DataCondenser.py

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from LogReader import LogReader
import math
import json
import pickle


class DataCondenser:
    def __init__(self):
        self.rdrLog = LogReader()
        
    def condenseData(self, strPath):
        dataOwl = None
        
        log = self.rdrLog.loadLog(strPath)
        dataOwl = log.getOwlData()
        
        self.tti = dataOwl["task-tree-individuals"]
        owlMeta = dataOwl["metadata"]
        owlAnnot = dataOwl["annotation"]
        
        if owlMeta:
            result = {"Toplevel" : self.condenseNodes("", owlMeta.subActions())};
            
            with open("out.json", "wb") as f:
                json.dump(result, f)
            
            with open("generalized_model.pkl", "wb") as f:
                pickle.dump({"model" : result,
                             "parameters" : owlAnnot.annotatedParameterTypes()},
                            f, pickle.HIGHEST_PROTOCOL)
        else:
            print "No meta data in file!"
    
    def condenseNodes(self, strParentNode, arrNodes, nLevel = 0):
        arrTypes = {}
        arrIndividuals = {}
        
        for strNode in arrNodes:
            owlNode = self.tti[strNode]
            ident = owlNode.taskContext()#.type()
            
            failures = owlNode.failures()
            failure = ""
            if len(failures) > 0:
                failure = self.tti[failures[0]].type()
            
            result = self.condenseNodes(strNode, owlNode.subActions(), nLevel + 1)
            if not ident in arrTypes:
                arrTypes[ident] = result
            else:
                arrTypes[ident] = self.unifyResults(arrTypes[ident], result)
            
            arrTypes[ident]["individuals"][strNode] = {"parameters" : owlNode.annotatedParameters(True),
                                                       "parent" : strParentNode,
                                                       "failure" : failure}
        
        return {"subTypes" : arrTypes,
                "individuals" : {}}
    
    def unifyResults(self, res1, res2):
        resparams = {}
        if len(res1["individuals"]) > 0:
            resparams = res1["individuals"]
        
        if len(res2["individuals"]) > 0:
            resparams = dict(resparams.items() + res2["individuals"].items())
        
        unified = {"subTypes" : {},
                   "individuals" : resparams}
        
        for ressub1 in res1["subTypes"]:
            if ressub1 in res2["subTypes"]:
                unified["subTypes"][ressub1] = self.unifyResults(res1["subTypes"][ressub1],
                                                                 res2["subTypes"][ressub1])
            else:
                unified["subTypes"][ressub1] = res1["subTypes"][ressub1]
        
        for ressub2 in res2["subTypes"]:
            if not ressub2 in res1["subTypes"]:
                unified["subTypes"][ressub2] = res2["subTypes"][ressub2]
        
        return unified