WellMet is pure Python framework for spatial structural reliability analysis. Or, more specifically, for "failure probability estimation and detection of failure surfaces by adaptive sequential decomposition of the design domain".
List of commits:
| Subject | Hash | Author | Date (UTC) |
|---|---|---|---|
| convex_hull: optimize orth basis generation | 4ebfde2955aa792fb31075bf9ac8af718ae07b29 | I am | 2022-11-26 15:35:21 |
| voronoi: WIP | 8f7f3bca7c4333ab579e057438c8c64208d1f60e | I am | 2022-11-26 11:22:55 |
| voronoi: remove the code already moved to wireframe module | fe57b4b699e8689d35afb510f704019152566f09 | I am | 2022-11-25 09:25:55 |
| qt_gui.qt_pairwise.ContactWidget: show Qhull request at most once. | db6af332a70786759692175384f4b7e779b4caca | I am | 2022-11-16 13:26:04 |
| wireframe.LinearSolver: polytope handle hotfix | bc48de3193ce9be482cc4dd068a26f48ea1e0203 | I am | 2022-11-16 13:24:41 |
| qt_gui.qt_pairwise.ContactWidget: add LP methods | 8dbd480ca80aabe1c7e0e689780767b78c86c1d1 | I am | 2022-11-16 11:33:32 |
| wireframe: implement two LP solvers | 8aecd55104ce85ccccf872ebef0052a5937415b5 | I am | 2022-11-16 11:32:31 |
| qt_gui.qt_pairwise.ContactWidget: add Gabriel adjacency | 86eaf302b176612fad072a2248e2c724d32be755 | I am | 2022-11-16 06:17:53 |
| wireframe: implement Gabriel adjacency | f0a74b4f28cec6c2ddc68d6811e0a812fc829d63 | I am | 2022-11-16 06:16:58 |
| qt_gui.qt_pairwise.ContactWidget: reflect updated wireframe | b00a9be23d92d999e0020426a7a5caa1206c5862 | I am | 2022-11-16 03:56:57 |
| wireframe: implement LocalizedHull class | 95c19144c5144461db241df1b2bd20d8950c8d57 | I am | 2022-11-16 03:55:59 |
| qt_gui.qt_pairwise.ContactWidget: polish UI | bd48c8a6342e20dac1d635b963dd44e62c371379 | I am | 2022-11-13 20:03:39 |
| wireframe: add convex_slice() method | efada4c62f88b6106c687217472e0b8c52374fed | I am | 2022-11-13 19:51:49 |
| qt_gui.qt_pairwise.ContactWidget: finish widget and clean up | 10753a104879d6b9c109735e959b18b5a60a4616 | I am | 2022-11-10 05:02:01 |
| wireframe: finish and clean up module. implement DirectContact. | f7ff9c02f25a3ed8fe04e99036da69766afa5a49 | I am | 2022-11-10 04:58:58 |
| qt_gui.qt_pairwise.ContactWidget: implement Qhull check | e362df1f8b87ab7c6a27643cd6f4091b9caec9a3 | I am | 2022-11-09 21:35:10 |
| wireframe: add Qframe class | cf52d85dd1663537cd8c52a98514c7d041913979 | I am | 2022-11-09 21:34:12 |
| qt_gui.qt_pairwise: add auto arnge and auto levels controls | 9bc2cf10a9a1695c2c1a5f6a9959f375d3808a71 | I am | 2022-11-09 18:16:47 |
| qt_gui.qt_pairwise: set blue -1 by default. Add force_update option in Contact widget | 7371575784df7fd41bd76ad29caf38f474a06a26 | I am | 2022-11-09 15:39:03 |
| qt_gui.qt_pairwise: prepare Contact widget | bfa78d7493f5eb04b6bd3a4ad26dd1a92d17a895 | I am | 2022-11-09 03:36:05 |
Commit 4ebfde2955aa792fb31075bf9ac8af718ae07b29
- convex_hull: optimize orth basis generation
Author: I am
Author date (UTC): 2022-11-26 15:35
Committer name: I am
Committer date (UTC): 2022-11-26 15:35
Parent(s): 8f7f3bca7c4333ab579e057438c8c64208d1f60e
Signer:
Signing key:
Signing status: N
Tree: f498b9edb404a08eac3fed6327cbe96ebe35bafe
Author: I am
Author date (UTC): 2022-11-26 15:35
Committer name: I am
Committer date (UTC): 2022-11-26 15:35
Parent(s): 8f7f3bca7c4333ab579e057438c8c64208d1f60e
Signer:
Signing key:
Signing status: N
Tree: f498b9edb404a08eac3fed6327cbe96ebe35bafe
| File | Lines added | Lines deleted |
|---|---|---|
| wellmet/convex_hull.py | 5 | 26 |
| File wellmet/convex_hull.py changed (mode: 100644) (index c6a9832..8ed6168) | |||
| ... | ... | def _get_nD_orth_basis_from_normals(A, b): | |
| 187 | 187 | to_fire = np.nanargmax(b) | to_fire = np.nanargmax(b) |
| 188 | 188 | a = A[to_fire] | a = A[to_fire] |
| 189 | 189 | ||
| 190 | Acos = a * A | ||
| 191 | 190 | ||
| 192 | 191 | #č QR rozklad jede po sloupcich | #č QR rozklad jede po sloupcich |
| 193 | 192 | basis_T = np.empty((ndim, ndim)) | basis_T = np.empty((ndim, ndim)) |
| 194 | 193 | basis_T[:,0] = a | basis_T[:,0] = a |
| 195 | 194 | ||
| 196 | 195 | #č skalarní součín | #č skalarní součín |
| 197 | cos = np.sum(Acos, axis=1) | ||
| 196 | cos = A @ a | ||
| 198 | 197 | bcos = b * (1 + np.abs(cos)) | bcos = b * (1 + np.abs(cos)) |
| 199 | 198 | ||
| 200 | 199 | for i in range(1, ndim-2): | for i in range(1, ndim-2): |
| ... | ... | def _get_nD_orth_basis_from_normals(A, b): | |
| 204 | 203 | ||
| 205 | 204 | #č QR rozklad komplikuje život transponovanim matic | #č QR rozklad komplikuje život transponovanim matic |
| 206 | 205 | basis_T[:,i] = next_vector | basis_T[:,i] = next_vector |
| 207 | #č není hezky pouštět v cyklu QR rozklad, ale zatím tak | ||
| 208 | ##č (jednotka dočasnosti - jeden furt) | ||
| 209 | basis_T, __R = np.linalg.qr(basis_T) | ||
| 206 | basis_T[:, :i+1], __R = np.linalg.qr(basis_T[:, :i+1]) | ||
| 210 | 207 | ||
| 211 | 208 | next_orth_vector = basis_T[:,i] | next_orth_vector = basis_T[:,i] |
| 212 | 209 | ||
| 213 | Acos = next_orth_vector * A | ||
| 214 | |||
| 215 | 210 | #č skalarní součín | #č skalarní součín |
| 216 | cos = np.sum(Acos, axis=1) | ||
| 217 | bcos = bcos * (1 + np.abs(cos)) | ||
| 218 | |||
| 219 | #č vzít ndim-1 nejbližších ke středu hyperrovin | ||
| 220 | #č (nepotřebuje nejlepší řešení, potřebuji jen porazit draka) | ||
| 221 | #_dim = dim - 1 | ||
| 222 | #č předpokládám, že vzdálenost je záporná | ||
| 223 | #č (hledáme zde tedy ndim největších čísel) | ||
| 224 | #index = np.argpartition(bcos, -_dim)[-_dim:] | ||
| 225 | # sort | ||
| 226 | #č dostaneme index indexů | ||
| 227 | #č argsort třídí od nějměnšího k většímu | ||
| 228 | #index2 = np.argsort(-bcos[index]) | ||
| 211 | cos = A @ next_orth_vector | ||
| 212 | bcos *= (1 + np.abs(cos)) | ||
| 213 | |||
| 229 | 214 | ||
| 230 | 215 | to_fire = np.nanargmax(bcos) | to_fire = np.nanargmax(bcos) |
| 231 | 216 | #č předposlední vektor | #č předposlední vektor |
| ... | ... | def _get_nD_orth_basis_from_normals(A, b): | |
| 234 | 219 | #č a už ready. Poslední vektor je určen předchozími | #č a už ready. Poslední vektor je určen předchozími |
| 235 | 220 | basis_T, __R = np.linalg.qr(basis_T) | basis_T, __R = np.linalg.qr(basis_T) |
| 236 | 221 | ||
| 237 | #č udělám to zjednodušeně - zvolim první největší vektor | ||
| 238 | #č a přídam k tomu co nejlepší nejortogonálnější další | ||
| 239 | #index3 = np.concatenate(([to_fire], index[index2])) | ||
| 240 | #č pomocí indexu indexů z normálových (směrových) vektorů uděláme maticu | ||
| 241 | #basis = hull.A[index3] | ||
| 242 | |||
| 243 | 222 | #č QR rozklad jede po sloupcich | #č QR rozklad jede po sloupcich |
| 244 | 223 | #č co jsem viděl, numpy matici Q normalizuje | #č co jsem viděl, numpy matici Q normalizuje |
| 245 | 224 | #č a první sloupec zůstavá (skoro) tím samým, co byl před tím | #č a první sloupec zůstavá (skoro) tím samým, co byl před tím |
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