12.3. Derivations and Reaction Networks¶
12.3.1. Rule Application¶
Transformation rules (reaction patterns) can be applied to graphs (molecules) to create new graphs (molecules). The transformations (reactions) implicitly form a directed (multi-)hypergraph (chemical reaction network).
1 include("0050_formoseGrammar.py")
2 # Reaction networks are expaned using a strategy:
3 strat = (
4 # A molecule can be active or passive during evaluation.
5 addUniverse(formaldehyde) # passive
6 >> addSubset(glycolaldehyde) # active
7 # Aach reaction must have a least 1 active educt.
8 >> inputRules
9 )
10 # We call a reaction network a 'derivation graph'.
11 dg = DG(graphDatabase=inputGraphs)
12 dg.build().execute(strat)
13 # They can also be visualised.
14 dg.print()
12.3.2. Repetition¶
A sub-strategy can be repeated.
1 include("0050_formoseGrammar.py")
2 strat = (
3 addUniverse(formaldehyde)
4 >> addSubset(glycolaldehyde)
5 # Iterate the rule application 4 times.
6 >> repeat[4](
7 inputRules
8 )
9 )
10 dg = DG(graphDatabase=inputGraphs)
11 dg.build().execute(strat)
12 dg.print()
12.3.3. Application Constraints¶
We may want to impose constraints on which reactions are accepted. E.g., in formose the molecules should not have too many carbon atoms.
1 include("0050_formoseGrammar.py")
2 strat = (
3 addUniverse(formaldehyde)
4 >> addSubset(glycolaldehyde)
5 # Constrain the reactions:
6 # No molecules with more than 20 atom can be created.
7 >> rightPredicate[
8 lambda derivation: all(g.numVertices <= 20 for g in derivation.right)
9 ](
10 # Iterate until nothing new is found.
11 repeat(
12 inputRules
13 )
14 )
15 )
16 dg = DG(graphDatabase=inputGraphs)
17 dg.build().execute(strat)
18 dg.print()
12.3.4. Advanced Printing¶
Reaction networks can become large, and often it is necessary to hide parts of the network, or in general change the appearance.
1 include("0212_dgPredicate.py")
2 # Create a printer with default options:
3 p = DGPrinter()
4 # Hide "large" molecules: those with > 4 Cs:
5 p.pushVertexVisible(lambda v: v.graph.vLabelCount("C") <= 4)
6 # Hide the reactions with the large molceules as well:
7 def edgePred(e):
8 if any(v.graph.vLabelCount("C") > 4 for v in e.sources): return False
9 if any(v.graph.vLabelCount("C") > 4 for v in e.targets): return False
10 return True
11 p.pushEdgeVisible(edgePred)
12 # Add the number of Cs to the molecule labels:
13 p.pushVertexLabel(lambda v: "\\#C=" + str(v.graph.vLabelCount("C")))
14 # Highlight the molecules with 4 Cs:
15 p.pushVertexColour(lambda v: "blue" if v.graph.vLabelCount("C") == 4 else "")
16 # Print the network with the customised printer.
17 dg.print(p)
12.3.5. Double Pushout Printing¶
Each reaction/derivation can be visualised as a DPO diagram.
1 include("0212_dgPredicate.py")
2 for e in dg.edges:
3 e.print()