iam-git / WellMet (public) (License: MIT) (since 2021-08-31) (hash sha1)
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".
Clone URLs: https://rocketgit.com/user/iam-git/WellMet ssh://rocketgit@ssh.rocketgit.com/user/iam-git/WellMet git://git.rocketgit.com/user/iam-git/WellMet
release trunk
List of commits:
Subject Hash Author Date (UTC)
voronoi.ConvexSpline: little fix of np.sign() for better sleep 968f649e54d97abd596cd60749872fa3ef788c06 I am 2022-10-19 06:28:46
implement even more optimized Convex.. now ConvexSpline class c08511aecb32b880e73c9f10d07feff4dc588e8b I am 2022-10-19 04:11:13
voronoi: WIP cbb61d4544367a67c9a5c3fe6d64a9dc284eb364 I am 2022-10-18 05:05:44
voronoi: WIP e9c247fef30cfb678bad9b955d6f6a94a0ff61e7 I am 2022-10-18 01:02:30
candynodex: hotfix to support numpy masking indexing f359123b72a3d998d5eed4b9e611a5e402a18c8d I am 2022-10-17 09:02:33
add new lightweight candynodes module to replace old heavy ugly candybox ca97509f47c100df90e1e06d82ed7c759627bfd0 I am 2022-10-17 06:31:33
qt_gui: move CandyBox creating to the individual DiceBox widgets 29253625129219b5e550f82cae3da0e53ef5bd47 I am 2022-10-16 03:17:55
voronoi: WIP 4855190869e06df740cc05c64e8b27b2c3c5a88d I am 2022-10-15 22:46:51
voronoi: WIP 0298b3a0563587ea4fc24fa99b51d2e01d517203 I am 2022-10-15 20:00:25
voronoi: WIP 4b80757fa26e3d40dceac888480ec3d814f6abc9 I am 2022-10-15 17:30:11
voronoi: WIP 7cadd1d01d7f4bc3f51e785fe6ebf8cae6ff839a I am 2022-10-15 03:12:54
voronoi: clean up and comment out ContactSolver 543f8482a925ff6cf8485b9be616b5bd3c714d13 I am 2022-10-14 04:24:40
qt_gui.qt_gui: use ConvexSolver instead of ContactSolver (which did not work correctly) c1e49a9b2c3cc13886d317b460d7598f661cf216 I am 2022-10-14 04:01:06
voronoi: add fantastic ConvexSolver 2853826e5ef1abc79d3ac2fb8289b13c45211a31 I am 2022-10-14 04:00:14
qt_gui.qt_plot: add (finally!) Numbers class 4cdc658c0fcc857c23c0e39e91e7a1ce5e1b30a1 I am 2022-10-13 06:23:46
qt_gui.qt_gui: show contacts in distance matrix. Based on ContactSolver for now 21bd6101888d9f06d7d6a7c6ba2732ff30fdd68d I am 2022-10-13 04:16:41
voronoi: ContactSolver is ready. Ale je to na nic. Pomalá, potvora 28e2442b0101eac2407ed4a69c6f757ffd579cf1 I am 2022-10-13 04:14:59
voronoi: add some preliminary lines of ContactSolver. WIP 6a203b278c9fa6de7da75c402c80f78d40164fdf I am 2022-10-12 23:38:59
voronoi: přídat pár těžkých smyček. WIP 7b0ffb602ae00ab2f12dc98d34c35ec20afa3cc4 I am 2022-10-12 00:13:36
add to repo voronoi module. WIP f43768c22d88fac7c7095be02d8c9b5620153976 I am 2022-10-11 03:29:49
Commit 968f649e54d97abd596cd60749872fa3ef788c06 - voronoi.ConvexSpline: little fix of np.sign() for better sleep
Author: I am
Author date (UTC): 2022-10-19 06:28
Committer name: I am
Committer date (UTC): 2022-10-19 06:28
Parent(s): c08511aecb32b880e73c9f10d07feff4dc588e8b
Signer:
Signing key:
Signing status: N
Tree: 8c6833feaa4e5449b99c631d4061bd4c806760f3
File Lines added Lines deleted
wellmet/voronoi.py 6 4
File wellmet/voronoi.py changed (mode: 100644) (index 8624ad2..fbb8d26)
... ... class ConvexSpline:
187 187 a = basis[:, 1] a = basis[:, 1]
188 188 ##č nejak tuším, že v poslední dimenzi ##č nejak tuším, že v poslední dimenzi
189 189 ##č znaménko normál musí být jednotné ##č znaménko normál musí být jednotné
190 a = a * -np.sign(a[-1])
190 a = a * (1 - 2 * (a[-1] > 0)) # a = a * -np.sign(a[-1])
191 191 b = X @ a b = X @ a
192 192
193 193 if np.max(b) - np.max(b[[i, j]]) < 1e-7: if np.max(b) - np.max(b[[i, j]]) < 1e-7:
 
... ... class ConvexSpline:
198 198 idx = np.argmax(b) idx = np.argmax(b)
199 199 #č nahradíme v jedničce naš odhad normálního vektoru #č nahradíme v jedničce naš odhad normálního vektoru
200 200 #č nalezenou překažkou. #č nalezenou překažkou.
201 #č Zbytek bazí je jíž byl ortogonální
201 #č Zbytek bazí jíž byl ortogonální
202 202 #č k té naši pomyšlené normále #č k té naši pomyšlené normále
203 203 basis[:, 1] = X[idx] - X[i] basis[:, 1] = X[idx] - X[i]
204 204
 
... ... class ConvexSpline:
211 211 a = basis[:, -1] a = basis[:, -1]
212 212 #č nejak tuším, že v poslední dimenzi #č nejak tuším, že v poslední dimenzi
213 213 #č znaménko normál musí být jednotné #č znaménko normál musí být jednotné
214 a = a * -np.sign(a[-1])
214 a = a * (1 - 2 * (a[-1] > 0)) # a = a * -np.sign(a[-1])
215 215 b = X @ a b = X @ a
216 216 if np.max(b) - np.max(b[[i, j]]) < 1e-7: if np.max(b) - np.max(b[[i, j]]) < 1e-7:
217 217 return True return True
 
... ... class ConvexSpline:
224 224 a = basis[:, -1] a = basis[:, -1]
225 225 #č nejak tuším, že v poslední dimenzi #č nejak tuším, že v poslední dimenzi
226 226 #č znaménko normál musí být jednotné #č znaménko normál musí být jednotné
227 a = a * -np.sign(a[-1])
227 a = a * (1 - 2 * (a[-1] > 0)) # a = a * -np.sign(a[-1])
228 228 b = X @ a b = X @ a
229 229 return np.max(b) - np.max(b[[i, j]]) < 1e-7 return np.max(b) - np.max(b[[i, j]]) < 1e-7
230 230
 
... ... class ContactVoronoi:
663 663 #č To ale nic neříká o hustotách #č To ale nic neříká o hustotách
664 664 mask = np.isfinite(w) mask = np.isfinite(w)
665 665 nodes.w = w * mask nodes.w = w * mask
666 #č p_couple není finální pravděpodobnost
667 #č bodíky ještě musejí projít konvexní obálkou
666 668 nodes.p_couple = np.sum(w[mask]) nodes.p_couple = np.sum(w[mask])
667 669 #č a doporučíme (dočasně) k integraci #č a doporučíme (dočasně) k integraci
668 670 self._recommend_to_integration(idx) self._recommend_to_integration(idx)
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