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MNGuenther / allesfitter

allesfitter is a convenient wrapper around the packages ellc (light curve and RV models), dynesty (static and dynamic nested sampling) emcee (Markov Chain Monte Carlo sampling) and celerite (Gaussian Process models).
MIT License
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TTVs preparation plots with gaps #46

Closed martindevora closed 2 years ago

martindevora commented 2 years ago

Hi,

when running the allesfitter ttvs module for curves with long gaps (like TESS ones for very separated sectors) the plots of the transits within the curve are very complicated to be assessed: less x-axis values and huge long horizontal monolithic plot. I don't know what can of improvements you could do (maybe splitting the document with n plots -one per dataset without gaps-), but just noticed that it is complicated to be read. I attach an example. ttv_preparation_SOI_2

Regards.

MNGuenther commented 2 years ago

Ah I see. We can probably just replace this plot with either the tessplot() or the monthplot() function from allesfitter.plotting. Will look into it.

MNGuenther commented 2 years ago

I have added the option to tailor this plot now in versions >=1.2.9 (freshly pushed to GitHub and PyPI). You can now use any of the following options:

allesfitter.prepare_ttv_fit('allesfit_with_ttvs')
allesfitter.prepare_ttv_fit('allesfit_with_ttvs', style='fullplot') #same as before, this is the default
allesfitter.prepare_ttv_fit('allesfit_with_ttvs', style='brokenplot')
allesfitter.prepare_ttv_fit('allesfit_with_ttvs', style='chunkplot')
allesfitter.prepare_ttv_fit('allesfit_with_ttvs', style='monthplot')
allesfitter.prepare_ttv_fit('allesfit_with_ttvs', style='tessplot')

All of these are explained and explored in the new tutorial_17 python script and Jupyter notebook. I will also update tutorial_11 with these changes soon.