def ABCpmc_HOD(T,
eps_val,
N_part=1000,
prior_name='first_try',
observables=['nbar', 'xi'],
data_dict={'Mr': 21},
output_dir=None):
'''
ABC-PMC implementation.
Parameters
----------
- T : Number of iterations
- eps_val :
- N_part : Number of particles
- observables : list of observables. Options are 'nbar', 'gmf', 'xi'
- data_dict : dictionary that specifies the observation keywords
'''
if output_dir is None:
output_dir = util.dat_dir()
else:
pass
#Initializing the vector of observables and inverse covariance matrix
if observables == ['xi']:
fake_obs = Data.data_xi(**data_dict)
fake_obs_cov = Data.data_cov(**data_dict)[1:16, 1:16]
xi_Cii = np.diag(fake_obs_cov)
elif observables == ['nbar', 'xi']:
fake_obs = np.hstack(
[Data.data_nbar(**data_dict),
Data.data_xi(**data_dict)])
fake_obs_cov = Data.data_cov(**data_dict)[:16, :16]
Cii = np.diag(fake_obs_cov)
xi_Cii = Cii[1:]
nbar_Cii = Cii[0]
elif observables == ['nbar', 'gmf']:
fake_obs = np.hstack(
[Data.data_nbar(**data_dict),
Data.data_gmf(**data_dict)])
fake_obs_cov = Data.data_cov('nbar_gmf', **data_dict)
Cii = np.diag(fake_obs_cov)
gmf_Cii = Cii[1:]
nbar_Cii = Cii[0]
# True HOD parameters
data_hod_dict = Data.data_hod_param(Mr=data_dict['Mr'])
data_hod = np.array([
data_hod_dict['logM0'], # log M0
np.log(data_hod_dict['sigma_logM']), # log(sigma)
data_hod_dict['logMmin'], # log Mmin
data_hod_dict['alpha'], # alpha
data_hod_dict['logM1'] # log M1
])
# Priors
prior_min, prior_max = PriorRange(prior_name)
prior = abcpmc.TophatPrior(prior_min, prior_max)
prior_range = np.zeros((len(prior_min), 2))
prior_range[:, 0] = prior_min
prior_range[:, 1] = prior_max
# simulator
our_model = HODsim(Mr=data_dict['Mr']) # initialize model
kwargs = {'prior_range': prior_range, 'observables': observables}
def simz(tt):
sim = our_model.sum_stat(tt, **kwargs)
if sim is None:
pickle.dump(tt, open("simz_crash_theta.p", 'wb'))
pickle.dump(kwargs, open('simz_crash_kwargs.p', 'wb'))
raise ValueError('Simulator is giving NonetType')
return sim
def multivariate_rho(datum, model):
#print datum , model
dists = []
if observables == ['nbar', 'xi']:
dist_nbar = (datum[0] - model[0])**2. / nbar_Cii
dist_xi = np.sum((datum[1:] - model[1:])**2. / xi_Cii)
dists = [dist_nbar, dist_xi]
elif observables == ['nbar', 'gmf']:
dist_nbar = (datum[0] - model[0])**2. / nbar_Cii
dist_gmf = np.sum((datum[1:] - model[1:])**2. / gmf_Cii)
dists = [dist_nbar, dist_gmf]
elif observables == ['xi']:
dist_xi = np.sum((datum - model)**2. / xi_Cii)
dists = [dist_xi]
#print np.array(dists)
return np.array(dists)
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=N_part, #N_particles
Y=fake_obs, #data
postfn=simz, #simulator
dist=multivariate_rho, #distance function
pool=mpi_pool)
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
eps = abcpmc.MultiConstEps(T, eps_val)
pools = []
f = open("abc_tolerance.dat", "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps):
#while pool.ratio > 0.01:
new_eps_str = '\t'.join(eps(pool.t).astype('str')) + '\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open("abc_tolerance.dat", "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)
# plot theta
plot_thetas(pool.thetas,
pool.ws,
pool.t,
Mr=data_dict["Mr"],
truths=data_hod,
plot_range=prior_range,
observables=observables,
output_dir=output_dir)
if (pool.t < 4) and (pool.t > 2):
pool.thetas = np.loadtxt(
"/home/mj/abc/halo/dat/gold/nbar_xi_Mr21_theta_t3.mercer.dat")
pool.ws = np.loadtxt(
"/home/mj/abc/halo/dat/gold/nbar_xi_Mr21_w_t3.mercer.dat")
eps.eps = [1.12132735353, 127.215586776]
# write theta and w to file
theta_file = ''.join([
output_dir,
util.observable_id_flag(observables), '_Mr',
str(data_dict["Mr"]), '_theta_t',
str(pool.t), '.mercer.dat'
])
w_file = ''.join([
output_dir,
util.observable_id_flag(observables), '_Mr',
str(data_dict["Mr"]), '_w_t',
str(pool.t), '.mercer.dat'
])
np.savetxt(theta_file, pool.thetas)
np.savetxt(w_file, pool.ws)
if pool.t < 3:
eps.eps = np.percentile(np.atleast_2d(pool.dists), 50, axis=0)
elif (pool.t > 2) and (pool.t < 20):
eps.eps = np.percentile(np.atleast_2d(pool.dists), 75, axis=0)
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
else:
eps.eps = np.percentile(np.atleast_2d(pool.dists), 90, axis=0)
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
#if eps.eps < eps_min:
# eps.eps = eps_min
pools.append(pool)
#abcpmc_sampler.close()
return pools
def ABC(T,
eps_input,
Npart=1000,
cen_tf=None,
cen_prior_name=None,
cen_abcrun=None):
''' ABC-PMC implementation.
