forked from simpeg/simpeg
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Directives.py
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Directives.py
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from __future__ import print_function
from . import Utils
from . import Regularization, DataMisfit, ObjectiveFunction
from . import Maps
import numpy as np
import matplotlib.pyplot as plt
import warnings
from .Utils import mkvc
class InversionDirective(object):
"""InversionDirective"""
debug = False #: Print debugging information
_regPair = [
Regularization.BaseComboRegularization,
Regularization.BaseRegularization,
ObjectiveFunction.ComboObjectiveFunction
]
_dmisfitPair = [
DataMisfit.BaseDataMisfit,
ObjectiveFunction.ComboObjectiveFunction
]
def __init__(self, **kwargs):
Utils.setKwargs(self, **kwargs)
@property
def inversion(self):
"""This is the inversion of the InversionDirective instance."""
return getattr(self, '_inversion', None)
@inversion.setter
def inversion(self, i):
if getattr(self, '_inversion', None) is not None:
warnings.warn(
'InversionDirective {0!s} has switched to a new inversion.'
.format(self.__class__.__name__)
)
self._inversion = i
@property
def invProb(self):
return self.inversion.invProb
@property
def opt(self):
return self.invProb.opt
@property
def reg(self):
if getattr(self, '_reg', None) is None:
self.reg = self.invProb.reg # go through the setter
return self._reg
@reg.setter
def reg(self, value):
assert any([isinstance(value, regtype) for regtype in self._regPair]), (
"Regularization must be in {}, not {}".format(
self._regPair, type(value)
)
)
if isinstance(value, Regularization.BaseComboRegularization):
value = 1*value # turn it into a combo objective function
self._reg = value
@property
def dmisfit(self):
if getattr(self, '_dmisfit', None) is None:
self.dmisfit = self.invProb.dmisfit # go through the setter
return self._dmisfit
@dmisfit.setter
def dmisfit(self, value):
assert any([
isinstance(value, dmisfittype) for dmisfittype in
self._dmisfitPair
]), "Regularization must be in {}, not {}".format(
self._dmisfitPair, type(value)
)
if not isinstance(value, ObjectiveFunction.ComboObjectiveFunction):
value = 1*value # turn it into a combo objective function
self._dmisfit = value
@property
def survey(self):
"""
Assuming that dmisfit is always a ComboObjectiveFunction,
return a list of surveys for each dmisfit [survey1, survey2, ... ]
"""
return [objfcts.survey for objfcts in self.dmisfit.objfcts]
@property
def prob(self):
"""
Assuming that dmisfit is always a ComboObjectiveFunction,
return a list of problems for each dmisfit [prob1, prob2, ...]
"""
return [objfcts.prob for objfcts in self.dmisfit.objfcts]
def initialize(self):
pass
def endIter(self):
pass
def finish(self):
pass
def validate(self, directiveList=None):
return True
class DirectiveList(object):
dList = None #: The list of Directives
def __init__(self, *directives, **kwargs):
self.dList = []
for d in directives:
assert isinstance(d, InversionDirective), (
'All directives must be InversionDirectives not {}'
.format(type(d))
)
self.dList.append(d)
Utils.setKwargs(self, **kwargs)
@property
def debug(self):
return getattr(self, '_debug', False)
@debug.setter
def debug(self, value):
for d in self.dList:
d.debug = value
self._debug = value
@property
def inversion(self):
"""This is the inversion of the InversionDirective instance."""
return getattr(self, '_inversion', None)
@inversion.setter
def inversion(self, i):
if self.inversion is i:
return
if getattr(self, '_inversion', None) is not None:
warnings.warn(
'{0!s} has switched to a new inversion.'
.format(self.__class__.__name__)
)
for d in self.dList:
d.inversion = i
self._inversion = i
def call(self, ruleType):
if self.dList is None:
if self.debug:
print('DirectiveList is None, no directives to call!')
return
directives = ['initialize', 'endIter', 'finish']
assert ruleType in directives, (
'Directive type must be in ["{0!s}"]'
.format('", "'.join(directives))
)
for r in self.dList:
getattr(r, ruleType)()
def validate(self):
[directive.validate(self) for directive in self.dList]
return True
class BetaEstimate_ByEig(InversionDirective):
"""BetaEstimate"""
beta0 = None #: The initial Beta (regularization parameter)
beta0_ratio = 1e2 #: estimateBeta0 is used with this ratio
def initialize(self):
"""
The initial beta is calculated by comparing the estimated
eigenvalues of JtJ and WtW.
To estimate the eigenvector of **A**, we will use one iteration
of the *Power Method*:
.. math::
\mathbf{x_1 = A x_0}
Given this (very course) approximation of the eigenvector, we can
use the *Rayleigh quotient* to approximate the largest eigenvalue.
.. math::
\lambda_0 = \\frac{\mathbf{x^\\top A x}}{\mathbf{x^\\top x}}
We will approximate the largest eigenvalue for both JtJ and WtW,
and use some ratio of the quotient to estimate beta0.
.. math::
\\beta_0 = \gamma \\frac{\mathbf{x^\\top J^\\top J x}}{\mathbf{x^\\top W^\\top W x}}
:rtype: float
:return: beta0
"""
if self.debug:
print('Calculating the beta0 parameter.')
m = self.invProb.model
f = self.invProb.getFields(m, store=True, deleteWarmstart=False)
# Fix the seed for random vector for consistent result
np.random.seed(1)
x0 = np.random.rand(*m.shape)
t, b = 0, 0
i_count = 0
for dmis, reg in zip(self.dmisfit.objfcts, self.reg.objfcts):
# check if f is list
if len(self.dmisfit.objfcts) > 1:
t += x0.dot(dmis.deriv2(m, x0, f=f[i_count]))
else:
t += x0.dot(dmis.deriv2(m, x0, f=f))
b += x0.dot(reg.deriv2(m, v=x0))
i_count += 1
self.beta0 = self.beta0_ratio*(t/b)
self.invProb.beta = self.beta0
class BetaSchedule(InversionDirective):
"""BetaSchedule"""
coolingFactor = 8.
coolingRate = 3
def endIter(self):
if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
if self.debug:
print(
'BetaSchedule is cooling Beta. Iteration: {0:d}'
.format(self.opt.iter)
)
self.invProb.beta /= self.coolingFactor
class TargetMisfit(InversionDirective):
"""
... note:: Currently the target misfit is not set up for joint inversions. Get `in touch <https://github.com/simpeg/simpeg/issues/new>`_ if you would like to help with the upgrade!
"""
chifact = 1.
phi_d_star = None
@property
def target(self):
if getattr(self, '_target', None) is None:
# the factor of 0.5 is because we do phid = 0.5*|| dpred - dobs||^2
if self.phi_d_star is None:
nD = 0
for survey in self.survey:
nD += survey.nD
self.phi_d_star = 0.5 * nD
self._target = self.chifact * self.phi_d_star
return self._target
@target.setter
def target(self, val):
self._target = val
def endIter(self):
if self.invProb.phi_d < self.target:
self.opt.stopNextIteration = True
class SaveEveryIteration(InversionDirective):
@property
def name(self):
if getattr(self, '_name', None) is None:
self._name = 'InversionModel'
return self._name
@name.setter
def name(self, value):
self._name = value
@property
def fileName(self):
if getattr(self, '_fileName', None) is None:
from datetime import datetime
self._fileName = '{0!s}-{1!s}'.format(
self.name, datetime.now().strftime('%Y-%m-%d-%H-%M')
)
return self._fileName
@fileName.setter
def fileName(self, value):
self._fileName = value
class SaveModelEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
