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)
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
simplex: implement GaussCubatureIntegration in the most memory-friendly way 689d253ae7e2a22242258fd5bef0a069caf7cf75 I am 2023-02-11 04:31:11
convex_hull.QHullCubature: implement memory-friendly outside (chi) integration ad8210a04b1e0903de7435cad16b1304707d0e6e I am 2023-02-09 22:22:05
qt_gui.qt_plot: require box recommendation to show next point 6f726047f7f08e884359020eaa1eac6f6cc125d2 I am 2023-02-09 11:51:44
dicebox.circumtri.CirQTri.get_circum_node: limit circumcenter by explore radius, not by just R 136ec73bb06da16c1f2bce64b3c349be4c8ba975 I am 2023-02-09 03:09:51
dicebox.circumtri: implement new brand CirQTri box 5879b8ad6317c65aa9481b59f76b6159f19e247a I am 2023-02-09 01:29:10
simplex.FullCubatureIntegration: store simplex probabilities in sorted dicts c0da90731ff3ede47d9b4eec0ad9b28a29027167 I am 2023-02-09 01:23:14
dicebox.circumtri: exploratory: even better idea 811ab11cd7172ff4a3807992f4928be2e8068ec0 I am 2023-02-08 15:31:23
Commit 5729723cfe5c93658c3739fbfec384e135b44073 - whitebox: implement method to .get_sensitivities() from 2D boundary
Author: I am
Author date (UTC): 2023-03-09 22:14
Committer name: I am
Committer date (UTC): 2023-03-09 22:14
Parent(s): 8278c48915c326e7731f1934732550c5b9c18e64
Signer:
Signing key:
Signing status: N
Tree: bf687d2d4dc9f5d9747bef15e81f610e57a9c265
File Lines added Lines deleted
wellmet/whitebox.py 50 1
File wellmet/whitebox.py changed (mode: 100644) (index 4b3c7d9..0a36430)
... ... from .samplebox import SampleBox
33 33 from . import f_models from . import f_models
34 34 import copy import copy
35 35 from . import g_models from . import g_models
36 from . import misc as wmisc
36 37
37 38
38 39
 
... ... class WhiteBox:
171 172 bounds_R = wt.get_2D_R_boundary(nrod, (xmin, xmax), (ymin, ymax)) bounds_R = wt.get_2D_R_boundary(nrod, (xmin, xmax), (ymin, ymax))
172 173 # transformuji na seznam vzorků # transformuji na seznam vzorků
173 174 return [wt.f_model.new_sample(bounds_R[i], extend=True) for i in range(len(bounds_R))] return [wt.f_model.new_sample(bounds_R[i], extend=True) for i in range(len(bounds_R))]
174
175
176
177 def get_sensitivities(wt, nrod=200, G_radius=7, space='R'):
178 #č nejdřív musíme získat relevantní ve skutečném prostoru rámec
179 view_sample = wmisc.get_isodistances(wt.f_model, G_radius, nrod)
180 view_sample_R = view_sample.R
181
182 #č jakože tímto vyfiltrujeme nejen NaN, ale i nekonečna?
183 xmin = np.ma.masked_invalid(view_sample_R[:,0]).min()
184 xmax = np.ma.masked_invalid(view_sample_R[:,0]).max()
185 ymin = np.ma.masked_invalid(view_sample_R[:,1]).min()
186 ymax = np.ma.masked_invalid(view_sample_R[:,1]).max()
187
188 #č skoro hotovo. Jsme připravení získat seznam hraničních bodů od g_modelu
189 boundaries = wt.get_2D_R_boundary(nrod, (xmin, xmax), (ymin, ymax))
190
191 sensitivity = np.zeros(2)
192 # boundaries are instances of an obscure Ingot class.
193 # Relicts from the WellMet's childhood
194 for boundary in boundaries:
195 #č podle ono to tam musí implicitně vzít podle R
196 sample = wt.f_model.new_sample(boundary, extend=True)
197 sample_space = getattr(sample, space)
198 vectors = np.diff(sample_space, axis=0)
199 #č k těm hustotám můžeme podstupovat několika způsoby
200 #č buď zprůměrovat uzly, v nich napočítat hustoty.
201 #č Nebo napočítat hustoty v tam, kde je máme,
202 #č zprůměrovat už jen hustoty.
203 #č Jdeme na tu druhou variantu
204 pdf = sample.pdf(space)
205 pdf[:-1] += pdf[1:]
206 #pdf /= 2
207 pdf = pdf[:-1]
208
209 #č pokud nebudeme chtit nic křeslit,
210 #č tak samotné vektory ani nepotřebujeme
211 np.square(vectors, out=vectors)
212
213 #č zdá se, že ani délky nepotřebujeme
214 #lengths = np.sum(vectors, axis=1)
215 #vectors /= lengths
216
217 vectors *= pdf.reshape(-1,1)
218
219 sensitivity += np.sum(vectors, axis=0)[:2]
220
221 sensitivity /= np.sum(sensitivity)
222 y, x = sensitivity
223 return x, y
175 224
176 225
177 226
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