def file(self):
""" FFT data file name
"""
#corrdict = (self.cat_corr)['correction']
specdict = (self.cat_corr)['spec']
fft_dir = direc('fft', self.cat_corr)
self.data_file = self.galdata.file_name # galaxy data file
# FFT label
fft_str = 'FFT_'
if specdict['ell'] != 0:
fft_str += 'Q_'
#if (corrdict['name'].lower() in ('floriansn', 'hectorsn')) & (self.type != 'random'):
# fft_corr_str = ''.join(['.', corrdict['name'].lower()])
# FFTs from data file
fft_file = ''.join([
fft_dir,
fft_str, (self.data_file).rsplit('/')[-1],
'.grid', str(specdict['Ngrid']),
'.P0', str(specdict['P0']),
'.box', str(int(specdict['Lbox']))
])
return fft_file
def file(self):
""" power/bispectrum file
"""
catdict = (self.cat_corr)['catalog']
corrdict = (self.cat_corr)['correction']
specdict = (self.cat_corr)['spec']
# powerspectrum or bispectrum
if self.type in ('pk'):
spec_str = 'POWER_'
elif self.type == 'bk':
spec_str = 'BISP_'
#if 'quad' not in specdict.keys():
# specdict['quad'] = False
#if specdict['quad']:
# spec_str += 'Q_'
if self.ell != 0:
spec_str += 'Q_'
gal_data = Data('data', self.cat_corr, **self.kwargs)
self.data_file = gal_data.file_name
gal_file = (gal_data.file_name).split('/')[-1]
rand_data = Data('random', self.cat_corr, **self.kwargs)
self.random_file = rand_data.file_name
spec_dir = direc('spec', self.cat_corr)
if self.type == 'pk':
specparam_str = ''.join([
'.grid', str(specdict['Ngrid']),
'.P0', str(specdict['P0']),
'.box', str(specdict['Lbox'])
])
elif self.type == 'bk':
# (hardcoded)
spectrum_str = ''.join([
'.grid', str(specdict['Ngrid']),
'.nmax40.ncut3.s3',
'.P0', str(specdict['P0']),
'.box', str(specdict['Lbox'])
])
else:
raise NotImplementedError()
file_name = ''.join([
spec_dir,
spec_str,
gal_file,
specparam_str
])
return file_name
def file(self):
'''
Override parent class file() method in order to return
file name of random catalog file. Everything is quite
hardcoded.
'''
cat_name = ((self.cat_corr)['catalog'])['name'].lower()
data_dir = direc('data', self.cat_corr)
if 'cmass' in cat_name: # CMASS
if cat_name == 'cmass':
# CMASS random catalog
file_name = 'cmass-dr12v4-N-Reid.ran.dat'
elif 'cmasslowz' in cat_name:
# CMASS LOWZ combined random catalog
if 'e2' in cat_name:
cmasslowz_str = 'e2'
elif 'e3' in cat_name:
cmasslowz_str = 'e3'
else:
cmasslowz_str = ''
if '_low' in cat_name:
zbin_str = '_LOW'
elif 'high' in cat_name:
zbin_str = '_HIGH'
else:
raise NameError("Must specify redshift bin of CMASS LOWZ sample")
file_name = ''.join([
'random0_DR12v5_CMASSLOWZ',
cmasslowz_str.upper(),
zbin_str,
'_North.ran.dat'
])
else:
raise NotImplementedError()
elif cat_name == 'nseries': # Nseries
file_name = 'Nseries_cutsky_randoms_50x_redshifts_comp.dat'
elif cat_name == 'qpm': # QPM
file_name = 'a0.6452_rand50x.dr12d_cmass_ngc.vetoed.dat'
else:
raise NotImplementedError()
return ''.join([data_dir, file_name])
def dlosenv_peakfit_sigma_env_fit(cat_corr, n_NN=3, fit='gauss', **kwargs):
""" Linear fit of the best-fit sigma as a function of environment.
Bestfit sigma values are computed for the dLOS distribution in bins of
galaxy environment. Linear fit is done using MPFit.
