| [25834] | 1 | Index: ../trunk-jpl/src/m/modules/Scotch.py
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| 2 | ===================================================================
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| 3 | --- ../trunk-jpl/src/m/modules/Scotch.py (revision 25010)
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| 4 | +++ ../trunk-jpl/src/m/modules/Scotch.py (revision 25011)
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| 5 | @@ -1,7 +1,7 @@
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| 6 | from Scotch_python import Scotch_python
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| 7 |
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| 8 |
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| 9 | -def Scotch(* varargin):
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| 10 | +def Scotch(*varargin):
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| 11 | '''SCOTCH - Scotch partitioner
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| 12 |
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| 13 | Usage:
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| 14 | @@ -8,6 +8,6 @@
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| 15 | maptab = Scotch(adjmat, vertlb, vertwt, edgewt, archtyp, archpar, Scotch - specific parameters)
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| 16 | '''
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| 17 | # Call mex module
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| 18 | - maptab = Scotch_python(* varargin)
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| 19 | + maptab = Scotch_python(*varargin)
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| 20 |
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| 21 | return maptab
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| 22 | Index: ../trunk-jpl/src/m/boundaryconditions/love_numbers.py
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| 23 | ===================================================================
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| 24 | --- ../trunk-jpl/src/m/boundaryconditions/love_numbers.py (revision 25010)
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| 25 | +++ ../trunk-jpl/src/m/boundaryconditions/love_numbers.py (revision 25011)
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| 26 | @@ -1,7 +1,7 @@
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| 27 | import numpy as np
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| 28 |
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| 29 |
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| 30 | -def love_numbers(value, * varargin):
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| 31 | +def love_numbers(value, *varargin):
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| 32 | '''LOVE_NUMBERS: provide love numbers (value 'h', 'k', 'l', 'gamma' and 'lambda'
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| 33 | retrieved from: http://www.srosat.com/iag-jsg/loveNb.php
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| 34 | Usage: series = love_numbers(value)
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| 35 | Index: ../trunk-jpl/src/m/plot/export_gl.py
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| 36 | ===================================================================
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| 37 | --- ../trunk-jpl/src/m/plot/export_gl.py (revision 25010)
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| 38 | +++ ../trunk-jpl/src/m/plot/export_gl.py (revision 25011)
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| 39 | @@ -6,7 +6,7 @@
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| 40 | from writejsfile import writejsfile
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| 41 |
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| 42 |
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| 43 | -def export_gl(md, * varargin):
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| 44 | +def export_gl(md, *varargin):
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| 45 | class ResultObj(object):
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| 46 | def __getattr__(self, attr):
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| 47 | return self.__dict__.get(attr)
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| 48 | Index: ../trunk-jpl/src/m/classes/qmu/response_function.py
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| 49 | ===================================================================
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| 50 | --- ../trunk-jpl/src/m/classes/qmu/response_function.py (revision 25010)
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| 51 | +++ ../trunk-jpl/src/m/classes/qmu/response_function.py (revision 25011)
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| 52 | @@ -1,7 +1,7 @@
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| 53 | import numpy as np
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| 54 |
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| 55 | +from fielddisplay import fielddisplay
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| 56 | from MatlabFuncs import *
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| 57 | -from fielddisplay import fielddisplay
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| 58 | from pairoptions import pairoptions
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| 59 | from partition_npart import *
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| 60 | from rlev_write import *
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| 61 | @@ -102,17 +102,17 @@
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| 62 |
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| 63 | return [rf] # Always return a list, so we have something akin to a MATLAB single row matrix
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| 64 |
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| 65 | - def __repr__(self): #{{{
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| 66 | + def __repr__(rf): #{{{
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| 67 | # display the object
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| 68 | string = 'class "response_function" object = \n'
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| 69 | - string = "%s\n%s" % (string, fielddisplay(self, 'descriptor', 'name tag'))
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| 70 | - string = "%s\n%s" % (string, fielddisplay(self, 'respl', 'response levels'))
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| 71 | - string = "%s\n%s" % (string, fielddisplay(self, 'probl', 'probability levels'))
