Hough indexing of lazy EBSD patterns errors with PyEBSDIndex 0.3 about kikuchipy HOT 11 CLOSED

I believe they are related to these changes in band_detect_cl.py: USNavalResearchLaboratory/PyEBSDIndex@88136e6#diff-b1a7457abbba36e579c00423396bc962373e6fcb044b4c43a3c2ad0384cca8be

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What is the data type of your dask array?

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In this example it is uint8.

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Trying to figure out exactly what is up ... I see Numba error, but seems like it is failing on making an opencl buffer?

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Ah - I see what is up ... I think. When it fails to run the OpenCL version, it attempts the numba version. Both are failing for similar reasons. Let me look at what I can do here.

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Thank you for having a look, really appreciate it! Any potentially useful information you have about the problem is useful.

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If only all the fixes where this easy (at least I hope it is this easy). Maybe not the most elegant solution, but it looks like throwing an image = np.asarray(image) at the top of the radon functions should work. This will make a copy of the chunk of patterns read in, but I think I was doing that before anyway. While I have made many improvements on making PyEBSDIndex faster, I have not worried much about the memory efficiency.

Some insight:

• Through some staring some Windows system monitoring tools, it looked like I was spending just about as much time transferring patterns to the GPU as calculating (as is the way with GPU compute). I figured it would not be that bad, that if the patterns are uint8, or uint16, I could just send them as-is to the GPU, and convert to float on the GPU. In fact - I made the decision that if the patterns were float -- I actually convert them to uint16, and then scale the results (anyone who wants 32-bit precision on pattern quality really needs to justify it to me I guess).
• Then I figured, well, I might as well do my data transpose on the GPU as well.

Thus, I stoped converting to float on the CPU (which would have made a copy in this case), and tried sending a version as is to the GPU. OpenCL and Numba will work with numpy, but not Dask arrays ... thus the issue on both pipelines.

I will send in a PR for you to test.

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BTW - old version on my machine: 780 pats/s, new 1180 pats/s

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Your changes in USNavalResearchLaboratory/PyEBSDIndex#60 fixes my issue here, thanks!

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PyEBSDIndex 0.3.2 is now out on PyPi and conda-forge, and should be the fix for this issue.

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Thank you so much for the quick handling of my issue here. Have a good weekend!

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