def initialize(self):
print("SimPEG.SaveModelEveryIteration will save your models as: '###-{0!s}.npy'".format(self.fileName))
def endIter(self):
np.save('{0:03d}-{1!s}'.format(
self.opt.iter, self.fileName), self.opt.xc
)
class SaveOutputEveryIteration(SaveEveryIteration):
"""SaveModelEveryIteration"""
header = None
save_txt = True
beta = None
phi_d = None
phi_m = None
phi_m_small = None
phi_m_smooth_x = None
phi_m_smooth_y = None
phi_m_smooth_z = None
phi = None
def initialize(self):
if self.save_txt is True:
print(
"SimPEG.SaveOutputEveryIteration will save your inversion "
"progress as: '###-{0!s}.txt'".format(self.fileName)
)
f = open(self.fileName+'.txt', 'w')
self.header = " # beta phi_d phi_m phi_m_small phi_m_smoomth_x phi_m_smoomth_y phi_m_smoomth_z phi\n"
f.write(self.header)
f.close()
# Create a list of each
self.beta = []
self.phi_d = []
self.phi_m = []
self.phi_m_small = []
self.phi_m_smooth_x = []
self.phi_m_smooth_y = []
self.phi_m_smooth_z = []
self.phi = []
def endIter(self):
phi_s, phi_x, phi_y, phi_z = 0, 0, 0, 0
for reg in self.reg.objfcts:
phi_s += (
reg.objfcts[0](self.invProb.model) * reg.alpha_s
)
phi_x += (
reg.objfcts[1](self.invProb.model) * reg.alpha_x
)
if reg.regmesh.dim == 2:
phi_y += (
reg.objfcts[2](self.invProb.model) * reg.alpha_y
)
elif reg.regmesh.dim == 3:
phi_y += (
reg.objfcts[2](self.invProb.model) * reg.alpha_y
)
phi_z += (
reg.objfcts[3](self.invProb.model) * reg.alpha_z
)
self.beta.append(self.invProb.beta)
self.phi_d.append(self.invProb.phi_d)
self.phi_m.append(self.invProb.phi_m)
self.phi_m_small.append(phi_s)
self.phi_m_smooth_x.append(phi_x)
self.phi_m_smooth_y.append(phi_y)
self.phi_m_smooth_z.append(phi_z)
self.phi.append(self.opt.f)
if self.save_txt:
f = open(self.fileName+'.txt', 'a')
f.write(
' {0:3d} {1:1.4e} {2:1.4e} {3:1.4e} {4:1.4e} {5:1.4e} '
'{6:1.4e} {7:1.4e} {8:1.4e}\n'.format(
self.opt.iter,
self.beta[self.opt.iter-1],
self.phi_d[self.opt.iter-1],
self.phi_m[self.opt.iter-1],
self.phi_m_small[self.opt.iter-1],
self.phi_m_smooth_x[self.opt.iter-1],
self.phi_m_smooth_y[self.opt.iter-1],
self.phi_m_smooth_z[self.opt.iter-1],
self.phi[self.opt.iter-1]
)
)
f.close()
def load_results(self):
results = np.loadtxt(self.fileName+str(".txt"), comments="#")
self.beta = results[:, 1]
self.phi_d = results[:, 2]
self.phi_m = results[:, 3]
self.phi_m_small = results[:, 4]
self.phi_m_smooth_x = results[:, 5]
self.phi_m_smooth_y = results[:, 6]
self.phi_m_smooth_z = results[:, 7]
self.phi_m_smooth = (
self.phi_m_smooth_x + self.phi_m_smooth_y + self.phi_m_smooth_z
)
self.f = results[:, 7]
self.target_misfit = self.invProb.dmisfit.prob.survey.nD / 2.
self.i_target = None
if self.invProb.phi_d < self.target_misfit:
i_target = 0
while self.phi_d[i_target] > self.target_misfit:
i_target += 1
self.i_target = i_target
def plot_misfit_curves(self, fname=None, plot_small_smooth=False):
self.target_misfit = self.invProb.dmisfit.prob.survey.nD / 2.
self.i_target = None
if self.invProb.phi_d < self.target_misfit:
i_target = 0
while self.phi_d[i_target] > self.target_misfit:
i_target += 1
self.i_target = i_target
fig = plt.figure(figsize=(5, 2))
ax = plt.subplot(111)
ax_1 = ax.twinx()
ax.semilogy(np.arange(len(self.phi_d)), self.phi_d, 'k-', lw=2)
ax_1.semilogy(np.arange(len(self.phi_d)), self.phi_m, 'r', lw=2)
if plot_small_smooth:
ax_1.semilogy(np.arange(len(self.phi_d)), self.phi_m_small, 'ro')
ax_1.semilogy(np.arange(len(self.phi_d)), self.phi_m_smooth, 'rx')
ax_1.legend(
("$\phi_m$", "small", "smooth"), bbox_to_anchor=(1.5, 1.)
