def fill(self):
import xastropy.spec.abs_line as xspa
# Data
self.atomic = xspa.abs_line_data(self.wrest)
#
self.analy['VLIM'] = [0., 0.] # km/s
self.analy['FLG_ANLY'] = 1 # Analyze
self.analy['FLG_EYE'] = 0
self.analy['FLG_LIMIT'] = 0 # No limit
self.analy['DATFIL'] = ''
self.analy['IONNM'] = self.atomic['name']
def fill(self):
import xastropy.spec.abs_line as xspa
# Data
self.atomic = xspa.abs_line_data(self.wrest)
#
self.analy['VLIM'] = [0., 0.] # km/s
self.analy['FLG_ANLY'] = 1 # Analyze
self.analy['FLG_EYE'] = 0
self.analy['FLG_LIMIT'] = 0 # No limit
self.analy['DATFIL'] = ''
self.analy['IONNM'] = self.atomic['name']
def fill(self):
import xastropy.spec.abs_line as xspa
# Data
self.atomic = xspa.abs_line_data(self.wrest)
#
self.analy["VLIM"] = [0.0, 0.0] # km/s
self.analy["FLG_ANLY"] = 1 # Analyze
self.analy["FLG_EYE"] = 0
self.analy["FLG_LIMIT"] = 0 # No limit
self.analy["DATFIL"] = ""
self.analy["IONNM"] = self.atomic["name"]
def fill_data(self):
import xastropy.spec.abs_line as xspa
# Data
self.atomic = xspa.abs_line_data(self.wrest)
#
self.analy['WVMNX'] = [0., 0.] # Wavelength interval about the line (observed)
self.analy['VLIM'] = [0., 0.]*u.km/u.s # Velocity limit of line
self.analy['FLG_ANLY'] = 1 # Analyze
self.analy['FLG_EYE'] = 0
self.analy['FLG_LIMIT'] = 0 # No limit
self.analy['DATFIL'] = ''
self.analy['IONNM'] = self.atomic['name']
self.analy['z'] = 0. # Redshift
# Characteristics
self.attrib = {'N': 0., 'Nsig': 0., 'flgN': 0, # Column
'b': 0., 'bsig': 0., # Doppler
'EW': 0., 'EWsig': 0., 'flgEW': 0 # EW
}
def mk_ew_lyman_spline(bval, ew_fil=None):
""" Generate a pickle file of a Spline of EW vs NHI for the Lyman series
Parameters:
bval: float
Doppler parameter (km/s)
ew_fil: string ('EW_SPLINE_b##.p')
Name of output pickle file
"""
from astropy import constants as const
from xastropy.spec import abs_line as xsab
from xastropy.spec import voigt as xsv
# Outfil
if ew_fil == None:
ew_fil = "EW_SPLINE_b" + str(int(bval)) + ".p"
# Units
if not isinstance(bval, u.quantity.Quantity):
bval = bval * u.km / u.s # km/s
# NHI
nspl = 100
log_NHI = 11.0 + 11 * np.arange(nspl) / (nspl - 1.0)
# Lines
wrest = tau_eff_llist()
# Output
outp = {"wrest": wrest, "tck": []}
# Setup
nvel = 60001
velo = (-30000.0 + np.arange(nvel, dtype="float64")) * u.km / u.s # km/s
dvel = 1.0 * u.km / u.s # km/s
uval = velo / bval
# Loop
for cnt, line in enumerate(wrest):
# Get atomic data
abl_data = xsab.abs_line_data(line.value)
# Wave array
dwv = dvel.to(u.cm / u.s) * line / const.c.cgs # Ang
# Voigt
vd = (bval / line).to(u.Hz) # Frequency
a = abl_data["gamma"] / (12.56637 * vd.value)
vgt = xsv.voigtking(uval, a)
# tau
tau = 0.014971475 * abl_data["fval"] * vgt / vd # Normalized to N_HI = 1 cm^-2
# Flux
tau_array = np.outer(tau, 10.0 ** log_NHI)
fx = np.exp(-1.0 * tau_array)
# EW
EW = np.sum(1.0 - fx, 0) * dwv
# EW_SPLINE[qq].EW = EW
# Spline
# EW_SPLINE[qq].splint = spl_init(NHI, EW, /double)
tck = interpolate.splrep(log_NHI, EW) # , s=0)
# Check?
chk = False
# if line == (1215.6701*u.AA):
# xdb.set_trace()
# chk=True
if chk:
from matplotlib import pyplot as plt
plt.clf()
plt.plot(log_NHI, EW, "o")
# Spline
xnew = np.linspace(np.amin(log_NHI), np.amax(log_NHI), nspl * 10)
ynew = interpolate.splev(xnew, tck, der=0)
plt.plot(xnew, ynew, "-")
plt.show()
# Output
print("line = %g" % line.value)
outp["tck"].append(tck)
# Write
print("Writing %s" % ew_fil)
pickle.dump(outp, open(ew_fil, "wb"))
def mk_ew_lyman_spline(bval, ew_fil=None):
""" Generate a pickle file of a Spline of EW vs NHI for the Lyman series
Parameters:
bval: float
Doppler parameter (km/s)
ew_fil: string ('EW_SPLINE_b##.p')
Name of output pickle file
"""
from astropy import constants as const
from xastropy.spec import abs_line as xsab
from xastropy.spec import voigt as xsv
# Outfil
if ew_fil == None:
ew_fil = 'EW_SPLINE_b' + str(int(bval)) + '.p'
# Units
if not isinstance(bval, u.quantity.Quantity):
bval = bval * u.km / u.s # km/s
# NHI
nspl = 100
log_NHI = 11.0 + 11 * np.arange(nspl) / (nspl - 1.)
# Lines
wrest = tau_eff_llist()
# Output
outp = {'wrest': wrest, 'tck': []}
# Setup
nvel = 60001
velo = (-30000. + np.arange(nvel, dtype='float64')) * u.km / u.s # km/s
dvel = 1. * u.km / u.s # km/s
uval = velo / bval
# Loop
for cnt, line in enumerate(wrest):
# Get atomic data
abl_data = xsab.abs_line_data(line.value)
# Wave array
dwv = dvel.to(u.cm / u.s) * line / const.c.cgs # Ang
# Voigt
vd = (bval / line).to(u.Hz) # Frequency
a = abl_data['gamma'] / (12.56637 * vd.value)
vgt = xsv.voigtking(uval, a)
# tau
tau = 0.014971475 * abl_data[
'fval'] * vgt / vd # Normalized to N_HI = 1 cm^-2
# Flux
tau_array = np.outer(tau, 10.**log_NHI)
fx = np.exp(-1. * tau_array)
# EW
EW = np.sum(1. - fx, 0) * dwv
#EW_SPLINE[qq].EW = EW
# Spline
#EW_SPLINE[qq].splint = spl_init(NHI, EW, /double)
tck = interpolate.splrep(log_NHI, EW) #, s=0)
# Check?
chk = False
#if line == (1215.6701*u.AA):
# xdb.set_trace()
# chk=True
if chk:
from matplotlib import pyplot as plt
plt.clf()
plt.plot(log_NHI, EW, 'o')
# Spline
xnew = np.linspace(np.amin(log_NHI), np.amax(log_NHI), nspl * 10)
ynew = interpolate.splev(xnew, tck, der=0)
plt.plot(xnew, ynew, '-')
plt.show()
# Output
print('line = %g' % line.value)
outp['tck'].append(tck)
# Write
print('Writing %s' % ew_fil)
pickle.dump(outp, open(ew_fil, "wb"))
