def calc_hapi():
''' Calc spectrum under HAPI '''
clean_after_run = not exists(HAPIdb) and False
try:
db_begin(HAPIdb)
if not molecule in tableList(): # only if data not downloaded already
fetch(molecule, mol_id, iso, wmin-broadening_max_width /
2, wmax+broadening_max_width/2)
# HAPI doesnt correct for side effects
# Calculate with HAPI
nu, coef = absorptionCoefficient_Voigt(SourceTables='CO2',
Environment={'T': T, # K
'p': p/1.01325, # atm
},
WavenumberStep=dnu,
HITRAN_units=False,
GammaL='gamma_self')
nu, trans = transmittanceSpectrum(nu, coef,
Environment={'l': L, # cm
})
s_hapi = Spectrum.from_array(nu, trans, 'transmittance_noslit', 'cm-1', 'I/I0',
conditions={'Tgas': T},
name='HAPI')
except:
raise
finally:
if clean_after_run:
shutil.rmtree(HAPIdb)
return s_hapi
def calc_hapi():
nu, coef = absorptionCoefficient_Voigt(
SourceTables=molecule,
Environment={"T": T, "p": pressure_bar / 1.01315,}, # K # atm
GammaL="gamma_self",
WavenumberStep=dnu,
HITRAN_units=False,
)
return nu, coef
def calc_hapi():
nu, coef = absorptionCoefficient_Voigt(SourceTables=molecule,
Environment={'T': T, # K
'p': pressure_bar/1.01315, # atm
},
GammaL='gamma_self',
WavenumberStep=dnu,
HITRAN_units=False)
return nu, coef
def calc_hapi():
""" Calc spectrum under HAPI """
clean_after_run = not exists(HAPIdb) and False
try:
db_begin(HAPIdb)
if not molecule in tableList(
): # only if data not downloaded already
fetch(
molecule,
mol_id,
iso,
wmin - broadening_max_width / 2,
wmax + broadening_max_width / 2,
)
# HAPI doesnt correct for side effects
# Calculate with HAPI
nu, coef = absorptionCoefficient_Voigt(
SourceTables="CO2",
Environment={
"T": T,
"p": p / 1.01325,
}, # K # atm
WavenumberStep=dnu,
HITRAN_units=False,
GammaL="gamma_self",
)
nu, trans = transmittanceSpectrum(
nu,
coef,
Environment={
"l": L,
},
) # cm
s_hapi = Spectrum.from_array(
nu,
trans,
"transmittance_noslit",
"cm-1",
"1",
conditions={"Tgas": T},
name="HAPI",
)
except:
raise
finally:
if clean_after_run:
shutil.rmtree(HAPIdb)
return s_hapi
def test_broadening(rtol=1e-2, verbose=True, plot=False, *args, **kwargs):
'''
Test broadening against HAPI and tabulated data
We're looking at CO(0->1) line 'R1' at 2150.86 cm-1
'''
from radis.io.hapi import db_begin, fetch, tableList, absorptionCoefficient_Voigt
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
plt.ion()
setup_test_line_databases() # add HITRAN-CO-TEST in ~/.radis if not there
# Conditions
T = 3000
p = 0.0001
wstep = 0.001
wmin = 2150 # cm-1
wmax = 2152 # cm-1
broadening_max_width = 10 # cm-1
# %% HITRAN calculation
# -----------
# Generate HAPI database locally
db_begin(join(dirname(__file__), __file__.replace('.py', '_HAPIdata')))
if not 'CO' in tableList(): # only if data not downloaded already
fetch('CO', 5, 1, wmin - broadening_max_width / 2,
wmax + broadening_max_width / 2)
# HAPI doesnt correct for side effects
# Calculate with HAPI
nu, coef = absorptionCoefficient_Voigt(
SourceTables='CO',
Environment={
'T': T, # K
'p': p / 1.01325, # atm
},
WavenumberStep=wstep,
HITRAN_units=False)
s_hapi = Spectrum.from_array(nu,
coef,
'abscoeff',
'cm-1',
'cm_1',
conditions={'Tgas': T},
name='HAPI')
# %% Calculate with RADIS
# ----------
sf = SpectrumFactory(
wavenum_min=wmin,
wavenum_max=wmax,
mole_fraction=1,
path_length=1, # doesnt change anything
wstep=wstep,
pressure=p,
broadening_max_width=broadening_max_width,
isotope=[1],
warnings={
'MissingSelfBroadeningWarning': 'ignore',
'NegativeEnergiesWarning': 'ignore',
'HighTemperatureWarning': 'ignore',
'GaussianBroadeningWarning': 'ignore'
}) # 0.2)
sf.load_databank('HITRAN-CO-TEST')
# s = pl.non_eq_spectrum(Tvib=T, Trot=T, Ttrans=T)
s = sf.eq_spectrum(Tgas=T, name='RADIS')
if plot: # plot broadening of line of largest linestrength
sf.plot_broadening(i=sf.df1.S.idxmax())
# Plot and compare
res = abs(get_residual_integral(s, s_hapi, 'abscoeff'))
if plot:
plot_diff(s,
s_hapi,
var='abscoeff',
title='{0} bar, {1} K (residual {2:.2g}%)'.format(
p, T, res * 100),
show_points=False)
plt.xlim((wmin, wmax))
if verbose:
printm('residual:', res)
assert res < rtol
