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)
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
whitebox: introduce Gaussian_Z_prod_2D class with respective exact solution 13e592f107918d4cc3daaeec1f08e38779a943aa I am 2022-01-18 18:51:23
mplot: polish maxes and mfigs dfb9193da5fcb633fbe58fb39ff7b4432de4a5c5 I am 2022-01-18 00:44:56
testcases: add gaussian_2D module c5e279c08bba39bdd82c79d15d00241c786c795d I am 2022-01-18 00:06:36
qt_gui.qt_testcases: simplify code for the unparametrized testcases 60b7678186738107b3346beae7fb0744570d60fc I am 2022-01-18 00:05:34
mplot.mart: move setup_labels to mart module 9d7783a3a9905a72c51b4fec76e367dffdbc4563 I am 2022-01-17 17:33:16
mplot.__init__: set up small non zero padding (0.01) f0d23235b109c4224599eb0426ad0d9ee8b2a9b8 I am 2022-01-17 17:31:44
mplot: raster candidates, polishing 63d6d00dfe7affc8ed3d05161fd4a5b50db62207 I am 2022-01-17 16:34:05
mplot: more figures 3a7f54e09cd78a2fc2ff50d7cf5c142f04ffdc28 I am 2022-01-09 00:42:33
testcases: add proxy_prod 2D testcase edb11ab54bcf677757974f133d2d66816b701ade I am 2022-01-09 00:41:27
stm_df: fix proxy, pandas nějak nám šťourá v našich boolean maticích 1d9f360edfc03932a08dff8aad97f189b155e708 I am 2022-01-09 00:39:24
mplot.maxes: a little bit more to labels d989a2d1b69b116a14f7ff81776f5f75d80cfea0 I am 2022-01-06 17:48:57
mplot.figs: add double and triple triangulations plots 091361cc326239b552078c489f9f164ccda4f54b I am 2022-01-06 16:17:50
Commit 4831d87d7bfb953656ed68028bffe73597752a66 - mplot: šťourání s diagramy
Author: I am
Author date (UTC): 2022-01-20 01:34
Committer name: I am
Committer date (UTC): 2022-01-20 01:34
Parent(s): dd9e0c8f79977a92615ef408862298ed5ee0d000
Signer:
Signing key:
Signing status: N
Tree: 025e4992bde01edba6f4d80a4cc41b9bbea9aed8
File Lines added Lines deleted
mplot/mfigs.py 9 2
mplot/mgraph.py 6 5
testcases/gaussian_2D.py 1 1
File mplot/mfigs.py changed (mode: 100644) (index a233f58..96b8ab7)
... ... from . import maxes
10 10 from . import maxes3d from . import maxes3d
11 11
12 12 __all__ = [ __all__ = [
13 'convergence_diagram', 'double_proxy_diagram',
13 'convergence_diagram', 'convergence_legend', 'double_proxy_diagram',
14 14 'double_tri_R_plot', 'double_tri_R_twins_plot', 'double_plot', 'triple_plot', 'double_tri_R_plot', 'double_tri_R_twins_plot', 'double_plot', 'triple_plot',
15 15 'qhull_under_density', 'plane_under_density', 'dhull_vs_complete' 'qhull_under_density', 'plane_under_density', 'dhull_vs_complete'
16 16 ] ]
 
... ... def convergence_diagram(fig, sample_box, space, lim=1000):
30 30 ax.sample_box = sample_box ax.sample_box = sample_box
31 31 maxes.convergence_diagram(ax) maxes.convergence_diagram(ax)
32 32 ax.set_xlim(0, lim) ax.set_xlim(0, lim)
33
33
34 def convergence_legend(fig, sample_box, space, lim=1000):
35 fig.set_figheight(3)
36 ax = fig.add_subplot(111)
37 ax.sample_box = sample_box
38 maxes.convergence_diagram(ax)
39 ax.legend(bbox_to_anchor=(0.5, -0.25), ncol=2, loc='upper center')
40 ax.set_xlim(0, lim)
34 41
35 42 def double_proxy_diagram(fig, sample_box, space, lim=1000): def double_proxy_diagram(fig, sample_box, space, lim=1000):
36 43 ax1 = ax = fig.add_subplot(211) ax1 = ax = fig.add_subplot(211)
File mplot/mgraph.py changed (mode: 100644) (index 7dfda89..0357958)
... ... def trii_estimation_fill(ax, df):
72 72
73 73 #č ok, tak uděláme všecko dohromady #č ok, tak uděláme všecko dohromady
74 74 #č datarám, jako vždy, uživatel donese svůj vlastní #č datarám, jako vždy, uživatel donese svůj vlastní
75 def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$", plot_outside=True):
75 def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$",
76 plot_outside=True, **kwargs):
76 77 # some default values # some default values
77 78 # if not ax.get_xlabel(): # if not ax.get_xlabel():
78 79 # ax.set_xlabel('Number of simulations') # ax.set_xlabel('Number of simulations')
 
... ... def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$", plot_out
87 88
88 89 v = df['vertex_estimation'].to_numpy() v = df['vertex_estimation'].to_numpy()
89 90 wv = df['weighted_vertex_estimation'].to_numpy() wv = df['weighted_vertex_estimation'].to_numpy()
90 ax.plot(df.index, wv, '-r', label="weighted $p_f$ estimation", zorder=100500)
91 ax.plot(df.index, v, '-m', label="simple $p_f$ estimation", zorder=1050)
91 ax.plot(df.index, wv, '-r', label="weighted $p_f$ estimation", zorder=100500, **kwargs)
92 ax.plot(df.index, v, '-m', label="simple $p_f$ estimation", zorder=1050, **kwargs)
92 93
93 94
94 95 #č teď čáry #č teď čáry
95 96 if plot_outside: if plot_outside:
96 97 o = df['outside'].to_numpy() o = df['outside'].to_numpy()
97 98 ax.plot(df.index, o, '-', color="#AAAAAA", \ ax.plot(df.index, o, '-', color="#AAAAAA", \
98 label="outside domain estimation", zorder=15)
99 label="outside domain estimation", zorder=15, **kwargs)
99 100
100 101 if pf_exact is not None: if pf_exact is not None:
101 ax.axhline(pf_exact, c='b', label=pf_exact_method)
102 ax.axhline(pf_exact, c='b', label=pf_exact_method, **kwargs)
102 103
103 104
104 105
File testcases/gaussian_2D.py changed (mode: 100644) (index f7da17c..cee5e27)
... ... add('four_branch')
36 36 def four_branch(): def four_branch():
37 37 wt = WhiteBox(f, gm.FourBranch2D(k1=3, k2=7)) wt = WhiteBox(f, gm.FourBranch2D(k1=3, k2=7))
38 38 wt.pf_exact = 2.34e-03 wt.pf_exact = 2.34e-03
39 wt.pf_exact_method = 'known value' #"some guys said me that"
39 wt.pf_exact_method = 'known $p_f$ value' #"some guys said me that"
40 40 wt.description = "Four branch system. Structural Safety 62 (2016) 66-75" wt.description = "Four branch system. Structural Safety 62 (2016) 66-75"
41 41 return wt return wt
42 42
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