)
ax.plot(np.r_[ax.get_xlim()[0], ax.get_xlim()[1]], np.ones(2)*self.target_misfit, 'k:')
ax.set_xlabel("Iteration")
ax.set_ylabel("$\phi_d$")
ax_1.set_ylabel("$\phi_m$", color='r')
for tl in ax_1.get_yticklabels():
tl.set_color('r')
plt.show()
def plot_tikhonov_curves(self, fname=None, dpi=200):
self.target_misfit = self.invProb.dmisfit.prob.survey.nD / 2.
self.i_target = None
if self.invProb.phi_d < self.target_misfit:
i_target = 0
while self.phi_d[i_target] > self.target_misfit:
i_target += 1
self.i_target = i_target
fig = plt.figure(figsize=(5, 8))
ax1 = plt.subplot(311)
ax2 = plt.subplot(312)
ax3 = plt.subplot(313)
ax1.plot(self.beta, self.phi_d, 'k-', lw=2, ms=4)
ax1.set_xlim(np.hstack(self.beta).min(), np.hstack(self.beta).max())
ax1.set_xlabel("$\\beta$", fontsize=14)
ax1.set_ylabel("$\phi_d$", fontsize=14)
ax2.plot(self.beta, self.phi_m, 'k-', lw=2)
ax2.set_xlim(np.hstack(self.beta).min(), np.hstack(self.beta).max())
ax2.set_xlabel("$\\beta$", fontsize=14)
ax2.set_ylabel("$\phi_m$", fontsize=14)
ax3.plot(self.phi_m, self.phi_d, 'k-', lw=2)
ax3.set_xlim(np.hstack(self.phi_m).min(), np.hstack(self.phi_m).max())
ax3.set_xlabel("$\phi_m$", fontsize=14)
ax3.set_ylabel("$\phi_d$", fontsize=14)
if self.i_target is not None:
ax1.plot(self.beta[self.i_target], self.phi_d[self.i_target], 'k*', ms=10)
ax2.plot(self.beta[self.i_target], self.phi_m[self.i_target], 'k*', ms=10)
ax3.plot(self.phi_m[self.i_target], self.phi_d[self.i_target], 'k*', ms=10)
for ax in [ax1, ax2, ax3]:
ax.set_xscale("linear")
ax.set_yscale("linear")
plt.tight_layout()
plt.show()
if fname is not None:
fig.savefig(fname, dpi=dpi)
class SaveOutputDictEveryIteration(SaveEveryIteration):
"""
Saves inversion parameters at every iteraion.
"""
# Initialize the output dict
outDict = None
outDict = {}
saveOnDisk = False
def initialize(self):
print("SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-{0!s}.npz'".format(self.fileName))
def endIter(self):
