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Remove the half life weights from the fill_spectrum functions and have it in its own function which can be applied seperately.
Edit 15/04/2015:
Jeanne's suggestion is to remove the timing axis from the Spectra class and have the timing applied as analytic function. This means that whenever you return a number of counts from the spectrum i.e. in Spectra.sum() you would need to multiply the number of events from the 2D spectrum by the result of the correct time model.
The text was updated successfully, but these errors were encountered:
I think we shouldn't do it. Realistically the best precision in time we can get counts in months or weeks(?), i.e. I doubt that anyone would want (would be able) to study the changes in spectra within days or hours. So what we can do is to
decide what would be a time unit: a week or a month
change the number of time bins from 10 years to 10_52.18 weeks or 10_12 months
fill the spectrum as we were doing it before
We have a function that slices spectrum, so it will be possible to study any time block.
Remove the half life weights from the fill_spectrum functions and have it in its own function which can be applied seperately.
Edit 15/04/2015:
Jeanne's suggestion is to remove the timing axis from the
Spectra
class and have the timing applied as analytic function. This means that whenever you return a number of counts from the spectrum i.e. inSpectra.sum()
you would need to multiply the number of events from the 2D spectrum by the result of the correct time model.The text was updated successfully, but these errors were encountered: