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)
testcases: clean up a little bit 979a70e10cda4786ef17d03217f5a57cd86a6ae9 I am 2023-05-31 16:57:49
g_models: fix quartic name, add parabola b80e98134d7a5c91ad9e44ad9b58e76ac0a3bb64 I am 2023-05-31 16:54:39
qt_gui.qt_gui: little tweaks 73c027858135753f99d2eabb3f080e936c4ff18d I am 2023-05-30 16:53:59
reader forgets Udmurt, learns English instead :'( 640ac860613b0d93fc037490c3a02da251eb8aca I am 2023-05-30 15:23:31
testcases: add __init__ file 0b942cd9028c3c5baea43aff61526b75c2f3ee43 I am 2023-05-28 14:04:55
mplot: add quiver plots a32b3d850fef99d6798b778140867416e17c16db I am 2023-03-30 13:21:30
mplot.mart: add simplex_vectors() function 7a90003e40bd459d2a3ef45a4867dfa392bd3fc8 I am 2023-03-30 13:20:10
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
Commit 979a70e10cda4786ef17d03217f5a57cd86a6ae9 - testcases: clean up a little bit
Author: I am
Author date (UTC): 2023-05-31 16:57
Committer name: I am
Committer date (UTC): 2023-05-31 16:57
Parent(s): b80e98134d7a5c91ad9e44ad9b58e76ac0a3bb64
Signer:
Signing key:
Signing status: N
Tree: ab3c105463394501cec8b3440b57aac0050ddb06
File Lines added Lines deleted
wellmet/testcases/gaussian_2D.py 14 91
wellmet/testcases/testcases_2D.py 60 0
wellmet/testcases/testcases_2D_papers.py 10 58
File wellmet/testcases/gaussian_2D.py changed (mode: 100644) (index 99940ff..752410b)
2 2 # coding: utf-8 # coding: utf-8
3 3
4 4 """ """
5 We will prepare here WhiteBox instances
5 WhiteBox instances
6 6 of different Gaussian 2D problems of different Gaussian 2D problems
7 7 """ """
8 8
 
... ... def hyperbola_2():
108 108 return Gaussian_Hyperbola_2D(const=2) return Gaussian_Hyperbola_2D(const=2)
109 109
110 110
111 class ProxyProd:
112 def __init__(self, const=0):
113 self.const = const
114 #assert (const * sign) > 0
115 #self.sign = sign
116 self.true_gm = gm.Z_prod(const=const, sign=np.sign(self.const))
117 # wrap
118 self.get_2D_R_boundary = self.true_gm.get_2D_R_boundary
119
120 # sign
121 def __repr__(self):
122 return 'ProxyProd(const=%s)' % repr(self.const)
123
124 def __call__(self, input_sample):
125 #č očekávam, že get_R_coordinates mně vrátí 2D pole
126 sample = gm.get_R_coordinates(input_sample, 2)
127 x, y = sample.T
128
129 #č osudná podmínka
130 mask = np.atleast_1d(np.sign(x)==(np.sign(y)*np.sign(self.const)))
131 #č mrdáme na kontrolu. současný startup candybox vytvoří vždycky
132 input_sample.candybox.proxy = mask
133 sweet_sample = input_sample
134 #sweet_sample = CandyBox(input_sample, proxy=mask)
135
136 #č zatím, pro jednoduchost, předpokladáme,
137 #č že dostaváme vzorky po jednom
138 if np.all(mask):
139 #č nevíme vůbec, co to je za funkci
140 #č ale veříme, že víme co tam bude
141 g = np.full(len(sweet_sample), 1)
142 #č s praznejm podpísem odmítá
143 return samplebox.SampleBox(sweet_sample, g, repr(self))
144
145 else: #č deme počítat, bez b
146 true_sample = self.true_gm(sweet_sample)
147 #č padelame pospís
148 true_sample.gm_signature = repr(self)
149 return true_sample
150
151
152 def _proxy_prod(const):
153 wt = Gaussian_Z_prod_2D(const=const)
154 wt.gm = ProxyProd(const)
155 #č padelame pospís
156 wt.samplebox.gm_signature = repr(wt.gm)
157 return wt
158
159 add('proxy_prod_1')
160 def proxy_prod_1():
161 return _proxy_prod(1)
162 111
163 add('proxy_prod_5')
164 def proxy_prod_5():
165 return _proxy_prod(-5)
166 112
167 113
168 114 """ """
 
