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 ''
SINGLE = halos['single']
SUB = halos['sub']
NC4 = halos['nc4']
RA_AVG,DEC_AVG,Z_AVG = [],[],[]
# Loop Over Halos
for i in range(len(halos)):
if i % 100 == 0: print i
# 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
# Project Galaxies
ang_d,lum_d = C.zdistance(clus_z,H0)
angles = C.findangle(gal_ra,gal_dec,clus_ra,clus_dec)
rdata = angles * ang_d
vdata = c * (gal_z - clus_z) / (1 + clus_z)
# Take Average Three times
cut1 = np.where((np.abs(vdata)<1000)&(rdata<1.5))[0]
clus_ra = astats.biweight_location(gal_ra[cut1])
clus_dec = astats.biweight_location(gal_dec[cut1])
clus_z = astats.biweight_location(gal_z[cut1])
ang_d,lum_d = C.zdistance(clus_z,H0)
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 ''
