Merge branch 'master' into parallel

Change name "dispatcher" to "problem".

.bumpversion.cfg

@@ -1,4 +1,4 @@
 [bumpversion]
-current_version = 0.1.10
+current_version = 0.1.3
 files = setup.py SimPEG/__init__.py docs/conf.py
 

.gitignore

@@ -38,4 +38,5 @@ nosetests.xml
 *.sublime-project
 *.sublime-workspace
 docs/_build/
+*_cython.c
 Makefile

.travis.yml

@@ -2,29 +2,6 @@ language: python
 python:
   - 2.7
 
-sudo: false
-
-addons:
-  apt:
-    packages:
-    - gcc
-    - gfortran
-    - libopenmpi-dev
-    - libmumps-seq-dev
-    - libblas-dev
-    - liblapack-dev
-
-env:
-    - TEST_DIR="tests/mesh tests/base tests/utils"
-    - TEST_DIR=tests/em/fdem/inverse/derivs
-    - TEST_DIR=tests/em/tdem
-    - TEST_DIR=tests/dcip
-    - TEST_DIR=tests/flow
-    - TEST_DIR=tests/mt
-    - TEST_DIR=tests/examples
-    - TEST_DIR=tests/em/fdem/inverse/adjoint
-    - TEST_DIR=tests/em/fdem/forward
-
 # Setup anaconda
 before_install:
   - if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh -O miniconda.sh; fi
@@ -32,21 +9,20 @@ before_install:
   - ./miniconda.sh -b
   - export PATH=/home/travis/anaconda/bin:/home/travis/miniconda/bin:$PATH
   - conda update --yes conda
+  # The next couple lines fix a crash with multiprocessing on Travis and are not specific to using Miniconda
+  - sudo rm -rf /dev/shm
+  - sudo ln -s /run/shm /dev/shm
 
 # Install packages
 install:
-  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk
+  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython networkx pyzmq
   - pip install nose-cov python-coveralls
-
-  - git clone https://github.com/rowanc1/pymatsolver.git
-  - cd pymatsolver; python setup.py install; cd ..
-
+  # - pip install -r requirements.txt
   - python setup.py install
-  - python setup.py build_ext --inplace
 
 # Run test
 script:
-  - nosetests $TEST_DIR --with-cov --cov SimPEG --cov-config .coveragerc -v -s
+  - nosetests --with-cov --cov SimPEG --cov-config .coveragerc -v -s
 
 # Calculate coverage
 after_success:
@@ -56,5 +32,3 @@ notifications:
   email:
     - rowanc1@gmail.com
     - lindseyheagy@gmail.com
-    - gkrosen@gmail.com
-    - sgkang09@gmail.com

AUTHORS.rst

@@ -1,13 +1,9 @@
+- Luz Angelica Caudillo-Mata, (`@lacmajedrez <https://github.com/lacmajedrez/>`_)
 - Rowan Cockett, (`@rowanc1 <https://github.com/rowanc1/>`_)
+- Eldad Haber, (`@ehaber99 <https://github.com/ehaber99/>`_)
 - Lindsey Heagy, (`@lheagy <https://github.com/lheagy/>`_)
 - Seogi Kang, (`@sgkang <https://github.com/sgkang/>`_)
-- Brendan Smithyman, (`@bsmithyman <https://github.com/bsmithyman/>`_)
-- Gudni Rosenkjaer, (`@grosenkj <https://github.com/grosenkj/>`_)
-- Dom Fournier, (`@fourndo <https://github.com/fourndo/>`_)
 - Dave Marchant, (`@dwfmarchant <https://github.com/dwfmarchant/>`_)
+- Gudni Rosenkjaer, (`@grosenkj <https://github.com/grosenkj/>`_)
 - Lars Ruthotto, (`@lruthotto <https://github.com/lruthotto/>`_)
 - Mike Wathen, (`@mrwathen <https://github.com/mrwathen/>`_)
-- Luz Angelica Caudillo-Mata, (`@lacmajedrez <https://github.com/lacmajedrez/>`_)
-- Eldad Haber, (`@ehaber99 <https://github.com/ehaber99/>`_)
-- Doug Oldenburg, (`@dougoldenburg <https://github.com/dougoldenburg/>`_)
-- Adam Pidlisecky, (`@aPid1 <https://github.com/aPid1/>`_)

CITATION.rst

@@ -1,21 +0,0 @@
-Citing SimPEG
--------------
-
-There is a `paper about SimPEG <http://dx.doi.org/10.1016/j.cageo.2015.09.015>`_, if you use this code, please help our scientific visibility by citing our work!
-
-
-    Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W. (2015). SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers & Geosciences.
-
-
-BibTex:
-
-.. code::
-
-    @article{cockett2015simpeg,
-      title={SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications},
-      author={Cockett, Rowan and Kang, Seogi and Heagy, Lindsey J and Pidlisecky, Adam and Oldenburg, Douglas W},
-      journal={Computers \& Geosciences},
-      year={2015},
-      publisher={Elsevier}
-    }
-

GCEtools/PETScIO.py

@@ -0,0 +1,470 @@
+#!/usr/bin/python
+"""
+Input and output functions.
+"""
+
+
+import os as _os
+import errno as _errno
+import sys as _sys
+import numpy as _np
+
+from petsc4py import PETSc as _PETSc
+import fileinput as _fl
+
+def vecToArray(obj):
+    """ Converts a PETSc vector to a numpy array, available on *all* MPI nodes.
+
+        Args:
+            obj (petsc4py.PETSc.Vec): input vector.
+
+        Returns:
+            numpy.array :
+        """
+    # scatter vector 'obj' to all processes
+    comm = obj.getComm()
+    scatter, obj0 = _PETSc.Scatter.toAll(obj)
+    scatter.scatter(obj, obj0, False, _PETSc.Scatter.Mode.FORWARD)
+
+    return _np.asarray(obj0)
+
+    # deallocate
+    comm.barrier()
+    scatter.destroy()
+    obj0.destroy()
+
+
+def vecToArray0(obj):
+    """ Converts a PETSc vector to a numpy array available on MPI node 0.
+
+        Args:
+            obj (petsc4py.PETSc.Vec): input vector.
+
+        Returns:
+            numpy.array :
+        """
+    # scatter vector 'obj' to process 0
+    comm = obj.getComm()
+    rank = comm.getRank()
+    scatter, obj0 = _PETSc.Scatter.toZero(obj)
+    scatter.scatter(obj, obj0, False, _PETSc.Scatter.Mode.FORWARD)
+
+    if rank == 0:   return _np.asarray(obj0)
+
+    # deallocate
+    comm.barrier()
+    scatter.destroy()
+    obj0.destroy()
+
+
+def arrayToVec(vecArray):
+    """ Converts a (global) array to a PETSc vector over :attr:`petsc4py.PETSc.COMM_WORLD`.
+
+        Args:
+            vecArray (array or numpy.array): input vector.
+
+        Returns:
+            petsc4py.PETSc.Vec() :
+        """
+    vec = _PETSc.Vec().create(comm=_PETSc.COMM_WORLD)
+    vec.setSizes(len(vecArray))
+    vec.setUp()
+    (Istart,Iend) = vec.getOwnershipRange()
+    return vec.createWithArray(vecArray[Istart:Iend],
+            comm=_PETSc.COMM_WORLD)
+    vec.destroy()
+
+
+def arrayToMat(matArray):
+    """ Converts a (global) 2D array to a PETSc matrix over :attr:`petsc4py.PETSc.COMM_WORLD`.
+
+        Args:
+            matArray (array or numpy.array): input square array.
+
+        :rtype: petsc4py.PETSc.Mat()
+
+        .. important::
+            Requires `SciPy <http://www.scipy.org>`_.
+
+        """
+    try:
+        import scipy.sparse as sparse
+    except:
+        print '\nERROR: loading matrices from txt files requires Scipy!'
+        return
+
+    matSparse =matArray
+
+    mat = _PETSc.Mat().createAIJ(size=matSparse.shape,comm=_PETSc.COMM_WORLD)
+    (Istart,Iend) = mat.getOwnershipRange()
+
+    ai = matSparse.indptr[Istart:Iend+1] - matSparse.indptr[Istart]
+    aj = matSparse.indices[matSparse.indptr[Istart]:matSparse.indptr[Iend]]
+    av = matSparse.data[matSparse.indptr[Istart]:matSparse.indptr[Iend]]
+
+    mat.setValuesCSR(ai,aj,av)
+    mat.assemble()
+
+    return mat
+    mat.destroy()
+
+def matToSparse(mat):
+    """ Converts a PETSc matrix to a (global) sparse matrix.
+
+        Args:
+            mat (petsc4py.PETSc.Mat): input PETSc matrix.
+
+        :rtype: scipy.sparse.csr_matrix
+
+        .. important::
+            Requires `SciPy <http://www.scipy.org>`_.
+
+        """
+    import scipy.sparse as sparse
+
+    data = mat.getValuesCSR()
+
+    (Istart,Iend) = mat.getOwnershipRange()
+    columns = mat.getSize()[0]
+    sparseSubMat = sparse.csr_matrix(data[::-1],shape=(Iend-Istart,columns))
+
+    comm = _PETSc.COMM_WORLD
+
+    sparseSubMat = comm.tompi4py().allgather(sparseSubMat)
+
+    return sparse.vstack(sparseSubMat)
+
+def adjToH(adj,d=[0],amp=[0.]):
+    """ Creates a 1 particle PETSc-type Hamiltonian matrix from a PETSc adjacency matrix.
+
+        Args:
+            adj (petsc4py.PETSc.Mat): input PETSc-type adjacency matrix.
+
+            d (array of ints): an array containing *integers* indicating the nodes
+                        where diagonal defects are to be placed (e.g. ``d=[0,1,4]``).
+
+            amp (array of floats):   an array containing *floats* indicating the diagonal defect
+                        amplitudes corresponding to each element in ``d`` (e.g. ``amp=[0.5,-1,4.2]``).
+
+        Returns:
+             : 1 particle Hamiltonian matrix
+        :rtype: petsc4py.PETSc.Mat()
+
+        Warning:
+            * The size of ``a`` and ``d`` must be identical
+
+                >>> amp = [0.5,-1.,4.2]
+                >>> len(d) == len(amp)
+                True
+
+            * Elements of ``d`` can range from :math:`[0,N-1]` where the adjacency matrix is :math:`N\\times N`.
+
+        """
+    (Istart,Iend) = adj.getOwnershipRange()
+    diagSum = []
+    for i in range(Istart,Iend):
+        diagSum.append(_np.sum(adj.getRow(i)[-1]))
+        for j,val in enumerate(d):
+            if i==val:  diagSum[i-Istart] += amp[j]
+
+    mat = _PETSc.Mat().create(comm=_PETSc.COMM_WORLD)
+    mat.setSizes(adj.getSize())
+    mat.setUp()
+
+    for i in range(Istart,Iend):
+        mat.setValue(i,i,diagSum[i-Istart])
+
+    mat.assemble()
+    mat.axpy(-1,adj)
+
+    return mat
+    mat.destroy()
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#---------------------- Vec I/O functions ---------------------------
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def exportVec(vec,filename,filetype):
+    """ Export a PETSc vector to a file.
+
+        Args:
+            vec (petsc4py.PETSc.Vec): input vector.
+            filename (str): path to desired output file.
+            filetype (str): the filetype of the exported vector.
+
+                            * ``'txt'`` - a column vector in text format.
+                            * ``'bin'`` - a PETSc binary vector.
+        """
+    if _os.path.isabs(filename):
+        outDir = _os.path.dirname(filename)
+    else:
+        outDir = './'+_os.path.dirname(filename)
+
+    # create output directory if it doesn't exist
+    try:
+        _os.mkdir(outDir)
+    except OSError as exception:
+        if exception.errno != _errno.EEXIST:
+            raise
+
+    if filetype == 'txt':
+        # scatter prob to process 0
+        comm = vec.getComm()
+        rank = comm.getRank()
+        scatter, vec0 = _PETSc.Scatter.toZero(vec)
+        scatter.scatter(vec, vec0, False, _PETSc.Scatter.Mode.FORWARD)
+
+        # use process 0 to write to text file
+        if rank == 0:
+            array0 = _np.asarray(vec0)
+            with open(filename,'w') as f:
+                for i in range(len(array0)):
+                    f.write('{0: .12e}\n'.format(array0[i]))
+
+        # deallocate
+        comm.barrier()
+        scatter.destroy()
+        vec0.destroy()
+
+    elif filetype == 'bin':
+        binSave = _PETSc.Viewer().createBinary(filename, 'w')
+        binSave(vec)
+        binSave.destroy()
+
+    vec.comm.barrier()
+
+def loadVec(filename,filetype):
+    """ Import a PETSc vector from a file.
+
+        Args:
+            filename (str): path to input file.
+            filetype (str): the filetype.
+
+                            * ``'txt'`` - a column vector in text format.
+                            * ``'bin'`` - a PETSc binary vector.
+        """
+    if filetype == 'txt':
+        try:
+            vecArray = _np.loadtxt(filename,dtype=_PETSc.ScalarType)
+            return arrayToVec(vecArray)
+        except:
+            print "\nERROR: input state space file " + filename\
+                + " does not exist or is in an incorrect format"
+            _sys.exit()
+
+    elif filetype == 'bin':
+        binLoad = _PETSc.Viewer().createBinary(filename, 'r')
+        try:
+            return _PETSc.Vec().load(binLoad)
+        except:
+            print "\nERROR: input state space file " + filename\
+                + " does not exist or is in an incorrect format"
+            _sys.exit()
+        binLoad.destroy()
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#---------------------- Mat I/O functions ---------------------------
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def exportMat(mat,filename,filetype,mattype=None):
+    """ Export a PETSc matrix to a file.
+
+        Args:
+            mat (petsc4py.PETSc.Mat): input matrix.
+            filename (str): path to desired output file.
+            filetype (str): the filetype of the exported vector.
+
+                            * ``'txt'`` - a 2D matrix array in text format.
+                            * ``'bin'`` - a PETSc binary matrix.
+            mattype (str): (``None``,``'adj'``) - if set to ``adj``, only
+                            integers ``0`` and ``1`` are written. Note
+                            that this only applied in ``txt`` mode.
+        """
+    rank = _PETSc.Comm.Get_rank(_PETSc.COMM_WORLD)
+
+    if _os.path.isabs(filename):
+        outDir = _os.path.dirname(filename)
+    else:
+        outDir = './'+_os.path.dirname(filename)
+
+    # create output directory if it doesn't exist
+    try:
+        _os.mkdir(outDir)
+    except OSError as exception:
+        if exception.errno != _errno.EEXIST:
+            raise
+
+    if filetype == 'txt':
+        txtSave = _PETSc.Viewer().createASCII(filename, 'w',
+            format=_PETSc.Viewer.Format.ASCII_DENSE, comm=_PETSc.COMM_WORLD)
+        txtSave(mat)
+        txtSave.destroy()
+
+        if rank == 0:
+            for line in _fl.FileInput(filename,inplace=1):
+                if line[2] != 't':
+                    if mattype == 'adj':
+                        line = line.replace(" i","j")
+                        line = line.replace(" -","-")
+                        line = line.replace("+-","-")
+                        line = line.replace("0000e+01+0.00000e+00j","")
+                        line = line.replace(".00000e+00+0.00000e+00j","")
+                        line = line.replace(".","")
+                        line = line.replace(" -","\t-")
+                        line = line.replace("  ","\t")
+                        line = line.replace(" ","")
+                        line = line.replace("\t"," ")
+                        print line,
+                    else:
+                        line = line.replace(" i","j")
+                        line = line.replace(" -","-")
+                        line = line.replace("+-","-")
+                        print line,
+
+    elif filetype == 'bin':
+        binSave = _PETSc.Viewer().createBinary(filename, 'w', comm=_PETSc.COMM_WORLD)
+        binSave(mat)
+        binSave.destroy()
+
+    mat.comm.barrier()
+
+def loadMat(filename,filetype,delimiter=None):
+    """ Import a PETSc matrix from a file.
+
+        Args:
+            filename (str): path to input file.
+            filetype (str): the filetype.
+
+                            * ``'txt'`` - a 2D matrix array in text format.
+                            * ``'bin'`` - a PETSc matrix vector.
+
+            delimiter (str): this is passed to `numpy.genfromtxt\
+                <http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html>`_
+                in the case of strange delimiters in an imported ``txt`` file.
+        """
+    if filetype == 'txt':
+        try:
+            try:
+                if delimiter is None:
+                    matArray = _np.genfromtxt(filename,dtype=_PETSc.ScalarType)
+                else:
+                    matArray = _np.genfromtxt(filename,dtype=_PETSc.ScalarType,delimiter=delimiter)
+            except:
+                filefix = []
+                for line in _fl.FileInput(filename,inplace=0):
+                    if line[2] != 't':
+                        line = line.replace(" i","j")
+                        line = line.replace(" -","-")
+                        line = line.replace("+-","-")
+                        filefix.append(line)
+
+                matArray = _np.genfromtxt(filefix,dtype=_PETSc.ScalarType)
+
+            return arrayToMat(matArray)
+        except:
+            print "\nERROR: input state space file " + filename\
+                + " does not exist or is in an incorrect format"
+            _sys.exit()
+
+    elif filetype == 'bin':
+        binLoad = _PETSc.Viewer().createBinary(filename, 'r')
+        try:
+            return _PETSc.Mat().load(binLoad)
+        except:
+            print "\nERROR: input state space file " + filename\
+                + " does not exist or is in an incorrect format"
+            _sys.exit()
+        binLoad.destroy()
+
+def exportVecToMat(vec,filename,filetype):
+    """ Export a :math:`N^2` element PETSc vector as a :math:`N\\times N` matrix.
+
+        This is useful when wanting to view the full statespace of a 2 particle
+        quantum walk.
+
+        Args:
+            vec (petsc4py.PETSc.Vec): input :math:`N^2` element vector.
+            filename (str): path to desired output file.
+            filetype (str): the filetype of the exported vector.
+
+                            * ``'txt'`` - an :math:`N\\times N` 2D matrix array in text format.
+                            * ``'bin'`` - an :math:`N\\times N` PETSc binary matrix.
+        """
+    rank = _PETSc.Comm.Get_rank(_PETSc.COMM_WORLD)
+
+    if _os.path.isabs(filename):
+        outDir = _os.path.dirname(filename)
+    else:
+        outDir = './'+_os.path.dirname(filename)
+
+    # create output directory if it doesn't exist
+    try:
+        _os.mkdir(outDir)
+    except OSError as exception:
+        if exception.errno != _errno.EEXIST:
+            raise
+
+    vecArray = vecToArray(vec)
+    matArray = vecArray.reshape([_np.sqrt(vecArray.size),_np.sqrt(vecArray.size)])
+
+    if filetype == 'txt':
+        #if rank == 0:  _np.savetxt(filename,matArray)
+        txtSave = _PETSc.Viewer().createASCII(filename, 'w',
+            format=_PETSc.Viewer.Format.ASCII_DENSE, comm=_PETSc.COMM_WORLD)
+        txtSave(arrayToMat(matArray))
+        txtSave.destroy()
+
+        if rank == 0:
+            for line in _fl.FileInput(filename,inplace=1):
+                if line[2] != 't':
+                    line = line.replace(" i","j")
+                    line = line.replace(" -","-")
+                    line = line.replace("+-","-")
+                    print line,
+
+    elif filetype == 'bin':
+        binSave = _PETSc.Viewer().createBinary(filename, 'w', comm=_PETSc.COMM_WORLD)
+        binSave(arrayToMat(matArray))
+        binSave.destroy()
+    vec.comm.barrier()
+
+
+def loadMatToVec(filename,filetype):
+    """ Load a :math:`N\\times N` matrix as a :math:`N^2` element PETSc vector.
+
+        This is useful when wanting to import the full statespace of a 2 particle
+        quantum walk to use for propagation.
+
+        Args:
+            filename (str): path to the input file.
+            filetype (str): the filetype
+
+                            * ``'txt'`` - an :math:`N\\times N` 2D matrix array in text format.
+                            * ``'bin'`` - **Not yet implemented! Please use a txt \
+                                         format for this type of import**.
+        """
+    if filetype == 'txt':
+        try:
+            try:
+                matArray = _np.loadtxt(filename,dtype=_PETSc.ScalarType)
+            except:
+                filefix = []
+                for line in _fl.FileInput(filename,inplace=0):
+                    if line[2] != 't':
+                        line = line.replace(" i","j")
+                        line = line.replace(" -","-")
+                        line = line.replace("+-","-")
+                        filefix.append(line)
+
+                matArray = _np.loadtxt(filefix,dtype=_PETSc.ScalarType)
+
+            vecArray = matArray.reshape(matArray.shape[0]**2)
+            return arrayToVec(vecArray)
+        except:
+            print "\nERROR: input state space file " + filename\
+                + " does not exist or is in an incorrect format"
+            _sys.exit()
+
+    elif filetype == 'bin':
+        print '\nERROR: only works for txt storage!'
+        _sys.exit()

GCEtools/gceStartup.txt

@@ -0,0 +1,11 @@
+# Check project status
+gcutil getproject --project=<ProjectName> --cache_flag_values
+
+# Start an instance
+gcutil addinstance <instanceName>
+
+# Log in 
+gcutil ssh <instanceName>
+
+# Shut down
+gcutil deleteinstance <instanceName>

GCEtools/petscStartup.sh

@@ -0,0 +1,145 @@
+#! /bin/bash
+
+locale-gen en_US en_US.UTF-8 hu_HU hu_HU.UTF-8 > output.t
+dpkg-reconfigure locales >> output.t
+
+sudo apt-get update  >> output.t
+echo " "
+echo " "
+echo "   ============================================"
+echo "   | Installing packages form package manager |"
+echo "   ============================================"
+echo " "
+echo " "
+
+sudo apt-get -y install aptitude >> output.t
+
+packages=(gcc gfortran git libopenmpi-dev python-pip python-dev git flex bison cmake vim cython ipython python-scipy python-numpy python-nose python-pip python-matplotlib python-vtk python-h5py libmumps-ptscotch-4.10.0 libmumps-ptscotch-dev libblas-dev liblapack-dev )
+
+
+for item in ${packages[*]}
+do
+    printf "     %-30s\n" $item
+
+done
+
+for item in ${packages[*]}
+do
+    tput cuu1
+done
+
+for item in ${packages[*]}
+do
+    sudo aptitude -y install $item >> output.t
+    printf "     %-30s              %-4s\n" $item done
+done
+
+
+echo " "
+echo " "
+echo "   ====================================="
+echo "   | Installing extra Python libraries |"
+echo "   ====================================="
+echo " "
+echo " "
+
+
+pipPackages=(mpi4py pymumps)
+
+for item in ${pipPackages[*]}
+do
+    printf "     %-30s\n" $item
+done
+
+for item in ${pipPackages[*]}
+do
+    tput cuu1
+done
+
+for item in ${pipPackages[*]}
+do
+        sudo pip install $item >> output.t
+        printf "     %-30s              %-4s\n" $item done
+done
+
+Upgrade=(scipy numpy ipython)
+
+for item in ${Upgrade[*]}
+do
+    printf "     %-8s%-7s\n" $item upgrade
+
+done
+
+for item in ${Upgrade[*]}
+do
+    tput cuu1
+done
+
+for item in ${Upgrade[*]}
+do
+    sudo pip install $item --upgrade >> output.t
+    printf "     %-8s%-7s                             %-4s\n" $item upgrade done
+done
+
+
+
+echo " "
+echo " "
+echo "   ====================="
+echo "   | Installing SimPEG |"
+echo "   ====================="
+echo " "
+echo " "
+cd ~
+
+
+git clone https://github.com/simpeg/simpeg.git >> output.t
+cd simpeg/SimPEG/
+python setup.py >> output.t
+cd ~
+
+mkdir petsc
+cd petsc
+
+echo " "
+echo " "
+echo "   ===================="
+echo "   | Installing PETSc |"
+echo "   ===================="
+echo " "
+echo " "
+wget http://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-3.4.3.tar.gz
+
+tar -zxf petsc-3.4.3.tar.gz
+
+cd petsc-3.4.3
+
+./configure --with-debugging=no  --dowload-mpich=yes --download-blacs=yes --download-f-blas-lapack=yes --download-scalapack=yes --download-mumps=yes --download-ml=yes --download-spooles=yes --download-hypre=yes --dowload-trilinos=yes --download-metis=yes --download-parmetis=yes --download-umfpack=yes --download-ptscotch=yes  --download-superlu=yes --download-superlu_dist=yes --download-essl=yes --download-eucild=yes  --download-spai=yes  --download-mpi4py=yes --download-petsc4py=yes --download-scientificpython=yes
+
+
+echo "export PETSC_DIR=/home/${USER}/petsc/petsc-3.4.3" >> ~/.bashrc
+echo "export PETSC_ARCH=arch-linux2-c-opt" >> ~/.bashrc
+export PETSC_DIR=/home/${USER}/petsc/petsc-3.4.3
+export PETSC_ARCH=arch-linux2-c-opt
+. ~/.bashrc
+
+make PETSC_DIR=/home/${USER}/petsc/petsc-3.4.3 PETSC_ARCH=arch-linux2-c-opt all
+make PETSC_DIR=/home/${USER}/petsc/petsc-3.4.3 PETSC_ARCH=arch-linux2-c-opt test
+
+cd ~/petsc
+echo " "
+echo " "
+echo "   ======================="
+echo "   | Installing PETSc4PY |"
+echo "   ======================="
+echo " "
+echo " "
+git clone https://bitbucket.org/petsc/petsc4py.git
+cd petsc4py/
+python setup.py build >> output.t
+python setup.py install --prefix=~/petsc >> output.t
+
+echo "export PYTHONPATH=~/petsc/lib/python2.7/site-packages:/home/$USER/simpeg:${PYTHONPATH}" >> ~/.bashrc
+
+cd ~
+source ~/.bashrc

GCEtools/startup.sh

@@ -0,0 +1,22 @@
+#! /bin/bash
+sudo aptitude -y update
+sudo aptitude -y upgrade
+sudo aptitude -y install gcc gfortran git libopenmpi-dev python-pip python-dev
+sudo aptitude -y install ipython python-scipy python-numpy python-nose python-pip python-matplotlib
+sudo aptitude -y install libmumps-ptscotch-4.10.0 libmumps-ptscotch-dev
+sudo aptitude -y install libblas-dev liblapack-dev
+
+sudo pip install mpi4py
+sudo pip install pymumps
+
+sudo pip install scipy --upgrade
+sudo pip install numpy --upgrade
+sudo pip install ipython --upgrade
+
+git clone https://github.com/simpeg/simpeg.git
+cd simpeg/SimPEG/
+python setup.py
+cd ~
+
+echo export PYTHONPATH=/home/$USER/simpeg/ >> .bashrc
+source .bashrc

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2013-2016 SimPEG Developers
+Copyright (c) 2013-2015 SimPEG Developers
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of
 this software and associated documentation files (the "Software"), to deal in

PROJECTS.rst

@@ -0,0 +1,36 @@
+- Electromagnetics (`simpegEM <http://simpegem.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegem.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegem
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegem/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegem?branch=master
+- Potential Fields (`simpegPF <http://simpegpf.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegpf.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegpf
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegpf/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegpf?branch=master
+- Ground Water Flow (`simpegFLOW <http://simpegflow.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegflow.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegflow
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegflow/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegflow?branch=master
+- Direct Current Resistivity (`simpegDC <http://simpeg-dc.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegdc.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegdc
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegdc/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegdc?branch=master
+- Electromagnetics 1D (`simpegEM1D <http://simpegem1d.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegEM1D.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegEM1D
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegEM1D/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegEM1D?branch=master
+- Magnetotellurics (`simpegMT <http://simpegmt.rtfd.org/>`_)
+    .. image:: https://travis-ci.org/simpeg/simpegmt.svg?branch=master
+        :target: https://travis-ci.org/simpeg/simpegmt
+        :alt: Master Branch
+    .. image:: https://coveralls.io/repos/simpeg/simpegmt/badge.png?branch=master
+        :target: https://coveralls.io/r/simpeg/simpegmt?branch=master

README.rst

@@ -17,7 +17,7 @@ SimPEG
     :target: https://github.com/simpeg/simpeg/blob/master/LICENSE
     :alt: BSD 3 clause license.
 
-.. image:: https://api.travis-ci.org/simpeg/simpeg.svg?branch=master
+.. image:: https://img.shields.io/travis/simpeg/simpeg.svg
     :target: https://travis-ci.org/simpeg/simpeg
     :alt: Travis CI build status
 
@@ -25,10 +25,6 @@ SimPEG
     :target: https://coveralls.io/r/simpeg/simpeg?branch=master
     :alt: Coverage status
 
-.. image:: http://img.shields.io/badge/GITTER-JOIN_CHAT-brightgreen.svg?style=flat-square
-    :alt: gitter chat room at https://gitter.im/simpeg/simpeg
-    :target: https://gitter.im/simpeg/simpeg
-    
 Simulation and Parameter Estimation in Geophysics  -  A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
 
 The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:
@@ -40,28 +36,6 @@ The vision is to create a package for finite volume simulation with applications
 * designed for large-scale inversions
 
 
-Citing SimPEG:
---------------
-
-There is a paper about SimPEG!
-
-
-    Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W. (2015). SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers & Geosciences.
-
-
-**BibTex:**
-
-.. code::
-
-    @article{cockett2015simpeg,
-      title={SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications},
-      author={Cockett, Rowan and Kang, Seogi and Heagy, Lindsey J and Pidlisecky, Adam and Oldenburg, Douglas W},
-      journal={Computers \& Geosciences},
-      year={2015},
-      publisher={Elsevier}
-    }
-
-
 Website:
 http://simpeg.xyz
 
@@ -83,4 +57,4 @@ https://github.com/simpeg/simpeg/issues
 
 
 Code Snippets & Tutorials:
-http://simpeg.xyz/Journal
+http://www.row1.ca/simpeg

SimPEG/DCIP/BaseDC.py

@@ -1,292 +0,0 @@
-from SimPEG import *
-
-class FieldsDC_CC(Problem.Fields):
-    knownFields = {'phi_sol':'CC'}
-    aliasFields = {
-                    'phi' : ['phi_sol','CC','_phi'],
-                    'e' : ['phi_sol','F','_e'],
-                    'j' : ['phi_sol','F','_j']
-                  }
-
-    def __init__(self,mesh,survey,**kwargs):
-        super(FieldsDC_CC, self).__init__(mesh, survey, **kwargs)
-
-    def startup(self):
-        self._cellGrad = self.survey.prob.mesh.cellGrad
-        self._Mfinv = self.survey.prob.mesh.getFaceInnerProduct(invMat=True)
-
-    def _phi(self, phi_sol, srcList):
-        phi = phi_sol
-        # for i, src in enumerate(srcList):
-        #     phi_p = src.phi_p(self.survey.prob)
-        #     if phi_p is not None:
-        #         phi[:,i] += phi_p
-        return phi
-
-    def _e(self, phi_sol, srcList):
-        e = -self._cellGrad*phi_sol
-        # for i, src in enumerate(srcList):
-        #     e_p = src.e_p(self.survey.prob)
-        #     if e_p is not None:
-        #         e[:,i] += e_p
-        return e
-
-    def _j(self, phi_sol, srcList):
-
-        j = -self._Mfinv*self.survey.prob.Msig*self._cellGrad*phi_sol
-        # for i, src in enumerate(srcList):
-        #     j_p = src.j_p(self.survey.prob)
-        #     if j_p is not None:
-        #         j[:,i] += j_p
-        return j
-
-
-
-class SrcDipole(Survey.BaseSrc):
-    """A dipole source, locA and locB are moved to the closest cell-centers"""
-
-    current = 1
-    loc  = None
-    # _rhsDict = None
-
-    def __init__(self, rxList, locA, locB, **kwargs):
-        self.loc = (locA, locB)
-        super(SrcDipole, self).__init__(rxList, **kwargs)
-
-    def eval(self, prob):
-        # Recompute rhs
-        # if getattr(self, '_rhsDict', None) is None:
-        #     self._rhsDict = {}
-        # if mesh not in self._rhsDict:
-        pts = [self.loc[0], self.loc[1]]
-        inds = Utils.closestPoints(prob.mesh, pts)
-        q = np.zeros(prob.mesh.nC)
-        q[inds] = - self.current * ( np.r_[1., -1.] / prob.mesh.vol[inds] )
-        # self._rhsDict[mesh] = q
-        # return self._rhsDict[mesh]
-        return q
-
-
-class RxDipole(Survey.BaseRx):
-    """A dipole source, locA and locB are moved to the closest cell-centers"""
-    def __init__(self, locsM, locsN, **kwargs):
-        locs = (locsM, locsN)
-        assert locsM.shape == locsN.shape, 'locs must be the same shape.'
-        super(RxDipole, self).__init__(locs, 'dipole', storeProjections=False, **kwargs)
-
-    @property
-    def nD(self):
-        """Number of data in the receiver."""
-        return self.locs[0].shape[0]
-
-    def getP(self, mesh):
-        P0 = mesh.getInterpolationMat(self.locs[0], self.projGLoc)
-        P1 = mesh.getInterpolationMat(self.locs[1], self.projGLoc)
-        return P0 - P1
-
-
-class SurveyDC(Survey.BaseSurvey):
-    """
-        **SurveyDC**
-
-        Geophysical DC resistivity data.
-
-    """
-    uncert = None
-    def __init__(self, srcList, **kwargs):
-        self.srcList = srcList
-        Survey.BaseSurvey.__init__(self, **kwargs)
-        # self._rhsDict = {}
-        self._Ps = {}
-
-    def eval(self, u):
-        """
-            Predicted data.
-
-            .. math::
-                d_\\text{pred} = Pu(m)
-        """
-        P = self.getP(self.prob.mesh)
-        return P*mkvc(u[self.srcList, 'phi_sol'])
-
-    def getP(self, mesh):
-        if mesh in self._Ps:
-            return self._Ps[mesh]
-
-        P_src = [sp.vstack([rx.getP(mesh) for rx in src.rxList]) for src in self.srcList]
-
-        self._Ps[mesh] = sp.block_diag(P_src)
-        return self._Ps[mesh]
-
-
-class ProblemDC_CC(Problem.BaseProblem):
-    """
-        **ProblemDC**
-
-        Geophysical DC resistivity problem.
-
-    """
-
-    surveyPair = SurveyDC
-    Solver     = Solver
-    fieldsPair = FieldsDC_CC
-    Ainv = None
-
-    def __init__(self, mesh, **kwargs):
-        Problem.BaseProblem.__init__(self, mesh)
-        self.mesh.setCellGradBC('neumann')
-        Utils.setKwargs(self, **kwargs)
-
-
-    deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
-
-    @property
-    def Msig(self):
-        if getattr(self, '_Msig', None) is None:
-            sigma = self.curModel.transform
-            Av = self.mesh.aveF2CC
-            self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
-        return self._Msig
-
-    @property
-    def dMdsig(self):
-        if getattr(self, '_dMdsig', None) is None:
-            sigma = self.curModel.transform
-            Av = self.mesh.aveF2CC
-            dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
-            self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
-        return self._dMdsig
-
-    @property
-    def A(self):
-        """
-            Makes the matrix A(m) for the DC resistivity problem.
-
-            :param numpy.array m: model
-            :rtype: scipy.csc_matrix
-            :return: A(m)
-
-            .. math::
-                c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
-
-            Where M() is the mass matrix and mT is the model transform.
-        """
-        if getattr(self, '_A', None) is None:
-            D = self.mesh.faceDiv
-            G = self.mesh.cellGrad
-            self._A = D*self.Msig*G
-            # Remove the null space from the matrix.
-            self._A[0,0] /= self.mesh.vol[0]
-            self._A = self._A.tocsc()
-        return self._A
-
-    def getRHS(self):
-        # if self.mesh not in self._rhsDict:
-        RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
-        # self._rhsDict[mesh] = RHS
-        # return self._rhsDict[mesh]
-        return RHS
-
-    def fields(self, m):
-
-        F = self.fieldsPair(self.mesh, self.survey)
-        self.curModel = m
-        A    = self.A
-        self.Ainv = self.Solver(A, **self.solverOpts)
-        RHS    = self.getRHS()
-        Phi  = self.Ainv * RHS
-        Srcs = self.survey.srcList
-        F[Srcs, 'phi_sol'] = Phi
-
-        return F
-
-    def Jvec(self, m, v, f=None):
-        """
-            :param numpy.array m: model
-            :param numpy.array v: vector to multiply
-            :param Fields f: fields
-            :rtype: numpy.array
-            :return: Jv
-
-            .. math::
-                c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
-
-                \\nabla_u (A(m)u - q) = A(m)
-
-                \\nabla_m (A(m)u - q) = G\\text{sdiag}(Du)\\nabla_m(M(mT(m)))
-
-            Where M() is the mass matrix and mT is the model transform.
-
-            .. math::
-                J = - P \left( \\nabla_u c(m, u) \\right)^{-1} \\nabla_m c(m, u)
-
-                J(v) = - P ( A(m)^{-1} ( G\\text{sdiag}(Du)\\nabla_m(M(mT(m))) v ) )
-        """
-        # Set current model; clear dependent property $\mathbf{A(m)}$
-        self.curModel = m
-        sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
-        if f is None:
-            # Run forward simulation if $u$ not provided
-            f = self.fields(self.curModel)
-        u = f[self.survey.srcList, 'phi_sol']
-
-        D = self.mesh.faceDiv
-        G = self.mesh.cellGrad
-        # Derivative of model transform, $\deriv{\sigma}{\m}$
-        dsigdm_x_v = self.curModel.transformDeriv * v
-
-        # Take derivative of $C(m,u)$ w.r.t. $m$
-        dCdm_x_v = np.empty_like(u)
-        # loop over fields for each source
-        for i in range(self.survey.nSrc):
-            # Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
-            dAdsig         = D * self.dMdsig( G * u[:,i] )
-            dCdm_x_v[:, i] = dAdsig *  dsigdm_x_v
-
-        # Take derivative of $C(m,u)$ w.r.t. $u$
-        dA_du = self.A
-        # Solve for $\deriv{u}{m}$
-        # dCdu_inv = self.Solver(dCdu, **self.solverOpts)
-        if self.Ainv is None:
-            self.Ainv = self.Solver(dA_du, **self.solverOpts)
-
-        P     = self.survey.getP(self.mesh)
-        Jv    = - P * mkvc( self.Ainv * dCdm_x_v )
-        return Jv
-
-    def Jtvec(self, m, v, f=None):
-
-        self.curModel = m
-        sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
-        if f is None:
-            # Run forward simulation if $f$ not provided
-            f = self.fields(self.curModel)
-        u = f[self.survey.srcList, 'phi_sol']
-
-        shp = u.shape
-        P = self.survey.getP(self.mesh)
-        PT_x_v = (P.T*v).reshape(shp, order='F')
-
-        D = self.mesh.faceDiv
-        G = self.mesh.cellGrad
-        dA_du = self.A
-        mT_dm = self.mapping.deriv(m)
-
-        # We probably always need this due to the linesearch .. (?)
-        self.Ainv = self.Solver(dA_du.T, **self.solverOpts)
-        # if self.Ainv is None:
-        #     self.Ainv = self.Solver(dCdu, **self.solverOpts)
-
-        w = self.Ainv * PT_x_v
-
-        Jtv = 0
-        for i, ui in enumerate(u.T):  # loop over each column
-            Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
-
-        Jtv = - mT_dm.T * ( Jtv )
-        return Jtv
-
-
-
-
-

