Post-process using SpecutilsΒΆ
Find peaks or uncertainties using the specutils library. A Radis Spectrum
object can easily be converted to a specutils specutils.spectra.spectrum1d.Spectrum1D
using to_specutils().
Below, we create a noisy spectrum based on a synthetic CO spectrum,
we convert it to specutils, add uncertainties by targeting a
noisy region, then determine the lines using find_lines_threshold() :
import astropy.units as u
import numpy as np
from radis import spectrum_test
""" We create a synthetic CO spectrum"""
s = (
spectrum_test(molecule="CO", wavenum_min=2000, wavenum_max=2030)
.apply_slit(1.5, "nm")
.take("radiance")
)
s.trim() # removes nans created by the slit convolution boundary effects
noise = np.random.normal(0.0, s.max().value * 0.03, len(s))
s_exp = s + noise
s_exp.plot()

--------------------------------------------------------------------------------
CO - HITRAN - Downloading database
--------------------------------------------------------------------------------
Download:
- All files already downloaded.
Caching to HDF5/H5 format:
- All files already cached.
0.03s - Loaded database
Calculating Equilibrium Spectrum
Physical Conditions
----------------------------------------
Tgas 700 K
isotope 1,2,3
medium air
mole_fraction 0.1
path_length 1 cm
pressure 1.01325 bar
self_absorption True
species CO
state X
wavenum_max 2030.0000 cm-1
wavenum_min 2000.0000 cm-1
Computation Parameters
----------------------------------------
Tref 296 K
add_at_used numpy
broadening_method voigt_poly
cutoff 1e-27 cm-1/(#.cm-2)
dbformat hitran
dbpath /home/docs/.radisdb/hitran/CO.h5
diluent air
folding_thresh 1e-06
include_neighbouring_lines True
isatom False
isneutral None
lbfunc None
memory_mapping_engine auto
neighbour_lines 0 cm-1
optimization simple
parsum_mode full summation
pfsource default
potential_lowering None
pseudo_continuum_threshold 0
sparse_ldm True
truncation 50 cm-1
waveunit cm-1
wstep 0.01 cm-1
zero_padding 3001
----------------------------------------
0.02s - Spectrum calculated
<matplotlib.lines.Line2D object at 0x784ca86caf90>
Determine the noise level by selecting a noisy region from the graph above :
spectrum = s_exp.to_specutils()
from specutils import SpectralRegion
from specutils.manipulation import noise_region_uncertainty
from specutils.spectra import Spectrum1D
noise_region = SpectralRegion(2010.5 / u.cm, 2009.5 / u.cm)
spectrum = noise_region_uncertainty(spectrum, noise_region)
if not isinstance(spectrum, Spectrum1D):
spectrum = Spectrum1D(
flux=spectrum.flux,
spectral_axis=spectrum.spectral_axis,
uncertainty=getattr(spectrum, "uncertainty", None),
wcs=getattr(spectrum, "wcs", None),
mask=getattr(spectrum, "mask", None),
meta=getattr(spectrum, "meta", None),
)
/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/develop/radis/spectrum/spectrum.py:4394: AstropyDeprecationWarning: The Spectrum1D class is deprecated and may be removed in a future version.
Use Spectrum instead.
return Spectrum1D(
/home/docs/checkouts/readthedocs.org/user_builds/radis/conda/develop/lib/python3.14/site-packages/astropy/nddata/mixins/ndslicing.py:68: AstropyDeprecationWarning: The Spectrum1D class is deprecated and may be removed in a future version.
Use Spectrum instead.
return self.__class__(**kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/radis/conda/develop/lib/python3.14/site-packages/specutils/spectra/spectrum.py:582: AstropyDeprecationWarning: The Spectrum1D class is deprecated and may be removed in a future version.
Use Spectrum instead.
return self.__class__(**alt_kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/develop/examples/2_Experimental_spectra/plot_specutils_processing.py:47: AstropyDeprecationWarning: The Spectrum1D class is deprecated and may be removed in a future version.
Use Spectrum instead.
spectrum = Spectrum1D(
Find lines :
from specutils.fitting import find_lines_threshold
lines = find_lines_threshold(spectrum, noise_factor=2)
print(lines)
s_exp.plot(lw=2, show_ruler=True)
import matplotlib.pyplot as plt
for line in lines.to_pandas().line_center.values:
plt.axvline(line, color="r", zorder=-1)
s.plot(nfig="same")
plt.axvspan(noise_region.lower.value, noise_region.upper.value, color="b", alpha=0.1)

/home/docs/checkouts/readthedocs.org/user_builds/radis/conda/develop/lib/python3.14/site-packages/specutils/analysis/flux.py:289: AstropyUserWarning: Spectrum is not below the threshold signal-to-noise 0.01. This may indicate you have not continuum subtracted this spectrum (or that you have but it has high SNR features).
If you want to suppress this warning either type 'specutils.conf.do_continuum_function_check = False' or see http://docs.astropy.org/en/stable/config/#adding-new-configuration-items for other ways to configure the warning.
warnings.warn(message, AstropyUserWarning)
line_center line_type line_center_index
1 / cm
------------------ ---------- -----------------
2000.6499999999994 emission 4
2001.3099999999988 emission 70
2001.7899999999984 emission 118
2001.8599999999983 emission 125
2001.9799999999982 emission 137
2001.9999999999982 emission 139
2002.0599999999981 emission 145
2002.149999999998 emission 154
2002.239999999998 emission 163
... ... ...
2001.6399999999985 absorption 103
2002.5599999999977 absorption 195
2004.6399999999958 absorption 403
2009.929999999991 absorption 932
2010.1299999999908 absorption 952
2010.1699999999908 absorption 956
2014.5999999999867 absorption 1399
2015.1899999999862 absorption 1458
2015.329999999986 absorption 1472
2019.5499999999822 absorption 1894
Length = 196 rows
/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/develop/radis/tools/plot_tools.py:615: UserWarning: Couldn't add Ruler tool (still an experimental feature in RADIS : please report the error !)
warn(
<matplotlib.patches.Rectangle object at 0x784ca94c4f50>
Note: we can also create a RADIS spectrum object from Specutils
specutils.spectra.spectrum1d.Spectrum1D :
from radis import Spectrum
s2 = Spectrum.from_specutils(spectrum)
s2.plot(Iunit="mW/cm2/sr/nm", wunit="nm")
s_exp.plot(Iunit="mW/cm2/sr/nm", wunit="nm", nfig="same")
assert s_exp == s2

Total running time of the script: (0 minutes 1.492 seconds)