def calc_all_observables(param):
model.param_dict.update(dict(zip(param_names, param))) ##update model.param_dict with pairs (param_names:params)
try:
model.mock.populate()
except:
model.populate_mock(halocat)
gc.collect()
output = []
pos_gals_d = return_xyz_formatted_array(*(model.mock.galaxy_table[ax] for ax in 'xyz'), \
velocity=model.mock.galaxy_table['vz'], velocity_distortion_dimension='z',\
period=Lbox) ##redshift space distorted
pos_gals_d = np.array(pos_gals_d,dtype=float)
pos_gals = return_xyz_formatted_array(*(model.mock.galaxy_table[ax] for ax in 'xyz'), period=Lbox)
pos_gals = np.array(pos_gals,dtype=float)
########one mock, different vpfcens and ptclposes.
np.random.rand(num_sphere,3)*250, ptclpos
particle_masses = halocat.particle_mass
total_num_ptcls_in_snapshot = halocat.num_ptcl_per_dim**3
downsampling_factor = total_num_ptcls_in_snapshot/float(num_ptcls_to_use)
vpf = void_prob_func(pos_gals_d, r_vpf, random_sphere_centers=vpf_centers, period=Lbox)
ggl = delta_sigma(pos_gals, ptclpos, particle_masses=particle_masses, downsampling_factor=downsampling_factor,\
rp_bins=rp_bins_ggl, period=Lbox)[1]/1e12
########
# parameter set
output.append(param)
return output
def calc_all_observables(param):
model.param_dict.update(dict(
zip(param_names,
param))) ##update model.param_dict with pairs (param_names:params)
c = Ngal_estimate(halocat, param)
n_est = c.ngal_estimate()
if n_est < 1e5 and n_est > 7.8e4:
try:
model.mock.populate()
except:
model.populate_mock(halocat)
gc.collect()
output = []
if model.mock.galaxy_table[
'x'].size < 9.8e4 and model.mock.galaxy_table['x'].size > 8e4:
pos_gals = return_xyz_formatted_array(*(model.mock.galaxy_table[ax]
for ax in 'xyz'),
period=Lbox)
pos_gals = np.array(pos_gals, dtype=float)
pos_gals_d = return_xyz_formatted_array(*(model.mock.galaxy_table[ax] for ax in 'xyz'), \
velocity=model.mock.galaxy_table['vz'], velocity_distortion_dimension='z',\
period=Lbox) ##redshift space distorted
pos_gals_d = np.array(pos_gals_d, dtype=float)
#ngals
output.append(model.mock.galaxy_table['x'].size)
#delta sigma
deltasigma = delta_sigma(pos_gals,
pos_part,
particle_masses=particle_masses,
downsampling_factor=downsampling_factor,
rp_bins=rp_bins,
period=Lbox)
output.append(deltasigma[1])
output.append(deltasigma[0])
# wprp
output.append(wp(pos_gals_d, r_wp, pi_max, period=Lbox))
# parameter set
output.append(param)
else:
output = 1
return output
def calc_all_observables(param, seed=seed):
model.param_dict.update(dict(
zip(param_names,
param))) ##update model.param_dict with pairs (param_names:params)
try:
model.mock.populate(seed=seed)
except:
model.populate_mock(halocat, seed=seed)
gc.collect()
output = []
pos_gals_d = return_xyz_formatted_array(*(model.mock.galaxy_table[ax] for ax in 'xyz'), \
velocity=model.mock.galaxy_table['vz'], velocity_distortion_dimension='z',\
period=Lbox) ##redshift space distorted
pos_gals_d = np.array(pos_gals_d, dtype=float)
pos_gals = return_xyz_formatted_array(*(model.mock.galaxy_table[ax]
for ax in 'xyz'),
period=Lbox)
pos_gals = np.array(pos_gals, dtype=float)
particle_masses = halocat.particle_mass
total_num_ptcls_in_snapshot = halocat.num_ptcl_per_dim**3
downsampling_factor = total_num_ptcls_in_snapshot / float(num_ptcls_to_use)
vpf = void_prob_func(pos_gals_d,
r_vpf,
random_sphere_centers=vpf_centers,
period=Lbox)
wprp = wp(pos_gals_d, r_wp, pi_max, period=Lbox)
Pcic = np.bincount(counts_in_cylinders(pos_gals_d, pos_gals_d, proj_search_radius, \
cylinder_half_length, period=Lbox), minlength=100)[1:71]/float(pos_gals_d.shape[0])
Pcic_40 = np.add.reduceat(Pcic, sum_40)
ggl = delta_sigma(pos_gals, ptclpos, particle_masses=particle_masses, downsampling_factor=downsampling_factor,\
rp_bins=rp_bins_ggl, period=Lbox)[1]/1e12
func = np.concatenate((np.array(
(pos_gals_d.shape[0] / float(Lbox**3), )), wprp, ggl, vpf, Pcic_40))
output.append(func)
# parameter set
output.append(param)
output.append(np.where(param - fid != 0)[0][0])
return output
def calc_ds(i):
ptclpos = downsample_ptclpos(ptcl_accept_rate)
num_ptcls_to_use = len(ptclpos)
downsampling_factor = total_num_ptcls_in_snapshot / float(num_ptcls_to_use)
return delta_sigma(pos_gals, ptclpos, particle_masses=particle_masses, downsampling_factor=downsampling_factor,\
rp_bins=rp_bins_ggl, period=Lbox)[1]/1e12
def get_deltasigma(galaxy_data, particle_data, min_sep=44, max_sep=2e3, binning='log', nbins=20, verbosity=1, downsampling_factor=1):
if verbosity>0:
print 'Will construct %s - %s \Delta \Sigma profile'%ctype
# Decide on an appropriate binning scheme
if (binning.lower()=='log'):
rbins = np.logspace(np.log10(min_sep), np.log10(max_sep), nbins )
elif (binning.lower()=='linear'):
rbins = np.linspace(min_sep, max_sep, nbins )
if verbosity>1:
print 'Will use %s binning:'%binning, rbins
# Parse the mask
#mask1 = tools.choose_cs_mask(data,ctype[0])
#mask2 = tools.choose_cs_mask(data,ctype[1])
pos1 = pretending.return_xyz_formatted_array(particle_data['x'], particle_data['y'], particle_data['z']) #, mask = mask1)
pos2 = pretending.return_xyz_formatted_array(galaxy_data['x'], galaxy_data['y'], galaxy_data['z']) #, mask = mask2)
R = np.sqrt(np.array(rbins)[1:]*np.array(rbins)[:-1])
rp, DeltaSigma = pretending.delta_sigma(pos2, pos1, particle_data['mass'], downsampling_factor,
rbins, info.Lbox, cosmology=info.cosmology, num_threads='max')
return rp, DeltaSigma
