def test_cut_recombine(verbose=True, *args, **kwargs):
"""
Use :func:`~radis.spectrum.operations.crop` and :func:`~radis.los.slabs.MergeSlabs`
to cut a Spectrum and recombine it
Assert we still get the same spectrum at the end
"""
s = load_spec(getTestFile("CO_Tgas1500K_mole_fraction0.01.spec"), binary=True)
# Cut in half
cut = 2177
s1 = crop(s, 2000, cut, "cm-1", inplace=False)
s2 = crop(s, cut, 2300, "cm-1", inplace=False)
# Recombine
s_new = MergeSlabs(s1, s2, resample="full", out="transparent")
# Compare
assert s.compare_with(s_new, spectra_only=True, plot=False, verbose=verbose)
def non_eq_spectrum(
self,
Tvib=None,
Trot=None,
Ttrans=None,
vib_distribution="boltzmann",
overpopulation=None,
mole_fraction=None,
path_length=None,
save_rescaled_bands=False,
):
"""See :py:meth:`~radis.lbl.factory.SpectrumFactory.non_eq_spectrum`
.. warning::
only valid under optically thin conditions!!
Parameters
----------
... same as usually. If None, then the reference value (used to
calculate bands) is used
Other Parameters
----------------
save_rescaled_bands: boolean
save updated bands. Take some time as it requires rescaling all
bands individually (which is only done on the MergedSlabs usually)
Default ``False``
Notes
-----
Implementation:
Generation of a new spectrum is done by recombination of the precalculated
bands with
"""
from radis.los import MergeSlabs
# Restart from a copy each time (else reference bands are modified by rescaling
# and we may loose information if rescaling to 0 for instance)
bands_ref = self.bands_ref
bands = {
br: bands_ref[br].copy(copy_lines=self.copy_lines)
for br in bands_ref
}
# Initialize inputs
if Trot is None:
Trot = self.Trot_ref
if Tvib is None:
Tvib = self.Tvib_ref
if Trot != self.Trot_ref:
raise ValueError(
"Trot {0} doesnt match the reference Trot: {1}".format(
Trot, self.Trot_ref))
if mole_fraction is None:
mole_fraction = self.mole_fraction_ref
if path_length is None:
path_length = self.path_length_ref
if overpopulation is None:
overpopulation = {}
# # Fill missing overpopulation with 1
# if overpopulation is None:
# overpopulation = dict.fromkeys(list(bands.keys()), 1)
# else:
# for br in bands.keys():
# if not br in overpopulation:
# overpopulation[br] = 1
# Tvib_ref = self.Tvib_ref
# pop_correction = dict.fromkeys(list(bands.keys()), 1)
# if Tvib != Tvib_ref:
# raise NotImplementedError
# E_bands = self.E_bands
# # Correct for vibrational temperature
# for br in bands:
# pop_correction[br] = exp(-E_bands[br]*hc_k/Tvib)/exp(-E_bands[br]*hc_k/Tvib_ref)
# Recalculate populations from reference everytime
vib_levels = self.vib_levels
# Recalculate partition function
Qvib = self._calc_vib_populations(Tvib=Tvib,
vib_distribution=vib_distribution,
overpopulation=overpopulation)
# TODO: note: Qvib doesnt take gvib into account. Wrong Qvib but correct populations
# if rescaled with ratio???
# ... warning: update_populations set to False here not updated (because we dont
# ... care much about all levels here. only the one from vib_levels matter)
if overpopulation is not None:
Q, _, _ = self.parfunc.at_noneq(
Tvib,
Trot,
overpopulation=overpopulation,
returnQvibQrot=True,
update_populations=False,
)
else:
Q = self.parfunc.at_noneq(Tvib, Trot, update_populations=False)
Qref = self.Qref
Qvib_ref = self.Qvib_ref
for br, band in bands.items(): # type(band): Spectrum
if br == "others":
continue
viblvl_u = band.conditions["viblvl_u"]
viblvl_l = band.conditions["viblvl_l"]
nu_vib = vib_levels.loc[viblvl_u, "nvib"]
nu_vib_old = vib_levels.loc[viblvl_u, "nvib_ref"]
nl_vib = vib_levels.loc[viblvl_l, "nvib"]
nl_vib_old = vib_levels.loc[viblvl_l, "nvib_ref"]
# with new, old = (2, 1):
# n2/n1 = exp(-Evib2/kT2)/exp(-Evib1/kT1) * Qvib1/Qvib2
# = n2vib / n1vib * Q1/Q2 * Q2vib / Q1vib
if vib_distribution == "boltzmann":
corfactor_u = nu_vib / nu_vib_old * Qref / Q * Qvib / Qvib_ref
corfactor_l = nl_vib / nl_vib_old * Qref / Q * Qvib / Qvib_ref
else:
raise NotImplementedError(
"vib_distribution: {0}".format(vib_distribution))
rescale_updown_levels(band, corfactor_u, 1, corfactor_l, 1)
# Update lines
if self.copy_lines:
band.lines["nu"] *= corfactor_u
band.lines["nl"] *= corfactor_l
band.lines["Qvib"] = Qvib
band.lines["nu_vib"] *= corfactor_u
band.lines["nl_vib"] *= corfactor_l
band.lines["Ei"] *= np.nan
band.lines["S"] = np.nan
# Get total spectrum
s = MergeSlabs(*list(bands.values()))
# populations
s.populations = pd.DataFrame(vib_levels[["nvib", "Evib"]])
# rebuild lines
if self.copy_lines:
s.lines = pd.concat([band.lines for band in bands.values()])
# Update total mole fraction if it changed
mole_fraction_ref = self.mole_fraction_ref
if mole_fraction != mole_fraction_ref:
s.rescale_mole_fraction(
mole_fraction,
mole_fraction_ref,
ignore_warnings=True, # mole_fraction is 'N/A'
force=True,
)
path_length_ref = self.path_length_ref
if path_length != path_length_ref:
s.rescale_path_length(path_length, path_length_ref, force=True)
# Add parameters in conditions:
s.conditions["overpopulation"] = overpopulation
s.conditions["mole_fraction"] = mole_fraction # was 'N/A' after Merge
# because it's different for all bands
s.conditions["path_length"] = path_length
s.conditions["Tvib"] = Tvib
s.conditions["Trot"] = Trot
if save_rescaled_bands:
for br, band in bands.items(): # type(band): Spectrum
if mole_fraction != mole_fraction_ref:
band.rescale_mole_fraction(
mole_fraction,
mole_fraction_ref,
ignore_warnings=True, # mole_fraction is 'N/A'
force=True,
)
if path_length != path_length_ref:
band.rescale_path_length(path_length,
path_length_ref,
force=True)
self.bands = bands
return s
def test_compare_torch_CO2(verbose=True,
plot=False,
save=False,
warnings=True,
use_cache=True,
*args,
**kwargs):
"""Reproduce the plasma torch experiment of Packan 2003 in in atmospheric air
Notes
-----
Thresholds parameters are reduced for a faster calculation time. For instance:
- broadening_max_width should be 50 rather than 20
- cutoff should be 1e-27 rather than 1e-25
- wstep should be 0.01 or even 0.008 rather than 0.1
Performance:
- neq==0.9.20: Finished test_compare_torch_CO2 in 758.640324s
- neq==0.9.21: Finished test_compare_torch_CO2 in 672s
- neq==0.9.21*: (with ParallelFactory) Finished test_compare_torch_CO2 in 298s
- neq==0.9.22: (Parallel + continuum) Finished in 65s
RADIS 0.9.9 == neq 0.9.24
- RADIS 0.9.26: Finished test_compare_torch_CO2 in 57s (DLM, no continuum)
Reference
--------
Packan et al 2003 "Measurement and Modeling of OH, NO, and CO Infrared Radiation
at 3400 K", JTHT
"""
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
plt.ion()
t0 = time()
# %% Conditions
wlmin = 4100
wlmax = 4900
wmin = nm2cm(wlmax)
wmax = nm2cm(wlmin)
wstep = 0.1
# Load concentration profile
conds = pd.read_csv(
getValidationCase(
"test_compare_torch_CO2_data/test_compare_torch_CO2_conditions_JTHT2003.dat"
),
comment="#",
delim_whitespace=True,
)
slab_width = 0.05 # cm, WARNING. Hardcoded (look up the table)
