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.testcases_2D: add more nataf ceses ff4873afb1722500e3faed3c7a7f2552496b6e08 I am 2022-04-02 18:48:42
dicebox.Goal._handle_candidates: trigger cache update 80d401d5c4aaab0894d92e6df5390aff12738880 I am 2022-03-30 22:11:24
mplot.mfigs: add G_U_distortion plot 6ed034375dfd042706a91a368c9483270095e75c I am 2022-03-20 14:42:50
mplot.mart: add number_points function dab89bdb9e5190600aa394c9b65cfa24d162be0e I am 2022-03-20 14:42:23
testcases:testcases_nD: add hyperplane_pf3 87176287101b0a15a86c7999ba9626674400d225 I am 2022-03-20 14:41:26
testcases:gaussian_2D: add pf estimation for sinshilds case 889b5aebae164ea3fb1f9375ae58d1afa4687696 I am 2022-03-17 20:42:13
mplot: add rbf_density_plot() 62ce990cb9418418879d0f5e084a26043b5bfdeb I am 2022-03-04 03:00:32
misc: little changes to the rbf class 058a0401db09f4e9e68af72c987c3b879077864d I am 2022-03-03 22:53:48
mplot.maxes: rbf, one more try 8303470585507772e8170504989835f04a7828b9 I am 2022-03-02 22:13:08
mplot.maxes: add rbf_plot 3485da2191b2f64fac76813d072a6b6f6e2fba71 I am 2022-03-02 03:35:27
mplot.maxes: add rbf plot bd5055adced9e0d8887fa213baa0ff9f9e038ba4 I am 2022-03-02 02:17:18
misc: add small class for rbf surrogate model f8beecf63302ceae6d3462ed279b796b54149bdf I am 2022-03-02 02:16:01
testcases: pf_exact fix for suspension 3d case 0f3318cac398c9195e0b21a6f13edc9475156b0d I am 2022-03-02 02:14:55
g_models.CosExp2D: add pf_expression() method 3ebf3098bcff1a73a649059b1539d622d58b7439 I am 2022-02-06 19:38:37
mplot.mfigs.double_diagram(): do not share x axis. User can set up limits itself after all e74145928c028479070af2fe766d55f180828cdf I am 2022-02-06 05:02:59
testcases: add reference solution bounds to the whitebox 3358b3ff7903417f6484a875abb154d3a7c04a06 I am 2022-02-06 05:00:50
mplot: we need more rasterization! 46df5af418dcab84e8e7e7e23724a74e86575806 I am 2022-02-05 02:20:22
testcases: provide more presice pf for vehicle suspension example e2c4bd1c7f2278b4d94b6f7515785528267c18bb I am 2022-02-05 02:18:45
g_models.PassiveSuspensionVehicle: reformulation and clean up 6e0f9ea7b97f12a2554d895be0b2b8a3f39a536d I am 2022-02-04 03:50:03
mplot: add beta diagram 5e346bdd2c5f06a41726e2ff264776dcc60eac19 I am 2022-02-01 04:08:34
Commit ff4873afb1722500e3faed3c7a7f2552496b6e08 - testcases.testcases_2D: add more nataf ceses
Author: I am
Author date (UTC): 2022-04-02 18:48
Committer name: I am
Committer date (UTC): 2022-04-02 18:48
Parent(s): 80d401d5c4aaab0894d92e6df5390aff12738880
Signer:
Signing key:
Signing status: N
Tree: bcc8b487fbc21509bd6bbf05b9f9cca93abcddca
File Lines added Lines deleted
testcases/testcases_2D.py 35 12
File testcases/testcases_2D.py changed (mode: 100644) (index dfece1e..e70060e)
... ... h = f_models.UnCorD((stats.norm(-12,5), stats.norm(12,5)))
16 16 #l = f_models.UnCorD([stats.lognorm(s=1), stats.lognorm(s=1)]) #l = f_models.UnCorD([stats.lognorm(s=1), stats.lognorm(s=1)])
17 17 #č pro různorodost #č pro různorodost
18 18 gew = f_models.Nataf((stats.gumbel_r, stats.weibull_min(c=1.5)), [[1,0.8], [0.8,1]]) gew = f_models.Nataf((stats.gumbel_r, stats.weibull_min(c=1.5)), [[1,0.8], [0.8,1]])
19 gewm = f_models.Nataf((stats.gumbel_r, stats.weibull_min(c=1.5)), [[1,-0.8], [-0.8,1]])
19 20 #gew = f_models.Nataf((stats.norm, stats.norm), [[1,0.8], [0.8,1]]) #gew = f_models.Nataf((stats.norm, stats.norm), [[1,0.8], [0.8,1]])
20 21 #gew = f_models.Nataf((stats.gumbel_r, stats.gumbel_l), [[1,0.8], [0.8,1]]) #gew = f_models.Nataf((stats.gumbel_r, stats.gumbel_l), [[1,0.8], [0.8,1]])
21 22 #č uniforma je na [0, 1], takže v cajku #č uniforma je na [0, 1], takže v cajku
 
