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convert basic statistics pp to use the new data object (#460)
* convert basicStatistics to use the new data objects * convert tests of basicStatisitics * convert more tests * clean up * move addMetaKeys to localInputAndChecks * resolve comments * fix checkIndexAlignment in DataSet * add unit test for checkIndexAlignment
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,227 @@ | ||
| # Copyright 2017 Battelle Energy Alliance, LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| import xml.etree.ElementTree as ET | ||
| import xml.dom.minidom as pxml | ||
| import os | ||
|
|
||
| def convert(tree,fileName=None): | ||
| """ | ||
| Converts input files to be compatible with merge request #460 | ||
| @ In, tree, xml.etree.ElementTree.ElementTree object, the contents of a RAVEN input file | ||
| @ In, fileName, the name for the raven input file | ||
| @Out, tree, xml.etree.ElementTree.ElementTree object, the modified RAVEN input file | ||
| """ | ||
| simulation = tree.getroot() | ||
| models = simulation.find('Models') | ||
|
|
||
| hasVariableGroups = True | ||
| hasDataObjects = True | ||
| hasOutStreams = True | ||
|
|
||
| variableGroups = simulation.find('VariableGroups') | ||
| if variableGroups is None: | ||
| variableGroups = ET.Element('VariableGroups') | ||
| hasVariableGroups = False | ||
| dataObjects = simulation.find('DataObjects') | ||
| if dataObjects is None: | ||
| dataObjects = ET.Element('DataObjects') | ||
| hasDataObjects = False | ||
| outStreams = simulation.find('OutStreams') | ||
| if outStreams is None: | ||
| outStreams = ET.Element('OutStreams') | ||
| hasOutStreams = False | ||
|
|
||
| steps = simulation.find('Steps') | ||
| postProcess = steps.findall('PostProcess') | ||
|
|
||
|
|
||
| if models is None: return tree # no models, no BasicStats | ||
| timeDep = {} | ||
| for model in models: | ||
| if model.tag == 'PostProcessor' and model.attrib['subType'] == 'BasicStatistics': | ||
| #note that this converts exactly, it asks for everything with respect to everything | ||
| params = [] | ||
|
|
||
| timeDep[model.attrib['name']] = model.find('pivotParameter') | ||
| if model.find('all') is not None: | ||
| anode = model.find('all') | ||
| targNode = anode.find('targets') | ||
| featNode = anode.find('features') | ||
| targs = targNode.text | ||
| feats = featNode.text | ||
|
|
||
| model.remove(model.find('all')) | ||
|
|
||
| metricDict = {'expectedValue':'mean', | ||
| 'minimum':'min', | ||
| 'maximum':'max', | ||
| 'median':'median', | ||
| 'variance':'var', | ||
| 'sigma':'sigma', | ||
| 'percentile':'percentile', | ||
| 'variationCoefficient':'vc', | ||
| 'skewness':'skew', | ||
| 'kurtosis':'kurt', | ||
| 'samples':'samp' | ||
| } | ||
| for metric, prefix in metricDict.items(): | ||
| node = ET.Element(metric) | ||
| node.text = targs | ||
| node.attrib['prefix'] = prefix | ||
| model.append(node) | ||
| for targ in targs.split(','): | ||
| if metric != 'percentile': | ||
| params.append(prefix+'_'+targ.strip()) | ||
| else: | ||
| params.append(prefix+'_5_'+targ.strip()) | ||
| params.append(prefix+'_95_'+targ.strip()) | ||
|
|
||
| metricDict = {'sensitivity': 'sen', | ||
| 'covariance':'cov', | ||
| 'pearson':'pear', | ||
| 'NormalizedSensitivity':'nsen', | ||
| 'VarianceDependentSensitivity':'vsen' | ||
| } | ||
|
|
||
| for metric, prefix in metricDict.items(): | ||
| node = ET.Element(metric) | ||
| node.attrib['prefix'] = prefix | ||
| node.append(targNode) | ||
| node.append(featNode) | ||
| model.append(node) | ||
| for targ in targs.split(','): | ||
| for feat in feats.split(','): | ||
| params.append(prefix+'_'+targ.strip()+'_'+feat.strip()) | ||
|
|
||
| else: | ||
| metricDict = {'expectedValue':'mean', | ||
| 'minimum':'min', | ||
| 'maximum':'max', | ||
| 'median':'median', | ||
| 'variance':'var', | ||
| 'sigma':'sigma', | ||
| 'percentile':'percentile', | ||
