[FIX] Correctly normalize off-axis projections for octree datasets #5077
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With the code from #5071 (adapted so that the same colorbar is used):
The results are now unbiased (within a few percent) and show only minor deviations (at cell edges, as one would expect due to the projection strategy). |
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@cphyc we haven't been backporting anything to the "stable" branch for years at this point, and backport- labels are not necessary with proper milestoning :) |
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Oopsy sorry! I keep on forgetting how the labels work :) |
chrishavlin
previously approved these changes
Dec 6, 2024
| @@ -483,6 +485,10 @@ def temp_weightfield(field, data): | |||
| Nx=resolution[0], | |||
| Ny=resolution[1], | |||
| ) | |||
| # Note: since dx was divided by wmax, we need to rescale by it | |||
| projected_weighted_qty *= wmax.d / np.sqrt(3) | |||
| projected_weight *= wmax.d / np.sqrt(3) | |||
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ah! that explains the mostly constant factor that values were off by! nice catch.
Co-authored-by: Chris Havlin <[email protected]>
chrishavlin
approved these changes
Dec 6, 2024
meeseeksmachine
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Dec 6, 2024
…ctions for octree datasets
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PR Summary
Fixes #5071.
PR Checklist