def return_mock(sm_lim=9.5, sigma=0.2, rho=-0.1, sim='Bolshoi'):
#get parameters to make mock
dm_sim = sim
sfr_cor = rho
sm_lim = sm_lim
sm_scat = sigma
sm_mock_key = 'Mpeak'
ssfr_mock_key = 'Vpeak'
#load halo catalogue
HC, Lbox = get_mock(dm_sim)
box_size=Lbox
vol = Lbox**3.0
#load sdss catalogue
GC = get_sdss_sample()
#get mock data structure
mock = get_mock_array(HC)
#define central moment SMHM distribution
from scipy.stats import norm
def sigma2_xy(y):
return sm_scat
def P_x(y,mu_xy,sigma2_xy=sigma2_xy):
mu_x = mu_xy(y)
sigma2_x = sigma2_xy(y)
p = norm(loc=mu_x, scale=sigma2_x)
return p
#define halo mass function
phi_star = 9.95231920*10**(-5)
m_star = 13.9833298
alpha1 = -1.85781918
alpha2 = 0.438785492
dndm_halo = cu.schechter_function.Log_Super_Schechter(phi_star, m_star, alpha1, alpha2)
#define stellar mass function
#phi_1 = 7.97407978*10**(-3)
#phi_2 = 1.10507295*10**(-3)
#m_1 = 10.5040374
#m_2 = 10.9083632
#alpha_1 = -0.956108368
#alpha_2 = -1.48157281
phi_1 = 6.94559219e-03
phi_2 = 2.76601643e-04
m_1 = 1.05518723e+01
m_2 = 1.08818830e+01
alpha_1 = -1.20094175e+00
alpha_2 = -7.40886755e-01
dndm_gal = cu.schechter_function.Log_Double_Schechter(m_1, m_2, phi_1, phi_2, alpha_1, alpha_2)
#solve for the mean SMHM relation
dm_star = np.linspace(5,12.5,100)
dm_halo = np.linspace(10,15.5,100)
dn_star = dndm_gal(dm_star)
dn_halo = dndm_halo(dm_halo)
if sm_scat==0.0:
SMHM = AM_nonparam_1(dn_star, dm_star, dn_halo, dm_halo,\
None, y_min = np.amin(dm_halo),\
y_max = np.amax(dm_halo), ny=30, tol=0.001)
else:
SMHM = AM_nonparam_1(dn_star, dm_star, dn_halo, dm_halo,\
P_x, y_min = np.amin(dm_halo),\
y_max = np.amax(dm_halo), ny=30, tol=0.001)
#apply the mean SMHM relation
P = lambda y: P_x(y, mu_xy=SMHM)
#trim mock
keep = (np.log10(mock['Mpeak'])>10.0)
mock = mock[keep]
#make mock
mock = make_SHAM_mock(mock, P, mock_prop=sm_mock_key, gal_prop='Mstar',\
use_log_mock_prop=True)
#make completeness cut
keep = (mock['Mstar']>sm_lim)
mock = mock[keep]
#add SSFRs
bins = np.arange(sm_lim,12.5,0.1)
mock = add_ssfrs(mock, GC, bins)
#correlate SSFR and Vpeak
mock = correlate(mock, 'Mstar', ssfr_mock_key, 'SSFR', bins=bins, rho=sfr_cor)
#identify host and sub haloes
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
#star forming and quenched
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<=LHS) #indicies of red galaxies
mock['red'] = 0
mock['red'][red]=1
#identify ejected haloes
if np.all(mock['First_Acc_Scale']==-99):
mock['ejected']=-99
else:
ejected_haloes = (mock['upid']==-1) & (mock['First_Acc_Scale']<1.0)
mock['ejected'] = 0
mock['ejected'][ejected_haloes] = 1
#identify abandoned satellites and satellites with ghost central
host_ids = np.unique(mock['id'][host])
has_host = np.in1d(mock['upid'], host_ids)
had_host = np.in1d(mock['upid'], mock['id'])
has_no_host = (has_host==False)
had_no_host = (had_host==False)
abandoned = (has_no_host & had_host)
ghost = (has_no_host & had_no_host & sub)
mock['abandoned'] = 0
mock['abandoned'][abandoned] = 1
mock['ghost'] = 0
mock['ghost'][ghost] = 1
####add some group properties#########################################################
#groupID
mock['group_ID'] = mock['upid']
mock['group_ID'][host] = mock['id'][host]
"""
from halotools.utils import aggregation
def broadcast(x):
central = (x['upid']==-1)
return x['mvir'][central]
mock = aggregation.add_members_property(mock, 'group_ID', 'Mvir_host', broadcast)
def broadcast(x):
central = (x['upid']==-1)
return x['SSFR'][central]
mock = aggregation.add_members_property(mock, 'group_ID', 'SSFR_host', broadcast)
def logsum(x):
vals = 10.0**(x['Mstar'])
return np.log10(np.sum(vals))
mock = aggregation.add_members_property(mock, 'group_ID', 'Mstar_all', logsum)
def N(x):
return len(x)
mock = aggregation.add_members_property(mock, 'group_ID', 'N', N)
def N(x):
cen = (x['upid']==-1)
return np.sum(cen)
mock = aggregation.add_members_property(mock, 'group_ID', 'N_cen', N)
def N(x):
sats = (x['upid']!=-1)
return np.sum(sats)
mock = aggregation.add_members_property(mock, 'group_ID', 'N_sat', N)
from scipy.stats import rankdata
def rank(x):
return rankdata(-1.0*x['Mstar'])-1
mock = aggregation.add_members_property(mock, 'group_ID', 'Rank', rank)
######################################################################################
"""
return mock
def return_base_mock(sm_lim=9.5, sigma=0.2, sim='Bolshoi'):
#get parameters to make mock
dm_sim = sim
sm_lim = sm_lim
sm_scat = sigma
sm_mock_key = 'Mpeak'
ssfr_mock_key = 'Vpeak'
#load halo catalogue
HC, Lbox = get_mock(dm_sim)
box_size=Lbox
vol = Lbox**3.0
#load sdss catalogue
GC = get_sdss_sample()
#get mock data structure
mock = get_mock_array(HC)
#define central moment SMHM distribution
from scipy.stats import norm
def sigma2_xy(y):
return sm_scat
def P_x(y,mu_xy,sigma2_xy=sigma2_xy):
mu_x = mu_xy(y)
sigma2_x = sigma2_xy(y)
p = norm(loc=mu_x, scale=sigma2_x)
return p
#define halo mass function
phi_star = 9.95231920*10**(-5)
m_star = 13.9833298
alpha1 = -1.85781918
alpha2 = 0.438785492
dndm_halo = cu.schechter_function.Log_Super_Schechter(phi_star, m_star, alpha1, alpha2)
#define stellar mass function
#phi_1 = 7.97407978*10**(-3)
#phi_2 = 1.10507295*10**(-3)
#m_1 = 10.5040374
#m_2 = 10.9083632
#alpha_1 = -0.956108368
#alpha_2 = -1.48157281
phi_1 = 6.94559219e-03
phi_2 = 2.76601643e-04
m_1 = 1.05518723e+01
m_2 = 1.08818830e+01
alpha_1 = -1.20094175e+00
alpha_2 = -7.40886755e-01
dndm_gal = cu.schechter_function.Log_Double_Schechter(m_1, m_2, phi_1, phi_2, alpha_1, alpha_2)
#solve for the mean SMHM relation
dm_star = np.linspace(5,12.5,100)
dm_halo = np.linspace(10,15.5,100)
dn_star = dndm_gal(dm_star)
dn_halo = dndm_halo(dm_halo)
if sm_scat==0.0:
SMHM = AM_nonparam_1(dn_star, dm_star, dn_halo, dm_halo,\
None, y_min = np.amin(dm_halo),\
y_max = np.amax(dm_halo), ny=30, tol=0.001)
else:
SMHM = AM_nonparam_1(dn_star, dm_star, dn_halo, dm_halo,\
P_x, y_min = np.amin(dm_halo),\
y_max = np.amax(dm_halo), ny=30, tol=0.001)
#apply the mean SMHM relation
P = lambda y: P_x(y, mu_xy=SMHM)
#trim mock
keep = (np.log10(mock['Mpeak'])>10.0)
mock = mock[keep]
#make mock
mock = make_SHAM_mock(mock, P, mock_prop=sm_mock_key, gal_prop='Mstar',\
use_log_mock_prop=True)
#make completeness cut
keep = (mock['Mstar']>sm_lim)
mock = mock[keep]
#add SSFRs
bins = np.arange(sm_lim,12.5,0.1)
mock = add_ssfrs(mock, GC, bins)
return mock
