def gaia_read(ra, dec, radius, dr='dr1'):
"""Partition all Gaia source fits files
Parameters
----------
ra : np.float64
right ascension to search around (degrees, J2000)
dec : np.float64
declination to search around (degrees, J2000)
radius : np.float64
radius of search (degrees)
dr : string
name of data release (default 'dr1')
Returns
-------
data : ndarray
array with Gaia data for matching objects
Comments
--------
Requires data in $GAIA_DATA to have been processed by gaia_run()
into files partitioned by RA/Dec
"""
gaia_path = os.path.join(os.getenv('GAIA_DATA'), dr, 'gaia_source',
'fits_sorted')
gaia_index = fitsio.read(os.path.join(gaia_path, 'gaia-index.fits'))
ra_arr = np.array([np.float64(ra)])
dec_arr = np.array([np.float64(dec)])
(iindex, i0, dindex) = spheregroup.spherematch(gaia_index['racen'],
gaia_index['deccen'],
ra_arr,
dec_arr,
np.float64(radius) + 1.,
maxmatch=0)
if (len(iindex) == 0):
return None
data = None
for (ira, idec) in zip(gaia_index['ira'][iindex],
gaia_index['idec'][iindex]):
filename = _partition_filename(ira, idec)
if (os.path.isfile(filename)):
gaia = fitsio.read(filename, ext=1)
(igaia, i0, dgaia) = spheregroup.spherematch(gaia['ra'],
gaia['dec'],
ra_arr,
dec_arr,
np.float64(radius),
maxmatch=0)
if (len(igaia) > 0):
tmp_data = gaia[igaia]
if (data is None):
data = np.zeros(0, dtype=gaia[0].dtype)
data = np.append(data, tmp_data)
return data
def _iSEDfitMatch(self):
''' Match the GroupCat galaxies with iSEDfit galaxy properties from
John Moustakas's MFData objects. The matching is done using PyDL's
spherematch
'''
# import SDSS MFdata catalog
mfdata = mrdfits(code_dir(), 'dat/observations/mfdata_all_supergrid01_sdss.fits.gz')
spherematch_time = time.time()
match1, match2, d_match = spherematch(
self.ra, self.dec, mfdata.ra, mfdata.dec, 0.001)
print 'spherematch took ', time.time() - spherematch_time
iSEDfit_mass = np.repeat(-999., len(self.ra))
iSEDfit_SFR = np.repeat(-999., len(self.ra))
iSEDfit_SSFR = np.repeat(-999., len(self.ra))
if np.max(self.z[match1] - mfdata.z[match2]) > 0.1:
raise ValueError
#wrong = np.argmax(self.z[match1] - mfdata.z[match2])
#print self.ra[match1[wrong]], self.dec[match1[wrong]], self.z[match1[wrong]]
#print mfdata.ra[match2[wrong]], mfdata.dec[match2[wrong]], mfdata.z[match2[wrong]]
iSEDfit_mass[match1] = mfdata.mass[match2]
iSEDfit_SFR[match1] = mfdata.sfr[match2]
iSEDfit_SSFR[match1] = iSEDfit_SFR[match1]-iSEDfit_mass[match1]
setattr(self, 'iSEDfit_mass', iSEDfit_mass)
setattr(self, 'iSEDfit_sfr', iSEDfit_SFR)
setattr(self, 'iSEDfit_ssfr', iSEDfit_SSFR)
return None
def ClosestStarsPoint(self, cRA, cDec, radius):
"""
Returns indices of stars that is
within radius of the center_index star
"""
tRA = self.tab['RA']
tDec = self.tab['Dec']
indices = spherematch(tRA, tDec, cRA, cDec, maxmatch = 0, matchlength = radius)[0]
return SubTable(self,self.tab['Index'][indices])
def ClosestStarsPoint(self, cRA, cDec, radius):
"""
Returns indices of stars that is
within radius of the center_index star
"""
tRA = self.tab['RA']
tDec = self.tab['Dec']
indices = spherematch(tRA,
tDec,
cRA,
cDec,
maxmatch=0,
matchlength=radius)[0]
return SubTable(self, self.tab['Index'][indices])
def BOSS_fibercollision(ra, dec):
''' apply BOSS fiber collisions
'''
fib_angscale = 0.01722 # 62'' fiber collision angular scale
t0 = time.time()
m1, m2, d12 = spherematch(ra, dec, ra, dec, fib_angscale, maxmatch=2)
print('spherematch takes %f sec' % (time.time() - t0))
notitself = (d12 > 0.0)
# only ~60% of galaxies within the angular scale are fiber collided
# since 40% are in overlapping regions with substantially lower
# fiber collision rates
notoverlap = (np.random.uniform(size=len(m1)) > 0.6)
fibcollided = np.zeros(len(ra)).astype(bool)
fibcollided[m1[notitself & notoverlap]] = True
return fibcollided
def ClosestStars(self, center_index, radius):
"""
Returns "SubTable" object of stars that is
within radius of the center_index star
"""
tRA = self.tab['RA']
tDec = self.tab['Dec']
cRA = np.array([self.tab['RA'][center_index]])
cDec = np.array([self.tab['Dec'][center_index]])
indices = spherematch(tRA,
tDec,
cRA,
cDec,
maxmatch=0,
matchlength=radius)[0]
return SubTable(self, self.tab['Index'][indices])
def _Match_OtherSFR(self, lit='salim2016'):
''' Match galaxies with UV SSFR and stellar masses from Salim et al. (2007).