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
'''
abcinh = ABCInherit(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
# Data (Group Catalog Satellite fQ)
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
qfrac = Fq()
m_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (m_bin[:-1] + m_bin[1:])
sfq = qfrac.Classify(grpcat.mass,
grpcat.sfr,
np.median(grpcat.z),
sfms_prop=abcinh.sim_kwargs['sfr_prop']['sfms'])
ngal, dum = np.histogram(grpcat.mass, bins=m_bin)
ngal_q, dum = np.histogram(grpcat.mass[sfq == 'quiescent'], bins=m_bin)
data_sum = [M_mid, ngal_q.astype('float') / ngal.astype('float')]
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
def Simz(tt): # Simulator (forward model)
tqdel_dict = {'name': 'explin', 'm': tt[0], 'b': tt[1]}
sat_evol = EvolveSatSFR(sat_cen, tqdelay_dict=tqdel_dict)
sfq_sim = qfrac.Classify(sat_evol.mass,
sat_evol.sfr,
sat_evol.zsnap,
sfms_prop=sat_evol.sfms_prop)
ngal_sim, dum = np.histogram(sat_evol.mass, bins=m_bin)
ngal_q_sim, dum = np.histogram(sat_evol.mass[sfq_sim == 'quiescent'],
bins=m_bin)
sim_sum = [
M_mid,
ngal_q_sim.astype('float') / ngal_sim.astype('float')
]
return sim_sum
# Priors
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def rho(simum, datum):
datum_dist = datum[1]
simum_dist = simum[1]
drho = np.sum((datum_dist - simum_dist)**2)
return drho
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
w_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.w_t',
str(pewl), '_', abcrun_flag, '.dat'
])
dist_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.dist_t',
str(pewl), '_', abcrun_flag, '.dat'
])
eps_file = ''.join([
code_dir(), 'dat/pmc_abc/Satellite.tQdelay.epsilon_', abcrun_flag,
'.dat'
])
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=rho, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=None):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = '\t' + str(pool.eps) + '\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='tQdelay_slope, tQdelay_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
eps.eps = np.median(pool.dists)
pools.append(pool)
return pools
bins=25,
smooth=True,
range=plot_range,
labels=[
r"$\log M_{0}$", r"$\sigma_{log M}$",
r"$\log M_{min}$", r"$\alpha$", r"$\log M_{1}$"
])
plt.savefig("/home/mj/public_html/nbar_wp_v1_now_t" + str(t) + ".png")
plt.close()
np.savetxt("/home/mj/public_html/nbar_wp_v1_theta_t" + str(t) + ".dat",
theta)
np.savetxt("/home/mj/public_html/nbar_wp_v1_w_t" + str(t) + ".dat", w)
mpi_pool = mpi_util.MpiPool()
def sample(T, eps_val, eps_min):
abcpmc_sampler = abcpmc.Sampler(N=100,
Y=data,
postfn=simz,
dist=distance,
pool=mpi_pool)
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
eps = abcpmc.MultiConstEps(T, [1.e6, 1.e6])
#eps = abcpmc.MultiExponentialEps(T,[1.e41 , 1.e12] , [eps_min , eps_min])
pools = []
for pool in abcpmc_sampler.sample(prior, eps):
print("T: {0}, ratio: {1:>.4f}".format(pool.t, pool.ratio))
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