6,1.5)],[(1000,6,-1.3),(1000.,6),(1000,6,1.3)]], x0=['C','C','C'])# Setup the model
+    M = simpeg.Mesh.TensorMesh([[(200,6,-1.5),(200.,4),(200,6,1.5)],[(200,6,-1.5),(200.,4),(200,6,1.5)],[(200,8,-1.5),(200.,8),(200,8,1.5)]], x0=['C','C','C'])# Setup the model
+    # Set the frequencies
+    freqs = np.logspace(1,-3,5)
+    elev = 0
+
+    ## Setup the the survey object
+    # Receiver locations
+    rx_x, rx_y = np.meshgrid(np.arange(-350,350,200),np.arange(-350,350,200))
+    rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))
+
+    return M, freqs, rx_loc, elev
+
+def random(conds):
+    ''' Returns a halfspace model based on the inputs'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Backround
+    sigBG = np.ones(M.nC)*conds
+    # Add randomness to the model (10% of the value).
+    sig = np.exp( np.log(sigBG) + np.random.randn(M.nC)*(conds)*1e-1 )
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def halfSpace(conds):
+    ''' Returns a halfspace model based on the inputs'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Model
+    ccM = M.gridCC
+    # conds = [1e-2]
+    groundInd = ccM[:,2] < elev
+    sig = np.zeros(M.nC) + 1e-8
+    sig[groundInd] = conds
+    # Set the background, not the same as the model
+    sigBG = np.zeros(M.nC) + 1e-8
+    sigBG[groundInd] = conds
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def blockInhalfSpace(conds):
+    ''' Returns a halfspace model based on the inputs'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Model
+    ccM = M.gridCC
+    # conds = [1e-2]
+    groundInd = ccM[:,2] < elev
+    sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,np.array([-1000,-1000,-1500]),np.array([1000,1000,-1000]),conds)
+    sig[~groundInd] = 1e-8
+    # Set the background, not the same as the model
+    sigBG = np.zeros(M.nC) + 1e-8
+    sigBG[groundInd] = conds[1]
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def twoLayer(conds):
+    ''' Returns a 2 layer model based on the conductivity values given'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Model
+    ccM = M.gridCC
+    groundInd = ccM[:,2] < elev
+    botInd = ccM[:,2] < -3000
+    sig = np.zeros(M.nC) + 1e-8
+    sig[groundInd] = conds[1]
+    sig[botInd] = conds[0]
+    # Set the background, not the same as the model
+    sigBG = np.zeros(M.nC) + 1e-8
+    sigBG[groundInd] = conds[1]
+
+
+    return (M, freqs, sig, sigBG, rx_loc)
+

SimPEG/NSEM/__init__.py

@@ -0,0 +1,5 @@
+import Utils
+from SurveyNSEM import Rx, Survey, Data
+from FieldsNSEM import Fields1D_ePrimSec, Fields3D_ePrimSec
+from ProblemNSEM import Problem1D_ePrimSec, Problem3D_ePrimSec
+import SrcNSEM

SimPEG/Optimization.py

@@ -1003,8 +1003,9 @@ class ProjectedGNCG(BFGS, Minimize, Remember):
                 # perturb inactive set off of bounds so that they are included in the step
                 delx = delx + self.stepOffBoundsFact * (rhs_a * dm_i / dm_a)
 
+
             # Only keep gradients going in the right direction on the active set
             indx = ((self.xc<=self.lower) & (delx < 0)) | ((self.xc>=self.upper) & (delx > 0))
             delx[indx] = 0.
 
-            return delx
+            return delx

SimPEG/PropMaps.py

@@ -74,7 +74,7 @@ class Property(object):
                 if linkedMap is None:
                     return None
                 linkMap = linkMapClass(None) * linkedMap
-                m = getattr(self, '%s'%linkName)
+                m = getattr(self, '%sModel'%linkName)
                 return linkMap.deriv( m )
 
             m = getattr(self, '%sModel'%prop.name)
@@ -239,7 +239,7 @@ class PropMap(object):
             setattr(self, '%sMap'%name, mapping)
             setattr(self, '%sIndex'%name, slices.get(name, slice(nP, nP + mapping.nP)))
             nP += mapping.nP
-        self.nP = nP 
+        self.nP = nP
 
     @property
     def defaultInvProp(self):

SimPEG/Regularization.py

@@ -1,4 +1,6 @@
-import Utils, Maps, Mesh, numpy as np, scipy.sparse as sp
+import Utils, Maps, Mesh
+import numpy as np
+import scipy.sparse as sp
 
 class RegularizationMesh(object):
     """
@@ -311,6 +313,9 @@ class BaseRegularization(object):
             tmp = indActive
             indActive = np.zeros(mesh.nC, dtype=bool)
             indActive[tmp] = True
+        if indActive is not None and mapping is None:
+            mapping = Maps.IdentityMap(nP=indActive.nonzero()[0].size)
+
         self.regmesh = RegularizationMesh(mesh,indActive)
         self.mapping = mapping or self.mapPair(mesh)
         self.mapping._assertMatchesPair(self.mapPair)
@@ -344,7 +349,6 @@ class BaseRegularization(object):
     def W(self):
         """Full regularization weighting matrix W."""
         return sp.identity(self.regmesh.nC)
-        # self.regmesh._Pac.T * sp.identity(self.regmesh.nC) * self.regmesh._Pac # or do we want sp.identity(self.mesh.nC) or even just Utils.Identity() ?
 
     @Utils.timeIt
     def eval(self, m):
@@ -375,11 +379,12 @@ class BaseRegularization(object):
     @Utils.timeIt
     def eval2Deriv(self, m, v=None):
         """
+        Second derivative
 
-            :param numpy.array m: geophysical model
-            :param numpy.array v: vector to multiply
-            :rtype: scipy.sparse.csr_matrix or numpy.ndarray
-            :return: WtW or WtW*v
+        :param numpy.array m: geophysical model
+        :param numpy.array v: vector to multiply
+        :rtype: scipy.sparse.csr_matrix or numpy.ndarray
+        :return: WtW or WtW*v
 
         The regularization is:
 
@@ -400,27 +405,281 @@ class BaseRegularization(object):
 
         return mD.T * ( self.W.T * ( self.W * ( mD * v) ) )
 
-class Tikhonov(BaseRegularization):
+class Simple(BaseRegularization):
     """
+    Simple regularization that does not include length scales in the derivatives.
     """
-    mrefInSmooth = True  #: SMOOTH and SMOOTH_MOD_DIF options
-    alpha_s  = Utils.dependentProperty('_alpha_s', 1e-6, ['_W', '_Ws'], "Smallness weight")
-    alpha_x  = Utils.dependentProperty('_alpha_x', 1.0, ['_W', '_Wx'], "Weight for the first derivative in the x direction")
-    alpha_y  = Utils.dependentProperty('_alpha_y', 1.0, ['_W', '_Wy'], "Weight for the first derivative in the y direction")
-    alpha_z  = Utils.dependentProperty('_alpha_z', 1.0, ['_W', '_Wz'], "Weight for the first derivative in the z direction")
-    alpha_xx = Utils.dependentProperty('_alpha_xx', 0.0, ['_W', '_Wxx'], "Weight for the second derivative in the x direction")
-    alpha_yy = Utils.dependentProperty('_alpha_yy', 0.0, ['_W', '_Wyy'], "Weight for the second derivative in the y direction")
-    alpha_zz = Utils.dependentProperty('_alpha_zz', 0.0, ['_W', '_Wzz'], "Weight for the second derivative in the z direction")
+
+    mrefInSmooth = False  #: include mref in the smoothness?
+    alpha_s      = Utils.dependentProperty('_alpha_s', 1.0, ['_W', '_Wsmall'], "Smallness weight")
+    alpha_x      = Utils.dependentProperty('_alpha_x', 1.0, ['_W', '_Wx'],     "Weight for the first derivative in the x direction")
+    alpha_y      = Utils.dependentProperty('_alpha_y', 1.0, ['_W', '_Wy'],     "Weight for the first derivative in the y direction")
+    alpha_z      = Utils.dependentProperty('_alpha_z', 1.0, ['_W', '_Wz'],     "Weight for the first derivative in the z direction")
+    cell_weights = 1.
+
+    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
+        BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+
+        if isinstance(self.cell_weights,float):
+            self.cell_weights = np.ones(self.regmesh.nC) * self.cell_weights
+
+    @property
+    def Wsmall(self):
+        """Regularization matrix Wsmall"""
+        if getattr(self,'_Wsmall', None) is None:
+            self._Wsmall = Utils.sdiag((self.alpha_s*self.cell_weights)**0.5)
+        return self._Wsmall
+
+    @property
+    def Wx(self):
+        """Regularization matrix Wx"""
+        if getattr(self, '_Wx', None) is None:
+            self._Wx = Utils.sdiag((self.alpha_x * (self.regmesh.aveCC2Fx*self.cell_weights))**0.5)*self.regmesh.cellDiffxStencil
+        return self._Wx
+
+    @property
+    def Wy(self):
+        """Regularization matrix Wy"""
+        if getattr(self, '_Wy', None) is None:
+            self._Wy = Utils.sdiag((self.alpha_y * (self.regmesh.aveCC2Fy*self.cell_weights))**0.5)*self.regmesh.cellDiffyStencil
+        return self._Wy
+
+    @property
+    def Wz(self):
+        """Regularization matrix Wz"""
+        if getattr(self, '_Wz', None) is None:
+            self._Wz = Utils.sdiag((self.alpha_z * (self.regmesh.aveCC2Fz*self.cell_weights))**0.5)*self.regmesh.cellDiffzStencil
+        return self._Wz
+
+#    @property
+#    def Wsmooth(self):
+#        """Full smoothness regularization matrix W"""
+#        print 'wtf why are we using Wsmooth'
+#        raise NotImplementedError
+#        if getattr(self, '_Wsmooth', None) is None:
+#            wlist = (self.Wx,)
+#            if self.regmesh.dim > 1:
+#                wlist += (self.Wy,)
+#            if self.regmesh.dim > 2:
+#                wlist += (self.Wz,)
+#            self._Wsmooth = sp.vstack(wlist)
+#        return self._Wsmooth
+#
+#    @property
+#    def W(self):
+#        """Full regularization matrix W"""
+#        print 'wtf why are we using W'
+#        if getattr(self, '_W', None) is None:
+#            wlist = (self.Wsmall, self.Wx)
+#            if self.regmesh.dim > 1:
+#                wlist += (self.Wy,)
+#            if self.regmesh.dim > 2:
+#                wlist += (self.Wz,)
+#            self._W = sp.vstack(wlist)
+#        return self._W
+
+
+    @Utils.timeIt
+    def _evalSmall(self, m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmallDeriv(self, m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return r.T * ( self.Wsmall * self.mapping.deriv(m - self.mref) )
+
+    @Utils.timeIt
+    def _evalSmall2Deriv(self, m, v = None):
+        rDeriv = self.Wsmall * ( self.mapping.deriv(m - self.mref) )
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothx(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wx * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wx * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothy(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wy * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wy * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothz(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wz * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wz * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmooth(self, m):
+        phiSmooth = self._evalSmoothx(m)
+        if self.regmesh.dim > 1:
+            phiSmooth += self._evalSmoothy(m)
+        if self.regmesh.dim > 2:
+            phiSmooth += self._evalSmoothz(m)
+        return phiSmooth
+
+    @Utils.timeIt
+    def _evalSmoothxDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wx * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wx * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wx * ( self.mapping * m )
+            return r.T * ( self.Wx * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothx2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wx * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wx * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothyDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wy * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wy * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wy * ( self.mapping * m )
+            return r.T * ( self.Wy * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothy2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wy * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wy * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothzDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wz * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wz * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wz * ( self.mapping * m )
+            return r.T * ( self.Wz * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothz2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wz * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wz * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothDeriv(self, m):
+        deriv = self._evalSmoothxDeriv(m)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyDeriv(m)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzDeriv(m)
+        return deriv
+
+    @Utils.timeIt
+    def _evalSmooth2Deriv(self, m, v=None):
+        deriv = self._evalSmoothx2Deriv(m, v)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothy2Deriv(m, v)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothz2Deriv(m, v)
+        return deriv
+
+
+    @Utils.timeIt
+    def eval(self, m):
+        return self._evalSmall(m) + self._evalSmooth(m)
+
+    @Utils.timeIt
+    def evalDeriv(self, m):
+        """
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
+
+        """
+        return self._evalSmallDeriv(m) + self._evalSmoothDeriv(m)
+
+    @Utils.timeIt
+    def eval2Deriv(self, m, v=None):
+        return self._evalSmall2Deriv(m, v) + self._evalSmooth2Deriv(m, v)
+
+
+
+class Tikhonov(Simple):
+    """
+    L2 Tikhonov regularization with both smallness and smoothness (first order
+    derivative) contributions.
+
+    .. math::
+        \phi_m(\mathbf{m}) = \\alpha_s \| W_s (\mathbf{m} - \mathbf{m_{ref}} ) \|^2
+        + \\alpha_x \| W_x \\frac{\partial}{\partial x} (\mathbf{m} - \mathbf{m_{ref}} ) \|^2
+        + \\alpha_y \| W_y \\frac{\partial}{\partial y} (\mathbf{m} - \mathbf{m_{ref}} ) \|^2
+        + \\alpha_z \| W_z \\frac{\partial}{\partial z} (\mathbf{m} - \mathbf{m_{ref}} ) \|^2
+
+    Note if the key word argument `mrefInSmooth` is False, then mref is not
+    included in the smoothness contribution.
+
+    :param Mesh mesh: SimPEG mesh
+    :param Maps mapping: regularization mapping, takes the model from model space to the thing you want to regularize
+    :param numpy.ndarray indActive: active cell indices for reducing the size of differential operators in the definition of a regularization mesh
+    :param bool mrefInSmooth: (default = False) put mref in the smoothness component?
+    :param float alpha_s: (default 1e-6) smallness weight
+    :param float alpha_x: (default 1) smoothness weight for first derivative in the x-direction
+    :param float alpha_y: (default 1) smoothness weight for first derivative in the y-direction
+    :param float alpha_z: (default 1) smoothness weight for first derivative in the z-direction
+    :param float alpha_xx: (default 1) smoothness weight for second derivative in the x-direction
+    :param float alpha_yy: (default 1) smoothness weight for second derivative in the y-direction
+    :param float alpha_zz: (default 1) smoothness weight for second derivative in the z-direction
+    """
+    mrefInSmooth = False  # put mref in the smoothness contribution
+    alpha_s      = Utils.dependentProperty('_alpha_s', 1e-6, ['_W', '_Wsmall'], "Smallness weight")
+    alpha_x      = Utils.dependentProperty('_alpha_x',  1.0, ['_W', '_Wx'],     "Weight for the first derivative in the x direction")
+    alpha_y      = Utils.dependentProperty('_alpha_y',  1.0, ['_W', '_Wy'],     "Weight for the first derivative in the y direction")
+    alpha_z      = Utils.dependentProperty('_alpha_z',  1.0, ['_W', '_Wz'],     "Weight for the first derivative in the z direction")
+    alpha_xx     = Utils.dependentProperty('_alpha_xx', 0.0, ['_W', '_Wxx'],    "Weight for the second derivative in the x direction")
+    alpha_yy     = Utils.dependentProperty('_alpha_yy', 0.0, ['_W', '_Wyy'],    "Weight for the second derivative in the y direction")
+    alpha_zz     = Utils.dependentProperty('_alpha_zz', 0.0, ['_W', '_Wzz'],    "Weight for the second derivative in the z direction")
 
     def __init__(self, mesh, mapping=None, indActive = None, **kwargs):
         BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
 
     @property
-    def Ws(self):
-        """Regularization matrix Ws"""
-        if getattr(self,'_Ws', None) is None:
-            self._Ws = Utils.sdiag((self.regmesh.vol*self.alpha_s)**0.5)
-        return self._Ws
+    def Wsmall(self):
+        """Regularization matrix Wsmall"""
+        if getattr(self,'_Wsmall', None) is None:
+            self._Wsmall = Utils.sdiag((self.regmesh.vol*self.alpha_s)**0.5)
+        return self._Wsmall
 
     @property
     def Wx(self):
@@ -467,41 +726,135 @@ class Tikhonov(BaseRegularization):
             self._Wzz = Utils.sdiag((self.regmesh.vol*self.alpha_zz)**0.5)*self.regmesh.faceDiffz*self.regmesh.cellDiffz
         return self._Wzz
 
+
     @property
-    def Wsmooth(self):
+    def Wsmooth2(self):
         """Full smoothness regularization matrix W"""
         if getattr(self, '_Wsmooth', None) is None:
-            wlist = (self.Wx, self.Wxx)
+            wlist = (self.Wxx)
             if self.regmesh.dim > 1:
-                wlist += (self.Wy, self.Wyy)
+                wlist += (self.Wyy)
             if self.regmesh.dim > 2:
-                wlist += (self.Wz, self.Wzz)
+                wlist += (self.Wzz)
             self._Wsmooth = sp.vstack(wlist)
         return self._Wsmooth
 
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        if getattr(self, '_W', None) is None:
-            wlist = (self.Ws, self.Wsmooth)
-            self._W = sp.vstack(wlist)
-        return self._W
+    @Utils.timeIt
+    def _evalSmoothxx(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wxx * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wxx * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothyy(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wyy * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wyy * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothzz(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wzz * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wzz * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmooth2(self, m):
+        phiSmooth2 = self._evalSmoothxx(m)
+        if self.regmesh.dim > 1:
+            phiSmooth2 += self._evalSmoothyy(m)
+        if self.regmesh.dim > 2:
+            phiSmooth2 += self._evalSmoothzz(m)
+        return phiSmooth2
+
+    @Utils.timeIt
+    def _evalSmoothxxDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wxx * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wxx * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wxx * ( self.mapping * m )
+            return r.T * ( self.Wxx * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothyyDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wyy * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wyy * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wyy * ( self.mapping * m )
+            return r.T * ( self.Wyy * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothzzDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wzz * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wzz * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wzz * ( self.mapping * m )
+            return r.T * ( self.Wzz * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothxx2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wxx * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wxx * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothyy2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wyy * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wyy * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothzz2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wzz * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wzz * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothDeriv2(self, m):
+        deriv = self._evalSmoothxxDeriv(m)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyyDeriv(m)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzzDeriv(m)
+        return deriv
+
+    @Utils.timeIt
+    def _evalSmooth2Deriv2(self, m, v=None):
+        deriv = self._evalSmoothxx2Deriv(m, v)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyy2Deriv(m, v)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzz2Deriv(m, v)
+        return deriv
+
 
     @Utils.timeIt
     def eval(self, m):
-        if self.mrefInSmooth == True:
-            r1 = self.Wsmooth * ( self.mapping * (m) )
-            r2 = self.Ws * ( self.mapping * (m - self.mref) )
-            return 0.5*(r1.dot(r1)+r2.dot(r2))
-        elif self.mrefInSmooth == False:
-            r = self.W * ( self.mapping * (m - self.mref) )
-            return 0.5*r.dot(r)
-
+        return self._evalSmall(m) + self._evalSmooth(m) + self._evalSmooth2(m)
 
     @Utils.timeIt
     def evalDeriv(self, m):
         """
-
         The regularization is:
 
         .. math::
@@ -515,231 +868,135 @@ class Tikhonov(BaseRegularization):
             R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
 
         """
-        if self.mrefInSmooth == True:
-            mD1 = self.mapping.deriv(m)
-            mD2 = self.mapping.deriv(m - self.mref)
-            r1 = self.Wsmooth * ( self.mapping * (m))
-            r2 = self.Ws * ( self.mapping * (m - self.mref) )
-            out1 = mD1.T * ( self.Wsmooth.T * r1 )
-            out2 = mD2.T * ( self.Ws.T * r2 )
-            out = out1+out2
-        elif self.mrefInSmooth == False:
-            mD = self.mapping.deriv(m - self.mref)
-            r = self.W * ( self.mapping * (m - self.mref) )
-            out = mD.T * ( self.W.T * r )
-        return out
+        return self._evalSmallDeriv(m) + self._evalSmoothDeriv(m) + self._evalSmoothDeriv2(m)
+
+    def eval2Deriv(self, m, v=None):
+        """
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
+
+        """
+        return self._evalSmall2Deriv(m, v) + self._evalSmooth2Deriv(m, v) + self._evalSmooth2Deriv2(m, v)
 
 
-class Simple(BaseRegularization):
+
+class Sparse(Simple):
     """
-        Only for tensor mesh
-    """
-
-    mrefInSmooth = True  #: SMOOTH and SMOOTH_MOD_DIF options
-    alpha_s     = Utils.dependentProperty('_alpha_s', 1.0, ['_W', '_Ws'], "Smallness weight")
-    alpha_x     = Utils.dependentProperty('_alpha_x', 1.0, ['_W', '_Wx'], "Weight for the first derivative in the x direction")
-    alpha_y     = Utils.dependentProperty('_alpha_y', 1.0, ['_W', '_Wy'], "Weight for the first derivative in the y direction")
-    alpha_z     = Utils.dependentProperty('_alpha_z', 1.0, ['_W', '_Wz'], "Weight for the first derivative in the z direction")
-    wght        = 1.
+        The regularization is:
     
-    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
-        BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+        .. math::
+    
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top R^\\top R W(m-m_\\text{ref})}
+    
+        where the IRLS weight
+    
+        .. math::
+    
+            R = \eta TO FINISH LATER!!!
+    
+        So the derivative is straight forward:
+    
+        .. math::
+    
+            R(m) = \mathbf{W^\\top R^\\top R W (m-m_\\text{ref})}
+    
+        The IRLS weights are recomputed after each beta solves.
+        It is strongly recommended to do a few Gauss-Newton iterations
+        before updating.
+    """
         
-        if isinstance(self.wght,float):
-            self.wght = np.ones(self.regmesh.nC) * self.wght
+    # set default values
+    eps_p = 1e-1        # Threshold value for the model norm
+    eps_q = 1e-1        # Threshold value for the model gradient norm
+    curModel = None     # Requires model to compute the weights
+    l2model = None
+    gamma = 1.          # Model norm scaling to smooth out convergence
+    norms = [0., 2., 2., 2.] # Values for norm on (m, dmdx, dmdy, dmdz)
+    cell_weights = 1.        # Consider overwriting with sensitivity weights
+
+    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
+        Simple.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+
+        if isinstance(self.cell_weights,float):
+            self.cell_weights = np.ones(self.regmesh.nC) * self.cell_weights
 
     @property
-    def Ws(self):
-        """Regularization matrix Ws"""
-        if getattr(self,'_Ws', None) is None:
-                self._Ws = Utils.sdiag((self.regmesh.vol*self.alpha_s*self.wght)**0.5)
-        return self._Ws
+    def Wsmall(self):
+        """Regularization matrix Wsmall"""
+        if getattr(self,'_Wsmall', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Rs = Utils.speye(self.regmesh.nC)
+
+            else:
+                f_m = self.mapping * (self.curModel - self.reg.mref)
+                self.rs = self.R(f_m , self.eps_p, self.norms[0])
+                self.Rs = Utils.sdiag( self.rs )
+
+            self._Wsmall = Utils.sdiag((self.alpha_s*self.gamma*self.cell_weights)**0.5)*self.Rs
+
+        return self._Wsmall
 
     @property
     def Wx(self):
         """Regularization matrix Wx"""
-        if getattr(self, '_Wx', None) is None:
-            self._Wx = Utils.sdiag((self.regmesh.aveCC2Fx * self.regmesh.vol*self.alpha_x*(self.regmesh.aveCC2Fx*self.wght))**0.5)*self.regmesh.cellDiffxStencil
+        if getattr(self,'_Wx', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Rx = Utils.speye(self.regmesh.cellDiffxStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffxStencil * (self.mapping * self.curModel)
+                self.rx = self.R( f_m , self.eps_q, self.norms[1])
+                self.Rx = Utils.sdiag( self.rx )
+
+            self._Wx = Utils.sdiag(( self.alpha_x*self.gamma*(self.regmesh.aveCC2Fx*self.cell_weights))**0.5)*self.Rx*self.regmesh.cellDiffxStencil
+
         return self._Wx
 
     @property
     def Wy(self):
         """Regularization matrix Wy"""
-        if getattr(self, '_Wy', None) is None:
-            self._Wy = Utils.sdiag((self.regmesh.aveCC2Fy * self.regmesh.vol * self.alpha_y*(self.regmesh.aveCC2Fy*self.wght))**0.5)*self.regmesh.cellDiffyStencil
+        if getattr(self,'_Wy', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Ry = Utils.speye(self.regmesh.cellDiffyStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffyStencil * (self.mapping * self.curModel)
+                self.ry = self.R( f_m , self.eps_q, self.norms[2])
+                self.Ry = Utils.sdiag( self.ry )
+
+            self._Wy = Utils.sdiag((self.alpha_y*self.gamma*(self.regmesh.aveCC2Fy*self.cell_weights))**0.5)*self.Ry*self.regmesh.cellDiffyStencil
+
         return self._Wy
 
     @property
     def Wz(self):
         """Regularization matrix Wz"""
-        if getattr(self, '_Wz', None) is None:
-            self._Wz = Utils.sdiag((self.regmesh.aveCC2Fz * self.regmesh.vol*self.alpha_z*(self.regmesh.aveCC2Fz*self.wght))**0.5)*self.regmesh.cellDiffzStencil
+        if getattr(self,'_Wz', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Rz = Utils.speye(self.regmesh.cellDiffzStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffzStencil * (self.mapping * self.curModel)
+                self.rz = self.R( f_m , self.eps_q, self.norms[3])
+                self.Rz = Utils.sdiag( self.rz )
+
+            self._Wz = Utils.sdiag((self.alpha_z*self.gamma*(self.regmesh.aveCC2Fz*self.cell_weights))**0.5)*self.Rz*self.regmesh.cellDiffzStencil
+
         return self._Wz
 
-    @property
-    def Wsmooth(self):
-        """Full smoothness regularization matrix W"""
-        if getattr(self, '_Wsmooth', None) is None:
-            wlist = (self.Wx,)
-            if self.regmesh.dim > 1:
-                wlist += (self.Wy,)
-            if self.regmesh.dim > 2:
-                wlist += (self.Wz,)
-            self._Wsmooth = sp.vstack(wlist)
-        return self._Wsmooth
+    def R(self, f_m , eps, exponent):
 
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        if getattr(self, '_W', None) is None:
-            wlist = (self.Ws, self.Wsmooth)
-            self._W = sp.vstack(wlist)
-        return self._W
-
-
-    @Utils.timeIt
-    def eval(self, m):
-        if self.mrefInSmooth == True:
-            r1 = self.Wsmooth * ( self.mapping * (m) )
-            r2 = self.Ws * ( self.mapping * (m - self.mref) )
-            return 0.5*(r1.dot(r1)+r2.dot(r2))
-        elif self.mrefInSmooth == False:
-            r = self.W * ( self.mapping * (m - self.mref) )
-            return 0.5*r.dot(r)
-        return phim
-
-
-
-    @Utils.timeIt
-    def evalDeriv(self, m):
-        """
-
-        The regularization is:
-
-        .. math::
-
-            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
-
-        So the derivative is straight forward:
-
-        .. math::
-
-            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
-
-        """
-        if self.mrefInSmooth == True:
-            mD1 = self.mapping.deriv(m)
-            mD2 = self.mapping.deriv(m - self.mref)
-            r1 = self.Wsmooth * ( self.mapping * (m))
-            r2 = self.Ws * ( self.mapping * (m - self.mref) )
-            out1 = mD1.T * ( self.Wsmooth.T * r1 )
-            out2 = mD2.T * ( self.Ws.T * r2 )
-            out = out1+out2
-        elif self.mrefInSmooth == False:
-            mD = self.mapping.deriv(m - self.mref)
-            r = self.W * ( self.mapping * (m - self.mref) )
-            out = mD.T * ( self.W.T * r )
-        return out
-
-
-class Sparse(Simple):
-
-    # set default values
-    eps      = 1e-1
-    curModel = None # use a model to compute the weights
-    gamma    = 1.
-    p        = 0.
-    qx       = 2.
-    qy       = 2.
-    qz       = 2.
-    wght     = 1.
-
-    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
-        Simple.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
-        
-        if isinstance(self.wght,float):
-            self.wght = np.ones(self.regmesh.nC) * self.wght
-
-    @property
-    def Ws(self):
-        """Regularization matrix Ws"""
-        if getattr(self, 'curModel', None) is None:
-            self.Rs = Utils.speye(self.regmesh.nC)
-
-        else:
-            f_m = self.curModel - self.reg.mref
-            self.rs = self.R(f_m , self.p)
-            #print "Min rs: " + str(np.max(self.rs)) + "Max rs: " + str(np.min(self.rs))
-            self.Rs = Utils.sdiag( self.rs )
-            
-        return Utils.sdiag((self.regmesh.vol*self.alpha_s*self.gamma*self.wght)**0.5)*self.Rs
-
-
-    @property
-    def Wx(self):
-        """Regularization matrix Wx"""
-            
-        if getattr(self, 'curModel', None) is None:
-            self.Rx = Utils.speye(self.regmesh.cellDiffxStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffxStencil * self.curModel
-            self.rx = self.R( f_m , self.qx)
-            self.Rx = Utils.sdiag( self.rx )
-
-        return Utils.sdiag(( (self.regmesh.aveCC2Fx * self.regmesh.vol) *self.alpha_x*self.gamma*(self.regmesh.aveCC2Fx*self.wght))**0.5)*self.Rx*self.regmesh.cellDiffxStencil
-
-    @property
-    def Wy(self):
-        """Regularization matrix Wy"""
-
-        if getattr(self, 'curModel', None) is None:
-            self.Ry = Utils.speye(self.regmesh.cellDiffyStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffyStencil * self.curModel
-            self.ry = self.R( f_m , self.qy)
-            self.Ry = Utils.sdiag( self.ry )
-            
-        return Utils.sdiag(((self.regmesh.aveCC2Fy * self.regmesh.vol)*self.alpha_y*self.gamma*(self.regmesh.aveCC2Fy*self.wght))**0.5)*self.Ry*self.regmesh.cellDiffyStencil
-
-    @property
-    def Wz(self):
-        """Regularization matrix Wz"""
-
-        if getattr(self, 'curModel', None) is None:
-            self.Rz = Utils.speye(self.regmesh.cellDiffzStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffzStencil * self.curModel
-            self.rz = self.R( f_m , self.qz)
-            self.Rz = Utils.sdiag( self.rz )
-
-        return Utils.sdiag(((self.regmesh.aveCC2Fz * self.regmesh.vol)*self.alpha_z*self.gamma*(self.regmesh.aveCC2Fz*self.wght))**0.5)*self.Rz*self.regmesh.cellDiffzStencil
-
-    @property
-    def Wsmooth(self):
-        """Full smoothness regularization matrix W"""
-        #if getattr(self, '_Wsmooth', None) is None:
-        wlist = (self.Wx,)
-        if self.regmesh.dim > 1:
-            wlist += (self.Wy,)
-        if self.regmesh.dim > 2:
-            wlist += (self.Wz,)
-        #self._Wsmooth = sp.vstack(wlist)
-        return sp.vstack(wlist)
-
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        #if getattr(self, '_W', None) is None:
-        wlist = (self.Ws, self.Wsmooth)
-        #self._W = sp.vstack(wlist)
-        return sp.vstack(wlist)
-        
-    def R(self, f_m , exponent):
-
-        eta = (self.eps**(1-exponent/2.))**0.5
-        r = eta / (f_m**2.+self.eps**2.)**((1-exponent/2.)/2.)
+        # Eta scaling is important for mix-norms...do not mess with it
+        eta = (eps**(1.-exponent/2.))**0.5
+        r = eta / (f_m**2.+ eps**2.)**((1.-exponent/2.)/2.)
 
         return r

SimPEG/Utils/ModelBuilder.py

@@ -88,12 +88,14 @@ def getIndicesBlock(p0,p1,ccMesh):
     # Return a tuple
     return ind
 
-def defineBlock(ccMesh,p0,p1,vals=[0,1]):
+def defineBlock(ccMesh,p0,p1,vals=None):
     """
         Build a block with the conductivity specified by condVal.  Returns an array.
         vals[0]  conductivity of the block
         vals[1]  conductivity of the ground
     """
+    if vals is None:
+        vals = [0,1]
     sigma = np.zeros(ccMesh.shape[0]) + vals[1]
     ind   = getIndicesBlock(p0,p1,ccMesh)
 
@@ -101,7 +103,11 @@ def defineBlock(ccMesh,p0,p1,vals=[0,1]):
 
     return mkvc(sigma)
 
-def defineElipse(ccMesh, center=[0,0,0], anisotropy=[1,1,1], slope=10., theta=0.):
+def defineElipse(ccMesh, center=None, anisotropy=None, slope=10., theta=0.):
+    if center is None:
+        center = [0,0,0]
+    if anisotropy is None:
+        anisotropy = [1,1,1]
     G = ccMesh.copy()
     dim = ccMesh.shape[1]
     for i in range(dim):
@@ -156,7 +162,7 @@ def getIndicesSphere(center,radius,ccMesh):
     # Return a tuple
     return ind
 
-def defineTwoLayers(ccMesh,depth,vals=[0,1]):
+def defineTwoLayers(ccMesh,depth,vals=None):
     """
     Define a two layered model.  Depth of the first layer must be specified.
     CondVals vector with the conductivity values of the layers.  Eg:
@@ -167,6 +173,8 @@ def defineTwoLayers(ccMesh,depth,vals=[0,1]):
         0                          depth                                 zf
              1st layer                       2nd layer
     """
+    if vals is None:
+        vals = [0,1]
     sigma = np.zeros(ccMesh.shape[0]) + vals[1]
 
     dim = np.size(ccMesh[0,:])
@@ -252,7 +260,7 @@ def layeredModel(ccMesh, layerTops, layerValues):
 
 
 
-def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=[0,1]):
+def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=None):
     """
         Create a random model by convolving a kernel with a
         uniformly distributed model.
@@ -276,6 +284,8 @@ def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=[0,1]):
 
 
     """
+    if bounds is None:
+        bounds = [0,1]
 
     if seed is None:
         seed = np.random.randint(1e3)

SimPEG/Utils/__init__.py

@@ -7,3 +7,4 @@ from CounterUtils import *
 import ModelBuilder
 import SolverUtils
 from coordutils import *
+from modelutils import *

SimPEG/Utils/codeutils.py

@@ -55,8 +55,10 @@ def hook(obj, method, name=None, overwrite=False, silent=False):
         print 'Method '+name+' was not overwritten.'
 
 
-def setKwargs(obj, ignore=[],  **kwargs):
+def setKwargs(obj, ignore=None,  **kwargs):
     """Sets key word arguments (kwargs) that are present in the object, throw an error if they don't exist."""
+    if ignore is None:
+        ignore = []
     for attr in kwargs:
         if attr in ignore:
             continue

SimPEG/Utils/io_utils.py

@@ -0,0 +1,137 @@
+from SimPEG import np, Mesh
+import time as tm
+import vtk, vtk.util.numpy_support as npsup
+import re
+
+def read_GOCAD_ts(tsfile):
+    """
+
+    Read GOCAD triangulated surface (*.ts) file
+    INPUT:
+    tsfile: Triangulated surface
+
+    OUTPUT:
+    vrts : Array of vertices in XYZ coordinates [n x 3]
+    trgl : Array of index for triangles [m x 3]. The order of the vertices
+            is important and describes the normal
+            n = cross( (P2 - P1 ) , (P3 - P1) )
+
+    Author: @fourndo
+
+
+    .. note::
+
+        Remove all attributes from the GoCAD surface before exporting it!
+
+    """
+
+
+    fid = open(tsfile,'r')
+    line = fid.readline()
+
+    # Skip all the lines until the vertices
+    while re.match('TFACE',line)==None:
+        line = fid.readline()
+
+    line = fid.readline()
+    vrtx = []
+
+    # Run down all the vertices and save in array
+    while re.match('VRTX',line):
+        l_input  = re.split('[\s*]',line)
+        temp = np.array(l_input[2:5])
+        vrtx.append(temp.astype(np.float))
+
+        # Read next line
+        line = fid.readline()
+
+    vrtx = np.asarray(vrtx)
+
+    # Skip lines to the triangles
+    while re.match('TRGL',line)==None:
+        line = fid.readline()
+
+    # Run down the list of triangles
+    trgl = []
+
+    # Run down all the vertices and save in array
+    while re.match('TRGL',line):
+        l_input  = re.split('[\s*]',line)
+        temp = np.array(l_input[1:4])
+        trgl.append(temp.astype(np.int))
+
+        # Read next line
+        line = fid.readline()
+
+    trgl = np.asarray(trgl)
+
+    return vrtx, trgl
+
+def surface2inds(vrtx, trgl, mesh, boundaries=True, internal=True):
+    """"
+    Function to read gocad polystructure file and output indexes of mesh with in the structure.
+
+    """
+    # Adjust the index
+    trgl = trgl - 1
+
+    # Make vtk pts
+    ptsvtk = vtk.vtkPoints()
+    ptsvtk.SetData(npsup.numpy_to_vtk(vrtx,deep=1))
+
+    # Make the polygon connection
+    polys = vtk.vtkCellArray()
+    for face in trgl:
+        poly = vtk.vtkPolygon()
+        poly.GetPointIds().SetNumberOfIds(len(face))
+        for nrv, vert in enumerate(face):
+            poly.GetPointIds().SetId(nrv,vert)
+        polys.InsertNextCell(poly)
+
+    # Make the polydata, structure of connections and vrtx
+    polyData = vtk.vtkPolyData()
+    polyData.SetPoints(ptsvtk)
+    polyData.SetPolys(polys)
+
+    # Make implicit func
+    ImpDistFunc = vtk.vtkImplicitPolyDataDistance()
+    ImpDistFunc.SetInput(polyData)
+
+    # Convert the mesh
+    vtkMesh = vtk.vtkRectilinearGrid()
+    vtkMesh.SetDimensions(mesh.nNx,mesh.nNy,mesh.nNz)
+    vtkMesh.SetXCoordinates(npsup.numpy_to_vtk(mesh.vectorNx, deep=1))
+    vtkMesh.SetYCoordinates(npsup.numpy_to_vtk(mesh.vectorNy, deep=1))
+    vtkMesh.SetZCoordinates(npsup.numpy_to_vtk(mesh.vectorNz, deep=1))
+    # Add indexes
+    vtkInd = npsup.numpy_to_vtk(np.arange(mesh.nC), deep=1)
+    vtkInd.SetName('Index')
+    vtkMesh.GetCellData().AddArray(vtkInd)
+
+    extractImpDistRectGridFilt = vtk.vtkExtractGeometry() # Object constructor
+    extractImpDistRectGridFilt.SetImplicitFunction(ImpDistFunc) #
+    extractImpDistRectGridFilt.SetInputData(vtkMesh)
+
+    if boundaries is True:
+        extractImpDistRectGridFilt.ExtractBoundaryCellsOn()
+
+    else:
+        extractImpDistRectGridFilt.ExtractBoundaryCellsOff()
+
+    if internal is True:
+        extractImpDistRectGridFilt.ExtractInsideOn()
+
+    else:
+        extractImpDistRectGridFilt.ExtractInsideOff()
+
+    print "Extracting indices from grid..."
+    # Executing the pipe
+    extractImpDistRectGridFilt.Update()
+
+    # Get index inside
+    insideGrid = extractImpDistRectGridFilt.GetOutput()
+    insideGrid = npsup.vtk_to_numpy(insideGrid.GetCellData().GetArray('Index'))
+
+
+    # Return the indexes inside
+    return insideGrid

SimPEG/Utils/modelutils.py

@@ -0,0 +1,63 @@
+from matutils import mkvc, ndgrid
+import numpy as np
+
+def surface2ind_topo(mesh, topo, gridLoc='CC'):
+# def genActiveindfromTopo(mesh, topo):
+    """
+    Get active indices from topography
+    """
+
+
+    if mesh.dim == 3:
+        from scipy.interpolate import NearestNDInterpolator
+        Ftopo = NearestNDInterpolator(topo[:,:2], topo[:,2])
+
+        if gridLoc == 'CC':
+            XY = ndgrid(mesh.vectorCCx, mesh.vectorCCy)
+            Zcc = mesh.gridCC[:,2].reshape((np.prod(mesh.vnC[:2]), mesh.nCz), order='F')
+
+            gridTopo = Ftopo(XY)
+            actind = [gridTopo[ixy] <= Zcc[ixy,:] for ixy in range(np.prod(mesh.vnC[0]))]
+            actind = np.hstack(actind)
+
+        elif gridLoc == 'N':
+
+            XY = ndgrid(mesh.vectorNx, mesh.vectorNy)
+            gridTopo = Ftopo(XY).reshape(mesh.vnN[:2], order='F')
+
+            if mesh._meshType not in ['TENSOR', 'CYL', 'BASETENSOR']:
+                raise NotImplementedError('Nodal surface2ind_topo not implemented for %s mesh'%mesh._meshType)
+
+            Nz = mesh.vectorNz[1:] # TODO: this will only work for tensor meshes
+            actind = np.array([False]*mesh.nC).reshape(mesh.vnC, order='F')
+
+            for ii in range(mesh.nCx):
+                for jj in range(mesh.nCy):
+                     actind[ii,jj,:] = [np.all(gridTopo[ii:ii+2, jj:jj+2] >= Nz[kk]) for kk in range(len(Nz)) ]
+
+    elif mesh.dim == 2:
+        from scipy.interpolate import interp1d
+        Ftopo = interp1d(topo[:,0], topo[:,1])
+
+        if gridLoc == 'CC':
+            gridTopo = Ftopo(mesh.gridCC[:,0])
+            actind = mesh.gridCC[:,1] <= gridTopo
+
+        elif gridLoc == 'N':
+
+            gridTopo = Ftopo(mesh.vectorNx)
+            if mesh._meshType not in ['TENSOR', 'CYL', 'BASETENSOR']:
+                raise NotImplementedError('Nodal surface2ind_topo not implemented for %s mesh'%mesh._meshType)
+
+            Ny = mesh.vectorNy[1:] # TODO: this will only work for tensor meshes
+            actind = np.array([False]*mesh.nC).reshape(mesh.vnC, order='F')
+
+            for ii in range(mesh.nCx):
+                actind[ii,:] = [np.all(gridTopo[ii:ii+2] > Ny[kk]) for kk in range(len(Ny)) ]
+
+    else:
+        raise NotImplementedError('surface2ind_topo not implemented for 1D mesh')
+
+    return mkvc(actind)
+
+

docs/examples/DC_Forward_PseudoSection.rst

@@ -12,9 +12,17 @@
 DC Forward Simulation
 =====================
 
-Forward model conductive spheres in a half-space and plot a pseudo-section
+Forward model two conductive spheres in a half-space and plot a
+pseudo-section. Assumes an infinite line source and measures along the
+center of the spheres.
 
-Created by @fourndo on Mon Feb 01 19:28:06 2016
+INPUT:
+loc     = Location of spheres [[x1,y1,z1],[x2,y2,z2]]
+radi    = Radius of spheres [r1,r2]
+param   = Conductivity of background and two spheres [m0,m1,m2]
+surveyType = survey type 'pole-dipole' or 'dipole-dipole'
+unitType = Data type "appResistivity" | "appConductivity"  | "volt"
+Created by @fourndo
 
 
 

docs/examples/Inversion_IRLS.rst

@@ -0,0 +1,26 @@
+.. _examples_Inversion_IRLS:
+
+.. --------------------------------- ..
+..                                   ..
+..    THIS FILE IS AUTO GENEREATED   ..
+..                                   ..
+..    SimPEG/Examples/__init__.py    ..
+..                                   ..
+.. --------------------------------- ..
+
+
+Inversion: Linear Problem
+=========================
+
+Here we go over the basics of creating a linear problem and inversion.
+
+
+
+.. plot::
+
+    from SimPEG import Examples
+    Examples.Inversion_IRLS.run()
+
+.. literalinclude:: ../../SimPEG/Examples/Inversion_IRLS.py
+    :language: python
+    :linenos:

docs/examples/MT_1D_analytic_nlayer_Earth.rst

@@ -1,4 +1,4 @@
-.. _examples_Forward_BasicDirectCurrent:
+.. _examples_MT_1D_analytic_nlayer_Earth:
 
 .. --------------------------------- ..
 ..                                   ..
@@ -8,14 +8,14 @@
 ..                                   ..
 .. --------------------------------- ..
 
-Forward BasicDirectCurrent
-==========================
+MT 1D analytic nlayer Earth
+===========================
 
 .. plot::
 
     from SimPEG import Examples
-    Examples.Forward_BasicDirectCurrent.run()
+    Examples.MT_1D_analytic_nlayer_Earth.run()
 
-.. literalinclude:: ../../SimPEG/Examples/Forward_BasicDirectCurrent.py
+.. literalinclude:: ../../SimPEG/Examples/MT_1D_analytic_nlayer_Earth.py
     :language: python
     :linenos:

docs/examples/Mesh_Basic_ForwardDC.rst

@@ -0,0 +1,25 @@
+.. _examples_Mesh_Basic_ForwardDC:
+
+.. --------------------------------- ..
+..                                   ..
+..    THIS FILE IS AUTO GENEREATED   ..
+..                                   ..
+..    SimPEG/Examples/__init__.py    ..
+..                                   ..
+.. --------------------------------- ..
+
+
+Mesh: Basic Forward 2D DC Resistivity
+=====================================
+
+2D DC forward modeling example with Tensor and Curvilinear Meshes
+
+
+.. plot::
+
+    from SimPEG import Examples
+    Examples.Mesh_Basic_ForwardDC.run()
+
+.. literalinclude:: ../../SimPEG/Examples/Mesh_Basic_ForwardDC.py
+    :language: python
+    :linenos:

docs/examples/Utils_surface2ind_topo.rst

@@ -0,0 +1,24 @@
+.. _examples_Utils_surface2ind_topo:
+
+.. --------------------------------- ..
+..                                   ..
+..    THIS FILE IS AUTO GENEREATED   ..
+..                                   ..
+..    SimPEG/Examples/__init__.py    ..
+..                                   ..
+.. --------------------------------- ..
+
+
+Here we show how to use :code:`Utils.surface2ind_topo` to identify cells below
+a topographic surface.
+
+
+
+.. plot::
+
+    from SimPEG import Examples
+    Examples.Utils_surface2ind_topo.run()
+
+.. literalinclude:: ../../SimPEG/Examples/Utils_surface2ind_topo.py
+    :language: python
+    :linenos:

setup.py

@@ -5,16 +5,17 @@ SimPEG is a python package for simulation and gradient based
 parameter estimation in the context of geophysical applications.
 """
 
-import numpy as np
-
 import os
 import sys
 import subprocess
 
 from distutils.core import setup
+from distutils.command.build_ext import build_ext
 from setuptools import find_packages
 from distutils.extension import Extension
 
+
+
 CLASSIFIERS = [
 'Development Status :: 4 - Beta',
 'Intended Audience :: Developers',
@@ -51,11 +52,16 @@ if args.count("build_ext") > 0 and args.count("--inplace") == 0:
 try:
     from Cython.Build import cythonize
     from Cython.Distutils import build_ext
-    cythonKwargs = dict(cmdclass={'build_ext': build_ext})
     USE_CYTHON = True
 except Exception, e:
     USE_CYTHON = False
-    cythonKwargs = dict()
+
+class NumpyBuild(build_ext):
+    def finalize_options(self):
+        build_ext.finalize_options(self)
+        __builtins__.__NUMPY_SETUP__ = False
+        import numpy
+        self.include_dirs.append(numpy.get_include())
 
 ext = '.pyx' if USE_CYTHON else '.c'
 