Matching is done using PyDL's spherematch
'''
# import other data
if lit == 'salim2016':
nsa_file = ''.join(['/mount/sirocco1/hahn/cenque/', 'GSWLC-A1.dat'])
nsa_ra, nsa_dec, nsa_z, nsa_mass, nsa_sfr = np.loadtxt(nsa_file, unpack=True, usecols=[5, 6, 7, 9, 11])
elif lit == 'uv':
nsa_file = ''.join(['/mount/sirocco1/hahn/cenque/', 'nsaid_ra_dec_z_mass_uvssfr'])
nsa_ra, nsa_dec, nsa_z, nsa_mass, nsa_uvssfr = np.loadtxt(nsa_file, unpack=True, usecols=[1,2,3,4,5])
print nsa_uvssfr.min(), nsa_uvssfr.max(), np.mean(nsa_uvssfr)
nsa_mass = np.log10(nsa_mass)
nsa_sfr = (nsa_mass + nsa_uvssfr) # UV SFR
spherematch_time = time.time()
match1, match2, d_match = spherematch(self.ra, self.dec, nsa_ra, nsa_dec, 0.001)
print 'spherematch took ', time.time() - spherematch_time
print np.float(len(match1))/np.float(len(self.ra))
salim_match = np.repeat(-999, len(self.ra))
salim_mass = np.repeat(-999., len(self.ra))
salim_SFR = np.repeat(-999., len(self.ra))
salim_SSFR = np.repeat(-999., len(self.ra))
if np.max(self.z[match1] - nsa_z[match2]) > 0.05:
raise ValueError
salim_match[match1] = match2
if lit == 'uv':
salim_mass[match1] = self.mass[match1]
else:
salim_mass[match1] = nsa_mass[match2]
salim_SFR[match1] = nsa_sfr[match2]
salim_SSFR[match1] = salim_SFR[match1] - salim_mass[match1]
setattr(self, lit+'_match', salim_match)
setattr(self, lit+'_mass', salim_mass)
setattr(self, lit+'_sfr', salim_SFR)
setattr(self, lit+'_ssfr', salim_SSFR)
return None
def Build_MPAJHU_TinkerCatalog_ASCII(Mrcut=18):
''' Append MPA-JHU SSFR values to the Tinker et al. (2011) catalog.
The main purpose is to try to reproduce the Kauffmann et al. (2013) results.
Galaxies are matched to each other through spherematch.
'''
# import Tinker et al. (2011) catalog with specified Mr cut
catalog = TinkerCatalog(Mrcut=Mrcut)
# import MPA-JHU catalog
mpajhu_gals = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_info_dr7_v5_2.fit']))
# SFR total
mpajhu_sfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totsfr_dr7_v5_2.fits']))
# SFR fiber
mpajhu_sfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibsfr_dr7_v5_2.fits']))
# SSFR total
mpajhu_ssfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totspecsfr_dr7_v5_2.fits']))
# SSFR fiber
mpajhu_ssfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibspecsfr_dr7_v5_2.fits']))
# stellar mass total
mpajhu_masstot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'totlgm_dr7_v5_2.fit']))
# stellar mass fiber
mpajhu_massfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'fiblgm_dr7_v5_2.fit']))
t_spherematch = time.time()
match = spherematch(catalog['ra'], catalog['dec'], mpajhu_gals.ra,
mpajhu_gals.dec, 0.000833333)
print 'Spherematch with matchlenght = ', 0.000833333
print 'takes ', time.time() - t_spherematch, 'seconds'
print 1. - np.float(len(match[0])) / np.float(len(
catalog['ra'])), 'of the VAGC galaxies'
print 'do not have matches, likely due to fiber collisions'
if len(match[0]) != len(np.unique(match[0])):
raise ValueError
# save the MPAJHU indices, jsut in case
catalog['mpajhu_index'] = np.repeat(-999, len(catalog['ra']))
catalog['mpajhu_index'][match[0]] = match[1]
# append SFR, SSFR, and mass values to catalog
for col in [
'sfr_tot_mpajhu', 'sfr_fib_mpajhu', 'ssfr_tot_mpajhu',
'ssfr_fib_mpajhu', 'mass_tot_mpajhu', 'mass_fib_mpajhu'
]: # initiate arrays
catalog[col] = np.repeat(-999., len(catalog['ra']))
catalog['sfr_tot_mpajhu'][match[0]] = mpajhu_sfrtot.median[match[1]]
catalog['sfr_fib_mpajhu'][match[0]] = mpajhu_sfrfib.median[match[1]]
catalog['ssfr_tot_mpajhu'][match[0]] = mpajhu_ssfrtot.median[match[1]]
catalog['ssfr_fib_mpajhu'][match[0]] = mpajhu_ssfrfib.median[match[1]]
catalog['mass_tot_mpajhu'][match[0]] = mpajhu_masstot.median[match[1]]
catalog['mass_fib_mpajhu'][match[0]] = mpajhu_massfib.median[match[1]]
print mpajhu_massfib.median[match[1]]
first_cols = [
'id_gal', 'ra', 'dec', 'z', 'mass', 'sfr', 'ssfr', 'mass_tot_mpajhu',
'mass_fib_mpajhu', 'sfr_tot_mpajhu', 'sfr_fib_mpajhu',
'ssfr_tot_mpajhu', 'ssfr_fib_mpajhu'
]
data_fmt = []
data_list = []
for i_key, key in enumerate(first_cols):
data_list.append(catalog[key])
if key == 'id_gal':
data_fmt.append('%i')
else:
data_fmt.append('%10.5f')
later_cols = []
for key in catalog.keys():
if key not in first_cols:
later_cols.append(key)
for key in later_cols:
data_list.append(catalog[key])
if 'id' in key:
data_fmt.append('%i')
elif 'index' in key:
data_fmt.append('%i')
elif key == 'n_sersic':
data_fmt.append('%i')
elif key == 'stellmass':
data_fmt.append('%1.5e')
else:
data_fmt.append('%10.5f')
str_header = ', '.join(first_cols + later_cols)
M_cut = Tinker_Masscut(Mrcut)
mpajhu_tinker_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'GroupCat.Mr',
str(Mrcut), '.Mass',
str(M_cut), '.D360.MPAJHU.dat'
])
np.savetxt(mpajhu_tinker_file, (np.vstack(np.array(data_list))).T,
fmt=data_fmt,
delimiter='\t',
header=str_header)
return None
def Build_VAGCdr72_MPAJHU(Ascii=False):
''' Build VAGC dr72 with cross referenced MPAJHU stellar masses
and SSFRs.
'''
# import VAGC dr72bright34
vagc_dr72 = VAGCdr72bright34_Catalog()
print len(vagc_dr72['ra']), ', VAGC dr72bright34 galaxies'
# import MPA-JHU catalog
mpajhu_gals = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_info_dr7_v5_2.fit']))
# SFR total
mpajhu_sfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totsfr_dr7_v5_2.fits']))
# SFR fiber
mpajhu_sfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibsfr_dr7_v5_2.fits']))
# SSFR total
mpajhu_ssfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totspecsfr_dr7_v5_2.fits']))
# SSFR fiber
mpajhu_ssfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibspecsfr_dr7_v5_2.fits']))
# stellar mass total
mpajhu_masstot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'totlgm_dr7_v5_2.fit']))
# stellar mass fiber
mpajhu_massfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'fiblgm_dr7_v5_2.fit']))
catalog = {}
catalog['ra'] = vagc_dr72['ra']
catalog['dec'] = vagc_dr72['dec']
catalog['z'] = vagc_dr72['z']
for i_band, band in enumerate(['u', 'g', 'r', 'i', 'z']):
catalog['M_' + band] = vagc_dr72['M_' + band]
t_spherematch = time.time()
match = spherematch(catalog['ra'], catalog['dec'], mpajhu_gals.ra,
mpajhu_gals.dec, 0.000833333)
print 'Spherematch with matchlenght = ', 0.000833333
print 'takes ', time.time() - t_spherematch, 'seconds'
print 1. - np.float(len(match[0])) / np.float(len(
catalog['ra'])), 'of the VAGC galaxies'
print 'do not have matches'
if len(match[0]) != len(np.unique(match[0])):
raise ValueError
# save the MPAJHU indices, jsut in case
catalog['mpajhu_index'] = np.repeat(-999, len(catalog['ra']))
catalog['mpajhu_index'][match[0]] = match[1]
# append SFR, SSFR, and mass values to catalog
for col in [
'sfr_tot', 'sfr_fib', 'ssfr_tot', 'ssfr_fib', 'mass_tot',
'mass_fib'
]: # initiate arrays
catalog[col] = np.repeat(-999., len(catalog['ra']))
catalog['sfr_tot'][match[0]] = mpajhu_sfrtot.median[match[1]]
catalog['sfr_fib'][match[0]] = mpajhu_sfrfib.median[match[1]]
catalog['ssfr_tot'][match[0]] = mpajhu_ssfrtot.median[match[1]]
catalog['ssfr_fib'][match[0]] = mpajhu_ssfrfib.median[match[1]]
catalog['mass_tot'][match[0]] = mpajhu_masstot.median[match[1]]
catalog['mass_fib'][match[0]] = mpajhu_massfib.median[match[1]]
mpajhu_file = ''.join(
[UT.dir_dat(), 'vagc/', 'VAGCdr72.MPAJHU.nocut.hdf5'])
f = h5py.File(mpajhu_file, 'w')
grp = f.create_group('data')
for key in catalog.keys():
grp.create_dataset(key, data=catalog[key])
f.close()
if Ascii: # write to Ascii (for jeremy)
mpajhu_file = ''.join(
[UT.dir_dat(), 'vagc/', 'VAGCdr72.MPAJHU.nocut.dat'])
column_order = [
'ra', 'dec', 'z', 'mass_tot', 'sfr_tot', 'ssfr_tot', 'mass_fib',
'sfr_fib', 'ssfr_fib'
]
data_list = []
data_fmt = ['%10.5f' for i in range(len(column_order))]
str_header = ''
for col in column_order:
data_list.append(catalog[col])
if 'mass' in col:
str_header += ' ' + col + ' (Msun),'
elif 'sfr' in col:
if 'ssfr' not in col:
str_header += ' ' + col + ' (Msun/yr),'
else:
str_header += ' ' + col + ','
else:
str_header += ' ' + col + ','
np.savetxt(mpajhu_file, (np.vstack(np.array(data_list))).T,
fmt=data_fmt,
delimiter='\t',
header=str_header)
return None
def Build_KauffmannParent():
''' Try to create the parent sample of Kauffmann et al.(2013)
'''
# import VAGC dr72bright34
vagc_dr72 = VAGCdr72bright34_Catalog()
# import MPA-JHU catalog
mpajhu_gals = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_info_dr7_v5_2.fit']))
# SFR total
mpajhu_sfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totsfr_dr7_v5_2.fits']))
# SFR fiber
mpajhu_sfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibsfr_dr7_v5_2.fits']))
# SSFR total
mpajhu_ssfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totspecsfr_dr7_v5_2.fits']))
# SSFR fiber
mpajhu_ssfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibspecsfr_dr7_v5_2.fits']))
# stellar mass total
mpajhu_masstot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'totlgm_dr7_v5_2.fit']))
# stellar mass fiber
mpajhu_massfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'fiblgm_dr7_v5_2.fit']))
catalog = {}
catalog['ra'] = vagc_dr72['ra']
catalog['dec'] = vagc_dr72['dec']
catalog['z'] = vagc_dr72['z']
for i_band, band in enumerate(['u', 'g', 'r', 'i', 'z']):
catalog['M_' + band] = vagc_dr72['M_' + band]
# pre cut
cut_z = (catalog['z'] > 0.017) & (catalog['z'] < 0.03)
pre_cuts = np.where(cut_z) #& cut_stellarmass & cut_absmag)
for key in catalog.keys():
catalog[key] = catalog[key][pre_cuts]
t_spherematch = time.time()
match = spherematch(catalog['ra'], catalog['dec'], mpajhu_gals.ra,
mpajhu_gals.dec, 0.000833333)
print 'Spherematch with matchlenght = ', 0.000833333
print 'takes ', time.time() - t_spherematch, 'seconds'
print 1. - np.float(len(match[0])) / np.float(len(
catalog['ra'])), 'of the VAGC galaxies'
print 'do not have matches'
if len(match[0]) != len(np.unique(match[0])):
raise ValueError
# save the MPAJHU indices, jsut in case
catalog['mpajhu_index'] = np.repeat(-999, len(catalog['ra']))
catalog['mpajhu_index'][match[0]] = match[1]
# append SFR, SSFR, and mass values to catalog
for col in [
'sfr_tot_mpajhu', 'sfr_fib_mpajhu', 'ssfr_tot_mpajhu',
'ssfr_fib_mpajhu', 'mass_tot_mpajhu', 'mass_fib_mpajhu'
]: # initiate arrays
catalog[col] = np.repeat(-999., len(catalog['ra']))
catalog['sfr_tot_mpajhu'][match[0]] = mpajhu_sfrtot.median[match[1]]
catalog['sfr_fib_mpajhu'][match[0]] = mpajhu_sfrfib.median[match[1]]
catalog['ssfr_tot_mpajhu'][match[0]] = mpajhu_ssfrtot.median[match[1]]
catalog['ssfr_fib_mpajhu'][match[0]] = mpajhu_ssfrfib.median[match[1]]
catalog['mass_tot_mpajhu'][match[0]] = mpajhu_masstot.median[match[1]]
catalog['mass_fib_mpajhu'][match[0]] = mpajhu_massfib.median[match[1]]
# kauffmann et al.(2013) cuts
cut_stellarmass = (catalog['mass_tot_mpajhu'] > 9.25)
cut_absmag = (catalog['M_r'] < -16.) & (catalog['M_r'] > -24.)
cut_match = (catalog['mpajhu_index'] != -999)
final_cuts = np.where(cut_stellarmass & cut_absmag & cut_match)
for key in catalog.keys():
catalog[key] = catalog[key][final_cuts]
mpajhu_file = ''.join(
[UT.dir_dat(), 'vagc/', 'VAGCdr72.Kauff2013cut.hdf5'])
f = h5py.File(mpajhu_file, 'w')
grp = f.create_group('data')
for key in catalog.keys():
grp.create_dataset(key, data=catalog[key])
f.close()
return None
def Build_MPAJHU_TinkerCatalog(Mrcut=18):
''' Append MPA-JHU SSFR values to the Tinker et al. (2011) catalog.
The main purpose is to try to reproduce the Kauffmann et al. (2013) results.
Galaxies are matched to each other through spherematch.
'''
# import Tinker et al. (2011) catalog with specified Mr cut
catalog = TinkerCatalog(Mrcut=Mrcut)
# import MPA-JHU catalog
mpajhu_gals = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_info_dr7_v5_2.fit']))
# SFR total
mpajhu_sfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totsfr_dr7_v5_2.fits']))
# SFR fiber
mpajhu_sfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibsfr_dr7_v5_2.fits']))
# SSFR total
mpajhu_ssfrtot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_totspecsfr_dr7_v5_2.fits']))
# SSFR fiber
mpajhu_ssfrfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'gal_fibspecsfr_dr7_v5_2.fits']))
# stellar mass total
mpajhu_masstot = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'totlgm_dr7_v5_2.fit']))
# stellar mass fiber
mpajhu_massfib = mrdfits(''.join(
[UT.dir_dat(), 'mpa_jhu/', 'fiblgm_dr7_v5_2.fit']))
t_spherematch = time.time()
match = spherematch(catalog['ra'], catalog['dec'], mpajhu_gals.ra,
mpajhu_gals.dec, 0.000833333)
print 'Spherematch with matchlenght = ', 0.000833333
print 'takes ', time.time() - t_spherematch, 'seconds'
print 1. - np.float(len(match[0])) / np.float(len(
catalog['ra'])), 'of the VAGC galaxies'
print 'do not have matches, likely due to fiber collisions'
if len(match[0]) != len(np.unique(match[0])):
raise ValueError
# save the MPAJHU indices, jsut in case
catalog['mpajhu_index'] = np.repeat(-999, len(catalog['ra']))
catalog['mpajhu_index'][match[0]] = match[1]
# append SFR, SSFR, and mass values to catalog
for col in [
'sfr_tot_mpajhu', 'sfr_fib_mpajhu', 'ssfr_tot_mpajhu',
'ssfr_fib_mpajhu', 'mass_tot_mpajhu', 'mass_fib_mpajhu'
]: # initiate arrays
catalog[col] = np.repeat(-999., len(catalog['ra']))
catalog['sfr_tot_mpajhu'][match[0]] = mpajhu_sfrtot.median[match[1]]
catalog['sfr_fib_mpajhu'][match[0]] = mpajhu_sfrfib.median[match[1]]
catalog['ssfr_tot_mpajhu'][match[0]] = mpajhu_ssfrtot.median[match[1]]
catalog['ssfr_fib_mpajhu'][match[0]] = mpajhu_ssfrfib.median[match[1]]
catalog['mass_tot_mpajhu'][match[0]] = mpajhu_masstot.median[match[1]]
catalog['mass_fib_mpajhu'][match[0]] = mpajhu_massfib.median[match[1]]
# trim galaxies without matches
hasmatch = np.where(catalog['mpajhu_index'] != -999)
for key in catalog.keys():
key_val = catalog[key]
catalog[key] = key_val[hasmatch]
catalog['mpajhu_tinker_index'] = hasmatch[0]
M_cut = Tinker_Masscut(Mrcut)
mpajhu_tinker_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'GroupCat.Mr',
str(Mrcut), '.Mass',
str(M_cut), '.D360.MPAJHU.hdf5'
])
f = h5py.File(mpajhu_tinker_file, 'w')
grp = f.create_group('data')
for key in catalog.keys():
grp.create_dataset(key, data=catalog[key])
f.close()
return None
def _fanuc_check(plateid=None):
"""Read in plDrillPos file and make sure holes do not overlap
Parameters
----------
plateid : np.int32, int
plate ID
Returns
-------
ok : boolean
True if OK, False if not
Notes
-----
Assumes 5 arcmin is biggest thing it needs to check
For the off-axis acquisition camera at LCO,
it assumes a 55 x 40 mm footprint.
"""
offaxis_xsize = 55.
offaxis_ysize = 40.
drillpos_template = 'plDrillPos-{plate}.par' + post_str
drillpos_name = drillpos_template.format(plate=plateid)
dpos = yanny.yanny(drillpos_name)
(m1, m2, d12) = spheregroup.spherematch(dpos['DRILLPOS']['ra'],
dpos['DRILLPOS']['dec'],
dpos['DRILLPOS']['ra'],
dpos['DRILLPOS']['dec'],
5. / 60., maxmatch=0)
for indx1, indx2 in zip(m1, m2):
if(indx1 != indx2):
dx = (dpos['DRILLPOS']['xDrill'][indx1] -
dpos['DRILLPOS']['xDrill'][indx2])
dy = (dpos['DRILLPOS']['yDrill'][indx1] -
dpos['DRILLPOS']['yDrill'][indx2])
d12 = np.sqrt(dx**2 + dy**2)
limit = 0.5 * (dpos['DRILLPOS']['holeDiam'][indx1] +
dpos['DRILLPOS']['holeDiam'][indx2])
if(d12 < limit):
conflict = True
holeType1 = dpos['DRILLPOS']['holeType'][indx1].decode()
holeType2 = dpos['DRILLPOS']['holeType'][indx2].decode()
# Special case for acquisition camera
if(holeType1 == "ACQUISITION_OFFAXIS" or
holeType2 == "ACQUISITION_OFFAXIS"):
conflict = False
x1 = dpos['DRILLPOS']['xDrill'][indx1]
x2 = dpos['DRILLPOS']['xDrill'][indx2]
y1 = dpos['DRILLPOS']['yDrill'][indx1]
y2 = dpos['DRILLPOS']['yDrill'][indx2]
if(holeType1 == "ACQUISITION_OFFAXIS"):
holeDiam = dpos['DRILLPOS']['holeDiam'][indx2]
else:
holeDiam = dpos['DRILLPOS']['holeDiam'][indx1]
if(np.abs(x1 - x2) < 0.5 * (offaxis_xsize + holeDiam) and
np.abs(y1 - y2) < 0.5 * (offaxis_ysize + holeDiam)):
conflict = True
if(conflict is True):
return(False)
return(True)
def _Build(self, field, dr_gama=3, dr_legacy=7, silent=True):
''' Get Legacy Survey photometry for objects in the GAMA DR`dr_gama`
photo+spec objects from the sweep files. This is meant to run on nersc
but you can also manually download the sweep files and specify the dir
where the sweep files are located in.
'''
from pydl.pydlutils.spheregroup import spherematch
if dr_legacy == 5:
sweep_n_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr5/sweep/5.0/'
sweep_s_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr5/sweep/5.0/'
tractor_n_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr5/tractor/'
elif dr_legacy == 7:
sweep_n_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr7/sweep/7.1/'
sweep_s_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr7/sweep/7.1/'
tractor_n_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr7/tractor/'
tractor_s_dir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr7/tractor/'
elif dr_legacy == 8:
sweep_n_dir = \
'/global/project/projectdirs/cosmo/data/legacysurvey/dr8/north/sweep/8.0/'
sweep_s_dir = \
'/global/project/projectdirs/cosmo/data/legacysurvey/dr8/south/sweep/8.0/'
tractor_n_dir = \
'/global/project/projectdirs/cosmo/data/legacysurvey/dr8/north/tractor/'
tractor_s_dir = \
'/global/project/projectdirs/cosmo/data/legacysurvey/dr7/south/tractor/'
# read in the names of the sweep files
fsweep = ''.join([UT.dat_dir(), 'legacy/', field, '.sweep_list.dat'])
if not os.path.isfile(fsweep):
_ = self._getSweeps(field, silent=silent)
sweep_files = np.loadtxt(fsweep, unpack=True, usecols=[0], dtype='S')
if not silent:
print("there are %i sweep files in the %s GAMA region" %
(len(sweep_files), field))
# read in GAMA objects
gama = GAMA()
gama_data = gama.Read(field, data_release=dr_gama, silent=silent)
sweep_dict = {}
gama_photo_dict, gama_spec_dict, gama_kcorr0_dict, gama_kcorr1_dict = {}, {}, {}, {}
# loop through the files and only keep ones that spherematch with GAMA objects
for i_f, f in enumerate(sweep_files):
# read in sweep object
for sweep_dir in [sweep_n_dir, sweep_s_dir]:
fsweep = os.path.join(sweep_dir, f.decode('unicode_escape'))
if os.path.isfile(fsweep): break
sweep = fits.open(fsweep)[1].data
if not silent: print('matching %s' % fsweep)
# spherematch the sweep objects with GAMA objects
if len(sweep['ra']) > len(gama_data['photo']['ra']):
match = spherematch(sweep['ra'], sweep['dec'],
gama_data['photo']['ra'],
gama_data['photo']['dec'], 0.000277778)
else:
match_inv = spherematch(gama_data['photo']['ra'],
gama_data['photo']['dec'], sweep['ra'],
sweep['dec'], 0.000277778)
match = [match_inv[1], match_inv[0], match_inv[2]]
if not silent:
print('%i matches from the %s sweep file' % (len(match[0]), f))
# save sweep photometry to `sweep_dict`
for key in sweep.names:
if i_f == 0:
sweep_dict[key.lower()] = sweep[key][match[0]]
else:
sweep_dict[key.lower()] = np.concatenate(
[sweep_dict[key.lower()], sweep[key][match[0]]])
# save matching GAMA data ('photo', 'spec', and kcorrects)
for gkey, gdict in zip(
['photo', 'spec', 'kcorr_z0.0', 'kcorr_z0.1'], [
gama_photo_dict, gama_spec_dict, gama_kcorr0_dict,
gama_kcorr1_dict
]):
for key in gama_data[gkey].keys():
if i_f == 0:
gdict[key] = gama_data[gkey][key][match[1]]
else:
gdict[key] = np.concatenate(
[gdict[key], gama_data[gkey][key][match[1]]])
del sweep # free memory? (apparently not really)
if not silent:
print('========================')
print('%i objects out of %i GAMA objects mached' %
(len(sweep_dict['ra']), len(gama_data['photo']['dec'])))
assert len(sweep_dict['ra']) == len(gama_photo_dict['ra'])
assert len(sweep_dict['ra']) == len(gama_spec_dict['ra'])
assert len(sweep_dict['ra']) == len(gama_kcorr0_dict['mass'])
assert len(sweep_dict['ra']) == len(gama_kcorr1_dict['mass'])
# writeout all the GAMA objects without sweep objects
if not silent:
nosweep = ~np.in1d(gama_data['photo']['objid'],
gama_photo_dict['objid'])
f_nosweep = ''.join([
UT.dat_dir(), 'GAMAdr',
str(dr_gama), '.', field, '.LEGACYdr',
str(dr_legacy), '.nosweep_match.fits'
])
print('========================')
print(
'Writing out RA, Dec of %i GAMA objects without Legacy sweep objects to %s'
% (np.sum(nosweep), f_nosweep))
tb = aTable([
gama_data['photo']['ra'][nosweep],
gama_data['photo']['dec'][nosweep]
],
names=('ra', 'dec'))
tb.meta[
'COMMENTS'] = 'RA, Dec of GAMA objects without matches in Legacy DR5 sweep'
tb.write(f_nosweep, format='fits', overwrite=True)
#np.savetxt(f_nosweep, np.array([gama_data['photo']['ra'], gama_data['photo']['dec']]).T, header='RA, Dec')
# read apfluxes from tractor catalogs
try:
apflux_dict = self._getTractorApflux(sweep_dict['brickname'],
sweep_dict['objid'],
tractor_dir=tractor_n_dir)
except ValueError:
apflux_dict = self._getTractorApflux(sweep_dict['brickname'],
sweep_dict['objid'],
tractor_dir=tractor_s_dir)
assert apflux_dict['apflux_g'].shape[0] == len(sweep_dict['brickname'])
# save data to hdf5 file
if not silent:
print('writing to %s' %
self._File(field, dr_gama=dr_gama, dr_legacy=dr_legacy))
f = h5py.File(self._File(field, dr_gama=dr_gama, dr_legacy=dr_legacy),
'w')
grp_gp = f.create_group('gama-photo')
grp_gs = f.create_group('gama-spec')
grp_k0 = f.create_group('gama-kcorr-z0.0')
grp_k1 = f.create_group('gama-kcorr-z0.1')
grp_lp = f.create_group('legacy-photo')
for key in sweep_dict.keys():
self._h5py_create_dataset(grp_lp, key, sweep_dict[key])
for key in apflux_dict.keys(): # additional apflux data.
self._h5py_create_dataset(grp_lp, key, apflux_dict[key])
for key in gama_photo_dict.keys():
grp_gp.create_dataset(key, data=gama_photo_dict[key])
for key in gama_spec_dict.keys():
grp_gs.create_dataset(key, data=gama_spec_dict[key])
for key in gama_kcorr0_dict.keys():
grp_k0.create_dataset(key, data=gama_kcorr0_dict[key])
for key in gama_kcorr1_dict.keys():
grp_k1.create_dataset(key, data=gama_kcorr1_dict[key])
f.close()
return None
def _construct(self, overwrite=False, silent=True):
''' construct postprocessed catalog from the ascii files described in
http://sdss.physics.nyu.edu/lss/dr72/bright/34/lss/README.dr72bright34
'''
if not overwrite and os.path.isfile(self.file):
print("%s already exists; to overwrite specify `overwrite=True`" %
self.file)
return None
# lss catalog
flss = os.path.join(os.path.dirname(self.file), 'lss.dr72bright34.dat')
# photometric data
fphoto = os.path.join(os.path.dirname(self.file),
'photoinfo.dr72bright34.dat')
# vmax data
fvmax = os.path.join(os.path.dirname(self.file),
'vmax.dr72bright34.dat')
# read the data
ra, dec, cz = np.loadtxt(flss, unpack=True, usecols=[3, 4, 5])
Mu, Mg, Mr, Mi, Mz, mu_50, r50_r90 = np.loadtxt(
fphoto, unpack=True, usecols=[1, 2, 3, 4, 5, 6, 7])
vmax, zmin, zmax = np.loadtxt(fvmax, unpack=True, usecols=[1, 2, 3])
cat, des = {}, {}
cat['ra'] = ra
des['ra'] = ['deg', 'right ascension']
cat['dec'] = dec
des['dec'] = ['deg', 'declination']
cat['cz'] = cz
des['cz'] = ['km/s', 'c * redshift']
cat['redshift'] = cz / 2.998e5
des['redshift'] = ['', 'redshift']
cat['M_u'] = Mu
des['M_u'] = ['mag', 'u-band absolute magnitude kcorrected to z=0.1']
cat['M_g'] = Mg
des['M_g'] = ['mag', 'g-band absolute magnitude kcorrected to z=0.1']
cat['M_r'] = Mr
des['M_r'] = ['mag', 'r-band absolute magnitude kcorrected to z=0.1']
cat['M_i'] = Mi
des['M_i'] = ['mag', 'i-band absolute magnitude kcorrected to z=0.1']
cat['M_z'] = Mz
des['M_z'] = ['mag', 'z-band absolute magnitude kcorrected to z=0.1']
cat['mu_50'] = mu_50
des['mu_50'] = [
'',
'r-band half-light Petrosian surface brightness, (1+z)^4- and K-corrected'
]
cat['r50_r90'] = r50_r90
des['r50_r90'] = ['', 'inverse concentration parameter']
cat['vmax'] = vmax
des['vmax'] = ['', 'total volume over which this galaxy is observable']
cat['zmin'] = zmin
des['zmin'] = ['', 'min redshift observable for a galaxy with absmag']
cat['zmax'] = zmax
des['zmax'] = ['', 'max redshift observable for a galaxy with absmag']
if self.cross_nsa:
from astropy import units as U
from pydl.pydlutils.spheregroup import spherematch
# read NSA
nsa = Astrologs('nsa')
# now lets spherematch VAGC with NSA with 3'' match length
match_length = (3 * U.arcsec).to(U.degree).value
m_nsa, m_vagc, dmatch = spherematch(nsa.data['ra'],
nsa.data['dec'],
cat['ra'],
cat['dec'],
match_length,
maxmatch=0)
if not silent:
print('%i of %i VAGC galaxies have NSA matches' %
(len(m_vagc), len(cat['ra'])))
i_nsa = np.zeros(len(ra)).astype(int)
for i in range(len(cat['ra'])):
targ = (m_vagc == i)
if np.sum(targ) > 1:
zmatch = (np.abs(nsa.data['redshift'][m_nsa[targ]] -
cat['redshift'][i]) < 0.001)
i_nsa[i] = m_nsa[targ][zmatch][0]
elif np.sum(targ) == 0:
i_nsa[i] = -999
else:
i_nsa[i] = m_nsa[targ]
hasmatch = (i_nsa != -999)
for name in nsa.data.keys():
nsa_col_data = nsa.data[name]
if 'S' in str(nsa_col_data.dtype):
blank = np.array(['-999'])
blank.astype(nsa_col_data.dtype)
else:
blank = np.array([-999.])
blank.astype(nsa_col_data.dtype)
empty_shape = list(nsa_col_data.shape)
empty_shape[0] = len(cat['ra'])
empty = np.tile(blank[0], empty_shape)
empty[hasmatch] = nsa_col_data[i_nsa[hasmatch]]
cat['NSA_%s' % name] = empty
des['NSA_%s' % name] = nsa.meta[name]
des['NSA_%s' %
name][1] = 'from NSA; ' + des['NSA_%s' % name][1]
# save to hdf5
self._save_to_hdf5(cat, self.file, meta=des, silent=silent)
return None
def _construct(self, overwrite=False, silent=True):
''' construct postprocessed catalogs from Jeremy's *.galdata_corr and *.prob
files
*.prob is the galaxy catalog with probability of being a satellite
(official stats assume >0.5 is a satellite).
*.galdata_corr are galaxy properties. This file is in the same order
as the *.prob file
'''
if not overwrite and os.path.isfile(self.file):
print("%s already exists; to overwrite specify `overwrite=True`" %
self.file)
return None
fprob = os.path.join(os.path.dirname(self.file),
'pca_groups_M%s.prob' % self.mlim)
fgald = os.path.join(os.path.dirname(self.file),
'pca_groups_M%s.galdata_corr' % self.mlim)
# read in .prob file, which has the following data columns
# 1) foo
# 2) gal id
# 3) group id
# 4) id of central galaxy
# 5) r-band magnitude h=1
# 6) P_sat (>0.5 means a satellite)
# 7) halo mass [Msol/h]
# 8) foo
# 9) foo
# 10) foo
# 11) projected separation of gal from central, in units of Rhalo
# 12) projected separation of gal in units of radians
# 13) angular radius of halo
galid, grpid, cenid, rmag, psat, mhalo, sep_rhalo, sep_rad, r_halo = \
np.loadtxt(fprob, unpack=True, usecols=[1, 2, 3, 4, 5, 6, 10, 11, 12])
# read in .galdata_corr file, which has the following data columns
# this file is line matched to the prob file
# 1) foo
# 2) gal id
# 3) M_r
# 4) M_g
# 5) cz [km/s]
# 6) Dn4000 (from MPA/JHU catalog)
# 7) H_delta EW (same source)
# 8) log sSFR (1/yr same source, but uses kcorrect stellar mass for "s")
# 9) stellar mass (Msol/h^2, from kcorrect)
# 10) ra
# 11) dec
# 12) velocity dispersion (from VAGC)
# 13) signal to noise of spectrum (from VAGC)
# 14) sersic index (from VAGC)
Mg, cz, Dn4000, Hdel_ew, logSSFR, Ms, ra, dec, vdisp, sn, sersic = \
np.loadtxt(fgald, unpack=True, usecols=range(3,14))
cat, des = {}, {}
cat['galid'] = galid
des['galid'] = ['', 'galaxy id']
cat['groupid'] = grpid
des['groupid'] = ['', 'group id']
cat['centralid'] = cenid
des['centralid'] = ['', 'id of central galaxy']
cat['M_r'] = rmag
des['M_r'] = ['mag', 'r-band magnitude h=1']
cat['M_g'] = Mg
des['M_g'] = ['mag', 'g-band magnitude h=1']
cat['P_sat'] = psat
des['P_sat'] = ['', 'P_sat > 0.5 are satellite']
cat['iscen'] = (psat <= 0.5) # is central
des['iscen'] = ['', 'True: central; False: satellite']
cat['M_halo'] = mhalo
des['M_halo'] = ['Msun/h', 'halo mass']
cat['sep_rhalo'] = sep_rhalo
des['sep_rhalo'] = ['R_halo', 'proj. sep. from central']
cat['sep_rad'] = sep_rhalo
des['sep_rad'] = ['radian', 'proj. sep. from central']
cat['r_halo'] = r_halo
des['r_halo'] = ['?', 'angular radius of halo']
cat['cz'] = cz
des['cz'] = ['km/s', 'c * redshift']
cat['redshift'] = cz / 2.998e5
des['redshift'] = ['', 'redshift']
cat['Dn4000'] = Dn4000
des['Dn4000'] = ['', 'Dn4000 from MPAJHU']
cat['H_delta_EW'] = Hdel_ew
des['H_delta_EW'] = ['', 'H_delta EW MPAJHU']
cat['log.ssfr'] = logSSFR
des['log.ssfr'] = ['dex', 'MPAJHU but using kcorrect M*']
cat['M_star'] = Ms * 0.7**2
des['M_star'] = ['Msun', 'kcorrect stellar mass']
cat['log.M_star'] = np.log10(cat['M_star'])
des['log.M_star'] = ['dex', 'log stellar mass']
cat['ra'] = ra * (180. / np.pi)
des['ra'] = ['deg', 'right ascension']
cat['dec'] = dec * (180. / np.pi)
des['dec'] = ['deg', 'declination']
cat['vdisp'] = vdisp
des['vdisp'] = ['km/s', 'velocity dispersion from VAGC']
cat['s2n'] = sn
des['s2n'] = ['', 'signal-to-noise of spectrum from VAGC']
cat['sersic'] = sersic
des['sersic'] = ['', 'sersic index from VAGC']
if self.cross_nsa:
from astropy import units as U
from pydl.pydlutils.spheregroup import spherematch
# read NSA
nsa = Astrologs('nsa')
# now lets spherematch VAGC with NSA with 3'' match length
match_length = (3 * U.arcsec).to(U.degree).value
m_nsa, m_vagc, dmatch = spherematch(nsa.data['ra'],
nsa.data['dec'],
cat['ra'],
cat['dec'],
match_length,
maxmatch=0)
if not silent:
print('%i of %i VAGC galaxies have NSA matches' %
(len(m_vagc), len(cat['ra'])))
i_nsa = np.zeros(len(ra)).astype(int)
for i in range(len(cat['ra'])):
targ = (m_vagc == i)
if np.sum(targ) > 1:
zmatch = (np.abs(nsa.data['redshift'][m_nsa[targ]] -
cat['redshift'][i]) < 0.001)
i_nsa[i] = m_nsa[targ][zmatch][0]
elif np.sum(targ) == 0:
i_nsa[i] = -999
else:
i_nsa[i] = m_nsa[targ]
hasmatch = (i_nsa != -999)
for name in nsa.data.keys():
nsa_col_data = nsa.data[name]
if 'S' in str(nsa_col_data.dtype):
blank = np.array(['-999'])
blank.astype(nsa_col_data.dtype)
else:
blank = np.array([-999.])
blank.astype(nsa_col_data.dtype)
empty_shape = list(nsa_col_data.shape)
empty_shape[0] = len(cat['ra'])
empty = np.tile(blank[0], empty_shape)
empty[hasmatch] = nsa_col_data[i_nsa[hasmatch]]
cat['NSA_%s' % name] = empty
des['NSA_%s' % name] = nsa.meta[name]
des['NSA_%s' %
name][1] = 'from NSA; ' + des['NSA_%s' % name][1]
# save to hdf5
self._save_to_hdf5(cat, self.file, meta=des, silent=silent)
return None
