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.mgraph: hračky s legendou 198cc40947d48b2a3cde990254def7d57c1535a6 I am 2022-01-05 09:04:23
mplot: apply default settings to figures 765d830e0230b3d85f079eb26c6eddfdb624b859 I am 2022-01-05 09:03:15
simplex: try garbage collection 45148d2665eea195c05de5852b15485d83cb7223 I am 2022-01-05 06:10:46
testcases: add Gaussian Z_min testcase 50bc3f27365e60db67f100c89ebc49b52e948645 I am 2022-01-04 13:55:59
whitebox: add Gaussian Z_min whitebox e989c87895d681920056558a0b9473b93c3b299b I am 2022-01-04 13:54:37
schemes: přídat pár komentářů k schematům e8f1665d43cfb70e89716f591852538df0e27d8c I am 2022-01-04 09:00:36
mplot.maxes: tri_nodes_plot is almost ready. Zbyvá to udělat hezky 251a0f4b7909a5468453cfb518c2bbb4064cce51 I am 2022-01-04 06:42:36
mplot.maxes: add complete tri plots 08c6e421188c4251cae1aa664a69bdc3a0afb314 I am 2022-01-04 05:45:21
mplot.mart: add tri_plot() function aace2f9d02b7744651a089c164af9b7b5824729e I am 2022-01-04 05:44:14
simplex: fix use before initialization ff8be3dcc679c4893948d7d1dc6c45402ccee81b I am 2022-01-04 05:43:17
mplot.maxes: prepare convex_hull_plot() function 732f16a441c1a5f1160b45d91a050683ee70ff5d I am 2022-01-03 15:26:59
mplot.mart: comment out old convex plot function, prepare qhull_plot instead 59de205056903adf87f84c5c1b022c3c83fa9c57 I am 2022-01-03 15:24:30
mplot.mgraph: prepare orth graph routines 74e30ed99fcc6e68c6b3bf7e55bbd93fe8f26d13 I am 2022-01-02 11:18:11
convex_hull: complicate orth estimator ba5f40eaf0f5f4012ef07f5684d58481cefbfe09 I am 2021-12-28 13:56:19
reader: explicitly use utf-8 encoding. (Nemělo by to nic pokazit.. Pitomé nastavení ve Windows) 17bc8a9692695c22f78f8a39ed8aaddd4bfa4808 I am 2021-12-26 22:59:41
dicebox: walk throw candidates in reversed order cadf89d0efc973d540b4ee6b337eb175ae962ee8 I am 2021-12-24 21:03:00
schemes: exclude walkington 5 and 7 (2D and 3D schemes) from offer in higher dimensions 4d7b7e249c18c41f9220c778ada21116cca02aa0 I am 2021-12-23 23:52:07
simplex: implement fallback integration 9a1048540d803a76cba8c92ca67efc16c932f1a9 I am 2021-12-23 22:36:38
schemes: oprava komentaře bfa1fd1f1e1fe2e86838ee2f63f43fab50d0d115 I am 2021-12-22 13:40:45
schemes: send degree parameter as is to quadpy. a1758193a21bf47031e376612418c26c1eb1d289 I am 2021-12-22 11:53:30
Commit 198cc40947d48b2a3cde990254def7d57c1535a6 - mplot.mgraph: hračky s legendou
Author: I am
Author date (UTC): 2022-01-05 09:04
Committer name: I am
Committer date (UTC): 2022-01-05 09:04
Parent(s): 765d830e0230b3d85f079eb26c6eddfdb624b859
Signer:
Signing key:
Signing status: N
Tree: d6c9e004180104313462e2b2ad33d76559f3a1b7
File Lines added Lines deleted
mplot/mgraph.py 15 9
File mplot/mgraph.py changed (mode: 100644) (index cc0da22..7dfda89)
... ... def tri_estimation_fill(ax, df):
25 25 s = df['success'].to_numpy() s = df['success'].to_numpy()
26 26 f = df['failure'].to_numpy() f = df['failure'].to_numpy()
27 27 m = df['mix'].to_numpy() m = df['mix'].to_numpy()
28 labels = ("failure domain estimation", "mixed simplices measure",\
29 "out of sampling domain estimation", "success domain estimation")
28 labels = ("failure domain", "mixed domain",\
29 "outside domain", "success domain")
30 30 return ax.stackplot(df.index, f, m, o, s, labels=labels, colors=colors) return ax.stackplot(df.index, f, m, o, s, labels=labels, colors=colors)
31 31
32 32
 
... ... def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$", plot_out
82 82 # fill # fill
83 83 tri_estimation_fill(ax, df) tri_estimation_fill(ax, df)
84 84
85 if pf_exact is not None:
86 ax.axhline(pf_exact, c='b', label=pf_exact_method)
85 #č blbost, ale uspořadal jsem tu prvky tak,
86 #č aby se hezky kreslily v legendě
87
88 v = df['vertex_estimation'].to_numpy()
89 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)
92
87 93
88 94 #č teď čáry #č teď čáry
89 95 if plot_outside: if plot_outside:
90 96 o = df['outside'].to_numpy() o = df['outside'].to_numpy()
91 ax.plot(df.index, o, '-', color="#AAAAAA", label="outside")
97 ax.plot(df.index, o, '-', color="#AAAAAA", \
98 label="outside domain estimation", zorder=15)
99
100 if pf_exact is not None:
101 ax.axhline(pf_exact, c='b', label=pf_exact_method)
92 102
93 v = df['vertex_estimation'].to_numpy()
94 wv = df['weighted_vertex_estimation'].to_numpy()
95 ax.plot(df.index, v, '-m', label="simple $p_f$ estimation")
96 ax.plot(df.index, wv, '-r', label="weighted $p_f$ estimation")
97 103
98 104
99 105
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