def quenched_fraction(mock,mass,bins,use_rank=False):
if use_rank==True:
#rank by stellar mass in groups
from halotools.utils import aggregation
from scipy.stats import rankdata
def function(x,key):
return rankdata(-x[key],method='ordinal')-1
mock = aggregation.add_members_property(mock, 'group_ID', 'rank', function, ['Mstar'])
host = np.where(mock['rank']==0)[0]
sub = np.where(mock['upid']!=0)[0]
host_bool = (mock['rank']==0)
sub_bool = (mock['rank']!=0)
else:
host = np.where(mock['upid']==-1)[0]
sub = np.where(mock['upid']!=-1)[0]
host_bool = (mock['upid']==-1)
sub_bool = (mock['upid']!=-1)
#star forming and quenched
LHS = -11.0
blue = np.where(mock['SSFR']>LHS)[0] #indices of blue galaxies
red = np.where(mock['SSFR']<LHS)[0] #indicies of red galaxies
#calculate the quenched fraction of centrals as a function of halo mass
from f_prop import f_prop
f_red_cen = f_prop(mass,bins,red,blue,host_bool)
#calculate how many hosts are in each mass bin
N = np.histogram(mass[host_bool],bins=bins)[0]
return f_red_cen, N
def return_quick_ssfr_mock(mock, rho=-0.1):
#get parameters to make mock
sfr_cor = rho
#correlate SSFR and Vpeak
bins = np.arange(np.min(mock['Mstar']),12.5,0.1)
mock = correlate(mock, 'Mstar', 'Vpeak', 'SSFR', bins=bins, rho=sfr_cor)
####add some group properties#########################################################
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
#groupID
mock['group_ID'] = mock['upid']
mock['group_ID'][host] = mock['id'][host]
from halotools.utils import aggregation
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)
return mock
def main():
catalogue = 'sm_9.49_s0.2_sfr_c0.0_250'
#catalogue = 'sm_9.49_s0.0_sfr_c-1.0_250_cen_shuffle'
#open mock
filepath = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
print(mock.dtype.names)
#open halo catalogue (used to account for empty haloes)
sys.path.insert(0, '../mocks/')
from make_mock import get_mock
HC, Lbox = get_mock('Bolshoi')
#define host and sub haloes
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
HC_host = (HC['upid']==-1)
HC_sub = (HC['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
#calculate the number of members in a halo
from halotools.utils import aggregation
def function(x):
return len(x)
mock = aggregation.add_members_property(mock, 'group_ID', 'N', function)
def function(x):
inds = (x['SSFR']<=-11.0)
red = np.zeros(len(x))
red[inds]=1.0
return red
mock = aggregation.add_members_property(mock, 'group_ID', 'red', function)
def function(x):
count = (x['Mstar']>=9.5)
return np.sum(x['red'][count])
mock = aggregation.add_members_property(mock, 'group_ID', 'N_red', function)
def function(x):
count = (x['Mstar']>=9.5)
return len(x[count])-np.sum(x['red'][count])
mock = aggregation.add_members_property(mock, 'group_ID', 'N_blue', function)
def function(x):
count = (x['Mstar']>=9.5)
sat = (x['upid']!=-1)
return np.sum(x['red'][count&sat])
mock = aggregation.add_members_property(mock, 'group_ID', 'N_red_sat', function)
def function(x):
count = (x['Mstar']>=9.5)
sat = (x['upid']!=-1)
return len(x[count&sat])-np.sum(x['red'][count&sat])
mock = aggregation.add_members_property(mock, 'group_ID', 'N_blue_sat', function)
#bin galaxies by halo mass
bins = np.arange(10,15,0.1)
bins = 10.0**bins
bin_centers = (bins[:-1]+bins[1:])/2.0
result = np.digitize(mock['Mvir_host'],bins)
N_cen = np.histogram(mock['mvir'][host],bins)[0]
N_haloes = np.histogram(HC['mvir'][HC_host],bins)[0]
N = np.zeros(len(bins)-1)
N_red = np.zeros(len(bins)-1)
N_blue= np.zeros(len(bins)-1)
Ncen = np.zeros(len(bins)-1)
Nsat = np.zeros(len(bins)-1)
Ncen_red = np.zeros(len(bins)-1)
Nsat_red = np.zeros(len(bins)-1)
Ncen_blue = np.zeros(len(bins)-1)
Nsat_blue = np.zeros(len(bins)-1)
for i in range(0,len(bins)-1):
inds = (result==i+1)
mock_c = mock[inds]
#empty haloes
N_empty = np.float(N_haloes[i]-N_cen[i])
zeros = np.zeros(N_empty)
ones = np.ones(N_cen[i])
#red galaxies
N_red[i] = np.mean(np.hstack((mock_c['N_red'],zeros)))
Nsat_red[i] = np.mean(np.hstack((mock_c['N_red_sat'],zeros)))
Ncen_red[i] = np.mean(np.hstack((mock_c['N_red']-mock_c['N_red_sat'],zeros)))
#blue galaxies
N_blue[i] = np.mean(np.hstack((mock_c['N_blue'],zeros)))
Nsat_blue[i] = np.mean(np.hstack((mock_c['N_blue_sat'],zeros)))
Ncen_blue[i] = np.mean(np.hstack((mock_c['N_blue']-mock_c['N_blue_sat'],zeros)))
#all galaxies
N[i] = np.mean(np.hstack((mock_c['N'],zeros)))
Ncen[i] = np.mean(np.hstack((ones,zeros)))
Nsat[i] = np.mean(np.hstack((N[i]-ones,zeros)))
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
plt.plot(bin_centers, N, color='black')
plt.plot(bin_centers, Ncen, '--',color='black')
plt.plot(bin_centers, Nsat, ':',color='black')
plt.xscale('log')
plt.yscale('log')
plt.ylim([0.1,100])
plt.xlim([10**10.5,10**15])
plt.xlabel(r'$M_{\rm vir} ~[h^{-1}M_{\odot}]$')
plt.ylabel(r'$\langle N \rangle$')
plt.show()
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
plt.plot(bin_centers, N, color='black')
plt.plot(bin_centers, N_red, color='red')
plt.plot(bin_centers, N_blue, color='blue')
plt.xscale('log')
plt.yscale('log')
plt.ylim([0.1,100])
plt.xlim([10**10.5,10**15])
plt.xlabel(r'$M_{\rm vir} ~[h^{-1}M_{\odot}]$')
plt.ylabel(r'$\langle N \rangle$')
plt.show()
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
plt.plot(bin_centers, N_red, color='red')
plt.plot(bin_centers, Ncen_red, '--', color='red')
plt.plot(bin_centers, Nsat_red, ':', color='red')
plt.xscale('log')
plt.yscale('log')
plt.ylim([0.1,100])
plt.xlim([10**10.5,10**15])
plt.xlabel(r'$M_{\rm vir} ~[h^{-1}M_{\odot}]$')
plt.ylabel(r'$\langle N \rangle$')
plt.show()
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
plt.plot(bin_centers, N_blue, color='blue')
plt.plot(bin_centers, Ncen_blue, '--', color='blue')
plt.plot(bin_centers, Nsat_blue, ':', color='blue')
plt.xscale('log')
plt.yscale('log')
plt.ylim([0.1,100])
plt.xlim([10**10.5,10**15])
plt.xlabel(r'$M_{\rm vir} ~[h^{-1}M_{\odot}]$')
plt.ylabel(r'$\langle N \rangle$')
plt.show()