Difference between revisions of "CNF Example Meshes"
From crtc.cs.odu.edu
(→OBS_ALU_Y50) |
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'''Uniform :''' | '''Uniform :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10 |
</pre> | </pre> | ||
'''Adaptive :''' | '''Adaptive :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10 |
</pre> | </pre> | ||
| Line 43: | Line 43: | ||
'''Uniform :''' | '''Uniform :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform |
</pre> | </pre> | ||
'''Adaptive :'''(min_edge = 2) | '''Adaptive :'''(min_edge = 2) | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 |
</pre> | </pre> | ||
| Line 54: | Line 54: | ||
'''Adaptive :'''(min_edge = 0.5) | '''Adaptive :'''(min_edge = 0.5) | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1 |
</pre> | </pre> | ||
| Line 76: | Line 76: | ||
'''Uniform :''' | '''Uniform :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform |
</pre> | </pre> | ||
'''Adaptive :''' | '''Adaptive :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 |
</pre> | </pre> | ||
| Line 102: | Line 102: | ||
'''Uniform :''' | '''Uniform :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform |
</pre> | </pre> | ||
'''Adaptive :''' | '''Adaptive :''' | ||
<pre> | <pre> | ||
| − | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output | + | docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 |
</pre> | </pre> | ||
Revision as of 21:01, 22 November 2019
Contents
2D Example Meshes
The directory containing the 2D input data is located here.
Synthetic Gaussian Data
- Input distribution size : 1,000,000 cells
- Uniform case: 30,949 triangles
- Adapted case: 3,788 triangles
Commands to generate meshes :
Uniform :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10
Adaptive :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/Gaussian2.vtk --output Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10
GPDGK16Numerical_140519
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519
- Input distribution size : 10,000 cells
- Uniform case: 7587 triangles
- Adapted (min_edge = 2) case: 623 triangles
- Adapted (min_edge = 0.5) case: 1,409 triangles
Commands to generate meshes :
Uniform :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive :(min_edge = 2)
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
Adaptive :(min_edge = 0.5)
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1
Note : By using `min-edge` less than 1 we are essentially generating triangles with an edge smaller that the input pixels. Using values much smaller than 1 is not expected to help the discitization since we are esetially packing more element into a pixel which has constant value.
OBS_ALU_Y50
- Input distribution size : 10,000 cells
- Uniform case: 7587 triangles
- Adapted case: 1018 triangles
The 2D case created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
Commands to generate meshes :
Uniform :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
NT_140519
- Input distribution size : 10,000 cells
- Uniform case: 7587 triangles
- Adapted case: 1038 triangles
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
Commands to generate meshes :
Uniform :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive :
docker run -v $(pwd):/data/ cnf_tools tessellate2d --input 2D_CASES/input/NT_140519_50_X.vtk --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
3D Example Meshes
CFF_14052019
GPDGK16Numerical_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDGK16Numerical_140519.nrrd --weight-limit 0.1 --min-edge 1 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk
GPDMMS13_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDMMS13_140519.nrrd --weight-limit 0.05 --min-edge 1 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
GPDVGG99_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDVGG99_140519.nrrd --weight-limit 0.05 --min-edge 1 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
NT_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/NT_140519.nrrd --weight-limit 0.07 --min-edge 1 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
OBS_ALU_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_ALU_140519.nrrd --weight-limit 0.13 --min-edge 1 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
OBS_CS_140519: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_CS_140519.nrrd --weight-limit 0.01 --min-edge 1 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
CFF_DATA
cff_E.data_IM: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_IM.nrrd --weight-limit 0.01 --min-edge 1 --output ./cff_E.data_IM,d=10,wl=0.01,me=1.vtk
cff_E.data_REAL: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_REAL.nrrd --weight-limit 0.1 --min-edge 1 --output ./cff_E.data_REAL,d=10,wl=0.1,me=1.vtk
cff_H.data_IM: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_IM.nrrd --weight-limit 0.05 --min-edge 1 --output ./cff_H.data_IM,d=10,wl=0.05,me=1.vtk
cff_H.data_REAL: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_REAL.nrrd --weight-limit 0.1 --min-edge 1 --output ./cff_H.data_REAL,d=10,wl=0.1,me=1.vtk
cff_Ht.data_IM: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_IM.nrrd --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_IM,d=10,wl=0.1,me=1.vtk
cff_Ht.data_REAL: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_REAL.nrrd --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=1.vtk
DATA_04252019
CFF_E_im: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_im.nrrd --weight-limit 0.04 --min-edge 1 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
CFF_E_re: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_re.nrrd --weight-limit 0.08 --min-edge 1 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
CFF_H_im: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_im.nrrd --weight-limit 0.06 --min-edge 1 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
CFF_H_re: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_re.nrrd --weight-limit 0.13 --min-edge 1 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
GPD_H_down: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_down.nrrd --weight-limit 0.1 --min-edge 1 --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk
GPD_H_up: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_up.nrrd --weight-limit 0.1 --min-edge 1 --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk
OBS_ALU: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd --background-value 0 --weight-limit 0.07 --min-edge 1 --output ./OBS_ALU,d=5,bg=0,wl=0.07,me=1.vtk
OBS_ALU: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd --delta 2 --background-value 0 --uniform --output ./OBS_ALU,d=5,bg=0,uniform.vtk
phase_space_000
phase_space_000: Input Image Output Mesh
docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/phase_space_000/phase_space_000.nrrd --delta 1.5 --weight-limit 0.005 --min-edge 1 --output ./phase_space_000,d=1.5,wl=0.005,me=1.vtk
