def comperr(Nnu,plotfig=False):
nus=np.linspace(1.e7/2700,1.e7/2100.,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)
sigmaD_grid=set_ditgrid(sigmaD)
gammaL_grid=set_ditgrid(gammaL)
cnu,indexnu,dLarray=initspec.init_dit(mdbCO.nu_lines,nus)
xs_dit_lp=dit_xsvector(cnu,indexnu,dLarray,sigmaD,gammaL,Sij,nus,sigmaD_grid,gammaL_grid)
wls_dit = 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_dit_lp/xsv_lpf_lp-1.
med=np.median(dif)
iju=22940.
ijd=26400.
limu,limd=np.searchsorted(wls_dit[::-1],[iju,ijd])
std=np.std(dif[::-1][limu:limd])
return med,std,ijd,iju,wls_dit,xs_dit_lp,xsv_lpf_lp,dif
def xs(Nc, Nline=10000):
nu0 = 2000.0
nu1 = 2100.0
nus = np.logspace(np.log10(nu0), np.log10(nu1), 10000, dtype=np.float64)
nu_lines = np.random.rand(Nline)*(nu1-nu0)+nu0
nsigmaD = 1.0
gammaL = np.random.rand(Nline)+0.1
cnu, indexnu, R, pmarray = initspec.init_modit(nu_lines, nus)
ngammaL = gammaL/(nu_lines/R)
ngammaL_grid = set_ditgrid(ngammaL, res=0.1)
S = jnp.array(np.random.normal(size=Nline))
ts = time.time()
a = []
for i in range(0, Nc):
tsx = time.time()
xsv = xsvector(cnu, indexnu, R, pmarray, nsigmaD,
ngammaL, S, nus, ngammaL_grid)
xsv.block_until_ready()
tex = time.time()
a.append(tex-tsx)
te = time.time()
a = np.array(a)
print(Nline, ',', np.mean(a[1:]), ',', np.std(a[1:]))
return (te-ts)/Nc
def xs(Nline):
nu0 = 2000.0
nu1 = 2100.0
nus = np.logspace(np.log10(nu0), np.log10(nu1), 10000, dtype=np.float64)
nu_lines = np.random.rand(Nline) * (nu1 - nu0) + nu0
nsigmaD = 1.0
gammaL = np.random.rand(Nline) + 0.1
cnu, indexnu, R, pmarray = initspec.init_modit(nu_lines, nus)
ngammaL = gammaL / (nu_lines / R)
ngammaL_grid = set_ditgrid(ngammaL, res=0.1)
S = jnp.array(np.random.normal(size=Nline))
xsv = xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, S, nus,
ngammaL_grid)
xsv.block_until_ready()
return True
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)
d = 10000
ll = mdbCO.nu_lines
xsv_lpf_lp = lpf_xsection(nus, ll, sigmaD, gammaL, Sij, memory_size=30)
config.update('jax_enable_x64', True)
xs_modit_lp_f64 = modit_xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL,
Sij, nus, ngammaL_grid)
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, pmarray = initspec.init_dit(mdbCO.nu_lines, nus)
sigmaD_grid = set_ditgrid(sigmaD, res=0.1)
gammaL_grid = set_ditgrid(gammaL, res=0.1)
xs_dit_lp = dit_xsvector(cnu, indexnu, pmarray, sigmaD,
gammaL, Sij, nus, sigmaD_grid, gammaL_grid)
wls_dit = 100000000/nus
#ref (direct)
d = 10000
ll = mdbCO.nu_lines
xsv_lpf_lp = lpf_xsection(nus, ll, sigmaD, gammaL, Sij, memory_size=30)
config.update('jax_enable_x64', True)
xs_dit_lp_f64 = dit_xsvector(
cnu, indexnu, pmarray, sigmaD, gammaL, Sij, nus, sigmaD_grid, gammaL_grid)