source: issm/trunk-jpl/src/m/classes/inversion.py@ 13856

#module imports
import numpy
import copy
from fielddisplay import fielddisplay
from EnumDefinitions import *
from StringToEnum import StringToEnum
from checkfield import *
from WriteData import *

class inversion(object):
        """
        INVERSION class definition

           Usage:
              inversion=inversion();
        """

        #properties
        def __init__(self):
                # {{{ Properties
                self.iscontrol                   = 0
                self.tao                         = 0
                self.incomplete_adjoint          = 0
                self.control_parameters          = float('NaN')
                self.nsteps                      = 0
                self.maxiter_per_step            = float('NaN')
                self.cost_functions              = float('NaN')
                self.cost_functions_coefficients = float('NaN')
                self.gradient_scaling            = float('NaN')
                self.cost_function_threshold     = 0
                self.min_parameters              = float('NaN')
                self.max_parameters              = float('NaN')
                self.step_threshold              = float('NaN')
                self.gradient_only               = 0
                self.vx_obs                      = float('NaN')
                self.vy_obs                      = float('NaN')
                self.vz_obs                      = float('NaN')
                self.vel_obs                     = float('NaN')
                self.thickness_obs               = float('NaN')

                #set defaults
                self.setdefaultparameters()

                #}}}
        def __repr__(self):
                # {{{ Display
                string='\n   Inversion parameters:'
                string="%s\n%s"%(string,fielddisplay(self,'iscontrol','is inversion activated?'))
                string="%s\n%s"%(string,fielddisplay(self,'incomplete_adjoint','do we assume linear viscosity?'))
                string="%s\n%s"%(string,fielddisplay(self,'control_parameters','parameter where inverse control is carried out; ex: {''FrictionCoefficient''}, or {''MaterialsRheologyBbar''}'))
                string="%s\n%s"%(string,fielddisplay(self,'nsteps','number of optimization searches'))
                string="%s\n%s"%(string,fielddisplay(self,'cost_functions','indicate the type of response for each optimization step'))
                string="%s\n%s"%(string,fielddisplay(self,'cost_functions_coefficients','cost_functions_coefficients applied to the misfit of each vertex and for each control_parameter'))
                string="%s\n%s"%(string,fielddisplay(self,'cost_function_threshold','misfit convergence criterion. Default is 1%, NaN if not applied'))
                string="%s\n%s"%(string,fielddisplay(self,'maxiter_per_step','maximum iterations during each optimization step'))
                string="%s\n%s"%(string,fielddisplay(self,'gradient_scaling','scaling factor on gradient direction during optimization, for each optimization step'))
                string="%s\n%s"%(string,fielddisplay(self,'step_threshold','decrease threshold for misfit, default is 30%'))
                string="%s\n%s"%(string,fielddisplay(self,'min_parameters','absolute minimum acceptable value of the inversed parameter on each vertex'))
                string="%s\n%s"%(string,fielddisplay(self,'max_parameters','absolute maximum acceptable value of the inversed parameter on each vertex'))
                string="%s\n%s"%(string,fielddisplay(self,'gradient_only','stop control method solution at gradient'))
                string="%s\n%s"%(string,fielddisplay(self,'vx_obs','observed velocity x component [m/a]'))
                string="%s\n%s"%(string,fielddisplay(self,'vy_obs','observed velocity y component [m/a]'))
                string="%s\n%s"%(string,fielddisplay(self,'vel_obs','observed velocity magnitude [m/a]'))
                string="%s\n%s"%(string,fielddisplay(self,'thickness_obs','observed thickness [m]'))
                string="%s\n%s"%(string,'Available cost functions:')
                string="%s\n%s"%(string,'   101: SurfaceAbsVelMisfit')
                string="%s\n%s"%(string,'   102: SurfaceRelVelMisfit')
                string="%s\n%s"%(string,'   103: SurfaceLogVelMisfit')
                string="%s\n%s"%(string,'   104: SurfaceLogVxVyMisfit')
                string="%s\n%s"%(string,'   105: SurfaceAverageVelMisfit')
                string="%s\n%s"%(string,'   201: ThicknessAbsMisfit')
                string="%s\n%s"%(string,'   501: DragCoefficientAbsGradient')
                string="%s\n%s"%(string,'   502: RheologyBbarAbsGradient')
                string="%s\n%s"%(string,'   503: ThicknessAbsGradient')
                return string
                #}}}

        def setdefaultparameters(self): # {{{
                
                #default is incomplete adjoint for now
                self.incomplete_adjoint=1

                #parameter to be inferred by control methods (only
                #drag and B are supported yet)
                self.control_parameters='FrictionCoefficient'

                #number of steps in the control methods
                self.nsteps=20

                #maximum number of iteration in the optimization algorithm for
                #each step
                self.maxiter_per_step=20*numpy.ones(self.nsteps)

                #the inversed parameter is updated as follows:
                #new_par=old_par + gradient_scaling(n)*C*gradient with C in [0 1];
                #usually the gradient_scaling must be of the order of magnitude of the 
                #inversed parameter (10^8 for B, 50 for drag) and can be decreased
                #after the first iterations
                self.gradient_scaling=50*numpy.ones((self.nsteps,1))

                #several responses can be used:
                self.cost_functions=101*numpy.ones((self.nsteps,1))

                #step_threshold is used to speed up control method. When
                #misfit(1)/misfit(0) < self.step_threshold, we go directly to
                #the next step
                self.step_threshold=.7*numpy.ones(self.nsteps) #30 per cent decrement

                #stop control solution at the gradient computation and return it? 
                self.gradient_only=0

                #cost_function_threshold is a criteria to stop the control methods.
                #if J[n]-J[n-1]/J[n] < criteria, the control run stops
                #NaN if not applied
                self.cost_function_threshold=float('NaN')    #not activated 

                return self
        #}}}

        def checkconsistency(self,md,solution,analyses):    # {{{

                #Early return
                if not self.iscontrol:
                        return md

                num_controls=numpy.size(md.inversion.control_parameters)
                num_costfunc=numpy.size(md.inversion.cost_functions,axis=1)

                md = checkfield(md,'inversion.iscontrol','values',[0,1])
                md = checkfield(md,'inversion.tao','values',[0,1])
                md = checkfield(md,'inversion.incomplete_adjoint','values',[0,1])
                md = checkfield(md,'inversion.control_parameters','cell',1,'values',['BalancethicknessThickeningRate','FrictionCoefficient','MaterialsRheologyBbar','MaterialsRheologyZbar','Vx','Vy'])
                md = checkfield(md,'inversion.nsteps','numel',[1],'>=',1)
                md = checkfield(md,'inversion.maxiter_per_step','size',[md.inversion.nsteps],'>=',0)
                md = checkfield(md,'inversion.step_threshold','size',[md.inversion.nsteps])
                md = checkfield(md,'inversion.cost_functions','size',[md.inversion.nsteps,num_costfunc],'values',[101,102,103,104,105,201,501,502,503,504,505])
                md = checkfield(md,'inversion.cost_functions_coefficients','size',[md.mesh.numberofvertices,num_costfunc],'>=',0)
                md = checkfield(md,'inversion.gradient_only','values',[0,1])
                md = checkfield(md,'inversion.gradient_scaling','size',[md.inversion.nsteps,num_controls])
                md = checkfield(md,'inversion.min_parameters','size',[md.mesh.numberofvertices,num_controls])
                md = checkfield(md,'inversion.max_parameters','size',[md.mesh.numberofvertices,num_controls])

                if solution==BalancethicknessSolutionEnum():
                        md = checkfield(md,'inversion.thickness_obs','size',[md.mesh.numberofvertices],'NaN',1)
                else:
                        md = checkfield(md,'inversion.vx_obs','size',[md.mesh.numberofvertices],'NaN',1)
                        md = checkfield(md,'inversion.vy_obs','size',[md.mesh.numberofvertices],'NaN',1)

                return md
        # }}}

        def marshall(self,fid):    # {{{

                WriteData(fid,'object',self,'fieldname','iscontrol','format','Boolean')
                WriteData(fid,'object',self,'fieldname','tao','format','Boolean')
                WriteData(fid,'object',self,'fieldname','incomplete_adjoint','format','Boolean')
                if not self.iscontrol:
                        return
                WriteData(fid,'object',self,'fieldname','nsteps','format','Integer')
                WriteData(fid,'object',self,'fieldname','maxiter_per_step','format','DoubleMat','mattype',3)
                WriteData(fid,'object',self,'fieldname','cost_functions_coefficients','format','DoubleMat','mattype',1)
                WriteData(fid,'object',self,'fieldname','gradient_scaling','format','DoubleMat','mattype',3)
                WriteData(fid,'object',self,'fieldname','cost_function_threshold','format','Double')
                WriteData(fid,'object',self,'fieldname','min_parameters','format','DoubleMat','mattype',3)
                WriteData(fid,'object',self,'fieldname','max_parameters','format','DoubleMat','mattype',3)
                WriteData(fid,'object',self,'fieldname','step_threshold','format','DoubleMat','mattype',3)
                WriteData(fid,'object',self,'fieldname','gradient_only','format','Boolean')
                WriteData(fid,'object',self,'fieldname','vx_obs','format','DoubleMat','mattype',1)
                WriteData(fid,'object',self,'fieldname','vy_obs','format','DoubleMat','mattype',1)
                WriteData(fid,'object',self,'fieldname','vz_obs','format','DoubleMat','mattype',1)
                WriteData(fid,'object',self,'fieldname','thickness_obs','format','DoubleMat','mattype',1)

                #process control parameters
                num_control_parameters=len(self.control_parameters)
                data=numpy.array([StringToEnum(control_parameter)[0] for control_parameter in self.control_parameters]).reshape(1,-1)
                WriteData(fid,'data',data,'enum',InversionControlParametersEnum(),'format','DoubleMat','mattype',3)
                WriteData(fid,'data',num_control_parameters,'enum',InversionNumControlParametersEnum(),'format','Integer')

                #process cost functions
                num_cost_functions=numpy.size(self.cost_functions,axis=1)
                data=copy.deepcopy(self.cost_functions)
                data[numpy.nonzero(data==101)]=SurfaceAbsVelMisfitEnum()
                data[numpy.nonzero(data==102)]=SurfaceRelVelMisfitEnum()
                data[numpy.nonzero(data==103)]=SurfaceLogVelMisfitEnum()
                data[numpy.nonzero(data==104)]=SurfaceLogVxVyMisfitEnum()
                data[numpy.nonzero(data==105)]=SurfaceAverageVelMisfitEnum()
                data[numpy.nonzero(data==201)]=ThicknessAbsMisfitEnum()
                data[numpy.nonzero(data==501)]=DragCoefficientAbsGradientEnum()
                data[numpy.nonzero(data==502)]=RheologyBbarAbsGradientEnum()
                data[numpy.nonzero(data==503)]=ThicknessAbsGradientEnum()
                data[numpy.nonzero(data==504)]=ThicknessAlongGradientEnum()
                data[numpy.nonzero(data==505)]=ThicknessAcrossGradientEnum()
                WriteData(fid,'data',data,'enum',InversionCostFunctionsEnum(),'format','DoubleMat','mattype',3)
                WriteData(fid,'data',num_cost_functions,'enum',InversionNumCostFunctionsEnum(),'format','Integer')
        # }}}