cleanup in mapping tests

Analytics

.bumpversion.cfg

@@ -1,4 +1,4 @@
 [bumpversion]
-current_version = 0.1.10
+current_version = 0.1.12
 files = setup.py SimPEG/__init__.py docs/conf.py
 

.gitignore

@@ -39,3 +39,5 @@ nosetests.xml
 *.sublime-workspace
 docs/_build/
 Makefile
+docs/warnings.txt
+.DS_Store

.travis.yml

@@ -24,18 +24,25 @@ env:
     - TEST_DIR=tests/examples
     - TEST_DIR=tests/em/fdem/inverse/adjoint
     - TEST_DIR=tests/em/fdem/forward
+    - TEST_DIR=tests/docs;
+      GAE_PYTHONPATH=${HOME}/.cache/google_appengine;
+      PATH=$PATH:${HOME}/google-cloud-sdk/bin;
+      PYTHONPATH=${PYTHONPATH}:${GAE_PYTHONPATH};
+      CLOUDSDK_CORE_DISABLE_PROMPTS=1
 
 # Setup anaconda
 before_install:
-  - if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh -O miniconda.sh; fi
+# Install packages
+  - if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh
+    -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh
+    -O miniconda.sh; fi
   - chmod +x miniconda.sh
   - ./miniconda.sh -b
   - export PATH=/home/travis/anaconda/bin:/home/travis/miniconda/bin:$PATH
   - conda update --yes conda
 
-# Install packages
 install:
-  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk
+  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk sphinx
   - pip install nose-cov python-coveralls
 
   - git clone https://github.com/rowanc1/pymatsolver.git
@@ -46,12 +53,28 @@ install:
 
 # Run test
 script:
+  # test docs
   - nosetests $TEST_DIR --with-cov --cov SimPEG --cov-config .coveragerc -v -s
 
 # Calculate coverage
 after_success:
   - coveralls --config_file .coveragerc
 
+  - if [ "$TRAVIS_BRANCH" = "master" -a "$TRAVIS_PULL_REQUEST" = "false" ]; then
+      if [ ${TEST_DIR} == "tests/docs" ]; then
+        python scripts/fetch_gae_sdk.py $(dirname "${GAE_PYTHONPATH}");
+        openssl aes-256-cbc -K $encrypted_93066031461c_key -iv $encrypted_93066031461c_iv
+          -in docs/credentials.tar.gz.enc -out credentials.tar.gz -d ;
+        if [ ! -d ${HOME}/google-cloud-sdk ]; then curl https://sdk.cloud.google.com | bash; fi ;
+        tar -xzf credentials.tar.gz ;
+        gcloud auth activate-service-account --key-file client-secret.json ;
+        gcloud config set project simpegdocs;
+        gcloud -q components update gae-python;
+        gcloud -q preview app deploy ./docs/app.yaml --version ${TRAVIS_COMMIT} --promote;
+      fi;
+    fi
+
+
 notifications:
   email:
     - rowanc1@gmail.com

README.rst

@@ -1,4 +1,4 @@
-.. image:: https://raw.github.com/simpeg/simpeg/master/docs/simpeg-logo.png
+.. image:: https://raw.github.com/simpeg/simpeg/master/docs/images/simpeg-logo.png
     :alt: SimPEG Logo
 
 ======

SimPEG/DCIP/BaseDC.py

@@ -162,8 +162,8 @@ class ProblemDC_CC(Problem.BaseProblem):
         """
             Makes the matrix A(m) for the DC resistivity problem.
 
-            :param numpy.array m: model
-            :rtype: scipy.csc_matrix
+            :param numpy.ndarray m: model
+            :rtype: scipy.sparse.csc_matrix
             :return: A(m)
 
             .. math::

SimPEG/DCIP/BaseIP.py

@@ -71,7 +71,7 @@ class ProblemIP(Problem.BaseProblem):
             Makes the matrix A(m) for the DC resistivity problem.
 
             :param numpy.array m: model
-            :rtype: scipy.csc_matrix
+            :rtype: scipy.sparse.csc_matrix
             :return: A(m)
 
             .. math::

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

@@ -144,12 +144,18 @@ class BetaSchedule(InversionDirective):
             if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
             self.invProb.beta /= self.coolingFactor
 
+
 class TargetMisfit(InversionDirective):
 
+    chifact = 1.
+    phi_d_star = None
+
     @property
     def target(self):
         if getattr(self, '_target', None) is None:
-            self._target = self.survey.nD*0.5
+            if self.phi_d_star is None:
+                self.phi_d_star = 0.5 * self.survey.nD
+            self._target = self.chifact * self.phi_d_star # the factor of 0.5 is because we do phid = 0.5*|| dpred - dobs||^2
         return self._target
     @target.setter
     def target(self, val):
@@ -161,7 +167,7 @@ class TargetMisfit(InversionDirective):
 
 
 
-class _SaveEveryIteration(InversionDirective):
+class SaveEveryIteration(InversionDirective):
     @property
     def name(self):
         if getattr(self, '_name', None) is None:
@@ -182,7 +188,7 @@ class _SaveEveryIteration(InversionDirective):
         self._fileName = value
 
 
-class SaveModelEveryIteration(_SaveEveryIteration):
+class SaveModelEveryIteration(SaveEveryIteration):
     """SaveModelEveryIteration"""
 
     def initialize(self):
@@ -192,7 +198,7 @@ class SaveModelEveryIteration(_SaveEveryIteration):
         np.save('%03d-%s' % (self.opt.iter, self.fileName), self.opt.xc)
 
 
-class SaveOutputEveryIteration(_SaveEveryIteration):
+class SaveOutputEveryIteration(SaveEveryIteration):
     """SaveModelEveryIteration"""
 
     def initialize(self):
@@ -206,7 +212,7 @@ class SaveOutputEveryIteration(_SaveEveryIteration):
         f.write(' %3d %1.4e %1.4e %1.4e %1.4e\n'%(self.opt.iter, self.invProb.beta, self.invProb.phi_d, self.invProb.phi_m, self.opt.f))
         f.close()
 
-class SaveOutputDictEveryIteration(_SaveEveryIteration):
+class SaveOutputDictEveryIteration(SaveEveryIteration):
     """SaveOutputDictEveryIteration"""
 
     def initialize(self):
@@ -222,7 +228,7 @@ 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:
@@ -237,47 +243,6 @@ 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):
-
-#     mref0 = None
-
-#     def nextIter(self):
 #         mref = getattr(self, 'm_prev', None)
 #         if mref is None:
 #             if self.debug: print 'UpdateReferenceModel is using mref0'
@@ -288,65 +253,177 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
 class Update_IRLS(InversionDirective):
 
     eps_min = None
+    eps = None
+    norms = [2.,2.,2.,2.]
     factor = None
     gamma = None
     phi_m_last = None
     phi_d_last = None
+    f_old = None
+    f_min_change = 1e-2
+    beta_tol = 5e-2
+    prctile = 95
+
+    # Solving parameter for IRLS (mode:2)
+    IRLSiter   = 0
+    minGNiter = 5
+    maxIRLSiter = 10
+    iterStart = 0
+
+    # Beta schedule
+    coolingFactor = 2.
+    coolingRate = 1
+
+    mode = 1
+
+    @property
+    def target(self):
+        if getattr(self, '_target', None) is None:
+            self._target = self.survey.nD*0.5
+        return self._target
+    @target.setter
+    def target(self, val):
+        self._target = val
 
     def initialize(self):
 
-        # Scale the regularization for changes in norm
-        if getattr(self, 'phi_m_last', None) is not None:
-
-            self.reg.curModel = self.invProb.curModel
-            self.reg.gamma = 1.
-            phim_new = self.reg.eval(self.invProb.curModel)
-            self.gamma = self.phi_m_last / phim_new
-
-        self.reg.curModel = self.invProb.curModel
-        self.reg.gamma = self.gamma
-
-        if getattr(self, 'phi_d_last', None) is None:
-            self.phi_d_last = self.invProb.phi_d
+        if self.mode == 1:
+            self.reg.norms = [2., 2., 2., 2.]
 
     def endIter(self):
-        # Cool the threshold parameter
-        if getattr(self, 'factor', None) is not None:
-            eps = self.reg.eps / self.factor
 
-            if getattr(self, 'eps_min', None) is not None:
-                self.reg.eps = np.max([self.eps_min,eps])
+        # After reaching target misfit with l2-norm, switch to IRLS (mode:2)
+        if self.invProb.phi_d < self.target and self.mode == 1:
+            print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
+
+            self.mode = 2
+
+            # Either use the supplied epsilon, or fix base on distribution of
+            # model values
+            if getattr(self, 'reg.eps', None) is None:
+                self.reg.eps_p = np.percentile(np.abs(self.invProb.curModel),self.prctile)
+            else:                 
+                self.reg.eps_p = self.eps[0]
+                
+            if getattr(self, 'reg.eps', None) is None:
+                self.reg.eps_q = np.percentile(np.abs(self.reg.regmesh.cellDiffxStencil*(self.reg.mapping * self.invProb.curModel)),self.prctile)
+            else:                 
+                self.reg.eps_q = self.eps[1]
+            
+            print "L[p qx qy qz]-norm : " + str(self.reg.norms)
+            print "eps_p: " + str(self.reg.eps_p) + " eps_q: " + str(self.reg.eps_q)
+            
+            self.reg.norms = self.norms
+            self.coolingFactor = 1.
+            self.coolingRate = 1
+            self.iterStart = self.opt.iter
+            self.phi_d_last = self.invProb.phi_d
+            self.phi_m_last = self.invProb.phi_m_last
+
+            self.reg.l2model = self.invProb.curModel
+            self.reg.curModel = self.invProb.curModel
+
+            if getattr(self, 'f_old', None) is None:
+                self.f_old = self.reg.eval(self.invProb.curModel)#self.invProb.evalFunction(self.invProb.curModel, return_g=False, return_H=False)
+
+        # Beta Schedule
+        if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
+            if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
+            self.invProb.beta /= self.coolingFactor
+
+
+        # Only update after GN iterations
+        if (self.opt.iter-self.iterStart) % self.minGNiter == 0 and self.mode==2:
+
+            self.IRLSiter += 1
+
+            phim_new = self.reg.eval(self.invProb.curModel)
+            self.f_change = np.abs(self.f_old - phim_new) / self.f_old
+
+            print "Regularization decrease: %6.3e" % (self.f_change)
+
+            # Check for maximum number of IRLS cycles
+            if self.IRLSiter == self.maxIRLSiter:
+                print "Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter
+                self.opt.stopNextIteration = True
+                return
+
+            # Check if the function has changed enough
+            if self.f_change < self.f_min_change and self.IRLSiter > 1:
+                print "Minimum decrease in regularization. End of IRLS"
+                self.opt.stopNextIteration = True
+                return
             else:
-                self.reg.eps = eps
+                self.f_old = phim_new
 
-        # Get phi_m at the end of current iteration
-        self.phi_m_last = self.invProb.phi_m_last
+#            # Cool the threshold parameter if required
+#            if getattr(self, 'factor', None) is not None:
+#                eps = self.reg.eps / self.factor
+#
+#                if getattr(self, 'eps_min', None) is not None:
+#                    self.reg.eps = np.max([self.eps_min,eps])
+#                else:
+#                    self.reg.eps = eps
 
-         # Update the model used for the IRLS weights
-        self.reg.curModel = self.invProb.curModel
+            # Get phi_m at the end of current iteration
+            self.phi_m_last = self.invProb.phi_m_last
 
-        # Temporarely set gamma to 1.
-        self.reg.gamma = 1.
+            # Reset the regularization matrices so that it is
+            # recalculated for current model
+            self.reg._Wsmall = None
+            self.reg._Wx = None
+            self.reg._Wy = None
+            self.reg._Wz = None
 
-        # Compute change in model objective function and update scaling
-        phim_new = self.reg.eval(self.invProb.curModel)
+             # Update the model used for the IRLS weights
+            self.reg.curModel = self.invProb.curModel
 
-        self.reg.gamma = self.phi_m_last / phim_new
+            # Temporarely set gamma to 1. to get raw phi_m
+            self.reg.gamma = 1.
 
-        self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
+            # Compute new model objective function value
+            phim_new = self.reg.eval(self.invProb.curModel)
+
+            # Update gamma to scale the regularization between IRLS iterations
+            self.reg.gamma = self.phi_m_last / phim_new
+
+            # Reset the regularization matrices again for new gamma
+            self.reg._Wsmall = None
+            self.reg._Wx = None
+            self.reg._Wy = None
+            self.reg._Wz = None
+
+            # Check if misfit is within the tolerance, otherwise scale beta
+            val = self.invProb.phi_d / (self.survey.nD*0.5)
+
+            if np.abs(1.-val) > self.beta_tol:
+                self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
 
 class Update_lin_PreCond(InversionDirective):
+    """
+    Create a Jacobi preconditioner for the linear problem
+    """
+    onlyOnStart=False
 
+    def initialize(self):
+
+        if getattr(self.opt, 'approxHinv', None) is None:
+            # Update the pre-conditioner
+            diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() #* (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
+            PC     = Utils.sdiag((self.prob.mapping.deriv(None).T *diagA)**-1.)
+            self.opt.approxHinv = PC
 
     def endIter(self):
         # Cool the threshold parameter
+        if 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):
     """

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/Analytics/FDEMDipolarfields.py

@@ -0,0 +1,302 @@
+from __future__ import division
+import numpy as np
+from scipy.constants import mu_0, pi, epsilon_0
+from scipy.special import erf
+from SimPEG import Utils
+
+omega = lambda f: 2.*np.pi*f
+# TODO:
+# r = lambda dx, dy, dz: np.sqrt( dx**2. + dy**2. + dz**2.)
+# k = lambda f, mu, epsilon, sig: np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+def E_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=0., epsr=1.):
+
+    """
+        Computing Analytic Electric fields from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+    mu = mu_0*(1+kappa)
+    epsilon = epsilon_0*epsr
+    sig_hat = sig + 1j*omega(f)*epsilon
+
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+    # Check
+    if XYZ.shape[0] > 1 & f.shape[0] > 1:
+        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
+
+    dx = XYZ[:,0]-srcLoc[0]
+    dy = XYZ[:,1]-srcLoc[1]
+    dz = XYZ[:,2]-srcLoc[2]
+
+    r  = np.sqrt( dx**2. + dy**2. + dz**2.)
+    # k  = np.sqrt( -1j*2.*np.pi*f*mu*sig )
+    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+    front = current * length / (4.*np.pi*sig_hat* r**3) * np.exp(-1j*k*r)
+    mid   = -k**2 * r**2 + 3*1j*k*r + 3
+
+    if orientation.upper() == 'X':
+        Ex = front*((dx**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
+        Ey = front*(dx*dy  / r**2)*mid
+        Ez = front*(dx*dz  / r**2)*mid
+        return Ex, Ey, Ez
+
+    elif orientation.upper() == 'Y':
+        #  x--> y, y--> z, z-->x
+        Ey = front*((dy**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
+        Ez = front*(dy*dz  / r**2)*mid
+        Ex = front*(dy*dx  / r**2)*mid
+        return Ex, Ey, Ez
+
+    elif orientation.upper() == 'Z':
+        # x --> z, y --> x, z --> y
+        Ez = front*((dz**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
+        Ex = front*(dz*dx  / r**2)*mid
+        Ey = front*(dz*dy  / r**2)*mid
+        return Ex, Ey, Ez
+
+
+def E_galvanic_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Galvanic portion of Electric fields from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+    mu = mu_0*(1+kappa)
+    epsilon = epsilon_0*epsr
+    sig_hat = sig + 1j*omega(f)*epsilon
+
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+    # Check
+    if XYZ.shape[0] > 1 & f.shape[0] > 1:
+        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
+
+    dx = XYZ[:,0]-srcLoc[0]
+    dy = XYZ[:,1]-srcLoc[1]
+    dz = XYZ[:,2]-srcLoc[2]
+
+    r  = np.sqrt( dx**2. + dy**2. + dz**2.)
+    # k  = np.sqrt( -1j*2.*np.pi*f*mu*sig )
+    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+    front = current * length / (4.*np.pi*sig_hat* r**3) * np.exp(-1j*k*r)
+    mid   = -k**2 * r**2 + 3*1j*k*r + 3
+
+    if orientation.upper() == 'X':
+        Ex_galvanic = front*((dx**2 / r**2)*mid + (-1j*k*r-1.))
+        Ey_galvanic = front*(dx*dy  / r**2)*mid
+        Ez_galvanic = front*(dx*dz  / r**2)*mid
+        return Ex_galvanic, Ey_galvanic, Ez_galvanic
+
+    elif orientation.upper() == 'Y':
+        #  x--> y, y--> z, z-->x
+        Ey_galvanic = front*((dy**2 / r**2)*mid + (-1j*k*r-1.))
+        Ez_galvanic = front*(dy*dz  / r**2)*mid
+        Ex_galvanic = front*(dy*dx  / r**2)*mid
+        return Ex_galvanic, Ey_galvanic, Ez_galvanic
+
+    elif orientation.upper() == 'Z':
+        # x --> z, y --> x, z --> y
+        Ez_galvanic = front*((dz**2 / r**2)*mid + (-1j*k*r-1.))
+        Ex_galvanic = front*(dz*dx  / r**2)*mid
+        Ey_galvanic = front*(dz*dy  / r**2)*mid
+        return Ex_galvanic, Ey_galvanic, Ez_galvanic
+
+
+def E_inductive_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Inductive portion of Electric fields from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+    mu = mu_0*(1+kappa)
+    epsilon = epsilon_0*epsr
+    sig_hat = sig + 1j*omega(f)*epsilon
+
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+    # Check
+    if XYZ.shape[0] > 1 & f.shape[0] > 1:
+        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
+
+    dx = XYZ[:,0]-srcLoc[0]
+    dy = XYZ[:,1]-srcLoc[1]
+    dz = XYZ[:,2]-srcLoc[2]
+
+    r  = np.sqrt( dx**2. + dy**2. + dz**2.)
+    # k  = np.sqrt( -1j*2.*np.pi*f*mu*sig )
+    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+    front = current * length / (4.*np.pi*sig_hat* r**3) * np.exp(-1j*k*r)
+
+    if orientation.upper() == 'X':
+        Ex_inductive = front*(k**2 * r**2)
+        Ey_inductive = np.zeros_like(Ex_inductive)
+        Ez_inductive = np.zeros_like(Ex_inductive)
+        return Ex_inductive, Ey_inductive, Ez_inductive
+
+    elif orientation.upper() == 'Y':
+        #  x--> y, y--> z, z-->x
+        Ey_inductive = front*(k**2 * r**2)
+        Ez_inductive = np.zeros_like(Ey_inductive)
+        Ex_inductive = np.zeros_like(Ey_inductive)
+        return Ex_inductive, Ey_inductive, Ez_inductive
+
+    elif orientation.upper() == 'Z':
+        # x --> z, y --> x, z --> y
+        Ez_inductive = front*(k**2 * r**2)
+        Ex_inductive = np.zeros_like(Ez_inductive)
+        Ey_inductive = np.zeros_like(Ez_inductive)
+        return Ex_inductive, Ey_inductive, Ez_inductive
+
+
+def J_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Current densities from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+
+    Ex, Ey, Ez = E_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=current, length=length, orientation=orientation, kappa=kappa, epsr=epsr)
+    Jx = sig*Ex
+    Jy = sig*Ey
+    Jz = sig*Ez
+    return Jx, Jy, Jz
+
+
+def J_galvanic_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Galvanic portion of Current densities from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+
+    Ex_galvanic, Ey_galvanic, Ez_galvanic = E_galvanic_from_ElectricDipoleWholeSpaced(XYZ, srcLoc, sig, f, current=current, length=length, orientation=orientation, kappa=kappa, epsr=epsr)
+    Jx_galvanic = sig*Ex_galvanic
+    Jy_galvanic = sig*Ey_galvanic
+    Jz_galvanic = sig*Ez_galvanic
+    return Jx_galvanic, Jy_galvanic, Jz_galvanic
+
+
+def J_inductive_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Inductive portion of Current densities from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+
+    Ex_inductive, Ey_inductive, Ez_inductive = E_inductive_from_ElectricDipoleWholeSpaced(XYZ, srcLoc, sig, f, current=current, length=length, orientation=orientation, kappa=kappa, epsr=epsr)
+    Jx_inductive = sig*Ex_inductive
+    Jy_inductive = sig*Ey_inductive
+    Jz_inductive = sig*Ez_inductive
+    return Jx_inductive, Jy_inductive, Jz_inductive
+
+
+def H_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Magnetic fields from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+    mu = mu_0*(1+kappa)
+    epsilon = epsilon_0*epsr
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+    # Check
+    if XYZ.shape[0] > 1 & f.shape[0] > 1:
+        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
+
+    dx = XYZ[:,0]-srcLoc[0]
+    dy = XYZ[:,1]-srcLoc[1]
+    dz = XYZ[:,2]-srcLoc[2]
+
+    r  = np.sqrt( dx**2. + dy**2. + dz**2.)
+    # k  = np.sqrt( -1j*2.*np.pi*f*mu*sig )
+    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+    front = current * length / (4.*np.pi* r**2) * (-1j*k*r + 1) * np.exp(-1j*k*r)
+
+    if orientation.upper() == 'X':
+        Hy = front*(-dz  / r)
+        Hz = front*(dy  / r)
+        Hx = np.zeros_like(Hy)
+        return Hx, Hy, Hz
+
+    elif orientation.upper() == 'Y':
+        Hx = front*(dz  / r)
+        Hz = front*(-dx  / r)
+        Hy = np.zeros_like(Hx)
+        return Hx, Hy, Hz
+
+    elif orientation.upper() == 'Z':
+        Hx = front*(-dy / r)
+        Hy = front*(dx  / r)
+        Hz = np.zeros_like(Hx)
+        return Hx, Hy, Hz
+
+
+def B_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Magnetic flux densites from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+
+    Hx, Hy, Hz = H_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=current, length=length, orientation=orientation, kappa=kappa, epsr=epsr)
+    Bx = mu*Hx
+    By = mu*Hy
+    Bz = mu*Hz
+    return Bx, By, Bz
+
+
+def A_from_ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', kappa=1., epsr=1.):
+
+    """
+        Computing Electric vector potentials from Electrical Dipole in a Wholespace
+        TODO:
+            Add description of parameters
+    """
+    mu = mu_0*(1+kappa)
+    epsilon = epsilon_0*epsr
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+    # Check
+    if XYZ.shape[0] > 1 & f.shape[0] > 1:
+        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
+
+    dx = XYZ[:,0]-srcLoc[0]
+    dy = XYZ[:,1]-srcLoc[1]
+    dz = XYZ[:,2]-srcLoc[2]
+
+    r  = np.sqrt( dx**2. + dy**2. + dz**2.)
+    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
+
+    front = current * length / (4.*np.pi*r)
+
+    if orientation.upper() == 'X':
+        Ax = front*np.exp(-1j*k*r)
+        Ay = np.zeros_like(Ax)
+        Az = np.zeros_like(Ax)
+        return Ax, Ay, Az
+
+    elif orientation.upper() == 'Y':
+        Ay = front*np.exp(-1j*k*r)
+        Ax = np.zeros_like(Ay)
+        Az = np.zeros_like(Ay)
+        return Ax, Ay, Az
+
+    elif orientation.upper() == 'Z':
+        Az = front*np.exp(-1j*k*r)
+        Ax = np.zeros_like(Ay)
+        Ay = np.zeros_like(Ay)
+        return Ax, Ay, Az
+
+
+
+
+

