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Merge pull request #337 from simpeg/feat/docs-gae-travis-deploy

Browse Source 
Feat/docs gae travis deploy
This commit is contained in:
Lindsey
2016-06-13 19:16:22 -06:00
committed by GitHub
parent 382b31bd12 7a82f57367
commit 64b0b4561f
104 changed files with 998 additions and 471 deletions
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.gitignore
+1
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@@ -39,3 +39,4 @@ nosetests.xml
*.sublime-workspace
docs/_build/
Makefile
docs/warnings.txt
.travis.yml
+26 -3
View File
@@ -24,18 +24,25 @@ env:
- TEST_DIR=tests/examples
- TEST_DIR=tests/em/fdem/inverse/adjoint
- TEST_DIR=tests/em/fdem/forward
- TEST_DIR=tests/docs;
GAE_PYTHONPATH=${HOME}/.cache/google_appengine;
PATH=$PATH:${HOME}/google-cloud-sdk/bin;
PYTHONPATH=${PYTHONPATH}:${GAE_PYTHONPATH};
CLOUDSDK_CORE_DISABLE_PROMPTS=1
# Setup anaconda
before_install:
- if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh -O miniconda.sh; fi
# Install packages
- if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh
-O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh
-O miniconda.sh; fi
- chmod +x miniconda.sh
- ./miniconda.sh -b
- export PATH=/home/travis/anaconda/bin:/home/travis/miniconda/bin:$PATH
- conda update --yes conda
# Install packages
install:
- conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk
- conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython ipython nose vtk sphinx
- pip install nose-cov python-coveralls
- git clone https://github.com/rowanc1/pymatsolver.git
@@ -46,12 +53,28 @@ install:
# Run test
script:
# test docs
- nosetests $TEST_DIR --with-cov --cov SimPEG --cov-config .coveragerc -v -s
# Calculate coverage
after_success:
- coveralls --config_file .coveragerc
- if [ "$TRAVIS_BRANCH" = "master" -a "$TRAVIS_PULL_REQUEST" = "false" ]; then
if [ ${TEST_DIR} == "tests/docs" ]; then
python scripts/fetch_gae_sdk.py $(dirname "${GAE_PYTHONPATH}");
openssl aes-256-cbc -K $encrypted_93066031461c_key -iv $encrypted_93066031461c_iv
-in docs/credentials.tar.gz.enc -out credentials.tar.gz -d ;
if [ ! -d ${HOME}/google-cloud-sdk ]; then curl https://sdk.cloud.google.com | bash; fi ;
tar -xzf credentials.tar.gz ;
gcloud auth activate-service-account --key-file client-secret.json ;
gcloud config set project simpegdocs;
gcloud -q components update gae-python;
gcloud -q preview app deploy ./docs/app.yaml --version ${TRAVIS_COMMIT} --promote;
fi;
fi
notifications:
email:
- rowanc1@gmail.com
SimPEG/DCIP/BaseDC.py
+2 -2
View File
@@ -162,8 +162,8 @@ class ProblemDC_CC(Problem.BaseProblem):
"""
Makes the matrix A(m) for the DC resistivity problem.
:param numpy.array m: model
:rtype: scipy.csc_matrix
:param numpy.ndarray m: model
:rtype: scipy.sparse.csc_matrix
:return: A(m)
.. math::
SimPEG/DCIP/BaseIP.py
+1 -1
View File
@@ -71,7 +71,7 @@ class ProblemIP(Problem.BaseProblem):
Makes the matrix A(m) for the DC resistivity problem.
:param numpy.array m: model
:rtype: scipy.csc_matrix
:rtype: scipy.sparse.csc_matrix
:return: A(m)
.. math::
SimPEG/Directives.py
+4 -4
View File
@@ -167,7 +167,7 @@ class TargetMisfit(InversionDirective):
class _SaveEveryIteration(InversionDirective):
class SaveEveryIteration(InversionDirective):
@property
def name(self):
if getattr(self, '_name', None) is None:
@@ -188,7 +188,7 @@ class _SaveEveryIteration(InversionDirective):
self._fileName = value
class SaveModelEveryIteration(_SaveEveryIteration):
class SaveModelEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
def initialize(self):
@@ -198,7 +198,7 @@ class SaveModelEveryIteration(_SaveEveryIteration):
np.save('%03d-%s' % (self.opt.iter, self.fileName), self.opt.xc)
class SaveOutputEveryIteration(_SaveEveryIteration):
class SaveOutputEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
def initialize(self):
@@ -212,7 +212,7 @@ class SaveOutputEveryIteration(_SaveEveryIteration):
f.write(' %3d %1.4e %1.4e %1.4e %1.4e\n'%(self.opt.iter, self.invProb.beta, self.invProb.phi_d, self.invProb.phi_m, self.opt.f))
f.close()
class SaveOutputDictEveryIteration(_SaveEveryIteration):
class SaveOutputDictEveryIteration(SaveEveryIteration):
"""SaveOutputDictEveryIteration"""
def initialize(self):
SimPEG/EM/Base.py
+4 -3
View File
@@ -20,10 +20,10 @@ class BaseEMProblem(Problem.BaseProblem):
Problem.BaseProblem.__init__(self, mesh, **kwargs)
surveyPair = Survey.BaseSurvey
dataPair = Survey.Data
surveyPair = Survey.BaseSurvey #: The survey to pair with.
dataPair = Survey.Data #: The data to pair with.
PropMap = EMPropMap
PropMap = EMPropMap #: The property mapping
Solver = SimpegSolver
solverOpts = {}
@@ -217,6 +217,7 @@ class BaseEMSurvey(Survey.BaseSurvey):
def eval(self, f):
"""
Project fields to receiver locations
:param Fields u: fields object
:rtype: numpy.ndarray
:return: data
SimPEG/EM/FDEM/FieldsFDEM.py
+18 -30
View File
@@ -6,11 +6,11 @@ from SimPEG.EM.Utils import omega
from SimPEG.Utils import Zero, Identity, sdiag
class Fields(SimPEG.Problem.Fields):
class FieldsFDEM(SimPEG.Problem.Fields):
"""
Fancy Field Storage for a FDEM survey. Only one field type is stored for
each problem, the rest are computed. The fields obejct acts like an array and is indexed by
each problem, the rest are computed. The fields object acts like an array and is indexed by
.. code-block:: python
@@ -92,7 +92,7 @@ class Fields(SimPEG.Problem.Fields):
"""
Total derivative of e with respect to the inversion model. Returns :math:`d\mathbf{e}/d\mathbf{m}` for forward and (:math:`d\mathbf{e}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
:param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
@@ -110,7 +110,7 @@ class Fields(SimPEG.Problem.Fields):
"""
Total derivative of b with respect to the inversion model. Returns :math:`d\mathbf{b}/d\mathbf{m}` for forward and (:math:`d\mathbf{b}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
:param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
@@ -128,7 +128,7 @@ class Fields(SimPEG.Problem.Fields):
"""
Total derivative of h with respect to the inversion model. Returns :math:`d\mathbf{h}/d\mathbf{m}` for forward and (:math:`d\mathbf{h}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
:param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
@@ -146,7 +146,7 @@ class Fields(SimPEG.Problem.Fields):
"""
Total derivative of j with respect to the inversion model. Returns :math:`d\mathbf{j}/d\mathbf{m}` for forward and (:math:`d\mathbf{j}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint
:param Src src: sorce
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: source
:param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint)
:param numpy.ndarray v: vector to take sensitivity product with
:param bool adjoint: adjoint?
@@ -160,12 +160,12 @@ class Fields(SimPEG.Problem.Fields):
return self._jDeriv_u(src, v, adjoint), self._jDeriv_m(src, v, adjoint)
return np.array(self._jDeriv_u(src, du_dm_v, adjoint) + self._jDeriv_m(src, v, adjoint), dtype = complex)
class Fields3D_e(Fields):
class Fields3D_e(FieldsFDEM):
"""
Fields object for Problem3D_e.
:param Mesh mesh: mesh
:param Survey survey: survey
:param BaseMesh mesh: mesh
:param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
"""
knownFields = {'eSolution':'E'}
@@ -180,9 +180,6 @@ class Fields3D_e(Fields):
'h' : ['eSolution','CCV','_h'],
}
def __init__(self, mesh, survey, **kwargs):
Fields.__init__(self, mesh, survey, **kwargs)
def startup(self):
self.prob = self.survey.prob
self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -426,12 +423,12 @@ class Fields3D_e(Fields):
class Fields3D_b(Fields):
class Fields3D_b(FieldsFDEM):
"""
Fields object for Problem3D_b.
:param Mesh mesh: mesh
:param Survey survey: survey
:param BaseMesh mesh: mesh
:param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
"""
knownFields = {'bSolution':'F'}
@@ -446,9 +443,6 @@ class Fields3D_b(Fields):
'h' : ['bSolution','CCV','_h'],
}
def __init__(self,mesh,survey,**kwargs):
Fields.__init__(self,mesh,survey,**kwargs)
def startup(self):
self.prob = self.survey.prob
self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -693,12 +687,12 @@ class Fields3D_b(Fields):
return Zero()
class Fields3D_j(Fields):
class Fields3D_j(FieldsFDEM):
"""
Fields object for Problem3D_j.
:param Mesh mesh: mesh
:param Survey survey: survey
:param BaseMesh mesh: mesh
:param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
"""
knownFields = {'jSolution':'F'}
@@ -713,9 +707,6 @@ class Fields3D_j(Fields):
'b' : ['jSolution','CCV','_b'],
}
def __init__(self,mesh,survey,**kwargs):
Fields.__init__(self,mesh,survey,**kwargs)
def startup(self):
self.prob = self.survey.prob
self._edgeCurl = self.survey.prob.mesh.edgeCurl
@@ -988,12 +979,12 @@ class Fields3D_j(Fields):
return 1./(1j * omega(src.freq)) * VI * (self._aveE2CCV * ( s_mDeriv(v) - self._edgeCurl.T * ( self._MfRhoDeriv(jSolution) * v ) ) )
class Fields3D_h(Fields):
class Fields3D_h(FieldsFDEM):
"""
Fields object for Problem3D_h.
:param Mesh mesh: mesh
:param Survey survey: survey
:param BaseMesh mesh: mesh
:param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey
"""
knownFields = {'hSolution':'E'}
@@ -1008,9 +999,6 @@ class Fields3D_h(Fields):
'b' : ['hSolution','CCV','_b'],
}
def __init__(self,mesh,survey,**kwargs):
Fields.__init__(self,mesh,survey,**kwargs)
def startup(self):
self.prob = self.survey.prob
self._edgeCurl = self.survey.prob.mesh.edgeCurl
SimPEG/EM/FDEM/ProblemFDEM.py
+21 -16
View File
@@ -1,7 +1,7 @@
from SimPEG import Problem, Utils, np, sp, Solver as SimpegSolver
from scipy.constants import mu_0
from SurveyFDEM import Survey as SurveyFDEM
from FieldsFDEM import Fields, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
from FieldsFDEM import FieldsFDEM, Fields3D_e, Fields3D_b, Fields3D_h, Fields3D_j
from SimPEG.EM.Base import BaseEMProblem
from SimPEG.EM.Utils import omega
@@ -31,10 +31,11 @@ class BaseFDEMProblem(BaseEMProblem):
if using the H-J formulation (:code:`Problem3D_j` or :code:`Problem3D_h`). Note that here, :math:`\mathbf{s_m}` is an integrated quantity.
The problem performs the elimination so that we are solving the system for \\\(\\\mathbf{e},\\\mathbf{b},\\\mathbf{j} \\\) or \\\(\\\mathbf{h}\\\)
"""
surveyPair = SurveyFDEM
fieldsPair = Fields
fieldsPair = FieldsFDEM
def fields(self, m):
"""
@@ -64,7 +65,7 @@ class BaseFDEMProblem(BaseEMProblem):
:param numpy.array m: inversion model (nP,)
:param numpy.array v: vector which we take sensitivity product with (nP,)
:param SimPEG.EM.FDEM.Fields u: fields object
:param SimPEG.EM.FDEM.FieldsFDEM.FieldsFDEM u: fields object
:rtype numpy.array:
:return: Jv (ndata,)
"""
@@ -99,7 +100,7 @@ class BaseFDEMProblem(BaseEMProblem):
:param numpy.array m: inversion model (nP,)
:param numpy.array v: vector which we take adjoint product with (nP,)
:param SimPEG.EM.FDEM.Fields u: fields object
:param SimPEG.EM.FDEM.FieldsFDEM.FieldsFDEM u: fields object
:rtype numpy.array:
:return: Jv (ndata,)
"""
@@ -153,8 +154,8 @@ class BaseFDEMProblem(BaseEMProblem):
Evaluates the sources for a given frequency and puts them in matrix form
:param float freq: Frequency
:rtype: (numpy.ndarray, numpy.ndarray)
:return: s_m, s_e (nE or nF, nSrc)
:rtype: tuple
:return: (s_m, s_e) (nE or nF, nSrc)
"""
Srcs = self.survey.getSrcByFreq(freq)
if self._formulation is 'EB':
@@ -194,7 +195,7 @@ class Problem3D_e(BaseFDEMProblem):
which we solve for :math:`\mathbf{e}`.
:param SimPEG.Mesh mesh: mesh
:param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
"""
_solutionType = 'eSolution'
@@ -269,7 +270,7 @@ class Problem3D_e(BaseFDEMProblem):
Derivative of the right hand side with respect to the model
:param float freq: frequency
:param SimPEG.EM.FDEM.Src src: FDEM source
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
@@ -305,7 +306,7 @@ class Problem3D_b(BaseFDEMProblem):
.. note ::
The inverse problem will not work with full anisotropy
:param SimPEG.Mesh mesh: mesh
:param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
"""
_solutionType = 'bSolution'
@@ -400,7 +401,7 @@ class Problem3D_b(BaseFDEMProblem):
Derivative of the right hand side with respect to the model
:param float freq: frequency
:param SimPEG.EM.FDEM.Src src: FDEM source
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
@@ -444,6 +445,7 @@ class Problem3D_j(BaseFDEMProblem):
\mathbf{h} = \\frac{1}{i \omega} \mathbf{M_{\mu}^e}^{-1} \\left(-\mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{j} + \mathbf{M^e} \mathbf{s_m} \\right)
and solve for \\\(\\\mathbf{j}\\\) using
.. math ::
@@ -453,7 +455,7 @@ class Problem3D_j(BaseFDEMProblem):
.. note::
This implementation does not yet work with full anisotropy!!
:param SimPEG.Mesh mesh: mesh
:param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
"""
_solutionType = 'jSolution'
@@ -529,8 +531,8 @@ class Problem3D_j(BaseFDEMProblem):
\mathbf{RHS} = \mathbf{C} \mathbf{M_{\mu}^e}^{-1}\mathbf{s_m} -i\omega \mathbf{s_e}
:param float freq: Frequency
:rtype: numpy.ndarray (nE, nSrc)
:return: RHS
:rtype: numpy.ndarray
:return: RHS (nE, nSrc)
"""
s_m, s_e = self.getSourceTerm(freq)
@@ -549,7 +551,7 @@ class Problem3D_j(BaseFDEMProblem):
Derivative of the right hand side with respect to the model
:param float freq: frequency
:param SimPEG.EM.FDEM.Src src: FDEM source
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
@@ -591,7 +593,7 @@ class Problem3D_h(BaseFDEMProblem):
\\left(\mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{C} + i \omega \mathbf{M_{\mu}^e}\\right) \mathbf{h} = \mathbf{M^e} \mathbf{s_m} + \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{s_e}
:param SimPEG.Mesh mesh: mesh
:param SimPEG.Mesh.BaseMesh.BaseMesh mesh: mesh
"""
_solutionType = 'hSolution'
@@ -608,9 +610,11 @@ class Problem3D_h(BaseFDEMProblem):
.. math::
\mathbf{A} = \mathbf{C}^{\\top} \mathbf{M_{\\rho}^f} \mathbf{C} + i \omega \mathbf{M_{\mu}^e}
:param float freq: Frequency
:rtype: scipy.sparse.csr_matrix
:return: A
"""
MeMu = self.MeMu
@@ -653,6 +657,7 @@ class Problem3D_h(BaseFDEMProblem):
:param float freq: Frequency
:rtype: numpy.ndarray
:return: RHS (nE, nSrc)
"""
s_m, s_e = self.getSourceTerm(freq)
@@ -666,7 +671,7 @@ class Problem3D_h(BaseFDEMProblem):
Derivative of the right hand side with respect to the model
:param float freq: frequency
:param SimPEG.EM.FDEM.Src src: FDEM source
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
SimPEG/EM/FDEM/RxFDEM.py
+5 -5
View File
@@ -25,10 +25,10 @@ class BaseRx(SimPEG.Survey.BaseRx):
def eval(self, src, mesh, f):
"""
Project fields to recievers to get data.
Project fields to receivers to get data.
:param Source src: FDEM source
:param Mesh mesh: mesh used
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param BaseMesh mesh: mesh used
:param Fields f: fields object
:rtype: numpy.ndarray
:return: fields projected to recievers
@@ -44,8 +44,8 @@ class BaseRx(SimPEG.Survey.BaseRx):
"""
Derivative of projected fields with respect to the inversion model times a vector.
:param Source src: FDEM source
:param Mesh mesh: mesh used
:param SimPEG.EM.FDEM.SrcFDEM.BaseSrc src: FDEM source
:param BaseMesh mesh: mesh used
:param Fields f: fields object
:param numpy.ndarray v: vector to multiply
:rtype: numpy.ndarray
SimPEG/EM/FDEM/SrcFDEM.py
+28 -28
View File
@@ -23,8 +23,8 @@ class BaseSrc(Survey.BaseSrc):
- :math:`s_m` : magnetic source term
- :math:`s_e` : electric source term
:param Problem prob: FDEM Problem
:rtype: (numpy.ndarray, numpy.ndarray)
:param BaseFDEMProblem prob: FDEM Problem
:rtype: tuple
:return: tuple with magnetic source term and electric source term
"""
s_m = self.s_m(prob)
@@ -37,10 +37,10 @@ class BaseSrc(Survey.BaseSrc):
- :code:`s_mDeriv` : derivative of the magnetic source term
- :code:`s_eDeriv` : derivative of the electric source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: (numpy.ndarray, numpy.ndarray)
:rtype: tuple
:return: tuple with magnetic source term and electric source term derivatives times a vector
"""
if v is not None:
@@ -52,7 +52,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Primary magnetic flux density
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: primary magnetic flux density
"""
@@ -64,7 +64,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Primary magnetic field
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -76,7 +76,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Primary electric field
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: primary electric field
"""
@@ -88,7 +88,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Primary current density
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: primary current density
"""
@@ -100,7 +100,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Magnetic source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: magnetic source term on mesh
"""
@@ -110,7 +110,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Electric source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: electric source term on mesh
"""
@@ -120,7 +120,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Derivative of magnetic source term with respect to the inversion model
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
@@ -133,7 +133,7 @@ class BaseSrc(Survey.BaseSrc):
"""
Derivative of electric source term with respect to the inversion model
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:param numpy.ndarray v: vector to take product with
:param bool adjoint: adjoint?
:rtype: numpy.ndarray
@@ -162,7 +162,7 @@ class RawVec_e(BaseSrc):
"""
Electric source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: electric source term on mesh
"""
@@ -191,7 +191,7 @@ class RawVec_m(BaseSrc):
"""
Magnetic source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: magnetic source term on mesh
"""
@@ -220,7 +220,7 @@ class RawVec(BaseSrc):
"""
Magnetic source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: magnetic source term on mesh
"""
@@ -232,7 +232,7 @@ class RawVec(BaseSrc):
"""
Electric source term
:param Problem prob: FDEM Problem
:param BaseFDEMProblem prob: FDEM Problem
:rtype: numpy.ndarray
:return: electric source term on mesh
"""
@@ -301,7 +301,7 @@ class MagDipole(BaseSrc):
"""
The primary magnetic flux density from a magnetic vector potential
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -339,7 +339,7 @@ class MagDipole(BaseSrc):
"""
The primary magnetic field from a magnetic vector potential
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -350,7 +350,7 @@ class MagDipole(BaseSrc):
"""
The magnetic source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -364,7 +364,7 @@ class MagDipole(BaseSrc):
"""
The electric source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -416,7 +416,7 @@ class MagDipole_Bfield(BaseSrc):
"""
The primary magnetic flux density from the analytic solution for magnetic fields from a dipole
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -455,7 +455,7 @@ class MagDipole_Bfield(BaseSrc):
"""
The primary magnetic field from a magnetic vector potential
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -466,7 +466,7 @@ class MagDipole_Bfield(BaseSrc):
"""
The magnetic source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -479,7 +479,7 @@ class MagDipole_Bfield(BaseSrc):
"""
The electric source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -530,7 +530,7 @@ class CircularLoop(BaseSrc):
"""
The primary magnetic flux density from a magnetic vector potential
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -567,7 +567,7 @@ class CircularLoop(BaseSrc):
"""
The primary magnetic field from a magnetic vector potential
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -578,7 +578,7 @@ class CircularLoop(BaseSrc):
"""
The magnetic source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
@@ -591,7 +591,7 @@ class CircularLoop(BaseSrc):
"""
The electric source term
:param Problem prob: FDEM problem
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
SimPEG/EM/TDEM/BaseTDEM.py
+3 -3
View File
@@ -112,7 +112,7 @@ class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
"""
:param numpy.array m: Conductivity model
:param numpy.ndarray v: vector (model object)
:param simpegEM.TDEM.FieldsTDEM f: Fields resulting from m
:param FieldsTDEM f: Fields resulting from m
:rtype: numpy.ndarray
:return: w (data object)
@@ -136,8 +136,8 @@ class BaseTDEMProblem(BaseTimeProblem, BaseEMProblem):
def Jtvec(self, m, v, f=None):
"""
:param numpy.array m: Conductivity model
:param numpy.ndarray,SimPEG.Survey.Data v: vector (data object)
:param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
:param numpy.ndarray v: vector (or a :class:`SimPEG.Survey.Data` object)
:param FieldsTDEM u: Fields resulting from m
:rtype: numpy.ndarray
:return: w (model object)
SimPEG/EM/TDEM/TDEM_b.py
+13 -13
View File
@@ -87,8 +87,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
"""
:param numpy.array m: Conductivity model
:param numpy.array vec: vector (like a model)
:param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
:rtype: simpegEM.TDEM.FieldsTDEM
:param FieldsTDEM u: Fields resulting from m
:rtype: FieldsTDEM
:return: f
Multiply G by a vector
@@ -125,9 +125,9 @@ class ProblemTDEM_b(BaseTDEMProblem):
"""
:param numpy.array m: Conductivity model
:param numpy.array vec: vector (like a fields)
:param simpegEM.TDEM.FieldsTDEM u: Fields resulting from m
:rtype: np.ndarray (like a model)
:return: p
:param FieldsTDEM u: Fields resulting from m
:rtype: numpy.ndarray
:return: p (like a model)
Multiply G.T by a vector
"""
@@ -153,8 +153,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
def solveAh(self, m, p):
"""
:param numpy.array m: Conductivity model
:param simpegEM.TDEM.FieldsTDEM p: Fields object
:rtype: simpegEM.TDEM.FieldsTDEM
:param FieldsTDEM p: Fields object
:rtype: FieldsTDEM
:return: y
Solve the block-matrix system \\\(\\\hat{A} \\\hat{y} = \\\hat{p}\\\):
@@ -200,8 +200,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
def solveAht(self, m, p):
"""
:param numpy.array m: Conductivity model
:param simpegEM.TDEM.FieldsTDEM p: Fields object
:rtype: simpegEM.TDEM.FieldsTDEM
:param FieldsTDEM p: Fields object
:rtype: FieldsTDEM
:return: y
Solve the block-matrix system \\\(\\\hat{A}^\\\\top \\\hat{y} = \\\hat{p}\\\):
@@ -270,8 +270,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
def _AhVec(self, m, vec):
"""
:param numpy.array m: Conductivity model
:param simpegEM.TDEM.FieldsTDEM vec: Fields object
:rtype: simpegEM.TDEM.FieldsTDEM
:param FieldsTDEM vec: Fields object
:rtype: FieldsTDEM
:return: f
Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where
@@ -315,8 +315,8 @@ class ProblemTDEM_b(BaseTDEMProblem):
def _AhtVec(self, m, vec):
"""
:param numpy.array m: Conductivity model
:param simpegEM.TDEM.FieldsTDEM vec: Fields object
:rtype: simpegEM.TDEM.FieldsTDEM
:param FieldsTDEM vec: Fields object
:rtype: FieldsTDEM
:return: f
Multiply the matrix \\\(\\\hat{A}\\\) by a fields vector where
SimPEG/Examples/DC_Analytic_Dipole.py
+5 -5
View File
@@ -1,5 +1,5 @@
from SimPEG import *
import SimPEG.DCIP as DC
import SimPEG.EM.Static.DC as DC
def run(plotIt=False):
cs = 25.
@@ -21,10 +21,10 @@ def run(plotIt=False):
# ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')
# ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)
rx = DC.RxDipole(xyz_rxP, xyz_rxN)
src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
survey = DC.SurveyDC([src])
problem = DC.ProblemDC_CC(mesh)
rx = DC.Rx.Dipole(xyz_rxP, xyz_rxN)
src = DC.Src.Dipole([rx], np.r_[-200, 0, -12.5], np.r_[+200, 0, -12.5])
survey = DC.Survey([src])
problem = DC.Problem3D_CC(mesh)
problem.pair(survey)
try:
from pymatsolver import MumpsSolver
SimPEG/Examples/EM_Schenkel_Morrison_Casing.py
+5 -2
View File
@@ -19,10 +19,13 @@ def run(plotIt=True):
Morrison Casing Model, and the results are used in a 2016 SEG abstract by
Yang et al.
- Schenkel, C.J., and H.F. Morrison, 1990, Effects of well casing on potential field measurements using downhole current sources: Geophysical prospecting, 38, 663-686.
.. code-block:: text
Schenkel, C.J., and H.F. Morrison, 1990, Effects of well casing on potential field measurements using downhole current sources: Geophysical prospecting, 38, 663-686.
The model consists of:
- Air: Conductivity 1e-8 S/m, above z = 0
- Background: conductivity 1e-2 S/m, below z = 0
- Casing: conductivity 1e6 S/m
@@ -215,7 +218,7 @@ def run(plotIt=True):
# ------------ Problem and Survey ---------------
survey = FDEM.Survey(sg_p + dg_p)
mapping = [('sigma', Maps.IdentityMap(mesh))]
problem = FDEM.Problem3D_h(mesh, mapping=mapping)
problem = FDEM.Problem3D_h(mesh, mapping=mapping, Solver=solver)
problem.pair(survey)
# ------------- Solve ---------------------------
SimPEG/Examples/MT_1D_ForwardAndInversion.py
+3 -3
View File
@@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
def run(plotIt=True):
"""
MT: 1D: Inversion
=======================
=================
Forward model 1D MT data.
Setup and run a MT 1D inversion.
@@ -50,7 +50,7 @@ def run(plotIt=True):
m_0 = np.log(sigma_0[active])
# Set the mapping
actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
actMap = simpeg.Maps.InjectActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)
mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap
## Setup the layout of the survey, set the sources and the connected receivers
@@ -76,7 +76,7 @@ def run(plotIt=True):
survey.dobs = survey.dtrue + 0.025*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)
if plotIt:
fig = MT.Utils.dataUtils.plotMT1DModelData(problem)
fig = MT.Utils.dataUtils.plotMT1DModelData(problem, [m_0])
fig.suptitle('Target - smooth true')
SimPEG/Examples/MT_3D_Foward.py
+3 -4
View File
@@ -12,7 +12,7 @@ except:
def run(plotIt=True, nFreq=1):
"""
MT: 3D: Forward
=======================
===============
Forward model 3D MT data.
@@ -46,16 +46,15 @@ def run(plotIt=True, nFreq=1):
survey = MT.Survey(srcList)
## Setup the problem object
problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG)
problem = MT.Problem3D.eForm_ps(M, sigmaPrimary=sigBG, Solver=Solver)
problem.pair(survey)
problem.Solver = Solver
# Calculate the data
fields = problem.fields(sig)
dataVec = survey.eval(fields)
# Make the data
mtData = MT.Data(survey,dataVec)
mtData = MT.Data(survey, dataVec)
# Add plots
if plotIt:
pass
SimPEG/Examples/Utils_surface2ind_topo.py
+9 -7
View File
@@ -2,8 +2,12 @@ from SimPEG import *
from SimPEG.Utils import surface2ind_topo
def run(plotIt=False, nx = 5, ny = 5):
def run(plotIt=False, nx=5, ny=5):
"""
Utils: surface2ind_topo
=======================
Here we show how to use :code:`Utils.surface2ind_topo` to identify cells below
a topographic surface.
