Refactor run temp dependant function #876
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| norm = Normalize(vmin=intensity_start, vmax=intensity_end) | ||
| slice = compute_slice(selected_ws, x_axis, y_axis, norm_to_one) | ||
| self._cache_slice(slice, colourmap, norm, sample_temp, x_axis, y_axis) | ||
| self._cache_slice(slice, colourmap, norm, None, x_axis, y_axis) |
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Would it be possible to check with the users that caching the sample temperature is no longer wanted before we remove it?
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This feature was accidently removed a couple of months ago, so I think we're safe to remove. I'll raise an issue to investigate whether this should be reimplemented. At this point it's more effort to fix than to remove.
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Yes, I think it's fine to remove it. In practice different slices often have different temperatures so the caching is not always useful.
Is _cache_slice referenced elsewhere where it needs the temperature? If not, maybe it can be refactored to remove this argument, instead of passing None?
Ideally what we could have in future is to cache the name of the log variable which holds the temperature and use that to determine the temperature of the slice. However (1) this was never implemented, and more importantly (2) the .nxspe file format we often use discards all the log information :( the reduced/processed .nxs files retains them but it's hard to get users to change to using it...
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It is not used elsewhere. I think I will leave it as is now, and implement the feature as you outline shortly #877.
Description of work:
Refactors a large function that was getting hard to work with.
Also, I've removed the caching of sample log fields on the
slice_plotter_presenter. This used to mean that you only had to select a sample log for temperature once, then it would be used for all other subsequent slices. I accidently bypassed this functionality a while ago when adding intensity change for cuts, taking the view that the temperature should be specified for every new dataset introduced.If users take issue with this, I can reimplement.
To test: