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)
simplex: use shares instead of probabilities in sinsitivity analysis 75ff4258b95fd568d1b4d163cdc04d40952e0029 I am 2023-03-16 18:46:44
whitebox.get_sensitivities: mask nans and infinities 1a754c51b3a1ef535e86b5b4cb6b75fe9e90840f I am 2023-03-14 22:33:52
simplex.SeparationAxis: fix previous commit 61abd70e69d2fecd74535738c96e74a634af0ad1 I am 2023-03-12 16:49:22
simplex.SeparationAxis: print linprog solution in case of an unexpected status af1aaac1dce3fbc51dc48418685d948a27cf72c6 I am 2023-03-12 15:18:07
g_models: add hyperbola LSF 5f6df35b4280a11c0df1d3cd110250305805ce66 I am 2023-03-12 15:07:20
simplex._Sense: implement piece of postprocessing to get unique vectors 4088852e703947643a8388b2ba8839a694211566 I am 2023-03-10 17:22:29
simplex.SeparationAxis: in case of an unexpected linprog status print result message too 1c9346233693533afb07022c78308b3be6ea9ec7 I am 2023-03-10 15:40:06
whitebox: implement method to .get_sensitivities() from 2D boundary 5729723cfe5c93658c3739fbfec384e135b44073 I am 2023-03-09 22:14:27
simplex.SeparationAxis: make an perfectionism-driven change 8278c48915c326e7731f1934732550c5b9c18e64 I am 2023-03-09 21:51:18
simplex.SeparationAxis: little fix for an hypotetical issue 838e476387c31f9c6847cf7ae8a2a01514038a03 I am 2023-03-09 15:19:48
simplex._Sense: bugfix 3ae53e8b178107b8beb6ad960ec683c8d38a3b3c I am 2023-03-07 23:23:36
simplex._Sense: totally rework, finalized vectors are introduced 829ba58a93481541a58cd4a9f6dae0ba9fb611c6 I am 2023-03-07 13:33:38
simplex: preliminary commit of an _Sense's auxiliary class - brand new SeparationAxis c0bbb714400df48abf7a398e018c5626803bf024 I am 2023-03-04 10:17:12
simplex._Sense: one more optimization a51f4ba65ad4a55ecc74fd82a6e011a4eeed8a5d I am 2023-02-26 15:21:23
qt_gui.qt_plot: implement sensitivity-related Arrows class b5365ae3ca3ab0ed8018e60d4e8711c5c30e4d96 I am 2023-02-26 14:31:10
simplex._Sense: rename "sensibility" to sensitivity a9d56d5386730263eb27e6e31f07b99bab0620fb I am 2023-02-26 14:29:54
g_models: add quadratic 013b4ddc108b94061eaebc1a10d18427f10a34d4 I am 2023-02-24 08:04:12
simplex._Sense: one more performance trick 59b423cca53b9975da67d876110927f233506de8 I am 2023-02-24 08:03:23
simplex: implement separability-based sensibility analysis (new brand _Sense class) 9c5d58f2301893ceaec1b0e90bff76035cfa15b2 I am 2023-02-23 18:49:11
dicebox.circumtri.CirQTri: switch to GaussCubatureIntegration 5b52dd25cb7a997af4953230116deb9efc577d56 I am 2023-02-11 04:32:48
Commit 75ff4258b95fd568d1b4d163cdc04d40952e0029 - simplex: use shares instead of probabilities in sinsitivity analysis
Author: I am
Author date (UTC): 2023-03-16 18:46
Committer name: I am
Committer date (UTC): 2023-03-16 18:46
Parent(s): 1a754c51b3a1ef535e86b5b4cb6b75fe9e90840f
Signer:
Signing key:
Signing status: N
Tree: 9dcb20431a7388ad2ff814d983942c9841587f27
File Lines added Lines deleted
wellmet/simplex.py 45 6
File wellmet/simplex.py changed (mode: 100644) (index ff793e3..a5dcf55)
... ... class FinalizedAxis(NamedTuple):
547 547 class SensitivityResult(NamedTuple): class SensitivityResult(NamedTuple):
548 548 global_gradient: np.ndarray global_gradient: np.ndarray
549 549 sensitivities: np.ndarray sensitivities: np.ndarray
550 probabilities: dict
550 shares: dict
551 551 depths: dict depths: dict
552 552 vectors:dict vectors:dict
553 553 coverings:dict coverings:dict
 