@@ -13,27 +17,25 @@ def run(plotIt=False, nx = 5, ny = 5):
xtopo = np.linspace(mesh.gridN[:,0].min(), mesh.gridN[:,0].max())
topo = 0.4*np.sin(xtopo*5) # define a topographic surface
Topo = np.hstack([Utils.mkvc(xtopo,2),Utils.mkvc(topo,2)]) #make it an array
Topo = np.hstack([Utils.mkvc(xtopo,2), Utils.mkvc(topo,2)]) #make it an array
indcc = surface2ind_topo(mesh, Topo,'CC')
indcc = surface2ind_topo(mesh, Topo, 'CC')
if plotIt:
from matplotlib.pylab import plt
from scipy.interpolate import interp1d
fig, ax = plt.subplots(1,1,figsize=(6,6))
fig, ax = plt.subplots(1,1, figsize=(6,6))
mesh.plotGrid(ax=ax, nodes=True, centers=True)
ax.plot(xtopo,topo,'k',linewidth=1)
# ax.plot(mesh.vectorNx, interp1d(xtopo,topo)(mesh.vectorNx),'--k',linewidth=3)
ax.plot(mesh.vectorCCx, interp1d(xtopo,topo)(mesh.vectorCCx),'--k',linewidth=3)
aveN2CC = Utils.sdiag(mesh.aveN2CC.T.sum(1))*mesh.aveN2CC.T
a = aveN2CC * indcc
a[a > 0] = 1.
a[a < 0.25] = np.nan
a = a.reshape(mesh.vnN, order='F')
masked_array = np.ma.array(a, mask=np.isnan(a))
ax.pcolor(mesh.vectorNx,mesh.vectorNy,masked_array.T, cmap = plt.cm.gray,alpha=0.2)
ax.pcolor(mesh.vectorNx,mesh.vectorNy,masked_array.T, cmap=plt.cm.gray, alpha=0.2)
plt.show()
SimPEG/Examples/__init__.py
+3 -3
View File
@@ -38,7 +38,7 @@ if __name__ == '__main__':
# Create the examples dir in the docs folder.
fName = os.path.realpath(__file__)
docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'examples'])
docExamplesDir = os.path.sep.join(fName.split(os.path.sep)[:-3] + ['docs', 'content', 'examples'])
shutil.rmtree(docExamplesDir)
os.makedirs(docExamplesDir)
@@ -95,12 +95,12 @@ if __name__ == '__main__':
from SimPEG import Examples
Examples.%s.run()
.. literalinclude:: ../../SimPEG/Examples/%s.py
.. literalinclude:: ../../../SimPEG/Examples/%s.py
:language: python
:linenos:
"""%(name,doc,name,name)
rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'examples', name + '.rst']))
rst = os.path.sep.join((filePath.split(os.path.sep)[:-3] + ['docs', 'content', 'examples', name + '.rst']))
print 'Creating: %s.rst'%name
f = open(rst, 'w')
SimPEG/FLOW/Richards/Empirical.py
+2 -2
View File
@@ -31,7 +31,7 @@ class NonLinearMap(object):
"""
:param numpy.array u: fields
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
The *transform* changes the model into the physical property.
@@ -44,7 +44,7 @@ class NonLinearMap(object):
"""
:param numpy.array u: fields
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
The *transform* changes the model into the physical property.
SimPEG/MT/SrcMT.py
+2 -2
View File
@@ -86,7 +86,7 @@ class polxy_1Dprimary(BaseMTSrc):
Get the electrical field source
"""
e_p = self.ePrimary(problem)
Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
sigma_p = Map_sigma_p._transform(self.sigma1d)
# Make mass matrix
# Note: M(sig) - M(sig_p) = M(sig - sig_p)
@@ -163,7 +163,7 @@ class polxy_3Dprimary(BaseMTSrc):
Get the electrical field source
"""
e_p = self.ePrimary(problem)
Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
Map_sigma_p = Maps.SurjectVertical1D(problem.mesh)
sigma_p = Map_sigma_p._transform(self.sigma1d)
# Make mass matrix
# Note: M(sig) - M(sig_p) = M(sig - sig_p)
SimPEG/MT/Utils/dataUtils.py
+6 -6
View File
@@ -19,7 +19,7 @@ def getAppRes(MTdata):
zList.append(zc)
return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
def rotateData(MTdata,rotAngle):
def rotateData(MTdata, rotAngle):
'''
Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
'''
@@ -44,19 +44,19 @@ def rotateData(MTdata,rotAngle):
return MT.Data.fromRecArray(outRec)
def appResPhs(freq,z):
def appResPhs(freq, z):
app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
return app_res, app_phs
def skindepth(rho,freq):
def skindepth(rho, freq):
''' Function to calculate the skindepth of EM waves'''
return np.sqrt( (rho*((1/(freq * mu_0 * np.pi )))))
def rec2ndarr(x,dt=float):
def rec2ndarr(x, dt=float):
return x.view((dt, len(x.dtype.names)))
def makeAnalyticSolution(mesh,model,elev,freqs):
def makeAnalyticSolution(mesh, model, elev, freqs):
from SimPEG import MT
data1D = []
for freq in freqs:
@@ -70,7 +70,7 @@ def makeAnalyticSolution(mesh,model,elev,freqs):
dataRec = np.array(data1D,dtype=[('freq',float),('x',float),('y',float),('z',float),('zyx',complex)])
return dataRec
def plotMT1DModelData(problem,models,symList=None):
def plotMT1DModelData(problem, models, symList=None):
from SimPEG import MT
# Setup the figure
fontSize = 15
SimPEG/Maps.py
+6 -6
View File
@@ -41,8 +41,8 @@ class IdentityMap(object):
If this is a meshless mapping (i.e. nP is defined independently)
the shape will be the the shape (nP,nP).
:rtype: (int,int)
:return: shape of the operator as a tuple
:rtype: tuple
:return: shape of the operator as a tuple (int,int)
"""
if self._nP is not None:
return (self.nP, self.nP)
@@ -86,7 +86,7 @@ class IdentityMap(object):
The derivative of the transformation.
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
"""
@@ -216,7 +216,7 @@ class ExpMap(IdentityMap):
def deriv(self, m):
"""
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
The *transform* changes the model into the physical property.
@@ -366,7 +366,7 @@ class SurjectVertical1D(IdentityMap):
def deriv(self, m):
"""
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
"""
repNum = self.mesh.vnC[:self.mesh.dim-1].prod()
@@ -427,7 +427,7 @@ class Surject2Dto3D(IdentityMap):
def deriv(self, m):
"""
:param numpy.array m: model
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: derivative of transformed model
"""
inds = self * np.arange(self.nP)
SimPEG/Mesh/BaseMesh.py
+26 -24
View File
@@ -7,8 +7,8 @@ class BaseMesh(object):
BaseMesh does all the counting you don't want to do.
BaseMesh should be inherited by meshes with a regular structure.
:param numpy.array,list n: number of cells in each direction (dim, )
:param numpy.array,list x0: Origin of the mesh (dim, )
:param numpy.array n: (or list) number of cells in each direction (dim, )
:param numpy.array x0: (or list) Origin of the mesh (dim, )
"""
@@ -34,8 +34,8 @@ class BaseMesh(object):
"""
Origin of the mesh
:rtype: numpy.array (dim, )
:return: x0
:rtype: numpy.array
:return: x0, (dim, )
"""
return self._x0
@@ -116,8 +116,8 @@ class BaseMesh(object):
"""
Total number of edges in each direction
:rtype: numpy.array (dim, )
:return: [nEx, nEy, nEz]
:rtype: numpy.array
:return: [nEx, nEy, nEz], (dim, )
.. plot::
:include-source:
@@ -173,8 +173,8 @@ class BaseMesh(object):
"""
Total number of faces in each direction
:rtype: numpy.array (dim, )
:return: [nFx, nFy, nFz]
:rtype: numpy.array
:return: [nFx, nFy, nFz], (dim, )
.. plot::
:include-source:
@@ -200,8 +200,8 @@ class BaseMesh(object):
"""
Face Normals
:rtype: numpy.array (sum(nF), dim)
:return: normals
:rtype: numpy.array
:return: normals, (sum(nF), dim)
"""
if self.dim == 2:
nX = np.c_[np.ones(self.nFx), np.zeros(self.nFx)]
@@ -218,8 +218,8 @@ class BaseMesh(object):
"""
Edge Tangents
:rtype: numpy.array (sum(nE), dim)
:return: normals
:rtype: numpy.array
:return: normals, (sum(nE), dim)
"""
if self.dim == 2:
tX = np.c_[np.ones(self.nEx), np.zeros(self.nEx)]
@@ -236,8 +236,9 @@ class BaseMesh(object):
Given a vector, fV, in cartesian coordinates, this will project it onto the mesh using the normals
:param numpy.array fV: face vector with shape (nF, dim)
:rtype: numpy.array with shape (nF, )
:return: projected face vector
:rtype: numpy.array
:return: projected face vector, (nF, )
"""
assert isinstance(fV, np.ndarray), 'fV must be an ndarray'
assert len(fV.shape) == 2 and fV.shape[0] == self.nF and fV.shape[1] == self.dim, 'fV must be an ndarray of shape (nF x dim)'
@@ -248,8 +249,9 @@ class BaseMesh(object):
Given a vector, eV, in cartesian coordinates, this will project it onto the mesh using the tangents
:param numpy.array eV: edge vector with shape (nE, dim)
:rtype: numpy.array with shape (nE, )
:return: projected edge vector
:rtype: numpy.array
:return: projected edge vector, (nE, )
"""
assert isinstance(eV, np.ndarray), 'eV must be an ndarray'
assert len(eV.shape) == 2 and eV.shape[0] == self.nE and eV.shape[1] == self.dim, 'eV must be an ndarray of shape (nE x dim)'
@@ -295,7 +297,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Total number of cells in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: [nCx, nCy, nCz]
"""
return np.array([x for x in [self.nCx, self.nCy, self.nCz] if not x is None])
@@ -335,7 +337,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Total number of nodes in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: [nNx, nNy, nNz]
"""
return np.array([x for x in [self.nNx, self.nNy, self.nNz] if not x is None])
@@ -345,7 +347,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of x-edges in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnEx
"""
return np.array([x for x in [self.nCx, self.nNy, self.nNz] if not x is None])
@@ -355,7 +357,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of y-edges in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnEy or None if dim < 2
"""
return None if self.dim < 2 else np.array([x for x in [self.nNx, self.nCy, self.nNz] if not x is None])
@@ -365,7 +367,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of z-edges in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnEz or None if dim < 3
"""
return None if self.dim < 3 else np.array([x for x in [self.nNx, self.nNy, self.nCz] if not x is None])
@@ -375,7 +377,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of x-faces in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnFx
"""
return np.array([x for x in [self.nNx, self.nCy, self.nCz] if not x is None])
@@ -385,7 +387,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of y-faces in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnFy or None if dim < 2
"""
return None if self.dim < 2 else np.array([x for x in [self.nCx, self.nNy, self.nCz] if not x is None])
@@ -395,7 +397,7 @@ class BaseRectangularMesh(BaseMesh):
"""
Number of z-faces in each direction
:rtype: numpy.array (dim, )
:rtype: numpy.array
:return: vnFz or None if dim < 3
"""
return None if self.dim < 3 else np.array([x for x in [self.nCx, self.nCy, self.nNz] if not x is None])
SimPEG/Mesh/CylMesh.py
+6 -6
View File
@@ -68,8 +68,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
"""
Number of x-faces in each direction
:rtype: numpy.array (dim, )
:return: vnFx
:rtype: numpy.array
:return: vnFx, (dim, )
"""
return self.vnC
@@ -78,8 +78,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
"""
Number of y-edges in each direction
:rtype: numpy.array (dim, )
:return: vnEy or None if dim < 2
:rtype: numpy.array
:return: vnEy or None if dim < 2, (dim, )
"""
nNx = self.nNx if self.isSymmetric else self.nNx - 1
return np.r_[nNx, self.nCy, self.nNz]
@@ -89,8 +89,8 @@ class CylMesh(BaseTensorMesh, BaseRectangularMesh, InnerProducts, CylView):
"""
Number of z-edges in each direction
:rtype: numpy.array (dim, )
:return: vnEz or None if nCy > 1
:rtype: numpy.array
:return: vnEz or None if nCy > 1, (dim, )
"""
if self.isSymmetric:
return np.r_[self.nNx, self.nNy, self.nCz]
SimPEG/Mesh/InnerProducts.py
+8 -9
View File
@@ -16,7 +16,7 @@ class InnerProducts(object):
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:param bool doFast: do a faster implementation if available.
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
return self._getInnerProduct('F', prop=prop, invProp=invProp, invMat=invMat, doFast=doFast)
@@ -27,7 +27,7 @@ class InnerProducts(object):
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:param bool doFast: do a faster implementation if available.
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nE, nE)
"""
return self._getInnerProduct('E', prop=prop, invProp=invProp, invMat=invMat, doFast=doFast)
@@ -39,7 +39,7 @@ class InnerProducts(object):
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:param bool doFast: do a faster implementation if available.
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nE, nE)
"""
assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"
@@ -115,13 +115,12 @@ class InnerProducts(object):
:param bool doFast: do a faster implementation if available.
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: function
:return: dMdmu(u), the derivative of the inner product matrix (u)
Given u, dMdmu returns (nF, nC*nA)
:param np.ndarray u: vector that multiplies dMdmu
:rtype: scipy.csr_matrix
:param numpy.ndarray u: vector that multiplies dMdmu
:rtype: scipy.sparse.csr_matrix
:return: dMdmu, the derivative of the inner product matrix for a certain u
"""
return self._getInnerProductDeriv(prop, 'F', doFast=doFast, invProp=invProp, invMat=invMat)
@@ -133,7 +132,7 @@ class InnerProducts(object):
:param bool doFast: do a faster implementation if available.