# regCombo = ["phi_ms", "phi_msx"]
# if self.prob[0].mesh.dim >= 2:
# regCombo += ["phi_msy"]
# if self.prob[0].mesh.dim == 3:
# regCombo += ["phi_msz"]
# Initialize the output dict
iterDict = None
iterDict = {}
# Save the data.
iterDict['iter'] = self.opt.iter
iterDict['beta'] = self.invProb.beta
iterDict['phi_d'] = self.invProb.phi_d
iterDict['phi_m'] = self.invProb.phi_m
# for label, fcts in zip(regCombo, self.reg.objfcts[0].objfcts):
# iterDict[label] = fcts(self.invProb.model)
iterDict['f'] = self.opt.f
iterDict['m'] = self.invProb.model
iterDict['dpred'] = self.invProb.dpred
if hasattr(self.reg.objfcts[0], 'eps_p') is True:
iterDict['eps_p'] = self.reg.objfcts[0].eps_p
iterDict['eps_q'] = self.reg.objfcts[0].eps_q
if hasattr(self.reg.objfcts[0], 'norms') is True:
iterDict['lps'] = self.reg.objfcts[0].norms[0][0]
iterDict['lpx'] = self.reg.objfcts[0].norms[0][1]
# Save the file as a npz
if self.saveOnDisk:
np.savez('{:03d}-{:s}'.format(self.opt.iter, self.fileName), iterDict)
self.outDict[self.opt.iter] = iterDict
class Update_IRLS(InversionDirective):
updateGamma = False
f_old = 0
f_min_change = 1e-2
beta_tol = 1e-1
beta_ratio_l2 = None
prctile = 100
chifact_start = 1.
chifact_target = 1.
# Solving parameter for IRLS (mode:2)
IRLSiter = 0
minGNiter = 1
maxIRLSiter = 20
iterStart = 0
sphericalDomain = False
# Beta schedule
updateBeta = True
betaSearch = True
coolingFactor = 2.
coolingRate = 1
ComboObjFun = False
mode = 1
coolEpsOptimized = True
coolEps_p = True
coolEps_q = True
floorEps_p = 1e-8
floorEps_q = 1e-8
coolEpsFact = 1.2
silent = False
fix_Jmatrix = False
@property
def target(self):
if getattr(self, '_target', None) is None:
nD = 0
for survey in self.survey:
nD += survey.nD
self._target = nD*0.5*self.chifact_target
return self._target
@target.setter
def target(self, val):
self._target = val
@property
def start(self):
if getattr(self, '_start', None) is None:
if isinstance(self.survey, list):
self._start = 0
for survey in self.survey:
self._start += survey.nD*0.5*self.chifact_start
else:
self._start = self.survey.nD*0.5*self.chifact_start
return self._start
@start.setter
def start(self, val):
self._start = val
def initialize(self):
if self.mode == 1:
self.norms = []
for reg in self.reg.objfcts:
self.norms.append(reg.norms)
reg.norms = np.c_[2., 2., 2., 2.]
reg.model = self.invProb.model
# Update the model used by the regularization
for reg in self.reg.objfcts:
reg.model = self.invProb.model
for reg in self.reg.objfcts:
for comp in reg.objfcts:
self.f_old += np.sum(comp.f_m**2. / (comp.f_m**2. + comp.epsilon**2.)**(1 - comp.norm/2.))
self.phi_dm = []
self.phi_dmx = []
# Look for cases where the block models in to be scaled
for prob in self.prob:
if getattr(prob, 'coordinate_system', None) is not None:
if prob.coordinate_system == 'spherical':
self.sphericalDomain = True
if self.sphericalDomain:
self._angleScale()
def endIter(self):
if self.sphericalDomain:
self._angleScale()
# Check if misfit is within the tolerance, otherwise scale beta
if np.all([
np.abs(1. - self.invProb.phi_d / self.target) > self.beta_tol,
self.updateBeta,
self.mode != 1
]):
ratio = (self.target / self.invProb.phi_d)
if ratio > 1:
ratio = np.mean([2.0, ratio])
else:
ratio = np.mean([0.75, ratio])
self.invProb.beta = self.invProb.beta * ratio
if np.all([self.mode != 1, self.betaSearch]):
print("Beta search step")
# self.updateBeta = False
# Re-use previous model and continue with new beta
self.invProb.model = self.reg.objfcts[0].model
self.opt.xc = self.reg.objfcts[0].model
return
elif np.all([self.mode == 1, self.opt.iter % self.coolingRate == 0]):
self.invProb.beta = self.invProb.beta / self.coolingFactor
phim_new = 0
for reg in self.reg.objfcts:
for comp in reg.objfcts:
phim_new += np.sum(
comp.f_m**2. /
(comp.f_m**2. + comp.epsilon**2.)**(1 - comp.norm/2.)
