def _galaxy_table_indices(source_halo_id, galaxy_host_halo_id):
""" For every halo in the source halo catalog, calculate the index
in the source galaxy catalog of the first appearance of a galaxy that
occupies the halo, reserving -1 for source halos with no resident galaxies.
Parameters
----------
source_halo_id : ndarray
Numpy integer array of shape (num_halos, )
galaxy_host_halo_id : ndarray
Numpy integer array of shape (num_gals, )
Returns
-------
indices : ndarray
Numpy integer array of shape (num_halos, ).
All values will be in the interval [-1, num_gals)
"""
uval_gals, indx_uval_gals = np.unique(galaxy_host_halo_id, return_index=True)
idxA, idxB = crossmatch(source_halo_id, uval_gals)
num_source_halos = len(source_halo_id)
indices = np.zeros(num_source_halos) - 1
indices[idxA] = indx_uval_gals[idxB]
return indices
def test2_bijective_case():
"""
Setup:
* Each source halo belongs to a unique bin.
* There exists 5 target halos for every source halo.
* Each source halo is populated with a single galaxy.
Verify:
* The target galaxy catalog is a 5x repetition of the source galaxy catalog
"""
num_source_halos = 10
num_galaxies = num_source_halos
num_target_halos = num_source_halos * 5
nhalo_min = 1
source_halo_dt_list = [(str('halo_id'), str('i8')),
(str('bin_number'), str('i4'))]
source_halos_dtype = np.dtype(source_halo_dt_list)
source_halos = np.zeros(num_source_halos, dtype=source_halos_dtype)
source_halos['halo_id'] = np.arange(num_source_halos).astype(int)
source_halos['bin_number'] = np.arange(num_source_halos).astype(int)
source_galaxy_dt_list = [(str('host_halo_id'), str('i8'))]
source_galaxies_dtype = np.dtype(source_galaxy_dt_list)
source_galaxies = np.zeros(num_galaxies, dtype=source_galaxies_dtype)
source_galaxies['host_halo_id'] = np.arange(num_galaxies).astype(int)
target_halo_dt_list = [(str('bin_number'), str('i4')),
(str('halo_id'), str('i8'))]
target_halos_dtype = np.dtype(target_halo_dt_list)
target_halos = np.zeros(num_target_halos, dtype=target_halos_dtype)
target_halos['bin_number'] = np.repeat(source_halos['bin_number'], 5)
target_halos['halo_id'] = np.arange(num_target_halos).astype(int)
fake_bins = np.arange(-0.5, num_source_halos + 0.5, 1)
# indices, matching_target_halo_ids = source_galaxy_selection_indices(source_galaxies['host_halo_id'],
# source_halos['halo_id'], source_halos['bin_number'], target_halos['bin_number'],
# target_halos['halo_id'], nhalo_min, fake_bins)
_result = source_galaxy_selection_indices(source_galaxies['host_halo_id'],
source_halos['halo_id'],
source_halos['bin_number'],
target_halos['bin_number'],
target_halos['halo_id'],
nhalo_min, fake_bins)
indices, target_galaxy_target_halo_ids, target_galaxy_source_halo_ids = _result
selected_galaxies = source_galaxies[indices]
assert len(selected_galaxies) == num_target_halos
assert np.all(selected_galaxies == np.repeat(source_galaxies, 5))
assert len(target_galaxy_target_halo_ids) == len(selected_galaxies)
idxA, idxB = crossmatch(target_galaxy_target_halo_ids,
target_halos['halo_id'])
target_halo_bins = target_halos['bin_number'][idxB]
assert np.all(
np.histogram(target_halo_bins)[0] == 5 *
np.histogram(source_halos['bin_number'])[0])
def add_hostid(catalog, halos):
catalog['hostid'] = catalog['upid']
cenmask = catalog['upid'] == -1
catalog['hostid'][cenmask] = catalog['id'][cenmask]
idxA, idxB = crossmatch(catalog['hostid'], halos['halo_id'])
catalog['has_matching_host'] = False
catalog['has_matching_host'][idxA] = True
return catalog
def match_centrals(simname='Illustris-1',
snapnum=135,
data_path=PROJECT_DIRECTORY + 'data/value_added_catalogs/'):
"""
match central galaxies and primary subhaloes between full physics and dmo simulations.
Returns
-------
fp_central_id, dmo_central_id : np.arrays
arrays of matching subfind halo IDs
Notes
-----
This function requires that host halo matches and halo catalogs have been precomputed.
The location of the precomputed files is sepcified by ``data_path``.
"""
# open host halo matching file
fname = simname + '_' + str(snapnum) + '_host_halo_matches.dat'
host_halo_match_table = Table.read(data_path + fname, format='ascii')
# open full physics host halo catalog
fname = simname + '_' + str(snapnum) + '_host_halo_catalog.dat'
fp_halo_table = Table.read(data_path + fname, format='ascii')
# open dmo host halo catalog
fname = simname + '-Dark' + '_' + str(snapnum) + '_host_halo_catalog.dat'
dmo_halo_table = Table.read(data_path + fname, format='ascii')
# match halo catalogs to matches
idx_1, idy_1 = crossmatch(fp_halo_table['host_halo_id'],
host_halo_match_table['host_halo_id'])
fp_central_id = np.array(fp_halo_table['central_id'][idx_1])
dmo_host_id = host_halo_match_table['dmo_host_halo_id'][idy_1]
idx_2, idy_2 = crossmatch(dmo_host_id, dmo_halo_table['host_halo_id'])
dmo_central_id = np.zeros(len(dmo_host_id)).astype('int') - 1
dmo_central_id[idx_2] = dmo_halo_table['central_id'][idy_2]
return fp_central_id, dmo_central_id
def compute_richness(unique_halo_ids, halo_id_of_galaxies):
"""
"""
unique_halo_ids = np.atleast_1d(unique_halo_ids)
halo_id_of_galaxies = np.atleast_1d(halo_id_of_galaxies)
richness_result = np.zeros_like(unique_halo_ids).astype(int)
vals, counts = np.unique(halo_id_of_galaxies, return_counts=True)
idxA, idxB = crossmatch(vals, unique_halo_ids)
richness_result[idxB] = counts[idxA]
return richness_result
def add_host_keys(catalog, host_keys_to_add=('mvir', 'vmax')):
"""
"""
idxA, idxB = crossmatch(catalog['hostid'], catalog['id'])
catalog['host_halo_is_in_catalog'] = False
catalog['host_halo_is_in_catalog'][idxA] = True
for key in host_keys_to_add:
catalog['host_halo_' + key] = catalog[key]
catalog['host_halo_' + key][idxA] = catalog[key][idxB]
return catalog
def create_galsampled_dc2(umachine,
target_halos,
halo_indices,
galaxy_indices,
Lbox_target=256.):
"""
"""
dc2 = Table()
dc2['source_halo_id'] = umachine['hostid'][galaxy_indices]
dc2['target_halo_id'] = np.repeat(target_halos['halo_id'][halo_indices],
target_halos['richness'][halo_indices])
idxA, idxB = crossmatch(dc2['target_halo_id'], target_halos['halo_id'])
msg = "target IDs do not match!"
assert np.all(dc2['source_halo_id'][idxA] == target_halos['source_halo_id']
[idxB]), msg
target_halo_keys = ('x', 'y', 'z', 'vx', 'vy', 'vz')
for key in target_halo_keys:
dc2['target_halo_' + key] = 0.
dc2['target_halo_' + key][idxA] = target_halos[key][idxB]
dc2['target_halo_mass'] = 0.
dc2['target_halo_mass'][idxA] = target_halos['fof_halo_mass'][idxB]
source_galaxy_keys = ('host_halo_mvir', 'upid', 'host_centric_x',
'host_centric_y', 'host_centric_z',
'host_centric_vx', 'host_centric_vy',
'host_centric_vz', 'obs_sm', 'obs_sfr',
'sfr_percentile_fixed_sm')
for key in source_galaxy_keys:
dc2[key] = umachine[key][galaxy_indices]
x_init = dc2['target_halo_x'] + dc2['host_centric_x']
vx_init = dc2['target_halo_vx'] + dc2['host_centric_vx']
dc2['x'], dc2['vx'] = enforce_periodicity_of_box(x_init,
Lbox_target,
velocity=vx_init)
y_init = dc2['target_halo_y'] + dc2['host_centric_y']
vy_init = dc2['target_halo_vy'] + dc2['host_centric_vy']
dc2['y'], dc2['vy'] = enforce_periodicity_of_box(y_init,
Lbox_target,
velocity=vy_init)
z_init = dc2['target_halo_z'] + dc2['host_centric_z']
vz_init = dc2['target_halo_vz'] + dc2['host_centric_vz']
dc2['z'], dc2['vz'] = enforce_periodicity_of_box(z_init,
Lbox_target,
velocity=vz_init)
return dc2
def test_empty_halos_case():
"""
"""
# Set up a source halo catalog with 100 halos in each mass bin
log_mhost_min, log_mhost_max, dlog_mhost = 10.5, 15.5, 0.5
log_mhost_bins = np.arange(log_mhost_min, log_mhost_max + dlog_mhost,
dlog_mhost)
log_mhost_mids = 0.5 * (log_mhost_bins[:-1] + log_mhost_bins[1:])
num_source_halos_per_bin = 20
source_halo_log_mhost = np.tile(log_mhost_mids, num_source_halos_per_bin)
num_source_halos = len(source_halo_log_mhost)
source_halo_id = np.arange(num_source_halos).astype(int)
source_halo_bin_number = halo_bin_indices(log_mhost=(source_halo_log_mhost,
log_mhost_bins))
source_halo_richness = np.tile([0, 3], int(num_source_halos / 2))
source_galaxy_host_halo_id = np.repeat(source_halo_id,
source_halo_richness)
source_galaxy_host_mass = np.repeat(source_halo_log_mhost,
source_halo_richness)
num_target_halos_per_source_halo = 11
target_halo_bin_number = np.repeat(source_halo_bin_number,
num_target_halos_per_source_halo)
target_halo_log_mhost = np.repeat(source_halo_log_mhost,
num_target_halos_per_source_halo)
num_target_halos = len(target_halo_bin_number)
target_halo_ids = np.arange(num_target_halos).astype('i8')
nhalo_min = 5
_result = source_galaxy_selection_indices(
source_galaxy_host_halo_id, source_halo_bin_number, source_halo_id,
target_halo_bin_number, target_halo_ids, nhalo_min, log_mhost_bins)
selection_indices, target_galaxy_target_halo_ids, target_galaxy_source_halo_ids = _result
selected_galaxies_source_halo_mass = source_galaxy_host_mass[
selection_indices]
idxA, idxB = crossmatch(target_galaxy_target_halo_ids, target_halo_ids)
selected_galaxies_target_halo_mass = target_halo_log_mhost[idxB]
assert np.allclose(selected_galaxies_source_halo_mass,
selected_galaxies_target_halo_mass)
gen = zip(selected_galaxies_source_halo_mass,
target_galaxy_target_halo_ids, target_galaxy_source_halo_ids)
for galmass, target_id, source_id in gen:
source_mask = source_halo_id == source_id
target_mask = target_halo_ids == target_id
source_halo_mass = source_halo_log_mhost[source_mask][0]
target_halo_mass = target_halo_log_mhost[target_mask][0]
assert source_halo_mass == target_halo_mass == galmass
def test_bin_distribution_recovery():
log_mhost_min, log_mhost_max, dlog_mhost = 10.5, 15.5, 0.5
log_mhost_bins = np.arange(log_mhost_min, log_mhost_max + dlog_mhost,
dlog_mhost)
log_mhost_mids = 0.5 * (log_mhost_bins[:-1] + log_mhost_bins[1:])
num_source_halos_per_bin = 10
source_halo_log_mhost = np.tile(log_mhost_mids, num_source_halos_per_bin)
source_halo_bin_number = halo_bin_indices(log_mhost=(source_halo_log_mhost,
log_mhost_bins))
num_target_halos_per_source_halo = 11
target_halo_bin_number = np.repeat(source_halo_bin_number,
num_target_halos_per_source_halo)
target_halo_log_mhost = np.repeat(source_halo_log_mhost,
num_target_halos_per_source_halo)
num_target_halos = len(target_halo_bin_number)
target_halo_ids = np.arange(num_target_halos).astype('i8')
nhalo_min = 5
source_halo_selection_indices, matching_target_halo_ids = source_halo_index_selection(
source_halo_bin_number, target_halo_bin_number, target_halo_ids,
nhalo_min, log_mhost_bins)
idxA, idxB = crossmatch(matching_target_halo_ids, target_halo_ids)
target_mass = target_halo_log_mhost[idxB]
source_mass = source_halo_log_mhost[source_halo_selection_indices]
assert np.allclose(target_mass, source_mass)
source_halo_selection_indices, matching_target_halo_ids = source_halo_index_selection(
source_halo_bin_number, target_halo_bin_number, target_halo_ids,
nhalo_min, log_mhost_bins)
idxA, idxB = crossmatch(matching_target_halo_ids, target_halo_ids)
target_mass = target_halo_log_mhost[idxB]
source_mass = source_halo_log_mhost[source_halo_selection_indices]
assert np.allclose(target_mass, source_mass)
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)
if args.central:
mask_cen = model.mock.galaxy_table['gal_type'] == 'centrals'
pos_gals_d = pos_gals_d[mask_cen]
if args.Vmax != 0:
idx_galaxies, idx_halos = crossmatch(
model.mock.galaxy_table['halo_id'], halocat.halo_table['halo_id'])
model.mock.galaxy_table['halo_vmax'] = np.zeros(
len(model.mock.galaxy_table),
dtype=halocat.halo_table['halo_vmax'].dtype)
model.mock.galaxy_table['halo_vmax'][
idx_galaxies] = halocat.halo_table['halo_vmax'][idx_halos]
mask_Vmax = model.mock.galaxy_table['halo_vmax'] > args.Vmax
pos_gals_d = pos_gals_d[mask_Vmax]
# ngals
output.append(model.mock.galaxy_table['x'].size)
# wprp
output.append(wp(pos_gals_d, r_wp, pi_max, period=Lbox))
# parameter set
output.append(param)
return output
def assign_positions(self, **kwargs):
"""
assign satellite positions based on subhalo positions.
"""
if 'table' in kwargs.keys():
table = kwargs['table']
halo_id = table['halo_id']
halo_hostid = table['halo_hostid']
halo_x = table['halo_x']
halo_y = table['halo_y']
halo_z = table['halo_z']
else:
halo_x = kwargs['halo_x']
halo_y = kwargs['halo_y']
halo_z = kwargs['halo_z']
# get satellite positions
x = halo_x * 1.0
y = halo_y * 1.0
z = halo_z * 1.0
# get host halo positions for satellites
inds1, inds2 = crossmatch(halo_hostid, halo_id)
halo_x[inds1] = halo_x[inds2]
halo_y[inds1] = halo_y[inds2]
halo_z[inds1] = halo_z[inds2]
if 'table' in kwargs.keys():
table['x'] = x * 1.0
table['y'] = y * 1.0
table['z'] = z * 1.0
table['halo_x'] = halo_x * 1.0
table['halo_y'] = halo_y * 1.0
table['halo_z'] = halo_z * 1.0
return table
else:
return np.vstack((x, y, z)).T, np.vstack(
(halo_x, halo_y, halo_z)).T
def add_hostpos(umachine, halos):
"""
"""
idxA, idxB = crossmatch(umachine['hostid'], halos['halo_id'])
umachine['host_halo_x'] = np.nan
umachine['host_halo_y'] = np.nan
umachine['host_halo_z'] = np.nan
umachine['host_halo_vx'] = np.nan
umachine['host_halo_vy'] = np.nan
umachine['host_halo_vz'] = np.nan
umachine['host_halo_mvir'] = np.nan
umachine['host_halo_x'][idxA] = halos['x'][idxB]
umachine['host_halo_y'][idxA] = halos['y'][idxB]
umachine['host_halo_z'][idxA] = halos['z'][idxB]
umachine['host_halo_vx'][idxA] = halos['vx'][idxB]
umachine['host_halo_vy'][idxA] = halos['vy'][idxB]
umachine['host_halo_vz'][idxA] = halos['vz'][idxB]
umachine['host_halo_mvir'][idxA] = halos['mvir'][idxB]
return umachine
def tabulate(cls,
halocat,
tpcf,
*tpcf_args,
mode='auto',
Num_ptcl_requirement=sim_defaults.Num_ptcl_requirement,
prim_haloprop_key=model_defaults.prim_haloprop_key,
prim_haloprop_bins=100,
sec_haloprop_key=model_defaults.sec_haloprop_key,
sec_haloprop_percentile_bins=None,
sats_per_prim_haloprop=3e-12,
downsample=1.0,
verbose=False,
redshift_space_distortions=True,
cens_prof_model=None,
sats_prof_model=None,
project_xyz=False,
cosmology_obs=None,
num_threads=1,
**tpcf_kwargs):
"""
Tabulates correlation functions for halos such that galaxy correlation
functions can be calculated rapidly.
Parameters
----------
halocat : object
Either an instance of `halotools.sim_manager.CachedHaloCatalog` or
`halotools.sim_manager.UserSuppliedHaloCatalog`. This halo catalog
is used to tabubulate correlation functions.
tpcf : function
The halotools correlation function for which values are tabulated.
Positional arguments should be passed after this function.
Additional keyword arguments for the correlation function are also
passed through this function.
*tpcf_args : tuple, optional
Positional arguments passed to the ``tpcf`` function.
mode : string, optional
String describing whether an auto- ('auto') or a cross-correlation
('cross') function is going to be tabulated.
Num_ptcl_requirement : int, optional
Requirement on the number of dark matter particles in the halo
catalog. The column defined by the ``prim_haloprop_key`` string
will have a cut placed on it: all halos with
halocat.halo_table[prim_haloprop_key] <
Num_ptcl_requirement*halocat.particle_mass will be thrown out
immediately after reading the original halo catalog in memory.
Default value is set in
`~halotools.sim_defaults.Num_ptcl_requirement`.
prim_haloprop_key : string, optional
String giving the column name of the primary halo property
governing the occupation statistics of gal_type galaxies. Default
value is specified in the model_defaults module.
prim_haloprop_bins : int or list, optional
Integer determining how many (logarithmic) bins in primary halo
property will be used. If a list or numpy array is provided, these
will be used as bins directly.
sec_haloprop_key : string, optional
String giving the column name of the secondary halo property
governing the assembly bias. Must be a key in the table passed to
the methods of `HeavisideAssembiasComponent`. Default value is
specified in the `~halotools.empirical_models.model_defaults`
module.
sec_haloprop_percentile_bins : int, float or None, optional
If an integer, it determines how many evenly spaced bins in the
secondary halo property percentiles are going to be used. If a
float between 0 and 1, it determines the split. If None is
provided, no binning is applied.
sats_per_prim_haloprop : float, optional
Float determing how many satellites sample each halo. For each
halo, the number is drawn from a Poisson distribution with an
expectation value of ``sats_per_prim_haloprop`` times the primary
halo property.
downsample : float or function, optional
Fraction between 0 and 1 used to downsample the total sample used
to tabulate correlation functions. Values below unity can be used
to reduce the computation time. It should not result in biases but
the resulting correlation functions will be less accurate. If
float, the same value is applied to all halos. If function, it
should return the fraction is a function of the primary halo
property.
verbose : boolean, optional
Boolean determing whether the progress should be displayed.
redshift_space_distortions : boolean, optional
Boolean determining whether redshift space distortions should be
applied to halos/galaxies.
cens_prof_model : object, optional
Instance of `halotools.empirical_models.MonteCarloGalProf` that
determines the phase space coordinates of centrals. If none is
provided, `halotools.empirical_models.TrivialPhaseSpace` will be
used.
sats_prof_model : object, optional
Instance of `halotools.empirical_models.MonteCarloGalProf` that
determines the phase space coordinates of satellites. If none is
provided, `halotools.empirical_models.NFWPhaseSpace` will be used.
project_xyz : bool, optional
If True, the coordinates will be projected along all three spatial
axes. By default, only the projection onto the z-axis is used.
cosmology_obs : object, optional
Instance of an astropy `~astropy.cosmology`. This can be used to
correct coordinates in the simulation for the Alcock-Paczynski (AP)
effect, i.e. a mismatch between the cosmology of the model
(simulation) and the cosmology used to interpret observations. Note
that the cosmology of the simulation is part of the halocat object.