| 'variationCoefficient':'vc', | ||
| 'skewness':'skew', | ||
| 'kurtosis':'kurt', | ||
| 'samples':'samp', | ||
| 'sensitivity': 'sen', | ||
| 'covariance':'cov', | ||
| 'pearson':'pear', | ||
| 'NormalizedSensitivity':'nsen', | ||
| 'VarianceDependentSensitivity':'vsen' | ||
| } | ||
|
|
||
| metricDict1 = {'expectedValue':'mean', | ||
| 'minimum':'min', | ||
| 'maximum':'max', | ||
| 'median':'median', | ||
| 'variance':'var', | ||
| 'sigma':'sigma', | ||
| 'percentile':'percentile', | ||
| 'variationCoefficient':'vc', | ||
| 'skewness':'skew', | ||
| 'kurtosis':'kurt', | ||
| 'samples':'samp' | ||
| } | ||
|
|
||
| for child in model: | ||
| if child.tag in metricDict.keys(): | ||
| child.attrib['prefix'] = metricDict[child.tag] | ||
| if child.tag in metricDict1.keys(): | ||
| for var in child.text.split(','): | ||
| if child.tag != 'percentile': | ||
| params.append(metricDict[child.tag] + '_' + var.strip()) | ||
| else: | ||
| if 'percent' in child.attrib.keys(): | ||
| params.append(metricDict[child.tag]+'_'+child.attrib['percent']+'_'+var.strip()) | ||
| else: | ||
| params.append(metricDict[child.tag]+'_5_'+var.strip()) | ||
| params.append(metricDict[child.tag]+'_95_'+var.strip()) | ||
|
|
||
| else: | ||
| targNode = child.find('targets') | ||
| featNode = child.find('features') | ||
| for targ in targNode.text.split(','): | ||
| for feat in featNode.text.split(','): | ||
| params.append(metricDict[child.tag]+'_'+targ.strip()+'_'+feat.strip()) | ||
|
|
||
| # add variable groups | ||
| group = ET.Element('Group') | ||
| group.attrib['name'] = model.attrib['name'] + '_vars' | ||
| group.text = ',\n '.join(params) | ||
| variableGroups.append(group) | ||
|
|
||
| if variableGroups.find('Group') is not None: | ||
| if not hasVariableGroups: | ||
| simulation.append(variableGroups) | ||
| for modelName, pivotParam in timeDep.items(): | ||
|
|
||
| dataSetName = modelName + '_basicStatPP' | ||
| if pivotParam is None: | ||
| dataSet = ET.Element('PointSet') | ||
| else: | ||
| dataSet = ET.Element('HistorySet') | ||
| option = ET.SubElement(dataSet, 'options') | ||
| pivotNode = ET.SubElement(option,'pivotParameter') | ||
| pivotNode.text = pivotParam.text | ||
|
|
||
| dataSet.attrib['name'] = dataSetName | ||
| outNode = ET.SubElement(dataSet,'Output') | ||
| outNode.text = modelName + '_vars' | ||
| dataObjects.append(dataSet) | ||
| if not hasDataObjects: | ||
| simulation.append(dataObjects) | ||
|
|
||
| printNode = ET.Element('Print') | ||
| printNode.attrib['name'] = dataSetName + '_dump' | ||
| typeNode = ET.SubElement(printNode,'type') | ||
| typeNode.text = 'csv' | ||
| sourceNode = ET.SubElement(printNode,'source') | ||
| sourceNode.text = dataSetName | ||
| outStreams.append(printNode) | ||
| if not hasOutStreams: | ||
| simulation.append(outStreams) | ||
|
|
||
| for pp in postProcess: | ||
| if modelName == pp.find('Model').text.strip(): | ||
| outputs = pp.findall('Output') | ||
| remove = False | ||
| hasPrint = False | ||
| for output in outputs: | ||
| if output.attrib['class'] == 'Files': | ||
| output.attrib['class'] = 'DataObjects' | ||
| output.attrib['type'] = 'PointSet' if pivotParam is None else 'HistorySet' | ||
| output.text = dataSetName | ||
| if remove: | ||
| pp.remove(output) | ||
| else: | ||
| remove = True | ||
| elif output.attrib['class'] == 'OutStreams' and output.attrib['type'] == 'Print': | ||
| output.text = dataSetName + '_dump' | ||
| hasPrint = True | ||
| elif output.attrib['class'] == 'DataObjects': | ||
| pp.remove(output) | ||
| if not hasPrint: | ||
| printNode = ET.SubElement(pp, 'Output') | ||
| printNode.attrib['class'] = 'OutStreams' | ||
| printNode.attrib['type'] = 'Print' | ||
| printNode.text = dataSetName + '_dump' | ||
|
|
||
| return tree | ||
|
|
||
| if __name__=='__main__': | ||
| import convert_utils | ||
| import sys | ||
| convert_utils.standardMain(sys.argv,convert) |
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