new_good = np.array(new_good)
Nspec = np.array(Nspec)
N200 = N200[new_good]
## Calculate Average Halo Positions from Galaxies
calc_avg = False
if calc_avg == True:
from stack_class_c4 import CFOUR
from causticpy import Caustic
root = '/nfs/christoq_ls/nkern'
data_loc = 'MassRich/TRUTH_CAUSTIC'
write_loc = 'individual'
C4 = CFOUR({'H0':70,'chris_data_root':'/nfs/christoq_ls/MILLENNIUM/Henriques/TRUTH_CAUSTIC'})
C = Caustic()
# Load Halos
halos = fits.open(root+'/C4/'+data_loc+'/halos.fits')[1].data
HaloID = halos['orig_order']
RA = halos['halo_ra']
DEC = halos['halo_dec']
Z = halos['halo_z']
Nspec = halos['Nspec']
N200 = halos['N200']
HVD = halos['HVD']
RVIR = halos['RVIR']
SINGLE = halos['single']
SUB = halos['sub']
NC4 = halos['nc4']
def cluster_rich(data_loc,
halo_file,
chris_data_root,
chris_data_file,
newfilename,
write_data=True,
clobber=True):
""" Calculate a Cluster's richness via Miller N200 and Kern N200
data_loc : e.g. MassRich/TRUTH_CAUSTIC
halo_file : e.g. halos.fits
chris_data_root : e.g. /nfs/christoq_ls/C4/sdssdr12
chris_data_file : e.g. DR12_GalaxyPhotoData_wabs_wedges.fits or m19.1_allgals_wsdss_specerrs_abs.fits
"""
# Run through Kern richness estimator, mainly to get cluster pairs
root = '/nfs/christoq_ls/nkern'
C4 = CFOUR({'H0': 70, 'chris_data_root': chris_data_root})
C = Caustic()
H0 = 70.0
c = 2.99792e5
Cosmo = cosmo.LambdaCDM(H0, 0.3, 0.7)
keys = ['C', 'H0', 'c', 'Cosmo', 'C4']
varib = ez.create(keys, locals())
R = RICHNESS(varib)
# Load Halos
halos = fits.open(data_loc + '/' + halo_file)[1].data
HaloID = halos['orig_order']
RA = halos['ra_avg']
DEC = halos['dec_avg']
Z = halos['z_avg']
RVIR = halos['RVIR']
# Load Galaxy Data
gals = fits.open(chris_data_root + '/' + chris_data_file)[1].data
# Change gals keys according to SDSSDR12 galaxy file
if data_loc[-4:] != 'DR12':
gals = dict(map(lambda x: (x, gals[x]), gals.names))
gals['objid'] = gals.pop('GAL_HALOID')
gals['ra'] = gals.pop('GAL_RA')
gals['dec'] = gals.pop('GAL_DEC')
gals['z'] = gals.pop('GAL_Z_APP')
gals['u_mag'] = gals.pop('GAL_SDSS_U')
gals['g_mag'] = gals.pop('GAL_SDSS_G')
gals['r_mag'] = gals.pop('GAL_SDSS_R')
gals['i_mag'] = gals.pop('GAL_SDSS_I')
gals['z_mag'] = gals.pop('GAL_SDSS_Z')
gals['r_absmag'] = gals.pop('R_ABSMAG')
# Derive Gal Cut Out Parameters
arcs = np.array(
Cosmo.arcsec_per_kpc_proper(Z)) * 15000. / 3600. # 15 Mpc in degrees
# Kern richness arrays
kern_N200 = []
HVD = []
pair_avg = []
Nspec = []
kern_obs_tot = []
kern_obs_back = [
] # obs_back is number of non-member galaxies in central aperture around cluster (aka. already scaled to inner aperture)
# Miller Richness Arrays
new = fits.open(chris_data_root + '/richness_cr200_bcg/new.fits')[0].data
newb = fits.open(chris_data_root + '/richness_cr200_bcg/newb.fits')[0].data
newb *= 0.2
miller_N200 = []
miller_obs_tot = []
miller_obs_back = []
colfac = 9
bakfac = 1
mag3 = 4
v = 2
radfac = 1
# Loop over clusters
print ''
print '-' * 40
print '...calculating cluster richnesses'
for i in range(len(HaloID)):
if i % 100 == 0:
print '...working on cluster ' + str(i) + ' out of ' + str(
len(HaloID))
# Define Cluster parameters
clus_ra = RA[i]
clus_dec = DEC[i]
clus_z = Z[i]
clus_rvir = RVIR[i]
haloid = HaloID[i]
if np.isnan(clus_ra) == True or np.isnan(clus_dec) == True or np.isnan(
clus_z) == True:
richness.append(0)
HVD.append(0)
pair_avg.append(False)
Nspec.append(0)
continue
# 15 Mpc in degrees of declination and degrees of RA
d_dec = arcs[i]
d_ra = d_dec / np.cos(clus_dec * np.pi / 180)
d_z = 0.04
# Cut Out Galaxy Data Around Cluster
cut = np.where((np.abs(gals['ra'] - clus_ra) < d_ra)
& (np.abs(gals['dec'] - clus_dec) < d_dec)
& (np.abs(gals['z'] - clus_z) < d_z))[0]
gal_ra = gals['ra'][cut]
gal_dec = gals['dec'][cut]
gal_z = gals['z'][cut]
gal_gmags = gals['g_mag'][cut]
gal_rmags = gals['r_mag'][cut]
gal_imags = gals['i_mag'][cut]
gal_absr = gals['r_absmag'][cut]
# Run Kern Richness Estimator
rich = R.richness_est(gal_ra,
gal_dec,
gal_z,
np.zeros(len(gal_z)),
gal_gmags,
gal_rmags,
gal_imags,
gal_absr,
haloid,
clus_ra,
clus_dec,
clus_z,
clus_rvir=clus_rvir,
spec_list=None,
use_specs=False,
use_bcg=False,
fit_rs=False,
fixed_vdisp=False,
fixed_aperture=False,
plot_sky=False,
plot_gr=False,
plot_phase=False,
find_pairs=True)
kern_N200.append(rich)
HVD.append(R.vel_disp)
pair_avg.append(R.pair)
Nspec.append(R.Nspec)
kern_obs_tot.append(R.obs_tot)
kern_obs_back.append(R.obs_back_scaled)
# Append Miller Richness Values
k = halos['orig_order'][i]
miller_N200.append(new[k, colfac, mag3, v, radfac] -
newb[k, bakfac, mag3, v, radfac])
miller_obs_tot.append(new[k, colfac, mag3, v, radfac])
miller_obs_back.append(newb[k, bakfac, mag3, v, radfac])
kern_N200 = np.array(kern_N200)
HVD = np.array(HVD)
pair_avg = np.array(pair_avg)
Nspec = np.array(Nspec)
kern_obs_tot = np.array(kern_obs_tot)
kern_obs_back = np.array(kern_obs_back)
miller_N200 = np.array(miller_N200)
miller_obs_tot = np.array(miller_obs_tot)
miller_obs_back = np.array(miller_obs_back)
print '...finished calculating richnesses'
## Write Data Out
if write_data == True:
print '...writing out halos.fits file'
# Dictionary of new columns
new_keys = [
'kern_N200', 'HVD', 'pair_avg', 'Nspec', 'kern_obs_tot',
'kern_obs_back', 'miller_N200', 'miller_obs_tot', 'miller_obs_back'
]
new_dic = ez.create(new_keys, locals())
# Original fits record file
orig_table = halos
# Write out own fits file
keys = ['HaloID', 'RVIR'] + new_keys
dic = ez.create(keys, locals())
fits_table(dic, keys, data_loc + '/richnesses.fits', clobber=True)
# Append new columns
fits_append(orig_table,
new_dic,
new_keys,
filename=data_loc + '/' + newfilename,
clobber=clobber)
print '-' * 40
print ''
def clus_avg(data_loc,halo_file,chris_data_root,newfilename,write_data=True,clobber=True):
C4 = CFOUR({'H0':70,'chris_data_root':chris_data_root})
C = Caustic()
# Load Halos
halos = fits.open(data_loc+'/'+halo_file)[1].data
HaloID = halos['orig_order']
RA = halos['ra_bcg']
DEC = halos['dec_bcg']
Z = halos['z_biwt']
RVIR = halos['RVIR']
SINGLE = halos['single']
SUB = halos['sub']
NC4 = halos['nc4']
RA_AVG,DEC_AVG,Z_AVG = [],[],[]
# Loop Over Halos
print ''
print '-'*40
print '...running average cluster center code'
for i in range(len(halos)):
if i % 100 == 0: print '...working on cluster '+str(i)+' out of '+str(len(halos))
try:
# Assign Halo Properties
clus_ra = RA[i]
clus_dec = DEC[i]
clus_z = Z[i]
# Load Galaxies
galdata = C4.load_chris_gals(HaloID[i])
gal_ra,gal_dec,gal_z,gal_gmags,gal_rmags,gal_imags = galdata
# Take Iterative Average, four times
# vlim = 1500, rlim = 1.5
clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1500,1.5,C)
# vlim = 1000, rlim = 1.5
clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1000,1.5,C)
# vlim = 1500, rlim = 1.5
clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1000,1.5,C)
# vlim = 2000, rlim = 1.5
clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,2000,1.5,C)
except:
print i
clus_ra,clus_dec,clus_z = 0, 0, 0
RA_AVG.append(clus_ra)
DEC_AVG.append(clus_dec)
Z_AVG.append(clus_z)
RA_AVG,DEC_AVG,Z_AVG = np.array(RA_AVG),np.array(DEC_AVG),np.array(Z_AVG)
print '...finished average cluster-center calculations'
## Write Data Out
if write_data == True:
print '...writing out cluster catalgoue with average centers included'
# Dictionary of new columns
new_keys = ['RA_AVG','DEC_AVG','Z_AVG']
new_dic = ez.create(new_keys,locals())
# Original fits record file
orig_table = halos
# Write own fits file
keys = ['HaloID','RA','DEC','Z','RVIR','RA_AVG','DEC_AVG','Z_AVG']
dic = ez.create(keys,locals())
fits_table(dic,keys,data_loc+'/avg_centers.fits',clobber=True)
# Append new columns
fits_append(orig_table,new_dic,new_keys,filename=data_loc+'/'+newfilename,clobber=clobber)
print '-'*40
print ''
def clus_avg(data_loc,
halo_file,
chris_data_root,
newfilename,
write_data=True,
clobber=True):
C4 = CFOUR({'H0': 70, 'chris_data_root': chris_data_root})
C = Caustic()
# Load Halos
halos = fits.open(data_loc + '/' + halo_file)[1].data
HaloID = halos['orig_order']
RA = halos['ra_bcg']
DEC = halos['dec_bcg']
Z = halos['z_biwt']
RVIR = halos['RVIR']
SINGLE = halos['single']
SUB = halos['sub']
NC4 = halos['nc4']
RA_AVG, DEC_AVG, Z_AVG = [], [], []
# Loop Over Halos
print ''
print '-' * 40
print '...running average cluster center code'
for i in range(len(halos)):
if i % 100 == 0:
print '...working on cluster ' + str(i) + ' out of ' + str(
len(halos))
try:
# Assign Halo Properties
clus_ra = RA[i]
clus_dec = DEC[i]
clus_z = Z[i]
# Load Galaxies
galdata = C4.load_chris_gals(HaloID[i])
gal_ra, gal_dec, gal_z, gal_gmags, gal_rmags, gal_imags = galdata
# Take Iterative Average, four times
# vlim = 1500, rlim = 1.5
clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
gal_ra, gal_dec, gal_z, 1500,
1.5, C)
# vlim = 1000, rlim = 1.5
clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
gal_ra, gal_dec, gal_z, 1000,
1.5, C)
# vlim = 1500, rlim = 1.5
clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
gal_ra, gal_dec, gal_z, 1000,
1.5, C)
# vlim = 2000, rlim = 1.5
clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
gal_ra, gal_dec, gal_z, 2000,
1.5, C)
except:
print i
clus_ra, clus_dec, clus_z = 0, 0, 0
RA_AVG.append(clus_ra)
DEC_AVG.append(clus_dec)
Z_AVG.append(clus_z)
RA_AVG, DEC_AVG, Z_AVG = np.array(RA_AVG), np.array(DEC_AVG), np.array(
Z_AVG)
print '...finished average cluster-center calculations'
## Write Data Out
if write_data == True:
print '...writing out cluster catalgoue with average centers included'
# Dictionary of new columns
new_keys = ['RA_AVG', 'DEC_AVG', 'Z_AVG']
new_dic = ez.create(new_keys, locals())
# Original fits record file
orig_table = halos
# Write own fits file
keys = [
'HaloID', 'RA', 'DEC', 'Z', 'RVIR', 'RA_AVG', 'DEC_AVG', 'Z_AVG'
]
dic = ez.create(keys, locals())
fits_table(dic, keys, data_loc + '/avg_centers.fits', clobber=True)
# Append new columns
fits_append(orig_table,
new_dic,
new_keys,
filename=data_loc + '/' + newfilename,
clobber=clobber)
print '-' * 40
print ''