def compute_ee(cat1, cat2, options, period=100., rbins=None):
avec2 = np.vstack((cat2['a1'], cat2['a2'], cat2['a3'])).T
avec1 = np.vstack((cat1['a1'], cat1['a2'], cat1['a3'])).T
pvec1 = np.vstack((cat1['x'], cat1['y'], cat1['z'])).T
pvec2 = np.vstack((cat2['x'], cat2['y'], cat2['z'])).T
hids1 = cat1['halo_id']
hids2 = cat2['halo_id']
if options['2pt']['binning'] == 'log' and (rbins is None):
rbins = np.logspace(np.log10(options['2pt']['rmin']),
np.log10(options['2pt']['rmax']),
options['2pt']['nbin'])
elif options['2pt']['binning'] == 'equal' and (rbins is None):
rbins = util.equalise_binning(cat1, cat2, options['2pt']['rmin'],
options['2pt']['rmax'],
options['2pt']['nbin'])
ee_1h, ee_2h = ee_3d(pvec2,
avec2,
hids2,
pvec1,
avec1,
hids1,
rbins,
period=period,
num_threads=1)
return ee_1h, ee_2h
def compute_ee(cat1, cat2, options, period=100., rbins=None, nbins=6):
aname = 'a%d'
if ('shapes_suffix' in options['2pt'].keys()):
aname += options['2pt']['shapes_suffix']
avec2 = np.vstack((cat2[aname % 1], cat2[aname % 2], cat2[aname % 3])).T
avec1 = np.vstack((cat1[aname % 1], cat1[aname % 2], cat1[aname % 3])).T
pvec1 = np.vstack((cat1['x'], cat1['y'], cat1['z'])).T
pvec2 = np.vstack((cat2['x'], cat2['y'], cat2['z'])).T
if options['2pt']['binning'] == 'log' and (rbins is None):
rbins = np.logspace(np.log10(options['2pt']['rmin']),
np.log10(options['2pt']['rmax']), nbins + 1)
elif options['2pt']['binning'] == 'equal' and (rbins is None):
rbins = util.equalise_binning(cat1, cat2, options['2pt']['rmin'],
options['2pt']['rmax'], nbins + 1)
mask1 = avec1.T[0] != 0.0
mask2 = avec2.T[0] != 0.0
ee = ee_3d(pvec2[mask2],
avec2[mask2],
pvec1[mask1],
avec1[mask1],
rbins,
period=period,
num_threads=1)
return ee
def jackknife_ee_3d(sample1, orientations1, sample2, orientations2, rbins,
Nsub=[5, 5, 5],
period=None, num_threads=1, verbose=False,
approx_cell1_size=None, approx_cell2_size=None):
"""
"""
sample1, sample2, j_index_1, j_index_2, Nsub, N_sub_vol, Lbox, PBCs =\
_process_args(sample1, sample2, period, Nsub)
# loop through jackknife samples (note that zero is not used as a label)
results = np.zeros((N_sub_vol+1, len(rbins)-1))
for i in range(0, N_sub_vol+1):
start = time.time()
# remove one sample at a time
mask1 = (j_index_1 != i)
mask2 = (j_index_2 != i)
results[i, :] = ee_3d(sample1[mask1], orientations1[mask1],
sample2[mask2], orientations2[mask2], rbins,
period=period, num_threads=num_threads,
approx_cell1_size=None, approx_cell2_size=None)
dt = time.time()-start
if (verbose is True) & (i == 0):
print("estimated time to complete jackknife",
"calculation (s): {0}".format(dt*N_sub_vol))
# get jackknife samples
result_sub = results[1:, :]
# get full sample result
# result = results[0, :]
result = np.mean(result_sub, axis=0)
# calculate the covariance matrix
cov = np.matrix(np.cov(result_sub.T, bias=True))*(N_sub_vol-1.0)
return result, cov
def calculate_ed_ee(halocat, central_alignment_strength, satellite_alignment_strength, rbins):
gal_sample_mask = (halocat.halo_table['halo_mpeak']>10**12.0)
table = halocat.halo_table[gal_sample_mask]
# satellite position model
satellite_position_model = SubHaloPositions()
table = Table(np.copy(satellite_position_model.assign_gal_type(table=table)))
table = Table(np.copy(satellite_position_model.assign_positions(table=table)))
# central alignment model
cen_alignment_model = CentralAlignment(central_alignment_strength=central_alignment_strength)
table = Table(np.copy(cen_alignment_model.assign_central_orientation(table=table)))
# satellite alignment model
sat_alignment_model = SatelliteAlignment(satellite_alignment_strength=satellite_alignment_strength)
table = Table(np.copy(sat_alignment_model.assign_satellite_orientation(table=table)))
# calculate observables
galaxy_coords = np.vstack((table['x'],
table['y'],
table['z'])).T
galaxy_orientations = np.vstack((table['galaxy_axisA_x'],
table['galaxy_axisA_y'],
table['galaxy_axisA_z'])).T
gal_omega = ed_3d(galaxy_coords, galaxy_orientations,
galaxy_coords,
rbins, period=halocat.Lbox, num_threads=4)
gal_eta = ee_3d(galaxy_coords, galaxy_orientations,
galaxy_coords, galaxy_orientations,
rbins, period=halocat.Lbox, num_threads=4)
return gal_omega, gal_eta
cat2 = data[np.invert(mask)]
cat0 = data
data_sym = fi.FITS(options['2pt']['shapes'].replace('.fits', '-symmetrised.fits'))[-1].read()
cat1_sym = data_sym[mask]
cat2_sym = data_sym[np.invert(mask)]
cat0_sym = data_sym
avec2 = np.vstack((cat2_sym['a1'], cat2_sym['a2'], cat2_sym['a3'])).T
avec1 = np.vstack((cat2_sym['a1'], cat2_sym['a2'], cat2_sym['a3'])).T
pvec1 = np.vstack((cat2['x'], cat2['y'], cat2['z'])).T
pvec2 = np.vstack((cat2['x'], cat2['y'], cat2['z'])).T
rbins = np.logspace(np.log10(options['2pt']['rmin']), np.log10(options['2pt']['rmax']), options['2pt']['nbin'])
period=100.
ee = ee_3d(pvec2, avec2, pvec1, avec1, rbins, period=period, num_threads=1)
pvec1 = np.vstack((cat2_sym['x'], cat2_sym['y'], cat2_sym['z'])).T
pvec2 = np.vstack((cat2_sym['x'], cat2_sym['y'], cat2_sym['z'])).T
ee0 = ee_3d(pvec2, avec2, pvec1, avec1, rbins, period=period, num_threads=1)
dee=ee0-ee
plt.close()
plt.subplot(211)
plt.plot(x, ee0, color='purple', ls='--', label='Symmetrised', lw=2.5)
plt.plot(x, ee, color='plum', ls=':', label='Symmetrised Shapes', lw=2.5)
plt.xscale('log')
plt.xlabel('$r$ / $h^{-1}$ Mpc', fontsize=18)
plt.ylim(1e-3,1e-1)
plt.ylabel('EE Correlation', fontsize=18)
plt.legend(loc='lower left')
def main():
# get simulation information
if len(sys.argv) > 1:
sim_name = sys.argv[1]
snapnum = int(sys.argv[2])
shape_type = sys.argv[3]
sample_name = sys.argv[4]
else:
sim_name = 'TNG300-1' # full physics high-res run
snapnum = 99 # z=0
shape_type = 'reduced' # non-reduced, reduced, iterative
sample_name = 'sample_3'
# load a test halo catalog
from halotools.sim_manager import CachedHaloCatalog
halocat = CachedHaloCatalog(simname='bolplanck',
halo_finder='rockstar',
redshift=0.0,
dz_tol=0.1,
version_name='halotools_v0p4')
from halotools.empirical_models import HodModelFactory
# define the central occupatoion model
from halotools.empirical_models import TrivialPhaseSpace, Zheng07Cens
cens_occ_model = Zheng07Cens()
cens_prof_model = TrivialPhaseSpace()
# define the satellite occupation model
from halotools.empirical_models import Zheng07Sats
from halotools.empirical_models import NFWPhaseSpace, SubhaloPhaseSpace
from intrinsic_alignments.ia_models.anisotropic_nfw_phase_space import AnisotropicNFWPhaseSpace
sats_occ_model = Zheng07Sats()
#sats_prof_model = AnisotropicNFWPhaseSpace()
sats_prof_model = SubhaloPhaseSpace('satellites',
np.logspace(10.5, 15.2, 15))
# define the alignment models
from intrinsic_alignments.ia_models.ia_model_components import CentralAlignment,\
RadialSatelliteAlignment, MajorAxisSatelliteAlignment, HybridSatelliteAlignment
central_orientation_model = CentralAlignment()
satellite_orientation_model = RadialSatelliteAlignment()
if sample_name == 'sample_1':
cens_occ_model.param_dict['logMmin'] = 12.54
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 12.68
sats_occ_model.param_dict['logM1'] = 13.48
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.755
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.279
elif sample_name == 'sample_2':
cens_occ_model.param_dict['logMmin'] = 11.93
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 12.05
sats_occ_model.param_dict['logM1'] = 12.85
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.64
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.084
elif sample_name == 'sample_3':
cens_occ_model.param_dict['logMmin'] = 11.61
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 11.8
sats_occ_model.param_dict['logM1'] = 12.6
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.57172919
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.01995
# combine model components
model_instance = HodModelFactory(
centrals_occupation=cens_occ_model,
centrals_profile=cens_prof_model,
satellites_occupation=sats_occ_model,
satellites_profile=sats_prof_model,
centrals_orientation=central_orientation_model,
satellites_orientation=satellite_orientation_model,
model_feature_calling_sequence=('centrals_occupation',
'centrals_profile',
'satellites_occupation',
'satellites_profile',
'centrals_orientation',
'satellites_orientation'))
from intrinsic_alignments.utils.jackknife_observables import jackknife_ee_3d
from halotools.mock_observables.alignments import ee_3d
rbins = np.logspace(-1, 1.5, 15)
rbin_centers = (rbins[:-1] + rbins[1:]) / 2.0
N = 10
ee = np.zeros((N, len(rbins) - 1))
for i in range(0, N):
# populate mock catalog
model_instance.populate_mock(halocat)
print("number of galaxies: ", len(model_instance.mock.galaxy_table))
mock = model_instance.mock.galaxy_table
# galaxy coordinates and orientations
coords = np.vstack((mock['x'], mock['y'], mock['z'])).T
orientations = np.vstack(
(mock['galaxy_axisA_x'], mock['galaxy_axisA_y'],
mock['galaxy_axisA_z'])).T
ee[i, :] = ee_3d(coords,
orientations,
coords,
orientations,
rbins,
period=halocat.Lbox)
err = np.zeros(len(ee))
err = np.std(ee, axis=0)
ee = np.mean(ee, axis=0)
# save measurements
fpath = fpath = PROJECT_DIRECTORY + 'modelling_illustris/data/'
fname = sim_name + '_' + str(snapnum) + '-' + sample_name + '_model_ee.dat'
ascii.write([rbin_centers, ee, err],
fpath + fname,
names=['r', 'ee', 'err'],
overwrite=True)