def readRunMeanFitness(runPath):
# Read the run file to get update and mean fitness lists
runData = avida.read_data(runPath, ['update', 'ave_fitness'])
updates = runData['update']
fitnesses = runData['ave_fitness']
# Convert data to dictionary {update: fitness}
fitnessData = {}
for update, fitness in zip(updates, fitnesses):
fitnessData[update] = fitness
return fitnessData
def readPopulation(runPath, z = False):
# Read population file
popData = avida.read_data(runPath, ['num_cpus', 'sequence'], z)
numbers = popData['num_cpus']
sequences = popData['sequence']
# Create genotypes based on the sequences and their number
genotypes = []
for number, sequence in zip(numbers, sequences):
for n in range(int(number)):
genotypes.append(Genotype(sequence))
return genotypes
def readRunGenotypes(runPath, auto_ancestor = False, ancestor = None):
# Read the run file to get update and sequence lists
runData = avida.read_data(runPath, ['update', 'dom_sequence'])
updates = runData['update']
sequences = runData['dom_sequence']
# Convert data to dictionary {update: genotype}
genotypes = {}
for update, sequence in zip(updates, sequences):
genotypes[update] = Genotype(sequence, ancestor)
# Set the first genotype (min update) as the ancestor
if auto_ancestor:
ancestor = genotypes[min(updates)]
for update in updates:
genotypes[update].ancestor = ancestor
return genotypes
