cnu_H2O, indexnu_H2O, R_H2O, pmarray_H2O = initspec.init_modit(
mdbH2O.nu_lines, nus)
cnu_CO, indexnu_CO, R_CO, pmarray_CO = initspec.init_modit(mdbCO.nu_lines, nus)
R = R_CO
# Precomputing gdm_ngammaL
def fT(T0, alpha):
return T0[:, None] * (Parr[None, :] / Pref)**alpha[:, None]
T0_test = np.array([1100.0, 1500.0, 1100.0, 1500.0])
alpha_test = np.array([0.2, 0.2, 0.05, 0.05])
res = 0.2
dgm_ngammaL_H2O = setdgm_exomol(mdbH2O, fT, Parr, R_H2O, molmassH2O, res,
T0_test, alpha_test)
dgm_ngammaL_CO = setdgm_exomol(mdbCO, fT, Parr, R_CO, molmassCO, res, T0_test,
alpha_test)
# check dgm
if False:
from exojax.plot.ditplot import plot_dgmn
Tarr = 1300. * (Parr / Pref)**0.1
SijM_H2O, ngammaLM_H2O, nsigmaDl_H2O = modit.exomol(
mdbH2O, Tarr, Parr, R_H2O, molmassH2O)
SijM_CO, ngammaLM_CO, nsigmaDl_CO = modit.exomol(mdbCO, Tarr, Parr, R_CO,
molmassCO)
plot_dgmn(Parr, dgm_ngammaL, ngammaLM_H2O, 0, 6)
plt.show()
# a core driver
def test_VALD_MODIT():
#wavelength range
wls, wll = 10395, 10405
#Set a model atmospheric layers, wavenumber range for the model, an instrument
NP = 100
Parr, dParr, k = pressure_layer(NP=NP)
Pref = 1.0 #bar
ONEARR = np.ones_like(Parr)
Nx = 2000
nus, wav, res = nugrid(wls - 5.0, wll + 5.0, Nx, unit="AA", xsmode="modit")
Rinst = 100000. #instrumental spectral resolution
beta_inst = R2STD(
Rinst) #equivalent to beta=c/(2.0*np.sqrt(2.0*np.log(2.0))*R)
#atoms and ions from VALD
adbV = moldb.AdbVald(
path_ValdLineList, nus, crit=1e-100
) #The crit is defined just in case some weak lines may cause an error that results in a gamma of 0... (220219)
asdb = moldb.AdbSepVald(adbV)
#molecules from exomol
mdbH2O = moldb.MdbExomol('.database/H2O/1H2-16O/POKAZATEL',
nus,
crit=1e-50) #,crit = 1e-40)
mdbTiO = moldb.MdbExomol('.database/TiO/48Ti-16O/Toto', nus,
crit=1e-50) #,crit = 1e-50)
mdbOH = moldb.MdbExomol('.database/OH/16O-1H/MoLLIST', nus)
mdbFeH = moldb.MdbExomol('.database/FeH/56Fe-1H/MoLLIST', nus)
#CIA
cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus)
#molecular mass
molmassH2O = molinfo.molmass("H2O")
molmassTiO = molinfo.molmass("TiO")
molmassOH = molinfo.molmass("OH")
molmassFeH = molinfo.molmass("FeH")
molmassH = molinfo.molmass("H")
molmassH2 = molinfo.molmass("H2")
#Initialization of MODIT (for separate VALD species, and exomol molecules(e.g., FeH))
cnuS, indexnuS, R, pmarray = initspec.init_modit_vald(
asdb.nu_lines, nus, asdb.N_usp)
cnu_FeH, indexnu_FeH, R, pmarray = initspec.init_modit(
mdbFeH.nu_lines, nus)
cnu_H2O, indexnu_H2O, R, pmarray = initspec.init_modit(
mdbH2O.nu_lines, nus)
cnu_OH, indexnu_OH, R, pmarray = initspec.init_modit(mdbOH.nu_lines, nus)
cnu_TiO, indexnu_TiO, R, pmarray = initspec.init_modit(
mdbTiO.nu_lines, nus)
#sampling the max/min of temperature profiles
fT = lambda T0, alpha: T0[:, None] * (Parr[None, :] / Pref)**alpha[:, None]
T0_test = np.array([1500.0, 4000.0, 1500.0, 4000.0])
alpha_test = np.array([0.2, 0.2, 0.05, 0.05])
res = 0.2
#Assume typical atmosphere
H_He_HH_VMR_ref = [0.1, 0.15, 0.75]
PH_ref = Parr * H_He_HH_VMR_ref[0]
PHe_ref = Parr * H_He_HH_VMR_ref[1]
PHH_ref = Parr * H_He_HH_VMR_ref[2]
#Precomputing dgm_ngammaL
dgm_ngammaL_VALD = setdgm_vald_all(asdb, PH_ref, PHe_ref, PHH_ref, R, fT,
res, T0_test, alpha_test)
dgm_ngammaL_FeH = setdgm_exomol(mdbFeH, fT, Parr, R, molmassFeH, res,
T0_test, alpha_test)
dgm_ngammaL_H2O = setdgm_exomol(mdbH2O, fT, Parr, R, molmassH2O, res,
T0_test, alpha_test)
dgm_ngammaL_OH = setdgm_exomol(mdbOH, fT, Parr, R, molmassOH, res, T0_test,
alpha_test)
dgm_ngammaL_TiO = setdgm_exomol(mdbTiO, fT, Parr, R, molmassTiO, res,
T0_test, alpha_test)
T0 = 3000.
alpha = 0.07
Mp = 0.155 * 1.99e33 / 1.90e30
Rp = 0.186 * 6.96e10 / 6.99e9
u1 = 0.0
u2 = 0.0
RV = 0.00
vsini = 2.0
mmw = 2.33 * ONEARR #mean molecular weight
log_e_H = -4.2
VMR_H = 0.09
VMR_H2 = 0.77
VMR_FeH = 10**-8
VMR_H2O = 10**-4
VMR_OH = 10**-4
VMR_TiO = 10**-8
A_Fe = 1.5
A_Ti = 1.2
adjust_continuum = 0.99
ga = 2478.57730044555 * Mp / Rp**2
Tarr = T0 * (Parr / Pref)**alpha
PH = Parr * VMR_H
PHe = Parr * (1 - VMR_H - VMR_H2)
PHH = Parr * VMR_H2
VMR_e = VMR_H * 10**log_e_H
#VMR of atoms and ions (+Abundance modification)
mods_ID = jnp.array([[26, 1], [22, 1]])
mods = jnp.array([A_Fe, A_Ti])
VMR_uspecies = atomll.get_VMR_uspecies(asdb.uspecies, mods_ID, mods)
VMR_uspecies = VMR_uspecies[:, None] * ONEARR
#Compute delta tau
#Atom & ions (VALD)
SijMS, ngammaLMS, nsigmaDlS = vald_all(asdb, Tarr, PH, PHe, PHH, R)
xsmS = xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS,
SijMS, nus, dgm_ngammaL_VALD)
dtauatom = dtauVALD(dParr, xsmS, VMR_uspecies, mmw, ga)
#FeH
SijM_FeH, ngammaLM_FeH, nsigmaDl_FeH = exomol(mdbFeH, Tarr, Parr, R,
molmassFeH)
xsm_FeH = xsmatrix(cnu_FeH, indexnu_FeH, R, pmarray, nsigmaDl_FeH,
