def phyat2model(phyat_file, model_file):
"""
generate a model_file file from a phyat_file, setting all the master lines to I=1.0
"""
list_phyat = read_data(phyat_file)
with open(model_file, 'w') as f:
for line in list_phyat:
if line['ref'] == 999:
new_num = line['num']-90000000000000
f.write('{0:14d} {1[id]:9s} 1.000 0.000 1.000e+04 1.000 0000000000000 1 1.00 {1[comment]:>15s}'.format(new_num, line))
def get_extra_atoms(extra_file=None, uniq=False):
if extra_file is None:
return []
extra_data = read_data(extra_file)
atoms = []
for ID in extra_data.id:
IDs = split_atom(ID)
if IDs[0] is None:
atoms.append('{0[1]}{0[2]}'.format(IDs))
else:
atoms.append('{0[0]}{0[1]}{0[2]}'.format(IDs))
if uniq:
res = unique(atoms)
else:
res = atoms
return res
def get_extra_atoms(extra_file=None,uniq=False):
if extra_file is None:
return []
extra_data = read_data(extra_file)
atoms=[]
for ID in extra_data.id:
IDs = split_atom(ID)
if IDs[0] is None:
atoms.append('{0[1]}{0[2]}'.format(IDs))
else:
atoms.append('{0[0]}{0[1]}{0[2]}'.format(IDs))
if uniq:
res = unique(atoms)
else:
res = atoms
return res
def phyat2model(phyat_file,
model_file,
ion_frac_file,
norm_hbeta=1e4,
abund_file='asplund_2009.dat',
ion_frac_min=1e-4,
verbose=False):
"""
generate a model_file file from a phyat_file, setting all the master lines to I=i_rel/norm
norm by default corresponds to Hbeta = 1e4
"""
ab_data = np.genfromtxt(abund_file, dtype='U3, float', names='elem, abund')
ion_frac_data = np.genfromtxt(ion_frac_file,
dtype='U4,float',
names='ion, ion_frac')
ion_frac_dic = {}
for record in ion_frac_data:
ion_frac_dic[record['ion']] = record['ion_frac']
list_phyat = read_data(phyat_file)
Hbeta_num = 90101000000000
Ibeta = list_phyat[list_phyat['num'] == Hbeta_num]['i_rel'][0]
norm = Ibeta / norm_hbeta * ion_frac_dic['H1']
num_tab = []
with open(model_file, 'w') as f:
for line in list_phyat:
if line['ref'] == 999:
num = line['num']
new_num = int(str(num)[1::])
if new_num in num_tab:
raise ValueError(
'Duplicate reference number {}'.format(new_num))
else:
num_tab.append(new_num)
Z = int(str(num)[1:3])
spec = int(str(num)[3:5])
type_ = int(str(num)[5:6])
if type_ == 3: # Collision lines
spec -= 1
elem = Z_inv[Z]
elem2 = line['id'].split('_')[0]
coeff_abund = 1.0
if elem2 == 'D':
elem = 'D'
elif elem2 == '3He':
coeff_abund = 1e-3
ion = elem + str(spec)
if Z < 90:
abund = 10**(ab_data[ab_data['elem'] == elem]['abund'][0] -
12) * coeff_abund
ion_frac = 1.0
if ion_frac_dic is not None:
if ion in ion_frac_dic:
ion_frac = ion_frac_dic[ion]
else:
# TODO we need to look for the available ion with similar structure
ionFe = 'Fe' + str(spec)
ion_frac = ion_frac_dic[ionFe]
else: # No element lines. Absorption. Set to 1.0 by canceling abund / norm
abund = norm
ion_frac = 1.0
if ion_frac < ion_frac_min:
ion_frac = ion_frac_min
if verbose:
print(line['num'], Z, Z_inv[Z], spec, abund, ion_frac)
intens = line['i_rel'] / norm * abund * ion_frac
line['comment'] = line['comment'].strip()
f.write(
'{0:14d} {1[id]:9s} 1.000 0.000{2:10.3e} 1.000 0000000000000 1 1.00 {1[comment]:<100s}\n'
.format(new_num, line, intens))
