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