Synthesizing and fitting coherent anti-Stokes Raman spectra in Python.

• Documentation
• Project homepage
• Live demo

Having no access to the source codes of any of the existing CARS programs, I started this project simply as a way to learn more about CARS. I ended up spending a lot of time sifting through literatures from the past decades, trying to figure out what were done to analyze experimental CARS spectra and how they were implemented. The latter proved rather challenging as specific implementations weren't always laid out in sufficient (mathematical) details to facilitate comprehension and replication (e.g., things as trivial as units for different constants weren't always made clear in some publications).

In an effort to put together a fully-functioning CARS fitting program, I thought it would perhaps benefit other CARS practitioners (especially the newcomers) if I open source my implementations. I hope to also benefit from this transparency and openness to public scrutiny. Although the "draft" code (not available in this public repo) already lives up to my original purpose (least-square fit of experimental broadband CARS spectra), it is most likely not error-free and has a lot of room left for improvement. Therefore, I plan to rewrite the important modules (spectrum synthesis and least-square fit) and slowly bring all features (see below) up to date. I am also looking forward to feedbacks and potential collaborators from the community.

$ pip install carspy

See installation guide for alternative methods.

To try out the basic functions of carspy, head over to the Live demo, which is a webapp built with Plotly/Dash and hosted on Heroku to showcase how CARS spectrum could be synthesized and fitted with carspy. The computation/loading speed is largely limited by the cloud server and Internet speed. For a much better performance, download or fork the carspy app repository and run the app locally.

• Synthesize CARS spectrum

• Least-square-fit of a synthesized spectrum

• CARSpy (stands for Coherent Anti-Stokes Raman Spectroscopy):

• The CARS model:

• Readily extendable for species other than N2 and for other CARS setup other than typical broadband CARS.
• Option to incorporate equilibrium composition using an external chemical equilibrium calculator (such as cantera), such that temperature is the only fitting parameter for thermometry.
• Vibrational and rotational nonequilibrium: vibrational temperature can be varied independently from rotational temperature.

Figure 1 Synthesized CARS spectra in N2 at 1 atm, 2400 K, with a pump linewidth of 0.5 cm-1, using Voigt lineshape and cross-coherence convolution.

Figure 2 Synthesized CARS spectra in N2 at 10 atm, 2400 K, with a pump linewidth of 0.5 cm-1, using modified exponential gap law (MEG) and cross-coherence convolution.

The above features currently present in the draft code will be gradually improved and included in the main branch. Here is a tentative plan:

• (Done) Module for synthesizing CARS spectra (optional with cantera)
• (Done) Module for least-square fit (optional with lmfit)
• (Done) Parallel processing example with joblib
• (Mid-term) EMEG and XMEG for high-pressure combustion environments
• (Long-term) Enrich documentation
• (Long-term) Other common diatomic species
• (Long-term) Dualpump/Wide CARS

Please consider citing this repository if you use carspy for your publications as:

@misc{Yin2021,
  author = {Yin, Zhiyao},
  title = {CARSpy: Synthesizing and fitting coherent anti-Stokes Raman spectra in Python},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/chuckedfromspace/carspy}}
}

• A copy of the NRC report (TR-GD-013_1989) was kindly provided by Dr. Gregory Smallwood and his colleagues at NRC, which has significantly eased the difficulty of understanding some of the key theories in synthesizing CARS spectra.
• This package was initially created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.