Merge branch 'dev' of https://github.com/simpeg/simpeg into analytics

This commit is contained in:
seogi_macbook
2016-06-30 00:23:12 -07:00
19 changed files with 258 additions and 97 deletions
+1 -1
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@@ -1,4 +1,4 @@
[bumpversion]
current_version = 0.1.11
current_version = 0.1.12
files = setup.py SimPEG/__init__.py docs/conf.py
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@@ -40,3 +40,4 @@ nosetests.xml
docs/_build/
Makefile
docs/warnings.txt
.DS_Store
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@@ -1,4 +1,4 @@
.. image:: https://raw.github.com/simpeg/simpeg/master/docs/simpeg-logo.png
.. image:: https://raw.github.com/simpeg/simpeg/master/docs/images/simpeg-logo.png
:alt: SimPEG Logo
======
+26 -13
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@@ -253,8 +253,7 @@ class SaveOutputDictEveryIteration(SaveEveryIteration):
class Update_IRLS(InversionDirective):
eps_min = None
eps_p = None
eps_q = None
eps = None
norms = [2.,2.,2.,2.]
factor = None
gamma = None
@@ -263,6 +262,7 @@ class Update_IRLS(InversionDirective):
f_old = None
f_min_change = 1e-2
beta_tol = 5e-2
prctile = 95
# Solving parameter for IRLS (mode:2)
IRLSiter = 0
@@ -297,9 +297,22 @@ class Update_IRLS(InversionDirective):
print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
self.mode = 2
print self.eps_p, self.eps_q, self.norms
self.reg.eps_p = self.eps_p
self.reg.eps_q = self.eps_q
# Either use the supplied epsilon, or fix base on distribution of
# model values
if getattr(self, 'reg.eps', None) is None:
self.reg.eps_p = np.percentile(np.abs(self.invProb.curModel),self.prctile)
else:
self.reg.eps_p = self.eps[0]
if getattr(self, 'reg.eps', None) is None:
self.reg.eps_q = np.percentile(np.abs(self.reg.regmesh.cellDiffxStencil*(self.reg.mapping * self.invProb.curModel)),self.prctile)
else:
self.reg.eps_q = self.eps[1]
print "L[p qx qy qz]-norm : " + str(self.reg.norms)
print "eps_p: " + str(self.reg.eps_p) + " eps_q: " + str(self.reg.eps_q)
self.reg.norms = self.norms
self.coolingFactor = 1.
self.coolingRate = 1
@@ -343,14 +356,14 @@ class Update_IRLS(InversionDirective):
else:
self.f_old = phim_new
# Cool the threshold parameter if required
if getattr(self, 'factor', None) is not None:
eps = self.reg.eps / self.factor
if getattr(self, 'eps_min', None) is not None:
self.reg.eps = np.max([self.eps_min,eps])
else:
self.reg.eps = eps
# # Cool the threshold parameter if required
# if getattr(self, 'factor', None) is not None:
# eps = self.reg.eps / self.factor
#
# if getattr(self, 'eps_min', None) is not None:
# self.reg.eps = np.max([self.eps_min,eps])
# else:
# self.reg.eps = eps
# Get phi_m at the end of current iteration
self.phi_m_last = self.invProb.phi_m_last
+2 -2
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@@ -1,7 +1,7 @@
from SimPEG import *
import SimPEG.EM.Static.DC as DC
def run(plotIt=False):
def run(plotIt=True):
cs = 25.
hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
@@ -65,4 +65,4 @@ def run(plotIt=False):
if __name__ == '__main__':
print run(plotIt=True)
print run()
+7 -29
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@@ -42,55 +42,33 @@ def run(N=100, plotIt=True):
survey = Survey.LinearSurvey()
survey.pair(prob)
survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk)
#survey.makeSyntheticData(mtrue, std=std_noise)
wd = np.ones(nk) * std_noise
#print survey.std[0]
#M = prob.mesh
# Distance weighting
wr = np.sum(prob.G**2.,axis=0)**0.5
wr = ( wr/np.max(wr) )
# reg = Regularization.Simple(mesh)
# reg.mref = mref
# reg.cell_weights = wr
#
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1./wd
#
# opt = Optimization.ProjectedGNCG(maxIter=20,lower=-2.,upper=2., maxIterCG= 10, tolCG = 1e-4)
# invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
# invProb.curModel = m0
#
# beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
# target = Directives.TargetMisfit()
#
betaest = Directives.BetaEstimate_ByEig()
# inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])
#
#
# mrec = inv.run(m0)
# ml2 = mrec
# print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
#
# # Switch regularization to sparse
# phim = invProb.phi_m_last
# phid = invProb.phi_d
reg = Regularization.Sparse(mesh)
reg.mref = mref
reg.cell_weights = wr
reg.mref = np.zeros(mesh.nC)
eps_p = 5e-2
eps_q = 5e-2
norms = [0., 0., 2., 2.]
opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
update_Jacobi = Directives.Update_lin_PreCond()
IRLS = Directives.Update_IRLS( norms=norms, eps_p=eps_p, eps_q=eps_q)
# Set the IRLS directive, penalize the lowest 25 percentile of model values
# Start with an l2-l2, then switch to lp-norms
norms = [0., 0., 2., 2.]
IRLS = Directives.Update_IRLS( norms=norms, prctile = 25, maxIRLSiter = 15, minGNiter=3)
inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
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@@ -0,0 +1,62 @@
from SimPEG import Mesh, Maps, np
def run(plotIt=True):
"""
Maps: ComboMaps
===============
We will use an example where we want a 1D layered earth as
our model, but we want to map this to a 2D discretization to do our forward
modeling. We will also assume that we are working in log conductivity still,
so after the transformation we want to map to conductivity space.
To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
which does the first part of what we just described. The second part will be
done by the :class:`SimPEG.Maps.ExpMap` described above.
.. code-block:: python
:linenos:
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
sig = myMap * m
If you noticed, it was pretty easy to combine maps. What is even cooler is
that the derivatives also are made for you (if everything goes right).
Just to be sure that the derivative is correct, you should always run the test
on the mapping that you create.
"""
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
sig = myMap * m
if not plotIt: return
import matplotlib.pyplot as plt
figs, axs = plt.subplots(1,2)
axs[0].plot(m, M.vectorCCy, 'b-o')
axs[0].set_title('Model')
axs[0].set_ylabel('Depth, y')
axs[0].set_xlabel('Value, $m_i$')
axs[0].set_xlim(0,3)
axs[0].set_ylim(0,1)
clbar = plt.colorbar(M.plotImage(sig,ax=axs[1],grid=True,gridOpts=dict(color='grey'))[0])
axs[1].set_title('Physical Property')
axs[1].set_ylabel('Depth, y')
clbar.set_label('$\sigma = \exp(\mathbf{P}m)$')
plt.tight_layout()
plt.show()
if __name__ == '__main__':
run()
+41
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@@ -0,0 +1,41 @@
from SimPEG import Mesh, Maps, Utils
def run(plotIt=True):
"""
Maps: Mesh2Mesh
===============
This mapping allows you to go from one mesh to another.
