def select_func(params): ''' Takes cluster catalogue and makes cleaning selection cuts based on redshift, N200, Nspec, cluster pairs etc. ''' print '...beginning clean selection cuts' # Load Halo Catalgoue halos = fits.open(data_loc+'/halos.fits')[1].data if params['data_loc'][-2:] == 'TH': # TRUTH cut = np.where( (halos['Z_BCG']>0.03)& (halos['Z_BCG']<0.12)& (halos['n10virm19']>5) )[0] if params['data_loc'][-2:] == 'IC': # TRUTH_CAUSTIC cut = np.where( (halos['miller_N200']>5)& (np.isnan(halos['miller_N200'])==False)& (halos['Z_AVG']>0.03)& (halos['Z_AVG']<0.12)& (halos['Nspec']>5)& (halos['pair_avg']!=True)& (halos['cata_fail']!=True) )[0] elif params['data_loc'][-2:] == 'C4': # TRUTH_CAUSTIC_C4 cut = np.where( (halos['miller_N200']>5)& (np.isnan(halos['miller_N200'])==False)& (halos['Z_AVG']>0.03)& (halos['Z_AVG']<0.12)& (halos['Nspec']>5)& (halos['pair_avg']!=True)& (halos['cata_fail']!=True) )[0] elif params['data_loc'][-2:] == '12': # DR12 cut = np.where( (halos['miller_N200']>=5)& (np.isnan(halos['miller_N200'])==False)& (halos['Z_AVG']>0.03)& (halos['Z_AVG']<0.12)& (halos['Nspec']>5)& (halos['pair_avg']!=True)& (halos['cata_fail']!=True) )[0] print '...size before cuts = '+str(len(halos))+', size after = '+str(len(cut)) new_halos = halos[cut] keys = new_halos.names dic = dict(map(lambda x: (x,new_halos[x]),keys)) fits_table(dic,keys,data_loc+'/cut_halos.fits',clobber=True)
def select_func(params):
''' Takes cluster catalogue and makes cleaning selection cuts based on redshift, N200, Nspec, cluster pairs etc. '''
print '...beginning clean selection cuts'
# Load Halo Catalgoue
halos = fits.open(data_loc + '/halos.fits')[1].data
if params['data_loc'][-2:] == 'TH': # TRUTH
cut = np.where((halos['Z_BCG'] > 0.03) & (halos['Z_BCG'] < 0.12)
& (halos['n10virm19'] > 5))[0]
if params['data_loc'][-2:] == 'IC': # TRUTH_CAUSTIC
cut = np.where((halos['miller_N200'] > 5)
& (np.isnan(halos['miller_N200']) == False)
& (halos['Z_AVG'] > 0.03) & (halos['Z_AVG'] < 0.12)
& (halos['Nspec'] > 5) & (halos['pair_avg'] != True)
& (halos['cata_fail'] != True))[0]
elif params['data_loc'][-2:] == 'C4': # TRUTH_CAUSTIC_C4
cut = np.where((halos['miller_N200'] > 5)
& (np.isnan(halos['miller_N200']) == False)
& (halos['Z_AVG'] > 0.03) & (halos['Z_AVG'] < 0.12)
& (halos['Nspec'] > 5) & (halos['pair_avg'] != True)
& (halos['cata_fail'] != True))[0]
elif params['data_loc'][-2:] == '12': # DR12
cut = np.where((halos['miller_N200'] >= 5)
& (np.isnan(halos['miller_N200']) == False)
& (halos['Z_AVG'] > 0.03) & (halos['Z_AVG'] < 0.12)
& (halos['Nspec'] > 5) & (halos['pair_avg'] != True)
& (halos['cata_fail'] != True))[0]
print '...size before cuts = ' + str(len(halos)) + ', size after = ' + str(
len(cut))
new_halos = halos[cut]
keys = new_halos.names
dic = dict(map(lambda x: (x, new_halos[x]), keys))
fits_table(dic, keys, data_loc + '/cut_halos.fits', clobber=True)
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 ''
## Truth Table (M-R for Henriques using 3D members and True Mass) ##
if panel1 == True:
# Constants
chris_data_root = '/nfs/christoq_ls/MILLENNIUM/Henriques/TRUTH'
data_loc = 'MassRich/TRUTH'
# Load Data
truth = fits.open(chris_data_root+'/c4_cluster_truth_revH100_rev5.fits')[1].data
truth['m200mean']*=1e10
truth['m200crit']*=1e10
# Write out halos.fits file
if init_cats == True:
keys = truth.names
dic = dict(map(lambda x: (x,truth[x]),keys))
fits_table(dic,keys,data_loc+'/truth.fits',clobber=True)
params = {'data_loc':data_loc}
## Perform clean selection function cuts ##
if sel_func == True:
select_func(params)
Rich = truth['n10virm19']
Mass = truth['m200crit']
rich_low,rich_high,rich_step = 3.3,4.8,0.3
richbins = np.arange(rich_low,rich_high,rich_step)
mass_low,mass_high,mass_step = 14.25,15.15,0.2
massbins = np.arange(mass_low,mass_high,mass_step)
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 ''
if panel1 == True:
# Constants
chris_data_root = '/nfs/christoq_ls/MILLENNIUM/Henriques/TRUTH'
data_loc = 'MassRich/TRUTH'
# Load Data
truth = fits.open(chris_data_root +
'/c4_cluster_truth_revH100_rev5.fits')[1].data
truth['m200mean'] *= 1e10
truth['m200crit'] *= 1e10
# Write out halos.fits file
if init_cats == True:
keys = truth.names
dic = dict(map(lambda x: (x, truth[x]), keys))
fits_table(dic, keys, data_loc + '/truth.fits', clobber=True)
params = {'data_loc': data_loc}
## Perform clean selection function cuts ##
if sel_func == True:
select_func(params)
Rich = truth['n10virm19']
Mass = truth['m200crit']
rich_low, rich_high, rich_step = 3.3, 4.8, 0.3
richbins = np.arange(rich_low, rich_high, rich_step)
mass_low, mass_high, mass_step = 14.25, 15.15, 0.2
massbins = np.arange(mass_low, mass_high, mass_step)