If None, no correction for the AP effect is applied. Also, a
correction for the AP effect is only applied for auto-correlation
functions.
num_threads : int, optional
How many threads to use for the tabulation.
**tpcf_kwargs : dict, optional
Keyword arguments passed to the ``tpcf`` function.
Returns
-------
halotab : TabCorr
Object containing all necessary information to calculate
correlation functions for arbitrary galaxy models.
"""
if 'period' in tpcf_kwargs:
print('Warning: TabCorr will pass the keyword argument "period" ' +
'to {} based on the Lbox argument of'.format(tpcf.__name__) +
' the halo catalog. The value you provided will be ignored.')
del tpcf_kwargs['period']
halotab = cls()
if cosmology_obs is not None and mode == 'auto':
rp_stretch = (
(cosmology_obs.comoving_distance(halocat.redshift) *
cosmology_obs.H0) /
(halocat.cosmology.comoving_distance(halocat.redshift) *
halocat.cosmology.H0))
pi_stretch = (halocat.cosmology.efunc(halocat.redshift) /
cosmology_obs.efunc(halocat.redshift))
lbox_stretch = np.array([rp_stretch, rp_stretch, pi_stretch])
else:
lbox_stretch = np.ones(3)
# First, we tabulate the halo number densities.
halos = halocat.halo_table
halos = halos[halos['halo_pid'] == -1]
halos = halos[halos[prim_haloprop_key] >=
(Num_ptcl_requirement + 0.5) * halocat.particle_mass]
if isinstance(prim_haloprop_bins, int):
prim_haloprop_bins = np.linspace(
np.log10(np.amin(halos[prim_haloprop_key])) - 1e-3,
np.log10(np.amax(halos[prim_haloprop_key])) + 1e-3,
prim_haloprop_bins + 1)
elif isinstance(prim_haloprop_bins, (list, np.ndarray)):
pass
else:
raise ValueError('prim_haloprop_bins must be an int, list or ' +
'numpy array.')
if sec_haloprop_percentile_bins is None:
sec_haloprop_percentile_bins = np.array([-1e-3, 1 + 1e-3])
elif isinstance(sec_haloprop_percentile_bins, float):
if not (0 < sec_haloprop_percentile_bins
and sec_haloprop_percentile_bins < 1):
raise ValueError('sec_haloprop_percentile_bins must be ' +
'between 0 and 1.')
sec_haloprop_percentile_bins = np.array(
[-1e-3, sec_haloprop_percentile_bins, 1 + 1e-3])
elif isinstance(sec_haloprop_percentile_bins, int):
sec_haloprop_percentile_bins = np.linspace(
-1e-3, 1 + 1e-3, sec_haloprop_percentile_bins + 1)
else:
raise ValueError('sec_haloprop_percentile_bins must be an int, ' +
'float, list or numpy array.')
halos[sec_haloprop_key +
'_percentile'] = (compute_conditional_percentiles(
table=halos,
prim_haloprop_key=prim_haloprop_key,
sec_haloprop_key=sec_haloprop_key))
halotab.gal_type = Table()
n_h, prim_haloprop_bins, sec_haloprop_percentile_bins = (
np.histogram2d(
np.log10(halos[prim_haloprop_key]),
halos[sec_haloprop_key + '_percentile'],
bins=[prim_haloprop_bins, sec_haloprop_percentile_bins]))
halotab.gal_type['n_h'] = n_h.ravel(order='F')
grid = np.meshgrid(prim_haloprop_bins, sec_haloprop_percentile_bins)
halotab.gal_type['log_prim_haloprop_min'] = grid[0][:-1, :-1].ravel()
halotab.gal_type['log_prim_haloprop_max'] = grid[0][:-1, 1:].ravel()
halotab.gal_type['sec_haloprop_percentile_min'] = (
grid[1][:-1, :-1].ravel())
halotab.gal_type['sec_haloprop_percentile_max'] = (
grid[1][1:, :-1].ravel())
halotab.gal_type = vstack([halotab.gal_type, halotab.gal_type])
halotab.gal_type['gal_type'] = np.concatenate(
(np.repeat('centrals'.encode('utf8'),
len(halotab.gal_type) // 2),
np.repeat('satellites'.encode('utf8'),
len(halotab.gal_type) // 2)))
halotab.gal_type['prim_haloprop'] = 10**(
0.5 * (halotab.gal_type['log_prim_haloprop_min'] +
halotab.gal_type['log_prim_haloprop_max']))
halotab.gal_type['sec_haloprop_percentile'] = (
0.5 * (halotab.gal_type['sec_haloprop_percentile_min'] +
halotab.gal_type['sec_haloprop_percentile_max']))
# Now, we tabulate the correlation functions.
cens_occ_model = Zheng07Cens(prim_haloprop_key=prim_haloprop_key)
if cens_prof_model is None:
cens_prof_model = TrivialPhaseSpace(redshift=halocat.redshift)
sats_occ_model = Zheng07Sats(prim_haloprop_key=prim_haloprop_key)
if sats_prof_model is None:
sats_prof_model = NFWPhaseSpace(redshift=halocat.redshift)
model = HodModelFactory(centrals_occupation=cens_occ_model,
centrals_profile=cens_prof_model,
satellites_occupation=sats_occ_model,
satellites_profile=sats_prof_model)
model.param_dict['logMmin'] = 0
model.param_dict['sigma_logM'] = 0.1
model.param_dict['alpha'] = 1.0
model.param_dict['logM0'] = 0
model.param_dict['logM1'] = -np.log10(sats_per_prim_haloprop)
model.populate_mock(halocat, Num_ptcl_requirement=Num_ptcl_requirement)
gals = model.mock.galaxy_table
idx_gals, idx_halos = crossmatch(gals['halo_id'], halos['halo_id'])
assert np.all(gals['halo_id'][idx_gals] == halos['halo_id'][idx_halos])
gals[sec_haloprop_key + '_percentile'] = np.zeros(len(gals))
gals[sec_haloprop_key +
'_percentile'][idx_gals] = (halos[sec_haloprop_key +
'_percentile'][idx_halos])
if verbose:
print("Number of tracer particles: {0}".format(len(gals)))
for xyz in ['xyz', 'yzx', 'zxy']:
if verbose and project_xyz:
print("Projecting onto {0}-axis...".format(xyz[2]))
pos_all = (return_xyz_formatted_array(
x=gals[xyz[0]],
y=gals[xyz[1]],
z=gals[xyz[2]],
velocity=gals['v' +
xyz[2]] if redshift_space_distortions else 0,
velocity_distortion_dimension='z',
period=halocat.Lbox,
redshift=halocat.redshift,
cosmology=halocat.cosmology) * lbox_stretch)
period = halocat.Lbox * lbox_stretch
# Get a list of the positions of each sub-population.
i_prim = np.digitize(np.log10(gals[prim_haloprop_key]),
bins=prim_haloprop_bins,
right=False) - 1
mask = (i_prim < 0) | (i_prim >= len(prim_haloprop_bins))
i_sec = np.digitize(gals[sec_haloprop_key + '_percentile'],
bins=sec_haloprop_percentile_bins,
right=False) - 1
i_type = np.where(gals['gal_type'] == 'centrals', 0, 1)
# Throw out those that don't fall into any bin.
pos_all = pos_all[~mask]
i = (i_prim + i_sec * (len(prim_haloprop_bins) - 1) + i_type *
((len(prim_haloprop_bins) - 1) *
(len(sec_haloprop_percentile_bins) - 1)))
pos_all = pos_all[np.argsort(i)]
counts = np.bincount(i, minlength=len(halotab.gal_type))
assert len(counts) == len(halotab.gal_type)
pos_bin = []
for i in range(len(halotab.gal_type)):
pos = pos_all[np.sum(counts[:i]):np.sum(counts[:i + 1]), :]
if halotab.gal_type['gal_type'][i] == 'centrals':
# Make sure the number of halos are consistent.
try:
assert len(pos) == int(halotab.gal_type['n_h'][i])
except AssertionError:
raise RuntimeError('There was an internal error in ' +
'TabCorr. If possible, please ' +
'report this bug in the TabCorr ' +
'GitHub repository.')
else:
if len(pos) == 0 and halotab.gal_type['n_h'][i] != 0:
raise RuntimeError(
'There was at least one bin without satellite ' +
'tracers. Increase sats_per_prim_haloprop.')
if len(pos) > 0:
if isinstance(downsample, float):
use = np.random.random(len(pos)) < downsample
else:
use = (np.random.random(len(pos)) < downsample(
halotab.gal_type['prim_haloprop'][i]))
# If the down-sampling reduced the number of tracers to at
# or below one, force at least 2 tracers to not bias the
# clustering estimates.
if np.sum(use) <= 1 and len(pos) > 1:
use = np.zeros(len(pos), dtype=bool)
use[np.random.choice(len(pos), size=2)] = True
pos = pos[use]
pos_bin.append(pos)
if mode == 'auto':
combinations = itertools.combinations_with_replacement(
range(len(halotab.gal_type)), 2)
else:
combinations = range(len(halotab.gal_type))
if xyz == 'xyz':
tpcf_matrix, tpcf_shape = compute_tpcf_matrix(
mode,
pos_bin,
tpcf,
period,
tpcf_args,
tpcf_kwargs,
combinations,
num_threads=num_threads,
verbose=verbose)
if not project_xyz or mode == 'cross':
break
elif xyz != 'xyz':
tpcf_matrix += compute_tpcf_matrix(mode,
pos_bin,
tpcf,
period,
tpcf_args,
tpcf_kwargs,
combinations,
num_threads=num_threads,
verbose=verbose)[0]
if project_xyz and mode == 'auto':
tpcf_matrix /= 3.0
if mode == 'auto':
tpcf_matrix_flat = []
for i in range(tpcf_matrix.shape[0]):
tpcf_matrix_flat.append(
symmetric_matrix_to_array(tpcf_matrix[i]))
tpcf_matrix = np.array(tpcf_matrix_flat)
# Remove entries that don't have any halos.
use = halotab.gal_type['n_h'] != 0
halotab.gal_type = halotab.gal_type[use]
if mode == 'auto':
use = symmetric_matrix_to_array(np.outer(use, use))
tpcf_matrix = tpcf_matrix[:, use]
halotab.gal_type['n_h'] /= np.prod(halocat.Lbox * lbox_stretch)
halotab.attrs = {}
halotab.attrs['tpcf'] = tpcf.__name__
halotab.attrs['mode'] = mode
halotab.attrs['simname'] = halocat.simname
halotab.attrs['redshift'] = halocat.redshift
halotab.attrs['Num_ptcl_requirement'] = Num_ptcl_requirement
halotab.attrs['prim_haloprop_key'] = prim_haloprop_key
halotab.attrs['sec_haloprop_key'] = sec_haloprop_key
halotab.tpcf_args = tpcf_args
halotab.tpcf_kwargs = tpcf_kwargs
halotab.tpcf_shape = tpcf_shape
halotab.tpcf_matrix = tpcf_matrix
halotab.init = True
return halotab
def tabulate(cls,
halocat,
tpcf,
*tpcf_args,
mode='auto',
Num_ptcl_requirement=sim_defaults.Num_ptcl_requirement,
cosmology=sim_defaults.default_cosmology,
prim_haloprop_key=model_defaults.prim_haloprop_key,
prim_haloprop_bins=100,
sec_haloprop_key=model_defaults.sec_haloprop_key,
sec_haloprop_percentile_bins=None,
sats_per_prim_haloprop=3e-12,
downsample=1.0,
verbose=False,
redshift_space_distortions=True,
cens_prof_model=None,
sats_prof_model=None,
project_xyz=False,
cosmology_ref=None,
comm=None,
**tpcf_kwargs):
"""
Tabulates correlation functions for halos such that galaxy correlation
functions can be calculated rapidly.
Parameters
----------
halocat : object
Either an instance of `halotools.sim_manager.CachedHaloCatalog` or
`halotools.sim_manager.UserSuppliedHaloCatalog`. This halo catalog
is used to tabubulate correlation functions.
tpcf : function
The halotools correlation function for which values are tabulated.
Positional arguments should be passed after this function.
Additional keyword arguments for the correlation function are also
passed through this function.
*tpcf_args : tuple, optional
Positional arguments passed to the ``tpcf`` function.
mode : string, optional
String describing whether an auto- ('auto') or a cross-correlation
('cross') function is going to be tabulated.
Num_ptcl_requirement : int, optional
Requirement on the number of dark matter particles in the halo
catalog. The column defined by the ``prim_haloprop_key`` string
will have a cut placed on it: all halos with
halocat.halo_table[prim_haloprop_key] <
Num_ptcl_requirement*halocat.particle_mass will be thrown out
immediately after reading the original halo catalog in memory.
Default value is set in
`~halotools.sim_defaults.Num_ptcl_requirement`.
cosmology : object, optional
Instance of an astropy `~astropy.cosmology`. Default cosmology is
set in `~halotools.sim_manager.sim_defaults`. This might be used to
calculate phase-space distributions and redshift space distortions.
prim_haloprop_key : string, optional
String giving the column name of the primary halo property
governing the occupation statistics of gal_type galaxies. Default
value is specified in the model_defaults module.
prim_haloprop_bins : int or list, optional
Integer determining how many (logarithmic) bins in primary halo
property will be used. If a list or numpy array is provided, these
will be used as bins directly.
sec_haloprop_key : string, optional
String giving the column name of the secondary halo property
governing the assembly bias. Must be a key in the table passed to
the methods of `HeavisideAssembiasComponent`. Default value is
specified in the `~halotools.empirical_models.model_defaults`
module.
sec_haloprop_percentile_bins : int, float, list or None, optional
If an integer, it determines how many evenly spaced bins in the
secondary halo property percentiles are going to be used. If a
float between 0 and 1, it determines the split. Finally, if a list
or numpy array, it directly describes the bins that are going to be
used. If None is provided, no binning is applied.
sats_per_prim_haloprop : float, optional
Float determing how many satellites sample each halo. For each
halo, the number is drawn from a Poisson distribution with an
expectation value of ``sats_per_prim_haloprop`` times the primary
halo property.
downsample : float, optional
Fraction between 0 and 1 used to downsample the total sample used
to tabulate correlation functions. Values below unity can be used
to reduce the computation time. It should not result in biases but
the resulting correlation functions will be less accurate.
verbose : boolean, optional
Boolean determing whether the progress should be displayed.
redshift_space_distortions : boolean, optional
Boolean determining whether redshift space distortions should be
applied to halos/galaxies.
cens_prof_model : object, optional
Instance of `halotools.empirical_models.MonteCarloGalProf` that
determines the phase space coordinates of centrals. If none is
provided, `halotools.empirical_models.TrivialPhaseSpace` will be
used.
sats_prof_model : object, optional
Instance of `halotools.empirical_models.MonteCarloGalProf` that
determines the phase space coordinates of satellites. If none is
provided, `halotools.empirical_models.NFWPhaseSpace` will be used.
project_xyz : bool, optional
If True, the coordinates will be projected along all three spatial
axes. By default, only the projection onto the z-axis is used.
comm : MPI communicator
If not None, then will distribute calculation via MPI
**tpcf_kwargs : dict, optional
Keyword arguments passed to the ``tpcf`` function.
Returns
-------
halotab : TabCorr
Object containing all necessary information to calculate
correlation functions for arbitrary galaxy models.
"""
if sec_haloprop_percentile_bins is None:
sec_haloprop_percentile_bins = np.array([0, 1])
elif isinstance(sec_haloprop_percentile_bins, float):
sec_haloprop_percentile_bins = np.array(
[0, sec_haloprop_percentile_bins, 1])
if 'period' in tpcf_kwargs:
print('Warning: TabCorr will pass the keyword argument "period" ' +
'to {} based on the Lbox argument of'.format(tpcf.__name__) +
' the halo catalog. The value you provided will be ignored.')
del tpcf_kwargs['period']
halotab = cls()
if cosmology_ref is not None and mode == 'auto':
rp_stretch = (
(cosmology_ref.comoving_distance(halocat.redshift) *
cosmology_ref.H0) /
(cosmology.comoving_distance(halocat.redshift) * cosmology.H0))
pi_stretch = (cosmology.efunc(halocat.redshift) /
cosmology_ref.efunc(halocat.redshift))
lbox_stretch = np.array([rp_stretch, rp_stretch, pi_stretch])
else:
lbox_stretch = np.ones(3)
# First, we tabulate the halo number densities.
halos = halocat.halo_table
halos = halos[halos['halo_pid'] == -1]
halos = halos[halos[prim_haloprop_key] >=
(Num_ptcl_requirement - 0.5) * halocat.particle_mass]
if isinstance(prim_haloprop_bins, int):
prim_haloprop_bins = np.linspace(
np.log10(np.amin(halos[prim_haloprop_key])) - 1e-3,
np.log10(np.amax(halos[prim_haloprop_key])) + 1e-3,
prim_haloprop_bins + 1)
elif not isinstance(prim_haloprop_bins, (list, np.ndarray)):
raise ValueError('prim_haloprop_bins must be an int, list or ' +
'numpy array.')
halos[sec_haloprop_key +
'_percentile'] = (compute_conditional_percentiles(
table=halos,
prim_haloprop_key=prim_haloprop_key,
sec_haloprop_key=sec_haloprop_key))
halotab.gal_type = Table()
n_h, prim_haloprop_bins, sec_haloprop_percentile_bins = (
np.histogram2d(
np.log10(halos[prim_haloprop_key]),
halos[sec_haloprop_key + '_percentile'],
bins=[prim_haloprop_bins, sec_haloprop_percentile_bins]))
halotab.gal_type['n_h'] = n_h.ravel(order='F') / np.prod(
halocat.Lbox * lbox_stretch)
grid = np.meshgrid(prim_haloprop_bins, sec_haloprop_percentile_bins)
halotab.gal_type['log_prim_haloprop_min'] = grid[0][:-1, :-1].ravel()
halotab.gal_type['log_prim_haloprop_max'] = grid[0][:-1, 1:].ravel()
halotab.gal_type['sec_haloprop_percentile_min'] = (
grid[1][:-1, :-1].ravel())
halotab.gal_type['sec_haloprop_percentile_max'] = (
grid[1][1:, :-1].ravel())
halotab.gal_type = vstack([halotab.gal_type, halotab.gal_type])
halotab.gal_type['gal_type'] = np.concatenate(
(np.repeat('centrals'.encode('utf8'),
len(halotab.gal_type) // 2),
np.repeat('satellites'.encode('utf8'),
len(halotab.gal_type) // 2)))
halotab.gal_type['prim_haloprop'] = 10**(
0.5 * (halotab.gal_type['log_prim_haloprop_min'] +
halotab.gal_type['log_prim_haloprop_max']))
halotab.gal_type['sec_haloprop_percentile'] = (
0.5 * (halotab.gal_type['sec_haloprop_percentile_min'] +
halotab.gal_type['sec_haloprop_percentile_max']))
# Now, we tabulate the correlation functions.
cens_occ_model = Zheng07Cens(prim_haloprop_key=prim_haloprop_key)
if cens_prof_model is None:
cens_prof_model = TrivialPhaseSpace(redshift=halocat.redshift)
sats_occ_model = Zheng07Sats(prim_haloprop_key=prim_haloprop_key)
if sats_prof_model is None:
sats_prof_model = NFWPhaseSpace(redshift=halocat.redshift)
model = HodModelFactory(centrals_occupation=cens_occ_model,
centrals_profile=cens_prof_model,
satellites_occupation=sats_occ_model,
satellites_profile=sats_prof_model)
model.param_dict['logMmin'] = 0
model.param_dict['sigma_logM'] = 0.1
model.param_dict['alpha'] = 1.0
model.param_dict['logM0'] = 0
model.param_dict['logM1'] = -np.log10(sats_per_prim_haloprop)
model.populate_mock(halocat, Num_ptcl_requirement=Num_ptcl_requirement)
gals = model.mock.galaxy_table
gals = gals[np.random.random(len(gals)) < downsample]
idx_gals, idx_halos = crossmatch(gals['halo_id'], halos['halo_id'])
assert np.all(gals['halo_id'][idx_gals] == halos['halo_id'][idx_halos])
gals[sec_haloprop_key + '_percentile'] = np.zeros(len(gals))
gals[sec_haloprop_key +
'_percentile'][idx_gals] = (halos[sec_haloprop_key +
'_percentile'][idx_halos])
if verbose:
print("Number of tracer particles: {0}".format(len(gals)))
for xyz in ['xyz', 'yzx', 'zxy']:
pos_all = return_xyz_formatted_array(
x=gals[xyz[0]],
y=gals[xyz[1]],
z=gals[xyz[2]],
velocity=gals['v' +
xyz[2]] if redshift_space_distortions else 0,
velocity_distortion_dimension='z',
period=halocat.Lbox,
redshift=halocat.redshift,
cosmology=cosmology) * lbox_stretch
pos = []
n_gals = []
for i in range(len(halotab.gal_type)):
mask = ((10**(halotab.gal_type['log_prim_haloprop_min'][i]) <
gals[prim_haloprop_key]) &
(10**(halotab.gal_type['log_prim_haloprop_max'][i]) >=
gals[prim_haloprop_key]) &
(halotab.gal_type['sec_haloprop_percentile_min'][i] <
gals[sec_haloprop_key + '_percentile']) &
(halotab.gal_type['sec_haloprop_percentile_max'][i] >=
gals[sec_haloprop_key + '_percentile']) &
(halotab.gal_type['gal_type'][i] == gals['gal_type']))
pos.append(pos_all[mask])
n_gals.append(np.sum(mask))
n_gals = np.array(n_gals)
n_done = 0
if verbose:
print("Projecting onto {0}-axis...".format(xyz[2]))
gal_type_index = np.arange(len(halotab.gal_type))
if (comm is not None) & (has_mpi):
size = comm.size
rank = comm.rank
gal_type_index = gal_type_index[rank::size]
print('{}: len(gal_type_index)={}'.format(
rank, len(gal_type_index)))
elif (comm is not None) & (not has_mpi):
raise (ImportError(
"You passed something to the comm argument, but I couldn't import mpi4py"
))
for i in gal_type_index:
if mode == 'auto':
for k in np.arange(i, len(halotab.gal_type)):
if len(pos[i]) * len(pos[k]) > 0:
if verbose:
if comm:
if comm.rank == 0:
n_done += (n_gals[i] * n_gals[k] *
(2 if k != i else 1))
print_progress(n_done /
np.sum(n_gals)**2)
else:
n_done += (n_gals[i] * n_gals[k] *
(2 if k != i else 1))
print_progress(n_done / np.sum(n_gals)**2)
if i == k:
xi = tpcf(pos[i],
*tpcf_args,
sample2=pos[k] if k != i else None,
do_auto=True,
do_cross=False,
period=halocat.Lbox * lbox_stretch,
**tpcf_kwargs)
else:
xi = tpcf(pos[i],
*tpcf_args,
sample2=pos[k] if k != i else None,
do_auto=False,
do_cross=True,
period=halocat.Lbox * lbox_stretch,
**tpcf_kwargs)
if 'tpcf_matrix' not in locals():
tpcf_matrix = np.zeros(
(len(xi.ravel()), len(halotab.gal_type),
len(halotab.gal_type)))
tpcf_shape = xi.shape
tpcf_matrix[:, i, k] += xi.ravel()
tpcf_matrix[:, k, i] = tpcf_matrix[:, i, k]
elif mode == 'cross':
if len(pos[i]) > 0:
if verbose:
n_done += n_gals[i]
print_progress(n_done / np.sum(n_gals))
xi = tpcf(pos[i],
*tpcf_args,
**tpcf_kwargs,
period=halocat.Lbox * lbox_stretch)
if tpcf.__name__ == 'delta_sigma':
xi = xi[1]
if 'tpcf_matrix' not in locals():
tpcf_matrix = np.zeros(
(len(xi.ravel()), len(halotab.gal_type)))
tpcf_shape = xi.shape
tpcf_matrix[:, i] = xi.ravel()
if not project_xyz or mode == 'cross':
break
if comm:
tpcf_matrix = comm.allreduce(tpcf_matrix, op=MPI.SUM)
if project_xyz and mode == 'auto':
tpcf_matrix /= 3.0
if mode == 'auto':
tpcf_matrix_flat = []
for i in range(tpcf_matrix.shape[0]):
tpcf_matrix_flat.append(
symmetric_matrix_to_array(tpcf_matrix[i]))
tpcf_matrix = np.array(tpcf_matrix_flat)
halotab.attrs = {}
halotab.attrs['tpcf'] = tpcf.__name__
halotab.attrs['mode'] = mode
halotab.attrs['simname'] = halocat.simname
halotab.attrs['redshift'] = halocat.redshift
halotab.attrs['Num_ptcl_requirement'] = Num_ptcl_requirement
halotab.attrs['prim_haloprop_key'] = prim_haloprop_key
halotab.attrs['sec_haloprop_key'] = sec_haloprop_key
halotab.tpcf_args = tpcf_args
halotab.tpcf_kwargs = tpcf_kwargs
halotab.tpcf_shape = tpcf_shape
halotab.tpcf_matrix = tpcf_matrix
halotab.init = True
return halotab
def assign_positions(self, **kwargs):
"""
assign satellite positions based on subhalo radial positions and random angular positions.
"""
if 'table' in kwargs.keys():
table = kwargs['table']
halo_x = table['halo_x']
halo_y = table['halo_y']
halo_z = table['halo_z']
halo_hostid = table['halo_hostid']
halo_id = table['halo_id']
b_to_a = table['halo_b_to_a']
c_to_a = table['halo_c_to_a']
halo_axisA_x = table['halo_axisA_x']
halo_axisA_y = table['halo_axisA_y']
halo_axisA_z = table['halo_axisA_z']
halo_axisC_x = table['halo_axisC_x']
halo_axisC_y = table['halo_axisC_y']
halo_axisC_z = table['halo_axisC_z']
concentration = table['halo_nfw_conc']
rvir = table['halo_rvir']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
else:
halo_x = kwargs['halo_x']
halo_y = kwargs['halo_y']
halo_z = kwargs['halo_z']
halo_hostid = kwargs['halo_hostid']
halo_id = kwargs['halo_id']
b_to_a = kwargs['halo_b_to_a']
c_to_a = kwargs['halo_c_to_a']
halo_axisA_x = kwargs['halo_axisA_x']
halo_axisA_y = kwargs['halo_axisA_y']
halo_axisA_z = kwargs['halo_axisA_z']
halo_axisC_x = kwargs['halo_axisC_x']
halo_axisC_y = kwargs['halo_axisC_y']
halo_axisC_z = kwargs['halo_axisC_z']
concentration = kwargs['halo_nfw_conc']
rvir = tabel['halo_rvir']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
Npts = len(halo_x)
# get host halo properties
inds1, inds2 = crossmatch(halo_hostid, halo_id)
# some sub-haloes point to a host that does not exist
no_host = ~np.in1d(halo_hostid, halo_id)
if np.sum(no_host) > 0:
msg = ("There are {0} sub-haloes with no host halo.".format(
np.sum(no_host)))
warn(msg)
host_halo_concentration = np.zeros(Npts)
host_halo_concentration[inds1] = concentration[inds2]
host_halo_rvir = np.zeros(Npts)
host_halo_rvir[inds1] = rvir[inds2]
host_b_to_a = np.zeros(Npts)
host_b_to_a[inds1] = b_to_a[inds2]
host_c_to_a = np.zeros(Npts)
host_c_to_a[inds1] = c_to_a[inds2]
major_axis = np.vstack((halo_axisA_x, halo_axisA_y, halo_axisA_z)).T
minor_axis = np.vstack((halo_axisC_x, halo_axisC_y, halo_axisC_z)).T
inter_axis = np.cross(major_axis, minor_axis)
host_major_axis = np.zeros((Npts, 3))
host_inter_axis = np.zeros((Npts, 3))
host_minor_axis = np.zeros((Npts, 3))
host_major_axis[inds1] = major_axis[inds2]
host_inter_axis[inds1] = inter_axis[inds2]
host_minor_axis[inds1] = minor_axis[inds2]
# host x,y,z-position
halo_x[inds1] = halo_x[inds2]
halo_y[inds1] = halo_y[inds2]
halo_z[inds1] = halo_z[inds2]
# host halo centric positions
phi = np.random.uniform(0, 2 * np.pi, Npts)
uran = np.random.rand(Npts) * 2 - 1
cos_t = uran
sin_t = np.sqrt((1. - cos_t * cos_t))
b_to_a, c_to_a = self.anisotropy_bias_response(host_b_to_a,
host_c_to_a)
c_to_b = c_to_a / b_to_a
# temporarily use x-axis as the major axis
x = 1.0 / c_to_a * sin_t * np.cos(phi)
y = 1.0 / c_to_b * sin_t * np.sin(phi)
z = cos_t
x_correlated_axes = np.vstack((x, y, z)).T
x_axes = np.tile((1, 0, 0), Npts).reshape((Npts, 3))
matrices = rotation_matrices_from_basis(host_major_axis,
host_inter_axis,
host_minor_axis)
# rotate x-axis into the major axis
#angles = angles_between_list_of_vectors(x_axes, major_axes)
#rotation_axes = vectors_normal_to_planes(x_axes, major_axes)
#matrices = rotation_matrices_from_angles(angles, rotation_axes)
correlated_axes = rotate_vector_collection(matrices, x_correlated_axes)
x, y, z = correlated_axes[:, 0], correlated_axes[:,
1], correlated_axes[:,
2]
nfw = NFWPhaseSpace(conc_mass_model='direct_from_halo_catalog', )
dimensionless_radial_distance = nfw._mc_dimensionless_radial_distance(
host_halo_concentration)
x *= dimensionless_radial_distance
y *= dimensionless_radial_distance
z *= dimensionless_radial_distance
x *= host_halo_rvir
y *= host_halo_rvir
z *= host_halo_rvir
a = 1
b = b_to_a * a
c = c_to_a * a
T = (c**2 - b**2) / (c**2 - a**2)
q = b / a
s = c / a
x *= np.sqrt(q * s)
y *= np.sqrt(q * s)
z *= np.sqrt(q * s)
# host-halo centric radial distance
r = np.sqrt(x * x + y * y + z * z)
# move back into original cordinate system
xx = halo_x + x
yy = halo_y + y
zz = halo_z + z
xx[no_host] = halo_x[no_host]
yy[no_host] = halo_y[no_host]
zz[no_host] = halo_z[no_host]
# account for PBCs
xx, yy, zz = wrap_coordinates(xx, yy, zz, Lbox)
if 'table' in kwargs.keys():
# assign satellite galaxy positions
try:
mask = (table['gal_type'] == 'satellites')
except KeyError:
mask = np.array([True] * len(table))
msg = (
"`gal_type` not indicated in `table`.",
"The orientation is being assigned for all galaxies in the `table`."
)
print(msg)
table['x'] = halo_x * 1.0
table['y'] = halo_y * 1.0
table['z'] = halo_z * 1.0
table['x'][mask] = xx[mask]
table['y'][mask] = yy[mask]
table['z'][mask] = zz[mask]
table['r'] = 0.0
table['r'][mask] = r[mask]
table['halo_x'][mask] = halo_x[mask]
table['halo_y'][mask] = halo_y[mask]
table['halo_z'][mask] = halo_z[mask]
return table
else:
x = xx
y = yy
z = zz
return np.vstack((x, y, z)).T
def assign_positions(self, **kwargs):
"""
assign satellite positions based on subhalo radial positions and random angular positions.
"""
if 'table' in kwargs.keys():
table = kwargs['table']
halo_x = table['halo_x']
halo_y = table['halo_y']
halo_z = table['halo_z']
halo_axisA_x = table['halo_axisA_x']
halo_axisA_y = table['halo_axisA_y']
halo_axisA_z = table['halo_axisA_z']
halo_hostid = table['halo_hostid']
halo_id = table['halo_id']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
else:
halo_x = kwargs['halo_x']
halo_y = kwargs['halo_y']
halo_z = kwargs['halo_z']
halo_hostid = kwargs['halo_hostid']
halo_id = kwargs['halo_id']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
# get subhalo positions
x = halo_x * 1.0
y = halo_y * 1.0
z = halo_z * 1.0
# get host halo positions
inds1, inds2 = crossmatch(halo_hostid, halo_id)
# x-position
halo_x[inds1] = halo_x[inds2]
# y-position
halo_y[inds1] = halo_y[inds2]
# z-position
halo_z[inds1] = halo_z[inds2]
# get host halo orientation
host_halo_axisA_x = halo_axisA_x
host_halo_axisA_x[inds1] = halo_axisA_x[inds2]
host_halo_axisA_y = halo_axisA_y
host_halo_axisA_y[inds1] = halo_axisA_y[inds2]
host_halo_axisA_z = halo_axisA_z
host_halo_axisA_z[inds1] = halo_axisA_z[inds2]
host_halo_mjor_axes = np.vstack(
(halo_axisA_x, halo_axisA_y, halo_axisA_z)).T
# calculate radial positions
vec_r, r = radial_distance(x, y, z, halo_x, halo_y, halo_z, Lbox)
# rotate radial vectors arond halo major axis
N = len(x)
rot_angles = np.random.uniform(0.0, 2 * np.pi, N)
rot_axes = host_halo_mjor_axes
rot_m = rotation_matrices_from_angles(rot_angles, rot_axes)
new_vec_r = rotate_vector_collection(rot_m, vec_r)
xx = new_vec_r[:, 0]
yy = new_vec_r[:, 1]
zz = new_vec_r[:, 2]
# move back into original cordinate system
xx = halo_x + xx
yy = halo_y + yy
zz = halo_z + zz
# account for PBCs
mask = (xx < 0.0)
xx[mask] = xx[mask] + Lbox[0]
mask = (xx > Lbox[0])
xx[mask] = xx[mask] - Lbox[0]
mask = (yy < 0.0)
yy[mask] = yy[mask] + Lbox[1]
mask = (yy > Lbox[1])
yy[mask] = yy[mask] - Lbox[1]
mask = (zz < 0.0)
zz[mask] = zz[mask] + Lbox[2]
mask = (zz > Lbox[2])
zz[mask] = zz[mask] - Lbox[2]
if 'table' in kwargs.keys():
# assign satellite galaxy positions
try:
mask = (table['gal_type'] == 'satellites')
except KeyError:
mask = np.array([True] * len(table))
msg = (
"`gal_type` not indicated in `table`.",
"The orientation is being assigned for all galaxies in the `table`."
)
print(msg)
table['x'] = halo_x * 1.0
table['y'] = halo_y * 1.0
table['z'] = halo_z * 1.0
table['x'][mask] = xx[mask]
table['y'][mask] = yy[mask]
table['z'][mask] = zz[mask]
table['halo_x'][mask] = halo_x[mask]
table['halo_y'][mask] = halo_y[mask]
table['halo_z'][mask] = halo_z[mask]
return table
else:
x = xx
y = yy
z = zz
return np.vstack((x, y, z)).T
def assign_positions(self, **kwargs):
"""
assign satellite positions based on subhalo radial positions and random angular positions.
"""
if 'table' in kwargs.keys():
table = kwargs['table']
halo_x = table['halo_x']
halo_y = table['halo_y']
halo_z = table['halo_z']
halo_hostid = table['halo_hostid']
halo_id = table['halo_id']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
else:
halo_x = kwargs['halo_x']
halo_y = kwargs['halo_y']
halo_z = kwargs['halo_z']
halo_hostid = kwargs['halo_hostid']
halo_id = kwargs['halo_id']
try:
Lbox = kwargs['Lbox']
except KeyError:
Lbox = self._Lbox
# get subhalo positions
x = halo_x * 1.0
y = halo_y * 1.0
z = halo_z * 1.0
# get host halo positions
inds1, inds2 = crossmatch(halo_hostid, halo_id)
# x-position
halo_x[inds1] = halo_x[inds2]
# y-position
halo_y[inds1] = halo_y[inds2]
# z-position
halo_z[inds1] = halo_z[inds2]
# calculate radial positions
vec_r, r = radial_distance(x, y, z, halo_x, halo_y, halo_z, Lbox)
# calculate new positions with same radial distance
# in the coordinatre systems centered on host haloes
npts = len(x)
uran = np.random.uniform(0, 1, npts)
theta = np.arccos(uran * 2.0 - 1.0)
phi = np.random.uniform(0, 2 * np.pi, npts)
xx = r * np.sin(theta) * np.cos(phi)
yy = r * np.sin(theta) * np.sin(phi)
zz = r * np.cos(theta)
# move back into original cordinate system
xx = halo_x + xx
yy = halo_y + yy
zz = halo_z + zz
# account for PBCs
mask = (xx < 0.0)
xx[mask] = xx[mask] + Lbox[0]
mask = (xx > Lbox[0])
xx[mask] = xx[mask] - Lbox[0]
mask = (yy < 0.0)
yy[mask] = yy[mask] + Lbox[1]
mask = (yy > Lbox[1])
yy[mask] = yy[mask] - Lbox[1]
mask = (zz < 0.0)
zz[mask] = zz[mask] + Lbox[2]
mask = (zz > Lbox[2])
zz[mask] = zz[mask] - Lbox[2]
if 'table' in kwargs.keys():
# assign satellite galaxy positions
try:
mask = (table['gal_type'] == 'satellites')
except KeyError:
mask = np.array([True] * len(table))
msg = (
"`gal_type` not indicated in `table`.",
"The orientation is being assigned for all galaxies in the `table`."
)
print(msg)
table['x'] = halo_x * 1.0
table['y'] = halo_y * 1.0
table['z'] = halo_z * 1.0
table['x'][mask] = xx[mask]
table['y'][mask] = yy[mask]
table['z'][mask] = zz[mask]
table['halo_x'][mask] = halo_x[mask]
table['halo_y'][mask] = halo_y[mask]
table['halo_z'][mask] = halo_z[mask]
return table
else:
x = xx
y = yy
z = zz
return np.vstack((x, y, z)).T
def load_umachine_z0(fname=FNAME, ssfr_q_loc=-11.8, ssfr_q_scale=0.5, seed=43):
"""These data can be downloaded from """
mock = Table(np.load(fname))
cenmask = mock["upid"] == -1
mock["hostid"] = mock["upid"]
mock["hostid"][cenmask] = mock["id"][cenmask]
mhost = np.copy(mock["mp"])
idxA, idxB = crossmatch(mock["upid"][~cenmask], mock["id"])
mhost_sats = mhost[~cenmask]
mhost_sats[idxA] = mock["mp"][idxB]
mhost[~cenmask] = mhost_sats
mock["logmhost"] = np.log10(mhost)
mask = mock["sm"] > 10**9.5
mock = mock[mask]
mock["logsm"] = np.log10(mock["sm"])
mock["ssfr"] = mock["sfr"] / mock["sm"]
mock.remove_column("sm")
zeromask = mock["ssfr"] == 0
nzero = np.count_nonzero(zeromask)
rng = np.random.RandomState(seed)
random_ssfr_quenched_gals = rng.normal(loc=ssfr_q_loc,
scale=ssfr_q_scale,
size=nzero)
mock["ssfr"][zeromask] = 10**random_ssfr_quenched_gals
mock["log_ssfr"] = np.log10(mock["ssfr"])
mock.remove_column("ssfr")
mock["x"] = np.mod(mock["pos"][:, 0], 250)
mock["y"] = np.mod(mock["pos"][:, 1], 250)
mock["z"] = np.mod(mock["pos"][:, 2], 250)
cols_to_remove = [
"flags",
"uparent_dist",
"pos",
"vmp",
"lvmp",
"m",
"descid",
"v",
"r",
"rank1",
"rank2",
"ra",
"rarank",
"A_UV",
"icl",
"obs_sm",
"obs_sfr",
"obs_uv",
"empty",
]
for col in cols_to_remove:
mock.remove_column(col)
mock["logmpeak"] = np.log10(mock["mp"])
mock.remove_column("mp")
mock["neg_logmhost"] = -mock["logmhost"]
mock.sort(("neg_logmhost", "hostid", "upid", "logmpeak"))
mock.remove_column("neg_logmhost")
return mock
def halocat_to_galaxy_table(halocat):
"""
transform a Halotools halocat.halo_table into a
test galaxy_table object, used for testing model componenets
Returns
-------
galaxy_table : astropy.table object
"""
halo_id = halocat.halo_table['halo_id']
halo_upid = halocat.halo_table['halo_upid']
host_id = halocat.halo_table['halo_hostid']
# create galaxy table
table = Table([halo_id, halo_upid, host_id], names=('halo_id', 'halo_upid', 'halo_hostid'))
# add position information
table['x'] = halocat.halo_table['halo_x']
table['y'] = halocat.halo_table['halo_y']
table['z'] = halocat.halo_table['halo_z']
table['vx'] = halocat.halo_table['halo_vx']
table['vy'] = halocat.halo_table['halo_vy']
table['vz'] = halocat.halo_table['halo_vz']
# add halo mass
table['halo_mpeak'] = halocat.halo_table['halo_mpeak']
# add orientation information
# place holders for now
table['galaxy_axisA_x'] = 0.0
table['galaxy_axisA_y'] = 0.0
table['galaxy_axisA_z'] = 0.0
table['galaxy_axisB_x'] = 0.0
table['galaxy_axisB_y'] = 0.0
table['galaxy_axisB_z'] = 0.0
table['galaxy_axisC_x'] = 0.0
table['galaxy_axisC_y'] = 0.0
table['galaxy_axisC_z'] = 0.0
# tag centrals vs satellites
hosts = (halocat.halo_table['halo_upid'] == -1)
subs = (halocat.halo_table['halo_upid'] != -1)
table['gal_type'] = 'satellites'
table['gal_type'][hosts] = 'centrals'
table['gal_type'][subs] = 'satellites'
# host halo properties
inds1, inds2 = crossmatch(halocat.halo_table['halo_hostid'], halocat.halo_table['halo_id'])
# host halo position
table['halo_x'] = 0.0
table['halo_x'][inds1] = halocat.halo_table['halo_x'][inds2]
table['halo_y'] = 0.0
table['halo_y'][inds1] = halocat.halo_table['halo_y'][inds2]
table['halo_z'] = 0.0
table['halo_z'][inds1] = halocat.halo_table['halo_z'][inds2]
# host haloo mass
table['halo_mvir'] = 0.0
table['halo_mvir'][inds1] = halocat.halo_table['halo_mvir'][inds2]
table['halo_rvir'] = 0.0
table['halo_rvir'][inds1] = halocat.halo_table['halo_rvir'][inds2]
# assign orientations
#table['halo_axisA_x'] = 0.0
#table['halo_axisA_x'][inds1] = halocat.halo_table['halo_axisA_x'][inds2]
#table['halo_axisA_y'] = 0.0
#table['halo_axisA_y'][inds1] = halocat.halo_table['halo_axisA_y'][inds2]
#table['halo_axisA_z'] = 0.0
#table['halo_axisA_z'][inds1] = halocat.halo_table['halo_axisA_z'][inds2]
table['halo_axisA_x'] = halocat.halo_table['halo_axisA_x']
table['halo_axisA_y'] = halocat.halo_table['halo_axisA_y']
table['halo_axisA_z'] = halocat.halo_table['halo_axisA_z']
return table
def load_orphan_subhalos():
"""
"""
dirname = "/Users/aphearin/work/sims/bolplanck/orphan_catalog_z0"
basename = "cross_matched_orphan_catalog.hdf5"
halo_table = Table.read(os.path.join(dirname, basename), path='data')
Lbox = 250.
halo_table['x'] = enforce_periodicity_of_box(halo_table['x'], Lbox)
halo_table['y'] = enforce_periodicity_of_box(halo_table['y'], Lbox)
halo_table['z'] = enforce_periodicity_of_box(halo_table['z'], Lbox)
halo_table['vmax_at_mpeak_percentile'] = np.load(
os.path.join(dirname, 'vmax_percentile.npy'))
halo_table['noisy_vmax_at_mpeak_percentile'] = noisy_percentile(
halo_table['vmax_at_mpeak_percentile'], 0.5)
halo_table['orphan_mass_loss_percentile'] = -1.
halo_table['orphan_mass_loss_percentile'][halo_table['orphan']] = np.load(
os.path.join(dirname, 'orphan_mass_loss_percentile.npy'))
halo_table['orphan_vmax_at_mpeak_percentile'] = -1.
halo_table['orphan_vmax_at_mpeak_percentile'][halo_table['orphan']] = np.load(
os.path.join(dirname, 'orphan_vmax_at_mpeak_percentile.npy'))
halo_table['orphan_vmax_loss_percentile'] = -1.
halo_table['orphan_vmax_loss_percentile'][halo_table['orphan']] = np.load(
os.path.join(dirname, 'orphan_vmax_loss_percentile.npy'))
halo_table['orphan_fixed_mpeak_mhost_percentile'] = -1.
halo_table['orphan_fixed_mpeak_mhost_percentile'][halo_table['orphan']] = np.load(
os.path.join(dirname, 'orphan_fixed_mpeak_mhost_percentile.npy'))
halo_table['zpeak'] = 1./halo_table['mpeak_scale']-1.
halo_table['zpeak_no_splashback'] = 0.
satmask = halo_table['upid'] != -1
halo_table['zpeak_no_splashback'][satmask] = halo_table['zpeak'][satmask]
rvir_peak_physical_unity_h = halo_mass_to_halo_radius(halo_table['mpeak'],
Planck15, halo_table['zpeak'], 'vir')
rvir_peak_physical = rvir_peak_physical_unity_h/Planck15.h
halo_table['rvir_zpeak'] = rvir_peak_physical*1000.
rvir_peak_no_spl_physical_unity_h = halo_mass_to_halo_radius(halo_table['mpeak'],
Planck15, halo_table['zpeak_no_splashback'], 'vir')
rvir_peak_no_spl_physical = rvir_peak_no_spl_physical_unity_h/Planck15.h
halo_table['rvir_zpeak_no_splashback'] = rvir_peak_no_spl_physical*1000.
halo_table['hostid'] = np.nan
hostmask = halo_table['upid'] == -1
halo_table['hostid'][hostmask] = halo_table['halo_id'][hostmask]
halo_table['hostid'][~hostmask] = halo_table['upid'][~hostmask]
idxA, idxB = crossmatch(halo_table['hostid'], halo_table['halo_id'])
halo_table['host_mvir'] = np.nan
halo_table['host_mvir'][idxA] = halo_table['mvir'][idxB]
halo_table = halo_table[~np.isnan(halo_table['host_mvir'])]
halo_table['frac_mpeak_remaining'] = halo_table['mvir']/halo_table['mpeak']
halo_table['frac_vpeak_remaining'] = halo_table['vmax']/halo_table['vmax_at_mpeak']
return halo_table
def build_output_snapshot_mock(umachine,
target_halos,
halo_indices,
galaxy_indices,
Lbox_target=256.):
"""
"""
dc2 = Table()
dc2['source_halo_id'] = umachine['hostid'][galaxy_indices]
dc2['target_halo_id'] = np.repeat(
target_halos['fof_halo_tag'][halo_indices],
target_halos['richness'][halo_indices])
idxA, idxB = crossmatch(dc2['target_halo_id'],
target_halos['fof_halo_tag'])
msg = "target IDs do not match!"
assert np.all(dc2['source_halo_id'][idxA] == target_halos['source_halo_id']
[idxB]), msg
dc2['target_halo_x'] = 0.
dc2['target_halo_y'] = 0.
dc2['target_halo_z'] = 0.
dc2['target_halo_vx'] = 0.
dc2['target_halo_vy'] = 0.
dc2['target_halo_vz'] = 0.
dc2['target_halo_x'][idxA] = target_halos['fof_halo_center_x'][idxB]
dc2['target_halo_y'][idxA] = target_halos['fof_halo_center_y'][idxB]
dc2['target_halo_z'][idxA] = target_halos['fof_halo_center_z'][idxB]
dc2['target_halo_vx'][idxA] = target_halos['fof_halo_mean_vx'][idxB]
dc2['target_halo_vy'][idxA] = target_halos['fof_halo_mean_vy'][idxB]
dc2['target_halo_vz'][idxA] = target_halos['fof_halo_mean_vz'][idxB]
dc2['target_halo_mass'] = 0.
dc2['target_halo_mass'][idxA] = target_halos['fof_halo_mass'][idxB]
source_galaxy_keys = ('host_halo_mvir', 'upid', 'host_centric_x',
'host_centric_y', 'host_centric_z',
'host_centric_vx', 'host_centric_vy',
'host_centric_vz', 'obs_sm', 'obs_sfr',
'sfr_percentile_fixed_sm', 'rmag',
'sdss_petrosian_gr', 'sdss_petrosian_ri', 'size_kpc')
for key in source_galaxy_keys:
dc2[key] = umachine[key][galaxy_indices]
x_init = dc2['target_halo_x'] + dc2['host_centric_x']
vx_init = dc2['target_halo_vx'] + dc2['host_centric_vx']
dc2['x'], dc2['vx'] = enforce_periodicity_of_box(x_init,
Lbox_target,
velocity=vx_init)
y_init = dc2['target_halo_y'] + dc2['host_centric_y']
vy_init = dc2['target_halo_vy'] + dc2['host_centric_vy']
dc2['y'], dc2['vy'] = enforce_periodicity_of_box(y_init,
Lbox_target,
velocity=vy_init)
z_init = dc2['target_halo_z'] + dc2['host_centric_z']
vz_init = dc2['target_halo_vz'] + dc2['host_centric_vz']
dc2['z'], dc2['vz'] = enforce_periodicity_of_box(z_init,
Lbox_target,
velocity=vz_init)
return dc2
def load_value_added_halocat(simname='bolplanck', redshift=0.0, version_name='halotools_v0p4'):
"""
adds properties to halotools rockstar halo catalogs
Returns
-------
halocat : Halotools halocat object
"""
halocat = CachedHaloCatalog(simname=simname, halo_finder='rockstar',
redshift=redshift, version_name=version_name)
halos = halocat.halo_table
inds1, inds2 = crossmatch(halos['halo_hostid'], halos['halo_id'])
x = halos['halo_x']
y = halos['halo_x']
z = halos['halo_z']
host_x = np.copy(x)
host_y = np.copy(y)
host_z = np.copy(z)
host_x[inds1] = halos['halo_x'][inds2]
host_y[inds1] = halos['halo_y'][inds2]
host_z[inds1] = halos['halo_z'][inds2]
dx = relative_positions_and_velocities(x, host_x, period=halocat.Lbox[0])
dy = relative_positions_and_velocities(y, host_y, period=halocat.Lbox[1])
dz = relative_positions_and_velocities(z, host_z, period=halocat.Lbox[2])
radius = np.sqrt(dx**2+dy**2+dz**2)
r = normalized_vectors(np.vstack((dx, dy, dz)).T)
r = np.nan_to_num(r)
halos['halo_centric_distance'] = radius
halos['halo_radial_unit_vector'] = r
# calculate scaled radial distance (r/r_vir)
scaled_radius = np.zeros(len(halos))
# ignore divide by zero in this case
scaled_radius[inds1] = np.divide(radius[inds1], halos['halo_rvir'][inds2],
out=np.zeros_like(radius[inds1]),
where=halos['halo_rvir'][inds2] != 0)
halos['halo_r_by_rvir'] = radius
#define major axis of (sub-)haloes
halos['halo_major_axis'] = normalized_vectors(np.vstack((halos['halo_axisA_x'],
halos['halo_axisA_y'],
halos['halo_axisA_z'])).T)
#define spin axis of (sub-)haloes
halos['halo_spin_axis'] = normalized_vectors(np.vstack((halos['halo_jx'],
halos['halo_jy'],
halos['halo_jz'])).T)
# define host orientation vectors for each (sub-)halo
# major axis
halos['halo_host_major_axis'] = np.copy(halos['halo_major_axis'])
halos['halo_host_major_axis'][inds1] = halos['halo_major_axis'][inds2]
# spin axis
halos['halo_host_spin_axis'] = np.copy(halos['halo_spin_axis'])
halos['halo_host_spin_axis'][inds1] = halos['halo_spin_axis'][inds2]
# major axis
#theta_ma_1 = angles_between_list_of_vectors(halos['halo_radial_unit_vector'], halos['halo_major_axis'])
#theta_ma_2 = angles_between_list_of_vectors(halos['halo_host_major_axis'], halos['halo_major_axis'])
# spin axis
#theta_ma_3 = angles_between_list_of_vectors(halos['halo_radial_unit_vector'], halos['halo_spin_axis'])
#theta_ma_4 = angles_between_list_of_vectors(halos['halo_host_spin_axis'], halos['halo_spin_axis'])
halocat.halo_table = halos
return halocat
def test_many_galaxies_per_source_halo():
""" Test case of mutliple source galaxies per source halo
"""
# Set up a source halo catalog with 100 halos in each mass bin
log_mhost_min, log_mhost_max, dlog_mhost = 10.5, 15.5, 0.5
log_mhost_bins = np.arange(log_mhost_min, log_mhost_max + dlog_mhost,
dlog_mhost)
log_mhost_mids = 0.5 * (log_mhost_bins[:-1] + log_mhost_bins[1:])
num_distinct_source_halo_masses = len(log_mhost_mids)
num_source_halos_per_bin = 20
source_halo_log_mhost = np.tile(log_mhost_mids, num_source_halos_per_bin)
num_source_halos = len(source_halo_log_mhost)
source_halo_id = np.arange(num_source_halos).astype(int)
source_halo_bin_number = halo_bin_indices(log_mhost=(source_halo_log_mhost,
log_mhost_bins))
assert len(
source_halo_bin_number
) == num_distinct_source_halo_masses * num_source_halos_per_bin, "Bad setup of source_halos"
ngals_per_source_halo = 3
num_source_galaxies = num_source_halos * ngals_per_source_halo
source_galaxy_host_halo_id = np.repeat(source_halo_id,
ngals_per_source_halo)
source_galaxy_host_mass = np.repeat(source_halo_log_mhost,
ngals_per_source_halo)
assert len(source_galaxy_host_mass
) == num_source_galaxies, "Bad setup of source_galaxies"
num_target_halos_per_source_halo = 11
target_halo_log_mhost = np.repeat(source_halo_log_mhost,
num_target_halos_per_source_halo)
target_halo_bin_number = halo_bin_indices(log_mhost=(target_halo_log_mhost,
log_mhost_bins))
num_target_halos = len(target_halo_bin_number)
target_halo_ids = np.arange(num_target_halos).astype('i8')
nhalo_min = 5
_result = source_galaxy_selection_indices(
source_galaxy_host_halo_id, source_halo_bin_number, source_halo_id,
target_halo_bin_number, target_halo_ids, nhalo_min, log_mhost_bins)
selection_indices, target_galaxy_target_halo_ids, target_galaxy_source_halo_ids = _result
correct_num_target_galaxies = int(num_target_halos * ngals_per_source_halo)
assert correct_num_target_galaxies == len(
target_galaxy_target_halo_ids) == len(selection_indices)
idxA, idxB = crossmatch(target_galaxy_target_halo_ids, target_halo_ids)
assert len(idxA) == len(target_galaxy_target_halo_ids)
target_halo_bins = target_halo_bin_number[idxB]
A = num_target_halos_per_source_halo * ngals_per_source_halo
assert np.all(
np.histogram(target_halo_bins)[0] == A *
np.histogram(source_halo_bin_number)[0])
selected_galaxies_target_halo_mass = target_halo_log_mhost[idxB]
a = halo_bin_indices(log_mhost=(selected_galaxies_target_halo_mass,
log_mhost_bins))
b = halo_bin_indices(log_mhost=(source_galaxy_host_mass, log_mhost_bins))
assert np.all(
np.histogram(a)[0] == num_target_halos_per_source_halo *
np.histogram(b)[0])
selected_galaxies_source_halo_mass = source_galaxy_host_mass[
selection_indices]
assert np.allclose(selected_galaxies_source_halo_mass,
selected_galaxies_target_halo_mass)
gen = zip(selected_galaxies_source_halo_mass,
target_galaxy_target_halo_ids, target_galaxy_source_halo_ids)
for galmass, target_id, source_id in gen:
source_mask = source_halo_id == source_id
target_mask = target_halo_ids == target_id
source_halo_mass = source_halo_log_mhost[source_mask][0]
target_halo_mass = target_halo_log_mhost[target_mask][0]
assert source_halo_mass == target_halo_mass == galmass
