def parseToReport( self, fileName, keyTag, header = None, unique = True):
'''
@var fileName: name of flat (delimited) in text format to be parsed
@type fileName: String
@var keyTag: ID of column to be used for report key row
@type keyTag: String
@summary:
Primary Function
Parses flat file returns report object.
'''
if header != None:
self.setHeader(header)
result = Report()
self.startFile(fileName)
d = self.getTagedLine()
while d != None:
if d != "":
keyName = d[keyTag]
del d[keyTag]
for valueTag in d.keys():
v = d[valueTag]
result.addElement(keyName,valueTag,v)
d = self.getTagedLine()
self.closeFile()
return result
def getReport(self):
report = Report()
fluxModel = self
reactionNames = fluxModel.network.getOrderedReactionNames()
reactionMap = fluxModel.network.getReactionMap()
for reactionName in reactionNames:
reaction = reactionMap[reactionName]
equation = reaction.getEquation()
pathway = reaction.getAnnotation("Subsystem")
name = reaction.getName()#Currently sensitivity values can be too large for control factors.
report.addElement(reactionName,"name",name)
report.addElement(reactionName,"equation", equation)
report.addElement(reactionName,"Subsystem", pathway)
return report
def modelReport(self,dir=1,prediction=None):
report = Report()
delta = 1e-6
for rowName in self.getRowNames():
rLimit = self.floatLimit(self.getRowLimit(rowName))
rValues =self.getRowValueMap(rowName)
rString = self._reportRow(rValues,rLimit,prediction)
report.addElement(rowName,"Type","row")
report.addElement(rowName,"Equation",rString)
rLimitS = "(%s,%s)" % (rLimit[0],rLimit[1])
report.addElement(rowName,"Limit",rLimitS)
if prediction != None:
rValue = self._vectorValue(rValues, prediction)
rValue = round(rValue,6)
rValid = rLimit[0]-delta < rValue < rLimit[1] + delta
report.addElement(rowName,"Value",rValue)
report.addElement(rowName,"Valid",rValid)
for colName in self.getColumnNames():
cLimit = self.floatLimit(self.getColumnLimit(colName))
cValues =self.getColumnValueMap(colName)
cString = self._reportRow(cValues,cLimit,prediction)
report.addElement(colName,"Type","column")
report.addElement(colName,"Equation",cString)
if prediction != None:
if colName in prediction.keys():
cValue = prediction[colName]
cValid = cLimit[0]-delta < cValue < cLimit[1] + delta
report.addElement(colName,"Value",cValue)
report.addElement(colName,"Valid",cValid)
return report