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| 72 | - string = "%s\n%s" % (string, fielddisplay(self, 'rell', 'reliability levels'))
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| 73 | - string = "%s\n%s" % (string, fielddisplay(self, 'grell', 'general reliability levels'))
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| 74 | + string = "%s\n%s" % (string, fielddisplay(rf, 'descriptor', 'name tag'))
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| 75 | + string = "%s\n%s" % (string, fielddisplay(rf, 'respl', 'response levels'))
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| 76 | + string = "%s\n%s" % (string, fielddisplay(rf, 'probl', 'probability levels'))
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| 77 | + string = "%s\n%s" % (string, fielddisplay(rf, 'rell', 'reliability levels'))
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| 78 | + string = "%s\n%s" % (string, fielddisplay(rf, 'grell', 'general reliability levels'))
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| 79 |
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| 80 | - if self.partition != []:
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| 81 | - string = "%s\n%s" % (string, fielddisplay(self, 'partition', 'partition'))
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| 82 | + if rf.partition != []:
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| 83 | + string = "%s\n%s" % (string, fielddisplay(rf, 'partition', 'partition vector defining where the response will be computed'))
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| 84 |
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| 85 | return string
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| 86 | #}}}
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| 87 | Index: ../trunk-jpl/src/m/coordsystems/gmtmask.py
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| 88 | ===================================================================
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| 89 | --- ../trunk-jpl/src/m/coordsystems/gmtmask.py (revision 25010)
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| 90 | +++ ../trunk-jpl/src/m/coordsystems/gmtmask.py (revision 25011)
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| 91 | @@ -5,7 +5,7 @@
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| 92 | import subprocess
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| 93 |
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| 94 |
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| 95 | -def gmtmask(lat, long, * varargin):
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| 96 | +def gmtmask(lat, long, *varargin):
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| 97 | '''GMTMASK - figure out which lat, long points are on the ocean
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| 98 |
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| 99 | Usage:
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| 100 | Index: ../trunk-jpl/src/m/qmu/qmupart2npart.py
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| 101 | ===================================================================
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| 102 | --- ../trunk-jpl/src/m/qmu/qmupart2npart.py (nonexistent)
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| 103 | +++ ../trunk-jpl/src/m/qmu/qmupart2npart.py (revision 25011)
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| 104 | @@ -0,0 +1,10 @@
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| 105 | +import numpy as np
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| 106 | +
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| 107 | +
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| 108 | +def qmupart2npart(vector):
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| 109 | + # Vector is full of -1 (no partition) and 0 to npart. We need to identify
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| 110 | + # npart.
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| 111 | +
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| 112 | + npart = vector.max() + 1
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| 113 | +
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| 114 | + return npart
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| 115 | Index: ../trunk-jpl/src/m/partition/partitioner.py
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| 116 | ===================================================================
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| 117 | --- ../trunk-jpl/src/m/partition/partitioner.py (revision 25010)
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| 118 | +++ ../trunk-jpl/src/m/partition/partitioner.py (revision 25011)
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| 119 | @@ -8,26 +8,26 @@
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| 120 | from mesh2d import *
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| 121 |
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| 122 |
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| 123 | -def partitioner(md, * varargin):
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| 124 | - help = '''
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| 125 | -PARTITIONER - partition mesh
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| 126 | +def partitioner(md, *varargin):
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| 127 | + '''
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| 128 | + PARTITIONER - partition mesh
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| 129 |
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| 130 | - List of options to partitioner:
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| 131 | + List of options to partitioner:
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| 132 |
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| 133 | - package: 'chaco', 'metis'
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| 134 | - npart: number of partitions.
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| 135 | - weighting: 'on' or 'off': default off
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| 136 | - section: 1 by defaults(1 = bisection, 2 = quadrisection, 3 = octasection)
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| 137 | - recomputeadjacency: 'on' by default (set to 'off' to compute existing one)
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| 138 | - type: 'node' or 'element' partition vector (default to 'node')
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| 139 | - Output: md.qmu.partition recover the partition vector
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| 140 | + package: 'chaco', 'metis'
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| 141 | + npart: number of partitions.
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| 142 | + weighting: 'on' or 'off': default off
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| 143 | + section: 1 by defaults(1 = bisection, 2 = quadrisection, 3 = octasection)
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| 144 | + recomputeadjacency: 'on' by default (set to 'off' to compute existing one)
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| 145 | + type: 'node' or 'element' partition vector (default to 'node')
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| 146 | + Output: partitionvector: the partition vector
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| 147 |
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| 148 | - Usage:
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| 149 | - md = partitioner(md, 'package', 'chaco', 'npart', 100, 'weighting', 'on')
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| 150 | + Usage:
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| 151 | + partitionvector = partitioner(md, 'package', 'chaco', 'npart', 100, 'weighting', 'on')
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| 152 | '''
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| 153 |
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| 154 | #get options:
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| 155 | - options = pairoptions(* varargin)
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| 156 | + options = pairoptions(*varargin)
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| 157 |
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| 158 | #get options:
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| 159 | section = options.getfieldvalue('section', 1)
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| 160 | @@ -121,12 +121,5 @@
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| 161 |
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| 162 | part = part.reshape(-1, 1)
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| 163 |
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| 164 | - if vectortype == 'element':
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| 165 | - md.qmu.epartition = part
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| 166 | - if np.size(md.qmu.vpartition) == 0 or (np.size(md.qmu.vpartition) == 1 and np.isnan(md.qmu.vpartition)):
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| 167 | - md.qmu.vpartition = np.zeros((md.mesh.numberofvertices, 1))
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| 168 | - else:
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| 169 | - md.qmu.vpartition = part
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| 170 | - if np.size(md.qmu.epartition) == 0 or (np.size(md.qmu.epartition) == 1 and np.isnan(md.qmu.epartition)):
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| 171 | - md.qmu.epartition = np.zeros((md.mesh.numberofelements, 1))
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| 172 | - return md
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| 173 | + # Output
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| 174 | + return part
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| 175 | Index: ../trunk-jpl/src/m/classes/slr.py
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| 176 | ===================================================================
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| 177 | --- ../trunk-jpl/src/m/classes/slr.py (revision 25010)
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| 178 | +++ ../trunk-jpl/src/m/classes/slr.py (revision 25011)
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| 179 | @@ -1,8 +1,9 @@
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| 180 | +import numpy as np
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| 181 | +
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| 182 | +from checkfield import checkfield
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| 183 | from fielddisplay import fielddisplay
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| 184 | from MatlabFuncs import *
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| 185 | from model import *
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| 186 | -import numpy as np
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| 187 | -from checkfield import checkfield
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| 188 | from WriteData import WriteData
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| 189 |
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| 190 |
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| 191 | @@ -38,10 +39,10 @@
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| 192 | self.geodetic_run_frequency = 1 #how many time steps we skip before we run the geodetic part of the solver during transient
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| 193 | self.geodetic = 0 #compute geodetic SLR? (in addition to steric?)
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| 194 | self.degacc = 0
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| 195 | - self.loop_increment = 0
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| 196 | self.horiz = 0
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| 197 | self.Ngia = float('NaN')
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| 198 | self.Ugia = float('NaN')
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| 199 | + self.planetradius = planetradius('earth')
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| 200 | self.requested_outputs = []
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| 201 | self.transitions = []
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| 202 |
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| 203 | @@ -71,7 +72,6 @@
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| 204 | string = "%s\n%s" % (string, fielddisplay(self, 'hydro_rate', 'rate of hydrological expansion [mm / yr]'))
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| 205 | string = "%s\n%s" % (string, fielddisplay(self, 'Ngia', 'rate of viscous (GIA) geoid expansion (in mm / yr)'))
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| 206 | string = "%s\n%s" % (string, fielddisplay(self, 'Ugia', 'rate of viscous (GIA) bedrock uplift (in mm / yr)'))
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| 207 | - string = "%s\n%s" % (string, fielddisplay(self, 'loop_increment', 'vector assembly (in the convolution) framentation'))
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| 208 | string = "%s\n%s" % (string, fielddisplay(self, 'geodetic', 'compute geodetic SLR? (in addition to steric?) default 0'))
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| 209 | string = "%s\n%s" % (string, fielddisplay(self, 'geodetic_run_frequency', 'how many time steps we skip before we run SLR solver during transient (default: 1)'))
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| 210 | string = "%s\n%s" % (string, fielddisplay(self, 'rigid', 'rigid earth graviational potential perturbation'))
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| 211 | @@ -90,7 +90,6 @@
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| 212 | self.abstol = float('NaN') #1 mm of sea level rise
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| 213 | #maximum of non - linear iterations.
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| 214 | self.maxiter = 5
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| 215 | - self.loop_increment = 200
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| 216 | #computational flags:
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| 217 | self.geodetic = 0
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| 218 | self.rigid = 1
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| 219 | @@ -120,6 +119,8 @@
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| 220 | self.transitions = []
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| 221 | #horizontal displacement? (not by default)
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| 222 | self.horiz = 0
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| 223 | + #earth area
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| 224 | + self.planetradius = planetradius('earth')
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| 225 |
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| 226 | return self
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| 227 | #}}}
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| 228 | @@ -149,7 +150,6 @@
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| 229 | md = checkfield(md, 'fieldname', 'slr.hydro_rate', 'NaN', 1, 'Inf', 1, 'size', [md.mesh.numberofvertices])
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| 230 | md = checkfield(md, 'fieldname', 'slr.degacc', 'size', [1, 1], '>=', 1e-10)
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| 231 | md = checkfield(md, 'fieldname', 'slr.requested_outputs', 'stringrow', 1)
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| 232 | - md = checkfield(md, 'fieldname', 'slr.loop_increment', 'NaN', 1, 'Inf', 1, '>=', 1)
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| 233 | md = checkfield(md, 'fieldname', 'slr.horiz', 'NaN', 1, 'Inf', 1, 'values', [0, 1])
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| 234 | md = checkfield(md, 'fieldname', 'slr.Ngia', 'NaN', 1, 'Inf', 1, 'size', [md.mesh.numberofvertices])
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| 235 | md = checkfield(md, 'fieldname', 'slr.Ugia', 'NaN', 1, 'Inf', 1, 'size', [md.mesh.numberofvertices])
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| 236 | @@ -203,9 +203,9 @@
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| 237 | WriteData(fid, prefix, 'object', self, 'fieldname', 'Ugia', 'format', 'DoubleMat', 'mattype', 1, 'scale', 1e-3 / md.constants.yts)
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| 238 | WriteData(fid, prefix, 'object', self, 'fieldname', 'degacc', 'format', 'Double')
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| 239 | WriteData(fid, prefix, 'object', self, 'fieldname', 'transitions', 'format', 'MatArray')
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| 240 | - WriteData(fid, prefix, 'object', self, 'fieldname', 'loop_increment', 'format', 'Integer')
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| 241 | WriteData(fid, prefix, 'object', self, 'fieldname', 'horiz', 'format', 'Integer')
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| 242 | WriteData(fid, prefix, 'object', self, 'fieldname', 'geodetic', 'format', 'Integer')
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| 243 | + WriteData(fid, prefix, 'object', self, 'fieldname', 'planetradius', 'format', 'Double')
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| 244 |
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| 245 | #process requested outputs
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| 246 | outputs = self.requested_outputs
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| 247 | Index: ../trunk-jpl/src/m/classes/qmu.py
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| 248 | ===================================================================
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| 249 | --- ../trunk-jpl/src/m/classes/qmu.py (revision 25010)
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| 250 | +++ ../trunk-jpl/src/m/classes/qmu.py (revision 25011)
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| 251 | @@ -1,13 +1,13 @@
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| 252 | import numpy as np
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| 253 | -from MatlabFuncs import *
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| 254 | -from IssmConfig import *
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| 255 | -from project3d import project3d
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| 256 | +
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| 257 | +from checkfield import checkfield
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| 258 | from collections import OrderedDict
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| 259 | +from dakota_method import *
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| 260 | from fielddisplay import fielddisplay
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| 261 | -from checkfield import checkfield
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| 262 | +from helpers import *
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| 263 | +from IssmConfig import *
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| 264 | +from MatlabFuncs import *
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| 265 | from WriteData import WriteData
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| 266 | -from helpers import *
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| 267 | -from dakota_method import *
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| 268 |
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| 269 |
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| 270 | class qmu(object):
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| 271 | @@ -26,10 +26,13 @@
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| 272 | self.method = OrderedDict()
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| 273 | self.params = OrderedStruct()
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| 274 | self.results = OrderedDict()
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| 275 | - self.numberofpartitions = 0
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| 276 | self.numberofresponses = 0
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| 277 | self.variabledescriptors = []
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| 278 | + self.variablepartitions = []
