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issm/oecreview/Archive/24684-25833/ISSM-25009-25010.diff@
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../trunk-jpl/test/NightlyRun/test444.py
65 65 version = float(version[0]) 66 66 67 67 #partitioning 68 md.qmu.numberofpartitions = 10 69 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 70 md.qmu.vpartition = md.qmu.vpartition - 1 68 npart = 10 69 partition = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 71 70 md.qmu.isdakota = 1 72 71 73 72 #variables 74 73 md.qmu.variables.drag_coefficient = normal_uncertain.normal_uncertain( 75 74 'descriptor', 'scaled_BasalforcingsFloatingiceMeltingRate', 76 'mean', np.ones( md.qmu.numberofpartitions),77 'stddev', .1 * np.ones( md.qmu.numberofpartitions),78 'partition', md.qmu.vpartition75 'mean', np.ones(npart), 76 'stddev', .1 * np.ones(npart), 77 'partition', partition 79 78 ) 80 79 81 80 … … 109 108 md.qmu.params.analysis_driver = 'stressbalance' 110 109 md.qmu.params.evaluation_concurrency = 1 111 110 112 #partitioning113 md.qmu.numberofpartitions = 10114 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on')115 md.qmu.vpartition = md.qmu.vpartition - 1116 md.qmu.isdakota = 1117 118 111 md.stressbalance.reltol = 10**-5 #tighten for qmu analyses 119 112 120 113 #solve -
../trunk-jpl/test/NightlyRun/test418.py
21 21 md.cluster = generic('name', gethostname(), 'np', 3) 22 22 23 23 #partitioning 24 md.qmu.numberofpartitions= 10024 npart = 100 25 25 26 # Partitioner seam d to generate the following message,26 # Partitioner seamed to generate the following message, 27 27 # 28 28 # corrupted size vs. prev_size 29 29 # Aborted (core dumped) … … 35 35 # TODO: 36 36 # - Run valgrind and fix the above 37 37 # 38 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions) 39 md.qmu.vpartition = md.qmu.vpartition - 1 38 partition = partitioner(md, 'package', 'chaco', 'npart', npart) - 1 40 39 41 40 vector = np.arange(1, 1 + md.mesh.numberofvertices, 1).reshape(-1, 1) 42 41 # double check this before committing: 43 vector_on_partition = AreaAverageOntoPartition(md, vector )44 vector_on_nodes = vector_on_partition[ md.qmu.vpartition]42 vector_on_partition = AreaAverageOntoPartition(md, vector, partition) 43 vector_on_nodes = vector_on_partition[partition + 1] 45 44 46 45 field_names = ['vector_on_nodes'] 47 46 field_tolerances = [1e-11] -
../trunk-jpl/test/NightlyRun/test420.m
47 47 md.qmu.results=md.results.dakota; 48 48 49 49 %test on thickness 50 h=zeros( part,1);50 h=zeros(npart,1); 51 51 for i=1:npart, 52 52 h(i)=md.qmu.results.dresp_out(i).mean; 53 53 end -
../trunk-jpl/test/NightlyRun/test420.py
16 16 md.cluster = generic('name', gethostname(), 'np', 3) 17 17 18 18 #partitioning 19 md.qmu.numberofpartitions = 10 20 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions) 21 md.qmu.vpartition = md.qmu.vpartition - 1 19 npart = 10 20 partition = partitioner(md, 'package', 'chaco', 'npart', npart) - 1 22 21 md.qmu.isdakota = 1 23 22 24 23 #Dakota options … … 37 36 #responses 38 37 md.qmu.responses.MaxVel = response_function.response_function( 39 38 'descriptor', 'scaled_Thickness', 40 'partition', md.qmu.vpartition39 'partition', partition 41 40 ) 42 41 43 42 #method … … 64 63 md.qmu.results = md.results.dakota 65 64 66 65 #test on thickness 67 h = np.zeros( (md.qmu.numberofpartitions, ))68 for i in range( md.qmu.numberofpartitions):66 h = np.zeros(npart) 67 for i in range(npart): 69 68 h[i] = md.qmu.results.dresp_out[i].mean 70 69 71 70 #project onto grid 72 thickness = h[(md.qmu.vpartition ).flatten()]71 thickness = h[(md.qmu.vpartition + 1).flatten()] 73 72 74 73 #Fields and tolerances to track changes 75 74 field_names = ['Thickness'] -
../trunk-jpl/test/NightlyRun/test234.py