"""
M = Mesh.TensorMesh([100,100])
h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
h1 = h1/h1.sum()
M2 = Mesh.TensorMesh([h1,h1])
V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
v = Utils.mkvc(V)
modh = Maps.Mesh2Mesh([M,M2])
modH = Maps.Mesh2Mesh([M2,M])
H = modH * v
h = modh * H
if not plotIt: return
import matplotlib.pyplot as plt
ax = plt.subplot(131)
M.plotImage(v, ax=ax)
ax.set_title('Fine Mesh (Original)')
ax = plt.subplot(132)
M2.plotImage(H,clim=[0,1],ax=ax)
ax.set_title('Course Mesh')
ax = plt.subplot(133)
M.plotImage(h,clim=[0,1],ax=ax)
ax.set_title('Fine Mesh (Interpolated)')
plt.show()
if __name__ == '__main__':
run()
+1 -1
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@@ -2,7 +2,7 @@ from SimPEG import *
from SimPEG.Utils import surface2ind_topo
def run(plotIt=False, nx=5, ny=5):
def run(plotIt=True, nx=5, ny=5):
"""
Utils: surface2ind_topo
+3 -1
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@@ -10,6 +10,8 @@ import EM_TDEM_1D_Inversion
import FLOW_Richards_1D_Celia1990
import Inversion_IRLS
import Inversion_Linear
import Maps_ComboMaps
import Maps_Mesh2Mesh
import Mesh_Basic_ForwardDC
import Mesh_Basic_PlotImage
import Mesh_Basic_Types
@@ -22,7 +24,7 @@ import MT_1D_ForwardAndInversion
import MT_3D_Foward
import Utils_surface2ind_topo
__examples__ = ["DC_Analytic_Dipole", "DC_Forward_PseudoSection", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_Schenkel_Morrison_Casing", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "Inversion_IRLS", "Inversion_Linear", "Mesh_Basic_ForwardDC", "Mesh_Basic_PlotImage", "Mesh_Basic_Types", "Mesh_Operators_CahnHilliard", "Mesh_QuadTree_Creation", "Mesh_QuadTree_FaceDiv", "Mesh_QuadTree_HangingNodes", "Mesh_Tensor_Creation", "MT_1D_ForwardAndInversion", "MT_3D_Foward", "Utils_surface2ind_topo"]
__examples__ = ["DC_Analytic_Dipole", "DC_Forward_PseudoSection", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_Schenkel_Morrison_Casing", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "Inversion_IRLS", "Inversion_Linear", "Maps_ComboMaps", "Maps_Mesh2Mesh", "Mesh_Basic_ForwardDC", "Mesh_Basic_PlotImage", "Mesh_Basic_Types", "Mesh_Operators_CahnHilliard", "Mesh_QuadTree_Creation", "Mesh_QuadTree_FaceDiv", "Mesh_QuadTree_HangingNodes", "Mesh_Tensor_Creation", "MT_1D_ForwardAndInversion", "MT_3D_Foward", "Utils_surface2ind_topo"]
##### AUTOIMPORTS #####
+3 -1
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@@ -502,7 +502,9 @@ class InjectActiveCells(IdentityMap):
if Utils.isScalar(valInactive):
self.valInactive = np.ones(self.nC)*float(valInactive)
else:
self.valInactive = valInactive.copy()
self.valInactive = np.ones(self.nC)
self.valInactive[self.indInactive] = valInactive.copy()
self.valInactive[self.indActive] = 0
inds = np.nonzero(self.indActive)[0]
+1 -1
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@@ -15,7 +15,7 @@ import Directives
import Inversion
import Tests
__version__ = '0.1.11'
__version__ = '0.1.12'
__author__ = 'Rowan Cockett'
__license__ = 'MIT'
__copyright__ = 'Copyright 2014 Rowan Cockett'
+2 -2
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@@ -51,9 +51,9 @@ copyright = u'2013 - 2016, SimPEG Developers'
# built documents.
#
# The short X.Y version.
version = '0.1.11'
version = '0.1.12'
# The full version, including alpha/beta/rc tags.
release = '0.1.11'
release = '0.1.12'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
+4 -43
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@@ -63,26 +63,8 @@ done by the :class:`SimPEG.Maps.ExpMap` described above.
.. plot::
from SimPEG import *
import matplotlib.pyplot as plt
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
sig = myMap * m
figs, axs = plt.subplots(1,2)
axs[0].plot(m, M.vectorCCy, 'b-o')
axs[0].set_title('Model')
axs[0].set_ylabel('Depth, y')
axs[0].set_xlabel('Value, $m_i$')
axs[0].set_xlim(0,3)
axs[0].set_ylim(0,1)
clbar = plt.colorbar(M.plotImage(sig,ax=axs[1],grid=True,gridOpts=dict(color='grey'))[0])
axs[1].set_title('Physical Property')
axs[1].set_ylabel('Depth, y')
clbar.set_label('$\sigma = \exp(\mathbf{P}m)$')
plt.tight_layout()
from SimPEG import Examples
Examples.Maps_ComboMaps.run()
If you noticed, it was pretty easy to combine maps. What is even cooler is
that the derivatives also are made for you (if everything goes right).
