source: issm/oecreview/Archive/24684-25833/ISSM-25089-25090.diff@ 25834

Last change on this file since 25834 was 25834, checked in by Mathieu Morlighem, 4 years ago

CHG: added 24684-25833
File size: 28.3 KB
RevLine 
[25834]1Index: ../trunk-jpl/src/m/classes/qmu.m
2===================================================================
3--- ../trunk-jpl/src/m/classes/qmu.m (revision 25089)
4+++ ../trunk-jpl/src/m/classes/qmu.m (revision 25090)
5@@ -122,7 +122,7 @@
6 end
7 end
8 if strcmpi(classlist{i},'uniform_uncertain')
9- if (h~=0),
10+ if (h~=0),
11 error('uniform_uncertain variables should be declared before histogram_bin uncertain variables');
12 else
13 u=1;
14Index: ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.py
15===================================================================
16--- ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.py (nonexistent)
17+++ ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.py (revision 25090)
18@@ -0,0 +1,66 @@
19+import numpy as np
20+
21+
22+class histogram_bin_uncertain(object):
23+ '''
24+ HISTOGRAM_BIN_UNCERTAIN class definition
25+
26+ Usage:
27+ [hbu] = histogram_bin_uncertain(
28+ 'descriptor', descriptor,
29+ 'pairs_per_variable', pairs_per_variable,
30+ 'abscissas', abscissas,
31+ 'counts', counts
32+ )
33+
34+ where the required args are:
35+ descriptor (char, description, '')
36+ pairs_per_variable (double list, [])
37+ abscissas (double list, [])
38+ counts (int list, [])
39+
40+ NOTE: A call to the constructor with zero arguments will return a default
41+ instance; one argument of the class copies the instance; three or more
42+ arguments constructs a new instance from the arguments.
43+ '''
44+
45+ def __init__(self): #{{{
46+ self.descriptor = ''
47+ self.pairs_per_variable = []
48+ self.abscissas = []
49+ self.counts = []
50+ #}}}
51+
52+ @staticmethod
53+ def histogram_bin_uncertain(*args): #{{{
54+ nargin = len(args)
55+
56+ # create a default object
57+ if nargin == 0:
58+ return histogram_bin_uncertain()
59+
60+ # copy the object
61+ elif nargin == 1:
62+ if isinstance(args[0], histogram_bin_uncertain):
63+ hbu = args[0]
64+ else:
65+ raise Exception("Object {} is a {} class object, not 'histogram_bin_uncertain'.".format(str(args[0]), str(type(args[0]))))
66+
67+ elif nargin == 2 or nargin == 3:
68+ raise Exception("Construction of 'histogram_bin_uncertain' class object requires at least {} inputs.".format(4))
69+
70+ # create the object from the input
71+ elif nargin == 4:
72+ hbu = histogram_bin_uncertain()
73+
74+ #recover options:
75+ options = pairoptions(*args)
76+
77+ #initialize fields:
78+ hbu.descriptor = options.getfieldvalue('descriptor')
79+ hbu.pairs_per_variable = options.getfieldvalue('pairs_per_variable')
80+ hbu.abscissas = options.getfieldvalue('abscissas')
81+ hbu.counts = options.getfieldvalue('counts')
82+
83+ else:
84+ raise Exception("Construction of histogram_bin_uncertain class object requires either (1) no arguments, (2) a histogram_bin_uncertain instance to copy from, or (3) a descriptor and pairs per variable, abscissas, and counts lists")
85Index: ../trunk-jpl/src/m/classes/qmu/normal_uncertain.py
86===================================================================
87--- ../trunk-jpl/src/m/classes/qmu/normal_uncertain.py (revision 25089)
88+++ ../trunk-jpl/src/m/classes/qmu/normal_uncertain.py (revision 25090)
89@@ -13,11 +13,18 @@
90 NORMAL_UNCERTAIN class definition
91
92 Usage:
93- nuv = normal_uncertain('descriptor',descriptor,'mean',mean,'stddev',stddev,'partition',partition)
94- where nuv is the normal_uncertain object returned by the constructor, mean and stddev are self
95- explanatory. partition is the partition vector for distributed variables. Can be a partition
96- vector over elements or vertices.
97+ [nuv] = normal_uncertain(
98+ 'descriptor', descriptor,
99+ 'mean', mean,
100+ 'stddev', stddev,
101+ 'partition', partition
102+ )
103
104+ where nuv is the normal_uncertain object returned by the constructor,
105+ mean and stddev are self explanatory, and partition is the partition
106+ vector for distributed variables. Can be a partition vector over
107+ elements or vertices.
108+
109 Example:
110 md.qmu.variables.rheology=normal_uncertain(
111 'descriptor','RheologyBBar',
112@@ -31,12 +38,13 @@
113 'partition',vpartition
114 )
115 '''
116- def __init__(self):
117+ def __init__(self): #{{{
118 self.descriptor = ''
119 self.mean = np.NaN
120 self.stddev = np.NaN
121 self.partition = []
122 self.nsteps = 0
123+ #}}}
124
125 @staticmethod
126 def normal_uncertain(*args): #{{{
127@@ -51,11 +59,10 @@
128 if isinstance(args[0], normal_uncertain):
129 nuv = args[0]
130 else:
131- raise RuntimeError('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "normal_uncertain".')
132+ raise Exception('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "normal_uncertain".')
133
134 # create the object from the input
135 else:
136- # lines differ here in other classes / tests; see asizec problem in notes
137 nuv = normal_uncertain()
138
139 #recover options:
140@@ -66,21 +73,21 @@
141 nuv.mean = options.getfieldvalue('mean')
142 nuv.stddev = options.getfieldvalue('stddev')
143
144- #if the variable is scaled, a partition vector should have been supplied, and
145- #that partition vector should have as many partitions as the mean and stddev
146- #vectors:
147+ #if the variable is scaled, a partition vector should have been
148+ #supplied, and that partition vector should have as many partitions
149+ #as the mean and stddev vectors:
150 if nuv.isscaled():
151 nuv.partition = options.getfieldvalue('partition')
152 nuv.nsteps = options.getfieldvalue('nsteps', 1)
153 npart = qmupart2npart(nuv.partition)
154 if npart != nuv.mean.shape[0]:
155- raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the row size of the mean field should be identical to the number of partitions" % nuv.descriptor)
156+ raise Exception("normal_uncertain constructor: for the scaled variable %s the row size of the mean field should be identical to the number of partitions" % nuv.descriptor)
157 if npart != nuv.stddev.shape[0]:
158- raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the row size of the stddev field should be identical to the number of partitions" % nuv.descriptor)
159+ raise Exception("normal_uncertain constructor: for the scaled variable %s the row size of the stddev field should be identical to the number of partitions" % nuv.descriptor)
160 if nuv.nsteps != nuv.mean.shape[1]:
161- raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the col size of the mean field should be identical to the number of time steps" % nuv.descriptor)
162+ raise Exception("normal_uncertain constructor: for the scaled variable %s the col size of the mean field should be identical to the number of time steps" % nuv.descriptor)
163 if nuv.nsteps != nuv.stddev.shape[1]:
164- raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the col size of the stddev field should be identical to the number of time steps" % nuv.descriptor)
165+ raise Exception("normal_uncertain constructor: for the scaled variable %s the col size of the stddev field should be identical to the number of time steps" % nuv.descriptor)
166
167 return [nuv] # Always return a list, so we have something akin to a MATLAB single row matrix
168 #}}}
169@@ -106,31 +113,31 @@
170
171 def checkconsistency(self, md, solution, analyses): #{{{
172 md = checkfield(md, 'field', self.mean, 'fieldname', 'normal_uncertain.mean', 'NaN', 1, 'Inf', 1, '>=', 0)
173- md = checkfield(md, 'field', self.stddev, 'fieldname', 'normal_uncertain.stddev', 'NaN', 1, 'Inf', 1, '>=', 0, 'numel', len(self.mean))
174+ md = checkfield(md, 'field', self.stddev, 'fieldname', 'normal_uncertain.stddev', 'NaN', 1, 'Inf', 1, '>=', 0)
175 if self.isscaled():
176 if self.partition == []:
177- raise RuntimeError("normal_uncertain is a scaled variable, but it's missing a partition vector")
178+ raise Exception("normal_uncertain is a scaled variable, but it's missing a partition vector")
179 #better have a partition vector that has as many partitions as stddev's size:
180 if self.stddev.shape[0] != partition_npart(self.partititon):
181- raise RuntimeError("normal_uncertain error message: row size of stddev and partition size should be identical")
182+ raise Exception("normal_uncertain error message: row size of stddev and partition size should be identical")
183 if self.mean.shape[0] != partition_npart(self.partition):