37 37 version = float(version[0]) 38 38 39 39 #partitioning 40 md.qmu.numberofpartitions = 20 41 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 42 md.qmu.vpartition = md.qmu.vpartition - 1 40 npart = 20 41 partition = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 43 42 44 43 #variables 45 44 md.qmu.variables.surface_mass_balance = normal_uncertain.normal_uncertain( 46 45 'descriptor', 'scaled_SmbMassBalance', 47 'mean', np.ones( md.qmu.numberofpartitions),48 'stddev', .1 * np.ones( md.qmu.numberofpartitions),49 'partition', md.qmu.vpartition46 'mean', np.ones(npart), 47 'stddev', .1 * np.ones(npart), 48 'partition', partition 50 49 ) 51 50 52 51 #responses -
../trunk-jpl/test/NightlyRun/test440.py
17 17 md.cluster = generic('name', oshostname(), 'np', 3) 18 18 19 19 #partitioning 20 md.qmu.numberofpartitions = md.mesh.numberofvertices 21 md = partitioner(md, 'package', 'linear') 22 md.qmu.vpartition = md.qmu.vpartition - 1 20 npart = md.mesh.numberofvertices 21 partition = partitioner(md, 'package', 'linear', 'npart', npart) - 1 23 22 md.qmu.isdakota = 1 24 23 25 24 #Dakota options … … 38 37 #responses 39 38 md.qmu.responses.MaxVel = response_function.response_function( 40 39 'descriptor', 'scaled_Thickness', 41 'partition', md.qmu.vpartition40 'partition', partition 42 41 ) 43 42 44 43 #method … … 65 64 md.qmu.results = md.results.dakota 66 65 67 66 #test on thickness 68 h = np.zeros( md.qmu.numberofpartitions)69 for i in range( md.qmu.numberofpartitions):67 h = np.zeros(npart) 68 for i in range(npart): 70 69 h[i] = md.qmu.results.dresp_out[i].mean 71 70 72 71 #project onto grid 73 thickness = h[ md.qmu.vpartition]72 thickness = h[partition] 74 73 75 74 #Fields and tolerances to track changes 76 75 field_names = ['Thickness'] -
../trunk-jpl/test/NightlyRun/test414.py
31 31 version = float(version[0]) 32 32 33 33 #partitioning 34 md.qmu.numberofpartitions = 20 35 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 36 md.qmu.vpartition = md.qmu.vpartition - 1 34 npart = 20 35 partition = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 37 36 38 37 #variables 39 38 md.qmu.variables.drag_coefficient = normal_uncertain.normal_uncertain( 40 39 'descriptor', 'scaled_FrictionCoefficient', 41 'mean', np.ones( md.qmu.numberofpartitions),42 'stddev', .01 * np.ones( md.qmu.numberofpartitions),43 'partition', md.qmu.vpartition40 'mean', np.ones(npart), 41 'stddev', .01 * np.ones(npart), 42 'partition', partition 44 43 ) 45 44 46 45 #responses -
../trunk-jpl/test/NightlyRun/test218.py
71 71 md.stressbalance.spcvy[pos] = 0. 72 72 73 73 #partitioning 74 md.qmu.numberofpartitions = md.mesh.numberofvertices 75 md = partitioner(md, 'package', 'linear', 'npart', md.qmu.numberofpartitions) 76 md.qmu.vpartition = md.qmu.vpartition - 1 74 npart = md.mesh.numberofvertices 75 partition = partitioner(md, 'package', 'linear', 'npart', npart) - 1 77 76 78 77 #Dakota options 79 78 … … 87 86 'descriptor', 'scaled_MaterialsRheologyB', 88 87 'mean', np.ones(md.mesh.numberofvertices), 89 88 'stddev', .05 * np.ones(md.mesh.numberofvertices), 90 'partition', md.qmu.vpartition89 'partition', partition 91 90 ) 92 91 93 92 #responses … … 120 119 121 120 #Fields and tolerances to track changes 122 121 md.qmu.results = md.results.dakota 123 md.results.dakota.importancefactors = importancefactors(md, 'scaled_MaterialsRheologyB', 'MaxVel' ).reshape(-1, 1)122 md.results.dakota.importancefactors = importancefactors(md, 'scaled_MaterialsRheologyB', 'MaxVel', partition).reshape(-1, 1) 124 123 field_names = ['importancefactors'] 125 124 field_tolerances = [1e-10] 126 125 field_values = [md.results.dakota.importancefactors] -
../trunk-jpl/test/NightlyRun/test417.py
31 31 version = float(version[0]) 32 32 33 33 #partitioning 34 md.qmu.numberofpartitions= 2035 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 36 md.qmu. vpartition = md.qmu.vpartition -134 npart = 20 35 partition = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 36 md.qmu.isdakota = 1 37 37 38 38 #variables 39 39 md.qmu.variables.drag_coefficient = normal_uncertain.normal_uncertain( 40 40 'descriptor', 'scaled_FrictionCoefficient', 41 'mean', np.ones( md.qmu.numberofpartitions),42 'stddev', .01 * np.ones( md.qmu.numberofpartitions),43 'partition', md.qmu.vpartition41 'mean', np.ones(npart), 42 'stddev', .01 * np.ones(npart), 43 'partition', partition 44 44 ) 45 45 46 46 #responses … … 58 58 md.qmu.mass_flux_profiles = ['../Exp/MassFlux1.exp', '../Exp/MassFlux2.exp', '../Exp/MassFlux3.exp', '../Exp/MassFlux4.exp', '../Exp/MassFlux5.exp', '../Exp/MassFlux6.exp', '../Exp/Square.exp'] 59 59 md.qmu.mass_flux_profile_directory = getcwd() 60 60 61 # method61 # nond_sampling study 62 62 md.qmu.method = dakota_method.dakota_method('nond_samp') 63 63 md.qmu.method = dmeth_params_set(md.qmu.method, 'seed', 1234, 'samples', 20, 'sample_type', 'lhs') 64 64 65 # parameters65 # parameters 66 66 md.qmu.params.interval_type = 'forward' 67 67 md.qmu.params.direct = True 68 68 md.qmu.params.tabular_graphics_data = True … … 75 75 md.qmu.params.analysis_driver = 'stressbalance' 76 76 md.qmu.params.evaluation_concurrency = 1 77 77 78 #partitioning79 md.qmu.numberofpartitions = 2080 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on')81 md.qmu.vpartition = md.qmu.vpartition - 182 md.qmu.isdakota = 183 84 78 md.stressbalance.reltol = 10**-5 #tighten for qmu analyses 85 79 86 80 #solve -
../trunk-jpl/test/NightlyRun/test445.py
34 34 version = float(version[0]) 35 35 36 36 #partitioning 37 md.qmu.numberofpartitions = 10 38 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 39 md.qmu.vpartition = md.qmu.vpartition - 1 37 npart = 10 38 partitioner = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 40 39 md.qmu.isdakota = 1 41 40 42 41 #variables 43 42 md.qmu.variables.neff = normal_uncertain.normal_uncertain( 44 43 'descriptor', 'scaled_FrictionEffectivePressure', 45 'mean', np.ones( md.qmu.numberofpartitions),46 'stddev', .05 * np .ones(md.qmu.numberofpartitions),47 'partition', md.qmu.vpartition44 'mean', np.ones(npart), 45 'stddev', .05 * npart), 46 'partition', partition 48 47 ) 49 48 md.qmu.variables.geoflux = normal_uncertain.normal_uncertain( 50 49 'descriptor', 'scaled_BasalforcingsGeothermalflux', 51 'mean', np.ones( md.qmu.numberofpartitions),52 'stddev', .05 * np.ones( md.qmu.numberofpartitions),53 'partition', md.qmu.vpartition50 'mean', np.ones(npart), 51 'stddev', .05 * np.ones(npart), 52 'partition', partition 54 53 ) 55 54 56 55 #responses -
../trunk-jpl/test/NightlyRun/test412.py
18 18 md.cluster = generic('name', gethostname(), 'np', 3) 19 19 20 20 #partitioning 21 md.qmu.numberofpartitions = md.mesh.numberofvertices 22 md = partitioner(md, 'package', 'linear', 'npart', md.qmu.numberofpartitions) 23 md.qmu.vpartition = md.qmu.vpartition - 1 21 md = partitioner(md, 'package', 'linear', 'npart', md.mesh.numberofvertices) - 1 24 22 md.qmu.isdakota = 1 25 23 26 24 #Dakota options … … 39 37 'descriptor', 'scaled_FrictionCoefficient', 40 38 'mean', np.ones(md.mesh.numberofvertices), 41 39 'stddev', .01 * np.ones(md.mesh.numberofvertices), 42 'partition', md.qmu.vpartition40 'partition', partition 43 41 ) 44 42 45 43 #responses … … 69 67 70 68 #Fields and tolerances to track changes 71 69 md.qmu.results = md.results.dakota 72 md.results.dakota.importancefactors = importancefactors(md, 'scaled_FrictionCoefficient', 'MaxVel' ).T70 md.results.dakota.importancefactors = importancefactors(md, 'scaled_FrictionCoefficient', 'MaxVel', partition).T 73 71 field_names = ['importancefactors'] 74 72 field_tolerances = [1e-10] 75 73 field_values = [md.results.dakota.importancefactors] -
../trunk-jpl/test/NightlyRun/test250.py