@@ -167,31 +149,10 @@ Map 2D Cross-Section to 3D Model
Mesh to Mesh Map
----------------
.. plot::
from SimPEG import *
import matplotlib.pyplot as plt
M = Mesh.TensorMesh([100,100])
h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
h1 = h1/h1.sum()
M2 = Mesh.TensorMesh([h1,h1])
V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
v = Utils.mkvc(V)
modh = Maps.Mesh2Mesh([M,M2])
modH = Maps.Mesh2Mesh([M2,M])
H = modH * v
h = modh * H
ax = plt.subplot(131)
M.plotImage(v, ax=ax)
ax.set_title('Fine Mesh (Original)')
ax = plt.subplot(132)
M2.plotImage(H,clim=[0,1],ax=ax)
ax.set_title('Course Mesh')
ax = plt.subplot(133)
M.plotImage(h,clim=[0,1],ax=ax)
ax.set_title('Fine Mesh (Interpolated)')
plt.show()
from SimPEG import Examples
Examples.Maps_Mesh2Mesh.run()
.. autoclass:: SimPEG.Maps.Mesh2Mesh
+26
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@@ -0,0 +1,26 @@
.. _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:
+48
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@@ -0,0 +1,48 @@
.. _examples_Maps_ComboMaps:
.. --------------------------------- ..
.. ..
.. THIS FILE IS AUTO GENEREATED ..
.. ..
.. SimPEG/Examples/__init__.py ..
.. ..
.. --------------------------------- ..
Maps: ComboMaps
===============
We will use an example where we want a 1D layered earth as
our model, but we want to map this to a 2D discretization to do our forward
modeling. We will also assume that we are working in log conductivity still,
so after the transformation we want to map to conductivity space.
To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
which does the first part of what we just described. The second part will be
done by the :class:`SimPEG.Maps.ExpMap` described above.
.. code-block:: python
:linenos:
M = Mesh.TensorMesh([7,5])
v1dMap = Maps.SurjectVertical1D(M)
expMap = Maps.ExpMap(M)
myMap = expMap * v1dMap
m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
sig = myMap * m
If you noticed, it was pretty easy to combine maps. What is even cooler is
that the derivatives also are made for you (if everything goes right).
Just to be sure that the derivative is correct, you should always run the test
on the mapping that you create.
.. plot::
from SimPEG import Examples
Examples.Maps_ComboMaps.run()
.. literalinclude:: ../../../SimPEG/Examples/Maps_ComboMaps.py
:language: python
:linenos:
+27
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@@ -0,0 +1,27 @@
.. _examples_Maps_Mesh2Mesh:
.. --------------------------------- ..
.. ..
.. THIS FILE IS AUTO GENEREATED ..
.. ..
.. SimPEG/Examples/__init__.py ..
.. ..
.. --------------------------------- ..
Maps: Mesh2Mesh
===============
This mapping allows you to go from one mesh to another.
.. plot::
from SimPEG import Examples
Examples.Maps_Mesh2Mesh.run()
.. literalinclude:: ../../../SimPEG/Examples/Maps_Mesh2Mesh.py
:language: python
:linenos:
+1 -1
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@@ -1,4 +1,4 @@
.. image:: https://raw.github.com/simpeg/simpeg/master/docs/simpeg-logo.png
.. image:: https://raw.github.com/simpeg/simpeg/master/docs/images/simpeg-logo.png
:alt: SimPEG Logo
SimPEG Documentation
+1 -1
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@@ -83,7 +83,7 @@ with open("README.rst") as f:
setup(
name = "SimPEG",
version = "0.1.11",
version = "0.1.12",
packages = find_packages(),
install_requires = ['numpy>=1.7',
'scipy>=0.13',