D.O. Rework: Topological PP

framework/BaseClasses.py

@@ -246,7 +246,7 @@ def provideExpectedMetaKeys(self):
     """
       Provides the registered list of metadata keys for this entity.
       @ In, None
-      @ Out, meta, list(str), expected keys (empty if none)
+      @ Out, meta, set(str), expected keys (empty if none)
     """
     return self.metadataKeys
 

framework/DataObjects/XDataSet.py

@@ -345,10 +345,11 @@ def getVars(self,subset=None):
     else:
       self.raiseAnError(KeyError,'Unrecognized subset choice: "{}"'.format(subset))
 
-  def getVarValues(self,var):
+  def getVarValues(self,var,asDict=False):
     """
       Returns the sampled values of "var"
       @ In, var, str or list(str), name(s) of variable(s)
+      @ In, asDict, bool, optional, if True then always returns a dictionary even if only one sample requested
       @ Out, res, xr.DataArray, samples (or dict of {var:xr.DataArray} if multiple variables requested)
     """
     ## NOTE TO DEVELOPER:
@@ -365,6 +366,8 @@ def getVarValues(self,var):
       #format as dataarray
       else:
         res = self._data[var]
+      if asDict:
+        res = {var:res}
     elif isinstance(var,list):
       res = dict((v,self.getVarValues(v)) for v in var)
     else:

framework/Models/PostProcessor.py

@@ -119,6 +119,21 @@ def __init__(self,runInfoDict):
     self.workingDir = ''
     self.printTag = 'POSTPROCESSOR MODEL'
 
+  def provideExpectedMetaKeys(self):
+    """
+      Overrides the base class method to assure child postprocessor is also polled for its keys.
+      @ In, None
+      @ Out, meta, set(str), expected keys (empty if none)
+    """
+    # get keys as per base class
+    keys = Model.provideExpectedMetaKeys(self)
+    # add postprocessor keys
+    try:
+      keys = keys.union(self.interface.provideExpectedMetaKeys())
+    except AttributeError:
+      pass # either "interface" has no method for returning meta keys, or "interface" is not established yet.
+    return keys
+
   def whatDoINeed(self):
     """
       This method is used mainly by the Simulation class at the Step construction stage.

framework/PostProcessors/PostProcessor.py

@@ -45,6 +45,7 @@ def __init__(self, messageHandler):
     self.type = self.__class__.__name__  # pp type
     self.name = self.__class__.__name__  # pp name
     self.messageHandler = messageHandler
+    self.metadataKeys = set()
 
   @classmethod
   def getInputSpecification(cls):
@@ -94,3 +95,22 @@ def run(self, input):
       @ Out, None
     """
     pass
+
+  ## TODO FIXME ##
+  # These two methods (addMetaKeys, provideExpectedMetaKeys) are made to be consistent with the BaseClasses.BaseType, and in
+  # that glorious day when the PostProcessors inherit from the BaseType, these implementations should be removed.
+  def addMetaKeys(self,*args):
+    """
+      Adds keywords to a list of expected metadata keys.
+      @ In, args, list(str), keywords to register
+      @ Out, None
+    """
+    self.metadataKeys = self.metadataKeys.union(set(args))
+
+  def provideExpectedMetaKeys(self):
+    """
+      Provides the registered list of metadata keys for this entity.
+      @ In, None
+      @ Out, meta, list(str), expected keys (empty if none)
+    """
+    return self.metadataKeys

