def load_lowz(cls, grab_spectra=False, isys_path=None):
""" LLS from Wotta+16 (includes Lehner+13)
Updated to include systems excluded by Wotta+16 (high NHI)
Parameters
----------
grab_spectra : bool, optional
Not implemented
Return
------
lls_survey
"""
# System files
l13_files = pyigm_path + '/data/LLS/Literature/lehner13.tar.gz'
w16_files = pyigm_path + '/data/LLS/Literature/wotta16.tar.gz'
# Load systems via the sys tarball. Includes transitions
L13_survey = pyisu.load_sys_files(l13_files, 'LLS', ref='Lehner+13')
W16_survey = pyisu.load_sys_files(w16_files, 'LLS', ref='Wotta+16')
lowz_LLS = L13_survey + W16_survey
# Spectra?
if grab_spectra:
raise NotImplementedError("NOPE")
specfils = glob.glob(spath + 'HD-LLS_J*.fits')
if len(specfils) < 100:
import tarfile
print('HD-LLS: Downloading a 155Mb file. Be patient..')
url = 'http://www.ucolick.org/~xavier/HD-LLS/DR1/HD-LLS_spectra.tar.gz'
spectra_fil = pyigm_path + '/data/LLS/HD-LLS/HD-LLS_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('HD-LLS: Unpacking..')
outdir = pyigm_path + "/data/LLS/HD-LLS"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Done
print('HD-LLS: All done')
else:
print('HD-LLS: Using files in {:s}'.format(spath))
return lowz_LLS
def load_lowz(cls, grab_spectra=False, isys_path=None):
""" LLS from Wotta+16 (includes Lehner+13)
Parameters
----------
grab_spectra : bool, optional
Not implemented
Return
------
lls_survey
"""
# System files
l13_files = pyigm_path+'/data/LLS/Literature/lehner13.tar.gz'
w16_files = pyigm_path+'/data/LLS/Literature/wotta16.tar.gz'
# Load systems via the sys tarball. Includes transitions
L13_survey = pyisu.load_sys_files(l13_files, 'LLS', ref='Lehner+13')
W16_survey = pyisu.load_sys_files(w16_files, 'LLS', ref='Wotta+16')
lowz_LLS = L13_survey+W16_survey
# Spectra?
if grab_spectra:
raise NotImplementedError("NOPE")
specfils = glob.glob(spath+'HD-LLS_J*.fits')
if len(specfils) < 100:
import tarfile
print('HD-LLS: Downloading a 155Mb file. Be patient..')
url = 'http://www.ucolick.org/~xavier/HD-LLS/DR1/HD-LLS_spectra.tar.gz'
spectra_fil = pyigm_path+'/data/LLS/HD-LLS/HD-LLS_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('HD-LLS: Unpacking..')
outdir = pyigm_path+"/data/LLS/HD-LLS"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Done
print('HD-LLS: All done')
else:
print('HD-LLS: Using files in {:s}'.format(spath))
return lowz_LLS
def load_HDLLS(cls, load_sys=True, grab_spectra=False, isys_path=None):
""" Default sample of LLS (HD-LLS, DR1)
Parameters
----------
grab_spectra : bool, optional
Grab 1D spectra? (155Mb)
load_sys : bool, optional
Load systems using the sys tarball
isys_path : str, optional
Read system files from this path
Return
------
lls_survey
"""
# Pull from Internet (as necessary)
summ_fil = pyigm_path + "/data/LLS/HD-LLS/HD-LLS_DR1.fits"
print('HD-LLS: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = pyigm_path + "/data/LLS/HD-LLS/HD-LLS_ions.json"
print('HD-LLS: Loading ions file {:s}'.format(ions_fil))
# System files
sys_files = pyigm_path + "/data/LLS/HD-LLS/HD-LLS_sys.tar.gz"
# Transitions
#clm_files = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_clms.tar.gz"
# Metallicity
ZH_fil = pyigm_path + "/data/LLS/HD-LLS/HD-LLS_DR1_dustnhi.hdf5"
print('HD-LLS: Loading metallicity file {:s}'.format(ZH_fil))
# Load systems via the sys tarball. Includes transitions
if load_sys: # This approach takes ~120s
if isys_path is not None:
lls_survey = pyisu.load_sys_files(isys_path,
'LLS',
sys_path=True,
ref='HD-LLS')
else:
lls_survey = pyisu.load_sys_files(sys_files,
'LLS',
ref='HD-LLS')
lls_survey.fill_ions(use_components=True)
else:
# Read
lls_survey = cls.from_sfits(summ_fil)
# Load ions
lls_survey.fill_ions(jfile=ions_fil)
lls_survey.ref = 'HD-LLS'
"""
# Load transitions
if not skip_trans:
print('HD-LLS: Loading transitions from {:s}'.format(clm_files))
tar = tarfile.open(clm_files)
for member in tar.getmembers():
if '.' not in member.name:
print('Skipping a likely folder: {:s}'.format(member.name))
continue
# Extract
f = tar.extractfile(member)
tdict = json.load(f)
# Find system
i0 = member.name.rfind('/')
i1 = member.name.rfind('_clm')
try:
idx = names.index(member.name[i0+1:i1])
except ValueError:
print('Skipping {:s}, not statistical in DR1'.format(member.name[i0+1:i1]))
continue
# Fill up
lls_survey._abs_sys[idx].load_components(tdict)
lls_survey._abs_sys[idx]._components = lls_survey._abs_sys[idx].subsys['A']._components
"""
# Load metallicity
fh5 = h5py.File(ZH_fil, 'r')
ras = []
decs = []
zval = []
mkeys = fh5['met'].keys()
mkeys.remove('left_edge_bins')
for key in mkeys:
radec, z = key.split('z')
coord = ltu.radec_to_coord(radec)
# Save
zval.append(float(z))
ras.append(coord.ra.value)
decs.append(coord.dec.value)
mcoords = SkyCoord(ras, decs, unit='deg')
zval = np.array(zval)
# Set data path and metallicity
spath = pyigm_path + "/data/LLS/HD-LLS/Spectra/"
for lls in lls_survey._abs_sys:
lls.spec_path = spath
# Match
sep = lls.coord.separation(mcoords)
mt = np.where((sep < 15 * u.arcsec)
& (np.abs(zval - lls.zabs) < 2e-3))[0]
if len(mt) == 0:
pdb.set_trace()
raise ValueError("Bad match")
elif len(mt) > 1: # Take closest
mt = np.argmin(sep)
else:
mt = mt[0]
# Save
lls.metallicity = MetallicityPDF(
fh5['met']['left_edge_bins'] +
fh5['met']['left_edge_bins'].attrs['BINSIZE'] / 2.,
fh5['met'][mkeys[mt]])
# Spectra?
if grab_spectra:
specfils = glob.glob(spath + 'HD-LLS_J*.fits')
if len(specfils) < 100:
import tarfile
print('HD-LLS: Downloading a 155Mb file. Be patient..')
url = 'http://www.ucolick.org/~xavier/HD-LLS/DR1/HD-LLS_spectra.tar.gz'
spectra_fil = pyigm_path + '/data/LLS/HD-LLS/HD-LLS_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('HD-LLS: Unpacking..')
outdir = pyigm_path + "/data/LLS/HD-LLS"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Done
print('HD-LLS: All done')
else:
print('HD-LLS: Using files in {:s}'.format(spath))
return lls_survey
def load_H100(cls, grab_spectra=False, load_sys=True, isys_path=None): #skip_trans=True):
""" Sample of unbiased HIRES DLAs compiled and analyzed by Neeleman+13
Neeleman, M. et al. 2013, ApJ, 769, 54
Parameters
----------
grab_spectra : bool, optional
Grab 1D spectra? (141Mb)
load_sys : bool, optional
Load systems? (takes ~60s for 100 systems)
skip_trans : bool, optional
Skip loading transitions (takes ~60s)?
isys_path : str, optional
Read system files from this path
Return
------
dla_survey
"""
# Pull from Internet (as necessary)
summ_fil = pyigm_path+"/data/DLA/H100/H100_DLA.fits"
print('H100: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = pyigm_path+"/data/DLA/H100/H100_DLA_ions.json"
print('H100: Loading ions file {:s}'.format(ions_fil))
# Transitions
trans_fil = pyigm_path+"/data/DLA/H100/H100_DLA_clms.tar.gz"
# System files
sys_files = pyigm_path+"/data/DLA/H100/H100_DLA_sys.tar.gz"
if load_sys: # This approach takes ~120s
print('H100: Loading systems. This takes ~120s')
if isys_path is not None:
dla_survey = pyisu.load_sys_files(isys_path, 'DLA', sys_path=True)
else:
dla_survey = pyisu.load_sys_files(sys_files, 'DLA')
dla_survey.fill_ions(use_components=True)
else:
# Read
dla_survey = cls.from_sfits(summ_fil)
# Load ions
dla_survey.fill_ions(jfile=ions_fil)
dla_survey.ref = 'Neeleman+13'
"""
# Load transitions
names = list(dla_survey.name)
if not skip_trans:
print('H100: Loading transitions file {:s}'.format(trans_fil))
tar = tarfile.open(trans_fil)
for member in tar.getmembers():
if '.' not in member.name:
print('Skipping a likely folder: {:s}'.format(member.name))
continue
# Extract
f = tar.extractfile(member)
# Need to fix for 3.4
tdict = json.load(f)
# Find system
i0 = member.name.rfind('/')
i1 = member.name.rfind('_clm')
try:
idx = names.index(member.name[i0+1:i1])
except ValueError:
pdb.set_trace()
# Fill up
dla_survey._abs_sys[idx].load_components(tdict)
"""
spath = pyigm_path+"/data/DLA/H100/Spectra/"
for dla in dla_survey._abs_sys:
dla.spec_path = spath
# Spectra?
if grab_spectra:
pdb.set_trace() # USE IGMSPEC!!
specfils = glob.glob(spath+'H100_J*.fits')
if len(specfils) < 100:
import tarfile
print('H100: Downloading a 141Mb file. Be patient..')
url = 'https://dl.dropboxusercontent.com/u/6285549/DLA/H100/H100_DLA_spectra.tar.gz'
spectra_fil = pyigm_path+'/data/DLA/H100/H100_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('H100: Unpacking..')
outdir = pyigm_path+"/data/DLA/H100"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Remove
print('H100: Removing tar file')
os.remove(spectra_fil)
# Done
print('H100: All done')
else:
print('H100: Using files in {:s}'.format(spath))
return dla_survey
def load_HDLLS(cls, load_sys=True, grab_spectra=False, isys_path=None):
""" Default sample of LLS (HD-LLS, DR1)
Parameters
----------
grab_spectra : bool, optional
Grab 1D spectra? (155Mb)
load_sys : bool, optional
Load systems using the sys tarball
isys_path : str, optional
Read system files from this path
Return
------
lls_survey
"""
# Pull from Internet (as necessary)
summ_fil = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_DR1.fits"
print('HD-LLS: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_ions.json"
print('HD-LLS: Loading ions file {:s}'.format(ions_fil))
# System files
sys_files = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_sys.tar.gz"
# Transitions
#clm_files = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_clms.tar.gz"
# Metallicity
ZH_fil = pyigm_path+"/data/LLS/HD-LLS/HD-LLS_DR1_dustnhi.hdf5"
print('HD-LLS: Loading metallicity file {:s}'.format(ZH_fil))
# Load systems via the sys tarball. Includes transitions
if load_sys: # This approach takes ~120s
if isys_path is not None:
lls_survey = pyisu.load_sys_files(isys_path, 'LLS',sys_path=True)
else:
lls_survey = pyisu.load_sys_files(sys_files, 'LLS')
lls_survey.fill_ions(use_components=True)
else:
# Read
lls_survey = cls.from_sfits(summ_fil)
# Load ions
lls_survey.fill_ions(jfile=ions_fil)
lls_survey.ref = 'HD-LLS'
"""
# Load transitions
if not skip_trans:
print('HD-LLS: Loading transitions from {:s}'.format(clm_files))
tar = tarfile.open(clm_files)
for member in tar.getmembers():
if '.' not in member.name:
print('Skipping a likely folder: {:s}'.format(member.name))
continue
# Extract
f = tar.extractfile(member)
tdict = json.load(f)
# Find system
i0 = member.name.rfind('/')
i1 = member.name.rfind('_clm')
try:
idx = names.index(member.name[i0+1:i1])
except ValueError:
print('Skipping {:s}, not statistical in DR1'.format(member.name[i0+1:i1]))
continue
# Fill up
lls_survey._abs_sys[idx].load_components(tdict)
lls_survey._abs_sys[idx]._components = lls_survey._abs_sys[idx].subsys['A']._components
"""
# Load metallicity
fh5=h5py.File(ZH_fil, 'r')
ras = []
decs = []
zval = []
mkeys = fh5['met'].keys()
mkeys.remove('left_edge_bins')
for key in mkeys:
radec, z = key.split('z')
coord = ltu.radec_to_coord(radec)
# Save
zval.append(float(z))
ras.append(coord.ra.value)
decs.append(coord.dec.value)
mcoords = SkyCoord(ras, decs, unit='deg')
zval = np.array(zval)
# Set data path and metallicity
spath = pyigm_path+"/data/LLS/HD-LLS/Spectra/"
for lls in lls_survey._abs_sys:
lls.spec_path = spath
# Match
sep = lls.coord.separation(mcoords)
mt = np.where((sep < 15*u.arcsec) & (np.abs(zval-lls.zabs) < 2e-3))[0]
if len(mt) == 0:
pdb.set_trace()
raise ValueError("Bad match")
elif len(mt) > 1: # Take closest
mt = np.argmin(sep)
# Save
lls.metallicity = MetallicityPDF(fh5['met']['left_edge_bins']+
fh5['met']['left_edge_bins'].attrs['BINSIZE']/2.,
fh5['met'][mkeys[mt]])
# Spectra?
if grab_spectra:
specfils = glob.glob(spath+'HD-LLS_J*.fits')
if len(specfils) < 100:
import tarfile
print('HD-LLS: Downloading a 155Mb file. Be patient..')
url = 'http://www.ucolick.org/~xavier/HD-LLS/DR1/HD-LLS_spectra.tar.gz'
spectra_fil = pyigm_path+'/data/LLS/HD-LLS/HD-LLS_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('HD-LLS: Unpacking..')
outdir = pyigm_path+"/data/LLS/HD-LLS"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Done
print('HD-LLS: All done')
else:
print('HD-LLS: Using files in {:s}'.format(spath))
return lls_survey
def load_H100(cls, grab_spectra=False, build_abs_sys=True, isys_path=None):
""" Sample of unbiased HIRES DLAs compiled and analyzed by Neeleman+13
Neeleman, M. et al. 2013, ApJ, 769, 54
Parameters
----------
build_abs_sys : bool, optional
Build AbsSystem objects (~10s)
Required for a fair bit of other things, e.g. kin
isys_path : str, optional
Read system files from this path
grab_spectra : bool, optional
Grab 1D spectra? (141Mb)
deprecated.. Use igmspec
Return
------
dla_survey : DLASurvey
"""
# Pull from Internet (as necessary)
summ_fil = resource_filename('pyigm', "/data/DLA/H100/H100_DLA.fits")
print('H100: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = resource_filename('pyigm',
"/data/DLA/H100/H100_DLA_ions.json")
print('H100: Loading ions file {:s}'.format(ions_fil))
# Transitions
trans_fil = resource_filename('pyigm',
"/data/DLA/H100/H100_DLA_clms.tar.gz")
# System files
sys_files = resource_filename('pyigm',
"/data/DLA/H100/H100_DLA_sys.tar.gz")
print('H100: Loading systems. This takes ~10s')
dla_survey = pyisu.load_sys_files(sys_files,
'DLA',
build_abs_sys=build_abs_sys)
# Reset flag_NHI (which has been wrong)
for key in dla_survey._dict.keys():
dla_survey._dict[key]['flag_NHI'] = 1
# Fill ion Tables
if build_abs_sys:
print("Filling the _ionN tables...")
dla_survey.fill_ions(use_components=True)
dla_survey.ref = 'Neeleman+13'
if not build_abs_sys:
print(
"Not loading up all the other data. Use build_abs_sys=True for that!"
)
return dla_survey
# Metallicities
tbl2_file = resource_filename('pyigm',
"/data/DLA/H100/H100_table2.dat")
tbl2 = Table.read(tbl2_file, format='cds')
# Parse for matching
names = dla_survey._data['Name']
qsonames = []
zabs = []
for name in names:
prs = name.split('_')
qsonames.append(prs[0])
try:
zabs.append(float(prs[-1][1:]))
except ValueError:
pdb.set_trace()
qsonames = np.array(qsonames)
zabs = np.array(zabs)
# Match
for ii, iqso, izabs in zip(range(len(tbl2)), tbl2['QSO'],
tbl2['zabs']):
mt = np.where((qsonames == iqso)
& (np.abs(izabs - zabs) < 1e-3))[0]
if len(mt) == 0:
pdb.set_trace()
elif len(mt) != 1:
pdb.set_trace()
# Metallicity
dla_survey._abs_sys[mt[0]].ZH = tbl2['[M/H]'][ii]
dla_survey._abs_sys[mt[0]].sig_ZH = tbl2['e_[M/H]'][ii]
if tbl2['M'][ii] in ['S', 'Si', 'O']:
dla_survey._abs_sys[mt[0]].flag_ZH = 1 # Alpha
elif tbl2['M'][ii] in ['Zn']:
dla_survey._abs_sys[mt[0]].flag_ZH = 2 # Zn
elif tbl2['M'][ii] in ['Fe']:
dla_survey._abs_sys[mt[0]].flag_ZH = 4 # Fe
else:
raise ValueError("Bad metal")
dla_survey._abs_sys[mt[0]].elm_Z = tbl2['M'][ii]
# Kin
dla_survey._abs_sys[mt[0]].kin['dv'] = tbl2['dv'][ii]
dla_survey._abs_sys[mt[0]].kin['trans'] = tbl2['trans'][ii]
dla_survey._abs_sys[mt[0]].selection = tbl2['Select'][ii]
spath = pyigm_path + "/data/DLA/H100/Spectra/"
for dla in dla_survey._abs_sys:
dla.spec_path = spath
# Spectra?
if grab_spectra:
warnings.warn(
"All of these spectra are in igmspec at https://github.com/specdb/specdb"
)
print("Grab them there!")
print("All done!!")
return dla_survey
def load_H100(cls, grab_spectra=False, load_sys=True, isys_path=None): #skip_trans=True):
""" Sample of unbiased HIRES DLAs compiled and analyzed by Neeleman+13
Neeleman, M. et al. 2013, ApJ, 769, 54
Parameters
----------
grab_spectra : bool, optional
Grab 1D spectra? (141Mb)
load_sys : bool, optional
Load systems? (takes ~60s for 100 systems)
skip_trans : bool, optional
Skip loading transitions (takes ~60s)?
isys_path : str, optional
Read system files from this path
Return
------
dla_survey
"""
# Pull from Internet (as necessary)
summ_fil = pyigm_path+"/data/DLA/H100/H100_DLA.fits"
print('H100: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = pyigm_path+"/data/DLA/H100/H100_DLA_ions.json"
print('H100: Loading ions file {:s}'.format(ions_fil))
# Transitions
trans_fil = pyigm_path+"/data/DLA/H100/H100_DLA_clms.tar.gz"
# System files
sys_files = pyigm_path+"/data/DLA/H100/H100_DLA_sys.tar.gz"
if load_sys: # This approach takes ~120s
print('H100: Loading systems. This takes ~120s')
if isys_path is not None:
dla_survey = pyisu.load_sys_files(isys_path, 'DLA', sys_path=True)
else:
dla_survey = pyisu.load_sys_files(sys_files, 'DLA')
dla_survey.fill_ions(use_components=True)
else:
# Read
dla_survey = cls.from_sfits(summ_fil)
# Load ions
dla_survey.fill_ions(jfile=ions_fil)
dla_survey.ref = 'Neeleman+13'
"""
# Load transitions
names = list(dla_survey.name)
if not skip_trans:
print('H100: Loading transitions file {:s}'.format(trans_fil))
tar = tarfile.open(trans_fil)
for member in tar.getmembers():
if '.' not in member.name:
print('Skipping a likely folder: {:s}'.format(member.name))
continue
# Extract
f = tar.extractfile(member)
# Need to fix for 3.4
tdict = json.load(f)
# Find system
i0 = member.name.rfind('/')
i1 = member.name.rfind('_clm')
try:
idx = names.index(member.name[i0+1:i1])
except ValueError:
pdb.set_trace()
# Fill up
dla_survey._abs_sys[idx].load_components(tdict)
"""
spath = pyigm_path+"/data/DLA/H100/Spectra/"
for dla in dla_survey._abs_sys:
dla.spec_path = spath
# Spectra?
if grab_spectra:
pdb.set_trace() # USE IGMSPEC!!
specfils = glob.glob(spath+'H100_J*.fits')
if len(specfils) < 100:
import tarfile
print('H100: Downloading a 141Mb file. Be patient..')
url = 'https://dl.dropboxusercontent.com/u/6285549/DLA/H100/H100_DLA_spectra.tar.gz'
spectra_fil = pyigm_path+'/data/DLA/H100/H100_spectra.tar.gz'
f = urllib2.urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('H100: Unpacking..')
outdir = pyigm_path+"/data/DLA/H100"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Remove
print('H100: Removing tar file')
os.remove(spectra_fil)
# Done
print('H100: All done')
else:
print('H100: Using files in {:s}'.format(spath))
return dla_survey
def load_H100(cls, grab_spectra=False, load_sys=True, isys_path=None): #skip_trans=True):
""" Sample of unbiased HIRES DLAs compiled and analyzed by Neeleman+13
Neeleman, M. et al. 2013, ApJ, 769, 54
Parameters
----------
grab_spectra : bool, optional
Grab 1D spectra? (141Mb)
load_sys : bool, optional
Load systems? (takes ~60s for 100 systems)
skip_trans : bool, optional
Skip loading transitions (takes ~60s)?
isys_path : str, optional
Read system files from this path
Return
------
dla_survey
"""
# Pull from Internet (as necessary)
summ_fil = resource_filename('pyigm', "/data/DLA/H100/H100_DLA.fits")
print('H100: Loading summary file {:s}'.format(summ_fil))
# Ions
ions_fil = resource_filename('pyigm', "/data/DLA/H100/H100_DLA_ions.json")
print('H100: Loading ions file {:s}'.format(ions_fil))
# Transitions
trans_fil = resource_filename('pyigm', "/data/DLA/H100/H100_DLA_clms.tar.gz")
# System files
sys_files = resource_filename('pyigm', "/data/DLA/H100/H100_DLA_sys.tar.gz")
if load_sys: # This approach takes ~120s
print('H100: Loading systems. This takes ~120s')
if isys_path is not None:
dla_survey = pyisu.load_sys_files(isys_path, 'DLA', sys_path=True, use_coord=True)
else:
dla_survey = pyisu.load_sys_files(sys_files, 'DLA', use_coord=True)
dla_survey.fill_ions(use_components=True)
else:
# Read
dla_survey = cls.from_sfits(summ_fil)
# Load ions
dla_survey.fill_ions(jfile=ions_fil)
# Metallicities
tbl2_file = resource_filename('pyigm', "/data/DLA/H100/H100_table2.dat")
tbl2 = Table.read(tbl2_file, format='cds')
names = dla_survey.name
qsonames = []
zabs = []
for name in names:
prs = name.split('_')
qsonames.append(prs[0])
zabs.append(float(prs[1][1:]))
qsonames = np.array(qsonames)
zabs = np.array(zabs)
for ii, iqso, izabs in zip(range(len(tbl2)), tbl2['QSO'], tbl2['zabs']):
mt = np.where((qsonames == iqso) & (np.abs(izabs-zabs) < 1e-3))[0]
if len(mt) == 0:
pdb.set_trace()
elif len(mt) != 1:
pdb.set_trace()
# Metallicity
dla_survey._abs_sys[mt[0]].ZH = tbl2['[M/H]'][ii]
dla_survey._abs_sys[mt[0]].sig_ZH = tbl2['e_[M/H]'][ii]
if tbl2['M'][ii] in ['S','Si','O']:
dla_survey._abs_sys[mt[0]].flag_ZH = 1 # Alpha
elif tbl2['M'][ii] in ['Zn']:
dla_survey._abs_sys[mt[0]].flag_ZH = 2 # Zn
elif tbl2['M'][ii] in ['Fe']:
dla_survey._abs_sys[mt[0]].flag_ZH = 4 # Fe
else:
raise ValueError("Bad metal")
dla_survey._abs_sys[mt[0]].elm_Z = tbl2['M'][ii]
# Kin
dla_survey._abs_sys[mt[0]].kin['dv'] = tbl2['dv'][ii]
dla_survey._abs_sys[mt[0]].kin['trans'] = tbl2['trans'][ii]
dla_survey._abs_sys[mt[0]].selection = tbl2['Select'][ii]
dla_survey.ref = 'Neeleman+13'
spath = pyigm_path+"/data/DLA/H100/Spectra/"
for dla in dla_survey._abs_sys:
dla.spec_path = spath
# Spectra?
if grab_spectra:
warnings.warn("All of these spectra are in igmspec at https://github.com/specdb/specdb")
specfils = glob.glob(spath+'H100_J*.fits')
if len(specfils) < 100:
import tarfile
print('H100: Downloading a 141Mb file. Be patient..')
url = 'https://dl.dropboxusercontent.com/u/6285549/DLA/H100/H100_DLA_spectra.tar.gz'
spectra_fil = pyigm_path+'/data/DLA/H100/H100_spectra.tar.gz'
f = urlopen(url)
with open(spectra_fil, "wb") as code:
code.write(f.read())
# Unpack
print('H100: Unpacking..')
outdir = pyigm_path+"/data/DLA/H100"
t = tarfile.open(spectra_fil, 'r:gz')
t.extractall(outdir)
# Remove
print('H100: Removing tar file')
os.remove(spectra_fil)
# Done
print('H100: All done')
else:
print('H100: Using files in {:s}'.format(spath))
return dla_survey