184- raise RuntimeError("normal_uncertain error message: row size of mean and partition size should be identical")
185+ raise Exception("normal_uncertain error message: row size of mean and partition size should be identical")
186 #we need as many steps in stddev and mean as there are in time steps
187 if self.stddev.shape[1] != self.nsteps:
188- raise RuntimeError("normal_uncertain error message: col size of stddev and partition size should be identical")
189+ raise Exception("normal_uncertain error message: col size of stddev and partition size should be identical")
190 if self.mean.shape[1] != self.nsteps:
191- raise RuntimeError("normal_uncertain error message: col size of mean and partition size should be identical")
192+ raise Exception("normal_uncertain error message: col size of mean and partition size should be identical")
193 md = checkfield(md, 'field', self.partition, 'fieldname', 'normal_uncertain.partition', 'NaN', 1, 'Inf', 1, '>=', -1, 'numel', [md.mesh.numberofvertices, md.mesh.numberofvertices])
194 if self.partition.shape[1] > 1:
195- raise RuntimeError("normal_uncertain error message: partition should be a column vector")
196+ raise Exception("normal_uncertain error message: partition should be a column vector")
197 partcheck = np.unique(self.partition)
198 partmin = min(partcheck)
199 partmax = max(partcheck)
200 if partmax < -1:
201- raise RuntimeError("normal_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
202+ raise Exception("normal_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
203 nmax = max(md.mesh.numberofelements, md.mesh.numberofvertices)
204 if partmax > nmax:
205- raise RuntimeError("normal_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
206+ raise Exception("normal_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
207 #}}}
208
209 #virtual functions needed by qmu processing algorithms
210Index: ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.m
211===================================================================
212--- ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.m (revision 25089)
213+++ ../trunk-jpl/src/m/classes/qmu/histogram_bin_uncertain.m (revision 25090)
214@@ -1,41 +1,38 @@
215-% % definition for the histogram_bin_uncertain class.
216+%HISTOGRAM BIN UNCERTAIN class definition
217 %
218-% [hbu]=histogram_bin_uncertain(varargin)
219+% [hbu]=histogram_bin_uncertain(varargin)
220 %
221-% where the required varargin are:
222-% descriptor (char, description, '')
223-% pairs_per_variable (double vector, [])
224-% abscissas (double vector, [])
225-% counts (int vector, [])
226+% where the required varargin are:
227+% descriptor (char, description, '')
228+% pairs_per_variable (double vector, [])
229+% abscissas (double vector, [])
230+% counts (int vector, [])
231 %
232-% note that zero arguments constructs a default instance; one
233-% argument of the class copies the instance; and three or more
234-% arguments constructs a new instance from the arguments.
235+% NOTE: A call to the constructor with zero arguments will return a default
236+% instance; one argument of the class copies the instance; three or more
237+% arguments constructs a new instance from the arguments.
238 %
239 classdef histogram_bin_uncertain
240- properties
241- descriptor='';
242+ properties
243+ descriptor='';
244 pairs_per_variable=[];
245- abscissas = [];
246- counts = [];
247- end
248-
249- methods
250- function [hbu]=histogram_bin_uncertain(varargin) % {{{
251-
252- switch nargin
253- case 0 % create a default object
254- case 1 % copy the object
255- if isa(varargin{1},'histogram_bin_uncertain')
256- hbu=varargin{1};
257- else
258- error('Object ''%s'' is a ''%s'' class object, not ''%s''.',...
259- inputname(1),class(varargin{1}),'histogram_bin_uncertain');
260- end
261- case {2,3} % not enough arguments
262- error('Construction of ''%s'' class object requires at least %d inputs.',...
263- 'histogram_bin_uncertain',4)
264- case 4 %
265+ abscissas = [];
266+ counts = [];
267+ end
268+ methods
269+ function [hbu]=histogram_bin_uncertain(varargin) % {{{
270+ switch nargin
271+ case 0 % create a default object
272+ case 1 % copy the object
273+ if isa(varargin{1},'histogram_bin_uncertain')
274+ hbu=varargin{1};
275+ else
276+ error('Object ''%s'' is a ''%s'' class object, not ''%s''.',...