framework/PostProcessors/TopologicalDecomposition.py

@@ -116,47 +116,27 @@ def inputToInternal(self, currentInp):
       @ In, currentInp, list or DataObjects, The input object to process
       @ Out, inputDict, dict, the converted input
     """
-    if type(currentInp) == list:
-      currentInput = currentInp [-1]
-    else:
-      currentInput = currentInp
-    if type(currentInput) == dict:
-      if 'features' in currentInput.keys():
-        return currentInput
-    inputDict = {'features':{}, 'targets':{}, 'metadata':{}}
-    if hasattr(currentInput, 'type'):
-      inType = currentInput.type
-    elif type(currentInput).__name__ == 'list':
-      inType = 'list'
-    else:
+    # TODO typechecking against what currentInp can be; so far it's a length=1 list with a dataobject inside
+    currentInp = currentInp[0]
+    currentInp.asDataset()
+    # nowadays, our only input should be DataObject
+    ## if no "type", then you're not a PointSet or HistorySet
+    if not hasattr(currentInp,'type') or currentInp.type != 'PointSet':
       self.raiseAnError(IOError, self.__class__.__name__,
-                        ' postprocessor accepts files, HDF5, Data(s) only. ',
-                        ' Requested: ', type(currentInput))
-
-    if inType not in ['HDF5', 'PointSet', 'list'] and not isinstance(currentInput,Files.File):
-      self.raiseAnError(IOError, self, self.__class__.__name__ + ' post-processor only accepts files, HDF5, or DataObjects! Got ' + str(inType) + '!!!!')
-    # FIXME: implement this feature
-    if isinstance(currentInput,Files.File):
-      if currentInput.subtype == 'csv':
-        pass
-    # FIXME: implement this feature
-    if inType == 'HDF5':
-      pass  # to be implemented
-    if inType in ['PointSet']:
-      for targetP in self.parameters['features']:
-        if   targetP in currentInput.getParaKeys('input'):
-          inputDict['features'][targetP] = currentInput.getParam('input' , targetP)
-        elif targetP in currentInput.getParaKeys('output'):
-          inputDict['features'][targetP] = currentInput.getParam('output', targetP)
-      for targetP in self.parameters['targets']:
-        if   targetP in currentInput.getParaKeys('input'):
-          inputDict['targets'][targetP] = currentInput.getParam('input' , targetP)
-        elif targetP in currentInput.getParaKeys('output'):
-          inputDict['targets'][targetP] = currentInput.getParam('output', targetP)
-      inputDict['metadata'] = currentInput.getAllMetadata()
-    # now we check if the sampler that genereted the samples are from adaptive... in case... create the grid
-    if 'SamplerType' in inputDict['metadata'].keys():
-      pass
+                        ' postprocessor only accepts PointSet DataObjects for input. ',
+                        ' Requested: ', type(currentInp))
+    # now we know we have a PointSet
+    ## TODO FIXME maintaining old structure for now, in the future convert to use DataObject directly
+    ##    and not bother with inputToInternal
+    ##    This works particularly well since we only accept point sets.
+    data = currentInp.asDataset(outType='dict')['data']
+    inputDict = {'features':dict((var,data[var]) for var in self.parameters['features']),
+                 'targets' :dict((var,data[var]) for var in self.parameters['targets' ]),
+                 'metadata':currentInp.getMeta(general=True)}
+    #if 'PointProbability' in currentInp.getVars():
+    inputDict['metadata']['PointProbability'] = currentInp.getVarValues('PointProbability').values
+    #else:
+    #  raise NotImplementedError # TODO
     return inputDict
 
   def _localReadMoreXML(self, xmlNode):
@@ -169,6 +149,8 @@ def _localReadMoreXML(self, xmlNode):
     paramInput = TopologicalDecomposition.getInputSpecification()()
     paramInput.parseNode(xmlNode)
     self._handleInput(paramInput)
+    # register metadata
+    self.addMetaKeys('maxLabel','minLabel')
 
   def _handleInput(self, paramInput):
     """
@@ -233,6 +215,20 @@ def collectOutput(self, finishedJob, output):
     inputList,outputDict = evaluation
 
     if output.type == 'PointSet':
+      # TODO this is a slow dict-based implementation.  It should be improved on need.
+      # TODO can inputList ever be multiple dataobjects?
+      if len(inputList) > 1:
+        self.raiseAnError(NotImplementedError, 'Need to implement looping over all inputs.')
+      fromInput = inputList[0].asDataset('dict')['data']
+      results = dict((var,fromInput[var]) for var in output.getVars() if var in fromInput.keys())
+      for label in ['minLabel','maxLabel']:
+        results[label] = outputDict[label]
+      output.load(results,style='dict')
+      output.addMeta(self.type,{'general':{'hierarchy':outputDict['hierarchy']}})
+      return
+
+
+      #### OLD ####
       requestedInput = output.getParaKeys('input')
       requestedOutput = output.getParaKeys('output')
       dataLength = None
@@ -318,7 +314,7 @@ def run(self, inputIn):
       self.inputData[:, i] = myDataIn[lbl.encode('UTF-8')]
 
     if self.weighted:
-      self.weights = inputIn[0].getMetadata('PointProbability')
+      self.weights = internalInput['metadata']['PointProbability']
     else:
       self.weights = None
 

framework/Samplers/Sampler.py

@@ -577,7 +577,7 @@ def initialize(self,externalSeeding=None,solutionExport=None):
           self.entitiesToRemove.append('transformation-'+distName)
 
     # Register expected metadata
-    meta = ['ProbabilityWeight','prefix']
+    meta = ['ProbabilityWeight','prefix','PointProbability']
     self.addMetaKeys(*meta)
 
   def localInitialize(self):

tests/framework/PostProcessors/TopologicalPostProcessor/test_topology_simple.xml

@@ -1,5 +1,5 @@
 <?xml version="1.0" ?>
-<Simulation>
+<Simulation verbosity='debug'>
   <TestInfo>
     <name>framework/PostProcessors/TopologicalPostProcessor.topology_simple</name>
     <author>maljdan</author>