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Futurize 1, futurize 2, pasteurize.

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This commit is contained in:
Brendan Smithyman
2016-07-16 14:17:02 -05:00
parent 362975d2bd
commit ca8d8f8c2d
197 changed files with 2618 additions and 1235 deletions
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SimPEG/Directives.py
+38 -26
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@@ -1,4 +1,16 @@
import Utils, numpy as np
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import open
from builtins import int
from future import standard_library
standard_library.install_aliases()
from builtins import str
from past.utils import old_div
from builtins import object
from . import Utils
import numpy as np
class InversionDirective(object):
"""InversionDirective"""
@@ -15,7 +27,7 @@ class InversionDirective(object):
@inversion.setter
def inversion(self, i):
if getattr(self,'_inversion',None) is not None:
print 'Warning: InversionDirective %s has switched to a new inversion.' % self.__name__
print('Warning: InversionDirective %s has switched to a new inversion.' % self.__name__)
self._inversion = i
@property
@@ -68,7 +80,7 @@ class DirectiveList(object):
def inversion(self, i):
if self.inversion is i: return
if getattr(self,'_inversion',None) is not None:
print 'Warning: %s has switched to a new inversion.' % self.__name__
print('Warning: %s has switched to a new inversion.' % self.__name__)
for d in self.dList:
d.inversion = i
self._inversion = i
@@ -120,7 +132,7 @@ class BetaEstimate_ByEig(InversionDirective):
:return: beta0
"""
if self.debug: print 'Calculating the beta0 parameter.'
if self.debug: print('Calculating the beta0 parameter.')
m = self.invProb.curModel
f = self.invProb.getFields(m, store=True, deleteWarmstart=False)
@@ -128,7 +140,7 @@ class BetaEstimate_ByEig(InversionDirective):
x0 = np.random.rand(*m.shape)
t = x0.dot(self.dmisfit.eval2Deriv(m,x0,f=f))
b = x0.dot(self.reg.eval2Deriv(m, v=x0))
self.beta0 = self.beta0_ratio*(t/b)
self.beta0 = self.beta0_ratio*(old_div(t,b))
self.invProb.beta = self.beta0
@@ -141,7 +153,7 @@ class BetaSchedule(InversionDirective):
def endIter(self):
if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
if self.debug: print('BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter)
self.invProb.beta /= self.coolingFactor
@@ -192,7 +204,7 @@ class SaveModelEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
def initialize(self):
print "SimPEG.SaveModelEveryIteration will save your models as: '###-%s.npy'"%self.fileName
print("SimPEG.SaveModelEveryIteration will save your models as: '###-%s.npy'"%self.fileName)
def endIter(self):
np.save('%03d-%s' % (self.opt.iter, self.fileName), self.opt.xc)
@@ -202,7 +214,7 @@ class SaveOutputEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
def initialize(self):
print "SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-%s.txt'"%self.fileName
print("SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-%s.txt'"%self.fileName)
f = open(self.fileName+'.txt', 'w')
f.write(" # beta phi_d phi_m f\n")
f.close()
@@ -216,7 +228,7 @@ class SaveOutputDictEveryIteration(SaveEveryIteration):
"""SaveOutputDictEveryIteration"""
def initialize(self):
print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-%s.npz'"%self.fileName
print("SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-%s.npz'"%self.fileName)
def endIter(self):
# Save the data.
@@ -294,7 +306,7 @@ class Update_IRLS(InversionDirective):
# After reaching target misfit with l2-norm, switch to IRLS (mode:2)
if self.invProb.phi_d < self.target and self.mode == 1:
print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
print("Convergence with smooth l2-norm regularization: Start IRLS steps...")
self.mode = 2
@@ -302,17 +314,17 @@ class Update_IRLS(InversionDirective):
# model values
if getattr(self, 'reg.eps', None) is None:
self.reg.eps_p = np.percentile(np.abs(self.invProb.curModel),self.prctile)
else:
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:
else:
self.reg.eps_q = self.eps[1]
print "L[p qx qy qz]-norm : " + str(self.reg.norms)
print "eps_p: " + str(self.reg.eps_p) + " eps_q: " + str(self.reg.eps_q)
print("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
@@ -328,7 +340,7 @@ class Update_IRLS(InversionDirective):
# Beta Schedule
if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
if self.debug: print('BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter)
self.invProb.beta /= self.coolingFactor
@@ -338,19 +350,19 @@ class Update_IRLS(InversionDirective):
self.IRLSiter += 1
phim_new = self.reg.eval(self.invProb.curModel)
self.f_change = np.abs(self.f_old - phim_new) / self.f_old
self.f_change = old_div(np.abs(self.f_old - phim_new), self.f_old)
print "Regularization decrease: %6.3e" % (self.f_change)
print("Regularization decrease: %6.3e" % (self.f_change))
# Check for maximum number of IRLS cycles
if self.IRLSiter == self.maxIRLSiter:
print "Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter
print("Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter)
self.opt.stopNextIteration = True
return
# Check if the function has changed enough
if self.f_change < self.f_min_change and self.IRLSiter > 1:
print "Minimum decrease in regularization. End of IRLS"
print("Minimum decrease in regularization. End of IRLS")
self.opt.stopNextIteration = True
return
else:
@@ -385,7 +397,7 @@ class Update_IRLS(InversionDirective):
phim_new = self.reg.eval(self.invProb.curModel)
# Update gamma to scale the regularization between IRLS iterations
self.reg.gamma = self.phi_m_last / phim_new
self.reg.gamma = old_div(self.phi_m_last, phim_new)
# Reset the regularization matrices again for new gamma
self.reg._Wsmall = None
@@ -394,7 +406,7 @@ class Update_IRLS(InversionDirective):
self.reg._Wz = None
# Check if misfit is within the tolerance, otherwise scale beta
val = self.invProb.phi_d / (self.survey.nD*0.5)
val = old_div(self.invProb.phi_d, (self.survey.nD*0.5))
if np.abs(1.-val) > self.beta_tol:
self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
@@ -438,7 +450,7 @@ class Update_Wj(InversionDirective):
m = self.invProb.curModel
if self.k is None:
self.k = int(self.survey.nD/10)
self.k = int(old_div(self.survey.nD,10))
def JtJv(v):
@@ -447,6 +459,6 @@ class Update_Wj(InversionDirective):
return self.prob.Jtvec(m,Jv)
JtJdiag = Utils.diagEst(JtJv,len(m),k=self.k)
JtJdiag = JtJdiag / max(JtJdiag)
JtJdiag = old_div(JtJdiag, max(JtJdiag))
self.reg.wght = JtJdiag