source:
issm/oecreview/Archive/23185-23389/ISSM-23256-23257.diff
Last change on this file was 23390, checked in by , 6 years ago | |
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../trunk-jpl/test/NightlyRun/test420.py
1 #Test Name: SquareSheetShelfDakotaScaledResponse 2 3 import numpy as np 4 from model import * 5 from socket import gethostname 6 from triangle import * 7 from setmask import * 8 from parameterize import * 9 from setflowequation import * 10 from solve import * 11 from partitioner import * 12 13 md = triangle(model(),'../Exp/Square.exp',200000.) 14 md = setmask(md,'../Exp/SquareShelf.exp','') 15 md = parameterize(md,'../Par/SquareSheetShelf.py') 16 md = setflowequation(md,'SSA','all') 17 md.cluster = generic('name',gethostname(),'np',3) 18 19 #partitioning 20 md.qmu.numberofpartitions = 10 21 md = partitioner(md,'package','chaco','npart',md.qmu.numberofpartitions) 22 md.qmu.partition = md.qmu.partition-1 23 md.qmu.isdakota = 1 24 25 #Dakota options 26 27 #dakota version 28 version = IssmConfig('_DAKOTA_VERSION_') 29 version = float(version[0]) 30 31 #variables 32 md.qmu.variables.rho_ice = normal_uncertain.normal_uncertain('MaterialsRhoIce',md.materials.rho_ice,0.01) 33 34 #responses 35 md.qmu.responses.MaxVel = response_function.response_function('scaled_Thickness',[],[0.0001,0.001,0.01,0.25,0.5,0.75,0.99,0.999,0.9999]) 36 37 #method 38 md.qmu.method = dakota_method.dakota_method('nond_l') 39 40 #parameters 41 md.qmu.params.direct = True 42 md.qmu.params.interval_type = 'forward' 43 44 if version >= 6: 45 md.qmu.params.analysis_driver = 'matlab' 46 md.qmu.params.evaluation_scheduling = 'master' 47 md.qmu.params.processors_per_evaluation = 2 48 else: 49 md.qmu.params.analysis_driver = 'stressbalance' 50 md.qmu.params.evaluation_concurrency = 1 51 52 #imperative! 53 md.stressbalance.reltol = 10**-5 #tighten for qmu analysese 54 55 #solve 56 md.verbose = verbose('000000000') # this line is recommended 57 md = solve(md,'Stressbalance','overwrite','y') 58 md.qmu.results = md.results.dakota 59 60 #test on thickness 61 h = np.zeros((md.qmu.numberofpartitions,)) 62 for i in range(md.qmu.numberofpartitions): 63 h[i] = md.qmu.results.dresp_out[i].mean 64 65 #project onto grid 66 thickness = h[(md.qmu.partition).flatten()] 67 68 #Fields and tolerances to track changes 69 field_names = ['Thickness'] 70 field_tolerances = [1e-10] 71 field_values = [thickness]
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