277+ inputname(1),class(varargin{1}),'histogram_bin_uncertain');
278+ end
279+ case {2,3} % not enough arguments
280+ error('Construction of ''histogram_bin_uncertain'' class object requires at least %d inputs.',4)
281+ case 4 %
282 % create the object from the input
283 hbu = histogram_bin_uncertain;
284 hbu.descriptor=varargin{1};
285@@ -43,19 +40,16 @@
286 hbu.abscissas=varargin{3};
287 hbu.counts=varargin{4};
288
289- otherwise
290- error('Construction of histogram_bin_uncertain class object requires three arguments, descriptor, abscissas and counts');
291- end
292-
293- end % }}}
294+ otherwise
295+ error('Construction of histogram_bin_uncertain class object requires either (1) no arguments, (2) a histogram_bin_uncertain instance to copy from, or (3) a descriptor and pairs per variable, abscissas, and counts lists');
296+ end
297+ end % }}}
298 function md=checkconsistency(self,md,solution,analyses) % {{{
299 end % }}}
300- function []=disp(hbu) % {{{
301-
302-% display the object
303-
304- disp(sprintf('\n'));
305- for i=1:numel(hbu)
306+ function []=disp(hbu) % {{{
307+ % display the object
308+ disp(sprintf('\n'));
309+ for i=1:numel(hbu)
310 disp(sprintf('class ''%s'' object ''%s%s'' = \n',...
311 class(hbu),inputname(1),string_dim(hbu,i)));
312 disp(sprintf(' descriptor: ''%s''' ,hbu(i).descriptor));
313@@ -63,9 +57,8 @@
314 disp(sprintf(' abscissas: %g' ,hbu(i).abscissas));
315 disp(sprintf(' counts: %g' ,hbu(i).counts));
316 end
317-
318 end % }}}
319- function [desc] =prop_desc(hbu,dstr) % {{{
320+ function [desc]=prop_desc(hbu,dstr) % {{{
321 desc=cell(1,numel(hbu));
322 for i=1:numel(hbu)
323 if ~isempty(hbu(i).descriptor)
324@@ -83,13 +76,13 @@
325 function [initpt]=prop_initpt(hbu) % {{{
326 initpt=[];
327 end % }}}
328- function [lower] =prop_lower(hbu) % {{{
329+ function [lower]=prop_lower(hbu) % {{{
330 lower=[];
331 end % }}}
332- function [upper] =prop_upper(hbu) % {{{
333+ function [upper]=prop_upper(hbu) % {{{
334 upper=[];
335 end % }}}
336- function [mean] =prop_mean(hbu) % {{{
337+ function [mean]=prop_mean(hbu) % {{{
338 mean=[];
339 end % }}}
340 function [stddev]=prop_stddev(hbu) % {{{
341@@ -98,20 +91,19 @@
342 function [initst]=prop_initst(hbu) % {{{
343 initst=[];
344 end % }}}
345- function [stype] =prop_stype(hbu) % {{{
346+ function [stype]=prop_stype(hbu) % {{{
347 stype={};
348 end % }}}
349- function [scale] =prop_scale(hbu) % {{{
350+ function [scale]=prop_scale(hbu) % {{{
351 scale=[];
352 end % }}}
353- function [abscissas] =prop_abscissas(hbu) % {{{
354- abscissas=[];
355- for i=1:numel(hbu)
356- abscissas=[abscissas hbu(i).abscissas];
357- end
358- abscissas=allequal(abscissas,-Inf);
359-
360- end % }}}
361+ function [abscissas]=prop_abscissas(hbu) % {{{
362+ abscissas=[];
363+ for i=1:numel(hbu)
364+ abscissas=[abscissas hbu(i).abscissas];
365+ end
366+ abscissas=allequal(abscissas,-Inf);
367+ end % }}}
368 function [pairs_per_variable] =prop_pairs_per_variable(hbu) % {{{
369 pairs_per_variable=zeros(1,numel(hbu));
370 for i=1:numel(hbu)
371@@ -120,12 +112,11 @@
372 pairs_per_variable=allequal(pairs_per_variable,-Inf);
373 end % }}}
374 function [counts] =prop_counts(hbu) % {{{
375- counts=[];
376- for i=1:numel(hbu)
377- counts=[counts hbu(i).counts];
378- end
379- counts=allequal(counts,-Inf);
380-
381+ counts=[];
382+ for i=1:numel(hbu)
383+ counts=[counts hbu(i).counts];
384+ end
385+ counts=allequal(counts,-Inf);
386 end % }}}
387 function scaled=isscaled(self) % {{{
388 if strncmp(self.descriptor,'scaled_',7),
389@@ -137,13 +128,10 @@
390 end
391 methods (Static)
392 function []=dakota_write(fidi,dvar) % {{{
393+ % collect only the variables of the appropriate class
394+ hbu=struc_class(dvar,'histogram_bin_uncertain');
395
396-% collect only the variables of the appropriate class
397-
398- hbu=struc_class(dvar,'histogram_bin_uncertain');
399-
400-% write variables
401-
402+ % write variables
403 vlist_write(fidi,'histogram_bin_uncertain','hbu',hbu);
404 end % }}}
405 end
406Index: ../trunk-jpl/src/m/classes/qmu/uniform_uncertain.py
407===================================================================
408--- ../trunk-jpl/src/m/classes/qmu/uniform_uncertain.py (revision 25089)
409+++ ../trunk-jpl/src/m/classes/qmu/uniform_uncertain.py (revision 25090)
410@@ -13,7 +13,7 @@
411 UNIFORM_UNCERTAIN class definition
412
413 Usage:
414- uuv = uniform_uncertain(
415+ [uuv] = uniform_uncertain(
416 'descriptor', descriptor,
417 'lower', lower,
418 'upper', upper,
419@@ -38,15 +38,16 @@
420 'partition', vpartition
421 )
422 '''
423- def __init__(self):
424+ def __init__(self): #{{{
425 self.descriptor = ''
426 self.lower = -np.Inf
427 self.upper = np.Inf
428 self.partition = []
429 self.nsteps = 0
430+ #}}}
431
432 @staticmethod
433- def uniform_uncertain(*args):
434+ def uniform_uncertain(*args): #{{{
435 nargin = len(args)
436
437 # create a default object
438@@ -58,7 +59,7 @@
439 if isinstance(args[0], uniform_uncertain):
440 uuv = args[0]
441 else:
442- raise RuntimeError('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "uniform_uncertain".')
443+ raise Exception('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "uniform_uncertain".')
444
445 # create the object from the input
446 else:
447@@ -73,22 +74,23 @@
448 uuv.upper = options.getfieldvalue('upper')
449
450 #if the variable is scaled, a partition vector should have been
451- #supplied, and that partition vector should have as many partitions as
452- #the lower and upper vectors:
453+ #supplied, and that partition vector should have as many
454+ #partitions as the lower and upper vectors:
455 if uuv.isscaled():
456 uuv.partition = options.getfieldvalue('partition')
457 uuv.nsteps = options.getfieldvalue('nsteps', 1)
458 npart = qmupart2npart(uuv.partition)
459 if npart != uuv.upper.shape[0]:
460- raise RuntimeError("uniform_uncertain constructor: for the scaled variable %s the upper field is not currently a vector of values for all the partitions described in the partition vector" % uuv.descriptor)
461+ raise Exception("uniform_uncertain constructor: for the scaled variable %s the upper field is not currently a vector of values for all the partitions described in the partition vector" % uuv.descriptor)
462 if npart != uuv.lower.shape[0]:
463- raise RuntimeError("uniform_uncertain constructor: for the scaled variable %s the lower field is not currently a vector of values for all the partitions described in the partition vector" % uuv.descriptor)
464+ raise Exception("uniform_uncertain constructor: for the scaled variable %s the lower field is not currently a vector of values for all the partitions described in the partition vector" % uuv.descriptor)
465 if uuv.nsteps != uuv.upper.shape[1]:
466- raise RuntimeError("uniform_uncertain constructor: for the scaled variable %s the col size of the upper field should be identical to the number of time steps" % uuv.descriptor)
467+ raise Exception("uniform_uncertain constructor: for the scaled variable %s the col size of the upper field should be identical to the number of time steps" % uuv.descriptor)
468 if uuv.nsteps != uuv.lower.shape[1]:
469- raise RuntimeError("uniform_uncertain constructor: for the scaled variable %s the col size of the lower field should be identical to the number of time steps" % uuv.descriptor)
470+ raise Exception("uniform_uncertain constructor: for the scaled variable %s the col size of the lower field should be identical to the number of time steps" % uuv.descriptor)
471
472 return [uuv] # Always return a list, so we have something akin to a MATLAB single row matrix
473+ #}}}
474
475 def __repr__(self): #{{{
476 string = ' uniform uncertain variable: '
477@@ -114,29 +116,29 @@
478 md = checkfield(md, 'field', self.lower, 'fieldname', 'uniform_uncertain.upper', 'NaN', 1, 'Inf', 1, '<', self.upper, 'numel', len(self.upper))
479 if self.isscaled():
480 if self.partition == []:
481- raise RuntimeError("uniform_uncertain is a scaled variable, but it's missing a partition vector")
482+ raise Exception("uniform_uncertain is a scaled variable, but it's missing a partition vector")
483 #better have a partition vector that has as many partitions as
484 #upper and lower's size:
485 if self.upper.shape[0] != partition_npart(self.partititon):
486- raise RuntimeError("uniform_uncertain error message: row size of upper and partition size should be identical")
487+ raise Exception("uniform_uncertain error message: row size of upper and partition size should be identical")
488 if self.lower.shape[0] != partition_npart(self.partition):
489- raise RuntimeError("uniform_uncertain error message: row size of lower and partition size should be identical")
490+ raise Exception("uniform_uncertain error message: row size of lower and partition size should be identical")
491 #we need as steps in upper and lower as there are time steps
492 if self.stddev.shape[1] != self.nsteps:
493- raise RuntimeError("uniform_uncertain error message: col size of upper and partition size should be identical")
494+ raise Exception("uniform_uncertain error message: col size of upper and partition size should be identical")
495 if self.mean.shape[1] != self.nsteps:
496- raise RuntimeError("uniform_uncertain error message: col size of lower and partition size should be identical")
497+ raise Exception("uniform_uncertain error message: col size of lower and partition size should be identical")
498 md = checkfield(md, 'field', self.partition, 'fieldname', 'uniform_uncertain.partition', 'NaN', 1, 'Inf', 1, '>=', -1, 'numel', [md.mesh.numberofvertices, md.mesh.numberofvertices])
499 if self.partition.shape[1] > 1:
500- raise RuntimeError("uniform_uncertain error message: partition should be a column vector")
501+ raise Exception("uniform_uncertain error message: partition should be a column vector")
502 partcheck = np.unique(self.partition)
503 partmin = min(partcheck)
504 partmax = max(partcheck)
505 if partmax < -1:
506- raise RuntimeError("uniform_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
507+ raise Exception("uniform_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
508 nmax = max(md.mesh.numberofelements, md.mesh.numberofvertices)
509 if partmax > nmax:
510- raise RuntimeError("uniform_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
511+ raise Exception("uniform_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
512 #}}}
513
514 #virtual functions needed by qmu processing algorithms:
515Index: ../trunk-jpl/src/m/classes/qmu.py
516===================================================================
517--- ../trunk-jpl/src/m/classes/qmu.py (revision 25089)
518+++ ../trunk-jpl/src/m/classes/qmu.py (revision 25090)
519@@ -22,6 +22,7 @@
520 self.isdakota = 0
521 self.output = 0
522 self.variables = OrderedStruct()
523+ self.correlation_matrix = []
524 self.responses = OrderedStruct()
525 self.method = OrderedDict()
526 self.params = OrderedStruct()
527@@ -154,7 +155,7 @@
528 md.checkmessage('in parallel library mode, Dakota needs to run at least one slave on one cpu (md.qmu.params.processors_per_evaluation >= 1)!')
529
530 if np.mod(md.cluster.np - 1, self.params.processors_per_evaluation):
531- 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')
532+ #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')
533
534 # Go through variables and check for consistency
535 fv = fieldnames(self.variables)
536@@ -163,6 +164,32 @@
537 if hasattr(variable, 'checkconsistency'):
538 variable.checkconsistency(md, solution, analyses)
539
540+ # Go through variables and check that we have normal uncertains first,
541+ # then uniform uncertains and finally histogram_bin_uncertain. Indeed,
542+ # Dakota will order them this waym, and when we send partitions for
543+ # scaled variables, they better show up in the order Dakota is feeding
544+ # them to us in InputUpdateFromDakotax!
545+ fv = fieldnames(self.variables)
546+ classlist = []
547+ for i in range(len(fv)):
548+ classlist.append(self.variables[fv[i]].__class__.__name__)
549+ n = 0
550+ u = 0
551+ h = 0
552+ for i in range(len(classlist)):
553+ if classlist[i] == 'normal_uncertain':
554+ if u != 0 or h != 0:
555+ raise Exception('normal uncertain variables should be declared before uniform and hhistogram_bin uncertain variables')
556+ else:
557+ n = 1
558+ if classlist[i] == 'uniform_uncertain':
559+ if h != 0:
560+ raise Exception('uniform_uncertain variables should be declared before histogram_bin uncertain variables')
561+ else:
562+ u = 1
563+ if classlist[i] == 'histogram_bin_uncertain':
564+ h = 1
565+
566 return md
567 # }}}
568 def marshall(self, prefix, md, fid): # {{{
Note: See TracBrowser for help on using the repository browser.