SimPEG/EM/Analytics/__init__.py

@@ -2,3 +2,4 @@ from TDEM import hzAnalyticDipoleT
 from FDEM import hzAnalyticDipoleF
 from FDEMcasing import *
 from DC import DCAnalyticHalf, DCAnalyticSphere
+from FDEMDipolarfields import *

SimPEG/EM/Base.py

@@ -20,10 +20,10 @@ class BaseEMProblem(Problem.BaseProblem):
         Problem.BaseProblem.__init__(self, mesh, **kwargs)
 
 
-    surveyPair = Survey.BaseSurvey
-    dataPair = Survey.Data
+    surveyPair = Survey.BaseSurvey #: The survey to pair with.
+    dataPair = Survey.Data #: The data to pair with.
 
-    PropMap = EMPropMap
+    PropMap = EMPropMap #: The property mapping
 
     Solver = SimpegSolver
     solverOpts = {}
@@ -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):
 
@@ -205,6 +217,7 @@ class BaseEMSurvey(Survey.BaseSurvey):
     def eval(self, f):
         """
         Project fields to receiver locations
+
         :param Fields u: fields object
         :rtype: numpy.ndarray
         :return: data

SimPEG/EM/FDEM/FieldsFDEM.py

@@ -6,11 +6,11 @@ from SimPEG.EM.Utils import omega
 from SimPEG.Utils import Zero, Identity, sdiag
 
 
-class Fields(SimPEG.Problem.Fields):
+class FieldsFDEM(SimPEG.Problem.Fields):
     """
 
     Fancy Field Storage for a FDEM survey. Only one field type is stored for
-    each problem, the rest are computed. The fields obejct acts like an array and is indexed by
+    each problem, the rest are computed. The fields object acts like an array and is indexed by
 
     .. code-block:: python
 
@@ -92,7 +92,7 @@ class Fields(SimPEG.Problem.Fields):
         """
         Total derivative of e with respect to the inversion model. Returns :math:`d\mathbf{e}/d\mathbf{m}` for forward and (:math:`d\mathbf{e}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
 
-        :param Src src: sorce
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
         :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
         :param numpy.ndarray v: vector to take sensitivity product with
         :param bool adjoint: adjoint?
@@ -110,7 +110,7 @@ class Fields(SimPEG.Problem.Fields):
         """
         Total derivative of b with respect to the inversion model. Returns :math:`d\mathbf{b}/d\mathbf{m}` for forward and (:math:`d\mathbf{b}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
 
-        :param Src src: sorce
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
         :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
         :param numpy.ndarray v: vector to take sensitivity product with
         :param bool adjoint: adjoint?
@@ -128,7 +128,7 @@ class Fields(SimPEG.Problem.Fields):
         """
         Total derivative of h with respect to the inversion model. Returns :math:`d\mathbf{h}/d\mathbf{m}` for forward and (:math:`d\mathbf{h}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
 
-        :param Src src: sorce
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
         :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
         :param numpy.ndarray v: vector to take sensitivity product with
         :param bool adjoint: adjoint?
@@ -146,7 +146,7 @@ class Fields(SimPEG.Problem.Fields):
         """
         Total derivative of j with respect to the inversion model. Returns :math:`d\mathbf{j}/d\mathbf{m}` for forward and (:math:`d\mathbf{j}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
 
-        :param Src src: sorce
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
         :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
         :param numpy.ndarray v: vector to take sensitivity product with
         :param bool adjoint: adjoint?
@@ -160,12 +160,12 @@ 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(FieldsFDEM):
     """
-    Fields object for Problem_e.
+    Fields object for Problem3D_e.
 
-    :param Mesh mesh: mesh
-    :param Survey survey: survey
+    :param BaseMesh mesh: mesh
+    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
     """
 
     knownFields = {'eSolution':'E'}
@@ -180,9 +180,6 @@ class Fields_e(Fields):
                     'h' : ['eSolution','CCV','_h'],
                   }
 
-    def __init__(self, mesh, survey, **kwargs):
-        Fields.__init__(self, mesh, survey, **kwargs)
-
     def startup(self):
         self.prob = self.survey.prob
         self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -426,12 +423,12 @@ class Fields_e(Fields):
 
 
 
-class Fields_b(Fields):
+class Fields3D_b(FieldsFDEM):
     """
-    Fields object for Problem_b.
+    Fields object for Problem3D_b.
 
-    :param Mesh mesh: mesh
-    :param Survey survey: survey
+    :param BaseMesh mesh: mesh
+    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
     """
 
     knownFields = {'bSolution':'F'}
@@ -446,9 +443,6 @@ class Fields_b(Fields):
                     'h' : ['bSolution','CCV','_h'],
                   }
 
-    def __init__(self,mesh,survey,**kwargs):
-        Fields.__init__(self,mesh,survey,**kwargs)
-
     def startup(self):
         self.prob = self.survey.prob
         self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -693,12 +687,12 @@ class Fields_b(Fields):
         return Zero()
 
 
-class Fields_j(Fields):
+class Fields3D_j(FieldsFDEM):
     """
-    Fields object for Problem_j.
+    Fields object for Problem3D_j.
 
-    :param Mesh mesh: mesh
-    :param Survey survey: survey
+    :param BaseMesh mesh: mesh
+    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
     """
 
     knownFields = {'jSolution':'F'}
@@ -713,9 +707,6 @@ class Fields_j(Fields):
                     'b' : ['jSolution','CCV','_b'],
                   }
 
-    def __init__(self,mesh,survey,**kwargs):
-        Fields.__init__(self,mesh,survey,**kwargs)
-
     def startup(self):
         self.prob = self.survey.prob
         self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -988,12 +979,12 @@ 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(FieldsFDEM):
     """
-    Fields object for Problem_h.
+    Fields object for Problem3D_h.
 
-    :param Mesh mesh: mesh
-    :param Survey survey: survey
+    :param BaseMesh mesh: mesh
+    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
     """
 
     knownFields = {'hSolution':'E'}
@@ -1008,9 +999,6 @@ class Fields_h(Fields):
                     'b' : ['hSolution','CCV','_b'],
                   }
 
-    def __init__(self,mesh,survey,**kwargs):
-        Fields.__init__(self,mesh,survey,**kwargs)
-
     def startup(self):
         self.prob = self.survey.prob
         self._edgeCurl = self.survey.prob.mesh.edgeCurl

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 FieldsFDEM, 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}\\\)
@@ -28,13 +28,14 @@ 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}\\\)
+
     """
 
     surveyPair = SurveyFDEM
-    fieldsPair = Fields
+    fieldsPair = FieldsFDEM
 
     def fields(self, m):
         """
@@ -64,7 +65,7 @@ class BaseFDEMProblem(BaseEMProblem):
 
         :param numpy.array m: inversion model (nP,)
         :param numpy.array v: vector which we take sensitivity product with (nP,)
-        :param SimPEG.EM.FDEM.Fields u: fields object
+        :param SimPEG.EM.FDEM.FieldsFDEM.FieldsFDEM u: fields object
         :rtype numpy.array:
         :return: Jv (ndata,)
         """
@@ -87,7 +88,7 @@ class BaseFDEMProblem(BaseEMProblem):
                 du_dm_v = Ainv * ( - dA_dm_v + dRHS_dm_v )
 
                 for rx in src.rxList:
-                    df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
+                    df_dmFun = getattr(f, '_{0}Deriv'.format(rx.projField), None)
                     df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
                     Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
             Ainv.clean()
@@ -99,7 +100,7 @@ class BaseFDEMProblem(BaseEMProblem):
 
         :param numpy.array m: inversion model (nP,)
         :param numpy.array v: vector which we take adjoint product with (nP,)
-        :param SimPEG.EM.FDEM.Fields u: fields object
+        :param SimPEG.EM.FDEM.FieldsFDEM.FieldsFDEM u: fields object
         :rtype numpy.array:
         :return: Jv (ndata,)
         """
@@ -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')
@@ -154,8 +154,8 @@ class BaseFDEMProblem(BaseEMProblem):
         Evaluates the sources for a given frequency and puts them in matrix form
 
         :param float freq: Frequency
-        :rtype: (numpy.ndarray, numpy.ndarray)
-        :return: s_m, s_e (nE or nF, nSrc)
+        :rtype: tuple
+        :return: (s_m, s_e) (nE or nF, nSrc)
         """
         Srcs = self.survey.getSrcByFreq(freq)
         if self._formulation is 'EB':
@@ -178,7 +178,7 @@ class BaseFDEMProblem(BaseEMProblem):
 ################################ E-B Formulation #########################################
 ##########################################################################################
 
-class Problem_e(BaseFDEMProblem):
+class Problem3D_e(BaseFDEMProblem):
     """
     By eliminating the magnetic flux density using
 
@@ -195,12 +195,12 @@ class Problem_e(BaseFDEMProblem):
 
     which we solve for :math:`\mathbf{e}`.
 
-    :param SimPEG.Mesh mesh: mesh
+    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
     """
 
     _solutionType = 'eSolution'
     _formulation  = 'EB'
-    fieldsPair    = Fields_e
+    fieldsPair    = Fields3D_e
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -270,7 +270,7 @@ class Problem_e(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -289,7 +289,7 @@ 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
 
@@ -306,12 +306,12 @@ class Problem_b(BaseFDEMProblem):
     .. note ::
         The inverse problem will not work with full anisotropy
 
-    :param SimPEG.Mesh mesh: mesh
+    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
     """
 
     _solutionType = 'bSolution'
     _formulation  = 'EB'
-    fieldsPair    = Fields_b
+    fieldsPair    = Fields3D_b
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -401,7 +401,7 @@ class Problem_b(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -437,7 +437,7 @@ class Problem_b(BaseFDEMProblem):
 ##########################################################################################
 
 
-class Problem_j(BaseFDEMProblem):
+class Problem3D_j(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{h}\\\) using
 
@@ -445,6 +445,7 @@ class Problem_j(BaseFDEMProblem):
 
         \mathbf{h} = \\frac{1}{i \omega} \mathbf{M_{\mu}^e}^{-1} \\left(-\mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{j}  + \mathbf{M^e} \mathbf{s_m} \\right)
 
+
     and solve for \\\(\\\mathbf{j}\\\) using
 
     .. math ::
@@ -454,12 +455,12 @@ class Problem_j(BaseFDEMProblem):
     .. note::
         This implementation does not yet work with full anisotropy!!
 
-    :param SimPEG.Mesh mesh: mesh
+    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
     """
 
     _solutionType = 'jSolution'
     _formulation  = 'HJ'
-    fieldsPair    = Fields_j
+    fieldsPair    = Fields3D_j
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -530,8 +531,8 @@ class Problem_j(BaseFDEMProblem):
             \mathbf{RHS} = \mathbf{C} \mathbf{M_{\mu}^e}^{-1}\mathbf{s_m} -i\omega \mathbf{s_e}
 
         :param float freq: Frequency
-        :rtype: numpy.ndarray (nE, nSrc)
-        :return: RHS
+        :rtype: numpy.ndarray
+        :return: RHS (nE, nSrc)
         """
 
         s_m, s_e = self.getSourceTerm(freq)
@@ -550,7 +551,7 @@ class Problem_j(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -578,7 +579,7 @@ class Problem_j(BaseFDEMProblem):
 
 
 
-class Problem_h(BaseFDEMProblem):
+class Problem3D_h(BaseFDEMProblem):
     """
     We eliminate \\\(\\\mathbf{j}\\\) using
 
@@ -592,12 +593,12 @@ class Problem_h(BaseFDEMProblem):
 
         \\left(\mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{C} + i \omega \mathbf{M_{\mu}^e}\\right) \mathbf{h} = \mathbf{M^e} \mathbf{s_m} + \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{s_e}
 
-    :param SimPEG.Mesh mesh: mesh
+    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
     """
 
     _solutionType = 'hSolution'
     _formulation  = 'HJ'
-    fieldsPair    = Fields_h
+    fieldsPair    = Fields3D_h
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
@@ -609,9 +610,11 @@ class Problem_h(BaseFDEMProblem):
         .. math::
             \mathbf{A} = \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{C} + i \omega \mathbf{M_{\mu}^e}
 
+
         :param float freq: Frequency
         :rtype: scipy.sparse.csr_matrix
         :return: A
+
         """
 
         MeMu = self.MeMu
@@ -654,6 +657,7 @@ class Problem_h(BaseFDEMProblem):
         :param float freq: Frequency
         :rtype: numpy.ndarray
         :return: RHS (nE, nSrc)
+
         """
 
         s_m, s_e = self.getSourceTerm(freq)
@@ -667,7 +671,7 @@ class Problem_h(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray

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 receivers to get data.
+
+        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param BaseMesh 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 SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param BaseMesh 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

@@ -9,16 +9,22 @@ 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):
         """
         - :math:`s_m` : magnetic source term
         - :math:`s_e` : electric source term
 
-        :param Problem prob: FDEM Problem
-        :rtype: (numpy.ndarray, numpy.ndarray)
+        :param BaseFDEMProblem prob: FDEM Problem
+        :rtype: tuple
         :return: tuple with magnetic source term and electric source term
         """
         s_m = self.s_m(prob)
@@ -31,10 +37,10 @@ class BaseSrc(Survey.BaseSrc):
         - :code:`s_mDeriv` : derivative of the magnetic source term
         - :code:`s_eDeriv` : derivative of the electric source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
-        :rtype: (numpy.ndarray, numpy.ndarray)
+        :rtype: tuple
         :return: tuple with magnetic source term and electric source term derivatives times a vector
         """
         if v is not None:
@@ -46,47 +52,55 @@ class BaseSrc(Survey.BaseSrc):
         """
         Primary magnetic flux density
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary magnetic flux density
         """
-        return Zero()
+        if self._bPrimary is None:
+            return Zero()
+        return self._bPrimary
 
     def hPrimary(self, prob):
         """
         Primary magnetic field
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
-        return Zero()
+        if self._hPrimary is None:
+            return Zero()
+        return self._hPrimary
 
     def ePrimary(self, prob):
         """
         Primary electric field
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary electric field
         """
-        return Zero()
+        if self._ePrimary is None:
+            return Zero()
+        return self._ePrimary
 
     def jPrimary(self, prob):
         """
         Primary current density
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary current density
         """
-        return Zero()
+        if self._jPrimary is None:
+            return Zero()
+        return self._jPrimary
 
     def s_m(self, prob):
         """
         Magnetic source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -96,7 +110,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Electric source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -106,7 +120,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Derivative of magnetic source term with respect to the inversion model
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -119,7 +133,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Derivative of electric source term with respect to the inversion model
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -135,21 +149,20 @@ 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):
         """
         Electric source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -165,21 +178,20 @@ 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):
         """
         Magnetic source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -196,20 +208,19 @@ 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):
         """
         Magnetic source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -221,7 +232,7 @@ class RawVec(BaseSrc):
         """
         Electric source term
 
-        :param Problem prob: FDEM Problem
+        :param BaseFDEMProblem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -277,21 +288,20 @@ 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):
         """
         The primary magnetic flux density from a magnetic vector potential
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -329,7 +339,7 @@ class MagDipole(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -340,7 +350,7 @@ class MagDipole(BaseSrc):
         """
         The magnetic source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -354,7 +364,7 @@ class MagDipole(BaseSrc):
         """
         The electric source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -406,7 +416,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The primary magnetic flux density from the analytic solution for magnetic fields from a dipole
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -445,7 +455,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -456,7 +466,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The magnetic source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -469,7 +479,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The electric source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -520,7 +530,7 @@ class CircularLoop(BaseSrc):
         """
         The primary magnetic flux density from a magnetic vector potential
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -542,7 +552,7 @@ class CircularLoop(BaseSrc):
             if not prob.mesh.isSymmetric:
                 # TODO ?
                 raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            a = MagneticDipoleVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
+            a = MagneticLoopVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
 
         else:
             srcfct = MagneticDipoleVectorPotential
@@ -557,7 +567,7 @@ class CircularLoop(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -568,7 +578,7 @@ class CircularLoop(BaseSrc):
         """
         The magnetic source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -581,7 +591,7 @@ class CircularLoop(BaseSrc):
         """
         The electric source term
 
-        :param Problem prob: FDEM problem
+        :param BaseFDEMProblem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """

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/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/TDEM/BaseTDEM.py

@@ -112,7 +112,7 @@ class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
         """
             :param numpy.array m: Conductivity model
             :param numpy.ndarray v: vector (model object)
-            :param simpegEM.TDEM.FieldsTDEM f: Fields resulting from m
+            :param FieldsTDEM f: Fields resulting from m
             :rtype: numpy.ndarray
             :return: w (data object)
 
@@ -136,8 +136,8 @@ class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
     def Jtvec(self, m, v, f=None):
         """
             :param numpy.array m: Conductivity model
-            :param numpy.ndarray,SimPEG.Survey.Data v: vector (data object)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
+            :param numpy.ndarray v: vector (or a :class:`SimPEG.Survey.Data` object)
+            :param FieldsTDEM u: Fields resulting from m
             :rtype: numpy.ndarray
             :return: w (model object)
 

