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Inca motion vector (#376) #409

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@aitaten aitaten commented Jul 23, 2024

Introduction of the correlation based motion vector computation as is currently computed in INCA. The error motion computation, and the NWP vector wind correction has not been included. However, this correlation based computation is similar to the original TREC computation adding a new OF method. The original idea is coming from #376

@aitaten aitaten added the enhancement New feature or request label Jul 23, 2024
@aitaten aitaten requested a review from dnerini July 23, 2024 15:13
@aitaten aitaten self-assigned this Jul 23, 2024
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Thanks @aitaten this looks very cool! I left few initial comments to get started with the review

pysteps/motion/correlation.py Outdated Show resolved Hide resolved
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"correlation" sounds a bit too vague, can we use a more precise name? is this approach equivalent to "TREC"?

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TREC stands for tracking radar echo by cross-correlation (TREC). So I would say it is quite equivalent. I will change the name accordingly

pysteps/motion/correlation.py Outdated Show resolved Hide resolved

return IS_tmp, JS_tmp

else:
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don't need this since the function won't be executed without numba

With *T* > 2, all the resulting obtained vectors are pooled together for
the final interpolation on a regular grid.

settings: dict, optional
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where are these settings documented? can't we convert this dictionary into optional arguments?

Optional dictionary containing keyword arguments for the correlation
based algorithm.

interp_method: {"idwinterp2d", "rbfinterp2d"}, optional
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out of curiosity, what approach uses INCA to densify the motion field?

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I does a cubic interpolation. I can also implemented if you want ... but again, It is done with numba :(

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@dnerini dnerini Aug 5, 2024

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do you think it could be added as an additional interpolator in the pysteps/utils/interpolate.py module? (I wouldn't mind the numba optional requirement)

if NUMBA_IMPORTED:

@jit(nopython=True)
def compute_motion_numba(image1, image2, NI, NJ, nthin, IS_tmp, JS_tmp):
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can't we simply have a non accelerated version in case numba is not installed instead of throwing an error?

nr_fields = input_images.shape[0]
domain_size = (input_images.shape[1], input_images.shape[2])

PMIN = settings.get("PMIN", 0.05)
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we usually use ALL_CAPS names for global constants only.

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Thanks ... I did some changes on these and other variables

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need to add numba to the test requirements

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2 participants