try using list for Jv instead of datapair - has been seen to cause memory leaks

- allow vector containing values in the inactive set to be passed (not just nC in length)

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 + (Tx[0][2] + Tx[1][2])/2 ])
 
-    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')
 
+    # 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))
+    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)
 
-    #ax.set_xticklabels([])
-    #ax.set_yticklabels([])
+    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

@@ -146,10 +146,15 @@ class BetaSchedule(InversionDirective):
 
 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):
@@ -222,7 +227,7 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
             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:
+        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:
@@ -237,41 +242,6 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
         # 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)
 
-#==============================================================================
-# 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):
 
@@ -293,6 +263,7 @@ class Update_IRLS(InversionDirective):
     phi_m_last = None
     phi_d_last = None
 
+
     def initialize(self):
 
         # Scale the regularization for changes in norm
@@ -310,7 +281,7 @@ class Update_IRLS(InversionDirective):
             self.phi_d_last = self.invProb.phi_d
 
     def endIter(self):
-        # Cool the threshold parameter
+        # Cool the threshold parameter if required
         if getattr(self, 'factor', None) is not None:
             eps = self.reg.eps / self.factor
 
@@ -325,28 +296,44 @@ class Update_IRLS(InversionDirective):
          # Update the model used for the IRLS weights
         self.reg.curModel = self.invProb.curModel
 
-        # Temporarely set gamma to 1.
+        # Temporarely set gamma to 1. to get raw phi_m
         self.reg.gamma = 1.
 
-        # Compute change in model objective function and update scaling
+        # 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
 
-        self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
+        # Set the weighting matrix to None so that it is recomputed next time
+        # it is called in the inversion
+        self.reg._W = None
 
 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 self.onlyOnStart==True:
+            return
 
         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(diagA**-1.)
+            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
-            print 'Updated pre-cond'
+
 
 class Update_Wj(InversionDirective):
     """
@@ -373,3 +360,19 @@ class Update_Wj(InversionDirective):
             JtJdiag = JtJdiag / max(JtJdiag)
 
             self.reg.wght = JtJdiag
+
+class Scale_Beta(InversionDirective):
+    """
+        Instead of a linear cooling schedule, beta is allowed to change based
+        on the ratio between the target misfit and the current data misfit. The
+        update is done only if the misfit is outside some threshold bounds.
+    """
+    tol = 0.05
+
+    def endIter(self):
+
+        # Check if misfit is within the tolerance, otherwise adjust beta
+        val = self.invProb.phi_d / (self.survey.nD*0.5)
+
+        if np.abs(1.-val) > self.tol:
+            self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d

SimPEG/EM/Analytics/DC.py

@@ -2,20 +2,20 @@ import numpy as np
 from scipy.constants import mu_0, pi
 from scipy import special
 
-def DCAnalyticHalf(txloc, rxlocs, sigma, flag="wholespace"):
+def DCAnalyticHalf(txloc, rxlocs, sigma, earth_type="wholespace"):
     """
         Analytic solution for electric potential from a postive pole
 
-        Input variables:
-
-            txloc =  a xyz location of A (+) electrode (np.r_[xa, ya, za])
+        :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])
+            M: xyz locations of M (+) electrode (np.c_[xmlocs, ymlocs, zmlocs])
+            N: xyz locations of N (-) electrode (np.c_[xnlocs, ynlocs, znlocs])
 
-            sigma = conductivity (either float or complex)
-            flag = "wholsespace" or "halfspace"
+        :param float or complex sigma: values of conductivity
+        :param string earth_type: values of conductivity ("wholsespace" or "halfspace")
 
     """
     M = rxlocs[0]
@@ -28,7 +28,7 @@ def DCAnalyticHalf(txloc, rxlocs, sigma, flag="wholespace"):
     phiN = 1./(4*np.pi*rN*sigma)
     phi = phiM - phiN
 
-    if flag == "halfspace":
+    if earth_type == "halfspace":
         phi *= 2
 
     return phi
@@ -37,27 +37,26 @@ deg2rad  = lambda deg: deg/180.*np.pi
 rad2deg  = lambda rad: rad*180./np.pi
 
 def DCAnalyticSphere(txloc, rxloc, xc, radius, sigma, sigma1, \
-                 flag = "sec", order=12, halfspace=False):
+                 field_type = "secondary", order=12, halfspace=False):
 # def DCSpherePointCurrent(txloc, rxloc, xc, radius, rho, rho1, \
-#                  flag = "sec", order=12):
+#                  field_type = "secondary", order=12):
     """
 
         Parameters:
 
-            txloc (array) : current electrode location (x,y,z)
-            xc (float)    : x center of depressed sphere
-            rxloc (array) : electrode locations
-                              (Nx3 array, # of electrodes)
-            radius (float): radius of the sphere (m)
-            rho (float)   : resistivity of the background (ohm-m)
-            rho1 (float)  : resistivity of the sphere
-            flag (string) : "sec", "total", "prim"
-                              (default="sec")
-                              "sec": secondary potential only due to sphere
-                              "prim": primary potential from the point source
-                              "total": "sec"+"prim"
-            order (float) : maximum order of Legendre polynomial
-                              (default=12)
+            :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
@@ -86,7 +85,7 @@ def DCAnalyticSphere(txloc, rxloc, xc, radius, sigma, sigma1, \
     # primary potential in a whole space
     prim = rho*1./(4*np.pi*R)
 
-    if flag =="prim":
+    if field_type =="primary":
         return prim
 
     sphind = r < radius
@@ -105,9 +104,9 @@ def DCAnalyticSphere(txloc, rxloc, xc, radius, sigma, sigma1, \
     else:
         scale = 1
 
-    if flag == "sec":
+    if field_type == "secondary":
         return scale*(out-prim)
-    elif flag == "total":
+    elif field_type == "total":
         return scale*out
 
 def AnBnfun(n, radius, x0, rho, rho1, I=1.):

SimPEG/EM/Base.py

@@ -62,6 +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):
         """
@@ -71,13 +80,21 @@ 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
     def MfMui(self):
@@ -152,9 +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):
@@ -170,8 +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):
@@ -191,9 +205,7 @@ class BaseEMProblem(Problem.BaseProblem):
 
         dMfRhoI_dI = -self.MfRhoI**2
         dMf_drho = self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u)
-        return dMfRhoI_dI * ( dMf_drho * (-Utils.sdiag(self.curModel.rho**2) * self.curModel.sigmaDeriv) )
-
-        # return self.mesh.getFaceInnerProductDeriv(self.curModel.rho, invMat=True)(u) * self.curModel.rhoDeriv
+        return dMfRhoI_dI * ( dMf_drho * self.curModel.rhoDeriv )
 
 class BaseEMSurvey(Survey.BaseSurvey):
 

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
@@ -257,7 +257,7 @@ class Fields_e(Fields):
         """
 
         # assuming primary does not depend on the model
-        return Zero()
+        return src.ePrimaryDeriv(self.prob, v, adjoint) #Zero()
 
     def _bPrimary(self, eSolution, srcList):
         """
@@ -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
@@ -600,8 +600,8 @@ class Fields_b(Fields):
 
 
         if adjoint:
-            return self._MeSigmaIDeriv(w).T * v - self._MeSigmaI.T * s_eDeriv
-        return  self._MeSigmaIDeriv(w) * v - self._MeSigmaI * s_eDeriv
+            return self._MeSigmaIDeriv(w).T * v - self._MeSigmaI.T * s_eDeriv + src.ePrimaryDeriv(self.prob, v, adjoint)
+        return  self._MeSigmaIDeriv(w) * v - self._MeSigmaI * s_eDeriv + src.ePrimaryDeriv(self.prob, v, adjoint)
 
     def _j(self, bSolution, srcList):
         """
@@ -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}\\\)
     """
@@ -74,7 +74,8 @@ class BaseFDEMProblem(BaseEMProblem):
 
         self.curModel = m
 
-        Jv = self.dataPair(self.survey)
+        # Jv = self.dataPair(self.survey)
+        Jv = []
 
         for freq in self.survey.freqs:
             A = self.getA(freq)
@@ -87,11 +88,11 @@ 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)
+                    Jv.append(rx.evalDeriv(src, self.mesh, f, df_dm_v))
             Ainv.clean()
-        return Utils.mkvc(Jv)
+        return np.hstack(Jv)
 
     def Jtvec(self, m, v, f=None):
         """
@@ -125,7 +126,7 @@ 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 +138,9 @@ 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')
@@ -167,7 +167,6 @@ 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
 
@@ -178,7 +177,7 @@ class BaseFDEMProblem(BaseEMProblem):
 ################################ E-B Formulation #########################################
 ##########################################################################################
 
-class Problem_e(BaseFDEMProblem):
+class Problem3D_e(BaseFDEMProblem):
     """
     By eliminating the magnetic flux density using
 
@@ -200,7 +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)
@@ -289,7 +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
 
@@ -311,7 +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)
@@ -437,7 +436,7 @@ class Problem_b(BaseFDEMProblem):
 ##########################################################################################
 
