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Vectorize coordinates._construct_face_centroids() #1117

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Vectorize coordinates._construct_face_centroids() #1117

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50c16e1
Vectorize coordinates._construct_face_centroids()
erogluorhan Dec 21, 2024
b13aa57
Merge branch 'main' into optimize_face_centroids
philipc2 Dec 23, 2024
95939fe
Merge branch 'main' into optimize_face_centroids
aaronzedwick Jan 10, 2025
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17 changes: 8 additions & 9 deletions uxarray/grid/coordinates.py
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Expand Up @@ -327,15 +327,14 @@ def _construct_face_centroids(node_x, node_y, node_z, face_nodes, n_nodes_per_fa
tuple
The x, y, and z coordinates of the centroids.
"""
centroid_x = np.zeros((face_nodes.shape[0]), dtype=np.float64)
centroid_y = np.zeros((face_nodes.shape[0]), dtype=np.float64)
centroid_z = np.zeros((face_nodes.shape[0]), dtype=np.float64)

for face_idx, n_max_nodes in enumerate(n_nodes_per_face):
# Compute Cartesian Average
centroid_x[face_idx] = np.mean(node_x[face_nodes[face_idx, 0:n_max_nodes]])
centroid_y[face_idx] = np.mean(node_y[face_nodes[face_idx, 0:n_max_nodes]])
centroid_z[face_idx] = np.mean(node_z[face_nodes[face_idx, 0:n_max_nodes]])

# Mask to ignore invalid indices
mask = np.arange(face_nodes.shape[1])[None, :] < n_nodes_per_face[:, None]

# Calculate centroids
centroid_x = np.sum(node_x[face_nodes] * mask, axis=1) / n_nodes_per_face
centroid_y = np.sum(node_y[face_nodes] * mask, axis=1) / n_nodes_per_face
centroid_z = np.sum(node_z[face_nodes] * mask, axis=1) / n_nodes_per_face
Comment on lines +335 to +337
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This here is problematic for grids that have variable element sizes.

face_node_connectivity will contain fill values, which will lead to an index error here.

IndexError: index -9223372036854775808 is out of bounds for axis 0 with size 83886080

This wasn't an issue for the SCREAM dataset because it is completely composed of quads.

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How about fixing this and changing/standardizing the way we handle connectivity?

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changing/standardizing the way we handle connectivity

Can you elaborate? I've explored storing connectivity as partitioned arrays in #978, although for something as simple as computing centroids, Numba seems to a significantly simpler implementation, performing closer to compiled language performance without needing to create an additional mask.

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My understanding is that with -1 etc. the mask approach fails, isn't that correct? so, I was thinking adopting a convention that allows for masking to work would simply things.

Not sure about the performance as I think NumPy's vectorized operations might be highly optimized for numerical computations and hopefully outperform the explicit loop here, even when those loops are parallelized with Numba.


return _normalize_xyz(centroid_x, centroid_y, centroid_z)

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