@@ -94,8 +100,8 @@ setup(
     classifiers=CLASSIFIERS,
     platforms = ["Windows", "Linux", "Solaris", "Mac OS-X", "Unix"],
     use_2to3 = False,
-    include_dirs=[np.get_include()],
+    cmdclass={'build_ext':NumpyBuild},
+    setup_requires=['numpy'],
     ext_modules = extensions,
     scripts=scripts,
-    **cythonKwargs
 )

tests/base/test_PropMaps.py

@@ -1,6 +1,7 @@
 import unittest
 from SimPEG import *
 from scipy.constants import mu_0
+from SimPEG import Tests
 
 
 class MyPropMap(Maps.PropMap):
@@ -187,6 +188,34 @@ class TestPropMaps(unittest.TestCase):
 
         MyReciprocalPropMap([('sigma', iMap), ('mu', iMap)]) # This should be fine
 
+    def test_linked_derivs_sigma(self):
+        mesh = Mesh.TensorMesh([4,5], x0='CC')
+
+        mapping = Maps.ExpMap(mesh)
+        propmap = MyReciprocalPropMap([('rho', mapping)])
+
+        x0 = np.random.rand(mesh.nC)
+        m  = propmap(x0)
+
+        # test Sigma
+        testme = lambda v: [1./(m.rhoMap*v), m.sigmaDeriv]
+        print 'Testing Rho from Sigma'
+        Tests.checkDerivative(testme, x0, dx=0.01*x0, num=5, plotIt=False)
+
+    def test_linked_derivs_rho(self):
+        mesh = Mesh.TensorMesh([4,5], x0='CC')
+
+        mapping = Maps.ExpMap(mesh)
+        propmap = MyReciprocalPropMap([('sigma', mapping)])
+
+        x0 = np.random.rand(mesh.nC)
+        m  = propmap(x0)
+
+        # test Sigma
+        testme = lambda v: [1./(m.sigmaMap*v), m.rhoDeriv]
+        print 'Testing Rho from Sigma'
+        Tests.checkDerivative(testme, x0, dx=0.01*x0, num=5, plotIt=False)
+
 if __name__ == '__main__':
     unittest.main()
 

tests/base/test_regularization.py

@@ -65,10 +65,8 @@ class RegularizationTests(unittest.TestCase):
                     elif mesh.dim == 3:
                         indActive = Utils.mkvc(mesh.gridCC[:,-1] <= 2*np.sin(2*np.pi*mesh.gridCC[:,0])+0.5 * 2*np.sin(2*np.pi*mesh.gridCC[:,1])+0.5)
 
-                    mapping = Maps.IdentityMap(nP=indActive.nonzero()[0].size)
-
                     for indAct in [indActive, indActive.nonzero()[0]]: # test both bool and integers
-                        reg = r(mesh, mapping=mapping, indActive=indAct)
+                        reg = r(mesh, indActive=indAct)
                         m = np.random.rand(mesh.nC)[indAct]
                         reg.mref = np.ones_like(m)*np.mean(m)
 

tests/em/fdem/forward/test_FDEM_analytics.py

@@ -28,12 +28,12 @@ class FDEM_analyticTests(unittest.TestCase):
 
         x = np.linspace(-10,10,5)
         XYZ = Utils.ndgrid(x,np.r_[0],np.r_[0])
-        rxList = EM.FDEM.Rx(XYZ, 'exi')
+        rxList = EM.FDEM.Rx.Point_e(XYZ, orientation='x', component='imag')
         Src0 = EM.FDEM.Src.MagDipole([rxList],loc=np.r_[0.,0.,0.], freq=freq)
 
         survey = EM.FDEM.Survey([Src0])
 
-        prb = EM.FDEM.Problem_b(mesh, mapping=mapping)
+        prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
         prb.pair(survey)
 
         try:
@@ -125,8 +125,8 @@ class FDEM_analyticTests(unittest.TestCase):
 
         mapping = [('sigma', Maps.IdentityMap(mesh)),('mu', Maps.IdentityMap(mesh))]
 
-        prbe = EM.FDEM.Problem_h(mesh, mapping=mapping)
-        prbm = EM.FDEM.Problem_e(mesh, mapping=mapping)
+        prbe = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
+        prbm = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
 
         prbe.pair(surveye) # pair problem and survey
         prbm.pair(surveym)

tests/em/fdem/forward/test_FDEM_forwardHB.py

@@ -12,7 +12,7 @@ testBH = True
 verbose = False
 
 TOLEJHB = 1 # averaging and more sensitive to boundary condition violations (ie. the impact of violating the boundary conditions in each case is different.)
-#TODO: choose better testing parameters to lower this 
+#TODO: choose better testing parameters to lower this
 
 SrcList = ['RawVec', 'MagDipole_Bfield', 'MagDipole', 'CircularLoop']
 
@@ -125,4 +125,4 @@ class FDEM_CrossCheck(unittest.TestCase):
             self.assertTrue(crossCheckTest(SrcList, 'b', 'h', 'hzi', verbose=verbose, TOL=TOLEJHB))
 
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()

tests/em/static/test_DC_2D_analytic.py

@@ -0,0 +1,69 @@
+import unittest
+from SimPEG import Mesh, Utils, EM, Maps, np
+import SimPEG.EM.Static.DC as DC
+
+class DCProblemAnalyticTests(unittest.TestCase):
+
+    def setUp(self):
+
+        cs = 12.5
+        hx = [(cs,7, -1.3),(cs,61),(cs,7, 1.3)]
+        hy = [(cs,7, -1.3),(cs,20)]
+        mesh = Mesh.TensorMesh([hx, hy],x0="CN")
+        sighalf = 1e-2
+        sigma = np.ones(mesh.nC)*sighalf
+        x = np.linspace(-135, 250., 20)
+        M = Utils.ndgrid(x-12.5, np.r_[0.])
+        N = Utils.ndgrid(x+12.5, np.r_[0.])
+        A0loc = np.r_[-150, 0.]
+        A1loc = np.r_[-130, 0.]
+        rxloc = [np.c_[M, np.zeros(20)], np.c_[N, np.zeros(20)]]
+        data_anal = EM.Analytics.DCAnalyticHalf(np.r_[A0loc, 0.], rxloc, sighalf, earth_type="halfspace")
+
+        rx = DC.Rx.Dipole_ky(M, N)
+        src0 = DC.Src.Pole([rx], A0loc)
+        survey = DC.Survey_ky([src0])
+
+        self.survey = survey
+        self.mesh = mesh
+        self.sigma = sigma
+        self.data_anal = data_anal
+
+        try:
+            from pymatsolver import MumpsSolver
+            self.Solver = MumpsSolver
+        except ImportError, e:
+            self.Solver = SolverLU
+
+    def test_Problem3D_N(self):
+
+        problem = DC.Problem2D_N(self.mesh)
+        problem.Solver = self.Solver
+        problem.pair(self.survey)
+        data = self.survey.dpred(self.sigma)
+        err= np.linalg.norm((data-self.data_anal)/self.data_anal)**2 / self.data_anal.size
+        if err < 0.05:
+            passed = True
+            print ">> DC analytic test for Problem3D_N is passed"
+        else:
+            passed = False
+            print ">> DC analytic test for Problem3D_N is failed"
+        self.assertTrue(passed)
+
+    def test_Problem3D_CC(self):
+        problem = DC.Problem2D_CC(self.mesh)
+        problem.Solver = self.Solver
+        problem.pair(self.survey)
+        data = self.survey.dpred(self.sigma)
+        err= np.linalg.norm((data-self.data_anal)/self.data_anal)**2 / self.data_anal.size
+        if err < 0.05:
+            passed = True
+            print ">> DC analytic test for Problem3D_CC is passed"
+        else:
+            passed = False
+            print ">> DC analytic test for Problem3D_CC is failed"
+        self.assertTrue(passed)
+
+if __name__ == '__main__':
+    unittest.main()
+

tests/em/static/test_DC_2D_jvecjtvecadj.py

@@ -0,0 +1,127 @@
+import unittest
+from SimPEG import *
+import SimPEG.EM.Static.DC as DC
+
+
+class DCProblem_2DTestsCC(unittest.TestCase):
+
+    def setUp(self):
+
+        cs = 12.5
+        hx = [(cs,7, -1.3),(cs,61),(cs,7, 1.3)]
+        hy = [(cs,7, -1.3),(cs,20)]
+        mesh = Mesh.TensorMesh([hx, hy],x0="CN")
+        x = np.linspace(-135, 250., 20)
+        M = Utils.ndgrid(x-12.5, np.r_[0.])
+        N = Utils.ndgrid(x+12.5, np.r_[0.])
+        A0loc = np.r_[-150, 0.]
+        A1loc = np.r_[-130, 0.]
+        rxloc = [np.c_[M, np.zeros(20)], np.c_[N, np.zeros(20)]]
+        rx = DC.Rx.Dipole_ky(M, N)
+        src0 = DC.Src.Pole([rx], A0loc)
+        src1 = DC.Src.Pole([rx], A1loc)
+        survey = DC.Survey_ky([src0, src1])
+        problem = DC.Problem2D_CC(mesh, mapping=[('rho', Maps.IdentityMap(mesh))])
+        problem.pair(survey)
+
+        mSynth = np.ones(mesh.nC)*1.
+        survey.makeSyntheticData(mSynth)
+
+        # Now set up the problem to do some minimization
+        dmis = DataMisfit.l2_DataMisfit(survey)
+        reg = Regularization.Tikhonov(mesh)
+        opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
+        invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e0)
+        inv = Inversion.BaseInversion(invProb)
+
+        self.inv    = inv
+        self.reg    = reg
+        self.p      = problem
+        self.mesh   = mesh
+        self.m0     = mSynth
+        self.survey = survey
+        self.dmis   = dmis
+
+    def test_misfit(self):
+        derChk = lambda m: [self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx)]
+        passed = Tests.checkDerivative(derChk, self.m0, plotIt=False, num=3)
+        self.assertTrue(passed)
+
+    def test_adjoint(self):
+        # Adjoint Test
+        u = np.random.rand(self.mesh.nC*self.survey.nSrc)
+        v = np.random.rand(self.mesh.nC)
+        w = np.random.rand(self.survey.dobs.shape[0])
+        wtJv = w.dot(self.p.Jvec(self.m0, v))
+        vtJtw = v.dot(self.p.Jtvec(self.m0, w))
+        passed = np.abs(wtJv - vtJtw) < 1e-10
+        print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
+        self.assertTrue(passed)
+
+    def test_dataObj(self):
+        derChk = lambda m: [self.dmis.eval(m), self.dmis.evalDeriv(m)]
+        passed = Tests.checkDerivative(derChk, self.m0, plotIt=False, num=3)
+        self.assertTrue(passed)
+
+class DCProblemTestsN(unittest.TestCase):
+
+    def setUp(self):
+
+        cs = 12.5
+        hx = [(cs,7, -1.3),(cs,61),(cs,7, 1.3)]
+        hy = [(cs,7, -1.3),(cs,20)]
+        mesh = Mesh.TensorMesh([hx, hy],x0="CN")
+        x = np.linspace(-135, 250., 20)
+        M = Utils.ndgrid(x-12.5, np.r_[0.])
+        N = Utils.ndgrid(x+12.5, np.r_[0.])
+        A0loc = np.r_[-150, 0.]
+        A1loc = np.r_[-130, 0.]
+        rxloc = [np.c_[M, np.zeros(20)], np.c_[N, np.zeros(20)]]
+        rx = DC.Rx.Dipole_ky(M, N)
+        src0 = DC.Src.Pole([rx], A0loc)
+        src1 = DC.Src.Pole([rx], A1loc)
+        survey = DC.Survey_ky([src0, src1])
+        problem = DC.Problem2D_N(mesh, mapping=[('rho', Maps.IdentityMap(mesh))])
+        problem.pair(survey)
+
+        mSynth = np.ones(mesh.nC)*1.
+        survey.makeSyntheticData(mSynth)
+
+        # Now set up the problem to do some minimization
+        dmis = DataMisfit.l2_DataMisfit(survey)
+        reg = Regularization.Tikhonov(mesh)
+        opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
+        invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e0)
+        inv = Inversion.BaseInversion(invProb)
+
+        self.inv = inv
+        self.reg = reg
+        self.p =     problem
+        self.mesh = mesh
+        self.m0 = mSynth
+        self.survey = survey
+        self.dmis = dmis
+
+    def test_misfit(self):
+        derChk = lambda m: [self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx)]
+        passed = Tests.checkDerivative(derChk, self.m0, plotIt=False, num=3)
+        self.assertTrue(passed)
+
+    def test_adjoint(self):
+        # Adjoint Test
+        u = np.random.rand(self.mesh.nC*self.survey.nSrc)
+        v = np.random.rand(self.mesh.nC)
+        w = np.random.rand(self.survey.dobs.shape[0])
+        wtJv = w.dot(self.p.Jvec(self.m0, v))
+        vtJtw = v.dot(self.p.Jtvec(self.m0, w))
+        passed = np.abs(wtJv - vtJtw) < 1e-8
+        print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
+        self.assertTrue(passed)
+
+    def test_dataObj(self):
+        derChk = lambda m: [self.dmis.eval(m), self.dmis.evalDeriv(m)]
+        passed = Tests.checkDerivative(derChk, self.m0, plotIt=False, num=3)
+        self.assertTrue(passed)
+
+if __name__ == '__main__':
+    unittest.main()

tests/em/static/test_DC_analytic.py

@@ -0,0 +1,71 @@
+import unittest
+from SimPEG import Mesh, Utils, EM, Maps, np
+import SimPEG.EM.Static.DC as DC
+
+class DCProblemAnalyticTests(unittest.TestCase):
+
+    def setUp(self):
+
+        cs = 25.
+        hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+        hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
+        hz = [(cs,7, -1.3),(cs,20)]
+        mesh = Mesh.TensorMesh([hx, hy, hz],x0="CCN")
+        sigma = np.ones(mesh.nC)*1e-2
+
+        x = mesh.vectorCCx[(mesh.vectorCCx>-155.)&(mesh.vectorCCx<155.)]
+        y = mesh.vectorCCx[(mesh.vectorCCy>-155.)&(mesh.vectorCCy<155.)]
+        Aloc = np.r_[-200., 0., 0.]
+        Bloc = np.r_[200., 0., 0.]
+        M = Utils.ndgrid(x-25.,y, np.r_[0.])
+        N = Utils.ndgrid(x+25.,y, np.r_[0.])
+        phiA = EM.Analytics.DCAnalyticHalf(Aloc, [M,N], 1e-2, earth_type="halfspace")
+        phiB = EM.Analytics.DCAnalyticHalf(Bloc, [M,N], 1e-2, earth_type="halfspace")
+        data_anal = phiA-phiB
+
+        rx = DC.Rx.Dipole(M, N)
+        src = DC.Src.Dipole([rx], Aloc, Bloc)
+        survey = DC.Survey([src])
+
+        self.survey = survey
+        self.mesh = mesh
+        self.sigma = sigma
+        self.data_anal = data_anal
+
+        try:
+            from pymatsolver import MumpsSolver
+            self.Solver = MumpsSolver
+        except ImportError, e:
+            self.Solver = SolverLU
+
+    def test_Problem3D_N(self):
+        problem = DC.Problem3D_N(self.mesh)
+        problem.Solver = self.Solver
+        problem.pair(self.survey)
+        data = self.survey.dpred(self.sigma)
+        err= np.linalg.norm(data-self.data_anal)/np.linalg.norm(self.data_anal)
+        if err < 0.2:
+            passed = True
+            print ">> DC analytic test for Problem3D_N is passed"
+        else:
+            passed = False
+            print ">> DC analytic test for Problem3D_N is failed"
+        self.assertTrue(passed)
+
+    def test_Problem3D_CC(self):
+        problem = DC.Problem3D_CC(self.mesh)
+        problem.Solver = self.Solver
+        problem.pair(self.survey)
+        data = self.survey.dpred(self.sigma)
+        err= np.linalg.norm(data-self.data_anal)/np.linalg.norm(self.data_anal)
+        if err < 0.2:
+            passed = True
+            print ">> DC analytic test for Problem3D_CC is passed"
+        else:
+            passed = False
+            print ">> DC analytic test for Problem3D_CC is failed"
+        self.assertTrue(passed)
+
+if __name__ == '__main__':
+    unittest.main()
+