| [21303] | 1 | import numpy as np
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| [25688] | 2 |
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| 3 | from checkfield import checkfield
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| 4 | from fielddisplay import fielddisplay
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| 5 | from marshallcostfunctions import marshallcostfunctions
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| [19048] | 6 | from project3d import project3d
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| [18994] | 7 | from supportedcontrols import supportedcontrols
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| 8 | from supportedcostfunctions import supportedcostfunctions
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| [25688] | 9 | from WriteData import WriteData
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| [12038] | 10 |
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| [24213] | 11 |
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| [12958] | 12 | class inversion(object):
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| [25688] | 13 | """INVERSION class definition
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| [13023] | 14 |
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| [25688] | 15 | Usage:
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| 16 | inversion = inversion()
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| [24213] | 17 | """
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| [13023] | 18 |
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| [24213] | 19 | def __init__(self): # {{{
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| 20 | self.iscontrol = 0
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| 21 | self.incomplete_adjoint = 0
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| [25688] | 22 | self.control_parameters = np.nan
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| [24213] | 23 | self.nsteps = 0
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| [25688] | 24 | self.maxiter_per_step = np.nan
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| [24213] | 25 | self.cost_functions = ''
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| [25688] | 26 | self.cost_functions_coefficients = np.nan
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| 27 | self.gradient_scaling = np.nan
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| [24213] | 28 | self.cost_function_threshold = 0
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| [25688] | 29 | self.min_parameters = np.nan
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| 30 | self.max_parameters = np.nan
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| 31 | self.step_threshold = np.nan
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| 32 | self.vx_obs = np.nan
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| 33 | self.vy_obs = np.nan
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| 34 | self.vz_obs = np.nan
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| 35 | self.vel_obs = np.nan
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| 36 | self.thickness_obs = np.nan
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| 37 | self.surface_obs = np.nan
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| [13093] | 38 |
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| [24213] | 39 | self.setdefaultparameters()
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| 40 | #}}}
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| [12123] | 41 |
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| [24213] | 42 | def __repr__(self): # {{{
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| [25688] | 43 | s = ' inversion parameters:\n'
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| 44 | s += '{}\n'.format(fielddisplay(self, 'iscontrol', 'is inversion activated?'))
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| 45 | s += '{}\n'.format(fielddisplay(self, 'incomplete_adjoint', '1: linear viscosity, 0: non - linear viscosity'))
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| 46 | s += '{}\n'.format(fielddisplay(self, 'control_parameters', 'ex: {''FrictionCoefficient''}, or {''MaterialsRheologyBbar''}'))
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| 47 | s += '{}\n'.format(fielddisplay(self, 'nsteps', 'number of optimization searches'))
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| 48 | s += '{}\n'.format(fielddisplay(self, 'cost_functions', 'indicate the type of response for each optimization step'))
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| 49 | s += '{}\n'.format(fielddisplay(self, 'cost_functions_coefficients', 'cost_functions_coefficients applied to the misfit of each vertex and for each control_parameter'))
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| 50 | s += '{}\n'.format(fielddisplay(self, 'cost_function_threshold', 'misfit convergence criterion. Default is 1%, NaN if not applied'))
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| 51 | s += '{}\n'.format(fielddisplay(self, 'maxiter_per_step', 'maximum iterations during each optimization step'))
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| 52 | s += '{}\n'.format(fielddisplay(self, 'gradient_scaling', 'scaling factor on gradient direction during optimization, for each optimization step'))
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| 53 | s += '{}\n'.format(fielddisplay(self, 'step_threshold', 'decrease threshold for misfit, default is 30%'))
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| 54 | s += '{}\n'.format(fielddisplay(self, 'min_parameters', 'absolute minimum acceptable value of the inversed parameter on each vertex'))
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| 55 | s += '{}\n'.format(fielddisplay(self, 'max_parameters', 'absolute maximum acceptable value of the inversed parameter on each vertex'))
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| [25806] | 56 | s += '{}\n'.format(fielddisplay(self, 'vx_obs', 'observed velocity x component [m/yr]'))
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| 57 | s += '{}\n'.format(fielddisplay(self, 'vy_obs', 'observed velocity y component [m/yr]'))
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| 58 | s += '{}\n'.format(fielddisplay(self, 'vel_obs', 'observed velocity magnitude [m/yr]'))
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| [25688] | 59 | s += '{}\n'.format(fielddisplay(self, 'thickness_obs', 'observed thickness [m]'))
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| 60 | s += '{}\n'.format(fielddisplay(self, 'surface_obs', 'observed surface elevation [m]'))
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| 61 | s += '{}\n'.format('Available cost functions:')
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| 62 | s += '{}\n'.format(' 101: SurfaceAbsVelMisfit')
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| 63 | s += '{}\n'.format(' 102: SurfaceRelVelMisfit')
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| 64 | s += '{}\n'.format(' 103: SurfaceLogVelMisfit')
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| 65 | s += '{}\n'.format(' 104: SurfaceLogVxVyMisfit')
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| 66 | s += '{}\n'.format(' 105: SurfaceAverageVelMisfit')
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| 67 | s += '{}\n'.format(' 201: ThicknessAbsMisfit')
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| 68 | s += '{}\n'.format(' 501: DragCoefficientAbsGradient')
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| 69 | s += '{}\n'.format(' 502: RheologyBbarAbsGradient')
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| 70 | s += '{}\n'.format(' 503: ThicknessAbsGradient')
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| 71 | return s
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| [24213] | 72 | #}}}
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| [12123] | 73 |
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| [24213] | 74 | def setdefaultparameters(self): # {{{
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| 75 | #default is incomplete adjoint for now
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| 76 | self.incomplete_adjoint = 1
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| 77 | #parameter to be inferred by control methods (only
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| 78 | #drag and B are supported yet)
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| 79 | self.control_parameters = 'FrictionCoefficient'
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| 80 | #number of steps in the control methods
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| 81 | self.nsteps = 20
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| 82 | #maximum number of iteration in the optimization algorithm for
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| 83 | #each step
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| 84 | self.maxiter_per_step = 20 * np.ones(self.nsteps)
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| 85 | #the inversed parameter is updated as follows:
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| 86 | #new_par = old_par + gradient_scaling(n) * C * gradient with C in [0 1]
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| 87 | #usually the gradient_scaling must be of the order of magnitude of the
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| [24261] | 88 | #inversed parameter (1.0e8 for B, 50 for drag) and can be decreased
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| [24213] | 89 | #after the first iterations
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| 90 | self.gradient_scaling = 50 * np.ones((self.nsteps, 1))
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| 91 | #several responses can be used:
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| 92 | self.cost_functions = [101, ]
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| 93 | #step_threshold is used to speed up control method. When
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| 94 | #misfit(1) / misfit(0) < self.step_threshold, we go directly to
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| 95 | #the next step
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| 96 | self.step_threshold = 0.7 * np.ones(self.nsteps) #30 per cent decrement
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| 97 | #cost_function_threshold is a criteria to stop the control methods.