SimPEG/EM/TDEM/TDEM_b.py

@@ -87,8 +87,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
         """
             :param numpy.array m: Conductivity model
             :param numpy.array vec: vector (like a model)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
-            :rtype: simpegEM.TDEM.FieldsTDEM
+            :param FieldsTDEM u: Fields resulting from m
+            :rtype: FieldsTDEM
             :return: f
 
             Multiply G by a vector
@@ -125,9 +125,9 @@ class ProblemTDEM_b(BaseTDEMProblem):
         """
             :param numpy.array m: Conductivity model
             :param numpy.array vec: vector (like a fields)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
-            :rtype: np.ndarray (like a model)
-            :return: p
+            :param FieldsTDEM u: Fields resulting from m
+            :rtype: numpy.ndarray
+            :return: p (like a model)
 
             Multiply G.T by a vector
         """
@@ -153,8 +153,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
     def solveAh(self, m, p):
         """
             :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM p: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
+            :param FieldsTDEM p: Fields object
+            :rtype: FieldsTDEM
             :return: y
 
             Solve the block-matrix system \\\(\\\hat{A} \\\hat{y} = \\\hat{p}\\\):
@@ -200,8 +200,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
     def solveAht(self, m, p):
         """
             :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM p: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
+            :param FieldsTDEM p: Fields object
+            :rtype: FieldsTDEM
             :return: y
 
             Solve the block-matrix system \\\(\\\hat{A}^\\\\top \\\hat{y} = \\\hat{p}\\\):
@@ -270,8 +270,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
     def _AhVec(self, m, vec):
         """
             :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
+            :param FieldsTDEM vec: Fields object
+            :rtype: FieldsTDEM
             :return: f
 
             Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where
@@ -315,8 +315,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
     def _AhtVec(self, m, vec):
         """
             :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
+            :param FieldsTDEM vec: Fields object
+            :rtype: FieldsTDEM
             :return: f
 
             Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where

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_Analytic_Dipole.py

@@ -1,7 +1,7 @@
 from SimPEG import *
-import SimPEG.DCIP as DC
+import SimPEG.EM.Static.DC as DC
 
-def run(plotIt=False):
+def run(plotIt=True):
     cs = 25.
     hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
     hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
@@ -21,10 +21,10 @@ def run(plotIt=False):
     #     ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')
     #     ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)
 
-    rx = DC.RxDipole(xyz_rxP, xyz_rxN)
-    src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
-    survey = DC.SurveyDC([src])
-    problem = DC.ProblemDC_CC(mesh)
+    rx = DC.Rx.Dipole(xyz_rxP, xyz_rxN)
+    src = DC.Src.Dipole([rx], np.r_[-200, 0, -12.5], np.r_[+200, 0, -12.5])
+    survey = DC.Survey([src])
+    problem = DC.Problem3D_CC(mesh)
     problem.pair(survey)
     try:
         from pymatsolver import MumpsSolver
@@ -65,4 +65,4 @@ def run(plotIt=False):
 
 
 if __name__ == '__main__':
-    print run(plotIt=True)
+    print run()

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

@@ -19,10 +19,13 @@ def run(plotIt=True):
         Morrison Casing Model, and the results are used in a 2016 SEG abstract by
         Yang et al.
 
-        - Schenkel, C.J., and H.F. Morrison, 1990, Effects of well casing on potential field measurements using downhole current sources: Geophysical prospecting, 38, 663-686.
+        .. code-block:: text
+
+            Schenkel, C.J., and H.F. Morrison, 1990, Effects of well casing on potential field measurements using downhole current sources: Geophysical prospecting, 38, 663-686.
 
 
         The model consists of:
+
         - Air: Conductivity 1e-8 S/m, above z = 0
         - Background: conductivity 1e-2 S/m, below z = 0
         - Casing: conductivity 1e6 S/m
@@ -215,7 +218,7 @@ 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, Solver=solver)
     problem.pair(survey)
 
     # ------------- Solve ---------------------------

SimPEG/Examples/Inversion_IRLS.py

@@ -1,7 +1,7 @@
 from SimPEG import *
 
 
-def run(N=200, plotIt=True):
+def run(N=100, plotIt=True):
     """
         Inversion: Linear Problem
         =========================
@@ -18,6 +18,8 @@ def run(N=200, plotIt=True):
     mesh = Mesh.TensorMesh([N])
 
     m0 = np.ones(mesh.nC) * 1e-4
+    mref = np.zeros(mesh.nC)
+
     nk = 10
     jk = np.linspace(1.,nk,nk)
     p = -2.
@@ -40,67 +42,35 @@ def run(N=200, plotIt=True):
     survey = Survey.LinearSurvey()
     survey.pair(prob)
     survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk)
-    #survey.makeSyntheticData(mtrue, std=std_noise)
 
     wd = np.ones(nk) * std_noise
 
-    #print survey.std[0]
-    #M = prob.mesh
     # Distance weighting
     wr = np.sum(prob.G**2.,axis=0)**0.5
     wr = ( wr/np.max(wr) )
 
-    reg = Regularization.Simple(mesh)
-    reg.wght = wr
-
     dmis = DataMisfit.l2_DataMisfit(survey)
     dmis.Wd = 1./wd
 
-    opt = Optimization.ProjectedGNCG(maxIter=30,lower=-2.,upper=2., maxIterCG= 20, tolCG = 1e-4)
-    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
-    invProb.curModel = m0
-
-    beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
-    target = Directives.TargetMisfit()
-
     betaest = Directives.BetaEstimate_ByEig()
-    inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])
-
-
-    mrec = inv.run(m0)
-    ml2 = mrec
-    print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
-
-    # Switch regularization to sparse
-    phim = invProb.phi_m_last
-    phid =  invProb.phi_d
 
     reg = Regularization.Sparse(mesh)
+    reg.mref = mref
+    reg.cell_weights = wr
 
-#==============================================================================
-#     fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
-#     dmdx = reg.mesh.cellDiffxStencil * mrec
-#     plt.plot(np.sort(dmdx))
-#==============================================================================
-
-    #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.norms   = [0., 0., 2., 2.]
-    reg.wght = wr
+    
 
-    opt = Optimization.ProjectedGNCG(maxIter=5 ,lower=-2.,upper=2., maxIterCG= 100, tolCG = 1e-3)
-    invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta*2.)
-    beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)
-    #betaest = Directives.BetaEstimate_ByEig()
-    target = Directives.TargetMisfit()
-    IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid )
+    opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
+    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
+    update_Jacobi = Directives.Update_lin_PreCond()
+    
+    # Set the IRLS directive, penalize the lowest 25 percentile of model values
+    # Start with an l2-l2, then switch to lp-norms
+    norms   = [0., 0., 2., 2.]    
+    IRLS = Directives.Update_IRLS( norms=norms, prctile = 25, maxIRLSiter = 15, minGNiter=3)
 
-    inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS])
-
-    m0 = mrec
+    inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
 
     # Run inversion
     mrec = inv.run(m0)
@@ -117,7 +87,7 @@ def run(N=200, plotIt=True):
         axes[0].set_title('Columns of matrix G')
 
         axes[1].plot(mesh.vectorCCx, mtrue, 'b-')
-        axes[1].plot(mesh.vectorCCx, ml2, 'r-')
+        axes[1].plot(mesh.vectorCCx, reg.l2model, 'r-')
         #axes[1].legend(('True Model', 'Recovered Model'))
         axes[1].set_ylim(-1.0,1.25)
 

SimPEG/Examples/MT_1D_ForwardAndInversion.py

@@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
 def run(plotIt=True):
     """
         MT: 1D: Inversion
-        =======================
+        =================
 
         Forward model 1D MT data.
         Setup and run a MT 1D inversion.
@@ -50,7 +50,7 @@ def run(plotIt=True):
     m_0 = np.log(sigma_0[active])
 
     # Set the mapping
-    actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
+    actMap = simpeg.Maps.InjectActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
     mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap
 
     ## Setup the layout of the survey, set the sources and the connected receivers
@@ -76,7 +76,7 @@ def run(plotIt=True):
     survey.dobs = survey.dtrue + 0.025*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)
 
     if plotIt:
-        fig = MT.Utils.dataUtils.plotMT1DModelData(problem)
+        fig = MT.Utils.dataUtils.plotMT1DModelData(problem, [m_0])
         fig.suptitle('Target - smooth true')
 
 

SimPEG/Examples/MT_3D_Foward.py

@@ -12,7 +12,7 @@ except:
 def run(plotIt=True, nFreq=1):
     """
         MT: 3D: Forward
-        =======================
+        ===============
 
         Forward model 3D MT data.
 
@@ -46,16 +46,15 @@ def run(plotIt=True, nFreq=1):
     survey = MT.Survey(srcList)
 
     ## Setup the problem object
-    problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG)
+    problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG, Solver=Solver)
     problem.pair(survey)
-    problem.Solver = Solver
 
     # Calculate the data
     fields = problem.fields(sig)
     dataVec = survey.eval(fields)
 
     # Make the data
-    mtData = MT.Data(survey,dataVec)
+    mtData = MT.Data(survey, dataVec)
     # Add plots
     if plotIt:
         pass

SimPEG/Examples/Maps_ComboMaps.py

@@ -0,0 +1,62 @@
+from SimPEG import Mesh, Maps, np
+
+def run(plotIt=True):
+    """
+
+        Maps: ComboMaps
+        ===============
+
+        We will use an example where we want a 1D layered earth as
+        our model, but we want to map this to a 2D discretization to do our forward
+        modeling. We will also assume that we are working in log conductivity still,
+        so after the transformation we want to map to conductivity space.
+        To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
+        which does the first part of what we just described. The second part will be
+        done by the :class:`SimPEG.Maps.ExpMap` described above.
+
+        .. code-block:: python
+            :linenos:
+
+            M = Mesh.TensorMesh([7,5])
+            v1dMap = Maps.SurjectVertical1D(M)
+            expMap = Maps.ExpMap(M)
+            myMap = expMap * v1dMap
+            m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
+            sig = myMap * m
+
+        If you noticed, it was pretty easy to combine maps. What is even cooler is
+        that the derivatives also are made for you (if everything goes right).
+        Just to be sure that the derivative is correct, you should always run the test
+        on the mapping that you create.
+
+    """
+
+
+    M = Mesh.TensorMesh([7,5])
+    v1dMap = Maps.SurjectVertical1D(M)
+    expMap = Maps.ExpMap(M)
+    myMap = expMap * v1dMap
+    m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
+    sig = myMap * m
+
+    if not plotIt: return
+
+    import matplotlib.pyplot as plt
+    figs, axs = plt.subplots(1,2)
+    axs[0].plot(m, M.vectorCCy, 'b-o')
+    axs[0].set_title('Model')
+    axs[0].set_ylabel('Depth, y')
+    axs[0].set_xlabel('Value, $m_i$')
+    axs[0].set_xlim(0,3)
+    axs[0].set_ylim(0,1)
+    clbar = plt.colorbar(M.plotImage(sig,ax=axs[1],grid=True,gridOpts=dict(color='grey'))[0])
+    axs[1].set_title('Physical Property')
+    axs[1].set_ylabel('Depth, y')
+    clbar.set_label('$\sigma = \exp(\mathbf{P}m)$')
+    plt.tight_layout()
+    plt.show()
+
+
+if __name__ == '__main__':
+    run()
+

SimPEG/Examples/Maps_Mesh2Mesh.py

@@ -0,0 +1,41 @@
+from SimPEG import Mesh, Maps, Utils
+
+def run(plotIt=True):
+    """
+
+        Maps: Mesh2Mesh
+        ===============
+
+        This mapping allows you to go from one mesh to another.
+
+    """
+
+    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
+
+    if not plotIt: return
+
+    import matplotlib.pyplot as plt
+    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()
+
+
+if __name__ == '__main__':
+    run()
+

SimPEG/Examples/Mesh_Basic_ForwardDC.py

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

SimPEG/Examples/Utils_surface2ind_topo.py

@@ -0,0 +1,43 @@
+from SimPEG import *
+from SimPEG.Utils import surface2ind_topo
+
+
+def run(plotIt=True, nx=5, ny=5):
+    """
+
+        Utils: surface2ind_topo
+        =======================
+
+        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.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

@@ -8,9 +8,11 @@ import EM_FDEM_Analytic_MagDipoleWholespace
 import EM_Schenkel_Morrison_Casing
 import EM_TDEM_1D_Inversion
 import FLOW_Richards_1D_Celia1990
-import Forward_BasicDirectCurrent
 import Inversion_IRLS
 import Inversion_Linear
+import Maps_ComboMaps
+import Maps_Mesh2Mesh
+import Mesh_Basic_ForwardDC
 import Mesh_Basic_PlotImage
 import Mesh_Basic_Types
 import Mesh_Operators_CahnHilliard
@@ -20,8 +22,9 @@ 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", "Inversion_IRLS", "Inversion_Linear", "Maps_ComboMaps", "Maps_Mesh2Mesh", "Mesh_Basic_ForwardDC", "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 #####
 
@@ -37,7 +40,7 @@ if __name__ == '__main__':
 
     # Create the examples dir in the docs folder.
     fName = os.path.realpath(__file__)
-    docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'examples'])
+    docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'content', 'examples'])
     shutil.rmtree(docExamplesDir)
     os.makedirs(docExamplesDir)
 
@@ -94,12 +97,12 @@ if __name__ == '__main__':
     from SimPEG import Examples
     Examples.%s.run()
 
-.. literalinclude:: ../../SimPEG/Examples/%s.py
+.. literalinclude:: ../../../SimPEG/Examples/%s.py
     :language: python
     :linenos:
 """%(name,doc,name,name)
 
-        rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'examples', name + '.rst']))
+        rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'content', 'examples', name + '.rst']))
 
         print 'Creating: %s.rst'%name
         f = open(rst, 'w')

SimPEG/FLOW/Richards/Empirical.py

@@ -31,7 +31,7 @@ class NonLinearMap(object):
         """
             :param numpy.array u: fields
             :param numpy.array m: model
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: derivative of transformed model
 
             The *transform* changes the model into the physical property.
@@ -44,7 +44,7 @@ class NonLinearMap(object):
         """
             :param numpy.array u: fields
             :param numpy.array m: model
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: derivative of transformed model
 
             The *transform* changes the model into the physical property.

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/SrcMT.py

@@ -86,7 +86,7 @@ class polxy_1Dprimary(BaseMTSrc):
         Get the electrical field source
         """
         e_p = self.ePrimary(problem)
-        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
         sigma_p = Map_sigma_p._transform(self.sigma1d)
         # Make mass matrix
         # Note: M(sig) - M(sig_p) = M(sig - sig_p)
@@ -163,7 +163,7 @@ class polxy_3Dprimary(BaseMTSrc):
         Get the electrical field source
         """
         e_p = self.ePrimary(problem)
-        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
         sigma_p = Map_sigma_p._transform(self.sigma1d)
         # Make mass matrix
         # Note: M(sig) - M(sig_p) = M(sig - sig_p)

SimPEG/MT/Utils/dataUtils.py

@@ -19,7 +19,7 @@ def getAppRes(MTdata):
         zList.append(zc)
     return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
 
-def rotateData(MTdata,rotAngle):
+def rotateData(MTdata, rotAngle):
     '''
     Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
     '''
@@ -44,19 +44,19 @@ def rotateData(MTdata,rotAngle):
     return MT.Data.fromRecArray(outRec)
 
 
-def appResPhs(freq,z):
+def appResPhs(freq, z):
     app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
     app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
     return app_res, app_phs
 
-def skindepth(rho,freq):
+def skindepth(rho, freq):
     ''' Function to calculate the skindepth of EM waves'''
     return np.sqrt( (rho*((1/(freq * mu_0 * np.pi )))))
 
-def rec2ndarr(x,dt=float):
+def rec2ndarr(x, dt=float):
     return x.view((dt, len(x.dtype.names)))
 
-def makeAnalyticSolution(mesh,model,elev,freqs):
+def makeAnalyticSolution(mesh, model, elev, freqs):
     from SimPEG import MT
     data1D = []
     for freq in freqs:
@@ -70,7 +70,7 @@ def makeAnalyticSolution(mesh,model,elev,freqs):
     dataRec = np.array(data1D,dtype=[('freq',float),('x',float),('y',float),('z',float),('zyx',complex)])
     return dataRec
 
-def plotMT1DModelData(problem,models,symList=None):
+def plotMT1DModelData(problem, models, symList=None):
     from SimPEG import MT
     # Setup the figure
     fontSize = 15

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/Mesh/BaseMesh.py

@@ -7,8 +7,8 @@ class BaseMesh(object):
     BaseMesh does all the counting you don't want to do.
     BaseMesh should be inherited by meshes with a regular structure.
 
-    :param numpy.array,list n: number of cells in each direction (dim, )
-    :param numpy.array,list x0: Origin of the mesh (dim, )
+    :param numpy.array n: (or list) number of cells in each direction (dim, )
+    :param numpy.array x0: (or list) Origin of the mesh (dim, )
 
     """
 
@@ -34,8 +34,8 @@ class BaseMesh(object):
         """
         Origin of the mesh
 
-        :rtype: numpy.array (dim, )
-        :return: x0
+        :rtype: numpy.array
+        :return: x0, (dim, )
         """
         return self._x0
 
@@ -116,8 +116,8 @@ class BaseMesh(object):
         """
         Total number of edges in each direction
 
-        :rtype: numpy.array (dim, )
-        :return: [nEx, nEy, nEz]
+        :rtype: numpy.array
+        :return: [nEx, nEy, nEz], (dim, )
 
         .. plot::
             :include-source:
@@ -173,8 +173,8 @@ class BaseMesh(object):
         """
         Total number of faces in each direction
 
-        :rtype: numpy.array (dim, )
-        :return: [nFx, nFy, nFz]
+        :rtype: numpy.array
+        :return: [nFx, nFy, nFz], (dim, )
 
         .. plot::
             :include-source:
@@ -200,8 +200,8 @@ class BaseMesh(object):
         """
         Face Normals
 
-        :rtype: numpy.array (sum(nF), dim)
-        :return: normals
+        :rtype: numpy.array
+        :return: normals, (sum(nF), dim)
         """
         if self.dim == 2:
             nX = np.c_[np.ones(self.nFx), np.zeros(self.nFx)]
@@ -218,8 +218,8 @@ class BaseMesh(object):
         """
         Edge Tangents
 
-        :rtype: numpy.array (sum(nE), dim)
-        :return: normals
+        :rtype: numpy.array
+        :return: normals, (sum(nE), dim)
         """
         if self.dim == 2:
             tX = np.c_[np.ones(self.nEx), np.zeros(self.nEx)]
@@ -236,8 +236,9 @@ class BaseMesh(object):
         Given a vector, fV, in cartesian coordinates, this will project it onto the mesh using the normals
 
         :param numpy.array fV: face vector with shape (nF, dim)
-        :rtype: numpy.array with shape (nF, )
-        :return: projected face vector
+        :rtype: numpy.array
+        :return: projected face vector, (nF, )
+
         """
         assert isinstance(fV, np.ndarray), 'fV must be an ndarray'
         assert len(fV.shape) == 2 and fV.shape[0] == self.nF and fV.shape[1] == self.dim, 'fV must be an ndarray of shape (nF x dim)'
@@ -248,8 +249,9 @@ class BaseMesh(object):
         Given a vector, eV, in cartesian coordinates, this will project it onto the mesh using the tangents
 
         :param numpy.array eV: edge vector with shape (nE, dim)
-        :rtype: numpy.array with shape (nE, )
-        :return: projected edge vector
+        :rtype: numpy.array
+        :return: projected edge vector, (nE, )
+
         """
         assert isinstance(eV, np.ndarray), 'eV must be an ndarray'
         assert len(eV.shape) == 2 and eV.shape[0] == self.nE and eV.shape[1] == self.dim, 'eV must be an ndarray of shape (nE x dim)'
@@ -295,7 +297,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Total number of cells in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: [nCx, nCy, nCz]
         """
         return np.array([x for x in [self.nCx, self.nCy, self.nCz] if not x is None])
@@ -335,7 +337,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Total number of nodes in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: [nNx, nNy, nNz]
         """
         return np.array([x for x in [self.nNx, self.nNy, self.nNz] if not x is None])
@@ -345,7 +347,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of x-edges in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnEx
         """
         return np.array([x for x in [self.nCx, self.nNy, self.nNz] if not x is None])
@@ -355,7 +357,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of y-edges in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnEy or None if dim < 2
         """
         return None if self.dim < 2 else np.array([x for x in [self.nNx, self.nCy, self.nNz] if not x is None])
@@ -365,7 +367,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of z-edges in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnEz or None if dim < 3
         """
         return None if self.dim < 3 else np.array([x for x in [self.nNx, self.nNy, self.nCz] if not x is None])
@@ -375,7 +377,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of x-faces in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnFx
         """
         return np.array([x for x in [self.nNx, self.nCy, self.nCz] if not x is None])
@@ -385,7 +387,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of y-faces in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnFy or None if dim < 2
         """
         return None if self.dim < 2 else np.array([x for x in [self.nCx, self.nNy, self.nCz] if not x is None])
@@ -395,7 +397,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of z-faces in each direction
 
-        :rtype: numpy.array (dim, )
+        :rtype: numpy.array
         :return: vnFz or None if dim < 3
         """
         return None if self.dim < 3 else np.array([x for x in [self.nCx, self.nCy, self.nNz] if not x is None])

SimPEG/Mesh/CurvilinearMesh.py

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

SimPEG/Mesh/CylMesh.py

@@ -68,8 +68,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
         """
         Number of x-faces in each direction
 
-        :rtype: numpy.array (dim, )
-        :return: vnFx
+        :rtype: numpy.array 
+        :return: vnFx, (dim, )
         """
         return self.vnC
 
@@ -78,8 +78,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
         """
         Number of y-edges in each direction
 
-        :rtype: numpy.array (dim, )
-        :return: vnEy or None if dim < 2
+        :rtype: numpy.array 
+        :return: vnEy or None if dim < 2, (dim, )
         """
         nNx = self.nNx if self.isSymmetric else self.nNx - 1
         return np.r_[nNx, self.nCy, self.nNz]
@@ -89,8 +89,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
         """
         Number of z-edges in each direction
 
-        :rtype: numpy.array (dim, )
-        :return: vnEz or None if nCy > 1
+        :rtype: numpy.array 
+        :return: vnEz or None if nCy > 1, (dim, )
         """
         if self.isSymmetric:
             return np.r_[self.nNx, self.nNy, self.nCz]
@@ -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/InnerProducts.py

@@ -16,7 +16,7 @@ class InnerProducts(object):
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
             :param bool doFast: do a faster implementation if available.
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: M, the inner product matrix (nF, nF)
         """
         return self._getInnerProduct('F', prop=prop, invProp=invProp, invMat=invMat, doFast=doFast)
@@ -27,7 +27,7 @@ class InnerProducts(object):
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
             :param bool doFast: do a faster implementation if available.
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: M, the inner product matrix (nE, nE)
         """
         return self._getInnerProduct('E', prop=prop, invProp=invProp, invMat=invMat, doFast=doFast)
@@ -39,7 +39,7 @@ class InnerProducts(object):
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
             :param bool doFast: do a faster implementation if available.
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: M, the inner product matrix (nE, nE)
         """
         assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"
@@ -115,13 +115,12 @@ class InnerProducts(object):
             :param bool doFast: do a faster implementation if available.
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
-            :rtype: function
             :return: dMdmu(u), the derivative of the inner product matrix (u)
 
             Given u, dMdmu returns (nF, nC*nA)
 
-            :param np.ndarray u: vector that multiplies dMdmu
-            :rtype: scipy.csr_matrix
+            :param numpy.ndarray u: vector that multiplies dMdmu
+            :rtype: scipy.sparse.csr_matrix
             :return: dMdmu, the derivative of the inner product matrix for a certain u
         """
         return self._getInnerProductDeriv(prop, 'F', doFast=doFast, invProp=invProp, invMat=invMat)
@@ -133,7 +132,7 @@ class InnerProducts(object):
             :param bool doFast: do a faster implementation if available.
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: dMdm, the derivative of the inner product matrix (nE, nC*nA)
         """
         return self._getInnerProductDeriv(prop, 'E', doFast=doFast, invProp=invProp, invMat=invMat)
@@ -145,7 +144,7 @@ class InnerProducts(object):
             :param bool doFast: do a faster implementation if available.
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: dMdm, the derivative of the inner product matrix (nE, nC*nA)
         """
         fast = None
@@ -169,7 +168,7 @@ class InnerProducts(object):
             :param numpy.array v: vector to multiply (required in the general implementation)
             :param list P: list of projection matrices
             :param str projType: 'F' for faces 'E' for edges
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: dMdm, the derivative of the inner product matrix (n, nC*nA)
         """
         assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"

SimPEG/Mesh/MeshIO.py

@@ -6,13 +6,11 @@ class TensorMeshIO(object):
     @classmethod
     def readUBC(TensorMesh, fileName):
         """
-            Read UBC GIF 3DTensor mesh and generate 3D Tensor mesh in simpegTD
+            Read UBC GIF 3D tensor mesh and generate 3D TensorMesh in SimPEG.
 
-            Input:
-            :param fileName, path to the UBC GIF mesh file
-
-            Output:
-            :param SimPEG TensorMesh object
+            :param string fileName: path to the UBC GIF mesh file
+            :rtype: TensorMesh
+            :return: The tensor mesh for the fileName.
         """
 
         # Interal function to read cell size lines for the UBC mesh files.
@@ -24,7 +22,6 @@ 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='!')
 
@@ -49,11 +46,9 @@ class TensorMeshIO(object):
             Read VTK Rectilinear (vtr xml file) and return SimPEG Tensor mesh and model
 
             Input:
-            :param vtrFileName, path to the vtr model file to write to
-
-            Output:
-            :return SimPEG TensorMesh object
-            :return SimPEG model dictionary
+            :param string fileName: path to the vtr model file to read
+            :rtype: tuple
+            :return: (TensorMesh, modelDictionary)
 
         """
         # Import
@@ -103,9 +98,8 @@ class TensorMeshIO(object):
         Makes and saves a VTK rectilinear file (vtr) for a simpeg Tensor mesh and model.
 
         Input:
-        :param str, path to the output vtk file
-        :param mesh, SimPEG TensorMesh object - mesh to be transfer to VTK
-        :param models, dictionary of numpy.array - Name('s) and array('s). Match number of cells
+        :param string fileName: path to the output vtk file
+        :param dict models: dictionary of numpy.array - Name('s) and array('s). Match number of cells
 
         """
         # Import
@@ -163,12 +157,9 @@ class TensorMeshIO(object):
         """
             Read UBC 3DTensor mesh model and generate 3D Tensor mesh model in simpeg
 
-            Input:
-            :param fileName, path to the UBC GIF mesh file to read
-            :param mesh, TensorMesh object, mesh that coresponds to the model
-
-            Output:
-            :return numpy array, model with TensorMesh ordered
+            :param string fileName: path to the UBC GIF mesh file to read
+            :rtype: numpy.ndarray
+            :return: model with TensorMesh ordered
         """
         f = open(fileName, 'r')
         model = np.array(map(float, f.readlines()))
@@ -184,8 +175,7 @@ class TensorMeshIO(object):
             Writes a model associated with a SimPEG TensorMesh
             to a UBC-GIF format model file.
 
-            :param str fileName: File to write to
-            :param simpeg.Mesh.TensorMesh mesh: The mesh
+            :param string fileName: File to write to
             :param numpy.ndarray model: The model
         """
 
@@ -202,8 +192,8 @@ class TensorMeshIO(object):
         """
             Writes a SimPEG TensorMesh to a UBC-GIF format mesh file.
 
-            :param str fileName: File to write to
-            :param simpeg.Mesh.TensorMesh mesh: The mesh
+            :param string fileName: File to write to
+            :param dict models: A dictionary of the models
 
         """
         assert mesh.dim == 3
@@ -232,9 +222,8 @@ class TreeMeshIO(object):
         """
             Write UBC ocTree mesh and model files from a simpeg ocTree mesh and model.
 
-            :param str fileName: File to write to
-            :param simpeg.Mesh.TreeMesh mesh: The mesh
-            :param dictionary models: The models in a dictionary, where the keys is the name of the of the model file
+            :param string fileName: File to write to
+            :param dict models: The models in a dictionary, where the keys is the name of the of the model file
         """
 
         # Calculate information to write in the file.
@@ -287,10 +276,9 @@ class TreeMeshIO(object):
 
             Input:
             :param str meshFile: path to the UBC GIF OcTree mesh file to read
+            :rtype: SimPEG.Mesh.TreeMesh
+            :return: The octree mesh
 
-            Output:
-            :return SimPEG.Mesh.TreeMesh mesh: The octree mesh
-            :return list of ndarray's: models as a list of numpy array's
         """
 
         ## Read the file lines
@@ -336,11 +324,9 @@ class TreeMeshIO(object):
         """
             Read UBC OcTree model and get vector
 
-            Input:
-            :param fileName, path to the UBC GIF model file to read
-
-            Output:
-            :return numpy array, OcTree model
+            :param string fileName: path to the UBC GIF model file to read
+            :rtype: numpy.ndarray
+            :return: OcTree model
         """
 
         if type(fileName) is list:

SimPEG/Mesh/TensorMesh.py

@@ -198,8 +198,8 @@ class BaseTensorMesh(BaseMesh):
         Determines if a set of points are inside a mesh.
 
         :param numpy.ndarray pts: Location of points to test
-        :rtype numpy.ndarray
-        :return inside, numpy array of booleans
+        :rtype numpy.ndarray:
+        :return: inside, numpy array of booleans
         """
         pts = Utils.asArray_N_x_Dim(pts, self.dim)
 
@@ -221,7 +221,7 @@ class BaseTensorMesh(BaseMesh):
 
         :param numpy.ndarray loc: Location of points to interpolate to
         :param str locType: What to interpolate (see below)
-        :rtype: scipy.sparse.csr.csr_matrix
+        :rtype: scipy.sparse.csr_matrix
         :return: M, the interpolation matrix
 
         locType can be::
@@ -289,7 +289,7 @@ class BaseTensorMesh(BaseMesh):
             :param bool returnP: returns the projection matrices
             :param bool invProp: inverts the material property
             :param bool invMat: inverts the matrix
-            :rtype: scipy.csr_matrix
+            :rtype: scipy.sparse.csr_matrix
             :return: M, the inner product matrix (nF, nF)
         """
         assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"

SimPEG/Mesh/TreeMesh.py

@@ -1875,7 +1875,7 @@ class TreeMesh(BaseTensorMesh, InnerProducts, TreeMeshIO):
 
         :param numpy.ndarray locs: Location of points to interpolate to
         :param str locType: What to interpolate (see below)
-        :rtype: scipy.sparse.csr.csr_matrix
+        :rtype: scipy.sparse.csr_matrix
         :return: M, the interpolation matrix
 
         locType can be::

SimPEG/Mesh/View.py

@@ -552,7 +552,8 @@ class CurvView(object):
     def __init__(self):
         pass
 
-    def plotGrid(self, length=0.05, showIt=False):
+
+    def plotGrid(self, ax=None, nodes=False, faces=False, centers=False, edges=False, lines=True,  showIt=False):
         """Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions.
 
 
@@ -560,60 +561,63 @@ class CurvView(object):
             :include-source:
 
             from SimPEG import Mesh, Utils
-            X, Y = Utils.exampleCurvGird([3,3],'rotate')
+            X, Y = Utils.exampleLrmGrid([3,3],'rotate')
             M = Mesh.CurvilinearMesh([X, Y])
             M.plotGrid(showIt=True)
 
         """
+        import matplotlib.pyplot as plt
+        import matplotlib
+        from mpl_toolkits.mplot3d import Axes3D
+
+        axOpts = {'projection':'3d'} if self.dim == 3 else {}
+        if ax is None: ax = plt.subplot(111, **axOpts)
+
         NN = self.r(self.gridN, 'N', 'N', 'M')
         if self.dim == 2:
-            fig = plt.figure(2)
-            fig.clf()
-            ax = plt.subplot(111)
-            X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
-            Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
 
-            X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
-            Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
+            if lines:
+                X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
+                Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
 
-            X = np.r_[X1, X2]
-            Y = np.r_[Y1, Y2]
+                X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
+                Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
 
-            plt.plot(X, Y)
+                X = np.r_[X1, X2]
+                Y = np.r_[Y1, Y2]
 
-            plt.hold(True)
-            Nx = self.r(self.normals, 'F', 'Fx', 'V')
-            Ny = self.r(self.normals, 'F', 'Fy', 'V')
-            Tx = self.r(self.tangents, 'E', 'Ex', 'V')
-            Ty = self.r(self.tangents, 'E', 'Ey', 'V')
+                ax.plot(X, Y, 'b-')
+            if centers:
+                ax.plot(self.gridCC[:,0],self.gridCC[:,1],'ro')
 
-            plt.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
+            # Nx = self.r(self.normals, 'F', 'Fx', 'V')
+            # Ny = self.r(self.normals, 'F', 'Fy', 'V')
+            # Tx = self.r(self.tangents, 'E', 'Ex', 'V')
+            # Ty = self.r(self.tangents, 'E', 'Ey', 'V')
 
-            nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
-            plt.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
-            plt.plot(nX, nY, 'r-')
+            # ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
 
-            nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
-            #plt.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
-            plt.plot(nX, nY, 'g-')
+            # nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
+            # ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
+            # ax.plot(nX, nY, 'r-')
 
-            tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
-            tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
-            plt.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
-            plt.plot(tX, tY, 'r-')
+            # nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
+            # #ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
+            # ax.plot(nX, nY, 'g-')
 
-            nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
-            nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
-            #plt.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
-            plt.plot(nX, nY, 'g-')
-            plt.axis('equal')
+            # tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
+            # tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
+            # ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
+            # ax.plot(tX, tY, 'r-')
+
+            # nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
+            # nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
+            # #ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
+            # ax.plot(nX, nY, 'g-')
 
         elif self.dim == 3:
-            fig = plt.figure(3)
-            fig.clf()
-            ax = fig.add_subplot(111, projection='3d')
             X1 = np.c_[mkvc(NN[0][:-1, :, :]), mkvc(NN[0][1:, :, :]), mkvc(NN[0][:-1, :, :])*np.nan].flatten()
             Y1 = np.c_[mkvc(NN[1][:-1, :, :]), mkvc(NN[1][1:, :, :]), mkvc(NN[1][:-1, :, :])*np.nan].flatten()
             Z1 = np.c_[mkvc(NN[2][:-1, :, :]), mkvc(NN[2][1:, :, :]), mkvc(NN[2][:-1, :, :])*np.nan].flatten()
@@ -630,16 +634,50 @@ class CurvView(object):
             Y = np.r_[Y1, Y2, Y3]
             Z = np.r_[Z1, Z2, Z3]
 
-            plt.plot(X, Y, 'b', zs=Z)
+            ax.plot(X, Y, 'b', zs=Z)
             ax.set_zlabel('x3')
 
         ax.grid(True)
-        ax.hold(False)
         ax.set_xlabel('x1')
         ax.set_ylabel('x2')
 
         if showIt: plt.show()
 
+    def plotImage(self, I, ax=None, showIt=False, grid=False, clim=None):
+        if self.dim == 3: raise NotImplementedError('This is not yet done!')
+
+        import matplotlib.pyplot as plt
+        import matplotlib
+        from mpl_toolkits.mplot3d import Axes3D
+        import matplotlib.colors as colors
+        import matplotlib.cm as cmx
+
+        if ax is None: ax = plt.subplot(111)
+        jet = cm = plt.get_cmap('jet')
+        cNorm  = colors.Normalize(
+            vmin=I.min() if clim is None else clim[0],
+            vmax=I.max() if clim is None else clim[1])
+
+        scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
+        # ax.set_xlim((self.x0[0], self.h[0].sum()))
+        # ax.set_ylim((self.x0[1], self.h[1].sum()))
+
+        Nx = self.r(self.gridN[:,0],'N','N','M')
+        Ny = self.r(self.gridN[:,1],'N','N','M')
+        cell = self.r(I,'CC','CC','M')
+
+        for ii in range(self.nCx):
+            for jj in range(self.nCy):
+                I = [ii,ii+1,ii+1,ii]
+                J = [jj,jj,jj+1,jj+1]
+                ax.add_patch(plt.Polygon(np.c_[Nx[I,J],Ny[I,J]], facecolor=scalarMap.to_rgba(cell[ii,jj]), edgecolor='k' if grid else 'none'))
+
+        scalarMap._A = []  # http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots
+        ax.set_xlabel('x')
+        ax.set_ylabel('y')
+        if showIt: plt.show()
+        return [scalarMap]
+
 
 if __name__ == '__main__':
     from SimPEG import *

SimPEG/Optimization.py

@@ -131,7 +131,7 @@ class Minimize(object):
 
         Minimizes the function (evalFunction) starting at the location x0.
 
-        :param def evalFunction: function handle that evaluates: f, g, H = F(x)
+        :param callable evalFunction: function handle that evaluates: f, g, H = F(x)
         :param numpy.ndarray x0: starting location
         :rtype: numpy.ndarray
         :return: x, the last iterate of the optimization algorithm
@@ -372,8 +372,8 @@ class Minimize(object):
             Else, a modifySearchDirectionBreak call is preformed.
 
             :param numpy.ndarray p: searchDirection
-            :rtype: numpy.ndarray,bool
-            :return: (xt, passLS)
+            :rtype: tuple
+            :return: (xt, passLS) numpy.ndarray, bool
         """
         # Projected Armijo linesearch
         self._LS_t = 1
@@ -408,8 +408,8 @@ class Minimize(object):
             evalFunction returns a False indicating the break was not caught.
 
             :param numpy.ndarray p: searchDirection
-            :rtype: numpy.ndarray,bool
-            :return: (xt, breakCaught)
+            :rtype: tuple
+            :return: (xt, breakCaught) numpy.ndarray, bool
         """
         self.printDone(inLS=True)
         print 'The linesearch got broken. Boo.'
@@ -1008,4 +1008,4 @@ class ProjectedGNCG(BFGS, Minimize, Remember):
             indx = ((self.xc<=self.lower) & (delx < 0)) | ((self.xc>=self.upper) & (delx > 0))
             delx[indx] = 0.
 
-            return delx
+            return delx

SimPEG/PropMaps.py

@@ -74,7 +74,7 @@ class Property(object):
                 if linkedMap is None:
                     return None
                 linkMap = linkMapClass(None) * linkedMap
-                m = getattr(self, '%s'%linkName)
+                m = getattr(self, '%sModel'%linkName)
                 return linkMap.deriv( m )
 
             m = getattr(self, '%sModel'%prop.name)
@@ -187,7 +187,7 @@ class _PropMapMetaClass(type):
             attrs[attr + 'Model'] = prop._getModelProperty()
             attrs[attr + 'Deriv'] = prop._getModelDerivProperty()
 
-        return type(name.replace('PropMap', 'PropModel'), (PropModel, ), attrs)
+        return type('PropModel', (PropModel, ), attrs)
 
 
 class PropMap(object):
@@ -239,7 +239,7 @@ class PropMap(object):
             setattr(self, '%sMap'%name, mapping)
             setattr(self, '%sIndex'%name, slices.get(name, slice(nP, nP + mapping.nP)))
             nP += mapping.nP
-        self.nP = nP 
+        self.nP = nP
 
     @property
     def defaultInvProp(self):

SimPEG/Regularization.py

@@ -1,4 +1,6 @@
-import Utils, Maps, Mesh, numpy as np, scipy.sparse as sp
+import Utils, Maps, Mesh
+import numpy as np
+import scipy.sparse as sp
 
 class RegularizationMesh(object):
     """
@@ -8,7 +10,7 @@ class RegularizationMesh(object):
     are not necessarily true differential operators, but are constructed from
     a SimPEG Mesh.
 
