This thread on astropy-dev might be of interest to people here, particularly those of you working on simulated observations.
---------- Forwarded message ---------- From: Perry Greenfield email@example.com Date: Fri, Mar 7, 2014 at 11:31 AM Subject: [astropy-dev] Adding optional information to models To: firstname.lastname@example.org
We've been looking how to merge the capabilities of models with the spectral objects we've been using for pysynphot (a topic that will be raised in a separate thread) and one of the issues that has surfaced has been how do we handle the "waveset" info we have some of our current analytic models in pysynphot.
To explain a bit, the analytic models have a method, that returns a set of input values that is judged to sample the feature reasonably well. The purpose of this is that when one is combining such models and then binning, integrating, or plotting them later, it is possible to lose fine structure in any random sampling scheme. A classic example is for a Gaussian. If the Gaussian has a small sigma such that if it is evaluated on a regular grid or some other source of points to sample, it may not appear in an integration or plot of the result if its main structure falls in between those samples.
Pysynphot gets around this by making a union of all wavelength samples present in all tabular data. But analytic components don't have a wavetable by default since they can be evaluated at any wavelength. Still, it is useful for them to have some equivalent to that so that a feature like a Gaussian isn't missed in the other existing wavelength tables. So we added an optional waveset to analytic models that had features that might be missed. For a Gaussian, that means giving a set of points that sample the peak reasonably well (what is "reasonable" is of course a somewhat murky concept). Perhaps it could be called the sampleset.
In our reworking of pysynphot, the design under consideration would need this info in the model itself. So we want to consider an attribute for models that provide a set of samples to cover such a need. If the analytic model doesn't need it (e.g., a power law), it can default to be an empty array. When models are combined (e.g., adding two different Gaussians), the resulting sample set would be the union of the two input models (this is a recursive model). For pysynphot, this union was computed recursively on the fly when requested, though a more static approach could be used as well, or some caching scheme.
People that wanted to add a new model wouldn't have to handle this if it wasn't needed for their use. But for some kinds of models, it can be quite useful for the same purposes (binning, integration, or plotting).
For 1-d models, it is a very straightforward thing to implement. For 2-d and higher dimensions, there are a few alternatives for saving the info (gridded vs ungridded?).
Any reason we shouldn't add this capability to models?
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