def __init__(self, fit_fun='pgg', inst=None, mutationBoundaries=True):
logging.basicConfig(level=logging.INFO,
format='[%(asctime)s]::%(levelname)s %(message)s',
datefmt='%Y.%m.%d - %H:%M:%S')
logging.info('Creating population')
if inst is None:
self.pathToInputFiles = PARAMETER_FOLDER
self.pathToOutputFiles = OUTPUT_FOLDER
else:
self.pathToInputFiles = inst
self.pathToOutputFiles = inst
self.pathToInitFile = fman.getPathToFile(filename=INITIALISATION_FILE,
dirname=self.pathToInputFiles)
self.attrs = fman.extractColumnFromFile(self.pathToInitFile, 0, str)
self.vals = fman.extractColumnFromFile(self.pathToInitFile, 1, int)
for attr, val in zip(self.attrs, self.vals):
setattr(self, attr, val)
self.pathToPhenFile = fman.getPathToFile(
filename=INITIAL_PHENOTYPES_FILE, dirname=self.pathToInputFiles)
with open(self.pathToPhenFile) as f:
self.initialPhenotypes = [float(line) for line in f.readlines()]
self.pathToTechFile = fman.getPathToFile(
filename=INITIAL_TECHNOLOGY_FILE, dirname=self.pathToInputFiles)
self.initialTechnologyLevel = fman.extractColumnFromFile(
self.pathToTechFile, 0, float)[0]
self.pathToParFile = fman.getPathToFile(filename=PARAMETER_FILE,
dirname=self.pathToInputFiles)
self.parattrs = fman.extractColumnFromFile(self.pathToParFile, 0, str)
self.parvals = fman.extractColumnFromFile(self.pathToParFile, 1, float)
for parattr, parval in zip(self.parattrs, self.parvals):
setattr(self, parattr, parval)
self.pathToFitFile = fman.getPathToFile(
filename=FITNESS_PARAMETERS_FILE, dirname=self.pathToInputFiles)
self.fitattrs = fman.extractColumnFromFile(self.pathToFitFile, 0, str)
self.fitvals = fman.extractColumnFromFile(self.pathToFitFile, 1, float)
self.fitnessParameters = {}
for fitattr, fitval in zip(self.fitattrs, self.fitvals):
self.fitnessParameters[fitattr] = fitval
if hasattr(self, "individualBaseResources") == False:
setattr(self, "individualBaseResources", 1)
self.fit_fun = fit_fun
self.mutationBoundaries = mutationBoundaries
def test_initial_phenotypes_format(self):
self.pathToFile = fman.getPathToFile(filename=INITIAL_PHENOTYPES_FILE,
dirname='test/test')
with open(self.pathToFile) as f:
lines = [float(x) for x in f.readlines()]
for line in lines:
assert type(line) is float
assert 0 <= line <= 1
def test_initialisation_values_provided(self):
self.pathToFile = fman.getPathToFile(filename=INITIALISATION_FILE,
dirname='test/test')
self.pars = fman.extractColumnFromFile(self.pathToFile, 1, int)
for par in self.pars:
assert type(
par
) is int, "Did you insert a non integer value for deme number, deme size or generation number?"
assert par > 0, "Did you insert a negative value for deme number, deme size or generation number?"
def test_population_is_initialised_with_right_values(
self, objectAttributesExist, objectAttributesValues):
# First, the population is initialised according to the initialisation settings
self.pathToFile = fman.getPathToFile(filename=INITIALISATION_FILE,
dirname='test/test')
self.attributeNames = fman.extractColumnFromFile(
self.pathToFile, 0, str)
self.attributeValues = fman.extractColumnFromFile(
self.pathToFile, 1, int)
self.population = Pop(inst='test/test')
testAttr, whichAttr = objectAttributesExist(self.population,
self.attributeNames)
assert testAttr, "Population does not have attribute(s) {0}".format(
whichAttr)
testVal, attributes, expected, observed = objectAttributesValues(
self.population, self.attributeNames, self.attributeValues)
assert testVal, "Population has {1}={2} instead of {3}".format(
attributes, expected, observed)
def runSimulation(self):
if self.numberOfDemes >= 2 and self.fit_fun in fitness.functions:
self.createAndPopulateDemes()
self.pathToOutputFolder = fman.getPathToFile(
self.pathToOutputFiles)
if not os.path.exists(self.pathToOutputFolder):
os.makedirs(self.pathToOutputFolder, exist_ok=True)
phenotypesfile = self.pathToOutputFolder + '/out_phenotypes.txt'
demographyfile = self.pathToOutputFolder + '/out_demography.txt'
technologyfile = self.pathToOutputFolder + '/out_technology.txt'
resourcesfile = self.pathToOutputFolder + '/out_resources.txt'
consensusfile = self.pathToOutputFolder + '/out_consensus.txt'
with open(phenotypesfile, "w", buffering=1) as fp, \
open(demographyfile, "w", buffering=1) as fd, \
open(technologyfile, "w", buffering=1) as ft, \
open(resourcesfile, "w", buffering=1) as fr, \
open(consensusfile, "w", buffering=1) as fc:
for gen in range(self.numberOfGenerations):
logging.info('Running generation {0}'.format(gen))
self.lifecycle()
if self.demography == 0:
#raise ValueError("The population went extinct after {0} generations".format(gen))
logging.info(
'Population went extinct after {0} generations'.
format(gen))
phenmeans = [None] * self.numberOfPhenotypes
break
else:
phenmeans = []
phenvars = []
for phen in range(self.numberOfPhenotypes):
tmpPhenotypes = [
ind.phenotypicValues[phen]
for ind in self.individuals
]
tmpMean = ar.specialmean(tmpPhenotypes)
tmpVar = ar.specialvariance(
sum(tmpPhenotypes),
sum(x**2 for x in tmpPhenotypes),
len(tmpPhenotypes))
phenmeans.append(str(round(tmpMean, 3)))
assert type(
tmpVar
) is float, "phenotype variance = {0}, phenotypes = {1}".format(
tmpVar, tmpPhenotypes)
phenvars.append(str(round(tmpVar, 3)))
sep = ','
fp.write('{0},{1}\n'.format(sep.join(phenmeans),
sep.join(phenvars)))
(demmean, demvar) = ar.extractMeanAndVariance(
lst=self.populationStructure, n=self.numberOfDemes)
assert all(
[x is not None for x in self.advances]
), "some or all deme tech entries are none at generation {1}: {0}".format(
self.advances, gen)
(techmean, techvar) = ar.extractMeanAndVariance(
lst=self.advances, n=self.numberOfDemes)
(resmean, resvar) = ar.extractMeanAndVariance(
lst=self.resources, n=self.numberOfDemes)
assert all(
[x is not None for x in self.debatetime]
), "some or all deme debate time entries are none at generation {1}: {0}".format(
self.debatetime, gen)
(consmean, consvar) = ar.extractMeanAndVariance(
lst=self.debatetime, n=self.numberOfDemes)
fd.write('{0},{1}\n'.format(demmean, demvar))
ft.write('{0},{1}\n'.format(techmean, techvar))
fr.write('{0},{1}\n'.format(resmean, resvar))
fc.write('{0},{1}\n'.format(consmean, consvar))
elif self.numberOfDemes < 2 and self.fit_fun in fitness.functions:
raise ValueError(
'This program runs simulations on well-mixed populations only. "numberOfDemes" in initialisation.txt must be > 1'
)
elif self.numberOfDemes >= 2 and self.fit_fun not in fitness.functions:
raise KeyError(
str('Fitness function "{0}" unknown. Add it to the functions in fitness.py'
).format(self.fit_fun))
else:
raise ValueError(
'This program runs simulations on well-mixed populations only. "numberOfDemes" in initialisation.txt must be > 1'
)
raise KeyError(
'Fitness function "{0}" unknown. Add it to the functions in fitness.py'
.format(self.fit_fun))
def test_initialisation_parameters_listed(self):
self.pathToFile = fman.getPathToFile(filename=INITIALISATION_FILE,
dirname='test/test')
for par in ['numberOfDemes', 'initialDemeSize', 'numberOfGenerations']:
fman.searchFile(self.pathToFile, par)