def run_auto(datfile, randfile, outfile):
fd = h5py.File(datfile)
fr = h5py.File(randfile)
cd = fd["cart_pts"]
cr = fr["cart_pts"]
dat = np.empty(cd.shape)
rands = np.empty(cr.shape)
dat[:, :] = cd[:, :]
rands[:, :] = cr[:, :]
wps = clustering.wp(dat,
rbins,
pibins,
randoms=rands,
do_cross=False,
estimator='Landy-Szalay',
N_threads='max')
outarr = np.empty((rbin_centers.shape[0], 2))
outarr[:, 0] = rbin_centers
outarr[:, 1] = wps
np.savetxt(outfile, outarr)
def sum_stat(self, theta):
self.model.param_dict["alpha"] = theta[3]
self.model.param_dict["logMmin"] = theta[2]
self.model.param_dict["sigma_logM"] = theta[1]
self.model.param_dict["logM0"] = theta[0]
self.model.param_dict["logM1"] = theta[4]
if np.all((prior_range[:, 0] < theta) & (theta < prior_range[:, 1])):
try:
self.model.populate_mock()
nbar = self.model.mock.number_density
pos = three_dim_pos_bundle(table=self.model.mock.galaxy_table, key1="x", key2="y", key3="z")
w_p = wp(pos, rbins, pi_bins, period=np.array([L, L, L]))
return [nbar, w_p]
except ValueError:
return [10.0, np.zeros(14)]
else:
return [10.0, np.zeros(14)]
def sum_stat(self, theta):
self.model.param_dict['alpha'] = theta[3]
self.model.param_dict['logMmin'] = theta[2]
self.model.param_dict['sigma_logM'] = theta[1]
self.model.param_dict['logM0'] = theta[0]
self.model.param_dict['logM1'] = theta[4]
if np.all((prior_range[:, 0] < theta) & (theta < prior_range[:, 1])):
try:
self.model.populate_mock()
nbar = self.model.mock.number_density
pos = three_dim_pos_bundle(table=self.model.mock.galaxy_table,
key1='x',
key2='y',
key3='z')
w_p = wp(pos, rbins, pi_bins, period=np.array([L, L, L]))
return [nbar, w_p]
except ValueError:
return [10., np.zeros(14)]
else:
return [10., np.zeros(14)]
def run_auto(datfile, randfile, outfile):
fd = h5py.File(datfile)
fr = h5py.File(randfile)
cd = fd["cart_pts"]
cr = fr["cart_pts"]
dat = np.empty(cd.shape)
rands = np.empty(cr.shape)
dat[:, :] = cd[:, :]
rands[:, :] = cr[:, :]
wps = clustering.wp(dat, rbins, pibins, randoms=rands,
do_cross=False, estimator='Landy-Szalay',
N_threads='max')
outarr = np.empty((rbin_centers.shape[0], 2))
outarr[:, 0] = rbin_centers
outarr[:, 1] = wps
np.savetxt(outfile, outarr)
def main():
N_threads = 1 #for calculating the TPCFs
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
if len(sys.argv)>1:
catalogue = sys.argv[1]
sm_low = float(sys.argv[2])
sm_high = float(sys.argv[3])
else:
catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250'
sm_low=9.5
sm_high=9.8
if catalogue[-3:]=='250':
period = [250.0,250.0,250.0]
if catalogue[-3:]=='125':
period = [125.0,125.0,125.0]
else: period = [250.0,250.0,250.0]
print(period)
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('opening mock catalogue:', catalogue+'.hdf5')
#open catalogue
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
print(mock.dtype.names)
#centrals and satellites
host = np.where(mock['upid']==-1)[0]
sub = np.where(mock['upid']!=-1)[0]
host_bool = (mock['upid']==-1)
sub_bool = (mock['upid']!=-1)
N_sat = len(sub)
N_cen = len(host)
N_gal = len(host)+len(sub)
print(N_gal, N_cen, N_sat)
#star forming and quenched
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
#calculate correlation functions
rp_bins = np.linspace(-2.0,1.4,25)
rp_bins = 10.0**rp_bins
rp_bin_centers = (rp_bins[:-1]+rp_bins[1:])/2.0
pi_bins = np.linspace(0,50,50)
pi_bin_centers = (pi_bins[:-1]+pi_bins[1:])/2.0
#full sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection = (selection_1 & selection_2 & sub_bool)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of all galaxies: {0}".format(len(sample1)))
result_all = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads, max_sample_size=int(1e7))
#quenched sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_1 = (selection_1 & selection_2)
selection_2 = (red)
selection = (selection_1 & selection_2 & sub_bool)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of red galaxies: {0}".format(len(sample1)))
result_q = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads, max_sample_size=int(1e7))
#star-forming sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_1 = (selection_1 & selection_2)
selection_2 = (blue)
selection = (selection_1 & selection_2 & sub_bool)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of blue galaxies: {0}".format(len(sample1)))
result_sf = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads, max_sample_size=int(1e7))
#save projected correlation functions
data_1 = Table([rp_bin_centers,result_all], names=['r', 'wp'])
data_2 = Table([rp_bin_centers,result_q], names=['r', 'wp'])
data_3 = Table([rp_bin_centers,result_sf], names=['r', 'wp'])
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
filename_1 = catalogue+'_wp_satellite_all_'+str(sm_low)+'_'+str(sm_high)+'.dat'
ascii.write(data_1, savepath+filename_1)
filename_2 = catalogue+'_wp_satellite_q_'+str(sm_low)+'_'+str(sm_high)+'.dat'
ascii.write(data_2, savepath+filename_2)
filename_3 = catalogue+'_wp_satellite_sf_'+str(sm_low)+'_'+str(sm_high)+'.dat'
ascii.write(data_3, savepath+filename_3)
def main():
#load in fiducial mock
#all galaxies
sample='all'
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
sm_bin = '9.5_10.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_all_a = np.array(data['wp'])
sm_bin = '10.0_10.5'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_all_b = np.array(data['wp'])
sm_bin = '10.5_11.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_all_c = np.array(data['wp'])
#quenched galaxies
sample='q'
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
sm_bin = '9.5_10.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_q_a = np.array(data['wp'])
sm_bin = '10.0_10.5'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_q_b = np.array(data['wp'])
sm_bin = '10.5_11.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_q_c = np.array(data['wp'])
#SF galaxies
sample='sf'
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
sm_bin = '9.5_10.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_sf_a = np.array(data['wp'])
sm_bin = '10.0_10.5'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_sf_b = np.array(data['wp'])
sm_bin = '10.5_11.0'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu_sf_c = np.array(data['wp'])
mu = np.hstack((mu_all_a, mu_all_b, mu_all_c,mu_q_a, mu_q_b, mu_q_c,mu_sf_a, mu_sf_b, mu_sf_c))
#load covariance matrix
filepath = './'
filename = 'big_cov_8_8_8.npy'
cov = np.matrix(np.load(filepath+filename))
inv_cov = cov.I
#mock parameters
rhos = np.linspace(0.0,-1.0,25)
sigmas = np.linspace(0.0,0.3,25)
dm_sim ='Chinchilla_250'
box_size=250.0
period = np.array([box_size]*3)
sm_lim = 9.5
save_filename_1 = 'chinchilla_rho_3_sigma_2_jacknife_8_8_8_chi_2_array'
save_filename_2 = 'chinchilla_rho_3_sigma_2_jacknife_8_8_8_uber_array'
#correlation function parameters
N_threads=4
rp_bins = np.linspace(-1.0,1.4,25)
rp_bins = 10.0**rp_bins
rp_bin_centers = (rp_bins[:-1]+rp_bins[1:])/2.0
pi_bins = np.linspace(0,50,50)
pi_bin_centers = (pi_bins[:-1]+pi_bins[1:])/2.0
X = np.zeros((len(sigmas),len(rhos)))
uber_result = np.zeros((len(sigmas),len(rhos), 24*3*3))
for i,sigma in enumerate(sigmas):
for j,rho in enumerate(rhos):
print(i,j,sigma, rho)
mock = return_mock(sm_lim = sm_lim, sigma = sigma, rho = rho, sim = dm_sim)
#calculate correlation functions
#star forming and quenched
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
sm_low=9.5
sm_high=10.0
#full sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of all galaxies: {0}".format(len(sample1)))
result_all_a = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#quenched sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (red)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of red galaxies: {0}".format(len(sample1)))
result_q_a = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#star-forming sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (blue)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of blue galaxies: {0}".format(len(sample1)))
result_sf_a = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
sm_low=10.0
sm_high=10.5
#full sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of all galaxies: {0}".format(len(sample1)))
result_all_b = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#quenched sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (red)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of red galaxies: {0}".format(len(sample1)))
result_q_b = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#star-forming sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (blue)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of blue galaxies: {0}".format(len(sample1)))
result_sf_b = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
sm_low=10.5
sm_high=11.0
#full sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of all galaxies: {0}".format(len(sample1)))
result_all_c = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#quenched sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (red)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of red galaxies: {0}".format(len(sample1)))
result_q_c = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#star-forming sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (blue)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of blue galaxies: {0}".format(len(sample1)))
result_sf_c = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
result = np.hstack((result_all_a, result_all_b, result_all_c,\
result_q_a, result_q_b, result_q_c,\
result_sf_a, result_sf_b, result_sf_c))
uber_result[i,j] = result
Y = np.matrix((result-mu))
X_p = Y*inv_cov*Y.T
X[i,j] = X_p
print(X_p)
np.save('./'+save_filename_1,X)
np.save('./'+save_filename_2,uber_result)
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(hspace=0, wspace=0, left=0.2, right=0.9, bottom=0.2, top=0.9)
plt.imshow(X, origin='lower',\
extent=[sigmas[0],sigmas[-1],rhos[0],rhos[-1]],interpolation='nearest',\
aspect='auto',cmap='cool')
plt.plot([0.2],[-0.8],'x',color='black',ms=10)
plt.xlabel(r'$\sigma_{\rm SMHM}$')
plt.ylabel(r'$\rho_{\rm SSFR}$')
plt.colorbar()
plt.show(block=False)
fig.savefig('/Users/duncan/Desktop/chi_squared_plot.pdf')
def main():
N_threads = 4 #for calculating the TPCFs
np.random.seed(0)
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
catalogue = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250'
Nran = 10**6 #number of randoms...
Nsub = np.array([8,8,8]) #jackknife sample splitting
calculate_RR = False
RR_filename = 'RR_8_8_8_10e6'
if len(sys.argv)==3:
sm_low=float(sys.argv[1])
sm_high=float(sys.argv[2])
else:
sm_low=10.5
sm_high=11.0
print("Running with {} to {} stellar mass bin.".format(sm_low,sm_high))
if catalogue[-3:]=='250':
period = np.array([250.0,250.0,250.0])
if catalogue[-3:]=='125':
period = np.array([125.0,125.0,125.0])
else: period = np.array([250.0,250.0,250.0])
print(period)
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('opening mock catalogue:', catalogue+'.hdf5')
#open catalogue
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
print(mock.dtype.names)
#centrals and satellites
host = np.where(mock['upid']==-1)[0]
sub = np.where(mock['upid']!=-1)[0]
host_bool = (mock['upid']==-1)
sub_bool = (mock['upid']!=-1)
N_sat = len(sub)
N_cen = len(host)
N_gal = len(host)+len(sub)
print(N_gal, N_cen, N_sat)
#star forming and quenched
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
#calculate correlation functions
rp_bins = np.linspace(-1.0,1.4,25)
rp_bins = 10.0**rp_bins
rp_bin_centers = (rp_bins[:-1]+rp_bins[1:])/2.0
pi_bins = np.linspace(0,50,50)
pi_bin_centers = (pi_bins[:-1]+pi_bins[1:])/2.0
#calculate jackknife samples
labels = get_subvolume_labels(np.vstack((mock['x'],mock['y'],mock['z'])).T, Nsub,\
np.array([250.0,250.0,250.0]))
#calculate RR once!
if calculate_RR==True:
print('calculating RR')
randoms = np.random.random((Nran,3))*250.0
random_labels = get_subvolume_labels(randoms, Nsub, np.array([250.0,250.0,250.0]))
r_selection = (random_labels!=1)
RR = xy_z_npairs(randoms[r_selection], randoms[r_selection], rp_bins, pi_bins,\
period=period, N_threads=N_threads)
RR = np.diff(np.diff(RR,axis=0),axis=1)
NR = len(randoms[r_selection])
np.save('./' + RR_filename,RR)
else:
print('loading RR')
randoms = np.random.random((Nran,3))*250.0
random_labels = get_subvolume_labels(randoms, Nsub, np.array([250.0,250.0,250.0]))
r_selection = (random_labels!=1)
NR = len(randoms[r_selection])
RR = np.load('./' + RR_filename+'.npy')
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
result_all = np.zeros((np.max(labels)+1,len(rp_bins)-1))
result_all[0,:] = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
#loops over jackknife samples
for i in range(1,np.max(labels)+1):
print("all", i)
#full sample auto correlation
selection_3 = (labels!=i)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
N1 = len(sample1)
print("number of all galaxies: {0}".format(N1))
DD = xy_z_npairs(sample1, sample1, rp_bins, pi_bins, period=period,\
N_threads=N_threads)
DD = np.diff(np.diff(DD,axis=0),axis=1)
xi = _TP_estimator(DD,None,RR,N1,N1,NR,NR,'Natural')
result_all[i,:] = integrate_2D_xi(xi,pi_bins)
result_q = np.zeros((np.max(labels)+1,len(rp_bins)-1))
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (red)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
result_q[0,:] = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
for i in range(1,np.max(labels)+1):
print("red", i)
#quenched sample auto correlation
selection_4 = (labels!=i)
selection = (selection_1 & selection_2 & selection_3 & selection_4)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
N1 = len(sample1)
print("number of red galaxies: {0}".format(N1))
DD = xy_z_npairs(sample1, sample1, rp_bins, pi_bins, period=period,\
N_threads=N_threads)
DD = np.diff(np.diff(DD,axis=0),axis=1)
xi = _TP_estimator(DD,None,RR,N1,N1,NR,NR,'Natural')
result_q[i,:] = integrate_2D_xi(xi,pi_bins)
result_sf = np.zeros((np.max(labels)+1,len(rp_bins)-1))
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (blue)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
result_sf[0,:] = wp(sample1, rp_bins, pi_bins, period=period,
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
for i in range(1,np.max(labels)+1):
print("blue", i)
#star-forming sample auto correlation
selection_4 = (labels!=i)
selection = (selection_1 & selection_2 & selection_3 & selection_4)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
N1 = len(sample1)
print("number of blue galaxies: {0}".format(N1))
DD = xy_z_npairs(sample1, sample1, rp_bins, pi_bins, period=period,\
N_threads=N_threads)
DD = np.diff(np.diff(DD,axis=0),axis=1)
xi = _TP_estimator(DD,None,RR,N1,N1,NR,NR,'Natural')
result_sf[i,:] = integrate_2D_xi(xi,pi_bins)
result_cross = np.zeros((np.max(labels)+1,len(rp_bins)-1))
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (blue)
selection_a = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection_a]
selection_3 = (red)
selection_b = (selection_1 & selection_2 & selection_3)
sample2 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection_b]
result_cross[0,:] = wp(sample1, rp_bins, pi_bins, sample2=sample2, period=period,
do_auto=False, do_cross=True, estimator='Natural',
N_threads=N_threads, max_sample_size=int(1e7))
for i in range(1,np.max(labels)+1):
print("cross", i)
#star-forming sample auto correlation
selection_4 = (labels!=i)
selection_aa = (selection_a & selection_4)
selection_bb = (selection_b & selection_4)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection_aa]
sample2 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection_bb]
N1 = len(sample1)
N2 = len(sample2)
print("number of blue by red galaxies: {0} x {1}".format(N1, N2))
DD = xy_z_npairs(sample1, sample2, rp_bins, pi_bins, period=period,\
N_threads=N_threads)
DD = np.diff(np.diff(DD,axis=0),axis=1)
xi = _TP_estimator(DD,None,RR,N1,N2,NR,NR,'Natural')
result_cross[i,:] = integrate_2D_xi(xi,pi_bins)
all_full = result_all[0,:]
all_sub = result_all[1:,:]
q_full = result_q[0,:]
q_sub = result_q[1:,:]
sf_full = result_sf[0,:]
sf_sub = result_sf[1:,:]
cross_full = result_cross[0,:]
cross_sub = result_cross[1:,:]
#save raw results
filename_1 = catalogue+'_wp_fiducial_all_'+str(sm_low)+'_'+str(sm_high)
np.save(filename_1+'_result', result_all)
filename_2 = catalogue+'_wp_fiducial_q_'+str(sm_low)+'_'+str(sm_high)
np.save(filename_2+'_result', result_q)
filename_3 = catalogue+'_wp_fiducial_sf_'+str(sm_low)+'_'+str(sm_high)
np.save(filename_3+'_result', result_sf)
filename_4 = catalogue+'_wp_fiducial_cross_'+str(sm_low)+'_'+str(sm_high)
np.save(filename_4+'_result', result_cross)
#calculate covariance matrices
cov_all = covariance_matrix(all_sub,all_full,np.max(labels))
cov_red = covariance_matrix(q_sub,q_full,np.max(labels))
cov_blue = covariance_matrix(sf_sub,sf_full,np.max(labels))
cov_cross = covariance_matrix(cross_sub,cross_full,np.max(labels))
#save projected correlation functions
data_1 = Table([rp_bin_centers,all_full], names=['r', 'wp'])
data_2 = Table([rp_bin_centers,q_full], names=['r', 'wp'])
data_3 = Table([rp_bin_centers,sf_full], names=['r', 'wp'])
data_4 = Table([rp_bin_centers,cross_full], names=['r', 'wp'])
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
filename_1 = catalogue+'_wp_fiducial_all_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_1, savepath+filename_1+'.dat')
np.save(filename_1+'_cov', cov_all)
filename_2 = catalogue+'_wp_fiducial_q_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_2, savepath+filename_2+'.dat')
np.save(filename_2+'_cov', cov_red)
filename_3 = catalogue+'_wp_fiducial_sf_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_3, savepath+filename_3+'.dat')
np.save(filename_3+'_cov', cov_blue)
filename_4 = catalogue+'_wp_fiducial_cross_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_4, savepath+filename_4+'.dat')
np.save(filename_4+'_cov', cov_cross)
plt.figure()
plt.errorbar(rp_bin_centers, all_full, yerr=np.sqrt(np.diagonal(cov_all)),\
color='black')
plt.errorbar(rp_bin_centers, q_full, yerr=np.sqrt(np.diagonal(cov_red)),\
color='red')
plt.errorbar(rp_bin_centers, sf_full, yerr=np.sqrt(np.diagonal(cov_blue)),\
color='blue')
plt.errorbar(rp_bin_centers, cross_full, yerr=np.sqrt(np.diagonal(cov_cross)),\
color='green')
plt.xscale('log')
plt.yscale('log')
plt.show(block=False)
def main():
N_threads = 4 #for calculating the TPCFs
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
catalogue = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250'
sm_low=10.0
sm_high=10.5
Nran = 10**6
Nsub = np.array([2,2,2])
if catalogue[-3:]=='250':
period = [250.0,250.0,250.0]
if catalogue[-3:]=='125':
period = [125.0,125.0,125.0]
else: period = [250.0,250.0,250.0]
print(period)
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('opening mock catalogue:', catalogue+'.hdf5')
#open catalogue
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
print(mock.dtype.names)
#centrals and satellites
host = np.where(mock['upid']==-1)[0]
sub = np.where(mock['upid']!=-1)[0]
host_bool = (mock['upid']==-1)
sub_bool = (mock['upid']!=-1)
N_sat = len(sub)
N_cen = len(host)
N_gal = len(host)+len(sub)
print(N_gal, N_cen, N_sat)
#star forming and quenched
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
#calculate correlation functions
rp_bins = np.linspace(-1.0,1.4,25)
rp_bins = 10.0**rp_bins
rp_bin_centers = (rp_bins[:-1]+rp_bins[1:])/2.0
pi_bins = np.linspace(0,50,50)
pi_bin_centers = (pi_bins[:-1]+pi_bins[1:])/2.0
#calculate jackknife samples
labels = get_subvolume_labels(np.vstack((mock['x'],mock['y'],mock['z'])).T, Nsub, np.array([250.0,250.0,250.0]))
randoms = np.random.random((Nran,3))*250.0
random_labels = get_subvolume_labels(randoms, Nsub, np.array([250.0,250.0,250.0]))
#loops over jackknife samples
result_all = np.zeros((np.max(labels)+1,len(rp_bins)-1))
for i in range(0,np.max(labels)+1):
print("all", i)
#full sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (labels!=i)
r_selection = (random_labels!=i)
selection = (selection_1 & selection_2 & selection_3)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of all galaxies: {0}".format(len(sample1)))
result_all[i,:] = wp(sample1, rp_bins, pi_bins, period=period, randoms=randoms[r_selection],
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
result_q = np.zeros((np.max(labels)+1,len(rp_bins)-1))
for i in range(0,np.max(labels)+1):
print("red", i)
#quenched sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (labels!=i)
selection_1 = (selection_1 & selection_2 & selection_3)
selection_2 = (red)
r_selection = (random_labels!=i)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of red galaxies: {0}".format(len(sample1)))
result_q[i,:] = wp(sample1, rp_bins, pi_bins, period=period, randoms=randoms[r_selection],
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
result_sf = np.zeros((np.max(labels)+1,len(rp_bins)-1))
for i in range(0,np.max(labels)+1):
print("blue", i)
#star-forming sample auto correlation
selection_1 = (mock['Mstar']>sm_low)
selection_2 = (mock['Mstar']<sm_high)
selection_3 = (labels!=i)
selection_1 = (selection_1 & selection_2 & selection_3)
selection_2 = (blue)
r_selection = (random_labels!=i)
selection = (selection_1 & selection_2)
sample1 = np.vstack((mock['x'],mock['y'],mock['z'])).T[selection]
print("number of blue galaxies: {0}".format(len(sample1)))
result_sf[i,:] = wp(sample1, rp_bins, pi_bins, period=period, randoms=randoms[r_selection],
do_auto=True, do_cross=False, estimator='Natural',
N_threads=N_threads,max_sample_size=int(1e7))
all_full = result_all[0,:]
all_sub = result_all[1:,:]
q_full = result_q[0,:]
q_sub = result_q[1:,:]
sf_full = result_sf[0,:]
sf_sub = result_sf[1:,:]
cov_all = covariance_matrix(all_sub,all_full,np.max(labels))
cov_red = covariance_matrix(q_sub,q_full,np.max(labels))
cov_blue = covariance_matrix(sf_sub,sf_full,np.max(labels))
#save projected correlation functions
data_1 = Table([rp_bin_centers,all_full], names=['r', 'wp'])
data_2 = Table([rp_bin_centers,q_full], names=['r', 'wp'])
data_3 = Table([rp_bin_centers,sf_full], names=['r', 'wp'])
savepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
filename_1 = catalogue+'_wp_fiducial_all_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_1, savepath+filename_1+'.dat')
np.save(filename_1+'_cov', cov_all)
filename_2 = catalogue+'_wp_fiducial_q_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_2, savepath+filename_2+'.dat')
np.save(filename_2+'_cov', cov_red)
filename_3 = catalogue+'_wp_fiducial_sf_'+str(sm_low)+'_'+str(sm_high)
ascii.write(data_3, savepath+filename_3+'.dat')
np.save(filename_3+'_cov', cov_blue)
plt.figure()
plt.errorbar(rp_bin_centers, all_full, yerr=np.sqrt(np.diagonal(cov_all)), color='black')
plt.errorbar(rp_bin_centers, q_full, yerr=np.sqrt(np.diagonal(cov_red)), color='red')
plt.errorbar(rp_bin_centers, sf_full, yerr=np.sqrt(np.diagonal(cov_blue)), color='blue')
plt.xscale('log')
plt.yscale('log')
plt.show()