 
-class Problem_j(BaseFDEMProblem):
+class Problem3D_j(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{h}\\\) using
 
@@ -459,7 +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)
@@ -578,7 +577,7 @@ class Problem_j(BaseFDEMProblem):
 
 
 
-class Problem_h(BaseFDEMProblem):
+class Problem3D_h(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{j}\\\) using
 
@@ -597,7 +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

@@ -9,8 +9,14 @@ 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):
         """
@@ -50,6 +56,20 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary magnetic flux density
         """
+        if self._bPrimary is None:
+            return Zero()
+        return self._bPrimary
+
+    def bPrimaryDeriv(self, prob, v, adjoint=False):
+        """
+        Derivative of the primary magnetic flux density
+
+        :param Problem prob: FDEM Problem
+        :param numpy.ndarray v: vector
+        :param bool adjoint: adjoint?
+        :rtype: numpy.ndarray
+        :return: primary magnetic flux density
+        """
         return Zero()
 
     def hPrimary(self, prob):
@@ -60,6 +80,20 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
+        if self._hPrimary is None:
+            return Zero()
+        return self._hPrimary
+
+    def hPrimaryDeriv(self, prob, v, adjoint=False):
+        """
+        Derivative of the primary magnetic field
+
+        :param Problem prob: FDEM Problem
+        :param numpy.ndarray v: vector
+        :param bool adjoint: adjoint?
+        :rtype: numpy.ndarray
+        :return: primary magnetic flux density
+        """
         return Zero()
 
     def ePrimary(self, prob):
@@ -70,6 +104,20 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary electric field
         """
+        if self._ePrimary is None:
+            return Zero()
+        return self._ePrimary
+
+    def ePrimaryDeriv(self, prob, v, adjoint=False):
+        """
+        Derivative of the primary electric field
+
+        :param Problem prob: FDEM Problem
+        :param numpy.ndarray v: vector
+        :param bool adjoint: adjoint?
+        :rtype: numpy.ndarray
+        :return: primary magnetic flux density
+        """
         return Zero()
 
     def jPrimary(self, prob):
@@ -80,6 +128,20 @@ class BaseSrc(Survey.BaseSrc):
         :rtype: numpy.ndarray
         :return: primary current density
         """
+        if self._jPrimary is None:
+            return Zero()
+        return self._jPrimary
+
+    def jPrimaryDeriv(self, prob, v, adjoint=False):
+        """
+        Derivative of the primary current density
+
+        :param Problem prob: FDEM Problem
+        :param numpy.ndarray v: vector
+        :param bool adjoint: adjoint?
+        :rtype: numpy.ndarray
+        :return: primary magnetic flux density
+        """
         return Zero()
 
     def s_m(self, prob):
@@ -135,15 +197,14 @@ 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):
         """
@@ -165,15 +226,14 @@ 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):
         """
@@ -196,14 +256,13 @@ 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):
         """
@@ -277,14 +336,13 @@ 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):
@@ -542,10 +600,10 @@ 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
+            srcfct = MagneticLoopVectorPotential
             ax = srcfct(self.loc, gridX, 'x', self.radius, mu=self.mu)
             ay = srcfct(self.loc, gridY, 'y', self.radius, mu=self.mu)
             az = srcfct(self.loc, gridZ, 'z', self.radius, mu=self.mu)
@@ -604,5 +662,155 @@ class CircularLoop(BaseSrc):
             return -C.T * (MMui_s * self.bPrimary(prob))
 
 
+class PrimSecSigma(BaseSrc):
+
+    def __init__(self, rxList, freq, sigBack, ePrimary, **kwargs):
+        self.sigBack = sigBack
+
+        BaseSrc.__init__(self, rxList, freq=freq, _ePrimary=ePrimary, **kwargs)
+
+    def s_e(self, prob):
+        return (prob.MeSigma -  prob.mesh.getEdgeInnerProduct(self.sigBack)) * self.ePrimary(prob)
+
+    def s_eDeriv(self, prob, v, adjoint=False):
+        if adjoint:
+            return prob.MeSigmaDeriv(self.ePrimary(prob)).T * v
+        return prob.MeSigmaDeriv(self.ePrimary(prob)) * v
+
+
+class PrimSecMappedSigma(BaseSrc):
+
+    """
+    Primary-Secondary Source in which a mapping is provided to put the current model
+    onto the primary mesh. This is solved on every model update.
+
+    There are a lot of layers to the derivatives here!
+
+    **Required**
+    :param list rxList: Receiver List
+    :param float freq: frequency
+    :param ProblemFDEM primaryProblem: FDEM primary problem
+    :param SurveyFDEM primarySurvey: FDEM primary survey
+
+    **Optional**
+    :param Mapping map2meshSecondary: mapping current model to act as primary model on the secondary mesh
+
+    """
+
+    def __init__(self, rxList, freq, primaryProblem, primarySurvey, map2meshSecondary = None ,**kwargs):
+
+        self.primaryProblem = primaryProblem
+        self.primarySurvey = primarySurvey
+
+        if self.primaryProblem.ispaired is False:
+            self.primaryProblem.pair(self.primarySurvey)
+
+        self.map2meshSecondary = map2meshSecondary
+
+        BaseSrc.__init__(self, rxList, freq=freq, **kwargs)
+
+    def _ProjPrimary(self, prob):
+        # if getattr(self, '__ProjPrimary', None) is None:
+        return self.primaryProblem.mesh.getInterpolationMatCartMesh(prob.mesh, locType='F', locTypeTo='E')
+        # return self.__ProjPrimary
+
+
+    def _primaryFields(self, prob, fieldType=None):
+
+        # TODO: cache and check if prob.curModel has changed
+        fields = self.primaryProblem.fields(prob.curModel.sigmaModel)
+
+        if fieldType is not None:
+            return fields[:,fieldType]
+        return fields
+
+    def _primaryFieldsDeriv(self, prob, v, adjoint=False, f=None):
+        if adjoint:
+            raise NotImplementedError
+
+        # TODO: this should not be hard-coded for j
+        # jp = self._primaryFields(prob)[:,'j']
+
+        # TODO: pull apart Jvec so that don't have to copy paste this code in
+        # A = self.primaryProblem.getA(self.freq)
+        # Ainv = self.primaryProblem.Solver(A, **self.primaryProblem.solverOpts) # create the concept of Ainv (actually a solve)
+
+        if f is None:
+           f = self._primaryFields(prob.curModel.sigmaModel)
+
+        freq = self.freq
+
+        A = self.primaryProblem.getA(freq)
+        Ainv = self.primaryProblem.Solver(A, **self.primaryProblem.solverOpts) # create the concept of Ainv (actually a solve)
+
+        src = self.primarySurvey.srcList[0]
+        # for src in self.survey.getSrcByFreq(freq):
+        u_src = Utils.mkvc(f[src, self.primaryProblem._solutionType])
+        dA_dm_v = self.primaryProblem.getADeriv(freq, u_src, v)
+        dRHS_dm_v = self.primaryProblem.getRHSDeriv(freq, src, v)
+        du_dm_v = Ainv * ( - dA_dm_v + dRHS_dm_v )
+
+        df_dmFun = getattr(f, '_{0}Deriv'.format('j'), 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()
+
+        return df_dm_v
+
+        # return self.primaryProblem.Jvec(prob.curModel, v, f=f)
+
+    def ePrimary(self, prob, f=None):
+        if f is None:
+            f = self._primaryFields(prob)
+
+        ep = self._ProjPrimary(prob) * (
+                self.primaryProblem.MfI * (
+                self.primaryProblem.MfRho * f[:,'j'])
+                )
+
+        return Utils.mkvc(ep)
+
+    def ePrimaryDeriv(self, prob, v, adjoint=False, f=None):
+
+        if adjoint is True:
+            raise NotImplementedError
+
+        if f is None:
+            f = self._primaryFields(prob)
+
+        epDeriv = self._ProjPrimary(prob) * (
+                    self.primaryProblem.MfI * (
+                    (self.primaryProblem.MfRhoDeriv(f[:,'j']) * v)
+                    +
+                    (self.primaryProblem.MfRho * self._primaryFieldsDeriv(prob, v, f=f))
+                    )
+                    )
+
+        return Utils.mkvc(epDeriv)
+
+
+    def s_e(self, prob):
+        sigmaPrimary = self.map2meshSecondary * prob.curModel.sigmaModel
+
+        return Utils.mkvc((prob.MeSigma -  prob.mesh.getEdgeInnerProduct(sigmaPrimary)) * self.ePrimary(prob))
+
+
+    def s_eDeriv(self, prob, v, adjoint=False):
+        if adjoint:
+            raise NotImplementedError
+            return prob.MeSigmaDeriv(self.ePrimary(prob)).T * v
+
+        sigmaPrimary = self.map2meshSecondary * prob.curModel.sigmaModel
+        sigmaPrimaryDeriv = self.map2meshSecondary.deriv(prob.curModel.sigmaModel)
+
+        f = self._primaryFields(prob)
+        ePrimary = self.ePrimary(prob,f=f)
+
+        return (prob.MeSigmaDeriv(ePrimary) * v
+                - prob.mesh.getEdgeInnerProductDeriv(sigmaPrimary)(ePrimary) * sigmaPrimaryDeriv * v
+                + (prob.MeSigma -  prob.mesh.getEdgeInnerProduct(sigmaPrimary)) * self.ePrimaryDeriv(prob, v, None, f=f)
+                )
+
+
 
 

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, f):
-        """Grid Location projection (e.g. Ex Fy ...)"""
-        return f._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/ProblemDC.py

@@ -122,13 +122,12 @@ class Problem3D_CC(BaseDCProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        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
 
@@ -144,13 +143,8 @@ class Problem3D_CC(BaseDCProblem):
         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):
@@ -162,10 +156,6 @@ class Problem3D_CC(BaseDCProblem):
 
         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):
@@ -255,11 +245,10 @@ class Problem3D_N(BaseDCProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        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

SimPEG/EM/Static/DC/ProblemDC_2D.py

@@ -161,14 +161,13 @@ class Problem2D_CC(BaseDCProblem_2D):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        A = D MfRhoI G
 
         """
 
         D = self.Div
         G = self.Grad
         vol = self.mesh.vol
-        # TODO: this won't work for full anisotropy
         MfRhoI = self.MfRhoI
         # Get resistivity rho
         rho = self.curModel.rho
@@ -304,11 +303,10 @@ class Problem2D_N(BaseDCProblem_2D):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        A = D MfRhoI G
 
         """
 
-        # TODO: this won't work for full anisotropy
         MeSigma = self.MeSigma
         MnSigma = self.MnSigma
         Grad = self.mesh.nodalGrad

SimPEG/EM/Static/DC/RxDC.py

@@ -43,7 +43,14 @@ class BaseRx(SimPEG.Survey.BaseRx):
         elif adjoint:
             return P.T*v
 
-# DC.Rx.Dipole(locs)
+# DC.Rx.Pole(locs)
+class Pole(BaseRx):
+
+    def __init__(self, locs, rxType = 'phi', **kwargs):
+        BaseRx.__init__(self, locs, rxType)
+
+
+# DC.Rx.Dipole(locsM, locsN)
 class Dipole(BaseRx):
 
     def __init__(self, locsM, locsN, rxType = 'phi', **kwargs):

SimPEG/EM/Static/IP/ProblemIP.py

@@ -180,13 +180,12 @@ class Problem3D_CC(BaseIPProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        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
 
@@ -313,11 +312,10 @@ class Problem3D_N(BaseIPProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        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

SimPEG/EM/Static/SIP/ProblemSIP.py

@@ -251,7 +251,7 @@ class Problem3D_CC(BaseSIPProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        A = D MfRhoI G
 
         """
 
@@ -384,7 +384,7 @@ class Problem3D_N(BaseSIPProblem):
 
         Make the A matrix for the cell centered DC resistivity problem
 
-        A = D MfRhoI D^\\top V
+        A = G.T MeSigma G
 
         """
 

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/Examples/DC_Forward_PseudoSection.py

@@ -2,7 +2,7 @@ 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
         =====================
@@ -15,14 +15,14 @@ def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', dtype='appc', p
         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)
+        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

@@ -86,12 +86,12 @@ def run(N=200, plotIt=True):
     #reg.recModel = mrec
     reg.wght = np.ones(mesh.nC)
     reg.mref = np.zeros(mesh.nC)
-    reg.eps_p = 2e-3
-    reg.eps_q = 2e-3
+    reg.eps_p = 5e-2
+    reg.eps_q = 1e-2
     reg.norms   = [0., 0., 2., 2.]
     reg.wght = wr
 
-    opt = Optimization.ProjectedGNCG(maxIter=5 ,lower=-2.,upper=2., maxIterCG= 100, tolCG = 1e-3)
+    opt = Optimization.ProjectedGNCG(maxIter=10 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 20, tolCG = 1e-3)
     invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta*2.)
     beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)
     #betaest = Directives.BetaEstimate_ByEig()

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

@@ -20,8 +20,9 @@ import Mesh_QuadTree_HangingNodes
 import Mesh_Tensor_Creation
 import MT_1D_ForwardAndInversion
 import MT_3D_Foward
+import Utils_surface2ind_topo
 
-__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_IRLS", "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__ = ["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_IRLS", "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", "Utils_surface2ind_topo"]
 
 ##### AUTOIMPORTS #####
 

SimPEG/MT/BaseMT.py

@@ -1,5 +1,5 @@
 from SimPEG import SolverLU as SimpegSolver, PropMaps, Utils, mkvc, sp, np
-from SimPEG.EM.FDEM.FDEM import BaseFDEMProblem
+from SimPEG.EM.FDEM.ProblemFDEM import BaseFDEMProblem
 from SurveyMT import Survey, Data
 from FieldsMT import BaseMTFields
 

SimPEG/MT/Utils/ediFilesUtils.py

@@ -7,17 +7,16 @@ from SimPEG.MT.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/Maps.py

@@ -1,3 +1,4 @@
+from __future__ import division
 import Utils, numpy as np, scipy.sparse as sp
 from scipy.sparse.linalg import LinearOperator
 from Tests import checkDerivative
@@ -5,6 +6,7 @@ from PropMaps import PropMap, Property
 from numpy.polynomial import polynomial
 from scipy.interpolate import UnivariateSpline
 import warnings
+from SimPEG.Utils import Zero
 
 class IdentityMap(object):
     """
@@ -17,7 +19,7 @@ class IdentityMap(object):
         Utils.setKwargs(self, **kwargs)
 
         if nP is not None:
-            assert type(nP) in [int, long], ' Number of parameters must be an integer.'
+            assert type(nP) in [int, long, np.int64], ' Number of parameters must be an integer.'
 
         self.mesh = mesh
         self._nP  = nP
@@ -129,7 +131,15 @@ class IdentityMap(object):
 
 
 class ComboMap(IdentityMap):
-    """Combination of various maps."""
+    """
+        Combination of various maps.
+
+        The ComboMap holds the information for multiplying and combining
+        maps. It also uses the chain rule to create the derivative.
+        Remember, any time that you make your own combination of mappings
+        be sure to test that the derivative is correct.
+
+    """
 
     def __init__(self, maps, **kwargs):
         IdentityMap.__init__(self, None, **kwargs)
@@ -178,6 +188,12 @@ class ComboMap(IdentityMap):
 
 class ExpMap(IdentityMap):
     """
+        Electrical conductivity varies over many orders of magnitude, so it is a common
+        technique when solving the inverse problem to parameterize and optimize in terms
+        of log conductivity. This makes sense not only because it ensures all conductivities
+        will be positive, but because this is fundamentally the space where conductivity
+        lives (i.e. it varies logarithmically).
+
         Changes the model into the physical property.
 
         A common example of this is to invert for electrical conductivity
@@ -449,6 +465,32 @@ class Mesh2Mesh(IdentityMap):
     """
         Takes a model on one mesh are translates it to another mesh.
 
+        .. plot::
+
+            from SimPEG import *
+            import matplotlib.pyplot as plt
+            M = Mesh.TensorMesh([100,100])
+            h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
+            h1 = h1/h1.sum()
+            M2 = Mesh.TensorMesh([h1,h1])
+            V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
+            v = Utils.mkvc(V)
+            modh = Maps.Mesh2Mesh([M,M2])
+            modH = Maps.Mesh2Mesh([M2,M])
+            H = modH * v
+            h = modh * H
+            ax = plt.subplot(131)
+            M.plotImage(v, ax=ax)
+            ax.set_title('Fine Mesh (Original)')
+            ax = plt.subplot(132)
+            M2.plotImage(H,clim=[0,1],ax=ax)
+            ax.set_title('Course Mesh')
+            ax = plt.subplot(133)
+            M.plotImage(h,clim=[0,1],ax=ax)
+            ax.set_title('Fine Mesh (Interpolated)')
+            plt.show()
+
+
     """
 
     def __init__(self, meshes, **kwargs):
@@ -501,11 +543,19 @@ class InjectActiveCells(IdentityMap):
         self.indInactive = np.logical_not(indActive)
         if Utils.isScalar(valInactive):
             self.valInactive = np.ones(self.nC)*float(valInactive)
+            self.valInactive[self.indActive] = 0.
         else:
-            self.valInactive = valInactive.copy()
-        self.valInactive[self.indActive] = 0
+            if len(valInactive) == sum(self.indInactive):
+                self.valInactive = np.zeros(nC)
+                self.valInactive[self.indInactive] = valInactive.copy()
+            else:
+                assert len(self.valInactive) == self.nC, 'valInactive must be the size of nC or nInactive'
+                self.valInactive = valInactive.copy()
+                if any(self.valInactive[self.indActive] != 0.):
+                    warnings.warn('the inactive has non-zero values in the active set.')
 
         inds = np.nonzero(self.indActive)[0]
+        # inds[self.indActive]
         self.P = sp.csr_matrix((np.ones(inds.size),(inds, range(inds.size))), shape=(self.nC, self.nP))
 
     @property
@@ -533,83 +583,6 @@ 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):
     """
@@ -651,6 +624,37 @@ class Weighting(IdentityMap):
     def deriv(self, m):
         return self.P
 
+class Projection(IdentityMap):
+    """
+        A map to rearrange parameters
+
+    """
+
+
+    def __init__(self, indTo, indFrom, shape, mesh=None, **kwargs):
+
+        assert len(indTo) == len(indFrom)
+
+        self.P = sp.csr_matrix((np.ones(len(indTo)), (indTo, indFrom)), shape=shape)
+        self._shape = shape
+
+        super(Projection, self).__init__(mesh, **kwargs)
+
+    @property
+    def shape(self):
+        return self._shape
+
+    @property
+    def nP(self):
+        """Number of parameters in the model."""
+        return self.shape[1]
+
+    def _transform(self, m):
+        return self.P*m
+
+    def deriv(self, m):
+        return self.P
+
 
 class ComplexMap(IdentityMap):
     """ComplexMap
@@ -693,13 +697,13 @@ class CircleMap(IdentityMap):
 
         Parameterize the model space using a circle in a wholespace.
 
-        ..math::
+        .. math::
 
             \sigma(m) = \sigma_1 + (\sigma_2 - \sigma_1)\left(\\arctan\left(100*\sqrt{(\\vec{x}-x_0)^2 + (\\vec{y}-y_0)}-r\\right) \pi^{-1} + 0.5\\right)
 
         Define the model as:
 
-        ..math::
+        .. math::
 
             m = [\sigma_1, \sigma_2, x_0, y_0, r]
 
@@ -1052,7 +1056,697 @@ class SplineMap(IdentityMap):
         return sp.csr_matrix(np.c_[g1,g2,g3])
 
 
+class ParametrizedLayer(IdentityMap):
+    """
+        Parametrized Layer Space
+
+        m = [val_background, val_layer, layer_center, layer_thickness]
+
+
+        .. plot::
+            :include-source:
+
+            from SimPEG import Mesh, Maps, np
+            import matplotlib.pyplot as plt
+
+            fig, ax = plt.subplots(1,1,figsize=(2,3))
+
+            mesh = Mesh.TensorMesh([50,50],x0='CC')
+            mapping = Maps.ParametrizedLayer(mesh)
+            m = np.hstack(np.r_[1., 2., -0.1, 0.2])
+            rho = mapping._transform(m)
+            mesh.plotImage(rho, ax=ax)
+
+        **Required**
+
+        :param Mesh mesh: SimPEG Mesh, 2D or 3D
+
+        **Optional**
+
+        :param float slopeFact: arctan slope factor - divided by the minimum h spacing to give the slope of the arctan functions
+        :param float slope: slope of the arctan function
+        :param numpy.ndarray indActive: bool vector with
+
+    """
+
+    slopeFact = 1e2 # will be scaled by the mesh.
+    slope = None
+    indActive = None
+
+    def __init__(self, mesh, **kwargs):
+
+        super(ParametrizedLayer, self).__init__(mesh, **kwargs)
+
+
+        if self.slope is None:
+            self.slope = self.slopeFact / np.hstack(self.mesh.h).min()
+
+        self.x = [self.mesh.gridCC[:,0] if self.indActive is None else self.mesh.gridCC[self.indActive,0]][0]
+
+        if self.mesh.dim > 1:
+            self.y = [self.mesh.gridCC[:,1] if self.indActive is None else self.mesh.gridCC[self.indActive,1]][0]
+
+        if self.mesh.dim > 2:
+            self.z = [self.mesh.gridCC[:,2] if self.indActive is None else self.mesh.gridCC[self.indActive,2]][0]
+
+    @property
+    def nP(self):
+        return 4
+
+    @property
+    def shape(self):
+        if self.indActive is not None:
+            return (sum(self.indActive), self.nP)
+        return (self.mesh.nC, self.nP)
+
+    def mDict(self, m):
+        return {
+                'val_background': m[0],
+                'val_layer': m[1],
+                'layer_center': m[2],
+                'layer_thickness': m[3],
+                }
+
+    def _atanfct(self, xyz, xyzi, slope):
+        return np.arctan(slope * (xyz - xyzi))/np.pi + 0.5
+
+    def _atanfctDeriv(self, xyz, xyzi, slope):
+        # d/dx(atan(x)) = 1/(1+x**2)
+        x = slope * (xyz - xyzi)
+        dx = - slope
+        return (1./(1 + x**2))/np.pi * dx
+
+    def _atanLayer(self, mDict):
+        if self.mesh.dim == 2:
+            z = self.y
+        elif self.mesh.dim == 3:
+            z = self.z
+
+        layer_bottom = mDict['layer_center'] - mDict['layer_thickness'] / 2.
+        layer_top = mDict['layer_center'] + mDict['layer_thickness'] / 2.
+        return self._atanfct(z, layer_bottom, self.slope)*self._atanfct(z, layer_top, -self.slope)
+
+    def _atanLayerDeriv_layer_center(self, mDict):
+        if self.mesh.dim == 2:
+            z = self.y
+        elif self.mesh.dim == 3:
+            z = self.z
+
+        layer_bottom = mDict['layer_center'] - mDict['layer_thickness'] / 2.
+        layer_top = mDict['layer_center'] + mDict['layer_thickness'] / 2.
+
+        return (self._atanfctDeriv(z, layer_bottom, self.slope)*self._atanfct(z, layer_top, -self.slope)
+                + self._atanfct(z, layer_bottom, self.slope)*self._atanfctDeriv(z, layer_top, -self.slope))
+
+    def _atanLayerDeriv_layer_thickness(self, mDict):
+        if self.mesh.dim == 2:
+            z = self.y
+        elif self.mesh.dim == 3:
+            z = self.z
+
+        layer_bottom = mDict['layer_center'] - mDict['layer_thickness'] / 2.
+        layer_top = mDict['layer_center'] + mDict['layer_thickness'] / 2.
+
+        return (-0.5*self._atanfctDeriv(z, layer_bottom, self.slope)*self._atanfct(z, layer_top, -self.slope)
+                + 0.5*self._atanfct(z, layer_bottom, self.slope)*self._atanfctDeriv(z, layer_top, -self.slope))
+
+    def layer_cont(self, mDict):
+        return mDict['val_background'] + (mDict['val_layer'] - mDict['val_background'])*self._atanLayer(mDict)
+
+    def _transform(self, m):
+        mDict = self.mDict(m)
+        return self.layer_cont(mDict)
+
+    def _deriv_val_background(self, mDict):
+        return np.ones_like(self.x) - self._atanLayer(mDict)
+
+    def _deriv_val_layer(self, mDict):
+        return self._atanLayer(mDict)
+
+    def _deriv_layer_center(self, mDict):
+        return (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_center(mDict)
+
+    def _deriv_layer_thickness(self, mDict):
+        return (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_thickness(mDict)
+
+    def deriv(self, m):
+
+        mDict = self.mDict(m)
+
+        return sp.csr_matrix(np.vstack([
+            self._deriv_val_background(mDict),
+            self._deriv_val_layer(mDict),
+            self._deriv_layer_center(mDict),
+            self._deriv_layer_thickness(mDict),
+            ]).T)
+
+
+class ParametrizedCasingAndLayer(ParametrizedLayer):
+    """
+        Parametrized layered space with casing.
+
+        m = [val_background, val_layer, val_casing, val_insideCasing, layer_center, layer_thickness, casing_radius, casing_thickness, casing_bottom, casing_top]
+    """
+
+    def __init__(self, mesh, **kwargs):
+
+        assert mesh._meshType == 'CYL', 'Parametrized Casing in a layer map only works for a cyl mesh.'
+
+        super(ParametrizedCasingAndLayer, self).__init__(mesh, **kwargs)
+
+
+    @property
+    def nP(self):
+        return 10
+
+    @property
+    def shape(self):
+        if self.indActive is not None:
+            return (sum(self.indActive), self.nP)
+        return (self.mesh.nC, self.nP)
+
+    def mDict(self, m):
+        #m = [val_background, val_layer, val_casing, val_insideCasing, layer_center, layer_thickness, casing_radius, casing_thickness, casing_bottom, casing_top]
+        return {
+                    'val_background': m[0],
+                    'val_layer': m[1],
+                    'val_casing': m[2],
+                    'val_insideCasing': m[3],
+                    'layer_center': m[4],
+                    'layer_thickness': m[5],
+                    'casing_radius': m[6],
+                    'casing_thickness': m[7],
+                    'casing_bottom': m[8],
+                    'casing_top': m[9]
+               }
+
+    def _atanCasingLength(self, mDict):
+        return (self._atanfct(self.z, mDict['casing_top'], -self.slope)
+                * self._atanfct(self.z, mDict['casing_bottom'], self.slope))
+
+    def _atanCasingLengthDeriv_casing_top(self, mDict):
+        return (self._atanfctDeriv(self.z, mDict['casing_top'], -self.slope)
+                * self._atanfct(self.z, mDict['casing_bottom'], self.slope))
+
+    def _atanCasingLengthDeriv_casing_bottom(self, mDict):
+        return (self._atanfct(self.z, mDict['casing_top'], -self.slope)
+                * self._atanfctDeriv(self.z, mDict['casing_bottom'], self.slope))
+
+    def _atanInsideCasing(self, mDict):
+        casing_a = mDict['casing_radius'] - 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict)
+                * self._atanfct(self.x, casing_a, -self.slope))
+
+    def _atanInsideCasingDeriv_casing_radius(self, mDict):
+        casing_a = mDict['casing_radius'] - 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict)
+                * self._atanfctDeriv(self.x, casing_a, -self.slope))
+
+    def _atanInsideCasingDeriv_casing_thickness(self, mDict):
+        casing_a = mDict['casing_radius'] - 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict)
+                * - 0.5*self._atanfctDeriv(self.x, casing_a, -self.slope))
+
+    def _atanInsideCasingDeriv_casing_top(self, mDict):
+        casing_a = mDict['casing_radius'] - 0.5*mDict['casing_thickness']
+        return (self._atanCasingLengthDeriv_casing_top(mDict)
+                * self._atanfct(self.x, casing_a, -self.slope))
+
+    def _atanInsideCasingDeriv_casing_bottom(self, mDict):
+        casing_a = mDict['casing_radius'] - 0.5*mDict['casing_thickness']
+        return (self._atanCasingLengthDeriv_casing_bottom(mDict)
+                * self._atanfct(self.x, casing_a, -self.slope))
+
+    def _atanCasing(self, mDict):
+        casing_a, casing_b = mDict['casing_radius'] - 0.5*mDict['casing_thickness'], mDict['casing_radius'] + 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict)
+                * self._atanfct(self.x, casing_a, self.slope)
+                * self._atanfct(self.x, casing_b, -self.slope))
+
+    def _atanCasingDeriv_casing_radius(self, mDict):
+        casing_a, casing_b = mDict['casing_radius'] - 0.5*mDict['casing_thickness'], mDict['casing_radius'] + 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict) * (
+                self._atanfctDeriv(self.x, casing_a, self.slope)
+                * self._atanfct(self.x, casing_b, -self.slope)
+                +
+                self._atanfct(self.x, casing_a, self.slope)
+                * self._atanfctDeriv(self.x, casing_b, -self.slope)
+                ))
+
+    def _atanCasingDeriv_casing_thickness(self, mDict):
+        casing_a, casing_b = mDict['casing_radius'] - 0.5*mDict['casing_thickness'], mDict['casing_radius'] + 0.5*mDict['casing_thickness']
+        return (self._atanCasingLength(mDict) * (
+                - 0.5*self._atanfctDeriv(self.x, casing_a, self.slope)
+                * 0.5*self._atanfct(self.x, casing_b, -self.slope)
+                +
+                - 0.5*self._atanfct(self.x, casing_a, self.slope)
+                * 0.5*self._atanfctDeriv(self.x, casing_b, -self.slope)
+                ))
+
+    def _atanCasingDeriv_casing_bottom(self, mDict):
+        casing_a, casing_b = mDict['casing_radius'] - 0.5*mDict['casing_thickness'], mDict['casing_radius'] + 0.5*mDict['casing_thickness']
+        return (self._atanCasingLengthDeriv_casing_bottom(mDict)
+                * self._atanfct(self.x, casing_a, self.slope)
+                * self._atanfct(self.x, casing_b, -self.slope))
+
+    def _atanCasingDeriv_casing_top(self, mDict):
+        casing_a, casing_b = mDict['casing_radius'] - 0.5*mDict['casing_thickness'], mDict['casing_radius'] + 0.5*mDict['casing_thickness']
+        return (self._atanCasingLengthDeriv_casing_top(mDict)
+                * self._atanfct(self.x, casing_a, self.slope)
+                * self._atanfct(self.x, casing_b, -self.slope))
+
+    def layer_cont(self, mDict):
+        return mDict['val_background'] + (mDict['val_layer']-mDict['val_background']) * self._atanLayer(mDict) # contribution from the layered background
+
+
+    def _transform(self, m):
+
+        mDict = self.mDict(m)
+
+        # assemble the model
+        layer = self.layer_cont(mDict)
+        casing = (mDict['val_casing'] - layer) * self._atanCasing(mDict)
+        insideCasing = (mDict['val_insideCasing'] - layer) * self._atanInsideCasing(mDict)
+
+        return layer + casing + insideCasing
+
+
+    def _deriv_val_background(self, mDict):
+        d_layer_cont_dval_background = 1. - self._atanLayer(mDict) # contribution from the layered background
+        d_casing_cont_dval_background = -1. * d_layer_cont_dval_background * self._atanCasing(mDict)
+        d_insideCasing_cont_dval_background = -1. * d_layer_cont_dval_background * self._atanInsideCasing(mDict)
+        return d_layer_cont_dval_background + d_casing_cont_dval_background + d_insideCasing_cont_dval_background
+
+    def _deriv_val_layer(self, mDict):
+        d_layer_cont_dval_layer = self._atanLayer(mDict)
+        d_casing_cont_dval_layer = -1. * d_layer_cont_dval_layer * self._atanCasing(mDict)
+        d_insideCasing_cont_dval_layer = -1. * d_layer_cont_dval_layer * self._atanInsideCasing(mDict)
+        return d_layer_cont_dval_layer + d_casing_cont_dval_layer + d_insideCasing_cont_dval_layer
+
+    def _deriv_val_casing(self, mDict):
+        d_layer_cont_dval_casing = 0.
+        d_casing_cont_dval_casing = self._atanCasing(mDict)
+        d_insideCasing_cont_dval_casing = 0.
+        return d_layer_cont_dval_casing + d_casing_cont_dval_casing + d_insideCasing_cont_dval_casing
+
+    def _deriv_val_insideCasing(self, mDict):
+        d_layer_cont_dval_insideCasing = 0.
+        d_casing_cont_dval_insideCasing = 0.
+        d_insideCasing_cont_dval_insideCasing = self._atanInsideCasing(mDict)
+        return d_layer_cont_dval_insideCasing + d_casing_cont_dval_insideCasing + d_insideCasing_cont_dval_insideCasing
+
+    def _deriv_layer_center(self, mDict):
+        d_layer_cont_dlayer_center = (mDict['val_layer'] - mDict['val_background']) * self._atanLayerDeriv_layer_center(mDict)
+        d_casing_cont_dlayer_center =  - d_layer_cont_dlayer_center * self._atanCasing(mDict)
+        d_insideCasing_cont_dlayer_center = - d_layer_cont_dlayer_center * self._atanInsideCasing(mDict)
+        return d_layer_cont_dlayer_center + d_casing_cont_dlayer_center + d_insideCasing_cont_dlayer_center
+
+    def _deriv_layer_thickness(self, mDict):
+        d_layer_cont_dlayer_thickness = (mDict['val_layer']-mDict['val_background']) * self._atanLayerDeriv_layer_thickness(mDict)
+        d_casing_cont_dlayer_thickness =  - d_layer_cont_dlayer_thickness * self._atanCasing(mDict)
+        d_insideCasing_cont_dlayer_thickness = - d_layer_cont_dlayer_thickness * self._atanInsideCasing(mDict)
+        return d_layer_cont_dlayer_thickness + d_casing_cont_dlayer_thickness + d_insideCasing_cont_dlayer_thickness
+
+    def _deriv_casing_radius(self, mDict):
+        layer = self.layer_cont(mDict)
+        d_layer_cont_dcasing_radius = 0.
+        d_casing_cont_dcasing_radius = (mDict['val_casing'] - layer) * self._atanCasingDeriv_casing_radius(mDict)
+        d_insideCasing_cont_dcasing_radius = (mDict['val_insideCasing'] - layer) * self._atanInsideCasingDeriv_casing_radius(mDict)
+        return d_layer_cont_dcasing_radius + d_casing_cont_dcasing_radius + d_insideCasing_cont_dcasing_radius
+
+    def _deriv_casing_thickness(self, mDict):
+        d_layer_cont_dcasing_thickness = 0.
+        d_casing_cont_dcasing_thickness = (mDict['val_casing'] - self.layer_cont(mDict)) * self._atanCasingDeriv_casing_thickness(mDict)
+        d_insideCasing_cont_dcasing_thickness = (mDict['val_insideCasing'] - self.layer_cont(mDict)) * self._atanInsideCasingDeriv_casing_thickness(mDict)
+        return d_layer_cont_dcasing_thickness + d_casing_cont_dcasing_thickness + d_insideCasing_cont_dcasing_thickness
+
+    def _deriv_casing_bottom(self, mDict):
+        d_layer_cont_dcasing_bottom = 0.
+        d_casing_cont_dcasing_bottom = (mDict['val_casing'] - self.layer_cont(mDict)) * self._atanCasingDeriv_casing_bottom(mDict)
+        d_insideCasing_cont_dcasing_bottom = (mDict['val_insideCasing'] - self.layer_cont(mDict)) * self._atanInsideCasingDeriv_casing_bottom(mDict)
+        return d_layer_cont_dcasing_bottom + d_casing_cont_dcasing_bottom + d_insideCasing_cont_dcasing_bottom
+
+    def _deriv_casing_top(self, mDict):
+        d_layer_cont_dcasing_top = 0.
+        d_casing_cont_dcasing_top = (mDict['val_casing'] - self.layer_cont(mDict)) * self._atanCasingDeriv_casing_top(mDict)
+        d_insideCasing_cont_dcasing_top = (mDict['val_insideCasing'] - self.layer_cont(mDict)) * self._atanInsideCasingDeriv_casing_top(mDict)
+        return d_layer_cont_dcasing_top + d_casing_cont_dcasing_top + d_insideCasing_cont_dcasing_top
+
+
+    def deriv(self, m):
+
+        mDict = self.mDict(m)
+
+        return sp.csr_matrix(np.vstack([
+                self._deriv_val_background(mDict),
+                self._deriv_val_layer(mDict),
+                self._deriv_val_casing(mDict),
+                self._deriv_val_insideCasing(mDict),
+                self._deriv_layer_center(mDict),
+                self._deriv_layer_thickness(mDict),
+                self._deriv_casing_radius(mDict),
+                self._deriv_casing_thickness(mDict),
+                self._deriv_casing_bottom(mDict),
+                self._deriv_casing_top(mDict),
+                ]).T)
 
 
 
+class ParametrizedBlockInLayer(ParametrizedLayer):
+    """
+        Parametrized Block in a Layered Space
+
+        For 2D:
+        m = [val_background, val_layer, val_block, layer_center, layer_thickness, block_x0, block_dx]
+
+        For 3D:
+        m = [val_background, val_layer, val_block, layer_center, layer_thickness, block_x0, block_y0, block_dx, block_dy]
+
+        .. plot::
+            :include-source:
+
+            from SimPEG import Mesh, Maps, np
+            import matplotlib.pyplot as plt
+
+            fig, ax = plt.subplots(1,1,figsize=(2,3))
+
+            mesh = Mesh.TensorMesh([50,50],x0='CC')
+            mapping = Maps.ParametrizedBlockInLayer(mesh)
+            m = np.hstack(np.r_[1., 2., 3., -0.1, 0.2, 0.3, 0.2])
+            rho = mapping._transform(m)
+            mesh.plotImage(rho, ax=ax)
+
+        **Required**
+
+        :param Mesh mesh: SimPEG Mesh, 2D or 3D
+
+        **Optional**
+
+        :param float slopeFact: arctan slope factor - divided by the minimum h spacing to give the slope of the arctan functions
+        :param float slope: slope of the arctan function
+        :param numpy.ndarray indActive: bool vector with
+
+    """
+
+    def __init__(self, mesh, **kwargs):
+
+        super(ParametrizedBlockInLayer, self).__init__(mesh, **kwargs)
+
+    @property
+    def nP(self):
+        if self.mesh.dim == 2:
+            return 7
+        elif self.mesh.dim == 3:
+            return 9
+
+    @property
+    def shape(self):
+        if self.indActive is not None:
+            return (sum(self.indActive), self.nP)
+        return (self.mesh.nC, self.nP)
+
+    def _mDict2d(self, m):
+        return{
+                'val_background': m[0],
+                'val_layer': m[1],
+                'val_block': m[2],
+                'layer_center': m[3],
+                'layer_thickness': m[4],
+                'x0_block': m[5],
+                'dx_block': m[6]
+              }
+
+    def _mDict3d(self, m):
+        return{
+                'val_background': m[0],
+                'val_layer': m[1],
+                'val_block': m[2],
+                'layer_center': m[3],
+                'layer_thickness': m[4],
+                'x0_block': m[5],
+                'y0_block': m[6],
+                'dx_block': m[7],
+                'dy_block': m[8]
+              }
+
+    def mDict(self, m):
+        if self.mesh.dim == 2:
+            return self._mDict2d(m)
+        elif self.mesh.dim == 3:
+            return self._mDict3d(m)
+
+    def _atanBlock2d(self, mDict):
+        return (self._atanLayer(mDict)
+                          * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                          * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+
+    def _atanBlock2dDeriv_layer_center(self, mDict):
+        return (self._atanLayerDeriv_layer_center(mDict)
+                          * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                          * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+
+    def _atanBlock2dDeriv_layer_thickness(self, mDict):
+        return (self._atanLayerDeriv_layer_thickness(mDict)
+                          * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                          * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+
+
+    def _atanBlock2dDeriv_x0(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfctDeriv(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfctDeriv(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+                    )
+
+    def _atanBlock2dDeriv_dx(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfctDeriv(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope) * -0.5
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfctDeriv(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope) * 0.5)
+                    )
+
+    def _atanBlock3d(self, mDict):
+        return (self._atanLayer(mDict)
+                      * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                      * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+
+
+    def _atanBlock3dDeriv_layer_center(self, mDict):
+        return (self._atanLayerDeriv_layer_center(mDict)
+                      * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                      * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+
+    def _atanBlock3dDeriv_layer_thickness(self, mDict):
+        return (self._atanLayerDeriv_layer_thickness(mDict)
+                      * self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                      * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                      * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+
+
+    def _atanBlock3dDeriv_x0(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfctDeriv(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfctDeriv(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    )
+
+    def _atanBlock3dDeriv_y0(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfctDeriv(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfctDeriv(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    )
+
+    def _atanBlock3dDeriv_dx(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfctDeriv(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope) * -0.5
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfctDeriv(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope) * 0.5
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    )
+
+    def _atanBlock3dDeriv_dy(self, mDict):
+        return self._atanLayer(mDict) * (
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfctDeriv(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope) * -0.5
+                    * self._atanfct(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope))
+                    +
+                    (self._atanfct(self.x, mDict['x0_block'] - 0.5*mDict['dx_block'], self.slope)
+                    * self._atanfct(self.x, mDict['x0_block'] + 0.5*mDict['dx_block'], -self.slope)
+                    * self._atanfct(self.y, mDict['y0_block'] - 0.5*mDict['dy_block'], self.slope)
+                    * self._atanfctDeriv(self.y, mDict['y0_block'] + 0.5*mDict['dy_block'], -self.slope) * 0.5)
+                    )
+
+
+    def _transform2d(self, m):
+        mDict = self.mDict(m)
+        # assemble the model
+        layer_cont = mDict['val_background'] + (mDict['val_layer']-mDict['val_background'])*self._atanLayer(mDict) # contribution from the layered background
+        block_cont = (mDict['val_block']-layer_cont)*self._atanBlock2d(mDict) # perturbation due to the block
+
+        return layer_cont + block_cont
+
+    def _deriv2d_val_background(self, mDict):
+        d_layer_dval_background = np.ones_like(self.x) - self._atanLayer(mDict)
+        d_block_dval_background = (-d_layer_dval_background)*self._atanBlock2d(mDict)
+        return d_layer_dval_background + d_block_dval_background
+
+    def _deriv2d_val_layer(self, mDict):
+        d_layer_dval_layer = self._atanLayer(mDict)
+        d_block_dval_layer = (-d_layer_dval_layer)*self._atanBlock2d(mDict)
+        return d_layer_dval_layer + d_block_dval_layer
+
+    def _deriv2d_val_block(self, mDict):
+        d_layer_dval_block = 0.
+        d_block_dval_block = (1.-d_layer_dval_block)*self._atanBlock2d(mDict)
+        return d_layer_dval_block + d_block_dval_block
+
+    def _deriv2d_layer_center(self, mDict):
+        d_layer_dlayer_center = (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_center(mDict)
+        d_block_dlayer_center = ((mDict['val_block']-self.layer_cont(mDict))*self._atanBlock2dDeriv_layer_center(mDict)
+                                    - d_layer_dlayer_center*self._atanBlock2d(mDict))
+        return d_layer_dlayer_center + d_block_dlayer_center
+
+    def _deriv2d_layer_thickness(self, mDict):
+        d_layer_dlayer_thickness = (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_thickness(mDict)
+        d_block_dlayer_thickness = ((mDict['val_block']-self.layer_cont(mDict))*self._atanBlock2dDeriv_layer_thickness(mDict)
+                                    - d_layer_dlayer_thickness*self._atanBlock2d(mDict))
+        return d_layer_dlayer_thickness + d_block_dlayer_thickness
+
+    def _deriv2d_x0_block(self, mDict):
+        d_layer_dx0 = 0.
+        d_block_dx0 = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock2dDeriv_x0(mDict)
+        return d_layer_dx0 + d_block_dx0
+
+    def _deriv2d_dx_block(self, mDict):
+        d_layer_ddx = 0.
+        d_block_ddx = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock2dDeriv_dx(mDict)
+        return d_layer_ddx + d_block_ddx
+
+    def _deriv2d(self, m):
+        mDict = self.mDict(m)
+
+        return np.vstack([
+                            self._deriv2d_val_background(mDict),
+                            self._deriv2d_val_layer(mDict),
+                            self._deriv2d_val_block(mDict),
+                            self._deriv2d_layer_center(mDict),
+                            self._deriv2d_layer_thickness(mDict),
+                            self._deriv2d_x0_block(mDict),
+                            self._deriv2d_dx_block(mDict)
+                        ]).T
+
+    def _transform3d(self, m):
+        # parse model
+        mDict = self.mDict(m)
+
+        # assemble the model
+        layer_cont = mDict['val_background'] + (mDict['val_layer']-mDict['val_background'])*self._atanLayer(mDict) # contribution from the layered background
+        block_cont = (mDict['val_block']-layer_cont)*self._atanBlock3d(mDict) # perturbation due to the block
+
+        return layer_cont + block_cont
+
+    def _deriv3d_val_background(self, mDict):
+        d_layer_dval_background = np.ones_like(self.x) - self._atanLayer(mDict)
+        d_block_dval_background = (-d_layer_dval_background)*self._atanBlock3d(mDict)
+        return d_layer_dval_background + d_block_dval_background
+
+    def _deriv3d_val_layer(self, mDict):
+        d_layer_dval_layer = self._atanLayer(mDict)
+        d_block_dval_layer = (-d_layer_dval_layer)*self._atanBlock3d(mDict)
+        return d_layer_dval_layer + d_block_dval_layer
+
+    def _deriv3d_val_block(self, mDict):
+        d_layer_dval_block = 0.
+        d_block_dval_block = (1.-d_layer_dval_block)*self._atanBlock3d(mDict)
+        return d_layer_dval_block + d_block_dval_block
+
+    def _deriv3d_layer_center(self, mDict):
+        d_layer_dlayer_center = (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_center(mDict)
+        d_block_dlayer_center = ((mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_layer_center(mDict)
+                                    - d_layer_dlayer_center*self._atanBlock3d(mDict))
+        return d_layer_dlayer_center + d_block_dlayer_center
+
+    def _deriv3d_layer_thickness(self, mDict):
+        d_layer_dlayer_thickness = (mDict['val_layer']-mDict['val_background'])*self._atanLayerDeriv_layer_thickness(mDict)
+        d_block_dlayer_thickness = ((mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_layer_thickness(mDict)
+                                - d_layer_dlayer_thickness*self._atanBlock3d(mDict))
+        return d_layer_dlayer_thickness + d_block_dlayer_thickness
+
+    def _deriv3d_x0_block(self, mDict):
+        d_layer_dx0 = 0.
+        d_block_dx0 = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_x0(mDict)
+        return d_layer_dx0 + d_block_dx0
+
+    def _deriv3d_y0_block(self, mDict):
+        d_layer_dy0 = 0.
+        d_block_dy0 = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_y0(mDict)
+        return d_layer_dy0 + d_block_dy0
+
+    def _deriv3d_dx_block(self, mDict):
+        d_layer_ddx = 0.
+        d_block_ddx = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_dx(mDict)
+        return d_layer_ddx + d_block_ddx
+
+    def _deriv3d_dy_block(self, mDict):
+        d_layer_ddy = 0.
+        d_block_ddy = (mDict['val_block']-self.layer_cont(mDict))*self._atanBlock3dDeriv_dy(mDict)
+        return d_layer_ddy + d_block_ddy
+
+    def _deriv3d(self, m):
+
+        mDict = self.mDict(m)
+
+        return np.vstack([
+                            self._deriv3d_val_background(mDict),
+                            self._deriv3d_val_layer(mDict),
+                            self._deriv3d_val_block(mDict),
+                            self._deriv3d_layer_center(mDict),
+                            self._deriv3d_layer_thickness(mDict),
+                            self._deriv3d_x0_block(mDict),
+                            self._deriv3d_y0_block(mDict),
+                            self._deriv3d_dx_block(mDict),
+                            self._deriv3d_dy_block(mDict),
+                        ]).T
+
+    def _transform(self, m):
+
+        if self.mesh.dim == 2:
+            return self._transform2d(m)
+        elif self.mesh.dim == 3:
+            return self._transform3d(m)
+
+    def deriv(self, m):
+
+        if self.mesh.dim == 2:
+            return sp.csr_matrix(self._deriv2d(m))
+        elif self.mesh.dim == 3:
+            return sp.csr_matrix(self._deriv3d(m))
 

