Closed gully closed 3 years ago
Hooray, lots of these methods now exist in our sibling-package gollum! 🍾
Two methods do not yet exist though: "fill_nans" or "fill_gaps" would be useful for applications where you want to maintain the pixel lengths, and .cross_correlate(input_spec)
would be useful for many science applications. I'll add those as standalone Issues
We should put NotImplementedError placeholders planned methods. This practice will help show what methods are possible but just happen not to be implemented since the package is so new. Here is a brainstorm of such methods:
.match_stellar_model(model_spec)
would resample an input high resolution synthetic stellar spectrum to the observed spectrum's spectral resolution and wavelength sampling.match_telluric_model(model_spec)
would resample an input synthetic telluric spectrum to the observed spectrum's spectral resolution and wavelength sampling.interact_model()
when called from a Jupyter notebook would display an interactive bokeh dashboard for either stellar or substellar spectral libraries. We have a working demo for substellar atmospheres called intuition..cross_correlate(input_spec)
would cross correlate an input spectrum with the observed spectrum, returning the cross correlation signal as a function of velocity separation..fill_nans()
would fillNaN
's with an interpolated signal rather than simply drop them..refine_telluric_correction()
would refine the telluric correction provided by default from e.g. IGRINS. Occasionally the telluric division is offset and yields spike artifacts from over and under correction. This method would adjust for such issues.Some of the methods require access to synthetic spectral models. We'll probably want to think about how to support synthetic spectral models. Do we want that to be a separate package or a module of
muler
?