minor fix

- make PTv out from the loop
- do not use Data class in the loop for Jv

.bumpversion.cfg

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

.gitignore

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

.travis.yml

@@ -24,25 +24,18 @@ 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:
-# 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
+  - 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=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk sphinx
+  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk
   - pip install nose-cov python-coveralls
 
   - git clone https://github.com/rowanc1/pymatsolver.git
@@ -53,26 +46,11 @@ install:
 
 # Run test
 script:
-  # test docs
   - nosetests $TEST_DIR --with-cov --cov SimPEG --cov-config .coveragerc -v -s
 
 # Calculate coverage
 after_success:
-  - bash <(curl -s https://codecov.io/bash)
-  - 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
-
+  - coveralls --config_file .coveragerc
 
 notifications:
   email:

README.rst

@@ -1,4 +1,4 @@
-.. image:: https://raw.github.com/simpeg/simpeg/master/docs/images/simpeg-logo.png
+.. image:: https://raw.github.com/simpeg/simpeg/master/docs/simpeg-logo.png
     :alt: SimPEG Logo
 
 ======
@@ -15,7 +15,7 @@ SimPEG
 
 .. image:: https://img.shields.io/badge/license-MIT-blue.svg
     :target: https://github.com/simpeg/simpeg/blob/master/LICENSE
-    :alt: MIT license
+    :alt: BSD 3 clause license.
 
 .. image:: https://api.travis-ci.org/simpeg/simpeg.svg?branch=master
     :target: https://travis-ci.org/simpeg/simpeg
@@ -28,12 +28,7 @@ SimPEG
 .. image:: http://img.shields.io/badge/GITTER-JOIN_CHAT-brightgreen.svg?style=flat-square
     :alt: gitter chat room at https://gitter.im/simpeg/simpeg
     :target: https://gitter.im/simpeg/simpeg
-
-.. image:: https://codecov.io/gh/simpeg/simpeg/branch/master/graph/badge.svg
-    :target: https://codecov.io/gh/simpeg/simpeg
-    :alt: Coverage status
-
-
+    
 Simulation and Parameter Estimation in Geophysics  -  A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
 
 The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:

SimPEG/DCIP/BaseDC.py

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

SimPEG/Directives.py

@@ -167,7 +167,7 @@ class TargetMisfit(InversionDirective):
 
 
 
-class SaveEveryIteration(InversionDirective):
+class _SaveEveryIteration(InversionDirective):
     @property
     def name(self):
         if getattr(self, '_name', None) is None:
@@ -188,7 +188,7 @@ class SaveEveryIteration(InversionDirective):
         self._fileName = value
 
 
-class SaveModelEveryIteration(SaveEveryIteration):
+class SaveModelEveryIteration(_SaveEveryIteration):
     """SaveModelEveryIteration"""
 
     def initialize(self):
@@ -198,7 +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):
@@ -212,7 +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):
@@ -253,7 +253,8 @@ class SaveOutputDictEveryIteration(SaveEveryIteration):
 class Update_IRLS(InversionDirective):
 
     eps_min = None
-    eps = None
+    eps_p = None
+    eps_q = None
     norms = [2.,2.,2.,2.]
     factor = None
     gamma = None
@@ -262,7 +263,6 @@ class Update_IRLS(InversionDirective):
     f_old = None
     f_min_change = 1e-2
     beta_tol = 5e-2
-    prctile = 95
 
     # Solving parameter for IRLS (mode:2)
     IRLSiter   = 0
@@ -297,22 +297,9 @@ class Update_IRLS(InversionDirective):
             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)
-            
+            print self.eps_p, self.eps_q, self.norms
+            self.reg.eps_p = self.eps_p
+            self.reg.eps_q = self.eps_q
             self.reg.norms = self.norms
             self.coolingFactor = 1.
             self.coolingRate = 1
@@ -356,14 +343,14 @@ class Update_IRLS(InversionDirective):
             else:
                 self.f_old = phim_new
 
-#            # Cool the threshold parameter if required
-#            if getattr(self, 'factor', None) is not None:
-#                eps = self.reg.eps / self.factor
-#
-#                if getattr(self, 'eps_min', None) is not None:
-#                    self.reg.eps = np.max([self.eps_min,eps])
-#                else:
-#                    self.reg.eps = eps
+            # Cool the threshold parameter if required
+            if getattr(self, 'factor', None) is not None:
+                eps = self.reg.eps / self.factor
+
+                if getattr(self, 'eps_min', None) is not None:
+                    self.reg.eps = np.max([self.eps_min,eps])
+                else:
+                    self.reg.eps = eps
 
             # Get phi_m at the end of current iteration
             self.phi_m_last = self.invProb.phi_m_last

SimPEG/EM/Analytics/FDEMDipolarfields.py

@@ -1,302 +0,0 @@
-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

@@ -2,4 +2,3 @@ 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 #: The survey to pair with.
-    dataPair = Survey.Data #: The data to pair with.
+    surveyPair = Survey.BaseSurvey
+    dataPair = Survey.Data
 
-    PropMap = EMPropMap #: The property mapping
+    PropMap = EMPropMap
 
     Solver = SimpegSolver
     solverOpts = {}
@@ -217,7 +217,6 @@ 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 FieldsFDEM(SimPEG.Problem.Fields):
+class Fields(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 object acts like an array and is indexed by
+    each problem, the rest are computed. The fields obejct acts like an array and is indexed by
 
     .. code-block:: python
 
@@ -60,6 +60,20 @@ class FieldsFDEM(SimPEG.Problem.Fields):
 
         return self._bPrimary(solution, srcList) + self._bSecondary(solution, srcList)
 
+    def _bSecondary(self, solution, srcList):
+        """
+        Total magnetic flux density is sum of primary and secondary
+
+        :param numpy.ndarray solution: field we solved for
+        :param list srcList: list of sources
+        :rtype: numpy.ndarray
+        :return: total magnetic flux density
+        """
+        if getattr(self, '_bSecondary', None) is None:
+            raise NotImplementedError ('Getting b from %s is not implemented' %self.knownFields.keys()[0])
+
+        return  self._bSecondary(solution, srcList)
+
     def _h(self, solution, srcList):
         """
         Total magnetic field is sum of primary and secondary
@@ -92,7 +106,7 @@ class FieldsFDEM(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 SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
+        :param Src src: sorce
         :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 +124,7 @@ class FieldsFDEM(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 SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
+        :param Src src: sorce
         :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?
@@ -124,11 +138,26 @@ class FieldsFDEM(SimPEG.Problem.Fields):
             return self._bDeriv_u(src, v, adjoint), self._bDeriv_m(src, v, adjoint)
         return np.array(self._bDeriv_u(src, du_dm_v, adjoint) + self._bDeriv_m(src, v, adjoint), dtype = complex)
 
+    def _bSecondaryDeriv(self, src, du_dm_v, v, adjoint = False):
+        """
+        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 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?
+        :rtype: numpy.ndarray
+        :return: derivative times a vector (or tuple for adjoint)
+        """
+        # TODO: modify when primary field is dependent on m
+
+        return self._bDeriv(src, du_dm_v, v, adjoint = adjoint)
+
     def _hDeriv(self, src, du_dm_v, v, adjoint = False):
         """
         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 SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
+        :param Src src: sorce
         :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 +175,7 @@ class FieldsFDEM(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 SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
+        :param Src src: sorce
         :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 +189,12 @@ class FieldsFDEM(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 Fields3D_e(FieldsFDEM):
+class Fields3D_e(Fields):
     """
     Fields object for Problem3D_e.
 
-    :param BaseMesh mesh: mesh
-    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
+    :param Mesh mesh: mesh
+    :param Survey survey: survey
     """
 
     knownFields = {'eSolution':'E'}
@@ -180,6 +209,9 @@ class Fields3D_e(FieldsFDEM):
                     '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
@@ -423,12 +455,12 @@ class Fields3D_e(FieldsFDEM):
 
 
 
-class Fields3D_b(FieldsFDEM):
+class Fields3D_b(Fields):
     """
     Fields object for Problem3D_b.
 
-    :param BaseMesh mesh: mesh
-    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
+    :param Mesh mesh: mesh
+    :param Survey survey: survey
     """
 
     knownFields = {'bSolution':'F'}
@@ -443,6 +475,9 @@ class Fields3D_b(FieldsFDEM):
                     '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
@@ -465,6 +500,8 @@ class Fields3D_b(FieldsFDEM):
             return 'E'
         elif fieldType == 'b':
             return 'F'
+        elif fieldType == 'bSecondary':
+            return 'F'
         elif (fieldType == 'h') or (fieldType == 'j'):
             return'CCV'
         else:
@@ -687,12 +724,12 @@ class Fields3D_b(FieldsFDEM):
         return Zero()
 
 
-class Fields3D_j(FieldsFDEM):
+class Fields3D_j(Fields):
     """
     Fields object for Problem3D_j.
 
-    :param BaseMesh mesh: mesh
-    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
+    :param Mesh mesh: mesh
+    :param Survey survey: survey
     """
 
     knownFields = {'jSolution':'F'}
@@ -707,6 +744,9 @@ class Fields3D_j(FieldsFDEM):
                     '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
@@ -979,12 +1019,12 @@ class Fields3D_j(FieldsFDEM):
         return 1./(1j * omega(src.freq)) * VI * (self._aveE2CCV * ( s_mDeriv(v) - self._edgeCurl.T * ( self._MfRhoDeriv(jSolution) * v ) ) )
 
 
-class Fields3D_h(FieldsFDEM):
+class Fields3D_h(Fields):
     """
     Fields object for Problem3D_h.
 
-    :param BaseMesh mesh: mesh
-    :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
+    :param Mesh mesh: mesh
+    :param Survey survey: survey
     """
 
     knownFields = {'hSolution':'E'}
@@ -999,6 +1039,9 @@ class Fields3D_h(FieldsFDEM):
                     '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 FieldsFDEM, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
+from FieldsFDEM import Fields, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
 from SimPEG.EM.Base import BaseEMProblem
 from SimPEG.EM.Utils import omega
 
@@ -31,11 +31,10 @@ class BaseFDEMProblem(BaseEMProblem):
         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 = FieldsFDEM
+    fieldsPair = Fields
 
     def fields(self, m):
         """
@@ -65,7 +64,7 @@ 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.FieldsFDEM.FieldsFDEM u: fields object
+        :param SimPEG.EM.FDEM.Fields u: fields object
         :rtype numpy.array:
         :return: Jv (ndata,)
         """
@@ -100,7 +99,7 @@ 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.FieldsFDEM.FieldsFDEM u: fields object
+        :param SimPEG.EM.FDEM.Fields u: fields object
         :rtype numpy.array:
         :return: Jv (ndata,)
         """
@@ -154,8 +153,8 @@ class BaseFDEMProblem(BaseEMProblem):
         Evaluates the sources for a given frequency and puts them in matrix form
 
         :param float freq: Frequency
-        :rtype: tuple
-        :return: (s_m, s_e) (nE or nF, nSrc)
+        :rtype: (numpy.ndarray, numpy.ndarray)
+        :return: s_m, s_e (nE or nF, nSrc)
         """
         Srcs = self.survey.getSrcByFreq(freq)
         if self._formulation is 'EB':
@@ -195,7 +194,7 @@ class Problem3D_e(BaseFDEMProblem):
 
     which we solve for :math:`\mathbf{e}`.
 
-    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
+    :param SimPEG.Mesh mesh: mesh
     """
 
     _solutionType = 'eSolution'
@@ -270,7 +269,7 @@ class Problem3D_e(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param SimPEG.EM.FDEM.Src src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -306,7 +305,7 @@ class Problem3D_b(BaseFDEMProblem):
     .. note ::
         The inverse problem will not work with full anisotropy
 
-    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
+    :param SimPEG.Mesh mesh: mesh
     """
 
     _solutionType = 'bSolution'
@@ -401,7 +400,7 @@ class Problem3D_b(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param SimPEG.EM.FDEM.Src src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -445,7 +444,6 @@ class Problem3D_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 ::
@@ -455,7 +453,7 @@ class Problem3D_j(BaseFDEMProblem):
     .. note::
         This implementation does not yet work with full anisotropy!!
 
-    :param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
+    :param SimPEG.Mesh mesh: mesh
     """
 
     _solutionType = 'jSolution'
@@ -531,8 +529,8 @@ class Problem3D_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
-        :return: RHS (nE, nSrc)
+        :rtype: numpy.ndarray (nE, nSrc)
+        :return: RHS
         """
 
         s_m, s_e = self.getSourceTerm(freq)
@@ -551,7 +549,7 @@ class Problem3D_j(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param SimPEG.EM.FDEM.Src src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -593,7 +591,7 @@ class Problem3D_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.BaseMesh.BaseMesh mesh: mesh
+    :param SimPEG.Mesh mesh: mesh
     """
 
     _solutionType = 'hSolution'
@@ -610,11 +608,9 @@ class Problem3D_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
@@ -657,7 +653,6 @@ class Problem3D_h(BaseFDEMProblem):
         :param float freq: Frequency
         :rtype: numpy.ndarray
         :return: RHS (nE, nSrc)
-
         """
 
         s_m, s_e = self.getSourceTerm(freq)
@@ -671,7 +666,7 @@ class Problem3D_h(BaseFDEMProblem):
         Derivative of the right hand side with respect to the model
 
         :param float freq: frequency
-        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
+        :param SimPEG.EM.FDEM.Src src: FDEM source
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray

SimPEG/EM/FDEM/RxFDEM.py

@@ -25,10 +25,10 @@ class BaseRx(SimPEG.Survey.BaseRx):
 
     def eval(self, src, mesh, f):
         """
-        Project fields to receivers to get data.
+        Project fields to recievers to get data.
 
-        :param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
-        :param BaseMesh mesh: mesh used
+        :param Source src: FDEM source
+        :param Mesh mesh: mesh used
         :param Fields f: fields object
         :rtype: numpy.ndarray
         :return: fields projected to recievers
@@ -44,8 +44,8 @@ class BaseRx(SimPEG.Survey.BaseRx):
         """
         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 Source src: FDEM source
+        :param Mesh mesh: mesh used
         :param Fields f: fields object
         :param numpy.ndarray v: vector to multiply
         :rtype: numpy.ndarray
@@ -97,6 +97,19 @@ class Point_b(BaseRx):
         self.projField = 'b'
         super(Point_b, self).__init__(locs, orientation, component)
 
+class Point_bSecondary(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 = 'bSecondary'
+        super(Point_bSecondary, self).__init__(locs, orientation, component)
+
 
 class Point_h(BaseRx):
     """

SimPEG/EM/FDEM/SrcFDEM.py

@@ -23,8 +23,8 @@ class BaseSrc(Survey.BaseSrc):
         - :math:`s_m` : magnetic source term
         - :math:`s_e` : electric source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
-        :rtype: tuple
+        :param Problem prob: FDEM Problem
+        :rtype: (numpy.ndarray, numpy.ndarray)
         :return: tuple with magnetic source term and electric source term
         """
         s_m = self.s_m(prob)
@@ -37,10 +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 BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
-        :rtype: tuple
+        :rtype: (numpy.ndarray, numpy.ndarray)
         :return: tuple with magnetic source term and electric source term derivatives times a vector
         """
         if v is not None:
@@ -52,7 +52,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Primary magnetic flux density
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary magnetic flux density
         """
@@ -64,7 +64,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Primary magnetic field
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -76,7 +76,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Primary electric field
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary electric field
         """
@@ -88,7 +88,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Primary current density
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: primary current density
         """
@@ -100,7 +100,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -110,7 +110,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Electric source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -120,7 +120,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Derivative of magnetic source term with respect to the inversion model
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -133,7 +133,7 @@ class BaseSrc(Survey.BaseSrc):
         """
         Derivative of electric source term with respect to the inversion model
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :param numpy.ndarray v: vector to take product with
         :param bool adjoint: adjoint?
         :rtype: numpy.ndarray
@@ -162,7 +162,7 @@ class RawVec_e(BaseSrc):
         """
         Electric source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -191,7 +191,7 @@ class RawVec_m(BaseSrc):
         """
         Magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -220,7 +220,7 @@ class RawVec(BaseSrc):
         """
         Magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: magnetic source term on mesh
         """
@@ -232,7 +232,7 @@ class RawVec(BaseSrc):
         """
         Electric source term
 
-        :param BaseFDEMProblem prob: FDEM Problem
+        :param Problem prob: FDEM Problem
         :rtype: numpy.ndarray
         :return: electric source term on mesh
         """
@@ -301,7 +301,7 @@ class MagDipole(BaseSrc):
         """
         The primary magnetic flux density from a magnetic vector potential
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -339,7 +339,7 @@ class MagDipole(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -350,7 +350,7 @@ class MagDipole(BaseSrc):
         """
         The magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -364,7 +364,7 @@ class MagDipole(BaseSrc):
         """
         The electric source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -416,7 +416,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The primary magnetic flux density from the analytic solution for magnetic fields from a dipole
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -455,7 +455,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -466,7 +466,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -479,7 +479,7 @@ class MagDipole_Bfield(BaseSrc):
         """
         The electric source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -530,7 +530,7 @@ class CircularLoop(BaseSrc):
         """
         The primary magnetic flux density from a magnetic vector potential
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -555,7 +555,7 @@ class CircularLoop(BaseSrc):
             a = MagneticLoopVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
 
         else:
-            srcfct = MagneticDipoleVectorPotential
+            srcfct = MagneticLoopVectorPotential
             ax = srcfct(self.loc, gridX, 'x', self.radius, mu=self.mu)
             ay = srcfct(self.loc, gridY, 'y', self.radius, mu=self.mu)
             az = srcfct(self.loc, gridZ, 'z', self.radius, mu=self.mu)
@@ -567,7 +567,7 @@ class CircularLoop(BaseSrc):
         """
         The primary magnetic field from a magnetic vector potential
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -578,7 +578,7 @@ class CircularLoop(BaseSrc):
         """
         The magnetic source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """
@@ -591,7 +591,7 @@ class CircularLoop(BaseSrc):
         """
         The electric source term
 
-        :param BaseFDEMProblem prob: FDEM problem
+        :param Problem prob: FDEM problem
         :rtype: numpy.ndarray
         :return: primary magnetic field
         """

SimPEG/EM/TDEM/FieldsTDEM.py

@@ -0,0 +1,142 @@
+import numpy as np
+import scipy.sparse as sp
+import SimPEG
+from SimPEG import Utils
+from SimPEG.EM.Utils import omega
+from SimPEG.Utils import Zero, Identity
+
+class Fields(SimPEG.Problem.TimeFields):
+    """
+
+    Fancy Field Storage for a TDEM 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
+
+    .. code-block:: python
+
+        f = problem.fields(m)
+        e = f[srcList,'e']
+        b = f[srcList,'b']
+
+    If accessing all sources for a given field, use the :code:`:`
+
+    .. code-block:: python
+
+        f = problem.fields(m)
+        e = f[:,'e']
+        b = f[:,'b']
+
+    The array returned will be size (nE or nF, nSrcs :math:`\\times` nFrequencies)
+    """
+
+    knownFields = {}
+    dtype = float
+
+    def _eDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+        if adjoint is True:
+            return self._eDeriv_u(tInd, src, v, adjoint), self._eDeriv_m(tInd, src, v, adjoint)
+        return self._eDeriv_u(tInd, src, dun_dm_v) + self._eDeriv_m(tInd, src, v)
+
+    def _bDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+        if adjoint is True:
+            return self._bDeriv_u(tInd, src, v, adjoint), self._bDeriv_m(tInd, src, v, adjoint)
+        return self._bDeriv_u(tInd, src, dun_dm_v) + self._bDeriv_m(tInd, src, v)
+
+class Fields_Derivs(Fields):
+    knownFields = {
+                    'bDeriv': 'F',
+                    'eDeriv': 'E',
+                    'hDeriv': 'E',
+                    'jDeriv': 'F'
+                  }
+
+
+class Fields_b(Fields):
+    """Fancy Field Storage for a TDEM survey."""
+    knownFields = {'bSolution': 'F'}
+    aliasFields = {
+                    'b': ['bSolution', 'F', '_b'],
+                    'e': ['bSolution', 'E', '_e'],
+                  }
+
+    def startup(self):
+        self.MeSigmaI      = self.survey.prob.MeSigmaI
+        self.MeSigmaIDeriv = self.survey.prob.MeSigmaIDeriv
+        self.edgeCurl      = self.survey.prob.mesh.edgeCurl
+        self.MfMui         = self.survey.prob.MfMui
+
+    def _b(self, bSolution, srcList, tInd):
+        return bSolution
+
+    def _bDeriv_u(self, tInd, src, dun_dm_v, adjoint=False):
+        return Identity()*dun_dm_v
+
+    def _bDeriv_m(self, tInd, src, v, adjoint=False):
+        return Zero()
+
+    # def _bDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+    #     if adjoint is True:
+    #         return self._bDeriv_u(tInd, src, v, adjoint), self._bDeriv_m(tInd, src, v, adjoint)
+    #     return self._bDeriv_u(tInd, src, dun_dm_v) + self._bDeriv_m(tInd, src, v)
+
+    def _e(self, bSolution, srcList, tInd):
+        e = self.MeSigmaI * ( self.edgeCurl.T * ( self.MfMui * bSolution ) )
+        for i, src in enumerate(srcList):
+            _, S_e = src.eval(self.survey.prob, self.survey.prob.times[tInd])
+            e[:,i] = e[:,i] - self.MeSigmaI * S_e
+        return e
+
+    def _eDeriv_u(self, tInd, src, dun_dm_v, adjoint = False):
+        if adjoint is True:
+            return self.MfMui.T * ( self.edgeCurl * ( self.MeSigmaI.T * dun_dm_v ) )
+        return self.MeSigmaI * ( self.edgeCurl.T * ( self.MfMui * dun_dm_v ) )
+
+    def _eDeriv_m(self, tInd, src, v, adjoint = False):
+        _, S_e = src.eval(self.survey.prob, self.survey.prob.times[tInd])
+        bSolution = self[[src],'bSolution',tInd]
+
+        _, S_eDeriv = src.evalDeriv(self.survey.prob.times[tInd], self, adjoint=adjoint)
+
+        if adjoint is True:
+            return self.MeSigmaIDeriv(-S_e + self.edgeCurl.T * ( self.MfMui * bSolution ) ).T * v - S_eDeriv(self.MeSigmaI.T * v)
+
+        return self.MeSigmaIDeriv(-S_e + self.edgeCurl.T * ( self.MfMui * bSolution)) * v - self.MeSigmaI * S_eDeriv(v)
+
+
+
+class Fields_e(Fields):
+    """Fancy Field Storage for a TDEM survey."""
+    knownFields = {'eSolution': 'E'}
+    aliasFields = {
+                    'e': ['eSolution', 'E', '_e'],
+                    'b': ['eSolution', 'F', '_b'],
+                  }
+
+    def startup(self):
+        self.MeSigmaI      = self.survey.prob.MeSigmaI
+        self.MeSigmaIDeriv = self.survey.prob.MeSigmaIDeriv
+        self.edgeCurl      = self.survey.prob.mesh.edgeCurl
+        self.MfMui         = self.survey.prob.MfMui
+
+
+    def _e(self, eSolution, srcList, tInd):
+        return eSolution
+
+    def _eDeriv_u(self, tInd, src, dun_dm_v, adjoint = False):
+        return dun_dm_v
+
+    def _eDeriv_m(self, tInd, src, v, adjoint = False):
+        return Zero()
+
+    def _b(self, eSolution, srcList, tInd):
+        raise NotImplementedError
+
+    def _bDeriv_u(self, tInd, src, dun_dm_v, adjoint=False):
+        raise NotImplementedError
+
+    def _bDeriv_m(self, tInd, src, v, adjoint=False):
+        raise NotImplementedError
+
+    # def _bDeriv(self, tInd, src, dun_dm_v, v, adjoint=False):
+    #     if adjoint is True:
+    #         return self._bDeriv_u(tInd, src, v, adjoint), self._bDeriv_m(tInd, src, v, adjoint)
+    #     return self._bDeriv_u(tInd, src, dun_dm_v) + self._bDeriv_m(tInd, src, v)

SimPEG/EM/TDEM/SrcTDEM.py

@@ -0,0 +1,246 @@
+import SimPEG
+from SimPEG import np, Utils
+from SimPEG.Utils import Zero, Identity
+from scipy.constants import mu_0
+from SimPEG.EM.Utils import *
+
+####################################################
+# Sources
+####################################################
+
+class BaseWaveform(object):
+
+    def __init__(self, offTime=0., hasInitialFields=False):
+        self.offTime = offTime
+        self.hasInitialFields = hasInitialFields
+
+    def _assertMatchesPair(self, pair):
+        assert (isinstance(self, pair)
+            ), "Waveform object must be an instance of a %s BaseWaveform class."%(pair.__name__)
+
+    def eval(self, time):
+        raise NotImplementedError
+
+    def evalDeriv(self, time):
+        raise NotImplementedError # needed for E-formulation
+
+
+class StepOffWaveform(BaseWaveform):
+
+    def __init__(self, offTime=0.):
+        BaseWaveform.__init__(self, offTime, hasInitialFields=True)
+
+    def eval(self, time):
+        return 0.
+
+
+class RawWaveform(BaseWaveform):
+
+    def __init__(self, offTime=0.):
+        BaseWaveform.__init__(self, offTime, hasInitialFields=True)
+
+    def eval(self, time):
+        raise NotImplementedError('RawWaveform has not been implemented, you should write it!')
+
+
+class TriangularWaveform(BaseWaveform):
+
+    def __init__(self, offTime=0.):
+        BaseWaveform.__init__(self, offTime, hasInitialFields=True)
+
+    def eval(self, time):
+        raise NotImplementedError('TriangularWaveform has not been implemented, you should write it!')
+
+
+
+class BaseSrc(SimPEG.Survey.BaseSrc):
+
+    # rxPair = Rx
+    integrate = True
+    waveformPair = BaseWaveform
+
+    @property
+    def waveform(self):
+        "A waveform instance is not None"
+        return getattr(self, '_waveform', None)
+    @waveform.setter
+    def waveform(self, val):
+        if self.waveform is None:
+            val._assertMatchesPair(self.waveformPair)
+            self._mapping = val
+        else:
+            self._mapping = self.PropMap(val)
+
+
+    def __init__(self, rxList, waveform = StepOffWaveform(), **kwargs):
+        self.waveform = waveform
+        SimPEG.Survey.BaseSrc.__init__(self, rxList, **kwargs)
+
+
+    def bInitial(self, prob):
+        return Zero()
+
+    def bInitialDeriv(self, prob, v=None, adjoint=False):
+        return Zero()
+
+    def eInitial(self, prob):
+        return Zero()
+
+    def eInitialDeriv(self, prob, v=None, adjoint=False):
+        return Zero()
+
+    def eval(self, prob, time):
+        S_m = self.S_m(prob, time)
+        S_e = self.S_e(prob, time)
+        return S_m, S_e
+
+    def evalDeriv(self, prob, time, v=None, adjoint=False):
+        if v is not None:
+            return self.S_mDeriv(prob, time, v, adjoint), self.S_eDeriv(prob, time, v, adjoint)
+        else:
+            return lambda v: self.S_mDeriv(prob, time, v, adjoint), lambda v: self.S_eDeriv(prob, time, v, adjoint)
+
+    def S_m(self, prob, time):
+        return Zero()
+
+    def S_e(self, prob, time):
+        return Zero()
+
+    def S_mDeriv(self, prob, time, v=None, adjoint=False):
+        return Zero()
+
+    def S_eDeriv(self, prob, time, v=None, adjoint=False):
+        return Zero()
+
+
+class MagDipole(BaseSrc):
+
+    waveform = None
+    loc = None
+    orientation = 'Z'
+    moment = 1.
+    mu = mu_0
+
+    def __init__(self, rxList, **kwargs):
+        assert self.orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
+        self.integrate = False
+        BaseSrc.__init__(self, rxList, **kwargs)
+
+    def _bfromVectorPotential(self, prob):
+        if prob._eqLocs is 'FE':
+            gridX = prob.mesh.gridEx
+            gridY = prob.mesh.gridEy
+            gridZ = prob.mesh.gridEz
+            C = prob.mesh.edgeCurl
+
+        elif prob._eqLocs is 'EF':
+            gridX = prob.mesh.gridFx
+            gridY = prob.mesh.gridFy
+            gridZ = prob.mesh.gridFz
+            C = prob.mesh.edgeCurl.T
+
+
+        if prob.mesh._meshType is 'CYL':
+            if not prob.mesh.isSymmetric:
+                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+            a = MagneticDipoleVectorPotential(self.loc, gridY, 'y', mu=self.mu, moment=self.moment)
+
+        else:
+            srcfct = MagneticDipoleVectorPotential
+            ax = srcfct(self.loc, gridX, 'x', mu=self.mu, moment=self.moment)
+            ay = srcfct(self.loc, gridY, 'y', mu=self.mu, moment=self.moment)
+            az = srcfct(self.loc, gridZ, 'z', mu=self.mu, moment=self.moment)
+            a = np.concatenate((ax, ay, az))
+
+        return C*a
+
+
+    def bInitial(self, prob):
+
+        if self.waveform.hasInitialFields is False:
+            return Zero()
+
+        return self._bfromVectorPotential(prob)
+
+    def eInitial(self, prob):
+
+        if self.waveform.hasInitialFields is False:
+            return Zero()
+
+        b = self.bInitial(prob)
+        MeSigmaI = prob.MeSigmaI
+        MfMui = prob.MfMui
+        C = prob.mesh.edgeCurl
+
+        return MeSigmaI * (C.T * (MfMui * b))
+
+    def eInitialDeriv(self, prob, v=None, adjoint=False):
+
+        if self.waveform.hasInitialFields is False:
+            return Zero()
+
+        b = self.bInitial(prob)
+        MeSigmaIDeriv = prob.MeSigmaIDeriv
+        MfMui = prob.MfMui
+        C = prob.mesh.edgeCurl
+        S_e = self.S_e(prob, prob.t0)
+
+        # S_e doesn't depend on the model
+
+        if adjoint:
+            return MeSigmaIDeriv( -S_e + C.T * ( MfMui * b ) ).T * v
+
+        return MeSigmaIDeriv( -S_e + C.T * ( MfMui * b ) ) * v
+
+
+    def S_m(self, prob, time):
+        if self.waveform.hasInitialFields is False:
+            raise NotImplementedError
+        return Zero()
+
+    def S_e(self, prob, time):
+        if self.waveform.hasInitialFields is False:
+            raise NotImplementedError
+        return Zero()
+
+class CircularLoop(MagDipole):
+
+    waveform = None
+    loc = None
+    orientation = 'Z'
+    radius = None
+    mu = mu_0
+
+    def __init__(self, rxList, **kwargs):
+        assert self.orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
+        self.integrate = False
+        BaseSrc.__init__(self, rxList, **kwargs)
+
+    def _bfromVectorPotential(self, prob):
+        if prob._eqLocs is 'FE':
+            gridX = prob.mesh.gridEx
+            gridY = prob.mesh.gridEy
+            gridZ = prob.mesh.gridEz
+            C = prob.mesh.edgeCurl
+
+        elif prob._eqLocs is 'EF':
+            gridX = prob.mesh.gridFx
+            gridY = prob.mesh.gridFy
+            gridZ = prob.mesh.gridFz
+            C = prob.mesh.edgeCurl.T
+
+
+        if prob.mesh._meshType is 'CYL':
+            if not prob.mesh.isSymmetric:
+                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+            a = MagneticLoopVectorPotential(self.loc, gridY, 'y', radius=self.radius, mu=self.mu)
+
+        else:
+            srcfct = MagneticLoopVectorPotential
+            ax = srcfct(self.loc, gridX, 'x', mu=self.mu, radius=self.radius)
+            ay = srcfct(self.loc, gridY, 'y', mu=self.mu, radius=self.radius)
+            az = srcfct(self.loc, gridZ, 'z', mu=self.mu, radius=self.radius)
+            a = np.concatenate((ax, ay, az))
+
+        return C*a
+

SimPEG/EM/TDEM/SurveyTDEM.py

@@ -1,10 +1,16 @@
-from SimPEG import Utils, Survey, np
-from SimPEG.Survey import BaseSurvey
+import SimPEG
+from SimPEG import np, Utils
+from SimPEG.Utils import Zero, Identity
+from scipy.constants import mu_0
 from SimPEG.EM.Utils import *
-from BaseTDEM import FieldsTDEM
+import SrcTDEM as Src
 
 
-class RxTDEM(Survey.BaseTimeRx):
+####################################################
+# Receivers
+####################################################
+
+class Rx(SimPEG.Survey.BaseTimeRx):
 
     knownRxTypes = {
                     'ex':['e', 'Ex', 'N'],
@@ -21,7 +27,7 @@ class RxTDEM(Survey.BaseTimeRx):
                    }
 
     def __init__(self, locs, times, rxType):
-        Survey.BaseTimeRx.__init__(self, locs, times, rxType)
+        SimPEG.Survey.BaseTimeRx.__init__(self, locs, times, rxType)
 
     @property
     def projField(self):
@@ -56,144 +62,60 @@ class RxTDEM(Survey.BaseTimeRx):
         u_part = Utils.mkvc(u[src, self.projField, :])
         return P*u_part
 
-    def evalDeriv(self, src, mesh, timeMesh, u, v, adjoint=False):
+    def evalDeriv(self, src, mesh, timeMesh, v, adjoint=False):
         P = self.getP(mesh, timeMesh)
-
         if not adjoint:
-            return P * Utils.mkvc(v[src, self.projField, :])
+            return P * v #Utils.mkvc(v[src, self.projField+'Deriv', :])
         elif adjoint:
-            return P.T * v[src, self]
+            # dP_dF_T = P.T * v #[src, self]
+            # newshape = (len(dP_dF_T)/timeMesh.nN, timeMesh.nN )
+            return P.T * v #np.reshape(dP_dF_T, newshape, order='F')
 
 
-class SrcTDEM(Survey.BaseSrc):
-    rxPair = RxTDEM
-    radius = None
+####################################################
+# Survey
+####################################################
 
-    def getInitialFields(self, mesh):
-        F0 = getattr(self, '_getInitialFields_' + self.srcType)(mesh)
-        return F0
-
-    def getJs(self, mesh, time):
-        return None
-
-
-class SrcTDEM_VMD_MVP(SrcTDEM):
-
-    def __init__(self,rxList,loc,waveformType="STEPOFF"):
-        self.loc = loc
-        self.waveformType = waveformType
-        SrcTDEM.__init__(self,rxList)
-
-    def getInitialFields(self, mesh):
-        """Vertical magnetic dipole, magnetic vector potential"""
-        if self.waveformType == "STEPOFF":
-            print ">> Step waveform: Non-zero initial condition"
-            if mesh._meshType is 'CYL':
-                if mesh.isSymmetric:
-                    MVP = MagneticDipoleVectorPotential(self.loc, mesh, 'Ey')
-                else:
-                    raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            elif mesh._meshType is 'TENSOR':
-                MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
-            else:
-                raise Exception('Unknown mesh for VMD')
-            return {"b": mesh.edgeCurl*MVP}
-        elif self.waveformType == "GENERAL":
-            print ">> General waveform: Zero initial condition"
-            return {"b": np.zeros(mesh.nF)}
-        else:
-            raise NotImplementedError("Only use STEPOFF or GENERAL")
-
-    def getMeS(self, mesh, MfMui):
-        if mesh._meshType is 'CYL':
-            if mesh.isSymmetric:
-                MVP = MagneticDipoleVectorPotential(self.loc, mesh, 'Ey')
-            else:
-                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-        elif mesh._meshType is 'TENSOR':
-            MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
-        else:
-            raise Exception('Unknown mesh for VMD')
-        return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
-
-
-class SrcTDEM_CircularLoop_MVP(SrcTDEM):
-    def __init__(self,rxList,loc,radius,waveformType="STEPOFF"):
-        self.loc = loc
-        self.radius = radius
-        self.waveformType = waveformType
-        SrcTDEM.__init__(self,rxList)
-
-    def getInitialFields(self, mesh):
-        """Circular Loop, magnetic vector potential"""
-        if self.waveformType == "STEPOFF":
-            print ">> Step waveform: Non-zero initial condition"
-            if mesh._meshType is 'CYL':
-                if mesh.isSymmetric:
-                    MVP = MagneticLoopVectorPotential(self.loc, mesh, 'Ey', self.radius)
-                else:
-                    raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            elif mesh._meshType is 'TENSOR':
-                MVP = MagneticLoopVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'], self.radius)
-            else:
-                raise Exception('Unknown mesh for CircularLoop')
-            return {"b": mesh.edgeCurl*MVP}
-        elif self.waveformType == "GENERAL":
-            print ">> General waveform: Zero initial condition"
-            return {"b": np.zeros(mesh.nF)}
-        else:
-            raise NotImplementedError("Only use STEPOFF or GENERAL")
-
-    def getMeS(self, mesh, MfMui):
-        if mesh._meshType is 'CYL':
-            if mesh.isSymmetric:
-                MVP = MagneticLoopVectorPotential(self.loc, mesh, 'Ey', self.radius)
-            else:
-                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-        elif mesh._meshType is 'TENSOR':
-            MVP = MagneticLoopVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'], self.radius)
-        else:
-            raise Exception('Unknown mesh for CircularLoop')
-        return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
-
-
-class SurveyTDEM(Survey.BaseSurvey):
+class Survey(SimPEG.Survey.BaseSurvey):
     """
-        docstring for SurveyTDEM
+    Time domain electromagnetic survey
     """
-    srcPair = SrcTDEM
+
+    srcPair = Src.BaseSrc
+    rxPair = Rx
 
     def __init__(self, srcList, **kwargs):
         # Sort these by frequency
         self.srcList = srcList
-        Survey.BaseSurvey.__init__(self, **kwargs)
+        SimPEG.Survey.BaseSurvey.__init__(self, **kwargs)
 
     def eval(self, u):
-        data = Survey.Data(self)
+        data = SimPEG.Survey.Data(self)
         for src in self.srcList:
             for rx in src.rxList:
                 data[src, rx] = rx.eval(src, self.mesh, self.prob.timeMesh, u)
         return data
 
     def evalDeriv(self, u, v=None, adjoint=False):
-        assert v is not None, 'v to multiply must be provided.'
+        raise Exception('Use Receivers to project fields deriv.')
+        # assert v is not None, 'v to multiply must be provided.'
 
-        if not adjoint:
-            data = Survey.Data(self)
-            for src in self.srcList:
-                for rx in src.rxList:
-                    data[src, rx] = rx.evalDeriv(src, self.mesh, self.prob.timeMesh, u, v)
-            return data
-        else:
-            f = FieldsTDEM(self.mesh, self)
-            for src in self.srcList:
-                for rx in src.rxList:
-                    Ptv = rx.evalDeriv(src, self.mesh, self.prob.timeMesh, u, v, adjoint=True)
-                    Ptv = Ptv.reshape((-1, self.prob.timeMesh.nN), order='F')
-                    if rx.projField not in f: # first time we are projecting
-                        f[src, rx.projField, :] = Ptv
-                    else: # there are already fields, so let's add to them!
-                        f[src, rx.projField, :] += Ptv
-            return f
+        # if not adjoint:
+        #     data = SimPEG.Survey.Data(self)
+        #     for src in self.srcList:
+        #         for rx in src.rxList:
+        #             data[src, rx] = rx.evalDeriv(src, self.mesh, self.prob.timeMesh, u, v)
+        #     return data
+        # else:
+        #     f = FieldsTDEM(self.mesh, self)
+        #     for src in self.srcList:
+        #         for rx in src.rxList:
+        #             Ptv = rx.evalDeriv(src, self.mesh, self.prob.timeMesh, u, v, adjoint=True)
+        #             Ptv = Ptv.reshape((-1, self.prob.timeMesh.nN), order='F')
+        #             if rx.projField not in f: # first time we are projecting
+        #                 f[src, rx.projField, :] = Ptv
+        #             else: # there are already fields, so let's add to them!
+        #                 f[src, rx.projField, :] += Ptv
+        #     return f
 
 

SimPEG/EM/TDEM/TDEM.py

@@ -0,0 +1,553 @@
+from SimPEG import Problem, Utils, np, sp, Solver as SimpegSolver
+from SimPEG.EM.Base import BaseEMProblem
+from SimPEG.EM.TDEM.SurveyTDEM import Survey as SurveyTDEM
+from SimPEG.EM.TDEM.FieldsTDEM import *
+from scipy.constants import mu_0
+import time
+
+class BaseTDEMProblem(Problem.BaseTimeProblem, BaseEMProblem):
+    """
+    We start with the first order form of Maxwell's equations
+    """
+    surveyPair = SurveyTDEM
+    fieldsPair = Fields
+
+    def __init__(self, mesh, mapping=None, **kwargs):
+        Problem.BaseTimeProblem.__init__(self, mesh, mapping=mapping, **kwargs)
+
+    def fields(self, m):
+        """
+        Solve the forward problem for the fields.
+
+        :param numpy.array m: inversion model (nP,)
+        :rtype numpy.array:
+        :return F: fields
+        """
+
+        tic = time.time()
+        self.curModel = m
+
+        F = self.fieldsPair(self.mesh, self.survey)
+
+        # set initial fields
+        F[:,self._fieldType+'Solution',0] = self.getInitialFields()
+
+        # timestep to solve forward
+        if self.verbose: print '%s\nCalculating fields(m)\n%s'%('*'*50,'*'*50)
+        Ainv = None
+        for tInd, dt in enumerate(self.timeSteps):
+            if Ainv is not None and (tInd > 0 and dt != self.timeSteps[tInd - 1]):# keep factors if dt is the same as previous step b/c A will be the same
+                Ainv.clean()
+                Ainv = None
+
+            if Ainv is None:
+                A = self.getAdiag(tInd)
+                if self.verbose: print 'Factoring...   (dt = %e)'%dt
+                Ainv = self.Solver(A, **self.solverOpts)
+                if self.verbose: print 'Done'
+
+            rhs = self.getRHS(tInd+1) # this is on the nodes of the time mesh
+            Asubdiag = self.getAsubdiag(tInd)
+
+            if self.verbose: print ('    Solving...   (tInd = %i)')% (tInd+1)
+            sol = Ainv * (rhs - Asubdiag * F[:,self._fieldType+'Solution',tInd]) # taking a step
+
+            if self.verbose: print '    Done...'
+
+            if sol.ndim == 1:
+                sol.shape = (sol.size,1)
+            F[:,self._fieldType+'Solution',tInd+1] = sol
+        if self.verbose: print '%s\nDone calculating fields(m)\n%s'%('*'*50,'*'*50)
+        Ainv.clean()
+        return F
+
+
+    def Jvec(self, m, v, f=None):
+        """
+        Jvec computes the sensitivity times a vector
+
+        .. math::
+            \mathbf{J} \mathbf{v} = \\frac{d\mathbf{P}}{d\mathbf{F}} \left( \\frac{d\mathbf{F}}{d\mathbf{u}} \\frac{d\mathbf{u}}{d\mathbf{m}} + \\frac{\partial\mathbf{F}}{\partial\mathbf{m}} \\right) \mathbf{v}
+
+        where
+
+        .. math::
+            \mathbf{A} \\frac{d\mathbf{u}}{d\mathbf{m}} + \\frac{d\mathbf{A}(\mathbf{u})}{d\mathbf{m}} = \\frac{d \mathbf{RHS}}{d \mathbf{m}}
+        """
+
+        if f is None:
+           f = self.fields(m)
+
+        ftype = self._fieldType + 'Solution' # the thing we solved for
+        self.curModel = m
+
+        # mat to store previous time-step's solution deriv times a vector for each source
+        # size: nu x nSrc
+
+        # this is a bit silly
+
+        # if self._fieldType is 'b' or self._fieldType is 'j':
+        #     ifields = np.zeros((self.mesh.nF, len(Srcs)))
+        # elif self._fieldType is 'e' or self._fieldType is 'h':
+        #     ifields = np.zeros((self.mesh.nE, len(Srcs)))
+
+        # for i, src in enumerate(self.survey.srcList):
+        dun_dm_v = np.hstack([Utils.mkvc(self.getInitialFieldsDeriv(src,v),2) for src in self.survey.srcList]) # can over-write this at each timestep
+        #
+        df_dm_v = Fields_Derivs(self.mesh, self.survey) # store the field derivs we need to project to calc full deriv
+
+        Adiaginv = None
+
+        for tInd, dt in zip(range(self.nT), self.timeSteps):
+            if Adiaginv is not None and (tInd > 0 and dt != self.timeSteps[tInd - 1]):# keep factors if dt is the same as previous step b/c A will be the same
+                Adiaginv.clean()
+                Adiaginv = None
+
+            if Adiaginv is None:
+                A = self.getAdiag(tInd)
+                Adiaginv = self.Solver(A, **self.solverOpts)
+
+            Asubdiag = self.getAsubdiag(tInd)
+
+            for i, src in enumerate(self.survey.srcList):
+
+                # here, we are lagging by a timestep, so filling in as we go
+                for projField in set([rx.projField for rx in src.rxList]):
+                    # Seogi: df_duFun?
+                    df_dmFun = getattr(f, '_%sDeriv'%projField, None)
+                    # df_dm_v is dense, but we only need the times at (rx.P.T * ones > 0)
+                    # This should be called rx.footprint
+                    df_dm_v[src, '%sDeriv'%projField , tInd] = df_dmFun(tInd, src, dun_dm_v[:,i], v)
+
+                un_src = f[src,ftype,tInd+1]
+
+                dA_dm_v   = self.getAdiagDeriv(tInd, un_src, v) # cell centered on time mesh
+                dRHS_dm_v = self.getRHSDeriv(tInd+1, src, v) # on nodes of time mesh
+
+                dAsubdiag_dm_v = self.getAsubdiagDeriv(tInd, f[src,ftype,tInd], v)
+
+                JRHS = dRHS_dm_v - dAsubdiag_dm_v - dA_dm_v
+
+                # step in time and overwrite
+                if tInd != len(self.timeSteps+1):
+                    dun_dm_v[:,i] = Adiaginv * (JRHS - Asubdiag * dun_dm_v[:,i])
+
+        # Seogi: suspcious spot
+        # Jv = self.dataPair(self.survey)
+        Jv = []
+        for src in self.survey.srcList:
+            for rx in src.rxList:
+                # Looping over data class append memory as well!!
+                # Jv[src,rx] = rx.evalDeriv(src, self.mesh, self.timeMesh, Utils.mkvc(df_dm_v[src,'%sDeriv'%rx.projField,:]))
+                Jv.append(rx.evalDeriv(src, self.mesh, self.timeMesh, Utils.mkvc(df_dm_v[src,'%sDeriv'%rx.projField,:])))
+        Adiaginv.clean()
+        # del df_dm_v, dun_dm_v, Asubdiag
+        # return Utils.mkvc(Jv)
+        return np.hstack(Jv)
+
+    def Jtvec(self, m, v, f=None):
+
+        """
+        Jvec computes the adjoint of the sensitivity times a vector
+
+        .. math::
+            \mathbf{J}^\\top \mathbf{v} =  \left( \\frac{d\mathbf{u}}{d\mathbf{m}} ^ \\top \\frac{d\mathbf{F}}{d\mathbf{u}} ^ \\top  + \\frac{\partial\mathbf{F}}{\partial\mathbf{m}} ^ \\top \\right) \\frac{d\mathbf{P}}{d\mathbf{F}} ^ \\top \mathbf{v}
+
+        where
+
+        .. math::
+            \\frac{d\mathbf{u}}{d\mathbf{m}} ^\\top \mathbf{A}^\\top  + \\frac{d\mathbf{A}(\mathbf{u})}{d\mathbf{m}} ^ \\top = \\frac{d \mathbf{RHS}}{d \mathbf{m}} ^ \\top
+        """
+
+        if f is None:
+            f = self.fields(m)
+
+        self.curModel = m
+        ftype = self._fieldType + 'Solution' # the thing we solved for
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        df_duT_v = Fields_Derivs(self.mesh, self.survey)
+        ATinv_df_duT_v = np.zeros((len(self.survey.srcList), len(f[self.survey.srcList[0],ftype,0])), dtype=float) # same size as fields at a single timestep
+
+        JTv = np.zeros(m.shape, dtype=float)
+
+        # Loop over sources and receivers to create a fields object: PT_v, df_duT_v, df_dmT_v
+        PT_v = Fields_Derivs(self.mesh, self.survey) # initialize storage for PT_v (don't need to preserve over sources)
+        for src in self.survey.srcList:
+            # Looping over initializing field class is appending memory!
+            # PT_v = Fields_Derivs(self.mesh, self.survey) # initialize storage for PT_v (don't need to preserve over sources)
+            # initialize size
+            df_duT_v[src, '%sDeriv'%self._fieldType, :] = np.zeros_like(f[src, self._fieldType, :])
+
+            for rx in src.rxList:
+                print ('_%sDeriv')%(rx.projField)
+                PT_v[src,'%sDeriv'%rx.projField,:] = rx.evalDeriv(src, self.mesh, self.timeMesh, Utils.mkvc(v[src,rx]), adjoint=True) # this is +=
+
+                # PT_v = np.reshape(curPT_v,(len(curPT_v)/self.timeMesh.nN, self.timeMesh.nN), order='F')
+                df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
+
+                for tInd in range(self.nT+1):
+                    cur = df_duTFun(tInd, src, None, Utils.mkvc(PT_v[src,'%sDeriv'%rx.projField,tInd]), adjoint=True)
+                    df_duT_v[src, '%sDeriv'%self._fieldType, tInd] = df_duT_v[src, '%sDeriv'%self._fieldType, tInd] + Utils.mkvc(cur[0],2)
+                    JTv = cur[1] + JTv
+
+        del PT_v # no longer need this
+
+        AdiagTinv = None
+
+        # Do the back-solve through time
+        for tIndP in reversed(range(self.nT + 1)):
+            tInd = tIndP - 1
+            if AdiagTinv is not None and (tInd <= self.nT and self.timeSteps[tInd] != self.timeSteps[tInd+1]): # if the previous timestep is the same --> no need to refactor the matrix
+                AdiagTinv.clean()
+                AdiagTinv = None
+
+            # refactor if we need to
+            if AdiagTinv is None and tInd > -1:
+                Adiag = self.getAdiag(tInd)
+                AdiagTinv = self.Solver(Adiag.T, **self.solverOpts)
+
+            dAsubdiag_dm_v = Zero()
+
+            if tInd < self.nT - 1:
+                Asubdiag = self.getAsubdiag(tInd+1)
+
+
+            for isrc, src in enumerate(self.survey.srcList):
+                # solve against df_duT_v
+                if tInd >= self.nT-1:
+                    # last timestep (first to be solved)
+                    ATinv_df_duT_v[isrc,:] = AdiagTinv * df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]
+                elif tInd > -1:
+                # else:
+                    ATinv_df_duT_v[isrc,:] = AdiagTinv * (Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]) - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]))
+                else:
+                    # AdiagTinv = I
+                    ATinv_df_duT_v[isrc,:] = Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]) - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:])
+                    # - Utils.mkvc(Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]))
+                    # (Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]) - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]))
+
+                if tInd < self.nT - 1:
+                    dAsubdiagT_dm_v = self.getAsubdiagDeriv(tInd+1, f[src,ftype,tInd+1], ATinv_df_duT_v[isrc,:], adjoint = True)
+
+                if tInd > -1:
+                    un_src = f[src,ftype,tInd+1]
+                    dAT_dm_v = self.getAdiagDeriv(tInd, un_src, ATinv_df_duT_v[isrc,:], adjoint=True) # cell centered on time mesh
+                    dRHST_dm_v = self.getRHSDeriv(tInd+1, src, ATinv_df_duT_v[isrc,:], adjoint=True) # on nodes of time mesh
+
+                    JTv = JTv + Utils.mkvc(- dAT_dm_v - dAsubdiag_dm_v + dRHST_dm_v)
+                else:
+                    # dA_dm_v = self.getInitialFieldsDeriv(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1], adjoint=True)
+                    # print np.linalg.norm(self.getInitialFieldsDeriv(src, df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1], adjoint=True))
+                    # print np.linalg.norm(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1])
+                    # vec = - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]) + Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1])
+                    # dAsubdiagT_dm_v = self.getAsubdiagDeriv(tInd+1, f[src,ftype,tInd+1], Utils.mkvc(ATinv_df_duT_v[isrc,:]), adjoint = True)
+                    dRHST_dm_v = Utils.mkvc(self.getInitialFieldsDeriv(src, Utils.mkvc(ATinv_df_duT_v[isrc,:]) , adjoint=True))
+
+                    JTv = JTv + Utils.mkvc( -dAsubdiagT_dm_v + dRHST_dm_v) #
+
+
+
+                    # # dAT_dm_v = self.getAdiagDeriv(tInd, un_src, ATinv_df_duT_v[isrc,:], adjoint=True) # cell centered on time mesh
+                    # dRHST_dm_v0 = self.getRHSDeriv(tInd+1, src, ATinv_df_duT_v[isrc,:], adjoint=True) # on nodes of time mesh
+                    # dRHST_dm_v1 = self.getInitialFieldsDeriv( Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]), adjoint=True)
+                    # JTv = JTv + Utils.mkvc(dRHST_dm_v0 + dRHST_dm_v1)
+
+                    # print 'here'
+                    # inFields = self.getInitialFieldsDeriv(f[src,ftype,tInd+1], Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,tInd+1]), adjoint=True)
+                    #  # - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]), adjoint=True)
+                    # print inFields.shape
+                    # JTv = JTv + inFields
+                # dAsubdiag_dm_v = 0
+
+
+
+        # Missing the 0 step
+
+        # adding du_dm^T * dF_du^T * P^T vfor time 0 (no dRHS_dm_v at time 0)
+        # Asubdiag = self.getAsubdiag(0)
+        # for src in self.survey.srcList:
+        #     for projField in set(rx.projField):
+        #         v = AdiagTinv * (Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,0]) - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:]))
+        #         JTv = JTv - Utils.mkvc(self.getAdiagDeriv(0, f[src, ftype, tInd], v, adjoint = True))
+        #         # JTv = JTv + self.getInitialFieldsDeriv(Utils.mkvc(df_duT_v[src,'%sDeriv'%self._fieldType,0] - Asubdiag.T * Utils.mkvc(ATinv_df_duT_v[isrc,:])), adjoint=True)
+
+        # del df_duT_v, ATinv_df_duT_v, A, Asubdiag
+        if AdiagTinv is not None:
+            AdiagTinv.clean()
+
+        return Utils.mkvc(JTv).astype(float)
+
+
+
+    def getSourceTerm(self, tInd):
+
+        Srcs = self.survey.srcList
+
+        if self._eqLocs is 'FE':
+            S_m = np.zeros((self.mesh.nF,len(Srcs)))
+            S_e = np.zeros((self.mesh.nE,len(Srcs)))
+        elif self._eqLocs is 'EF':
+            S_m = np.zeros((self.mesh.nE,len(Srcs)))
+            S_e = np.zeros((self.mesh.nF,len(Srcs)))
+
+        for i, src in enumerate(Srcs):
+            smi, sei = src.eval(self, self.times[tInd])
+            S_m[:,i] = S_m[:,i] + smi
+            S_e[:,i] = S_e[:,i] + sei
+
+        return S_m, S_e
+
+    def getInitialFields(self):
+
+        Srcs = self.survey.srcList
+
+        if self._fieldType is 'b' or self._fieldType is 'j':
+            ifields = np.zeros((self.mesh.nF, len(Srcs)))
+        elif self._fieldType is 'e' or self._fieldType is 'h':
+            ifields = np.zeros((self.mesh.nE, len(Srcs)))
+
+        for i,src in enumerate(Srcs):
+            ifields[:,i] = ifields[:,i] + getattr(src, '%sInitial'%self._fieldType, None)(self)
+
+        return ifields
+
+    def getInitialFieldsDeriv(self, src, v, adjoint=False):
+
+        if adjoint is False:
+            if self._fieldType is 'b' or self._fieldType is 'j':
+                ifieldsDeriv = np.zeros(self.mesh.nF)
+            elif self._fieldType is 'e' or self._fieldType is 'h':
+                ifieldsDeriv = np.zeros(self.mesh.nE)
+
+        elif adjoint is True:
+            ifieldsDeriv = np.zeros(self.mapping.nP)
+
+        ifieldsDeriv = Utils.mkvc(getattr(src, '%sInitialDeriv'%self._fieldType, None)(self,v,adjoint)) + ifieldsDeriv
+
+            # ifieldsDeriv = Utils.mkvc(getattr(src, '%sInitialDeriv'%self._fieldType, None)(self,v,adjoint)) + ifieldsDeriv
+            # ifieldsDeriv = self.getAdiagDeriv(None, u, v, adjoint)
+            # ifieldsDeriv = ifieldsDeriv.sum()
+
+        return ifieldsDeriv
+
+
+##########################################################################################
+################################ E-B Formulation #########################################
+##########################################################################################
+
+# ------------------------------- Problem_b -------------------------------------------- #
+
+class Problem_b(BaseTDEMProblem):
+    """
+    Starting from the quasi-static E-B formulation of Maxwell's equations (semi-discretized)
+
+    .. math::
+
+        \mathbf{C} \mathbf{e} + \\frac{\partial \mathbf{b}}{\partial t} = \mathbf{s_m} \\\\
+        \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{M_{\sigma}^e} \mathbf{e} = \mathbf{s_e}
+
+    where :math:`\mathbf{s_e}` is an integrated quantity, we eliminate :math:`\mathbf{e}` using
+
+    .. math::
+        \mathbf{e} = \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{M_{\sigma}^e}^{-1} \mathbf{s_e}
+
+    to obtain a second order semi-discretized system in :math:`\mathbf{b}`
+
+    .. math::
+        \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b}  + \\frac{\partial \mathbf{b}}{\partial t} = \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{s_e} + \mathbf{s_m}
+
+    and moving everything except the time derivative to the rhs gives
+
+    .. math::
+        \\frac{\partial \mathbf{b}}{\partial t} = -\mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} + \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{s_e} + \mathbf{s_m}
+
+    For the time discretization, we use backward euler. To solve for the :math:`n+1`th time step, we have
+
+    .. math::
+        \\frac{\mathbf{b}^{n+1} - \mathbf{b}^{n}}{\mathbf{dt}} = -\mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b}^{n+1} + \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{s_e}^{n+1} + \mathbf{s_m}^{n+1}
+
+    re-arranging to put :math:`\mathbf{b}^{n+1}` on the left hand side gives
+
+    .. math::
+        (\mathbf{I} + \mathbf{dt} \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f}) \mathbf{b}^{n+1} = \mathbf{b}^{n} + \mathbf{dt}(\mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{s_e}^{n+1} + \mathbf{s_m}^{n+1})
+
+    :param Mesh mesh: mesh
+    :param Mapping mapping: mapping
+    """
+
+    _fieldType = 'b'
+    _eqLocs    = 'FE'
+    fieldsPair = Fields_b
+    surveyPair = SurveyTDEM
+
+    def __init__(self, mesh, mapping=None, **kwargs):
+        BaseTDEMProblem.__init__(self, mesh, mapping=mapping, **kwargs)
+
+    def getAdiag(self, tInd):
+        """
+        System matrix at a given time index
+
+        .. math::
+            (\mathbf{I} + \mathbf{dt} \mathbf{C} \mathbf{M_{\sigma}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f})
+
+        """
+        assert tInd >= 0 and tInd < self.nT
+
+        dt = self.timeSteps[tInd]
+        C = self.mesh.edgeCurl
+        MeSigmaI = self.MeSigmaI
+        MfMui = self.MfMui
+        I = Utils.speye(self.mesh.nF)
+
+        A = 1./dt * I + ( C * ( MeSigmaI * (C.T * MfMui ) ) )
+
+        if self._makeASymmetric is True:
+            return MfMui.T * A
+        return A
+
+    def getAdiagDeriv(self, tInd, u, v, adjoint=False):
+        C = self.mesh.edgeCurl
+        MeSigmaIDeriv = lambda x: self.MeSigmaIDeriv(x)
+        MfMui = self.MfMui
+
+        if adjoint:
+            if self._makeASymmetric is True:
+                v = MfMui * v
+            return  MeSigmaIDeriv(C.T * ( MfMui * u )).T * ( C.T * v )
+
+        ADeriv = ( C * ( MeSigmaIDeriv(C.T * ( MfMui * u )) * v ) )
+        if self._makeASymmetric is True:
+            return MfMui.T * ADeriv
+        return ADeriv
+
+
+    def getAsubdiag(self, tInd):
+
+        dt = self.timeSteps[tInd]
+        MfMui = self.MfMui
+        Asubdiag = - 1./dt * sp.eye(self.mesh.nF)
+
+        if self._makeASymmetric is True:
+            return MfMui.T * Asubdiag
+
+        return Asubdiag
+
+    def getAsubdiagDeriv(self, tInd, u, v, adjoint=False):
+        return Zero() * v
+
+
+
+    def getRHS(self, tInd):
+        C = self.mesh.edgeCurl
+        MeSigmaI = self.MeSigmaI
+        MfMui = self.MfMui
+
+        S_m, S_e = self.getSourceTerm(tInd)
+
+        rhs =  (C * (MeSigmaI * S_e) + S_m)
+        if self._makeASymmetric is True:
+            return MfMui.T * rhs
+        return rhs
+
+    def getRHSDeriv(self, tInd, src, v, adjoint=False):
+
+        C = self.mesh.edgeCurl
+        MeSigmaI = self.MeSigmaI
+        MeSigmaIDeriv = lambda u: self.MeSigmaIDeriv(u)
+        MfMui = self.MfMui
+
+        _, S_e = src.eval(tInd, self)
+        S_mDeriv, S_eDeriv = src.evalDeriv(self.times[tInd], self, adjoint=adjoint)
+
+        if adjoint:
+            if self._makeASymmetric is True:
+                v = self.MfMui * v
+            if isinstance(S_e, Utils.Zero):
+                MeSigmaIDerivT_v = Utils.Zero()
+            else:
+                MeSigmaIDerivT_v = MeSigmaIDeriv(S_e).T * v
+            RHSDeriv = MeSigmaIDerivT_v + S_eDeriv( MeSigmaI.T *  ( C.T * v ) ) + S_mDeriv(v)
+            return RHSDeriv
+
+        if isinstance(S_e, Utils.Zero):
+            MeSigmaIDeriv_v = Utils.Zero()
+        else:
+            MeSigmaIDeriv_v = MeSigmaIDeriv(S_e) * v
+
+        RHSDeriv = (C * (MeSigmaIDeriv_v + MeSigmaI * S_eDeriv(v) + S_mDeriv(v)))
+
+        if self._makeASymmetric is True:
+            return self.MfMui.T * RHSDeriv
+        return RHSDeriv
+
+
+# ------------------------------- Problem_e -------------------------------------------- #
+
+class Problem_e(BaseTDEMProblem):
+
+    _fieldType = 'e'
+    _eqLocs    = 'FE'
+    fieldsPair = Fields_e
+    surveyPair = SurveyTDEM
+
+    def __init__(self, mesh, mapping=None, **kwargs):
+        BaseTDEMProblem.__init__(self, mesh, mapping=mapping, **kwargs)
+
+    def getAdiag(self, tInd):
+        """
+        System matrix at a given time index
+
+        """
+        assert tInd >= 0 and tInd < self.nT
+
+        dt = self.timeSteps[tInd]
+        C = self.mesh.edgeCurl
+        MfMui = self.MfMui
+        MeSigma = self.MeSigma
+
+        return C.T * ( MfMui * C ) + 1./dt * MeSigma
+
+
+    def getAdiagDeriv(self, tInd, u, v, adjoint=False):
+        assert tInd >= 0 and tInd < self.nT
+
+        dt = self.timeSteps[tInd]
+        C = self.mesh.edgeCurl
+        MfMui = self.MfMui
+        MeSigmaDeriv = self.MeSigmaDeriv(u)
+
+        if adjoint:
+            return 1./dt * MeSigmaDeriv.T * v
+
+        return 1./dt * MeSigmaDeriv * v
+
+
+    def getAsubdiag(self, tInd):
+        assert tInd >= 0 and tInd < self.nT
+
+        dt = self.timeSteps[tInd]
+
+        return - 1./dt * self.MeSigma
+
+    def getAsubdiagDeriv(self, tInd, u, v, adjoint=False):
+        dt = self.timeSteps[tInd]
+
+        if adjoint:
+            return - 1./dt * self.MeSigmaDeriv(u).T * v
+
+        return - 1./dt * self.MeSigmaDeriv(u) * v
+
+    def getRHS(self, tInd):
+        return Zero()
+
+    def getRHSDeriv(self, tInd, src, v, adjoint=False):
+        return Zero()
+
+
+
+
+

SimPEG/EM/TDEM/__init__.py

@@ -1,3 +1,3 @@
-from SurveyTDEM import * #SurveyTDEM, RxTDEM, SrcTDEM
-from BaseTDEM import BaseTDEMProblem, FieldsTDEM
-from TDEM_b import ProblemTDEM_b
+from TDEM import BaseTDEMProblem, Problem_b, Problem_e
+from FieldsTDEM import Fields, Fields_b
+from SurveyTDEM import Survey, Src, Rx

SimPEG/EM/TDEM_old/BaseTDEM.py

@@ -112,7 +112,7 @@ class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
         """
             :param numpy.array m: Conductivity model
             :param numpy.ndarray v: vector (model object)
-            :param FieldsTDEM f: Fields resulting from m
+            :param simpegEM.TDEM.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 v: vector (or a :class:`SimPEG.Survey.Data` object)
-            :param FieldsTDEM u: Fields resulting from m
+            :param numpy.ndarray,SimPEG.Survey.Data v: vector (data object)
+            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
             :rtype: numpy.ndarray
             :return: w (model object)
 

SimPEG/EM/TDEM_old/SurveyTDEM.py

@@ -0,0 +1,199 @@
+from SimPEG import Utils, Survey, np
+from SimPEG.Survey import BaseSurvey
+from SimPEG.EM.Utils import *
+from BaseTDEM import FieldsTDEM
+import SrcTDEM as Src
+
+class RxTDEM(Survey.BaseTimeRx):
+
+    knownRxTypes = {
+                    'ex':['e', 'Ex', 'N'],
+                    'ey':['e', 'Ey', 'N'],
+                    'ez':['e', 'Ez', 'N'],
+
+                    'bx':['b', 'Fx', 'N'],
+                    'by':['b', 'Fy', 'N'],
+                    'bz':['b', 'Fz', 'N'],
+
+                    'dbxdt':['b', 'Fx', 'CC'],
+                    'dbydt':['b', 'Fy', 'CC'],
+                    'dbzdt':['b', 'Fz', 'CC'],
+                   }
+
+    def __init__(self, locs, times, rxType):
+        Survey.BaseTimeRx.__init__(self, locs, times, rxType)
+
+    @property
+    def projField(self):
+        """Field Type projection (e.g. e b ...)"""
+        return self.knownRxTypes[self.rxType][0]
+
+    @property
+    def projGLoc(self):
+        """Grid Location projection (e.g. Ex Fy ...)"""
+        return self.knownRxTypes[self.rxType][1]
+
+    @property
+    def projTLoc(self):
+        """Time Location projection (e.g. CC N)"""
+        return self.knownRxTypes[self.rxType][2]
+
+    def getTimeP(self, timeMesh):
+        """
+            Returns the time projection matrix.
+
+            .. note::
+
+                This is not stored in memory, but is created on demand.
+        """
+        if self.rxType in ['dbxdt','dbydt','dbzdt']:
+            return timeMesh.getInterpolationMat(self.times, self.projTLoc)*timeMesh.faceDiv
+        else:
+            return timeMesh.getInterpolationMat(self.times, self.projTLoc)
+
+    def eval(self, src, mesh, timeMesh, u):
+        P = self.getP(mesh, timeMesh)
+        u_part = Utils.mkvc(u[src, self.projField, :])
+        return P*u_part
+
+    def evalDeriv(self, src, mesh, timeMesh, u, v, adjoint=False):
+        P = self.getP(mesh, timeMesh)
+
+        if not adjoint:
+            return P * Utils.mkvc(v[src, self.projField, :])
+        elif adjoint:
+            return P.T * v[src, self]
+
+
+class SrcTDEM(Survey.BaseSrc):
+    rxPair = RxTDEM
+    radius = None
+
+    def getInitialFields(self, mesh):
+        F0 = getattr(self, '_getInitialFields_' + self.srcType)(mesh)
+        return F0
+
+    def getJs(self, mesh, time):
+        return None
+
+
+class SrcTDEM_VMD_MVP(SrcTDEM):
+
+    def __init__(self,rxList,loc,waveformType="STEPOFF"):
+        self.loc = loc
+        self.waveformType = waveformType
+        SrcTDEM.__init__(self,rxList)
+
+    def getInitialFields(self, mesh):
+        """Vertical magnetic dipole, magnetic vector potential"""
+        if self.waveformType == "STEPOFF":
+            print ">> Step waveform: Non-zero initial condition"
+            if mesh._meshType is 'CYL':
+                if mesh.isSymmetric:
+                    MVP = MagneticDipoleVectorPotential(self.loc, mesh, 'Ey')
+                else:
+                    raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+            elif mesh._meshType is 'TENSOR':
+                MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
+            else:
+                raise Exception('Unknown mesh for VMD')
+            return {"b": mesh.edgeCurl*MVP}
+        elif self.waveformType == "GENERAL":
+            print ">> General waveform: Zero initial condition"
+            return {"b": np.zeros(mesh.nF)}
+        else:
+            raise NotImplementedError("Only use STEPOFF or GENERAL")
+
+    def getMeS(self, mesh, MfMui):
+        if mesh._meshType is 'CYL':
+            if mesh.isSymmetric:
+                MVP = MagneticDipoleVectorPotential(self.loc, mesh, 'Ey')
+            else:
+                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+        elif mesh._meshType is 'TENSOR':
+            MVP = MagneticDipoleVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'])
+        else:
+            raise Exception('Unknown mesh for VMD')
+        return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
+
+
+class SrcTDEM_CircularLoop_MVP(SrcTDEM):
+    def __init__(self,rxList,loc,radius,waveformType="STEPOFF"):
+        self.loc = loc
+        self.radius = radius
+        self.waveformType = waveformType
+        SrcTDEM.__init__(self,rxList)
+
+    def getInitialFields(self, mesh):
+        """Circular Loop, magnetic vector potential"""
+        if self.waveformType == "STEPOFF":
+            print ">> Step waveform: Non-zero initial condition"
+            if mesh._meshType is 'CYL':
+                if mesh.isSymmetric:
+                    MVP = MagneticLoopVectorPotential(self.loc, mesh, 'Ey', self.radius)
+                else:
+                    raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+            elif mesh._meshType is 'TENSOR':
+                MVP = MagneticLoopVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'], self.radius)
+            else:
+                raise Exception('Unknown mesh for CircularLoop')
+            return {"b": mesh.edgeCurl*MVP}
+        elif self.waveformType == "GENERAL":
+            print ">> General waveform: Zero initial condition"
+            return {"b": np.zeros(mesh.nF)}
+        else:
+            raise NotImplementedError("Only use STEPOFF or GENERAL")
+
+    def getMeS(self, mesh, MfMui):
+        if mesh._meshType is 'CYL':
+            if mesh.isSymmetric:
+                MVP = MagneticLoopVectorPotential(self.loc, mesh, 'Ey', self.radius)
+            else:
+                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
+        elif mesh._meshType is 'TENSOR':
+            MVP = MagneticLoopVectorPotential(self.loc, mesh, ['Ex','Ey','Ez'], self.radius)
+        else:
+            raise Exception('Unknown mesh for CircularLoop')
+        return mesh.edgeCurl.T*MfMui*mesh.edgeCurl*MVP
+
+
+class SurveyTDEM(Survey.BaseSurvey):
+    """
+        docstring for SurveyTDEM
+    """
+    srcPair = SrcTDEM
+
+    def __init__(self, srcList, **kwargs):
+        # Sort these by frequency
+        self.srcList = srcList
+        Survey.BaseSurvey.__init__(self, **kwargs)
+
+    def projectFields(self, u):
+        data = Survey.Data(self)
+        for src in self.srcList:
+            for rx in src.rxList:
+                data[src, rx] = rx.projectFields(src, self.mesh, self.prob.timeMesh, u)
+        return data
+
+    def projectFieldsDeriv(self, u, v=None, adjoint=False):
+        assert v is not None, 'v to multiply must be provided.'
+
+        if not adjoint:
+            data = Survey.Data(self)
+            for src in self.srcList:
+                for rx in src.rxList:
+                    data[src, rx] = rx.projectFieldsDeriv(src, self.mesh, self.prob.timeMesh, u, v)
+            return data
+        else:
+            f = FieldsTDEM(self.mesh, self)
+            for src in self.srcList:
+                for rx in src.rxList:
+                    Ptv = rx.projectFieldsDeriv(src, self.mesh, self.prob.timeMesh, u, v, adjoint=True)
+                    Ptv = Ptv.reshape((-1, self.prob.timeMesh.nN), order='F')
+                    if rx.projField not in f: # first time we are projecting
+                        f[src, rx.projField, :] = Ptv
+                    else: # there are already fields, so let's add to them!
+                        f[src, rx.projField, :] += Ptv
+            return f
+
+

SimPEG/EM/TDEM_old/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 FieldsTDEM u: Fields resulting from m
-            :rtype: FieldsTDEM
+            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
+            :rtype: simpegEM.TDEM.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 FieldsTDEM u: Fields resulting from m
-            :rtype: numpy.ndarray
-            :return: p (like a model)
+            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
+            :rtype: np.ndarray (like a model)
+            :return: p
 
             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 FieldsTDEM p: Fields object
-            :rtype: FieldsTDEM
+            :param simpegEM.TDEM.FieldsTDEM p: Fields object
+            :rtype: simpegEM.TDEM.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 FieldsTDEM p: Fields object
-            :rtype: FieldsTDEM
+            :param simpegEM.TDEM.FieldsTDEM p: Fields object
+            :rtype: simpegEM.TDEM.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 FieldsTDEM vec: Fields object
-            :rtype: FieldsTDEM
+            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
+            :rtype: simpegEM.TDEM.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 FieldsTDEM vec: Fields object
-            :rtype: FieldsTDEM
+            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
+            :rtype: simpegEM.TDEM.FieldsTDEM
             :return: f
 
             Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where

SimPEG/EM/TDEM_old/__init__.py

@@ -0,0 +1,3 @@
+from SurveyTDEM import * #SurveyTDEM, RxTDEM, SrcTDEM
+from BaseTDEM import BaseTDEMProblem, FieldsTDEM
+from TDEM_b import ProblemTDEM_b

SimPEG/Examples/DC_Analytic_Dipole.py

@@ -1,7 +1,7 @@
 from SimPEG import *
-import SimPEG.EM.Static.DC as DC
+import SimPEG.DCIP as DC
 
-def run(plotIt=True):
+def run(plotIt=False):
     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=True):
     #     ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')
     #     ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)
 
-    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)
+    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)
     problem.pair(survey)
     try:
         from pymatsolver import MumpsSolver
@@ -65,4 +65,4 @@ def run(plotIt=True):
 
 
 if __name__ == '__main__':
-    print run()
+    print run(plotIt=True)

SimPEG/Examples/EM_Schenkel_Morrison_Casing.py

@@ -19,13 +19,10 @@ def run(plotIt=True):
         Morrison Casing Model, and the results are used in a 2016 SEG abstract by
         Yang et al.
 
