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Min size autodetect #2325
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Min size autodetect #2325
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Regarding deeply nested field, maybe Another questions is what would be good default values for those resolutions? |
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Went through it again, and now makes more sense to me: your default value in the code is probably good enough.
A high-level question: this PR looks also partially resolve the short-issue. I guess they will work together?
) | ||
convex_resolution: pd.NonNegativeInt = pd.Field( | ||
0, | ||
title="Concave Region Resolution.", |
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convex
2, | ||
title="Concave Region Resolution.", | ||
description="Specifies number of steps to use for determining `dl_min` based on concave featues." | ||
"If set to 0, then the corresponding `dl_min` reduction is not applied.", |
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Not sure if we should use 0
or None
when not applied
@@ -136,5 +221,12 @@ def normalize(v): | |||
inner_product = np.where(inner_product > 1, 1, inner_product) | |||
inner_product = np.where(inner_product < -1, -1, inner_product) | |||
angle = np.arccos(inner_product) | |||
num_vs = len(vs_orig) | |||
cross_product = np.cross( |
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np.cross
seems to have issues in photonforge: #2317
yeah, it does partially resolve it, but not as robustly as the other one. For example, if structure has rounded corners everywhere this autodetection feature would not see anything. But given that the other approach is more computationally intensive, might still be good to have a rough estimation like this one |
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I am not sure about naming
SmallestFeatureSpec
and also how deep it is tucked away:would appreciate any suggestions