def main():
rho = -1.0
sigma = 0.2
#open halo catalogue (used to account for empty haloes)
sys.path.insert(0, '../mocks/')
from make_mock import get_mock
mock, Lbox = get_mock('Bolshoi')
mock = np.array(mock)
print(mock.dtype.names)
hosts = (mock['upid']==-1)
subs = (mock['upid']!=-1)
never_sub = ((mock['First_Acc_Scale']==1.0) & (hosts))
print(np.sum(hosts),np.sum(never_sub))
sys.exit()
fig = plt.figure()
plt.plot(mock['Mpeak'][hosts], mock['mvir'][hosts], '.', ms=2, alpha=0.1, color='green')
plt.plot(mock['Mpeak'][never_sub], mock['mvir'][never_sub], '.', ms=2, alpha=0.1, color='red')
plt.xscale('log')
plt.yscale('log')
plt.show(block=True)
fig = plt.figure()
plt.plot(mock['Mpeak'][subs], mock['mvir'][subs], '.', ms=2, alpha=0.1)
plt.xscale('log')
plt.yscale('log')
plt.show(block=False)
bins=np.arange(10,15,0.1)
inds = np.digitize(np.log10(mock['Mpeak']),bins=bins)
sub_inds = (inds==15)
host_counts, bins = np.histogram(np.log10(mock['mvir'][sub_inds & hosts]),bins)
sub_counts, bins = np.histogram(np.log10(mock['mvir'][sub_inds & subs]),bins)
bin_centers = (bins[:-1]+bins[1:])/2.0
bin_widths = bins[1:]-bins[:-1]
left,right = bins[:-1],bins[1:]
X = np.array([left,right]).T.flatten()
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
p1 = plt.bar(bins[:-1], host_counts, width=bin_widths, color='green', alpha=0.5, lw=0)
Y = np.array([host_counts,host_counts]).T.flatten()
p1 = plt.bar(bins[:-1], sub_counts, width=bin_widths, color='orange', alpha=0.5, lw=0)
Y = np.array([sub_counts,sub_counts]).T.flatten()
plt.plot(X,Y,color='orange')
plt.show()
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()
