You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Really interesting tool that you have there, thank you for making it available!
I wonder if it would be possible to introduce a query_pairs type method in a future version, like the one provided by cKDTree in the scipy.spatial module. Ideally, it would return a numpy array of index pairs.
This could prove particularly useful for quickly accessing colliding pairs of AABBs without having to perform any further manipulation in Python.
Currently, to do this I need to use np.vectorize, np.repeat and np.concatenate to convert the nested lists from batch_query into flat arrays and then apply a boolean mask to remove duplicate and undesired pairs. Unfortunately, this whole process cancels out any performance gains from the parallelized C++ calculations.
Please, let me know!
The text was updated successfully, but these errors were encountered:
Thank you for your suggestion! I will adding a method query_intersections() that would efficiently enumerate pairs of intersecting AABBs.
Since AABB intersection checks are based on simple bounding box overlap rather than distance calculations, I believe parameters like r (radius) and p (norm) from query_pairs() are unnecessary for this method.
Would this approach meet your needs? Let me know if you have any specific requirements or use cases I should consider!
Hi,
Really interesting tool that you have there, thank you for making it available!
I wonder if it would be possible to introduce a
query_pairs
type method in a future version, like the one provided by cKDTree in thescipy.spatial
module. Ideally, it would return a numpy array of index pairs.This could prove particularly useful for quickly accessing colliding pairs of AABBs without having to perform any further manipulation in Python.
Currently, to do this I need to use
np.vectorize
,np.repeat
andnp.concatenate
to convert the nested lists frombatch_query
into flat arrays and then apply a boolean mask to remove duplicate and undesired pairs. Unfortunately, this whole process cancels out any performance gains from the parallelized C++ calculations.Please, let me know!
The text was updated successfully, but these errors were encountered: