def test_opacity_Fe_vald3(T, P):
PH = P * H_He_HH_VMR[0]
PHe = P * H_He_HH_VMR[1]
PHH = P * H_He_HH_VMR[2]
qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T))
# ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes.
Sij = SijT(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt)
sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol)
gammaL = atomll.gamma_vald3(T,
PH,
PHH,
PHe,
adbFe.ielem,
adbFe.iion,
adbFe.dev_nu_lines,
adbFe.elower,
adbFe.eupper,
adbFe.atomicmass,
adbFe.ionE,
adbFe.gamRad,
adbFe.gamSta,
adbFe.vdWdamp,
enh_damp=1.0)
val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)])
diff = np.abs(REFS[0, :] - val)
print(diff)
assert (diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3)
def comperr(Nnu, plotfig=False):
nus = np.logspace(np.log10(1.e7 / 2700),
np.log10(1.e7 / 2100.),
Nnu,
dtype=np.float64)
# nus=np.logspace(np.log10(3000),np.log10(6000.0),Nnu,dtype=np.float64)
mdbCO = moldb.MdbHit('/home/kawahara/exojax/data/CO/05_hit12.par', nus)
Mmol = 28.010446441149536
Tref = 296.0
Tfix = 1000.0
Pfix = 1.e-3 #
#USE TIPS partition function
Q296=np.array([107.25937215917970,224.38496958496091,112.61710362499998,\
660.22969049609367,236.14433662109374,1382.8672147421873])
Q1000=np.array([382.19096582031250,802.30952197265628,402.80326733398437,\
2357.1041210937501,847.84866308593757,4928.7215078125000])
qr = Q1000 / Q296
qt = np.ones_like(mdbCO.isoid, dtype=np.float64)
for idx, iso in enumerate(mdbCO.uniqiso):
mask = mdbCO.isoid == iso
qt[mask] = qr[idx]
Sij = SijT(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt)
gammaL = gamma_hitran(Pfix, Tfix, Pfix, mdbCO.n_air, mdbCO.gamma_air,
mdbCO.gamma_self)
#+ gamma_natural(A) #uncomment if you inclide a natural width
sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol)
cnu, indexnu, R, dq = initspec.init_modit(mdbCO.nu_lines, nus)
nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R)
ngammaL = gammaL / (mdbCO.nu_lines / R)
ngammaL_grid = set_ditgrid(ngammaL)
xs_modit_lp = modit_xsvector(cnu, indexnu, R, dq, nsigmaD, ngammaL, Sij,
nus, ngammaL_grid)
wls_modit = 100000000 / nus
#ref (direct)
d = 10000
ll = mdbCO.nu_lines
xsv_lpf_lp = lpf_xsection(nus, ll, sigmaD, gammaL, Sij, memory_size=30)
dif = xs_modit_lp / xsv_lpf_lp - 1.
med = np.median(dif)
iju = 22940.
ijd = 26400.
limu, limd = np.searchsorted(wls_modit[::-1], [iju, ijd])
std = np.std(dif[::-1][limu:limd])
return med, std, R, ijd, iju, wls_modit, xs_modit_lp, xsv_lpf_lp, dif
def linest(self, T):
"""line strength.
Args:
T: temperature (K)
Returns:
line strength (cm)
"""
if self.database == 'ExoMol':
qt = self.mdb.qr_interp(T)
elif self.database == 'HITRAN' or self.database == 'HITEMP':
qt = self.mdb.Qr_line_HAPI(T)
return SijT(T, self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower,
qt)
Q296 = np.array([
107.25937215917970, 224.38496958496091, 112.61710362499998,
660.22969049609367, 236.14433662109374, 1382.8672147421873
])
Q1000 = np.array([
382.19096582031250, 802.30952197265628, 402.80326733398437,
2357.1041210937501, 847.84866308593757, 4928.7215078125000
])
qr = Q1000 / Q296
qt = np.ones_like(mdbCO.isoid, dtype=np.float64)
for idx, iso in enumerate(mdbCO.uniqiso):
mask = mdbCO.isoid == iso
qt[mask] = qr[idx]
Sij = SijT(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt)
gammaL = gamma_hitran(Pfix, Tfix, Pfix, mdbCO.n_air, mdbCO.gamma_air,
mdbCO.gamma_self)
# + gamma_natural(A) #uncomment if you inclide a natural width
sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol)
cnu, indexnu, R, pmarray = initspec.init_modit(mdbCO.nu_lines, nus)
nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R)
ngammaL = gammaL / (mdbCO.nu_lines / R)
ngammaL_grid = set_ditgrid(ngammaL)
xs_modit_lp = modit_xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij,
nus, ngammaL_grid)
wls_modit = 100000000 / nus
#ref (direct)