def load_elements(self):
#Set atomic data
#atomicData.setDataFile('he_i_rec_Pal12-Pal13.fits')
atomicData.setDataFile('s_iii_coll_HRS12.dat')
#Default: 's_iii_atom_PKW09.dat'
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Tayal & Gupta 1999, ApJ, 526, 544'
#New Atomic data s_iii_coll_HRS12.dat
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Hudson, Ramsbottom & Scott 2012, ApJ, 750, 65'
#Declare ions
self.S2_atom = Atom('S', 2)
self.S3_atom = Atom('S', 3)
self.Ar3_atom = Atom('Ar', 3)
self.Ar4_atom = Atom('Ar', 4)
self.N2_atom = Atom('N', 2)
self.O2_atom = Atom('O', 2)
self.O3_atom = Atom('O', 3)
self.H1_atom = RecAtom('H', 1)
self.He1_atom = RecAtom('He', 1)
self.He2_atom = RecAtom('He', 2)
#Pyneb objects
self.diags = Diagnostics()
#Ohrs 2016 relation for the OI_SI gradient
self.logSI_OI_Gradient = random.normal(
-1.53, 0.05, size=self.MC_array_len
) # random.normal(-1.78, 0.03, size = self.MC_array_len)
self.OI_SI = power(10, -self.logSI_OI_Gradient)
#Theoretical ratios
self.S3_ratio = self.S3_atom.getEmissivity(
10000, 100, wave=9531) / self.S3_atom.getEmissivity(
10000, 100, wave=9069)
self.S3_9000_ratio = random.normal(
self.S3_atom.getEmissivity(10000, 100, wave=9531) /
self.S3_atom.getEmissivity(10000, 100, wave=9069),
0.01,
size=self.MC_array_len)
self.N2_6000_ratio = self.N2_atom.getEmissivity(
10000, 100, wave=6584) / self.N2_atom.getEmissivity(
10000, 100, wave=6548)
self.O3_5000_ratio = self.O3_atom.getEmissivity(
10000, 100, wave=5007) / self.O3_atom.getEmissivity(
10000, 100, wave=4959)
#Factors to speed calculations
self.lines_factors = {}
self.lines_factors['S3_9069A'] = 1 + self.S3_ratio
self.lines_factors['S3_9531A'] = 1 + 1 / self.S3_ratio
#Cloudy models for the SIV contribution
self.m_SIV_correction = random.normal(1.1628,
0.00559,
size=self.MC_array_len)
self.n_SIV_correction = random.normal(0.0470,
0.0097,
size=self.MC_array_len)
#self.m_SIV_correction = random.normal(1.109, 0.01, size = self.MC_array_len)
#self.n_SIV_correction = random.normal(0.135, 0.0173, size = self.MC_array_len)
#CHAOS relation TNII-TSIII
#T[SIII] = 1.312(+-0.075)T[NII]-0.313(+-0.058)
#TNII = (0.762+-0.044)*TSIII + 0.239+-0.046
self.m_TNII_correction = random.normal(0.762,
0.044,
size=self.MC_array_len)
self.n_TNII_correction = random.normal(0.239,
0.046,
size=self.MC_array_len)
#Truncated gaussian for the density
lower_trunc, upper_trunc = (1.0 - 50.0) / 25.0, (100 - 50) / 25.0
self.Truncated_gaussian = truncnorm(lower_trunc,
upper_trunc,
loc=50,
scale=25)
print '-Elements loaded\n'
return
