Hi- your interpolation library is one of the fastest around with the C++ implementation. I was hoping to use some of these functions for a project where I have a large shape=(1540, 1049, 521) array with scattered data of 3D vectors (~10,000 scattered points). Your functions work very quickly, but the casting to spherical coordinates is throwing off my results! Is there a manual way around this? I see the system parameter for Rtree, but I'm having a hard time figuring out what the valid options are.
Thanks for your time!
Here is my code snippet- force_locations is an mx3 array, where m is the number of scattered data
import pyinterp
field = np.zeros((vessel_seg.shape[0], vessel_seg.shape[1], vessel_seg.shape[2], 3))
n, p, q = vessel_seg.shape
Hi- your interpolation library is one of the fastest around with the C++ implementation. I was hoping to use some of these functions for a project where I have a large shape=(1540, 1049, 521) array with scattered data of 3D vectors (~10,000 scattered points). Your functions work very quickly, but the casting to spherical coordinates is throwing off my results! Is there a manual way around this? I see the
system
parameter for Rtree, but I'm having a hard time figuring out what the valid options are. Thanks for your time!Here is my code snippet-
force_locations
is an mx3 array, where m is the number of scattered data