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)
f_models & whitebox: object generation (creation) fix 3b2e17e86e14f0eaea0e3264e8d6a4f0930505f5 Олёш 2020-07-10 20:54:23
estimation: WIP, Voronoi_2_point worked 364c89027063f0bd3a1af80d7d53db059184dd68 Олёш 2020-07-10 02:51:33
IS_stat: one more IS implementation a23ab3ff5eac9a039f415b9281a02ebbbea778f2 Олёш 2020-07-09 03:09:14
f_models: .pdf() and .sample_pdf() redesigned 40dfc578e14f630c1735a279a3ec41d169cc7606 Олёш 2020-07-08 01:18:29
f_models: linear tranformations and .pdf function added 7ce23a722836eaa3488329870691a332641a92b1 Олёш 2020-07-07 03:36:07
estimation: WIP 602b24888d5b22e7a994a14119af3a12c53a374d Alex 2020-06-29 08:58:54
g_models: ConicSection added e603e051a42dec4694766d536e71300dc62cb3a8 Олёш 2020-06-27 11:11:31
qt_plot: slider and triangulation fix 04a8bf659873bed50b82dc94497f2fc473c883dd Олёш 2020-06-26 19:56:58
qt_plot: show triangulation 0aa36d61be95b6f40f4a01e77a61a701c78789ce Олёш 2020-06-26 10:10:39
f_models: přidany chybějící pdf-ka 1f3c99bcd1eaeed9b8b84175be9189449a7dec5f Олёш 2020-06-25 20:28:33
qt_plot: connection to BlackBox feature added fcff3099dc61e568981e1f1236c29b5326547fd5 Олёш 2020-06-23 09:52:56
qt_plot: estimation graphs are implemented a31c5b3c344893f0f0b7022ee54544493e97c79a Олёш 2020-06-21 22:21:55
qt_plot: WIP 7cf97855991f717873d02ce65058790b324a319d Олёш 2020-06-21 10:17:04
qt_plot: minor changes 931ca9629256f37588e7ed4cb497f71c93954a7b Олёш 2020-06-15 06:01:10
candybox introduced, stores additional data 2d7e227d114a60ab47456bfc62308b16dba4eacf Олёш 2020-06-11 23:54:54
qt_plot: konečně něco interaktivního... 2bcc8842afe87d1e44e066fe168fcfedb8043947 Олёш 2020-06-07 07:35:57
WIP: matplotlib and QtGui ce3d38363636ba60fcddc0ebfe45c1cefadfc7a5 Олёш 2020-06-07 01:57:04
tri_estimations_graph added (by plot.ly) 1d307b1a8e26277c6259596eb4c912b3262d3ee7 Олёш 2020-06-04 15:03:57
directory structure changed d67e975a7f4c7b01388f469324f635c74cf17995 Олёш 2020-06-03 21:17:35
Commit 3b2e17e86e14f0eaea0e3264e8d6a4f0930505f5 - f_models & whitebox: object generation (creation) fix
Author: Олёш
Author date (UTC): 2020-07-10 20:54
Committer name: Олёш
Committer date (UTC): 2020-07-10 20:54
Parent(s): 364c89027063f0bd3a1af80d7d53db059184dd68
Signer:
Signing key:
Signing status: N
Tree: cecd5862ef039c53b8b55add770cf974570c5c39
File Lines added Lines deleted
f_models.py 3 1
whitebox.py 5 5
File f_models.py changed (mode: 100644) (index dc3be53..a3b1857)
... ... class SNorm:
128 128 pdfs_R = stats.norm.pdf(sample_R) pdfs_R = stats.norm.pdf(sample_R)
129 129 pdf_R = np.prod(pdfs_R, axis=1).reshape(-1, 1) pdf_R = np.prod(pdfs_R, axis=1).reshape(-1, 1)
130 130 f_copy._data = np.hstack((sample_R, sample_P, pdf_R)) f_copy._data = np.hstack((sample_R, sample_P, pdf_R))
131 else:
132 f_copy._data = np.empty((0, f_copy._data.shape[1]), dtype=float)
131 133 return f_copy return f_copy
132 134
133 135 def __len__(f): def __len__(f):
 
... ... class SNorm:
228 230
229 231 # drobná pomucka # drobná pomucka
230 232 def new_sample(f, sample=None, space='R'): def new_sample(f, sample=None, space='R'):
231 f_copy = copy.copy(f)
233 f_copy = f()
232 234 if sample is not None: if sample is not None:
233 235 f_copy.add_sample(sample, space) f_copy.add_sample(sample, space)
234 236 return f_copy return f_copy
File whitebox.py changed (mode: 100644) (index c9f8f89..f62c675)
... ... class HyperPlane(WhiteBox): # куда ж без него...
256 256 self.gm = g_models.Linear_nD(betas) self.gm = g_models.Linear_nD(betas)
257 257 self.f = f_models.SNorm(len(betas)-1) self.f = f_models.SNorm(len(betas)-1)
258 258 # na začatku nemáme vzorky - pouze rozdělení a podpís # na začatku nemáme vzorky - pouze rozdělení a podpís
259 self.sample_box = SampleBox(self.f, gm_signature=self.gm_signature)
259 self.sample_box = SampleBox(self.f(), gm_signature=self.gm_signature)
260 260
261 261 # tady už je to ta, "náše" beta ) # tady už je to ta, "náše" beta )
262 262 # beta = c/np.sqrt(a**2 + b**2) # beta = c/np.sqrt(a**2 + b**2)
 
... ... class Weibull_Z_min(WhiteBox):
310 310
311 311 self.gm = g_models.Z_min(self.const) self.gm = g_models.Z_min(self.const)
312 312 # na začatku nemáme vzorky - pouze rozdělení a podpís # na začatku nemáme vzorky - pouze rozdělení a podpís
313 self.sample_box = SampleBox(self.f, gm_signature=self.gm_signature)
313 self.sample_box = SampleBox(self.f(), gm_signature=self.gm_signature)
314 314
315 315 def __str__(self): def __str__(self):
316 316 return 'Weibull_Z_min%sD'%(len(self.wb_scales)) return 'Weibull_Z_min%sD'%(len(self.wb_scales))
 
... ... class Gaussian_Z_sumexp(WhiteBox):
359 359 self.const = self.C self.const = self.C
360 360 self.gm = g_models.Z_sumexp(self.const) self.gm = g_models.Z_sumexp(self.const)
361 361 # na začatku nemáme vzorky - pouze rozdělení a podpís # na začatku nemáme vzorky - pouze rozdělení a podpís
362 self.sample_box = SampleBox(self.f, gm_signature=self.gm_signature)
362 self.sample_box = SampleBox(self.f(), gm_signature=self.gm_signature)
363 363
364 364 def __str__(self): def __str__(self):
365 365 return 'Gaussian_Z_sumexp%sD'%(self.nvar) return 'Gaussian_Z_sumexp%sD'%(self.nvar)
 
... ... class SNorm_Z_sumsq(WhiteBox):
404 404 self.const = self.C self.const = self.C
405 405 self.gm = g_models.Z_sumsq(self.C) self.gm = g_models.Z_sumsq(self.C)
406 406 # na začatku nemáme vzorky - pouze rozdělení a podpís # na začatku nemáme vzorky - pouze rozdělení a podpís
407 self.sample_box = SampleBox(self.f, gm_signature=self.gm_signature)
407 self.sample_box = SampleBox(self.f(), gm_signature=self.gm_signature)
408 408
409 409 def __str__(self): def __str__(self):
410 410 return 'SNorm_Z_sumsq%sD'%(self.nvar) return 'SNorm_Z_sumsq%sD'%(self.nvar)
 
... ... class SNorm_S_ball(WhiteBox):
461 461 self.r = r self.r = r
462 462 self.gm = g_models.S_ball(r) self.gm = g_models.S_ball(r)
463 463 # na začatku nemáme vzorky - pouze rozdělení a podpís # na začatku nemáme vzorky - pouze rozdělení a podpís
464 self.sample_box = SampleBox(self.f, gm_signature=self.gm_signature)
464 self.sample_box = SampleBox(self.f(), gm_signature=self.gm_signature)
465 465
466 466 def __str__(self): def __str__(self):
467 467 return 'SNorm_S_ball%sD'%(self.nvar) return 'SNorm_S_ball%sD'%(self.nvar)
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