# %% Calculate slabs
# CO2
sf = ParallelFactory(
wavenum_min=wmin,
wavenum_max=wmax,
mole_fraction=None,
path_length=None,
molecule="CO2",
isotope="1,2",
parallel=False,
wstep=wstep,
db_use_cached=use_cache,
export_lines=False, # saves some memory
export_populations=False, # saves some memory
cutoff=1e-25,
Nprocs=cpu_count() - 1, # warning with memory if too many procs
broadening_max_width=20,
# pseudo_continuum_threshold=0.01, # use pseudo-continuum, no DLM. Note : 56s on 20/08.
# optimization=None,
pseudo_continuum_threshold=
0, # use DLM, no pseudo-continuum. Note : 84s on 20/08
optimization="min-RMS",
verbose=False,
)
sf.warnings["MissingSelfBroadeningWarning"] = "ignore"
sf.warnings["NegativeEnergiesWarning"] = "ignore"
# # Init database: only if we want to save all spectra being calculated
# (discarded for the moment)
# if use_cache:
# sf.init_database('test_compare_torch_CO2_SpectrumDatabase_HITEMP_1e25',
# add_info=['Tgas'])
sf.init_databank("HITEMP-CO2-DUNHAM",
load_energies=False) # saves some memory
# sf.init_databank('CDSD-4000')
# CO
sfco = ParallelFactory(
wavenum_min=wmin,
wavenum_max=wmax,
mole_fraction=None,
path_length=None,
molecule="CO",
isotope="1,2",
parallel=False,
wstep=wstep,
db_use_cached=use_cache,
export_lines=False, # saves some memory
export_populations=False, # saves some memory
cutoff=1e-25,
Nprocs=cpu_count() - 1,
broadening_max_width=20,
optimization=None,
verbose=False,
)
sfco.warnings["MissingSelfBroadeningWarning"] = "ignore"
# Init database: only if we want to save all spectra being calculated
# (discarded for the moment)
# if use_cache:
# sfco.init_database(
# 'test_compare_torch_CO_SpectrumDatabase_HITEMP_1e25', add_info=['Tgas'])
sfco.init_databank("HITEMP-CO-DUNHAM")
# Calculate all slabs
# .. CO2 at equilibrium
slabsco2 = sf.eq_spectrum(list(conds.T_K),
mole_fraction=list(conds.CO2),
path_length=slab_width)
# .. CO at equilibrium, but with non_eq to calculate PartitionFunctions instead of using
# ... tabulated ones (which are limited to 3000 K in HAPI)
slabsco = sfco.non_eq_spectrum(
list(conds.T_K),
list(conds.T_K),
mole_fraction=list(conds.CO),
path_length=slab_width,
)
# Room absorption
with pytest.warns(
UserWarning
): # we expect a "using ParallelFactory for single case" warning
s0 = sf.eq_spectrum(Tgas=300, mole_fraction=330e-6, path_length=600)[0]
# Warning: This slab includes the CO2 emission from the 300 K air
# within the spectrometer. But experimentally it is substracted
# by there the chopper (this is corrected below)
# ... see RADIS Article for more details
# Add radiance for everyone if not calculated (ex: loaded from database)
for s in slabsco2 + slabsco + [s0]:
s.update()
# Merge CO + CO2 for each slab
slabstot = [MergeSlabs(s, sco) for (s, sco) in zip(slabsco2, slabsco)]
# %% Line-of-sight
# Solve ETR along line of sight
# --------
# two semi profile, + room absortion
line_of_sight = slabstot[1:][::-1] + slabstot + [s0]
stot = SerialSlabs(*line_of_sight)