... ... class _Sense:
607 607 neighbors_masked[id] = neis[neis_mask] neighbors_masked[id] = neis[neis_mask]
608 608
609 609
610 def get_finalized_supports(sx, finalized_nornal):
611 ED = sx.tri.points
612 failsi = sx.sample_box.failsi
613 simplices = sx.tri.simplices
614
615 point_scope = set()
616 for key in finalized_nornal.separated_simplices:
617 point_scope.update(simplices[key])
618
619
620 # set supports
621 max_red = -np.inf
622 min_green = np.inf
623 for point in point_scope:
624 failure = failsi[point]
625 value = np.inner(finalized_nornal.normal, ED[point])
626 if failure and (value > max_red):
627 max_red = value
628 #red_supp = point
629 elif not failure and (value < min_green):
630 min_green = value
631 #green_supp = point
632
633 assert max_red < min_green
634
635 return max_red, min_green
636
610 637
611 638 def perform_sensitivity_analysis(self): def perform_sensitivity_analysis(self):
612 639 # I'll try to use dual name convention here # I'll try to use dual name convention here
 
... ... class _Sense:
678 705
679 706 for simplex_id, normal in vectors.items(): for simplex_id, normal in vectors.items():
680 707 indices = simplices[simplex_id] indices = simplices[simplex_id]
681 p_mixed = sx.get_simplex_probability(indices)
708 p_mixed = sx.get_simplex_share(indices)
682 709 mixed_probability += p_mixed mixed_probability += p_mixed
683 710 probabilities[simplex_id] = p_mixed probabilities[simplex_id] = p_mixed
684 711
 
... ... class SeparationAxis:
1178 1205
1179 1206
1180 1207
1208
1209
1181 1210
1182
1183
1184
1211
1185 1212
1186 1213
1187 1214
 
... ... class GaussCubatureIntegration(_Triangulation, _Sense):
2988 3015
2989 3016
2990 3017 def integrate_simplex(sx, indices): def integrate_simplex(sx, indices):
2991
2992 3018 sx.newly_estimated += 1 sx.newly_estimated += 1
3019 return sx._preintegrate_simplex(indices)
2993 3020
3021 def _preintegrate_simplex(sx, indices):
2994 3022 vertices_model = sx.tri.points[indices] vertices_model = sx.tri.points[indices]
2995 3023
2996 3024 vol = quadpy.tn.get_vol(vertices_model) vol = quadpy.tn.get_vol(vertices_model)
 
... ... class GaussCubatureIntegration(_Triangulation, _Sense):
3028 3056 p_mixed, pfv, pfw, pf = sx.mixed_simplices[key] p_mixed, pfv, pfw, pf = sx.mixed_simplices[key]
3029 3057 return p_mixed / sx._norm_pdf_C return p_mixed / sx._norm_pdf_C
3030 3058
3059 # share = p_simplex * V**((ndim-1)/ndim)
3060 # component of sensitivity intagral f(x)dS
3061 def get_simplex_share(sx, indices):
3062 vol, fx = sx._preintegrate_simplex(indices)
3063 #č matematicky korektně musíme hustoty ještě vydělit
3064 #sx._norm_pdf_C
3065 #č ale u těch citlovostí jde nám jen o podíly
3066 #č a nemusíme provadět jednu operaci navíc
3067 ndim = len(indices) - 1
3068 return fx * vol**((ndim-1)/ndim)
3069
3031 3070
3032 3071 def get_pf_estimation(sx): def get_pf_estimation(sx):
3033 3072 if len(sx.tri.points) < sx.sample_box.nsim: if len(sx.tri.points) < sx.sample_box.nsim:
Hints:
Before first commit, do not forget to setup your git environment:
git config --global user.name "your_name_here"
git config --global user.email "your@email_here"

Clone this repository using HTTP(S):
git clone https://rocketgit.com/user/iam-git/WellMet

Clone this repository using ssh (do not forget to upload a key first):
git clone ssh://rocketgit@ssh.rocketgit.com/user/iam-git/WellMet

Clone this repository using git:
git clone git://git.rocketgit.com/user/iam-git/WellMet

You are allowed to anonymously push to this repository.
This means that your pushed commits will automatically be transformed into a merge request:
... clone the repository ...
... make some changes and some commits ...
git push origin main