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| 279 | + self.variablepartitions_npart = []
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| 280 | self.responsedescriptors = []
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| 281 | + self.responsepartitions = []
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| 282 | + self.responsepartitions_npart = []
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| 283 | self.mass_flux_profile_directory = float('NaN')
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| 284 | self.mass_flux_profiles = float('NaN')
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| 285 | self.mass_flux_segments = []
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| 286 | @@ -108,9 +111,8 @@
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| 287 | size = np.shape(getattr(result, fname))
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| 288 | s += " %-*s: [%ix%i] '%s'\n" % (maxlen + 1, fname, a, b, type(getattr(result, fname)))
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| 289 |
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| 290 | - s += "%s\n" % fielddisplay(self, 'vpartition', 'user provided mesh partitioning (vertex based)')
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| 291 | - s += "%s\n" % fielddisplay(self, 'epartition', 'user provided mesh partitioning (element based)')
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| 292 | - s += "%s\n" % fielddisplay(self, 'numberofpartitions', 'number of partitions for semi - discrete qmu')
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| 293 | + s += "%s\n" % fielddisplay(self, 'variablepartitions', '')
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| 294 | + s += "%s\n" % fielddisplay(self, 'variablepartitions_npart', '')
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| 295 | s += "%s\n" % fielddisplay(self, 'variabledescriptors', '')
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| 296 | s += "%s\n" % fielddisplay(self, 'responsedescriptors', '')
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| 297 | s += "%s\n" % fielddisplay(self, 'method', 'array of dakota_method class')
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| 298 | @@ -123,8 +125,6 @@
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| 299 | return s
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| 300 | # }}}
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| 301 | def extrude(self, md): # {{{
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| 302 | - self.vpartition = project3d(md, 'vector', np.transpose(self.vpartition), 'type', 'node')
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| 303 | - self.epartition = project3d(md, 'vector', np.transpose(self.epartition), 'type', 'element')
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| 304 | return self
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| 305 | #}}}
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| 306 | def setdefaultparameters(self): # {{{
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| 307 | @@ -154,25 +154,11 @@
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| 308 | if np.mod(md.cluster.np - 1, self.params.processors_per_evaluation):
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| 309 | md.checkmessage('in parallel library mode, the requirement is for md.cluster.np = md.qmu.params.processors_per_evaluation * number_of_slaves, where number_of_slaves will automatically be determined by Dakota. Modify md.cluster.np accordingly')
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| 310 |
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| 311 | - if np.size(md.qmu.vpartition) > 0:
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| 312 | - if np.size(md.qmu.vpartition, 0) != md.mesh.numberofvertices:
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| 313 | - md.checkmessage("user supplied vertex partition for qmu analysis should have size (md.mesh.numberofvertices x 1)")
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| 314 | - if not min(md.qmu.vpartition.flatten()) == 0:
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| 315 | - md.checkmessage("vertex partition vector not indexed from 0 on")
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| 316 | - if max(md.qmu.vpartition.flatten()) >= md.qmu.numberofpartitions:
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| 317 | - md.checkmessage("for qmu analysis, vertex partitioning vector cannot go over npart, number of partition areas")
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| 318 | + # Go through variables and check for consistency
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| 319 | + fv = fieldnames(self.variables)
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| 320 | + for i in range(len(fv)):
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| 321 | + getattr(self.variables, fv[i]).checkconsistency(md, solution, analyses)
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| 322 |
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| 323 | - if np.size(md.qmu.epartition) > 0:
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| 324 | - if np.size(md.qmu.epartition, 0) != md.mesh.numberofelements:
|
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| 325 | - md.checkmessage("user supplied element partition for qmu analysis should have size (md.mesh.numberofelements x 1)")
|
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| 326 | - if not min(md.qmu.epartition.flatten()) == 0:
|
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| 327 | - md.checkmessage("elememtn partition vector not indexed from 0 on")
|
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| 328 | - if max(md.qmu.epartition.flatten()) >= md.qmu.numberofpartitions:
|
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| 329 | - md.checkmessage("for qmu analysis, element partitioning vector cannot go over npart, number of partition areas")
|
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| 330 | -
|
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| 331 | - if np.size(md.qmu.vpartition) == 0 or np.any(np.isnan(md.qmu.vpartition)) or np.size(md.qmu.epartition) == 0 or np.any(np.isnan(md.qmu.epartition)):
|
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| 332 | - md.checkmessage("for qmu analysis, both an element and partitioning vectors need to be supplied with no nan values! One can be defaulted to all zeros.")
