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removed examples that are in Examples PR

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Lindsey Heagy
2016-04-06 16:19:39 -07:00
parent ae9ca6cec9
commit 636d178fbf
7 changed files with 25 additions and 1456 deletions
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SimPEG/Directives.py
+24 -24
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@@ -242,10 +242,10 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
# """SaveOutputDictEveryIteration
# A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
# """
#
#
# def initialize(self):
# print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
#
#
# def endIter(self):
# # Save the data.
# ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
@@ -266,11 +266,11 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
# phi_mz = 0.5 * mz.dot(mz)
# else:
# phi_mz = 'NaN'
#
#
#
#
# # Save the file as a npz
# np.savez('{:s}-{:03d}'.format(self.fileName,self.opt.iter), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
#
#
#==============================================================================
# class UpdateReferenceModel(Parameter):
@@ -292,12 +292,12 @@ class update_IRLS(InversionDirective):
gamma = None
phi_m_last = None
phi_d_last = None
def initialize(self):
# Scale the regularization for changes in norm
if getattr(self, 'phi_m_last', None) is not None:
self.reg.curModel = self.invProb.curModel
self.reg.gamma = 1.
phim_new = self.reg.eval(self.invProb.curModel)
@@ -305,10 +305,10 @@ class update_IRLS(InversionDirective):
self.reg.curModel = self.invProb.curModel
self.reg.gamma = self.gamma
if getattr(self, 'phi_d_last', None) is None:
self.phi_d_last = self.invProb.phi_d
def endIter(self):
# Cool the threshold parameter
if getattr(self, 'factor', None) is not None:
@@ -321,35 +321,35 @@ class update_IRLS(InversionDirective):
# Get phi_m at the end of current iteration
self.phi_m_last = self.invProb.phi_m_last
# Update the model used for the IRLS weights
self.reg.curModel = self.invProb.curModel
# Temporarely set gamma to 1.
self.reg.gamma = 1.
# Compute change in model objective function and update scaling
phim_new = self.reg.eval(self.invProb.curModel)
self.reg.gamma = self.phi_m_last / phim_new
# TO DO: Check optimization class, data misfit not matching reality
# TO DO: Check optimization class, data misfit not matching reality
#dpred = self.prob.fields(self.invProb.curModel)
#phid = self.invProb.dmisfit.eval(self.invProb.curModel)
#print self.survey.std[0]
#print phid
#print self.invProb.phi_d
#print self.invProb.phi_d_last
self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
class update_lin_PreCond(InversionDirective):
def endIter(self):
# Cool the threshold parameter
if getattr(self.opt, 'approxHinv', None) is not None:
# Update the pre-conditioner
diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() * (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
@@ -362,20 +362,20 @@ class update_Wj(InversionDirective):
Create approx-sensitivity base weighting
"""
k = None
def endIter(self):
if self.opt.iter == 2:
m = self.invProb.curModel
if self.k is None:
self.k = int(self.survey.nD/10)
def JtJv(v):
Jv = self.prob.Jvec(m, v)
return self.prob.Jtvec(m,Jv)
JtJdiag = Utils.diagEst(JtJv,len(m),k=self.k)
JtJdiag = JtJdiag / max(JtJdiag)