SimPEG/DCIP/BaseIP.py

@@ -1,182 +0,0 @@
-from SimPEG import *
-from BaseDC import SurveyDC, FieldsDC_CC
-
-class SurveyIP(SurveyDC):
-    """
-        **SurveyDC**
-
-        Geophysical DC resistivity data.
-
-    """
-
-    def __init__(self, srcList, **kwargs):
-        self.srcList = srcList
-        Survey.BaseSurvey.__init__(self, **kwargs)
-        self._Ps = {}
-
-    def dpred(self, m, f=None):
-        """
-            Predicted data.
-
-            .. math::
-                d_\\text{pred} = Pf(m)
-        """
-
-        return self.prob.forward(m)
-
-
-class ProblemIP(Problem.BaseProblem):
-    """
-        **ProblemIP**
-
-        Geophysical IP resistivity problem.
-
-    """
-
-    surveyPair = SurveyDC
-    Solver     = Solver
-    sigma = None
-    Ainv = None
-    u = None
-
-    def __init__(self, mesh, **kwargs):
-        Problem.BaseProblem.__init__(self, mesh)
-        self.mesh.setCellGradBC('neumann')
-        Utils.setKwargs(self, **kwargs)
-
-    # deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
-
-    @property
-    def Msig(self):
-        if getattr(self, '_Msig', None) is None:
-            # sigma = self.curModel.transform
-            sigma = self.sigma
-            Av = self.mesh.aveF2CC
-            self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
-        return self._Msig
-
-    @property
-    def dMdsig(self):
-        if getattr(self, '_dMdsig', None) is None:
-            # sigma = self.curModel.transform
-            sigma = self.sigma
-            Av = self.mesh.aveF2CC
-            dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
-            self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
-        return self._dMdsig
-
-    @property
-    def A(self):
-        """
-            Makes the matrix A(m) for the DC resistivity problem.
-
-            :param numpy.array m: model
-            :rtype: scipy.csc_matrix
-            :return: A(m)
-
-            .. math::
-                c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
-
-            Where M() is the mass matrix and mT is the model transform.
-        """
-        if getattr(self, '_A', None) is None:
-            D = self.mesh.faceDiv
-            G = self.mesh.cellGrad
-            self._A = D*self.Msig*G
-            # Remove the null space from the matrix.
-            self._A[-1,-1] /= self.mesh.vol[-1]
-            self._A = self._A.tocsc()
-        return self._A
-
-    def getRHS(self):
-        # if self.mesh not in self._rhsDict:
-        RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
-        # self._rhsDict[mesh] = RHS
-        # return self._rhsDict[mesh]
-        return RHS
-
-    def fields(self, m):
-        if self.u is None:
-	        A    = self.A
-	        if self.Ainv == None:
-	        	self.Ainv = self.Solver(A, **self.solverOpts)
-	        Q    = self.getRHS()
-	        self.u  = self.Ainv * Q
-        return self.u
-
-    def forward(self, m, u=None):
-        # Set current model; clear dependent property $\mathbf{A(m)}$
-        self.curModel = m
-        # sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
-        sigma = self.sigma
-        if self.u is None:
-            # Run forward simulation if $u$ not provided
-            u = self.fields(sigma)
-
-        shp = (self.mesh.nC, self.survey.nSrc)
-        u = self.u.reshape(shp, order='F')
-
-        D = self.mesh.faceDiv
-        G = self.mesh.cellGrad
-        # Derivative of model transform, $\deriv{\sigma}{\m}$
-        # dsigdm_x_v = self.curModel.transformDeriv * v
-
-        dsigdm_x_v = Utils.sdiag(sigma) * self.curModel.transformDeriv *  m
-
-        # Take derivative of $C(m,u)$ w.r.t. $m$
-        dCdm_x_v = np.empty_like(u)
-        # loop over fields for each source
-        for i in range(self.survey.nSrc):
-            # Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
-            dAdsig         = D * self.dMdsig( G * u[:,i] )
-            dCdm_x_v[:, i] = dAdsig *  dsigdm_x_v
-
-        # Take derivative of $C(m,u)$ w.r.t. $u$
-
-        if self.Ainv == None:
-        	self.Ainv = self.Solver(A, **self.solverOpts)
-
-        # dCdu = self.A
-        # Solve for $\deriv{u}{m}$
-        # dCdu_inv = self.Solver(dCdu, **self.solverOpts)
-        P        = self.survey.getP(self.mesh)
-        J_x_v    = - P * mkvc( self.Ainv * dCdm_x_v )
-        return -J_x_v
-
-    def Jvec(self, m, v, f=None):
-        return self.forward(v)
-
-    def Jtvec(self, m, v, f=None):
-
-        self.curModel = m
-        # sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
-        sigma = self.sigma
-        if self.u is None:
-            u = self.fields(sigma)
-        else:
-        	u = self.u
-        shp = (self.mesh.nC, self.survey.nSrc)
-        u = u.reshape(shp, order='F')
-        P = self.survey.getP(self.mesh)
-        PT_x_v = (P.T*v).reshape(shp, order='F')
-
-        D = self.mesh.faceDiv
-        G = self.mesh.cellGrad
-        A = self.A
-        mT_dm = Utils.sdiag(sigma)*self.mapping.deriv(m)
-        # mT_dm = self.mapping.deriv(m)
-
-        # dCdu = A.T
-        # Ainv = self.Solver(dCdu, **self.solverOpts)
-        # if self.Ainv == None:
-    	self.Ainv = self.Solver(A.T, **self.solverOpts)
-
-        w = self.Ainv * PT_x_v
-
-        Jtv = 0
-        for i, ui in enumerate(u.T):  # loop over each column
-            Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
-
-        Jtv = - mT_dm.T * ( Jtv )
-        return -Jtv
-

SimPEG/DCIP/Utils.py

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

SimPEG/DCIP/__init__.py

@@ -1,4 +0,0 @@
-from BaseDC import *
-from BaseIP import *
-from DCIPUtils import *
-import Utils

SimPEG/DataMisfit.py

@@ -22,11 +22,11 @@ class BaseDataMisfit(object):
         Utils.setKwargs(self,**kwargs)
 
     @Utils.timeIt
-    def eval(self, m, f=None):
-        """eval(m, f=None)
+    def eval(self, m, u=None):
+        """eval(m, u=None)
 
             :param numpy.array m: geophysical model
-            :param Fields f: fields
+            :param numpy.array u: fields
             :rtype: float
             :return: data misfit
 
@@ -34,11 +34,11 @@ class BaseDataMisfit(object):
         raise NotImplementedError('This method should be overwritten.')
 
     @Utils.timeIt
-    def evalDeriv(self, m, f=None):
-        """evalDeriv(m, f=None)
+    def evalDeriv(self, m, u=None):
+        """evalDeriv(m, u=None)
 
             :param numpy.array m: geophysical model
-            :param Fields f: fields
+            :param numpy.array u: fields
             :rtype: numpy.array
             :return: data misfit derivative
 
@@ -47,18 +47,32 @@ class BaseDataMisfit(object):
 
 
     @Utils.timeIt
-    def eval2Deriv(self, m, v, f=None):
-        """eval2Deriv(m, v, f=None)
+    def eval2Deriv(self, m, v, u=None):
+        """eval2Deriv(m, v, u=None)
 
             :param numpy.array m: geophysical model
             :param numpy.array v: vector to multiply
-            :param Fields f: fields
+            :param numpy.array u: fields
             :rtype: numpy.array
             :return: data misfit derivative
 
         """
         raise NotImplementedError('This method should be overwritten.')
 
+    # TODO: implement target misfit as a property, or possibly as an inversion directive.
+
+    # def target(self, forward):
+    #     """target(forward)
+
+    #         Target for data misfit. By default this is the number of data,
+    #         which satisfies the Discrepancy Principle.
+
+    #         :rtype: float
+    #         :return: data misfit target
+
+    #     """
+    #     prob, survey = self.splitForward(forward)
+    #     return survey.nD
 
 
 class l2_DataMisfit(BaseDataMisfit):
@@ -89,18 +103,10 @@ class l2_DataMisfit(BaseDataMisfit):
         """
 
         if getattr(self, '_Wd', None) is None:
-
+            print 'SimPEG.l2_DataMisfit is creating default weightings for Wd.'
             survey = self.survey
-
-            if getattr(survey,'std', None) is None:
-                print 'SimPEG.DataMisfit.l2_DataMisfit assigning default std of 5%'
-                survey.std = 0.05
-
-            if getattr(survey, 'eps', None) is None:
-                print 'SimPEG.DataMisfit.l2_DataMisfit assigning default eps of 1e-5 * ||dobs||'
-                survey.eps = np.linalg.norm(Utils.mkvc(survey.dobs),2)*1e-5
-
-            self._Wd = Utils.sdiag(1/(abs(survey.dobs)*survey.std+survey.eps))
+            eps = np.linalg.norm(Utils.mkvc(survey.dobs),2)*1e-5
+            self._Wd = Utils.sdiag(1/(abs(survey.dobs)*survey.std+eps))
         return self._Wd
 
     @Wd.setter
@@ -108,20 +114,24 @@ class l2_DataMisfit(BaseDataMisfit):
         self._Wd = value
 
     @Utils.timeIt
-    def eval(self, m, f=None):
-        "eval(m, f=None)"
-        if f is None: f = self.prob.fields(m)
-        R = self.Wd * self.survey.residual(m, f)
+    def eval(self, m, u=None):
+        "eval(m, u=None)"
+        prob   = self.prob
+        survey = self.survey
+        R = self.Wd * survey.residual(m, u=u)
         return 0.5*np.vdot(R, R)
 
     @Utils.timeIt
-    def evalDeriv(self, m, f=None):
-        "evalDeriv(m, f=None)"
-        if f is None: f = self.prob.fields(m)
-        return self.prob.Jtvec(m, self.Wd * (self.Wd * self.survey.residual(m, f=f)), f=f)
+    def evalDeriv(self, m, u=None):
+        "evalDeriv(m, u=None)"
+        prob   = self.prob
+        survey = self.survey
+        if u is None: u = prob.fields(m)
+        return prob.Jtvec(m, self.Wd * (self.Wd * survey.residual(m, u=u)), u=u)
 
     @Utils.timeIt
-    def eval2Deriv(self, m, v, f=None):
-        "eval2Deriv(m, v, f=None)"
-        if f is None: f = self.prob.fields(m)
-        return self.prob.Jtvec_approx(m, self.Wd * (self.Wd * self.prob.Jvec_approx(m, v, f=f)), f=f)
+    def eval2Deriv(self, m, v, u=None):
+        "eval2Deriv(m, v, u=None)"
+        prob   = self.prob
+        if u is None: u = prob.fields(m)
+        return prob.Jtvec_approx(m, self.Wd * (self.Wd * prob.Jvec_approx(m, v, u=u)), u=u)

SimPEG/Directives.py

@@ -123,10 +123,10 @@ class BetaEstimate_ByEig(InversionDirective):
         if self.debug: print 'Calculating the beta0 parameter.'
 
         m = self.invProb.curModel
-        f = self.invProb.getFields(m, store=True, deleteWarmstart=False)
+        u = self.invProb.getFields(m, store=True, deleteWarmstart=False)
 
         x0 = np.random.rand(*m.shape)
-        t = x0.dot(self.dmisfit.eval2Deriv(m,x0,f=f))
+        t = x0.dot(self.dmisfit.eval2Deriv(m,x0,u=u))
         b = x0.dot(self.reg.eval2Deriv(m, v=x0))
         self.beta0 = self.beta0_ratio*(t/b)
 
@@ -144,18 +144,12 @@ class BetaSchedule(InversionDirective):
             if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
             self.invProb.beta /= self.coolingFactor
 
-
 class TargetMisfit(InversionDirective):
 
-    chifact = 1.
-    phi_d_star = None
-
     @property
     def target(self):
         if getattr(self, '_target', None) is None:
-            if self.phi_d_star is None:
-                self.phi_d_star = 0.5 * self.survey.nD
-            self._target = self.chifact * self.phi_d_star # the factor of 0.5 is because we do phid = 0.5*|| dpred - dobs||^2
+            self._target = self.survey.nD
         return self._target
     @target.setter
     def target(self, val):
@@ -212,228 +206,17 @@ 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):
-    """SaveOutputDictEveryIteration"""
-
-    def initialize(self):
-        print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-%s.npz'"%self.fileName
-
-    def endIter(self):
-        # Save the data.
-        ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
-        phi_ms = 0.5*ms.dot(ms)
-        if self.reg.mrefInSmooth == True:
-            mref = self.reg.mref
-        else:
-            mref = 0
-        mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
-        phi_mx = 0.5 * mx.dot(mx)
-        if self.prob.mesh.dim >= 2:
-            my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_my = 0.5 * my.dot(my)
-        else:
-            phi_my = 'NaN'
-        if self.prob.mesh.dim==3:
-            mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
-            phi_mz = 0.5 * mz.dot(mz)
-        else:
-            phi_mz = 'NaN'
 
 
-        # Save the file as a npz
-        np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
 
+# class UpdateReferenceModel(Parameter):
+
+#     mref0 = None
+
+#     def nextIter(self):
 #         mref = getattr(self, 'm_prev', None)
 #         if mref is None:
 #             if self.debug: print 'UpdateReferenceModel is using mref0'
 #             mref = self.mref0
 #         self.m_prev = self.invProb.m_current
 #         return mref
-
-class Update_IRLS(InversionDirective):
-
-    eps_min = None
-    eps_p = None
-    eps_q = None
-    norms = [2.,2.,2.,2.]
-    factor = None
-    gamma = None
-    phi_m_last = None
-    phi_d_last = None
-    f_old = None
-    f_min_change = 1e-2
-    beta_tol = 5e-2
-
-    # Solving parameter for IRLS (mode:2)
-    IRLSiter   = 0
-    minGNiter = 5
-    maxIRLSiter = 10
-    iterStart = 0
-
-    # Beta schedule
-    coolingFactor = 2.
-    coolingRate = 1
-
-    mode = 1
-
-    @property
-    def target(self):
-        if getattr(self, '_target', None) is None:
-            self._target = self.survey.nD*0.5
-        return self._target
-    @target.setter
-    def target(self, val):
-        self._target = val
-
-    def initialize(self):
-
-        if self.mode == 1:
-            self.reg.norms = [2., 2., 2., 2.]
-
-    def endIter(self):
-
-        # After reaching target misfit with l2-norm, switch to IRLS (mode:2)
-        if self.invProb.phi_d < self.target and self.mode == 1:
-            print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
-
-            self.mode = 2
-            print self.eps_p, self.eps_q, self.norms
-            self.reg.eps_p = self.eps_p
-            self.reg.eps_q = self.eps_q
-            self.reg.norms = self.norms
-            self.coolingFactor = 1.
-            self.coolingRate = 1
-            self.iterStart = self.opt.iter
-            self.phi_d_last = self.invProb.phi_d
-            self.phi_m_last = self.invProb.phi_m_last
-
-            self.reg.l2model = self.invProb.curModel
-            self.reg.curModel = self.invProb.curModel
-
-            if getattr(self, 'f_old', None) is None:
-                self.f_old = self.reg.eval(self.invProb.curModel)#self.invProb.evalFunction(self.invProb.curModel, return_g=False, return_H=False)
-
-        # Beta Schedule
-        if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
-            if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
-            self.invProb.beta /= self.coolingFactor
-
-
-        # Only update after GN iterations
-        if (self.opt.iter-self.iterStart) % self.minGNiter == 0 and self.mode==2:
-
-            self.IRLSiter += 1
-
-            phim_new = self.reg.eval(self.invProb.curModel)
-            self.f_change = np.abs(self.f_old - phim_new) / self.f_old
-
-            print "Regularization decrease: %6.3e" % (self.f_change)
-
-            # Check for maximum number of IRLS cycles
-            if self.IRLSiter == self.maxIRLSiter:
-                print "Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter
-                self.opt.stopNextIteration = True
-                return
-
-            # Check if the function has changed enough
-            if self.f_change < self.f_min_change and self.IRLSiter > 1:
-                print "Minimum decrease in regularization. End of IRLS"
-                self.opt.stopNextIteration = True
-                return
-            else:
-                self.f_old = phim_new
-
-            # Cool the threshold parameter if required
-            if getattr(self, 'factor', None) is not None:
-                eps = self.reg.eps / self.factor
-
-                if getattr(self, 'eps_min', None) is not None:
-                    self.reg.eps = np.max([self.eps_min,eps])
-                else:
-                    self.reg.eps = eps
-
-            # Get phi_m at the end of current iteration
-            self.phi_m_last = self.invProb.phi_m_last
-
-            # Reset the regularization matrices so that it is
-            # recalculated for current model
-            self.reg._Wsmall = None
-            self.reg._Wx = None
-            self.reg._Wy = None
-            self.reg._Wz = None
-
-             # Update the model used for the IRLS weights
-            self.reg.curModel = self.invProb.curModel
-
-            # Temporarely set gamma to 1. to get raw phi_m
-            self.reg.gamma = 1.
-
-            # Compute new model objective function value
-            phim_new = self.reg.eval(self.invProb.curModel)
-
-            # Update gamma to scale the regularization between IRLS iterations
-            self.reg.gamma = self.phi_m_last / phim_new
-
-            # Reset the regularization matrices again for new gamma
-            self.reg._Wsmall = None
-            self.reg._Wx = None
-            self.reg._Wy = None
-            self.reg._Wz = None
-
-            # Check if misfit is within the tolerance, otherwise scale beta
-            val = self.invProb.phi_d / (self.survey.nD*0.5)
-
-            if np.abs(1.-val) > self.beta_tol:
-                self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
-
-class Update_lin_PreCond(InversionDirective):
-    """
-    Create a Jacobi preconditioner for the linear problem
-    """
-    onlyOnStart=False
-
-    def initialize(self):
-
-        if getattr(self.opt, 'approxHinv', None) is None:
-            # Update the pre-conditioner
-            diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() #* (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
-            PC     = Utils.sdiag((self.prob.mapping.deriv(None).T *diagA)**-1.)
-            self.opt.approxHinv = PC
-
-    def endIter(self):
-        # Cool the threshold parameter
-        if self.onlyOnStart==True:
-            return
-
-        if getattr(self.opt, 'approxHinv', None) is not None:
-            # Update the pre-conditioner
-            diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() #* (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
-            PC     = Utils.sdiag((self.prob.mapping.deriv(None).T *diagA)**-1.)
-            self.opt.approxHinv = PC
-
-
-class Update_Wj(InversionDirective):
-    """
-        Create approx-sensitivity base weighting using the probing method
-    """
-    k = None # Number of probing cycles
-    itr = None # Iteration number to update Wj, or always update if None
-
-    def endIter(self):
-
-        if self.itr is None or self.itr == self.opt.iter:
-
-            m = self.invProb.curModel
-            if self.k is None:
-                self.k = int(self.survey.nD/10)
-
-            def JtJv(v):
-
-                Jv = self.prob.Jvec(m, v)
-
-                return self.prob.Jtvec(m,Jv)
-
-            JtJdiag = Utils.diagEst(JtJv,len(m),k=self.k)
-            JtJdiag = JtJdiag / max(JtJdiag)
-
-            self.reg.wght = JtJdiag

SimPEG/Dispatcher.py

@@ -0,0 +1,112 @@
+### PROTOTYPE INTERFACE FOR PARALLEL DISPATCHER ###
+
+from functools import wraps
+
+def synchronize(fn):
+    @wraps(fn)
+    def wrapper(*args, **kwargs):
+        self = args[0]
+        
+        pr = isinstance(getattr(self, '_dispatcher', None), ParallelDispatcher)
+        if pr:
+            print('Parallel stuff: (start) %(prob)s.%(fn)s'%{'prob': self.__class__.__name__, 'fn': fn.__name__})
+
+        result =  fn(*args, **kwargs)
+        
+        if pr:
+            print('Parallel stuff: ( end ) %(prob)s.%(fn)s'%{'prob': self.__class__.__name__, 'fn': fn.__name__})
+        
+        return result
+
+    return wrapper
+
+
+class BaseDispatcher(object):
+    
+    def __init__(self, *args, **kwargs):
+        print('INIT: Dispatcher!')
+
+    def pair(self, problem):
+        self._prob = problem
+        print('PAIR: Dispatcher setup...')
+
+class SerialDispatcher(BaseDispatcher):
+    
+    def __init__(self, *args, **kwargs):
+        BaseDispatcher.__init__(self, *args, **kwargs)
+        print('INIT: Serial dispatcher...')
+
+class ParallelDispatcher(BaseDispatcher):
+
+    remoteOnly = ['someotherattribute']
+    
+    def __init__(self, *args, **kwargs):
+        BaseDispatcher.__init__(self, *args, **kwargs)
+        print('INIT: Parallel dispatcher...')
+
+    def pair(self, problem):
+        BaseDispatcher.pair(self, problem)
+        print('PAIR: Parallel dispatcher setup...')
+
+    def interceptSetattr(self, prob, name, value):
+        print('SET: Parallel dispatcher set %(prob)s.%(name)s = %(value)r'
+            %{'prob': prob.__class__.__name__, 'name': name, 'value': value})
+
+        if name in self.remoteOnly:
+            print('Setting remote state...')
+        else:
+            raise AttributeError('Set local copy!')
+
+    def interceptGetattr(self, prob, name):
+        print('GET: Parallel dispatcher get %(prob)s.%(name)s'
+            %{'prob': prob.__class__.__name__, 'name': name})
+
+        if name in self.remoteOnly:
+            return '***Value from remote state***'
+        else:
+            raise AttributeError('Attribute %s not in parallel namespace!'%(name,))
+
+class StandinSurvey(object):
+    
+    def pair(self, problem):
+        self._prob = problem
+        
+class StandinProblem(object):
+    
+    def __init__(self):
+        print('INIT: Problem!')
+        self._dispatcher = SerialDispatcher()
+
+    def __setattr__(self, name, value):
+        d = getattr(self, '_dispatcher', None)
+
+        if isinstance(d, ParallelDispatcher):
+            try:
+                d.interceptSetattr(self, name, value)
+            except AttributeError:
+                super(self.__class__, self).__setattr__(name, value)
+            finally:
+                return
+
+        else:
+            super(self.__class__, self).__setattr__(name, value)
+
+    def __getattr__(self, name):
+        d = super(self.__class__, self).__getattribute__('_dispatcher')
+
+        if isinstance(d, ParallelDispatcher):
+            return d.interceptGetattr(self, name)
+    
+    def pair(self, survey, dispatcher=None):
+        
+        self._survey = survey
+        self._survey.pair(self)
+        
+        if dispatcher is not None:
+            self._dispatcher = dispatcher
+            print('PAIR: Problem setup...')
+            self._dispatcher.pair(self)
+    
+    @synchronize
+    def dosomething(self):
+        print('Doing something!')

SimPEG/EM/Analytics/DC.py

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

SimPEG/EM/Analytics/FDEM.py

@@ -1,153 +0,0 @@
-from __future__ import division
-import numpy as np
-from scipy.constants import mu_0, pi
-from scipy.special import erf
-from SimPEG import Utils
-
-
-def hzAnalyticDipoleF(r, freq, sigma, secondary=True, mu=mu_0):
-    """
-    4.56 in Ward and Hohmann
-
-    .. plot::
-
-        import matplotlib.pyplot as plt
-        from SimPEG import EM
-        freq = np.logspace(-1, 6, 61)
-        test = EM.Analytics.FDEM.hzAnalyticDipoleF(100, freq, 0.001, secondary=False)
-        plt.loglog(freq, abs(test.real))
-        plt.loglog(freq, abs(test.imag))
-        plt.title('Response at $r$=100m')
-        plt.xlabel('Frequency')
-        plt.ylabel('Response')
-        plt.legend(('real','imag'))
-        plt.show()
-
-    """
-    r = np.abs(r)
-    k = np.sqrt(-1j*2.*np.pi*freq*mu*sigma)
-
-    m = 1
-    front = m / (2. * np.pi * (k**2) * (r**5) )
-    back = 9 - ( 9 + 9j * k * r - 4 * (k**2) * (r**2) - 1j * (k**3) * (r**3)) * np.exp(-1j*k*r)
-    hz = front*back
-
-    if secondary:
-        hp =-1/(4*np.pi*r**3)
-        hz = hz-hp
-
-    if hz.ndim == 1:
-        hz = Utils.mkvc(hz,2)
-
-    return hz
-
-def MagneticDipoleWholeSpace(XYZ, srcLoc, sig, f, moment=1., orientation='X', mu = mu_0):
-    """
-    Analytical solution for a dipole in a whole-space.
-
-    Equation 2.57 of Ward and Hohmann
-
-    TODOs:
-        - set it up to instead take a mesh & survey
-        - add E-fields
-        - handle multiple frequencies
-        - add divide by zero safety
-
-
-    .. plot::
-
-        from SimPEG import EM
-        import matplotlib.pyplot as plt
-        from scipy.constants import mu_0
-        freqs = np.logspace(-2,5,100)
-        Bx, By, Bz = EM.Analytics.FDEM.MagneticDipoleWholeSpace([0,100,0], [0,0,0], 1e-2, freqs, moment=1, orientation='Z')
-        plt.loglog(freqs, np.abs(Bz.real)/mu_0, 'b')
-        plt.loglog(freqs, np.abs(Bz.imag)/mu_0, 'r')
-        plt.legend(('real','imag'))
-        plt.show()
-
-
-    """
-
-    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
-
-    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 )
-    kr = k*r
-
-    front = moment / (4.*pi * r**3.) * np.exp(-1j*kr)
-    mid   = -kr**2. + 3.*1j*kr + 3.
-
-    if orientation.upper() == 'X':
-        Hx = front*( (dx/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
-        Hy = front*( (dx*dy/r**2.) * mid )
-        Hz = front*( (dx*dz/r**2.) * mid )
-
-    elif orientation.upper() == 'Y':
-        Hx = front*( (dy*dx/r**2.) * mid )
-        Hy = front*( (dy/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
-        Hz = front*( (dy*dz/r**2.) * mid )
-
-    elif orientation.upper() == 'Z':
-        Hx = front*( (dx*dz/r**2.) * mid )
-        Hy = front*( (dy*dz/r**2.) * mid )
-        Hz = front*( (dz/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
-
-    Bx = mu*Hx
-    By = mu*Hy
-    Bz = mu*Hz
-
-    if Bx.ndim is 1:
-        Bx = Utils.mkvc(Bx,2)
-
-    if By.ndim is 1:
-        By = Utils.mkvc(By,2)
-
-    if Bz.ndim is 1:
-        Bz = Utils.mkvc(Bz,2)
-
-    return Bx, By, Bz
-
-
-def ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', mu=mu_0):
-    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
-
-    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 )
-    kr = k*r
-
-    front = current * length / (4. * np.pi * sig * r**3) * np.exp(-1j*k*r)
-    mid   = -k**2 * r**2 + 3*1j*k*r + 3
-
-    # Ex = front*((dx**2 / r**2)*mid + (k**2 * r**2 -1j*k*r))
-    # Ey = front*(dx*dy  / r**2)*mid
-    # Ez = front*(dx*dz  / r**2)*mid
-
-    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
-        # return Ey, Ez, Ex

SimPEG/EM/Analytics/FDEMcasing.py

@@ -1,98 +0,0 @@
-from SimPEG import Utils, np
-from scipy.constants import mu_0, epsilon_0
-from SimPEG.EM.Utils.EMUtils import k
-
-def getKc(freq,sigma,a,b,mu=mu_0,eps=epsilon_0):
-    a = float(a)
-    b = float(b)
-    # return 1./(2*np.pi) * np.sqrt(b / a) * np.exp(-1j*k(freq,sigma,mu,eps)*(b-a))
-    return np.sqrt(b / a) * np.exp(-1j*k(freq,sigma,mu,eps)*(b-a))
-
-def _r2(xyz):
-    return np.sum(xyz**2,1)
-
-def _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    Kc1 = getKc(freq,sigma[1],a,b,mu[1],eps)
-
-    nobs = obsloc.shape[0]
-    dxyz = obsloc - np.c_[np.ones(nobs)]*np.r_[srcloc]
-
-    r2 = _r2(dxyz[:,:2])
-    sqrtr2z2 = np.sqrt(r2 + dxyz[:,2]**2)
-    k2 = k(freq,sigma[2],mu[2],eps)
-
-    return Kc1 * moment / (4.*np.pi) *np.exp(-1j*k2*sqrtr2z2) / sqrtr2z2
-
-
-def _getCasingHertzMagDipoleDeriv_r(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    HertzZ = _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-    nobs = obsloc.shape[0]
-    dxyz = obsloc - np.c_[np.ones(nobs)]*np.r_[srcloc]
-
-    r2 = _r2(dxyz[:,:2])
-    sqrtr2z2 = np.sqrt(r2 + dxyz[:,2]**2)
-    k2 = k(freq,sigma[2],mu[2],eps)
-
-    return -HertzZ * np.sqrt(r2) / sqrtr2z2 * (1j*k2 + 1./ sqrtr2z2)
-
-
-def _getCasingHertzMagDipoleDeriv_z(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    HertzZ = _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-    nobs = obsloc.shape[0]
-    dxyz = obsloc - np.c_[np.ones(nobs)]*np.r_[srcloc]
-
-    r2z2 = _r2(dxyz)
-    sqrtr2z2 = np.sqrt(r2z2)
-    k2 = k(freq,sigma[2],mu[2],eps)
-
-    return -HertzZ*dxyz[:,2] /sqrtr2z2 * (1j*k2 + 1./sqrtr2z2)
-
-def _getCasingHertzMagDipole2Deriv_z_r(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    HertzZ = _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-    dHertzZdr = _getCasingHertzMagDipoleDeriv_r(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-    nobs = obsloc.shape[0]
-    dxyz = obsloc - np.c_[np.ones(nobs)]*np.r_[srcloc]
-
-    r2 = _r2(dxyz[:,:2])
-    r = np.sqrt(r2)
-    z = dxyz[:,2]
-    sqrtr2z2 = np.sqrt(r2 + z**2)
-    k2 = k(freq,sigma[2],mu[2],eps)
-
-    return dHertzZdr*(-z/sqrtr2z2)*(1j*k2+1./sqrtr2z2) + HertzZ*(z*r/sqrtr2z2**3)*(1j*k2 + 2./sqrtr2z2)
-
-def _getCasingHertzMagDipole2Deriv_z_z(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    HertzZ = _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-    dHertzZdz = _getCasingHertzMagDipoleDeriv_z(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-    nobs = obsloc.shape[0]
-    dxyz = obsloc - np.c_[np.ones(nobs)]*np.r_[srcloc]
-
-    r2 = _r2(dxyz[:,:2])
-    r = np.sqrt(r2)
-    z = dxyz[:,2]
-    sqrtr2z2 = np.sqrt(r2 + z**2)
-    k2 = k(freq,sigma[2],mu[2],eps)
-
-    return (dHertzZdz*z + HertzZ)/sqrtr2z2*(-1j*k2 - 1./sqrtr2z2) + HertzZ*z/sqrtr2z2**3*(1j*k2*z + 2.*z/sqrtr2z2)
-
-def getCasingEphiMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    return 1j * omega(freq) * mu * _getCasingHertzMagDipoleDeriv_r(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-def getCasingHrMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    return _getCasingHertzMagDipole2Deriv_z_r(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-def getCasingHzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    d2HertzZdz2 = _getCasingHertzMagDipole2Deriv_z_z(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-    k2 = k(freq,sigma[2],mu[2],eps)
-    HertzZ = _getCasingHertzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-    return d2HertzZdz2 + k2**2 * HertzZ
-
-def getCasingBrMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    return mu_0 * getCasingHrMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)
-
-def getCasingBzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu=mu_0*np.ones(3),eps=epsilon_0,moment=1.):
-    return mu_0 * getCasingHzMagDipole(srcloc,obsloc,freq,sigma,a,b,mu,eps,moment)

SimPEG/EM/Analytics/TDEM.py

@@ -1,12 +0,0 @@
-import numpy as np
-from scipy.constants import mu_0, pi
-from scipy.special import erf
-
-def hzAnalyticDipoleT(r, t, sigma):
-    theta = np.sqrt((sigma*mu_0)/(4*t))
-    tr = theta*r
-    etr = erf(tr)
-    t1 = (9/(2*tr**2) - 1)*etr
-    t2 = (1/np.sqrt(pi))*(9/tr + 4*tr)*np.exp(-tr**2)
-    hz = (t1 - t2)/(4*pi*r**3)
-    return hz

SimPEG/EM/Analytics/__init__.py

@@ -1,4 +0,0 @@
-from TDEM import hzAnalyticDipoleT
-from FDEM import hzAnalyticDipoleF
-from FDEMcasing import *
-from DC import DCAnalyticHalf, DCAnalyticSphere

SimPEG/EM/Base.py

@@ -1,231 +0,0 @@
-from SimPEG import Survey, Problem, Utils, Models, Maps, PropMaps, np, sp, Solver as SimpegSolver
-from scipy.constants import mu_0
-
-
-class EMPropMap(Maps.PropMap):
-    """
-        Property Map for EM Problems. The electrical conductivity (\\(\\sigma\\)) is the default inversion property, and the default value of the magnetic permeability is that of free space (\\(\\mu = 4\\pi\\times 10^{-7} \\) H/m)
-    """
-
-    sigma = Maps.Property("Electrical Conductivity", defaultInvProp = True, propertyLink=('rho',Maps.ReciprocalMap))
-    mu = Maps.Property("Inverse Magnetic Permeability", defaultVal = mu_0, propertyLink=('mui',Maps.ReciprocalMap))
-
-    rho = Maps.Property("Electrical Resistivity", propertyLink=('sigma', Maps.ReciprocalMap))
-    mui = Maps.Property("Inverse Magnetic Permeability", defaultVal = 1./mu_0, propertyLink=('mu', Maps.ReciprocalMap))
-
-
-class BaseEMProblem(Problem.BaseProblem):
-
-    def __init__(self, mesh, **kwargs):
-        Problem.BaseProblem.__init__(self, mesh, **kwargs)
-
-
-    surveyPair = Survey.BaseSurvey
-    dataPair = Survey.Data
-
-    PropMap = EMPropMap
-
-    Solver = SimpegSolver
-    solverOpts = {}
-
-    verbose = False
-
-    ####################################################
-    # Make A Symmetric
-    ####################################################
-    @property
-    def _makeASymmetric(self):
-        if getattr(self, '__makeASymmetric', None) is None:
-            self.__makeASymmetric = True
-        return self.__makeASymmetric
-
-
-    ####################################################
-    # Mass Matrices
-    ####################################################
-
-    @property
-    def deleteTheseOnModelUpdate(self):
-        toDelete = []
-        if self.mapping.sigmaMap is not None or self.mapping.rhoMap is not None:
-            toDelete += ['_MeSigma', '_MeSigmaI','_MfRho','_MfRhoI']
-        if self.mapping.muMap is not None or self.mapping.muiMap is not None:
-            toDelete += ['_MeMu', '_MeMuI','_MfMui','_MfMuiI']
-        return toDelete
-
-    @property
-    def Me(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_Me', None) is None:
-            self._Me = self.mesh.getEdgeInnerProduct()
-        return self._Me
-
-    @property
-    def MeI(self):
-        """
-            Edge inner product matrix
-        """
-        if getattr(self, '_MeI', None) is None:
-            self._MeI = self.mesh.getEdgeInnerProduct(invMat=True)
-        return self._MeI
-
-    @property
-    def Mf(self):
-        """
-            Face inner product matrix
-        """
-        if getattr(self, '_Mf', None) is None:
-            self._Mf = self.mesh.getFaceInnerProduct()
-        return self._Mf
-
-    @property
-    def MfI(self):
-        """
-            Face inner product matrix
-        """
-        if getattr(self, '_MfI', None) is None:
-            self._MfI = self.mesh.getFaceInnerProduct(invMat=True)
-        return self._MfI
-
-    @property
-    def Vol(self):
-        if getattr(self, '_Vol', None) is None:
-            self._Vol = Utils.sdiag(self.mesh.vol)
-        return self._Vol
-
-    # ----- Magnetic Permeability ----- #
-    @property
-    def MfMui(self):
-        """
-            Face inner product matrix for \\(\\mu^{-1}\\). Used in the E-B formulation
-        """
-        if getattr(self, '_MfMui', None) is None:
-            self._MfMui = self.mesh.getFaceInnerProduct(self.curModel.mui)
-        return self._MfMui
-
-    @property
-    def MfMuiI(self):
-        """
-            Inverse of :code:`MfMui`.
-        """
-        if getattr(self, '_MfMuiI', None) is None:
-            self._MfMuiI = self.mesh.getFaceInnerProduct(self.curModel.mui, invMat=True)
-        return self._MfMuiI
-
-    @property
-    def MeMu(self):
-        """
-            Edge inner product matrix for \\(\\mu\\). Used in the H-J formulation
-        """
-        if getattr(self, '_MeMu', None) is None:
-            self._MeMu = self.mesh.getEdgeInnerProduct(self.curModel.mu)
-        return self._MeMu
-
-    @property
-    def MeMuI(self):
-        """
-            Inverse of :code:`MeMu`
-        """
-        if getattr(self, '_MeMuI', None) is None:
-            self._MeMuI = self.mesh.getEdgeInnerProduct(self.curModel.mu, invMat=True)
-        return self._MeMuI
-
-
-    # ----- Electrical Conductivity ----- #
-    #TODO: hardcoded to sigma as the model
-    @property
-    def MeSigma(self):
-        """
-            Edge inner product matrix for \\(\\sigma\\). Used in the E-B formulation
-        """
-        if getattr(self, '_MeSigma', None) is None:
-            self._MeSigma = self.mesh.getEdgeInnerProduct(self.curModel.sigma)
-        return self._MeSigma
-
-    # TODO: This should take a vector
-    def MeSigmaDeriv(self, u):
-        """
-            Derivative of MeSigma with respect to the model
-        """
-        return self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma)(u) * self.curModel.sigmaDeriv
-
-    @property
-    def MeSigmaI(self):
-        """
-            Inverse of the edge inner product matrix for \\(\\sigma\\).
-        """
-        if getattr(self, '_MeSigmaI', None) is None:
-            self._MeSigmaI = self.mesh.getEdgeInnerProduct(self.curModel.sigma, invMat=True)
-        return self._MeSigmaI
-
-    # TODO: This should take a vector
-    def MeSigmaIDeriv(self, u):
-        """
-            Derivative of :code:`MeSigma` with respect to the model
-        """
-        # TODO: only works for diagonal tensors. getEdgeInnerProductDeriv, invMat=True should be implemented in SimPEG
-
-        dMeSigmaI_dI = -self.MeSigmaI**2
-        dMe_dsig = self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma)(u)
-        return dMeSigmaI_dI * ( dMe_dsig * self.curModel.sigmaDeriv )
-
-    @property
-    def MfRho(self):
-        """
-            Face inner product matrix for \\(\\rho\\). Used in the H-J formulation
-        """
-        if getattr(self, '_MfRho', None) is None:
-            self._MfRho = self.mesh.getFaceInnerProduct(self.curModel.rho)
-        return self._MfRho
-
-    # TODO: This should take a vector
-    def MfRhoDeriv(self,u):
-        """
-            Derivative of :code:`MfRho` with respect to the model.
-        """
-        return self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u) * self.curModel.rhoDeriv
-
-    @property
-    def MfRhoI(self):
-        """
-            Inverse of :code:`MfRho`
-        """
-        if getattr(self, '_MfRhoI', None) is None:
-            self._MfRhoI = self.mesh.getFaceInnerProduct(self.curModel.rho, invMat=True)
-        return self._MfRhoI
-
-    # TODO: This isn't going to work yet
-    # TODO: This should take a vector
-    def MfRhoIDeriv(self,u):
-        """
-            Derivative of :code:`MfRhoI` with respect to the model.
-        """
-
-        dMfRhoI_dI = -self.MfRhoI**2
-        dMf_drho = self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u)
-        return dMfRhoI_dI * ( dMf_drho * self.curModel.rhoDeriv )
-
-class BaseEMSurvey(Survey.BaseSurvey):
-
-    def __init__(self, srcList, **kwargs):
-        # Sort these by frequency
-        self.srcList = srcList
-        Survey.BaseSurvey.__init__(self, **kwargs)
-
-    def eval(self, f):
-        """
-        Project fields to receiver locations
-        :param Fields u: fields object
-        :rtype: numpy.ndarray
-        :return: data
-        """
-        data = Survey.Data(self)
-        for src in self.srcList:
-            for rx in src.rxList:
-                data[src, rx] = rx.eval(src, self.mesh, f)
-        return data
-
-    def evalDeriv(self, f):
-        raise Exception('Use Receivers to project fields deriv.')

SimPEG/EM/FDEM/ProblemFDEM.py

@@ -1,689 +0,0 @@
-from SimPEG import Problem, Utils, np, sp, Solver as SimpegSolver
-from scipy.constants import mu_0
-from SurveyFDEM import Survey as SurveyFDEM
-from FieldsFDEM import Fields, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
-from SimPEG.EM.Base import BaseEMProblem
-from SimPEG.EM.Utils import omega
-
-
-class BaseFDEMProblem(BaseEMProblem):
-    """
-        We start by looking at Maxwell's equations in the electric
-        field \\\(\\\mathbf{e}\\\) and the magnetic flux
-        density \\\(\\\mathbf{b}\\\)
-
-        .. math ::
-
-            \mathbf{C} \mathbf{e} + i \omega \mathbf{b} = \mathbf{s_m} \\\\
-            {\mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{M_{\sigma}^e} \mathbf{e} = \mathbf{s_e}}
-
-        if using the E-B formulation (:code:`Problem3D_e`
-        or :code:`Problem3D_b`). Note that in this case, :math:`\mathbf{s_e}` is an integrated quantity.
-
-        If we write Maxwell's equations in terms of
-        \\\(\\\mathbf{h}\\\) and current density \\\(\\\mathbf{j}\\\)
-
-        .. math ::
-
-            \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{j} + i \omega \mathbf{M_{\mu}^e} \mathbf{h} = \mathbf{s_m} \\\\
-            \mathbf{C} \mathbf{h} - \mathbf{j} = \mathbf{s_e}
-
-        if using the H-J formulation (:code:`Problem3D_j` or :code:`Problem3D_h`). Note that here, :math:`\mathbf{s_m}` is an integrated quantity.
-
-        The problem performs the elimination so that we are solving the system for \\\(\\\mathbf{e},\\\mathbf{b},\\\mathbf{j} \\\) or \\\(\\\mathbf{h}\\\)
-    """
-
-    surveyPair = SurveyFDEM
-    fieldsPair = Fields
-
-    def fields(self, m):
-        """
-        Solve the forward problem for the fields.
-
-        :param numpy.array m: inversion model (nP,)
-        :rtype numpy.array:
-        :return f: forward solution
-        """
-
-        self.curModel = m
-        f = self.fieldsPair(self.mesh, self.survey)
-
-        for freq in self.survey.freqs:
-            A = self.getA(freq)
-            rhs = self.getRHS(freq)
-            Ainv = self.Solver(A, **self.solverOpts)
-            u = Ainv * rhs
-            Srcs = self.survey.getSrcByFreq(freq)
-            f[Srcs, self._solutionType] = u
-            Ainv.clean()
-        return f
-
-    def Jvec(self, m, v, f=None):
-        """
-        Sensitivity times a vector.
-
-        :param numpy.array m: inversion model (nP,)
-        :param numpy.array v: vector which we take sensitivity product with (nP,)
-        :param SimPEG.EM.FDEM.Fields u: fields object
-        :rtype numpy.array:
-        :return: Jv (ndata,)
-        """
-
-        if f is None:
-           f = self.fields(m)
-
-        self.curModel = m
-
-        Jv = self.dataPair(self.survey)
-
-        for freq in self.survey.freqs:
-            A = self.getA(freq)
-            Ainv = self.Solver(A, **self.solverOpts) # create the concept of Ainv (actually a solve)
-
-            for src in self.survey.getSrcByFreq(freq):
-                u_src = f[src, self._solutionType]
-                dA_dm_v = self.getADeriv(freq, u_src, v)
-                dRHS_dm_v = self.getRHSDeriv(freq, src, v)
-                du_dm_v = Ainv * ( - dA_dm_v + dRHS_dm_v )
-
-                for rx in src.rxList:
-                    df_dmFun = getattr(f, '_{0}Deriv'.format(rx.projField), None)
-                    df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
-                    Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
-            Ainv.clean()
-        return Utils.mkvc(Jv)
-
-    def Jtvec(self, m, v, f=None):
-        """
-        Sensitivity transpose times a vector
-
-        :param numpy.array m: inversion model (nP,)
-        :param numpy.array v: vector which we take adjoint product with (nP,)
-        :param SimPEG.EM.FDEM.Fields u: fields object
-        :rtype numpy.array:
-        :return: Jv (ndata,)
-        """
-
-        if f is None:
-            f = self.fields(m)
-
-        self.curModel = m
-
-        # Ensure v is a data object.
-        if not isinstance(v, self.dataPair):
-            v = self.dataPair(self.survey, v)
-
-        Jtv = np.zeros(m.size)
-
-        for freq in self.survey.freqs:
-            AT = self.getA(freq).T
-            ATinv = self.Solver(AT, **self.solverOpts)
-
-            for src in self.survey.getSrcByFreq(freq):
-                u_src = f[src, self._solutionType]
-
-                for rx in src.rxList:
-                    PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
-
-                    df_duTFun = getattr(f, '_{0}Deriv'.format(rx.projField), None)
-                    df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
-
-                    ATinvdf_duT = ATinv * df_duT
-
-                    dA_dmT = self.getADeriv(freq, u_src, ATinvdf_duT, adjoint=True)
-                    dRHS_dmT = self.getRHSDeriv(freq, src, ATinvdf_duT, adjoint=True)
-                    du_dmT = -dA_dmT + dRHS_dmT
-
-                    df_dmT = df_dmT + du_dmT
-
-                    # TODO: this should be taken care of by the reciever?
-                    if rx.component is 'real':
-                        Jtv +=   np.array(df_dmT, dtype=complex).real
-                    elif rx.component is 'imag':
-                        Jtv += - np.array(df_dmT, dtype=complex).real
-                    else:
-                        raise Exception('Must be real or imag')
-
-            ATinv.clean()
-
-        return Utils.mkvc(Jtv)
-
-    def getSourceTerm(self, freq):
-        """
-        Evaluates the sources for a given frequency and puts them in matrix form
-
-        :param float freq: Frequency
-        :rtype: (numpy.ndarray, numpy.ndarray)
-        :return: s_m, s_e (nE or nF, nSrc)
-        """
-        Srcs = self.survey.getSrcByFreq(freq)
-        if self._formulation is 'EB':
-            s_m = np.zeros((self.mesh.nF,len(Srcs)), dtype=complex)
-            s_e = np.zeros((self.mesh.nE,len(Srcs)), dtype=complex)
-        elif self._formulation is 'HJ':
-            s_m = np.zeros((self.mesh.nE,len(Srcs)), dtype=complex)
-            s_e = np.zeros((self.mesh.nF,len(Srcs)), dtype=complex)
-
-        for i, src in enumerate(Srcs):
-            smi, sei = src.eval(self)
-            #Why are you adding?
-            s_m[:,i] = s_m[:,i] + smi
-            s_e[:,i] = s_e[:,i] + sei
-
-        return s_m, s_e
-
-
-##########################################################################################
-################################ E-B Formulation #########################################
-##########################################################################################
-
-class Problem3D_e(BaseFDEMProblem):
-    """
-    By eliminating the magnetic flux density using
-
-        .. math ::
-
-            \mathbf{b} = \\frac{1}{i \omega}\\left(-\mathbf{C} \mathbf{e} + \mathbf{s_m}\\right)
-
-
-    we can write Maxwell's equations as a second order system in \\\(\\\mathbf{e}\\\) only:
-
-    .. math ::
-
-        \\left(\mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{C}+ i \omega \mathbf{M^e_{\sigma}} \\right)\mathbf{e} = \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f}\mathbf{s_m} -i\omega\mathbf{M^e}\mathbf{s_e}
-
-    which we solve for :math:`\mathbf{e}`.
-
-    :param SimPEG.Mesh mesh: mesh
-    """
-
-    _solutionType = 'eSolution'
-    _formulation  = 'EB'
-    fieldsPair    = Fields3D_e
-
-    def __init__(self, mesh, **kwargs):
-        BaseFDEMProblem.__init__(self, mesh, **kwargs)
-
-    def getA(self, freq):
-        """
-        System matrix
-
-        .. math ::
-            \mathbf{A} = \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{C} + i \omega \mathbf{M^e_{\sigma}}
-
-        :param float freq: Frequency
-        :rtype: scipy.sparse.csr_matrix
-        :return: A
-        """
-
-        MfMui = self.MfMui
-        MeSigma = self.MeSigma
-        C = self.mesh.edgeCurl
-
-        return C.T*MfMui*C + 1j*omega(freq)*MeSigma
-
-
-    def getADeriv(self, freq, u, v, adjoint=False):
-        """
-        Product of the derivative of our system matrix with respect to the model and a vector
-
-        .. math ::
-            \\frac{\mathbf{A}(\mathbf{m}) \mathbf{v}}{d \mathbf{m}} = i \omega \\frac{d \mathbf{M^e_{\sigma}}\mathbf{v} }{d\mathbf{m}}
-
-        :param float freq: frequency
-        :param numpy.ndarray u: solution vector (nE,)
-        :param numpy.ndarray v: vector to take prodct with (nP,) or (nD,) for adjoint
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: derivative of the system matrix times a vector (nP,) or adjoint (nD,)
-        """
-
-        dsig_dm = self.curModel.sigmaDeriv
-        dMe_dsig = self.MeSigmaDeriv(u)
-
-        if adjoint:
-            return 1j * omega(freq) * ( dMe_dsig.T * v )
-
-        return 1j * omega(freq) * ( dMe_dsig * v )
-
-    def getRHS(self, freq):
-        """
-        Right hand side for the system
-
-        .. math ::
-            \mathbf{RHS} = \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f}\mathbf{s_m} -i\omega\mathbf{M_e}\mathbf{s_e}
-
-        :param float freq: Frequency
-        :rtype: numpy.ndarray
-        :return: RHS (nE, nSrc)
-        """
-
-        s_m, s_e = self.getSourceTerm(freq)
-        C = self.mesh.edgeCurl
-        MfMui = self.MfMui
-
-        return C.T * (MfMui * s_m) -1j * omega(freq) * s_e
-
-    def getRHSDeriv(self, freq, src, v, adjoint=False):
-        """
-        Derivative of the right hand side with respect to the model
-
-        :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of rhs deriv with a vector
-        """
-
-        C = self.mesh.edgeCurl
-        MfMui = self.MfMui
-        s_mDeriv, s_eDeriv = src.evalDeriv(self, adjoint=adjoint)
-
-        if adjoint:
-            dRHS = MfMui * (C * v)
-            return s_mDeriv(dRHS) - 1j * omega(freq) * s_eDeriv(v)
-
-        else:
-            return C.T * (MfMui * s_mDeriv(v)) -1j * omega(freq) * s_eDeriv(v)
-
-
-class Problem3D_b(BaseFDEMProblem):
-    """
-    We eliminate :math:`\mathbf{e}` using
-
-    .. math ::
-
-         \mathbf{e} = \mathbf{M^e_{\sigma}}^{-1} \\left(\mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{s_e}\\right)
-
-    and solve for :math:`\mathbf{b}` using:
-
-    .. math ::
-
-        \\left(\mathbf{C} \mathbf{M^e_{\sigma}}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f}  + i \omega \\right)\mathbf{b} = \mathbf{s_m} + \mathbf{M^e_{\sigma}}^{-1}\mathbf{M^e}\mathbf{s_e}
-
-    .. note ::
-        The inverse problem will not work with full anisotropy
-
-    :param SimPEG.Mesh mesh: mesh
-    """
-
-    _solutionType = 'bSolution'
-    _formulation  = 'EB'
-    fieldsPair    = Fields3D_b
-
-    def __init__(self, mesh, **kwargs):
-        BaseFDEMProblem.__init__(self, mesh, **kwargs)
-
-    def getA(self, freq):
-        """
-        System matrix
-
-        .. math ::
-            \mathbf{A} = \mathbf{C} \mathbf{M^e_{\sigma}}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\mu^{-1}}^f}  + i \omega
-
-        :param float freq: Frequency
-        :rtype: scipy.sparse.csr_matrix
-        :return: A
-        """
-
-        MfMui = self.MfMui
-        MeSigmaI = self.MeSigmaI
-        C = self.mesh.edgeCurl
-        iomega = 1j * omega(freq) * sp.eye(self.mesh.nF)
-
-        A = C * (MeSigmaI * (C.T * MfMui)) + iomega
-
-        if self._makeASymmetric is True:
-            return MfMui.T*A
-        return A
-
-    def getADeriv(self, freq, u, v, adjoint=False):
-
-        """
-        Product of the derivative of our system matrix with respect to the model and a vector
-
-        .. math ::
-            \\frac{\mathbf{A}(\mathbf{m}) \mathbf{v}}{d \mathbf{m}} = \mathbf{C} \\frac{\mathbf{M^e_{\sigma}} \mathbf{v}}{d\mathbf{m}}
-
-        :param float freq: frequency
-        :param numpy.ndarray u: solution vector (nF,)
-        :param numpy.ndarray v: vector to take prodct with (nP,) or (nD,) for adjoint
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: derivative of the system matrix times a vector (nP,) or adjoint (nD,)
-        """
-
-        MfMui = self.MfMui
-        C = self.mesh.edgeCurl
-        MeSigmaIDeriv = self.MeSigmaIDeriv
-        vec = C.T * (MfMui * u)
-
-        MeSigmaIDeriv = MeSigmaIDeriv(vec)
-
-        if adjoint:
-            if self._makeASymmetric is True:
-                v = MfMui * v
-            return MeSigmaIDeriv.T * (C.T * v)
-
-        if self._makeASymmetric is True:
-            return MfMui.T * ( C * ( MeSigmaIDeriv * v ) )
-        return C * ( MeSigmaIDeriv * v )
-
-
-    def getRHS(self, freq):
-        """
-        Right hand side for the system
-
-        .. math ::
-            \mathbf{RHS} = \mathbf{s_m} + \mathbf{M^e_{\sigma}}^{-1}\mathbf{s_e}
-
-        :param float freq: Frequency
-        :rtype: numpy.ndarray
-        :return: RHS (nE, nSrc)
-        """
-
-        s_m, s_e = self.getSourceTerm(freq)
-        C = self.mesh.edgeCurl
-        MeSigmaI = self.MeSigmaI
-
-        RHS = s_m + C * ( MeSigmaI * s_e )
-
-        if self._makeASymmetric is True:
-            MfMui = self.MfMui
-            return MfMui.T * RHS
-
-        return RHS
-
-    def getRHSDeriv(self, freq, src, v, adjoint=False):
-        """
-        Derivative of the right hand side with respect to the model
-
-        :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of rhs deriv with a vector
-        """
-
-        C = self.mesh.edgeCurl
-        s_m, s_e = src.eval(self)
-        MfMui = self.MfMui
-
-        if self._makeASymmetric and adjoint:
-            v = self.MfMui * v
-
-        MeSigmaIDeriv = self.MeSigmaIDeriv(s_e)
-        s_mDeriv, s_eDeriv = src.evalDeriv(self, adjoint=adjoint)
-
-        if not adjoint:
-            RHSderiv = C * (MeSigmaIDeriv * v)
-            SrcDeriv = s_mDeriv(v) + C * (self.MeSigmaI * s_eDeriv(v))
-        elif adjoint:
-            RHSderiv = MeSigmaIDeriv.T * (C.T * v)
-            SrcDeriv = s_mDeriv(v) + self.MeSigmaI.T * (C.T * s_eDeriv(v))
-
-        if self._makeASymmetric is True and not adjoint:
-            return MfMui.T * (SrcDeriv + RHSderiv)
-
-        return RHSderiv + SrcDeriv
-
-
-
-##########################################################################################
-################################ H-J Formulation #########################################
-##########################################################################################
-
-
-class Problem3D_j(BaseFDEMProblem):
-    """
-    We eliminate \\\(\\\mathbf{h}\\\) using
-
-    .. math ::
-
-        \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 ::
-
-        \\left(\mathbf{C} \mathbf{M_{\mu}^e}^{-1} \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} + i \omega\\right)\mathbf{j} = \mathbf{C} \mathbf{M_{\mu}^e}^{-1} \mathbf{M^e} \mathbf{s_m} -i\omega\mathbf{s_e}
-
-    .. note::
-        This implementation does not yet work with full anisotropy!!
-
-    :param SimPEG.Mesh mesh: mesh
-    """
-
-    _solutionType = 'jSolution'
-    _formulation  = 'HJ'
-    fieldsPair    = Fields3D_j
-
-    def __init__(self, mesh, **kwargs):
-        BaseFDEMProblem.__init__(self, mesh, **kwargs)
-
-    def getA(self, freq):
-        """
-        System matrix
-
-        .. math ::
-                \\mathbf{A} = \\mathbf{C}  \\mathbf{M^e_{\\mu^{-1}}} \\mathbf{C}^{\\top} \\mathbf{M^f_{\\sigma^{-1}}}  + i\\omega
-
-        :param float freq: Frequency
-        :rtype: scipy.sparse.csr_matrix
-        :return: A
-        """
-
-        MeMuI = self.MeMuI
-        MfRho = self.MfRho
-        C = self.mesh.edgeCurl
-        iomega = 1j * omega(freq) * sp.eye(self.mesh.nF)
-
-        A = C * MeMuI * C.T * MfRho + iomega
-
-        if self._makeASymmetric is True:
-            return MfRho.T*A
-        return A
-
-
-    def getADeriv(self, freq, u, v, adjoint=False):
-        """
-        Product of the derivative of our system matrix with respect to the model and a vector
-
-        In this case, we assume that electrical conductivity, :math:`\sigma` is the physical property of interest (i.e. :math:`\sigma` = model.transform). Then we want
-
-        .. math ::
-
-            \\frac{\mathbf{A(\sigma)} \mathbf{v}}{d \mathbf{m}} = \mathbf{C}  \mathbf{M^e_{mu^{-1}}} \mathbf{C^{\\top}} \\frac{d \mathbf{M^f_{\sigma^{-1}}}\mathbf{v} }{d \mathbf{m}}
-
-        :param float freq: frequency
-        :param numpy.ndarray u: solution vector (nF,)
-        :param numpy.ndarray v: vector to take prodct with (nP,) or (nD,) for adjoint
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: derivative of the system matrix times a vector (nP,) or adjoint (nD,)
-        """
-
-        MeMuI = self.MeMuI
-        MfRho = self.MfRho
-        C = self.mesh.edgeCurl
-        MfRhoDeriv = self.MfRhoDeriv(u)
-
-        if adjoint:
-            if self._makeASymmetric is True:
-                v = MfRho * v
-            return MfRhoDeriv.T * (C * (MeMuI.T * (C.T * v)))
-
-        if self._makeASymmetric is True:
-            return MfRho.T * (C * ( MeMuI * (C.T * (MfRhoDeriv * v) )))
-        return C * (MeMuI * (C.T * (MfRhoDeriv * v)))
-
-
-    def getRHS(self, freq):
-        """
-        Right hand side for the system
-
-        .. math ::
-
-            \mathbf{RHS} = \mathbf{C} \mathbf{M_{\mu}^e}^{-1}\mathbf{s_m} -i\omega \mathbf{s_e}
-
-        :param float freq: Frequency
-        :rtype: numpy.ndarray (nE, nSrc)
-        :return: RHS
-        """
-
-        s_m, s_e = self.getSourceTerm(freq)
-        C = self.mesh.edgeCurl
-        MeMuI = self.MeMuI
-
-        RHS = C * (MeMuI * s_m) - 1j * omega(freq) * s_e
-        if self._makeASymmetric is True:
-            MfRho = self.MfRho
-            return MfRho.T*RHS
-
-        return RHS
-
-    def getRHSDeriv(self, freq, src, v, adjoint=False):
-        """
-        Derivative of the right hand side with respect to the model
-
-        :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of rhs deriv with a vector
-        """
-
-        C = self.mesh.edgeCurl
-        MeMuI = self.MeMuI
-        s_mDeriv, s_eDeriv = src.evalDeriv(self, adjoint=adjoint)
-
-        if adjoint:
-            if self._makeASymmetric:
-                MfRho = self.MfRho
-                v = MfRho*v
-            return s_mDeriv(MeMuI.T * (C.T * v)) - 1j * omega(freq) * s_eDeriv(v)
-
-        else:
-            RHSDeriv = C * (MeMuI * s_mDeriv(v)) - 1j * omega(freq) * s_eDeriv(v)
-
-            if self._makeASymmetric:
-                MfRho = self.MfRho
-                return MfRho.T * RHSDeriv
-            return RHSDeriv
-
-
-
-
-class Problem3D_h(BaseFDEMProblem):
-    """
-    We eliminate \\\(\\\mathbf{j}\\\) using
-
-    .. math ::
-
-        \mathbf{j} = \mathbf{C} \mathbf{h} - \mathbf{s_e}
-
-    and solve for \\\(\\\mathbf{h}\\\) using
-
-    .. math ::
-
-        \\left(\mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{C} + i \omega \mathbf{M_{\mu}^e}\\right) \mathbf{h} = \mathbf{M^e} \mathbf{s_m} + \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{s_e}
-
-    :param SimPEG.Mesh mesh: mesh
-    """
-
-    _solutionType = 'hSolution'
-    _formulation  = 'HJ'
-    fieldsPair    = Fields3D_h
-
-    def __init__(self, mesh, **kwargs):
-        BaseFDEMProblem.__init__(self, mesh, **kwargs)
-
-    def getA(self, freq):
-        """
-        System matrix
-
-        .. 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
-        MfRho = self.MfRho
-        C = self.mesh.edgeCurl
-
-        return C.T * (MfRho * C) + 1j*omega(freq)*MeMu
-
-    def getADeriv(self, freq, u, v, adjoint=False):
-        """
-        Product of the derivative of our system matrix with respect to the model and a vector
-
-        .. math::
-            \\frac{\mathbf{A}(\mathbf{m}) \mathbf{v}}{d \mathbf{m}} = \mathbf{C}^{\\top}\\frac{d \mathbf{M^f_{\\rho}}\mathbf{v} }{d\mathbf{m}}
-
-        :param float freq: frequency
-        :param numpy.ndarray u: solution vector (nE,)
-        :param numpy.ndarray v: vector to take prodct with (nP,) or (nD,) for adjoint
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: derivative of the system matrix times a vector (nP,) or adjoint (nD,)
-        """
-
-        MeMu = self.MeMu
-        C = self.mesh.edgeCurl
-        MfRhoDeriv = self.MfRhoDeriv(C*u)
-
-        if adjoint:
-            return MfRhoDeriv.T * (C * v)
-        return C.T * (MfRhoDeriv * v)
-
-    def getRHS(self, freq):
-        """
-        Right hand side for the system
-
-        .. math ::
-
-            \mathbf{RHS} = \mathbf{M^e} \mathbf{s_m} + \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{s_e}
-
-        :param float freq: Frequency
-        :rtype: numpy.ndarray
-        :return: RHS (nE, nSrc)
-        """
-
-        s_m, s_e = self.getSourceTerm(freq)
-        C = self.mesh.edgeCurl
-        MfRho  = self.MfRho
-
-        return s_m + C.T * ( MfRho * s_e )
-
-    def getRHSDeriv(self, freq, src, v, adjoint=False):
-        """
-        Derivative of the right hand side with respect to the model
-
-        :param float freq: frequency
-        :param SimPEG.EM.FDEM.Src src: FDEM source
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of rhs deriv with a vector
-        """
-
-        _, s_e = src.eval(self)
-        C = self.mesh.edgeCurl
-        MfRho  = self.MfRho
-
-        MfRhoDeriv = self.MfRhoDeriv(s_e)
-        if not adjoint:
-            RHSDeriv = C.T * (MfRhoDeriv * v)
-        elif adjoint:
-            RHSDeriv = MfRhoDeriv.T * (C * v)
-
-        s_mDeriv, s_eDeriv = src.evalDeriv(self, adjoint=adjoint)
-
-        return RHSDeriv + s_mDeriv(v) + C.T * (MfRho * s_eDeriv(v))
-

SimPEG/EM/FDEM/RxFDEM.py

@@ -1,139 +0,0 @@
-import SimPEG
-from SimPEG import sp
-
-class BaseRx(SimPEG.Survey.BaseRx):
-    """
-    Frequency domain receiver base class
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string orientation: receiver orientation 'x', 'y' or 'z'
-    :param string component: real or imaginary component 'real' or 'imag'
-    """
-
-    def __init__(self, locs, orientation=None, component=None):
-        assert(orientation in ['x','y','z']), "Orientation %s not known. Orientation must be in 'x', 'y', 'z'. Arbitrary orientations have not yet been implemented."%orientation
-        assert(component in ['real', 'imag']), "'component' must be 'real' or 'imag', not %s"%component
-
-        self.projComp = orientation
-        self.component = component
-
-        SimPEG.Survey.BaseRx.__init__(self, locs, rxType=None) #TODO: remove rxType from baseRx
-
-    def projGLoc(self, u):
-        """Grid Location projection (e.g. Ex Fy ...)"""
-        return u._GLoc(self.projField) + self.projComp
-
-    def eval(self, src, mesh, f):
-        """
-        Project fields to recievers to get data.
-
-        :param Source src: FDEM source
-        :param Mesh mesh: mesh used
-        :param Fields f: fields object
-        :rtype: numpy.ndarray
-        :return: fields projected to recievers
-        """
-
-        P = self.getP(mesh, self.projGLoc(f))
-        f_part_complex = f[src, self.projField]
-        f_part = getattr(f_part_complex, self.component) # get the real or imag component
-
-        return P*f_part
-
-    def evalDeriv(self, src, mesh, f, v, adjoint=False):
-        """
-        Derivative of projected fields with respect to the inversion model times a vector.
-
-        :param Source src: FDEM source
-        :param Mesh mesh: mesh used
-        :param Fields f: fields object
-        :param numpy.ndarray v: vector to multiply
-        :rtype: numpy.ndarray
-        :return: fields projected to recievers
-        """
-
-        P = self.getP(mesh, self.projGLoc(f))
-
-        if not adjoint:
-            Pv_complex = P * v
-            Pv = getattr(Pv_complex, self.component)
-        elif adjoint:
-            Pv_real = P.T * v
-
-            if self.component == 'imag':
-                Pv = 1j*Pv_real
-            elif self.component == 'real':
-                Pv = Pv_real.astype(complex)
-            else:
-                raise NotImplementedError('must be real or imag')
-
-        return Pv
-
-
-class Point_e(BaseRx):
-    """
-    Electric field FDEM receiver
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string orientation: receiver orientation 'x', 'y' or 'z'
-    :param string component: real or imaginary component 'real' or 'imag'
-    """
-
-    def __init__(self, locs, orientation=None, component=None):
-        self.projField = 'e'
-        super(Point_e, self).__init__(locs, orientation, component)
-
-
-class Point_b(BaseRx):
-    """
-    Magnetic flux FDEM receiver
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string orientation: receiver orientation 'x', 'y' or 'z'
-    :param string component: real or imaginary component 'real' or 'imag'
-    """
-
-    def __init__(self, locs, orientation=None, component=None):
-        self.projField = 'b'
-        super(Point_b, self).__init__(locs, orientation, component)
-
-class Point_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):
-    """
-    Magnetic field FDEM receiver
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string orientation: receiver orientation 'x', 'y' or 'z'
-    :param string component: real or imaginary component 'real' or 'imag'
-    """
-
-    def __init__(self, locs, orientation=None, component=None):
-        self.projField = 'h'
-        super(Point_h, self).__init__(locs, orientation, component)
-
-
-class Point_j(BaseRx):
-    """
-    Current density FDEM receiver
-
-    :param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
-    :param string orientation: receiver orientation 'x', 'y' or 'z'
-    :param string component: real or imaginary component 'real' or 'imag'
-    """
-
-    def __init__(self, locs, orientation=None, component=None):
-        self.projField = 'j'
-        super(Point_j, self).__init__(locs, orientation, component)

SimPEG/EM/FDEM/SrcFDEM.py

@@ -1,618 +0,0 @@
-from SimPEG import Survey, Problem, Utils, np, sp
-from scipy.constants import mu_0
-from SimPEG.EM.Utils import *
-from SimPEG.Utils import Zero
-
-class BaseSrc(Survey.BaseSrc):
-    """
-    Base source class for FDEM Survey
-    """
-
-    freq = None
-    integrate = False
-    _ePrimary = None
-    _bPrimary = None
-    _hPrimary = None
-    _jPrimary = None
-
-    def __init__(self, rxList, **kwargs):
-        Survey.BaseSrc.__init__(self, rxList, **kwargs)
-
-    def eval(self, prob):
-        """
-        - :math:`s_m` : magnetic source term
-        - :math:`s_e` : electric source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: (numpy.ndarray, numpy.ndarray)
-        :return: tuple with magnetic source term and electric source term
-        """
-        s_m = self.s_m(prob)
-        s_e = self.s_e(prob)
-        return s_m, s_e
-
-    def evalDeriv(self, prob, v=None, adjoint=False):
-        """
-        Derivatives of the source terms with respect to the inversion model
-        - :code:`s_mDeriv` : derivative of the magnetic source term
-        - :code:`s_eDeriv` : derivative of the electric source term
-
-        :param Problem prob: FDEM Problem
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: (numpy.ndarray, numpy.ndarray)
-        :return: tuple with magnetic source term and electric source term derivatives times a vector
-        """
-        if v is not None:
-            return self.s_mDeriv(prob, v, adjoint), self.s_eDeriv(prob, v, adjoint)
-        else:
-            return lambda v: self.s_mDeriv(prob, v, adjoint), lambda v: self.s_eDeriv(prob, v, adjoint)
-
-    def bPrimary(self, prob):
-        """
-        Primary magnetic flux density
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic flux density
-        """
-        if self._bPrimary is None:
-            return Zero()
-        return self._bPrimary
-
-    def hPrimary(self, prob):
-        """
-        Primary magnetic field
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        if self._hPrimary is None:
-            return Zero()
-        return self._hPrimary
-
-    def ePrimary(self, prob):
-        """
-        Primary electric field
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: primary electric field
-        """
-        if self._ePrimary is None:
-            return Zero()
-        return self._ePrimary
-
-    def jPrimary(self, prob):
-        """
-        Primary current density
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: primary current density
-        """
-        if self._jPrimary is None:
-            return Zero()
-        return self._jPrimary
-
-    def s_m(self, prob):
-        """
-        Magnetic source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: magnetic source term on mesh
-        """
-        return Zero()
-
-    def s_e(self, prob):
-        """
-        Electric source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: electric source term on mesh
-        """
-        return Zero()
-
-    def s_mDeriv(self, prob, v, adjoint = False):
-        """
-        Derivative of magnetic source term with respect to the inversion model
-
-        :param Problem prob: FDEM Problem
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of magnetic source term derivative with a vector
-        """
-
-        return Zero()
-
-    def s_eDeriv(self, prob, v, adjoint = False):
-        """
-        Derivative of electric source term with respect to the inversion model
-
-        :param Problem prob: FDEM Problem
-        :param numpy.ndarray v: vector to take product with
-        :param bool adjoint: adjoint?
-        :rtype: numpy.ndarray
-        :return: product of electric source term derivative with a vector
-        """
-        return Zero()
-
-
-class RawVec_e(BaseSrc):
-    """
-    RawVec electric source. It is defined by the user provided vector s_e
-
-    :param list rxList: receiver list
-    :param float freq: frequency
-    :param numpy.array s_e: electric source term
-    :param bool integrate: Integrate the source term (multiply by Me) [False]
-    """
-
-    def __init__(self, rxList, freq, s_e, **kwargs):
-        self._s_e = np.array(s_e, dtype=complex)
-        self.freq = float(freq)
-
-        BaseSrc.__init__(self, rxList, **kwargs)
-
-    def s_e(self, prob):
-        """
-        Electric source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: electric source term on mesh
-        """
-        if prob._formulation is 'EB' and self.integrate is True:
-            return prob.Me * self._s_e
-        return self._s_e
-
-
-class RawVec_m(BaseSrc):
-    """
-    RawVec magnetic source. It is defined by the user provided vector s_m
-
-    :param float freq: frequency
-    :param rxList: receiver list
-    :param numpy.array s_m: magnetic source term
-    :param bool integrate: Integrate the source term (multiply by Me) [False]
-    """
-
-    def __init__(self, rxList, freq, s_m, **kwargs):  #ePrimary=Zero(), bPrimary=Zero(), hPrimary=Zero(), jPrimary=Zero()):
-        self._s_m = np.array(s_m, dtype=complex)
-        self.freq = float(freq)
-
-        BaseSrc.__init__(self, rxList, **kwargs)
-
-    def s_m(self, prob):
-        """
-        Magnetic source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: magnetic source term on mesh
-        """
-        if prob._formulation is 'HJ' and self.integrate is True:
-            return prob.Me * self._s_m
-        return self._s_m
-
-
-class RawVec(BaseSrc):
-    """
-    RawVec source. It is defined by the user provided vectors s_m, s_e
-
-    :param rxList: receiver list
-    :param float freq: frequency
-    :param numpy.array s_m: magnetic source term
-    :param numpy.array s_e: electric source term
-    :param bool integrate: Integrate the source term (multiply by Me) [False]
-    """
-    def __init__(self, rxList, freq, s_m, s_e, **kwargs):
-        self._s_m = np.array(s_m, dtype=complex)
-        self._s_e = np.array(s_e, dtype=complex)
-        self.freq = float(freq)
-        BaseSrc.__init__(self, rxList, **kwargs)
-
-    def s_m(self, prob):
-        """
-        Magnetic source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: magnetic source term on mesh
-        """
-        if prob._formulation is 'HJ' and self.integrate is True:
-            return prob.Me * self._s_m
-        return self._s_m
-
-    def s_e(self, prob):
-        """
-        Electric source term
-
-        :param Problem prob: FDEM Problem
-        :rtype: numpy.ndarray
-        :return: electric source term on mesh
-        """
-        if prob._formulation is 'EB' and self.integrate is True:
-            return prob.Me * self._s_e
-        return self._s_e
-
-
-class MagDipole(BaseSrc):
-    """
-    Point magnetic dipole source calculated by taking the curl of a magnetic
-    vector potential. By taking the discrete curl, we ensure that the magnetic
-    flux density is divergence free (no magnetic monopoles!).
-
-    This approach uses a primary-secondary in frequency. Here we show the
-    derivation for E-B formulation noting that similar steps are followed for
-    the H-J formulation.
-
-    .. math::
-        \mathbf{C} \mathbf{e} + i \omega \mathbf{b} = \mathbf{s_m} \\\\
-            {\mathbf{C}^T \mathbf{M_{\mu^{-1}}^f} \mathbf{b} - \mathbf{M_{\sigma}^e} \mathbf{e} = \mathbf{s_e}}
-
-    We split up the fields and :math:`\mu^{-1}` into primary (:math:`\mathbf{P}`) and secondary (:math:`\mathbf{S}`) components
-
-    - :math:`\mathbf{e} = \mathbf{e^P} + \mathbf{e^S}`
-    - :math:`\mathbf{b} = \mathbf{b^P} + \mathbf{b^S}`
-    - :math:`\\boldsymbol{\mu}^{\mathbf{-1}} = \\boldsymbol{\mu}^{\mathbf{-1}^\mathbf{P}} + \\boldsymbol{\mu}^{\mathbf{-1}^\mathbf{S}}`
-
-    and define a zero-frequency primary problem, noting that the source is
-    generated by a divergence free electric current
-
-    .. math::
-        \mathbf{C} \mathbf{e^P} = \mathbf{s_m^P} = 0 \\\\
-            {\mathbf{C}^T \mathbf{{M_{\mu^{-1}}^f}^P} \mathbf{b^P} - \mathbf{M_{\sigma}^e} \mathbf{e^P} = \mathbf{M^e} \mathbf{s_e^P}}
-
-    Since :math:`\mathbf{e^P}` is curl-free, divergence-free, we assume that there is no constant field background, the :math:`\mathbf{e^P} = 0`, so our primary problem is
-
-    .. math::
-        \mathbf{e^P} =  0 \\\\
-            {\mathbf{C}^T \mathbf{{M_{\mu^{-1}}^f}^P} \mathbf{b^P} = \mathbf{s_e^P}}
-
-    Our secondary problem is then
-
-    .. math::
-        \mathbf{C} \mathbf{e^S} + i \omega \mathbf{b^S} = - i \omega \mathbf{b^P} \\\\
-            {\mathbf{C}^T \mathbf{M_{\mu^{-1}}^f} \mathbf{b^S} - \mathbf{M_{\sigma}^e} \mathbf{e^S} = -\mathbf{C}^T \mathbf{{M_{\mu^{-1}}^f}^S} \mathbf{b^P}}
-
-    :param list rxList: receiver list
-    :param float freq: frequency
-    :param numpy.ndarray loc: source location (ie: :code:`np.r_[xloc,yloc,zloc]`)
-    :param string orientation: 'X', 'Y', 'Z'
-    :param float moment: magnetic dipole moment
-    :param float mu: background magnetic permeability
-    """
-
-    def __init__(self, rxList, freq, loc, orientation='Z', moment=1., mu=mu_0, **kwargs):
-        self.freq = float(freq)
-        self.loc = loc
-        self.orientation = orientation
-        assert orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
-        self.moment = moment
-        self.mu = mu
-        BaseSrc.__init__(self, rxList)
-
-    def bPrimary(self, prob):
-        """
-        The primary magnetic flux density from a magnetic vector potential
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        formulation = prob._formulation
-
-        if formulation is 'EB':
-            gridX = prob.mesh.gridEx
-            gridY = prob.mesh.gridEy
-            gridZ = prob.mesh.gridEz
-            C = prob.mesh.edgeCurl
-
-        elif formulation is 'HJ':
-            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:
-                # TODO ?
-                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 hPrimary(self, prob):
-        """
-        The primary magnetic field from a magnetic vector potential
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        b = self.bPrimary(prob)
-        return 1./self.mu * b
-
-    def s_m(self, prob):
-        """
-        The magnetic source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-
-        b_p = self.bPrimary(prob)
-        if prob._formulation is 'HJ':
-            b_p = prob.Me * b_p
-        return -1j*omega(self.freq)*b_p
-
-    def s_e(self, prob):
-        """
-        The electric source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-
-        if all(np.r_[self.mu] == np.r_[prob.curModel.mu]):
-            return Zero()
-        else:
-            formulation = prob._formulation
-
-            if formulation is 'EB':
-                mui_s = prob.curModel.mui - 1./self.mu
-                MMui_s = prob.mesh.getFaceInnerProduct(mui_s)
-                C = prob.mesh.edgeCurl
-            elif formulation is 'HJ':
-                mu_s = prob.curModel.mu - self.mu
-                MMui_s = prob.mesh.getEdgeInnerProduct(mu_s, invMat=True)
-                C = prob.mesh.edgeCurl.T
-
-            return -C.T * (MMui_s * self.bPrimary(prob))
-
-
-class MagDipole_Bfield(BaseSrc):
-
-    """
-    Point magnetic dipole source calculated with the analytic solution for the
-    fields from a magnetic dipole. No discrete curl is taken, so the magnetic
-    flux density may not be strictly divergence free.
-
-    This approach uses a primary-secondary in frequency in the same fashion as the MagDipole.
-
-    :param list rxList: receiver list
-    :param float freq: frequency
-    :param numpy.ndarray loc: source location (ie: :code:`np.r_[xloc,yloc,zloc]`)
-    :param string orientation: 'X', 'Y', 'Z'
-    :param float moment: magnetic dipole moment
-    :param float mu: background magnetic permeability
-    """
-
-    def __init__(self, rxList, freq, loc, orientation='Z', moment=1., mu = mu_0):
-        self.freq = float(freq)
-        self.loc = loc
-        assert orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
-        self.orientation = orientation
-        self.moment = moment
-        self.mu = mu
-        BaseSrc.__init__(self, rxList)
-
-    def bPrimary(self, prob):
-        """
-        The primary magnetic flux density from the analytic solution for magnetic fields from a dipole
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-
-        formulation = prob._formulation
-
-        if formulation is 'EB':
-            gridX = prob.mesh.gridFx
-            gridY = prob.mesh.gridFy
-            gridZ = prob.mesh.gridFz
-            C = prob.mesh.edgeCurl
-
-        elif formulation is 'HJ':
-            gridX = prob.mesh.gridEx
-            gridY = prob.mesh.gridEy
-            gridZ = prob.mesh.gridEz
-            C = prob.mesh.edgeCurl.T
-
-        srcfct = MagneticDipoleFields
-        if prob.mesh._meshType is 'CYL':
-            if not prob.mesh.isSymmetric:
-                # TODO ?
-                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            bx = srcfct(self.loc, gridX, 'x', mu=self.mu, moment=self.moment)
-            bz = srcfct(self.loc, gridZ, 'z', mu=self.mu, moment=self.moment)
-            b = np.concatenate((bx,bz))
-        else:
-            bx = srcfct(self.loc, gridX, 'x', mu=self.mu, moment=self.moment)
-            by = srcfct(self.loc, gridY, 'y', mu=self.mu, moment=self.moment)
-            bz = srcfct(self.loc, gridZ, 'z', mu=self.mu, moment=self.moment)
-            b = np.concatenate((bx,by,bz))
-
-        return b
-
-    def hPrimary(self, prob):
-        """
-        The primary magnetic field from a magnetic vector potential
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        b = self.bPrimary(prob)
-        return 1/self.mu * b
-
-    def s_m(self, prob):
-        """
-        The magnetic source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        b = self.bPrimary(prob)
-        if prob._formulation is 'HJ':
-            b = prob.Me * b
-        return -1j*omega(self.freq)*b
-
-    def s_e(self, prob):
-        """
-        The electric source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        if all(np.r_[self.mu] == np.r_[prob.curModel.mu]):
-            return Zero()
-        else:
-            formulation = prob._formulation
-
-            if formulation is 'EB':
-                mui_s = prob.curModel.mui - 1./self.mu
-                MMui_s = prob.mesh.getFaceInnerProduct(mui_s)
-                C = prob.mesh.edgeCurl
-            elif formulation is 'HJ':
-                mu_s = prob.curModel.mu - self.mu
-                MMui_s = prob.mesh.getEdgeInnerProduct(mu_s, invMat=True)
-                C = prob.mesh.edgeCurl.T
-
-            return -C.T * (MMui_s * self.bPrimary(prob))
-
-
-class CircularLoop(BaseSrc):
-    """
-    Circular loop magnetic source calculated by taking the curl of a magnetic
-    vector potential. By taking the discrete curl, we ensure that the magnetic
-    flux density is divergence free (no magnetic monopoles!).
-
-    This approach uses a primary-secondary in frequency in the same fashion as the MagDipole.
-
-    :param list rxList: receiver list
-    :param float freq: frequency
-    :param numpy.ndarray loc: source location (ie: :code:`np.r_[xloc,yloc,zloc]`)
-    :param string orientation: 'X', 'Y', 'Z'
-    :param float moment: magnetic dipole moment
-    :param float mu: background magnetic permeability
-    """
-
-    def __init__(self, rxList, freq, loc, orientation='Z', radius=1., mu=mu_0):
-        self.freq = float(freq)
-        self.orientation = orientation
-        assert orientation in ['X','Y','Z'], "Orientation (right now) doesn't actually do anything! The methods in SrcUtils should take care of this..."
-        self.radius = radius
-        self.mu = mu
-        self.loc = loc
-        self.integrate = False
-        BaseSrc.__init__(self, rxList)
-
-    def bPrimary(self, prob):
-        """
-        The primary magnetic flux density from a magnetic vector potential
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        formulation = prob._formulation
-
-        if formulation is 'EB':
-            gridX = prob.mesh.gridEx
-            gridY = prob.mesh.gridEy
-            gridZ = prob.mesh.gridEz
-            C = prob.mesh.edgeCurl
-
-        elif formulation is 'HJ':
-            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:
-                # TODO ?
-                raise NotImplementedError('Non-symmetric cyl mesh not implemented yet!')
-            a = MagneticLoopVectorPotential(self.loc, gridY, 'y', moment=self.radius, mu=self.mu)
-
-        else:
-            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)
-            a = np.concatenate((ax, ay, az))
-
-        return C*a
-
-    def hPrimary(self, prob):
-        """
-        The primary magnetic field from a magnetic vector potential
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        b = self.bPrimary(prob)
-        return 1./self.mu*b
-
-    def s_m(self, prob):
-        """
-        The magnetic source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        b = self.bPrimary(prob)
-        if prob._formulation is 'HJ':
-            b =  prob.Me *  b
-        return -1j*omega(self.freq)*b
-
-    def s_e(self, prob):
-        """
-        The electric source term
-
-        :param Problem prob: FDEM problem
-        :rtype: numpy.ndarray
-        :return: primary magnetic field
-        """
-        if all(np.r_[self.mu] == np.r_[prob.curModel.mu]):
-            return Zero()
-        else:
-            formulation = prob._formulation
-
-            if formulation is 'EB':
-                mui_s = prob.curModel.mui - 1./self.mu
-                MMui_s = prob.mesh.getFaceInnerProduct(mui_s)
-                C = prob.mesh.edgeCurl
-
-
-            elif formulation is 'HJ':
-                mu_s = prob.curModel.mu - self.mu
-                MMui_s = prob.mesh.getEdgeInnerProduct(mu_s, invMat=True)
-                C = prob.mesh.edgeCurl.T
-
-            return -C.T * (MMui_s * self.bPrimary(prob))
-
-
-
-

SimPEG/EM/FDEM/SurveyFDEM.py

@@ -1,62 +0,0 @@
-import SimPEG
-from SimPEG.EM.Utils import *
-from SimPEG.EM.Base import BaseEMSurvey
-from scipy.constants import mu_0
-from SimPEG.Utils import Zero, Identity
-import SrcFDEM as Src
-import RxFDEM as Rx
-from SimPEG import sp
-
-class Survey(BaseEMSurvey):
-    """
-    Frequency domain electromagnetic survey
-
-    :param list srcList: list of FDEM sources used in the survey
-    """
-
-    srcPair = Src.BaseSrc
-    rxPair = Rx.BaseRx
-
-    def __init__(self, srcList, **kwargs):
-        # Sort these by frequency
-        self.srcList = srcList
-        BaseEMSurvey.__init__(self, srcList, **kwargs)
-
-        _freqDict = {}
-        for src in srcList:
-            if src.freq not in _freqDict:
-                _freqDict[src.freq] = []
-            _freqDict[src.freq] += [src]
-
-        self._freqDict = _freqDict
-        self._freqs = sorted([f for f in self._freqDict])
-
-    @property
-    def freqs(self):
-        """Frequencies"""
-        return self._freqs
-
-    @property
-    def nFreq(self):
-        """Number of frequencies"""
-        return len(self._freqDict)
-
-    @property
-    def nSrcByFreq(self):
-        """Number of sources at each frequency"""
-        if getattr(self, '_nSrcByFreq', None) is None:
-            self._nSrcByFreq = {}
-            for freq in self.freqs:
-                self._nSrcByFreq[freq] = len(self.getSrcByFreq(freq))
-        return self._nSrcByFreq
-
-    def getSrcByFreq(self, freq):
-        """
-        Returns the sources associated with a specific frequency.
-        :param float freq: frequency for which we look up sources
-        :rtype: dictionary
-        :return: sources at the sepcified frequency
-        """
-        assert freq in self._freqDict, "The requested frequency is not in this survey."
-        return self._freqDict[freq]
-

SimPEG/EM/FDEM/__init__.py

@@ -1,5 +0,0 @@
-from SurveyFDEM import Survey
-import SrcFDEM as Src
-import RxFDEM as Rx
-from ProblemFDEM import Problem3D_e, Problem3D_b, Problem3D_j, Problem3D_h
-from FieldsFDEM import Fields3D_e, Fields3D_b, Fields3D_j, Fields3D_h

SimPEG/EM/Static/DC/BoundaryUtils.py

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

SimPEG/EM/Static/DC/FieldsDC.py

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

SimPEG/EM/Static/DC/FieldsDC_2D.py

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

SimPEG/EM/Static/DC/ProblemDC.py

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

SimPEG/EM/Static/DC/ProblemDC_2D.py

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

SimPEG/EM/Static/DC/RxDC.py

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

SimPEG/EM/Static/DC/SrcDC.py

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

SimPEG/EM/Static/DC/SurveyDC.py

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

SimPEG/EM/Static/DC/Utils.py

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

SimPEG/EM/Static/DC/__init__.py

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

SimPEG/EM/Static/IP/ProblemIP.py

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

SimPEG/EM/Static/IP/SurveyIP.py

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

SimPEG/EM/Static/IP/__init__.py

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

SimPEG/EM/Static/SIP/ProblemSIP.py

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

SimPEG/EM/Static/SIP/Regularization.py

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

SimPEG/EM/Static/SIP/RxSIP.py

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

SimPEG/EM/Static/SIP/SrcSIP.py

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

SimPEG/EM/Static/SIP/SurveySIP.py

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

SimPEG/EM/Static/SIP/__init__.py

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

SimPEG/EM/Static/Utils/StaticUtils.py

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

SimPEG/EM/Static/Utils/__init__.py

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

SimPEG/EM/Static/__init__.py

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

SimPEG/EM/TDEM/FieldsTDEM.py

@@ -1,142 +0,0 @@
-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

@@ -1,246 +0,0 @@
-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

@@ -1,121 +0,0 @@
-import SimPEG
-from SimPEG import np, Utils
-from SimPEG.Utils import Zero, Identity
-from scipy.constants import mu_0
-from SimPEG.EM.Utils import *
-import SrcTDEM as Src
-
-
-####################################################
-# Receivers
-####################################################
-
-class Rx(SimPEG.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):
-        SimPEG.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, v, adjoint=False):
-        P = self.getP(mesh, timeMesh)
-        if not adjoint:
-            return P * v #Utils.mkvc(v[src, self.projField+'Deriv', :])
-        elif adjoint:
-            # 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')
-
-
-####################################################
-# Survey
-####################################################
-
-class Survey(SimPEG.Survey.BaseSurvey):
-    """
-    Time domain electromagnetic survey
-    """
-
-    srcPair = Src.BaseSrc
-    rxPair = Rx
-
-    def __init__(self, srcList, **kwargs):
-        # Sort these by frequency
-        self.srcList = srcList
-        SimPEG.Survey.BaseSurvey.__init__(self, **kwargs)
-
-    def eval(self, u):
-        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):
-        raise Exception('Use Receivers to project fields deriv.')
-        # assert v is not None, 'v to multiply must be provided.'
-
-        # 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

@@ -1,553 +0,0 @@
-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 +0,0 @@
-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

@@ -1,164 +0,0 @@
-from SimPEG import Solver, Problem
-from SimPEG.Problem import BaseTimeProblem
-from SimPEG.EM.Utils import *
-from scipy.constants import mu_0
-from SimPEG.Utils import sdiag, mkvc
-from SimPEG import Utils, Mesh
-from SimPEG.EM.Base import BaseEMProblem
-import numpy as np
-
-
-class FieldsTDEM(Problem.TimeFields):
-    """Fancy Field Storage for a TDEM survey."""
-    knownFields = {'b': 'F', 'e': 'E'}
-
-    def tovec(self):
-        nSrc, nF, nE = self.survey.nSrc, self.mesh.nF, self.mesh.nE
-        u = np.empty((0,nSrc)) #((0,1) if nSrc == 1 else (0, nSrc))
-
-        for i in range(self.survey.prob.nT):
-            if 'b' in self:
-                b = self[:,'b',i+1]
-            else:
-                b = np.zeros((nF,nSrc)) # if nSrc == 1 else (nF, nSrc))
-
-            if 'e' in self:
-                e = self[:,'e',i+1]
-            else:
-                e = np.zeros((nE,nSrc)) # if nSrc == 1 else (nE, nSrc))
-            u = np.concatenate((u, b, e))
-
-        return Utils.mkvc(u,nSrc)
-
-
-class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
-    """docstring for BaseTDEMProblem"""
-    def __init__(self, mesh, mapping=None, **kwargs):
-        BaseTimeProblem.__init__(self, mesh, mapping=mapping, **kwargs)
-
-    _FieldsForward_pair = FieldsTDEM  #: used for the forward calculation only
-
-    waveformType = "STEPOFF"
-    current = None
-
-    def currentwaveform(self, wave):
-        self._timeSteps = np.diff(wave[:,0])
-        self.current = wave[:,1]
-        self.waveformType = "GENERAL"
-
-    def fields(self, m):
-        if self.verbose: print '%s\nCalculating fields(m)\n%s'%('*'*50,'*'*50)
-        self.curModel = m
-        # Create a fields storage object
-        F = self._FieldsForward_pair(self.mesh, self.survey)
-        for src in self.survey.srcList:
-            # Set the initial conditions
-            F[src,:,0] = src.getInitialFields(self.mesh)
-        F = self.forward(m, self.getRHS, F=F)
-        if self.verbose: print '%s\nDone calculating fields(m)\n%s'%('*'*50,'*'*50)
-        return F
-
-    def forward(self, m, RHS, F=None):
-        self.curModel = m
-        F = F or FieldsTDEM(self.mesh, self.survey)
-
-        dtFact = None
-        Ainv   = None
-        for tInd, dt in enumerate(self.timeSteps):
-            if dt != dtFact:
-                dtFact = dt
-                if Ainv is not None:
-                    Ainv.clean()
-                A = self.getA(tInd)
-                if self.verbose: print 'Factoring...   (dt = %e)'%dt
-                Ainv = self.Solver(A, **self.solverOpts)
-                if self.verbose: print 'Done'
-            rhs = RHS(tInd, F)
-            if self.verbose: print '    Solving...   (tInd = %d)'%tInd
-            sol = Ainv * rhs
-            if self.verbose: print '    Done...'
-            if sol.ndim == 1:
-                sol.shape = (sol.size,1)
-            F[:,self.solType,tInd+1] = sol
-        Ainv.clean()
-        return F
-
-    def adjoint(self, m, RHS, F=None):
-        self.curModel = m
-        F = F or FieldsTDEM(self.mesh, self.survey)
-
-        dtFact = None
-        Ainv   = None
-        for tInd, dt in reversed(list(enumerate(self.timeSteps))):
-            if dt != dtFact:
-                dtFact = dt
-                if Ainv is not None:
-                    Ainv.clean()
-                A = self.getA(tInd)
-                if self.verbose: print 'Factoring (Adjoint)...   (dt = %e)'%dt
-                Ainv = self.Solver(A, **self.solverOpts)
-                if self.verbose: print 'Done'
-            rhs = RHS(tInd, F)
-            if self.verbose: print '    Solving (Adjoint)...   (tInd = %d)'%tInd
-            sol = Ainv * rhs
-            if self.verbose: print '    Done...'
-            if sol.ndim == 1:
-                sol.shape = (sol.size,1)
-            F[:,self.solType,tInd+1] = sol
-        Ainv.clean()
-        return F
-
-    def Jvec(self, m, v, f=None):
-        """
-            :param numpy.array m: Conductivity model
-            :param numpy.ndarray v: vector (model object)
-            :param simpegEM.TDEM.FieldsTDEM f: Fields resulting from m
-            :rtype: numpy.ndarray
-            :return: w (data object)
-
-            Multiplying \\\(\\\mathbf{J}\\\) onto a vector can be broken into three steps
-
-            * Compute \\\(\\\\vec{p} = \\\mathbf{G}v\\\)
-            * Solve \\\(\\\hat{\\\mathbf{A}} \\\\vec{y} = \\\\vec{p}\\\)
-            * Compute \\\(\\\\vec{w} = -\\\mathbf{Q} \\\\vec{y}\\\)
-
-        """
-        if self.verbose: print '%s\nCalculating J(v)\n%s'%('*'*50,'*'*50)
-        self.curModel = m
-        if f is None:
-            f = self.fields(m)
-        p = self.Gvec(m, v, f)
-        y = self.solveAh(m, p)
-        Jv = self.survey.evalDeriv(f, v=y)
-        if self.verbose: print '%s\nDone calculating J(v)\n%s'%('*'*50,'*'*50)
-        return - mkvc(Jv)
-
-    def Jtvec(self, m, v, f=None):
-        """
-            :param numpy.array m: Conductivity model
-            :param numpy.ndarray,SimPEG.Survey.Data v: vector (data object)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
-            :rtype: numpy.ndarray
-            :return: w (model object)
-
-            Multiplying \\\(\\\mathbf{J}^\\\\top\\\) onto a vector can be broken into three steps
-
-            * Compute \\\(\\\\vec{p} = \\\mathbf{Q}^\\\\top \\\\vec{v}\\\)
-            * Solve \\\(\\\hat{\\\mathbf{A}}^\\\\top \\\\vec{y} = \\\\vec{p}\\\)
-            * Compute \\\(\\\\vec{w} = -\\\mathbf{G}^\\\\top y\\\)
-
-        """
-        if self.verbose: print '%s\nCalculating J^T(v)\n%s'%('*'*50,'*'*50)
-        self.curModel = m
-        if f is None:
-            f = self.fields(m)
-
-        if not isinstance(v, self.dataPair):
-            v = self.dataPair(self.survey, v)
-
-        p = self.survey.evalDeriv(f, v=v, adjoint=True)
-        y = self.solveAht(m, p)
-        w = self.Gtvec(m, y, f)
-        if self.verbose: print '%s\nDone calculating J^T(v)\n%s'%('*'*50,'*'*50)
-        return - mkvc(w)
-

