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)
g_models: add boundary finding for metaball function 5ea3011d11572d2fa98d77819cc1ba333e6b4016 I am 2022-01-26 00:38:21
mplot: polish ad5819ced9f1be64c8d731813c575c1ec854421d I am 2022-01-25 03:39:22
testcases.gaussian_2D: add pf_exact to the black swan 38e19e42884234afc467a0ee7af9699af1f84e96 I am 2022-01-25 03:37:59
mplot.maxes: pass nrod to plot_boundaries() b87c6c58900b2b7f9a11f238eb9b7f2b054e9abf I am 2022-01-24 21:06:35
mplot: fix global settings overwrite 28a6304a895b0f2f53482ae086f0f2900adfd65e I am 2022-01-24 21:02:56
qt_gui.qt_dicebox: add dumb DiceBox widget d609cbbd928859a7c2ebf05ecdec2878d5356f9c I am 2022-01-24 18:43:53
mplot.maxes: parametrize tri plots 395bbad17802accecdc978462b0aed5a8828534b I am 2022-01-23 04:35:10
mplot.maxes: adjust triple plot marker sizes and lineweights 7e8b6eea3a27569542b5d15a2002ee1b77205672 I am 2022-01-22 21:39:02
mplot.maxes: add convergence legend 59405e29e76faf2a74d5bddd994c0252f7dfdb7d I am 2022-01-22 01:48:40
mplot.mart: keep frame in U space b6fda7b143da2c7a129ac0db12cf33e5325547d5 I am 2022-01-21 03:22:38
mplot.mfigs: add more of that points + triangulation plots, you know... 68072e812fb7ed4a39cfbf138cd216e7d4214d8a I am 2022-01-20 19:25:43
testcases.gaussian_2D: add fajvka, circle, parabola. Justify sinball 330595743710deee9924cd077302f5c45fae7f83 I am 2022-01-20 19:24:41
mplot: šťourání s diagramy 4831d87d7bfb953656ed68028bffe73597752a66 I am 2022-01-20 01:34:22
mplot: use empty markers for proxy points dd9e0c8f79977a92615ef408862298ed5ee0d000 I am 2022-01-19 22:30:37
mplot.mart: set up spines linewidth 4220ec6f715c78c1feac9966b5804ee995029bdf I am 2022-01-19 19:43:18
mplot: reduce arrow and axis linewidth 666b1455f15ec803106fbfd773c1d3aa3877a1f9 I am 2022-01-19 18:04:37
mplot.maxes: přídáme férové cenzurováné vzorkování 4b488e62a969f972882f982e903c6a3d252274a6 I am 2022-01-19 17:39:14
simplex: add simple filter function for rejection sampling a58d3814dfd3366ef536c90b37fd50e3edccc9e9 I am 2022-01-19 17:38:08
whitebox: add rude approximation for sumexp. (Je teda opravdu hodně nepřesná) 281f66ac70e21756415c8efecfdee50873b34b58 I am 2022-01-18 22:25:51
whitebox: add four betas class 57a25000259e74f1ac28bb1bac6b0cc01dd5201c I am 2022-01-18 21:43:00
Commit 5ea3011d11572d2fa98d77819cc1ba333e6b4016 - g_models: add boundary finding for metaball function
Author: I am
Author date (UTC): 2022-01-26 00:38
Committer name: I am
Committer date (UTC): 2022-01-26 00:38
Parent(s): ad5819ced9f1be64c8d731813c575c1ec854421d
Signer:
Signing key:
Signing status: N
Tree: 438b838e61f00331bc597872ed167bf41bcc1449
File Lines added Lines deleted
g_models.py 31 1
File g_models.py changed (mode: 100644) (index 3e14ac4..9cf6b77)
... ... class Metaballs2D:
718 718 g = 30.0/( y1**2 + 1.0 ) + 20.0/( y2**2 + 1.0 ) - self._const g = 30.0/( y1**2 + 1.0 ) + 20.0/( y2**2 + 1.0 ) - self._const
719 719 return SampleBox(input_sample, g, repr(self)) return SampleBox(input_sample, g, repr(self))
720 720
721 #hranice poruchy ӧвӧл :(
722 721
722 # Fence off!
723 def get_2D_R_boundary(self, nrod=100, *args):
724 """
725 Fence off!
726 nrod - number of rods in fencing
727 """
728 #č myšlenkou je vygenerovat rovnoměrně spoustu teček v každém směru,
729 #č pro každé phi zvolit nejblížší k nule r-ko
730 phi = np.linspace(0, 6.283185307, nrod , endpoint=True)
731 #č obecná hranice poruchy ӧвӧл :(
732 #č tyhle meze vícemené platí jen pro const=5
733 r = np.linspace(4, 8, nrod//2 , endpoint=True)
734 X = np.atleast_2d(r).T @ np.cos(np.atleast_2d(phi))
735 Y = np.atleast_2d(r).T @ np.sin(np.atleast_2d(phi))
736 # auxiliary variables
737 y1 = 4/9*(X + 2 )**2 + 1/25 * (Y )**2
738 y2 = 1/4*(X - 2.5)**2 + 1/25 * (Y-0.5)**2
739 g = 30.0/( y1**2 + 1.0 ) + 20.0/( y2**2 + 1.0 ) - self._const
740
741 #č nechapu úplně přoč tam není axis=1, ale O, ale to funguje
742 arg_r = np.argmin(np.abs(g), axis=0)
743 bound_r = r[arg_r]
744
745 bound_x = bound_r * np.cos(phi)
746 bound_y = bound_r * np.sin(phi)
747
748 # sample compatible
749 # малы транспонировать кароно? Озьы кулэ!
750 bound_R = np.array((bound_x, bound_y)).T
751 # tuple of samples
752 return (Ingot(bound_R),)
723 753
724 754
725 755 class Logistic2D: class Logistic2D:
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