36 36 37 37 #partitioning 38 38 md.qmu.numberofpartitions = md.mesh.numberofvertices 39 md = partitioner(md, 'package', 'linear') 39 partition = partitioner(md, 'package', 'linear', 'npart', md.mesh.numberofvertices) - 1 40 40 md.qmu.vpartition = md.qmu.vpartition - 1 41 41 42 42 #variables 43 43 md.qmu.variables.surface_mass_balance = normal_uncertain.normal_uncertain( 44 44 'descriptor', 'scaled_SmbMassBalance', 45 'mean', np.ones(md. qmu.numberofpartitions),46 'stddev', .1 * np.ones(md. qmu.numberofpartitions),47 'partition', md.qmu.vpartition45 'mean', np.ones(md.mesh.numberofvertices), 46 'stddev', .1 * np.ones(md.mesh.numberofvertices), 47 'partition', partition 48 48 ) 49 49 50 50 #responses -
../trunk-jpl/test/NightlyRun/test413.py
23 23 24 24 #partitioning 25 25 md.qmu.numberofpartitions = 20 26 md = partitioner(md, 'package', 'chaco', 'npart', md.qmu.numberofpartitions, 'weighting', 'on') 27 md.qmu.vpartition = md.qmu.vpartition - 1 26 partition = partitioner(md, 'package', 'chaco', 'npart', npart, 'weighting', 'on') - 1 28 27 29 28 #variables 30 29 md.qmu.variables.rho_ice = normal_uncertain.normal_uncertain( … … 34 33 ) 35 34 md.qmu.variables.drag_coefficient = normal_uncertain.normal_uncertain( 36 35 'descriptor', 'scaled_FrictionCoefficient', 37 'mean', np.ones( md.qmu.numberofpartitions),38 'stddev', .01 * np.ones( md.qmu.numberofpartitions),39 'partition', md.qmu.vpartition36 'mean', np.ones(npart), 37 'stddev', .01 * np.ones(npart), 38 'partition', partition 40 39 ) 41 40 42 41 #responses … … 68 67 69 68 #Fields and tolerances to track changes 70 69 md.qmu.results = md.results.dakota 71 md.results.dakota.importancefactors = importancefactors(md, 'scaled_FrictionCoefficient', 'MaxVel' ).T70 md.results.dakota.importancefactors = importancefactors(md, 'scaled_FrictionCoefficient', 'MaxVel', partition).T 72 71 field_names = ['importancefactors'] 73 72 field_tolerances = [1e-10] 74 73 field_values = [md.results.dakota.importancefactors] -
../trunk-jpl/test/NightlyRun/test251.py
36 36 version = float(version[0]) 37 37 38 38 #partitioning 39 md.qmu.numberofpartitions = md.mesh.numberofvertices 40 md = partitioner(md, 'package', 'linear') 41 md.qmu.vpartition = md.qmu.vpartition - 1 39 partition = partitioner(md, 'package', 'linear', 'npart', md.mesh.numberofvertices) - 1 42 40 43 41 #variables 44 42 md.qmu.variables.surface_mass_balance = normal_uncertain.normal_uncertain( … … 45 43 'descriptor', 'scaled_SmbMassBalance', 46 44 'mean', np.ones(md.qmu.numberofpartitions), 47 45 'stddev', 100 * np.ones(md.qmu.numberofpartitions), 48 'partition', md.qmu.vpartition46 'partition', partition 49 47 ) 50 48 51 49 #responses -
../trunk-jpl/src/m/classes/qmu.py
26 26 self.method = OrderedDict() 27 27 self.params = OrderedStruct() 28 28 self.results = OrderedDict() 29 self.vpartition = float('NaN')30 self.epartition = float('NaN')31 29 self.numberofpartitions = 0 32 30 self.numberofresponses = 0 33 31 self.variabledescriptors = [] … … 77 75 if isinstance(method, dakota_method): 78 76 s += " method : '%s'\n" % (method.method) 79 77 80 # params could behave a number of forms (mainly 1 struct or many)78 # params could have a number of forms (mainly 1 struct or many) 81 79 if type(self.params) == OrderedStruct: 82 80 params = [self.params] 83 81 else: … … 183 181 if not self.isdakota: 184 182 WriteData(fid, prefix, 'data', False, 'name', 'md.qmu.mass_flux_segments_present', 'format', 'Boolean') 185 183 return 186 WriteData(fid, prefix, 'object', self, 'fieldname', 'vpartition', 'format', 'DoubleMat', 'mattype', 2)187 WriteData(fid, prefix, 'object', self, 'fieldname', 'epartition', 'format', 'DoubleMat', 'mattype', 2)188 184 WriteData(fid, prefix, 'object', self, 'fieldname', 'numberofpartitions', 'format', 'Integer') 189 185 WriteData(fid, prefix, 'object', self, 'fieldname', 'numberofresponses', 'format', 'Integer') 190 186 WriteData(fid, prefix, 'object', self, 'fieldname', 'variabledescriptors', 'format', 'StringArray')
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