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='!')
 

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

@@ -39,7 +39,7 @@ class RegularizationMesh(object):
             if self.indActive is None:
                 self._nC = self.mesh.nC
             else:
-                self._nC = sum(self.indActive)
+                self._nC = int(sum(self.indActive))
         return self._nC
 
     @property
@@ -304,18 +304,29 @@ class BaseRegularization(object):
     mesh    = None    #: A SimPEG.Mesh instance.
     mref    = None    #: Reference model.
 
-    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
+    def __init__(self, mesh=None, nP=None, mapping=None, indActive=None, **kwargs):
         Utils.setKwargs(self, **kwargs)
         assert isinstance(mesh, Mesh.BaseMesh), "mesh must be a SimPEG.Mesh object."
         if indActive is not None and indActive.dtype != 'bool':
             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)
+
+        if mesh is None and nP is None:
+            raise Exception, 'either Mesh or number of parameters must be provided to the BaseRegularization'
+
         self.regmesh = RegularizationMesh(mesh,indActive)
-        self.mapping = mapping or self.mapPair(mesh)
-        self.mapping._assertMatchesPair(self.mapPair)
         self.indActive = indActive
 
+        if mesh is not None and nP is None:
+            nP = self.regmesh.nC
+        self.nP = nP
+
+        self.mapping = mapping or self.mapPair(nP=self.nP)
+        self.mapping._assertMatchesPair(self.mapPair)
+
     @property
     def parent(self):
         """This is the parent of the regularization."""
@@ -343,7 +354,7 @@ class BaseRegularization(object):
     @property
     def W(self):
         """Full regularization weighting matrix W."""
-        return sp.identity(self.regmesh.nC)
+        return sp.identity(self.nP)
 
     @Utils.timeIt
     def eval(self, m):
@@ -728,14 +739,14 @@ class Sparse(Simple):
     @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 sp.vstack(wlist)
+        if getattr(self, '_W', None) is None:
+            wlist = (self.Wsmall, self.Wsmooth)
+            self._W = sp.vstack(wlist)
+        return self._W
 
     def R(self, f_m , eps, exponent):
 
-        eta = (eps**(1-exponent/2.))**0.5
-        r = eta / (f_m**2.+ eps**2.)**((1-exponent/2.)/2.)
+        eta = (eps**(1.-exponent/2.))**0.5
+        r = eta / (f_m**2.+ eps**2.)**((1.-exponent/2.)/2.)
 
         return r

SimPEG/Utils/__init__.py

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

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/api_Maps.rst

@@ -122,92 +122,10 @@ When these are used in the inverse problem, this is extremely important!!
 The API
 =======
 
-.. autoclass:: SimPEG.Maps.IdentityMap
+.. automodule:: SimPEG.Maps
     :members:
     :undoc-members:
 
 
-Common Maps
-===========
 
 
-Exponential Map
----------------
-
-Electrical conductivity varies over many orders of magnitude, so it is a common
-technique when solving the inverse problem to parameterize and optimize in terms
-of log conductivity. This makes sense not only because it ensures all conductivities
-will be positive, but because this is fundamentally the space where conductivity
-lives (i.e. it varies logarithmically).
-
-.. autoclass:: SimPEG.Maps.ExpMap
-    :members:
-    :undoc-members:
-
-
-Vertical 1D Map
----------------
-
-.. autoclass:: SimPEG.Maps.Vertical1DMap
-    :members:
-    :undoc-members:
-
-
-Map 2D Cross-Section to 3D Model
---------------------------------
-
-.. autoclass:: SimPEG.Maps.Map2Dto3D
-    :members:
-    :undoc-members:
-
-
-Mesh to Mesh Map
-----------------
-
-
-.. plot::
-
-    from SimPEG import *
-    import matplotlib.pyplot as plt
-    M = Mesh.TensorMesh([100,100])
-    h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
-    h1 = h1/h1.sum()
-    M2 = Mesh.TensorMesh([h1,h1])
-    V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
-    v = Utils.mkvc(V)
-    modh = Maps.Mesh2Mesh([M,M2])
-    modH = Maps.Mesh2Mesh([M2,M])
-    H = modH * v
-    h = modh * H
-    ax = plt.subplot(131)
-    M.plotImage(v, ax=ax)
-    ax.set_title('Fine Mesh (Original)')
-    ax = plt.subplot(132)
-    M2.plotImage(H,clim=[0,1],ax=ax)
-    ax.set_title('Course Mesh')
-    ax = plt.subplot(133)
-    M.plotImage(h,clim=[0,1],ax=ax)
-    ax.set_title('Fine Mesh (Interpolated)')
-    plt.show()
-
-
-.. autoclass:: SimPEG.Maps.Mesh2Mesh
-    :members:
-    :undoc-members:
-
-
-Some Extras
-===========
-
-Combo Map
----------
-
-The ComboMap holds the information for multiplying and combining
-maps. It also uses the chain rule to create the derivative.
-Remember, any time that you make your own combination of mappings
-be sure to test that the derivative is correct.
-
-.. autoclass:: SimPEG.Maps.ComboMap
-    :members:
-    :undoc-members:
-

docs/examples/DC_Forward_PseudoSection.rst

@@ -20,9 +20,10 @@ 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
 
 
 

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_maps.py

@@ -5,8 +5,10 @@ from scipy.sparse.linalg import dsolve
 
 TOL = 1e-14
 
-MAPS_TO_TEST_2D = ["CircleMap", "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull","FullMap","Vertical1DMap"]
-MAPS_TO_TEST_3D = [             "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull","FullMap","Vertical1DMap"]
+MAPS_TO_TEST_2D = ["CircleMap", "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
+MAPS_TO_TEST_3D = [             "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
+MAPS_TO_TEST_CYL = [             "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer"]
+
 
 class MapTests(unittest.TestCase):
 
@@ -17,6 +19,8 @@ class MapTests(unittest.TestCase):
         self.mesh2 = Mesh.TensorMesh([a, b], x0=np.array([3, 5]))
         self.mesh3 = Mesh.TensorMesh([a, b, [3,4]], x0=np.array([3, 5, 2]))
         self.mesh22 = Mesh.TensorMesh([b, a], x0=np.array([3, 5]))
+        self.meshCyl = Mesh.CylMesh([10.,1.,10.], x0='00C')
+        print self.meshCyl._meshType
 
     def test_transforms2D(self):
         for M in MAPS_TO_TEST_2D:
@@ -28,6 +32,15 @@ class MapTests(unittest.TestCase):
             maps = getattr(Maps, M)(self.mesh3)
             self.assertTrue(maps.test())
 
+    def test_transformsCyl(self):
+        for M in MAPS_TO_TEST_CYL:
+            maps = getattr(Maps, M)(self.meshCyl)
+            self.assertTrue(maps.test())
+
+    def test_ParametricCasingAndLayer(self):
+        mapping = Maps.ParametrizedCasingAndLayer(self.meshCyl)
+        m = np.r_[-2., 1., 6., 2., -0.1, 0.2, 0.5, 0.2, -0.2, 0.2]
+        self.assertTrue(mapping.test(m))
 
     def test_transforms_logMap_reciprocalMap(self):
         # Note that log/reciprocal maps can be kinda finicky, so we are being explicit about the random seed.

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

@@ -18,9 +18,9 @@ class DCProblemAnalyticTests(unittest.TestCase):
         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, flag="halfspace")
+        data_anal = EM.Analytics.DCAnalyticHalf(np.r_[A0loc, 0.], rxloc, sighalf, earth_type="halfspace")
 
-        rx = DC.Rx.Dipole(M, N)
+        rx = DC.Rx.Dipole_ky(M, N)
         src0 = DC.Src.Pole([rx], A0loc)
         survey = DC.Survey_ky([src0])
 

tests/em/static/test_DC_analytic.py

@@ -19,8 +19,8 @@ class DCProblemAnalyticTests(unittest.TestCase):
         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, flag="halfspace")
-        phiB = EM.Analytics.DCAnalyticHalf(Bloc, [M,N], 1e-2, flag="halfspace")
+        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)

tests/mesh/test_cylMesh.py

@@ -146,6 +146,20 @@ class TestCyl2DMesh(unittest.TestCase):
 
         assert np.abs(Pr*(Pc2r*mc) - Pc*mc).max() < 1e-3
 
+    def test_getInterpMatCartMesh_Cells2Nodes(self):
+
+        Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
+        Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
+
+        mc = np.arange(Mc.nC)
+        xr = np.linspace(0,0.4,50)
+        xc = np.linspace(0,0.4,50) + 0.2
+        Pr = Mr.getInterpolationMat(np.c_[xr,np.ones(50)*-0.2,np.ones(50)*0.5],'N')
+        Pc = Mc.getInterpolationMat(np.c_[xc,np.zeros(50),np.ones(50)*0.5],'CC')
+        Pc2r = Mc.getInterpolationMatCartMesh(Mr, 'CC', locTypeTo='N')
+
+        assert np.abs(Pr*(Pc2r*mc) - Pc*mc).max() < 1e-3
+
     def test_getInterpMatCartMesh_Faces(self):
 
         Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
@@ -177,6 +191,37 @@ class TestCyl2DMesh(unittest.TestCase):
         assert np.abs(mag[dist > 0.1].min() - 1) < TOL
 
 
+    def test_getInterpMatCartMesh_Faces2Edges(self):
+
+        Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
+        Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
+
+        Pf2e = Mc.getInterpolationMatCartMesh(Mr, 'F', locTypeTo='E')
+        mf = np.ones(Mc.nF)
+
+        ecart = Pf2e * mf
+
+        excc = Mr.aveEx2CC*Mr.r(ecart, 'E', 'Ex')
+        eycc = Mr.aveEy2CC*Mr.r(ecart, 'E', 'Ey')
+        ezcc = Mr.r(ecart, 'E', 'Ez')
+
+        indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
+        indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])
+
+        TOL = 1e-2
+        assert np.abs(float(excc[indX]) - 1) < TOL
+        assert np.abs(float(excc[indY]) - 0) < TOL
+        assert np.abs(float(eycc[indX]) - 0) < TOL
+        assert np.abs(float(eycc[indY]) - 1) < TOL
+        assert np.abs((ezcc - 1).sum()) < TOL
+
+        mag = (excc**2 + eycc**2)**0.5
+        dist = ((Mr.gridCC[:,0] + 0.2)**2  + (Mr.gridCC[:,1] + 0.2)**2)**0.5
+
+        assert np.abs(mag[dist > 0.1].max() - 1) < TOL
+        assert np.abs(mag[dist > 0.1].min() - 1) < TOL
+
+
     def test_getInterpMatCartMesh_Edges(self):
 
         Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
@@ -185,11 +230,42 @@ class TestCyl2DMesh(unittest.TestCase):
         Pe = Mc.getInterpolationMatCartMesh(Mr, 'E')
         me = np.ones(Mc.nE)
 
-        erect = Pe * me
+        ecart = Pe * me
 
-        excc = Mr.aveEx2CC*Mr.r(erect, 'E', 'Ex')
-        eycc = Mr.aveEy2CC*Mr.r(erect, 'E', 'Ey')
-        ezcc = Mr.r(erect, 'E', 'Ez')
+        excc = Mr.aveEx2CC*Mr.r(ecart, 'E', 'Ex')
+        eycc = Mr.aveEy2CC*Mr.r(ecart, 'E', 'Ey')
+        ezcc = Mr.aveEz2CC*Mr.r(ecart, 'E', 'Ez')
+
+        indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
+        indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])
+
+        TOL = 1e-2
+        assert np.abs(float(excc[indX]) - 0) < TOL
+        assert np.abs(float(excc[indY]) + 1) < TOL
+        assert np.abs(float(eycc[indX]) - 1) < TOL
+        assert np.abs(float(eycc[indY]) - 0) < TOL
+        assert np.abs(ezcc.sum()) < TOL
+
+        mag = (excc**2 + eycc**2)**0.5
+        dist = ((Mr.gridCC[:,0] + 0.2)**2  + (Mr.gridCC[:,1] + 0.2)**2)**0.5
+
+        assert np.abs(mag[dist > 0.1].max() - 1) < TOL
+        assert np.abs(mag[dist > 0.1].min() - 1) < TOL
+
+
+    def test_getInterpMatCartMesh_Edges2Faces(self):
+
+        Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
+        Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
+
+        Pe2f = Mc.getInterpolationMatCartMesh(Mr, 'E', locTypeTo='F')
+        me = np.ones(Mc.nE)
+
+        frect = Pe2f * me
+
+        excc = Mr.aveFx2CC*Mr.r(frect, 'F', 'Fx')
+        eycc = Mr.aveFy2CC*Mr.r(frect, 'F', 'Fy')
+        ezcc = Mr.r(frect, 'F', 'Fz')
 
         indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
         indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])

tests/mt/test_Problem3D_againstAnalytic.py

@@ -242,9 +242,6 @@ class TestAnalytics(unittest.TestCase):
     def test_appRes1en3(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-3))
     def test_appPhs1en3(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-3,False))
 
-    # Do a derivative test
-    def test_derivProj1(self):self.assertTrue(DerivProjfieldsTest(halfSpace(1e-2)))
-
     # Do a derivative test of Jvec
     # def test_derivJvec_zxxr(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxxr',.1))
     # def test_derivJvec_zxxi(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxxi',.1))