|
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| 333 | -
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| 334 | return md
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| 335 | # }}}
|
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| 336 | def marshall(self, prefix, md, fid): # {{{
|
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| 337 | @@ -181,10 +167,13 @@
|
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| 338 | if not self.isdakota:
|
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| 339 | WriteData(fid, prefix, 'data', False, 'name', 'md.qmu.mass_flux_segments_present', 'format', 'Boolean')
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| 340 | return
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| 341 | - WriteData(fid, prefix, 'object', self, 'fieldname', 'numberofpartitions', 'format', 'Integer')
|
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| 342 | WriteData(fid, prefix, 'object', self, 'fieldname', 'numberofresponses', 'format', 'Integer')
|
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| 343 | WriteData(fid, prefix, 'object', self, 'fieldname', 'variabledescriptors', 'format', 'StringArray')
|
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| 344 | + WriteData(fid, prefix, 'object', self, 'fieldname', 'variablepartitions', 'format', 'MatArray')
|
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| 345 | + WriteData(fid, prefix, 'object', self, 'fieldname', 'variablepartitions_npart', 'format', 'IntMat', 'mattype', 3)
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| 346 | WriteData(fid, prefix, 'object', self, 'fieldname', 'responsedescriptors', 'format', 'StringArray')
|
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| 347 | + WriteData(fid, prefix, 'object', self, 'fieldname', 'responsepartitions', 'format', 'MatArray')
|
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| 348 | + WriteData(fid, prefix, 'object', self, 'fieldname', 'responsepartitions_npart', 'format', 'IntMat', 'mattype', 3)
|
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| 349 | if not isempty(self.mass_flux_segments):
|
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| 350 | WriteData(fid, prefix, 'data', self.mass_flux_segments, 'name', 'md.qmu.mass_flux_segments', 'format', 'MatArray')
|
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| 351 | flag = True
|
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| 352 | Index: ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupVariables.py
|
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| 353 | ===================================================================
|
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| 354 | --- ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupVariables.py (revision 25010)
|
|---|
| 355 | +++ ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupVariables.py (revision 25011)
|
|---|
| 356 | @@ -1,7 +1,8 @@
|
|---|
| 357 | -from MatlabFuncs import *
|
|---|
| 358 | from copy import deepcopy
|
|---|
| 359 | from helpers import *
|
|---|
| 360 | +from MatlabFuncs import *
|
|---|
| 361 | from normal_uncertain import *
|
|---|
| 362 | +from qmupart2npart import *
|
|---|
| 363 | from uniform_uncertain import *
|
|---|
| 364 |
|
|---|
| 365 |
|
|---|
| 366 | @@ -16,17 +17,25 @@
|
|---|
| 367 |
|
|---|
| 368 | #ok, key off according to type of descriptor:
|
|---|
| 369 | if strncmp(descriptor, 'scaled_', 7):
|
|---|
| 370 | - #we have a scaled variable, expand it over the partition.
|
|---|
| 371 | + #we have a scaled variable, expand it over the partition. First recover the partition.
|
|---|
| 372 | + partition = variables.partition
|
|---|
| 373 | + #figure out number of partitions
|
|---|
| 374 | + npart=qmupart2npart(partition)
|
|---|
| 375 | +
|
|---|
| 376 | if isinstance(variables, uniform_uncertain):
|
|---|
| 377 | - if ((type(variables.lower) in [list, np.ndarray] and len(variables.lower) > md.qmu.numberofpartitions) or (type(variables.upper) in [list, np.ndarray] and len(variables.upper) > md.qmu.numberofpartitions)):
|
|---|
| 378 | - raise RuntimeError('QmuSetupDesign error message: upper and lower should be either a scalar or a "npart" length vector')
|
|---|
| 379 | + nlower=len(variables.lower)
|
|---|
| 380 | + nupper=len(variables.upper)
