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'],
abcrun=None, data_dict={'Mr':21, 'b_normal':0.25}):
'''
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 abcrun is None:
raise ValueError("Specify the name of the abcrun!")
#Initializing the vector of observables and inverse covariance matrix
fake_obs, Cii_list = getObvs(observables, **data_dict)
# 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 = ABC_HODsim(Mr=data_dict['Mr'], b_normal=data_dict['b_normal']) # initialize model
kwargs = {'prior_range': prior_range, 'observables': observables}
def simz(tt):
sim = our_model(tt, **kwargs)
if sim is None:
pickle.dump(tt, open(util.crash_dir()+"simz_crash_theta.p", 'wb'))
pickle.dump(kwargs, open(util.crash_dir()+'simz_crash_kwargs.p', 'wb'))
raise ValueError('Simulator is giving NonetType')
return sim
def multivariate_rho(model, datum):
dists = []
if observables == ['nbar','xi']:
nbar_Cii = Cii_list[0]
xi_Cii = Cii_list[1]
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']:
nbar_Cii = Cii_list[0]
gmf_Cii = Cii_list[1]
dist_nbar = (datum[0] - model[0])**2. / nbar_Cii
# omitting the first GMF bin in the model ([1:])
dist_gmf = np.sum((datum[1:] - model[1][1:])**2. / gmf_Cii)
dists = [dist_nbar , dist_gmf]
elif observables == ['xi']:
xi_Cii = Cii_list[0]
dist_xi = np.sum((datum- model)**2. / xi_Cii)
dists = [dist_xi]
return np.array(dists)
tolerance_file = lambda name: ''.join([util.abc_dir(), "abc_tolerance", '.', name, '.dat'])
theta_file = lambda tt, name: ''.join([util.abc_dir(),
util.observable_id_flag(observables), '_theta_t', str(tt), '.', name, '.dat'])
w_file = lambda tt, name: ''.join([util.abc_dir(),
util.observable_id_flag(observables), '_w_t', str(tt), '.', name, '.dat'])
dist_file = lambda tt, name: ''.join([util.abc_dir(),
util.observable_id_flag(observables), '_dist_t', str(tt), '.', name, '.dat'])
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
print "Initial launch of the sampler"
pools = launch(eps_val)
