source: issm/trunk-jpl/src/m/classes/inversion.py@ 18994

Last change on this file since 18994 was 18994, checked in by Mathieu Morlighem, 10 years ago

NEW: merging cost functions
File size: 9.4 KB
Line 
1import numpy
2from fielddisplay import fielddisplay
3from EnumDefinitions import *
4from StringToEnum import StringToEnum
5from checkfield import checkfield
6from WriteData import WriteData
7from supportedcontrols import supportedcontrols
8from supportedcostfunctions import supportedcostfunctions
9from marshallcostfunctions import marshallcostfunctions
10
11class inversion(object):
12 """
13 INVERSION class definition
14
15 Usage:
16 inversion=inversion()
17 """
18
19 def __init__(self): # {{{
20 self.iscontrol = 0
21 self.incomplete_adjoint = 0
22 self.control_parameters = float('NaN')
23 self.nsteps = 0
24 self.maxiter_per_step = float('NaN')
25 self.cost_functions = float('NaN')
26 self.cost_functions_coefficients = float('NaN')
27 self.gradient_scaling = float('NaN')
28 self.cost_function_threshold = 0
29 self.min_parameters = float('NaN')
30 self.max_parameters = float('NaN')
31 self.step_threshold = float('NaN')
32 self.vx_obs = float('NaN')
33 self.vy_obs = float('NaN')
34 self.vz_obs = float('NaN')
35 self.vel_obs = float('NaN')
36 self.thickness_obs = float('NaN')
37 self.surface_obs = float('NaN')
38
39 #set defaults
40 self.setdefaultparameters()
41
42 #}}}
43 def __repr__(self): # {{{
44 string=' inversion parameters:'
45 string="%s\n%s"%(string,fielddisplay(self,'iscontrol','is inversion activated?'))
46 string="%s\n%s"%(string,fielddisplay(self,'incomplete_adjoint','1: linear viscosity, 0: non-linear viscosity'))
47 string="%s\n%s"%(string,fielddisplay(self,'control_parameters','ex: {''FrictionCoefficient''}, or {''MaterialsRheologyBbar''}'))
48 string="%s\n%s"%(string,fielddisplay(self,'nsteps','number of optimization searches'))
49 string="%s\n%s"%(string,fielddisplay(self,'cost_functions','indicate the type of response for each optimization step'))
50 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'))
51 string="%s\n%s"%(string,fielddisplay(self,'cost_function_threshold','misfit convergence criterion. Default is 1%, NaN if not applied'))
52 string="%s\n%s"%(string,fielddisplay(self,'maxiter_per_step','maximum iterations during each optimization step'))
53 string="%s\n%s"%(string,fielddisplay(self,'gradient_scaling','scaling factor on gradient direction during optimization, for each optimization step'))
54 string="%s\n%s"%(string,fielddisplay(self,'step_threshold','decrease threshold for misfit, default is 30%'))
55 string="%s\n%s"%(string,fielddisplay(self,'min_parameters','absolute minimum acceptable value of the inversed parameter on each vertex'))
56 string="%s\n%s"%(string,fielddisplay(self,'max_parameters','absolute maximum acceptable value of the inversed parameter on each vertex'))
57 string="%s\n%s"%(string,fielddisplay(self,'vx_obs','observed velocity x component [m/yr]'))
58 string="%s\n%s"%(string,fielddisplay(self,'vy_obs','observed velocity y component [m/yr]'))
59 string="%s\n%s"%(string,fielddisplay(self,'vel_obs','observed velocity magnitude [m/yr]'))
60 string="%s\n%s"%(string,fielddisplay(self,'thickness_obs','observed thickness [m]'))
61 string="%s\n%s"%(string,fielddisplay(self,'surface_obs','observed surface elevation [m]'))
62 string="%s\n%s"%(string,'Available cost functions:')
63 string="%s\n%s"%(string,' 101: SurfaceAbsVelMisfit')
64 string="%s\n%s"%(string,' 102: SurfaceRelVelMisfit')
65 string="%s\n%s"%(string,' 103: SurfaceLogVelMisfit')
66 string="%s\n%s"%(string,' 104: SurfaceLogVxVyMisfit')
67 string="%s\n%s"%(string,' 105: SurfaceAverageVelMisfit')
68 string="%s\n%s"%(string,' 201: ThicknessAbsMisfit')
69 string="%s\n%s"%(string,' 501: DragCoefficientAbsGradient')
70 string="%s\n%s"%(string,' 502: RheologyBbarAbsGradient')
71 string="%s\n%s"%(string,' 503: ThicknessAbsGradient')
72 return string
73 #}}}
74 def setdefaultparameters(self): # {{{
75
76 #default is incomplete adjoint for now
77 self.incomplete_adjoint=1
78
79 #parameter to be inferred by control methods (only
80 #drag and B are supported yet)
81 self.control_parameters='FrictionCoefficient'
82
83 #number of steps in the control methods
84 self.nsteps=20
85
86 #maximum number of iteration in the optimization algorithm for
87 #each step
88 self.maxiter_per_step=20*numpy.ones(self.nsteps)
89
90 #the inversed parameter is updated as follows:
91 #new_par=old_par + gradient_scaling(n)*C*gradient with C in [0 1];
92 #usually the gradient_scaling must be of the order of magnitude of the
93 #inversed parameter (10^8 for B, 50 for drag) and can be decreased
94 #after the first iterations
95 self.gradient_scaling=50*numpy.ones((self.nsteps,1))
96
97 #several responses can be used:
98 self.cost_functions=101
99
100 #step_threshold is used to speed up control method. When
101 #misfit(1)/misfit(0) < self.step_threshold, we go directly to
102 #the next step
103 self.step_threshold=.7*numpy.ones(self.nsteps) #30 per cent decrement
104
105 #cost_function_threshold is a criteria to stop the control methods.
