def ap(fobs, nusd, ws, we, Nx):
mask = (ws < wavd[::-1]) * (wavd[::-1] < we)
#additional mask
mask = mask * ((22898.5 > wavd[::-1]) + (wavd[::-1] > 22899.5))
fobsx = fobs[mask]
nusdx = nusd[mask]
wavdx = 1.e8 / nusdx[::-1]
errx = err[mask]
nus, wav, res = nugrid(ws - 5.0, we + 5.0, Nx, unit="AA")
#loading molecular database
#mdbCO=moldb.MdbExomol('.database/CO/12C-16O/Li2015',nus)
mdbCO = moldb.MdbHit('05_HITEMP2019.par.bz2', nus)
mdbCO.ExomolQT('.database/CO/12C-16O/Li2015')
##use HITEMP later
mdbH2O = moldb.MdbExomol('H2O/1H2-16O/POKAZATEL', nus, crit=1.e-45)
# mdbH2O=moldb.MdbExomol('.database/H2O/1H2-16O/POKAZATEL',nus)
print("resolution=", res)
#LOADING CIA
cdbH2H2 = contdb.CdbCIA('H2-H2_2011.cia', nus)
cdbH2He = contdb.CdbCIA('H2-He_2011.cia', nus)
### REDUCING UNNECESSARY LINES
#######################################################
#1. CO
Tarr = T0c * np.ones_like(Parr)
qt = vmap(mdbCO.qr_interp)(Tarr)
# gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, \
# mdbCO.gamma_air, mdbCO.gamma_self) \
gammaLMP = jit(vmap(gamma_hitran,(0,0,0,None,None,None)))\
(Parr*0.99,Tarr,Parr*0.01,mdbCO.n_air,mdbCO.gamma_air,mdbCO.gamma_self)
gammaLMN = gamma_natural(mdbCO.A)
#(Nlayer, Nlines)+(None, Nlines)=(Nlayers, Nlines)
gammaLM = gammaLMP + gammaLMN[None, :]
SijM=jit(vmap(SijT,(0,None,None,None,0)))\
(Tarr,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt)
sigmaDM=jit(vmap(doppler_sigma,(None,0,None)))\
(mdbCO.nu_lines,Tarr,molmassCO)
mask_CO, maxcf, maxcia = mask_weakline(mdbCO, Parr, dParr, Tarr, SijM,
gammaLM, sigmaDM,
maxMMR_CO * ONEARR, molmassCO, mmw,
g, vmrH2, cdbH2H2)
mdbCO.masking(mask_CO)
plot_maxpoint(mask_CO, Parr, maxcf, maxcia, mol="CO")
plt.savefig("npz/maxpoint_CO.pdf", bbox_inches="tight", pad_inches=0.0)
#2. H2O
T0xarr = list(range(500, 1800, 100))
for k, T0x in enumerate(T0xarr):
Tarr = T0x * np.ones_like(Parr)
qt = vmap(mdbH2O.qr_interp)(Tarr)
gammaLMP = jit(vmap(gamma_exomol,(0,0,None,None)))\
(Parr,Tarr,mdbH2O.n_Texp,mdbH2O.alpha_ref)
gammaLMN = gamma_natural(mdbH2O.A)
gammaLM = gammaLMP + gammaLMN[None, :]
SijM=jit(vmap(SijT,(0,None,None,None,0)))\
(Tarr,mdbH2O.logsij0,mdbH2O.nu_lines,mdbH2O.elower,qt)
sigmaDM=jit(vmap(doppler_sigma,(None,0,None)))\
(mdbH2O.nu_lines,Tarr,molmassH2O)
mask_H2O_tmp, maxcf, maxcia = mask_weakline(mdbH2O, Parr, dParr, Tarr,
SijM, gammaLM, sigmaDM,
maxMMR_H2O * ONEARR,
molmassH2O, mmw, g, vmrH2,
cdbH2H2)
if k == 0:
mask_H2O = np.copy(mask_H2O_tmp)
else:
mask_H2O = mask_H2O + mask_H2O_tmp
if T0x == 1700.0:
plot_maxpoint(mask_H2O_tmp, Parr, maxcf, maxcia, mol="H2O")
plt.savefig("maxpoint_H2O.pdf",
bbox_inches="tight",
pad_inches=0.0)
mdbH2O.masking(mask_H2O)
print("Final:", len(mask_H2O), "->", np.sum(mask_H2O))
#nu matrix
numatrix_CO = make_numatrix0(nus, mdbCO.nu_lines)
numatrix_H2O = make_numatrix0(nus, mdbH2O.nu_lines)
cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus)
cdbH2He = contdb.CdbCIA('.database/H2-He_2011.cia', nus)
return fobsx, nusdx, wavdx, errx, nus, wav, res, mdbCO, mdbH2O, numatrix_CO, numatrix_H2O, cdbH2H2, cdbH2He
def ap(fobs, nusd, ws, we, Nx):
mask = (ws < wavd[::-1]) * (wavd[::-1] < we)
#additional mask to remove a strong telluric
mask = mask * ((22898.5 > wavd[::-1]) + (wavd[::-1] > 22899.5))