-        .. 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.
+        - 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
@@ -218,7 +215,7 @@ def run(plotIt=True):
     # ------------ Problem and Survey ---------------
     survey = FDEM.Survey(sg_p + dg_p)
     mapping = [('sigma', Maps.IdentityMap(mesh))]
-    problem = FDEM.Problem3D_h(mesh, mapping=mapping, Solver=solver)
+    problem = FDEM.Problem3D_h(mesh, mapping=mapping)
     problem.pair(survey)
 
     # ------------- Solve ---------------------------

SimPEG/Examples/EM_TDEM_1D_Inversion.py

@@ -42,10 +42,10 @@ def run(plotIt=True):
 
 
     rxOffset=1e-3
-    rx = EM.TDEM.RxTDEM(np.array([[rxOffset, 0., 30]]), np.logspace(-5,-3, 31), 'bz')
-    src = EM.TDEM.SrcTDEM_VMD_MVP([rx], np.array([0., 0., 80]))
-    survey = EM.TDEM.SurveyTDEM([src])
-    prb = EM.TDEM.ProblemTDEM_b(mesh, mapping=mapping)
+    rx = EM.TDEM.Rx(np.array([[rxOffset, 0., 30]]), np.logspace(-5,-3, 31), 'bz')
+    src = EM.TDEM.Src.MagDipole([rx], loc=np.array([0., 0., 80]))
+    survey = EM.TDEM.Survey([src])
+    prb = EM.TDEM.Problem_b(mesh, mapping=mapping)
 
     prb.Solver = SolverLU
     prb.timeSteps = [(1e-06, 20),(1e-05, 20), (0.0001, 20)]
@@ -53,9 +53,9 @@ def run(plotIt=True):
 
     # create observed data
     std = 0.05
-    
+
     survey.dobs = survey.makeSyntheticData(mtrue,std)
-    survey.std = std 
+    survey.std = std
     survey.eps = 1e-5*np.linalg.norm(survey.dobs)
 
     if plotIt:

SimPEG/Examples/Inversion_IRLS.py

@@ -42,33 +42,55 @@ def run(N=100, 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.mref = mref
+#    reg.cell_weights = wr
+#
     dmis = DataMisfit.l2_DataMisfit(survey)
     dmis.Wd = 1./wd
-
+#
+#    opt = Optimization.ProjectedGNCG(maxIter=20,lower=-2.,upper=2., maxIterCG= 10, tolCG = 1e-4)
+#    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
+#    invProb.curModel = m0
+#
+#    beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
+#    target = Directives.TargetMisfit()
+#
     betaest = Directives.BetaEstimate_ByEig()
+#    inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])
+#
+#
+#    mrec = inv.run(m0)
+#    ml2 = mrec
+#    print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
+#
+#    # Switch regularization to sparse
+#    phim = invProb.phi_m_last
+#    phid =  invProb.phi_d
 
     reg = Regularization.Sparse(mesh)
     reg.mref = mref
     reg.cell_weights = wr
 
     reg.mref = np.zeros(mesh.nC)
-    
+    eps_p = 5e-2
+    eps_q = 5e-2
+    norms   = [0., 0., 2., 2.]
 
     opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
     invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
     update_Jacobi = Directives.Update_lin_PreCond()
-    
-    # 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)
+    IRLS = Directives.Update_IRLS( norms=norms,  eps_p=eps_p, eps_q=eps_q)
 
     inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
 

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.InjectActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
+    actMap = simpeg.Maps.ActiveCells(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, [m_0])
+        fig = MT.Utils.dataUtils.plotMT1DModelData(problem)
         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.
 
@@ -46,15 +46,16 @@ def run(plotIt=True, nFreq=1):
     survey = MT.Survey(srcList)
 
     ## Setup the problem object
-    problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG, Solver=Solver)
+    problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG)
     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

@@ -1,62 +0,0 @@
-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

@@ -1,41 +0,0 @@
-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/Utils_surface2ind_topo.py

@@ -2,12 +2,8 @@ from SimPEG import *
 from SimPEG.Utils import surface2ind_topo
 
 
-def run(plotIt=True, nx=5, ny=5):
+def run(plotIt=False, nx = 5, ny = 5):
     """
-
-        Utils: surface2ind_topo
-        =======================
-
         Here we show how to use :code:`Utils.surface2ind_topo` to identify cells below
         a topographic surface.
 
@@ -17,25 +13,27 @@ def run(plotIt=True, nx=5, ny=5):
     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
+    Topo = np.hstack([Utils.mkvc(xtopo,2),Utils.mkvc(topo,2)]) #make it an array
 
-    indcc = surface2ind_topo(mesh, Topo, 'CC')
+    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))
+        fig, ax = plt.subplots(1,1,figsize=(6,6))
         mesh.plotGrid(ax=ax, nodes=True, centers=True)
         ax.plot(xtopo,topo,'k',linewidth=1)
+        # ax.plot(mesh.vectorNx, interp1d(xtopo,topo)(mesh.vectorNx),'--k',linewidth=3)
         ax.plot(mesh.vectorCCx, interp1d(xtopo,topo)(mesh.vectorCCx),'--k',linewidth=3)
 
+
         aveN2CC = Utils.sdiag(mesh.aveN2CC.T.sum(1))*mesh.aveN2CC.T
         a = aveN2CC * indcc
         a[a > 0] = 1.
         a[a < 0.25] = np.nan
         a = a.reshape(mesh.vnN, order='F')
         masked_array = np.ma.array(a, mask=np.isnan(a))
-        ax.pcolor(mesh.vectorNx,mesh.vectorNy,masked_array.T, cmap=plt.cm.gray, alpha=0.2)
+        ax.pcolor(mesh.vectorNx,mesh.vectorNy,masked_array.T, cmap = plt.cm.gray,alpha=0.2)
         plt.show()
 
 

SimPEG/Examples/__init__.py

@@ -10,8 +10,6 @@ import EM_TDEM_1D_Inversion
 import FLOW_Richards_1D_Celia1990
 import Inversion_IRLS
 import Inversion_Linear
-import Maps_ComboMaps
-import Maps_Mesh2Mesh
 import Mesh_Basic_ForwardDC
 import Mesh_Basic_PlotImage
 import Mesh_Basic_Types
@@ -24,7 +22,7 @@ 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", "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"]
+__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", "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 #####
 
@@ -40,7 +38,7 @@ 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', 'content', 'examples'])
+    docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'examples'])
     shutil.rmtree(docExamplesDir)
     os.makedirs(docExamplesDir)
 
@@ -97,12 +95,12 @@ 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', 'content', 'examples', name + '.rst']))
+        rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'examples', name + '.rst']))
 
         print 'Creating: %s.rst'%name
         f = open(rst, 'w')

SimPEG/Examples/simpegEMpaper_example_1.py

@@ -0,0 +1,157 @@
+from SimPEG import np, Mesh, Maps, Utils, DataMisfit, Regularization, Optimization, Inversion, InvProblem, Directives
+from SimPEG import SolverLU
+from SimPEG.EM import FDEM, TDEM, mu_0
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.rcParams['font.size'] = 14
+
+def run(plotIt=True):
+    # Set up cylindrically symmeric mesh
+    cs, ncx, ncz, npad = 10., 15, 25, 13  # padded cyl mesh
+    hx = [(cs,ncx), (cs,npad,1.3)]
+    hz = [(cs,npad,-1.3), (cs,ncz), (cs,npad,1.3)]
+    mesh = Mesh.CylMesh([hx,1,hz], '00C')
+
+    # Conductivity model
+    layerz = np.r_[-200., -100.]
+    layer = (mesh.vectorCCz>=layerz[0]) & (mesh.vectorCCz<=layerz[1])
+    active = mesh.vectorCCz<0.
+    sig_half  = 1e-2  # Half-space conductivity
+    sig_air   = 1e-8  # Air conductivity
+    sig_layer = 5e-2  # Layer conductivity
+    sigma = np.ones(mesh.nCz)*sig_air
+    sigma[active] = sig_half
+    sigma[layer] = sig_layer
+
+    # Mapping
+    actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
+    mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
+    mtrue = np.log(sigma[active])
+
+    # FDEM problem & survey
+    rxlocs = Utils.ndgrid([np.r_[50.], np.r_[0], np.r_[0.]])
+    bzi = FDEM.Rx.Point_bSecondary(rxlocs, 'z', 'real')
+    bzr = FDEM.Rx.Point_bSecondary(rxlocs, 'z', 'imag')
+
+    freqs = np.logspace(2, 3, 5)
+    srcLoc = np.array([0., 0., 0.])
+
+    print 'min skin depth = ', 500./np.sqrt(freqs.max() * sig_half), 'max skin depth = ', 500./np.sqrt(freqs.min() * sig_half)
+    print 'max x ', mesh.vectorCCx.max(), 'min z ', mesh.vectorCCz.min(), 'max z ', mesh.vectorCCz.max()
+
+    srcList = []
+    [srcList.append(FDEM.Src.MagDipole([bzr, bzi],freq, srcLoc,orientation='Z')) for freq in freqs]
+
+    surveyFD = FDEM.Survey(srcList)
+    prbFD = FDEM.Problem3D_b(mesh, mapping=mapping)
+    prbFD.pair(surveyFD)
+    std = 0.03
+    surveyFD.makeSyntheticData(mtrue, std)
+    surveyFD.eps = np.linalg.norm(surveyFD.dtrue)*1e-5
+
+    # FDEM inversion
+    np.random.seed(1)
+    dmisfit = DataMisfit.l2_DataMisfit(surveyFD)
+    regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
+    reg = Regularization.Simple(regMesh)
+    opt = Optimization.InexactGaussNewton(maxIterCG=10, maxIter=4)
+    invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
+    # Inversion Directives
+    beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=3)
+    # betaest = Directives.BetaEstimate_ByEig(beta0_ratio=10.)
+    invProb.beta = 1.
+    target = Directives.TargetMisfit()
+
+    inv = Inversion.BaseInversion(invProb, directiveList=[beta,target])
+    m0 = np.log(np.ones(mtrue.size)*sig_half)
+    reg.alpha_s = 5e-1
+    reg.alpha_x = 1.
+    prbFD.counter = opt.counter = Utils.Counter()
+    opt.LSshorten = 0.5
+    opt.tolG = 1e-10
+    opt.eps = 1e-10
+    opt.remember('xc')
+    moptFD = inv.run(m0)
+
+    # TDEM problem
+    times = np.logspace(-4, np.log10(2e-3), 10)
+    print 'min diffusion distance ', 1.28*np.sqrt(times.min()/(sig_half*mu_0)), 'max diffusion distance ', 1.28*np.sqrt(times.max()/(sig_half*mu_0))
+    rx = TDEM.Rx(rxlocs, times, 'bz')
+    src = TDEM.Src.MagDipole([rx], waveform=TDEM.Src.StepOffWaveform(), loc=srcLoc) # same src location as FDEM problem
+
+    surveyTD = TDEM.Survey([src])
+    prbTD = TDEM.Problem_b(mesh, mapping=mapping)
+    prbTD.timeSteps = [(5e-5, 10),(1e-4, 10),(5e-4, 10)]
+    prbTD.pair(surveyTD)
+    prbTD.Solver = SolverLU
+
+    std = 0.03
+    surveyTD.makeSyntheticData(mtrue, std)
+    surveyTD.std = std
+    surveyTD.eps = np.linalg.norm(surveyTD.dtrue)*1e-5
+
+    # TDEM inversion
+    dmisfit = DataMisfit.l2_DataMisfit(surveyTD)
+    regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
+    reg = Regularization.Simple(regMesh)
+    opt = Optimization.InexactGaussNewton(maxIterCG=10, maxIter=4)
+    invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
+
+    # Inversion Directives
+    beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=3)
+    invProb.beta = 1.
+    # betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1.)
+    target = Directives.TargetMisfit()
+
+    inv = Inversion.BaseInversion(invProb, directiveList=[beta, target])
+    m0 = np.log(np.ones(mtrue.size)*sig_half)
+    reg.alpha_s = 5e-1
+    reg.alpha_x = 1.
+    prbTD.counter = opt.counter = Utils.Counter()
+    opt.LSshorten = 0.5
+    opt.remember('xc')
+    moptTD = inv.run(m0)
+
+    if plotIt:
+        fig, ax = plt.subplots(1,1, figsize = (4, 6))
+        plt.semilogx(sigma[active], mesh.vectorCCz[active], 'k-', lw=2)
+        plt.semilogx(np.exp(moptFD), mesh.vectorCCz[active], 'ko', ms=3)
+        plt.semilogx(np.exp(moptTD), mesh.vectorCCz[active], 'k*')
+        ax.set_ylim(-1000, 0)
+        ax.set_xlim(5e-3, 1e-1)
+
+        ax.set_xlabel('Conductivity (S/m)', fontsize = 14)
+        ax.set_ylabel('Depth (m)', fontsize = 14)
+        ax.grid(color='k', alpha=0.5, linestyle='dashed', linewidth=0.5)
+        plt.legend(['True', 'Pred (FD)', 'Pred (TD)'], fontsize=13, loc=4)
+        plt.show()
+
+        fig = plt.figure(figsize = (10*1.3, 5*1.3))
+        ax2 = plt.subplot(122)
+        ax2.plot(times, surveyTD.dobs, 'k-', lw=2)
+        ax2.plot(times, surveyTD.dpred(moptTD), 'ko', ms=4)
+        ax2.set_xscale('log')
+        ax2.set_yscale('log')
+        ax2.set_xlim(times.min(), times.max())
+        ax1 = plt.subplot(121)
+        ax1.plot(freqs, -surveyFD.dobs[::2], 'k-', lw=2)
+        ax1.plot(freqs, -surveyFD.dobs[1::2], 'k--', lw=2)
+        dpredFD = surveyFD.dpred(moptTD)
+        ax1.plot(freqs, -dpredFD[::2], 'ko', ms=4)
+        ax1.plot(freqs, -dpredFD[1::2], 'k+', markeredgewidth=2., ms=10)
+        ax1.set_xscale('log')
+        ax1.set_yscale('log')
+        ax2.set_xlabel('Time (s)', fontsize = 14)
+        ax1.set_xlabel('Frequency (Hz)', fontsize = 14)
+        ax1.set_ylabel('Vertical magnetic field (T)', fontsize = 14)
+        ax2.grid(True,which='minor')
+        ax1.grid(True,which='minor')
+        ax2.set_title("(b) TD observed vs. predicted", fontsize = 14)
+        ax1.set_title("(a) FD observed vs. predicted", fontsize = 14)
+        ax2.legend(("Obs", "Pred"), fontsize = 12)
+        ax1.legend(("Obs", "Pred (real)", "Pred (imag)"), fontsize = 12, loc=3)
+        ax1.set_xlim(freqs.max(), freqs.min())
+        plt.show()
+
+if __name__ == '__main__':
+    run()

SimPEG/FLOW/Richards/Empirical.py

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

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.SurjectVertical1D(problem.mesh)
+        Map_sigma_p = Maps.Vertical1DMap(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.SurjectVertical1D(problem.mesh)
+        Map_sigma_p = Maps.Vertical1DMap(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/Maps.py

@@ -41,8 +41,8 @@ class IdentityMap(object):
             If this is a meshless mapping (i.e. nP is defined independently)
             the shape will be the the shape (nP,nP).
 