|
|---|
| 381 | + if nlower != npart or nupper != npart:
|
|---|
| 382 | + raise RuntimeError('QmuSetupVariables error message: upper and lower fields should be same size as the number of partitions')
|
|---|
| 383 | elif isinstance(variables, normal_uncertain):
|
|---|
| 384 | - if type(variables.stddev) in [list, np.ndarray] and len(variables.stddev) > md.qmu.numberofpartitions:
|
|---|
| 385 | - raise RuntimeError('QmuSetupDesign error message: stddev should be either a scalar or a "npart" length vector')
|
|---|
| 386 | + nstddev=len(variables.stddev)
|
|---|
| 387 | + nmean=len(variables.mean)
|
|---|
| 388 | + if nstddev != npart or nmean != npart:
|
|---|
| 389 | + raise RuntimeError('QmuSetupVariables error message: stddev and mean fields should be same size as the number of partitions')
|
|---|
| 390 |
|
|---|
| 391 | #ok, dealing with semi-discrete distributed variable. Distribute according to how many
|
|---|
| 392 | #partitions we want
|
|---|
| 393 | - for j in range(md.qmu.numberofpartitions):
|
|---|
| 394 | + for j in range(npart):
|
|---|
| 395 | dvar.append(deepcopy(variables))
|
|---|
| 396 |
|
|---|
| 397 | # text parsing in dakota requires literal "'identifier'" not just "identifier"
|
|---|
| 398 | @@ -33,23 +42,11 @@
|
|---|
| 399 | dvar[-1].descriptor = "'" + str(variables.descriptor) + '_' + str(j + 1) + "'"
|
|---|
| 400 |
|
|---|
| 401 | if isinstance(variables, uniform_uncertain):
|
|---|
| 402 | - if type(variables.lower) in [list, np.ndarray]:
|
|---|
| 403 | - dvar[-1].lower = variables.lower[j]
|
|---|
| 404 | - else:
|
|---|
| 405 | - dvar[-1].lower = variables.lower
|
|---|
| 406 | - if type(variables.upper) in [list, np.ndarray]:
|
|---|
| 407 | - dvar[-1].upper = variables.upper[j]
|
|---|
| 408 | - else:
|
|---|
| 409 | - dvar[-1].upper = variables.upper
|
|---|
| 410 | + dvar[-1].lower = variables.lower[j]
|
|---|
| 411 | + dvar[-1].upper = variables.upper[j]
|
|---|
| 412 | elif isinstance(variables, normal_uncertain):
|
|---|
| 413 | - if type(variables.stddev) in [list, np.ndarray]:
|
|---|
| 414 | - dvar[-1].stddev = variables.stddev[j]
|
|---|
| 415 | - else:
|
|---|
| 416 | - dvar[-1].stddev = variables.stddev
|
|---|
| 417 | - if type(variables.mean) in [list, np.ndarray]:
|
|---|
| 418 | - dvar[-1].mean = variables.mean[j]
|
|---|
| 419 | - else:
|
|---|
| 420 | - dvar[-1].mean = variables.mean
|
|---|
| 421 | + dvar[-1].stddev = variables.stddev[j]
|
|---|
| 422 | + dvar[-1].mean = variables.mean[j]
|
|---|
| 423 | else:
|
|---|
| 424 | dvar.append(deepcopy(variables))
|
|---|
| 425 |
|
|---|
| 426 | Index: ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupResponses.py
|
|---|
| 427 | ===================================================================
|
|---|
| 428 | --- ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupResponses.py (revision 25010)
|
|---|
| 429 | +++ ../trunk-jpl/src/m/qmu/setupdesign/QmuSetupResponses.py (revision 25011)
|
|---|
| 430 | @@ -1,5 +1,6 @@
|
|---|
| 431 | +from copy import deepcopy
|
|---|
| 432 | from MatlabFuncs import *
|
|---|
| 433 | -from copy import deepcopy
|
|---|
| 434 | +from qmupart2npart import *
|
|---|
| 435 |
|
|---|
| 436 |
|
|---|
| 437 | def QmuSetupResponses(md, dresp, responses):
|
|---|
| 438 | @@ -7,15 +8,18 @@
|
|---|
| 439 | #get descriptor
|
|---|
| 440 | descriptor = responses.descriptor
|
|---|
| 441 |
|
|---|
| 442 | - #decide whether this is a distributed response, which will drive whether we expand it into npart values,
|
|---|
| 443 | - #or if we just carry it forward as is.
|
|---|
| 444 | + # Decide whether this is a distributed response, which will drive whether
|
|---|
| 445 | + # we expand it into npart values, or if we just carry it forward as is.
|
|---|
| 446 |
|
|---|
| 447 | #ok, key off according to type of descriptor:
|
|---|
| 448 | if strncmp(descriptor, 'scaled_', 7):
|
|---|
| 449 | #we have a scaled response, expand it over the partition.
|
|---|
| 450 | - #ok, dealing with semi - discrete distributed response. Distribute according to how many
|
|---|
| 451 | - #partitions we want
|
|---|
| 452 | - for j in range(md.qmu.numberofpartitions):
|
|---|
| 453 | +
|
|---|
| 454 | + # Ok, dealing with semi-discrete distributed response. Distribute
|
|---|
| 455 | + # according to how many partitions we want.