fobsx = fobs[mask]
nusdx = nusd[mask]
wavdx = 1.e8 / nusdx[::-1]
errx = err[mask]
nus, wav, res = nugrid(ws - 5.0, we + 5.0, Nx, unit="AA")
#loading molecular database
mdbCO = moldb.MdbExomol('.database/CO/12C-16O/Li2015', nus)
mdbH2O = moldb.MdbExomol('.database/H2O/1H2-16O/POKAZATEL',
nus,
crit=1.e-46)
#LOADING CIA
cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus)
cdbH2He = contdb.CdbCIA('.database/H2-He_2011.cia', nus)
#REDUCING UNNECESSARY LINES
#1. CO
Tarr = T0c * np.ones_like(Parr)
qt = vmap(mdbCO.qr_interp)(Tarr)
gammaLMP = jit(vmap(gamma_exomol,(0,0,None,None)))\
(Parr,Tarr,mdbCO.n_Texp,mdbCO.alpha_ref)
gammaLMN = gamma_natural(mdbCO.A)
gammaLM = gammaLMP + gammaLMN[None, :]
SijM=jit(vmap(SijT,(0,None,None,None,0)))\
(Tarr,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt)
sigmaDM=jit(vmap(doppler_sigma,(None,0,None)))\
(mdbCO.nu_lines,Tarr,molmassCO)
mask_CO, maxcf, maxcia = mask_weakline(mdbCO,
Parr,
dParr,
Tarr,
SijM,
gammaLM,
sigmaDM,
maxMMR_CO * ONEARR,
molmassCO,
mmw,
g,
vmrH2,
cdbH2H2,
margin=2)
mdbCO.masking(mask_CO)
#plot_maxpoint(mask_CO,Parr,maxcf,maxcia,mol="CO")
#plt.savefig("maxpoint_CO.pdf", bbox_inches="tight", pad_inches=0.0)
#2. H2O
T0xarr = list(range(500, 1800, 100))
for k, T0x in enumerate(T0xarr):
Tarr = T0x * np.ones_like(Parr)
qt = vmap(mdbH2O.qr_interp)(Tarr)
gammaLMP = jit(vmap(gamma_exomol,(0,0,None,None)))\
(Parr,Tarr,mdbH2O.n_Texp,mdbH2O.alpha_ref)
gammaLMN = gamma_natural(mdbH2O.A)
gammaLM = gammaLMP + gammaLMN[None, :]
SijM=jit(vmap(SijT,(0,None,None,None,0)))\
(Tarr,mdbH2O.logsij0,mdbH2O.nu_lines,mdbH2O.elower,qt)
sigmaDM=jit(vmap(doppler_sigma,(None,0,None)))\
(mdbH2O.nu_lines,Tarr,molmassH2O)
mask_H2O_tmp, maxcf, maxcia = mask_weakline(mdbH2O,
Parr,
dParr,
Tarr,
SijM,
gammaLM,
sigmaDM,
maxMMR_H2O * ONEARR,
molmassH2O,
mmw,
g,
vmrH2,
cdbH2H2,
margin=2)
if k == 0:
mask_H2O = np.copy(mask_H2O_tmp)
else:
mask_H2O = mask_H2O + mask_H2O_tmp
if k == len(T0xarr) - 1:
print("H2O ")
plot_maxpoint(mask_H2O_tmp, Parr, maxcf, maxcia, mol="H2O")
plt.savefig("maxpoint_H2O.pdf",
bbox_inches="tight",
pad_inches=0.0)
print("H2O saved")
mdbH2O.masking(mask_H2O)
print("Final:", len(mask_H2O), "->", np.sum(mask_H2O))
#nu matrix
numatrix_CO = make_numatrix0(nus, mdbCO.nu_lines)
numatrix_H2O = make_numatrix0(nus, mdbH2O.nu_lines)
return fobsx, nusdx, wavdx, errx, nus, wav, res, mdbCO, mdbH2O, numatrix_CO, numatrix_H2O, cdbH2H2, cdbH2He
mmrH2 = 0.74
molmassH2 = molinfo.molmass('H2')
vmrH2 = (mmrH2 * mmw / molmassH2) # VMR
Mp = 33.2 # fixing mass...
# Loading the molecular database of CO and the CIA
# In[8]:
mdbCO = moldb.MdbExomol('.database/CO/12C-16O/Li2015', nus, crit=1.e-46)
cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus)
# We have only 39 CO lines.
# Again, numatrix should be precomputed prior to HMC-NUTS.
numatrix_CO = make_numatrix0(nus, mdbCO.nu_lines)
# Or you can use initspec.init_lpf instead.
numatrix_CO = initspec.init_lpf(mdbCO.nu_lines, nus)
# reference pressure for a T-P model
Pref = 1.0 # bar
ONEARR = np.ones_like(Parr)
ONEWAV = jnp.ones_like(nflux)
# In[14]:
smalla = 1.0
smalldiag = smalla**2 * jnp.identity(NP)
# Now we write the model, which is used in HMC-NUTS.