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| 98 | #if J[n] - J[n - 1] / J[n] < criteria, the control run stops
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| 99 | #NaN if not applied
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| [25688] | 100 | self.cost_function_threshold = np.nan #not activated
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| 101 | return self
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| 102 | #}}}
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| [12123] | 103 |
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| [25688] | 104 | def extrude(self, md): # {{{
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| 105 | self.vx_obs = project3d(md, 'vector', self.vx_obs, 'type', 'node')
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| 106 | self.vy_obs = project3d(md, 'vector', self.vy_obs, 'type', 'node')
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| 107 | self.vel_obs = project3d(md, 'vector', self.vel_obs, 'type', 'node')
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| 108 | self.thickness_obs = project3d(md, 'vector', self.thickness_obs, 'type', 'node')
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| 109 | if not np.any(np.isnan(self.cost_functions_coefficients)):
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| 110 | self.cost_functions_coefficients = project3d(md, 'vector', self.cost_functions_coefficients, 'type', 'node')
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| 111 | if not np.any(np.isnan(self.min_parameters)):
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| 112 | self.min_parameters = project3d(md, 'vector', self.min_parameters, 'type', 'node')
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| 113 | if not np.any(np.isnan(self.max_parameters)):
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| 114 | self.max_parameters = project3d(md, 'vector', self.max_parameters, 'type', 'node')
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| [24213] | 115 | return self
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| 116 | #}}}
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| [12123] | 117 |
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| [24213] | 118 | def checkconsistency(self, md, solution, analyses): # {{{
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| [25688] | 119 | # Early return
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| [24213] | 120 | if not self.iscontrol:
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| 121 | return md
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| [12123] | 122 |
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| [24213] | 123 | num_controls = np.size(md.inversion.control_parameters)
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| 124 | num_costfunc = np.size(md.inversion.cost_functions)
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| [12123] | 125 |
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| [24213] | 126 | md = checkfield(md, 'fieldname', 'inversion.iscontrol', 'values', [0, 1])
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| 127 | md = checkfield(md, 'fieldname', 'inversion.incomplete_adjoint', 'values', [0, 1])
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| 128 | md = checkfield(md, 'fieldname', 'inversion.control_parameters', 'cell', 1, 'values', supportedcontrols())
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| 129 | md = checkfield(md, 'fieldname', 'inversion.nsteps', 'numel', [1], '>=', 0)
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| 130 | md = checkfield(md, 'fieldname', 'inversion.maxiter_per_step', 'size', [md.inversion.nsteps], '>=', 0)
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| 131 | md = checkfield(md, 'fieldname', 'inversion.step_threshold', 'size', [md.inversion.nsteps])
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| 132 | md = checkfield(md, 'fieldname', 'inversion.cost_functions', 'size', [num_costfunc], 'values', supportedcostfunctions())
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| 133 | md = checkfield(md, 'fieldname', 'inversion.cost_functions_coefficients', 'size', [md.mesh.numberofvertices, num_costfunc], '>=', 0)
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| 134 | md = checkfield(md, 'fieldname', 'inversion.gradient_scaling', 'size', [md.inversion.nsteps, num_controls])
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| 135 | md = checkfield(md, 'fieldname', 'inversion.min_parameters', 'size', [md.mesh.numberofvertices, num_controls])
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| 136 | md = checkfield(md, 'fieldname', 'inversion.max_parameters', 'size', [md.mesh.numberofvertices, num_controls])
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| [12123] | 137 |
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| [25688] | 138 | # Only SSA, HO and FS are supported right now
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| [24213] | 139 | if solution == 'StressbalanceSolution':
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| [26480] | 140 | if not (md.flowequation.isSSA or md.flowequation.isMLHO or md.flowequation.isHO or md.flowequation.isFS or md.flowequation.isL1L2):
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| 141 | md.checkmessage("'inversion can only be performed for SSA, MLHO, HO or FS ice flow models")
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| [24213] | 142 | if solution == 'BalancethicknessSolution':
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| 143 | md = checkfield(md, 'fieldname', 'inversion.thickness_obs', 'size', [md.mesh.numberofvertices], 'NaN', 1, 'Inf', 1)