"""
env_low, env_high, fpeaks, fpeak_errs, sigmas, sigma_errs, amps, nbins = \
dlos_envbin_peakfit(
cat_corr,
n_NN = n_NN,
fit = fit,
**kwargs
)
env_mid = np.array( 0.5 * (env_low + env_high) )
if n_NN == 5:
fit_range = np.where(
env_mid < 40.0
)
else:
raise NotImplementedError()
p0 = [ -0.03, 4.0 ] # guess
fa = {'x': env_mid[fit_range], 'y': sigmas[fit_range], 'err': sigma_errs[fit_range]}
fit_param = mpfit.mpfit(mpfit_linear, p0, functkw=fa, quiet=1)
best_slope = fit_param.params[0]
best_yint = fit_param.params[1]
try:
if kwargs['writeout']:
dlos_fpeak_envbin_fit_file = ''.join([
direc('data', cat_corr),
'DLOS_sigma_env_d', str(n_NN), 'NN_bin_bestfit.dat'
])
data_hdr = "Columns : bestfit_slope, bestfit_yint"
data_fmt = ['%10.5f', '%10.5f']
np.savetxt(
dlos_fpeak_envbin_fit_file,
np.c_[best_slope, best_yint],
fmt = data_fmt,
delimiter = '\t',
header = data_hdr
)
except KeyError:
pass
return best_slope, best_yint
def file(self):
""" power/bispectrum file
"""
specdict = (self.cat_corr)['spec']
# powerspectrum or bispectrum
if self.type in ('pk'):
spec_str = 'POWER_'
elif self.type == 'bk':
spec_str = 'BISP_'
#if 'quad' not in specdict.keys():
# specdict['quad'] = False
#if specdict['quad']:
# spec_str += 'Q_'
if (self.type == 'pk') and (self.ell != 0):
spec_str += 'Q_'
self.data_file = self.gal_data.file_name
gal_file = (self.gal_data.file_name).split('/')[-1]
self.random_file = self.rand_data.file_name
spec_dir = direc('spec', self.cat_corr)
if self.type == 'pk':
specparam_str = ''.join([
'.grid', str(specdict['Ngrid']),
'.P0', str(specdict['P0']),
'.box', str(specdict['Lbox'])
])
elif self.type == 'bk':
specparam_str = ''.join([
'.grid', str(specdict['Ngrid']),
'.nmax40.ncut3.s3',
'.P0', str(specdict['P0']),
'.box', str(specdict['Lbox'])
])
else:
raise NotImplementedError
file_name = ''.join([
spec_dir,
spec_str,
gal_file,
specparam_str
])
return file_name
def file(self):
""" Override parent class file attribute
Random catalog file
"""
cat_name = ((self.cat_corr)['catalog'])['name'].lower()
data_dir = direc('data', self.cat_corr)
if 'cmass' in cat_name: # CMASS
if cat_name == 'cmass':
# CMASS random catalog
file_name = 'cmass-dr12v4-N-Reid.ran.dat'
elif 'cmasslowz' in cat_name:
# CMASS LOWZ combined random catalog
if 'e2' in cat_name:
cmasslowz_str = 'e2'
elif 'e3' in cat_name:
cmasslowz_str = 'e3'
else:
cmasslowz_str = ''
if '_low' in cat_name:
zbin_str = '_LOW'
elif 'high' in cat_name:
zbin_str = '_HIGH'
else:
raise NameError("Must specify redshift bin of CMASS LOWZ sample")
file_name = ''.join([
'random0_DR12v5_CMASSLOWZ',
cmasslowz_str.upper(),
zbin_str,
'_North.ran.dat'
])
else:
raise NotImplementedError()
elif cat_name == 'nseries': # Nseries
file_name = 'Nseries_cutsky_randoms_50x_redshifts_comp.dat'
else:
raise NotImplementedError()
return ''.join([data_dir, file_name])
def file(self):
"""
Override parent class file() method in order to return
file name of random catalog file. Everything is quite
hardcoded.
"""
cat_name = ((self.cat_corr)["catalog"])["name"].lower()
data_dir = direc("data", self.cat_corr)
if "cmass" in cat_name: # CMASS
if cat_name == "cmass":
# CMASS random catalog
file_name = "cmass-dr12v4-N-Reid.ran.dat"
elif "cmasslowz" in cat_name:
# CMASS LOWZ combined random catalog
if "e2" in cat_name:
cmasslowz_str = "e2"
elif "e3" in cat_name:
cmasslowz_str = "e3"
else:
cmasslowz_str = ""
if "_low" in cat_name:
zbin_str = "_LOW"
elif "high" in cat_name:
zbin_str = "_HIGH"
else:
raise NameError("Must specify redshift bin of CMASS LOWZ sample")
file_name = "".join(["random0_DR12v5_CMASSLOWZ", cmasslowz_str.upper(), zbin_str, "_North.ran.dat"])
else:
raise NotImplementedError()
elif cat_name == "nseries": # Nseries
file_name = "Nseries_cutsky_randoms_50x_redshifts_comp.dat"
else:
raise NotImplementedError()
return "".join([data_dir, file_name])
def file(self):
''' Get file name of nbar(z) file
'''
gal_data = Data('data', self.cat_corr, **self.kwargs)
self.data_file = gal_data.file_name
data_dir = direc('data', self.cat_corr) # data directory
gal_file = (gal_data.file_name).split('/')[-1]
nbar_file = ''.join([
data_dir,
'nbar_',
gal_file
])
return nbar_file
def file(self):
""" FFT data file name
"""
corrdict = (self.cat_corr)['correction']
specdict = (self.cat_corr)['spec']
fft_dir = direc('fft', self.cat_corr)
if self.type == 'data':
galdata = Data(self.type, self.cat_corr, **self.kwargs) # data class
elif self.type == 'random':
galdata = Random(self.type, self.cat_corr, **self.kwargs) # data class
self.data_file = galdata.file_name # galaxy data file
# FFT label
fft_str = 'FFT_'
if specdict['ell'] != 0:
fft_str += 'Q_'
fft_corr_str = ''
if (corrdict['name'].lower() in ('floriansn', 'hectorsn')) & (self.type != 'random'):
fft_corr_str = ''.join(['.', corrdict['name'].lower()])
# FFTs from data file
fft_file = ''.join([
fft_dir,
fft_str, (self.data_file).rsplit('/')[-1],
fft_corr_str,
'.grid', str(specdict['Ngrid']),
'.P0', str(specdict['P0']),
'.box', str(specdict['Lbox'])
])
return fft_file
def file(self):
""" Name of line-of-sight displacement file
"""
catdict = self.cat_corr['catalog']
corrdict = self.cat_corr['correction']
dlos_dir = direc('data', self.cat_corr)
dlos_str = 'DLOS_'
dataclass = CorrCorrData(self.cat_corr, **self.kwargs) # data class
data_file = dataclass.file_name # galaxy data file
self.data_file = data_file
# add bells and whistles here later
# add bells and whistles here later
# add bells and whistles here later
file_name = ''.join([
dlos_dir,
dlos_str,
data_file.split('/')[-1]
])
return file_name
def build(self):
''' Build Fibercollided mock catalogs using specific idl routines or by using the given fiber collision weights
Parameters
----------
cat_corr : catalog correction dictionary
Notes
-----
'''
catdict = (self.cat_corr)['catalog']
catalog_name = catdict['name'].lower()
data_cols = self.datacolumns()
data_fmts = self.datacols_fmt()
data_hdrs = self.datacols_header()
data_dir = direc('data', self.cat_corr)
if catalog_name == 'nseries':
# N-series mocks (high quality mocks with actual CMASS tiling)
# original file
orig_file = ''.join([
data_dir,
'CutskyN', str(catdict['n_mock']), '.rdzwc'
])
orig_ra, orig_dec, orig_z, orig_wfc, orig_zupw, orig_upw_index = np.loadtxt(
orig_file,
unpack = True,
usecols = [0,1,2,4,5,6]
)
# file with mask completeness
mask_file = ''.join([data_dir, 'CutskyN', str(catdict['n_mock']), '.mask_info'])
orig_wcomp = np.loadtxt(mask_file, unpack=True, usecols=[0])
coll = np.where(orig_wfc == 0.0)
# data column list
data_list = [
orig_ra,
orig_dec,
orig_z,
orig_wfc,
orig_wcomp,
orig_zupw,
orig_upw_index
]
# handle upweighted redshift/index discrepancies by simply ignoring them.
if not np.array(orig_z[orig_upw_index.astype(int)[coll]] == orig_zupw[coll]).all():
wrong_index = (coll[0])[np.where(orig_z[orig_upw_index.astype(int)[coll]] != orig_zupw[coll])[0]]
warn_message = ''.join([
'upweighted galaxy redshift and index data discrepancies in ',
self.file(),
' ',
str(len(wrong_index)),
' galaxies affected'
])
warnings.warn(warn_message, Warning)
if len(wrong_index) > 0:
for i_data, datum in enumerate(data_list):
data_list[i_data] = np.delete(datum, wrong_index)
elif catalog_name == 'qpm':
# Quick Particle Mesh mocks from Jeremy (quantity over quality mocks)
orig_file = ''.join([
'/mount/riachuelo2/rs123/BOSS/QPM/cmass/mocks/dr12d/ngc/data/',
'a0.6452_', str("%04d" % catdict['n_mock']), '.dr12d_cmass_ngc.rdz'])
ra, dec, z, wfc = np.loadtxt(orig_file, unpack=True, usecols=[0,1,2,4])
orig_info_file = ''.join([
'/mount/riachuelo2/rs123/BOSS/QPM/cmass/mocks/dr12d/ngc/data/',
'a0.6452_', str("%04d" % catdict['n_mock']), '.dr12d_cmass_ngc.rdz.info'])
# gal_id, comp, z_real, z_red, mass_halo, flag_sta, id_halo
comp = np.loadtxt(orig_info_file, unpack=True, skiprows=3, usecols=[1])
if catdict['n_mock'] in (44, 46, 52, 53, 54, 56, 61, 707, 756, 794, 819, 831, 835, 838):
orig_veto_file = ''.join([
'/mount/riachuelo1/hahn/data/QPM/dr12d/',
'a0.6452_', str("%04d" % catdict['n_mock']), '.dr12d_cmass_ngc.veto'])
else:
orig_veto_file = ''.join([
'/mount/riachuelo2/rs123/BOSS/QPM/cmass/mocks/dr12d/ngc/data/',
'a0.6452_', str("%04d" % catdict['n_mock']), '.dr12d_cmass_ngc.veto'])
veto = np.loadtxt(orig_veto_file)
n_gal = len(veto)
if len(ra) != n_gal:
print orig_file
print orig_veto_file
raise ValueError('veto mask doesnt match')
vetomask = np.where(veto == 0)
# data column list
data_list = [
ra[vetomask],
dec[vetomask],
z[vetomask],
wfc[vetomask],
comp[vetomask]
]
elif catalog_name == 'bigmd':
# Big MultiDark
P0 = 20000.0
# read original random catalog
data_dir = '/mount/riachuelo1/hahn/data/BigMD/'
if 'version' in catdict.keys():
if catdict['version'] == 'nowiggles':
# simulation with no wiggles
orig_file = ''.join([data_dir, 'nowiggles/BigMD-cmass-dr12v4-nowiggle-veto.dat'])
else:
raise NotImplementedError
#orig_file = ''.join([data_dir, 'bigMD-cmass-dr12v4-wcp-veto.dat']) # hardcoded
#orig_file = ''.join([data_dir, 'bigMD-cmass-dr12v4-RST-standardHAM-veto.dat'])
#orig_file = ''.join([data_dir, 'bigMD-cmass-dr12v4-RST-quadru-veto.dat'])
else: # default
orig_file = ''.join([data_dir, 'BigMD-cmass-dr12v4-RST-standHAM-Vpeak-veto.dat'])
# RA, Decl, Redhsift, veto
ra, dec, z, wfkp, veto, wfc = np.loadtxt(
orig_file,
unpack=True,
usecols=[0,1,2,3,4,5]
)
n_gal = len(ra)
#nbar = (1.0 / P0) * (1.0/wfkp - 1.0)
vetomask = np.where(veto == 1) # impose vetomask
data_list = [
ra[vetomask],
dec[vetomask],
z[vetomask],
wfc[vetomask]
]
elif catalog_name == 'tilingmock': # tiling mock
input_file = ''.join([
'/mount/riachuelo1/hahn/data/tiling_mocks/',
'cmass-boss5003sector-icoll012.fidcosmo.dat'])
output_file = self.file()
idl_cmd = ' '.join([
'idl', '-e', '"',
"build_wcp_assign, 'tilingmock', input_file='"+input_file+"', output_file='"+output_file+"'", '"'])
os.system(idl_cmd)
return None
elif 'cmass' in catalog_name:
if catalog_name == 'cmass':
# CMASS DR12v4 galaxy data
data_file = ''.join([
data_dir,
'cmass-dr12v4-N-Reid.dat.fits'
])
data = mrdfits(data_file) # fits data object
zlimit = np.where((data.z >= 0.43) & (data.z <= 0.7))
elif 'cmasslowz' in catalog_name:
# CMASS LOWZ DR12v5 combined sample
# for Ariel's sample has three separate
# set of sectors '', 'e2', and 'e3'
cmasslowz_str = ''
if 'e2' in catalog_name:
cmasslowz_str = 'E2'
elif 'e3' in catalog_name:
cmasslowz_str = 'E3'
# Divide combined sample in two
# two bins of redshift
if '_low' in catalog_name:
zmin, zmax = 0.2, 0.5
elif '_high' in catalog_name:
zmin, zmax = 0.5, 0.75
else:
raise NameError("redshift bin must be specified")
# .fits data files from mk_catalog pipeline
data_file = ''.join([
data_dir,
'galaxy_DR12v5_CMASSLOWZ', cmasslowz_str, '_North.fits.gz'
])
data = mrdfits(data_file)
zlimit = np.where((data.z >= zmin) & (data.z < zmax)) # redshift limit
else:
raise NameError()
data_list = [
(data.ra)[zlimit],
(data.dec)[zlimit],
(data.z)[zlimit],
(data.nz)[zlimit],
(data.weight_systot)[zlimit],
(data.weight_noz)[zlimit],
(data.weight_cp)[zlimit],
(data.comp)[zlimit]
]
# write to corrected file
output_file = self.file()
np.savetxt(
output_file,
(np.vstack(np.array(data_list))).T,
fmt=data_fmts,
delimiter='\t',
header=data_hdrs
)
return None
def build(self):
''' Build the random catalogs from original data
Override parent class build attribute
'''
cat_name = ((self.cat_corr)['catalog'])['name'].lower()
if cat_name == 'nseries': # Nseries ----------------------------
# original random catalog
data_dir = '/mount/riachuelo1/hahn/data/Nseries/'
orig_rand_file = ''.join([data_dir, 'Nseries_cutsky_randoms_50x_redshifts.dat'])
ra, dec, z = np.loadtxt(orig_rand_file, unpack=True, usecols=[0,1,2]) # RA, Decl, Redhsift
# sector completeness catalog
orig_comp_file = ''.join([data_dir, 'Nseries_cutsky_randoms_50x_maskinfo.dat'])
comp = np.loadtxt(orig_comp_file, unpack=True, usecols=[0])
header_str = 'Columns : ra, dec, z, comp'
data_list = [ra, dec, z, comp]
data_fmt=['%10.5f', '%10.5f', '%10.5f', '%10.5f']
elif 'cmass' in cat_name: # CMASS --------------------------------
data_dir = direc('data', self.cat_corr)
if cat_name == 'cmass':
# random data fits file
data_file = ''.join([data_dir, 'cmass-dr12v4-N-Reid.ran.fits'])
cmass = mrdfits(data_file)
# mask file
mask_file = ''.join([data_dir, 'mask-cmass-dr12v4-N-Reid.fits'])
mask = mrdfits(mask_file)
ipoly = cmass.ipoly # polygon index
comp = mask.weight[ipoly]
# redshift limit
zlimit = np.where((cmass.z >= 0.43) & (cmass.z <= 0.7))
elif 'cmasslowz' in cat_name:
# CMASS LOWZ combined data
# three different CMASS LOWZ
if 'e2' in cat_name:
cmasslowz_str = 'E2'
elif 'e3' in cat_name:
cmasslowz_str = 'E3'
else:
cmasslowz_str = ''
if 'high' in cat_name:
zmin, zmax = 0.5, 0.75
elif '_low' in cat_name:
zmin, zmax = 0.2, 0.5
else:
raise NameError("CMASSLOWZ Catalog must specify high or lowr edshift bin")
# mask file
mask_file = ''.join([
data_dir,
'mask_DR12v5_CMASSLOWZ',
cmasslowz_str,
'_North.fits.gz'
])
start_time = time.time()
print 'Reading ', mask_file
mask = mrdfits(mask_file)
print 'took ', (time.time() - start_time)/60.0, ' minutes'
# random data fits file
data_file = ''.join([
data_dir,
'random0_DR12v5_CMASSLOWZ',
cmasslowz_str,
'_North.fits.gz'
])
start_time = time.time()
print 'Reading ', data_file
cmass = mrdfits(data_file)
print 'took ', (time.time() - start_time)/60.0, ' minutes'
ipoly = cmass.ipoly # polygon index
comp = mask.weight[ipoly]
# redshift limit
zlimit = np.where((cmass.z >= zmin) & (cmass.z < zmax))
else:
raise NotImplementedError("Only CMASS and CMASS+LOWZ combined sample implemented")
header_str = 'columns : ra, dec, z, nbar, comp' #ra, dec, z, nz, comp
data_list = [(cmass.ra)[zlimit], (cmass.dec)[zlimit], (cmass.z)[zlimit], (cmass.nz)[zlimit], comp[zlimit]]
data_fmt=['%10.5f', '%10.5f', '%10.5f', '%.5e', '%10.5f']
else:
raise NotImplementedError()
# write to corrected file
output_file = self.file()
np.savetxt(
output_file,
(np.vstack(np.array(data_list))).T,
fmt=data_fmt,
delimiter='\t',
header=header_str
)
return None
def build(self):
"""
Completely replace parent class build attribute in order
to ruild the random catalogs from original survey data.
Everything is hardcoded.
"""
cat_name = ((self.cat_corr)["catalog"])["name"].lower()
if cat_name == "nseries": # Nseries ----------------------------
# original random catalog
data_dir = "/mount/riachuelo1/hahn/data/Nseries/"
orig_rand_file = "".join([data_dir, "Nseries_cutsky_randoms_50x_redshifts.dat"])
ra, dec, z = np.loadtxt(orig_rand_file, unpack=True, usecols=[0, 1, 2]) # RA, Decl, Redhsift
# sector completeness catalog
orig_comp_file = "".join([data_dir, "Nseries_cutsky_randoms_50x_maskinfo.dat"])
comp = np.loadtxt(orig_comp_file, unpack=True, usecols=[0])
header_str = "Columns : ra, dec, z, comp"
data_list = [ra, dec, z, comp]
data_fmt = ["%10.5f", "%10.5f", "%10.5f", "%10.5f"]
elif "cmass" in cat_name: # CMASS --------------------------------
data_dir = direc("data", self.cat_corr)
if cat_name == "cmass":
# random data fits file
data_file = "".join([data_dir, "cmass-dr12v4-N-Reid.ran.fits"])
cmass = mrdfits(data_file)
# mask file
mask_file = "".join([data_dir, "mask-cmass-dr12v4-N-Reid.fits"])
mask = mrdfits(mask_file)
ipoly = cmass.ipoly # polygon index
comp = mask.weight[ipoly]
# redshift limit
zlimit = np.where((cmass.z >= 0.43) & (cmass.z <= 0.7))
elif "cmasslowz" in cat_name:
# CMASS LOWZ combined data
# three different CMASS LOWZ
if "e2" in cat_name:
cmasslowz_str = "E2"
elif "e3" in cat_name:
cmasslowz_str = "E3"
else:
cmasslowz_str = ""
if "high" in cat_name:
zmin, zmax = 0.5, 0.75
elif "_low" in cat_name:
zmin, zmax = 0.2, 0.5
else:
raise NameError("CMASSLOWZ Catalog must specify high or lowr edshift bin")
# mask file
mask_file = "".join([data_dir, "mask_DR12v5_CMASSLOWZ", cmasslowz_str, "_North.fits.gz"])
start_time = time.time()
print "Reading ", mask_file
mask = mrdfits(mask_file)
print "took ", (time.time() - start_time) / 60.0, " minutes"
# random data fits file
data_file = "".join([data_dir, "random0_DR12v5_CMASSLOWZ", cmasslowz_str, "_North.fits.gz"])
start_time = time.time()
print "Reading ", data_file
cmass = mrdfits(data_file)
print "took ", (time.time() - start_time) / 60.0, " minutes"
ipoly = cmass.ipoly # polygon index
comp = mask.weight[ipoly]
# redshift limit
zlimit = np.where((cmass.z >= zmin) & (cmass.z < zmax))
else:
raise NotImplementedError("Only CMASS and CMASS+LOWZ combined sample implemented")
header_str = "columns : ra, dec, z, nbar, comp" # ra, dec, z, nz, comp
data_list = [(cmass.ra)[zlimit], (cmass.dec)[zlimit], (cmass.z)[zlimit], (cmass.nz)[zlimit], comp[zlimit]]
data_fmt = ["%10.5f", "%10.5f", "%10.5f", "%.5e", "%10.5f"]
else:
raise NotImplementedError()
# write to corrected file
output_file = self.file()
np.savetxt(output_file, (np.vstack(np.array(data_list))).T, fmt=data_fmt, delimiter="\t", header=header_str)
return None
