def launch(eps_start, init_pool=None):
print eps_start
eps = abcpmc.ConstEps(T, eps_start)
mpi_pool = mpi_util.MpiPool()
pools = []
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
f = open(tolerance_file(abcrun), "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps):
#while pool.ratio > 0.01:
new_eps_str = '\t'.join(np.array(pool.eps).astype('str'))+'\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(tolerance_file(abcrun) , "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print pool.eps
# write theta, w, and rhos to file
np.savetxt(theta_file(pool.t, abcrun), pool.thetas)
np.savetxt(w_file(pool.t, abcrun), pool.ws)
np.savetxt(dist_file(pool.t, abcrun) , pool.dists)
# plot theta
plot_thetas(pool.thetas, pool.ws , pool.t,
truths=data_hod, plot_range=prior_range,
theta_filename=theta_file(pool.t, abcrun),
output_dir=util.abc_dir())
eps.eps = np.median(np.atleast_2d(pool.dists), axis = 0)
pools.append(pool)
abcpmc_sampler.close()
return pools
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 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):
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)
# 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