# stot = SerialSlabs(*slabstot[1:][::-1], *slabstot, s0) # Python 3 syntax only
# Also calculate the contribution of pure CO
# ---------
s0tr = s0.copy() # transmittance only
s0tr.conditions["thermal_equilibrium"] = False
s0tr._q["radiance_noslit"] *= 0 # hack to remove CO2 emission
s0tr._q["emisscoeff"] *= 0 # hack to remove CO2 emission
line_of_sight = slabsco[1:][::-1] + slabsco + [s0tr]
sco = SerialSlabs(*line_of_sight)
# sco = SerialSlabs(*slabsco[1:][::-1], *slabsco, s0tr) # Python 3 syntax only
# Generate Slit
# ------
disp = 4 # dispersion conversion function (nm/mm)
s_in = 1 # entrance slit (mm)
s_out = 2.8 # exit slit (mm)
top = (s_out - s_in) * disp
base = (s_out + s_in) * disp
slit_function = (top, base) # (nm)
# Final plot unit
unit = "µW/cm2/sr"
norm_by = "max" # should be 'max' if unit ~ 'µW/cm2/sr',
# 'area' if unit ~ 'µW/cm2/sr/nm'
# Convolve with Slit
# -------
sco.apply_slit(slit_function,
unit="nm",
norm_by=norm_by,
shape="trapezoidal")
stot.apply_slit(slit_function,
unit="nm",
norm_by=norm_by,
shape="trapezoidal")
# Remove emission from within the spectrometer (substracted
# by the chopper experimentaly)
# -------
s0spectro = s0.copy()
s0spectro.rescale_path_length(75 * 4) # spectrometer length
s0spectro.apply_slit(slit_function,
unit="nm",
norm_by=norm_by,
shape="trapezoidal")
_, I0spectro = s0spectro.get("radiance", Iunit=unit)
wtot, Itot = stot.get("radiance", Iunit=unit)
stot_corr = experimental_spectrum(
wtot,
Itot - I0spectro, # hack to remove
# emission from within
# spectrometer
Iunit=unit,
conditions={"medium": "air"},
)
# %% Compare with experiment data
# Plot experimental data
# ------
exp = pd.read_csv(
getValidationCase(
"test_compare_torch_CO2_data/test_compare_torch_CO2_spectrum_JTHT2003.dat"
),
skiprows=5,
delim_whitespace=True,
)
exp.w /= 10 # Angstrom to nm
exp = exp[(exp.w > wlmin) & (exp.w < wlmax)]
sexp = experimental_spectrum(exp.w,
exp.I,
Iunit="mW/cm2/sr",
conditions={"medium": "air"})
if plot:
sexp.plot("radiance",
wunit="nm",
Iunit=unit,
lw=2,
label="Packan 2003")
# Plot calculated
# ----------
stot.plot(
"radiance",
wunit="nm",
Iunit=unit,
nfig="same",
color="r",
ls="--",
label="All: CO$_2$+CO",
)
# Plot internal spectrometer emission
s0spectro.plot(
"radiance",
wunit="nm",
Iunit=unit,
nfig="same",
color="b",
label="CO$_2$ in Spectrometer",
)
stot_corr.plot(
"radiance",
wunit="nm",
Iunit=unit,
nfig="same",
color="r",
lw=2,
label="All-CO$_2$ in Spectro",
zorder=10,
)
sco.plot(
"radiance",
wunit="nm",
Iunit=unit,
nfig="same",
color="g",
ls="-",
label="CO",
)
plt.ylim((0, 9))
plt.legend(loc="upper right")
if save:
plt.savefig("test_compare_torch_CO2_brd20.png")
plt.savefig("test_compare_torch_CO2_brd20k.pdf")
assert get_residual(stot_corr, sexp, "radiance") < 0.02
if verbose:
print(("Finished test_compare_torch_CO2 in {0:.0f}s".format(time() -
t0)))
def test_plot_all_CO2_bandheads(verbose=True, plot=False, *args, **kwargs):
"""In this test we use the :meth:`~radis.lbl.bands.BandFactory.non_eq_bands`
method to calculate separately all vibrational bands of CO2, and compare
them with the final Spectrum.