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: dMdm, the derivative of the inner product matrix (nE, nC*nA)
"""
return self._getInnerProductDeriv(prop, 'E', doFast=doFast, invProp=invProp, invMat=invMat)
@@ -145,7 +144,7 @@ class InnerProducts(object):
:param bool doFast: do a faster implementation if available.
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: dMdm, the derivative of the inner product matrix (nE, nC*nA)
"""
fast = None
@@ -169,7 +168,7 @@ class InnerProducts(object):
:param numpy.array v: vector to multiply (required in the general implementation)
:param list P: list of projection matrices
:param str projType: 'F' for faces 'E' for edges
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: dMdm, the derivative of the inner product matrix (n, nC*nA)
"""
assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"
SimPEG/Mesh/MeshIO.py
+22 -35
View File
@@ -6,13 +6,11 @@ class TensorMeshIO(object):
@classmethod
def readUBC(TensorMesh, fileName):
"""
Read UBC GIF 3DTensor mesh and generate 3D Tensor mesh in simpegTD
Read UBC GIF 3D tensor mesh and generate 3D TensorMesh in SimPEG.
Input:
:param fileName, path to the UBC GIF mesh file
Output:
:param SimPEG TensorMesh object
:param string fileName: path to the UBC GIF mesh file
:rtype: TensorMesh
:return: The tensor mesh for the fileName.
"""
# Interal function to read cell size lines for the UBC mesh files.
@@ -48,11 +46,9 @@ class TensorMeshIO(object):
Read VTK Rectilinear (vtr xml file) and return SimPEG Tensor mesh and model
Input:
:param vtrFileName, path to the vtr model file to write to
Output:
:return SimPEG TensorMesh object
:return SimPEG model dictionary
:param string fileName: path to the vtr model file to read
:rtype: tuple
:return: (TensorMesh, modelDictionary)
"""
# Import
@@ -102,9 +98,8 @@ class TensorMeshIO(object):
Makes and saves a VTK rectilinear file (vtr) for a simpeg Tensor mesh and model.
Input:
:param str, path to the output vtk file
:param mesh, SimPEG TensorMesh object - mesh to be transfer to VTK
:param models, dictionary of numpy.array - Name('s) and array('s). Match number of cells
:param string fileName: path to the output vtk file
:param dict models: dictionary of numpy.array - Name('s) and array('s). Match number of cells
"""
# Import
@@ -162,12 +157,9 @@ class TensorMeshIO(object):
"""
Read UBC 3DTensor mesh model and generate 3D Tensor mesh model in simpeg
Input:
:param fileName, path to the UBC GIF mesh file to read
:param mesh, TensorMesh object, mesh that coresponds to the model
Output:
:return numpy array, model with TensorMesh ordered
:param string fileName: path to the UBC GIF mesh file to read
:rtype: numpy.ndarray
:return: model with TensorMesh ordered
"""
f = open(fileName, 'r')
model = np.array(map(float, f.readlines()))
@@ -183,8 +175,7 @@ class TensorMeshIO(object):
Writes a model associated with a SimPEG TensorMesh
to a UBC-GIF format model file.
:param str fileName: File to write to
:param simpeg.Mesh.TensorMesh mesh: The mesh
:param string fileName: File to write to
:param numpy.ndarray model: The model
"""
@@ -201,8 +192,8 @@ class TensorMeshIO(object):
"""
Writes a SimPEG TensorMesh to a UBC-GIF format mesh file.
:param str fileName: File to write to
:param simpeg.Mesh.TensorMesh mesh: The mesh
:param string fileName: File to write to
:param dict models: A dictionary of the models
"""
assert mesh.dim == 3
@@ -231,9 +222,8 @@ class TreeMeshIO(object):
"""
Write UBC ocTree mesh and model files from a simpeg ocTree mesh and model.
:param str fileName: File to write to
:param simpeg.Mesh.TreeMesh mesh: The mesh
:param dictionary models: The models in a dictionary, where the keys is the name of the of the model file
:param string fileName: File to write to
:param dict models: The models in a dictionary, where the keys is the name of the of the model file
"""
# Calculate information to write in the file.
@@ -286,10 +276,9 @@ class TreeMeshIO(object):
Input:
:param str meshFile: path to the UBC GIF OcTree mesh file to read
:rtype: SimPEG.Mesh.TreeMesh
:return: The octree mesh
Output:
:return SimPEG.Mesh.TreeMesh mesh: The octree mesh
:return list of ndarray's: models as a list of numpy array's
"""
## Read the file lines
@@ -335,11 +324,9 @@ class TreeMeshIO(object):
"""
Read UBC OcTree model and get vector
Input:
:param fileName, path to the UBC GIF model file to read
Output:
:return numpy array, OcTree model
:param string fileName: path to the UBC GIF model file to read
:rtype: numpy.ndarray
:return: OcTree model
"""
if type(fileName) is list:
SimPEG/Mesh/TensorMesh.py
+4 -4
View File
@@ -198,8 +198,8 @@ class BaseTensorMesh(BaseMesh):
Determines if a set of points are inside a mesh.
:param numpy.ndarray pts: Location of points to test
:rtype numpy.ndarray
:return inside, numpy array of booleans
:rtype numpy.ndarray:
:return: inside, numpy array of booleans
"""
pts = Utils.asArray_N_x_Dim(pts, self.dim)
@@ -221,7 +221,7 @@ class BaseTensorMesh(BaseMesh):
:param numpy.ndarray loc: Location of points to interpolate to
:param str locType: What to interpolate (see below)
:rtype: scipy.sparse.csr.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the interpolation matrix
locType can be::
@@ -289,7 +289,7 @@ class BaseTensorMesh(BaseMesh):
:param bool returnP: returns the projection matrices
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"
SimPEG/Mesh/TreeMesh.py
+1 -1
View File
@@ -1875,7 +1875,7 @@ class TreeMesh(BaseTensorMesh, InnerProducts, TreeMeshIO):
:param numpy.ndarray locs: Location of points to interpolate to
:param str locType: What to interpolate (see below)
:rtype: scipy.sparse.csr.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the interpolation matrix
locType can be::
SimPEG/Optimization.py
+5 -5
View File
@@ -131,7 +131,7 @@ class Minimize(object):
Minimizes the function (evalFunction) starting at the location x0.
:param def evalFunction: function handle that evaluates: f, g, H = F(x)
:param callable evalFunction: function handle that evaluates: f, g, H = F(x)
:param numpy.ndarray x0: starting location
:rtype: numpy.ndarray
:return: x, the last iterate of the optimization algorithm
@@ -372,8 +372,8 @@ class Minimize(object):
Else, a modifySearchDirectionBreak call is preformed.
:param numpy.ndarray p: searchDirection
:rtype: numpy.ndarray,bool
:return: (xt, passLS)
:rtype: tuple
:return: (xt, passLS) numpy.ndarray, bool
"""
# Projected Armijo linesearch
self._LS_t = 1
@@ -408,8 +408,8 @@ class Minimize(object):
evalFunction returns a False indicating the break was not caught.
:param numpy.ndarray p: searchDirection
:rtype: numpy.ndarray,bool
:return: (xt, breakCaught)
:rtype: tuple
:return: (xt, breakCaught) numpy.ndarray, bool
"""
self.printDone(inLS=True)
print 'The linesearch got broken. Boo.'
SimPEG/PropMaps.py
+1 -1
View File
@@ -187,7 +187,7 @@ class _PropMapMetaClass(type):
attrs[attr + 'Model'] = prop._getModelProperty()
attrs[attr + 'Deriv'] = prop._getModelDerivProperty()
return type(name.replace('PropMap', 'PropModel'), (PropModel, ), attrs)
return type('PropModel', (PropModel, ), attrs)
class PropMap(object):
SimPEG/Regularization.py
+6 -6
View File
@@ -10,7 +10,7 @@ class RegularizationMesh(object):
are not necessarily true differential operators, but are constructed from
a SimPEG Mesh.
:param Mesh mesh: problem mesh
:param BaseMesh mesh: problem mesh
:param numpy.array indActive: bool array, size nC, that is True where we have active cells. Used to reduce the operators so we regularize only on active cells
"""
@@ -383,8 +383,8 @@ class BaseRegularization(object):
:param numpy.array m: geophysical model
:param numpy.array v: vector to multiply
:rtype: scipy.sparse.csr_matrix or numpy.ndarray
:return: WtW or WtW*v
:rtype: scipy.sparse.csr_matrix
:return: WtW, or if v is supplied WtW*v (numpy.ndarray)
The regularization is:
@@ -650,8 +650,8 @@ class Tikhonov(Simple):
Note if the key word argument `mrefInSmooth` is False, then mref is not
included in the smoothness contribution.
:param Mesh mesh: SimPEG mesh
:param Maps mapping: regularization mapping, takes the model from model space to the thing you want to regularize
:param BaseMesh mesh: SimPEG mesh
:param IdentityMap mapping: regularization mapping, takes the model from model space to the thing you want to regularize
:param numpy.ndarray indActive: active cell indices for reducing the size of differential operators in the definition of a regularization mesh
:param bool mrefInSmooth: (default = False) put mref in the smoothness component?
:param float alpha_s: (default 1e-6) smallness weight
@@ -671,7 +671,7 @@ class Tikhonov(Simple):
alpha_yy = Utils.dependentProperty('_alpha_yy', 0.0, ['_W', '_Wyy'], "Weight for the second derivative in the y direction")
alpha_zz = Utils.dependentProperty('_alpha_zz', 0.0, ['_W', '_Wzz'], "Weight for the second derivative in the z direction")
def __init__(self, mesh, mapping=None, indActive = None, **kwargs):
def __init__(self, mesh, mapping=None, indActive=None, **kwargs):
BaseRegularization.__init__(self, mesh, mapping=mapping, indActive=indActive, **kwargs)
@property
SimPEG/Tests.py
+1 -1
View File
@@ -237,7 +237,7 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None, expectedOrder=2, tole
Compares error decay of 0th and 1st order Taylor approximation at point
x0 for a randomized search direction.
:param lambda fctn: function handle
:param callable fctn: function handle
:param numpy.array x0: point at which to check derivative
:param int num: number of times to reduce step length, h
:param bool plotIt: if you would like to plot
SimPEG/Utils/ModelBuilder.py
+13 -13
View File
@@ -7,11 +7,11 @@ def addBlock(gridCC, modelCC, p0, p1, blockProp):
"""
Add a block to an exsisting cell centered model, modelCC
:param numpy.array, gridCC: mesh.gridCC is the cell centered grid
:param numpy.array, modelCC: cell centered model
:param numpy.array, p0: bottom, southwest corner of block
:param numpy.array, p1: top, northeast corner of block
:blockProp float, blockProp: property to assign to the model
:param numpy.array gridCC: mesh.gridCC is the cell centered grid
:param numpy.array modelCC: cell centered model
:param numpy.array p0: bottom, southwest corner of block
:param numpy.array p1: top, northeast corner of block
:blockProp float blockProp: property to assign to the model
:return numpy.array, modelBlock: model with block
"""
@@ -147,7 +147,7 @@ def getIndicesSphere(center,radius,ccMesh):
if dimMesh == 1:
# Define the reference points
ind = np.abs(center[0] - ccMesh[:,0]) < radius
elif dimMesh == 2:
@@ -222,14 +222,14 @@ def layeredModel(ccMesh, layerTops, layerValues):
:param numpy.array ccMesh: cell-centered mesh
:param numpy.array layerTops: z-locations of the tops of each layer
:param numpy.array layerValue: values of the property to assign for each layer (starting at the top)
:param numpy.array layerValue: values of the property to assign for each layer (starting at the top)
:rtype: numpy.array
:return: M, layered model on the mesh
:return: M, layered model on the mesh
"""
descending = np.linalg.norm(sorted(layerTops, reverse=True) - layerTops) < 1e-20
# TODO: put an error check to make sure that there is an ordering... needs to work with inf elts
# TODO: put an error check to make sure that there is an ordering... needs to work with inf elts
# assert ascending or descending, "Layers must be listed in either ascending or descending order"
# start from bottom up
@@ -253,10 +253,10 @@ def layeredModel(ccMesh, layerTops, layerValues):
model = np.zeros(ccMesh.shape[0])
for i, top in enumerate(layerTops):
zind = z <= top
zind = z <= top
model[zind] = layerValues[i]
return model
return model
@@ -265,9 +265,9 @@ def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=None):
Create a random model by convolving a kernel with a
uniformly distributed model.
:param int,tuple shape: shape of the model.
:param tuple shape: shape of the model.
:param int seed: pick which model to produce, prints the seed if you don't choose.
:param numpy.ndarray,list anisotropy: this is the (3 x n) blurring kernel that is used.
:param numpy.ndarray anisotropy: this is the (3 x n) blurring kernel that is used.
:param int its: number of smoothing iterations
:param list bounds: bounds on the model, len(list) == 2
:rtype: numpy.ndarray
SimPEG/Utils/SolverUtils.py
+7 -7
View File
@@ -13,7 +13,7 @@ def _checkAccuracy(A, b, X, accuracyTol):
warnings.warn(msg, RuntimeWarning)
def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6):
def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6, name=None):
"""
Wraps a direct Solver.
@@ -72,11 +72,11 @@ def SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-6):
if factorize and hasattr(self.solver, 'clean'):
return self.solver.clean()
return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5, name=None):
"""
Wraps an iterative Solver.
@@ -128,13 +128,13 @@ def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
def clean(self):
pass
return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
return type(name if name is not None else fun.__name__, (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
from scipy.sparse import linalg
Solver = SolverWrapD(linalg.spsolve, factorize=False)
SolverLU = SolverWrapD(linalg.splu, factorize=True)
SolverCG = SolverWrapI(linalg.cg)
Solver = SolverWrapD(linalg.spsolve, factorize=False, name="Solver")
SolverLU = SolverWrapD(linalg.splu, factorize=True, name="SolverLU")
SolverCG = SolverWrapI(linalg.cg, name="SolverCG")
class SolverDiag(object):
SimPEG/Utils/interputils.py
+1 -1
View File
@@ -25,7 +25,7 @@ def interpmat(locs, x, y=None, z=None):
:param numpy.ndarray x: Tensor vector of 1st dimension of grid.
:param numpy.ndarray y: Tensor vector of 2nd dimension of grid. None by default.
:param numpy.ndarray z: Tensor vector of 3rd dimension of grid. None by default.