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| [25688] | 144 | elif solution == 'BalancethicknessSoftSolution':
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| 145 | md = checkfield(md, 'fieldname', 'inversion.thickness_obs', 'size', [md.mesh.numberofvertices], 'NaN', 1, 'Inf', 1)
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| [24213] | 146 | else:
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| 147 | md = checkfield(md, 'fieldname', 'inversion.vx_obs', 'size', [md.mesh.numberofvertices], 'NaN', 1, 'Inf', 1)
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| 148 | md = checkfield(md, 'fieldname', 'inversion.vy_obs', 'size', [md.mesh.numberofvertices], 'NaN', 1, 'Inf', 1)
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| 149 | return md
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| 150 | # }}}
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| [13023] | 151 |
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| [24213] | 152 | def marshall(self, prefix, md, fid): # {{{
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| 153 | yts = md.constants.yts
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| [15860] | 154 |
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| [24213] | 155 | WriteData(fid, prefix, 'name', 'md.inversion.type', 'data', 0, 'format', 'Integer')
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| 156 | WriteData(fid, prefix, 'object', self, 'fieldname', 'iscontrol', 'format', 'Boolean')
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| 157 | WriteData(fid, prefix, 'object', self, 'fieldname', 'incomplete_adjoint', 'format', 'Boolean')
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| 158 | WriteData(fid, prefix, 'object', self, 'fieldname', 'vel_obs', 'format', 'DoubleMat', 'mattype', 1, 'scale', 1. / yts)
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| 159 | if not self.iscontrol:
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| 160 | return
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| 161 | WriteData(fid, prefix, 'object', self, 'fieldname', 'nsteps', 'format', 'Integer')
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| [25806] | 162 | WriteData(fid, prefix, 'object', self, 'fieldname', 'maxiter_per_step', 'format', 'IntMat', 'mattype', 3)
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| [24213] | 163 | WriteData(fid, prefix, 'object', self, 'fieldname', 'cost_functions_coefficients', 'format', 'DoubleMat', 'mattype', 1)
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| 164 | WriteData(fid, prefix, 'object', self, 'fieldname', 'gradient_scaling', 'format', 'DoubleMat', 'mattype', 3)
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| 165 | WriteData(fid, prefix, 'object', self, 'fieldname', 'cost_function_threshold', 'format', 'Double')
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| 166 | WriteData(fid, prefix, 'object', self, 'fieldname', 'min_parameters', 'format', 'DoubleMat', 'mattype', 3)
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| 167 | WriteData(fid, prefix, 'object', self, 'fieldname', 'max_parameters', 'format', 'DoubleMat', 'mattype', 3)
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| 168 | WriteData(fid, prefix, 'object', self, 'fieldname', 'step_threshold', 'format', 'DoubleMat', 'mattype', 3)
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| 169 | WriteData(fid, prefix, 'object', self, 'fieldname', 'vx_obs', 'format', 'DoubleMat', 'mattype', 1, 'scale', 1. / yts)
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| 170 | WriteData(fid, prefix, 'object', self, 'fieldname', 'vy_obs', 'format', 'DoubleMat', 'mattype', 1, 'scale', 1. / yts)
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| 171 | WriteData(fid, prefix, 'object', self, 'fieldname', 'vz_obs', 'format', 'DoubleMat', 'mattype', 1, 'scale', 1. / yts)
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| 172 | WriteData(fid, prefix, 'object', self, 'fieldname', 'thickness_obs', 'format', 'DoubleMat', 'mattype', 1)
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| 173 | WriteData(fid, prefix, 'object', self, 'fieldname', 'surface_obs', 'format', 'DoubleMat', 'mattype', 1)
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| [13023] | 174 |
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| [25688] | 175 | # Process control parameters
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| [24213] | 176 | num_control_parameters = len(self.control_parameters)
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| 177 | WriteData(fid, prefix, 'object', self, 'fieldname', 'control_parameters', 'format', 'StringArray')
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| 178 | WriteData(fid, prefix, 'data', num_control_parameters, 'name', 'md.inversion.num_control_parameters', 'format', 'Integer')
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| [13023] | 179 |
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| [25688] | 180 | # Process cost functions
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| [24213] | 181 | num_cost_functions = np.size(self.cost_functions)
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| 182 | data = marshallcostfunctions(self.cost_functions)
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| 183 | WriteData(fid, prefix, 'data', data, 'name', 'md.inversion.cost_functions', 'format', 'StringArray')
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| 184 | WriteData(fid, prefix, 'data', num_cost_functions, 'name', 'md.inversion.num_cost_functions', 'format', 'Integer')
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| 185 | # }}}
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