... ... The same as Linear_nD, but defined via
83 84 beta in sense of reliability index""" beta in sense of reliability index"""
84 85 # Z_sum s divokým rozdělením # Z_sum s divokým rozdělením
85 86 # pro obyčejný norm dist viz. HyperPlane # pro obyčejný norm dist viz. HyperPlane
86 add('sum_2D')
87 def sum_2D():
88 return whitebox.WhiteBox(gew, gm.Z_sum(nvar=2, beta_exact=2))
89
87 add('nataf_sum_2D')
88 def nataf_sum_2D():
89 wt = whitebox.WhiteBox(gew, gm.Z_sum(nvar=2, beta_exact=np.sqrt(2)))
90 wt.pf_exact = 0.000495
91 wt.Nsim = int(10e6)
92 wt.pf_exact_method = 'MC'
93 return wt
90 94
91 95
92 96 # soucin velicin plus nějaká konstanta # soucin velicin plus nějaká konstanta
 
... ... def min_2D():
144 148
145 149
146 150 # g = np.sum(np.exp(-(sample**2)), axis=1) + self._const # g = np.sum(np.exp(-(sample**2)), axis=1) + self._const
147 add('sumexp_2D')
148 def sumexp_2D():
149 return whitebox.WhiteBox(f, gm.Z_sumexp(const=-0.1))
150 #whitebox.Gaussian_Z_sumexp(nvar=2, co=-2)
151
151 add('nataf_supexp_2D')
152 def nataf_supexp_2D():
153 return whitebox.WhiteBox(gew, gm.Z_sumexp(const=-0.003))
154 add('nataf_sumexp_2D')
155 def nataf_sumexp_2D():
156 wt = whitebox.WhiteBox(gewm, gm.Z_sumexp(const=-0.003))
157 wt.pf_exact = 1.2e-05
158 wt.Nsim = int(10e6)
159 wt.pf_exact_method = 'MC'
160 return wt
152 161
153 162
154 163 """ """
 
... ... def nataf_sumsq_2D():
175 184 # obecný S_ball bez výpočtu pf # obecný S_ball bez výpočtu pf
176 185 add('nataf_ball_2D') add('nataf_ball_2D')
177 186 def nataf_ball_2D(): def nataf_ball_2D():
178 return whitebox.WhiteBox(gew, gm.S_ball(r=5))
179
187 wt = whitebox.WhiteBox(gew, gm.S_ball(r=5))
188 wt.pf_exact = 0.012403
189 wt.Nsim = int(10e6)
190 wt.pf_exact_method = 'MC'
191 return wt
192
193 add('natafm_ball_2D')
194 def natafm_ball_2D():
195 wt = whitebox.WhiteBox(gewm, gm.S_ball(r=5))
196 wt.pf_exact = 0.006812
197 wt.Nsim = int(10e6)
198 wt.pf_exact_method = 'MC'
199 return wt
200
180 201
181 202
182 203
 
... ... add('exp_P_2D')
202 223 def exp_P_2D(): def exp_P_2D():
203 224 return whitebox.WhiteBox(u, gm.Exp_P(k=1., pow=5)) return whitebox.WhiteBox(u, gm.Exp_P(k=1., pow=5))
204 225
205
226 add('nataf_exp_2D')
227 def nataf_exp_2D():
228 return whitebox.WhiteBox(gew, gm.Exp_P(k=1., pow=-0.5))
206 229
207 230
208 231 # Sin2D # Sin2D
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