Closed CSSFrancis closed 1 year ago
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SyHuang19 commented on 2023-06-08T20:15:14Z ----------------------------------------------------------------
Line #3. label=["Chi^2","y_offset", "Orientation", "Peak1 Amplitude", "Peak2 Amplitude", "Sigma"],
It's probably better to change "y_offset" to "Background" since its more inline with other labels which have physical meanings to thems.
Good idea!
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SyHuang19 commented on 2023-06-08T20:15:16Z ----------------------------------------------------------------
I'm not sure why this line is repeated again
CSSFrancis commented on 2023-06-08T20:24:06Z ----------------------------------------------------------------
Yea I think did that accidentally
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SyHuang19 commented on 2023-06-08T20:15:17Z ----------------------------------------------------------------
Line #3. both.plot(navigator_kwds={"cmap":cmap,
Can you add a label to either the color bar or the image?
I think that is one of the limitations to plotting interactively with hyperspy. I don't know if you can set a label or custom ticks for a color bar.
I was going to add that upstream to hyperspy
eventually but never got around to it.
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SyHuang19 commented on 2023-06-08T20:15:19Z ----------------------------------------------------------------
Line #3. im = axs.imshow(peaks.isig[2].data,
Same as above
Here I can label the color bar! That is a good idea
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SyHuang19 commented on 2023-06-08T20:19:26Z ----------------------------------------------------------------
I wouldn't say these are helper functions. They are actual fitting functions.
I think that is one of the limitations to plotting interactively with hyperspy. I don't know if you can set a label or custom ticks for a color bar.
I was going to add that upstream to hyperspy
eventually but never got around to it.
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@SyHuang19 @TimothyLambdenCambridge Is there a chance you can look at this notebook for doing Orientation analysis on molecular glasses and make sure things look good?
I think that this is a good example of how to integrate some custom functions in with hyperspy/ pyxem so while these features might be added later I think a good first step is to show how this can be done. In order to add this completely into hyperspy/pyxem we would probably have to make some changes into hyperspy as to how the model fitting works when you want to use multiple cores.
As these datasets can get a little large it is important to have the ability to operate in parallel.