-    :param Mesh mesh: problem mesh
+    :param BaseMesh mesh: problem mesh
     :param numpy.array indActive: bool array, size nC, that is True where we have active cells. Used to reduce the operators so we regularize only on active cells
     """
 
@@ -311,6 +313,9 @@ class BaseRegularization(object):
             tmp = indActive
             indActive = np.zeros(mesh.nC, dtype=bool)
             indActive[tmp] = True
+        if indActive is not None and mapping is None:
+            mapping = Maps.IdentityMap(nP=indActive.nonzero()[0].size)
+
         self.regmesh = RegularizationMesh(mesh,indActive)
         self.mapping = mapping or self.mapPair(mesh)
         self.mapping._assertMatchesPair(self.mapPair)
@@ -378,8 +383,8 @@ class BaseRegularization(object):
 
         :param numpy.array m: geophysical model
         :param numpy.array v: vector to multiply
-        :rtype: scipy.sparse.csr_matrix or numpy.ndarray
-        :return: WtW or WtW*v
+        :rtype: scipy.sparse.csr_matrix
+        :return: WtW, or if v is supplied WtW*v (numpy.ndarray)
 
         The regularization is:
 
@@ -400,7 +405,238 @@ class BaseRegularization(object):
 
         return mD.T * ( self.W.T * ( self.W * ( mD * v) ) )
 
-class Tikhonov(BaseRegularization):
+class Simple(BaseRegularization):
+    """
+    Simple regularization that does not include length scales in the derivatives.
+    """
+
+    mrefInSmooth = False  #: include mref in the smoothness?
+    alpha_s      = Utils.dependentProperty('_alpha_s', 1.0, ['_W', '_Wsmall'], "Smallness weight")
+    alpha_x      = Utils.dependentProperty('_alpha_x', 1.0, ['_W', '_Wx'],     "Weight for the first derivative in the x direction")
+    alpha_y      = Utils.dependentProperty('_alpha_y', 1.0, ['_W', '_Wy'],     "Weight for the first derivative in the y direction")
+    alpha_z      = Utils.dependentProperty('_alpha_z', 1.0, ['_W', '_Wz'],     "Weight for the first derivative in the z direction")
+    cell_weights = 1.
+
+    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
+        BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+
+        if isinstance(self.cell_weights,float):
+            self.cell_weights = np.ones(self.regmesh.nC) * self.cell_weights
+
+    @property
+    def Wsmall(self):
+        """Regularization matrix Wsmall"""
+        if getattr(self,'_Wsmall', None) is None:
+            self._Wsmall = Utils.sdiag((self.alpha_s*self.cell_weights)**0.5)
+        return self._Wsmall
+
+    @property
+    def Wx(self):
+        """Regularization matrix Wx"""
+        if getattr(self, '_Wx', None) is None:
+            self._Wx = Utils.sdiag((self.alpha_x * (self.regmesh.aveCC2Fx*self.cell_weights))**0.5)*self.regmesh.cellDiffxStencil
+        return self._Wx
+
+    @property
+    def Wy(self):
+        """Regularization matrix Wy"""
+        if getattr(self, '_Wy', None) is None:
+            self._Wy = Utils.sdiag((self.alpha_y * (self.regmesh.aveCC2Fy*self.cell_weights))**0.5)*self.regmesh.cellDiffyStencil
+        return self._Wy
+
+    @property
+    def Wz(self):
+        """Regularization matrix Wz"""
+        if getattr(self, '_Wz', None) is None:
+            self._Wz = Utils.sdiag((self.alpha_z * (self.regmesh.aveCC2Fz*self.cell_weights))**0.5)*self.regmesh.cellDiffzStencil
+        return self._Wz
+
+#    @property
+#    def Wsmooth(self):
+#        """Full smoothness regularization matrix W"""
+#        print 'wtf why are we using Wsmooth'
+#        raise NotImplementedError
+#        if getattr(self, '_Wsmooth', None) is None:
+#            wlist = (self.Wx,)
+#            if self.regmesh.dim > 1:
+#                wlist += (self.Wy,)
+#            if self.regmesh.dim > 2:
+#                wlist += (self.Wz,)
+#            self._Wsmooth = sp.vstack(wlist)
+#        return self._Wsmooth
+#
+#    @property
+#    def W(self):
+#        """Full regularization matrix W"""
+#        print 'wtf why are we using W'
+#        if getattr(self, '_W', None) is None:
+#            wlist = (self.Wsmall, self.Wx)
+#            if self.regmesh.dim > 1:
+#                wlist += (self.Wy,)
+#            if self.regmesh.dim > 2:
+#                wlist += (self.Wz,)
+#            self._W = sp.vstack(wlist)
+#        return self._W
+
+
+    @Utils.timeIt
+    def _evalSmall(self, m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmallDeriv(self, m):
+        r = self.Wsmall * ( self.mapping * (m - self.mref) )
+        return r.T * ( self.Wsmall * self.mapping.deriv(m - self.mref) )
+
+    @Utils.timeIt
+    def _evalSmall2Deriv(self, m, v = None):
+        rDeriv = self.Wsmall * ( self.mapping.deriv(m - self.mref) )
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothx(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wx * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wx * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothy(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wy * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wy * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothz(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wz * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wz * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmooth(self, m):
+        phiSmooth = self._evalSmoothx(m)
+        if self.regmesh.dim > 1:
+            phiSmooth += self._evalSmoothy(m)
+        if self.regmesh.dim > 2:
+            phiSmooth += self._evalSmoothz(m)
+        return phiSmooth
+
+    @Utils.timeIt
+    def _evalSmoothxDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wx * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wx * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wx * ( self.mapping * m )
+            return r.T * ( self.Wx * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothx2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wx * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wx * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothyDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wy * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wy * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wy * ( self.mapping * m )
+            return r.T * ( self.Wy * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothy2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wy * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wy * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothzDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wz * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wz * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wz * ( self.mapping * m )
+            return r.T * ( self.Wz * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothz2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wz * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wz * ( self.mapping.deriv(m) )
+
+        if v is not None:
+            return rDeriv.T * ( rDeriv * v )
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothDeriv(self, m):
+        deriv = self._evalSmoothxDeriv(m)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyDeriv(m)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzDeriv(m)
+        return deriv
+
+    @Utils.timeIt
+    def _evalSmooth2Deriv(self, m, v=None):
+        deriv = self._evalSmoothx2Deriv(m, v)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothy2Deriv(m, v)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothz2Deriv(m, v)
+        return deriv
+
+
+    @Utils.timeIt
+    def eval(self, m):
+        return self._evalSmall(m) + self._evalSmooth(m)
+
+    @Utils.timeIt
+    def evalDeriv(self, m):
+        """
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
+
+        """
+        return self._evalSmallDeriv(m) + self._evalSmoothDeriv(m)
+
+    @Utils.timeIt
+    def eval2Deriv(self, m, v=None):
+        return self._evalSmall2Deriv(m, v) + self._evalSmooth2Deriv(m, v)
+
+
+
+class Tikhonov(Simple):
     """
     L2 Tikhonov regularization with both smallness and smoothness (first order
     derivative) contributions.
@@ -414,8 +650,8 @@ class Tikhonov(BaseRegularization):
     Note if the key word argument `mrefInSmooth` is False, then mref is not
     included in the smoothness contribution.
 
-    :param Mesh mesh: SimPEG mesh
-    :param Maps mapping: regularization mapping, takes the model from model space to the thing you want to regularize
+    :param BaseMesh mesh: SimPEG mesh
+    :param IdentityMap mapping: regularization mapping, takes the model from model space to the thing you want to regularize
     :param numpy.ndarray indActive: active cell indices for reducing the size of differential operators in the definition of a regularization mesh
     :param bool mrefInSmooth: (default = False) put mref in the smoothness component?
     :param float alpha_s: (default 1e-6) smallness weight
@@ -435,7 +671,7 @@ class Tikhonov(BaseRegularization):
     alpha_yy     = Utils.dependentProperty('_alpha_yy', 0.0, ['_W', '_Wyy'],    "Weight for the second derivative in the y direction")
     alpha_zz     = Utils.dependentProperty('_alpha_zz', 0.0, ['_W', '_Wzz'],    "Weight for the second derivative in the z direction")
 
-    def __init__(self, mesh, mapping=None, indActive = None, **kwargs):
+    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
         BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
 
     @property
@@ -490,56 +726,131 @@ class Tikhonov(BaseRegularization):
             self._Wzz = Utils.sdiag((self.regmesh.vol*self.alpha_zz)**0.5)*self.regmesh.faceDiffz*self.regmesh.cellDiffz
         return self._Wzz
 
+
     @property
-    def Wsmooth(self):
+    def Wsmooth2(self):
         """Full smoothness regularization matrix W"""
         if getattr(self, '_Wsmooth', None) is None:
-            wlist = (self.Wx, self.Wxx)
+            wlist = (self.Wxx)
             if self.regmesh.dim > 1:
-                wlist += (self.Wy, self.Wyy)
+                wlist += (self.Wyy)
             if self.regmesh.dim > 2:
-                wlist += (self.Wz, self.Wzz)
+                wlist += (self.Wzz)
             self._Wsmooth = sp.vstack(wlist)
         return self._Wsmooth
 
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        if getattr(self, '_W', None) is None:
-            wlist = (self.Wsmall, self.Wsmooth)
-            self._W = sp.vstack(wlist)
-        return self._W
-
     @Utils.timeIt
-    def _evalSmall(self, m):
-        r = self.Wsmall * ( self.mapping * (m - self.mref) )
-        return 0.5 * r.dot(r)
-
-    @Utils.timeIt
-    def _evalSmooth(self, m):
+    def _evalSmoothxx(self, m):
         if self.mrefInSmooth == True:
-            r = self.Wsmooth * ( self.mapping * (m - self.mref) )
+            r = self.Wxx * ( self.mapping * (m - self.mref) )
         elif self.mrefInSmooth == False:
-            r = self.Wsmooth * ( self.mapping * (m) )
+            r = self.Wxx * ( self.mapping * (m) )
         return 0.5 * r.dot(r)
 
+    @Utils.timeIt
+    def _evalSmoothyy(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wyy * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wyy * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmoothzz(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wzz * ( self.mapping * (m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wzz * ( self.mapping * (m) )
+        return 0.5 * r.dot(r)
+
+    @Utils.timeIt
+    def _evalSmooth2(self, m):
+        phiSmooth2 = self._evalSmoothxx(m)
+        if self.regmesh.dim > 1:
+            phiSmooth2 += self._evalSmoothyy(m)
+        if self.regmesh.dim > 2:
+            phiSmooth2 += self._evalSmoothzz(m)
+        return phiSmooth2
+
+    @Utils.timeIt
+    def _evalSmoothxxDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wxx * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wxx * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wxx * ( self.mapping * m )
+            return r.T * ( self.Wxx * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothyyDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wyy * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wyy * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wyy * ( self.mapping * m )
+            return r.T * ( self.Wyy * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothzzDeriv(self, m):
+        if self.mrefInSmooth == True:
+            r = self.Wzz * ( self.mapping * ( m - self.mref ) )
+            return r.T * ( self.Wzz * self.mapping.deriv(m - self.mref) )
+        elif self.mrefInSmooth == False:
+            r = self.Wzz * ( self.mapping * m )
+            return r.T * ( self.Wzz * self.mapping.deriv(m) )
+
+    @Utils.timeIt
+    def _evalSmoothxx2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wxx * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wxx * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothyy2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wyy * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wyy * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothzz2Deriv(self, m, v=None):
+        if self.mrefInSmooth == True:
+            rDeriv = self.Wzz * ( self.mapping.deriv( m - self.mref ) )
+        elif self.mrefInSmooth == False:
+            rDeriv = self.Wzz * self.mapping.deriv(m)
+        if v is not None:
+            return rDeriv.T * (rDeriv * v)
+        return rDeriv.T * rDeriv
+
+    @Utils.timeIt
+    def _evalSmoothDeriv2(self, m):
+        deriv = self._evalSmoothxxDeriv(m)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyyDeriv(m)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzzDeriv(m)
+        return deriv
+
+    @Utils.timeIt
+    def _evalSmooth2Deriv2(self, m, v=None):
+        deriv = self._evalSmoothxx2Deriv(m, v)
+        if self.regmesh.dim > 1:
+            deriv += self._evalSmoothyy2Deriv(m, v)
+        if self.regmesh.dim > 2:
+            deriv += self._evalSmoothzz2Deriv(m, v)
+        return deriv
+
+
     @Utils.timeIt
     def eval(self, m):
-        return self._evalSmall(m) + self._evalSmooth(m)
-
-    @Utils.timeIt
-    def _evalSmallDeriv(self,m):
-        r = self.Wsmall * ( self.mapping * (m - self.mref) )
-        return r.T * ( self.Wsmall * self.mapping.deriv(m - self.mref) )
-
-    @Utils.timeIt
-    def _evalSmoothDeriv(self,m):
-        if self.mrefInSmooth == True:
-            r = self.Wsmooth * ( self.mapping * ( m - self.mref ) )
-            return r.T * ( self.Wsmooth * self.mapping.deriv(m - self.mref) )
-        elif self.mrefInSmooth == False:
-            r = self.Wsmooth * ( self.mapping * m )
-            return r.T * ( self.Wsmooth * self.mapping.deriv(m) )
+        return self._evalSmall(m) + self._evalSmooth(m) + self._evalSmooth2(m)
 
     @Utils.timeIt
     def evalDeriv(self, m):
@@ -557,185 +868,135 @@ class Tikhonov(BaseRegularization):
             R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
 
         """
-        return self._evalSmallDeriv(m) + self._evalSmoothDeriv(m)
+        return self._evalSmallDeriv(m) + self._evalSmoothDeriv(m) + self._evalSmoothDeriv2(m)
+
+    def eval2Deriv(self, m, v=None):
+        """
+        The regularization is:
+
+        .. math::
+
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top W(m-m_\\text{ref})}
+
+        So the derivative is straight forward:
+
+        .. math::
+
+            R(m) = \mathbf{W^\\top W (m-m_\\text{ref})}
+
+        """
+        return self._evalSmall2Deriv(m, v) + self._evalSmooth2Deriv(m, v) + self._evalSmooth2Deriv2(m, v)
 
 
-class Simple(Tikhonov):
+
+class Sparse(Simple):
     """
-    Simple regularization that does not include length scales in the derivatives.
+        The regularization is:
+    
+        .. math::
+    
+            R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top R^\\top R W(m-m_\\text{ref})}
+    
+        where the IRLS weight
+    
+        .. math::
+    
+            R = \eta TO FINISH LATER!!!
+    
+        So the derivative is straight forward:
+    
+        .. math::
+    
+            R(m) = \mathbf{W^\\top R^\\top R W (m-m_\\text{ref})}
+    
+        The IRLS weights are recomputed after each beta solves.
+        It is strongly recommended to do a few Gauss-Newton iterations
+        before updating.
     """
-
-    mrefInSmooth = False  #: SMOOTH and SMOOTH_MOD_DIF options
-    alpha_s     = Utils.dependentProperty('_alpha_s', 1.0, ['_W', '_Wsmall'], "Smallness weight")
-    alpha_x     = Utils.dependentProperty('_alpha_x', 1.0, ['_W', '_Wx'], "Weight for the first derivative in the x direction")
-    alpha_y     = Utils.dependentProperty('_alpha_y', 1.0, ['_W', '_Wy'], "Weight for the first derivative in the y direction")
-    alpha_z     = Utils.dependentProperty('_alpha_z', 1.0, ['_W', '_Wz'], "Weight for the first derivative in the z direction")
-    wght        = 1.
+        
+    # set default values
+    eps_p = 1e-1        # Threshold value for the model norm
+    eps_q = 1e-1        # Threshold value for the model gradient norm
+    curModel = None     # Requires model to compute the weights
+    l2model = None
+    gamma = 1.          # Model norm scaling to smooth out convergence
+    norms = [0., 2., 2., 2.] # Values for norm on (m, dmdx, dmdy, dmdz)
+    cell_weights = 1.        # Consider overwriting with sensitivity weights
 
     def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
-        BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
+        Simple.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
 
-        if isinstance(self.wght,float):
-            self.wght = np.ones(self.regmesh.nC) * self.wght
+        if isinstance(self.cell_weights,float):
+            self.cell_weights = np.ones(self.regmesh.nC) * self.cell_weights
 
     @property
     def Wsmall(self):
         """Regularization matrix Wsmall"""
         if getattr(self,'_Wsmall', None) is None:
-            self._Wsmall = Utils.sdiag((self.regmesh.vol*self.alpha_s*self.wght)**0.5)
+            if getattr(self, 'curModel', None) is None:
+                self.Rs = Utils.speye(self.regmesh.nC)
+
+            else:
+                f_m = self.mapping * (self.curModel - self.reg.mref)
+                self.rs = self.R(f_m , self.eps_p, self.norms[0])
+                self.Rs = Utils.sdiag( self.rs )
+
+            self._Wsmall = Utils.sdiag((self.alpha_s*self.gamma*self.cell_weights)**0.5)*self.Rs
+
         return self._Wsmall
 
     @property
     def Wx(self):
         """Regularization matrix Wx"""
-        if getattr(self, '_Wx', None) is None:
-            self._Wx = Utils.sdiag((self.regmesh.aveCC2Fx * self.regmesh.vol*self.alpha_x*(self.regmesh.aveCC2Fx*self.wght))**0.5)*self.regmesh.cellDiffxStencil
+        if getattr(self,'_Wx', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Rx = Utils.speye(self.regmesh.cellDiffxStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffxStencil * (self.mapping * self.curModel)
+                self.rx = self.R( f_m , self.eps_q, self.norms[1])
+                self.Rx = Utils.sdiag( self.rx )
+
+            self._Wx = Utils.sdiag(( self.alpha_x*self.gamma*(self.regmesh.aveCC2Fx*self.cell_weights))**0.5)*self.Rx*self.regmesh.cellDiffxStencil
+
         return self._Wx
 
     @property
     def Wy(self):
         """Regularization matrix Wy"""
-        if getattr(self, '_Wy', None) is None:
-            self._Wy = Utils.sdiag((self.regmesh.aveCC2Fy * self.regmesh.vol * self.alpha_y*(self.regmesh.aveCC2Fy*self.wght))**0.5)*self.regmesh.cellDiffyStencil
+        if getattr(self,'_Wy', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Ry = Utils.speye(self.regmesh.cellDiffyStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffyStencil * (self.mapping * self.curModel)
+                self.ry = self.R( f_m , self.eps_q, self.norms[2])
+                self.Ry = Utils.sdiag( self.ry )
+
+            self._Wy = Utils.sdiag((self.alpha_y*self.gamma*(self.regmesh.aveCC2Fy*self.cell_weights))**0.5)*self.Ry*self.regmesh.cellDiffyStencil
+
         return self._Wy
 
     @property
     def Wz(self):
         """Regularization matrix Wz"""
-        if getattr(self, '_Wz', None) is None:
-            self._Wz = Utils.sdiag((self.regmesh.aveCC2Fz * self.regmesh.vol*self.alpha_z*(self.regmesh.aveCC2Fz*self.wght))**0.5)*self.regmesh.cellDiffzStencil
+        if getattr(self,'_Wz', None) is None:
+            if getattr(self, 'curModel', None) is None:
+                self.Rz = Utils.speye(self.regmesh.cellDiffzStencil.shape[0])
+
+            else:
+                f_m = self.regmesh.cellDiffzStencil * (self.mapping * self.curModel)
+                self.rz = self.R( f_m , self.eps_q, self.norms[3])
+                self.Rz = Utils.sdiag( self.rz )
+
+            self._Wz = Utils.sdiag((self.alpha_z*self.gamma*(self.regmesh.aveCC2Fz*self.cell_weights))**0.5)*self.Rz*self.regmesh.cellDiffzStencil
+
         return self._Wz
 
-    @property
-    def Wsmooth(self):
-        """Full smoothness regularization matrix W"""
-        if getattr(self, '_Wsmooth', None) is None:
-            wlist = (self.Wx,)
-            if self.regmesh.dim > 1:
-                wlist += (self.Wy,)
-            if self.regmesh.dim > 2:
-                wlist += (self.Wz,)
-            self._Wsmooth = sp.vstack(wlist)
-        return self._Wsmooth
-
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        if getattr(self, '_W', None) is None:
-            wlist = (self.Wsmall, self.Wsmooth)
-            self._W = sp.vstack(wlist)
-        return self._W
-
-    @Utils.timeIt
-    def _evalSmall(self, m):
-        r = self.Wsmall * ( self.mapping * (m - self.mref) )
-        return 0.5 * r.dot(r)
-
-    @Utils.timeIt
-    def _evalSmooth(self, m):
-        if self.mrefInSmooth == True:
-            r = self.Wsmooth * ( self.mapping * (m - self.mref) )
-        elif self.mrefInSmooth == False:
-            r = self.Wsmooth * ( self.mapping * m)
-        return 0.5 * r.dot(r)
-
-
-class Sparse(Simple):
-
-    # set default values
-    eps_p      = 1e-1
-    eps_q      = 1e-1
-    curModel = None # use a model to compute the weights
-    gamma    = 1.
-    norms    = [0., 2., 2., 2.]
-    wght     = 1.
-
-    def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
-        Simple.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
-
-        if isinstance(self.wght,float):
-            self.wght = np.ones(self.regmesh.nC) * self.wght
-
-    @property
-    def Wsmall(self):
-        """Regularization matrix Wsmall"""
-        if getattr(self, 'curModel', None) is None:
-            self.Rs = Utils.speye(self.regmesh.nC)
-
-        else:
-            f_m = self.curModel - self.reg.mref
-            self.rs = self.R(f_m , self.eps_p, self.norms[0])
-            #print "Min rs: " + str(np.max(self.rs)) + "Max rs: " + str(np.min(self.rs))
-            self.Rs = Utils.sdiag( self.rs )
-
-        return Utils.sdiag((self.regmesh.vol*self.alpha_s*self.gamma*self.wght)**0.5)*self.Rs
-
-
-    @property
-    def Wx(self):
-        """Regularization matrix Wx"""
-
-        if getattr(self, 'curModel', None) is None:
-            self.Rx = Utils.speye(self.regmesh.cellDiffxStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffxStencil * self.curModel
-            self.rx = self.R( f_m , self.eps_q, self.norms[1])
-            self.Rx = Utils.sdiag( self.rx )
-
-        return Utils.sdiag(( (self.regmesh.aveCC2Fx * self.regmesh.vol) *self.alpha_x*self.gamma*(self.regmesh.aveCC2Fx*self.wght))**0.5)*self.Rx*self.regmesh.cellDiffxStencil
-
-    @property
-    def Wy(self):
-        """Regularization matrix Wy"""
-
-        if getattr(self, 'curModel', None) is None:
-            self.Ry = Utils.speye(self.regmesh.cellDiffyStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffyStencil * self.curModel
-            self.ry = self.R( f_m , self.eps_q, self.norms[2])
-            self.Ry = Utils.sdiag( self.ry )
-
-        return Utils.sdiag(((self.regmesh.aveCC2Fy * self.regmesh.vol)*self.alpha_y*self.gamma*(self.regmesh.aveCC2Fy*self.wght))**0.5)*self.Ry*self.regmesh.cellDiffyStencil
-
-    @property
-    def Wz(self):
-        """Regularization matrix Wz"""
-
-        if getattr(self, 'curModel', None) is None:
-            self.Rz = Utils.speye(self.regmesh.cellDiffzStencil.shape[0])
-
-        else:
-            f_m = self.regmesh.cellDiffzStencil * self.curModel
-            self.rz = self.R( f_m , self.eps_q, self.norms[3])
-            self.Rz = Utils.sdiag( self.rz )
-
-        return Utils.sdiag(((self.regmesh.aveCC2Fz * self.regmesh.vol)*self.alpha_z*self.gamma*(self.regmesh.aveCC2Fz*self.wght))**0.5)*self.Rz*self.regmesh.cellDiffzStencil
-
-    @property
-    def Wsmooth(self):
-        """Full smoothness regularization matrix W"""
-        #if getattr(self, '_Wsmooth', None) is None:
-        wlist = (self.Wx,)
-        if self.regmesh.dim > 1:
-            wlist += (self.Wy,)
-        if self.regmesh.dim > 2:
-            wlist += (self.Wz,)
-        #self._Wsmooth = sp.vstack(wlist)
-        return sp.vstack(wlist)
-
-    @property
-    def W(self):
-        """Full regularization matrix W"""
-        #if getattr(self, '_W', None) is None:
-        wlist = (self.Wsmall, self.Wsmooth)
-        #self._W = sp.vstack(wlist)
-        return sp.vstack(wlist)
-
     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 scaling is important for mix-norms...do not mess with it
+        eta = (eps**(1.-exponent/2.))**0.5
+        r = eta / (f_m**2.+ eps**2.)**((1.-exponent/2.)/2.)
 
         return r

SimPEG/Survey.py

@@ -311,7 +311,6 @@ class BaseSurvey(object):
         if f is None: f = self.prob.fields(m)
         return Utils.mkvc(self.eval(f))
 
-
     @Utils.count
     def eval(self, f):
         """eval(f)
@@ -322,7 +321,7 @@ class BaseSurvey(object):
 
                 d_\\text{pred} = \mathbf{P} f(m)
         """
-        raise NotImplemented('eval is not yet implemented.')
+        raise NotImplementedError('eval is not yet implemented.')
 
     @Utils.count
     def evalDeriv(self, f):
@@ -334,7 +333,7 @@ class BaseSurvey(object):
 
                 \\frac{\partial d_\\text{pred}}{\partial u} = \mathbf{P}
         """
-        raise NotImplemented('eval is not yet implemented.')
+        raise NotImplementedError('eval is not yet implemented.')
 
     @Utils.count
     def residual(self, m, f=None):

SimPEG/Tests.py

@@ -237,7 +237,7 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None, expectedOrder=2, tole
         Compares error decay of 0th and 1st order Taylor approximation at point
         x0 for a randomized search direction.
 
-        :param lambda fctn: function handle
+        :param callable fctn: function handle
         :param numpy.array x0: point at which to check derivative
         :param int num: number of times to reduce step length, h
         :param bool plotIt: if you would like to plot

SimPEG/Utils/ModelBuilder.py

@@ -7,11 +7,11 @@ def addBlock(gridCC, modelCC, p0, p1, blockProp):
     """
         Add a block to an exsisting cell centered model, modelCC
 
-        :param numpy.array, gridCC: mesh.gridCC is the cell centered grid
-        :param numpy.array, modelCC: cell centered model
-        :param numpy.array, p0: bottom, southwest corner of block
-        :param numpy.array, p1: top, northeast corner of block
-        :blockProp float, blockProp: property to assign to the model 
+        :param numpy.array gridCC: mesh.gridCC is the cell centered grid
+        :param numpy.array modelCC: cell centered model
+        :param numpy.array p0: bottom, southwest corner of block
+        :param numpy.array p1: top, northeast corner of block
+        :blockProp float blockProp: property to assign to the model
 
         :return numpy.array, modelBlock: model with block
     """
@@ -147,7 +147,7 @@ def getIndicesSphere(center,radius,ccMesh):
 
     if dimMesh == 1:
        # Define the reference points
-        
+
         ind  = np.abs(center[0] - ccMesh[:,0]) < radius
 
     elif dimMesh == 2:
@@ -222,14 +222,14 @@ def layeredModel(ccMesh, layerTops, layerValues):
 
         :param numpy.array ccMesh: cell-centered mesh
         :param numpy.array layerTops: z-locations of the tops of each layer
-        :param numpy.array layerValue: values of the property to assign for each layer (starting at the top) 
+        :param numpy.array layerValue: values of the property to assign for each layer (starting at the top)
         :rtype: numpy.array
-        :return: M, layered model on the mesh 
+        :return: M, layered model on the mesh
     """
 
     descending = np.linalg.norm(sorted(layerTops, reverse=True) - layerTops) < 1e-20
 
-    # TODO: put an error check to make sure that there is an ordering... needs to work with inf elts 
+    # TODO: put an error check to make sure that there is an ordering... needs to work with inf elts
     # assert ascending or descending, "Layers must be listed in either ascending or descending order"
 
     # start from bottom up
@@ -253,10 +253,10 @@ def layeredModel(ccMesh, layerTops, layerValues):
     model = np.zeros(ccMesh.shape[0])
 
     for i, top in enumerate(layerTops):
-        zind = z <= top 
+        zind = z <= top
         model[zind] = layerValues[i]
 
-    return model 
+    return model
 
 
 
@@ -265,9 +265,9 @@ def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=None):
         Create a random model by convolving a kernel with a
         uniformly distributed model.
 
-        :param int,tuple shape: shape of the model.
+        :param tuple shape: shape of the model.
         :param int seed: pick which model to produce, prints the seed if you don't choose.
-        :param numpy.ndarray,list anisotropy: this is the (3 x n) blurring kernel that is used.
+        :param numpy.ndarray anisotropy: this is the (3 x n) blurring kernel that is used.
         :param int its: number of smoothing iterations
         :param list bounds: bounds on the model, len(list) == 2
         :rtype: numpy.ndarray

SimPEG/Utils/SolverUtils.py

@@ -13,7 +13,7 @@ def _checkAccuracy(A, b, X, accuracyTol):
         warnings.warn(msg, RuntimeWarning)
 
 
-def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6):
+def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6, name=None):
     """
     Wraps a direct Solver.
 
@@ -72,11 +72,11 @@ def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6):
         if factorize and hasattr(self.solver, 'clean'):
             return self.solver.clean()
 
-    return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
+    return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
 
 
 
-def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
+def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5, name=None):
     """
     Wraps an iterative Solver.
 
@@ -128,13 +128,13 @@ def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
     def clean(self):
         pass
 
-    return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
+    return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
 
 
 from scipy.sparse import linalg
-Solver   = SolverWrapD(linalg.spsolve, factorize=False)
-SolverLU = SolverWrapD(linalg.splu, factorize=True)
-SolverCG = SolverWrapI(linalg.cg)
+Solver   = SolverWrapD(linalg.spsolve, factorize=False, name="Solver")
+SolverLU = SolverWrapD(linalg.splu, factorize=True, name="SolverLU")
+SolverCG = SolverWrapI(linalg.cg, name="SolverCG")
 
 
 class SolverDiag(object):

SimPEG/Utils/__init__.py

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

SimPEG/Utils/interputils.py

@@ -25,7 +25,7 @@ def interpmat(locs, x, y=None, z=None):
         :param numpy.ndarray x: Tensor vector of 1st dimension of grid.
         :param numpy.ndarray y: Tensor vector of 2nd dimension of grid. None by default.
         :param numpy.ndarray z: Tensor vector of 3rd dimension of grid. None by default.
-        :rtype: scipy.sparse.csr.csr_matrix
+        :rtype: scipy.sparse.csr_matrix
         :return: Interpolation matrix
 
         .. plot::

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/matutils.py

@@ -27,7 +27,7 @@ def mkvc(x, numDims=1):
 
     if isinstance(x, Zero):
         return x
-    
+
     assert isinstance(x, np.ndarray), "Vector must be a numpy array"
 
     if numDims == 1:
@@ -355,9 +355,9 @@ def diagEst(matFun, n, k=None, approach='Probing'):
         2. Ones : random +/- 1 entries
         3. Random : random vectors
 
-    :param lambda (numpy.array) matFun: matrix to estimate the diagonal of
-    :param int64 n: size of the vector that should be used to compute matFun(v)
-    :param int64 k: number of vectors to be used to estimate the diagonal
+    :param callable matFun: takes a (numpy.array) and multiplies it by a matrix to estimate the diagonal
+    :param int n: size of the vector that should be used to compute matFun(v)
+    :param int k: number of vectors to be used to estimate the diagonal
     :param str approach: approach to be used for getting vectors
     :rtype: numpy.array
     :return: est_diag(A)
@@ -422,9 +422,9 @@ class Zero(object):
     def __ge__(self, v):return 0 >= v
     def __gt__(self, v):return 0 > v
 
-    @property 
+    @property
     def transpose(self): return Zero()
-    
+
     @property
     def T(self): return Zero()
 

SimPEG/Utils/meshutils.py

@@ -83,7 +83,7 @@ def closestPoints(mesh, pts, gridLoc='CC'):
     """
         Move a list of points to the closest points on a grid.
 
-        :param simpeg.Mesh.BaseMesh mesh: The mesh
+        :param BaseMesh mesh: The mesh
         :param numpy.ndarray pts: Points to move
         :param string gridLoc: ['CC', 'N', 'Fx', 'Fy', 'Fz', 'Ex', 'Ex', 'Ey', 'Ez']
         :rtype: numpy.ndarray
@@ -104,16 +104,20 @@ def closestPoints(mesh, pts, gridLoc='CC'):
 
 def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
     """
-        Extracts Core Mesh from Global mesh
-        xyzlim: 2D array [ndim x 2]
-        mesh: SimPEG mesh
-        This function ouputs:
-            - actind: corresponding boolean index from global to core
-            - meshcore: core SimPEG mesh
-        Warning: 1D and 2D has not been tested
+    Extracts Core Mesh from Global mesh
+
+    :param numpy.ndarray xyzlim: 2D array [ndim x 2]
+    :param BaseMesh mesh: The mesh
+
+    This function ouputs::
+
+        - actind: corresponding boolean index from global to core
+        - meshcore: core SimPEG mesh
+
+    Warning: 1D and 2D has not been tested
     """
     from SimPEG import Mesh
-    if mesh.dim ==1:
+    if mesh.dim == 1:
         xyzlim = xyzlim.flatten()
         xmin, xmax = xyzlim[0], xyzlim[1]
 
@@ -125,11 +129,11 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
 
         x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5]
 
-        meshCore = Mesh.TensorMesh([hx, hy] ,x0=x0)
+        meshCore = Mesh.TensorMesh([hx, hy], x0=x0)
 
         actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax)
 
-    elif mesh.dim ==2:
+    elif mesh.dim == 2:
         xmin, xmax = xyzlim[0,0], xyzlim[0,1]
         ymin, ymax = xyzlim[1,0], xyzlim[1,1]
 
@@ -144,12 +148,12 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
 
         x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5]
 
-        meshCore = Mesh.TensorMesh([hx, hy] ,x0=x0)
+        meshCore = Mesh.TensorMesh([hx, hy], x0=x0)
 
         actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax) \
                & (mesh.gridCC[:,1]>ymin) & (mesh.gridCC[:,1]<ymax) \
 
-    elif mesh.dim==3:
+    elif mesh.dim == 3:
         xmin, xmax = xyzlim[0,0], xyzlim[0,1]
         ymin, ymax = xyzlim[1,0], xyzlim[1,1]
         zmin, zmax = xyzlim[2,0], xyzlim[2,1]
@@ -168,7 +172,7 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
 
         x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5, zc[0]-hz[0]*0.5]
 
-        meshCore = Mesh.TensorMesh([hx, hy, hz] ,x0=x0)
+        meshCore = Mesh.TensorMesh([hx, hy, hz], x0=x0)
 
         actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax) \
                & (mesh.gridCC[:,1]>ymin) & (mesh.gridCC[:,1]<ymax) \

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)
+
+

SimPEG/__init__.py

@@ -15,7 +15,7 @@ import Directives
 import Inversion
 import Tests
 
-__version__   = '0.1.10'
+__version__   = '0.1.12'
 __author__    = 'Rowan Cockett'
 __license__   = 'MIT'
 __copyright__ = 'Copyright 2014 Rowan Cockett'

docs/Makefile

@@ -2,7 +2,7 @@
 #
 
 # You can set these variables from the command line.
-SPHINXOPTS    =
+SPHINXOPTS    = -n -w warnings.txt
 SPHINXBUILD   = sphinx-build
 PAPER         =
 BUILDDIR      = _build

docs/_templates/layout.html

@@ -0,0 +1,22 @@
+{# Import the theme's layout. #}
+{% extends "!layout.html" %}
+
+{% block extrahead %}
+{{ super() }}
+
+<meta name="description" content="Simulation and Parameter Estimation in Geophysics">
+<meta name="author" content="SimPEG Developers">
+<meta name="keywords" content="python, geophysics, inversion, electromagnetics, magnetotellurics, magnetics, gravity, DC, flow inverse problems, open source, finite volume">
+
+
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
+
+  ga('create', 'UA-45185336-1', 'auto');
+  ga('send', 'pageview');
+
+</script>
+{% endblock %}

docs/api_FiniteVolume.rst

@@ -1,19 +0,0 @@
-.. _api_FiniteVolume:
-
-Finite Volume
-*************
-
-Any numerical implementation requires the discretization of continuous functions into discrete approximations. These approximations are typically organized in a mesh, which defines boundaries, locations, and connectivity. Of specific interest to geophysical simulations, we require that averaging, interpolation and differential operators be defined for any mesh. In SimPEG, we have implemented a staggered mimetic finite volume approach (`Hyman and Shashkov, 1999 <http://math.lanl.gov/~mac/papers/numerics/HS99B.pdf>`_). This approach requires the definitions of variables at either cell-centers, nodes, faces, or edges as seen in the figure below.
-
-.. image:: images/finitevolrealestate.png
-   :width: 400 px
-   :alt: FiniteVolume
-   :align: center
-
-
-.. toctree::
-   :maxdepth: 2
-
-   api_Mesh
-   api_DiffOps
-   api_InnerProducts

docs/api_MeshCode.rst

@@ -1,36 +0,0 @@
-.. _api_MeshCode:
-
-Tensor Mesh
-===========
-
-.. automodule:: SimPEG.Mesh.TensorMesh
-    :show-inheritance:
-    :members:
-    :undoc-members:
-
-
-Cylindrical Mesh
-================
-
-.. automodule:: SimPEG.Mesh.CylMesh
-    :show-inheritance:
-    :members:
-    :undoc-members:
-
-
-Tree Mesh
-=========
-
-.. autoclass:: SimPEG.Mesh.TreeMesh.TreeMesh
-    :show-inheritance:
-    :members:
-    :undoc-members:
-
-
-Curvilinear Mesh
-================
-
-.. automodule:: SimPEG.Mesh.CurvilinearMesh
-    :show-inheritance:
-    :members:
-    :undoc-members:

docs/app.yaml

@@ -0,0 +1,95 @@
+# application: simpegdocs
+# version: 1
+runtime: python27
+api_version: 1
+threadsafe: yes
+
+handlers:
+
+# favicon
+- url: /images/logo-block\.ico
+  static_files: /images/logo-block.ico
+  upload: /images/logo-block\.ico
+
+# all css
+- url: /(.*\.css)
+  mime_type: text/css
+  static_files: _build/html/\1
+  upload: _build/html/(.*\.css)
+
+# webfonts
+- url: /(.*\.(eot|svg|ttf|woff|woff2|otf))
+  static_files: _build/html/\1
+  upload: _build/html/(.*\.(eot|svg|ttf|woff|woff2|otf))
+
+# javascript
+- url: /(.*\.js)
+  mime_type: text/javascript
+  static_files: _build/html/\1
+  upload: _build/html/(.*\.js)
+
+# plain text source
+- url: /(.*\.txt)
+  mime_type: text/plain
+  static_files: _build/html/\1
+  upload: _build/html/(.*\.txt)
+
+# images
+- url: /_images/(.*\.(gif|png|jpg|ico))
+  static_files: _build/html/_images/\1
+  upload: _build/html/_images/(.*\.(gif|png|jpg|ico))
+
+# redirect en/latest traffic
+- url: /en/latest/(.*\.html)
+  script: simpegdocs.app
+
+# raw html
+- url: /(.*\.html)
+  mime_type: text/html
+  static_files: _build/html/\1
+  upload: _build/html/(.*\.html)
+
+# serve index files
+- url: /(.+)/
+  static_files: _build/html/\1/index.html
+  upload: _build/html/(.+)/index.html
+
+- url: /(.+)
+  static_files: _build/html/\1/index.html
+  upload: _build/html/(.+)/index.html
+
+- url: /
+  static_files: _build/html/index.html
+  upload: _build/html/index.html
+
+- url: .*
+  script: simpegdocs.app
+
+# Recommended file skipping declaration from the GAE tutorials
+skip_files:
+  - ^(.*/)?app\.yaml
+  - ^(.*/)?app\.yml
+  - ^(.*/)?#.*#
+  - ^(.*/)?.*~
+  - ^(.*/)?.*\.py[co]
+  - ^(.*/)?.*/RCS/.*
+  - ^(.*/)?\..*
+  - ^(.*/)?tests$
+  - ^(.*/)?test$
+  - ^test/(.*/)?
+  - ^COPYING.LESSER
+  - ^README\..*
+  - \.gitignore
+  - ^\.git/.*
+  - \.*\.lint$
+  - ^(.*/)?.*\.doctree$
+
+libraries:
+- name: webapp2
+  version: "2.5.2"
+- name: PIL
+  version: "1.1.7"
+- name: numpy
+  version: "latest"
+- name: jinja2
+  version: "latest"

docs/conf.py

@@ -28,7 +28,7 @@ sys.path.append('../')
 