... ... def circle():
195 141 return WhiteBox(f, gm.ConicSection(l=1.4, e=0, teta=0, c=(1.3, 0.6), sign=1)) return WhiteBox(f, gm.ConicSection(l=1.4, e=0, teta=0, c=(1.3, 0.6), sign=1))
196 142
197 143
198 # parabola
199 #č nemá dostatečnou pro g_models uroveň
200 #č proto ji celou dáme tady
201 def Z(X, Y):
202 '''Rezerva spolehlivosti Z=Y+1-X**2'''
203 return - Y + 2.5 + 10.1*(X-1)**2
204 144
205 def get_bound_y(x):
206 '''Hranice poruchy Z=0'''
207 return 10.1*(x-1)**2 + 2.5
208
209 def parabola_2D(input_sample):
210 # očekávam, že get_R_coordinates mně vrátí 2D pole
211 sample = gm.get_R_coordinates(input_sample)
212 x, y, *__ = (*sample.T,)
213 g = Z(x, y)
214 return samplebox.SampleBox(input_sample, g, 'parabola_2D')
215
216 def parabola_boundary(nrod=100, xlim=(-5,5), *args):
217 """
218 Fence off!
219 nrod - number of rods in fencing
220 """
221
222 xbound = np.linspace(xlim[0], xlim[1], nrod, endpoint=True)
223
224 bound_y = get_bound_y(xbound)
225
226 # sample compatible
227 # малы транспонировать кароно? Озьы кулэ!
228 bound_R = np.array((xbound, bound_y)).T
229 # tuple of samples
230 return (f_models.Ingot(bound_R),)
231
232
233 parabola_2D.get_2D_R_boundary = parabola_boundary
234 145
235 146 add('parabola') add('parabola')
236 147 def parabola(): def parabola():
237 return WhiteBox(f, parabola_2D)
148 return WhiteBox(f, gm.parabola_2D)
238 149
239 150
240 151 # Sin2D # Sin2D
 
... ... add('sumexp')
315 226 def sumexp(): def sumexp():
316 227 return Gaussian_Z_sumexp_2D(-0.003) return Gaussian_Z_sumexp_2D(-0.003)
317 228
229 add('modified_rastrigin')
230 def modified_rastrigin():
231 wt = WhiteBox(f, gm.modified_rastrigin)
232 wt.description = "Modified Rastrigin"
233 wt.pf_exact = 7.34e-02
234 wt.pf_exact_method = 'known value'
235 return wt
236
237 add('quartic')
238 def quartic():
239 wt = WhiteBox(f, gm.quartic)
240 return wt
318 241
319 242
320 243 add('max') add('max')
File wellmet/testcases/testcases_2D.py changed (mode: 100644) (index 54c5bd1..52d8dd8)
... ... def add(str):
31 31 __all__.append(str) __all__.append(str)
32 32
33 33
34
35
36 class ProxyProd:
37 def __init__(self, const=0):
38 self.const = const
39 #assert (const * sign) > 0
40 #self.sign = sign
41 self.true_gm = gm.Z_prod(const=const, sign=np.sign(self.const))
42 # wrap
43 self.get_2D_R_boundary = self.true_gm.get_2D_R_boundary
44
45 # sign
46 def __repr__(self):
47 return 'ProxyProd(const=%s)' % repr(self.const)
48
49 def __call__(self, input_sample):
50 #č očekávam, že get_R_coordinates mně vrátí 2D pole
51 sample = gm.get_R_coordinates(input_sample, 2)
52 x, y = sample.T
53
54 #č osudná podmínka
55 mask = np.atleast_1d(np.sign(x)==(np.sign(y)*np.sign(self.const)))
56 #č mrdáme na kontrolu. současný startup candybox vytvoří vždycky
57 input_sample.candybox.proxy = mask
58 sweet_sample = input_sample
59 #sweet_sample = CandyBox(input_sample, proxy=mask)
60
61 #č zatím, pro jednoduchost, předpokladáme,
62 #č že dostaváme vzorky po jednom
63 if np.all(mask):
64 #č nevíme vůbec, co to je za funkci
65 #č ale veříme, že víme co tam bude
66 g = np.full(len(sweet_sample), 1)
67 #č s praznejm podpísem odmítá
68 return samplebox.SampleBox(sweet_sample, g, repr(self))
69
70 else: #č deme počítat, bez b
71 true_sample = self.true_gm(sweet_sample)
72 #č padelame pospís
73 true_sample.gm_signature = repr(self)
74 return true_sample
75
76
77 def _proxy_prod(const):
78 wt = Gaussian_Z_prod_2D(const=const)
79 wt.gm = ProxyProd(const)
80 #č padelame pospís
81 wt.samplebox.gm_signature = repr(wt.gm)
82 return wt
83
84 add('proxy_prod_1')
85 def proxy_prod_1():
86 return _proxy_prod(1)
87
88 add('proxy_prod_5')
89 def proxy_prod_5():
90 return _proxy_prod(-5)
91
92
93
34 94 # HyperRovina, куда ж без неё? # HyperRovina, куда ж без неё?
35 95 ############# #############
36 96 # g= a*X1 + b*X2 + c # g= a*X1 + b*X2 + c
File wellmet/testcases/testcases_2D_papers.py changed (mode: 100644) (index c00c746..57f8b67)
2 2 # coding: utf-8 # coding: utf-8
3 3
4 4 """ """
5 We will prepare here WhiteBox instances
6 of different well-known 2D problems
5 WhiteBox instances
6 of different non-Gaussian 2D problems
7 7 """ """
8 8
9 9 import numpy as np import numpy as np
 