SimPEG/EM/TDEM_old/SurveyTDEM.py

@@ -1,199 +0,0 @@
-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

@@ -1,356 +0,0 @@
-from BaseTDEM import BaseTDEMProblem, FieldsTDEM
-from SimPEG.Utils import mkvc, sdiag
-import numpy as np
-from SurveyTDEM import SurveyTDEM
-
-
-class FieldsTDEM_e_from_b(FieldsTDEM):
-    """Fancy Field Storage for a TDEM survey."""
-    knownFields = {'b': 'F'}
-    aliasFields = {'e': ['b','E','e_from_b']}
-
-    def startup(self):
-        self.MeSigmaI  = self.survey.prob.MeSigmaI
-        self.edgeCurlT = self.survey.prob.mesh.edgeCurl.T
-        self.MfMui     = self.survey.prob.MfMui
-
-    def e_from_b(self, b, srcInd, timeInd):
-        # TODO: implement non-zero js
-        return self.MeSigmaI*(self.edgeCurlT*(self.MfMui*b))
-
-class FieldsTDEM_e_from_b_Ah(FieldsTDEM):
-    """Fancy Field Storage for a TDEM survey.
-
-        This is used when solving Ahat and AhatT
-    """
-    knownFields = {'b': 'F'}
-    aliasFields = {'e': ['b','E','e_from_b']}
-    p = None
-
-    def startup(self):
-        self.MeSigmaI  = self.survey.prob.MeSigmaI
-        self.edgeCurlT = self.survey.prob.mesh.edgeCurl.T
-        self.MfMui     = self.survey.prob.MfMui
-
-    def e_from_b(self, y_b, srcInd, tInd):
-        y_e = self.MeSigmaI*(self.edgeCurlT*(self.MfMui*y_b))
-        if 'e' in self.p:
-            y_e = y_e - self.MeSigmaI*self.p[srcInd,'e',tInd]
-        return y_e
-
-class ProblemTDEM_b(BaseTDEMProblem):
-    """
-        Time-Domain EM problem - B-formulation
-
-        TDEM_b treats the following discretization of Maxwell's equations
-
-        .. math::
-            \dcurl \e^{(t+1)} + \\frac{\\b^{(t+1)} - \\b^{(t)}}{\delta t} = 0 \\\\
-            \dcurl^\\top \MfMui \\b^{(t+1)} - \MeSig \e^{(t+1)} = \Me \j_s^{(t+1)}
-
-        with \\\(\\b\\\) defined on cell faces and \\\(\e\\\) defined on edges.
-    """
-    def __init__(self, mesh, mapping=None, **kwargs):
-        BaseTDEMProblem.__init__(self, mesh, mapping=mapping, **kwargs)
-
-    solType = 'b' #: Type of the solution, in this case the 'b' field
-
-    surveyPair = SurveyTDEM
-    _FieldsForward_pair = FieldsTDEM_e_from_b     #: used for the forward calculation only
-
-    ####################################################
-    # Internal Methods
-    ####################################################
-
-    def getA(self, tInd):
-        """
-            :param int tInd: Time index
-            :rtype: scipy.sparse.csr_matrix
-            :return: A
-        """
-        dt = self.timeSteps[tInd]
-        return self.MfMui*self.mesh.edgeCurl*self.MeSigmaI*self.mesh.edgeCurl.T*self.MfMui + (1.0/dt)*self.MfMui
-
-    def getRHS(self, tInd, F):
-        dt = self.timeSteps[tInd]
-        B_n = np.c_[[F[src,'b',tInd] for src in self.survey.srcList]].T
-        if B_n.shape[0] is not 1:
-            raise NotImplementedError('getRHS not implemented for this shape of B_n')
-        RHS = (1.0/dt)*self.MfMui*B_n[0,:,:] #TODO: This is a hack
-        return RHS
-
-    ####################################################
-    # Derivatives
-    ####################################################
-
-    def Gvec(self, m, vec, u=None):
-        """
-            :param numpy.array m: Conductivity model
-            :param numpy.array vec: vector (like a model)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
-            :rtype: simpegEM.TDEM.FieldsTDEM
-            :return: f
-
-            Multiply G by a vector
-        """
-        if u is None:
-            u = self.fields(m)
-        self.curModel = m
-
-        # Note: Fields has shape (nF/E, nSrc, nT+1)
-        #       However, p will only really fill (:,:,1:nT+1)
-        #       meaning the 'initial fields' are zero (:,:,0)
-        p = FieldsTDEM(self.mesh, self.survey)
-        # 'b' at all times is zero.
-        #       However, to save memory we will **not** do:
-        #
-        #               p[:, 'b', :] = 0.0
-
-        # fake initial 'e' fields
-        p[:, 'e', 0] = 0.0
-        dMdsig = self.MeSigmaDeriv
-        # self.mesh.getEdgeInnerProductDeriv(self.curModel.transform)
-        # dsigdm_x_v = self.curModel.sigmaDeriv*vec
-        # dsigdm_x_v = self.curModel.transformDeriv*vec
-        for i in range(1,self.nT+1):
-            # TODO: G[1] may be dependent on the model
-            #       for a galvanic source (deriv of the dc problem)
-            #
-            # Do multiplication for all src in self.survey.srcList
-            for src in self.survey.srcList:
-                p[src, 'e', i] = - dMdsig(u[src,'e',i]) *  vec
-        return p
-
-    def Gtvec(self, m, vec, u=None):
-        """
-            :param numpy.array m: Conductivity model
-            :param numpy.array vec: vector (like a fields)
-            :param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
-            :rtype: np.ndarray (like a model)
-            :return: p
-
-            Multiply G.T by a vector
-        """
-        if u is None:
-            u = self.fields(m)
-        self.curModel = m
-        # dMdsig = self.mesh.getEdgeInnerProductDeriv(self.curModel.transform)
-        # dsigdm = self.curModel.transformDeriv
-        MeSigmaDeriv = self.MeSigmaDeriv
-
-        nSrc = self.survey.nSrc
-        VUs = None
-        # Here we can do internal multiplications of Gt*v and then multiply by MsigDeriv.T in one go.
-        for i in range(1,self.nT+1):
-            vu = None
-            for src in self.survey.srcList:
-                vusrc = MeSigmaDeriv(u[src,'e',i]).T * vec[src,'e',i]
-                vu = vusrc if vu is None else vu + vusrc
-            VUs = vu if VUs is None else VUs + vu
-        # p = -dsigdm.T*VUs
-        return -VUs
-
-    def solveAh(self, m, p):
-        """
-            :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM p: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
-            :return: y
-
-            Solve the block-matrix system \\\(\\\hat{A} \\\hat{y} = \\\hat{p}\\\):
-
-            .. math::
-                \mathbf{\hat{A}} = \left[
-                    \\begin{array}{cccc}
-                        A & 0 & & \\\\
-                        B & A & & \\\\
-                        & \ddots & \ddots & \\\\
-                        & & B & A
-                    \end{array}
-                \\right] \\\\
-                \mathbf{A} =
-                \left[
-                    \\begin{array}{cc}
-                        \\frac{1}{\delta t} \MfMui & \MfMui\dcurl \\\\
-                        \dcurl^\\top \MfMui & -\MeSig
-                    \end{array}
-                \\right] \\\\
-                \mathbf{B} =
-                \left[
-                    \\begin{array}{cc}
-                        -\\frac{1}{\delta t} \MfMui & 0 \\\\
-                        0 & 0
-                    \end{array}
-                \\right] \\\\
-        """
-
-        def AhRHS(tInd, y):
-            rhs = self.MfMui*(self.mesh.edgeCurl*(self.MeSigmaI*p[:,'e',tInd+1]))
-            if 'b' in p:
-                rhs = rhs + p[:,'b',tInd+1]
-            if tInd == 0:
-                return rhs
-            dt = self.timeSteps[tInd]
-            return rhs + 1.0/dt*self.MfMui*y[:,'b',tInd]
-
-        F = FieldsTDEM_e_from_b_Ah(self.mesh, self.survey, p=p)
-
-        return self.forward(m, AhRHS, F)
-
-    def solveAht(self, m, p):
-        """
-            :param numpy.array m: Conductivity model
-            :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}\\\):
-
-            .. math::
-                \mathbf{\hat{A}}^\\top = \left[
-                    \\begin{array}{cccc}
-                        A & B & & \\\\
-                          & \ddots & \ddots & \\\\
-                          & & A & B \\\\
-                          & & 0 & A
-                    \end{array}
-                \\right] \\\\
-                \mathbf{A} =
-                \left[
-                    \\begin{array}{cc}
-                        \\frac{1}{\delta t} \MfMui & \MfMui\dcurl \\\\
-                        \dcurl^\\top \MfMui & -\MeSig
-                    \end{array}
-                \\right] \\\\
-                \mathbf{B} =
-                \left[
-                    \\begin{array}{cc}
-                        -\\frac{1}{\delta t} \MfMui & 0 \\\\
-                        0 & 0
-                    \end{array}
-                \\right] \\\\
-        """
-
-        #  Mini Example:
-        #
-        #       nT = 3, len(times) == 4, fields stored in F[:,:,1:4]
-        #
-        #       0 is held for initial conditions (this shifts the storage by +1)
-        #       ^
-        #  fLoc 0     1     2     3
-        #       |-----|-----|-----|
-        #  tInd    0     1     2
-        #                        / ___/
-        #                      2          (tInd=2 uses fields 3 and would use 4 but it doesn't exist)
-        #                 / ___/
-        #                1                (tInd=1 uses fields 2 and 3)
-
-        def AhtRHS(tInd, y):
-            nSrc, nF = self.survey.nSrc, self.mesh.nF
-            rhs = np.zeros((nF,1) if nSrc == 1 else (nF, nSrc))
-
-            if 'e' in p:
-                rhs += self.MfMui*(self.mesh.edgeCurl*(self.MeSigmaI*p[:,'e',tInd+1]))
-            if 'b' in p:
-                rhs += p[:,'b',tInd+1]
-
-            if tInd == self.nT-1:
-                return rhs
-            dt = self.timeSteps[tInd+1]
-            return rhs + 1.0/dt*self.MfMui*y[:,'b',tInd+2]
-
-        F = FieldsTDEM_e_from_b_Ah(self.mesh, self.survey, p=p)
-
-        return self.adjoint(m, AhtRHS, F)
-
-    ####################################################
-    # Functions for tests
-    ####################################################
-
-    def _AhVec(self, m, vec):
-        """
-            :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
-            :return: f
-
-            Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where
-
-            .. math::
-                \mathbf{\hat{A}} = \left[
-                    \\begin{array}{cccc}
-                        A & 0 & & \\\\
-                        B & A & & \\\\
-                        & \ddots & \ddots & \\\\
-                        & & B & A
-                    \end{array}
-                \\right] \\\\
-                \mathbf{A} =
-                \left[
-                    \\begin{array}{cc}
-                        \\frac{1}{\delta t} \MfMui & \MfMui\dcurl \\\\
-                        \dcurl^\\top \MfMui & -\MeSig
-                    \end{array}
-                \\right] \\\\
-                \mathbf{B} =
-                \left[
-                    \\begin{array}{cc}
-                        -\\frac{1}{\delta t} \MfMui & 0 \\\\
-                        0 & 0
-                    \end{array}
-                \\right] \\\\
-        """
-
-        self.curModel = m
-        f = FieldsTDEM(self.mesh, self.survey)
-        for i in range(1,self.nT+1):
-            dt = self.timeSteps[i-1]
-            b = 1.0/dt*self.MfMui*vec[:,'b',i] + self.MfMui*(self.mesh.edgeCurl*vec[:,'e',i])
-            if i > 1:
-                b = b - 1.0/dt*self.MfMui*vec[:,'b',i-1]
-            f[:,'b',i] = b
-            f[:,'e',i] = self.mesh.edgeCurl.T*(self.MfMui*vec[:,'b',i]) - self.MeSigma*vec[:,'e',i]
-        return f
-
-    def _AhtVec(self, m, vec):
-        """
-            :param numpy.array m: Conductivity model
-            :param simpegEM.TDEM.FieldsTDEM vec: Fields object
-            :rtype: simpegEM.TDEM.FieldsTDEM
-            :return: f
-
-            Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where
-
-            .. math::
-                \mathbf{\hat{A}}^\\top = \left[
-                    \\begin{array}{cccc}
-                        A & B & & \\\\
-                          & \ddots & \ddots & \\\\
-                          & & A & B \\\\
-                          & & 0 & A
-                    \end{array}
-                \\right] \\\\
-                \mathbf{A} =
-                \left[
-                    \\begin{array}{cc}
-                        \\frac{1}{\delta t} \MfMui & \MfMui\dcurl \\\\
-                        \dcurl^\\top \MfMui & -\MeSig
-                    \end{array}
-                \\right] \\\\
-                \mathbf{B} =
-                \left[
-                    \\begin{array}{cc}
-                        -\\frac{1}{\delta t} \MfMui & 0 \\\\
-                        0 & 0
-                    \end{array}
-                \\right] \\\\
-        """
-        self.curModel = m
-        f = FieldsTDEM(self.mesh, self.survey)
-        for i in range(self.nT):
-            b = 1.0/self.timeSteps[i]*self.MfMui*vec[:,'b',i+1] + self.MfMui*(self.mesh.edgeCurl*vec[:,'e',i+1])
-            if i < self.nT-1:
-                b = b - 1.0/self.timeSteps[i+1]*self.MfMui*vec[:,'b',i+2]
-            f[:,'b', i+1] = b
-            f[:,'e', i+1] = self.mesh.edgeCurl.T*(self.MfMui*vec[:,'b',i+1]) - self.MeSigma*vec[:,'e',i+1]
-        return f

SimPEG/EM/TDEM_old/__init__.py

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

SimPEG/EM/Utils/AnalyticUtils.py

@@ -1,203 +0,0 @@
-from SimPEG import *
-from scipy.special import ellipk, ellipe
-from scipy.constants import mu_0, pi
-
-def MagneticDipoleVectorPotential(srcLoc, obsLoc, component, moment=1., dipoleMoment=(0., 0., 1.), mu = mu_0):
-    """
-        Calculate the vector potential of a set of magnetic dipoles
-        at given locations 'ref. <http://en.wikipedia.org/wiki/Dipole#Magnetic_vector_potential>'
-
-        :param numpy.ndarray srcLoc: Location of the source(s) (x, y, z)
-        :param numpy.ndarray,SimPEG.Mesh obsLoc: Where the potentials will be calculated (x, y, z) or a SimPEG Mesh
-        :param str,list component: The component to calculate - 'x', 'y', or 'z' if an array, or grid type if mesh, can be a list
-        :param numpy.ndarray dipoleMoment: The vector dipole moment
-        :rtype: numpy.ndarray
-        :return: The vector potential each dipole at each observation location
-    """
-    #TODO: break this out!
-
-    if type(component) in [list, tuple]:
-        out = range(len(component))
-        for i, comp in enumerate(component):
-            out[i] = MagneticDipoleVectorPotential(srcLoc, obsLoc, comp, dipoleMoment=dipoleMoment)
-        return np.concatenate(out)
-
-    if isinstance(obsLoc, Mesh.BaseMesh):
-        mesh = obsLoc
-        assert component in ['Ex','Ey','Ez','Fx','Fy','Fz'], "Components must be in: ['Ex','Ey','Ez','Fx','Fy','Fz']"
-        return MagneticDipoleVectorPotential(srcLoc, getattr(mesh,'grid'+component), component[1], dipoleMoment=dipoleMoment)
-
-    if component == 'x':
-        dimInd = 0
-    elif component == 'y':
-        dimInd = 1
-    elif component == 'z':
-        dimInd = 2
-    else:
-        raise ValueError('Invalid component')
-
-    srcLoc = np.atleast_2d(srcLoc)
-    obsLoc = np.atleast_2d(obsLoc)
-    dipoleMoment = np.atleast_2d(dipoleMoment)
-
-    nEdges = obsLoc.shape[0]
-    nSrc = srcLoc.shape[0]
-
-    m = np.array(dipoleMoment).repeat(nEdges, axis=0)
-    A = np.empty((nEdges, nSrc))
-    for i in range(nSrc):
-        dR = obsLoc - srcLoc[i, np.newaxis].repeat(nEdges, axis=0)
-        mCr = np.cross(m, dR)
-        r = np.sqrt((dR**2).sum(axis=1))
-        A[:, i] = +(mu/(4*pi)) * mCr[:,dimInd]/(r**3)
-    if nSrc == 1:
-        return A.flatten()
-    return A
-
-
-def MagneticDipoleFields(srcLoc, obsLoc, component, moment=1., mu = mu_0):
-    """
-        Calculate the vector potential of a set of magnetic dipoles
-        at given locations 'ref. <http://en.wikipedia.org/wiki/Dipole#Magnetic_vector_potential>'
-
-        :param numpy.ndarray srcLoc: Location of the source(s) (x, y, z)
-        :param numpy.ndarray obsLoc: Where the potentials will be calculated (x, y, z)
-        :param str component: The component to calculate - 'x', 'y', or 'z'
-        :param numpy.ndarray moment: The vector dipole moment (vertical)
-        :rtype: numpy.ndarray
-        :return: The vector potential each dipole at each observation location
-    """
-
-    if component=='x':
-        dimInd = 0
-    elif component=='y':
-        dimInd = 1
-    elif component=='z':
-        dimInd = 2
-    else:
-        raise ValueError('Invalid component')
-
-    srcLoc = np.atleast_2d(srcLoc)
-    obsLoc = np.atleast_2d(obsLoc)
-    moment = np.atleast_2d(moment)
-
-    nFaces = obsLoc.shape[0]
-    nSrc = srcLoc.shape[0]
-
-    m = np.array(moment).repeat(nFaces, axis=0)
-    B = np.empty((nFaces, nSrc))
-    for i in range(nSrc):
-        dR = obsLoc - srcLoc[i, np.newaxis].repeat(nFaces, axis=0)
-        r = np.sqrt((dR**2).sum(axis=1))
-        if dimInd == 0:
-            B[:, i] = +(mu/(4*pi)) /(r**3) * (3*dR[:,2]*dR[:,0]/r**2)
-        elif dimInd == 1:
-            B[:, i] = +(mu/(4*pi)) /(r**3) * (3*dR[:,2]*dR[:,1]/r**2)
-        elif dimInd == 2:
-            B[:, i] = +(mu/(4*pi)) /(r**3) * (3*dR[:,2]**2/r**2-1)
-        else:
-            raise Exception("Not Implemented")
-    if nSrc == 1:
-        return B.flatten()
-    return B
-
-
-
-def MagneticLoopVectorPotential(srcLoc, obsLoc, component, radius, mu=mu_0):
-    """
-        Calculate the vector potential of horizontal circular loop
-        at given locations
-
-        :param numpy.ndarray srcLoc: Location of the source(s) (x, y, z)
-        :param numpy.ndarray,SimPEG.Mesh obsLoc: Where the potentials will be calculated (x, y, z) or a SimPEG Mesh
-        :param str,list component: The component to calculate - 'x', 'y', or 'z' if an array, or grid type if mesh, can be a list
-        :param numpy.ndarray I: Input current of the loop
-        :param numpy.ndarray radius: radius of the loop
-        :rtype: numpy.ndarray
-        :return: The vector potential each dipole at each observation location
-    """
-
-    if type(component) in [list, tuple]:
-        out = range(len(component))
-        for i, comp in enumerate(component):
-            out[i] = MagneticLoopVectorPotential(srcLoc, obsLoc, comp, radius, mu)
-        return np.concatenate(out)
-
-    if isinstance(obsLoc, Mesh.BaseMesh):
-        mesh = obsLoc
-        assert component in ['Ex','Ey','Ez','Fx','Fy','Fz'], "Components must be in: ['Ex','Ey','Ez','Fx','Fy','Fz']"
-        return MagneticLoopVectorPotential(srcLoc, getattr(mesh,'grid'+component), component[1], radius, mu)
-
-    srcLoc = np.atleast_2d(srcLoc)
-    obsLoc = np.atleast_2d(obsLoc)
-
-    n = obsLoc.shape[0]
-    nSrc = srcLoc.shape[0]
-
-    if component=='z':
-        A = np.zeros((n, nSrc))
-        if nSrc ==1:
-            return A.flatten()
-        return A
-
-    else:
-
-        A = np.zeros((n, nSrc))
-        for i in range (nSrc):
-            x = obsLoc[:, 0] - srcLoc[i, 0]
-            y = obsLoc[:, 1] - srcLoc[i, 1]
-            z = obsLoc[:, 2] - srcLoc[i, 2]
-            r = np.sqrt(x**2 + y**2)
-            m = (4 * radius * r) / ((radius + r)**2 + z**2)
-            m[m > 1.] = 1.
-            # m might be slightly larger than 1 due to rounding errors
-            # but ellipke requires 0 <= m <= 1
-            K = ellipk(m)
-            E = ellipe(m)
-            ind = (r > 0) & (m < 1)
-            # % 1/r singular at r = 0 and K(m) singular at m = 1
-            Aphi = np.zeros(n)
-            # % Common factor is (mu * I) / pi with I = 1 and mu = 4e-7 * pi.
-            Aphi[ind] = 4e-7 / np.sqrt(m[ind])  * np.sqrt(radius / r[ind]) *((1. - m[ind] / 2.) * K[ind] - E[ind])
-            if component == 'x':
-                A[ind, i] = Aphi[ind] * (-y[ind] / r[ind] )
-            elif component == 'y':
-                A[ind, i] = Aphi[ind] * ( x[ind] / r[ind] )
-            else:
-                raise ValueError('Invalid component')
-
-        if nSrc == 1:
-            return A.flatten()
-        return A
-
-if __name__ == '__main__':
-    from SimPEG import Mesh
-    import matplotlib.pyplot as plt
-    cs = 20
-    ncx, ncy, ncz = 41, 41, 40
-    hx = np.ones(ncx)*cs
-    hy = np.ones(ncy)*cs
-    hz = np.ones(ncz)*cs
-    mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')
-    srcLoc = np.r_[0., 0., 0.]
-    Ax = MagneticLoopVectorPotential(srcLoc, mesh.gridEx, 'x', 200)
-    Ay = MagneticLoopVectorPotential(srcLoc, mesh.gridEy, 'y', 200)
-    Az = MagneticLoopVectorPotential(srcLoc, mesh.gridEz, 'z', 200)
-    A = np.r_[Ax, Ay, Az]
-    B0 = mesh.edgeCurl*A
-    J0 = mesh.edgeCurl.T*B0
-
-    # mesh.plotImage(A, vType = 'Ex')
-    # mesh.plotImage(A, vType = 'Ey')
-
-    mesh.plotImage(B0, vType = 'Fx')
-    mesh.plotImage(B0, vType = 'Fy')
-    mesh.plotImage(B0, vType = 'Fz')
-
-    # # mesh.plotImage(J0, vType = 'Ex')
-    # mesh.plotImage(J0, vType = 'Ey')
-    # mesh.plotImage(J0, vType = 'Ez')
-
-    plt.show()
-
-

SimPEG/EM/Utils/EMUtils.py

@@ -1,16 +0,0 @@
-import numpy as np
-from scipy.constants import mu_0, epsilon_0
-
-# useful params
-def omega(freq):
-    """Angular frequency, omega"""
-    return 2.*np.pi*freq
-
-def k(freq, sigma, mu=mu_0, eps=epsilon_0):
-    """ Eq 1.47 - 1.49 in Ward and Hohmann """
-    w = omega(freq)
-    alp  = w * np.sqrt( mu*eps/2 * ( np.sqrt(1. + (sigma / (eps*w))**2 ) + 1) ) 
-    beta = w * np.sqrt( mu*eps/2 * ( np.sqrt(1. + (sigma / (eps*w))**2 ) - 1) ) 
-    return alp - 1j*beta
-
-

SimPEG/EM/Utils/__init__.py

@@ -1,2 +0,0 @@
-from EMUtils import omega, k
-from AnalyticUtils import MagneticDipoleFields, MagneticDipoleVectorPotential, MagneticLoopVectorPotential

SimPEG/EM/Utils/testingUtils.py

@@ -1,131 +0,0 @@
-import unittest
-from SimPEG import *
-from SimPEG import EM
-import sys
-from scipy.constants import mu_0
-
-FLR = 1e-20 # "zero", so if residual below this --> pass regardless of order
-CONDUCTIVITY = 1e1
-MU = mu_0
-freq = 5e-1
-
-
-def getFDEMProblem(fdemType, comp, SrcList, freq, useMu=False, verbose=False):
-    cs = 10.
-    ncx, ncy, ncz = 0, 0, 0
-    npad = 8
-    hx = [(cs,npad,-1.3), (cs,ncx), (cs,npad,1.3)]
-    hy = [(cs,npad,-1.3), (cs,ncy), (cs,npad,1.3)]
-    hz = [(cs,npad,-1.3), (cs,ncz), (cs,npad,1.3)]
-    mesh = Mesh.TensorMesh([hx,hy,hz],['C','C','C'])
-
-    if useMu is True:
-        mapping = [('sigma', Maps.ExpMap(mesh)), ('mu', Maps.IdentityMap(mesh))]
-    else:
-        mapping = Maps.ExpMap(mesh)
-
-    x = np.array([np.linspace(-5.*cs,-2.*cs,3),np.linspace(5.*cs,2.*cs,3)]) + cs/4. #don't sample right by the source, slightly off alignment from either staggered grid
-    XYZ = Utils.ndgrid(x,x,np.linspace(-2.*cs,2.*cs,5))
-    Rx0 = getattr(EM.FDEM.Rx, 'Point_' + comp[0])
-    if comp[2] == 'r':
-        real_or_imag = 'real'
-    elif comp[2] == 'i':
-        real_or_imag = 'imag'
-    rx0 = Rx0(XYZ, comp[1], 'imag')
-
-    Src = []
-
-    for SrcType in SrcList:
-        if SrcType is 'MagDipole':
-            Src.append(EM.FDEM.Src.MagDipole([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
-        elif SrcType is 'MagDipole_Bfield':
-            Src.append(EM.FDEM.Src.MagDipole_Bfield([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
-        elif SrcType is 'CircularLoop':
-            Src.append(EM.FDEM.Src.CircularLoop([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
-        elif SrcType is 'RawVec':
-            if fdemType is 'e' or fdemType is 'b':
-                S_m = np.zeros(mesh.nF)
-                S_e = np.zeros(mesh.nE)
-                S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
-                S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
-                Src.append(EM.FDEM.Src.RawVec([rx0], freq, S_m, mesh.getEdgeInnerProduct()*S_e))
-
-            elif fdemType is 'h' or fdemType is 'j':
-                S_m = np.zeros(mesh.nE)
-                S_e = np.zeros(mesh.nF)
-                S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
-                S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
-                Src.append(EM.FDEM.Src.RawVec([rx0], freq, mesh.getEdgeInnerProduct()*S_m, S_e))
-
-    if verbose:
-        print '  Fetching %s problem' % (fdemType)
-
-    if fdemType == 'e':
-        survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
-
-    elif fdemType == 'b':
-        survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
-
-    elif fdemType == 'j':
-        survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem3D_j(mesh, mapping=mapping)
-
-    elif fdemType == 'h':
-        survey = EM.FDEM.Survey(Src)
-        prb = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
-
-    else:
-        raise NotImplementedError()
-    prb.pair(survey)
-
-    try:
-        from pymatsolver import MumpsSolver
-        prb.Solver = MumpsSolver
-    except ImportError, e:
-        prb.Solver = SolverLU
-
-    return prb
-
-def crossCheckTest(SrcList, fdemType1, fdemType2, comp, addrandoms = False, useMu=False, TOL=1e-5, verbose=False):
-
-    l2norm = lambda r: np.sqrt(r.dot(r))
-
-    prb1 = getFDEMProblem(fdemType1, comp, SrcList, freq, useMu, verbose)
-    mesh = prb1.mesh
-    print 'Cross Checking Forward: %s, %s formulations - %s' % (fdemType1, fdemType2, comp)
-
-    logsig = np.log(np.ones(mesh.nC)*CONDUCTIVITY)
-    mu = np.ones(mesh.nC)*MU
-
-    if addrandoms is True:
-        logsig  += np.random.randn(mesh.nC)*np.log(CONDUCTIVITY)*1e-1
-        mu += np.random.randn(mesh.nC)*MU*1e-1
-
-    if useMu is True:
-        m = np.r_[logsig, mu]
-    else:
-        m = logsig
-
-    survey1 = prb1.survey
-    d1 = survey1.dpred(m)
-
-    if verbose:
-        print '  Problem 1 solved'
-
-
-    prb2 = getFDEMProblem(fdemType2, comp, SrcList, freq, useMu, verbose)
-
-    survey2 = prb2.survey
-    d2 = survey2.dpred(m)
-
-    if verbose:
-        print '  Problem 2 solved'
-
-    r = d2-d1
-    l2r = l2norm(r)
-
-    tol = np.max([TOL*(10**int(np.log10(0.5* (l2norm(d1) + l2norm(d2)) ))),FLR])
-    print l2norm(d1), l2norm(d2),  l2r , tol, l2r < tol
-    return l2r < tol

SimPEG/EM/__init__.py

@@ -1,7 +0,0 @@
-import TDEM
-import FDEM
-import Static
-import Base
-import Analytics
-import Utils
-from scipy.constants import mu_0, epsilon_0

SimPEG/Examples/DC_Analytic_Dipole.py

@@ -1,68 +0,0 @@
-from SimPEG import *
-import SimPEG.DCIP as DC
-
-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)]
-    hz = [(cs,7, -1.3),(cs,20)]
-    mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
-    sighalf = 1e-2
-    sigma = np.ones(mesh.nC)*sighalf
-    xtemp = np.linspace(-150, 150, 21)
-    ytemp = np.linspace(-150, 150, 21)
-    xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])
-    xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])
-    xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])
-
-    # if plotIt:
-    #     fig, ax = plt.subplots(1,1, figsize = (5,5))
-    #     mesh.plotSlice(sigma, grid=True, ax = ax)
-    #     ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')
-    #     ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)
-
-    rx = DC.RxDipole(xyz_rxP, xyz_rxN)
-    src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
-    survey = DC.SurveyDC([src])
-    problem = DC.ProblemDC_CC(mesh)
-    problem.pair(survey)
-    try:
-        from pymatsolver import MumpsSolver
-        problem.Solver = MumpsSolver
-    except Exception, e:
-        pass
-    data = survey.dpred(sigma)
-
-    def DChalf(srclocP, srclocN, rxloc, sigma, I=1.):
-        rp = (srclocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)
-        rn = (srclocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)
-        rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))
-        rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))
-        return I/(sigma*2.*np.pi)*(1/rP-1/rN)
-
-    data_anaP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)
-    data_anaN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)
-    data_ana = data_anaP-data_anaN
-    Data_ana = data_ana.reshape((21, 21), order = 'F')
-    Data = data.reshape((21, 21), order = 'F')
-    X = xyz_rxM[:,0].reshape((21, 21), order = 'F')
-    Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,2, figsize = (12, 5))
-        vmin = np.r_[data, data_ana].min()
-        vmax = np.r_[data, data_ana].max()
-        dat1 = ax[1].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)
-        dat0 = ax[0].contourf(X, Y, Data_ana, 60, vmin = vmin, vmax = vmax)
-        cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])
-        cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])
-        ax[1].set_title('Analytic')
-        ax[0].set_title('Computed')
-        plt.show()
-
-    return np.linalg.norm(data-data_ana)/np.linalg.norm(data_ana)
-
-
-if __name__ == '__main__':
-    print run(plotIt=True)

SimPEG/Examples/DC_Forward_PseudoSection.py

@@ -1,208 +0,0 @@
-from SimPEG import Mesh, Utils, np, sp
-import SimPEG.DCIP as DC
-import time
-
-def run(loc=None, sig=None, radi=None, param=None, surveyType='dipole-dipole', unitType='appConductivity', plotIt=True):
-    """
-        DC Forward Simulation
-        =====================
-
-        Forward model two conductive spheres in a half-space and plot a
-        pseudo-section. Assumes an infinite line source and measures along the
-        center of the spheres.
-
-        INPUT:
-        loc     = Location of spheres [[x1,y1,z1],[x2,y2,z2]]
-        radi    = Radius of spheres [r1,r2]
-        param   = Conductivity of background and two spheres [m0,m1,m2]
-        surveyType = survey type 'pole-dipole' or 'dipole-dipole'
-        unitType = Data type "appResistivity" | "appConductivity"  | "volt"
-        Created by @fourndo
-
-    """
-
-    assert surveyType in ['pole-dipole', 'dipole-dipole'], "Source type (surveyType) must be pdp or dpdp (pole dipole or dipole dipole)"
-    assert unitType in ['appResistivity', 'appConductivity', 'volt'], "Unit type (unitType) must be appResistivity or appConductivity or volt (potential)"
-
-    if loc is None:
-        loc = np.c_[[-50.,0.,-50.],[50.,0.,-50.]]
-    if sig is None:
-        sig = np.r_[1e-2,1e-1,1e-3]
-    if radi is None:
-        radi = np.r_[25.,25.]
-    if param is None:
-        param = np.r_[30.,30.,5]
-
-
-    # First we need to create a mesh and a model.
-    # This is our mesh
-    dx    = 5.
-
-    hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]
-    hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]
-    hzind = [(dx,15,-1.3),(dx, 15)]
-
-    mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
-
-
-    # Set background conductivity
-    model = np.ones(mesh.nC) * sig[0]
-
-    # First anomaly
-    ind = Utils.ModelBuilder.getIndicesSphere(loc[:,0],radi[0],mesh.gridCC)
-    model[ind] = sig[1]
-
-    # Second anomaly
-    ind = Utils.ModelBuilder.getIndicesSphere(loc[:,1],radi[1],mesh.gridCC)
-    model[ind] = sig[2]
-
-    # Get index of the center
-    indy = int(mesh.nCy/2)
-
-    # Plot the model for reference
-    # Define core mesh extent
-    xlim = 200
-    zlim = 100
-
-    # Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh
-    ends = [(-175,0),(175,0)]
-    ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]
-
-    # Snap the endpoints to the grid. Easier to create 2D section.
-    indx = Utils.closestPoints(mesh, ends )
-    locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]
-
-    # We will handle the geometry of the survey for you and create all the combination of tx-rx along line
-    # [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, surveyType, param[0], param[1], param[2])
-    survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, surveyType, param[0], param[1], param[2])
-
-    # Define some global geometry
-    dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) )
-    dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
-    dl_y = ( Tx[-1][1,1] - Tx[0][1,0]  ) / dl_len
-    #azm =  np.arctan(dl_y/dl_x)
-
-    #Set boundary conditions
-    mesh.setCellGradBC('neumann')
-
-    # Define the linear system needed for the DC problem. We assume an infitite
-    # line source for simplicity.
-    Div = mesh.faceDiv
-    Grad = mesh.cellGrad
-    Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-    A = Div*Msig*Grad
-
-    # Change one corner to deal with nullspace
-    A[0,0] = 1
-    A = sp.csc_matrix(A)
-
-    # We will solve the system iteratively, so a pre-conditioner is helpful
-    # This is simply a Jacobi preconditioner (inverse of the main diagonal)
-    dA = A.diagonal()
-    P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-
-    # Now we can solve the system for all the transmitters
-    # We want to store the data
-    data = []
-
-    # There is probably a more elegant way to do this, but we can just for-loop through the transmitters
-    for ii in range(len(Tx)):
-
-        start_time = time.time() # Let's time the calculations
-
-        #print("Transmitter %i / %i\r" % (ii+1,len(Tx)))
-
-        # Select dipole locations for receiver
-        rxloc_M = np.asarray(Rx[ii][:,0:3])
-        rxloc_N = np.asarray(Rx[ii][:,3:])
-
-
-        # For usual cases 'dipole-dipole' or "gradient"
-        if surveyType == 'pole-dipole':
-            # Create an "inifinity" pole
-            tx =  np.squeeze(Tx[ii][:,0:1])
-            tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
-            inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
-            RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
-        else:
-            inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
-            RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
-
-        # Iterative Solve
-        Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
-
-        # We now have the potential everywhere
-        phi = Utils.mkvc(Ainvb[0])
-
-        # Solve for phi on pole locations
-        P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
-        P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
-
-        # Compute the potential difference
-        dtemp = (P1*phi - P2*phi)*np.pi
-
-        data.append( dtemp )
-        print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),
-
-    print 'Transmitter {0} of {1}'.format(ii,len(Tx))
-    print 'Forward completed'
-
-    # Let's just convert the 3D format into 2D (distance along line) and plot
-    survey2D = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc) , 'Xloc')
-    survey2D.dobs =np.hstack(data)
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig = plt.figure(figsize=(7,7))
-        ax = plt.subplot(2,1,1, aspect='equal')
-        # Plot the location of the spheres for reference
-        circle1=plt.Circle((loc[0,0], loc[2,0]), radi[0], color='w', fill=False, lw=3)
-        circle2=plt.Circle((loc[0,1], loc[2,1]), radi[1], color='k', fill=False, lw=3)
-        ax.add_artist(circle1)
-        ax.add_artist(circle2)
-
-        dat = mesh.plotSlice(np.log10(model), ax = ax, normal = 'Y',
-                             ind = indy,grid=True, clim = np.log10([sig.min(),sig.max()]))
-
-        ax.set_title('3-D model')
-        plt.gca().set_aspect('equal', adjustable='box')
-
-        plt.scatter(Tx[0][0,:],Tx[0][2,:],s=40,c='g', marker='v')
-        plt.scatter(Rx[0][:,0::3],Rx[0][:,2::3],s=40,c='y')
-        plt.xlim([-xlim,xlim])
-        plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
-
-
-        pos =  ax.get_position()
-        ax.set_position([pos.x0 , pos.y0 + 0.025 ,  pos.width, pos.height])
-        pos =  ax.get_position()
-        cbarax = fig.add_axes([pos.x0 , pos.y0 + 0.025 ,  pos.width, pos.height * 0.04])  ## the parameters are the specified position you set
-        cb = fig.colorbar(dat[0],cax=cbarax, orientation="horizontal",
-                          ax = ax, ticks=np.linspace(np.log10(sig.min()),
-                         np.log10(sig.max()), 3), format="$10^{%.1f}$")
-        cb.set_label("Conductivity (S/m)",size=12)
-        cb.ax.tick_params(labelsize=12)
-
-        # Second plot for the predicted apparent resistivity data
-        ax2 = plt.subplot(2,1,2, aspect='equal')
-
-        # Plot the location of the spheres for reference
-        circle1=plt.Circle((loc[0,0], loc[2,0]), radi[0], color='w', fill=False, lw=3)
-        circle2=plt.Circle((loc[0,1], loc[2,1]), radi[1], color='k', fill=False, lw=3)
-        ax2.add_artist(circle1)
-        ax2.add_artist(circle2)
-
-        # Add the speudo section
-        dat = DC.plot_pseudoSection(survey2D, ax2, surveyType=surveyType, unitType=unitType)        # plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
-        # plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
-        # plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
-        ax2.set_title('Apparent Conductivity data')
-
-        plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
-        plt.show()
-
-        return fig, ax
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/DCfwd.py

@@ -0,0 +1,71 @@
+from SimPEG import Mesh, Utils, np, SolverLU
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib.mlab import griddata
+
+## 2D DC forward modeling example with Tensor and Curvilinear Meshes
+
+# Step1: Generate Tensor and Curvilinear Mesh
+sz = [40,40]
+# Tensor Mesh
+tM = Mesh.TensorMesh(sz)
+# Curvilinear Mesh
+rM = Mesh.CurvilinearMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))
+
+# Step2: Direct Current (DC) operator
+def DCfun(mesh, pts):
+    D = mesh.faceDiv
+    G = D.T
+    sigma = 1e-2*np.ones(mesh.nC)
+    Msigi = mesh.getFaceInnerProduct(1./sigma)
+    MsigI = Utils.sdInv(Msigi)
+    A = D*MsigI*G
+    A[-1,-1] /= mesh.vol[-1] # Remove null space
+    rhs = np.zeros(mesh.nC)
+    txind = Utils.meshutils.closestPoints(mesh, pts)
+    rhs[txind] = np.r_[1,-1]
+    return A, rhs
+
+pts = np.vstack((np.r_[0.25, 0.5], np.r_[0.75, 0.5]))
+
+#Step3: Solve DC problem (LU solver)
+AtM, rhstM = DCfun(tM, pts)
+AinvtM = SolverLU(AtM)
+phitM = AinvtM*rhstM
+
+ArM, rhsrM = DCfun(rM, pts)
+AinvrM = SolverLU(ArM)
+phirM = AinvrM*rhsrM
+
+#Step4: Making Figure
+fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
+label = ["(a)", "(b)"]
+opts = {}
+vmin, vmax = phitM.min(), phitM.max()
+dat = tM.plotImage(phitM, ax=axes[0], clim=(vmin, vmax), grid=True)
+
+#TODO: At the moment Curvilinear Mesh do not have plotimage 
+
+Xi = tM.gridCC[:,0].reshape(sz[0], sz[1], order='F')
+Yi = tM.gridCC[:,1].reshape(sz[0], sz[1], order='F')
+PHIrM = griddata(rM.gridCC[:,0], rM.gridCC[:,1], phirM, Xi, Yi, interp='linear')
+axes[1].contourf(Xi, Yi, PHIrM, 100, vmin=vmin, vmax=vmax)
+
+cb = plt.colorbar(dat[0], ax=axes[0]); cb.set_label("Voltage (V)")
+cb = plt.colorbar(dat[0], ax=axes[1]); cb.set_label("Voltage (V)")
+
+tM.plotGrid(ax=axes[0], **opts)
+axes[0].set_title('TensorMesh')
+rM.plotGrid(ax=axes[1], **opts)
+axes[1].set_title('CurvilinearMesh')
+for i in range(2):
+    axes[i].set_xlim(0.025, 0.975)
+    axes[i].set_ylim(0.025, 0.975)
+    axes[i].text(0., 1.0, label[i], fontsize=20)
+    if i==0:        
+        axes[i].set_ylabel("y")
+    else:
+        axes[i].set_ylabel(" ")
+    axes[i].set_xlabel("x")
+
+plt.show()

