Feature request: number of orbits and time to merger in NRPyPN about nrpytutorial HOT 3 OPEN

Thanks for the request Gabriele! I really want this feature as well.

To this end, we are working on an optimized version of a state-of-the-art gravitational wave approximant, SEOBNRv4P, which is calibrated against NR binary black hole simulations. Once this is ready, I think it'll provide a pretty good estimate of the duration of a simulation.

For now, barring downloading & installing LALSuite (which will give direct access to the official SEOBNRv4P), another idea would be to download a version of
https://bitbucket.org/eob_ihes/teobresums/src/master/
to get an estimate.

from nrpytutorial.

Thanks for your response.

To this end, we are working on an optimized version of a state-of-the-art gravitational wave approximant, SEOBNRv4P, which is calibrated against NR binary black hole simulations. Once this is ready, I think it'll provide a pretty good estimate of the duration of a simulation.

I am (very slowly) developing a package to prepare Einstein Toolkit simulations with a much higher interface than the .par files (https://github.com/Sbozzolo/Jhuki). At the moment, I use NRPyPN to compute the momenta for quasi-circular inspirals. This is where I would like to have functions to estimate number of orbits and merger time.

I can already use PyCBC to estimate the number of orbits and the time to merger using existing approximants. However, PyCBC comes with loads of dependencies (including LALSuite) that I would like to avoid to keep my package easily installable on as many machines as possible (I already had a lot of dependency annoyances with PyCBC/LALSuite for kuibit). So, for my use case, the most ideal solution would be to have a couple of additional functions in NRPyPN. Given what is already in the code, this sounds something that should be doable, but I don't know anything about post-Newtonian theory, so maybe I am completely wrong.

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This looks like a very interesting and useful package Gabriele. Indeed dependencies can make a mess for new users, and we'll continue to try to minimize them in NRPy+ development. In fact it's the reason I pointed to TEOBResumS as a possible option (though, it's not in Python).

Speaking of things not in Python, I recently wrote a very small C code that does the same thing as NRPyPN, only ~1000x faster (using NRPy+ C code output capabilities). I'd be happy to share if you are interested.

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