:rtype: scipy.sparse.csr.csr_matrix
:rtype: scipy.sparse.csr_matrix
:return: Interpolation matrix
.. plot::
SimPEG/Utils/matutils.py
+6 -6
View File
@@ -27,7 +27,7 @@ def mkvc(x, numDims=1):
if isinstance(x, Zero):
return x
assert isinstance(x, np.ndarray), "Vector must be a numpy array"
if numDims == 1:
@@ -355,9 +355,9 @@ def diagEst(matFun, n, k=None, approach='Probing'):
2. Ones : random +/- 1 entries
3. Random : random vectors
:param lambda (numpy.array) matFun: matrix to estimate the diagonal of
:param int64 n: size of the vector that should be used to compute matFun(v)
:param int64 k: number of vectors to be used to estimate the diagonal
:param callable matFun: takes a (numpy.array) and multiplies it by a matrix to estimate the diagonal
:param int n: size of the vector that should be used to compute matFun(v)
:param int k: number of vectors to be used to estimate the diagonal
:param str approach: approach to be used for getting vectors
:rtype: numpy.array
:return: est_diag(A)
@@ -422,9 +422,9 @@ class Zero(object):
def __ge__(self, v):return 0 >= v
def __gt__(self, v):return 0 > v
@property
@property
def transpose(self): return Zero()
@property
def T(self): return Zero()
SimPEG/Utils/meshutils.py
+18 -14
View File
@@ -83,7 +83,7 @@ def closestPoints(mesh, pts, gridLoc='CC'):
"""
Move a list of points to the closest points on a grid.
:param simpeg.Mesh.BaseMesh mesh: The mesh
:param BaseMesh mesh: The mesh
:param numpy.ndarray pts: Points to move
:param string gridLoc: ['CC', 'N', 'Fx', 'Fy', 'Fz', 'Ex', 'Ex', 'Ey', 'Ez']
:rtype: numpy.ndarray
@@ -104,16 +104,20 @@ def closestPoints(mesh, pts, gridLoc='CC'):
def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
"""
Extracts Core Mesh from Global mesh
xyzlim: 2D array [ndim x 2]
mesh: SimPEG mesh
This function ouputs:
- actind: corresponding boolean index from global to core
- meshcore: core SimPEG mesh
Warning: 1D and 2D has not been tested
Extracts Core Mesh from Global mesh
:param numpy.ndarray xyzlim: 2D array [ndim x 2]
:param BaseMesh mesh: The mesh
This function ouputs::
- actind: corresponding boolean index from global to core
- meshcore: core SimPEG mesh
Warning: 1D and 2D has not been tested
"""
from SimPEG import Mesh
if mesh.dim ==1:
if mesh.dim == 1:
xyzlim = xyzlim.flatten()
xmin, xmax = xyzlim[0], xyzlim[1]
@@ -125,11 +129,11 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5]
meshCore = Mesh.TensorMesh([hx, hy] ,x0=x0)
meshCore = Mesh.TensorMesh([hx, hy], x0=x0)
actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax)
elif mesh.dim ==2:
elif mesh.dim == 2:
xmin, xmax = xyzlim[0,0], xyzlim[0,1]
ymin, ymax = xyzlim[1,0], xyzlim[1,1]
@@ -144,12 +148,12 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5]
meshCore = Mesh.TensorMesh([hx, hy] ,x0=x0)
meshCore = Mesh.TensorMesh([hx, hy], x0=x0)
actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax) \
& (mesh.gridCC[:,1]>ymin) & (mesh.gridCC[:,1]<ymax) \
elif mesh.dim==3:
elif mesh.dim == 3:
xmin, xmax = xyzlim[0,0], xyzlim[0,1]
ymin, ymax = xyzlim[1,0], xyzlim[1,1]
zmin, zmax = xyzlim[2,0], xyzlim[2,1]
@@ -168,7 +172,7 @@ def ExtractCoreMesh(xyzlim, mesh, meshType='tensor'):
x0 = [xc[0]-hx[0]*0.5, yc[0]-hy[0]*0.5, zc[0]-hz[0]*0.5]
meshCore = Mesh.TensorMesh([hx, hy, hz] ,x0=x0)
meshCore = Mesh.TensorMesh([hx, hy, hz], x0=x0)
actind = (mesh.gridCC[:,0]>xmin) & (mesh.gridCC[:,0]<xmax) \
& (mesh.gridCC[:,1]>ymin) & (mesh.gridCC[:,1]<ymax) \
docs/Makefile
+1 -1
View File
@@ -2,7 +2,7 @@
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXOPTS = -n -w warnings.txt
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = _build
docs/_templates/layout.html
Vendored
+22
View File
@@ -0,0 +1,22 @@
{# Import the theme's layout. #}
{% extends "!layout.html" %}
{% block extrahead %}
{{ super() }}
<meta name="description" content="Simulation and Parameter Estimation in Geophysics">
<meta name="author" content="SimPEG Developers">
<meta name="keywords" content="python, geophysics, inversion, electromagnetics, magnetotellurics, magnetics, gravity, DC, flow inverse problems, open source, finite volume">
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-45185336-1', 'auto');
ga('send', 'pageview');
</script>
{% endblock %}
docs/api_FiniteVolume.rst
-19
View File
@@ -1,19 +0,0 @@
.. _api_FiniteVolume:
Finite Volume
*************
Any numerical implementation requires the discretization of continuous functions into discrete approximations. These approximations are typically organized in a mesh, which defines boundaries, locations, and connectivity. Of specific interest to geophysical simulations, we require that averaging, interpolation and differential operators be defined for any mesh. In SimPEG, we have implemented a staggered mimetic finite volume approach (`Hyman and Shashkov, 1999 <http://math.lanl.gov/~mac/papers/numerics/HS99B.pdf>`_). This approach requires the definitions of variables at either cell-centers, nodes, faces, or edges as seen in the figure below.
.. image:: images/finitevolrealestate.png
:width: 400 px
:alt: FiniteVolume
:align: center
.. toctree::
:maxdepth: 2
api_Mesh
api_DiffOps
api_InnerProducts
docs/api_MeshCode.rst
-36
View File
@@ -1,36 +0,0 @@
.. _api_MeshCode:
Tensor Mesh
===========
.. automodule:: SimPEG.Mesh.TensorMesh
:show-inheritance:
:members:
:undoc-members:
Cylindrical Mesh
================
.. automodule:: SimPEG.Mesh.CylMesh
:show-inheritance:
:members:
:undoc-members:
Tree Mesh
=========
.. autoclass:: SimPEG.Mesh.TreeMesh.TreeMesh
:show-inheritance:
:members:
:undoc-members:
Curvilinear Mesh
================
.. automodule:: SimPEG.Mesh.CurvilinearMesh
:show-inheritance:
:members:
:undoc-members:
docs/app.yaml
+95
View File
@@ -0,0 +1,95 @@
# application: simpegdocs
# version: 1
runtime: python27
api_version: 1
threadsafe: yes
handlers:
# favicon
- url: /images/logo-block\.ico
static_files: /images/logo-block.ico
upload: /images/logo-block\.ico
# all css
- url: /(.*\.css)
mime_type: text/css
static_files: _build/html/\1
upload: _build/html/(.*\.css)
# webfonts
- url: /(.*\.(eot|svg|ttf|woff|woff2|otf))
static_files: _build/html/\1
upload: _build/html/(.*\.(eot|svg|ttf|woff|woff2|otf))
# javascript
- url: /(.*\.js)
mime_type: text/javascript
static_files: _build/html/\1
upload: _build/html/(.*\.js)
# plain text source
- url: /(.*\.txt)
mime_type: text/plain
static_files: _build/html/\1
upload: _build/html/(.*\.txt)
# images
- url: /_images/(.*\.(gif|png|jpg|ico))
static_files: _build/html/_images/\1
upload: _build/html/_images/(.*\.(gif|png|jpg|ico))
# redirect en/latest traffic
- url: /en/latest/(.*\.html)
script: simpegdocs.app
# raw html
- url: /(.*\.html)
mime_type: text/html
static_files: _build/html/\1
upload: _build/html/(.*\.html)
# serve index files
- url: /(.+)/
static_files: _build/html/\1/index.html
upload: _build/html/(.+)/index.html
- url: /(.+)
static_files: _build/html/\1/index.html
upload: _build/html/(.+)/index.html
- url: /
static_files: _build/html/index.html
upload: _build/html/index.html
- url: .*
script: simpegdocs.app
# Recommended file skipping declaration from the GAE tutorials
skip_files:
- ^(.*/)?app\.yaml
- ^(.*/)?app\.yml
- ^(.*/)?#.*#
- ^(.*/)?.*~
- ^(.*/)?.*\.py[co]
- ^(.*/)?.*/RCS/.*
- ^(.*/)?\..*
- ^(.*/)?tests$
- ^(.*/)?test$
- ^test/(.*/)?
- ^COPYING.LESSER
- ^README\..*
- \.gitignore
- ^\.git/.*
- \.*\.lint$
- ^(.*/)?.*\.doctree$
libraries:
- name: webapp2
version: "2.5.2"
- name: PIL
version: "1.1.7"
- name: numpy
version: "latest"
- name: jinja2
version: "latest"
docs/conf.py
+43 -4
View File
@@ -28,7 +28,7 @@ sys.path.append('../')
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc', 'matplotlib.sphinxext.plot_directive']
extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'matplotlib.sphinxext.plot_directive']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
@@ -44,7 +44,7 @@ master_doc = 'index'
# General information about the project.
project = u'SimPEG'
copyright = u'2013, SimPEG Developers'
copyright = u'2013 - 2016, SimPEG Developers'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
@@ -124,12 +124,12 @@ except Exception, e:
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
html_favicon = './images/logo-block.ico'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
html_static_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
@@ -229,6 +229,12 @@ man_pages = [
# If true, show URL addresses after external links.
#man_show_urls = False
# Intersphinx
intersphinx_mapping = {'python': ('http://docs.python.org/2', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
'matplotlib': ('http://matplotlib.sourceforge.net/', None)}
# -- Options for Texinfo output ------------------------------------------------
@@ -251,3 +257,36 @@ texinfo_documents = [
#texinfo_show_urls = 'footnote'
autodoc_member_order = 'bysource'
def supress_nonlocal_image_warn():
import sphinx.environment
sphinx.environment.BuildEnvironment.warn_node = _supress_nonlocal_image_warn
def _supress_nonlocal_image_warn(self, msg, node):
from docutils.utils import get_source_line
if not msg.startswith('nonlocal image URI found:'):
self._warnfunc(msg, '%s:%s' % get_source_line(node))
supress_nonlocal_image_warn()
nitpick_ignore = [
('py:class', 'IdentityMap'),
('py:class', 'BaseSurvey'),
('py:class', 'BaseSrc'),
('py:class', 'BaseRx'),
('py:class', 'Survey'),
('py:class', 'FieldsFDEM'),
('py:class', 'Fields3D_e'),
('py:class', 'Fields3D_b'),
('py:class', 'Fields3D_j'),
('py:class', 'Fields3D_h'),
('py:class', 'SurveyTDEM'),
('py:class', 'SrcTDEM'),
('py:class', 'EMPropMap'),
('py:class', 'Data'),
('py:class', 'SurveyDC'),
('py:class', 'BaseMTFields'),
('py:class', 'SolverLU'),
]
docs/api_DataMisfit.rst → docs/content/api_core/api_DataMisfit.rst
View File
docs/api_DiffOps.rst → docs/content/api_core/api_DiffOps.rst
View File
docs/api_Examples.rst → docs/content/api_core/api_Examples.rst
+1 -1
View File
@@ -7,7 +7,7 @@ Examples
:maxdepth: 1
:glob:
examples/*
../examples/*
External Notebooks
docs/content/api_core/api_FiniteVolume.rst
+27
View File
@@ -0,0 +1,27 @@
.. _api_FiniteVolume:
Finite Volume
*************
Any numerical implementation requires the discretization of continuous
functions into discrete approximations. These approximations are typically
organized in a mesh, which defines boundaries, locations, and connectivity. Of
specific interest to geophysical simulations, we require that averaging,
interpolation and differential operators be defined for any mesh. In SimPEG,
we have implemented a staggered mimetic finite volume approach (`Hyman and
Shashkov, 1999 <http://math.lanl.gov/~mac/papers/numerics/HS99B.pdf>`_). This
approach requires the definitions of variables at either cell-centers, nodes,
faces, or edges as seen in the figure below.
.. image:: ../../images/finitevolrealestate.png
:width: 400 px
:alt: FiniteVolume
:align: center
.. toctree::
:maxdepth: 2
api_Mesh
api_DiffOps
api_InnerProducts
docs/api_ForwardProblem.rst → docs/content/api_core/api_ForwardProblem.rst
+38 -4
View File
@@ -52,13 +52,15 @@ We can take the derivative of the PDE:
\nabla_m c(m, u) \partial m + \nabla_u c(m, u) \partial u = 0
If the forward problem is invertible, then we can rearrange for \\(\\frac{\\partial u}{\\partial m}\\):
If the forward problem is invertible, then we can rearrange for
\\(\\frac{\\partial u}{\\partial m}\\):
.. math::
J = - P \left( \nabla_u c(m, u) \right)^{-1} \nabla_m c(m, u)
This can often be computed given a vector (i.e. \\(J(v)\\)) rather than stored, as \\(J\\) is a large dense matrix.
This can often be computed given a vector (i.e. \\(J(v)\\)) rather than
stored, as \\(J\\) is a large dense matrix.
@@ -67,13 +69,45 @@ The API
Problem
-------
.. automodule:: SimPEG.Problem
.. autoclass:: SimPEG.Problem.BaseProblem
:members:
:undoc-members:
.. autoclass:: SimPEG.Problem.BaseTimeProblem
:members:
:undoc-members:
Fields
------
.. autoclass:: SimPEG.Fields.Fields
:members:
:undoc-members:
.. autoclass:: SimPEG.Fields.TimeFields
:members:
:undoc-members:
Survey
------
.. automodule:: SimPEG.Survey
.. autoclass:: SimPEG.Survey.BaseSurvey
:members:
:undoc-members:
.. autoclass:: SimPEG.Survey.BaseSrc
:members:
:undoc-members:
.. autoclass:: SimPEG.Survey.BaseRx
:members:
:undoc-members:
.. autoclass:: SimPEG.Survey.BaseTimeRx
:members:
:undoc-members:
.. autoclass:: SimPEG.Survey.Data
:members:
:undoc-members:
docs/api_InnerProducts.rst → docs/content/api_core/api_InnerProducts.rst
+52 -22
View File
@@ -4,7 +4,10 @@
Inner Products
**************
By using the weak formulation of many of the PDEs in geophysical applications, we can rapidly develop discretizations. Much of this work, however, needs a good understanding of how to approximate inner products on our discretized meshes. We will define the inner product as:
By using the weak formulation of many of the PDEs in geophysical applications,
we can rapidly develop discretizations. Much of this work, however, needs a
good understanding of how to approximate inner products on our discretized
meshes. We will define the inner product as:
.. math::
@@ -14,12 +17,15 @@ where a and b are either scalars or vectors.
