def evolvability(self, repertoire):
# Get the dominant genotype network for the repertoire
giant = self.caller.dominant_network(repertoire)
# Construct a EvolvabilityAnalyzer object
evoAnalyzer = EvolvabilityAnalyzer(giant,
self.inDataDict,
self.seqToRepDict_evo,
self.repToGiantDict,
self.rcToSeqDict,
self.bitsToSeqDict,
self.netBuilder,
self.isDoubleStranded)
# Compute repertoire evolvability and set it as a network
# attribute
repertoireEvo, targetRepertoires = evoAnalyzer.getReportoireEvo()
giant["Evolvability"] = repertoireEvo
# Stringify the list, since pythons lists cannot be written to GML.
giant["Evolvability_targets"] = json.dumps(targetRepertoires)
# Set evolvability values for all vertices, i.e., sequences
evoTuples = evoAnalyzer.getEvoAll()
evoScores = [evoTuples[i][0] for i in range(len(evoTuples))]
evoTargets = [evoTuples[i][1] for i in range(len(evoTuples))]
giant.vs["Evolvability"] = evoScores
giant.vs["Evolves_to_genotypes_in"] = [
evoTargets[i].keys()
for i in range(len(evoTargets))
]
giant.vs["Evolvability_targets"] = evoTargets
def init_evolvability(self):
self.seqToRepDict_evo = EvolvabilityAnalyzer.updateSeqToRepDict(
self.seqToRepDict, self.repToGiantDict)
if self.isDoubleStranded:
self.rcToSeqDict = EvolvabilityAnalyzer.buildRcToSeqDict(
self.seqToRepDict_evo, self.bitManip)
self.bitsToSeqDict = EvolvabilityAnalyzer.buildBitsToSeqDict(
self.seqToRepDict_evo, self.rcToSeqDict, self.bitManip,
self.isDoubleStranded)