"""
# Note: only with iso1 at the moment
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
plt.ion()
Tgas = 1000
sf = SpectrumFactory(
wavelength_min=4160,
wavelength_max=4220,
mole_fraction=1,
path_length=0.3,
cutoff=1e-23,
molecule="CO2",
isotope=1,
optimization=None,
verbose=verbose,
)
sf.warnings["MissingSelfBroadeningWarning"] = "ignore"
sf.warnings["NegativeEnergiesWarning"] = "ignore"
sf.warnings["HighTemperatureWarning"] = "ignore"
sf.fetch_databank("hitran")
s_tot = sf.non_eq_spectrum(Tvib=Tgas, Trot=Tgas)
s_bands = sf.non_eq_bands(Tvib=Tgas, Trot=Tgas)
if verbose:
printm("{0} bands in spectrum".format(len(s_bands)))
assert len(s_bands) == 6
# Ensure that recombining gives the same
s_merged = MergeSlabs(*list(s_bands.values()))
assert s_tot.compare_with(s_merged, "radiance_noslit", plot=False)
if verbose:
printm("Recombining bands give the same Spectrum")
# %%
if plot:
s_tot.apply_slit(1, "nm")
s_tot.name = "Full spectrum"
s_tot.plot(wunit="nm", lw=3)
for band, s in s_bands.items():
s.plot(wunit="nm", nfig="same")
plt.legend(loc="upper left")
plt.ylim(ymax=0.25)
# %% Compare with equilibrium bands now
s_bands_eq = sf.eq_bands(Tgas)
s_merged_eq = MergeSlabs(*list(s_bands_eq.values()))
assert get_residual(s_tot, s_merged_eq, "radiance_noslit") < 1.5e-5
return True
def test_plot_all_CO2_bandheads(verbose=True, plot=False, *args, **kwargs):
''' In this test we use the :meth:`~radis.lbl.bands.BandFactory.non_eq_bands`
method to calculate separately all vibrational bands of CO2, and compare
them with the final Spectrum.
'''
# Note: only with iso1 at the moment
if plot: # Make sure matplotlib is interactive so that test are not stuck in pytest
plt.ion()
verbose = True
Tgas = 1000
sf = SpectrumFactory(wavelength_min=4160,
wavelength_max=4220,
mole_fraction=1,
path_length=0.3,
cutoff=1e-23,
molecule='CO2',
isotope=1,
db_use_cached=True,
lvl_use_cached=True,
verbose=verbose)
sf.warnings['MissingSelfBroadeningWarning'] = 'ignore'
sf.warnings['NegativeEnergiesWarning'] = 'ignore'
sf.warnings['HighTemperatureWarning'] = 'ignore'
sf.fetch_databank()
s_tot = sf.non_eq_spectrum(Tvib=Tgas, Trot=Tgas)
s_bands = sf.non_eq_bands(Tvib=Tgas, Trot=Tgas)
if verbose:
printm('{0} bands in spectrum'.format(len(s_bands)))
assert len(s_bands) == 6
# Ensure that recombining gives the same
s_merged = MergeSlabs(*list(s_bands.values()))
assert s_tot.compare_with(s_merged, 'radiance_noslit', plot=False)
if verbose:
printm('Recombining bands give the same Spectrum')
# %%
if plot:
s_tot.apply_slit(1, 'nm')
s_tot.name = 'Full spectrum'
s_tot.plot(wunit='nm', lw=3)
for band, s in s_bands.items():
s.plot(wunit='nm', nfig='same')
plt.legend(loc='upper left')
plt.ylim(ymax=0.25)
# %% Compare with equilibrium bands now
s_bands_eq = sf.eq_bands(Tgas)
s_merged_eq = MergeSlabs(*list(s_bands_eq.values()))
assert get_residual(s_tot, s_merged_eq, 'radiance_noslit') < 1e-5
return True
