def do_fit(system, verbose):
try:
degree = system.model['d']
except TypeError:
msg = red('Error: ') + 'Need to run mod before fit. '
clogger.error(msg)
return
with warnings.catch_warnings(record=True) as w:
p = polyfit(system.time, system.vrad, degree)
if len(w):
msg = yellow('Warning: ') + 'Polyfit may be poorly conditioned. ' \
+ 'Maybe try a lower degree drift?'
clogger.info(msg)
return p
def do_genetic(system, just_gen=False):
""" Carry out the fit using a genetic algorithm and if
just_gen=False try to improve it with a run of the LM algorithm """
try:
degree = system.model['d']
keplerians = system.model['k']
except TypeError:
msg = red('Error: ') + 'Need to run mod before gen. '
clogger.error(msg)
return
maxP = system.per.get_peaks(output_period=True)[1]
size_maxP = 10**(len(str(int(maxP)))-1)
system.fit = {}
msg = blue('INFO: ') + 'Initializing genetic algorithm...'
clogger.info(msg)
msg = blue(' : ') + 'Model is: %d keplerians + %d drift' % (keplerians, degree)
clogger.info(msg)
vel = zeros_like(system.time)
def chi2_1(individual):
""" Fitness function for 1 planet model """
P, K, ecc, omega, T0, gam = individual
get_rvn(system.time, P, K, ecc, omega, T0, gam, vel)
chi2 = sum(((system.vrad - vel)/system.error)**2)
#print chi2
return chi2,
def chi2_n(individual):
""" Fitness function for N planet model """
P, K, ecc, omega, T0, gam = [individual[i::6] for i in range(6)]
#print ecc
get_rvn(system.time, P, K, ecc, omega, T0, gam[0], vel)
#print 'out of get_rvn'
chi2 = sum(((system.vrad - vel)/system.error)**2)
#print chi2
return chi2,
## create the required types -- the fitness and the individual.
creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) # minimization of a single objective
creator.create("Individual", list, fitness=creator.FitnessMin)
## create parameters by sampling from their priors
def P_prior():
return random.uniform(5, 1000)
# return random.gauss(maxP, size_maxP)
def K_prior():
return random.uniform(0, 150)
def ecc_prior():
return random.uniform(0, 0.9)
def om_prior():
return random.uniform(0, 360)
def t0_prior():
return random.uniform(2350000, 2550000)
def gamma_prior():
return random.uniform(-100, 100)
priors = [P_prior, K_prior, ecc_prior, om_prior, t0_prior, gamma_prior]
toolbox = base.Toolbox()
toolbox.register("individual", tools.initCycle, creator.Individual, priors, n=keplerians)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def mutPrior(individual, indpb):
for i, fcn in enumerate(zip(individual, priors)):
if random.random() < indpb:
individual[i] = fcn[1]()
return individual,
toolbox.register("evaluate", chi2_n)
toolbox.register("mate", tools.cxTwoPoints)
toolbox.register("mutate", mutPrior, indpb=0.10)
toolbox.register("select", tools.selTournament, tournsize=3)
npop = 500
ngen = 150
npar = 5*keplerians+1
## build the population
pop = toolbox.population(n=npop)
## helper functions
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", nanmean)
# stats.register("std", nanstd)
stats.register("min", np.nanmin)
# stats.register("max", np.nanmax)
# stats.register("total", sigma3)
stats.register("red", lambda v: min(v)/(len(system.time)-npar) )
msg = blue('INFO: ') + 'Created population with N=%d. Going to evolve for %d generations...' % (npop,ngen)
clogger.info(msg)
## run the genetic algorithm
algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=ngen, stats=stats, halloffame=hof, verbose=True)
## output results information
msg = yellow('RESULT: ') + 'Best individual is'
clogger.info(msg)
## loop over planets
print("%3s %12s %10s %10s %10s %15s %9s" % \
('', 'P[days]', 'K[km/s]', 'e', unichr(0x3c9).encode('utf-8')+'[deg]', 'T0[days]', 'gam') )
for i, planet in enumerate(list(ascii_lowercase)[:keplerians]):
P, K, ecc, omega, T0, gam = [hof[0][j::6] for j in range(6)]
print("%3s %12.1f %10.2f %10.2f %10.2f %15.2f %9.2f" % (planet, P[i], K[i], ecc[i], omega[i], T0[i], gam[i]) )
msg = yellow('RESULT: ') + 'Best fitness value: %s\n' % (hof[0].fitness)
clogger.info(msg)
if just_gen:
# save fit in the system and return, no need for LM
system.fit['params'] = hof[0]
system.fit['chi2'] = hof[0].fitness/(len(system.time)-npar)
return
msg = blue('INFO: ') + 'Calling LM to improve result...'
clogger.info(msg)
## call levenberg markardt fit
lm = do_lm(system, [hof[0][j::6] for j in range(6)])
lm_par = lm[0]
## loop over planets
msg = yellow('RESULT: ') + 'Best fit is'
clogger.info(msg)
print("%3s %12s %10s %10s %10s %15s %9s" % \
('', 'P[days]', 'K[km/s]', 'e', unichr(0x3c9).encode('utf-8')+'[deg]', 'T0[days]', 'gam') )
for i, planet in enumerate(list(ascii_lowercase)[:keplerians]):
P, K, ecc, omega, T0, gam = [lm_par[j::6] for j in range(6)]
print("%3s %12.1f %10.2f %10.2f %10.2f %15.2f %9.2f" % (planet, P[i], K[i], ecc[i], omega[i], T0[i], gam[i]) )
chi2 = chi2_n(lm_par)[0]
msg = yellow('RESULT: ') + 'Best fitness value: %f, %f' % (chi2, chi2/(len(system.time)-npar))
clogger.info(msg)
# save fit in the system
system.fit['params'] = lm_par
system.fit['chi2'] = chi2
# # print p.minFitness, p.maxFitness, p.avgFitness, p.sumFitness
# print 'Genetic:', p.bestFitIndividual, p.bestFitIndividual.fitness
# lm = do_lm(system, p.bestFitIndividual.genes)
# lm_par = lm[0]
# print 'LM:', lm_par
# # get best solution curve
# new_time = system.get_time_to_plot()
# vel = zeros_like(new_time)
# P, K, ecc, omega, t0 = p.bestFitIndividual.genes
# get_rv(new_time, P, K, ecc, omega, t0, vel)
# # plot RV with time
# plot(system.time, system.vrad, 'o')
# plot(new_time, vel, '-')
# P, K, ecc, omega, t0 = lm_par
# get_rv(new_time, P, K, ecc, omega, t0, vel)
# plot(new_time, vel, 'r-')
# show()
return
