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rowanc1
120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
import SimPEG
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from SimPEG import Utils, sp, np
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from Optimization import Remember, IterationPrinters, StoppingCriteria
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class BaseInversion(object):
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"""BaseInversion(objFunc, opt, **kwargs)
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"""
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__metaclass__ = Utils.SimPEGMetaClass
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name = 'BaseInversion'
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debug = False #: Print debugging information
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counter = None #: Set this to a SimPEG.Utils.Counter() if you want to count things
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def __init__(self, objFunc, opt, **kwargs):
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Utils.setKwargs(self, **kwargs)
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self.objFunc = objFunc
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self.objFunc.parent = self
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self.opt = opt
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self.opt.parent = self
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self.stoppers = [StoppingCriteria.iteration]
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# Check if we have inserted printers into the optimization
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if IterationPrinters.phi_d not in self.opt.printers:
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self.opt.printers.insert(1,IterationPrinters.beta)
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self.opt.printers.insert(2,IterationPrinters.phi_d)
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self.opt.printers.insert(3,IterationPrinters.phi_m)
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#TODO: Move this to the data class?
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@property
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def phi_d_target(self):
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"""
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target for phi_d
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By default this is the number of data.
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Note that we do not set the target if it is None, but we return the default value.
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"""
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if getattr(self, '_phi_d_target', None) is None:
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return self.data.dobs.size #
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return self._phi_d_target
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@phi_d_target.setter
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def phi_d_target(self, value):
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self._phi_d_target = value
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@Utils.timeIt
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def run(self, m0):
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"""run(m0)
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Runs the inversion!
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"""
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self.objFunc.startup(m0)
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self.m = self.opt.minimize(self.objFunc.evalFunction, m0)
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self.finish()
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return self.m
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@Utils.callHooks('finish')
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def finish(self):
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"""finish()
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**finish** is called at the end of the optimization.
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"""
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pass
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def save(self, group):
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group.attrs['phi_d'] = self.phi_d
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group.attrs['phi_m'] = self.phi_m
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group.setArray('m', self.m)
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group.setArray('dpred', self.dpred)
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# class Inversion(Cooling, Remember, BaseInversion):
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# maxIter = 10
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# name = "SimPEG Inversion"
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# def __init__(self, prob, reg, opt, data, **kwargs):
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# BaseInversion.__init__(self, prob, reg, opt, data, **kwargs)
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# self.stoppers.append(StoppingCriteria.phi_d_target_Inversion)
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# if StoppingCriteria.phi_d_target_Minimize not in self.opt.stoppers:
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# self.opt.stoppers.append(StoppingCriteria.phi_d_target_Minimize)
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# class TimeSteppingInversion(Remember, BaseInversion):
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# """
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# A slightly different view on regularization parameters,
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# let Beta be viewed as 1/dt, and timestep by updating the
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# reference model every optimization iteration.
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# """
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# maxIter = 1
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# name = "Time-Stepping SimPEG Inversion"
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# def __init__(self, prob, reg, opt, data, **kwargs):
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# BaseInversion.__init__(self, prob, reg, opt, data, **kwargs)
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# self.stoppers.append(StoppingCriteria.phi_d_target_Inversion)
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# if StoppingCriteria.phi_d_target_Minimize not in self.opt.stoppers:
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# self.opt.stoppers.append(StoppingCriteria.phi_d_target_Minimize)
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# def _startup_TimeSteppingInversion(self, m0):
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# def _doEndIteration_updateMref(self, xt):
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# if self.debug: 'Updating the reference model.'
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# self.parent.reg.mref = self.xc
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# self.opt.hook(_doEndIteration_updateMref, overwrite=True)
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