 # Add any Sphinx extension module names here, as strings. They can be extensions
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
-extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc', 'matplotlib.sphinxext.plot_directive']
+extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'matplotlib.sphinxext.plot_directive']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -44,16 +44,16 @@ master_doc = 'index'
 
 # General information about the project.
 project = u'SimPEG'
-copyright = u'2013, SimPEG Developers'
+copyright = u'2013 - 2016, SimPEG Developers'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.1.10'
+version = '0.1.12'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.10'
+release = '0.1.12'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -124,12 +124,12 @@ except Exception, e:
 # The name of an image file (within the static path) to use as favicon of the
 # docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
 # pixels large.
-#html_favicon = None
+html_favicon = './images/logo-block.ico'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = []
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
@@ -229,6 +229,12 @@ man_pages = [
 # If true, show URL addresses after external links.
 #man_show_urls = False
 
+# Intersphinx
+intersphinx_mapping = {'python': ('http://docs.python.org/2', None),
+                       'numpy': ('http://docs.scipy.org/doc/numpy/', None),
+                       'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
+                       'matplotlib': ('http://matplotlib.sourceforge.net/', None)}
+
 
 # -- Options for Texinfo output ------------------------------------------------
 
@@ -251,3 +257,36 @@ texinfo_documents = [
 #texinfo_show_urls = 'footnote'
 
 autodoc_member_order = 'bysource'
+
+def supress_nonlocal_image_warn():
+    import sphinx.environment
+    sphinx.environment.BuildEnvironment.warn_node = _supress_nonlocal_image_warn
+
+def _supress_nonlocal_image_warn(self, msg, node):
+    from docutils.utils import get_source_line
+
+    if not msg.startswith('nonlocal image URI found:'):
+        self._warnfunc(msg, '%s:%s' % get_source_line(node))
+
+supress_nonlocal_image_warn()
+
+
+nitpick_ignore = [
+    ('py:class', 'IdentityMap'),
+    ('py:class', 'BaseSurvey'),
+    ('py:class', 'BaseSrc'),
+    ('py:class', 'BaseRx'),
+    ('py:class', 'Survey'),
+    ('py:class', 'FieldsFDEM'),
+    ('py:class', 'Fields3D_e'),
+    ('py:class', 'Fields3D_b'),
+    ('py:class', 'Fields3D_j'),
+    ('py:class', 'Fields3D_h'),
+    ('py:class', 'SurveyTDEM'),
+    ('py:class', 'SrcTDEM'),
+    ('py:class', 'EMPropMap'),
+    ('py:class', 'Data'),
+    ('py:class', 'SurveyDC'),
+    ('py:class', 'BaseMTFields'),
+    ('py:class', 'SolverLU'),
+]

docs/content/api_core/api_Examples.rst

@@ -7,7 +7,7 @@ Examples
     :maxdepth: 1
     :glob:
 
-    examples/*
+    ../examples/*
 
 
 External Notebooks

docs/content/api_core/api_FiniteVolume.rst

@@ -0,0 +1,27 @@
+.. _api_FiniteVolume:
+
+Finite Volume
+*************
+
+Any numerical implementation requires the discretization of continuous
+functions into discrete approximations. These approximations are typically
+organized in a mesh, which defines boundaries, locations, and connectivity. Of
+specific interest to geophysical simulations, we require that averaging,
+interpolation and differential operators be defined for any mesh. In SimPEG,
+we have implemented a staggered mimetic finite volume approach (`Hyman and
+Shashkov, 1999 <http://math.lanl.gov/~mac/papers/numerics/HS99B.pdf>`_). This
+approach requires the definitions of variables at either cell-centers, nodes,
+faces, or edges as seen in the figure below.
+
+.. image:: ../../images/finitevolrealestate.png
+   :width: 400 px
+   :alt: FiniteVolume
+   :align: center
+
+
+.. toctree::
+   :maxdepth: 2
+
+   api_Mesh
+   api_DiffOps
+   api_InnerProducts

docs/content/api_core/api_ForwardProblem.rst

@@ -52,13 +52,15 @@ We can take the derivative of the PDE:
 
     \nabla_m c(m, u) \partial m + \nabla_u c(m, u) \partial u = 0
 
-If the forward problem is invertible, then we can rearrange for \\(\\frac{\\partial u}{\\partial m}\\):
+If the forward problem is invertible, then we can rearrange for
+\\(\\frac{\\partial u}{\\partial m}\\):
 
 .. math::
 
     J = - P \left( \nabla_u c(m, u) \right)^{-1} \nabla_m c(m, u)
 
-This can often be computed given a vector (i.e. \\(J(v)\\)) rather than stored, as \\(J\\) is a large dense matrix.
+This can often be computed given a vector (i.e. \\(J(v)\\)) rather than
+stored, as \\(J\\) is a large dense matrix.
 
 
 
@@ -67,13 +69,45 @@ The API
 
 Problem
 -------
-.. automodule:: SimPEG.Problem
+
+.. autoclass:: SimPEG.Problem.BaseProblem
+    :members:
+    :undoc-members:
+
+.. autoclass:: SimPEG.Problem.BaseTimeProblem
+    :members:
+    :undoc-members:
+
+Fields
+------
+
+.. autoclass:: SimPEG.Fields.Fields
+    :members:
+    :undoc-members:
+
+.. autoclass:: SimPEG.Fields.TimeFields
     :members:
     :undoc-members:
 
 Survey
 ------
-.. automodule:: SimPEG.Survey
+
+.. autoclass:: SimPEG.Survey.BaseSurvey
     :members:
     :undoc-members:
 
+.. autoclass:: SimPEG.Survey.BaseSrc
+    :members:
+    :undoc-members:
+
+.. autoclass:: SimPEG.Survey.BaseRx
+    :members:
+    :undoc-members:
+
+.. autoclass:: SimPEG.Survey.BaseTimeRx
+    :members:
+    :undoc-members:
+
+.. autoclass:: SimPEG.Survey.Data
+    :members:
+    :undoc-members:

docs/content/api_core/api_InnerProducts.rst

@@ -4,7 +4,10 @@
 Inner Products
 **************
 
-By using the weak formulation of many of the PDEs in geophysical applications, we can rapidly develop discretizations. Much of this work, however, needs a good understanding of how to approximate inner products on our discretized meshes. We will define the inner product as:
+By using the weak formulation of many of the PDEs in geophysical applications,
+we can rapidly develop discretizations. Much of this work, however, needs a
+good understanding of how to approximate inner products on our discretized
+meshes. We will define the inner product as:
 
 .. math::
 
@@ -14,12 +17,15 @@ where a and b are either scalars or vectors.
 
 .. note::
 
-    The InnerProducts class is a base class providing inner product matrices for meshes and cannot run on its own.
+    The InnerProducts class is a base class providing inner product matrices
+    for meshes and cannot run on its own.
 
 
 Example problem for DC resistivity
 ----------------------------------
-We will start with the formulation of the Direct Current (DC) resistivity problem in geophysics.
+
+We will start with the formulation of the Direct Current (DC) resistivity
+problem in geophysics.
 
 
 .. math::
@@ -28,12 +34,13 @@ We will start with the formulation of the Direct Current (DC) resistivity proble
 
         \nabla\cdot \vec{j} = q
 
-In the following discretization, \\\( \\sigma \\\) and \\\( \\phi \\\)
-will be discretized on the cell-centers and the flux, \\\(\\vec{j}\\\),
+In the following discretization, :math:`\sigma` and :math:`\phi`
+will be discretized on the cell-centers and the flux, :math:`\vec{j}`,
 will be on the faces. We will use the weak formulation to discretize
 the DC resistivity equation.
 
-We can define in weak form by integrating with a general face function \\\(\\vec{f}\\\):
+We can define in weak form by integrating with a general face function
+:math:`\vec{f}`:
 
 .. math::
 
@@ -61,9 +68,16 @@ We can then discretize for every cell:
 
 .. note::
 
-    We have discretized the dot product above, but remember that we do not really have a single vector \\\(\\mathbf{J}\\\), but approximations of \\\(\\vec{j}\\\) on each face of our cell. In 2D that means 2 approximations of \\\(\\mathbf{J}_x\\\) and 2 approximations of \\\(\\mathbf{J}_y\\\). In 3D we also have 2 approximations of \\\(\\mathbf{J}_z\\\).
+    We have discretized the dot product above, but remember that we do not
+    really have a single vector :math:`\mathbf{J}`, but approximations of
+    :math:`\vec{j}` on each face of our cell. In 2D that means 2
+    approximations of :math:`\mathbf{J}_x` and 2 approximations of
+    :math:`\mathbf{J}_y`. In 3D we also have 2 approximations of
+    :math:`\mathbf{J}_z`.
 
-Regardless of how we choose to approximate this dot product, we can represent this in vector form (again this is for every cell), and will generalize for the case of anisotropic (tensor) sigma.
+Regardless of how we choose to approximate this dot product, we can represent
+this in vector form (again this is for every cell), and will generalize for
+the case of anisotropic (tensor) sigma.
 
 .. math::
 
@@ -71,14 +85,17 @@ Regardless of how we choose to approximate this dot product, we can represent th
     -\phi^{\top} v_{\text{cell}} \mathbf{D}_{\text{cell}} \mathbf{F})
     + \text{BC}
 
-We multiply by  square-root of volume on each side of the tensor conductivity to keep symmetry in the system. Here \\\(\\mathbf{J}_c\\\) is the Cartesian \\\(\\mathbf{J}\\\) (on the faces that we choose to use in our approximation) and must be calculated differently depending on the mesh:
+We multiply by  square-root of volume on each side of the tensor conductivity
+to keep symmetry in the system. Here :math:`\mathbf{J}_c` is the Cartesian
+:math:`\mathbf{J}` (on the faces that we choose to use in our approximation)
+and must be calculated differently depending on the mesh:
 
 .. math::
     \mathbf{J}_c = \mathbf{Q}_{(i)}\mathbf{J}_\text{TENSOR} \\
     \mathbf{J}_c = \mathbf{N}_{(i)}^{-1}\mathbf{Q}_{(i)}\mathbf{J}_\text{Curv}
 
-Here the \\\(i\\\) index refers to where we choose to approximate this integral, as discussed in the note above.
-We will approximate this integral by taking the fluxes clustered around every node of the cell, there are 8 combinations in 3D, and 4 in 2D. We will use a projection matrix \\\( \\mathbf{Q}_{(i)} \\\) to pick the appropriate fluxes. So, now that we have 8 approximations of this integral, we will just take the average. For the TensorMesh, this looks like:
+Here the :math:`i` index refers to where we choose to approximate this integral, as discussed in the note above.
+We will approximate this integral by taking the fluxes clustered around every node of the cell, there are 8 combinations in 3D, and 4 in 2D. We will use a projection matrix :math:`\mathbf{Q}_{(i)}` to pick the appropriate fluxes. So, now that we have 8 approximations of this integral, we will just take the average. For the TensorMesh, this looks like:
 
 .. math::
 
@@ -107,10 +124,12 @@ By defining the faceInnerProduct (8 combinations of fluxes in 3D, 4 in 2D, 2 in
         \sum_{i=1}^{2^d}
         \mathbf{P}_{(i)}^{\top} \Sigma^{-1} \mathbf{P}_{(i)}
 
-Where \\\(d\\\) is the dimension of the mesh.
-The \\\( \\mathbf{M}^f \\\) is returned when given the input of \\\( \\Sigma^{-1} \\\).
+Where :math:`d` is the dimension of the mesh.
+The :math:`\mathbf{M}^f` is returned when given the input of :math:`\Sigma^{-1}`.
 
-Here each \\( \\mathbf{P} \\in \\mathbb{R}^{(d*nC, nF)} \\\) is a combination of the projection, volume, and any normalization to Cartesian coordinates (where the dot product is well defined):
+Here each :math:`\mathbf{P} ~ \in ~ \mathbb{R}^{(d*nC, nF)}` is a combination
+of the projection, volume, and any normalization to Cartesian coordinates
+(where the dot product is well defined):
 
 .. math::
 
@@ -129,7 +148,10 @@ If ``returnP=True`` is requested in any of these methods the projection matrices
     # In 1D
     P = [P0, P1]
 
-The derivation for ``edgeInnerProducts`` is exactly the same, however, when we approximate the integral using the fields around each node, the projection matrices look a bit different because we have 12 edges in 3D instead of just 6 faces. The interface to the code is exactly the same.
+The derivation for ``edgeInnerProducts`` is exactly the same, however, when we
+approximate the integral using the fields around each node, the projection
+matrices look a bit different because we have 12 edges in 3D instead of just 6
+faces. The interface to the code is exactly the same.
 
 
 Defining Tensor Properties
@@ -137,7 +159,8 @@ Defining Tensor Properties
 
 **For 3D:**
 
-Depending on the number of columns (either 1, 3, or 6) of mu, the material property is interpreted as follows:
+Depending on the number of columns (either 1, 3, or 6) of mu, the material
+property is interpreted as follows:
 
 .. math::
 
@@ -188,13 +211,16 @@ Which is nice and easy to invert if necessary, however, in the fully anisotropic
 Taking Derivatives
 ------------------
 
-We will take the derivative of the fully anisotropic tensor for a 3D mesh, the other cases are easier and will not be discussed here. Let us start with one part of the sum which makes up \\\(\\mathbf{M}^f_\\Sigma\\\) and take the derivative when this is multiplied by some vector \\\(\\mathbf{v}\\\):
+We will take the derivative of the fully anisotropic tensor for a 3D mesh, the
+other cases are easier and will not be discussed here. Let us start with one
+part of the sum which makes up :math:`\mathbf{M}^f_\Sigma` and take the
+derivative when this is multiplied by some vector :math:`\mathbf{v}`:
 
 .. math::
 
     \mathbf{P}^\top \boldsymbol{\Sigma} \mathbf{Pv}
 
-Here we will let \\\( \\mathbf{Pv} = \\mathbf{y} \\\) and \\\(\\mathbf{y}\\\) will have the form:
+Here we will let :math:`\mathbf{Pv} = \mathbf{y}` and :math:`\mathbf{y}` will have the form:
 
 .. math::
 
@@ -233,7 +259,9 @@ Here we will let \\\( \\mathbf{Pv} = \\mathbf{y} \\\) and \\\(\\mathbf{y}\\\) wi
         \end{matrix}
     \right]
 
-Now it is easy to take the derivative with respect to any one of the parameters, for example, \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_1}\\\)
+Now it is easy to take the derivative with respect to any one of the
+parameters, for example,
+:math:`\frac{\partial}{\partial\boldsymbol{\sigma}_1}`
 
 .. math::
     \frac{\partial}{\partial \boldsymbol{\sigma}_1}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -247,7 +275,8 @@ Now it is easy to take the derivative with respect to any one of the parameters,
         \end{matrix}
     \right]
 
-Whereas \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_4}\\\), for example, is:
+Whereas :math:`\frac{\partial}{\partial\boldsymbol{\sigma}_4}`, for
+example, is:
 
 .. math::
     \frac{\partial}{\partial \boldsymbol{\sigma}_4}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -261,11 +290,12 @@ Whereas \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_4}\\\), for example
         \end{matrix}
     \right]
 
-These are computed for each of the 8 projections, horizontally concatenated, and returned.
+These are computed for each of the 8 projections, horizontally concatenated,
+and returned.
 
 The API
 -------
 
-.. automodule:: SimPEG.Mesh.InnerProducts
+.. autoclass:: SimPEG.Mesh.InnerProducts.InnerProducts
     :members:
     :undoc-members:

docs/content/api_core/api_Inversion.rst

@@ -3,7 +3,7 @@
 InvProblem
 **********
 
-.. automodule:: SimPEG.InvProblem
+.. autoclass:: SimPEG.InvProblem.BaseInvProblem
     :show-inheritance:
     :members:
     :undoc-members:
@@ -12,7 +12,7 @@ InvProblem
 Inversion
 *********
 
-.. automodule:: SimPEG.Inversion
+.. autoclass:: SimPEG.Inversion.BaseInversion
     :show-inheritance:
     :members:
     :undoc-members:

docs/content/api_core/api_Maps.rst

@@ -27,7 +27,8 @@ back to conductivity. This is a relatively trivial example (we are just taking
 the exponential!) but by defining maps we can start to combine and manipulate
 exactly what we think about as our model, \\\(m\\\). In code, this looks like
 
-::
+.. code-block:: python
+    :linenos:
 
     M = Mesh.TensorMesh([100]) # Create a mesh
     expMap = Maps.ExpMap(M)    # Create a mapping
@@ -46,14 +47,15 @@ We will use an example where we want a 1D layered earth as
 our model, but we want to map this to a 2D discretization to do our forward
 modeling. We will also assume that we are working in log conductivity still,
 so after the transformation we want to map to conductivity space.
-To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.Vertical1DMap`),
+To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
 which does the first part of what we just described. The second part will be
 done by the :class:`SimPEG.Maps.ExpMap` described above.
 
-::
+.. code-block:: python
+    :linenos:
 
     M = Mesh.TensorMesh([7,5])
-    v1dMap = Maps.Vertical1DMap(M)
+    v1dMap = Maps.SurjectVertical1D(M)
     expMap = Maps.ExpMap(M)
     myMap = expMap * v1dMap
     m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
@@ -61,26 +63,8 @@ done by the :class:`SimPEG.Maps.ExpMap` described above.
 