SimPEG/Examples/EM_FDEM_1D_Inversion.py

@@ -1,115 +0,0 @@
-from SimPEG import *
-import SimPEG.EM as EM
-from SimPEG.EM import mu_0
-
-
-def run(plotIt=True):
-    """
-        EM: FDEM: 1D: Inversion
-        =======================
-
-        Here we will create and run a FDEM 1D inversion.
-
-    """
-
-    cs, ncx, ncz, npad = 5., 25, 15, 15
-    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')
-
-    layerz = -100.
-
-    active = mesh.vectorCCz<0.
-    layer = (mesh.vectorCCz<0.) & (mesh.vectorCCz>=layerz)
-    actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
-    mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
-    sig_half = 2e-2
-    sig_air = 1e-8
-    sig_layer = 1e-2
-    sigma = np.ones(mesh.nCz)*sig_air
-    sigma[active] = sig_half
-    sigma[layer] = sig_layer
-    mtrue = np.log(sigma[active])
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (3, 6))
-        plt.semilogx(sigma[active], mesh.vectorCCz[active])
-        ax.set_ylim(-500, 0)
-        ax.set_xlim(1e-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)
-
-
-    rxOffset=10.
-    bzi = EM.FDEM.Rx.Point_b(np.array([[rxOffset, 0., 1e-3]]), orientation='z', component='imag')
-
-    freqs = np.logspace(1,3,10)
-    srcLoc = np.array([0., 0., 10.])
-
-    srcList = [EM.FDEM.Src.MagDipole([bzi],freq, srcLoc,orientation='Z') for freq in freqs]
-
-    survey = EM.FDEM.Survey(srcList)
-    prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
-
-    try:
-        from pymatsolver import MumpsSolver
-        prb.Solver = MumpsSolver
-    except ImportError, e:
-        prb.Solver = SolverLU
-
-    prb.pair(survey)
-
-    std = 0.05
-    survey.makeSyntheticData(mtrue, std)
-
-    survey.std = std
-    survey.eps = np.linalg.norm(survey.dtrue)*1e-5
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (6, 6))
-        ax.semilogx(freqs,survey.dtrue[:freqs.size], 'b.-')
-        ax.semilogx(freqs,survey.dobs[:freqs.size], 'r.-')
-        ax.legend(('Noisefree', '$d^{obs}$'), fontsize = 16)
-        ax.set_xlabel('Time (s)', fontsize = 14)
-        ax.set_ylabel('$B_z$ (T)', fontsize = 16)
-        ax.set_xlabel('Time (s)', fontsize = 14)
-        ax.grid(color='k', alpha=0.5, linestyle='dashed', linewidth=0.5)
-
-    dmisfit = DataMisfit.l2_DataMisfit(survey)
-    regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
-    reg = Regularization.Tikhonov(regMesh)
-    opt = Optimization.InexactGaussNewton(maxIter = 6)
-    invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
-
-    # Create an inversion object
-    beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)
-    betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)
-    inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])
-    m0 = np.log(np.ones(mtrue.size)*sig_half)
-    reg.alpha_s = 1e-3
-    reg.alpha_x = 1.
-    prb.counter = opt.counter = Utils.Counter()
-    opt.LSshorten = 0.5
-    opt.remember('xc')
-
-    mopt = inv.run(m0)
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (3, 6))
-        plt.semilogx(sigma[active], mesh.vectorCCz[active])
-        plt.semilogx(np.exp(mopt), mesh.vectorCCz[active])
-        ax.set_ylim(-500, 0)
-        ax.set_xlim(1e-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(['$\sigma_{true}$', '$\sigma_{pred}$'],loc='best')
-        plt.show()
-
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/EM_FDEM_Analytic_MagDipoleWholespace.py

@@ -1,43 +0,0 @@
-from SimPEG import *
-import SimPEG.EM as EM
-
-def run(XYZ=None, loc=np.r_[0.,0.,0.], sig=1.0, freq=1.0, orientation='Z', plotIt=True):
-    """
-        EM: Magnetic Dipole in a Whole-Space
-        ====================================
-
-        Here we plot the magnetic flux density from a harmonic dipole in a wholespace.
-
-    """
-
-    if XYZ is None:
-        x = np.arange(-100.5,100.5,step = 1.) #(avoid putting measurement points where source is located)
-        y = np.r_[0]
-        z = x
-        XYZ = Utils.ndgrid(x,y,z)
-
-
-    Bx, By, Bz = EM.Analytics.FDEM.MagneticDipoleWholeSpace(XYZ, loc, sig, freq, orientation=orientation)
-    absB = np.sqrt(Bx*Bx.conj()+By*By.conj()+Bz*Bz.conj()).real
-
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        from matplotlib.colors import LogNorm
-        fig, ax = plt.subplots(1,1,figsize=(6,5))
-        bxplt = Bx.reshape(x.size,z.size)
-        bzplt = Bz.reshape(x.size,z.size)
-        pc = ax.pcolor(x,z,absB.reshape(x.size,z.size),norm=LogNorm())
-        ax.streamplot(x,z,bxplt.real,bzplt.real,color='k',density=1)
-        ax.set_xlim([x.min(),x.max()])
-        ax.set_ylim([z.min(),z.max()])
-        ax.set_xlabel('x')
-        ax.set_ylabel('z')
-        cb = plt.colorbar(pc,ax = ax)
-        cb.set_label('|B| (T)')
-        plt.show()
-
-        return fig, ax
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/EM_Schenkel_Morrison_Casing.py

@@ -1,275 +0,0 @@
-from SimPEG import *
-from SimPEG.EM import FDEM, Analytics, mu_0
-import time
-
-try:
-    from pymatsolver import MumpsSolver
-    solver = MumpsSolver
-except Exception:
-    solver = SolverLU
-    pass
-
-def run(plotIt=True):
-    """
-        EM: Schenkel and Morrison Casing Model
-        ======================================
-
-        Here we create and run a FDEM forward simulation to calculate the vertical
-        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.
-
-        - 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
-            - 300m long
-            - radius of 0.1m
-            - thickness of 6e-3m
-
-        Inside the casing, we take the same conductivity as the background.
-
-        We are using an EM code to simulate DC, so we use frequency low enough
-        that the skin depth inside the casing is longer than the casing length (f
-        = 1e-6 Hz). The plot produced is of the current inside the casing.
-
-        These results are shown in the SEG abstract by Yang et al., 2016: 3D DC
-        resistivity modeling of steel casing for reservoir monitoring using
-        equivalent resistor network. The solver used to produce these results and
-        achieve the CPU time of ~30s is Mumps, which was installed using pymatsolver_
-
-        .. _pymatsolver: https://github.com/rowanc1/pymatsolver
-
-        This example is on figshare: https://dx.doi.org/10.6084/m9.figshare.3126961.v1
-
-        If you would use this example for a code comparison, or build upon it, a
-        citation would be much appreciated!
-
-    """
-
-    if plotIt:
-        import matplotlib.pylab as plt
-
-    # ------------------ MODEL ------------------
-    sigmaair = 1e-8 # air
-    sigmaback = 1e-2 # background
-    sigmacasing = 1e6 # casing
-    sigmainside = sigmaback # inside the casing
-
-
-    casing_t = 0.006  # 1cm thickness
-    casing_l = 300   # length of the casing
-
-    casing_r = 0.1
-    casing_a = casing_r - casing_t/2. # inner radius
-    casing_b = casing_r + casing_t/2. # outer radius
-    casing_z = np.r_[-casing_l,0.]
-
-
-    # ------------------ SURVEY PARAMETERS ------------------
-    freqs = np.r_[1e-6] #[1e-1, 1, 5] # frequencies
-    dsz = -300 # down-hole z source location
-    src_loc = np.r_[0.,0.,dsz]
-    inf_loc = np.r_[0.,0.,1e4]
-
-    print 'Skin Depth: ', [(500./np.sqrt(sigmaback*_)) for _ in freqs]
-
-
-    # ------------------ MESH ------------------
-    # fine cells near well bore
-    csx1, csx2 = 2e-3, 60.
-    pfx1, pfx2 = 1.3, 1.3
-    ncx1 = np.ceil(casing_b/csx1+2)
-
-    # pad nicely to second cell size
-    npadx1 = np.floor(np.log(csx2/csx1) / np.log(pfx1))
-    hx1a,hx1b = Utils.meshTensor([(csx1,ncx1)]),Utils.meshTensor([(csx1,npadx1,pfx1)])
-    dx1 = sum(hx1a)+sum(hx1b)
-    dx1 = np.floor(dx1/csx2)
-    hx1b *= (dx1*csx2 - sum(hx1a))/sum(hx1b)
-
-    # second chunk of mesh
-    dx2 = 300. # uniform mesh out to here
-    ncx2 = np.ceil((dx2 - dx1)/csx2)
-    npadx2 = 45
-    hx2a, hx2b = Utils.meshTensor([(csx2,ncx2)]), Utils.meshTensor([(csx2,npadx2,pfx2)])
-    hx = np.hstack([hx1a,hx1b,hx2a,hx2b])
-
-    # z-direction
-    csz = 0.05
-    nza = 10
-    ncz, npadzu, npadzd = np.int(np.ceil(np.diff(casing_z)[0]/csz))+10, 68, 68 # cell size, number of core cells, number of padding cells in the x- direction
-    hz = Utils.meshTensor([(csz,npadzd,-1.3), (csz,ncz), (csz,npadzu,1.3)]) # vector of cell widths in the z-direction
-
-    # Mesh
-    mesh = Mesh.CylMesh([hx,1.,hz], [0.,0.,-np.sum(hz[:npadzu+ncz-nza])])
-
-    print 'Mesh Extent xmax: %f,: zmin: %f, zmax: %f'%(mesh.vectorCCx.max(), mesh.vectorCCz.min(), mesh.vectorCCz.max())
-    print 'Number of cells', mesh.nC
-
-    if plotIt is True:
-        fig, ax = plt.subplots(1, 1, figsize=(6, 4))
-        ax.set_title('Simulation Mesh')
-        mesh.plotGrid(ax=ax)
-        plt.show()
-
-    # Put the model on the mesh
-    sigWholespace = sigmaback*np.ones((mesh.nC))
-
-    sigBack = sigWholespace.copy()
-    sigBack[mesh.gridCC[:,2] > 0.] = sigmaair
-
-    sigCasing = sigBack.copy()
-    iCasingZ = (mesh.gridCC[:,2] <= casing_z[1]) & (mesh.gridCC[:,2] >= casing_z[0])
-    iCasingX = (mesh.gridCC[:,0] >= casing_a) & (mesh.gridCC[:,0] <= casing_b)
-    iCasing = iCasingX & iCasingZ
-    sigCasing[iCasing] = sigmacasing
-
-
-    if plotIt is True:
-
-        # plotting parameters
-        xlim = np.r_[0., 0.2]
-        zlim = np.r_[-350., 10.]
-        clim_sig = np.r_[-8,6]
-
-        # plot models
-        fig, ax = plt.subplots(1,1,figsize=(4,4))
-
-        f = plt.colorbar(mesh.plotImage(np.log10(sigCasing),ax=ax)[0], ax=ax)
-        ax.grid(which='both')
-        ax.set_title('Log_10 (Sigma)')
-        ax.set_xlim(xlim)
-        ax.set_ylim(zlim)
-        f.set_clim(clim_sig)
-
-        plt.show()
-
-
-    # -------------- Sources --------------------
-    # Define Custom Current Sources
-
-    # surface source
-    sg_x = np.zeros(mesh.vnF[0],dtype=complex)
-    sg_y = np.zeros(mesh.vnF[1],dtype=complex)
-    sg_z = np.zeros(mesh.vnF[2],dtype=complex)
-
-    nza = 2 # put the wire two cells above the surface
-    ncin = 2
-
-    # vertically directed wire
-    sgv_indx = (mesh.gridFz[:,0] > casing_a) & (mesh.gridFz[:,0] < casing_a + csx1)  # hook it up to casing at the surface
-    sgv_indz = (mesh.gridFz[:,2] <= +csz*nza) & (mesh.gridFz[:,2] >= -csz*2)
-    sgv_ind = sgv_indx & sgv_indz
-    sg_z[sgv_ind] = -1.
-
-    # horizontally directed wire
-    sgh_indx = (mesh.gridFx[:,0] > casing_a)    & (mesh.gridFx[:,0] <= inf_loc[2])
-    sgh_indz = (mesh.gridFx[:,2] > csz*(nza-0.5)) & (mesh.gridFx[:,2] < csz*(nza+0.5))
-    sgh_ind = sgh_indx & sgh_indz
-    sg_x[sgh_ind] = -1.
-
-    sgv2_indx = (mesh.gridFz[:,0] >= mesh.gridFx[sgh_ind,0].max()) & (mesh.gridFz[:,0] <= inf_loc[2]*1.2)  # hook it up to casing at the surface
-    sgv2_indz = (mesh.gridFz[:,2] <= +csz*nza) & (mesh.gridFz[:,2] >= -csz*2)
-    sgv2_ind = sgv2_indx & sgv2_indz
-    sg_z[sgv2_ind] = 1.
-
-    # assemble the source
-    sg = np.hstack([sg_x,sg_y,sg_z])
-    sg_p = [FDEM.Src.RawVec_e([],_,sg/mesh.area) for _ in freqs]
-
-    # downhole source
-    dg_x = np.zeros(mesh.vnF[0],dtype=complex)
-    dg_y = np.zeros(mesh.vnF[1],dtype=complex)
-    dg_z = np.zeros(mesh.vnF[2],dtype=complex)
-
-    # vertically directed wire
-    dgv_indx = (mesh.gridFz[:,0] < csx1)  # go through the center of the well
-    dgv_indz = (mesh.gridFz[:,2] <= +csz*nza) & (mesh.gridFz[:,2] > dsz + csz/2.)
-    dgv_ind = dgv_indx & dgv_indz
-    dg_z[dgv_ind] = -1.
-
-    # couple to the casing downhole
-    dgh_indx = mesh.gridFx[:,0] < casing_a + csx1
-    dgh_indz = (mesh.gridFx[:,2] < dsz + csz)  & (mesh.gridFx[:,2] >= dsz)
-    dgh_ind = dgh_indx & dgh_indz
-    dg_x[dgh_ind] = 1.
-
-    # horizontal part at surface
-    dgh2_indx = mesh.gridFx[:,0] <= inf_loc[2]*1.2
-    dgh2_indz = sgh_indz.copy()
-    dgh2_ind = dgh2_indx & dgh2_indz
-    dg_x[dgh2_ind] = -1.
-
-    # vertical part at surface
-    dgv2_ind = sgv2_ind.copy()
-    dg_z[dgv2_ind] = 1.
-
-    # assemble the source
-    dg = np.hstack([dg_x,dg_y,dg_z])
-    dg_p = [FDEM.Src.RawVec_e([],_,dg/mesh.area) for _ in freqs]
-
-    # ------------ Problem and Survey ---------------
-    survey = FDEM.Survey(sg_p + dg_p)
-    mapping = [('sigma', Maps.IdentityMap(mesh))]
-    problem = FDEM.Problem3D_h(mesh, mapping=mapping)
-    problem.pair(survey)
-
-    # ------------- Solve ---------------------------
-    t0 = time.time()
-    fieldsCasing = problem.fields(sigCasing)
-    print 'Time to solve 2 sources', time.time() - t0
-
-    # Plot current
-
-    # current density
-    jn0 = fieldsCasing[dg_p,'j']
-    jn1 = fieldsCasing[sg_p,'j']
-
-    # current
-    in0 = [mesh.area*fieldsCasing[dg_p,'j'][:,i] for i in range(len(freqs))]
-    in1 = [mesh.area*fieldsCasing[sg_p,'j'][:,i] for i in range(len(freqs))]
-
-    in0 = np.vstack(in0).T
-    in1 = np.vstack(in1).T
-
-    # integrate to get z-current inside casing
-    inds_inx = (mesh.gridFz[:,0] >= casing_a) & (mesh.gridFz[:,0] <= casing_b)
-    inds_inz = (mesh.gridFz[:,2] >= dsz )     & (mesh.gridFz[:,2] <= 0)
-    inds_fz = inds_inx & inds_inz
-
-    indsx = [False]*mesh.nFx
-    inds = list(indsx) + list(inds_fz)
-
-    in0_in = in0[np.r_[inds]]
-    in1_in = in1[np.r_[inds]]
-    z_in = mesh.gridFz[inds_fz,2]
-
-    in0_in = in0_in.reshape([in0_in.shape[0]/3,3])
-    in1_in = in1_in.reshape([in1_in.shape[0]/3,3])
-    z_in = z_in.reshape([z_in.shape[0]/3,3])
-
-    I0 = in0_in.sum(1).real
-    I1 = in1_in.sum(1).real
-    z_in = z_in[:,0]
-
-    if plotIt is True:
-        fig, ax = plt.subplots(1,2,figsize=(12,4))
-
-        ax[0].plot(z_in,np.absolute(I0), z_in,np.absolute(I1))
-        ax[0].legend(['top casing', 'bottom casing'],loc='best')
-        ax[0].set_title('Magnitude of Vertical Current in Casing')
-
-        ax[1].semilogy(z_in,np.absolute(I0), z_in,np.absolute(I1))
-        ax[1].legend(['top casing', 'bottom casing'],loc='best')
-        ax[1].set_title('Magnitude of Vertical Current in Casing')
-        ax[1].set_ylim([1e-2, 1.])
-
-        plt.show()
-
-if __name__ == '__main__':
-    run()
-

SimPEG/Examples/EM_TDEM_1D_Inversion.py

@@ -1,106 +0,0 @@
-from SimPEG import *
-import SimPEG.EM as EM
-from SimPEG.EM import mu_0
-
-
-def run(plotIt=True):
-    """
-        EM: TDEM: 1D: Inversion
-        =======================
-
-        Here we will create and run a TDEM 1D inversion.
-
-    """
-
-    cs, ncx, ncz, npad = 5., 25, 15, 15
-    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')
-
-    active = mesh.vectorCCz<0.
-    layer = (mesh.vectorCCz<0.) & (mesh.vectorCCz>=-100.)
-    actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
-    mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
-    sig_half = 2e-3
-    sig_air = 1e-8
-    sig_layer = 1e-3
-    sigma = np.ones(mesh.nCz)*sig_air
-    sigma[active] = sig_half
-    sigma[layer] = sig_layer
-    mtrue = np.log(sigma[active])
-
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (3, 6))
-        plt.semilogx(sigma[active], mesh.vectorCCz[active])
-        ax.set_ylim(-600, 0)
-        ax.set_xlim(1e-4, 1e-2)
-        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)
-
-
-    rxOffset=1e-3
-    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)]
-    prb.pair(survey)
-
-    # create observed data
-    std = 0.05
-
-    survey.dobs = survey.makeSyntheticData(mtrue,std)
-    survey.std = std
-    survey.eps = 1e-5*np.linalg.norm(survey.dobs)
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (10, 6))
-        ax.loglog(rx.times, survey.dtrue, 'b.-')
-        ax.loglog(rx.times, survey.dobs, 'r.-')
-        ax.legend(('Noisefree', '$d^{obs}$'), fontsize = 16)
-        ax.set_xlabel('Time (s)', fontsize = 14)
-        ax.set_ylabel('$B_z$ (T)', fontsize = 16)
-        ax.set_xlabel('Time (s)', fontsize = 14)
-        ax.grid(color='k', alpha=0.5, linestyle='dashed', linewidth=0.5)
-
-    dmisfit = DataMisfit.l2_DataMisfit(survey)
-    regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
-    reg = Regularization.Tikhonov(regMesh)
-    opt = Optimization.InexactGaussNewton(maxIter = 5)
-    invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
-
-    # Create an inversion object
-    beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)
-    betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)
-    inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])
-    m0 = np.log(np.ones(mtrue.size)*sig_half)
-    reg.alpha_s = 1e-2
-    reg.alpha_x = 1.
-    prb.counter = opt.counter = Utils.Counter()
-    opt.LSshorten = 0.5
-    opt.remember('xc')
-
-    mopt = inv.run(m0)
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, ax = plt.subplots(1,1, figsize = (3, 6))
-        plt.semilogx(sigma[active], mesh.vectorCCz[active])
-        plt.semilogx(np.exp(mopt), mesh.vectorCCz[active])
-        ax.set_ylim(-600, 0)
-        ax.set_xlim(1e-4, 1e-2)
-        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(['$\sigma_{true}$', '$\sigma_{pred}$'])
-        plt.show()
-
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/FLOW_Richards_1D_Celia1990.py

@@ -1,86 +0,0 @@
-from SimPEG import *
-from SimPEG.FLOW import Richards
-
-def run(plotIt=True):
-    """
-        FLOW: Richards: 1D: Celia1990
-        =============================
-
-        There are two different forms of Richards equation that differ
-        on how they deal with the non-linearity in the time-stepping term.
-
-        The most fundamental form, referred to as the
-        'mixed'-form of Richards Equation Celia1990_
-
-        .. math::
-
-            \\frac{\partial \\theta(\psi)}{\partial t} - \\nabla \cdot k(\psi) \\nabla \psi - \\frac{\partial k(\psi)}{\partial z} = 0
-            \quad \psi \in \Omega
-
-        where \\\\(\\\\theta\\\\) is water content, and \\\\(\\\\psi\\\\) is pressure head.
-        This formulation of Richards equation is called the
-        'mixed'-form because the equation is parameterized in \\\\(\\\\psi\\\\)
-        but the time-stepping is in terms of \\\\(\\\\theta\\\\).
-
-        As noted in Celia1990_ the 'head'-based form of Richards
-        equation can be written in the continuous form as:
-
-        .. math::
-
-            \\frac{\partial \\theta}{\partial \psi}\\frac{\partial \psi}{\partial t} - \\nabla \cdot k(\psi) \\nabla \psi - \\frac{\partial k(\psi)}{\partial z} = 0 \quad \psi \in \Omega
-
-        However, it can be shown that this does not conserve mass in the discrete formulation.
-
-        Here we reproduce the results from Celia1990_ demonstrating the head-based formulation and the mixed-formulation.
-
-        .. _Celia1990: http://www.webpages.uidaho.edu/ch/papers/Celia.pdf
-    """
-    M = Mesh.TensorMesh([np.ones(40)])
-    M.setCellGradBC('dirichlet')
-    params = Richards.Empirical.HaverkampParams().celia1990
-    params['Ks'] = np.log(params['Ks'])
-    E = Richards.Empirical.Haverkamp(M, **params)
-
-    bc = np.array([-61.5,-20.7])
-    h = np.zeros(M.nC) + bc[0]
-
-
-    def getFields(timeStep,method):
-        timeSteps = np.ones(360/timeStep)*timeStep
-        prob = Richards.RichardsProblem(M, mapping=E, timeSteps=timeSteps,
-                                        boundaryConditions=bc, initialConditions=h,
-                                        doNewton=False, method=method)
-        return prob.fields(params['Ks'])
-
-    Hs_M10 = getFields(10., 'mixed')
-    Hs_M30 = getFields(30., 'mixed')
-    Hs_M120= getFields(120.,'mixed')
-    Hs_H10 = getFields(10., 'head')
-    Hs_H30 = getFields(30., 'head')
-    Hs_H120= getFields(120.,'head')
-
-    if not plotIt:return
-    import matplotlib.pyplot as plt
-    plt.figure(figsize=(13,5))
-    plt.subplot(121)
-    plt.plot(40-M.gridCC, Hs_M10[-1],'b-')
-    plt.plot(40-M.gridCC, Hs_M30[-1],'r-')
-    plt.plot(40-M.gridCC, Hs_M120[-1],'k-')
-    plt.ylim([-70,-10])
-    plt.title('Mixed Method')
-    plt.xlabel('Depth, cm')
-    plt.ylabel('Pressure Head, cm')
-    plt.legend(('$\Delta t$ = 10 sec','$\Delta t$ = 30 sec','$\Delta t$ = 120 sec'))
-    plt.subplot(122)
-    plt.plot(40-M.gridCC, Hs_H10[-1],'b-')
-    plt.plot(40-M.gridCC, Hs_H30[-1],'r-')
-    plt.plot(40-M.gridCC, Hs_H120[-1],'k-')
-    plt.ylim([-70,-10])
-    plt.title('Head-Based Method')
-    plt.xlabel('Depth, cm')
-    plt.ylabel('Pressure Head, cm')
-    plt.legend(('$\Delta t$ = 10 sec','$\Delta t$ = 30 sec','$\Delta t$ = 120 sec'))
-    plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Inversion_IRLS.py

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

SimPEG/Examples/Linear.py

@@ -1,17 +1,29 @@
 from SimPEG import *
 
+class LinearSurvey(Survey.BaseSurvey):
+    def projectFields(self, u):
+        return u
 
-def run(N=100, plotIt=True):
-    """
-        Inversion: Linear Problem
-        =========================
+class LinearProblem(Problem.BaseProblem):
+    """docstring for LinearProblem"""
 
-        Here we go over the basics of creating a linear problem and inversion.
+    surveyPair = LinearSurvey
 
-    """
+    def __init__(self, mesh, G, **kwargs):
+        Problem.BaseProblem.__init__(self, mesh, **kwargs)
+        self.G = G
 
-    np.random.seed(1)
+    def fields(self, m, u=None):
+        return self.G.dot(m)
 
+    def Jvec(self, m, v, u=None):
+        return self.G.dot(v)
+
+    def Jtvec(self, m, v, u=None):
+        return self.G.T.dot(v)
+
+
+def run(N, plotIt=True):
     mesh = Mesh.TensorMesh([N])
 
     nk = 20
@@ -31,8 +43,8 @@ def run(N=100, plotIt=True):
     mtrue[mesh.vectorCCx > 0.45] = -0.5
     mtrue[mesh.vectorCCx > 0.6] = 0
 
-    prob = Problem.LinearProblem(mesh, G)
-    survey = Survey.LinearSurvey()
+    prob = LinearProblem(mesh, G)
+    survey = LinearSurvey()
     survey.pair(prob)
     survey.makeSyntheticData(mtrue, std=0.01)
 
@@ -40,7 +52,7 @@ def run(N=100, plotIt=True):
 
     reg = Regularization.Tikhonov(mesh)
     dmis = DataMisfit.l2_DataMisfit(survey)
-    opt = Optimization.InexactGaussNewton(maxIter=35)
+    opt = Optimization.InexactGaussNewton(maxIter=20)
     invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
     beta = Directives.BetaSchedule()
     betaest = Directives.BetaEstimate_ByEig()
@@ -51,18 +63,16 @@ def run(N=100, plotIt=True):
 
     if plotIt:
         import matplotlib.pyplot as plt
-
-        fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
+        plt.figure(1)
         for i in range(prob.G.shape[0]):
-            axes[0].plot(prob.G[i,:])
-        axes[0].set_title('Columns of matrix G')
+            plt.plot(prob.G[i,:])
 
-        axes[1].plot(M.vectorCCx, survey.mtrue, 'b-')
-        axes[1].plot(M.vectorCCx, mrec, 'r-')
-        axes[1].legend(('True Model', 'Recovered Model'))
+        plt.figure(2)
+        plt.plot(M.vectorCCx, survey.mtrue, 'b-')
+        plt.plot(M.vectorCCx, mrec, 'r-')
         plt.show()
 
     return prob, survey, mesh, mrec
 
 if __name__ == '__main__':
-    run()
+    run(100)

SimPEG/Examples/MT_1D_ForwardAndInversion.py

@@ -1,129 +0,0 @@
-import SimPEG as simpeg
-import numpy as np
-import SimPEG.MT as MT
-from scipy.constants import mu_0
-import matplotlib.pyplot as plt
-
-def run(plotIt=True):
-    """
-        MT: 1D: Inversion
-        =======================
-
-        Forward model 1D MT data.
-        Setup and run a MT 1D inversion.
-
-    """
-
-    ## Setup the forward modeling
-    # Setting up 1D mesh and conductivity models to forward model data.
-    # Frequency
-    nFreq = 31
-    freqs = np.logspace(3,-3,nFreq)
-    # Set mesh parameters
-    ct = 20
-    air = simpeg.Utils.meshTensor([(ct,16,1.4)])
-    core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )
-    bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])
-    x0 = -np.array([np.sum(np.concatenate((core,bot)))])
-    # Make the model
-    m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)
-
-    # Setup model varibles
-    active = m1d.vectorCCx<0.
-    layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)
-    layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)
-    # Set the conductivity values
-    sig_half = 2e-3
-    sig_air = 1e-8
-    sig_layer1 = .2
-    sig_layer2 = .2
-    # Make the true model
-    sigma_true = np.ones(m1d.nCx)*sig_air
-    sigma_true[active] = sig_half
-    sigma_true[layer1] = sig_layer1
-    sigma_true[layer2] = sig_layer2
-    # Extract the model
-    m_true = np.log(sigma_true[active])
-    # Make the background model
-    sigma_0 = np.ones(m1d.nCx)*sig_air
-    sigma_0[active] = sig_half
-    m_0 = np.log(sigma_0[active])
-
-    # Set the mapping
-    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
-    # Receivers
-    rxList = []
-    for rxType in ['z1dr','z1di']:
-        rxList.append(MT.Rx(simpeg.mkvc(np.array([0.0]),2).T,rxType))
-    # Source list
-    srcList =[]
-    for freq in freqs:
-            srcList.append(MT.SrcMT.polxy_1Dprimary(rxList,freq))
-    # Make the survey
-    survey = MT.Survey(srcList)
-    survey.mtrue = m_true
-
-    ## Set the problem
-    problem = MT.Problem1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)
-    problem.pair(survey)
-
-    ## Forward model data
-    # Project the data
-    survey.dtrue = survey.dpred(m_true)
-    survey.dobs = survey.dtrue + 0.025*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)
-
-    if plotIt:
-        fig = MT.Utils.dataUtils.plotMT1DModelData(problem)
-        fig.suptitle('Target - smooth true')
-
-
-    # Assign uncertainties
-    std = 0.05 # 5% std
-    survey.std = np.abs(survey.dobs*std)
-    # Assign the data weight
-    Wd = 1./survey.std
-
-    ## Setup the inversion proceedure
-    # Define a counter
-    C =  simpeg.Utils.Counter()
-    # Set the optimization
-    opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)
-    opt.counter = C
-    opt.LSshorten = 0.5
-    opt.remember('xc')
-    # Data misfit
-    dmis = simpeg.DataMisfit.l2_DataMisfit(survey)
-    dmis.Wd = Wd
-    # Regularization - with a regularization mesh
-    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)
-    reg = simpeg.Regularization.Tikhonov(regMesh)
-    reg.mrefInSmooth = True
-    reg.alpha_s = 1e-7
-    reg.alpha_x = 1.
-    # Inversion problem
-    invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)
-    invProb.counter = C
-    # Beta cooling
-    beta = simpeg.Directives.BetaSchedule()
-    beta.coolingRate = 4
-    betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)
-    targmis = simpeg.Directives.TargetMisfit()
-    targmis.target = survey.nD
-    saveModel = simpeg.Directives.SaveModelEveryIteration()
-    saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue'
-    # Create an inversion object
-    inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis])
-
-    ## Run the inversion
-    mopt = inv.run(m_0)
-
-    if plotIt:
-        fig = MT.Utils.dataUtils.plotMT1DModelData(problem,[mopt])
-        fig.suptitle('Target - smooth true')
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/MT_3D_Foward.py

@@ -1,64 +0,0 @@
-# Test script to use SimPEG.MT platform to forward model synthetic data.
-
-# Import
-import SimPEG as simpeg
-from SimPEG import MT
-import numpy as np
-try:
-    from pymatsolver import MumpsSolver as Solver
-except:
-    from SimPEG import Solver
-
-def run(plotIt=True, nFreq=1):
-    """
-        MT: 3D: Forward
-        =======================
-
-        Forward model 3D MT data.
-
-    """
-
-    # Make a mesh
-    M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])
-    # Setup the model
-    conds = [1e-2,1]
-    sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-1000,-1000,-400],[1000,1000,-200],conds)
-    sig[M.gridCC[:,2]>0] = 1e-8
-    sig[M.gridCC[:,2]<-600] = 1e-1
-    sigBG = np.zeros(M.nC) + conds[0]
-    sigBG[M.gridCC[:,2]>0] = 1e-8
-
-    ## Setup the the survey object
-    # Receiver locations
-    rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))
-    rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),np.zeros((np.prod(rx_x.shape),1))))
-    # Make a receiver list
-    rxList = []
-    for loc in rx_loc:
-        # NOTE: loc has to be a (1,3) np.ndarray otherwise errors accure
-        for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
-            rxList.append(MT.Rx(simpeg.mkvc(loc,2).T,rxType))
-    # Source list
-    srcList =[]
-    for freq in np.logspace(3,-3,nFreq):
-        srcList.append(MT.SrcMT.polxy_1Dprimary(rxList,freq))
-    # Survey MT
-    survey = MT.Survey(srcList)
-
-    ## Setup the problem object
-    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)
-    # Add plots
-    if plotIt:
-        pass
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_Basic_ForwardDC.py

@@ -1,58 +0,0 @@
-from SimPEG import Mesh, Utils, np, SolverLU
-
-def run(plotIt=True):
-
-    """
-        Mesh: Basic Forward 2D DC Resistivity
-        =====================================
-
-        2D DC forward modeling example with Tensor and Curvilinear Meshes
-    """
-
-    # Step1: Generate Tensor and Curvilinear Mesh
-    sz = [40,40]
-    tM = Mesh.TensorMesh(sz)
-    rM = Mesh.CurvilinearMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))
-
-    # Step2: Direct Current (DC) operator
-    def DCfun(mesh, pts):
-        D = mesh.faceDiv
-        sigma = 1e-2*np.ones(mesh.nC)
-        MsigI = mesh.getFaceInnerProduct(sigma, invProp=True, invMat=True)
-        A = -D*MsigI*D.T
-        A[-1,-1] /= mesh.vol[-1] # Remove null space
-        rhs = np.zeros(mesh.nC)
-        txind = Utils.meshutils.closestPoints(mesh, pts)
-        rhs[txind] = np.r_[1,-1]
-        return A, rhs
-
-    pts = np.vstack((np.r_[0.25, 0.5], np.r_[0.75, 0.5]))
-
-    #Step3: Solve DC problem (LU solver)
-    AtM, rhstM = DCfun(tM, pts)
-    AinvtM = SolverLU(AtM)
-    phitM = AinvtM*rhstM
-
-    ArM, rhsrM = DCfun(rM, pts)
-    AinvrM = SolverLU(ArM)
-    phirM = AinvrM*rhsrM
-
-    if not plotIt: return
-
-    import matplotlib.pyplot as plt
-
-    #Step4: Making Figure
-    fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
-    vmin, vmax = phitM.min(), phitM.max()
-    dat = tM.plotImage(phitM, ax=axes[0], clim=(vmin, vmax), grid=True)
-    dat = rM.plotImage(phirM, ax=axes[1], clim=(vmin, vmax), grid=True)
-    cb = plt.colorbar(dat[0], ax=axes[0]); cb.set_label("Voltage (V)")
-    cb = plt.colorbar(dat[0], ax=axes[1]); cb.set_label("Voltage (V)")
-
-    axes[0].set_title('TensorMesh')
-    axes[1].set_title('CurvilinearMesh')
-    plt.show()
-
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_Basic_PlotImage.py

@@ -1,46 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True):
-    """
-        Mesh: Basic: PlotImage
-        ======================
-
-        You can use M.PlotImage to plot images on all of the Meshes.
-
-
-    """
-    M = Mesh.TensorMesh([32,32])
-    v = Utils.ModelBuilder.randomModel(M.vnC, seed=789)
-    v = Utils.mkvc(v)
-
-    O = Mesh.TreeMesh([32,32])
-    O.refine(1)
-    def function(cell):
-        if (cell.center[0] < 0.75 and cell.center[0] > 0.25 and
-            cell.center[1] < 0.75 and cell.center[1] > 0.25):return 5
-        if (cell.center[0] < 0.9 and cell.center[0] > 0.1 and
-            cell.center[1] < 0.9 and cell.center[1] > 0.1):return 4
-        return 3
-    O.refine(function)
-
-    P = M.getInterpolationMat(O.gridCC, 'CC')
-
-    ov = P * v
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-
-        fig, axes = plt.subplots(1,2,figsize=(10,5))
-
-        out = M.plotImage(v, grid=True, ax=axes[0])
-        cb = plt.colorbar(out[0], ax=axes[0]); cb.set_label("Random Field")
-        axes[0].set_title('TensorMesh')
-
-        out = O.plotImage(ov, grid=True, ax=axes[1], clim=[0,1])
-        cb = plt.colorbar(out[0], ax=axes[1]); cb.set_label("Random Field")
-        axes[1].set_title('TreeMesh')
-
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_Basic_Types.py

@@ -1,30 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True):
-    """
-        Mesh: Basic: Types
-        ==================
-
-        Here we show SimPEG used to create three different types of meshes.
-
-    """
-    sz = [16,16]
-    tM = Mesh.TensorMesh(sz)
-    qM = Mesh.TreeMesh(sz)
-    qM.refine(lambda cell: 4 if np.sqrt(((np.r_[cell.center]-0.5)**2).sum()) < 0.4 else 3)
-    rM = Mesh.CurvilinearMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, axes = plt.subplots(1,3,figsize=(14,5))
-        opts = {}
-        tM.plotGrid(ax=axes[0], **opts)
-        axes[0].set_title('TensorMesh')
-        qM.plotGrid(ax=axes[1], **opts)
-        axes[1].set_title('TreeMesh')
-        rM.plotGrid(ax=axes[2], **opts)
-        axes[2].set_title('CurvilinearMesh')
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_Operators_CahnHilliard.py

@@ -1,105 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True, n=60):
-    """
-        Mesh: Operators: Cahn Hilliard
-        ==============================
-
-        This example is based on the example in the FiPy_ library.
-        Please see their documentation for more information about the Cahn-Hilliard equation.
-
-        The "Cahn-Hilliard" equation separates a field \\\\( \\\\phi \\\\) into 0 and 1 with smooth transitions.
-
-        .. math::
-
-            \\frac{\partial \phi}{\partial t} = \\nabla \cdot D \\nabla \left( \\frac{\partial f}{\partial \phi} - \epsilon^2 \\nabla^2 \phi \\right)
-
-        Where \\\\( f \\\\) is the energy function \\\\( f = ( a^2 / 2 )\\\\phi^2(1 - \\\\phi)^2 \\\\)
-        which drives \\\\( \\\\phi \\\\) towards either 0 or 1, this competes with the term
-        \\\\(\\\\epsilon^2 \\\\nabla^2 \\\\phi \\\\) which is a diffusion term that creates smooth changes in \\\\( \\\\phi \\\\).
-        The equation can be factored:
-
-        .. math::
-
-            \\frac{\partial \phi}{\partial t} = \\nabla \cdot D \\nabla \psi \\\\
-            \psi = \\frac{\partial^2 f}{\partial \phi^2} (\phi - \phi^{\\text{old}}) + \\frac{\partial f}{\partial \phi} - \epsilon^2 \\nabla^2 \phi
-
-        Here we will need the derivatives of \\\\( f \\\\):
-
-        .. math::
-
-            \\frac{\partial f}{\partial \phi} = (a^2/2)2\phi(1-\phi)(1-2\phi)
-            \\frac{\partial^2 f}{\partial \phi^2} = (a^2/2)2[1-6\phi(1-\phi)]
-
-        The implementation below uses backwards Euler in time with an exponentially increasing time step.
-        The initial \\\\( \\\\phi \\\\) is a normally distributed field with a standard deviation of 0.1 and mean of 0.5.
-        The grid is 60x60 and takes a few seconds to solve ~130 times. The results are seen below, and you can see the
-        field separating as the time increases.
-
-        .. _FiPy: http://www.ctcms.nist.gov/fipy/examples/cahnHilliard/generated/examples.cahnHilliard.mesh2DCoupled.html
-
-    """
-
-    np.random.seed(5)
-
-    # Here we are going to rearrange the equations:
-
-    # (phi_ - phi)/dt = A*(d2fdphi2*(phi_ - phi) + dfdphi - L*phi_)
-    # (phi_ - phi)/dt = A*(d2fdphi2*phi_ - d2fdphi2*phi + dfdphi - L*phi_)
-    # (phi_ - phi)/dt = A*d2fdphi2*phi_ + A*( - d2fdphi2*phi + dfdphi - L*phi_)
-    # phi_ - phi = dt*A*d2fdphi2*phi_ + dt*A*(- d2fdphi2*phi + dfdphi - L*phi_)
-    # phi_ - dt*A*d2fdphi2 * phi_ =  dt*A*(- d2fdphi2*phi + dfdphi - L*phi_) + phi
-    # (I - dt*A*d2fdphi2) * phi_ =  dt*A*(- d2fdphi2*phi + dfdphi - L*phi_) + phi
-    # (I - dt*A*d2fdphi2) * phi_ =  dt*A*dfdphi - dt*A*d2fdphi2*phi - dt*A*L*phi_ + phi
-    # (dt*A*d2fdphi2 - I) * phi_ =  dt*A*d2fdphi2*phi + dt*A*L*phi_ - phi - dt*A*dfdphi
-    # (dt*A*d2fdphi2 - I - dt*A*L) * phi_ =  (dt*A*d2fdphi2 - I)*phi - dt*A*dfdphi
-
-    h = [(0.25,n)]
-    M = Mesh.TensorMesh([h,h])
-
-    # Constants
-    D = a = epsilon = 1.
-    I = Utils.speye(M.nC)
-
-    # Operators
-    A = D * M.faceDiv * M.cellGrad
-    L = epsilon**2 * M.faceDiv * M.cellGrad
-
-    duration = 75
-    elapsed = 0.
-    dexp = -5
-    phi = np.random.normal(loc=0.5,scale=0.01,size=M.nC)
-    ii, jj = 0, 0
-    PHIS = []
-    capture = np.logspace(-1,np.log10(duration),8)
-    while elapsed < duration:
-        dt = min(100, np.exp(dexp))
-        elapsed += dt
-        dexp += 0.05
-
-        dfdphi   = a**2 * 2 * phi * (1 - phi) * (1 - 2 * phi)
-        d2fdphi2 = Utils.sdiag(a**2 * 2 * (1 - 6 * phi * (1 - phi)))
-
-        MAT = (dt*A*d2fdphi2 - I - dt*A*L)
-        rhs = (dt*A*d2fdphi2 - I)*phi - dt*A*dfdphi
-        phi = Solver(MAT)*rhs
-
-        if elapsed > capture[jj]:
-            PHIS += [(elapsed, phi.copy())]
-            jj += 1
-        if ii % 10 == 0: print ii, elapsed
-        ii += 1
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, axes = plt.subplots(2,4,figsize=(14,6))
-        axes = np.array(axes).flatten().tolist()
-        for ii, ax in zip(np.linspace(0,len(PHIS)-1,len(axes)),axes):
-            ii = int(ii)
-            out = M.plotImage(PHIS[ii][1],ax=ax)
-            ax.axis('off')
-            ax.set_title('Elapsed Time: %4.1f'%PHIS[ii][0])
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_QuadTree_Creation.py

@@ -1,28 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True):
-    """
-        Mesh: QuadTree: Creation
-        ========================
-
-        You can give the refine method a function, which is evaluated on every cell
-        of the TreeMesh.
-
-        Occasionally it is useful to initially refine to a constant level
-        (e.g. 3 in this 32x32 mesh). This means the function is first evaluated
-        on an 8x8 mesh (2^3).
-
-    """
-    M = Mesh.TreeMesh([32,32])
-    M.refine(3)
-    def function(cell):
-        xyz = cell.center
-        for i in range(3):
-            if np.abs(np.sin(xyz[0]*np.pi*2)*0.5 + 0.5 - xyz[1]) < 0.2*i:
-                return 6-i
-        return 0
-    M.refine(function);
-    if plotIt: M.plotGrid(showIt=True)
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_QuadTree_FaceDiv.py

@@ -1,49 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True, n=60):
-    """
-        Mesh: QuadTree: FaceDiv
-        =======================
-
-
-
-    """
-
-
-    M = Mesh.TreeMesh([[(1,16)],[(1,16)]], levels=4)
-    M._refineCell([0,0,0])
-    M._refineCell([0,0,1])
-    M._refineCell([4,4,2])
-    M.__dirty__ = True
-    M.number()
-
-
-    if plotIt:
-        import matplotlib.pyplot as plt
-        fig, axes = plt.subplots(2,1,figsize=(10,10))
-
-        M.plotGrid(cells=True, nodes=False, ax=axes[0])
-        axes[0].axis('off')
-        axes[0].set_title('Simple QuadTree Mesh')
-        axes[0].set_xlim([-1,17])
-        axes[0].set_ylim([-1,17])
-
-        for ii, loc in zip(range(M.nC),M.gridCC):
-            axes[0].text(loc[0]+0.2,loc[1],'%d'%ii, color='r')
-
-        axes[0].plot(M.gridFx[:,0],M.gridFx[:,1], 'g>')
-        for ii, loc in zip(range(M.nFx),M.gridFx):
-            axes[0].text(loc[0]+0.2,loc[1],'%d'%ii, color='g')
-
-        axes[0].plot(M.gridFy[:,0],M.gridFy[:,1], 'm^')
-        for ii, loc in zip(range(M.nFy),M.gridFy):
-            axes[0].text(loc[0]+0.2,loc[1]+0.2,'%d'%(ii+M.nFx), color='m')
-
-        axes[1].spy(M.faceDiv)
-        axes[1].set_title('Face Divergence')
-        axes[1].set_ylabel('Cell Number')
-        axes[1].set_xlabel('Face Number')
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_QuadTree_HangingNodes.py

@@ -1,32 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True):
-    """
-        Mesh: QuadTree: Hanging Nodes
-        =============================
-
-        You can give the refine method a function, which is evaluated on every cell
-        of the TreeMesh.
-
-        Occasionally it is useful to initially refine to a constant level
-        (e.g. 3 in this 32x32 mesh). This means the function is first evaluated
-        on an 8x8 mesh (2^3).
-
-    """
-    M = Mesh.TreeMesh([8,8])
-    def function(cell):
-        xyz = cell.center
-        dist = ((xyz - [0.25,0.25])**2).sum()**0.5
-        if dist < 0.25:
-            return 3
-        return 2
-    M.refine(function);
-    M.number()
-    if plotIt:
-        import matplotlib.pyplot as plt
-        M.plotGrid(nodes=True, cells=True, facesX=True)
-        plt.legend(('Grid', 'Cell Centers', 'Nodes', 'Hanging Nodes', 'X faces', 'Hanging X faces'))
-        plt.show()
-
-if __name__ == '__main__':
-    run()

SimPEG/Examples/Mesh_Tensor_Creation.py

@@ -1,35 +0,0 @@
-from SimPEG import *
-
-def run(plotIt=True):
-    """
-
-        Mesh: Tensor: Creation
-        ======================
-
-        For tensor meshes, there are some functions that can come
-        in handy. For example, creating mesh tensors can be a bit time
-        consuming, these can be created speedily by just giving numbers
-        and sizes of padding. See the example below, that follows this
-        notation::
-
-            h1 = (
-                   (cellSize, numPad, [, increaseFactor]),
-                   (cellSize, numCore),
-                   (cellSize, numPad, [, increaseFactor])
-                 )
-
-        .. note::
-
-            You can center your mesh by passing a 'C' for the x0[i] position.
-            A 'N' will make the entire mesh negative, and a '0' (or a 0) will
-            make the mesh start at zero.
-
-    """
-    h1 = [(10, 5, -1.3), (5, 20), (10, 3, 1.3)]
-    M = Mesh.TensorMesh([h1, h1], x0='CN')
-    if plotIt:
-        M.plotGrid(showIt=True)
-
-if __name__ == '__main__':
-    run()
-

SimPEG/Examples/Utils_surface2ind_topo.py

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

SimPEG/Examples/__init__.py

@@ -1,113 +1 @@
-# Run this file to add imports.
-
-##### AUTOIMPORTS #####
-import DC_Analytic_Dipole
-import DC_Forward_PseudoSection
-import EM_FDEM_1D_Inversion
-import EM_FDEM_Analytic_MagDipoleWholespace
-import EM_Schenkel_Morrison_Casing
-import EM_TDEM_1D_Inversion
-import FLOW_Richards_1D_Celia1990
-import Inversion_IRLS
-import Inversion_Linear
-import Mesh_Basic_ForwardDC
-import Mesh_Basic_PlotImage
-import Mesh_Basic_Types
-import Mesh_Operators_CahnHilliard
-import Mesh_QuadTree_Creation
-import Mesh_QuadTree_FaceDiv
-import Mesh_QuadTree_HangingNodes
-import Mesh_Tensor_Creation
-import MT_1D_ForwardAndInversion
-import MT_3D_Foward
-import Utils_surface2ind_topo
-
-__examples__ = ["DC_Analytic_Dipole", "DC_Forward_PseudoSection", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_Schenkel_Morrison_Casing", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "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 #####
-
-if __name__ == '__main__':
-    """
-
-        Run the following to create the examples documentation and add to the imports at the top.
-
-    """
-
-    import shutil, os
-    from SimPEG import Examples
-
-    # Create the examples dir in the docs folder.
-    fName = os.path.realpath(__file__)
-    docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'examples'])
-    shutil.rmtree(docExamplesDir)
-    os.makedirs(docExamplesDir)
-
-    # Get all the python examples in this folder
-    thispath = os.path.sep.join(fName.split(os.path.sep)[:-1])
-    exfiles  = [f[:-3] for f in os.listdir(thispath) if os.path.isfile(os.path.join(thispath, f)) and f.endswith('.py') and not f.startswith('_')]
-
-    # Add the imports to the top in the AUTOIMPORTS section
-    f = file(fName, 'r')
-    inimports = False
-    out = ''
-    for line in f:
-        if not inimports:
-            out += line
-
-        if line == "##### AUTOIMPORTS #####\n":
-            inimports = not inimports
-            if inimports:
-                out += '\n'.join(["import %s"%_ for _ in exfiles])
-                out += '\n\n__examples__ = ["' + '", "'.join(exfiles)+ '"]\n'
-                out += '\n##### AUTOIMPORTS #####\n'
-    f.close()
-
-    f = file(fName, 'w')
-    f.write(out)
-    f.close()
-
-
-    def _makeExample(filePath, runFunction):
-        """Makes the example given a path of the file and the run function."""
-        filePath = os.path.realpath(filePath)
-        name = filePath.split(os.path.sep)[-1].rstrip('.pyc').rstrip('.py')
-
-        docstr = runFunction.__doc__
-        if docstr is None:
-            doc = '%s\n%s'%(name.replace('_',' '),'='*len(name))
-        else:
-            doc = '\n'.join([_[8:].rstrip() for _ in docstr.split('\n')])
-
-        out = """.. _examples_%s:
-
-.. --------------------------------- ..
-..                                   ..
-..    THIS FILE IS AUTO GENEREATED   ..
-..                                   ..
-..    SimPEG/Examples/__init__.py    ..
-..                                   ..
-.. --------------------------------- ..
-
-%s
-
-.. plot::
-
-    from SimPEG import Examples
-    Examples.%s.run()
-
-.. literalinclude:: ../../SimPEG/Examples/%s.py
-    :language: python
-    :linenos:
-"""%(name,doc,name,name)
-
-        rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'examples', name + '.rst']))
-
-        print 'Creating: %s.rst'%name
-        f = open(rst, 'w')
-        f.write(out)
-        f.close()
-
-    for ex in dir(Examples):
-        if ex.startswith('_'): continue
-        E = getattr(Examples,ex)
-        _makeExample(E.__file__, E.run)
+import Linear

SimPEG/Examples/simpegEMpaper_example_1.py

@@ -1,157 +0,0 @@
-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

@@ -1,578 +0,0 @@
-from SimPEG import Mesh, Maps, Utils, np
-
-
-class NonLinearMap(object):
-    """
-    SimPEG NonLinearMap
-
-    """
-
-    __metaclass__ = Utils.SimPEGMetaClass
-
-    counter = None   #: A SimPEG.Utils.Counter object
-    mesh = None      #: A SimPEG Mesh
-
-    def __init__(self, mesh):
-        self.mesh = mesh
-
-    def _transform(self, u, m):
-        """
-            :param numpy.array u: fields
-            :param numpy.array m: model
-            :rtype: numpy.array
-            :return: transformed model
-
-            The *transform* changes the model into the physical property.
-
-        """
-        return m
-
-    def derivU(self, u, m):
-        """
-            :param numpy.array u: fields
-            :param numpy.array m: model
-            :rtype: scipy.csr_matrix
-            :return: derivative of transformed model
-
-            The *transform* changes the model into the physical property.
-            The *transformDerivU* provides the derivative of the *transform* with respect to the fields.
-        """
-        raise NotImplementedError('The transformDerivU is not implemented.')
-
-
-    def derivM(self, u, m):
-        """
-            :param numpy.array u: fields
-            :param numpy.array m: model
-            :rtype: scipy.csr_matrix
-            :return: derivative of transformed model
-
-            The *transform* changes the model into the physical property.
-            The *transformDerivU* provides the derivative of the *transform* with respect to the model.
-        """
-        raise NotImplementedError('The transformDerivM is not implemented.')
-
-    @property
-    def nP(self):
-        """Number of parameters in the model."""
-        return self.mesh.nC
-
-    def example(self):
-        raise NotImplementedError('The example is not implemented.')
-
-    def test(self, m=None):
-        raise NotImplementedError('The test is not implemented.')
-
-
-class RichardsMap(object):
-    """docstring for RichardsMap"""
-
-    mesh       = None  #: SimPEG mesh
-
-    @property
-    def thetaModel(self):
-        """Model for moisture content"""
-        return self._thetaModel
-
-    @property
-    def kModel(self):
-        """Model for hydraulic conductivity"""
-        return self._kModel
-
-    def __init__(self, mesh, thetaModel, kModel):
-        self.mesh = mesh
-        assert isinstance(thetaModel, NonLinearMap)
-        assert isinstance(kModel, NonLinearMap)
-
-        self._thetaModel = thetaModel
-        self._kModel = kModel
-
-    def theta(self, u, m):
-        return self.thetaModel.transform(u, m)
-
-    def thetaDerivM(self, u, m):
-        return self.thetaModel.transformDerivM(u, m)
-
-    def thetaDerivU(self, u, m):
-        return self.thetaModel.transformDerivU(u, m)
-
-    def k(self, u, m):
-        return self.kModel.transform(u, m)
-
-    def kDerivM(self, u, m):
-        return self.kModel.transformDerivM(u, m)
-
-    def kDerivU(self, u, m):
-        return self.kModel.transformDerivU(u, m)
-
-    def plot(self, m):
-        import matplotlib.pyplot as plt
-
-        m = m[0]
-        h = np.linspace(-100, 20, 1000)
-        ax = plt.subplot(121)
-        ax.plot(self.theta(h, m), h)
-        ax = plt.subplot(122)
-        ax.semilogx(self.k(h, m), h)
-
-    def _assertMatchesPair(self, pair):
-        assert isinstance(self, pair), "Mapping object must be an instance of a %s class."%(pair.__name__)
-
-
-
-def _ModelProperty(name, models, doc=None, default=None):
-
-    def fget(self):
-        model = models[0]
-        if getattr(self, model, None) is not None:
-            MOD = getattr(self, model)
-            return getattr(MOD, name, default)
-        return default
-
-    def fset(self, value):
-        for model in models:
-            if getattr(self, model, None) is not None:
-                MOD = getattr(self, model)
-                setattr(MOD, name, value)
-
-    return property(fget, fset=fset, doc=doc)
-
-
-class HaverkampParams(object):
-    """Holds some default parameterizations for the Haverkamp model."""
-    def __init__(self): pass
-    @property
-    def celia1990(self):
-        """
-            Parameters used in:
-
-                Celia, Michael A., Efthimios T. Bouloutas, and Rebecca L. Zarba.
-                "A general mass-conservative numerical solution for the unsaturated flow equation."
-                Water Resources Research 26.7 (1990): 1483-1496.
-
-        """
-        return {'alpha':1.611e+06, 'beta':3.96,
-                'theta_r':0.075, 'theta_s':0.287,
-                'Ks':9.44e-03, 'A':1.175e+06,
-                'gamma':4.74}
-
-
-class _haverkamp_theta(NonLinearMap):
-
-    theta_s = 0.430
-    theta_r = 0.078
-    alpha   = 0.036
-    beta    = 3.960
-
-    def __init__(self, mesh, **kwargs):
-        NonLinearMap.__init__(self, mesh)
-        Utils.setKwargs(self, **kwargs)
-
-    def setModel(self, m):
-        self._currentModel = m
-
-    def transform(self, u, m):
-        self.setModel(m)
-        f = (self.alpha*(self.theta_s  -    self.theta_r  )/
-                        (self.alpha    + abs(u)**self.beta) + self.theta_r)
-        if Utils.isScalar(self.theta_s):
-            f[u >= 0] = self.theta_s
-        else:
-            f[u >= 0] = self.theta_s[u >= 0]
-        return f
-
-    def transformDerivM(self, u, m):
-        self.setModel(m)
-
-    def transformDerivU(self, u, m):
-        self.setModel(m)
-        g = (self.alpha*((self.theta_s - self.theta_r)/
-             (self.alpha + abs(u)**self.beta)**2)
-             *(-self.beta*abs(u)**(self.beta-1)*np.sign(u)))
-        g[u >= 0] = 0
-        g = Utils.sdiag(g)
-        return g
-
-
-class _haverkamp_k(NonLinearMap):
-
-    A       = 1.175e+06
-    gamma   = 4.74
-    Ks      = np.log(24.96)
-
-    def __init__(self, mesh, **kwargs):
-        NonLinearMap.__init__(self, mesh)
-        Utils.setKwargs(self, **kwargs)
-
-    def setModel(self, m):
-        self._currentModel = m
-        #TODO: Fix me!
-        self.Ks = m
-
-    def transform(self, u, m):
-        self.setModel(m)
-        f = np.exp(self.Ks)*self.A/(self.A+abs(u)**self.gamma)
-        if Utils.isScalar(self.Ks):
-            f[u >= 0] = np.exp(self.Ks)
-        else:
-            f[u >= 0] = np.exp(self.Ks[u >= 0])
-        return f
-
-    def transformDerivM(self, u, m):
-        self.setModel(m)
-        #A
-        # dA = np.exp(self.Ks)/(self.A+abs(u)**self.gamma) - np.exp(self.Ks)*self.A/(self.A+abs(u)**self.gamma)**2
-        #gamma
-        # dgamma = -(self.A*np.exp(self.Ks)*np.log(abs(u))*abs(u)**self.gamma)/(self.A + abs(u)**self.gamma)**2
-
-        # This assumes that the the model is Ks
-        return Utils.sdiag(self.transform(u, m))
-
-    def transformDerivU(self, u, m):
-        self.setModel(m)
-        g = -(np.exp(self.Ks)*self.A*self.gamma*abs(u)**(self.gamma-1)*np.sign(u))/((self.A+abs(u)**self.gamma)**2)
-        g[u >= 0] = 0
-        g = Utils.sdiag(g)
-        return g
-
-class Haverkamp(RichardsMap):
-    """Haverkamp Model"""
-
-    alpha   = _ModelProperty('alpha',   ['thetaModel'], default=1.6110e+06)
-    beta    = _ModelProperty('beta',    ['thetaModel'], default=3.96)
-    theta_r = _ModelProperty('theta_r', ['thetaModel'], default=0.075)
-    theta_s = _ModelProperty('theta_s', ['thetaModel'], default=0.287)
-
-    Ks    = _ModelProperty('Ks',    ['kModel'], default=np.log(24.96))
-    A     = _ModelProperty('A',     ['kModel'], default=1.1750e+06)
-    gamma = _ModelProperty('gamma', ['kModel'], default=4.74)
-
-    def __init__(self, mesh, **kwargs):
-        RichardsMap.__init__(self, mesh,
-                               _haverkamp_theta(mesh),
-                               _haverkamp_k(mesh))
-        Utils.setKwargs(self, **kwargs)
-
-
-
-
-class _vangenuchten_theta(NonLinearMap):
-
-    theta_s = 0.430
-    theta_r = 0.078
-    alpha   = 0.036
-    n       = 1.560
-
-    def __init__(self, mesh, **kwargs):
-        NonLinearMap.__init__(self, mesh)
-        Utils.setKwargs(self, **kwargs)
-
-    def setModel(self, m):
-        self._currentModel = m
-
-    def transform(self, u, m):
-        self.setModel(m)
-        m = 1 - 1.0/self.n
-        f = ((  self.theta_s  -  self.theta_r  )/
-             ((1+abs(self.alpha*u)**self.n)**m)   +  self.theta_r)
-        if Utils.isScalar(self.theta_s):
-            f[u >= 0] = self.theta_s
-        else:
-            f[u >= 0] = self.theta_s[u >= 0]
-
-        return f
-
-    def transformDerivM(self, u, m):
-        self.setModel(m)
-
-    def transformDerivU(self, u, m):
-        g = -self.alpha*self.n*abs(self.alpha*u)**(self.n - 1)*np.sign(self.alpha*u)*(1./self.n - 1)*(self.theta_r - self.theta_s)*(abs(self.alpha*u)**self.n + 1)**(1./self.n - 2)
-        g[u >= 0] = 0
-        g = Utils.sdiag(g)
-        return g
-
-
-class _vangenuchten_k(NonLinearMap):
-
-    I       = 0.500
-    alpha   = 0.036
-    n       = 1.560
-    Ks      = np.log(24.96)
-
-    def __init__(self, mesh, **kwargs):
-        NonLinearMap.__init__(self, mesh)
-        Utils.setKwargs(self, **kwargs)
-
-    def setModel(self, m):
-        self._currentModel = m
-        #TODO: Fix me!
-        self.Ks = m
-
-    def transform(self, u, m):
-        self.setModel(m)
-
-        alpha = self.alpha
-        I = self.I
-        n = self.n
-        Ks = self.Ks
-        m = 1.0 - 1.0/n
-
-        theta_e = 1.0/((1.0+abs(alpha*u)**n)**m)
-        f = np.exp(Ks)*theta_e**I* ( ( 1.0 - ( 1.0 - theta_e**(1.0/m) )**m )**2 )
-        if Utils.isScalar(self.Ks):
-            f[u >= 0] = np.exp(self.Ks)
-        else:
-            f[u >= 0] = np.exp(self.Ks[u >= 0])
-        return f
-
-    def transformDerivM(self, u, m):
-        self.setModel(m)
-#         #alpha
-#         # dA = I*u*n*np.exp(Ks)*abs(alpha*u)**(n - 1)*np.sign(alpha*u)*(1.0/n - 1)*((abs(alpha*u)**n + 1)**(1.0/n - 1))**(I - 1)*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)**2*(abs(alpha*u)**n + 1)**(1.0/n - 2) - (2*u*n*np.exp(Ks)*abs(alpha*u)**(n - 1)*np.sign(alpha*u)*(1.0/n - 1)*((abs(alpha*u)**n + 1)**(1.0/n - 1))**I*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)*(abs(alpha*u)**n + 1)**(1.0/n - 2))/(((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1) + 1)*(1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1.0/n));
-#         #n
-#         # dn = 2*np.exp(Ks)*((np.log(1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))*(1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n))/n**2 + ((1.0/n - 1)*(((np.log(abs(alpha*u)**n + 1)*(abs(alpha*u)**n + 1)**(1.0/n - 1))/n**2 - abs(alpha*u)**n*np.log(abs(alpha*u))*(1.0/n - 1)*(abs(alpha*u)**n + 1)**(1.0/n - 2))/((1.0/n - 1)*((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1) + 1)) - np.log((abs(alpha*u)**n + 1)**(1.0/n - 1))/(n**2*(1.0/n - 1)**2*((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))))/(1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1.0/n))*((abs(alpha*u)**n + 1)**(1.0/n - 1))**I*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1) - I*np.exp(Ks)*((np.log(abs(alpha*u)**n + 1)*(abs(alpha*u)**n + 1)**(1.0/n - 1))/n**2 - abs(alpha*u)**n*np.log(abs(alpha*u))*(1.0/n - 1)*(abs(alpha*u)**n + 1)**(1.0/n - 2))*((abs(alpha*u)**n + 1)**(1.0/n - 1))**(I - 1)*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)**2;
-#         #I
-#         # dI = np.exp(Ks)*np.log((abs(alpha*u)**n + 1)**(1.0/n - 1))*((abs(alpha*u)**n + 1)**(1.0/n - 1))**I*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)**2;
-        return Utils.sdiag(self.transform(u, m)) # This assumes that the the model is Ks
-
-    def transformDerivU(self, u, m):
-        self.setModel(m)
-        alpha = self.alpha
-        I = self.I
-        n = self.n
-        Ks = self.Ks
-        m = 1.0 - 1.0/n
-
-        g = I*alpha*n*np.exp(Ks)*abs(alpha*u)**(n - 1.0)*np.sign(alpha*u)*(1.0/n - 1.0)*((abs(alpha*u)**n + 1)**(1.0/n - 1))**(I - 1)*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)**2*(abs(alpha*u)**n + 1)**(1.0/n - 2) - (2*alpha*n*np.exp(Ks)*abs(alpha*u)**(n - 1)*np.sign(alpha*u)*(1.0/n - 1)*((abs(alpha*u)**n + 1)**(1.0/n - 1))**I*((1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1 - 1.0/n) - 1)*(abs(alpha*u)**n + 1)**(1.0/n - 2))/(((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1) + 1)*(1 - 1.0/((abs(alpha*u)**n + 1)**(1.0/n - 1))**(1.0/(1.0/n - 1)))**(1.0/n))
-        g[u >= 0] = 0
-        g = Utils.sdiag(g)
-        return g
-
-class VanGenuchten(RichardsMap):
-    """vanGenuchten Model"""
-
-    theta_r = _ModelProperty('theta_r', ['thetaModel'], default=0.075)
-    theta_s = _ModelProperty('theta_s', ['thetaModel'], default=0.287)
-
-    alpha   = _ModelProperty('alpha',   ['thetaModel', 'kModel'], default=0.036)
-    n       = _ModelProperty('n',       ['thetaModel', 'kModel'], default=1.560)
-
-    Ks    = _ModelProperty('Ks',    ['kModel'], default=np.log(24.96))
-    I     = _ModelProperty('I',     ['kModel'], default=0.500)
-
-    def __init__(self, mesh, **kwargs):
-        RichardsMap.__init__(self, mesh,
-                               _vangenuchten_theta(mesh),
-                               _vangenuchten_k(mesh))
-        Utils.setKwargs(self, **kwargs)
-
-
-class VanGenuchtenParams(object):
-    """
-        The RETC code for quantifying the hydraulic functions of unsaturated soils,
-        Van Genuchten, M Th, Leij, F J, Yates, S R
-
-        Table 3: Average values for selected soil water retention and hydraulic
-        conductivity parameters for 11 major soil textural groups
-        according to Rawls et al. [1982]
-
-    """
-    def __init__(self): pass
-    @property
-    def sand(self):
-        return {"theta_r": 0.020, "theta_s": 0.417, "alpha": 0.138*100., "n": 1.592, "Ks": 504.0/100./24./60./60.}
-    @property
-    def loamySand(self):
-        return {"theta_r": 0.035, "theta_s": 0.401, "alpha": 0.115*100., "n": 1.474, "Ks": 146.6/100./24./60./60.}
-    @property
-    def sandyLoam(self):
-        return {"theta_r": 0.041, "theta_s": 0.412, "alpha": 0.068*100., "n": 1.322, "Ks": 62.16/100./24./60./60.}
-    @property
-    def loam(self):
-        return {"theta_r": 0.027, "theta_s": 0.434, "alpha": 0.090*100., "n": 1.220, "Ks": 16.32/100./24./60./60.}
-    @property
-    def siltLoam(self):
-        return {"theta_r": 0.015, "theta_s": 0.486, "alpha": 0.048*100., "n": 1.211, "Ks": 31.68/100./24./60./60.}
-    @property
-    def sandyClayLoam(self):
-        return {"theta_r": 0.068, "theta_s": 0.330, "alpha": 0.036*100., "n": 1.250, "Ks": 10.32/100./24./60./60.}
-    @property
-    def clayLoam(self):
-        return {"theta_r": 0.075, "theta_s": 0.390, "alpha": 0.039*100., "n": 1.194, "Ks": 5.52/100./24./60./60.}
-    @property
-    def siltyClayLoam(self):
-        return {"theta_r": 0.040, "theta_s": 0.432, "alpha": 0.031*100., "n": 1.151, "Ks": 3.60/100./24./60./60.}
-    @property
-    def sandyClay(self):
-        return {"theta_r": 0.109, "theta_s": 0.321, "alpha": 0.034*100., "n": 1.168, "Ks": 2.88/100./24./60./60.}
-    @property
-    def siltyClay(self):
-        return {"theta_r": 0.056, "theta_s": 0.423, "alpha": 0.029*100., "n": 1.127, "Ks": 2.16/100./24./60./60.}
-    @property
-    def clay(self):
-        return {"theta_r": 0.090, "theta_s": 0.385, "alpha": 0.027*100., "n": 1.131, "Ks": 1.44/100./24./60./60.}
-
-
-    # From: INDIRECT METHODS FOR ESTIMATING THE HYDRAULIC PROPERTIES OF UNSATURATED SOILS
-    # @property
-    # def siltLoamGE3(self):
-    #     """Soil Index: 3310"""
-    #     return {"theta_r": 0.139, "theta_s": 0.394, "alpha": 0.00414, "n": 2.15}
-    # @property
-    # def yoloLightClayK_WC(self):
-    #     """Soil Index: None"""
-    #     return {"theta_r": 0.205, "theta_s": 0.499, "alpha": 0.02793, "n": 1.71}
-    # @property
-    # def yoloLightClayK_H(self):
-    #     """Soil Index: None"""
-    #     return {"theta_r": 0.205, "theta_s": 0.499, "alpha": 0.02793, "n": 1.71}
-    # @property
-    # def hygieneSandstone(self):
-    #     """Soil Index: 4130"""
-    #     return {"theta_r": 0.000, "theta_s": 0.256, "alpha": 0.00562, "n": 3.27}
-    # @property
-    # def lambcrgClay(self):
-    #     """Soil Index: 1003"""
-    #     return {"theta_r": 0.000, "theta_s": 0.502, "alpha": 0.140, "n": 1.93}
-    # @property
-    # def beitNetofaClaySoil(self):
-    #     """Soil Index: 1006"""
-    #     return {"theta_r": 0.000, "theta_s": 0.447, "alpha": 0.00156, "n": 1.17}
-    # @property
-    # def shiohotSiltyClay(self):
-    #     """Soil Index: 1101"""
-    #     return {"theta_r": 0.000, "theta_s": 0.456, "alpha": 183, "n":1.17}
-    # @property
-    # def siltColumbia(self):
-    #     """Soil Index: 2001"""
-    #     return {"theta_r": 0.146, "theta_s": 0.397,  "alpha": 0.0145, "n": 1.85}
-    # @property
-    # def siltMontCenis(self):
-    #     """Soil Index: 2002"""
-    #     return {"theta_r": 0.000, "theta_s": 0.425, "alpha": 0.0103, "n": 1.34}
-    # @property
-    # def slateDust(self):
-    #     """Soil Index: 2004"""
-    #     return {"theta_r": 0.000, "theta_s": 0.498, "alpha": 0.00981, "n": 6.75}
-    # @property
-    # def weldSiltyClayLoam(self):
-    #     """Soil Index: 3001"""
-    #     return {"theta_r": 0.159, "theta_s": 0.496, "alpha": 0.0136, "n": 5.45}
-    # @property
-    # def rideauClayLoam_Wetting(self):
-    #     """Soil Index: 3101a"""
-    #     return {"theta_r": 0.279, "theta_s": 0.419, "alpha": 0.0661, "n": 1.89}
-    # @property
-    # def rideauClayLoam_Drying(self):
-    #     """Soil Index: 3101b"""
-    #     return {"theta_r": 0.290, "theta_s": 0.419, "alpha": 0.0177, "n": 3.18}
-    # @property
-    # def caribouSiltLoam_Drying(self):
-    #     """Soil Index: 3301a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.451, "alpha": 0.00845, "n": 1.29}
-    # @property
-    # def caribouSiltLoam_Wetting(self):
-    #     """Soil Index: 3301b"""
-    #     return {"theta_r": 0.000, "theta_s": 0.450, "alpha": 0.140, "n": 1.09}
-    # @property
-    # def grenvilleSiltLoam_Wetting(self):
-    #     """Soil Index: 3302a"""
-    #     return {"theta_r": 0.013, "theta_s": 0523,  "alpha": 0.0630, "n": 1.24}
-    # @property
-    # def grenvilleSiltLoam_Drying(self):
-    #     """Soil Index: 3302c"""
-    #     return {"theta_r": 0.000, "theta_s": 0.488, "alpha": 0.0112, "n": 1.23}
-    # @property
-    # def touchetSiltLoam(self):
-    #     """Soil Index: 3304"""
-    #     return {"theta_r": 0.183, "theta_s": 0.498, "alpha": 0.0104, "n": 5.78}
-    # @property
-    # def gilatLoam(self):
-    #     """Soil Index: 3402a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.454, "alpha": 0.0291, "n": 1.47}
-    # @property
-    # def pachapaLoam(self):
-    #     """Soil Index: 3403"""
-    #     return {"theta_r": 0.000, "theta_s": 0.472, "alpha": 0.00829, "n": 1.62}
-    # @property
-    # def adelantoLoam(self):
-    #     """Soil Index: 3404"""
-    #     return {"theta_r": 0.000, "theta_s": 0.444, "alpha": 0.00710, "n": 1.26}
-    # @property
-    # def indioLoam(self):
-    #     """Soil Index: 3405a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.507, "alpha": 0.00847, "n": 1.60}
-    # @property
-    # def guclphLoam(self):
-    #     """Soil Index: 3407a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.563, "alpha": 0.0275, "n": 1.27}
-    # @property
-    # def guclphLoam(self):
-    #     """Soil Index: 3407b"""
-    #     return {"theta_r": 0.236, "theta_s": 0.435, "alpha": 0.0271, "n": 262}
-    # @property
-    # def rubiconSandyLoam(self):
-    #     """Soil Index: 3501a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.393,  "alpha": 0.00972, "n": 2.18}
-    # @property
-    # def rubiconSandyLoam(self):
-    #     """Soil Index: 350lb"""
-    #     return {"theta_r": 0.000, "theta_s": 0.433, "alpha": 0.147, "n": 1.28}
-    # @property
-    # def pachapaFmeSandyClay(self):
-    #     """Soil Index: 3503a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.340, "alpha": 0.0194, "n": 1.45}
-    # @property
-    # def gilatSandyLoam(self):
-    #     """Soil Index: 3504"""
-    #     return {"theta_r": 0.000, "theta_s": 0.432, "alpha": 0.0103, "n": 1.48}
-    # @property
-    # def plainfieldSand_210to250(self):
-    #     """Soil Index: 4101a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.351, "alpha": 0.0236, "n": 12.30}
-    # @property
-    # def plainfieldSand_210to250(self):
-    #     """Soil Index: 4101b"""
-    #     return {"theta_r": 0.000, "theta_s": 0.312, "alpha": 0.0387, "n": 4.48}
-    # @property
-    # def plainfieldSand_177to210(self):
-    #     """Soil Index: 4102a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.361, "alpha": 0.0207, "n": 10.0}
-    # @property
-    # def plainfieldSand_177to210(self):
-    #     """Soil Index: 4102b"""
-    #     return {"theta_r": 0.022, "theta_s": 0.309, "alpha": 0.0328, "n": 6.23}
-    # @property
-    # def plainfieldSand_149to177(self):
-    #     """Soil Index: 4103a"""
-    #     return {"theta_r": 0.000, "theta_s": 0.387, "alpha": 0.0173, "n": 7.80}
-    # @property
-    # def plainfieldSand_149to177(self):
-    #     """Soil Index: 4103b"""
-    #     return {"theta_r": 0.025, "theta_s": 0.321, "alpha": 0.0272, "n": 6.69}
-    # @property
-    # def plainfieldSand_l25to149(self):
-    #     """Soil Index: 4104a"""
-    #     return {"theta_r": 0.000, "theta_s": 03770, "alpha": 0.0145, "n": 10.60}
-    # @property
-    # def plainfieldSand_125to149(self):
-    #     """Soil Index: 4104b"""
-    #     return {"theta_r": 0.000, "theta_s": 0.342, "alpha": 0.0230, "n": 5.18}
-
-
-if __name__ == '__main__':
-    import matplotlib.pyplot as plt
-    M = Mesh.TensorMesh([10])
-    VGparams = VanGenuchtenParams()
-    leg = []
-    for p in dir(VGparams):
-        if p[0] == '_': continue
-        leg += [p]
-        params = getattr(VGparams, p)
-        model = VanGenuchten(M, **params)
-        ks = np.log(np.r_[params['Ks']])
-        model.plot(ks)
-
-    plt.legend(leg)
-
-    plt.show()

