Hannah Robarts

Hi Laura, that's a good point. I think it would have to depend on the reader - so if we have use cases where we know everything is within one...

Some examples: Normalise all data with a scalar value ``` processor = FluxNormaliser(flux=10) processor.set_input(data) data_norm = processor.get_output() show2D([data, data_norm], ['Data','Normalised data']) ```  Normalise data with a scalar value per...

Updated to plot the mean, minimum and maximum in the roi 

Updated so `preview_configuration()` still works on data with one angle  Also works on 2D data where the roi is specified with one of horizontal or vertical. Plot the roi...

@gfardell we discussed whether the preview_configuration should just be an option in logging. At the moment I've left it as a function the users can access - what do you...

Hi @lauramurgatroyd thank you for your review! I have made lots of updates - More efficiently access data in process (following conversation with @gfardell ) - Error if horizontal and...

@lauramurgatroyd and I tried to test a simplified version of the FluxNormaliser using numba to speed up the for loop in `process()` ``` @numba.njit(parallel=True) def parallel_FN(data, flux, N): out =...

Thank you for review @gfardell ! I think I've updated out so it can process in place. Here are some measurements of the execution time for the real FluxNormaliser class....

Compare different methods of indexing the data array with numba - Test 1 ravel at each projection then scale whole projection ``` @numba.njit(parallel=True) def numba_loop_1(flux, target, num_proj, proj_size, out): for...

Repeat the above tests on windows with 20 CPUs 1024x1024 pixels - Compare different numba and serial implementations  - Using numba implementation flatten array then scale whole projection (test...