Post-process using Specutils

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 test_spectrum

""" We create a synthetic CO spectrum"""

s = (
    test_spectrum(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()

/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/latest/radis/db/molparam.py:252: FutureWarning:

The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead

Calculating Equilibrium Spectrum
Physical Conditions
----------------------------------------
   Tgas                 700 K
   Trot                 700 K
   Tvib                 700 K
   isotope              1,2,3
   mole_fraction        0.1
   molecule             CO
   overpopulation       None
   path_length          1 cm
   pressure             1.01325 bar
   rot_distribution     boltzmann
   self_absorption      True
   state                X
   vib_distribution     boltzmann
   wavenum_max          2030.0000 cm-1
   wavenum_min          2000.0000 cm-1
Computation Parameters
----------------------------------------
   Tref                 296 K
   add_at_used          numpy
   broadening_method    voigt
   cutoff               1e-27 cm-1/(#.cm-2)
   dbformat             hitran
   dbpath               /home/docs/.radisdb/hitran/CO.hdf5
   folding_thresh       1e-06
   include_neighbouring_lines  True
   memory_mapping_engine  auto
   neighbour_lines      0 cm-1
   optimization         simple
   parfuncfmt           hapi
   parsum_mode          full summation
   pseudo_continuum_threshold  0
   sparse_ldm           True
   truncation           50 cm-1
   waveunit             cm-1
   wstep                0.01 cm-1
   zero_padding         3001
----------------------------------------
0.03s - Spectrum calculated

<matplotlib.lines.Line2D object at 0x7fbde62613f0>

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

noise_region = SpectralRegion(2010.5 / u.cm, 2009.5 / u.cm)
spectrum = noise_region_uncertainty(spectrum, noise_region)

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/python3.10/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.

   line_center     line_type  line_center_index
      1 / cm
------------------ ---------- -----------------
2000.7499999999993   emission                14
2000.7899999999993   emission                18
2000.8999999999992   emission                29
 2001.139999999999   emission                53
 2001.199999999999   emission                59
2001.6199999999985   emission               101
2001.6999999999985   emission               109
2001.8599999999983   emission               125
2001.9299999999982   emission               132
2002.0099999999982   emission               140
               ...        ...               ...
2010.5199999999904 absorption               991
2015.2599999999861 absorption              1465
2016.8199999999847 absorption              1621
2019.1899999999825 absorption              1858
 2019.679999999982 absorption              1907
2019.9499999999819 absorption              1934
2021.5199999999804 absorption              2091
 2024.229999999978 absorption              2362
2026.1799999999762 absorption              2557
2028.8099999999738 absorption              2820
Length = 247 rows
/home/docs/checkouts/readthedocs.org/user_builds/radis/checkouts/latest/radis/tools/plot_tools.py:619: UserWarning:

Couldn't add Ruler tool (still an experimental feature in RADIS : please report the error !)


<matplotlib.patches.Polygon object at 0x7fbdf62cacb0>

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