fix pca related tests.

framework/Samplers/AdaptiveSobol.py

@@ -375,7 +375,7 @@ def localGenerateInput(self,model,oldInput):
         for key in varName.strip().split(','):
           self.values[key] = pt[v]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(pt[v])
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","-")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+varName] = self.inputInfo['SampledVarsPb'][varName]
       # compute the SampledVarsPb for N-D distribution
       elif self.variables2distributionsMapping[varName]['totDim'] > 1 and self.variables2distributionsMapping[varName]['reducedDim'] == 1:
         dist = self.variables2distributionsMapping[varName]['name']
@@ -396,11 +396,13 @@ def localGenerateInput(self,model,oldInput):
           for key in var.strip().split(','):
             self.values[key] = pt[location]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(ndCoordinates)
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")] = self.inputInfo['SampledVarsPb'][varName]
-        self.inputInfo['ProbabilityWeight']*=self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")]
+        self.inputInfo['ProbabilityWeight-'+dist] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight']*=self.inputInfo['ProbabilityWeight-'+dist]
     self.inputInfo['PointProbability'] = reduce(mul,self.inputInfo['SampledVarsPb'].values())
     # reassign SampledVarsPb to fully correlated variables
     self._reassignSampledVarsPbToFullyCorrVars()
+    # reassign probability weight to correlated variables
+    self._reassignPbWeightToCorrelatedVars()
     self.inputInfo['SamplerType'] = 'Adaptive Sparse Grids for Sobol'
 
   def _addPointToDataObject(self,subset,point):

framework/Samplers/AdaptiveSparseGrid.py

@@ -333,7 +333,7 @@ def localGenerateInput(self,model,myInput):
         for key in varName.strip().split(','):
           self.values[key] = pt[v]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(pt[v])
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","-")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+varName] = self.inputInfo['SampledVarsPb'][varName]
         # compute the SampledVarsPb for N-D distribution
       elif self.variables2distributionsMapping[varName]['totDim'] > 1 and self.variables2distributionsMapping[varName]['reducedDim'] ==1:
         dist = self.variables2distributionsMapping[varName]['name']
@@ -354,11 +354,13 @@ def localGenerateInput(self,model,myInput):
           for key in var.strip().split(','):
             self.values[key] = pt[location]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(ndCoordinates)
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")] = self.inputInfo['SampledVarsPb'][varName]
-        self.inputInfo['ProbabilityWeight']*=self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")]
+        self.inputInfo['ProbabilityWeight-'+dist] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight']*=self.inputInfo['ProbabilityWeight-'+dist]
     self.inputInfo['PointProbability'] = reduce(mul,self.inputInfo['SampledVarsPb'].values())
     # reassign SampledVarsPb to fully correlated variables
     self._reassignSampledVarsPbToFullyCorrVars()
+    # reassign probability weight to correlated variables
+    self._reassignPbWeightToCorrelatedVars()
     self.inputInfo['SamplerType'] = self.type
 
   def localFinalizeActualSampling(self,jobObject,model,myInput):

framework/Samplers/Sampler.py

@@ -608,7 +608,7 @@ def _constantVariables(self):
       # we consider that CDF of the constant variables is equal to 1 (same as its Pb Weight)
       self.inputInfo['SampledVarsPb'].update(dict.fromkeys(self.constants.keys(),1.0))
       pbKey = ['ProbabilityWeight-'+key for key in self.constants.keys()]
-      self.addMetaKeys(pbKey)
+      self.addMetaKeys(*pbKey)
       self.inputInfo.update(dict.fromkeys(['ProbabilityWeight-'+key for key in self.constants.keys()],1.0))
 
   def amIreadyToProvideAnInput(self): #inLastOutput=None):

framework/Samplers/Sobol.py

@@ -199,7 +199,7 @@ def localGenerateInput(self,model,myInput):
         for key in varName.strip().split(','):
           self.values[key] = pt[v]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(pt[v])
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","-")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+varName] = self.inputInfo['SampledVarsPb'][varName]
       # compute the SampledVarsPb for N-D distribution
       elif self.variables2distributionsMapping[varName]['totDim'] > 1 and self.variables2distributionsMapping[varName]['reducedDim'] == 1:
         dist = self.variables2distributionsMapping[varName]['name']
@@ -220,8 +220,12 @@ def localGenerateInput(self,model,myInput):
           for key in var.strip().split(','):
             self.values[key] = pt[location]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(ndCoordinates)
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+dist] = self.inputInfo['SampledVarsPb'][varName]
     self.inputInfo['PointProbability'] = reduce(mul,self.inputInfo['SampledVarsPb'].values())
     self.inputInfo['ProbabilityWeight'] = np.atleast_1d(1.0) # weight has no meaning for sobol
+    # reassign SampledVarsPb to fully correlated variables
+    self._reassignSampledVarsPbToFullyCorrVars()
+    # reassign probability weight to correlated variables
+    self._reassignPbWeightToCorrelatedVars()
     self.inputInfo['SamplerType'] = 'Sparse Grids for Sobol'
 

