def abc(pewl, name=None, niter=None, npart=None, restart=None):
if restart is not None:
# read pool
theta_init = np.loadtxt(
os.path.join(abc_dir, 'theta.t%i.dat' % restart))
rho_init = np.loadtxt(
os.path.join(abc_dir, 'rho.t%i.dat' % restart))
w_init = np.loadtxt(
os.path.join(abc_dir, 'w.t%i.dat' % restart))
init_pool = abcpmc.PoolSpec(restart, None, None, theta_init, rho_init, w_init)
npart = len(theta_init)
print('%i particles' % npart)
else:
init_pool = None
#--- inference with ABC-PMC below ---
# prior
prior = abcpmc.TophatPrior(prior_min, prior_max)
# sampler
abcpmc_sampler = abcpmc.Sampler(
N=npart, # N_particles
Y=x_obs, # data
postfn=_sumstat_model_wrap, # simulator
dist=_distance_metric_wrap, # distance metric
pool=pewl,
postfn_kwargs={'dem': dem}#, dist_kwargs={'method': 'L2', 'phi_err': phi_err}
)
# threshold
eps = abcpmc.ConstEps(niter, eps0)
print('eps0', eps.eps)
for pool in abcpmc_sampler.sample(prior, eps, pool=init_pool):
eps_str = ", ".join(["{0:>.4f}".format(e) for e in pool.eps])
print("T: {0}, eps: [{1}], ratio: {2:>.4f}".format(pool.t, eps_str, pool.ratio))
for i, (mean, std) in enumerate(zip(*abcpmc.weighted_avg_and_std(pool.thetas, pool.ws, axis=0))):
print(u" theta[{0}]: {1:>.4f} \u00B1 {2:>.4f}".format(i, mean,std))
print('dist', pool.dists)
# write out theta, weights, and distances to file
dustInfer.writeABC('eps', pool, abc_dir=abc_dir)
dustInfer.writeABC('theta', pool, abc_dir=abc_dir)
dustInfer.writeABC('w', pool, abc_dir=abc_dir)
dustInfer.writeABC('rho', pool, abc_dir=abc_dir)
# update epsilon based on median thresholding
eps.eps = np.median(pool.dists, axis=0)
print('eps%i' % pool.t, eps.eps)
print('----------------------------------------')
#if pool.ratio <0.2: break
abcpmc_sampler.close()
return None
def plot_pool(T, prior=None, dem='slab_calzetti', abc_dir=None):
''' plot ABC pool
'''
# read pool
theta_T = np.loadtxt(os.path.join(abc_dir, 'theta.t%i.dat' % T))
rho_T = np.loadtxt(os.path.join(abc_dir, 'rho.t%i.dat' % T))
w_T = np.loadtxt(os.path.join(abc_dir, 'w.t%i.dat' % T))
pool = abcpmc.PoolSpec(T, None, None, theta_T, rho_T, w_T)
dustInfer.plotABC(pool, prior=prior, dem=dem, abc_dir=abc_dir)
return None
def FixedTauABC(T, eps_input, fixtau='satellite', Npart=1000, prior_name='try0',
observables=['fqz_multi'], abcrun=None,
restart=False, t_restart=None, eps_restart=None, **sim_kwargs):
''' Run ABC-PMC analysis for central galaxy SFH model with *FIXED* quenching
timescale
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
if isinstance(eps_input, list):
if len(eps_input) != len(observables):
raise ValueError
if len(observables) > 1 and isinstance(eps_input, float):
raise ValueError
# output abc run details
sfinherit_kwargs, abcrun_flag = MakeABCrun(
abcrun=abcrun, Niter=T, Npart=Npart, prior_name=prior_name,
eps_val=eps_input, restart=restart, **sim_kwargs)
# Data
data_sum = DataSummary(observables=observables)
# Priors
prior_min, prior_max = PriorRange(prior_name)
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def Simz(tt): # Simulator (forward model)
gv_slope = tt[0]
gv_offset = tt[1]
fudge_slope = tt[2]
fudge_offset = tt[3]
sim_kwargs = sfinherit_kwargs.copy()
sim_kwargs['sfr_prop']['gv'] = {'slope': gv_slope, 'fidmass': 10.5, 'offset': gv_offset}
sim_kwargs['evol_prop']['fudge'] = {'slope': fudge_slope, 'fidmass': 10.5, 'offset': fudge_offset}
sim_kwargs['evol_prop']['tau'] = {'name': fixtau}
sim_output = SimSummary(observables=observables, **sim_kwargs)
return sim_output
theta_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_theta_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
w_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_w_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
dist_file = lambda pewl: ''.join([code_dir(),
'dat/pmc_abc/', 'CenQue_dist_t', str(pewl), '_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
eps_file = ''.join([code_dir(),
'dat/pmc_abc/epsilon_', abcrun_flag,
'.fixedtau.', fixtau, '.dat'])
distfn = RhoFq
if restart:
if t_restart is None:
raise ValueError
last_thetas = np.loadtxt(theta_file(t_restart))
last_ws = np.loadtxt(w_file(t_restart))
last_dist = np.loadtxt(dist_file(t_restart))
init_pool = abcpmc.PoolSpec(t_restart, None, None, last_thetas, last_dist, last_ws)
else:
init_pool = None
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=distfn) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
if init_pool is None:
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=init_pool):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = str(pool.eps)+'\t'+str(pool.ratio)+'\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t), pool.thetas,
header='gv_slope, gv_offset, fudge_slope, fudge_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
if len(observables) == 1:
eps.eps = np.median(pool.dists)
else:
#print pool.dists
print np.median(np.atleast_2d(pool.dists), axis = 0)
eps.eps = np.median(np.atleast_2d(pool.dists), axis = 0)
print '----------------------------------------'
pools.append(pool)
return pools