|
|---|
| 456 | + npart = qmupart2npart(responses.partition)
|
|---|
| 457 | +
|
|---|
| 458 | + for j in range(npart):
|
|---|
| 459 | dresp.append(deepcopy(responses))
|
|---|
| 460 | dresp[-1].descriptor = str(responses.descriptor) + '_' + str(j + 1)
|
|---|
| 461 | else:
|
|---|
| 462 | Index: ../trunk-jpl/src/m/qmu/preqmu.py
|
|---|
| 463 | ===================================================================
|
|---|
| 464 | --- ../trunk-jpl/src/m/qmu/preqmu.py (revision 25010)
|
|---|
| 465 | +++ ../trunk-jpl/src/m/qmu/preqmu.py (revision 25011)
|
|---|
| 466 | @@ -1,10 +1,12 @@
|
|---|
| 467 | import os
|
|---|
| 468 | -from MatlabFuncs import *
|
|---|
| 469 | +
|
|---|
| 470 | +from dakota_in_data import *
|
|---|
| 471 | +from expandresponses import *
|
|---|
| 472 | from expandvariables import *
|
|---|
| 473 | -from expandresponses import *
|
|---|
| 474 | from helpers import *
|
|---|
| 475 | -from dakota_in_data import *
|
|---|
| 476 | +from MatlabFuncs import *
|
|---|
| 477 | from process_qmu_response_data import *
|
|---|
| 478 | +from qmupart2npart import *
|
|---|
| 479 |
|
|---|
| 480 |
|
|---|
| 481 | def preqmu(md, options):
|
|---|
| 482 | @@ -105,10 +107,42 @@
|
|---|
| 483 | responsedescriptors.append(fieldresponses[j].descriptor)
|
|---|
| 484 | #}}}
|
|---|
| 485 |
|
|---|
| 486 | + # Build a list of variable partitions
|
|---|
| 487 | + variablepartitions = []
|
|---|
| 488 | + variablepartitions_npart = []
|
|---|
| 489 | + variable_fieldnames = fieldnames(md.qmu.variables)
|
|---|
| 490 | + for in range(len(variable_fieldnames)):
|
|---|
| 491 | + field_name = variable_fieldnames[i]
|
|---|
| 492 | + fieldvariable = getattr(md.qmu.variables, field_name)
|
|---|
| 493 | + if fieldvariable.isscaled():
|
|---|
| 494 | + variablepartitions.append(fieldvariable.partition)
|
|---|
| 495 | + variablepartitions_npart.append(qmupart2npart(fieldvariable.partition))
|
|---|
| 496 | + else:
|
|---|
| 497 | + variablepartitions.append([])
|
|---|
| 498 | + variablepartitions_npart.append(0)
|
|---|
| 499 | +
|
|---|
| 500 | + # Build a list of response partitions
|
|---|
| 501 | + responsepartitions = []
|
|---|
| 502 | + responsepartitions_npart = []
|
|---|
| 503 | + response_fieldnames = fieldnames(md.qmu.responses)
|
|---|
| 504 | + for in range(len(response_fieldnames)):
|
|---|
| 505 | + field_name = response_fieldnames[i]
|
|---|
| 506 | + fieldresponse = getattr(md.qmu.variables, field_name)
|
|---|
| 507 | + if fieldresponse.isscaled():
|
|---|
| 508 | + responsepartitions.append(fieldresponse.partition)
|
|---|
| 509 | + responsepartitions_npart.append(qmupart2npart(fieldresponse.partition))
|
|---|
| 510 | + else:
|
|---|
| 511 | + responsepartitions.append([])
|
|---|
| 512 | + responsepartitions_npart.append(0)
|
|---|
| 513 | +
|
|---|
| 514 | # register the fields that will be needed by the Qmu model.
|
|---|
| 515 | md.qmu.numberofresponses = numresponses
|
|---|
| 516 | md.qmu.variabledescriptors = variabledescriptors
|
|---|
| 517 | + md.qmu.variablepartitions = variablepartitions
|
|---|
| 518 | + md.qmu.variablepartitions_npart = variablepartitions_npart
|
|---|
| 519 | md.qmu.responsedescriptors = responsedescriptors
|
|---|
| 520 | + md.qmu.responsepartitions = responsepartitions
|
|---|
| 521 | + md.qmu.responsepartitions_npart = responsepartitions_npart
|
|---|
| 522 |
|
|---|
| 523 | # now, we have to provide all the info necessary for the solutions to compute the
|
|---|
| 524 | # responses. For ex, if mass_flux is a response, we need a profile of points.
|
|---|
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