moose-spectra 0.3.0

A package to simulate diatomic optical emission spectra relying on line-by-line databases compiled for MassiveOES

Moose: Molecular optical emission spectroscopy for Python

Welcome to Moose, a python package for simulating optical emission spectra for diatomic molecules.

Moose was born out of a need to fit rotational and vibrational temperatures for a large set of data.

For this it uses line-by-line databases, assuming a Boltzmann distribution for rotational and/or vibrational temperatures.

On top of that Moose is intended to be minimal: it provides you some basic tools to do just that, simulate some spectra.

It is up to you to read and sanitize experimental data, that you would like to fit based on these simulations, for instance.

In addition, Moose is quite adaptable: you can use it as a foundation for more elaborate models and analysis.

Put differently, Moose is aimed at helping you: you can integrate and adapt it to your workflow, rather than the reverse.

To get a better grasp of how it works under the hood, see the online documentation for code reference and examples.

Supported bands

Since Moose reuses the databases from MassiveOES, it supports the same bands for fitting. Details can be found in data_sources.txt, and the page with information on citing.

Molecule	Band
OH	A-X
N2+	B-X
NH	A-X
NO	B-X
N2	C-B
C2	Swan
CN	B-X

Other bands

If you have suitable line-by-line data available for other molecular bands, you can also use Moose to fit those species.

You'd need to provide the data as a Dataframe that the function [create_stick_spectrum][https://antoinetue.github.io/Moose/latest/reference/Moose/Simulation/#Moose.Simulation.create_stick_spectrum] can work with, or write your own function.

Dependencies

Dependencies are mainly specified in the pyproject.toml. See below for a table of the core dependencies, which will be installed along with Moose if they are not present yet. Version numbers are just indications of the package versions that were used to develop the code, your mileage may vary using either older versions. Part of the automated test suite checks for compatibility of numpy and pandas version <= 2.0 and >= 2.0, to ensure these older versions remain supported.

Furthermore, even though the project does not import by default lmfit and contains no functions that specifically rely on it, the code contains convenience functions that were specifically written for usage with the lmfit.Model and lmfit.Parameters classes. If lmfit is installed in the active environment, some additional convenience function will be imported.

Further optional dependecies can be installed by specifying the appropriate feature flags when installing Moose, see also this doc page.

These dependencies will be installed when you install Moose using pip or uv.

Package	Version
Numpy	>= 1.24.2
Pandas	>= 1.5.3
scipy	>= 1.11.1
xxhash	>= 3.6.0
lmfit (optional)	>= 1.2.0

Installation

To simply install Moose itself, running the following suffices (note that on PyPI it is known as moose-spectra):

pip install moose-spectra

Installing Moose with additional optional dependencies is described on the documentation page on getting started.

You can also install the lastest development version of Moose, which may not be released to PyPI yet, using this git repository directly:

pip install git+https://github.com/AntoineTUE/Moose.git

The docs for this development version can be found here

Basic usage

A basic example demonstrating the usage is as follows. It assumes that there is a pandas DataFrame (called data) containing several spectra normalized on the interval (0,1), with the first column being a shared wavelength axis. It is important that the wavelength range over which we query the database (wl_interval) plus the padding range wl_pad, is larger than the experimental range plus the possible shift (mu) between simulation and experiment.

Extending the fitting over multiple cores/processes can be done by using for instance the excellent Dask library, via i.e. client.map. More elaborate examples are available via the online documentation.

import Moose, lmfit

wl_interval = (320,345) # Wavelength interval over which to simulate the spectrum
db = Moose.query_DB('N2CB.db', wl_interval)


# Create the lmfit.Model and lmfit.Parameters object needed for the fit.
model = lmfit.Model(Moose.model_for_fit, sim_db=db)
params = lmfit.create_params(**Moose.default_params)
# Or use some default suggested parameters for thermal fitting, i.e. T_rot==T_vib
# params = lmfit.create_params(**Moose.thermal_default_params)

# Perform the fit
fits = []
for col in data.columns[1:]:
    fits.append(model.Fit(data[col].values, x=data['Wavelength'].values, params=params))

You can also try a cloud instance provided by Binder to run the Moose examples, or try it with your own data: .

Copyright notice from MassiveOES

The original software is massiveOES developed by the Masaryk University available from: https://bitbucket.org/OES_muni/massiveoes.

Publications

VORÁČ, Jan; SYNEK, Petr; PROCHÁZKA, Vojtěch; HODER, Tomáš. State-by-state emission spectra fitting for non-equilibrium plasmas: OH spectra of surface barrier discharge at argon/water interface. Journal of Physics D: Applied Physics. 2017, 50(29), 294002. DOI: https://doi.org/10.1088/1361-6463/aa7570.

VORÁČ, Jan; SYNEK, Petr; POTOČŇÁKOVÁ, Lucia; HNILICA, Jaroslav; KUDRLE, Vít. Batch processing of overlapping molecular spectra as a tool for spatio-temporal diagnostics of power modulated microwave plasma jet. Plasma Sources Science and Technology 26.2 (2017), 025010. DOI: https://doi.org/10.1088/1361-6595/aa51f0.

Original sources for databases

Database	Source	DOI
OHAX	J. Luque and D.R. Crosley, J. Chem. Phys., 109, 439 (1998)	https://dx.doi.org/10.1088/0022-3727/39/17/015
OHAX	L.R. Williams and D.R. Crosley, J. Chem. Phys, 104, 6507 (1996)	https://doi.org/10.1063/1.471371
N$_2^+$BX	J. Luque and D.R. Crosley, “LIFBASE: Database and Spectral Simulation Program (Version 1.5) ”, SRI International Report MP 99-009 (1999).
NHAX	Western C 2016 PGOPHER—a program for simulating rotational structure, Version 9.0.100, University of Bristol
NHAX	Ram R and Bernath P 2010 J. Mol. Spectrosc. 260 115–9	https://doi.org/10.1016/j.jms.2010.01.006
NHAX	Lents J 1973 J. Quant. Spectrosc. Radiat. Transfer 13 297–310	https://doi.org/10.1016/0022-4073(73)90061-7
NHAX	Seong J, Park J K and Sun H 1994 Chem. Phys. Lett. 228 443–50	https://doi.org/10.1016/j.jms.2010.01.006
NOBX	J. Luque and D.R. Crosley, “LIFBASE: Database and Spectral Simulation Program (Version 1.5) ”, SRI International Report MP 99-009 (1999).
N$_2$CB	Nassar H, Pellerin S, Musiol K, Martinie O, Pellerin N and Cormier J 2004. Phys. D: Appl. Phys. 37 1904	https://doi.org/10.1088/0022-3727/37/14/005
N$_2$CB	Laux C O and Kruger C H 1992 J. Quant. Spectrosc. Radiat. Transfer 48 9–24	https://doi.org/10.1016/0022-4073(92)90003-M
N$_2$CB	Faure G and Shko’nik S 1998 J. Phys. D: Appl. Phys. 31 1212	https://doi.org/10.1088/0022-3727/31/10/013
C$_2$ Swan	James S.A. Brooke and Peter F. Bernath and Timothy W. Schmidt and George B. Bacskay; Journal of Quantitative Spectroscopy and Radiative Transfer; 2013	https://doi.org/10.1016/j.jqsrt.2013.02.025
C$_2$ Swan	Carbone, Emile and D'Isa, Federico and Hecimovic, Ante and Fantz, Ursel; PSST; 2020;	https://doi.org/10.1088/1361-6595/ab74b4

License information

This project is licensed under the MIT license. See LICENSE.