.. note::
The InnerProducts class is a base class providing inner product matrices for meshes and cannot run on its own.
The InnerProducts class is a base class providing inner product matrices
for meshes and cannot run on its own.
Example problem for DC resistivity
----------------------------------
We will start with the formulation of the Direct Current (DC) resistivity problem in geophysics.
We will start with the formulation of the Direct Current (DC) resistivity
problem in geophysics.
.. math::
@@ -28,12 +34,13 @@ We will start with the formulation of the Direct Current (DC) resistivity proble
\nabla\cdot \vec{j} = q
In the following discretization, \\\( \\sigma \\\) and \\\( \\phi \\\)
will be discretized on the cell-centers and the flux, \\\(\\vec{j}\\\),
In the following discretization, :math:`\sigma` and :math:`\phi`
will be discretized on the cell-centers and the flux, :math:`\vec{j}`,
will be on the faces. We will use the weak formulation to discretize
the DC resistivity equation.
We can define in weak form by integrating with a general face function \\\(\\vec{f}\\\):
We can define in weak form by integrating with a general face function
:math:`\vec{f}`:
.. math::
@@ -61,9 +68,16 @@ We can then discretize for every cell:
.. note::
We have discretized the dot product above, but remember that we do not really have a single vector \\\(\\mathbf{J}\\\), but approximations of \\\(\\vec{j}\\\) on each face of our cell. In 2D that means 2 approximations of \\\(\\mathbf{J}_x\\\) and 2 approximations of \\\(\\mathbf{J}_y\\\). In 3D we also have 2 approximations of \\\(\\mathbf{J}_z\\\).
We have discretized the dot product above, but remember that we do not
really have a single vector :math:`\mathbf{J}`, but approximations of
:math:`\vec{j}` on each face of our cell. In 2D that means 2
approximations of :math:`\mathbf{J}_x` and 2 approximations of
:math:`\mathbf{J}_y`. In 3D we also have 2 approximations of
:math:`\mathbf{J}_z`.
Regardless of how we choose to approximate this dot product, we can represent this in vector form (again this is for every cell), and will generalize for the case of anisotropic (tensor) sigma.
Regardless of how we choose to approximate this dot product, we can represent
this in vector form (again this is for every cell), and will generalize for
the case of anisotropic (tensor) sigma.
.. math::
@@ -71,14 +85,17 @@ Regardless of how we choose to approximate this dot product, we can represent th
-\phi^{\top} v_{\text{cell}} \mathbf{D}_{\text{cell}} \mathbf{F})
+ \text{BC}
We multiply by square-root of volume on each side of the tensor conductivity to keep symmetry in the system. Here \\\(\\mathbf{J}_c\\\) is the Cartesian \\\(\\mathbf{J}\\\) (on the faces that we choose to use in our approximation) and must be calculated differently depending on the mesh:
We multiply by square-root of volume on each side of the tensor conductivity
to keep symmetry in the system. Here :math:`\mathbf{J}_c` is the Cartesian
:math:`\mathbf{J}` (on the faces that we choose to use in our approximation)
and must be calculated differently depending on the mesh:
.. math::
\mathbf{J}_c = \mathbf{Q}_{(i)}\mathbf{J}_\text{TENSOR} \\
\mathbf{J}_c = \mathbf{N}_{(i)}^{-1}\mathbf{Q}_{(i)}\mathbf{J}_\text{Curv}
Here the \\\(i\\\) index refers to where we choose to approximate this integral, as discussed in the note above.
We will approximate this integral by taking the fluxes clustered around every node of the cell, there are 8 combinations in 3D, and 4 in 2D. We will use a projection matrix \\\( \\mathbf{Q}_{(i)} \\\) to pick the appropriate fluxes. So, now that we have 8 approximations of this integral, we will just take the average. For the TensorMesh, this looks like:
Here the :math:`i` index refers to where we choose to approximate this integral, as discussed in the note above.
We will approximate this integral by taking the fluxes clustered around every node of the cell, there are 8 combinations in 3D, and 4 in 2D. We will use a projection matrix :math:`\mathbf{Q}_{(i)}` to pick the appropriate fluxes. So, now that we have 8 approximations of this integral, we will just take the average. For the TensorMesh, this looks like:
.. math::
@@ -107,10 +124,12 @@ By defining the faceInnerProduct (8 combinations of fluxes in 3D, 4 in 2D, 2 in
\sum_{i=1}^{2^d}
\mathbf{P}_{(i)}^{\top} \Sigma^{-1} \mathbf{P}_{(i)}
Where \\\(d\\\) is the dimension of the mesh.
The \\\( \\mathbf{M}^f \\\) is returned when given the input of \\\( \\Sigma^{-1} \\\).
Where :math:`d` is the dimension of the mesh.
The :math:`\mathbf{M}^f` is returned when given the input of :math:`\Sigma^{-1}`.
Here each \\( \\mathbf{P} \\in \\mathbb{R}^{(d*nC, nF)} \\\) is a combination of the projection, volume, and any normalization to Cartesian coordinates (where the dot product is well defined):
Here each :math:`\mathbf{P} ~ \in ~ \mathbb{R}^{(d*nC, nF)}` is a combination
of the projection, volume, and any normalization to Cartesian coordinates
(where the dot product is well defined):
.. math::
@@ -129,7 +148,10 @@ If ``returnP=True`` is requested in any of these methods the projection matrices
# In 1D
P = [P0, P1]
The derivation for ``edgeInnerProducts`` is exactly the same, however, when we approximate the integral using the fields around each node, the projection matrices look a bit different because we have 12 edges in 3D instead of just 6 faces. The interface to the code is exactly the same.
The derivation for ``edgeInnerProducts`` is exactly the same, however, when we
approximate the integral using the fields around each node, the projection
matrices look a bit different because we have 12 edges in 3D instead of just 6
faces. The interface to the code is exactly the same.
Defining Tensor Properties
@@ -137,7 +159,8 @@ Defining Tensor Properties
**For 3D:**
Depending on the number of columns (either 1, 3, or 6) of mu, the material property is interpreted as follows:
Depending on the number of columns (either 1, 3, or 6) of mu, the material
property is interpreted as follows:
.. math::
@@ -188,13 +211,16 @@ Which is nice and easy to invert if necessary, however, in the fully anisotropic
Taking Derivatives
------------------
We will take the derivative of the fully anisotropic tensor for a 3D mesh, the other cases are easier and will not be discussed here. Let us start with one part of the sum which makes up \\\(\\mathbf{M}^f_\\Sigma\\\) and take the derivative when this is multiplied by some vector \\\(\\mathbf{v}\\\):
We will take the derivative of the fully anisotropic tensor for a 3D mesh, the
other cases are easier and will not be discussed here. Let us start with one
part of the sum which makes up :math:`\mathbf{M}^f_\Sigma` and take the
derivative when this is multiplied by some vector :math:`\mathbf{v}`:
.. math::
\mathbf{P}^\top \boldsymbol{\Sigma} \mathbf{Pv}
Here we will let \\\( \\mathbf{Pv} = \\mathbf{y} \\\) and \\\(\\mathbf{y}\\\) will have the form:
Here we will let :math:`\mathbf{Pv} = \mathbf{y}` and :math:`\mathbf{y}` will have the form:
.. math::
@@ -233,7 +259,9 @@ Here we will let \\\( \\mathbf{Pv} = \\mathbf{y} \\\) and \\\(\\mathbf{y}\\\) wi
\end{matrix}
\right]
Now it is easy to take the derivative with respect to any one of the parameters, for example, \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_1}\\\)
Now it is easy to take the derivative with respect to any one of the
parameters, for example,
:math:`\frac{\partial}{\partial\boldsymbol{\sigma}_1}`
.. math::
\frac{\partial}{\partial \boldsymbol{\sigma}_1}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -247,7 +275,8 @@ Now it is easy to take the derivative with respect to any one of the parameters,
\end{matrix}
\right]
Whereas \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_4}\\\), for example, is:
Whereas :math:`\frac{\partial}{\partial\boldsymbol{\sigma}_4}`, for
example, is:
.. math::
\frac{\partial}{\partial \boldsymbol{\sigma}_4}\left(\mathbf{P}^\top\Sigma\mathbf{y}\right)
@@ -261,11 +290,12 @@ Whereas \\\(\\frac{\\partial}{\\partial\\boldsymbol{\\sigma}_4}\\\), for example
\end{matrix}
\right]
These are computed for each of the 8 projections, horizontally concatenated, and returned.
These are computed for each of the 8 projections, horizontally concatenated,
and returned.
The API
-------
.. automodule:: SimPEG.Mesh.InnerProducts
.. autoclass:: SimPEG.Mesh.InnerProducts.InnerProducts
:members:
:undoc-members:
docs/api_Inversion.rst → docs/content/api_core/api_Inversion.rst
+2 -2
View File
@@ -3,7 +3,7 @@
InvProblem
**********
.. automodule:: SimPEG.InvProblem
.. autoclass:: SimPEG.InvProblem.BaseInvProblem
:show-inheritance:
:members:
:undoc-members:
@@ -12,7 +12,7 @@ InvProblem
Inversion
*********
.. automodule:: SimPEG.Inversion
.. autoclass:: SimPEG.Inversion.BaseInversion
:show-inheritance:
:members:
:undoc-members:
docs/api_InversionComponents.rst → docs/content/api_core/api_InversionComponents.rst
View File
docs/api_Maps.rst → docs/content/api_core/api_Maps.rst
+12 -9
View File
@@ -27,7 +27,8 @@ back to conductivity. This is a relatively trivial example (we are just taking
the exponential!) but by defining maps we can start to combine and manipulate
exactly what we think about as our model, \\\(m\\\). In code, this looks like
::
.. code-block:: python
:linenos:
M = Mesh.TensorMesh([100]) # Create a mesh
expMap = Maps.ExpMap(M) # Create a mapping
@@ -46,14 +47,15 @@ We will use an example where we want a 1D layered earth as
our model, but we want to map this to a 2D discretization to do our forward
modeling. We will also assume that we are working in log conductivity still,
so after the transformation we want to map to conductivity space.
To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.Vertical1DMap`),
To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
which does the first part of what we just described. The second part will be
done by the :class:`SimPEG.Maps.ExpMap` described above.
::
.. code-block:: python
:linenos:
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.Vertical1DMap(M)
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
@@ -64,7 +66,7 @@ done by the :class:`SimPEG.Maps.ExpMap` described above.
from SimPEG import *
import matplotlib.pyplot as plt
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.Vertical1DMap(M)
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
@@ -122,6 +124,8 @@ When these are used in the inverse problem, this is extremely important!!
The API
=======
The :code:`IdentityMap` is the base class for all mappings, and it does absolutely nothing.
.. autoclass:: SimPEG.Maps.IdentityMap
:members:
:undoc-members:
@@ -130,7 +134,6 @@ The API
Common Maps
===========
Exponential Map
---------------
@@ -148,7 +151,7 @@ lives (i.e. it varies logarithmically).
Vertical 1D Map
---------------
.. autoclass:: SimPEG.Maps.Vertical1DMap
.. autoclass:: SimPEG.Maps.SurjectVertical1D
:members:
:undoc-members:
@@ -196,8 +199,8 @@ Mesh to Mesh Map
:undoc-members:
Some Extras
===========
Under the Hood
==============
Combo Map
---------
docs/api_Mesh.rst → docs/content/api_core/api_Mesh.rst
+1 -1
View File
@@ -188,6 +188,6 @@ other types of meshes in this SimPEG framework.
The API
=======
.. automodule:: SimPEG.Mesh.BaseMesh
.. autoclass:: SimPEG.Mesh.BaseMesh.BaseMesh
:members:
:undoc-members:
docs/content/api_core/api_MeshCode.rst
+68
View File
@@ -0,0 +1,68 @@
.. _api_MeshCode:
Tensor Mesh
===========
.. autoclass:: SimPEG.Mesh.TensorMesh
:members:
:undoc-members:
:show-inheritance:
Cylindrical Mesh
================
.. autoclass:: SimPEG.Mesh.CylMesh
:members:
:undoc-members:
:show-inheritance:
Tree Mesh
=========
.. autoclass:: SimPEG.Mesh.TreeMesh
:members:
:undoc-members:
:show-inheritance:
Curvilinear Mesh
================
.. autoclass:: SimPEG.Mesh.CurvilinearMesh
:members:
:undoc-members:
:show-inheritance:
Base Rectangular Mesh
=====================
.. autoclass:: SimPEG.Mesh.BaseMesh.BaseRectangularMesh
:members:
:undoc-members:
:show-inheritance:
Base Tensor Mesh
================
.. autoclass:: SimPEG.Mesh.TensorMesh.BaseTensorMesh
:members:
:undoc-members:
:show-inheritance:
Mesh IO
=======
.. automodule:: SimPEG.Mesh.MeshIO
:members:
:undoc-members:
:show-inheritance:
Mesh Viewing
============
.. automodule:: SimPEG.Mesh.View
:members:
:undoc-members:
:show-inheritance:
docs/api_Optimization.rst → docs/content/api_core/api_Optimization.rst
View File
docs/content/api_core/api_PropMaps.rst
+29
View File
@@ -0,0 +1,29 @@
SimPEG PropMaps
***************
The API
=======
Property
--------
.. autoclass:: SimPEG.PropMaps.Property
:members:
:undoc-members:
PropMap
-------
.. autoclass:: SimPEG.PropMaps.PropMap
:members:
:undoc-members:
PropModel
---------
.. autoclass:: SimPEG.PropMaps.PropModel
:members:
:undoc-members:
docs/api_Regularization.rst → docs/content/api_core/api_Regularization.rst
+11
View File
@@ -91,10 +91,21 @@ The API
:members:
:undoc-members:
.. autoclass:: SimPEG.Regularization.Simple
:show-inheritance:
:members:
.. autoclass:: SimPEG.Regularization.Tikhonov
:show-inheritance:
:members:
.. autoclass:: SimPEG.Regularization.Sparse
:show-inheritance:
:members:
.. autoclass:: SimPEG.Regularization.RegularizationMesh
:show-inheritance:
:members:
docs/api_Solver.rst → docs/content/api_core/api_Solver.rst
+2
View File
@@ -46,6 +46,8 @@ The API
=======
.. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapD
:noindex:
.. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapI
:noindex:
docs/api_Tests.rst → docs/content/api_core/api_Tests.rst
View File
docs/api_Utilities.rst → docs/content/api_core/api_Utilities.rst
+1
View File
@@ -6,5 +6,6 @@ Utilities
api_Solver
api_Maps
api_PropMaps
api_Utils
api_Tests
docs/api_Utils.rst → docs/content/api_core/api_Utils.rst
+9 -3
View File
@@ -21,7 +21,7 @@ Solver Utilities
:undoc-members:
Curv Utilities
=============
==============
.. automodule:: SimPEG.Utils.curvutils
:members:
@@ -51,7 +51,9 @@ Interpolation Utilities
Counter Utilities
=================
::
.. code-block:: python
:linenos:
class MyClass(object):
def __init__(self, url):
self.counter = Counter()
@@ -69,7 +71,9 @@ Counter Utilities
for i in range(300): c.MySecondMethod()
c.counter.summary()
::
.. code-block:: text
:linenos:
Counters:
MyClass.MyMethod : 100
@@ -77,6 +81,8 @@ Counter Utilities
Times: mean sum
MyClass.MySecondMethod : 1.70e-06, 5.10e-04, 300x
The API
-------
docs/api_bigPicture.rst → docs/content/api_core/api_bigPicture.rst
+7 -7
View File
@@ -35,7 +35,7 @@ The Big Picture
Defining a well-posed inverse problem and solving it is a complex task that requires many components that must interact. It is helpful
to view this task as a workflow in which various elements are explicitly identified and integrated. The figure below outlines the inversion components that consists of inputs, implementation, and evaluation. The inputs are composed of the geophysical data, the equations which are a mathematical description of the governing physics, and prior knowledge or assumptions about the setting. The implementation consists of two broad categories: the forward simulation and the inversion. The **forward simulation** is the means by which we solve the governing equations given a model and the **inversion components** evaluate and update this model. We are considering a gradient based approach, which updates the model through an optimization routine. The output of this implementation is a model, which, prior to interpretation, must be evaluated. This requires considering, and often re-assessing, the choices and assumptions made in both the input and implementation stages.
.. image:: InversionWorkflow-PreSimPEG.png
.. image:: ../../images/InversionWorkflow-PreSimPEG.png
:width: 400 px
:alt: Components
:align: center
@@ -46,24 +46,24 @@ A Comprehensive Framework
There are an overwhelming amount of choices to be made as one works through the forward modeling and inversion process (see figure above). As a result, software implementations of this workflow often become complex and highly interdependent, making it difficult to interact with and to ask other scientists to pick up and change. Our approach to handling this complexity is to propose a framework, (see below), that compartmentalizes the implementation of inversions into various units. We present it in this specific modular style, as each unit contains a targeted subset of choices crucial to the inversion process.
.. image:: InversionWorkflow.png
.. image:: ../../images/InversionWorkflow.png
:width: 400 px
:alt: Framework
:align: center
The process of obtaining an acceptable model from an inversion generally requires the geophysicist to perform several iterations of the inversion workflow, rethinking and redesigning each piece of the framework to ensure it is appropriate in the current context. Inversions are experimental and empirical by nature and our software package is designed to facilitate this iterative process. To accomplish this, we have divided the inversion methodology into eight major components (See figure above). The (:class:`SimPEG.Mesh.BaseMesh`) class handles the discretization of the earth and also provides numerical operators. The forward simulation is split into two classes, the (:class:`SimPEG.Survey.BaseSurvey`) and the (:class:`SimPEG.Problem.BaseProblem`). The (:class:`SimPEG.Survey.BaseSurvey`) class handles the geometry of a geophysical problem as well as sources. The (:class:`SimPEG.Problem.BaseProblem`) class handles the simulation of the physics for the geophysical problem of interest. Although created independently, these two classes must be paired to form all of the components necessary for a geophysical forward simulation and calculation of the sensitivity. The (:class:`SimPEG.Problem.BaseProblem`) creates geophysical fields given a source from the (:class:`SimPEG.Survey.BaseSurvey`). The (:class:`SimPEG.Survey.BaseSurvey`) interpolates these fields to the receiver locations and converts them to the appropriate data type, for example, by selecting only the measured components of the field. Each of these operations may have associated derivatives with respect to the model and the computed field; these are included in the calculation of the sensitivity. For the inversion, a (:class:`SimPEG.DataMisfit.BaseDataMisfit`) is chosen to capture the goodness of fit of the predicted data and a (:class:`SimPEG.Regularization.BaseRegularization`) is chosen to handle the non-uniqueness. These inversion elements and an Optimization routine are combined into an inverse problem class (:class:`SimPEG.InvProblem.BaseInvProblem`). (:class:`SimPEG.InvProblem.BaseInvProblem`) is the mathematical statement that will be numerically solved by running an Inversion. The (:class:`SimPEG.Inversion.BaseInversion`) class handles organization and dispatch of directives between all of the various pieces of the framework.
The process of obtaining an acceptable model from an inversion generally requires the geophysicist to perform several iterations of the inversion workflow, rethinking and redesigning each piece of the framework to ensure it is appropriate in the current context. Inversions are experimental and empirical by nature and our software package is designed to facilitate this iterative process. To accomplish this, we have divided the inversion methodology into eight major components (See figure above). The :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class handles the discretization of the earth and also provides numerical operators. The forward simulation is split into two classes, the :class:`SimPEG.Survey.BaseSurvey` and the :class:`SimPEG.Problem.BaseProblem`. The :class:`SimPEG.Survey.BaseSurvey` class handles the geometry of a geophysical problem as well as sources. The :class:`SimPEG.Problem.BaseProblem` class handles the simulation of the physics for the geophysical problem of interest. Although created independently, these two classes must be paired to form all of the components necessary for a geophysical forward simulation and calculation of the sensitivity. The :class:`SimPEG.Problem.BaseProblem` creates geophysical fields given a source from the :class:`SimPEG.Survey.BaseSurvey`. The :class:`SimPEG.Survey.BaseSurvey` interpolates these fields to the receiver locations and converts them to the appropriate data type, for example, by selecting only the measured components of the field. Each of these operations may have associated derivatives with respect to the model and the computed field; these are included in the calculation of the sensitivity. For the inversion, a :class:`SimPEG.DataMisfit.BaseDataMisfit` is chosen to capture the goodness of fit of the predicted data and a :class:`SimPEG.Regularization.BaseRegularization` is chosen to handle the non-uniqueness. These inversion elements and an Optimization routine are combined into an inverse problem class :class:`SimPEG.InvProblem.BaseInvProblem`. :class:`SimPEG.InvProblem.BaseInvProblem` is the mathematical statement that will be numerically solved by running an Inversion. The :class:`SimPEG.Inversion.BaseInversion` class handles organization and dispatch of directives between all of the various pieces of the framework.
The arrows in the figure above indicate what each class takes as a primary argument. For example, both the (:class:`SimPEG.Problem.BaseProblem`) and (:class:`SimPEG.Regularization.BaseRegularization`) classes take a (:class:`SimPEG.Mesh.BaseMesh`) class as an argument. The diagram does not show class inheritance, as each of the base classes outlined have many subtypes that can be interchanged. The (:class:`SimPEG.Mesh.BaseMesh`) class, for example, could be a regular Cartesian mesh (:class:`SimPEG.Mesh.TensorMesh`) or a cylindrical coordinate mesh (:class:`SimPEG.Mesh.CylMesh`), which have many properties in common. These common features, such as both meshes being created from tensor products, can be exploited through inheritance of base classes, and differences can be expressed through subtype polymorphism. Please look at the documentation here for more in-depth information.
The arrows in the figure above indicate what each class takes as a primary argument. For example, both the :class:`SimPEG.Problem.BaseProblem` and :class:`SimPEG.Regularization.BaseRegularization` classes take a :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class as an argument. The diagram does not show class inheritance, as each of the base classes outlined have many subtypes that can be interchanged. The :class:`SimPEG.Mesh.BaseMesh.BaseMesh` class, for example, could be a regular Cartesian mesh :class:`SimPEG.Mesh.TensorMesh` or a cylindrical coordinate mesh :class:`SimPEG.Mesh.CylMesh`, which have many properties in common. These common features, such as both meshes being created from tensor products, can be exploited through inheritance of base classes, and differences can be expressed through subtype polymorphism. Please look at the documentation here for more in-depth information.
.. include:: ../CITATION.rst
.. include:: ../../../CITATION.rst
Authors
-------
.. include:: ../AUTHORS.rst
.. include:: ../../../AUTHORS.rst
License
-------
.. include:: ../LICENSE
.. include:: ../../../LICENSE
docs/api_installing.rst → docs/content/api_core/api_installing.rst
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docs/api_DC.rst → docs/content/dc/index.rst
+16 -9
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@@ -1,5 +1,3 @@
.. _api_DC:
.. math::
\renewcommand{\div}{\nabla\cdot\,}
@@ -38,8 +36,16 @@
\renewcommand {\u} { {\vec u} }
\newcommand{\I}{\vec{I}}
Direct Current Resistivity
**************************
`SimPEG.DCIP` uses SimPEG as the framework for the forward and inverse
direct current (DC) resistivity and induced polarization (IP) geophysical problems.
DC resistivity survey
*********************
=====================
Electrical resistivity of subsurface materials is measured by causing an electrical current to flow in the earth between one pair of electrodes while the voltage across a second pair of electrodes is measured. The result is an "apparent" resistivity which is a value representing the weighted average resistivity over a volume of the earth. Variations in this measurement are caused by variations in the soil, rock, and pore fluid electrical resistivity. Surveys require contact with the ground, so they can be labour intensive. Results are sometimes interpreted directly, but more commonly, 1D, 2D or 3D models are estimated using inversion procedures (`GPG <http://www.eos.ubc.ca/courses/eosc350/content/>`_).
@@ -55,7 +61,7 @@ As direct current (DC) implies, in DC resistivity survey, we assume steady-state
\curl \e = 0
Then by taking \\(\\curl\\) for the first equation, we have
Then by taking \\(\\div\\) of the first equation, we have
.. math::
@@ -137,13 +143,14 @@ Comparing to the analytic function:
.. plot::
import simpegDC as DC
DC.Examples.Verification.run(plotIt=True)
from SimPEG import Examples
Examples.DC_Analytic_Dipole.run(plotIt=True)
API
===
.. automodule:: simpegDC.BaseDC
API for DC codes
================
.. automodule:: SimPEG.DCIP.BaseDC
:show-inheritance:
:members:
:undoc-members:
docs/em/api_FDEM.rst → docs/content/em/api_FDEM.rst
+24 -6
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@@ -9,17 +9,28 @@
Frequency Domain Electromagnetics
*********************************
Electromagnetic (EM) geophysical methods are used in a variety of applications from resource exploration, including for hydrocarbons and minerals, to environmental applications, such as groundwater monitoring. The primary physical property of interest in EM is electrical conductivity, which describes the ease with which electric current flows through a material.
Electromagnetic (EM) geophysical methods are used in a variety of applications
from resource exploration, including for hydrocarbons and minerals, to
environmental applications, such as groundwater monitoring. The primary
physical property of interest in EM is electrical conductivity, which
describes the ease with which electric current flows through a material.
Background
==========
Electromagnetic phenomena are governed by Maxwell's equations. They describe the behavior of EM fields and fluxes. Electromagnetic theory for geophysical applications by Ward and Hohmann (1988) is a highly recommended resource on this topic.
Electromagnetic phenomena are governed by Maxwell's equations. They describe
the behavior of EM fields and fluxes. Electromagnetic theory for geophysical
applications by Ward and Hohmann (1988) is a highly recommended resource on
this topic.
Fourier Transform Convention
----------------------------
In order to examine Maxwell's equations in the frequency domain, we must first define our choice of harmonic time-dependence by choosing a Fourier transform convention. We use the :math:`e^{i \omega t}` convention, so we define our Fourier Transform pair as
In order to examine Maxwell's equations in the frequency domain, we must first
define our choice of harmonic time-dependence by choosing a Fourier transform
convention. We use the :math:`e^{i \omega t}` convention, so we define our
Fourier Transform pair as
.. math ::
F(\omega) = \int_{-\infty}^{\infty} f(t) e^{- i \omega t} dt \\
@@ -31,6 +42,7 @@ where :math:`\omega` is angular frequency, :math:`t` is time, :math:`F(\omega)`
Maxwell's Equations
===================
In the frequency domain, Maxwell's equations are given by
.. math ::
@@ -104,19 +116,20 @@ The H-J formulation is in terms of the current density and the magnetic field:
Discretizing
------------
For both formulations, we use a finite volume discretization
and discretize fields on cell edges, fluxes on cell faces and
physical properties in cell centers. This is particularly
important when using symmetry to reduce the dimensionality of a problem
(for instance on a 2D CylMesh, there are :math:`r`, :math:`z` faces and :math:`\theta` edges)
.. figure:: ../images/finitevolrealestate.png
.. figure:: ../../images/finitevolrealestate.png
:align: center
:scale: 60 %
For the two formulations, the discretization of the physical properties, fields and fluxes are summarized below.