-            :rtype: tuple
-            :return: shape of the operator as a tuple (int,int)
+            :rtype: (int,int)
+            :return: shape of the operator as a tuple
         """
         if self._nP is not None:
             return (self.nP, self.nP)
@@ -86,7 +86,7 @@ class IdentityMap(object):
             The derivative of the transformation.
 
             :param numpy.array m: model
-            :rtype: scipy.sparse.csr_matrix
+            :rtype: scipy.csr_matrix
             :return: derivative of transformed model
 
         """
@@ -216,7 +216,7 @@ class ExpMap(IdentityMap):
     def deriv(self, m):
         """
             :param numpy.array m: model
-            :rtype: scipy.sparse.csr_matrix
+            :rtype: scipy.csr_matrix
             :return: derivative of transformed model
 
             The *transform* changes the model into the physical property.
@@ -366,7 +366,7 @@ class SurjectVertical1D(IdentityMap):
     def deriv(self, m):
         """
             :param numpy.array m: model
-            :rtype: scipy.sparse.csr_matrix
+            :rtype: scipy.csr_matrix
             :return: derivative of transformed model
         """
         repNum = self.mesh.vnC[:self.mesh.dim-1].prod()
@@ -427,7 +427,7 @@ class Surject2Dto3D(IdentityMap):
     def deriv(self, m):
         """
             :param numpy.array m: model
-            :rtype: scipy.sparse.csr_matrix
+            :rtype: scipy.csr_matrix
             :return: derivative of transformed model
         """
         inds = self * np.arange(self.nP)
@@ -502,9 +502,7 @@ class InjectActiveCells(IdentityMap):
         if Utils.isScalar(valInactive):
             self.valInactive = np.ones(self.nC)*float(valInactive)
         else:
-            self.valInactive = np.ones(self.nC)
-            self.valInactive[self.indInactive] = valInactive.copy()
-            
+            self.valInactive = valInactive.copy()
         self.valInactive[self.indActive] = 0
 
         inds = np.nonzero(self.indActive)[0]

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 n: (or list) number of cells in each direction (dim, )
-    :param numpy.array x0: (or list) Origin of the mesh (dim, )
+    :param numpy.array,list n: number of cells in each direction (dim, )
+    :param numpy.array,list x0: Origin of the mesh (dim, )
 
     """
 
@@ -34,8 +34,8 @@ class BaseMesh(object):
         """
         Origin of the mesh
 
-        :rtype: numpy.array
-        :return: x0, (dim, )
+        :rtype: numpy.array (dim, )
+        :return: x0
         """
         return self._x0
 
@@ -116,8 +116,8 @@ class BaseMesh(object):
         """
         Total number of edges in each direction
 
-        :rtype: numpy.array
-        :return: [nEx, nEy, nEz], (dim, )
+        :rtype: numpy.array (dim, )
+        :return: [nEx, nEy, nEz]
 
         .. plot::
             :include-source:
@@ -173,8 +173,8 @@ class BaseMesh(object):
         """
         Total number of faces in each direction
 
-        :rtype: numpy.array
-        :return: [nFx, nFy, nFz], (dim, )
+        :rtype: numpy.array (dim, )
+        :return: [nFx, nFy, nFz]
 
         .. plot::
             :include-source:
@@ -200,8 +200,8 @@ class BaseMesh(object):
         """
         Face Normals
 
-        :rtype: numpy.array
-        :return: normals, (sum(nF), dim)
+        :rtype: numpy.array (sum(nF), dim)
+        :return: normals
         """
         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
-        :return: normals, (sum(nE), dim)
+        :rtype: numpy.array (sum(nE), dim)
+        :return: normals
         """
         if self.dim == 2:
             tX = np.c_[np.ones(self.nEx), np.zeros(self.nEx)]
@@ -236,9 +236,8 @@ 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
-        :return: projected face vector, (nF, )
-
+        :rtype: numpy.array with shape (nF, )
+        :return: projected face vector
         """
         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)'
@@ -249,9 +248,8 @@ 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
-        :return: projected edge vector, (nE, )
-
+        :rtype: numpy.array with shape (nE, )
+        :return: projected edge vector
         """
         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)'
@@ -297,7 +295,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Total number of cells in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :return: [nCx, nCy, nCz]
         """
         return np.array([x for x in [self.nCx, self.nCy, self.nCz] if not x is None])
@@ -337,7 +335,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Total number of nodes in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :return: [nNx, nNy, nNz]
         """
         return np.array([x for x in [self.nNx, self.nNy, self.nNz] if not x is None])
@@ -347,7 +345,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of x-edges in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :return: vnEx
         """
         return np.array([x for x in [self.nCx, self.nNy, self.nNz] if not x is None])
@@ -357,7 +355,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of y-edges in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :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])
@@ -367,7 +365,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of z-edges in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :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])
@@ -377,7 +375,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of x-faces in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :return: vnFx
         """
         return np.array([x for x in [self.nNx, self.nCy, self.nCz] if not x is None])
@@ -387,7 +385,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of y-faces in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :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])
@@ -397,7 +395,7 @@ class BaseRectangularMesh(BaseMesh):
         """
         Number of z-faces in each direction
 
-        :rtype: numpy.array
+        :rtype: numpy.array (dim, )
         :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/CylMesh.py

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

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.sparse.csr_matrix
+            :rtype: scipy.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.sparse.csr_matrix
+            :rtype: scipy.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.sparse.csr_matrix
+            :rtype: scipy.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"
@@ -115,12 +115,13 @@ 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 numpy.ndarray u: vector that multiplies dMdmu
-            :rtype: scipy.sparse.csr_matrix
+            :param np.ndarray u: vector that multiplies dMdmu
+            :rtype: scipy.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)
@@ -132,7 +133,7 @@ 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.sparse.csr_matrix
+            :rtype: scipy.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)
@@ -144,7 +145,7 @@ 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.sparse.csr_matrix
+            :rtype: scipy.csr_matrix
             :return: dMdm, the derivative of the inner product matrix (nE, nC*nA)
         """
         fast = None
@@ -168,7 +169,7 @@ 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.sparse.csr_matrix
+            :rtype: scipy.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

@@ -6,11 +6,13 @@ class TensorMeshIO(object):
     @classmethod
     def readUBC(TensorMesh, fileName):
         """
-            Read UBC GIF 3D tensor mesh and generate 3D TensorMesh in SimPEG.
+            Read UBC GIF 3DTensor mesh and generate 3D Tensor mesh in simpegTD
 
-            :param string fileName: path to the UBC GIF mesh file
-            :rtype: TensorMesh
-            :return: The tensor mesh for the fileName.
+            Input:
+            :param fileName, path to the UBC GIF mesh file
+
+            Output:
+            :param SimPEG TensorMesh object
         """
 
         # Interal function to read cell size lines for the UBC mesh files.
@@ -46,9 +48,11 @@ class TensorMeshIO(object):
             Read VTK Rectilinear (vtr xml file) and return SimPEG Tensor mesh and model
 
             Input:
-            :param string fileName: path to the vtr model file to read
-            :rtype: tuple
-            :return: (TensorMesh, modelDictionary)
+            :param vtrFileName, path to the vtr model file to write to
+
+            Output:
+            :return SimPEG TensorMesh object
+            :return SimPEG model dictionary
 
         """
         # Import
@@ -98,8 +102,9 @@ class TensorMeshIO(object):
         Makes and saves a VTK rectilinear file (vtr) for a simpeg Tensor mesh and model.
 
         Input:
-        :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
+        :param str, path to the output vtk file
+        :param mesh, SimPEG TensorMesh object - mesh to be transfer to VTK
+        :param models, dictionary of numpy.array - Name('s) and array('s). Match number of cells
 
         """
         # Import
@@ -157,9 +162,12 @@ class TensorMeshIO(object):
         """
             Read UBC 3DTensor mesh model and generate 3D Tensor mesh model in simpeg
 
-            :param string fileName: path to the UBC GIF mesh file to read
-            :rtype: numpy.ndarray
-            :return: model with TensorMesh ordered
+            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
         """
         f = open(fileName, 'r')
         model = np.array(map(float, f.readlines()))
@@ -175,7 +183,8 @@ class TensorMeshIO(object):
             Writes a model associated with a SimPEG TensorMesh
             to a UBC-GIF format model file.
 
-            :param string fileName: File to write to
+            :param str fileName: File to write to
+            :param simpeg.Mesh.TensorMesh mesh: The mesh
             :param numpy.ndarray model: The model
         """
 
@@ -192,8 +201,8 @@ class TensorMeshIO(object):
         """
             Writes a SimPEG TensorMesh to a UBC-GIF format mesh file.
 
-            :param string fileName: File to write to
-            :param dict models: A dictionary of the models
+            :param str fileName: File to write to
+            :param simpeg.Mesh.TensorMesh mesh: The mesh
 
         """
         assert mesh.dim == 3
@@ -222,8 +231,9 @@ class TreeMeshIO(object):
         """
             Write UBC ocTree mesh and model files from a simpeg ocTree mesh and model.
 
-            :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
+            :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
         """
 
         # Calculate information to write in the file.
@@ -276,9 +286,10 @@ 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
@@ -324,9 +335,11 @@ class TreeMeshIO(object):
         """
             Read UBC OcTree model and get vector
 
-            :param string fileName: path to the UBC GIF model file to read
-            :rtype: numpy.ndarray
-            :return: OcTree model
+            Input:
+            :param fileName, path to the UBC GIF model file to read
+
+            Output:
+            :return numpy array, 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_matrix
+        :rtype: scipy.sparse.csr.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.sparse.csr_matrix
+            :rtype: scipy.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_matrix
+        :rtype: scipy.sparse.csr.csr_matrix
         :return: M, the interpolation matrix
 
         locType can be::

SimPEG/Optimization.py

@@ -131,7 +131,7 @@ class Minimize(object):
 
         Minimizes the function (evalFunction) starting at the location x0.
 
-        :param callable evalFunction: function handle that evaluates: f, g, H = F(x)
+        :param def 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: tuple
-            :return: (xt, passLS) numpy.ndarray, bool
+            :rtype: numpy.ndarray,bool
+            :return: (xt, passLS)
         """
         # 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: tuple
-            :return: (xt, breakCaught) numpy.ndarray, bool
+            :rtype: numpy.ndarray,bool
+            :return: (xt, breakCaught)
         """
         self.printDone(inLS=True)
         print 'The linesearch got broken. Boo.'

SimPEG/PropMaps.py

@@ -187,7 +187,7 @@ class _PropMapMetaClass(type):
             attrs[attr + 'Model'] = prop._getModelProperty()
             attrs[attr + 'Deriv'] = prop._getModelDerivProperty()
 
-        return type('PropModel', (PropModel, ), attrs)
+        return type(name.replace('PropMap', 'PropModel'), (PropModel, ), attrs)
 
 
 class PropMap(object):

SimPEG/Regularization.py

@@ -10,7 +10,7 @@ class RegularizationMesh(object):
     are not necessarily true differential operators, but are constructed from
     a SimPEG Mesh.
 
-    :param BaseMesh mesh: problem mesh
+    :param Mesh 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
     """
 
@@ -383,8 +383,8 @@ class BaseRegularization(object):
 
         :param numpy.array m: geophysical model
         :param numpy.array v: vector to multiply
-        :rtype: scipy.sparse.csr_matrix
-        :return: WtW, or if v is supplied WtW*v (numpy.ndarray)
+        :rtype: scipy.sparse.csr_matrix or numpy.ndarray
+        :return: WtW or WtW*v
 
         The regularization is:
 
@@ -650,8 +650,8 @@ class Tikhonov(Simple):
     Note if the key word argument `mrefInSmooth` is False, then mref is not
     included in the smoothness contribution.
 
-    :param BaseMesh mesh: SimPEG mesh
-    :param IdentityMap mapping: regularization mapping, takes the model from model space to the thing you want to regularize
+    :param Mesh mesh: SimPEG mesh
+    :param Maps mapping: regularization mapping, takes the model from model space to the thing you want to regularize
     :param numpy.ndarray indActive: active cell indices for reducing the size of differential operators in the definition of a regularization mesh
     :param bool mrefInSmooth: (default = False) put mref in the smoothness component?
     :param float alpha_s: (default 1e-6) smallness weight
@@ -671,7 +671,7 @@ class Tikhonov(Simple):
     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

SimPEG/Survey.py

@@ -1,4 +1,5 @@
 import Utils, numpy as np, scipy.sparse as sp, uuid
+import gc
 
 class BaseRx(object):
     """SimPEG Receiver Object"""
@@ -311,6 +312,7 @@ 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)
@@ -321,7 +323,7 @@ class BaseSurvey(object):
 
                 d_\\text{pred} = \mathbf{P} f(m)
         """
-        raise NotImplementedError('eval is not yet implemented.')
+        raise NotImplemented('eval is not yet implemented.')
 
     @Utils.count
     def evalDeriv(self, f):
@@ -333,7 +335,7 @@ class BaseSurvey(object):
 
                 \\frac{\partial d_\\text{pred}}{\partial u} = \mathbf{P}
         """
-        raise NotImplementedError('eval is not yet implemented.')
+        raise NotImplemented('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 callable fctn: function handle
+        :param lambda 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 tuple shape: shape of the model.
+        :param int,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 anisotropy: this is the (3 x n) blurring kernel that is used.
+        :param numpy.ndarray,list 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, name=None):
+def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6):
     """
     Wraps a direct Solver.
 
@@ -72,11 +72,11 @@ def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6, name=
         if factorize and hasattr(self.solver, 'clean'):
             return self.solver.clean()
 
-    return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
+    return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
 
 
 
-def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5, name=None):
+def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
     """
     Wraps an iterative Solver.
 
@@ -128,13 +128,13 @@ def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5, name=None):
     def clean(self):
         pass
 
-    return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
+    return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
 
 
 from scipy.sparse import linalg
-Solver   = SolverWrapD(linalg.spsolve, factorize=False, name="Solver")
-SolverLU = SolverWrapD(linalg.splu, factorize=True, name="SolverLU")
-SolverCG = SolverWrapI(linalg.cg, name="SolverCG")
+Solver   = SolverWrapD(linalg.spsolve, factorize=False)
+SolverLU = SolverWrapD(linalg.splu, factorize=True)
+SolverCG = SolverWrapI(linalg.cg)
 
 
 class SolverDiag(object):

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_matrix
+        :rtype: scipy.sparse.csr.csr_matrix
         :return: Interpolation matrix
 
         .. plot::

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 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 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 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 BaseMesh mesh: The mesh
+        :param simpeg.Mesh.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
@@ -104,20 +104,16 @@ def closestPoints(mesh, pts, gridLoc='CC'):
 
 def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
     """
-    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
+        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
     """
     from SimPEG import Mesh
-    if mesh.dim == 1:
+    if mesh.dim ==1:
         xyzlim = xyzlim.flatten()
         xmin, xmax = xyzlim[0], xyzlim[1]
 
@@ -129,11 +125,11 @@ 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]
 
@@ -148,12 +144,12 @@ 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]
@@ -172,7 +168,7 @@ 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/__init__.py

@@ -15,7 +15,7 @@ import Directives
 import Inversion
 import Tests
 
-__version__   = '0.1.12'
+__version__   = '0.1.10'
 __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    = -n -w warnings.txt
+SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
 PAPER         =
 BUILDDIR      = _build

docs/_templates/layout.html

@@ -1,22 +0,0 @@
-{# 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_DC.rst

@@ -1,3 +1,5 @@
+.. _api_DC:
+
 .. math::
 
     \renewcommand{\div}{\nabla\cdot\,}
@@ -36,16 +38,8 @@
     \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/>`_).
 
@@ -61,7 +55,7 @@ As direct current (DC) implies, in DC resistivity survey, we assume steady-state
 
     \curl \e = 0
 
-Then by taking \\(\\div\\) of the first equation, we have
+Then by taking \\(\\curl\\) for the first equation, we have
 
 .. math::
 
@@ -143,14 +137,13 @@ Comparing to the analytic function:
 
 .. plot::
 
-    from SimPEG import Examples
-    Examples.DC_Analytic_Dipole.run(plotIt=True)
+    import simpegDC as DC
+    DC.Examples.Verification.run(plotIt=True)
 
+API
+===
 
-API for DC codes
-================
-
-.. automodule:: SimPEG.DCIP.BaseDC
+.. automodule:: simpegDC.BaseDC
     :show-inheritance:
     :members:
     :undoc-members:

docs/api_Examples.rst

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

docs/api_FiniteVolume.rst

@@ -0,0 +1,19 @@
+.. _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_ForwardProblem.rst

@@ -52,15 +52,13 @@ 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.
 
 
 
@@ -69,45 +67,13 @@ The API
 
 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
+.. automodule:: SimPEG.Problem
     :members:
     :undoc-members:
 
 Survey
 ------
-
-.. autoclass:: SimPEG.Survey.BaseSurvey
+.. automodule:: SimPEG.Survey
     :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/api_InnerProducts.rst

@@ -4,10 +4,7 @@
 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::
 
@@ -17,15 +14,12 @@ 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::
@@ -34,13 +28,12 @@ problem in geophysics.
 
         \nabla\cdot \vec{j} = q
 
-In the following discretization, :math:`\sigma` and :math:`\phi`
-will be discretized on the cell-centers and the flux, :math:`\vec{j}`,
+In the following discretization, \\\( \\sigma \\\) and \\\( \\phi \\\)
+will be discretized on the cell-centers and the flux, \\\(\\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
-:math:`\vec{f}`:
+We can define in weak form by integrating with a general face function \\\(\\vec{f}\\\):
 
 .. math::
 
@@ -68,16 +61,9 @@ 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 :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`.
+    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\\\).
 
-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::
 
@@ -85,17 +71,14 @@ the case of anisotropic (tensor) sigma.
     -\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 :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:
+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:
 
 .. 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 :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:
+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:
 
 .. math::
 
@@ -124,12 +107,10 @@ 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 :math:`d` is the dimension of the mesh.
-The :math:`\mathbf{M}^f` is returned when given the input of :math:`\Sigma^{-1}`.
+Where \\\(d\\\) is the dimension of the mesh.
+The \\\( \\mathbf{M}^f \\\) is returned when given the input of \\\( \\Sigma^{-1} \\\).
 
-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):
+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):
 
 .. math::
 
@@ -148,10 +129,7 @@ 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
@@ -159,8 +137,7 @@ 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::
 
@@ -211,16 +188,13 @@ 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 :math:`\mathbf{M}^f_\Sigma` and take the
-derivative when this is multiplied by some vector :math:`\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 \\\(\\mathbf{M}^f_\\Sigma\\\) and take the derivative when this is multiplied by some vector \\\(\\mathbf{v}\\\):
 
 .. math::
 
     \mathbf{P}^\top \boldsymbol{\Sigma} \mathbf{Pv}
 
-Here we will let :math:`\mathbf{Pv} = \mathbf{y}` and :math:`\mathbf{y}` will have the form:
+Here we will let \\\( \\mathbf{Pv} = \\mathbf{y} \\\) and \\\(\\mathbf{y}\\\) will have the form:
 
 .. math::
 
@@ -259,9 +233,7 @@ Here we will let :math:`\mathbf{Pv} = \mathbf{y}` and :math:`\mathbf{y}` will ha
         \end{matrix}
     \right]
 
-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}`
+Now it is easy to take the derivative with respect to any one of the parameters, for example, \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_1}\\\)
 
 .. math::
     \frac{\partial}{\partial \boldsymbol{\sigma}_1}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -275,8 +247,7 @@ parameters, for example,
         \end{matrix}
     \right]
 
-Whereas :math:`\frac{\partial}{\partial\boldsymbol{\sigma}_4}`, for
-example, is:
+Whereas \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_4}\\\), for example, is:
 
 .. math::
     \frac{\partial}{\partial \boldsymbol{\sigma}_4}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -290,12 +261,11 @@ example, is:
         \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
 -------
 
-.. autoclass:: SimPEG.Mesh.InnerProducts.InnerProducts
+.. automodule:: SimPEG.Mesh.InnerProducts
     :members:
     :undoc-members:

docs/api_Inversion.rst

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

docs/api_Maps.rst

@@ -27,8 +27,7 @@ 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
@@ -47,15 +46,14 @@ 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`),
+To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.Vertical1DMap`),
 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)
+    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!
@@ -63,8 +61,26 @@ done by the :class:`SimPEG.Maps.ExpMap` described above.
 
 .. plot::
 
-    from SimPEG import Examples
-    Examples.Maps_ComboMaps.run()
+    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()
 
 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).
@@ -106,8 +122,6 @@ 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:
@@ -116,6 +130,7 @@ The :code:`IdentityMap` is the base class for all mappings, and it does absolute
 Common Maps
 ===========
 
+
 Exponential Map
 ---------------
 
@@ -133,7 +148,7 @@ lives (i.e. it varies logarithmically).
 Vertical 1D Map
 ---------------
 
-.. autoclass:: SimPEG.Maps.SurjectVertical1D
+.. autoclass:: SimPEG.Maps.Vertical1DMap
     :members:
     :undoc-members:
 
@@ -149,10 +164,31 @@ Map 2D Cross-Section to 3D Model
 Mesh to Mesh Map
 ----------------
 
+
 .. plot::
 
-    from SimPEG import Examples
-    Examples.Maps_Mesh2Mesh.run()
+    from SimPEG import *
+    import matplotlib.pyplot as plt
+    M = Mesh.TensorMesh([100,100])
+    h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
+    h1 = h1/h1.sum()
+    M2 = Mesh.TensorMesh([h1,h1])
+    V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
+    v = Utils.mkvc(V)
+    modh = Maps.Mesh2Mesh([M,M2])
+    modH = Maps.Mesh2Mesh([M2,M])
+    H = modH * v
+    h = modh * H
+    ax = plt.subplot(131)
+    M.plotImage(v, ax=ax)
+    ax.set_title('Fine Mesh (Original)')
+    ax = plt.subplot(132)
+    M2.plotImage(H,clim=[0,1],ax=ax)
+    ax.set_title('Course Mesh')
+    ax = plt.subplot(133)
+    M.plotImage(h,clim=[0,1],ax=ax)
+    ax.set_title('Fine Mesh (Interpolated)')
+    plt.show()
 
 
 .. autoclass:: SimPEG.Maps.Mesh2Mesh
@@ -160,8 +196,8 @@ Mesh to Mesh Map
     :undoc-members:
 
 
-Under the Hood
-==============
+Some Extras
+===========
 
 Combo Map
 ---------

docs/api_Mesh.rst

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

docs/api_MeshCode.rst

@@ -0,0 +1,36 @@
+.. _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/api_Regularization.rst

@@ -91,21 +91,10 @@ 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/api_Solver.rst

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

docs/api_Utilities.rst

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

docs/api_Utils.rst

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

docs/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:: ../../images/InversionWorkflow-PreSimPEG.png
+.. image:: 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:: ../../images/InversionWorkflow.png
+.. image:: 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.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`) 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.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.
+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.
 
 
-.. include:: ../../../CITATION.rst
+.. include:: ../CITATION.rst
 
 Authors
 -------
 
-.. include:: ../../../AUTHORS.rst
+.. include:: ../AUTHORS.rst
 
 License
 -------
 
-.. include:: ../../../LICENSE
+.. include:: ../LICENSE

docs/app.yaml

@@ -1,95 +0,0 @@
-# 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', 'sphinx.ext.intersphinx', 'matplotlib.sphinxext.plot_directive']
+extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc', '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 - 2016, SimPEG Developers'
+copyright = u'2013, 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 = '0.1.12'
+version = '0.1.10'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.12'
+release = '0.1.10'
 
 # 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 = './images/logo-block.ico'
+#html_favicon = None
 
 # 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 = []
+html_static_path = ['_static']
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
@@ -229,12 +229,6 @@ 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 ------------------------------------------------
 
@@ -257,36 +251,3 @@ 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_FiniteVolume.rst

@@ -1,27 +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/content/api_core/api_MeshCode.rst

@@ -1,68 +0,0 @@
-.. _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

@@ -1,29 +0,0 @@
-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/em/api_basic.rst

@@ -1,33 +0,0 @@
-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/examples/Maps_ComboMaps.rst

@@ -1,48 +0,0 @@
-.. _examples_Maps_ComboMaps:
-
-.. --------------------------------- ..
-..                                   ..
-..    THIS FILE IS AUTO GENEREATED   ..
-..                                   ..
-..    SimPEG/Examples/__init__.py    ..
-..                                   ..
-.. --------------------------------- ..
-
-
-
-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.
-
-
-
-.. plot::
-
-    from SimPEG import Examples
-    Examples.Maps_ComboMaps.run()
-
-.. literalinclude:: ../../../SimPEG/Examples/Maps_ComboMaps.py
-    :language: python
-    :linenos:

docs/content/examples/Maps_Mesh2Mesh.rst

@@ -1,27 +0,0 @@
-.. _examples_Maps_Mesh2Mesh:
-
-.. --------------------------------- ..
-..                                   ..
-..    THIS FILE IS AUTO GENEREATED   ..
-..                                   ..
-..    SimPEG/Examples/__init__.py    ..
-..                                   ..
-.. --------------------------------- ..
-
-
-
-Maps: Mesh2Mesh
-===============
-
-This mapping allows you to go from one mesh to another.
-
-
-
-.. plot::
-
-    from SimPEG import Examples
-    Examples.Maps_Mesh2Mesh.run()
-
-.. literalinclude:: ../../../SimPEG/Examples/Maps_Mesh2Mesh.py
-    :language: python
-    :linenos:

docs/content/ip/index.rst

@@ -1,14 +0,0 @@
-Induced Polarization
-********************
-
-Todo: docs for IP!
-
-
-API for IP codes
-================
-
-.. automodule:: SimPEG.DCIP.BaseIP
-    :show-inheritance:
-    :members:
-    :undoc-members:
-    :inherited-members:

docs/em/api_FDEM.rst

@@ -9,28 +9,17 @@
 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 \\
@@ -42,7 +31,6 @@ 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 ::
@@ -116,20 +104,19 @@ 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 %
 
@@ -163,7 +150,7 @@ API
 FDEM Problem
 ------------
 
-.. automodule:: SimPEG.EM.FDEM.ProblemFDEM
+.. automodule:: SimPEG.EM.FDEM.FDEM
     :show-inheritance:
     :members:
     :undoc-members:
@@ -182,11 +169,6 @@ FDEM Survey
     :members:
     :undoc-members:
 
-.. automodule:: SimPEG.EM.FDEM.RxFDEM
-    :show-inheritance:
-    :members:
-    :undoc-members:
-
 FDEM Fields
 -----------
 

docs/em/api_TDEM.rst

@@ -347,10 +347,10 @@ and
 
 
 
-TDEM - B formulation
-====================
+TDEM Problem
+============
 
-.. automodule:: SimPEG.EM.TDEM.TDEM_b
+.. automodule:: SimPEG.EM.TDEM.TDEM
     :show-inheritance:
     :members:
     :undoc-members:
@@ -359,7 +359,7 @@ TDEM - B formulation
 Field Storage
 =============
 
-.. autoclass:: SimPEG.EM.TDEM.BaseTDEM.FieldsTDEM
+.. autoclass:: SimPEG.EM.TDEM.SurveyTDEM.Fields
     :show-inheritance:
     :members:
     :undoc-members:
@@ -369,19 +369,19 @@ Field Storage
 TDEM Survey Classes
 ===================
 
-.. autoclass:: SimPEG.EM.TDEM.SurveyTDEM.SurveyTDEM
+.. autoclass:: SimPEG.EM.TDEM.SurveyTDEM.Survey
     :show-inheritance:
     :members:
     :undoc-members:
     :inherited-members:
 
 
-Base Classes
-============
+.. Base Classes
+.. ============
 
-.. automodule:: SimPEG.EM.TDEM.BaseTDEM
-    :show-inheritance:
-    :members:
-    :undoc-members:
-    :inherited-members:
+.. .. automodule:: SimPEG.EM.TDEM.BaseTDEM
+..     :show-inheritance:
+..     :members:
+..     :undoc-members:
+..     :inherited-members:
 

docs/em/index.rst

@@ -3,23 +3,22 @@ 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/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/examples/DC_Forward_PseudoSection.rst

@@ -31,6 +31,6 @@ Created by @fourndo
     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/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:

docs/examples/EM_FDEM_Analytic_MagDipoleWholespace.rst

@@ -21,6 +21,6 @@ Here we plot the magnetic flux density from a harmonic dipole in a wholespace.
     from SimPEG import Examples
     Examples.EM_FDEM_Analytic_MagDipoleWholespace.run()
 
-.. literalinclude:: ../../../SimPEG/Examples/EM_FDEM_Analytic_MagDipoleWholespace.py
+.. literalinclude:: ../../SimPEG/Examples/EM_FDEM_Analytic_MagDipoleWholespace.py
     :language: python
     :linenos:

docs/examples/EM_Schenkel_Morrison_Casing.rst

@@ -17,13 +17,10 @@ current inside a steel-cased. The model is based on the Schenkel and
 Morrison Casing Model, and the results are used in a 2016 SEG abstract by
 Yang et al.
 
-.. 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.
+- 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
@@ -56,6 +53,6 @@ citation would be much appreciated!
     from SimPEG import Examples
     Examples.EM_Schenkel_Morrison_Casing.run()
 
-.. literalinclude:: ../../../SimPEG/Examples/EM_Schenkel_Morrison_Casing.py
+.. literalinclude:: ../../SimPEG/Examples/EM_Schenkel_Morrison_Casing.py
     :language: python
     :linenos:

docs/examples/EM_TDEM_1D_Inversion.rst

@@ -21,6 +21,6 @@ Here we will create and run a TDEM 1D inversion.
     from SimPEG import Examples
     Examples.EM_TDEM_1D_Inversion.run()
 
-.. literalinclude:: ../../../SimPEG/Examples/EM_TDEM_1D_Inversion.py
+.. literalinclude:: ../../SimPEG/Examples/EM_TDEM_1D_Inversion.py
     :language: python
     :linenos: