Difference between revisions of "CNF Example Meshes"
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=3D Example Meshes= | =3D Example Meshes= | ||
− | The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/ | + | The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data]. |
==CFF_14052019== | ==CFF_14052019== | ||
===GPDGK16Numerical_140519=== | ===GPDGK16Numerical_140519=== | ||
+ | * [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
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</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh] | '''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh] | ||
Line 139: | Line 35: | ||
===GPDMMS13_140519=== | ===GPDMMS13_140519=== | ||
+ | * [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
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</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh] | '''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh] | ||
Line 161: | Line 58: | ||
===GPDVGG99_140519=== | ===GPDVGG99_140519=== | ||
+ | * [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
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</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh] | '''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh] | ||
Line 183: | Line 81: | ||
===NT_140519=== | ===NT_140519=== | ||
+ | * [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 194: | Line 93: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh] | '''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh] | ||
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===OBS_ALU_140519=== | ===OBS_ALU_140519=== | ||
+ | * [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 221: | Line 121: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh] | '''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh] | ||
Line 234: | Line 134: | ||
===OBS_CS_140519=== | ===OBS_CS_140519=== | ||
+ | * [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 243: | Line 144: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh] | '''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh] | ||
Line 257: | Line 158: | ||
==CFF_DATA== | ==CFF_DATA== | ||
===cff_E.data_IM=== | ===cff_E.data_IM=== | ||
+ | * [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 268: | Line 170: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh] | '''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh] | ||
Line 281: | Line 183: | ||
===cff_E.data_REAL=== | ===cff_E.data_REAL=== | ||
+ | * [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 290: | Line 193: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh] | '''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh] | ||
Line 303: | Line 206: | ||
===cff_H.data_IM=== | ===cff_H.data_IM=== | ||
+ | * [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 312: | Line 216: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh] | '''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh] | ||
Line 325: | Line 229: | ||
===cff_H.data_REAL=== | ===cff_H.data_REAL=== | ||
+ | * [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 334: | Line 239: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh] | '''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh] | ||
Line 347: | Line 252: | ||
===cff_Ht.data_IM=== | ===cff_Ht.data_IM=== | ||
+ | * [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 356: | Line 262: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh] | '''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh] | ||
Line 369: | Line 275: | ||
===cff_Ht.data_REAL=== | ===cff_Ht.data_REAL=== | ||
+ | * [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
Line 378: | Line 285: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh] | '''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh] | ||
Line 392: | Line 299: | ||
==DATA_04252019== | ==DATA_04252019== | ||
===CFF_E_im=== | ===CFF_E_im=== | ||
+ | * [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 401: | Line 309: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh] | '''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh] | ||
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===CFF_E_re=== | ===CFF_E_re=== | ||
+ | * [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 423: | Line 332: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh] | '''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh] | ||
Line 436: | Line 345: | ||
===CFF_H_im=== | ===CFF_H_im=== | ||
+ | * [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 445: | Line 355: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh] | '''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh] | ||
Line 458: | Line 368: | ||
===CFF_H_re=== | ===CFF_H_re=== | ||
+ | * [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 467: | Line 378: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh] | '''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh] | ||
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===GPD_H_down=== | ===GPD_H_down=== | ||
+ | * [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 489: | Line 401: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh] | '''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh] | ||
Line 502: | Line 414: | ||
===GPD_H_up=== | ===GPD_H_up=== | ||
+ | * [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
Line 511: | Line 424: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh] | '''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh] | ||
Line 524: | Line 437: | ||
===OBS_ALU=== | ===OBS_ALU=== | ||
+ | * [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform with background-value = 0: 301,772 tetrahedra | * Uniform with background-value = 0: 301,772 tetrahedra | ||
* Adaptive with background-value = 0: 279,721 tetrahedra | * Adaptive with background-value = 0: 279,721 tetrahedra | ||
− | * Uniform with background-value = | + | * Uniform with background-value = default: 768,033 tetrahedra |
− | * Adaptive with background-value = | + | * Adaptive with background-value = default: 284,256 tetrahedra |
<gallery mode="packed" heights=350px> | <gallery mode="packed" heights=350px> | ||
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</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh] | '''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh] | ||
Line 551: | Line 465: | ||
</pre> | </pre> | ||
− | + | '''Uniform with background-value = default:''' [https://odu.box.com/s/b5licz0d25mb0ed0ttlz80adpsdoth4z Output Mesh] | |
− | |||
− | |||
− | '''Uniform with background-value = | ||
<pre> | <pre> | ||
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --output ./OBS_ALU,d=1,uniform.vtk | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --output ./OBS_ALU,d=1,uniform.vtk | ||
</pre> | </pre> | ||
− | '''Adaptive with background-value = | + | '''Adaptive with background-value = default:''' [https://odu.box.com/s/lz86mqwrukhupuhq9okfwim5vx6a2avr Output Mesh] |
<pre> | <pre> | ||
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk | ||
</pre> | </pre> | ||
+ | |||
+ | Note: In this case, we want to exclude the entries with value 0 (lower part, see figure) since they are not of interest. Using the flag --background-value 0, the | ||
+ | entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive. | ||
==phase_space_000== | ==phase_space_000== | ||
===phase_space_000=== | ===phase_space_000=== | ||
+ | * [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image] | ||
* Input distribution size: 15,625 cells | * Input distribution size: 15,625 cells | ||
* Uniform: 17,961 tetrahedra | * Uniform: 17,961 tetrahedra | ||
Line 575: | Line 490: | ||
</gallery> | </gallery> | ||
− | Commands to generate meshes : | + | Commands to generate meshes: |
'''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh] | '''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh] | ||
Line 585: | Line 500: | ||
<pre> | <pre> | ||
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk | ||
+ | </pre> | ||
+ | |||
+ | =2D Example Meshes= | ||
+ | The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data]. | ||
+ | == Synthetic Gaussian Data == | ||
+ | * [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 30,949 triangles | ||
+ | * Adaptive: 3,788 triangles | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:Gaussian_me_10_uniform.png | ||
+ | File:Gaussian me 10 wl 1e-1 adapted.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/Gaussian2.vtk --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10 | ||
+ | </pre> | ||
+ | '''Adaptive:''' [https://odu.box.com/s/1omr8szilea3r6fde39w49gu7u5xyui8 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/Gaussian2.vtk --output Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10 | ||
+ | </pre> | ||
+ | |||
+ | == 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 | ||
+ | |||
+ | * [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image] | ||
+ | * Input distribution size: 10,000 cells | ||
+ | * Uniform: 7,587 triangles | ||
+ | * Adaptive (min edge = 2): 623 triangles | ||
+ | * Adaptive (min edge = 0.5): 1,409 triangles | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:GPDGK16Numerical 140519 X50 me2 uniform.png | ||
+ | File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png | ||
+ | File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive (min edge = 2):''' [https://odu.box.com/s/3yp9jod0hcjxipfu81ywk0jrxphonasa Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 | ||
+ | </pre> | ||
+ | |||
+ | |||
+ | '''Adaptive (min edge = 0.5):''' [https://odu.box.com/s/7zuszll7jn8tt8bpge6vau2tkbatkihz Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1 | ||
+ | </pre> | ||
+ | |||
+ | '''Note: ''' By using ''min-edge'' less than 1 we are essentially generating triangles with an edge smaller than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value. | ||
+ | |||
+ | == NT_140519 == | ||
+ | The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519 | ||
+ | |||
+ | * [https://odu.box.com/s/nzhrcmfhrmi64ria7vldlb591797n7ph Input Image] | ||
+ | * Input distribution size: 10,000 cells | ||
+ | * Uniform: 7,587 triangles | ||
+ | * Adaptive: 1,038 triangles | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:NT 140519 X50 me2 uniform.png | ||
+ | File:NT 140519 X50 me2 me2 wl 1e-1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/oytjqxeque11wvbxu62fhwc3830fbpcy Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 | ||
+ | </pre> | ||
+ | |||
+ | == OBS_ALU_Y50 == | ||
+ | The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU | ||
+ | |||
+ | * [https://odu.box.com/s/o4qxxjebb3rxu71ncmm8kdgh9mvvsvqr Input Image] | ||
+ | * Input distribution size: 10,000 cells | ||
+ | * Uniform: 7,587 triangles | ||
+ | * Adaptive: 1,018 triangles | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:OBS ALU Y50 me 2 uniform.vtk.png | ||
+ | File:OBS ALU Y50 me 2 wl 1e-1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/8369kd2q52weqp76811h6p54sax3nj37 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1 | ||
</pre> | </pre> |
Revision as of 16:02, 2 June 2020
Contents
3D Example Meshes
The directory containing the 3D input data is located in the 3D folder of CNF_Data.
CFF_14052019
GPDGK16Numerical_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive (min edge = 1): 273,716 tetrahedra
- Adaptive (min edge = 2): 67,935 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
Adaptive (min edge = 1): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 2 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=2.vtk
GPDMMS13_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 264,762 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform --output ./GPDMMS13_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
GPDVGG99_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 261,485 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform --output ./GPDVGG99_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
NT_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive (min edge = 1): 253,965 tetrahedra
- Adaptive (min edge = 2): 120,168 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform --output ./NT_140519,d=1,uniform.vtk
Adaptive (min edge = 1): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk
OBS_ALU_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 259,269 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform --output ./OBS_ALU_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
OBS_CS_140519
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 25,168 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform --output ./OBS_CS_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
CFF_DATA
cff_E.data_IM
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 358,637 tetrahedra
- Adaptive: 358,637 tetrahedra (other side of the same adaptive case)
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform --output ./cff_E.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
cff_E.data_REAL
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 314,990 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform --output ./cff_E.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive --output ./cff_E.data_REAL,d=10,wl=0.1,me=2.vtk
cff_H.data_IM
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 289,855 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform --output ./cff_H.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
cff_H.data_REAL
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 372,016 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform --output ./cff_H.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive --output ./cff_H.data_REAL,d=10,wl=0.1,me=2.vtk
cff_Ht.data_IM
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 337,772 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform --output ./cff_Ht.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive --output ./cff_Ht.data_IM,d=10,wl=0.1,me=2.vtk
cff_Ht.data_REAL
- Input Image
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 394,632 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform --output ./cff_Ht.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=2.vtk
DATA_04252019
CFF_E_im
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 236,512 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform --output ./CFF_E_im,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
CFF_E_re
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 260,349 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform --output ./CFF_E_re,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
CFF_H_im
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 263,040 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform --output ./CFF_H_im,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
CFF_H_re
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 249,257 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform --output ./CFF_H_re,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
GPD_H_down
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 300,117 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform --output ./GPD_H_down,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk
GPD_H_up
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 295,671 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform --output ./GPD_H_up,d=1,uniform.vtk
Adaptive:Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk
OBS_ALU
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform with background-value = 0: 301,772 tetrahedra
- Adaptive with background-value = 0: 279,721 tetrahedra
- Uniform with background-value = default: 768,033 tetrahedra
- Adaptive with background-value = default: 284,256 tetrahedra
Commands to generate meshes:
Uniform with background-value = 0: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
Adaptive with background-value = 0: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07 --output ./OBS_ALU,d=5,bv=0,wl=0.07,me=1.vtk
Uniform with background-value = default: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --output ./OBS_ALU,d=1,uniform.vtk
Adaptive with background-value = default: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk
Note: In this case, we want to exclude the entries with value 0 (lower part, see figure) since they are not of interest. Using the flag --background-value 0, the entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.
phase_space_000
phase_space_000
- Input Image
- Input distribution size: 15,625 cells
- Uniform: 17,961 tetrahedra
- Adaptive: 10,593 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform --output ./phase_space_000,d=0.25,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk
2D Example Meshes
The directory containing the 2D input data is located in the 2D folder of CNF_Data.
Synthetic Gaussian Data
- Input Image
- Input distribution size: 1,000,000 cells
- Uniform: 30,949 triangles
- Adaptive: 3,788 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/Gaussian2.vtk --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/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 Image
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive (min edge = 2): 623 triangles
- Adaptive (min edge = 0.5): 1,409 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/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): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/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 than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value.
NT_140519
The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
- Input Image
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive: 1,038 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
OBS_ALU_Y50
The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
- Input Image
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive: 1,018 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1