... ... from scipy import integrate # for Pareto tail
15 15
16 16 __all__ = [ __all__ = [
17 17 'uniform_branin_2D', 'uniform_branin_2D',
18 'snorm_four_branch_2D',
19 'snorm_four_branch_2D_2',
20 'snorm_piecewise_2D_linear',
21 18 'piecewise_pareto_tail', 'piecewise_pareto_tail',
22 'snorm_min_2D_linear',
23 'snorm_min_2D_logistic',
24 'modified_rastrigin',
25 'quadratic'
19 'natafm_plane_2D',
26 20 ] ]
27 21
28 f = f_models.SNorm(2)
22
29 23 # Uniform-uniform # Uniform-uniform
30 24 # from scipy: "In the standard form, the distribution is uniform on [0, 1]." # from scipy: "In the standard form, the distribution is uniform on [0, 1]."
31 25 u = f_models.UnCorD((stats.uniform, stats.uniform)) u = f_models.UnCorD((stats.uniform, stats.uniform))
32 26
27 gewm = f_models.Nataf((stats.gumbel_r, stats.weibull_min(c=1.5)), [[1,-0.8], [-0.8,1]])
33 28
34 29
35 30 """ """
 
... ... def uniform_branin_2D():
44 39 wt.description = "Rescaled Branin function" wt.description = "Rescaled Branin function"
45 40 return wt return wt
46 41
47 # Four branch system
48 def snorm_four_branch_2D():
49 wt = WhiteBox(f, gm.FourBranch2D(k1=3, k2=7))
50 wt.pf_exact = 2.34e-03
51 wt.pf_exact_method = 'known value' #"some guys said me that"
52 wt.description = "Four branch system from some paper"
53 return wt
54
55
56 def snorm_four_branch_2D_2():
57 wt = WhiteBox(f, gm.FourBranch2D(k1=5.5, k2=11))
58 # TODO calculate
59 #wt.pf_exact = 0.257
60 #wt.pf_exact_method = 'known value' #"some guys said me that" it's 0.256
61 wt.description = "Four branch system from another paper"
62 return wt
63
64
65
66
67
68 # Breitung
69 # Piecewise linear function
70 def snorm_piecewise_2D_linear():
71 wt = WhiteBox(f, gm.piecewise_2D_linear)
72 wt.description = "Breitung. Piecewise linear function"
73 return wt
74
75 42
76 43
77 44 # Pareto tail # Pareto tail
 
... ... def piecewise_pareto_tail():
109 76
110 77
111 78
112 # Logistic 2D function
113 def snorm_min_2D_linear():
114 wt = WhiteBox(f, gm.Logistic2D())
115 wt.description = "Breitung. 2D linear (easy version for SuS)"
116 return wt
117
118 def snorm_min_2D_logistic():
119 wt = WhiteBox(f, gm.Logistic2D(easy_version=False))
120 wt.description = "Breitung. Logistic 2D function (hard version for SuS)"
121 return wt
122
123 def modified_rastrigin():
124 wt = WhiteBox(f, gm.modified_rastrigin)
125 wt.description = "Modified Rastrigin"
126 wt.pf_exact = 7.34e-02
127 wt.pf_exact_method = 'known value'
128 return wt
129
130 def quadratic():
131 wt = WhiteBox(f, gm.quadratic)
79 def natafm_plane_2D():
80 wt = whitebox.WhiteBox(gewm, gm.Linear_nD((-1, -2, 7)))
81 wt.pf_exact = 0.00114
82 wt.Nsim = int(10e8)
83 wt.pf_exact_method = 'MC'
132 84 return wt return wt
133 85
134 86
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