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 0x71bd67d7ef90>
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/latest/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/latest/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/latest/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/latest/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/latest/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
------------------ ---------- -----------------
2000.6599999999994 emission 5
2001.109999999999 emission 50
2001.159999999999 emission 55
2001.6799999999985 emission 107
2001.9299999999982 emission 132
2001.9799999999982 emission 137
2002.0299999999982 emission 142
2002.0499999999981 emission 144
2002.209999999998 emission 160
... ... ...
2005.1899999999953 absorption 458
2005.579999999995 absorption 497
2007.3999999999933 absorption 679
2010.0799999999908 absorption 947
2010.3299999999906 absorption 972
2014.5599999999868 absorption 1395
2019.6499999999821 absorption 1904
2021.6699999999803 absorption 2106
2022.10999999998 absorption 2150
2023.8599999999783 absorption 2325
Length = 221 rows
/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/latest/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 0x71bd67844f50>
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 1.502 seconds)