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 init_database(self):
molpath = pathlib.Path(self.databasedir) / pathlib.Path(
self.identifier)
if self.database == 'HITRAN' or self.database == 'HITEMP':
self.mdb = moldb.MdbHit(molpath,
nurange=[self.nus[0], self.nus[-1]],
crit=self.crit)
elif self.database == 'ExoMol':
print('broadf=', self.broadf)
self.mdb = moldb.MdbExomol(molpath,
nurange=[self.nus[0], self.nus[-1]],
broadf=self.broadf,
crit=self.crit)
else:
print('Select database from HITRAN, HITEMP, ExoMol.')
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
from exojax.spec import xsection as lpf_xsection
from exojax.spec import initspec
from exojax.spec.modit import xsvector as modit_xsvector
from exojax.spec.lpf import xsvector as lpf_xsvector
from exojax.spec import make_numatrix0
import numpy as np
import tqdm
import jax.numpy as jnp
from jax import vmap
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('bmh')
nus = np.logspace(np.log10(4000), np.log10(4500.0), 3000000, 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
])
from exojax.spec import xsection
from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural
from exojax.spec.exomol import gamma_exomol
from exojax.spec import moldb
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import time
# Setting wavenumber bins and loading HITEMP database
wav = np.linspace(16370.0, 16390.0, 2000, dtype=np.float64) # AA
nus = 1.e8/wav[::-1] # cm-1
ts = time.time()
mdbCO_HITEMP = moldb.MdbHit(
'/home/kawahara/exojax/data/CH4/06_HITEMP2020.par.bz2', nus, extract=True)
te = time.time()
print(te-ts, 'sec')