framework/Samplers/SparseGridCollocation.py

@@ -291,7 +291,7 @@ def localGenerateInput(self,model,myInput):
         for key in varName.strip().split(','):
           self.values[key] = pt[v]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(pt[v])
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","-")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+varName] = self.inputInfo['SampledVarsPb'][varName]
       # compute the SampledVarsPb for N-D distribution
       # Assume only one N-D distribution is associated with sparse grid collocation method
       elif self.variables2distributionsMapping[varName]['totDim'] > 1 and self.variables2distributionsMapping[varName]['reducedDim'] ==1:
@@ -313,12 +313,14 @@ def localGenerateInput(self,model,myInput):
           for key in var.strip().split(','):
             self.values[key] = pt[location]
         self.inputInfo['SampledVarsPb'][varName] = self.distDict[varName].pdf(ndCoordinates)
-        self.inputInfo['ProbabilityWeight-'+varName.replace(",","!")] = self.inputInfo['SampledVarsPb'][varName]
+        self.inputInfo['ProbabilityWeight-'+dist] = self.inputInfo['SampledVarsPb'][varName]
 
     self.inputInfo['ProbabilityWeight'] = weight
     self.inputInfo['PointProbability'] = reduce(mul,self.inputInfo['SampledVarsPb'].values())
     # reassign SampledVarsPb to fully correlated variables
     self._reassignSampledVarsPbToFullyCorrVars()
+    # reassign probability weight to correlated variables
+    self._reassignPbWeightToCorrelatedVars()
     self.inputInfo['SamplerType'] = 'Sparse Grid Collocation'
 
   def readFromROM(self):

scripts/conversionScripts/toBasicStatsPP.py

@@ -44,7 +44,21 @@ def convert(tree,fileName=None):
 
   steps = simulation.find('Steps')
   postProcess = steps.findall('PostProcess')
-
+  TestInfo = simulation.find('TestInfo')
+  revisions = TestInfo.find('revisions')
+  hasRev = True
+  if revisions is None:
+    revisions = ET.Element('revisions')
+    hasRev = False
+  rev = ET.Element('revision')
+  rev.attrib['author'] = 'wangc'
+  rev.attrib['date'] = '2017-12-20'
+  rev.text = 'convert test to use the new DataObjects with the new structure of basic statistic'
+  revisions.append(rev)
+  if not hasRev:
+    TestInfo.append(revisions)
+
+  toRemove = []
 
   if models is None: return tree # no models, no BasicStats
   timeDep = {}
@@ -201,6 +215,7 @@ def convert(tree,fileName=None):
           hasPrint = False
           for output in outputs:
             if output.attrib['class'] == 'Files':
+              toRemove.append(output.text)
               output.attrib['class'] = 'DataObjects'
               output.attrib['type'] = 'PointSet' if pivotParam is None else 'HistorySet'
               output.text = dataSetName
@@ -218,6 +233,12 @@ def convert(tree,fileName=None):
             printNode.attrib['class'] = 'OutStreams'
             printNode.attrib['type'] = 'Print'
             printNode.text = dataSetName + '_dump'
+  # move unused files
+  if len(toRemove) > 0:
+    files = simulation.find('Files')
+    for inputFile in files:
+      if inputFile.attrib['name'] in toRemove:
+        files.remove(inputFile)
 
   return tree
 

tests/framework/PostProcessors/Metrics/test_metric.xml

@@ -104,10 +104,6 @@
           <type>csv</type>
           <source>pp1_metric</source>
       </Print>
-      <Print name="pp1_metric_dump">
-          <type>csv</type>
-          <source>pp1_metric</source>
-      </Print>
   </OutStreams>
 
 </Simulation>

tests/framework/Samplers/EnsembledSampler/gold/EnsembleForwardTestConstants/PointsetEnsembleForward.csv

@@ -1,7 +1,7 @@
-x2,x1,constantine,OutputPlaceHolder
-0.02,0.777044571556,3.14159,1.0
-0.02,0.373049301773,3.14159,2.0
-0.02,0.407090028487,3.14159,3.0
-0.6,0.777044571556,3.14159,4.0
-0.6,0.373049301773,3.14159,5.0
-0.6,0.407090028487,3.14159,6.0
+x2,x1,constantine,OutputPlaceHolder,ProbabilityWeight
+0.02,0.777044571556,3.14159,1.0,0.31
+0.02,0.373049301773,3.14159,2.0,0.31
+0.02,0.407090028487,3.14159,3.0,0.31
+0.6,0.777044571556,3.14159,4.0,0.31
+0.6,0.373049301773,3.14159,5.0,0.31
+0.6,0.407090028487,3.14159,6.0,0.31

tests/framework/Samplers/EnsembledSampler/test_EnsembleSamplerConstants.xml

@@ -56,6 +56,7 @@
     <Print name='PointsetEnsembleForward'>
       <type>csv</type>
       <source>PointsetEnsembleForward</source>
+      <what>input, output, metadata|ProbabilityWeight</what>
     </Print>
   </OutStreams>
 
@@ -74,7 +75,7 @@
 
   <DataObjects>
     <PointSet name='PointsetEnsembleForward'>
-      <Input>x1,x2,constantine</Input>
+      <Input>x2,x1,constantine</Input>
       <Output>OutputPlaceHolder</Output>
     </PointSet>
   </DataObjects>

tests/framework/pca_LHS/gold/LHS/testPrintPointSet_dump.csv

@@ -1,11 +1,11 @@
-x1,y1,z1,x0,y0,z0,PointProbability,ProbabilityWeight,x,y,z
--0.53105898794,-0.569259389682,3.22893095003,-1.0913123605,0.899681849331,1.04029440408,0.000255355698073,1.31295915554e-05,-0.743344602433,0.349632634293,0.97067349756
-0.445025704532,1.26351501403,-2.17818337975,0.608051846435,1.61265799898,3.3588520593,0.00241437203718,0.000213147185818,0.812297452923,1.90404948914,3.20401930369
-1.24579814031,-0.0876059462285,-4.00510478781,2.0978643737,1.17733859737,2.37653038289,9.56794906349e-06,3.12591774386e-07,1.97554170164,2.20081407068,2.3096735992
--1.00397803541,-1.3874935316,1.71331609343,-1.00199872676,-0.229362887287,0.771562803628,0.00337588854906,0.00135362045116,-0.882251413774,-0.746858813451,0.737821112961
--1.46557772951,-0.986101660274,0.573811760616,-1.27787951207,-0.617228954739,1.44513538013,0.0113152619358,0.00339980732463,-1.18567259467,-1.26305898464,1.38828328975
--0.425684337499,-0.327671122605,4.06143380958,-1.23656660264,1.28144786679,1.05433777113,1.43936178528e-05,3.12591774385e-07,-0.792745407311,0.660293593444,0.974220965504
-0.0460394675712,2.04616646561,-0.277225472711,-0.383031935436,2.08526636488,3.74883044634,0.00752367171833,0.00339980732463,0.0745705028493,1.91863109543,3.5410991572
--0.0491383622033,0.0881018431042,-1.09705713377,0.179069729999,0.789398859874,2.29747910141,0.0346079784075,0.00135362045116,0.298845409293,0.881111142501,2.17994344681
-2.10377576773,0.372641118679,2.36918706333,1.3659604187,3.39352026782,1.31141113685,0.000391429078475,0.000213147185818,1.78425882579,4.10538892065,1.34594100663
-0.675431749619,0.82782638421,-3.32436118114,1.18737346547,1.33889328799,3.15655260213,0.000142915570757,1.31295915554e-05,1.24432178869,1.90385379507,3.02540830647
+x1,y1,z1,x0,y0,z0,x,y,z,ProbabilityWeight,PointProbability
+-0.53105898794,-0.569259389682,3.22893095003,-1.0913123605,0.899681849331,1.04029440408,-0.743344602433,0.349632634293,0.97067349756,1.31295915554e-05,0.000255355698073
+0.445025704532,1.26351501403,-2.17818337975,0.608051846435,1.61265799898,3.3588520593,0.812297452923,1.90404948914,3.20401930369,0.000213147185818,0.00241437203718
+1.24579814031,-0.0876059462285,-4.00510478781,2.0978643737,1.17733859737,2.37653038289,1.97554170164,2.20081407068,2.3096735992,3.12591774386e-07,9.56794906349e-06
+-1.00397803541,-1.3874935316,1.71331609343,-1.00199872676,-0.229362887287,0.771562803628,-0.882251413774,-0.746858813451,0.737821112961,0.00135362045116,0.00337588854906
+-1.46557772951,-0.986101660274,0.573811760616,-1.27787951207,-0.617228954739,1.44513538013,-1.18567259467,-1.26305898464,1.38828328975,0.00339980732463,0.0113152619358
+-0.425684337499,-0.327671122605,4.06143380958,-1.23656660264,1.28144786679,1.05433777113,-0.792745407311,0.660293593444,0.974220965504,3.12591774385e-07,1.43936178528e-05
+0.0460394675712,2.04616646561,-0.277225472711,-0.383031935436,2.08526636488,3.74883044634,0.0745705028493,1.91863109543,3.5410991572,0.00339980732463,0.00752367171833
+-0.0491383622033,0.0881018431042,-1.09705713377,0.179069729999,0.789398859874,2.29747910141,0.298845409293,0.881111142501,2.17994344681,0.00135362045116,0.0346079784075
+2.10377576773,0.372641118679,2.36918706333,1.3659604187,3.39352026782,1.31141113685,1.78425882579,4.10538892065,1.34594100663,0.000213147185818,0.000391429078475
+0.675431749619,0.82782638421,-3.32436118114,1.18737346547,1.33889328799,3.15655260213,1.24432178869,1.90385379507,3.02540830647,1.31295915554e-05,0.000142915570757