def peroux06b():
"""Peroux, C. et al. 2006b, A&A, 450, 53
SDSS J1323-0021
Metal rich
Metal columns copied by JXP from Table 1
Total NHI from damping wings
"""
# Setup
radec = '132323.78-002155.2' # SDSS Name
lls = LLSSystem(name='SDSSJ1323-0021_z0.716', radec=radec, zem=1.390,
zabs=0.716, vlim=[-200., 200.]*u.km/u.s, NHI=20.21,
sig_NHI=np.array([0.20,0.20]))
# Parse table file
tab_fil = pyigm_path+"/data/LLS/Literature/peroux06b.tb1.ascii"
with open(tab_fil,'r') as f:
flines = f.readlines()
ion_dict = {}
for iline in flines:
isplit = iline.split('\t')
if len(isplit[0]) == 0:
# Grab ions and init
ions = isplit[3:10]
for ion in ions:
Zion = ltai.name_ion(ion)
ion_dict[ion] = dict(clm=0., sig_clm=0.,flg_clm=1,Z=Zion[0],ion=Zion[1])
continue
# Column or sigma?
if isplit[0][0] == 'N': # Column
for kk,iis in enumerate(isplit[3:10]):
ion = ions[kk]
if iis[0] == '>':
ion_dict[ion]['flg_clm'] = 2
ion_dict[ion]['clm'] += float(iis[1:])
elif iis[0] == '<':
pass
elif iis[0] == '.':
pass
else:
ion_dict[ion]['clm'] += float(iis)
else: # Sigma
for kk,iis in enumerate(isplit[3:10]):
ion = ions[kk]
if iis[0] == '.':
pass
else:
ion_dict[ion]['sig_clm'] += float(iis)**2
# Convert to log
for ion in ions:
N = ion_dict[ion]['clm']
sig = np.sqrt(ion_dict[ion]['sig_clm'])
#
ion_dict[ion]['clm'] = np.log10(N)
if ion_dict[ion]['flg_clm'] == 2:
ion_dict[ion]['sig_clm'] = 0.
else:
ion_dict[ion]['sig_clm'] = sig/N/np.log(10)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Prx06b')
return lls
def peroux06b():
"""Peroux, C. et al. 2006b, A&A, 450, 53
SDSS J1323-0021
Metal rich
Metal columns copied by JXP from Table 1
Total NHI from damping wings
"""
# Setup
radec = '132323.78-002155.2' # SDSS Name
lls = LLSSystem(name='SDSSJ1323-0021_z0.716', radec=radec, zem=1.390,
zabs=0.716, vlim=[-200., 200.]*u.km/u.s, NHI=20.21,
sig_NHI=np.array([0.20,0.20]))
# Parse table file
tab_fil = pyigm_path+"/data/LLS/Literature/peroux06b.tb1.ascii"
with open(tab_fil,'r') as f:
flines = f.readlines()
ion_dict = {}
for iline in flines:
isplit = iline.split('\t')
if len(isplit[0]) == 0:
# Grab ions and init
ions = isplit[3:10]
for ion in ions:
Zion = ltai.name_ion(ion)
ion_dict[ion] = dict(clm=0., sig_clm=0.,flg_clm=1,Z=Zion[0],ion=Zion[1])
continue
# Column or sigma?
if isplit[0][0] == 'N': # Column
for kk,iis in enumerate(isplit[3:10]):
ion = ions[kk]
if iis[0] == '>':
ion_dict[ion]['flg_clm'] = 2
ion_dict[ion]['clm'] += float(iis[1:])
elif iis[0] == '<':
pass
elif iis[0] == '.':
pass
else:
ion_dict[ion]['clm'] += float(iis)
else: # Sigma
for kk,iis in enumerate(isplit[3:10]):
ion = ions[kk]
if iis[0] == '.':
pass
else:
ion_dict[ion]['sig_clm'] += float(iis)**2
# Convert to log
for ion in ions:
N = ion_dict[ion]['clm']
sig = np.sqrt(ion_dict[ion]['sig_clm'])
#
ion_dict[ion]['clm'] = np.log10(N)
if ion_dict[ion]['flg_clm'] == 2:
ion_dict[ion]['sig_clm'] = 0.
else:
ion_dict[ion]['sig_clm'] = sig/N/np.log(10)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Prx06b')
return lls
def kacprzak12():
'''Kacprzak, G. et al. 2012, MNRAS, 427, 3029-3043
TON 153
Taken from Table 1 by JXP
NHI from Churchill+2007
RA/DEC from Simbad
'''
# Setup
radec = '131956.2209+272808.271'
lls = LLSSystem(name='TON153_z1.002', radec=radec, zem=0.6610,
zabs=1.0023, vlim=[-250., 200.]*u.km/u.s, NHI=18.30, sig_NHI=np.array([0.30,0.30]))
# Table 1 (total)
ion_dict = {}
ion_dict['Mg II'] = dict(clm=13.11, sig_clm=0.07,flg_clm=1,Z=12,ion=2)
ion_dict['Mg I'] = dict(clm=11.54, sig_clm=0.06,flg_clm=1,Z=12,ion=1)
ion_dict['Si I'] = dict(clm=11.8, sig_clm=0.00,flg_clm=3,Z=14,ion=1)
ion_dict['Si II'] = dict(clm=13.16, sig_clm=0.11,flg_clm=1,Z=14,ion=2)
ion_dict['Si IV'] = dict(clm=12.4, sig_clm=0.0,flg_clm=3,Z=14,ion=4)
ion_dict['C II'] = dict(clm=13.39, sig_clm=0.0,flg_clm=2,Z=6,ion=2)
ion_dict['C III'] = dict(clm=14.20, sig_clm=0.05,flg_clm=1,Z=6,ion=3)
ion_dict['C III'] = dict(clm=14.41, sig_clm=0.05,flg_clm=1,Z=6,ion=4)
ion_dict['O VI'] = dict(clm=14.49, sig_clm=0.05,flg_clm=1,Z=8,ion=6)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Kcz12')
return lls
def kacprzak12():
'''Kacprzak, G. et al. 2012, MNRAS, 427, 3029-3043
TON 153
Taken from Table 1 by JXP
NHI from Churchill+2007
RA/DEC from Simbad
'''
# Setup
radec = '131956.2209+272808.271'
lls = LLSSystem(name='TON153_z1.002',
radec=radec,
zem=0.6610,
zabs=1.0023,
vlim=[-250., 200.] * u.km / u.s,
NHI=18.30,
sig_NHI=np.array([0.30, 0.30]))
# Table 1 (total)
ion_dict = {}
ion_dict['Mg II'] = dict(clm=13.11, sig_clm=0.07, flg_clm=1, Z=12, ion=2)
ion_dict['Mg I'] = dict(clm=11.54, sig_clm=0.06, flg_clm=1, Z=12, ion=1)
ion_dict['Si I'] = dict(clm=11.8, sig_clm=0.00, flg_clm=3, Z=14, ion=1)
ion_dict['Si II'] = dict(clm=13.16, sig_clm=0.11, flg_clm=1, Z=14, ion=2)
ion_dict['Si IV'] = dict(clm=12.4, sig_clm=0.0, flg_clm=3, Z=14, ion=4)
ion_dict['C II'] = dict(clm=13.39, sig_clm=0.0, flg_clm=2, Z=6, ion=2)
ion_dict['C III'] = dict(clm=14.20, sig_clm=0.05, flg_clm=1, Z=6, ion=3)
ion_dict['C III'] = dict(clm=14.41, sig_clm=0.05, flg_clm=1, Z=6, ion=4)
ion_dict['O VI'] = dict(clm=14.49, sig_clm=0.05, flg_clm=1, Z=8, ion=6)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Kcz12')
return lls
def peroux06a():
"""Peroux, C. et al. 2006a, MNRAS, 372, 369
SDSS J0134+0051
One of her sample
Metal columns taken by JXP from Table 2 (no online data)
Total NHI from damping wings
"""
# Setup
radec = '013405.75+005109.4' # SDSS Name
lls = LLSSystem(name='SDSSJ0134+0051_z0.842', radec=radec, zem=1.522,
zabs=0.842, vlim=[-150., 150.]*u.km/u.s, NHI=19.93, sig_NHI=np.array([0.15,0.15]))
# Table 2
ion_dict = {}
N = np.sum(np.array([5.56,12.6,13.7,23.5,61.4,39.8,6,9.14])*1e10)
sig = np.sqrt(np.sum((np.array([2.32,3.1,3.68,4.13,8.02,6.65,3.37,2.82])*1e10)**2))
ion_dict['Mg I'] = dict(clm=np.log10(N), sig_clm=sig/N/np.log(10),flg_clm=1,Z=12,ion=1)
ion_dict['Mg II'] = dict(clm=np.log10(5e13), sig_clm=0.,flg_clm=2,Z=12,ion=2)
N = np.sum(np.array([8.17,4.28,32.1,125,710,301,893,600,263,65.7])*1e11)
sig = np.sqrt(np.sum((np.array([2.63,1.40,2.37,8.6,53.2,28.4,73.5,61.7,14.0,2.95])*1e11)**2))
ion_dict['Fe II'] = dict(clm=np.log10(N), sig_clm=sig/N/np.log(10),flg_clm=1,Z=26,ion=2)
sig = np.sqrt(np.sum((np.array([3.72,1.84,2.36,3.83])*1e11)**2))
ion_dict['Zn II'] = dict(clm=np.log10(2*sig), sig_clm=0.,flg_clm=3,Z=30,ion=2)
sig = np.sqrt(np.sum((np.array([19.4,9.79])*1e11)**2))
ion_dict['Cr II'] = dict(clm=np.log10(2*sig), sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Not including MnII. Appears as a detection but also given as a limit..
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Prx06a')
return lls
def peroux06a():
"""Peroux, C. et al. 2006a, MNRAS, 372, 369
SDSS J0134+0051
One of her sample
Metal columns taken by JXP from Table 2 (no online data)
Total NHI from damping wings
"""
# Setup
radec = '013405.75+005109.4' # SDSS Name
lls = LLSSystem(name='SDSSJ0134+0051_z0.842', radec=radec, zem=1.522,
zabs=0.842, vlim=[-150., 150.]*u.km/u.s, NHI=19.93, sig_NHI=np.array([0.15,0.15]))
# Table 2
ion_dict = {}
N = np.sum(np.array([5.56,12.6,13.7,23.5,61.4,39.8,6,9.14])*1e10)
sig = np.sqrt(np.sum((np.array([2.32,3.1,3.68,4.13,8.02,6.65,3.37,2.82])*1e10)**2))
ion_dict['Mg I'] = dict(clm=np.log10(N), sig_clm=sig/N/np.log(10),flg_clm=1,Z=12,ion=1)
ion_dict['Mg II'] = dict(clm=np.log10(5e13), sig_clm=0.,flg_clm=2,Z=12,ion=2)
N = np.sum(np.array([8.17,4.28,32.1,125,710,301,893,600,263,65.7])*1e11)
sig = np.sqrt(np.sum((np.array([2.63,1.40,2.37,8.6,53.2,28.4,73.5,61.7,14.0,2.95])*1e11)**2))
ion_dict['Fe II'] = dict(clm=np.log10(N), sig_clm=sig/N/np.log(10),flg_clm=1,Z=26,ion=2)
sig = np.sqrt(np.sum((np.array([3.72,1.84,2.36,3.83])*1e11)**2))
ion_dict['Zn II'] = dict(clm=np.log10(2*sig), sig_clm=0.,flg_clm=3,Z=30,ion=2)
sig = np.sqrt(np.sum((np.array([19.4,9.79])*1e11)**2))
ion_dict['Cr II'] = dict(clm=np.log10(2*sig), sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Not including MnII. Appears as a detection but also given as a limit..
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Prx06a')
return lls
def test_fluxmodel():
# Init
lls = LLSSystem((0.*u.deg, 0.*u.deg), 2.5, None, NHI=17.9)
# Fill LLS lines
lls.fill_lls_lines()
# Generate a spectrum
wave = np.arange(3000., 6500)
npix = len(wave)
spec = XSpectrum1D.from_tuple((wave*u.AA,np.ones(npix)))
# Model
model = lls.flux_model(spec)
np.testing.assert_allclose(model.flux[100].value,0.009424664763760516)
def test_fluxmodel():
# Init
lls = LLSSystem((0. * u.deg, 0. * u.deg), 2.5, None, NHI=17.9)
# Fill LLS lines
lls.fill_lls_lines()
# Generate a spectrum
wave = np.arange(3000., 6500)
npix = len(wave)
spec = XSpectrum1D.from_tuple((wave * u.AA, np.ones(npix)))
# Model
model = lls.flux_model(spec)
np.testing.assert_allclose(model.flux[100].value,
0.009424664763760516,
rtol=1e-5)
def main(args=None):
from astropy.coordinates import SkyCoord
from astropy import units as u
from linetools import utils as ltu
from pyigm.abssys.dla import DLASystem
from pyigm.abssys.lls import LLSSystem
if args is None:
pargs = parser()
else:
pargs = args
# Coordinates
if pargs.jcoord is not None:
coord = ltu.radec_to_coord(pargs.jcoord)
else:
coord = SkyCoord(ra=0., dec=0., unit='deg')
# vlim
if pargs.vlim is not None:
vlims = [float(vlim) for vlim in pargs.vlim.split(',')]*u.km/u.s
else:
vlims = None
# go
if pargs.itype == 'dla':
isys = DLASystem(coord, pargs.zabs, vlims, pargs.NHI, zem=pargs.zem, sig_NHI=pargs.sigNHI)
elif pargs.itype == 'lls':
isys = LLSSystem(coord, pargs.zabs, vlims, NHI=pargs.NHI, zem=pargs.zem, sig_NHI=pargs.sigNHI)
else:
raise IOError("Not prepared for this type of IGMSystem")
# Write
isys.write_json(pargs.outfile)
def test_simple_init():
# Init
lls = LLSSystem((0. * u.deg, 0. * u.deg), 2.0, None, NHI=17.9)
#
np.testing.assert_allclose(lls.vlim[0].value, -500.)
np.testing.assert_allclose(lls.NHI, 17.9)
np.testing.assert_allclose(lls.tau_LL, 5.035377286841938, rtol=1e-5)
def __init__(self, abssys_list, parent=None,
only_one=False, linelist=None, no_buttons=False):
'''
only_one: bool, optional
Restrict to one selection at a time? [False]
no_buttons: bool, optional
Eliminate Refine/Reload buttons?
'''
super(AbsSysWidget, self).__init__(parent)
#if not status is None:
# self.statusBar = status
self.abssys_list = abssys_list
# Speeds things up
if linelist is None:
self.linelist = LineList('ISM')
else:
self.linelist = linelist
# Create the line list
list_label = QtGui.QLabel('Abs Systems:')
self.abslist_widget = QtGui.QListWidget(self)
if not only_one:
self.abslist_widget.setSelectionMode(QtGui.QAbstractItemView.ExtendedSelection)
self.abslist_widget.addItem('None')
#self.abslist_widget.addItem('Test')
# Lists
self.abs_sys = []
self.items = []
self.all_items = []
self.all_abssys = []
for abssys_fil in self.abssys_list:
self.all_abssys.append(LLSSystem.from_absid_fil(abssys_fil,
linelist=self.linelist))
self.add_item(abssys_fil)
self.abslist_widget.setCurrentRow(0)
self.abslist_widget.itemSelectionChanged.connect(self.on_list_change)
# Layout
vbox = QtGui.QVBoxLayout()
vbox.addWidget(list_label)
# Buttons
if not no_buttons:
buttons = QtGui.QWidget()
self.refine_button = QtGui.QPushButton('Refine', self)
#self.refine_button.clicked.connect(self.refine) # CONNECTS TO A PARENT
reload_btn = QtGui.QPushButton('Reload', self)
reload_btn.clicked.connect(self.reload)
hbox1 = QtGui.QHBoxLayout()
hbox1.addWidget(self.refine_button)
hbox1.addWidget(reload_btn)
buttons.setLayout(hbox1)
vbox.addWidget(buttons)
vbox.addWidget(self.abslist_widget)
self.setLayout(vbox)
def reload(self):
print('AbsSysWidget: Reloading systems..')
self.all_abssys = []
for abssys_fil in self.abssys_list:
self.all_abssys.append(
LLSSystem.from_absid_fil(abssys_fil, linelist=self.linelist))
#self.add_item(abssys_fil)
self.on_list_change()
def reload(self):
print('AbsSysWidget: Reloading systems..')
self.all_abssys = []
for abssys_fil in self.abssys_list:
self.all_abssys.append(LLSSystem.from_absid_fil(abssys_fil,
linelist=self.linelist))
#self.add_item(abssys_fil)
self.on_list_change()
def add_LLS(self,
z,
NHI=17.3,
bval=20. * u.km / u.s,
comment='None',
model=True):
"""Generate a new LLS
"""
#
new_sys = LLSSystem((0 * u.deg, 0 * u.deg),
z, [-300., 300] * u.km / u.s,
NHI=NHI)
new_sys.bval = bval # This is not standard, but for convenience
new_sys.comment = comment
new_sys.fill_lls_lines(bval=bval, do_analysis=0)
#QtCore.pyqtRemoveInputHook()
#pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
# Name
self.count_lls += 1
new_sys.label = 'LLS_Sys_{:d}'.format(self.count_lls)
# Add
self.abssys_widg.add_fil(new_sys.label)
self.abssys_widg.all_abssys.append(new_sys)
self.abssys_widg.abslist_widget.item(len(
self.abssys_widg.all_abssys)).setSelected(True)
# Update
self.llist['Plot'] = False # Turn off metal-lines
if model: # For dealing with initialization
self.update_model()
def test_dat_init():
# JXP .dat files
if os.getenv('LLSTREE') is None:
assert True
return
# Read
datfil = 'Data/UM184.z2929.dat'
lls = LLSSystem.from_datfile(datfil, tree=os.getenv('LLSTREE'))
#
np.testing.assert_allclose(lls.zabs, 2.93012)
def test_dat_init():
# JXP .dat files
if os.getenv('LLSTREE') is None:
assert True
return
# Read
datfil = 'Data/UM184.z2929.dat'
lls = LLSSystem.from_datfile(datfil, tree=os.getenv('LLSTREE'))
#
np.testing.assert_allclose(lls.zabs, 2.93012)
def test_parse_ion():
# JXP .ion file
if os.getenv('LLSTREE') is None:
assert True
return
# Read
datfil = 'Data/UM184.z2929.dat'
lls = LLSSystem.from_datfile(datfil, tree=os.getenv('LLSTREE'))
#
lls.get_ions(use_Nfile=True)
assert len(lls._ionN) == 13
def insert_dlas(sightline,
overlap=False,
rstate=None,
slls=False,
mix=False,
high=False,
noise=False):
""" Insert a DLA into input spectrum
Also adjusts the noise
Will also add noise 'everywhere' if requested
Parameters
----------
sightline:dla_cnn.data_model.sightline.Sightline object
overlap: bool
noise: bool, optional
Returns
-------
None
"""
#init
if rstate is None:
rstate = np.random.RandomState()
spec = XSpectrum1D.from_tuple(
(10**sightline.loglam, sightline.flux)) #generate xspectrum1d
# Generate DLAs
dlas = []
spec_dlas = []
zabslist = init_zabs(sightline, overlap)
for zabs in zabslist:
# Random NHI
NHI = uniform_NHI(slls=slls, mix=mix, high=high)
spec_dla = Dla((1 + zabs) * 1215.6701, NHI, '00' + str(jj))
if (slls or mix):
dla = LLSSystem((sightline.ra, sightline.dec), zabs, None, NHI=NHI)
else:
dla = DLASystem((sightline.ra, sightline.dec), zabs, None, NHI)
dlas.append(dla)
spec_dlas.append(spec_dla)
# Insert dlas to one sightline
vmodel, _ = hi_model(dlas, spec, fwhm=3.)
#add noise
if noise:
rand = rstate.randn(len(sightline.flux))
noise = rand * sightline.error * np.sqrt(1 - vmodel.flux.value**2)
else:
noise = 0
final_spec = XSpectrum1D.from_tuple(
(vmodel.wavelength, spec.flux.value * vmodel.flux.value + noise))
#generate new sightline
sightline.flux = final_spec.flux.value
sightline.dlas = spec_dlas
sightline.s2n = estimate_s2n(sightline)
def test_parse_ion():
# JXP .ion file
if os.getenv('LLSTREE') is None:
assert True
return
# Read
datfil = 'Data/UM184.z2929.dat'
lls = LLSSystem.from_datfile(datfil, tree=os.getenv('LLSTREE'))
#
lls.get_ions(use_Nfile=True)
assert len(lls._ionN) == 13
def nestor08():
'''Nestor, D. et al. 2008, MNRAS, 390, 1670-1682
Q2149+212
Taken from Table 1 by JXP
NHI from RTN06 (damping wings)
RA/DEC from STIS header
'''
# Setup
lls = LLSSystem(name='SDSSJ2151+2130_z1.002', radec=(327.94096*u.deg,
21.503750*u.deg),
zem=1.534, zabs=1.0023,
vlim=[-300., 300.]*u.km/u.s, NHI=19.30,
sig_NHI=np.array([0.10,0.10]))
# Meiring06, Table 4
ion_dict = {}
ion_dict['Zn II'] = dict(clm=12.13, sig_clm=0.,flg_clm=3,Z=30,ion=2)
ion_dict['Cr II'] = dict(clm=12.59, sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Nes08')
return lls
def get_dla(zabs, NHI, matrix_lam, matrix_flux, wvoff=60.):
spec = XSpectrum1D.from_tuple((matrix_lam, matrix_flux))
if NHI < 20.3:
dla = LLSSystem((0, 0), zabs, None, NHI=NHI)
else:
dla = DLASystem((0, 0), zabs, None, NHI)
wvcen = (1 + zabs) * 1215.67
gd_wv = (spec.wavelength.value >
wvcen - wvoff - 30) & (spec.wavelength.value < wvcen + wvoff + 30)
co = 1.5 #np.mean(spec.flux[gd_wv])#amax
lya, lines = hi_model(dla, spec, lya_only=True)
return lya.wavelength[gd_wv], co * lya.flux[gd_wv]
def nestor08():
'''Nestor, D. et al. 2008, MNRAS, 390, 1670-1682
Q2149+212
Taken from Table 1 by JXP
NHI from RTN06 (damping wings)
RA/DEC from STIS header
'''
# Setup
lls = LLSSystem(name='SDSSJ2151+2130_z1.002', radec=(327.94096*u.deg,
21.503750*u.deg),
zem=1.534, zabs=1.0023,
vlim=[-300., 300.]*u.km/u.s, NHI=19.30,
sig_NHI=np.array([0.10,0.10]))
# Meiring06, Table 4
ion_dict = {}
ion_dict['Zn II'] = dict(clm=12.13, sig_clm=0.,flg_clm=3,Z=30,ion=2)
ion_dict['Cr II'] = dict(clm=12.59, sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Nes08')
return lls
def main(args=None):
from linetools import utils as ltu
pargs = parser()
# Read
jdict = ltu.loadjson(pargs.jsonfile)
if 'class' not in jdict.keys():
raise KeyError("This script only works with JSON files with named classes")
if jdict['class'] == 'IGMSightline':
from pyigm.igm.igmsightline import IGMSightline
obj = IGMSightline.from_dict(jdict)
flg_tbl = True
elif jdict['class'] == 'DLASystem':
from pyigm.abssys.dla import DLASystem
obj = DLASystem.from_dict(jdict)
flg_tbl = False
elif jdict['class'] == 'LLSSystem':
from pyigm.abssys.lls import LLSSystem
obj = LLSSystem.from_dict(jdict)
obj.fill_ionN()
flg_tbl = True # Column density table
else:
raise IOError("Not prepared for this class: {:s}".format(jdict['class']))
# name
try:
name = jdict['name']
except KeyError:
try:
name = jdict['Name']
except:
name = 'None'
print("Name of object: {:s}".format(name))
# Generate table
if flg_tbl:
if jdict['class'] == 'IGMSightline':
tbl = obj.build_table()
elif jdict['class'] == 'LLSSystem':
tbl = obj._ionN
tbl['logN'].format = '5.2f'
tbl['sig_logN'].format = '5.2f'
tbl['vmin'].format = '8.1f'
tbl['vmax'].format = '8.1f'
else:
tbl = None
# Print
if len(tbl) > 0:
tbl.pprint(99999, max_width=120)
else:
print("Table was empty..")
def meiring06():
"""Meiring et al. 2006, MNRAS, 370, 43
Q1107+0003
Taken from Table 4 by JXP
NHI from RTN06 (damping wings)
RA/DEC from STIS header
"""
# Setup
lls = LLSSystem(name='SDSSJ1107+0003_z0.954', radec=(166.90273*u.deg,
0.05795000*u.deg),
zem=1.726, zabs=0.9542,
vlim=[-300., 300.]*u.km/u.s, NHI=20.26,
sig_NHI=np.array([0.14,0.09]))
# Meiring06, Table 4
ion_dict = {}
ion_dict['Zn II'] = dict(clm=12.08, sig_clm=0.,flg_clm=3,Z=30,ion=2)
ion_dict['Ti II'] = dict(clm=13.01, sig_clm=0.,flg_clm=3,Z=22,ion=2)
ion_dict['Cr II'] = dict(clm=12.76, sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei06')
return lls
def meiring06():
"""Meiring et al. 2006, MNRAS, 370, 43
Q1107+0003
Taken from Table 4 by JXP
NHI from RTN06 (damping wings)
RA/DEC from STIS header
"""
# Setup
lls = LLSSystem(name='SDSSJ1107+0003_z0.954', radec=(166.90273*u.deg,
0.05795000*u.deg),
zem=1.726, zabs=0.9542,
vlim=[-300., 300.]*u.km/u.s, NHI=20.26,
sig_NHI=np.array([0.14,0.09]))
# Meiring06, Table 4
ion_dict = {}
ion_dict['Zn II'] = dict(clm=12.08, sig_clm=0.,flg_clm=3,Z=30,ion=2)
ion_dict['Ti II'] = dict(clm=13.01, sig_clm=0.,flg_clm=3,Z=22,ion=2)
ion_dict['Cr II'] = dict(clm=12.76, sig_clm=0.,flg_clm=3,Z=24,ion=2)
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei06')
return lls
def zonak2004():
"""Zonak, S. et al. 2004, ApJ, 2004, 606, 196
PG1634+706
HST+Keck spectra
MgII, SiIV, SiIII from Table 2. Summing Subsystems A (Model 2) and B
Errors estimated by JXP (not reported)
SiIII in A may be a model
SiIV in B may be a model
Total NHI from LL. Taken from Fig 3 caption.
Error estimated by JXP
Not all EWs in Table 1 included
Adopting their M/H
"""
# Setup
radec = '163428.9897+703132.422' # SIMBAD
lls = LLSSystem(name='PG1634+706_z1.041', radec=radec, zem=1.337,
zabs=1.0414, vlim=[-250., 100.]*u.km/u.s, NHI=17.23, ZH=-1.4,
sig_NHI=np.array([0.15,0.15]))
# SubSystems
lls.nsub = 2
# Abundances
adict = dict(MgII={'clm': log_sum([11.45,11.90,12.02,11.68]), 'sig_clm': 0.05, 'flg_clm': 1},
SiIII={'clm': log_sum([12.5,12.5,12.8,12.7]), 'sig_clm': 0.25, 'flg_clm': 1},
SiIV={'clm': log_sum([10.9,10.8,11.2,11.1]), 'sig_clm': 0.15, 'flg_clm': 1} )
lls.subsys['A'] = AbsSubSystem(lls, 1.0414, [-80, 100]*u.km/u.s, 'A')
lls.subsys['A']._ionN = pyiau.dict_to_ions(adict)
bdict = dict(SiIII={'clm': log_sum([11.8,12.8,12.4]), 'sig_clm': 0.15, 'flg_clm': 1},
SiIV={'clm': log_sum([11.2,12.2,11.8]), 'sig_clm': 0.15, 'flg_clm': 1} )
lls.subsys['B'] = AbsSubSystem(lls, 1.0414, [-240, -80]*u.km/u.s, 'B')
lls.subsys['B']._ionN = pyiau.dict_to_ions(bdict)
# Total
lls._ionN = pyiau.sum_ionN(lls.subsys['A']._ionN, lls.subsys['B']._ionN)
lls.Refs.append('Zon04')
# Return
return lls
def add_LLS(self, z, NHI=17.3, bval=20.0 * u.km / u.s, comment="None", model=True):
"""Generate a new LLS
"""
#
new_sys = LLSSystem((0 * u.deg, 0 * u.deg), z, [-300.0, 300] * u.km / u.s, NHI=NHI)
new_sys.bval = bval # This is not standard, but for convenience
new_sys.comment = comment
new_sys.fill_lls_lines(bval=bval, do_analysis=0)
# Name
self.count_lls += 1
new_sys.label = "LLS_Sys_{:d}".format(self.count_lls)
# Add
self.abssys_widg.add_fil(new_sys.label)
self.abssys_widg.all_abssys.append(new_sys)
self.abssys_widg.abslist_widget.item(len(self.abssys_widg.all_abssys)).setSelected(True)
# Update
self.llist["Plot"] = False # Turn off metal-lines
if model: # For dealing with initialization
self.update_model()
def zonak2004():
"""Zonak, S. et al. 2004, ApJ, 2004, 606, 196
PG1634+706
HST+Keck spectra
MgII, SiIV, SiIII from Table 2. Summing Subsystems A (Model 2) and B
Errors estimated by JXP (not reported)
SiIII in A may be a model
SiIV in B may be a model
Total NHI from LL. Taken from Fig 3 caption.
Error estimated by JXP
Not all EWs in Table 1 included
Adopting their M/H
"""
# Setup
radec = '163428.9897+703132.422' # SIMBAD
lls = LLSSystem(name='PG1634+706_z1.041', radec=radec, zem=1.337,
zabs=1.0414, vlim=[-250., 100.]*u.km/u.s, NHI=17.23, ZH=-1.4,
sig_NHI=np.array([0.15,0.15]))
# SubSystems
lls.nsub = 2
# Abundances
adict = dict(MgII={'clm': log_sum([11.45,11.90,12.02,11.68]), 'sig_clm': 0.05, 'flg_clm': 1},
SiIII={'clm': log_sum([12.5,12.5,12.8,12.7]), 'sig_clm': 0.25, 'flg_clm': 1},
SiIV={'clm': log_sum([10.9,10.8,11.2,11.1]), 'sig_clm': 0.15, 'flg_clm': 1} )
lls.subsys['A'] = AbsSubSystem(lls, 1.0414, [-80, 100]*u.km/u.s, 'A')
lls.subsys['A']._ionN = pyiau.dict_to_ions(adict)
bdict = dict(SiIII={'clm': log_sum([11.8,12.8,12.4]), 'sig_clm': 0.15, 'flg_clm': 1},
SiIV={'clm': log_sum([11.2,12.2,11.8]), 'sig_clm': 0.15, 'flg_clm': 1} )
lls.subsys['B'] = AbsSubSystem(lls, 1.0414, [-240, -80]*u.km/u.s, 'B')
lls.subsys['B']._ionN = pyiau.dict_to_ions(bdict)
# Total
lls._ionN = pyiau.sum_ionN(lls.subsys['A']._ionN, lls.subsys['B']._ionN)
lls.Refs.append('Zon04')
# Return
return lls
def tripp2005():
'''Tripp, T. et al. 2005, ApJ, 2005, 619, 714
PG 1216+069 (LLS in Virgo)
HST/STIS, FUSE
Metal columns parsed from Tables 2 and 3
Total NHI from damping wings
M/H from O/H
'''
# Grab ASCII files from ApJ
tab_fils = [
pyigm_path + "/data/LLS/tripp2005.tb3.ascii",
pyigm_path + "/data/LLS/tripp2005.tb2.ascii"
]
urls = [
'http://iopscience.iop.org/0004-637X/619/2/714/fulltext/60797.tb3.txt',
'http://iopscience.iop.org/0004-637X/619/2/714/fulltext/60797.tb2.txt'
]
for jj, tab_fil in enumerate(tab_fils):
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = urls[jj]
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '121920.9320+063838.476' # SIMBAD
lls = LLSSystem(name='PG1216+069_z0.006',
radec=radec,
zem=0.3313,
zabs=0.00632,
vlim=[-100., 100.] * u.km / u.s,
NHI=19.32,
ZH=-1.6,
sig_NHI=np.array([0.03, 0.03]))
# Columns
# Start with Table 3 (VPFIT)
with open(tab_fils[0], 'r') as f:
flines3 = f.readlines()
ion_dict = {}
for iline in flines3:
if (len(iline.strip()) == 0):
continue
isplit = iline.split('\t')
# Ion
flg = 2
if (len(isplit[0].strip()) > 0): # & (isplit[0][0] not in ['1','2']):
ipos = isplit[0].find('1')
ionc = isplit[0][0:ipos - 1].strip()
try:
Zion = ltai.name_ion(ionc)
except KeyError:
pdb.set_trace()
flg = 1
# Column
csplit = isplit[3].split(' ')
clm = float(csplit[0])
sig = float(csplit[2])
if flg == 1:
ion_dict[ionc] = dict(logN=clm,
sig_logN=sig,
flag_N=1,
Z=Zion[0],
ion=Zion[1])
else: # Add it in
tmp_dict = dict(logN=clm,
sig_logN=sig,
flag_N=1,
Z=Zion[0],
ion=Zion[1])
flagN, logN, siglogN = ltaa.sum_logN(ion_dict[ionc], tmp_dict)
ion_dict[ionc]['logN'] = logN
ion_dict[ionc]['sig_logN'] = siglogN
ions = ion_dict.keys()
# Now Table 2 for the extras
with open(tab_fils[1], 'r') as f:
flines2 = f.readlines()
# Trim the first 10 lines
flines2 = flines2[10:]
# Loop
for iline in flines2:
isplit = iline.split('\t')
#
ionc = isplit[0].strip()
if (len(ionc) == 0) or (ionc in ions):
continue
#
Zion = ltai.name_ion(ionc)
ion_dict[ionc] = dict(Z=Zion[0], ion=Zion[1], sig_logN=0.)
if isplit[4][0] == '<':
ion_dict[ionc]['logN'] = float(isplit[4][1:])
ion_dict[ionc]['flag_N'] = 3
else:
raise ValueError('Should not get here')
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Tri05')
return lls
def battisti12():
'''Battisti, A. et al. 2012, ApJ, 744, 93
HST/COS
QSO info from Table 1
Metal columns parsed from Table 3
NHI from Lya
'''
all_lls = []
# Grab ASCII files from ApJ
tab_fils = [
pyigm_path + "/data/LLS/Literature/battisti12.tb1.ascii",
pyigm_path + "/data/LLS/Literature/battisti12.tb3.ascii"
]
urls = [
'http://iopscience.iop.org/0004-637X/744/2/93/suppdata/apj413924t1_ascii.txt',
'http://iopscience.iop.org/0004-637X/744/2/93/suppdata/apj413924t3_ascii.txt'
]
for jj, tab_fil in enumerate(tab_fils):
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = urls[jj]
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# QSO info
with open(tab_fils[0], 'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
all_idict = []
for iline in flines1:
if iline[0:2] != 'SD':
continue
# Parse
isplit = iline.split('\t')
name = isplit[0].split(' ')[1]
radec = name[1:]
zem = float(isplit[1].strip())
zabs = float(isplit[2].strip())
NHI = float(isplit[3].strip()[0:4])
sigNHI = np.array([float(isplit[3].strip()[11:])] * 2)
# Save
lls = LLSSystem(name=name,
radec=radec,
zem=zem,
zabs=zabs,
NHI=NHI,
sig_NHI=sigNHI,
vlim=[-500, 500] * u.km / u.s)
#
all_lls.append(lls)
all_idict.append({})
# Abundances
with open(tab_fils[1], 'r') as f:
flines3 = f.readlines()
flines3 = flines3[5:]
ion = None
for iline in flines3:
if ion == 'Ni II':
break
isplit = iline.split('\t')
if isplit[0] == 'C II*': # Skipping CII*
continue
# ion
ipos = -1
while (isplit[0][ipos] not in ['I', 'V']):
ipos -= 1
ion = isplit[0][0:ipos + 1 + len(isplit[0])]
Zion = ltai.name_ion(ion)
# Loop on systems
for kk, iis in enumerate(isplit[1:-1]):
if iis.strip()[0] == '.':
continue
all_idict[kk][ion] = dict(Z=Zion[0], ion=Zion[1], sig_clm=0.)
if iis[0] == '>':
all_idict[kk][ion]['flg_clm'] = 2
all_idict[kk][ion]['clm'] = float(iis[1:6])
elif iis[0] == '<':
all_idict[kk][ion]['flg_clm'] = 3
all_idict[kk][ion]['clm'] = float(iis[1:])
else:
all_idict[kk][ion]['flg_clm'] = 1
all_idict[kk][ion]['clm'] = float(iis[0:5])
all_idict[kk][ion]['sig_clm'] = float(iis[-4:])
# Return SLLS only
for kk, lls in enumerate(all_lls):
try:
lls._ionN = pyiau.dict_to_ions(all_idict[kk])
except ValueError:
pdb.set_trace()
lls.Refs.append('Bat12')
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def jenkins2005():
"""Jenkins, E. et al. 2005, ApJ, 2005, 623, 767
PHL 1811
HST/STIS, FUSE
Metals parsed from Table 1
OI taken from text
Had to input error on columns by hand (JXP)
Total NHI from Lyman series. see Fig 3
M/H from O/H
"""
# Grab ASCII file from ApJ
tab_fil = pyigm_path + "/data/LLS/Literature/jenkins2005.tb1.ascii"
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = 'http://iopscience.iop.org/0004-637X/623/2/767/fulltext/61520.tb1.txt'
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '215501.5152-092224.688' # SIMBAD
lls = LLSSystem(name='PHL1811_z0.081',
radec=radec,
zem=0.192,
zabs=0.080923,
vlim=[-100., 100.] * u.km / u.s,
NHI=17.98,
ZH=-0.19,
sig_NHI=np.array([0.05, 0.05]))
lls.lines = [] # Probably not used
# AbsLines
ism = LineList('ISM')
Nsig = {
'C IV': 0.4,
'N II': 0.4,
'Si II': 0.05,
'Si IV': 0.25,
'S II': 0.2,
'Fe II': 0.12,
'H I': 0.05,
'S III': 0.06
}
# Parse Table
with open(tab_fil, 'r') as f:
flines = f.readlines()
ion_dict = {}
for iline in flines:
iline = iline.strip()
if (len(iline) == 0):
continue
# Split on tabs
isplit = iline.split('\t')
# Offset?
ioff = 0
if isplit[0][0] in ['1', '2']:
ioff = -1
# Catch bad lines
if (isplit[1 + ioff][0:6]
in ['1442.0', '1443.7',
'1120.9']): # Skip goofy CII line and CII*
continue
if len(isplit[2 + ioff]) == 0:
continue
# Ion
if (len(isplit[0].strip()) > 0) & (isplit[0][0] not in ['1', '2']):
ionc = isplit[0].strip()
try:
Zion = ltai.name_ion(ionc)
except KeyError:
pdb.set_trace()
# Generate the Line
try:
newline = AbsLine(float(isplit[2 + ioff]) * u.AA,
linelist=ism,
closest=True)
except ValueError:
pdb.set_trace()
newline.attrib['z'] = lls.zabs
# Spectrum
newline.analy['datafile'] = 'STIS' if 'S' in isplit[1] else 'FUSE'
# EW
try:
EWvals = isplit[4 + ioff].split(' ')
except IndexError:
pdb.set_trace()
newline.attrib['EW'] = float(EWvals[0]) * u.AA / 1e3
newline.attrib['sig_EW'] = float(EWvals[2]) * u.AA / 1e3
newline.attrib['flag_EW'] = 1
if len(isplit) < (5 + ioff + 1):
continue
# Colm?
#xdb.set_trace()
newline.attrib['sig_logN'] = 0.
if (len(isplit[5 + ioff].strip()) > 0) & (isplit[5 + ioff].strip() !=
'\\ldots'):
if isplit[5 + ioff][0] == '\\':
ipos = isplit[5 + ioff].find(' ')
newline.attrib['logN'] = float(isplit[5 + ioff][ipos + 1:])
newline.attrib['flag_N'] = 2
elif isplit[5 + ioff][0] == '<':
ipos = 0
newline.attrib['logN'] = float(isplit[5 + ioff][ipos + 1:])
newline.attrib['flag_N'] = 3
elif isplit[5 + ioff][0] == '1':
try:
newline.attrib['logN'] = float(isplit[5 + ioff][0:5])
except ValueError:
pdb.set_trace()
newline.attrib['flag_N'] = 1
try:
newline.attrib['sig_logN'] = Nsig[ionc]
except KeyError:
print('No error for {:s}'.format(ionc))
else:
raise ValueError('Bad character')
# ion_dict
ion_dict[ionc] = dict(clm=newline.attrib['logN'],
sig_clm=newline.attrib['sig_logN'],
flg_clm=newline.attrib['flag_N'],
Z=Zion[0],
ion=Zion[1])
# Append
lls.lines.append(newline)
# Fix NI, OI
ion_dict['O I']['clm'] = 14.47
ion_dict['O I']['sig_clm'] = 0.05
ion_dict['N I']['flg_clm'] = 3
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Jen05')
# Return
return lls
def meiring07():
"""Meiring et al. 2007, MNRAS, 376, 557
SLLS with Magellan
Abundances from Table 11 from astro-ph (LateX) by JXP [AODM]
RA/DEC from Table 1
"""
all_lls = []
# Table 1
tab_fil = pyigm_path+"/data/LLS/Literature/meiring07.tb1.ascii"
with open(tab_fil,'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
qso_dict = {}
for iline in flines1:
if iline[0:2] in ['QS','\h','$\\', 'J2']:
continue
# Parse
isplit = iline.split('&')
if '-' not in isplit[3]:
sgn = '+'
else:
sgn = ''
radec = isplit[2].strip()+sgn+isplit[3].strip()
radec = radec.replace(':','')
# zem
if isplit[0].strip() != 'Q0826-2230':
zem = float(isplit[5].strip())
else:
zem = 0.911
# Save
qso_dict[isplit[0].strip()] = dict(radec=radec, zem=zem,
vlim=[-500.,500]*u.km/u.s)
# Abundances (AODM)
# Table 11
tab_fil = pyigm_path+"/data/LLS/Literature/meiring07.tb11.ascii"
with open(tab_fil,'r') as f:
flines11 = f.readlines()
#
for iline in flines11:
if iline[0:2] in ['\h',' ']:
continue
# Parse
isplit = iline.split('&')
# Ions
if iline[0:2] == 'QS':
ioncs = []
Zions = []
for iis in isplit[3:-1]: # Skipping HI
# Parse
is2 = iis.split('\\')
ip2 = is2[2].find('}')
ionc = is2[1][2:].strip()+' '+is2[2][0:ip2].strip()
# Zion
Zion = ltai.name_ion(ionc)
# Append
ioncs.append(ionc)
Zions.append(Zion)
continue
if iline[0] == 'Q':
# QSO
qso = isplit[0].strip()
# zabs and name
zabs = float(isplit[1].strip())
qso_dict[qso]['name']=qso+'z_{:.3f}'.format(zabs)
qso_dict[qso]['zabs']=zabs
# NHI
is2 = isplit[2].strip()
qso_dict[qso]['NHI'] = float(is2[0:5])
#if qso_dict[qso]['NHI'] >= 20.3:
# print('Uh oh. DLA')
qso_dict[qso]['sig_NHI'] = np.array([float(is2[10:])]*2)
# Generate LLS
lls = LLSSystem(**qso_dict[qso])
continue
else:
# ADOM Columns
ion_dict = {}
for kk,iis in enumerate(isplit[3:-1]):
is2 = iis.strip()
if is2[0:3] == '$>$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,flg_clm=2,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif is2[0:3] == '$<$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,flg_clm=3,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif len(is2) == 0:
pass
else:
ion_dict[ioncs[kk]] = dict(flg_clm=1,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[0:5])
ion_dict[ioncs[kk]]['sig_clm'] = float(is2[10:])
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei07')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def insert_dlas(spec,
zem,
fNHI=None,
rstate=None,
slls=False,
mix=False,
high=False,
low_s2n=False,
noise_boost=4.):
""" Insert a DLA into input spectrum
Also adjusts the noise
Will also add noise 'everywhere' if requested
Parameters
----------
spec
fNHI
rstate
low_s2n : bool, optional
Reduce the S/N everywhere. By a factor of noise_boost
noise_boost : float, optional
Factor to *increase* the noise by
Returns
-------
final_spec : XSpectrum1D
dlas : list
List of DLAs inserted
"""
from pyigm.fN import dla as pyi_fd
from pyigm.abssys.dla import DLASystem
from pyigm.abssys.lls import LLSSystem
from pyigm.abssys.utils import hi_model
# Init
if rstate is None:
rstate = np.random.RandomState()
if fNHI is None:
fNHI = init_fNHI(slls=slls, mix=mix, high=high)
# Allowed redshift placement
## Cut on zem and 910A rest-frame
zlya = spec.wavelength.value / 1215.67 - 1
dz = np.roll(zlya, -1) - zlya
dz[-1] = dz[-2]
gdz = (zlya < zem) & (spec.wavelength > 910. * u.AA * (1 + zem))
# l(z) -- Uses DLA for SLLS too which is fine
lz = pyi_fd.lX(zlya[gdz], extrap=True, calc_lz=True)
cum_lz = np.cumsum(lz * dz[gdz])
tot_lz = cum_lz[-1]
fzdla = interpolate.interp1d(cum_lz / tot_lz,
zlya[gdz],
bounds_error=False,
fill_value=np.min(zlya[gdz])) #
# n DLA
nDLA = 0
while nDLA == 0:
nval = rstate.poisson(tot_lz, 100)
gdv = nval > 0
if np.sum(gdv) == 0:
continue
else:
nDLA = nval[np.where(gdv)[0][0]]
# Generate DLAs
dlas = []
for jj in range(nDLA):
# Random z
zabs = float(fzdla(rstate.random_sample()))
# Random NHI
NHI = float(fNHI(rstate.random_sample()))
if (slls or mix):
dla = LLSSystem((0., 0), zabs, None, NHI=NHI)
else:
dla = DLASystem((0., 0), zabs, (None, None), NHI)
dlas.append(dla)
# Insert
vmodel, _ = hi_model(dlas, spec, fwhm=3., llist=llist)
# Add noise
rand = rstate.randn(spec.npix)
noise = rand * spec.sig * (1 - vmodel.flux.value)
# More noise??
if low_s2n:
rand2 = rstate.randn(spec.npix)
more_noise = noise_boost * rand2 * spec.sig
noise += more_noise
else:
s2n_boost = 1.
final_spec = XSpectrum1D.from_tuple(
(vmodel.wavelength, spec.flux.value * vmodel.flux.value + noise,
noise_boost * spec.sig))
# Return
return final_spec, dlas
def tripp2005():
'''Tripp, T. et al. 2005, ApJ, 2005, 619, 714
PG 1216+069 (LLS in Virgo)
HST/STIS, FUSE
Metal columns parsed from Tables 2 and 3
Total NHI from damping wings
M/H from O/H
'''
# Grab ASCII files from ApJ
tab_fils = [pyigm_path+"/data/LLS/tripp2005.tb3.ascii",
pyigm_path+"/data/LLS/tripp2005.tb2.ascii"]
urls = ['http://iopscience.iop.org/0004-637X/619/2/714/fulltext/60797.tb3.txt',
'http://iopscience.iop.org/0004-637X/619/2/714/fulltext/60797.tb2.txt']
for jj,tab_fil in enumerate(tab_fils):
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = urls[jj]
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urllib2.urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '121920.9320+063838.476' # SIMBAD
lls = LLSSystem(name='PG1216+069_z0.006', radec=radec, zem=0.3313,
zabs=0.00632, vlim=[-100., 100.]*u.km/u.s, NHI=19.32, ZH=-1.6,
sig_NHI=np.array([0.03, 0.03]))
# Columns
# Start with Table 3 (VPFIT)
with open(tab_fils[0],'r') as f:
flines3 = f.readlines()
ion_dict = {}
for iline in flines3:
if (len(iline.strip()) == 0):
continue
isplit = iline.split('\t')
# Ion
flg = 2
if (len(isplit[0].strip()) > 0):# & (isplit[0][0] not in ['1','2']):
ipos = isplit[0].find('1')
ionc = isplit[0][0:ipos-1].strip()
try:
Zion = ltai.name_ion(ionc)
except KeyError:
pdb.set_trace()
flg = 1
# Column
csplit = isplit[3].split(' ')
clm = float(csplit[0])
sig = float(csplit[2])
if flg == 1:
ion_dict[ionc] = dict(logN=clm, sig_logN=sig, flag_N=1,
Z=Zion[0], ion=Zion[1])
else: # Add it in
tmp_dict = dict(logN=clm, sig_logN=sig, flag_N=1, Z=Zion[0],
ion=Zion[1])
flagN, logN, siglogN = ltaa.sum_logN(ion_dict[ionc], tmp_dict)
ion_dict[ionc]['logN'] = logN
ion_dict[ionc]['sig_logN'] = siglogN
ions = ion_dict.keys()
# Now Table 2 for the extras
with open(tab_fils[1],'r') as f:
flines2 = f.readlines()
# Trim the first 10 lines
flines2 = flines2[10:]
# Loop
for iline in flines2:
isplit = iline.split('\t')
#
ionc = isplit[0].strip()
if (len(ionc) == 0) or (ionc in ions):
continue
#
Zion = ltai.name_ion(ionc)
ion_dict[ionc] = dict(Z=Zion[0], ion=Zion[1], sig_logN=0.)
if isplit[4][0] == '<':
ion_dict[ionc]['logN'] = float(isplit[4][1:])
ion_dict[ionc]['flag_N'] = 3
else:
raise ValueError('Should not get here')
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Tri05')
return lls
def dessauges09():
'''Dessauges-Zavadsky et al. 2009, MNRAS, 396, L96
SLLS with UVES
Zn,Fe abundances from Table 1 from astro-ph (LateX) by JXP [AODM]
Taken from the Zn/H and Fe/H assuming *no* ionization corrections
RA/DEC from the 'other' name
'''
# Solar abundances
eZn = 4.63
eFe = 7.45
sol = [eFe, eZn]
#
all_lls = []
# Table 1
tab_fil = pyigm_path + "/data/LLS/Literature/dessauges09.tb1.ascii"
with open(tab_fil, 'r') as f:
flines1 = f.readlines()
# Trim the first few lines
flines1 = flines1[3:]
for iline in flines1:
# Parse
isplit = iline.split('&')
# QSO
if iline[0:2] == 'QS':
# QSO, RA/DEC, zem
qso = isplit[0][4:].strip()
radec = isplit[1].strip()[1:].replace('$', '')
zem = float(isplit[3].strip())
# NHI, zabs
zabs = float(isplit[4].strip())
is2 = isplit[6].strip()
NHI = float(is2[1:6])
sigNHI = np.array([float(is2[10:14])] * 2)
# name
name = qso + 'z_{:.3f}'.format(zabs)
lls = LLSSystem(name=name,
radec=radec,
vlim=[-500, 500] * u.km / u.s,
zem=zem,
zabs=zabs,
NHI=NHI,
sig_NHI=sigNHI)
# ADOM Columns
ion_dict = {}
for kk, ion in enumerate(['Fe II', 'Zn II']):
Zion = ltai.name_ion(ion)
is2 = isplit[7 + kk].strip()
if is2[0:2] == '$>':
ion_dict[ion] = dict(sig_clm=0.,
flg_clm=2,
Z=Zion[0],
ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[2:7]) + NHI - 12 + sol[kk]
elif is2[0:2] == '$<':
ion_dict[ion] = dict(sig_clm=0.,
flg_clm=3,
Z=Zion[0],
ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[2:7]) + NHI - 12 + sol[kk]
elif is2[0:2] == '..':
pass
else:
ion_dict[ion] = dict(flg_clm=1, Z=Zion[0], ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[1:6]) + NHI - 12 + sol[kk]
ion_dict[ion]['sig_clm'] = float(is2[10:14])
#xdb.set_trace()
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('DZ09')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def auto_plls(self,x,y):
'''Automatically fit a pLLS
Parameters:
----------
x,y: floats
x,y values in the GUI
'''
spec = self.spec_widg.spec # For convenience
if len(self.abssys_widg.all_abssys) > 0:
conti= self.full_model
else:
conti= self.continuum
# Generate toy LLS from click
ximn = np.argmin(np.abs(spec.dispersion.value-x))
NHI = 17.29 + np.log10(-1.*np.log(y/conti.flux.value[ximn]))
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
#print('NHI={:g}'.format(NHI))
z = x/(911.7)-1
plls = LLSSystem((0*u.deg,0*u.deg),z,[-300.,300]*u.km/u.s,NHI=NHI)
plls.bval = 20*u.km/u.s
plls.fill_lls_lines(bval=20*u.km/u.s, do_analysis=0)
# wrest, Tau model, flux
wrest = spec.dispersion/(1+plls.zabs)
tau = igmlls.tau_multi_lls(spec.dispersion,[plls])
emtau = np.exp(-1. * tau)
lls_flux = lsc.convolve_psf(emtau, 3.)
#xdb.xplot(wrest, lls_flux)
# zmin (next highest LLS or zem)
if len(self.abssys_widg.all_abssys) != 0:
zlls = [lls.zabs for lls in self.abssys_widg.all_abssys if lls.zabs > plls.zabs]
if len(zlls) == 0:
zmin = self.zqso+0.01
else:
zmin = np.min(np.array(zlls)) - 0.01
else:
zmin = self.zqso+0.01
# Pixels for analysis and rolling
# NEED TO CUT ON X-Shooter ARM
apix = np.where( (wrest > 914*u.AA) & #(spec.dispersion<5600*u.AA) &
(spec.dispersion<(1+zmin)*1026.*u.AA))[0] # Might go to Lyb
nroll = (np.argmin(np.abs(spec.dispersion-(911.7*u.AA*(1+zmin))))- # Extra 0.01 for bad z
np.argmin(np.abs(spec.dispersion-(911.7*u.AA*(1+plls.zabs)))))
# Require nroll does not exceed length of spectrum
if np.max(apix)+nroll > len(spec.dispersion):
nroll = len(spec.dispersion) - np.max(apix) - 1
gdpix = np.arange(np.min(apix)-nroll,np.max(apix)+nroll+1)
roll_flux = np.concatenate([np.ones(nroll),lls_flux[apix], np.ones(nroll)])
roll_msk = roll_flux < 0.7
# Generate data arrays
wave_pad = spec.dispersion[gdpix]
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
flux_pad = spec.flux[gdpix]
sig_pad = spec.sig[gdpix]
if len(self.abssys_widg.all_abssys) > 0:
conti_pad = conti.flux[gdpix]
else:
conti_pad = conti.flux[gdpix]
# Generate matricies
flux_matrix = np.zeros((len(roll_flux),nroll))
sig_matrix = np.zeros((len(roll_flux),nroll))
conti_matrix = np.zeros((len(roll_flux),nroll))
roll_matrix = np.zeros((len(roll_flux),nroll))
mask_matrix = np.zeros((len(roll_flux),nroll))
for kk in range(nroll):
roll_matrix[:,kk] = np.roll(roll_flux,kk)
mask_matrix[:,kk] = np.roll(roll_msk,kk)
flux_matrix[:,kk] = flux_pad
conti_matrix[:,kk] = conti_pad
sig_matrix[:,kk] = sig_pad
# Model -- Multiply by continuum
model = roll_matrix * conti_matrix
# Condition
idx = np.where( (model < (flux_matrix-sig_matrix*1.5)) & (mask_matrix==True))
bad_matrix = np.zeros((len(roll_flux),nroll))
bad_matrix[idx] = 1
# Sum on offsets and get redshift
bad = np.sum(bad_matrix,0)
ibest = np.argmin(bad)
zbest = spec.dispersion[ibest+ximn]/(911.7*u.AA)-1 # Quantity
# Add pLLS?
if bad[ibest] < 10:
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
self.add_LLS(zbest.value, bval=20.*u.km/u.s, NHI=NHI)
else:
print('No viable pLLS found with our criteria!')
def meiring09():
'''Meiring et al. 2009, MNRAS, 393, 1513
SLLS with Magellan
Abundances from Table 3 from astro-ph (LateX) by JXP [AODM]
RA/DEC from Table 1
'''
all_lls = []
# Table 1
tab_fil = pyigm_path+"/data/LLS/Literature/meiring09.tb1.ascii"
with open(tab_fil,'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
qso_dict = {}
for iline in flines1:
if iline[0:3] in [' QS','\hl','$\\c', ' J2', ' ']:
continue
# Parse
isplit = iline.split('&')
#xdb.set_trace()
if '$' in isplit[3].strip():
isplit[3] = '-'+(isplit[3].strip())[3:]
radec = isplit[2].strip()+isplit[3].strip()
radec = radec.replace(':','')
# zem
zem = float(isplit[5].strip())
# Save
qso_dict[isplit[0].strip()] = dict(radec=radec,zem=zem,
vlim=[-500,500.]*u.km/u.s)
# Abundances (AODM)
# Table 3
tab_fil = pyigm_path+"/data/LLS/Literature/meiring09.tb3.ascii"
with open(tab_fil,'r') as f:
flines3 = f.readlines()
#
for iline in flines3:
if iline[0:2] in ['\h',' ']:
continue
# Parse
isplit = iline.split('&')
# Ions
if iline[0:2] == 'QS':
ioncs = []
Zions = []
for iis in isplit[3:-1]: # Skipping HI
# Parse
#is2 = iis.split('\\')
#ip2 = is2[2].find('}')
ionc = iis.strip()
# Zion
Zion = ltai.name_ion(ionc)
# Append
ioncs.append(ionc)
Zions.append(Zion)
continue
if iline[0] == 'Q':
# QSO
qso = isplit[0].strip()
if qso[-1] in ['A','B','C']:
qso = qso[0:-1]
# zabs and name
zabs = float(isplit[1].strip())
qso_dict[qso]['name']=qso+'z_{:.3f}'.format(zabs)
qso_dict[qso]['zabs']=zabs
# NHI
is2 = isplit[2].strip()
if is2[0] == '$':
qso_dict[qso]['NHI'] = 99.99 # THROW OUT Q1436-0051B
qso_dict[qso]['sig_NHI'] = np.array([0.,0.])
else:
qso_dict[qso]['NHI'] = float(is2[0:5])
qso_dict[qso]['sig_NHI'] = np.array([float(is2[10:])]*2)
#if qso_dict[qso]['NHI'] >= 20.3:
# print('Uh oh. DLA')
# Generate LLS
lls = LLSSystem(**qso_dict[qso])
continue
else:
# ADOM Columns
ion_dict = {}
for kk,iis in enumerate(isplit[3:-1]):
is2 = iis.strip()
if is2[0:3] == '$>$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,flg_clm=2,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif is2[0:3] == '$<$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,flg_clm=3,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif len(is2) == 0:
pass
else:
ion_dict[ioncs[kk]] = dict(flg_clm=1,Z=Zions[kk][0],ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[0:5])
ion_dict[ioncs[kk]]['sig_clm'] = float(is2[10:])
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei09')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def __init__(self,
abssys_list,
parent=None,
only_one=False,
linelist=None,
no_buttons=False):
'''
only_one: bool, optional
Restrict to one selection at a time? [False]
no_buttons: bool, optional
Eliminate Refine/Reload buttons?
'''
super(AbsSysWidget, self).__init__(parent)
#if not status is None:
# self.statusBar = status
self.abssys_list = abssys_list
# Speeds things up
if linelist is None:
self.linelist = LineList('ISM')
else:
self.linelist = linelist
# Create the line list
list_label = QtGui.QLabel('Abs Systems:')
self.abslist_widget = QtGui.QListWidget(self)
if not only_one:
self.abslist_widget.setSelectionMode(
QtGui.QAbstractItemView.ExtendedSelection)
self.abslist_widget.addItem('None')
#self.abslist_widget.addItem('Test')
# Lists
self.abs_sys = []
self.items = []
self.all_items = []
self.all_abssys = []
for abssys_fil in self.abssys_list:
self.all_abssys.append(
LLSSystem.from_absid_fil(abssys_fil, linelist=self.linelist))
self.add_item(abssys_fil)
self.abslist_widget.setCurrentRow(0)
self.abslist_widget.itemSelectionChanged.connect(self.on_list_change)
# Layout
vbox = QtGui.QVBoxLayout()
vbox.addWidget(list_label)
# Buttons
if not no_buttons:
buttons = QtGui.QWidget()
self.refine_button = QtGui.QPushButton('Refine', self)
#self.refine_button.clicked.connect(self.refine) # CONNECTS TO A PARENT
reload_btn = QtGui.QPushButton('Reload', self)
reload_btn.clicked.connect(self.reload)
hbox1 = QtGui.QHBoxLayout()
hbox1.addWidget(self.refine_button)
hbox1.addWidget(reload_btn)
buttons.setLayout(hbox1)
vbox.addWidget(buttons)
vbox.addWidget(self.abslist_widget)
self.setLayout(vbox)
def jenkins2005():
"""Jenkins, E. et al. 2005, ApJ, 2005, 623, 767
PHL 1811
HST/STIS, FUSE
Metals parsed from Table 1
OI taken from text
Had to input error on columns by hand (JXP)
Total NHI from Lyman series. see Fig 3
M/H from O/H
"""
# Grab ASCII file from ApJ
tab_fil = pyigm_path+"/data/LLS/Literature/jenkins2005.tb1.ascii"
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = 'http://iopscience.iop.org/0004-637X/623/2/767/fulltext/61520.tb1.txt'
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urllib2.urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '215501.5152-092224.688' # SIMBAD
lls = LLSSystem(name='PHL1811_z0.081', radec=radec, zem=0.192,
zabs=0.080923, vlim=[-100., 100.]*u.km/u.s, NHI=17.98, ZH=-0.19,
sig_NHI=np.array([0.05,0.05]))
lls.lines = [] # Probably not used
# AbsLines
ism = LineList('ISM')
Nsig = {'C IV': 0.4, 'N II': 0.4, 'Si II': 0.05, 'Si IV': 0.25,
'S II': 0.2, 'Fe II': 0.12, 'H I': 0.05, 'S III': 0.06}
# Parse Table
with open(tab_fil,'r') as f:
flines = f.readlines()
ion_dict = {}
for iline in flines:
iline = iline.strip()
if (len(iline) == 0):
continue
# Split on tabs
isplit = iline.split('\t')
# Offset?
ioff = 0
if isplit[0][0] in ['1','2']:
ioff = -1
# Catch bad lines
if (isplit[1+ioff][0:6] in ['1442.0','1443.7','1120.9']): # Skip goofy CII line and CII*
continue
if len(isplit[2+ioff]) == 0:
continue
# Ion
if (len(isplit[0].strip()) > 0) & (isplit[0][0] not in ['1','2']):
ionc = isplit[0].strip()
try:
Zion = ltai.name_ion(ionc)
except KeyError:
pdb.set_trace()
# Generate the Line
try:
newline = AbsLine(float(isplit[2+ioff])*u.AA,linelist=ism, closest=True)
except ValueError:
pdb.set_trace()
newline.attrib['z'] = lls.zabs
# Spectrum
newline.analy['datafile'] = 'STIS' if 'S' in isplit[1] else 'FUSE'
# EW
try:
EWvals = isplit[4+ioff].split(' ')
except IndexError:
pdb.set_trace()
newline.attrib['EW'] = float(EWvals[0])*u.AA/1e3
newline.attrib['sig_EW'] = float(EWvals[2])*u.AA/1e3
newline.attrib['flag_EW'] = 1
if len(isplit) < (5+ioff+1):
continue
# Colm?
#xdb.set_trace()
newline.attrib['sig_logN'] = 0.
if (len(isplit[5+ioff].strip()) > 0) & (isplit[5+ioff].strip() != '\\ldots'):
if isplit[5+ioff][0] == '\\':
ipos = isplit[5+ioff].find(' ')
newline.attrib['logN'] = float(isplit[5+ioff][ipos+1:])
newline.attrib['flag_N'] = 2
elif isplit[5+ioff][0] == '<':
ipos = 0
newline.attrib['logN'] = float(isplit[5+ioff][ipos+1:])
newline.attrib['flag_N'] = 3
elif isplit[5+ioff][0] == '1':
try:
newline.attrib['logN'] = float(isplit[5+ioff][0:5])
except ValueError:
pdb.set_trace()
newline.attrib['flag_N'] = 1
try:
newline.attrib['sig_logN'] = Nsig[ionc]
except KeyError:
print('No error for {:s}'.format(ionc))
else:
raise ValueError('Bad character')
# ion_dict
ion_dict[ionc] = dict(clm=newline.attrib['logN'], sig_clm=newline.attrib['sig_logN'],
flg_clm=newline.attrib['flag_N'], Z=Zion[0], ion=Zion[1])
# Append
lls.lines.append(newline)
# Fix NI, OI
ion_dict['O I']['clm'] = 14.47
ion_dict['O I']['sig_clm'] = 0.05
ion_dict['N I']['flg_clm'] = 3
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Jen05')
# Return
return lls
def tumlinson11():
"""Tumlinson, J. et al. 2011, ApJ, 733, 111
J1009+0713
HST/COS
Metal columns parsed from Table 1
NHI from LL+Lyman series (uncertain)
"""
# Grab ASCII file from ApJ
tab_fil = pyigm_path+"/data/LLS/Literature/tumlinson11.tb1.ascii"
url = 'http://iopscience.iop.org/0004-637X/733/2/111/suppdata/apj388927t1_ascii.txt'
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urllib2.urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '100902.06+071343.8' # From paper
lls = LLSSystem(name='J1009+0713_z0.356', radec=radec, zem=0.456,
zabs=0.3558, vlim=[-200., 250.]*u.km/u.s, NHI=18.4,
sig_NHI=np.array([0.41,0.41]))
# Columns
# Start with Table 3 (VPFIT)
with open(tab_fil,'r') as f:
flines1 = f.readlines()
# Trim
flines1 = flines1[18:]
#
ion_dict = {}
line_dict = dict(OI='1302',OVI='1038',MgII='2803^b',SiII='1190',
CaII='3934',FeII='2586')
ion = None
for iline in flines1:
isplit = iline.split('\t')
if ion=='FeIII': # Last line
break
# Ion
is2 = isplit[0].split(' ')
ion = is2[0]+is2[1]
try:
gdl = line_dict[ion]
except:
pass
#print('Taking {:s}'.format(isplit[0]))
else:
if is2[2] != gdl:
continue
Zion = ltai.name_ion(ion)
ion_dict[ion] = dict(logN=0., sig_logN=0., flag_N=0, Z=Zion[0],ion=Zion[1])
# Combine components [could replace with SubSystems some day]
for iis in isplit[1:-1]:
# Upper limit
if (iis.strip()[0] == '<') & (ion_dict[ion]['flag_N']==0):
ion_dict[ion]['flag_N']=3
ion_dict[ion]['logN']=float(iis[1:])
elif (iis.strip()[0] == '>'): # Saturated
ion_dict[ion]['flag_N']=2
ion_dict[ion]['logN']=log_sum([ion_dict[ion]['logN'],float(iis[1:5])])
elif iis.strip()[0] in ['.','<']:
pass
else:
if ion_dict[ion]['flag_N']==2: # Add to saturated
ion_dict[ion]['logN']=log_sum([ion_dict[ion]['logN'],float(iis[0:4])])
else:
ion_dict[ion]['flag_N']=1
obj = dict(logN=float(iis[0:4]),sig_logN=float(iis[-4:]),
flag_N=1)
# Add
flag,N,sig = ltaa.sum_logN(ion_dict[ion],obj)
ion_dict[ion]['logN']=N
ion_dict[ion]['sig_logN']=sig
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Tum11')
return lls
def meiring07():
"""Meiring et al. 2007, MNRAS, 376, 557
SLLS with Magellan
Abundances from Table 11 from astro-ph (LateX) by JXP [AODM]
RA/DEC from Table 1
"""
all_lls = []
# Table 1
tab_fil = pyigm_path + "/data/LLS/Literature/meiring07.tb1.ascii"
with open(tab_fil, 'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
qso_dict = {}
for iline in flines1:
if iline[0:2] in ['QS', '\h', '$\\', 'J2']:
continue
# Parse
isplit = iline.split('&')
if '-' not in isplit[3]:
sgn = '+'
else:
sgn = ''
radec = isplit[2].strip() + sgn + isplit[3].strip()
radec = radec.replace(':', '')
# zem
if isplit[0].strip() != 'Q0826-2230':
zem = float(isplit[5].strip())
else:
zem = 0.911
# Save
qso_dict[isplit[0].strip()] = dict(radec=radec,
zem=zem,
vlim=[-500., 500] * u.km / u.s)
# Abundances (AODM)
# Table 11
tab_fil = pyigm_path + "/data/LLS/Literature/meiring07.tb11.ascii"
with open(tab_fil, 'r') as f:
flines11 = f.readlines()
#
for iline in flines11:
if iline[0:2] in ['\h', ' ']:
continue
# Parse
isplit = iline.split('&')
# Ions
if iline[0:2] == 'QS':
ioncs = []
Zions = []
for iis in isplit[3:-1]: # Skipping HI
# Parse
is2 = iis.split('\\')
ip2 = is2[2].find('}')
ionc = is2[1][2:].strip() + ' ' + is2[2][0:ip2].strip()
# Zion
Zion = ltai.name_ion(ionc)
# Append
ioncs.append(ionc)
Zions.append(Zion)
continue
if iline[0] == 'Q':
# QSO
qso = isplit[0].strip()
# zabs and name
zabs = float(isplit[1].strip())
qso_dict[qso]['name'] = qso + 'z_{:.3f}'.format(zabs)
qso_dict[qso]['zabs'] = zabs
# NHI
is2 = isplit[2].strip()
qso_dict[qso]['NHI'] = float(is2[0:5])
#if qso_dict[qso]['NHI'] >= 20.3:
# print('Uh oh. DLA')
qso_dict[qso]['sig_NHI'] = np.array([float(is2[10:])] * 2)
# Generate LLS
lls = LLSSystem(**qso_dict[qso])
continue
else:
# ADOM Columns
ion_dict = {}
for kk, iis in enumerate(isplit[3:-1]):
is2 = iis.strip()
if is2[0:3] == '$>$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,
flg_clm=2,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif is2[0:3] == '$<$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,
flg_clm=3,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif len(is2) == 0:
pass
else:
ion_dict[ioncs[kk]] = dict(flg_clm=1,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[0:5])
ion_dict[ioncs[kk]]['sig_clm'] = float(is2[10:])
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei07')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def dessauges09():
'''Dessauges-Zavadsky et al. 2009, MNRAS, 396, L96
SLLS with UVES
Zn,Fe abundances from Table 1 from astro-ph (LateX) by JXP [AODM]
Taken from the Zn/H and Fe/H assuming *no* ionization corrections
RA/DEC from the 'other' name
'''
# Solar abundances
eZn = 4.63
eFe = 7.45
sol = [eFe,eZn]
#
all_lls = []
# Table 1
tab_fil = pyigm_path+"/data/LLS/Literature/dessauges09.tb1.ascii"
with open(tab_fil,'r') as f:
flines1 = f.readlines()
# Trim the first few lines
flines1 = flines1[3:]
for iline in flines1:
# Parse
isplit = iline.split('&')
# QSO
if iline[0:2] == 'QS':
# QSO, RA/DEC, zem
qso = isplit[0][4:].strip()
radec = isplit[1].strip()[1:].replace('$','')
zem = float(isplit[3].strip())
# NHI, zabs
zabs = float(isplit[4].strip())
is2 = isplit[6].strip()
NHI = float(is2[1:6])
sigNHI = np.array([float(is2[10:14])]*2)
# name
name = qso+'z_{:.3f}'.format(zabs)
lls = LLSSystem(name=name, radec=radec, vlim=[-500,500]*u.km/u.s,
zem=zem, zabs=zabs, NHI=NHI, sig_NHI=sigNHI)
# ADOM Columns
ion_dict = {}
for kk,ion in enumerate(['Fe II','Zn II']):
Zion = ltai.name_ion(ion)
is2 = isplit[7+kk].strip()
if is2[0:2] == '$>':
ion_dict[ion] = dict(sig_clm=0.,flg_clm=2,Z=Zion[0],ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[2:7]) + NHI - 12 + sol[kk]
elif is2[0:2] == '$<':
ion_dict[ion] = dict(sig_clm=0.,flg_clm=3,Z=Zion[0],ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[2:7]) + NHI - 12 + sol[kk]
elif is2[0:2] == '..':
pass
else:
ion_dict[ion] = dict(flg_clm=1,Z=Zion[0],ion=Zion[1])
ion_dict[ion]['clm'] = float(is2[1:6]) + NHI - 12 + sol[kk]
ion_dict[ion]['sig_clm'] = float(is2[10:14])
#xdb.set_trace()
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('DZ09')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def meiring09():
'''Meiring et al. 2009, MNRAS, 393, 1513
SLLS with Magellan
Abundances from Table 3 from astro-ph (LateX) by JXP [AODM]
RA/DEC from Table 1
'''
all_lls = []
# Table 1
tab_fil = pyigm_path + "/data/LLS/Literature/meiring09.tb1.ascii"
with open(tab_fil, 'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
qso_dict = {}
for iline in flines1:
if iline[0:3] in [' QS', '\hl', '$\\c', ' J2', ' ']:
continue
# Parse
isplit = iline.split('&')
#xdb.set_trace()
if '$' in isplit[3].strip():
isplit[3] = '-' + (isplit[3].strip())[3:]
radec = isplit[2].strip() + isplit[3].strip()
radec = radec.replace(':', '')
# zem
zem = float(isplit[5].strip())
# Save
qso_dict[isplit[0].strip()] = dict(radec=radec,
zem=zem,
vlim=[-500, 500.] * u.km / u.s)
# Abundances (AODM)
# Table 3
tab_fil = pyigm_path + "/data/LLS/Literature/meiring09.tb3.ascii"
with open(tab_fil, 'r') as f:
flines3 = f.readlines()
#
for iline in flines3:
if iline[0:2] in ['\h', ' ']:
continue
# Parse
isplit = iline.split('&')
# Ions
if iline[0:2] == 'QS':
ioncs = []
Zions = []
for iis in isplit[3:-1]: # Skipping HI
# Parse
#is2 = iis.split('\\')
#ip2 = is2[2].find('}')
ionc = iis.strip()
# Zion
Zion = ltai.name_ion(ionc)
# Append
ioncs.append(ionc)
Zions.append(Zion)
continue
if iline[0] == 'Q':
# QSO
qso = isplit[0].strip()
if qso[-1] in ['A', 'B', 'C']:
qso = qso[0:-1]
# zabs and name
zabs = float(isplit[1].strip())
qso_dict[qso]['name'] = qso + 'z_{:.3f}'.format(zabs)
qso_dict[qso]['zabs'] = zabs
# NHI
is2 = isplit[2].strip()
if is2[0] == '$':
qso_dict[qso]['NHI'] = 99.99 # THROW OUT Q1436-0051B
qso_dict[qso]['sig_NHI'] = np.array([0., 0.])
else:
qso_dict[qso]['NHI'] = float(is2[0:5])
qso_dict[qso]['sig_NHI'] = np.array([float(is2[10:])] * 2)
#if qso_dict[qso]['NHI'] >= 20.3:
# print('Uh oh. DLA')
# Generate LLS
lls = LLSSystem(**qso_dict[qso])
continue
else:
# ADOM Columns
ion_dict = {}
for kk, iis in enumerate(isplit[3:-1]):
is2 = iis.strip()
if is2[0:3] == '$>$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,
flg_clm=2,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif is2[0:3] == '$<$':
ion_dict[ioncs[kk]] = dict(sig_clm=0.,
flg_clm=3,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[3:])
elif len(is2) == 0:
pass
else:
ion_dict[ioncs[kk]] = dict(flg_clm=1,
Z=Zions[kk][0],
ion=Zions[kk][1])
ion_dict[ioncs[kk]]['clm'] = float(is2[0:5])
ion_dict[ioncs[kk]]['sig_clm'] = float(is2[10:])
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Mei09')
all_lls.append(lls)
# Return SLLS only
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def auto_plls(self, x, y):
"""Automatically fit a pLLS
Parameters:
----------
x,y: floats
x,y values in the GUI
"""
spec = self.spec_widg.spec # For convenience
if len(self.abssys_widg.all_abssys) > 0:
conti = self.full_model
else:
conti = self.continuum
# Generate toy LLS from click
ximn = np.argmin(np.abs(spec.wavelength.value - x))
if y > conti.flux.value[ximn]:
print("Type F below the continuum fool!")
return
NHI = 17.29 + np.log10(-1. * np.log(y / conti.flux.value[ximn]))
#QtCore.pyqtRemoveInputHook()
#pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
#print('NHI={:g}'.format(NHI))
z = x / (911.7) - 1
plls = LLSSystem((0 * u.deg, 0 * u.deg),
z, [-300., 300] * u.km / u.s,
NHI=NHI)
plls.bval = 20 * u.km / u.s
plls.fill_lls_lines(bval=20 * u.km / u.s, do_analysis=0)
# wrest, Tau model, flux
wrest = spec.wavelength / (1 + plls.zabs)
tau = igmlls.tau_multi_lls(spec.wavelength, [plls])
emtau = np.exp(-1. * tau)
lls_flux = lsc.convolve_psf(emtau, 3.)
#xdb.xplot(wrest, lls_flux)
# zmin (next highest LLS or zem)
if len(self.abssys_widg.all_abssys) != 0:
zlls = [
lls.zabs for lls in self.abssys_widg.all_abssys
if lls.zabs > plls.zabs
]
if len(zlls) == 0:
zmin = self.zqso + 0.05
else:
zmin = np.min(np.array(zlls)) - 0.01
else:
zmin = self.zqso + 0.05
# Pixels for analysis and rolling
# NEED TO CUT ON X-Shooter ARM
apix = np.where((wrest > 914 * u.AA) & #(spec.wavelength<5600*u.AA) &
(spec.wavelength < (1 + zmin) * 1026. * u.AA))[
0] # Might go to Lyb
nroll = (
np.argmin(np.abs(spec.wavelength -
(911.7 * u.AA *
(1 + zmin)))) - # Extra 0.01 for bad z
np.argmin(
np.abs(spec.wavelength - (911.7 * u.AA * (1 + plls.zabs)))))
# Require nroll does not exceed length of spectrum
if np.max(apix) + nroll > len(spec.wavelength):
nroll = len(spec.wavelength) - np.max(apix) - 1
gdpix = np.arange(np.min(apix) - nroll, np.max(apix) + nroll + 1)
roll_flux = np.concatenate(
[np.ones(nroll), lls_flux[apix],
np.ones(nroll)])
roll_msk = roll_flux < 0.7
# Generate data arrays
wave_pad = spec.wavelength[gdpix]
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
flux_pad = spec.flux[gdpix]
sig_pad = spec.sig[gdpix]
if len(self.abssys_widg.all_abssys) > 0:
conti_pad = conti.flux[gdpix]
else:
conti_pad = conti.flux[gdpix]
# Generate matricies
flux_matrix = np.zeros((len(roll_flux), nroll))
sig_matrix = np.zeros((len(roll_flux), nroll))
conti_matrix = np.zeros((len(roll_flux), nroll))
roll_matrix = np.zeros((len(roll_flux), nroll))
mask_matrix = np.zeros((len(roll_flux), nroll))
for kk in range(nroll):
roll_matrix[:, kk] = np.roll(roll_flux, kk)
mask_matrix[:, kk] = np.roll(roll_msk, kk)
flux_matrix[:, kk] = flux_pad
conti_matrix[:, kk] = conti_pad
sig_matrix[:, kk] = sig_pad
# Model -- Multiply by continuum
model = roll_matrix * conti_matrix
# Condition
idx = np.where((model < (flux_matrix - sig_matrix * 1.5))
& (mask_matrix == True))
bad_matrix = np.zeros((len(roll_flux), nroll))
bad_matrix[idx] = 1
# Sum on offsets and get redshift
bad = np.sum(bad_matrix, 0)
ibest = np.argmin(bad)
zbest = spec.wavelength[ibest + ximn] / (911.7 * u.AA) - 1 # Quantity
# Add pLLS?
if bad[ibest] < 10:
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
self.add_LLS(zbest.value, bval=20. * u.km / u.s, NHI=NHI)
else:
print('No viable pLLS found with our criteria!')
def tumlinson11():
"""Tumlinson, J. et al. 2011, ApJ, 733, 111
J1009+0713
HST/COS
Metal columns parsed from Table 1
NHI from LL+Lyman series (uncertain)
"""
# Grab ASCII file from ApJ
tab_fil = pyigm_path + "/data/LLS/Literature/tumlinson11.tb1.ascii"
url = 'http://iopscience.iop.org/0004-637X/733/2/111/suppdata/apj388927t1_ascii.txt'
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# Setup
radec = '100902.06+071343.8' # From paper
lls = LLSSystem(name='J1009+0713_z0.356',
radec=radec,
zem=0.456,
zabs=0.3558,
vlim=[-200., 250.] * u.km / u.s,
NHI=18.4,
sig_NHI=np.array([0.41, 0.41]))
# Columns
# Start with Table 3 (VPFIT)
with open(tab_fil, 'r') as f:
flines1 = f.readlines()
# Trim
flines1 = flines1[18:]
#
ion_dict = {}
line_dict = dict(OI='1302',
OVI='1038',
MgII='2803^b',
SiII='1190',
CaII='3934',
FeII='2586')
ion = None
for iline in flines1:
isplit = iline.split('\t')
if ion == 'FeIII': # Last line
break
# Ion
is2 = isplit[0].split(' ')
ion = is2[0] + is2[1]
try:
gdl = line_dict[ion]
except:
pass
#print('Taking {:s}'.format(isplit[0]))
else:
if is2[2] != gdl:
continue
Zion = ltai.name_ion(ion)
ion_dict[ion] = dict(logN=0.,
sig_logN=0.,
flag_N=0,
Z=Zion[0],
ion=Zion[1])
# Combine components [could replace with SubSystems some day]
for iis in isplit[1:-1]:
# Upper limit
if (iis.strip()[0] == '<') & (ion_dict[ion]['flag_N'] == 0):
ion_dict[ion]['flag_N'] = 3
ion_dict[ion]['logN'] = float(iis[1:])
elif (iis.strip()[0] == '>'): # Saturated
ion_dict[ion]['flag_N'] = 2
ion_dict[ion]['logN'] = log_sum(
[ion_dict[ion]['logN'],
float(iis[1:5])])
elif iis.strip()[0] in ['.', '<']:
pass
else:
if ion_dict[ion]['flag_N'] == 2: # Add to saturated
ion_dict[ion]['logN'] = log_sum(
[ion_dict[ion]['logN'],
float(iis[0:4])])
else:
ion_dict[ion]['flag_N'] = 1
obj = dict(logN=float(iis[0:4]),
sig_logN=float(iis[-4:]),
flag_N=1)
# Add
flag, N, sig = ltaa.sum_logN(ion_dict[ion], obj)
ion_dict[ion]['logN'] = N
ion_dict[ion]['sig_logN'] = sig
# Finish
lls._ionN = pyiau.dict_to_ions(ion_dict)
lls.Refs.append('Tum11')
return lls
def load_ml_file(pred_file):
""" Load the search results from the CNN into a DLASurvey object
Parameters
----------
pred_file
Returns
-------
ml_llssurvey: LLSSurvey
ml_dlasusrvey: DLASurvey
"""
print("Loading {:s}. Please be patient..".format(pred_file))
# Read
ml_results = ltu.loadjson(pred_file)
use_platef = False
if 'plate' in ml_results[0].keys():
use_platef = True
else:
if 'id' in ml_results[0].keys():
use_id = True
# Init
idict = dict(ra=[], dec=[], plate=[], fiber=[])
if use_platef:
for key in ['plate', 'fiber', 'mjd']:
idict[key] = []
dlasystems = []
llssystems = []
# Generate coords to speed things up
for obj in ml_results:
for key in ['ra', 'dec']:
idict[key].append(obj[key])
ml_coords = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
ra_names = ml_coords.icrs.ra.to_string(unit=u.hour, sep='', pad=True)
dec_names = ml_coords.icrs.dec.to_string(sep='', pad=True, alwayssign=True)
vlim = [-500., 500.] * u.km / u.s
dcoord = SkyCoord(ra=0., dec=0., unit='deg')
# Loop on list
didx, lidx = [], []
print("Looping on sightlines..")
for tt, obj in enumerate(ml_results):
#if (tt % 100) == 0:
# print('tt: {:d}'.format(tt))
# Sightline
if use_id:
plate, fiber = [int(spl) for spl in obj['id'].split('-')]
idict['plate'].append(plate)
idict['fiber'].append(fiber)
# Systems
for ss, syskey in enumerate(['dlas', 'subdlas']):
for idla in obj[syskey]:
name = 'J{:s}{:s}_z{:.3f}'.format(ra_names[tt], dec_names[tt],
idla['z_dla'])
if ss == 0:
isys = DLASystem(dcoord,
idla['z_dla'],
vlim,
NHI=idla['column_density'],
zem=obj['z_qso'],
name=name)
else:
isys = LLSSystem(dcoord,
idla['z_dla'],
vlim,
NHI=idla['column_density'],
zem=obj['z_qso'],
name=name)
isys.confidence = idla['dla_confidence']
isys.s2n = idla['s2n']
if use_platef:
isys.plate = obj['plate']
isys.fiber = obj['fiber']
elif use_id:
isys.plate = plate
isys.fiber = fiber
# Save
if ss == 0:
didx.append(tt)
dlasystems.append(isys)
else:
lidx.append(tt)
llssystems.append(isys)
# Generate sightline tables
sightlines = Table()
sightlines['RA'] = idict['ra']
sightlines['DEC'] = idict['dec']
sightlines['PLATE'] = idict['plate']
sightlines['FIBERID'] = idict['fiber']
# Surveys
ml_llssurvey = LLSSurvey()
ml_llssurvey.sightlines = sightlines.copy()
ml_llssurvey._abs_sys = llssystems
ml_llssurvey.coords = ml_coords[np.array(lidx)]
ml_dlasurvey = DLASurvey()
ml_dlasurvey.sightlines = sightlines.copy()
ml_dlasurvey._abs_sys = dlasystems
ml_dlasurvey.coords = ml_coords[np.array(didx)]
# Return
return ml_llssurvey, ml_dlasurvey
def battisti12():
'''Battisti, A. et al. 2012, ApJ, 744, 93
HST/COS
QSO info from Table 1
Metal columns parsed from Table 3
NHI from Lya
'''
all_lls = []
# Grab ASCII files from ApJ
tab_fils = [pyigm_path+"/data/LLS/Literature/battisti12.tb1.ascii",
pyigm_path+"/data/LLS/Literature/battisti12.tb3.ascii"]
urls = ['http://iopscience.iop.org/0004-637X/744/2/93/suppdata/apj413924t1_ascii.txt',
'http://iopscience.iop.org/0004-637X/744/2/93/suppdata/apj413924t3_ascii.txt']
for jj,tab_fil in enumerate(tab_fils):
chk_fil = glob.glob(tab_fil)
if len(chk_fil) > 0:
tab_fil = chk_fil[0]
else:
url = urls[jj]
print('LLSSurvey: Grabbing table file from {:s}'.format(url))
f = urllib2.urlopen(url)
with open(tab_fil, "wb") as code:
code.write(f.read())
# QSO info
with open(tab_fils[0],'r') as f:
flines1 = f.readlines()
# Grab RA/DEC
all_idict = []
for iline in flines1:
if iline[0:2] != 'SD':
continue
# Parse
isplit = iline.split('\t')
name = isplit[0].split(' ')[1]
radec = name[1:]
zem = float(isplit[1].strip())
zabs = float(isplit[2].strip())
NHI = float(isplit[3].strip()[0:4])
sigNHI = np.array([float(isplit[3].strip()[11:])]*2)
# Save
lls = LLSSystem(name=name,radec=radec,zem=zem,
zabs=zabs,NHI=NHI,sig_NHI=sigNHI, vlim=[-500,500]*u.km/u.s)
#
all_lls.append(lls)
all_idict.append({})
# Abundances
with open(tab_fils[1],'r') as f:
flines3 = f.readlines()
flines3 = flines3[5:]
ion = None
for iline in flines3:
if ion == 'Ni II':
break
isplit = iline.split('\t')
if isplit[0] == 'C II*': # Skipping CII*
continue
# ion
ipos = -1
while (isplit[0][ipos] not in ['I','V']):
ipos -= 1
ion = isplit[0][0:ipos+1+len(isplit[0])]
Zion = ltai.name_ion(ion)
# Loop on systems
for kk,iis in enumerate(isplit[1:-1]):
if iis.strip()[0] == '.':
continue
all_idict[kk][ion] = dict(Z=Zion[0], ion=Zion[1],sig_clm=0.)
if iis[0] == '>':
all_idict[kk][ion]['flg_clm'] = 2
all_idict[kk][ion]['clm'] = float(iis[1:6])
elif iis[0] == '<':
all_idict[kk][ion]['flg_clm'] = 3
all_idict[kk][ion]['clm'] = float(iis[1:])
else:
all_idict[kk][ion]['flg_clm'] = 1
all_idict[kk][ion]['clm'] = float(iis[0:5])
all_idict[kk][ion]['sig_clm'] = float(iis[-4:])
# Return SLLS only
for kk,lls in enumerate(all_lls):
try:
lls._ionN = pyiau.dict_to_ions(all_idict[kk])
except ValueError:
pdb.set_trace()
lls.Refs.append('Bat12')
fin_slls = [ills for ills in all_lls if ills.NHI < 20.3]
return fin_slls
def json_to_sdss_dlasurvey(json_file, sdss_survey, add_pf=True, debug=False):
""" Convert JSON output file to a DLASurvey object
Assumes SDSS bookkeeping for sightlines (i.e. PLATE, FIBER)
Parameters
----------
json_file : str
Full path to the JSON results file
sdss_survey : DLASurvey
SDSS survey, usually human (e.g. JXP for DR5)
add_pf : bool, optional
Add plate/fiber to DLAs in sdss_survey
Returns
-------
ml_survey : LLSSurvey
Survey object for the LLS
"""
print("Loading SDSS Survey from JSON file {:s}".format(json_file))
# imports
from pyigm.abssys.dla import DLASystem
from pyigm.abssys.lls import LLSSystem
# Fiber key
for fkey in ['FIBER', 'FIBER_ID', 'FIB']:
if fkey in sdss_survey.sightlines.keys():
break
# Read
ml_results = ltu.loadjson(json_file)
use_platef = False
if 'plate' in ml_results[0].keys():
use_platef = True
else:
if 'id' in ml_results[0].keys():
use_id = True
# Init
#idict = dict(plate=[], fiber=[], classification_confidence=[], # FOR v2
# classification=[], ra=[], dec=[])
idict = dict(ra=[], dec=[])
if use_platef:
for key in ['plate', 'fiber', 'mjd']:
idict[key] = []
ml_tbl = Table()
ml_survey = LLSSurvey()
systems = []
in_ml = np.array([False]*len(sdss_survey.sightlines))
# Loop
for obj in ml_results:
# Sightline
for key in idict.keys():
idict[key].append(obj[key])
# DLAs
#if debug:
# if (obj['plate'] == 1366) & (obj['fiber'] == 614):
# sv_coord = SkyCoord(ra=obj['ra'], dec=obj['dec'], unit='deg')
# print("GOT A MATCH IN RESULTS FILE")
for idla in obj['dlas']:
"""
dla = DLASystem((sdss_survey.sightlines['RA'][mt[0]],
sdss_survey.sightlines['DEC'][mt[0]]),
idla['spectrum']/(1215.6701)-1., None,
idla['column_density'])
"""
if idla['z_dla'] < 1.8:
continue
isys = LLSSystem((obj['ra'],obj['dec']),
idla['z_dla'], None, NHI=idla['column_density'], zem=obj['z_qso'])
isys.confidence = idla['dla_confidence']
if use_platef:
isys.plate = obj['plate']
isys.fiber = obj['fiber']
elif use_id:
plate, fiber = [int(spl) for spl in obj['id'].split('-')]
isys.plate = plate
isys.fiber = fiber
# Save
systems.append(isys)
# Connect to sightlines
ml_coord = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
s_coord = SkyCoord(ra=sdss_survey.sightlines['RA'], dec=sdss_survey.sightlines['DEC'], unit='deg')
idx, d2d, d3d = match_coordinates_sky(s_coord, ml_coord, nthneighbor=1)
used = d2d < 1.*u.arcsec
for iidx in np.where(~used)[0]:
print("Sightline RA={:g}, DEC={:g} was not used".format(sdss_survey.sightlines['RA'][iidx],
sdss_survey.sightlines['DEC'][iidx]))
# Add plate/fiber to statistical DLAs
if add_pf:
dla_coord = sdss_survey.coord
idx2, d2d, d3d = match_coordinates_sky(dla_coord, s_coord, nthneighbor=1)
if np.min(d2d.to('arcsec').value) > 1.:
raise ValueError("Bad match to sightlines")
for jj,igd in enumerate(np.where(sdss_survey.mask)[0]):
dla = sdss_survey._abs_sys[igd]
try:
dla.plate = sdss_survey.sightlines['PLATE'][idx2[jj]]
except IndexError:
pdb.set_trace()
dla.fiber = sdss_survey.sightlines[fkey][idx2[jj]]
# Finish
ml_survey._abs_sys = systems
if debug:
ml2_coord = ml_survey.coord
minsep = np.min(sv_coord.separation(ml2_coord))
minsep2 = np.min(sv_coord.separation(s_coord))
tmp = sdss_survey.sightlines[used]
t_coord = SkyCoord(ra=tmp['RA'], dec=tmp['DEC'], unit='deg')
minsep3 = np.min(sv_coord.separation(t_coord))
pdb.set_trace()
ml_survey.sightlines = sdss_survey.sightlines[used]
for key in idict.keys():
ml_tbl[key] = idict[key]
ml_survey.ml_tbl = ml_tbl
# Return
return ml_survey
