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)
convex_hull: last node fix in fire() function ca938c11398dfb2e16441b989902dab1558336b3 I am 2021-06-05 13:25:55
convex_hull: small fix for QHull under single simplex integration b0b236cd14ae614ecd1310b8c7cfcafe8d1b0851 I am 2021-05-30 05:57:45
convex_hull: ensure Ghull to always have some nodes for expansion 7110d521d9fece58639a359c83773df904b1177f I am 2021-05-29 07:03:49
dicebox: throw away plot dependency, simplify increment function 1cf0c481b8df1937ff7eda6e0373d5d6a72387db I am 2021-05-28 16:42:19
simplex: add JustCubatureTriangulation class separated from Shull ea291f9deee9f3b5b8615ae1f14a49aecd327461 I am 2021-05-28 16:39:58
mart: add convex_hull routines 90105552e7aecf3df8e84a1f5c4bdc1b04be249b I am 2021-04-25 20:11:34
convex_hull.Ghull: try to outthink OS's memory management 116444dc08cc0261e149de02f21f14f74dc8816b I am 2021-04-25 08:02:25
convex_hull.Ghull: in case of memory error divide ns by 3 0f629139f7926107b0b4eeb55452e745a2c23487 I am 2021-04-25 06:50:02
convex_hull: memory failsafe integration 7624e4b81526d3944f1a5ac74d298dee792c3085 I am 2021-04-24 08:06:27
simplex: delete ghull-related stuff (moved to convex_hull module already) f514adf959a9a2a9b7806b3cde81f78faf04539d I am 2021-04-19 17:04:54
convex_hull.QHull: change enough_points into property ae4f9b4d70bb43556874bc6d698cb1cc09ad9430 I am 2021-04-19 17:02:35
qt_plot: add support for "just nodes" da0e307ab0a21e565031789260a9160c5ff1b011 I am 2021-04-19 07:33:12
sball.Shell.rvs(): inverse linspace (wouldn't produce NaNs) 58452888a47230ab30c56bc33f5e3c780e60c752 I am 2021-04-18 03:26:21
convex_hull: move QHull from simplex module e5c67ce54fa4c9f3ce15714ebeff7d363d918494 I am 2021-04-17 19:04:00
schemes: comment out dups 8d22480a42926c3a78e62a07be4418e1f8ba350f I am 2021-04-16 23:35:53
convex_hull: split DirectHull into simplified DirectHull itself and CompleteHull b109bbdc325a2a88bf22e9893fa162690bf190f9 I am 2021-04-16 23:26:03
rework convex hull 60185dce0403ba941e849a60b5e43da6ce1fffd4 I am 2021-04-12 17:09:25
sball: add .get_random_directions() function d41131f28937e40eb853a30047de3cfc43cf5fa8 I am 2021-03-24 03:24:41
mplot: plot2D swithed to matplotlib 366326fb53bcbc54d7e6fb108b9dc256833bf679 I am 2021-03-18 01:44:07
three-ways image WIP a2987cde393c4e795342f6dc40d5920760b468d4 I am 2021-03-17 16:37:18
Commit ca938c11398dfb2e16441b989902dab1558336b3 - convex_hull: last node fix in fire() function
Author: I am
Author date (UTC): 2021-06-05 13:25
Committer name: I am
Committer date (UTC): 2021-06-05 13:25
Parent(s): b0b236cd14ae614ecd1310b8c7cfcafe8d1b0851
Signer:
Signing key:
Signing status: N
Tree: eb91cb9e705ac4832d34917bef83d902f5675b0e
File Lines added Lines deleted
convex_hull.py 6 6
File convex_hull.py changed (mode: 100644) (index 73252a9..0021711)
... ... class DirectHull:
289 289 A = hull.equations[:,:-1] A = hull.equations[:,:-1]
290 290 b = hull.equations[:,-1] b = hull.equations[:,-1]
291 291
292 to_fire = np.argmax(b)
292 to_fire = np.nanargmax(b)
293 293 a = A[to_fire] a = A[to_fire]
294 294 fire_from = stats.norm.cdf(hull.get_r()) fire_from = stats.norm.cdf(hull.get_r())
295 t = np.linspace(fire_from, 1, ns)
295 t = np.linspace(fire_from, 1, ns, endpoint=False)
296 296 t = stats.norm.ppf(t) t = stats.norm.ppf(t)
297 297 fire_G = t.reshape(-1,1) @ a.reshape(1,-1) fire_G = t.reshape(-1,1) @ a.reshape(1,-1)
298 298
 
... ... class CompleteHull:
415 415 A = hull.equations[:,:-1] A = hull.equations[:,:-1]
416 416 b = hull.equations[:,-1] b = hull.equations[:,-1]
417 417
418 to_fire = np.argmax(b)
418 to_fire = np.nanargmax(b)
419 419 a = A[to_fire] a = A[to_fire]
420 420 fire_from = stats.norm.cdf(hull.get_r()) fire_from = stats.norm.cdf(hull.get_r())
421 t = np.linspace(fire_from, 1, ns)
421 t = np.linspace(fire_from, 1, ns, endpoint=False)
422 422 t = stats.norm.ppf(t) t = stats.norm.ppf(t)
423 423 fire_G = t.reshape(-1,1) @ a.reshape(1,-1) fire_G = t.reshape(-1,1) @ a.reshape(1,-1)
424 424
 
... ... class QHull:
536 536 A = hull.equations[:,:-1] A = hull.equations[:,:-1]
537 537 b = hull.equations[:,-1] b = hull.equations[:,-1]
538 538
539 to_fire = np.argmax(b)
539 to_fire = np.nanargmax(b) #č tak, pro jistotu
540 540 a = A[to_fire] a = A[to_fire]
541 541 fire_from = stats.norm.cdf(hull.get_r()) fire_from = stats.norm.cdf(hull.get_r())
542 t = np.linspace(fire_from, 1, ns)
542 t = np.linspace(fire_from, 1, ns, endpoint=False)
543 543 t = stats.norm.ppf(t) t = stats.norm.ppf(t)
544 544 fire_G = t.reshape(-1,1) @ a.reshape(1,-1) fire_G = t.reshape(-1,1) @ a.reshape(1,-1)
545 545
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