)
# Update the model used by the regularization
phi_m_last = []
for reg in self.reg.objfcts:
reg.model = self.invProb.model
phi_m_last += [reg(self.invProb.model)]
# After reaching target misfit with l2-norm, switch to IRLS (mode:2)
if np.all([self.invProb.phi_d < self.start, self.mode == 1]):
self.startIRLS()
# Only update after GN iterations
if np.all([
(self.opt.iter-self.iterStart) % self.minGNiter == 0,
self.mode != 1
]):
if self.fix_Jmatrix:
print (">> Fix Jmatrix")
self.invProb.dmisfit.prob.fix_Jmatrix = True
# Check for maximum number of IRLS cycles
if self.IRLSiter == self.maxIRLSiter:
if not self.silent:
print(
"Reach maximum number of IRLS cycles:" +
" {0:d}".format(self.maxIRLSiter)
)
self.opt.stopNextIteration = True
return
# Print to screen
for reg in self.reg.objfcts:
if reg.eps_p > self.floorEps_p and self.coolEps_p:
reg.eps_p /= self.coolEpsFact
print('Eps_p: ' + str(reg.eps_p))
if reg.eps_q > self.floorEps_q and self.coolEps_q:
reg.eps_q /= self.coolEpsFact
print('Eps_q: ' + str(reg.eps_q))
# Remember the value of the norm from previous R matrices
# self.f_old = self.reg(self.invProb.model)
self.IRLSiter += 1
# Reset the regularization matrices so that it is
# recalculated for current model. Do it to all levels of comboObj
for reg in self.reg.objfcts:
# If comboObj, go down one more level
for comp in reg.objfcts:
comp.stashedR = None
for dmis in self.dmisfit.objfcts:
if getattr(dmis, 'stashedR', None) is not None:
dmis.stashedR = None
# Compute new model objective function value
phi_m_new = []
for reg in self.reg.objfcts:
phi_m_new += [reg(self.invProb.model)]
self.f_change = np.abs(self.f_old - phim_new) / self.f_old
if not self.silent:
print("delta phim: {0:6.3e}".format(self.f_change))
# Check if the function has changed enough
if np.all([
self.f_change < self.f_min_change,
self.IRLSiter > 1,
np.abs(1. - self.invProb.phi_d / self.target) < self.beta_tol
]):
print("Minimum decrease in regularization. End of IRLS")
self.opt.stopNextIteration = True
return
self.f_old = phim_new
# Update gamma to scale the regularization between IRLS iterations
for reg, phim_old, phim_now in zip(self.reg.objfcts,
phi_m_last, phi_m_new
):
# Now optional for extra care
if self.updateGamma:
gamma = phim_old / phim_now
else:
gamma = 1
# If comboObj, go down one more level
for comp in reg.objfcts:
comp.gamma = gamma
self.updateBeta = True
self.invProb.phi_m_last = self.reg(self.invProb.model)
def startIRLS(self):
if not self.silent:
print("Reached starting chifact with l2-norm regularization:" +
" Start IRLS steps...")
self.mode = 2
if getattr(self.opt, 'iter', None) is None:
self.iterStart = 0
else:
self.iterStart = self.opt.iter
self.invProb.phi_m_last = self.reg(self.invProb.model)
# Either use the supplied epsilon, or fix base on distribution of
# model values
for reg in self.reg.objfcts:
if getattr(reg, 'eps_p', None) is None:
reg.eps_p = np.percentile(
np.abs(reg.mapping*reg._delta_m(
self.invProb.model)
), self.prctile
)
if getattr(reg, 'eps_q', None) is None:
reg.eps_q = np.percentile(
np.abs(reg.mapping*reg._delta_m(
self.invProb.model)
), self.prctile
)
# Re-assign the norms supplied by user l2 -> lp
for reg, norms in zip(self.reg.objfcts, self.norms):
reg.norms = norms
# Save l2-model
self.invProb.l2model = self.invProb.model.copy()
# Print to screen
for reg in self.reg.objfcts:
if not self.silent:
print("eps_p: " + str(reg.eps_p) +
" eps_q: " + str(reg.eps_q))
@property
def _angleScale(self):
"""
Update the scales used by regularization for the
different block of models
"""
# Currently implemented for MVI-S only
max_p = []
for reg in self.reg.objfcts[0].objfcts:
eps_p = reg.epsilon
norm_p = 2 # self.reg.objfcts[0].norms[0]
f_m = abs(reg.f_m)
max_p += [np.max(eps_p**(1-norm_p/2.)*f_m /
(f_m**2. + eps_p**2.)**(1-norm_p/2.))]
