Selecting faces of a mesh based on vertices coordinates in numpy
I have two numpy arrays, one is for 3D vertices of a mesh, call it vert and one is for the triangular faces, call it faces:
The vert array is a N x 3 shape array of float, hence N three dimensional points. The x coordinate of each point can have both positive and negative values.
As a pure example this can be the vert array:
[[ 2.886495 24.886948 15.909558]
[ -13.916695 -58.985245 19.655312]
[ 40.415527 8.968353 8.515955]
...
[ 13.392465 -58.20602 18.752457]
[ -12.504704 -58.307934 18.912386]
[ 13.322185 -58.52817 19.165733]]
Since the mesh is centered, the left part of the mesh is the one with positive x component and the corresponding vertex indices are found by a np.where
i_vert_left = np.where(vert[:,0]>0)[0]
I now would like to filter out those faces made of triangles with coordinates entirely in the positive x axis.
However I have a problem in doing this indexing operation correctly.
My first attempt was to subset the faces such that their corresponding vertices have x>0
faces_left = np.asarray([f for f in faces if np.all(np.isin(i_vert_left,f)) ])
but the operation is incredibly slow on large meshes.
How can I exploit a smart indexing of the faces?
python numpy indexing mesh matrix-indexing
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
add a comment |
I have two numpy arrays, one is for 3D vertices of a mesh, call it vert and one is for the triangular faces, call it faces:
The vert array is a N x 3 shape array of float, hence N three dimensional points. The x coordinate of each point can have both positive and negative values.
As a pure example this can be the vert array:
[[ 2.886495 24.886948 15.909558]
[ -13.916695 -58.985245 19.655312]
[ 40.415527 8.968353 8.515955]
...
[ 13.392465 -58.20602 18.752457]
[ -12.504704 -58.307934 18.912386]
[ 13.322185 -58.52817 19.165733]]
Since the mesh is centered, the left part of the mesh is the one with positive x component and the corresponding vertex indices are found by a np.where
i_vert_left = np.where(vert[:,0]>0)[0]
I now would like to filter out those faces made of triangles with coordinates entirely in the positive x axis.
However I have a problem in doing this indexing operation correctly.
My first attempt was to subset the faces such that their corresponding vertices have x>0
faces_left = np.asarray([f for f in faces if np.all(np.isin(i_vert_left,f)) ])
but the operation is incredibly slow on large meshes.
How can I exploit a smart indexing of the faces?
python numpy indexing mesh matrix-indexing
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
What'sfaceslike? Shape?
– Divakar
Nov 19 '18 at 14:37
add a comment |
I have two numpy arrays, one is for 3D vertices of a mesh, call it vert and one is for the triangular faces, call it faces:
The vert array is a N x 3 shape array of float, hence N three dimensional points. The x coordinate of each point can have both positive and negative values.
As a pure example this can be the vert array:
[[ 2.886495 24.886948 15.909558]
[ -13.916695 -58.985245 19.655312]
[ 40.415527 8.968353 8.515955]
...
[ 13.392465 -58.20602 18.752457]
[ -12.504704 -58.307934 18.912386]
[ 13.322185 -58.52817 19.165733]]
Since the mesh is centered, the left part of the mesh is the one with positive x component and the corresponding vertex indices are found by a np.where
i_vert_left = np.where(vert[:,0]>0)[0]
I now would like to filter out those faces made of triangles with coordinates entirely in the positive x axis.
However I have a problem in doing this indexing operation correctly.
My first attempt was to subset the faces such that their corresponding vertices have x>0
faces_left = np.asarray([f for f in faces if np.all(np.isin(i_vert_left,f)) ])
but the operation is incredibly slow on large meshes.
How can I exploit a smart indexing of the faces?
python numpy indexing mesh matrix-indexing
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
I have two numpy arrays, one is for 3D vertices of a mesh, call it vert and one is for the triangular faces, call it faces:
The vert array is a N x 3 shape array of float, hence N three dimensional points. The x coordinate of each point can have both positive and negative values.
As a pure example this can be the vert array:
[[ 2.886495 24.886948 15.909558]
[ -13.916695 -58.985245 19.655312]
[ 40.415527 8.968353 8.515955]
...
[ 13.392465 -58.20602 18.752457]
[ -12.504704 -58.307934 18.912386]
[ 13.322185 -58.52817 19.165733]]
Since the mesh is centered, the left part of the mesh is the one with positive x component and the corresponding vertex indices are found by a np.where
i_vert_left = np.where(vert[:,0]>0)[0]
I now would like to filter out those faces made of triangles with coordinates entirely in the positive x axis.
However I have a problem in doing this indexing operation correctly.
My first attempt was to subset the faces such that their corresponding vertices have x>0
faces_left = np.asarray([f for f in faces if np.all(np.isin(i_vert_left,f)) ])
but the operation is incredibly slow on large meshes.
How can I exploit a smart indexing of the faces?
python numpy indexing mesh matrix-indexing
python numpy indexing mesh matrix-indexing
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
asked Nov 19 '18 at 14:33
linellolinello
3,51774982
3,51774982
What'sfaceslike? Shape?
– Divakar
Nov 19 '18 at 14:37
add a comment |
What'sfaceslike? Shape?
– Divakar
Nov 19 '18 at 14:37
What's
faces like? Shape?– Divakar
Nov 19 '18 at 14:37
What's
faces like? Shape?– Divakar
Nov 19 '18 at 14:37
add a comment |
1 Answer
1
active
oldest
votes
Assuming faces is a Nx3 array of integers indexing the three vertices of each triangle, I think you should just need:
# Check whether each vertex is left or not
vert_left_mask = vert[:, 0] > 0
# Check whether each face has all vertices on left or not
faces_left_mask = np.all(vert_left_mask[faces], axis=1)
# Select resulting left faces
faces_left = faces[faces_left_mask]
The main "trick" here is in vert_left_mask[faces], which replaces each integer vertex number with a boolean indicating whether the vertex is left or not, so it's easy to tell which face is fully left with np.all.
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Assuming faces is a Nx3 array of integers indexing the three vertices of each triangle, I think you should just need:
# Check whether each vertex is left or not
vert_left_mask = vert[:, 0] > 0
# Check whether each face has all vertices on left or not
faces_left_mask = np.all(vert_left_mask[faces], axis=1)
# Select resulting left faces
faces_left = faces[faces_left_mask]
The main "trick" here is in vert_left_mask[faces], which replaces each integer vertex number with a boolean indicating whether the vertex is left or not, so it's easy to tell which face is fully left with np.all.
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
add a comment |
Assuming faces is a Nx3 array of integers indexing the three vertices of each triangle, I think you should just need:
# Check whether each vertex is left or not
vert_left_mask = vert[:, 0] > 0
# Check whether each face has all vertices on left or not
faces_left_mask = np.all(vert_left_mask[faces], axis=1)
# Select resulting left faces
faces_left = faces[faces_left_mask]
The main "trick" here is in vert_left_mask[faces], which replaces each integer vertex number with a boolean indicating whether the vertex is left or not, so it's easy to tell which face is fully left with np.all.
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
add a comment |
Assuming faces is a Nx3 array of integers indexing the three vertices of each triangle, I think you should just need:
# Check whether each vertex is left or not
vert_left_mask = vert[:, 0] > 0
# Check whether each face has all vertices on left or not
faces_left_mask = np.all(vert_left_mask[faces], axis=1)
# Select resulting left faces
faces_left = faces[faces_left_mask]
The main "trick" here is in vert_left_mask[faces], which replaces each integer vertex number with a boolean indicating whether the vertex is left or not, so it's easy to tell which face is fully left with np.all.
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
Assuming faces is a Nx3 array of integers indexing the three vertices of each triangle, I think you should just need:
# Check whether each vertex is left or not
vert_left_mask = vert[:, 0] > 0
# Check whether each face has all vertices on left or not
faces_left_mask = np.all(vert_left_mask[faces], axis=1)
# Select resulting left faces
faces_left = faces[faces_left_mask]
The main "trick" here is in vert_left_mask[faces], which replaces each integer vertex number with a boolean indicating whether the vertex is left or not, so it's easy to tell which face is fully left with np.all.
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
answered Nov 19 '18 at 14:41
jdehesajdehesa
23.1k43252
23.1k43252
add a comment |
add a comment |
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What's
faceslike? Shape?– Divakar
Nov 19 '18 at 14:37