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.maxes: polish tri plots a little bit 9a4e0a18061e2d9f48c0c32c200bd1a408ceb77f I am 2022-01-05 13:15:00
mplot.mart: add plot_points() function 2c799e22d9f45b825a7f8a795ce6b9fe5fbd9c76 I am 2022-01-05 13:14:12
mplot.maxes: add convergence diagrams 813b1e8e9dd9d1e51951c51234910cdf2281cde8 I am 2022-01-05 09:07:18
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
Commit 9a4e0a18061e2d9f48c0c32c200bd1a408ceb77f - mplot.maxes: polish tri plots a little bit
Author: I am
Author date (UTC): 2022-01-05 13:15
Committer name: I am
Committer date (UTC): 2022-01-05 13:15
Parent(s): 2c799e22d9f45b825a7f8a795ce6b9fe5fbd9c76
Signer:
Signing key:
Signing status: N
Tree: ca03366bad39c10d027dfcbded5cc4d5be50534b
File Lines added Lines deleted
mplot/maxes.py 13 12
File mplot/maxes.py changed (mode: 100644) (index e4e137f..0707721)
... ... def tri_nodes_plot(ax, tri_space=None, tn_scheme=None,\
86 86 else: else:
87 87 assert 100500 < 0 #оӵ мар лэсьтӥське татын? � assert 100500 < 0 #оӵ мар лэсьтӥське татын? �
88 88
89 mart.plot_sample(ax, kwargs['nodes'], color=color)
89 mart.plot_sample(ax, kwargs['nodes'], ls='', marker='.',\
90 mec=color, mfc=color, ms=1, alpha=0.5)
90 91
91 92
92 93
 
... ... def tri_nodes_plot(ax, tri_space=None, tn_scheme=None,\
105 106 mart.curly(ax, linewidths=linewidths) mart.curly(ax, linewidths=linewidths)
106 107 Tri.integrate() Tri.integrate()
107 108 if tri_space == ax.space: if tri_space == ax.space:
108 mart.triplot(ax, color="#B2B2B2", zorder=100)
109 mart.triplot(ax, color="#B2B2B2", lw=0.5, zorder=100)
109 110 else: else:
110 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", zorder=100)
111 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=0.5, zorder=100)
111 112
112 mart.scatter_points(ax, zorder=100500)
113 mart.plot_boundaries(ax, zorder=1050)
113 mart.plot_points(ax, ms=2.5, zorder=100500)
114 mart.plot_boundaries(ax, lw=0.7, zorder=1050)
114 115
115 116 def tri_R_nodes_plot(ax, **kwargs): def tri_R_nodes_plot(ax, **kwargs):
116 117 tri_nodes_plot(ax, tri_space='R', **kwargs) tri_nodes_plot(ax, tri_space='R', **kwargs)
 
... ... def tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
137 138 mart.setup(ax) mart.setup(ax)
138 139 mart.curly(ax, linewidths=linewidths) mart.curly(ax, linewidths=linewidths)
139 140 if tri_space == ax.space: if tri_space == ax.space:
140 mart.triplot(ax, color="#B2B2B2", zorder=100)
141 mart.triplot(ax, color="#B2B2B2", lw=0.5, zorder=100)
141 142 else: else:
142 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", zorder=100)
143 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=0.5, zorder=100)
143 144
144 mart.scatter_points(ax, zorder=100500)
145 mart.plot_boundaries(ax, zorder=1050)
145 mart.plot_points(ax, ms=2.5, zorder=100500)
146 mart.plot_boundaries(ax, lw=0.7, zorder=1050)
146 147
147 148 def tri_R_plot(ax, **kwargs): def tri_R_plot(ax, **kwargs):
148 149 tri_plot(ax, tri_space='R', **kwargs) tri_plot(ax, tri_space='R', **kwargs)
 
... ... def convex_hull_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.
161 162 mart.setup(ax) mart.setup(ax)
162 163 mart.curly(ax, linewidths=linewidths) mart.curly(ax, linewidths=linewidths)
163 164 qhull = khull.QHull(ax.sample_box, space=tri_space, incremental=False) qhull = khull.QHull(ax.sample_box, space=tri_space, incremental=False)
164 mart.qhull_plot(ax, qhull, color="#B2B2B2", zorder=100)
165 mart.scatter_points(ax, zorder=100500)
166 mart.plot_boundaries(ax)
165 mart.qhull_plot(ax, qhull, color="#B2B2B2", lw=0.7, zorder=100)
166 mart.plot_points(ax, ms=2.5, zorder=100500)
167 mart.plot_boundaries(ax, lw=0.7, zorder=1050)
167 168
168 169 def just_points(ax): def just_points(ax):
169 170 ax.set_xlabel('$x_{1}$') ax.set_xlabel('$x_{1}$')
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