SimPEG/FLOW/Richards/RichardsProblem.py

@@ -1,304 +0,0 @@
-from SimPEG import *
-from Empirical import RichardsMap
-import time
-
-
-class RichardsRx(Survey.BaseTimeRx):
-    """Richards Receiver Object"""
-
-    knownRxTypes = ['saturation','pressureHead']
-
-    def eval(self, U, m, mapping, mesh, timeMesh):
-
-        if self.rxType == 'pressureHead':
-            u = np.concatenate(U)
-        elif self.rxType == 'saturation':
-            u = np.concatenate([mapping.theta(ui, m) for ui in U])
-
-        return self.getP(mesh, timeMesh) * u
-
-    def evalDeriv(self, U, m, mapping, mesh, timeMesh):
-
-        P = self.getP(mesh, timeMesh)
-        if self.rxType == 'pressureHead':
-            return P
-        elif self.rxType == 'saturation':
-            #TODO: if m is a parameter in the theta
-            #      distribution, we may need to do
-            #      some more chain rule here.
-            dT = sp.block_diag([mapping.thetaDerivU(ui, m) for ui in U])
-            return P*dT
-
-
-class RichardsSurvey(Survey.BaseSurvey):
-    """docstring for RichardsSurvey"""
-
-    rxList = None
-
-    def __init__(self, rxList, **kwargs):
-        self.rxList = rxList
-        Survey.BaseSurvey.__init__(self, **kwargs)
-
-    @property
-    def nD(self):
-        return np.array([rx.nD for rx in self.rxList]).sum()
-
-    @Utils.count
-    @Utils.requires('prob')
-    def dpred(self, m, f=None):
-        """
-            Create the projected data from a model.
-            The field, f, (if provided) will be used for the predicted data
-            instead of recalculating the fields (which may be expensive!).
-
-            .. math::
-                d_\\text{pred} = P(f(m), m)
-
-            Where P is a projection of the fields onto the data space.
-        """
-        if f is None: f = self.prob.fields(m)
-        return Utils.mkvc(self.eval(f, m))
-
-    @Utils.requires('prob')
-    def eval(self, U, m):
-        Ds = range(len(self.rxList))
-        for ii, rx in enumerate(self.rxList):
-            Ds[ii] = rx.eval(U, m,
-                                self.prob.mapping,
-                                self.prob.mesh,
-                                self.prob.timeMesh)
-
-        return np.concatenate(Ds)
-
-    @Utils.requires('prob')
-    def evalDeriv(self, U, m):
-        """The Derivative with respect to the fields."""
-        Ds = range(len(self.rxList))
-        for ii, rx in enumerate(self.rxList):
-            Ds[ii] = rx.evalDeriv(U, m,
-                                self.prob.mapping,
-                                self.prob.mesh,
-                                self.prob.timeMesh)
-
-        return sp.vstack(Ds)
-
-class RichardsProblem(Problem.BaseTimeProblem):
-    """docstring for RichardsProblem"""
-
-    boundaryConditions = None
-    initialConditions  = None
-
-    surveyPair  = RichardsSurvey
-    mapPair = RichardsMap
-
-    debug=True
-
-    Solver = Solver
-    solverOpts = {}
-
-    def __init__(self, mesh, mapping=None, **kwargs):
-        Problem.BaseTimeProblem.__init__(self, mesh, mapping=mapping, **kwargs)
-
-    def getBoundaryConditions(self, ii, u_ii):
-        if type(self.boundaryConditions) is np.ndarray:
-            return self.boundaryConditions
-
-        time = self.timeMesh.vectorCCx[ii]
-
-        return self.boundaryConditions(time, u_ii)
-
-    @property
-    def method(self):
-        """Method must be either 'mixed' or 'head'. See notes in Celia et al., 1990."""
-        return getattr(self, '_method', 'mixed')
-    @method.setter
-    def method(self, value):
-        assert value in ['mixed','head'], "method must be 'mixed' or 'head'."
-        self._method = value
-
-    # Setting doNewton will clear the rootFinder, which will be reinitialized when called
-    doNewton = Utils.dependentProperty('_doNewton', False, ['_rootFinder'],
-                "Do a Newton iteration. If False, a Picard iteration will be completed.")
-
-    maxIterRootFinder = Utils.dependentProperty('_maxIterRootFinder', 30, ['_rootFinder'],
-                "Maximum iterations for rootFinder iteration.")
-    tolRootFinder = Utils.dependentProperty('_tolRootFinder', 1e-4, ['_rootFinder'],
-                "Maximum iterations for rootFinder iteration.")
-
-    @property
-    def rootFinder(self):
-        """Root-finding Algorithm"""
-        if getattr(self, '_rootFinder', None) is None:
-            self._rootFinder = Optimization.NewtonRoot(doLS=self.doNewton, maxIter=self.maxIterRootFinder, tol=self.tolRootFinder, Solver=self.Solver)
-        return self._rootFinder
-
-    @Utils.timeIt
-    def fields(self, m):
-        tic = time.time()
-        u = range(self.nT+1)
-        u[0] = self.initialConditions
-        for ii, dt in enumerate(self.timeSteps):
-            bc = self.getBoundaryConditions(ii, u[ii])
-            u[ii+1] = self.rootFinder.root(lambda hn1m, return_g=True: self.getResidual(m, u[ii], hn1m, dt, bc, return_g=return_g), u[ii])
-            if self.debug: print "Solving Fields (%4d/%d - %3.1f%% Done) %d Iterations, %4.2f seconds"%(ii+1, self.nT, 100.0*(ii+1)/self.nT, self.rootFinder.iter, time.time() - tic)
-        return u
-
-    @Utils.timeIt
-    def diagsJacobian(self, m, hn, hn1, dt, bc):
-
-        DIV  = self.mesh.faceDiv
-        GRAD = self.mesh.cellGrad
-        BC   = self.mesh.cellGradBC
-        AV   = self.mesh.aveF2CC.T
-        if self.mesh.dim == 1:
-            Dz = self.mesh.faceDivx
-        elif self.mesh.dim == 2:
-            Dz = sp.hstack((Utils.spzeros(self.mesh.nC,self.mesh.vnF[0]), self.mesh.faceDivy),format='csr')
-        elif self.mesh.dim == 3:
-            Dz = sp.hstack((Utils.spzeros(self.mesh.nC,self.mesh.vnF[0]+self.mesh.vnF[1]), self.mesh.faceDivz),format='csr')
-
-        dT   = self.mapping.thetaDerivU(hn, m)
-        dT1  = self.mapping.thetaDerivU(hn1, m)
-        K1   = self.mapping.k(hn1, m)
-        dK1  = self.mapping.kDerivU(hn1, m)
-        dKm1 = self.mapping.kDerivM(hn1, m)
-
-        # Compute part of the derivative of:
-        #
-        #       DIV*diag(GRAD*hn1+BC*bc)*(AV*(1.0/K))^-1
-
-        DdiagGh1 = DIV*Utils.sdiag(GRAD*hn1+BC*bc)
-        diagAVk2_AVdiagK2 = Utils.sdiag((AV*(1./K1))**(-2)) * AV*Utils.sdiag(K1**(-2))
-
-        # The matrix that we are computing has the form:
-        #
-        #   -                                      -   -  -     -  -
-        #  |  Adiag                                 | | h1 |   | b1 |
-        #  |   Asub    Adiag                        | | h2 |   | b2 |
-        #  |            Asub    Adiag               | | h3 | = | b3 |
-        #  |                 ...     ...            | | .. |   | .. |
-        #  |                         Asub    Adiag  | | hn |   | bn |
-        #   -                                      -   -  -     -  -
-
-        Asub = (-1.0/dt)*dT
-
-        Adiag = (
-                  (1.0/dt)*dT1
-                 -DdiagGh1*diagAVk2_AVdiagK2*dK1
-                 -DIV*Utils.sdiag(1./(AV*(1./K1)))*GRAD
-                 -Dz*diagAVk2_AVdiagK2*dK1
-                )
-
-        B = DdiagGh1*diagAVk2_AVdiagK2*dKm1 + Dz*diagAVk2_AVdiagK2*dKm1
-
-        return Asub, Adiag, B
-
-    @Utils.timeIt
-    def getResidual(self, m, hn, h, dt, bc, return_g=True):
-        """
-            Where h is the proposed value for the next time iterate (h_{n+1})
-        """
-        DIV  = self.mesh.faceDiv
-        GRAD = self.mesh.cellGrad
-        BC   = self.mesh.cellGradBC
-        AV   = self.mesh.aveF2CC.T
-        if self.mesh.dim == 1:
-            Dz = self.mesh.faceDivx
-        elif self.mesh.dim == 2:
-            Dz = sp.hstack((Utils.spzeros(self.mesh.nC,self.mesh.vnF[0]), self.mesh.faceDivy),format='csr')
-        elif self.mesh.dim == 3:
-            Dz = sp.hstack((Utils.spzeros(self.mesh.nC,self.mesh.vnF[0]+self.mesh.vnF[1]), self.mesh.faceDivz),format='csr')
-
-        T  = self.mapping.theta(h, m)
-        dT = self.mapping.thetaDerivU(h, m)
-        Tn = self.mapping.theta(hn, m)
-        K  = self.mapping.k(h, m)
-        dK = self.mapping.kDerivU(h, m)
-
-        aveK = 1./(AV*(1./K))
-
-        RHS = DIV*Utils.sdiag(aveK)*(GRAD*h+BC*bc) + Dz*aveK
-        if self.method == 'mixed':
-            r = (T-Tn)/dt - RHS
-        elif self.method == 'head':
-            r = dT*(h - hn)/dt - RHS
-
-        if not return_g: return r
-
-        J = dT/dt - DIV*Utils.sdiag(aveK)*GRAD
-        if self.doNewton:
-            DDharmAve = Utils.sdiag(aveK**2)*AV*Utils.sdiag(K**(-2)) * dK
-            J = J - DIV*Utils.sdiag(GRAD*h + BC*bc)*DDharmAve - Dz*DDharmAve
-
-        return r, J
-
-    @Utils.timeIt
-    def Jfull(self, m, f=None):
-        if f is None:
-            f = self.fields(m)
-
-        nn = len(f)-1
-        Asubs, Adiags, Bs = range(nn), range(nn), range(nn)
-        for ii in range(nn):
-            dt = self.timeSteps[ii]
-            bc = self.getBoundaryConditions(ii, f[ii])
-            Asubs[ii], Adiags[ii], Bs[ii] = self.diagsJacobian(m, f[ii], f[ii+1], dt, bc)
-        Ad = sp.block_diag(Adiags)
-        zRight = Utils.spzeros((len(Asubs)-1)*Asubs[0].shape[0],Adiags[0].shape[1])
-        zTop = Utils.spzeros(Adiags[0].shape[0], len(Adiags)*Adiags[0].shape[1])
-        As = sp.vstack((zTop,sp.hstack((sp.block_diag(Asubs[1:]),zRight))))
-        A = As + Ad
-        B = np.array(sp.vstack(Bs).todense())
-
-        Ainv = self.Solver(A, **self.solverOpts)
-        P = self.survey.evalDeriv(f, m)
-        AinvB = Ainv * B
-        z = np.zeros((self.mesh.nC, B.shape[1]))
-        zAinvB = np.vstack((z, AinvB))
-        J = P * zAinvB
-        return J
-
-    @Utils.timeIt
-    def Jvec(self, m, v, f=None):
-        if f is None:
-            f = self.fields(m)
-
-        JvC = range(len(f)-1) # Cell to hold each row of the long vector.
-
-        # This is done via forward substitution.
-        bc = self.getBoundaryConditions(0, f[0])
-        temp, Adiag, B = self.diagsJacobian(m, f[0], f[1], self.timeSteps[0], bc)
-        Adiaginv = self.Solver(Adiag, **self.solverOpts)
-        JvC[0] = Adiaginv * (B*v)
-
-        for ii in range(1,len(f)-1):
-            bc = self.getBoundaryConditions(ii, f[ii])
-            Asub, Adiag, B = self.diagsJacobian(m, f[ii], f[ii+1], self.timeSteps[ii], bc)
-            Adiaginv = self.Solver(Adiag, **self.solverOpts)
-            JvC[ii] = Adiaginv * (B*v - Asub*JvC[ii-1])
-
-        P = self.survey.evalDeriv(f, m)
-        return P * np.concatenate([np.zeros(self.mesh.nC)] + JvC)
-
-    @Utils.timeIt
-    def Jtvec(self, m, v, f=None):
-        if f is None:
-            f = self.field(m)
-
-        P = self.survey.evalDeriv(f, m)
-        PTv = P.T*v
-
-        # This is done via backward substitution.
-        minus = 0
-        BJtv = 0
-        for ii in range(len(f)-1,0,-1):
-            bc = self.getBoundaryConditions(ii-1, f[ii-1])
-            Asub, Adiag, B = self.diagsJacobian(m, f[ii-1], f[ii], self.timeSteps[ii-1], bc)
-            #select the correct part of v
-            vpart = range((ii)*Adiag.shape[0], (ii+1)*Adiag.shape[0])
-            AdiaginvT = self.Solver(Adiag.T, **self.solverOpts)
-            JTvC = AdiaginvT * (PTv[vpart] - minus)
-            minus = Asub.T*JTvC  # this is now the super diagonal.
-            BJtv = BJtv + B.T*JTvC
-
-        return BJtv

SimPEG/FLOW/Richards/__init__.py

@@ -1,2 +0,0 @@
-import Empirical
-from RichardsProblem import *

SimPEG/FLOW/__init__.py

@@ -1 +0,0 @@
-import Richards

SimPEG/InvProblem.py

@@ -66,8 +66,8 @@ class BaseInvProblem(object):
         self.curModel = m0
 
         print """SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
-                    ***Done using same Solver and solverOpts as the problem***"""
-        self.opt.bfgsH0 = self.prob.Solver(self.reg.eval2Deriv(self.curModel), **self.prob.solverOpts)
+                    ***Done using same solver as the problem***"""
+        self.opt.bfgsH0 = self.prob.Solver(self.reg.eval2Deriv(self.curModel))
 
     @property
     def warmstart(self):
@@ -82,23 +82,23 @@ class BaseInvProblem(object):
         self._warmstart = value
 
     def getFields(self, m, store=False, deleteWarmstart=True):
-        f = None
+        u = None
 
         for mtest, u_ofmtest in self.warmstart:
             if m is mtest:
-                f = u_ofmtest
+                u = u_ofmtest
                 if self.debug: print 'InvProb is Warm Starting!'
                 break
 
-        if f is None:
-            f = self.prob.fields(m)
+        if u is None:
+            u = self.prob.fields(m)
 
         if deleteWarmstart:
             self.warmstart = []
         if store:
-            self.warmstart += [(m,f)]
+            self.warmstart += [(m,u)]
 
-        return f
+        return u
 
     @Utils.timeIt
     def evalFunction(self, m, return_g=True, return_H=True):
@@ -109,21 +109,21 @@ class BaseInvProblem(object):
         gc.collect()
 
         # Store fields if doing a line-search
-        f = self.getFields(m, store=(return_g==False and return_H==False))
+        u = self.getFields(m, store=(return_g==False and return_H==False))
 
-        phi_d = self.dmisfit.eval(m, f=f)
+        phi_d = self.dmisfit.eval(m, u=u)
         phi_m = self.reg.eval(m)
 
-        self.dpred = self.survey.dpred(m, f=f)  # This is a cheap matrix vector calculation.
+        self.dpred = self.survey.dpred(m, u=u)  # This is a cheap matrix vector calculation.
 
         self.phi_d, self.phi_d_last  = phi_d, self.phi_d
         self.phi_m, self.phi_m_last  = phi_m, self.phi_m
 
-        phi = phi_d + self.beta * phi_m
+        f = phi_d + self.beta * phi_m
 
-        out = (phi,)
+        out = (f,)
         if return_g:
-            phi_dDeriv = self.dmisfit.evalDeriv(m, f=f)
+            phi_dDeriv = self.dmisfit.evalDeriv(m, u=u)
             phi_mDeriv = self.reg.evalDeriv(m)
 
             g = phi_dDeriv + self.beta * phi_mDeriv
@@ -131,7 +131,7 @@ class BaseInvProblem(object):
 
         if return_H:
             def H_fun(v):
-                phi_d2Deriv = self.dmisfit.eval2Deriv(m, v, f=f)
+                phi_d2Deriv = self.dmisfit.eval2Deriv(m, v, u=u)
                 phi_m2Deriv = self.reg.eval2Deriv(m, v=v)
 
                 return phi_d2Deriv + self.beta * phi_m2Deriv

SimPEG/Inversion.py

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

SimPEG/MT/BaseMT.py

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

SimPEG/MT/FieldsMT.py

@@ -1,351 +0,0 @@
-from SimPEG import Survey, Utils, Problem, np, sp, mkvc
-from scipy.constants import mu_0
-import sys
-from numpy.lib import recfunctions as recFunc
-from SimPEG.EM.Utils import omega
-
-##############
-### Fields ###
-##############
-class BaseMTFields(Problem.Fields):
-    """Field Storage for a MT survey."""
-    knownFields = {}
-    dtype = complex
-
-
-class Fields1D_e(BaseMTFields):
-    """
-    Fields storage for the 1D MT solution.
-    """
-    knownFields = {'e_1dSolution':'F'}
-    aliasFields = {
-                    'e_1d' : ['e_1dSolution','F','_e'],
-                    'e_1dPrimary' : ['e_1dSolution','F','_ePrimary'],
-                    'e_1dSecondary' : ['e_1dSolution','F','_eSecondary'],
-                    'b_1d' : ['e_1dSolution','E','_b'],
-                    'b_1dPrimary' : ['e_1dSolution','E','_bPrimary'],
-                    'b_1dSecondary' : ['e_1dSolution','E','_bSecondary']
-                  }
-
-    def __init__(self,mesh,survey,**kwargs):
-        BaseMTFields.__init__(self,mesh,survey,**kwargs)
-
-    def _ePrimary(self, eSolution, srcList):
-        ePrimary = np.zeros_like(eSolution)
-        for i, src in enumerate(srcList):
-            ep = src.ePrimary(self.survey.prob)
-            if ep is not None:
-                ePrimary[:,i] = ep[:,-1]
-        return ePrimary
-
-    def _eSecondary(self, eSolution, srcList):
-        return eSolution
-
-    def _e(self, eSolution, srcList):
-        return self._ePrimary(eSolution,srcList) + self._eSecondary(eSolution,srcList)
-
-    def _eDeriv_u(self, src, v, adjoint = False):
-        return v
-
-    def _eDeriv_m(self, src, v, adjoint = False):
-        # assuming primary does not depend on the model
-        return None
-
-    def _bPrimary(self, eSolution, srcList):
-        bPrimary = np.zeros([self.survey.mesh.nE,eSolution.shape[1]], dtype = complex)
-        for i, src in enumerate(srcList):
-            bp = src.bPrimary(self.survey.prob)
-            if bp is not None:
-                bPrimary[:,i] += bp[:,-1]
-        return bPrimary
-
-    def _bSecondary(self, eSolution, srcList):
-        C = self.mesh.nodalGrad
-        b = (C * eSolution)
-        for i, src in enumerate(srcList):
-            b[:,i] *= - 1./(1j*omega(src.freq))
-            # There is no magnetic source in the MT problem
-            # S_m, _ = src.eval(self.survey.prob)
-            # if S_m is not None:
-            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
-        return b
-
-    def _b(self, eSolution, srcList):
-        return self._bPrimary(eSolution, srcList) + self._bSecondary(eSolution, srcList)
-
-    def _bSecondaryDeriv_u(self, src, v, adjoint = False):
-        C = self.mesh.nodalGrad
-        if adjoint:
-            return - 1./(1j*omega(src.freq)) * (C.T * v)
-        return - 1./(1j*omega(src.freq)) * (C * v)
-
-    def _bSecondaryDeriv_m(self, src, v, adjoint = False):
-        # Doesn't depend on m
-        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
-        # S_eDeriv = S_eDeriv(v)
-        # if S_eDeriv is not None:
-        #     return 1./(1j * omega(src.freq)) * S_eDeriv
-        return None
-
-    def _bDeriv_u(self, src, v, adjoint=False):
-        # Primary does not depend on u
-        return self._bSecondaryDeriv_u(src, v, adjoint)
-
-    def _bDeriv_m(self, src, v, adjoint=False):
-        # Assuming the primary does not depend on the model
-        return self._bSecondaryDeriv_m(src, v, adjoint)
-
-    def _fDeriv_u(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt u.
-
-        :param MTsrc src: MT source
-        :param numpy.ndarray v: random vector of f_sol.size
-        This function stacks the fields derivatives appropriately
-
-        return a vector of size (nreEle+nrbEle)
-        """
-
-        de_du = v #Utils.spdiag(np.ones((self.nF,)))
-        db_du = self._bDeriv_u(src, v, adjoint)
-        # Return the stack
-        # This doesn't work...
-        return np.vstack((de_du,db_du))
-
-    def _fDeriv_m(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt m.
-
-        This function stacks the fields derivatives appropriately
-        """
-        return None
-
-class Fields3D_e(BaseMTFields):
-    """
-    Fields storage for the 3D MT solution. Labels polarizations by px and py.
-
-        :param SimPEG object mesh: The solution mesh
-        :param SimPEG object survey: A survey object
-    """
-    # Define the known the alias fields
-    # Assume that the solution of e on the E.
-    ## NOTE: Need to make this more general, to allow for other solutions formats.
-    knownFields = {'e_pxSolution':'E','e_pySolution':'E'}
-    aliasFields = {
-                    'e_px' : ['e_pxSolution','E','_e_px'],
-                    'e_pxPrimary' : ['e_pxSolution','E','_e_pxPrimary'],
-                    'e_pxSecondary' : ['e_pxSolution','E','_e_pxSecondary'],
-                    'e_py' : ['e_pySolution','E','_e_py'],
-                    'e_pyPrimary' : ['e_pySolution','E','_e_pyPrimary'],
-                    'e_pySecondary' : ['e_pySolution','E','_e_pySecondary'],
-                    'b_px' : ['e_pxSolution','F','_b_px'],
-                    'b_pxPrimary' : ['e_pxSolution','F','_b_pxPrimary'],
-                    'b_pxSecondary' : ['e_pxSolution','F','_b_pxSecondary'],
-                    'b_py' : ['e_pySolution','F','_b_py'],
-                    'b_pyPrimary' : ['e_pySolution','F','_b_pyPrimary'],
-                    'b_pySecondary' : ['e_pySolution','F','_b_pySecondary']
-                  }
-
-    def __init__(self,mesh,survey,**kwargs):
-        BaseMTFields.__init__(self,mesh,survey,**kwargs)
-
-    def _e_pxPrimary(self, e_pxSolution, srcList):
-        e_pxPrimary = np.zeros_like(e_pxSolution)
-        for i, src in enumerate(srcList):
-            ep = src.ePrimary(self.survey.prob)
-            if ep is not None:
-                e_pxPrimary[:,i] = ep[:,0]
-        return e_pxPrimary
-
-    def _e_pyPrimary(self, e_pySolution, srcList):
-        e_pyPrimary = np.zeros_like(e_pySolution)
-        for i, src in enumerate(srcList):
-            ep = src.ePrimary(self.survey.prob)
-            if ep is not None:
-                e_pyPrimary[:,i] = ep[:,1]
-        return e_pyPrimary
-
-    def _e_pxSecondary(self, e_pxSolution, srcList):
-        return e_pxSolution
-
-    def _e_pySecondary(self, e_pySolution, srcList):
-        return e_pySolution
-
-    def _e_px(self, e_pxSolution, srcList):
-        return self._e_pxPrimary(e_pxSolution,srcList) + self._e_pxSecondary(e_pxSolution,srcList)
-
-    def _e_py(self, e_pySolution, srcList):
-        return self._e_pyPrimary(e_pySolution,srcList) + self._e_pySecondary(e_pySolution,srcList)
-
-    #NOTE: For e_p?Deriv_u,
-    # v has to be u(2*nE) long for the not adjoint and nE long for adjoint.
-    # Returns nE long for not adjoint and 2*nE long for adjoint
-    def _e_pxDeriv_u(self, src, v, adjoint = False):
-        '''
-        Takes the derivative of e_px wrt u
-        '''
-        if adjoint:
-            # adjoint: returns a 2*nE long vector with zero's for py
-            return np.vstack((v,np.zeros_like(v)))
-        # Not adjoint: return only the px part of the vector
-        return v[:len(v)/2]
-
-    def _e_pyDeriv_u(self, src, v, adjoint = False):
-        '''
-        Takes the derivative of e_py wrt u
-        '''
-        if adjoint:
-            # adjoint: returns a 2*nE long vector with zero's for px
-            return np.vstack((np.zeros_like(v),v))
-        # Not adjoint: return only the px part of the vector
-        return v[len(v)/2::]
-
-    def _e_pxDeriv_m(self, src, v, adjoint = False):
-        # assuming primary does not depend on the model
-        return None
-    def _e_pyDeriv_m(self, src, v, adjoint = False):
-        # assuming primary does not depend on the model
-        return None
-
-    def _b_pxPrimary(self, e_pxSolution, srcList):
-        b_pxPrimary = np.zeros([self.survey.mesh.nF,e_pxSolution.shape[1]], dtype = complex)
-        for i, src in enumerate(srcList):
-            bp = src.bPrimary(self.survey.prob)
-            if bp is not None:
-                b_pxPrimary[:,i] += bp[:,0]
-        return b_pxPrimary
-
-    def _b_pyPrimary(self, e_pySolution, srcList):
-        b_pyPrimary = np.zeros([self.survey.mesh.nF,e_pySolution.shape[1]], dtype = complex)
-        for i, src in enumerate(srcList):
-            bp = src.bPrimary(self.survey.prob)
-            if bp is not None:
-                b_pyPrimary[:,i] += bp[:,1]
-        return b_pyPrimary
-
-    def _b_pxSecondary(self, e_pxSolution, srcList):
-        C = self.mesh.edgeCurl
-        b = (C * e_pxSolution)
-        for i, src in enumerate(srcList):
-            b[:,i] *= - 1./(1j*omega(src.freq))
-            # There is no magnetic source in the MT problem
-            # S_m, _ = src.eval(self.survey.prob)
-            # if S_m is not None:
-            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
-        return b
-
-    def _b_pySecondary(self, e_pySolution, srcList):
-        C = self.mesh.edgeCurl
-        b = (C * e_pySolution)
-        for i, src in enumerate(srcList):
-            b[:,i] *= - 1./(1j*omega(src.freq))
-            # There is no magnetic source in the MT problem
-            # S_m, _ = src.eval(self.survey.prob)
-            # if S_m is not None:
-            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
-        return b
-
-    def _b_px(self, eSolution, srcList):
-        return self._b_pxPrimary(eSolution, srcList) + self._b_pxSecondary(eSolution, srcList)
-
-    def _b_py(self, eSolution, srcList):
-        return self._b_pyPrimary(eSolution, srcList) + self._b_pySecondary(eSolution, srcList)
-
-    # NOTE: v needs to be length 2*nE to account for both polarizations
-    def _b_pxSecondaryDeriv_u(self, src, v, adjoint = False):
-        # C = sp.kron(self.mesh.edgeCurl,[[1,0],[0,0]])
-        C = sp.hstack((self.mesh.edgeCurl,Utils.spzeros(self.mesh.nF,self.mesh.nE))) # This works for adjoint = None
-        if adjoint:
-            return - 1./(1j*omega(src.freq)) * (C.T * v)
-        return - 1./(1j*omega(src.freq)) * (C * v)
-
-    def _b_pySecondaryDeriv_u(self, src, v, adjoint = False):
-        # C = sp.kron(self.mesh.edgeCurl,[[0,0],[0,1]])
-        C = sp.hstack((Utils.spzeros(self.mesh.nF,self.mesh.nE),self.mesh.edgeCurl)) # This works for adjoint = None
-        if adjoint:
-            return - 1./(1j*omega(src.freq)) * (C.T * v)
-        return - 1./(1j*omega(src.freq)) * (C * v)
-
-    def _b_pxSecondaryDeriv_m(self, src, v, adjoint = False):
-        # Doesn't depend on m
-        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
-        # S_eDeriv = S_eDeriv(v)
-        # if S_eDeriv is not None:
-        #     return 1./(1j * omega(src.freq)) * S_eDeriv
-        return None
-
-    def _b_pySecondaryDeriv_m(self, src, v, adjoint = False):
-        # Doesn't depend on m
-        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
-        # S_eDeriv = S_eDeriv(v)
-        # if S_eDeriv is not None:
-        #     return 1./(1j * omega(src.freq)) * S_eDeriv
-        return None
-
-    def _b_pxDeriv_u(self, src, v, adjoint=False):
-        # Primary does not depend on u
-        return self._b_pxSecondaryDeriv_u(src, v, adjoint)
-
-    def _b_pyDeriv_u(self, src, v, adjoint=False):
-        # Primary does not depend on u
-        return self._b_pySecondaryDeriv_u(src, v, adjoint)
-
-    def _b_pxDeriv_m(self, src, v, adjoint=False):
-        # Assuming the primary does not depend on the model
-        return self._b_pxSecondaryDeriv_m(src, v, adjoint)
-
-    def _b_pyDeriv_m(self, src, v, adjoint=False):
-        # Assuming the primary does not depend on the model
-        return self._b_pySecondaryDeriv_m(src, v, adjoint)
-
-    def _f_pxDeriv_u(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt u.
-
-        :param MTsrc src: MT source
-        :param numpy.ndarray v: random vector of f_sol.size
-        This function stacks the fields derivatives appropriately
-
-        return a vector of size (nreEle+nrbEle)
-        """
-
-        de_du = v #Utils.spdiag(np.ones((self.nF,)))
-        db_du = self._b_pxDeriv_u(src, v, adjoint)
-        # Return the stack
-        # This doesn't work...
-        return np.vstack((de_du,db_du))
-
-    def _f_pyDeriv_u(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt u.
-
-        :param MTsrc src: MT source
-        :param numpy.ndarray v: random vector of f_sol.size
-        This function stacks the fields derivatives appropriately
-
-        return a vector of size (nreEle+nrbEle)
-        """
-
-        de_du = v #Utils.spdiag(np.ones((self.nF,)))
-        db_du = self._b_pyDeriv_u(src, v, adjoint)
-        # Return the stack
-        # This doesn't work...
-        return np.vstack((de_du,db_du))
-
-    def _f_pxDeriv_m(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt m.
-
-        This function stacks the fields derivatives appropriately
-        """
-        # The fields have no dependance to the model.
-        return None
-
-    def _f_pyDeriv_m(self, src, v, adjoint=False):
-        """
-        Derivative of the fields object wrt m.
-
-        This function stacks the fields derivatives appropriately
-        """
-        # The fields have no dependance to the model.
-        return None