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 voidprobfunc(rbins, n_ran, period, filename):
global path
if filename.endswith(".npy"):
sample = np.load(os.path.join(path, filename))
vpf = void_prob_func(sample, rbins=rbins, n_ran=n_ran, period=period)
np.save(os.path.join('/home/ajana/VPF/', 'vpf_' + str(filename)),
(rbins.astype('float64'), vpf.astype('float64')))
else:
raise TypeError("File should be in .npy format")
def voidprobfunc(rbins, n_ran, period, filename):
global path
if filename.endswith(".npy"):
sample = np.load(os.path.join(path, filename))
vpf = void_prob_func(sample, rbins=rbins, n_ran=n_ran, period=period)
np.save(
os.path.join('/mnt/data4/Abhishek/fidmock/vpf',
'vpf_' + str(filename)), (rbins, vpf))
#np.save(os.path.join('/mnt/data4/Abhishek/VPF/random','vpf_'+str(filename)),(rbins.astype('float64'),vpf.astype('float64')))
else:
raise TypeError("File should be in .npy format")
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_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)
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])
func = np.concatenate((np.array(
(pos_gals_d.shape[0] / float(Lbox**3), )), wprp, vpf, Pcic))
output.append(func)
# parameter set
output.append(param)
output.append(np.where(param - median_w != 0)[0][0])
return output
def calc_vpf(i):
vpf_centers = random_vpfcen(num_sphere)
return void_prob_func(pos_gals_d,
r_vpf,
random_sphere_centers=vpf_centers,
period=Lbox)