def Build_VAGCdr72bright34():
''' Build hdf5 file of VAGC dr72brigh34, which is the
parent sample of Jeremy's group catalog
'''
# import VAGC dr72bright34
vagc_photoinfo = np.loadtxt(''.join(
[UT.dir_dat(), 'vagc/', 'photoinfo_nonan.dr72bright34.dat']),
unpack=True,
usecols=[0, 1, 2, 3, 4, 5])
vagc_lss = np.loadtxt(''.join(
[UT.dir_dat(), 'vagc/', 'lss.dr72bright34.dat']),
unpack=True,
usecols=[0, 3, 4, 5])
if not np.array_equal(vagc_photoinfo[0], vagc_lss[0]):
raise ValueError
catalog = {}
catalog['id'] = vagc_photoinfo[0]
for i_band, band in enumerate(['u', 'g', 'r', 'i', 'z']):
catalog['M_' + band] = vagc_photoinfo[i_band + 1]
catalog['ra'] = vagc_lss[1]
catalog['dec'] = vagc_lss[2]
catalog['cz'] = vagc_lss[3]
catalog['z'] = vagc_lss[3] / 299792.458
print len(catalog['z']), ' total galaxies'
dr72_file = ''.join([UT.dir_dat(), 'vagc/', 'VAGCdr72bright34.hdf5'])
f = h5py.File(dr72_file, 'w')
grp = f.create_group('data')
for key in catalog.keys():
grp.create_dataset(key, data=catalog[key])
f.close()
return None
def File(self):
''' Conformity catalog file name
'''
if self.catalog_name == 'tinker':
conform_file = ''.join([
UT.dir_dat(), 'conform_catalog/', 'MPAJHU_TinkerGroupCat.Mr',
str(self.Mrcut), '.Mass',
str(self.M_cut),
self._FileSpec(), '.p'
])
elif self.catalog_name == 'tinkauff':
conform_file = ''.join([
UT.dir_dat(), 'conform_catalog/',
'VAGCdr72brigh34_MPAJHU.GroupCat.Mass',
str(self.M_cut),
self._FileSpec(), '.p'
])
elif self.catalog_name == 'tinkauff_iso':
conform_file = ''.join([
UT.dir_dat(), 'conform_catalog/',
'VAGCdr72brigh34_MPAJHU.IsoGroupCat.Mass',
str(self.M_cut),
self._FileSpec(), '.p'
])
elif self.catalog_name == 'kauff':
conform_file = ''.join([
UT.dir_dat(), 'conform_catalog/',
'VAGCdr72brigh34_MPAJHU.Kauffmann',
self._FileSpec(), '.p'
])
else:
raise ValueError
return conform_file
def Build_TinKauffGroupCat(Mass_cut=9.25):
''' Compile the outputs of Jeremy's Group Catalog algorithm
in order to generate catalogs analogous to Kauffmann et al.(2013).
Hence TinKauff.
'''
# galdata_corr file
galdata_file = ''.join([
UT.dir_dat(), 'tinkauff/', 'clf_groups_JHU_M',
str(Mass_cut), '_z0.017_fibcoll.galdata_corr'
])
gal_data = np.loadtxt(galdata_file, unpack=True, usecols=range(1, 14))
catalog = {
'id': gal_data[0],
'ra': gal_data[5] * 57.2957795,
'dec': gal_data[6] * 57.2957795,
'M_r': gal_data[1],
'M_g': gal_data[2],
'z': gal_data[3] / 299792.458,
'mass_tot_mpajhu': np.log10(gal_data[4]),
'Dn4000': gal_data[7],
'ssfr_tot_mpajhu': gal_data[8],
'ssfr_fib_mpajhu': gal_data[9],
'sfr_tot_mpajhu': gal_data[10],
'sfr_fib_mpajhu': gal_data[11],
'mass_fib_mpajhu': gal_data[12],
}
# prob data
prob_file = ''.join([
UT.dir_dat(), 'tinkauff/', 'clf_groups_JHU_M',
str(Mass_cut), '_z0.017_fibcoll.prob'
])
prob_data = np.loadtxt(prob_file, unpack=True, usecols=[1, 2, 5, 12])
if not np.array_equal(catalog['id'], prob_data[0]):
raise ValueError
catalog['p_sat'] = prob_data[2]
catalog['group_id'] = prob_data[1]
catalog['angradius_halo'] = prob_data[3]
# cuts
if catalog['ssfr_tot_mpajhu'].min() == -999.:
N_cat = len(catalog['p_sat'])
nan_cuts = np.where(catalog['ssfr_tot_mpajhu'] != -999.)
for key in catalog.keys():
catalog[key] = catalog[key][nan_cuts]
print 'removed = ', N_cat - len(catalog['p_sat'])
tinkauff_file = ''.join([
UT.dir_dat(), 'tinkauff/', 'VAGCdr72_MPAJHU.GroupCat.Mass',
str(Mass_cut), '.hdf5'
])
f = h5py.File(tinkauff_file, 'w')
grp = f.create_group('data')
for column in catalog.keys():
grp.create_dataset(column, data=catalog[column])
f.close()
return None
def Build_TinkerCatalog(Mrcut=18):
''' Preprocess the group catalog data into a more python friendly format
with appropriate *little h* corrections!
'''
h = 0.7
M_cut = Tinker_Masscut(Mrcut)
# Read Group Catalog GalData
galdata_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'clf_groups_M',
str(Mrcut), '_',
str(M_cut), '_D360.', 'galdata_corr.fits'
])
gal_data = mrdfits(galdata_file)
catalog = {}
for column in gal_data.__dict__.keys():
column_data = getattr(gal_data, column)
if column == 'stellmass':
# stellmass is in units of Msol/h^2
# why jeremy why?/?
column_data = column_data / h**2
catalog['mass'] = np.log10(column_data) # convert to log Mass
elif column == 'ssfr':
column_data += np.log10(h**2) # little h #*(@*#$
catalog['ssfr'] = column_data
elif column == 'cz': # convert to z
catalog['z'] = column_data / 299792.458
elif column in ['ra', 'dec']:
catalog[column] = column_data * 57.2957795
else:
catalog[column] = column_data
catalog['sfr'] = catalog['mass'] + catalog[
'ssfr'] # calculate SFR form mass and ssfr
# Read Group Catalog probability
prob_file = ''.join([
UT.dir_dat(), '/tinker2011catalogs/', 'clf_groups_M',
str(Mrcut), '_',
str(M_cut), '_D360.', 'prob.fits'
])
prob_data = mrdfits(prob_file) # import probability file
for column in prob_data.__dict__.keys():
catalog[column] = getattr(prob_data, column)
tinker_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'GroupCat.Mr',
str(Mrcut), '.Mass',
str(M_cut), '.D360.hdf5'
])
f = h5py.File(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 VAGCdr72bright34_Catalog():
''' Read in the VAGC dr72bright34 catalog hdf5 file
'''
dr72_file = ''.join([UT.dir_dat(), 'vagc/', 'VAGCdr72bright34.hdf5'])
catalog = {}
f = h5py.File(dr72_file, 'r')
grp = f['data']
for col in grp.keys():
catalog[col] = grp[col].value
f.close()
return catalog
def KauffmannParent():
''' Read in the the Kauffmannn et al. (2013) parent sample constructed from
VAGC dr72bright34 catalog
'''
dr72_file = ''.join([UT.dir_dat(), 'vagc/', 'VAGCdr72.Kauff2013cut.hdf5'])
catalog = {}
f = h5py.File(dr72_file, 'r')
grp = f['data']
for col in grp.keys():
catalog[col] = grp[col].value
f.close()
return catalog
def TinKauffGroupCat(Mass_cut=9.25):
''' Read in the Tinker-Kauffmann Group catalog generated from VAGC
dr72bright34 with MPA-JHU galaxy property values.
'''
dr72_file = ''.join([
UT.dir_dat(), '/tinkauff/', 'VAGCdr72_MPAJHU.GroupCat.Mass',
str(Mass_cut), '.hdf5'
])
catalog = {}
f = h5py.File(dr72_file, 'r')
grp = f['data']
for col in grp.keys():
catalog[col] = grp[col].value
f.close()
return catalog
def MPAJHU_TinkerCatalog(Mrcut=18):
''' Read in the Tinker et al. (2011) matched up to the MPA-JHU catalog
and return catalog dictionary
'''
M_cut = Tinker_Masscut(Mrcut)
mpajhu_tinker_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'GroupCat.Mr',
str(Mrcut), '.Mass',
str(M_cut), '.D360.MPAJHU.hdf5'
])
catalog = {}
f = h5py.File(mpajhu_tinker_file, 'r')
grp = f['data']
for col in grp.keys():
catalog[col] = grp[col].value
f.close()
return catalog
def TinkerCatalog(Mrcut=18):
''' Tinker et al. (2011) group catalog combined into a
volume-limited galaxy catalog and return a dictionary with
all the value.
'''
M_cut = Tinker_Masscut(Mrcut)
# read in h5py file
tinker_file = ''.join([
UT.dir_dat(), 'tinker2011catalogs/', 'GroupCat.Mr',
str(Mrcut), '.Mass',
str(M_cut), '.D360.hdf5'
])
catalog = {}
f = h5py.File(tinker_file, 'r')
grp = f['data']
for col in grp.keys():
catalog[col] = grp[col].value
f.close()
return catalog
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