106 #if J[n]-J[n-1]/J[n] < criteria, the control run stops
107 #NaN if not applied
108 self.cost_function_threshold=float('NaN') #not activated
109
110 return self
111 #}}}
112 def checkconsistency(self,md,solution,analyses): # {{{
113
114 #Early return
115 if not self.iscontrol:
116 return md
117
118 num_controls=numpy.size(md.inversion.control_parameters)
119 num_costfunc=numpy.size(md.inversion.cost_functions)
120
121 md = checkfield(md,'fieldname','inversion.iscontrol','values',[0,1])
122 md = checkfield(md,'fieldname','inversion.incomplete_adjoint','values',[0,1])
123 md = checkfield(md,'fieldname','inversion.control_parameters','cell',1,'values',supportedcontrols())
124 md = checkfield(md,'fieldname','inversion.nsteps','numel',[1],'>=',0)
125 md = checkfield(md,'fieldname','inversion.maxiter_per_step','size',[md.inversion.nsteps],'>=',0)
126 md = checkfield(md,'fieldname','inversion.step_threshold','size',[md.inversion.nsteps])
127 md = checkfield(md,'fieldname','inversion.cost_functions','size',[num_costfunc],'values',supportedcostfunctions())
128 md = checkfield(md,'fieldname','inversion.cost_functions_coefficients','size',[md.mesh.numberofvertices,num_costfunc],'>=',0)
129 md = checkfield(md,'fieldname','inversion.gradient_scaling','size',[md.inversion.nsteps,num_controls])
130 md = checkfield(md,'fieldname','inversion.min_parameters','size',[md.mesh.numberofvertices,num_controls])
131 md = checkfield(md,'fieldname','inversion.max_parameters','size',[md.mesh.numberofvertices,num_controls])
132
133 #Only SSA, HO and FS are supported right now
134 if solution==StressbalanceSolutionEnum():
135 if not (md.flowequation.isSSA or md.flowequation.isHO or md.flowequation.isFS or md.flowequation.isL1L2):
136 md.checkmessage("'inversion can only be performed for SSA, HO or FS ice flow models");
137
138 if solution==BalancethicknessSolutionEnum():
139 md = checkfield(md,'fieldname','inversion.thickness_obs','size',[md.mesh.numberofvertices],'NaN',1)
140 else:
141 md = checkfield(md,'fieldname','inversion.vx_obs','size',[md.mesh.numberofvertices],'NaN',1)
142 md = checkfield(md,'fieldname','inversion.vy_obs','size',[md.mesh.numberofvertices],'NaN',1)
143
144 return md
145 # }}}
146 def marshall(self,md,fid): # {{{
147
148 yts=365.0*24.0*3600.0
149
150 WriteData(fid,'enum',InversionTypeEnum(),'data',0,'format','Integer')
151 WriteData(fid,'object',self,'fieldname','iscontrol','format','Boolean')
152 WriteData(fid,'object',self,'fieldname','incomplete_adjoint','format','Boolean')
153 if not self.iscontrol:
154 return
155 WriteData(fid,'object',self,'fieldname','nsteps','format','Integer')
156 WriteData(fid,'object',self,'fieldname','maxiter_per_step','format','DoubleMat','mattype',3)
157 WriteData(fid,'object',self,'fieldname','cost_functions_coefficients','format','DoubleMat','mattype',1)
158 WriteData(fid,'object',self,'fieldname','gradient_scaling','format','DoubleMat','mattype',3)
159 WriteData(fid,'object',self,'fieldname','cost_function_threshold','format','Double')
160 WriteData(fid,'object',self,'fieldname','min_parameters','format','DoubleMat','mattype',3)
161 WriteData(fid,'object',self,'fieldname','max_parameters','format','DoubleMat','mattype',3)
162 WriteData(fid,'object',self,'fieldname','step_threshold','format','DoubleMat','mattype',3)
163 WriteData(fid,'object',self,'fieldname','vx_obs','format','DoubleMat','mattype',1,'scale',1./yts)
164 WriteData(fid,'object',self,'fieldname','vy_obs','format','DoubleMat','mattype',1,'scale',1./yts)
165 WriteData(fid,'object',self,'fieldname','vz_obs','format','DoubleMat','mattype',1,'scale',1./yts)
166 WriteData(fid,'object',self,'fieldname','thickness_obs','format','DoubleMat','mattype',1)
167 WriteData(fid,'object',self,'fieldname','surface_obs','format','DoubleMat','mattype',1)
168
169 #process control parameters
170 num_control_parameters=len(self.control_parameters)
171 data=numpy.array([StringToEnum(control_parameter)[0] for control_parameter in self.control_parameters]).reshape(1,-1)
172 WriteData(fid,'data',data,'enum',InversionControlParametersEnum(),'format','DoubleMat','mattype',3)
173 WriteData(fid,'data',num_control_parameters,'enum',InversionNumControlParametersEnum(),'format','Integer')
174
175 #process cost functions
176 num_cost_functions=numpy.size(self.cost_functions)
177 data=marshallcostfunctions(self.cost_functions)
178 WriteData(fid,'data',numpy.array(data).reshape(1,-1),'enum',InversionCostFunctionsEnum(),'format','DoubleMat','mattype',3)
179 WriteData(fid,'data',num_cost_functions,'enum',InversionNumCostFunctionsEnum(),'format','Integer')
180 # }}}
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