 .. plot::
 
-    from SimPEG import *
-    import matplotlib.pyplot as plt
-    M = Mesh.TensorMesh([7,5])
-    v1dMap = Maps.Vertical1DMap(M)
-    expMap = Maps.ExpMap(M)
-    myMap = expMap * v1dMap
-    m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
-    sig = myMap * m
-    figs, axs = plt.subplots(1,2)
-    axs[0].plot(m, M.vectorCCy, 'b-o')
-    axs[0].set_title('Model')
-    axs[0].set_ylabel('Depth, y')
-    axs[0].set_xlabel('Value, $m_i$')
-    axs[0].set_xlim(0,3)
-    axs[0].set_ylim(0,1)
-    clbar = plt.colorbar(M.plotImage(sig,ax=axs[1],grid=True,gridOpts=dict(color='grey'))[0])
-    axs[1].set_title('Physical Property')
-    axs[1].set_ylabel('Depth, y')
-    clbar.set_label('$\sigma = \exp(\mathbf{P}m)$')
-    plt.tight_layout()
+    from SimPEG import Examples
+    Examples.Maps_ComboMaps.run()
 
 If you noticed, it was pretty easy to combine maps. What is even cooler is
 that the derivatives also are made for you (if everything goes right).
@@ -122,6 +106,8 @@ When these are used in the inverse problem, this is extremely important!!
 The API
 =======
 
+The :code:`IdentityMap` is the base class for all mappings, and it does absolutely nothing.
+
 .. autoclass:: SimPEG.Maps.IdentityMap
     :members:
     :undoc-members:
@@ -130,7 +116,6 @@ The API
 Common Maps
 ===========
 
-
 Exponential Map
 ---------------
 
@@ -148,7 +133,7 @@ lives (i.e. it varies logarithmically).
 Vertical 1D Map
 ---------------
 
-.. autoclass:: SimPEG.Maps.Vertical1DMap
+.. autoclass:: SimPEG.Maps.SurjectVertical1D
     :members:
     :undoc-members:
 
@@ -164,31 +149,10 @@ Map 2D Cross-Section to 3D Model
 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()
+    from SimPEG import Examples
+    Examples.Maps_Mesh2Mesh.run()
 
 
 .. autoclass:: SimPEG.Maps.Mesh2Mesh
@@ -196,8 +160,8 @@ Mesh to Mesh Map
     :undoc-members:
 
 
-Some Extras
-===========
+Under the Hood
+==============
 
 Combo Map
 ---------

docs/content/api_core/api_Mesh.rst

@@ -188,6 +188,6 @@ other types of meshes in this SimPEG framework.
 The API
 =======
 
-.. automodule:: SimPEG.Mesh.BaseMesh
+.. autoclass:: SimPEG.Mesh.BaseMesh.BaseMesh
     :members:
     :undoc-members:

docs/content/api_core/api_MeshCode.rst

@@ -0,0 +1,68 @@
+.. _api_MeshCode:
+
+Tensor Mesh
+===========
+
+.. autoclass:: SimPEG.Mesh.TensorMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Cylindrical Mesh
+================
+
+.. autoclass:: SimPEG.Mesh.CylMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Tree Mesh
+=========
+
+.. autoclass:: SimPEG.Mesh.TreeMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Curvilinear Mesh
+================
+
+.. autoclass:: SimPEG.Mesh.CurvilinearMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Base Rectangular Mesh
+=====================
+
+.. autoclass:: SimPEG.Mesh.BaseMesh.BaseRectangularMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Base Tensor Mesh
+================
+
+.. autoclass:: SimPEG.Mesh.TensorMesh.BaseTensorMesh
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Mesh IO
+=======
+
+.. automodule:: SimPEG.Mesh.MeshIO
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Mesh Viewing
+============
+
+.. automodule:: SimPEG.Mesh.View
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/content/api_core/api_PropMaps.rst

@@ -0,0 +1,29 @@
+SimPEG PropMaps
+***************
+
+The API
+=======
+
+Property
+--------
+
+.. autoclass:: SimPEG.PropMaps.Property
+    :members:
+    :undoc-members:
+
+
+PropMap
+-------
+
+.. autoclass:: SimPEG.PropMaps.PropMap
+    :members:
+    :undoc-members:
+
+
+PropModel
+---------
+
+.. autoclass:: SimPEG.PropMaps.PropModel
+    :members:
+    :undoc-members:
+

docs/content/api_core/api_Regularization.rst

@@ -91,10 +91,21 @@ The API
     :members:
     :undoc-members:
 
+.. autoclass:: SimPEG.Regularization.Simple
+    :show-inheritance:
+    :members:
 
 .. autoclass:: SimPEG.Regularization.Tikhonov
     :show-inheritance:
     :members:
 
+.. autoclass:: SimPEG.Regularization.Sparse
+    :show-inheritance:
+    :members:
+
+.. autoclass:: SimPEG.Regularization.RegularizationMesh
+    :show-inheritance:
+    :members:
+
 
 

docs/content/api_core/api_Solver.rst

@@ -46,6 +46,8 @@ The API
 =======
 
 .. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapD
+    :noindex:
 
 .. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapI
+    :noindex:
 

docs/content/api_core/api_Utilities.rst

@@ -6,5 +6,6 @@ Utilities
 
    api_Solver
    api_Maps
+   api_PropMaps
    api_Utils
    api_Tests

docs/content/api_core/api_Utils.rst

@@ -21,7 +21,7 @@ Solver Utilities
     :undoc-members:
 
 Curv Utilities
-=============
+==============
 
 .. automodule:: SimPEG.Utils.curvutils
     :members:
@@ -51,7 +51,9 @@ Interpolation Utilities
 Counter Utilities
 =================
 
-::
+.. code-block:: python
+    :linenos:
+
     class MyClass(object):
         def __init__(self, url):
             self.counter = Counter()
@@ -69,7 +71,9 @@ Counter Utilities
     for i in range(300): c.MySecondMethod()
     c.counter.summary()
 
-::
+
+.. code-block:: text
+    :linenos:
 
     Counters:
       MyClass.MyMethod                        :      100
@@ -77,6 +81,8 @@ Counter Utilities
     Times:                                        mean      sum
       MyClass.MySecondMethod                  : 1.70e-06, 5.10e-04,  300x
 
+
+
 The API
 -------
 

docs/content/api_core/api_bigPicture.rst

@@ -35,7 +35,7 @@ The Big Picture
 Defining a well-posed inverse problem and solving it is a complex task that requires many components that must interact. It is helpful
 to view this task as a workflow in which various elements are explicitly identified and integrated. The figure below outlines the inversion components that consists of inputs, implementation, and evaluation. The inputs are composed of the geophysical data, the equations which are a mathematical description of the governing physics, and prior knowledge or assumptions about the setting. The implementation consists of two broad categories: the forward simulation and the inversion. The **forward simulation** is the means by which we solve the governing equations given a model and the **inversion components** evaluate and update this model. We are considering a gradient based approach, which updates the model through an optimization routine. The output of this implementation is a model, which, prior to interpretation, must be evaluated. This requires considering, and often re-assessing, the choices and assumptions made in both the input and implementation stages.
 
-.. image:: InversionWorkflow-PreSimPEG.png
+.. image:: ../../images/InversionWorkflow-PreSimPEG.png
    :width: 400 px
    :alt: Components
    :align: center
@@ -46,24 +46,24 @@ A Comprehensive Framework
 
 There are an overwhelming amount of choices to be made as one works through the forward modeling and inversion process (see figure above). As a result, software implementations of this workflow often become complex and highly interdependent, making it difficult to interact with and to ask other scientists to pick up and change. Our approach to handling this complexity is to propose a framework, (see below), that compartmentalizes the implementation of inversions into various units. We present it in this specific modular style, as each unit contains a targeted subset of choices crucial to the inversion process.
 
-.. image:: InversionWorkflow.png
+.. image:: ../../images/InversionWorkflow.png
    :width: 400 px
    :alt: Framework
    :align: center
 
-The process of obtaining an acceptable model from an inversion generally requires the geophysicist to perform several iterations of the inversion workflow, rethinking and redesigning each piece of the framework to ensure it is appropriate in the current context. Inversions are experimental and empirical by nature and our software package is designed to facilitate this iterative process. To accomplish this, we have divided the inversion methodology into eight major components (See figure above). The (:class:`SimPEG.Mesh.BaseMesh`) class handles the discretization of the earth and also provides numerical operators. The forward simulation is split into two classes, the (:class:`SimPEG.Survey.BaseSurvey`) and the (:class:`SimPEG.Problem.BaseProblem`). The (:class:`SimPEG.Survey.BaseSurvey`) class handles the geometry of a geophysical problem as well as sources. The (:class:`SimPEG.Problem.BaseProblem`) class handles the simulation of the physics for the geophysical problem of interest. Although created independently, these two classes must be paired to form all of the components necessary for a geophysical forward simulation and calculation of the sensitivity. The (:class:`SimPEG.Problem.BaseProblem`) creates geophysical fields given a source from the (:class:`SimPEG.Survey.BaseSurvey`). The (:class:`SimPEG.Survey.BaseSurvey`) interpolates these fields to the receiver locations and converts them to the appropriate data type, for example, by selecting only the measured components of the field. Each of these operations may have associated derivatives with respect to the model and the computed field; these are included in the calculation of the sensitivity. For the inversion, a (:class:`SimPEG.DataMisfit.BaseDataMisfit`) is chosen to capture the goodness of fit of the predicted data and a (:class:`SimPEG.Regularization.BaseRegularization`) is chosen to handle the non-uniqueness. These inversion elements and an Optimization routine are combined into an inverse problem class (:class:`SimPEG.InvProblem.BaseInvProblem`). (:class:`SimPEG.InvProblem.BaseInvProblem`) is the mathematical statement that will be numerically solved by running an Inversion. The (:class:`SimPEG.Inversion.BaseInversion`) class handles organization and dispatch of directives between all of the various pieces of the framework.
+The process of obtaining an acceptable model from an inversion generally requires the geophysicist to perform several iterations of the inversion workflow, rethinking and redesigning each piece of the framework to ensure it is appropriate in the current context. Inversions are experimental and empirical by nature and our software package is designed to facilitate this iterative process. To accomplish this, we have divided the inversion methodology into eight major components (See figure above). The :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class handles the discretization of the earth and also provides numerical operators. The forward simulation is split into two classes, the :class:`SimPEG.Survey.BaseSurvey` and the :class:`SimPEG.Problem.BaseProblem`. The :class:`SimPEG.Survey.BaseSurvey` class handles the geometry of a geophysical problem as well as sources. The :class:`SimPEG.Problem.BaseProblem` class handles the simulation of the physics for the geophysical problem of interest. Although created independently, these two classes must be paired to form all of the components necessary for a geophysical forward simulation and calculation of the sensitivity. The :class:`SimPEG.Problem.BaseProblem` creates geophysical fields given a source from the :class:`SimPEG.Survey.BaseSurvey`. The :class:`SimPEG.Survey.BaseSurvey` interpolates these fields to the receiver locations and converts them to the appropriate data type, for example, by selecting only the measured components of the field. Each of these operations may have associated derivatives with respect to the model and the computed field; these are included in the calculation of the sensitivity. For the inversion, a :class:`SimPEG.DataMisfit.BaseDataMisfit` is chosen to capture the goodness of fit of the predicted data and a :class:`SimPEG.Regularization.BaseRegularization` is chosen to handle the non-uniqueness. These inversion elements and an Optimization routine are combined into an inverse problem class :class:`SimPEG.InvProblem.BaseInvProblem`. :class:`SimPEG.InvProblem.BaseInvProblem` is the mathematical statement that will be numerically solved by running an Inversion. The :class:`SimPEG.Inversion.BaseInversion` class handles organization and dispatch of directives between all of the various pieces of the framework.
 
-The arrows in the figure above indicate what each class takes as a primary argument. For example, both the (:class:`SimPEG.Problem.BaseProblem`) and (:class:`SimPEG.Regularization.BaseRegularization`) classes take a (:class:`SimPEG.Mesh.BaseMesh`) class as an argument. The diagram does not show class inheritance, as each of the base classes outlined have many subtypes that can be interchanged. The (:class:`SimPEG.Mesh.BaseMesh`) class, for example, could be a regular Cartesian mesh (:class:`SimPEG.Mesh.TensorMesh`) or a cylindrical coordinate mesh (:class:`SimPEG.Mesh.CylMesh`), which have many properties in common. These common features, such as both meshes being created from tensor products, can be exploited through inheritance of base classes, and differences can be expressed through subtype polymorphism. Please look at the documentation here for more in-depth information.
+The arrows in the figure above indicate what each class takes as a primary argument. For example, both the :class:`SimPEG.Problem.BaseProblem` and :class:`SimPEG.Regularization.BaseRegularization` classes take a :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class as an argument. The diagram does not show class inheritance, as each of the base classes outlined have many subtypes that can be interchanged. The :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class, for example, could be a regular Cartesian mesh :class:`SimPEG.Mesh.TensorMesh` or a cylindrical coordinate mesh :class:`SimPEG.Mesh.CylMesh`, which have many properties in common. These common features, such as both meshes being created from tensor products, can be exploited through inheritance of base classes, and differences can be expressed through subtype polymorphism. Please look at the documentation here for more in-depth information.
 
 
-.. include:: ../CITATION.rst
+.. include:: ../../../CITATION.rst
 
 Authors
 -------
 
-.. include:: ../AUTHORS.rst
+.. include:: ../../../AUTHORS.rst
 
 License
 -------
 
-.. include:: ../LICENSE
+.. include:: ../../../LICENSE

docs/content/dc/index.rst

@@ -1,5 +1,3 @@
-.. _api_DC:
-
 .. math::
 
     \renewcommand{\div}{\nabla\cdot\,}
@@ -38,8 +36,16 @@
     \renewcommand {\u}  { {\vec u} }
     \newcommand{\I}{\vec{I}}
 
+
+Direct Current Resistivity
+**************************
+
+`SimPEG.DCIP` uses SimPEG as the framework for the forward and inverse
+direct current (DC) resistivity and induced polarization (IP) geophysical problems.
+
+
 DC resistivity survey
-*********************
+=====================
 
 Electrical resistivity of subsurface materials is measured by causing an electrical current to flow in the earth between one pair of electrodes while the voltage across a second pair of electrodes is measured. The result is an "apparent" resistivity which is a value representing the weighted average resistivity over a volume of the earth. Variations in this measurement are caused by variations in the soil, rock, and pore fluid electrical resistivity. Surveys require contact with the ground, so they can be labour intensive. Results are sometimes interpreted directly, but more commonly, 1D, 2D or 3D models are estimated using inversion procedures (`GPG <http://www.eos.ubc.ca/courses/eosc350/content/>`_).
 
@@ -55,7 +61,7 @@ As direct current (DC) implies, in DC resistivity survey, we assume steady-state
 
     \curl \e = 0
 
-Then by taking \\(\\curl\\) for the first equation, we have
+Then by taking \\(\\div\\) of the first equation, we have
 
 .. math::
 
@@ -137,13 +143,14 @@ Comparing to the analytic function:
 
 .. plot::
 
-    import simpegDC as DC
-    DC.Examples.Verification.run(plotIt=True)
+    from SimPEG import Examples
+    Examples.DC_Analytic_Dipole.run(plotIt=True)
 
-API
-===
 
-.. automodule:: simpegDC.BaseDC
+API for DC codes
+================
+
+.. automodule:: SimPEG.DCIP.BaseDC
     :show-inheritance:
     :members:
     :undoc-members:

docs/content/em/api_FDEM.rst

@@ -9,17 +9,28 @@
 Frequency Domain Electromagnetics
 *********************************
 
-Electromagnetic (EM) geophysical methods are used in a variety of applications from resource exploration, including for hydrocarbons and minerals, to environmental applications, such as groundwater monitoring. The primary physical property of interest in EM is electrical conductivity, which describes the ease with which electric current flows through a material.
+Electromagnetic (EM) geophysical methods are used in a variety of applications
+from resource exploration, including for hydrocarbons and minerals, to
+environmental applications, such as groundwater monitoring. The primary
+physical property of interest in EM is electrical conductivity, which
+describes the ease with which electric current flows through a material.
 
 
 Background
 ==========
 
-Electromagnetic phenomena are governed by Maxwell's equations. They describe the behavior of EM fields and fluxes. Electromagnetic theory for geophysical applications by Ward and Hohmann (1988) is a highly recommended resource on this topic.
+Electromagnetic phenomena are governed by Maxwell's equations. They describe
+the behavior of EM fields and fluxes. Electromagnetic theory for geophysical
+applications by Ward and Hohmann (1988) is a highly recommended resource on
+this topic.
 
 Fourier Transform Convention
 ----------------------------
-In order to examine Maxwell's equations in the frequency domain, we must first define our choice of harmonic time-dependence by choosing a Fourier transform convention. We use the :math:`e^{i \omega t}` convention, so we define our Fourier Transform pair as
+
+In order to examine Maxwell's equations in the frequency domain, we must first
+define our choice of harmonic time-dependence by choosing a Fourier transform
+convention. We use the :math:`e^{i \omega t}` convention, so we define our
+Fourier Transform pair as
 
 .. math ::
     F(\omega) = \int_{-\infty}^{\infty} f(t) e^{- i \omega t} dt \\
@@ -31,6 +42,7 @@ where :math:`\omega` is angular frequency, :math:`t` is time, :math:`F(\omega)`
 
 Maxwell's Equations
 ===================
+
 In the frequency domain, Maxwell's equations are given by
 
 .. math ::
@@ -104,19 +116,20 @@ The H-J formulation is in terms of the current density and the magnetic field:
 
 Discretizing
 ------------
+
 For both formulations, we use a finite volume discretization
 and discretize fields on cell edges, fluxes on cell faces and
 physical properties in cell centers. This is particularly
 important when using symmetry to reduce the dimensionality of a problem
 (for instance on a 2D CylMesh, there are :math:`r`, :math:`z` faces and :math:`\theta` edges)
 
-.. figure:: ../images/finitevolrealestate.png
+.. figure:: ../../images/finitevolrealestate.png
     :align: center
     :scale: 60 %
 
 For the two formulations, the discretization of the physical properties, fields and fluxes are summarized below.
 
-.. figure:: ../images/ebjhdiscretizations.png
+.. figure:: ../../images/ebjhdiscretizations.png
     :align: center
     :scale: 60 %
 
@@ -150,7 +163,7 @@ API
 FDEM Problem
 ------------
 
-.. automodule:: SimPEG.EM.FDEM.FDEM
+.. automodule:: SimPEG.EM.FDEM.ProblemFDEM
     :show-inheritance:
     :members:
     :undoc-members:
@@ -169,6 +182,11 @@ FDEM Survey
     :members:
     :undoc-members:
 
+.. automodule:: SimPEG.EM.FDEM.RxFDEM
+    :show-inheritance:
+    :members:
+    :undoc-members:
+
 FDEM Fields
 -----------
 

docs/content/em/api_TDEM.rst

@@ -359,7 +359,7 @@ TDEM - B formulation
 Field Storage
 =============
 
-.. autoclass:: SimPEG.EM.TDEM.SurveyTDEM.FieldsTDEM
+.. autoclass:: SimPEG.EM.TDEM.BaseTDEM.FieldsTDEM
     :show-inheritance:
     :members:
     :undoc-members:

docs/content/em/api_basic.rst

@@ -0,0 +1,33 @@
+Overview of Electromagnetics in SimPEG
+**************************************
+
+
+The API
+=======
+
+Physical Properties
+-------------------
+
+.. autoclass:: SimPEG.EM.Base.EMPropMap
+    :show-inheritance:
+    :members:
+    :undoc-members:
+
+Problem
+-------
+
+.. autoclass:: SimPEG.EM.Base.BaseEMProblem
+    :show-inheritance:
+    :members:
+    :undoc-members:
+
+
+Survey
+------
+
+.. autoclass:: SimPEG.EM.Base.BaseEMSurvey
+    :show-inheritance:
+    :members:
+    :undoc-members:
+
+

docs/content/em/index.rst

@@ -3,22 +3,23 @@ Electromagnetics
 ================
 
 `SimPEG.EM` uses SimPEG as the framework for the forward and inverse
-electromagnetics geophysical problems. 
+electromagnetics geophysical problems.
 
 To solve for predicted data, we follow the framework shown below. The model is
 what we invert for. This is mapped to a physical property on the simulation
 mesh. A source which is used to excite the system is specified. Having a model
 and a source, we can solve Maxwell's equations for fields. We sample these
-fields with recievers to give us predicted data. 
+fields with recievers to give us predicted data.
 
 
-.. image:: ../images/simpegEM_noMath.png
+.. image:: ../../images/simpegEM_noMath.png
     :scale: 50%
 
 
 .. toctree::
    :maxdepth: 2
 
+   api_basic
    api_FDEM
    api_TDEM
    api_Utils

docs/content/examples/DC_Analytic_Dipole.rst

@@ -16,6 +16,6 @@ DC Analytic Dipole
     from SimPEG import Examples
     Examples.DC_Analytic_Dipole.run()
 
-.. literalinclude:: ../../SimPEG/Examples/DC_Analytic_Dipole.py
+.. literalinclude:: ../../../SimPEG/Examples/DC_Analytic_Dipole.py
     :language: python
     :linenos:

docs/content/examples/DC_Forward_PseudoSection.rst

@@ -20,9 +20,9 @@ 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
 
 
 
@@ -31,6 +31,6 @@ Created by @fourndo on Mon Feb 01 19:28:06 2016
     from SimPEG import Examples
     Examples.DC_Forward_PseudoSection.run()
 
-.. literalinclude:: ../../SimPEG/Examples/DC_Forward_PseudoSection.py
+.. literalinclude:: ../../../SimPEG/Examples/DC_Forward_PseudoSection.py
     :language: python
     :linenos:

docs/content/examples/EM_FDEM_1D_Inversion.rst

@@ -21,6 +21,6 @@ Here we will create and run a FDEM 1D inversion.
     from SimPEG import Examples
     Examples.EM_FDEM_1D_Inversion.run()
 
-.. literalinclude:: ../../SimPEG/Examples/EM_FDEM_1D_Inversion.py
+.. literalinclude:: ../../../SimPEG/Examples/EM_FDEM_1D_Inversion.py
     :language: python
     :linenos: