def fill_lls_lines(self, bval=20.):
"""
Generate an HI line list for an LLS.
Goes into self.lls_lines
Parameters
----------
bval : float (20.) Doppler parameter in km/s
"""
from barak import absorb as ba
atom = ba.readatom()
self.lls_lines = []
for line in atom['HI']:
tmp = abs_line.Abs_Line(line['wa']*u.AA,fill=False)
# Atomic data
tmp.atomic = {'fval': line['osc'], 'gamma': line['gam'],
'name': 'HI %s' % line['wa'], 'wrest': line['wa']}
tmp.name = tmp.atomic['name']
# Attributes
tmp.attrib['N'] = self.NHI
tmp.attrib['b'] = bval
tmp.z = self.zabs
self.lls_lines.append(tmp)
from barak.pyvpfit import readf26
from barak.absorb import findtrans, readatom, find_tau
from barak.constants import c_kms
from barak.sed import make_constant_dv_wa_scale
from barak.convolve import convolve_constant_dv
from COS import convolve_with_COS_FOS
import barak.spec
from barak.utilities import between, adict
from barak.plot import puttext
import pylab as pl
import numpy as np
import matplotlib.transforms as mtransforms
atomdat = readatom(molecules=1)
def get_fig_axes(nrows, ncols, npar, width=11.7, height=None, aspect=0.5):
""" Generate a figure with a number of subplots.
Parameters
----------
nrows : int
Number of rows
ncols : int
Number of columns
npar : int
Number of plots in total
width : float
Width of the figure in inches
aspect : float
width / height of each sub-plot.
def mk_line_list_fits_table(outfil=None,XIDL=False):
from barak import absorb as ba
if XIDL is True:
lindat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/grb.lst'
finedat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/fine_strct.lst'
else:
lindat = 'grb.lst' # This pulls from xastropy/data/spec_lines first
finedat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/fine_strct.lst'
# Read XIDL line list
llist = Abs_Line_List(lindat)
ndata = len(llist.data)
# Add columns
from astropy.table import Column
gamma = Column(np.zeros(ndata),name='gamma')
A = Column(np.zeros(ndata),name='A') # Einstein coefficient
j = Column(np.zeros(ndata),name='j') # Tot ang mom (z projection)
Ex = Column(np.zeros(ndata),name='Ex') # Excitation energy (cm^-1)
Elow = Column(np.zeros(ndata),name='Elow') # Energy of lower level
Eup = Column(np.zeros(ndata),name='Eup') # Energy of upper level
Z = Column(np.zeros(ndata,dtype='int'),name='Z') # Atomic number
ion = Column(np.zeros(ndata,dtype='int'),name='ion') # Ionic state
llist.data.add_columns([gamma,A,j,Ex,Elow,Eup,Z,ion])
# Z,ion
for ii in range(ndata):
nm = llist.data['name'][ii]
# Z
if nm[1] == 'I' or nm[1] == 'V':
ielm = 1
else:
ielm = 2
elm = nm[:ielm]
try:
Zv = ELEMENTS[elm].number
except KeyError:
if elm in ['CO','CC','HH']: # Molecules
Zv = 999
elif elm in ['D']: # Deuterium
Zv = 1
else:
xdb.set_trace()
llist.data['Z'][ii] = Zv
# ion
ispc = nm.find(' ')
cion = nm[ielm:ispc].strip('*')
if len(cion) == 0:
ionv =0
else:
ionv = roman.fromRoman(cion)
llist.data['ion'][ii] = ionv
# #######
# Fill in matches
# Read atom.dat using barak
atom, atomflat = ba.readatom(flat=True)
#pdb.set_trace()
llist.sources.append('atom.dat') # I wish I could pull this from ba.readatom
# Fine structure
fdata = ascii.read(finedat)
llist.sources.append(finedat)
# Loop
for ii in range(ndata):
# Atom.dat
mt = np.where(np.fabs(llist.data['wrest'][ii]-atomflat['wa']) < 1e-3)[0]
if len(mt) > 0: llist.data['gamma'][ii] = atomflat['gam'][mt[0]] # Takes the first match
# Fine structure
mt = np.where(np.fabs(llist.data['wrest'][ii]-fdata['wrest']) < 1e-3)[0]
if len(mt) > 0:
llist.data['A'][ii] = fdata['A'][mt[0]] # Takes the first match
# Output file
if outfil is None:
outfil = xa_path+'/data/atomic/spec_atomic_lines.fits'
# Header
'''
prihdr = fits.Header()
prihdr['COMMENT'] = "Above are the data sources"
for ii in range(len(llist.sources)):
card = 'SOURCE'+str(ii+1)
prihdr[card] = llist.sources[ii]
prihdu = fits.PrimaryHDU(header=prihdr)
# Table
table_hdu = fits.BinTableHDU.from_columns(np.array(llist.data.filled()))
'''
# Write
llist.data.write(outfil, overwrite=True, format='fits')
print('mk_line_list: Wrote {:s}'.format(outfil))
def process_options(args):
opt = adict()
filename = os.path.abspath(__file__).rsplit('/', 1)[0] + '/default.cfg'
opt = parse_config(filename)
if os.path.lexists('./plot.cfg'):
opt = parse_config('./plot.cfg', opt)
opt.atom = readatom(molecules=True)
if opt.Rfwhm is not None:
if isinstance(opt.Rfwhm, basestring):
if opt.Rfwhm == 'convolve_with_COS_FOS':
if convolve_with_COS_FOS is None:
raise ValueError('convolve_with_COS_FOS() not available')
print('Using tailored FWHM for COS/FOS data')
opt.Rfwhm = 'convolve_with_COS_FOS'
elif opt.Rfwhm.endswith('fits'):
print('Reading Resolution FWHM from', opt.Rfwhm)
res = readtabfits(opt.Rfwhm)
opt.Rfwhm = res.res / 2.354
else:
print('Reading Resolution FWHM from', opt.Rfwhm)
fh = open(opt.Rfwhm)
opt.Rfwhm = 1 / 2.354 * np.array([float(r) for r in fh])
fh.close()
else:
opt.Rfwhm = float(opt.Rfwhm)
if opt.features is not None:
print('Reading feature list from', opt.features)
opt.features = readtabfits(opt.features)
if opt.f26 is not None:
name = opt.f26
print('Reading ions and fitting regions from', name)
opt.f26 = readf26(name)
opt.f26.filename = name
if opt.transitions is not None:
print('Reading transitions from', opt.transitions)
fh = open(opt.transitions)
trans = list(fh)
fh.close()
temp = []
for tr in trans:
tr = tr.strip()
if tr and not tr.startswith('#'):
junk = tr.split()
tr = junk[0] + ' ' + junk[1]
t = findtrans(tr, atomdat=opt.atom)
temp.append(dict(name=t[0], wa=t[1][0], tr=t[1]))
opt.linelist = temp
else:
opt.linelist = readtxt(get_data_path() + 'linelists/qsoabs_lines',
names='wa,name,select')
if opt.f26 is None and opt.taulines is not None:
print('Reading ions from', opt.taulines)
fh = open(opt.taulines)
lines = []
for row in fh:
if row.lstrip().startswith('#'):
continue
items = row.split()
lines.append([items[0]] + list(map(float, items[1:])))
fh.close()
opt.lines = lines
if opt.show_regions is None:
opt.show_regions = True
if hasattr(opt, 'aodname'):
opt.aod = Table.read(opt.aodname)
return opt
import barak.spec
from barak.plot import A4PORTRAIT
from barak.utilities import indexnear, stats, between, adict
from barak.convolve import convolve_psf
from barak.absorb import calc_Wr, findtrans, \
calc_iontau, guess_logN_b, calc_N_AOD, readatom, split_trans_name
from barak.pyvpfit import readf26
from plotspec.utils import \
process_options, plot_velocity_regions, process_Rfwhm, plot_tick_vel, \
ATMOS, lines_from_f26
# expand continuum adjsutments this many Ang either side of the
# fitting region.
ATOMDAT = readatom()
HIwavs = ATOMDAT['HI']['wa'][::-1]
expand_cont_adjustment = 0
np.seterr(divide='ignore', invalid='ignore')
unrelated = []
# ( 3863.451, 3865.529),
# ( 3855.399, 3859.330),
# ( 4075.662, 4076.668),
# ( 4079.430, 4082.960),
# ( 3906.116, 3908.287),
# ( 3898.097, 3899.218),
# ( 4509.955, 4512.281),
# ( 4503.099, 4507.387),
# ( 4532.218, 4544.106),
def mk_line_list_fits_table(outfil=None,XIDL=False):
from barak import absorb as ba
if XIDL is True:
lindat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/grb.lst'
finedat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/fine_strct.lst'
else:
lindat = 'grb.lst' # This pulls from xastropy/data/spec_lines first
finedat = os.getenv('XIDL_DIR')+'/Spec/Lines/Lists/fine_strct.lst'
# Read XIDL line list
llist = Abs_Line_List(lindat)
ndata = len(llist.data)
# Add columns
from astropy.table import Column
gamma = Column(np.zeros(ndata),name='gamma')
A = Column(np.zeros(ndata),name='A') # Einstein coefficient
j = Column(np.zeros(ndata),name='j') # Tot ang mom (z projection)
Ex = Column(np.zeros(ndata),name='Ex') # Excitation energy (cm^-1)
Elow = Column(np.zeros(ndata),name='Elow') # Energy of lower level
Eup = Column(np.zeros(ndata),name='Eup') # Energy of upper level
Z = Column(np.zeros(ndata,dtype='int'),name='Z') # Atomic number
ion = Column(np.zeros(ndata,dtype='int'),name='ion') # Ionic state
llist.data.add_columns([gamma,A,j,Ex,Elow,Eup,Z,ion])
# Z,ion
for ii in range(ndata):
nm = llist.data['name'][ii]
# Z
if nm[1] == 'I' or nm[1] == 'V':
ielm = 1
else:
ielm = 2
elm = nm[:ielm]
try:
Zv = ELEMENTS[elm].number
except KeyError:
if elm in ['CO','CC','HH']: # Molecules
Zv = 999
elif elm in ['D']: # Deuterium
Zv = 1
else:
xdb.set_trace()
llist.data['Z'][ii] = Zv
# ion
ispc = nm.find(' ')
cion = nm[ielm:ispc].strip('*')
if len(cion) == 0:
ionv =0
else:
ionv = roman.fromRoman(cion)
llist.data['ion'][ii] = ionv
# #######
# Fill in matches
# Read atom.dat using barak
atom, atomflat = ba.readatom(flat=True)
#pdb.set_trace()
llist.sources.append('atom.dat') # I wish I could pull this from ba.readatom
# Fine structure
fdata = ascii.read(finedat)
llist.sources.append(finedat)
# Loop
for ii in range(ndata):
# Atom.dat
mt = np.where(np.fabs(llist.data['wrest'][ii]-atomflat['wa']) < 1e-3)[0]
if len(mt) > 0: llist.data['gamma'][ii] = atomflat['gam'][mt[0]] # Takes the first match
# Fine structure
mt = np.where(np.fabs(llist.data['wrest'][ii]-fdata['wrest']) < 1e-3)[0]
if len(mt) > 0:
llist.data['A'][ii] = fdata['A'][mt[0]] # Takes the first match
# Output file
if outfil is None:
outfil = xa_path+'/data/atomic/spec_atomic_lines.fits'
# Header
'''
prihdr = fits.Header()
prihdr['COMMENT'] = "Above are the data sources"
for ii in range(len(llist.sources)):
card = 'SOURCE'+str(ii+1)
prihdr[card] = llist.sources[ii]
prihdu = fits.PrimaryHDU(header=prihdr)
# Table
table_hdu = fits.BinTableHDU.from_columns(np.array(llist.data.filled()))
'''
# Write
llist.data.write(outfil, overwrite=True, format='fits')
print('mk_line_list: Wrote {:s}'.format(outfil))
from barak.io import writetxt
import pylab as pl
pl.rc('xtick', labelsize=11)
pl.rc('xtick.major', size=3, pad=5)
pl.rc('xtick.minor', size=1.5)
pl.rc('ytick', labelsize=11)
pl.rc('ytick.major', size=3, pad=4)
pl.rc('ytick.minor', size=1.5)
# divide wavelength pixels into this many sub-pixels
nwadiv = 10
# cache for spectra
SPECTRA = {}
atom = readatom(molecules=True)
options = {}
options['wa_vary'] = True
###################################################################
# Read initial model parameters along with fitting regions from vpfit
# f26-style file
###################################################################
def readf26file(filename):
""" Read a f26 style file, and return with regions sorted by
minimum wavelength.
"""
vpin = readf26(filename)
isort = np.argsort([r.wmin for r in vpin.regions])
def process_options(args):
opt = adict()
filename = os.path.abspath(__file__).rsplit('/', 1)[0] + '/default.cfg'
opt = parse_config(filename)
if os.path.lexists('./plot.cfg'):
opt = parse_config('./plot.cfg', opt)
opt.atom = readatom(molecules=True)
if opt.Rfwhm is not None:
if isinstance(opt.Rfwhm, basestring):
if opt.Rfwhm == 'convolve_with_COS_FOS':
if convolve_with_COS_FOS is None:
raise ValueError('convolve_with_COS_FOS() not available')
print('Using tailored FWHM for COS/FOS data')
opt.Rfwhm = 'convolve_with_COS_FOS'
elif opt.Rfwhm.endswith('fits'):
print('Reading Resolution FWHM from', opt.Rfwhm)
res = readtabfits(opt.Rfwhm)
opt.Rfwhm = res.res / 2.354
else:
print('Reading Resolution FWHM from', opt.Rfwhm)
fh = open(opt.Rfwhm)
opt.Rfwhm = 1 / 2.354 * np.array([float(r) for r in fh])
fh.close()
else:
opt.Rfwhm = float(opt.Rfwhm)
if opt.features is not None:
print('Reading feature list from', opt.features)
opt.features = readtabfits(opt.features)
if opt.f26 is not None:
name = opt.f26
print('Reading ions and fitting regions from', name)
opt.f26 = readf26(name)
opt.f26.filename = name
if opt.transitions is not None:
print('Reading transitions from', opt.transitions)
fh = open(opt.transitions)
trans = list(fh)
fh.close()
temp = []
for tr in trans:
tr = tr.strip()
if tr and not tr.startswith('#'):
junk = tr.split()
tr = junk[0] + ' ' + junk[1]
t = findtrans(tr, atomdat=opt.atom)
temp.append(dict(name=t[0], wa=t[1][0], tr=t[1]))
opt.linelist = temp
else:
opt.linelist = readtxt(get_data_path() + 'linelists/qsoabs_lines',
names='wa,name,select')
if opt.f26 is None and opt.taulines is not None:
print('Reading ions from', opt.taulines)
fh = open(opt.taulines)
lines = []
for row in fh:
if row.lstrip().startswith('#'):
continue
items = row.split()
lines.append([items[0]] + list(map(float, items[1:])))
fh.close()
opt.lines = lines
if opt.show_regions is None:
opt.show_regions = True
if hasattr(opt, 'aodname'):
opt.aod = Table.read(opt.aodname)
return opt
from barak.absorb import readatom, find_tau, findtrans, split_trans_name
from barak.sed import vel_from_wa, make_constant_dv_wa_scale
from barak.utilities import indexnear, between
from barak.plot import puttext, get_fig_axes
import barak.spec
from cStringIO import StringIO
from subprocess import call
import numpy as np
import pylab as plt
import velplot.velplot
reload(velplot.velplot)
from velplot.velplot import plot_tick_vel, read_transitions
ATOMDAT = readatom()
lw_model = 0.7
lw_data = 1
vzero = -219
#plt.ioff()
plt.rc('font', size=11)
plt.rc('xtick', labelsize=8.5)
plt.rc('ytick', labelsize=8.5)
plt.rc('xtick.major', size=3) # major tick size in points
plt.rc('xtick.minor', size=1.5) # minor tick size in points
plt.rc('xtick.major', pad=3.5) # distance to major tick label in points
plt.rc('ytick.major', size=3) # major tick size in points
plt.rc('ytick.minor', size=1.5) # minor tick size in points
# these are the upper and lower limits on the flat prior ranges. these
# will be used to generate guess positions for the sampler. If you get
# these wrong, the sampler can get confused (poor acceptance fractions
# or walkers getting lost in very low likelihood regions of parameter
# space). It will take some trial and error and inspection of the
# burn-in parameter distribution to find input ranges that give
# plausible results.
P.min = 2.4997, 12.5, 5, 2.500, 12.5, 5
P.max = 2.5003, 16.5, 70, 2.501, 15.5, 70
Npar = len(P.names)
# function to generate the model at each data point from parameters
atom = readatom()
trans = atom['HI']
def model(*par):
tau = np.zeros_like(X)
for i in xrange(len(par) // 3):
z, logN, b = par[3 * i:3 * i + 3]
tau += calc_iontau(X, trans, z + 1, logN, b)
return np.exp(-tau)
def make_data(ptrue):
""" Generate the data x, ydata, ysigma (in a real problem these
would usually all be given)."""
from barak.pyvpfit import readf26 from barak.absorb import findtrans, readatom, find_tau from barak.constants import c_kms from barak.spec import make_constant_dv_wa_scale, convolve_constant_dv from barak.convolve import convolve_psf import pylab as pl import numpy as np f26 = readf26(f26name) atomdat = readatom(molecules=1) spfilename = '' trfilename = '' vmin = vmax = 399 wadiv = None Rfwhm = 6.6 osc = False residuals = True redshift = 0.56 unrelated = [] # ( 3863.451, 3865.529), # ( 3855.399, 3859.330), # ( 4075.662, 4076.668), # ( 4079.430, 4082.960), # ( 3906.116, 3908.287), # ( 3898.097, 3899.218), # ( 4509.955, 4512.281), # ( 4503.099, 4507.387), # ( 4532.218, 4544.106), # ( 4314.625, 4315.922),
# these are the upper and lower limits on the flat prior ranges. these
# will be used to generate guess positions for the sampler. If you get
# these wrong, the sampler can get confused (poor acceptance fractions
# or walkers getting lost in very low likelihood regions of parameter
# space). It will take some trial and error and inspection of the
# burn-in parameter distribution to find input ranges that give
# plausible results.
P.min = 2.4997, 12.5, 5, 2.500, 12.5, 5
P.max = 2.5003, 16.5, 70, 2.501, 15.5, 70
Npar = len(P.names)
# function to generate the model at each data point from parameters
atom = readatom()
trans = atom['HI']
def model(*par):
tau = np.zeros_like(X)
for i in xrange(len(par)//3):
z,logN,b = par[3*i:3*i+3]
tau += calc_iontau(X, trans, z+1, logN, b)
return np.exp(-tau)
def make_data(ptrue):
""" Generate the data x, ydata, ysigma (in a real problem these
would usually all be given)."""
# for generating the wavelength scale
vrange = 500.
from astropy.constants import c as C
from scipy.ndimage import uniform_filter as uf
from astro.sampledist import RanDist
from pyntejos.utils import find_edges, clean_array
from astropy import constants as const
import astropy.units as u
from barak.absorb import readatom
from linetools.analysis import absline as ltaa
from linetools.lists.linelist import LineList
#Constant
e2_me_c = ((const.e.esu)**2 / (const.c.to('cm/s') * const.m_e.to('g'))
).value # from Draine (eq. 9.8 and 9.9)
#read atomic data
atomdat = readatom('/home/ntejos/software/vpfit10/atom.dat')
def get_tau0_peak(wa0, fosc, logN, b):
"""Get the value of the optical depth at the line center,
tau0. From Draine 2011 (see Chapter 9). It neglects estimulated
emission which is fine for IGM or ISM except for radio-frecuency
transitions.
Inputs
------
wa0: rest-frame wavelenght of the transition in A
fosc: oscillator strenght for the transition
logN: log10 of the column density in cm^-2
b: Doppler parameter in km/s
import matplotlib.pyplot as pl
from astropy.constants import c as C
from scipy.ndimage import uniform_filter as uf
from astro.sampledist import RanDist
from pyntejos.utils import find_edges,clean_array
from astropy import constants as const
import astropy.units as u
from barak.absorb import readatom
from linetools.analysis import absline as ltaa
from linetools.lists.linelist import LineList
#Constant
e2_me_c = ((const.e.esu)**2/(const.c.to('cm/s')*const.m_e.to('g'))).value # from Draine (eq. 9.8 and 9.9)
#read atomic data
atomdat = readatom('/home/ntejos/software/vpfit10/atom.dat')
def get_tau0_peak(wa0,fosc,logN,b):
"""Get the value of the optical depth at the line center,
tau0. From Draine 2011 (see Chapter 9). It neglects estimulated
emission which is fine for IGM or ISM except for radio-frecuency
transitions.
Inputs
------
wa0: rest-frame wavelenght of the transition in A
fosc: oscillator strenght for the transition
logN: log10 of the column density in cm^-2
b: Doppler parameter in km/s
