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)
estimation: little fix in Voronoi_2_point 84115e8d544a78bdaabcd624336ae181e61c845e Олёш 2020-08-01 19:35:48
candybox: reworked 07551958091f48d8f7eb62d27468c2abad030a33 Олёш 2020-08-01 17:24:33
IS_stat: defaultdict replaced by the own one, ISSI.add_single_event_data method added 131e8c23d8f164ae92b838451fc9976a888bffeb Олёш 2020-08-01 00:46:53
IS_stat: IS_like() and ISSI.delete_event_data() methods added a9b4514c9a802797bc15f518b3aeef61f4e3f24e Олёш 2020-07-31 19:58:21
blackbox: MinEnergyCensoredSampling at very begining a924d5d2991f624932d2f0165078e930bda65c58 Олёш 2020-07-31 01:04:09
estimation: --dtto-- 3816060e9da61b99593b7887ec66d6b0a8b239b9 Олёш 2020-07-28 11:27:22
estimation: nejde cell sampling pořadně zlepšit 824d5a2cf7bd10c0fec4b152549d61873cb897dc Олёш 2020-07-28 08:50:45
estimation: playing with cell sampling 78a8dc9a52993876b8a7991590590990efd98812 Олёш 2020-07-28 01:52:35
qt_plot: draw failure boundary by 1000 points d8737247d6cbdb52cc1b1a3d0935edd60234b515 Олёш 2020-07-27 10:38:57
estimation: Voronoi_2_point estimation node_pf_coloring fixed 35fc3d622fe9d5cfa519f1484862108b4003688b Олёш 2020-07-27 10:35:06
candybox: pandas index mess fix, __getattr__ will return numpyed array 10e7c39a69bd105780924319bbafb58602251a9d Олёш 2020-07-27 10:31:43
qt_plot: SimplexEstimationWidget: nodes size increased, VoronoiGraph: redraw item added 8176d66545bfcfa6ee4ea64cbdf3ffd9bbf5ca11 Олёш 2020-07-24 07:01:13
qt_plot: grafy aspoň něco kreslí c2c628d6edf48eb607e44902fa14022c97832cb1 Олёш 2020-07-22 14:40:30
qt_plot: с тех виӝетů на пока хватит 55ee0ccf1d72d6515a66b673c809b9548a2b1ecb Олёш 2020-07-22 09:01:26
qt_plot: WIP 65694b44be8b8f1da9d5c385d37964e65c3824e7 Олёш 2020-07-22 07:21:30
qt_plot: WIP 185d6b48000c43da723ea27055fa5d9540ae2a2a Олёш 2020-07-21 21:45:00
qt_plot: WIP 72bd348eeb85a1ee52d38978a37369e412e959f5 Олёш 2020-07-21 13:24:26
qt_plot: WIP 52ef45375a5851c74111b33223eab093c1e563c2 Олёш 2020-07-20 15:10:06
estimation: simplex_estimation fix. Less wrong) 6fe77618a0654cf107f48f6441738670355dc0a7 Олёш 2020-07-20 06:25:41
qt_plot: WIP ebbad200e0692e667b828a4c2bcc70229fa797e6 Олёш 2020-07-19 11:20:05
Commit 84115e8d544a78bdaabcd624336ae181e61c845e - estimation: little fix in Voronoi_2_point
Author: Олёш
Author date (UTC): 2020-08-01 19:35
Committer name: Олёш
Committer date (UTC): 2020-08-01 19:35
Parent(s): 07551958091f48d8f7eb62d27468c2abad030a33
Signer:
Signing key:
Signing status: N
Tree: 5cb154912486008f1e601725d85210f4fe82a482
File Lines added Lines deleted
estimation.py 3 2
File estimation.py changed (mode: 100644) (index 30e2e91..63e826a)
... ... def Voronoi_2_point_estimation(sample_box, model_space='Rn', sampling_space=None
356 356 # mně příjde, že je to legalní # mně příjde, že je to legalní
357 357 # sice samply podporujou maskovaní, to je ale drahé # sice samply podporujou maskovaní, to je ale drahé
358 358 weights_sim_part = h_plan_part.pdf(sampling_space) / h_L.pdf('R') # snad je to správně weights_sim_part = h_plan_part.pdf(sampling_space) / h_L.pdf('R') # snad je to správně
359 h_plan.add_sample(CandyBox(h_plan_part, w=weights_sim_part))
359 360
360 361 # vyfiltrujeme vzorky # vyfiltrujeme vzorky
361 362 h_plan_model_part = getattr(h_plan_part, model_space) h_plan_model_part = getattr(h_plan_part, model_space)
 
... ... def Voronoi_2_point_estimation(sample_box, model_space='Rn', sampling_space=None
370 371 # zajišťovat Vor_mask je docela zbytečně, je to jen pro out_nodes, # zajišťovat Vor_mask je docela zbytečně, je to jen pro out_nodes,
371 372 # které se zatím nikdě nepouživá # které se zatím nikdě nepouživá
372 373 Vor_mask = np.append(Vor_mask, Vor_mask_part) Vor_mask = np.append(Vor_mask, Vor_mask_part)
373 h_plan.add_sample(h_plan_part)
374
374 375
375 376
376 377
 
... ... def Voronoi_2_point_estimation(sample_box, model_space='Rn', sampling_space=None
431 432 cell_stats['Voronoi_2_point_pure_failure_rate'] = np.sum(weights_sim * node_pf_pure_estimations) / nis_eff cell_stats['Voronoi_2_point_pure_failure_rate'] = np.sum(weights_sim * node_pf_pure_estimations) / nis_eff
432 433 cell_stats['Voronoi_2_point_lower_bound'] = np.sum(weights_sim[failsii_2]) / nis_eff cell_stats['Voronoi_2_point_lower_bound'] = np.sum(weights_sim[failsii_2]) / nis_eff
433 434 cell_stats['Voronoi_failure_rate'] = cell_stats['cell_probability'] cell_stats['Voronoi_failure_rate'] = cell_stats['cell_probability']
434 nodes=CandyBox(h_plan.sampling_plan[Vor_mask], w=h_plan.w[Vor_mask], node_pf_estimations=node_pf_estimations,\
435 nodes=CandyBox(h_plan.sampling_plan[Vor_mask], w=weights_sim, node_pf_estimations=node_pf_estimations,\
435 436 node_pf_pure_estimations=node_pf_pure_estimations) node_pf_pure_estimations=node_pf_pure_estimations)
436 437
437 438 # neporucha # neporucha
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