191 lines
5.9 KiB
Python
191 lines
5.9 KiB
Python
config.ilp.solver="CPLEX"
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xanthine = Graph.fromGMLString("""graph [
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node [ id 0 label "N" ]
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node [ id 1 label "C" ]
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node [ id 2 label "N" ]
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node [ id 3 label "C" ]
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node [ id 4 label "C" ]
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node [ id 5 label "C" ]
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node [ id 6 label "N" ]
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node [ id 7 label "C" ]
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node [ id 8 label "N" ]
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node [ id 9 label "O" ]
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node [ id 10 label "O" ]
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node [ id 11 label "H" ]
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node [ id 12 label "H" ]
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node [ id 13 label "H" ]
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node [ id 14 label "H" ]
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edge [ source 1 target 0 label "-" ]
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edge [ source 2 target 1 label "-" ]
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edge [ source 3 target 2 label "-" ]
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edge [ source 4 target 3 label "=" ]
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edge [ source 5 target 4 label "-" ]
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edge [ source 0 target 5 label "-" ]
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edge [ source 8 target 3 label "-" ]
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edge [ source 7 target 8 label "=" ]
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edge [ source 6 target 7 label "-" ]
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edge [ source 4 target 6 label "-" ]
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edge [ source 9 target 1 label "=" ]
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edge [ source 10 target 5 label "=" ]
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edge [ source 11 target 0 label "-" ]
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edge [ source 12 target 2 label "-" ]
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edge [ source 13 target 6 label "-" ]
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edge [ source 14 target 7 label "-" ]
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]""", name="Xanthine")
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caffeine = Graph.fromGMLString("""graph [
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node [ id 0 label "N" ]
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node [ id 1 label "C" ]
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node [ id 2 label "N" ]
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node [ id 3 label "C" ]
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node [ id 4 label "C" ]
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node [ id 5 label "C" ]
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node [ id 6 label "N" ]
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node [ id 7 label "C" ]
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node [ id 8 label "N" ]
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node [ id 9 label "O" ]
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node [ id 10 label "O" ]
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node [ id 11 label "C" ]
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node [ id 12 label "C" ]
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node [ id 13 label "C" ]
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node [ id 14 label "H" ]
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node [ id 15 label "H" ]
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node [ id 16 label "H" ]
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node [ id 17 label "H" ]
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node [ id 18 label "H" ]
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node [ id 19 label "H" ]
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node [ id 20 label "H" ]
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node [ id 21 label "H" ]
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node [ id 22 label "H" ]
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node [ id 23 label "H" ]
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edge [ source 1 target 0 label "-" ]
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edge [ source 2 target 1 label "-" ]
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edge [ source 3 target 2 label "-" ]
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edge [ source 4 target 3 label "=" ]
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edge [ source 5 target 4 label "-" ]
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edge [ source 0 target 5 label "-" ]
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edge [ source 8 target 3 label "-" ]
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edge [ source 7 target 8 label "=" ]
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edge [ source 6 target 7 label "-" ]
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edge [ source 4 target 6 label "-" ]
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edge [ source 9 target 1 label "=" ]
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edge [ source 10 target 5 label "=" ]
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edge [ source 11 target 0 label "-" ]
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edge [ source 12 target 2 label "-" ]
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edge [ source 13 target 6 label "-" ]
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edge [ source 14 target 7 label "-" ]
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edge [ source 11 target 15 label "-" ]
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edge [ source 11 target 16 label "-" ]
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edge [ source 11 target 17 label "-" ]
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edge [ source 12 target 18 label "-" ]
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edge [ source 12 target 19 label "-" ]
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edge [ source 12 target 20 label "-" ]
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edge [ source 13 target 21 label "-" ]
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edge [ source 13 target 22 label "-" ]
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edge [ source 13 target 23 label "-" ]
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]""", name="Caffeine")
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nmethylation = Rule.fromGMLString(
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"""rule [
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left [
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node [ id 1 label "H" ]
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edge [ source 1 target 2 label "-" ]
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]
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context [
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node [ id 2 label "N" ]
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node [ id 3 label "C"]
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node [ id 4 label "C"]
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edge [ source 3 target 2 label "-" ]
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edge [ source 4 target 2 label "-" ]
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]
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right [
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node [ id 5 label "C" ]
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node [ id 6 label "H" ]
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node [ id 7 label "H" ]
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node [ id 8 label "H" ]
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edge [ source 2 target 5 label "-" ]
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edge [ source 5 target 6 label "-" ]
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edge [ source 5 target 7 label "-" ]
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edge [ source 5 target 8 label "-" ]
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]
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]"""
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)
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ndemethylation = Rule.fromGMLString("""rule [
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left [
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node [ id 5 label "C" ]
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node [ id 6 label "H" ]
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node [ id 7 label "H" ]
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node [ id 8 label "H" ]
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edge [ source 2 target 5 label "-" ]
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edge [ source 5 target 6 label "-" ]
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edge [ source 5 target 7 label "-" ]
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edge [ source 5 target 8 label "-" ]
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]
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context [
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node [ id 2 label "N" ]
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node [ id 3 label "C"]
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node [ id 4 label "C"]
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edge [ source 3 target 2 label "-" ]
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edge [ source 4 target 2 label "-" ]
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]
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right [
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node [ id 1 label "H" ]
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edge [ source 1 target 2 label "-" ]
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]
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]""")
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flowPrinter = FlowPrinter()
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flowPrinter.printUnfiltered = False
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postSection("Loaded Graphs")
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for a in inputGraphs:
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a.print()
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postSection("Loaded Rules")
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for a in inputRules:
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a.print()
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dg = DG(graphDatabase=inputGraphs)
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dg.build().execute(
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addSubset(inputGraphs)
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>> rightPredicate[
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lambda d: all(g.vLabelCount("C") <= 10 for g in d.right)
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](
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repeat(inputRules)
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)
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)
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dg.print()
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postSection("Product Graphs")
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for a in dg.vertices:
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a.graph.print()
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flowAutocata = Flow(dg)
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flowAutocata.overallAutocatalysis.enable()
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for a in {xanthine}:
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flowAutocata.addSource(a)
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flowAutocata.addSink(caffeine)
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flowAutocata.findSolutions()
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flowAutocata.solutions.list()
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flowAutocata.solutions.print(flowPrinter)
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sys.exit(0)
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rc = rcEvaluator(inputRules)
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for dRef in dg.derivations:
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der = dRef.derivation
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educt = rcId(der.left[0])
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for i in range(1, len(der.left)):
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educt = educt *rcParallel* rcId(der.left[i])
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product = rcId(der.right[0])
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for i in range(1, len(der.right)):
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product = product *rcParallel* rcId(der.right[i])
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rcExp = educt *rcSuper(allowPartial=False)* der.rule *rcSuper(allowPartial=False)* product
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res = rc.eval(rcExp)
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dRef.print()
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for a in res:
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a.print()
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a.printGML() |