max_p = np.asarray(max_p)
max_s = [np.pi, np.pi]
for obj, var in zip(self.reg.objfcts[1:], max_s):
obj.scale = max_p.max()/var
def validate(self, directiveList):
# check if a linear preconditioner is in the list, if not warn else
# assert that it is listed after the IRLS directive
dList = directiveList.dList
self_ind = dList.index(self)
lin_precond_ind = [
isinstance(d, UpdatePreconditioner) for d in dList
]
if any(lin_precond_ind):
assert(lin_precond_ind.index(True) > self_ind), (
"The directive 'UpdatePreconditioner' must be after Update_IRLS "
"in the directiveList"
)
else:
warnings.warn(
"Without a Linear preconditioner, convergence may be slow. "
"Consider adding `Directives.UpdatePreconditioner` to your "
"directives list"
)
return True
class UpdatePreconditioner(InversionDirective):
"""
Create a Jacobi preconditioner for the linear problem
"""
onlyOnStart = False
mapping = None
ComboObjFun = False
def initialize(self):
if getattr(self.opt, 'approxHinv', None) is None:
if getattr(self.opt, 'JtJdiag', None) is None:
JtJdiag = np.zeros_like(self.invProb.model)
for prob, dmisfit in zip(self.prob, self.dmisfit.objfcts):
m = self.invProb.model
if getattr(prob, 'getJtJdiag', None) is None:
assert getattr(prob, 'getJ', None) is not None, (
"Problem does not have a getJ attribute." +
"Cannot form the sensitivity explicitely"
)
JtJdiag += np.sum(np.power((dmisfit.W*prob.getJ(m)), 2), axis=0)
else:
JtJdiag += prob.getJtJdiag(m)
self.opt.JtJdiag = JtJdiag
# Update the pre-conditioner
reg_diag = np.zeros_like(self.invProb.model)
for reg in self.reg.objfcts:
reg_diag += self.invProb.beta*(reg.W.T*reg.W).diagonal()
Hdiag = self.opt.JtJdiag + reg_diag
PC = Utils.sdiag(Hdiag**-1.)
self.opt.approxHinv = PC
def endIter(self):
# Cool the threshold parameter
if self.onlyOnStart is True:
return
if getattr(self.opt, 'approxHinv', None) is not None:
# Update the pre-conditioner
reg_diag = np.zeros_like(self.invProb.model)
for reg in self.reg.objfcts:
reg_diag += self.invProb.beta*(reg.W.T*reg.W).diagonal()
Hdiag = self.opt.JtJdiag + reg_diag
PC = Utils.sdiag(Hdiag**-1.)
self.opt.approxHinv = PC
class Update_Wj(InversionDirective):
"""
Create approx-sensitivity base weighting using the probing method
"""
k = None # Number of probing cycles
itr = None # Iteration number to update Wj, or always update if None
def endIter(self):
if self.itr is None or self.itr == self.opt.iter:
m = self.invProb.model
if self.k is None:
self.k = int(self.survey.nD/10)
def JtJv(v):
Jv = self.prob.Jvec(m, v)
return self.prob.Jtvec(m, Jv)
JtJdiag = Utils.diagEst(JtJv, len(m), k=self.k)
JtJdiag = JtJdiag / max(JtJdiag)
self.reg.wght = JtJdiag
class UpdateSensitivityWeights(InversionDirective):
"""
Directive to take care of re-weighting
the non-linear magnetic problems.
"""
mapping = None
JtJdiag = None
everyIter = True
threshold = 1e-12
switch = True
def initialize(self):
# Calculate and update sensitivity
# for optimization and regularization
self.update()
def endIter(self):
if self.everyIter:
# Update inverse problem
self.update()