ngammaLM_FeH, SijM_FeH, nus, dgm_ngammaL_FeH)
dtaum_FeH = dtauM_mmwl(dParr, jnp.abs(xsm_FeH), VMR_FeH * ONEARR, mmw, ga)
#H2O
SijM_H2O, ngammaLM_H2O, nsigmaDl_H2O = exomol(mdbH2O, Tarr, Parr, R,
molmassH2O)
xsm_H2O = xsmatrix(cnu_H2O, indexnu_H2O, R, pmarray, nsigmaDl_H2O,
ngammaLM_H2O, SijM_H2O, nus, dgm_ngammaL_H2O)
dtaum_H2O = dtauM_mmwl(dParr, jnp.abs(xsm_H2O), VMR_H2O * ONEARR, mmw, ga)
#OH
SijM_OH, ngammaLM_OH, nsigmaDl_OH = exomol(mdbOH, Tarr, Parr, R, molmassOH)
xsm_OH = xsmatrix(cnu_OH, indexnu_OH, R, pmarray, nsigmaDl_OH, ngammaLM_OH,
SijM_OH, nus, dgm_ngammaL_OH)
dtaum_OH = dtauM_mmwl(dParr, jnp.abs(xsm_OH), VMR_OH * ONEARR, mmw, ga)
#TiO
SijM_TiO, ngammaLM_TiO, nsigmaDl_TiO = exomol(mdbTiO, Tarr, Parr, R,
molmassTiO)
xsm_TiO = xsmatrix(cnu_TiO, indexnu_TiO, R, pmarray, nsigmaDl_TiO,
ngammaLM_TiO, SijM_TiO, nus, dgm_ngammaL_TiO)
dtaum_TiO = dtauM_mmwl(dParr, jnp.abs(xsm_TiO), VMR_TiO * ONEARR, mmw, ga)
#Hminus
dtau_Hm = dtauHminus_mmwl(nus, Tarr, Parr, dParr, VMR_e * ONEARR,
VMR_H * ONEARR, mmw, ga)
#CIA
dtauc_H2H2 = dtauCIA_mmwl(nus, Tarr, Parr, dParr, VMR_H2 * ONEARR,
VMR_H2 * ONEARR, mmw, ga, cdbH2H2.nucia,
cdbH2H2.tcia, cdbH2H2.logac)
#Summations
dtau = dtauatom + dtaum_FeH + dtaum_H2O + dtaum_OH + dtaum_TiO + dtau_Hm + dtauc_H2H2
sourcef = planck.piBarr(Tarr, nus)
F0 = rtrun(dtau, sourcef)
Frot = response.rigidrot(nus, F0, vsini, u1, u2)
wavd = jnp.linspace(wls, wll, 500)
nusd = jnp.array(1.e8 / wavd[::-1])
mu = response.ipgauss_sampling(nusd, nus, Frot, beta_inst, RV)
mu = mu / jnp.nanmax(mu) * adjust_continuum
assert (np.all(~np.isnan(mu)) * \
np.all(mu != 0) * \
np.all(abs(mu) != np.inf))
#
cnu, indexnu, R, pmarray = initspec.init_modit(mdbCH4.nu_lines, nus)
# Precomputing gdm_ngammaL
def fT(T0, alpha):
return T0[:, None] * (Parr[None, :] / Pref)**alpha[:, None]
T0_test = np.array([1100.0, 1500.0, 1100.0, 1500.0])
alpha_test = np.array([0.2, 0.2, 0.05, 0.05])
res = 0.2
dgm_ngammaL = setdgm_exomol(mdbCH4, fT, Parr, R, molmassCH4, res, T0_test,
alpha_test)
# check dgm
if False:
from exojax.plot.ditplot import plot_dgmn
Tarr = 1300. * (Parr / Pref)**0.1
SijM_CH4, ngammaLM_CH4, nsigmaDl_CH4 = modit.exomol(
mdbCH4, Tarr, Parr, R, molmassCH4)
plot_dgmn(Parr, dgm_ngammaL, ngammaLM_CH4, 0, 6)
plt.show()
# a core driver
def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini):
g = 2478.57730044555 * Mp / Rp**2