.. figure:: ../images/ebjhdiscretizations.png
.. figure:: ../../images/ebjhdiscretizations.png
:align: center
:scale: 60 %
@@ -150,7 +163,7 @@ API
FDEM Problem
------------
.. automodule:: SimPEG.EM.FDEM.FDEM
.. automodule:: SimPEG.EM.FDEM.ProblemFDEM
:show-inheritance:
:members:
:undoc-members:
@@ -169,6 +182,11 @@ FDEM Survey
:members:
:undoc-members:
.. automodule:: SimPEG.EM.FDEM.RxFDEM
:show-inheritance:
:members:
:undoc-members:
FDEM Fields
-----------
docs/em/api_TDEM.rst → docs/content/em/api_TDEM.rst
+1 -1
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@@ -359,7 +359,7 @@ TDEM - B formulation
Field Storage
=============
.. autoclass:: SimPEG.EM.TDEM.SurveyTDEM.FieldsTDEM
.. autoclass:: SimPEG.EM.TDEM.BaseTDEM.FieldsTDEM
:show-inheritance:
:members:
:undoc-members:
docs/em/api_Utils.rst → docs/content/em/api_Utils.rst
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docs/content/em/api_basic.rst
+33
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@@ -0,0 +1,33 @@
Overview of Electromagnetics in SimPEG
**************************************
The API
=======
Physical Properties
-------------------
.. autoclass:: SimPEG.EM.Base.EMPropMap
:show-inheritance:
:members:
:undoc-members:
Problem
-------
.. autoclass:: SimPEG.EM.Base.BaseEMProblem
:show-inheritance:
:members:
:undoc-members:
Survey
------
.. autoclass:: SimPEG.EM.Base.BaseEMSurvey
:show-inheritance:
:members:
:undoc-members:
docs/em/index.rst → docs/content/em/index.rst
+4 -3
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@@ -3,22 +3,23 @@ Electromagnetics
================
`SimPEG.EM` uses SimPEG as the framework for the forward and inverse
electromagnetics geophysical problems.
electromagnetics geophysical problems.
To solve for predicted data, we follow the framework shown below. The model is
what we invert for. This is mapped to a physical property on the simulation
mesh. A source which is used to excite the system is specified. Having a model
and a source, we can solve Maxwell's equations for fields. We sample these
fields with recievers to give us predicted data.
fields with recievers to give us predicted data.
.. image:: ../images/simpegEM_noMath.png
.. image:: ../../images/simpegEM_noMath.png
:scale: 50%
.. toctree::
:maxdepth: 2
api_basic
api_FDEM
api_TDEM
api_Utils
docs/examples/DC_Analytic_Dipole.rst → docs/content/examples/DC_Analytic_Dipole.rst
+1 -1
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@@ -16,6 +16,6 @@ DC Analytic Dipole
from SimPEG import Examples
Examples.DC_Analytic_Dipole.run()
.. literalinclude:: ../../SimPEG/Examples/DC_Analytic_Dipole.py
.. literalinclude:: ../../../SimPEG/Examples/DC_Analytic_Dipole.py
:language: python
:linenos:
docs/examples/DC_Forward_PseudoSection.rst → docs/content/examples/DC_Forward_PseudoSection.rst
+1 -1
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@@ -31,6 +31,6 @@ Created by @fourndo
from SimPEG import Examples
Examples.DC_Forward_PseudoSection.run()
.. literalinclude:: ../../SimPEG/Examples/DC_Forward_PseudoSection.py
.. literalinclude:: ../../../SimPEG/Examples/DC_Forward_PseudoSection.py
:language: python
:linenos:
docs/examples/EM_FDEM_1D_Inversion.rst → docs/content/examples/EM_FDEM_1D_Inversion.rst
+1 -1
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@@ -21,6 +21,6 @@ Here we will create and run a FDEM 1D inversion.
from SimPEG import Examples
Examples.EM_FDEM_1D_Inversion.run()
.. literalinclude:: ../../SimPEG/Examples/EM_FDEM_1D_Inversion.py
.. literalinclude:: ../../../SimPEG/Examples/EM_FDEM_1D_Inversion.py
:language: python
:linenos:
docs/examples/EM_FDEM_Analytic_MagDipoleWholespace.rst → docs/content/examples/EM_FDEM_Analytic_MagDipoleWholespace.rst
+1 -1
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@@ -21,6 +21,6 @@ Here we plot the magnetic flux density from a harmonic dipole in a wholespace.
from SimPEG import Examples
Examples.EM_FDEM_Analytic_MagDipoleWholespace.run()
.. literalinclude:: ../../SimPEG/Examples/EM_FDEM_Analytic_MagDipoleWholespace.py
.. literalinclude:: ../../../SimPEG/Examples/EM_FDEM_Analytic_MagDipoleWholespace.py
:language: python
:linenos:
docs/examples/EM_Schenkel_Morrison_Casing.rst → docs/content/examples/EM_Schenkel_Morrison_Casing.rst
+5 -2
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@@ -17,10 +17,13 @@ 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.
.. code-block:: text
Schenkel, C.J., and H.F. Morrison, 1990, Effects of well casing on potential field measurements using downhole current sources: Geophysical prospecting, 38, 663-686.
The model consists of:
- Air: Conductivity 1e-8 S/m, above z = 0
- Background: conductivity 1e-2 S/m, below z = 0
- Casing: conductivity 1e6 S/m
@@ -53,6 +56,6 @@ citation would be much appreciated!
from SimPEG import Examples
Examples.EM_Schenkel_Morrison_Casing.run()
.. literalinclude:: ../../SimPEG/Examples/EM_Schenkel_Morrison_Casing.py
.. literalinclude:: ../../../SimPEG/Examples/EM_Schenkel_Morrison_Casing.py
:language: python
:linenos:
docs/examples/EM_TDEM_1D_Inversion.rst → docs/content/examples/EM_TDEM_1D_Inversion.rst
+1 -1
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@@ -21,6 +21,6 @@ Here we will create and run a TDEM 1D inversion.
from SimPEG import Examples
Examples.EM_TDEM_1D_Inversion.run()
.. literalinclude:: ../../SimPEG/Examples/EM_TDEM_1D_Inversion.py
.. literalinclude:: ../../../SimPEG/Examples/EM_TDEM_1D_Inversion.py
:language: python
:linenos:
docs/examples/FLOW_Richards_1D_Celia1990.rst → docs/content/examples/FLOW_Richards_1D_Celia1990.rst
+1 -1
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@@ -47,6 +47,6 @@ Here we reproduce the results from Celia1990_ demonstrating the head-based formu
from SimPEG import Examples
Examples.FLOW_Richards_1D_Celia1990.run()
.. literalinclude:: ../../SimPEG/Examples/FLOW_Richards_1D_Celia1990.py
.. literalinclude:: ../../../SimPEG/Examples/FLOW_Richards_1D_Celia1990.py
:language: python
:linenos:
docs/examples/Inversion_Linear.rst → docs/content/examples/Inversion_Linear.rst
+1 -1
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@@ -21,6 +21,6 @@ Here we go over the basics of creating a linear problem and inversion.
from SimPEG import Examples
Examples.Inversion_Linear.run()
.. literalinclude:: ../../SimPEG/Examples/Inversion_Linear.py
.. literalinclude:: ../../../SimPEG/Examples/Inversion_Linear.py
:language: python
:linenos:
docs/examples/MT_1D_ForwardAndInversion.rst → docs/content/examples/MT_1D_ForwardAndInversion.rst
+2 -2
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@@ -10,7 +10,7 @@
MT: 1D: Inversion
=======================
=================
Forward model 1D MT data.
Setup and run a MT 1D inversion.
@@ -22,6 +22,6 @@ Setup and run a MT 1D inversion.
from SimPEG import Examples
Examples.MT_1D_ForwardAndInversion.run()
.. literalinclude:: ../../SimPEG/Examples/MT_1D_ForwardAndInversion.py
.. literalinclude:: ../../../SimPEG/Examples/MT_1D_ForwardAndInversion.py
:language: python
:linenos:
docs/examples/MT_3D_Foward.rst → docs/content/examples/MT_3D_Foward.rst
+2 -2
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@@ -10,7 +10,7 @@
MT: 3D: Forward
=======================
===============
Forward model 3D MT data.
@@ -21,6 +21,6 @@ Forward model 3D MT data.
from SimPEG import Examples
Examples.MT_3D_Foward.run()
.. literalinclude:: ../../SimPEG/Examples/MT_3D_Foward.py
.. literalinclude:: ../../../SimPEG/Examples/MT_3D_Foward.py
:language: python
:linenos:
docs/examples/Mesh_Basic_ForwardDC.rst → docs/content/examples/Mesh_Basic_ForwardDC.rst
+1 -1
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@@ -20,6 +20,6 @@ Mesh: Basic Forward 2D DC Resistivity
from SimPEG import Examples
Examples.Mesh_Basic_ForwardDC.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_Basic_ForwardDC.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_Basic_ForwardDC.py
:language: python
:linenos:
docs/examples/Mesh_Basic_PlotImage.rst → docs/content/examples/Mesh_Basic_PlotImage.rst
+1 -1
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@@ -22,6 +22,6 @@ You can use M.PlotImage to plot images on all of the Meshes.
from SimPEG import Examples
Examples.Mesh_Basic_PlotImage.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_Basic_PlotImage.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_Basic_PlotImage.py
:language: python
:linenos:
docs/examples/Mesh_Basic_Types.rst → docs/content/examples/Mesh_Basic_Types.rst
+1 -1
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@@ -21,6 +21,6 @@ Here we show SimPEG used to create three different types of meshes.
from SimPEG import Examples
Examples.Mesh_Basic_Types.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_Basic_Types.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_Basic_Types.py
:language: python
:linenos:
docs/examples/Mesh_Operators_CahnHilliard.rst → docs/content/examples/Mesh_Operators_CahnHilliard.rst
+1 -1
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@@ -52,6 +52,6 @@ field separating as the time increases.
from SimPEG import Examples
Examples.Mesh_Operators_CahnHilliard.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_Operators_CahnHilliard.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_Operators_CahnHilliard.py
:language: python
:linenos:
docs/examples/Mesh_QuadTree_Creation.rst → docs/content/examples/Mesh_QuadTree_Creation.rst
+1 -1
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@@ -26,6 +26,6 @@ on an 8x8 mesh (2^3).
from SimPEG import Examples
Examples.Mesh_QuadTree_Creation.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_QuadTree_Creation.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_QuadTree_Creation.py
:language: python
:linenos:
docs/examples/Mesh_QuadTree_FaceDiv.rst → docs/content/examples/Mesh_QuadTree_FaceDiv.rst
+1 -1
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@@ -21,6 +21,6 @@ Mesh: QuadTree: FaceDiv
from SimPEG import Examples
Examples.Mesh_QuadTree_FaceDiv.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_QuadTree_FaceDiv.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_QuadTree_FaceDiv.py
:language: python
:linenos:
docs/examples/Mesh_QuadTree_HangingNodes.rst → docs/content/examples/Mesh_QuadTree_HangingNodes.rst
+1 -1
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@@ -26,6 +26,6 @@ on an 8x8 mesh (2^3).
from SimPEG import Examples
Examples.Mesh_QuadTree_HangingNodes.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_QuadTree_HangingNodes.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_QuadTree_HangingNodes.py
:language: python
:linenos:
docs/examples/Mesh_Tensor_Creation.rst → docs/content/examples/Mesh_Tensor_Creation.rst
+1 -1
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@@ -38,6 +38,6 @@ notation::
from SimPEG import Examples
Examples.Mesh_Tensor_Creation.run()
.. literalinclude:: ../../SimPEG/Examples/Mesh_Tensor_Creation.py
.. literalinclude:: ../../../SimPEG/Examples/Mesh_Tensor_Creation.py
:language: python
:linenos:
docs/examples/Utils_surface2ind_topo.rst → docs/content/examples/Utils_surface2ind_topo.rst
+5 -1
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@@ -9,6 +9,10 @@
.. --------------------------------- ..
Utils: surface2ind_topo
=======================
Here we show how to use :code:`Utils.surface2ind_topo` to identify cells below
a topographic surface.
@@ -19,6 +23,6 @@ a topographic surface.
from SimPEG import Examples
Examples.Utils_surface2ind_topo.run()
.. literalinclude:: ../../SimPEG/Examples/Utils_surface2ind_topo.py
.. literalinclude:: ../../../SimPEG/Examples/Utils_surface2ind_topo.py
:language: python
:linenos:
docs/flow/index.rst → docs/content/flow/index.rst
+2 -2
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@@ -35,8 +35,8 @@ Here we reproduce the results from Celia et al. (1990):
.. plot::
from SimPEG.FLOW.Examples import Celia1990
Celia1990.run()
from SimPEG import Examples
Examples.FLOW_Richards_1D_Celia1990.run()
Richards
========
docs/content/ip/index.rst
+14
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@@ -0,0 +1,14 @@
Induced Polarization
********************
Todo: docs for IP!
API for IP codes
================
.. automodule:: SimPEG.DCIP.BaseIP
:show-inheritance:
:members:
:undoc-members:
:inherited-members:
docs/mt/index.rst → docs/content/mt/index.rst
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.. _examples_Inversion_IRLS:
.. --------------------------------- ..
.. ..
.. THIS FILE IS AUTO GENEREATED ..
.. ..
.. SimPEG/Examples/__init__.py ..
.. ..
.. --------------------------------- ..
Inversion: Linear Problem
=========================
Here we go over the basics of creating a linear problem and inversion.
.. plot::
from SimPEG import Examples
Examples.Inversion_IRLS.run()
.. literalinclude:: ../../SimPEG/Examples/Inversion_IRLS.py
:language: python
:linenos:
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@@ -41,15 +41,16 @@ About SimPEG
.. toctree::
:maxdepth: 2
api_bigPicture
api_installing
content/api_core/api_bigPicture
content/api_core/api_installing
Examples
********
.. toctree::
:maxdepth: 2
api_Examples
content/api_core/api_Examples
Packages
********
@@ -57,9 +58,11 @@ Packages
.. toctree::
:maxdepth: 3
em/index
mt/index
flow/index
content/em/index
content/dc/index
content/ip/index
content/mt/index
content/flow/index
Finite Volume
*************
@@ -67,7 +70,7 @@ Finite Volume
.. toctree::
:maxdepth: 3
api_FiniteVolume
content/api_core/api_FiniteVolume
Forward Problems
****************
@@ -75,7 +78,7 @@ Forward Problems
.. toctree::
:maxdepth: 3
api_ForwardProblem
content/api_core/api_ForwardProblem
Inversion Components
********************
@@ -83,7 +86,7 @@ Inversion Components
.. toctree::
:maxdepth: 3
api_InversionComponents
content/api_core/api_InversionComponents
Utility Codes
*************
@@ -91,7 +94,7 @@ Utility Codes
.. toctree::
:maxdepth: 3
api_Utilities
content/api_core/api_Utilities
Project Index & Search

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