This is a repository for the reduction pipeline detailed in Wakeford, et al., 2016, ApJ. The method implements marginalization across a series of models to represent stochastic models for observatory and instrument systematics. This is primarily for HST WFC3, however, may be extended to STIS in the future.
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Use model fit returned by the Sherpa fit instead of recalculating it manually #36
After we run the second fit with Sherpa, we currently recalculate the fitted model (fit_model) manually, instead of reading it directly from the Sherpa objects. We need to figure out how to do this directly with Sherpa.
I believe that all we have to do is
fit_model = tdata.eval_model(tmodel)
but when I compare this array to the result of our manually calculated fit_model array, there is a difference in numerical accuracy, with the manual one seeming more accurate. We have to check why that is and whether we can change that.
After we run the second fit with Sherpa, we currently recalculate the fitted model (
fit_model) manually, instead of reading it directly from the Sherpa objects. We need to figure out how to do this directly with Sherpa.I believe that all we have to do is
but when I compare this array to the result of our manually calculated
fit_modelarray, there is a difference in numerical accuracy, with the manual one seeming more accurate. We have to check why that is and whether we can change that.