This class is the core of model generation in modelMaGe. It creates a
graph of from the model and provides methods to generate candidate models
from this.
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__init__(self,
model,
functionsIdName,
verbose=None)
Init method of Generator class. |
source code
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list
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__checkSelfLoop(self,
removed,
predecessor,
successor)
This method checks for resulting self-loops in case a species is
removed. |
source code
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none
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graph
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none
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dict
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__combine(self,
DG,
i,
j,
v,
newNode)
Combines two reactions if they have more than one reactant or
product. |
source code
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generate(self,
remove=[ ] ,
kinetics=False,
REVERSEMACROS=None,
verbose=False)
Returns model objects that are generated according to give kinetics
and remove commands. |
source code
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{str:{str:[str]}}
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__checkConsistentReverseMacros(self,
ReverseMacros,
ReverseKineticMacros)
For case ca1 = species_2 | species_5, there will be ca1.cps,
ca1K1.cps, ca1K2.cps the keys in 2 ReverseMacros should be
correspondingly changed ReverseMacros: {"['species_2',
'species_5']": 'ca1', "['species_5']": 'ca1',
"['species_2']": 'ca1'} ReverseKineticMacros:
{'reaction_6:MM:ca1': 'ca1', 'reaction_5:mMM:ca1': 'ca1'} |
source code
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list
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boolean
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libSBML:KineticLaw
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__lawToSBML(self,
law,
reaction,
graph,
assignModel,
fIdName)
Constructs an SBML KineticLaw object out of an XML Node |
source code
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{str:libSBML.SBMLDocument}
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__updateReaction(self,
node,
reactants,
products,
modifiers,
model,
add)
Decides if a reaction must be updated or added according to the
changes in the graph or not. |
source code
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