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: 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
mplot.maxes: aspoň nějak nastavit popísky os 50e8e467c24d73d9522166183275deab547243f8 I am 2022-01-06 16:16:36
mplot.maxes: add labels to candidates plot f8aa0f271a8d30cb7d79206290409f862c406a51 I am 2022-01-06 12:14:48
mplot.maxes: add candidates sampling (rejection-like) plot 5a15e10a9a462dd409e0627528bcb8910df77c7e I am 2022-01-06 09:40:38
mplot.maxes: add candidates plot 11e156edc351ca84fde742edf7ea1d85c3b0bc38 I am 2022-01-06 08:15:43
Commit 4b488e62a969f972882f982e903c6a3d252274a6 - mplot.maxes: přídáme férové cenzurováné vzorkování
Author: I am
Author date (UTC): 2022-01-19 17:39
Committer name: I am
Committer date (UTC): 2022-01-19 17:39
Parent(s): a58d3814dfd3366ef536c90b37fd50e3edccc9e9
Signer:
Signing key:
Signing status: N
Tree: 26191b985722332622bf1c114fef695b63397f0e
File Lines added Lines deleted
mplot/maxes.py 35 3
File mplot/maxes.py changed (mode: 100644) (index d024c01..306a360)
... ... from . import mart
17 17 from . import mgraph from . import mgraph
18 18
19 19 from matplotlib import colors as mcolors from matplotlib import colors as mcolors
20 from matplotlib import ticker
20 21
21 22
22 23 # it is mostly for qt_plot, it offers availiable options to user # it is mostly for qt_plot, it offers availiable options to user
23 24 __all__ = [ __all__ = [
24 'candidates_plot', 'candidates_sampling_plot',
25 'candidates_plot', 'rejection_sampling_plot',
26 'candidates_sampling_plot',
25 27 'convex_hull_plot', 'tri_plot', 'tri_nodes_plot', 'convex_hull_plot', 'tri_plot', 'tri_nodes_plot',
26 28 'tri_R_plot', 'tri_GK_plot', 'tri_R_plot', 'tri_GK_plot',
27 29 'tri_R_nodes_plot', 'tri_GK_nodes_plot', 'tri_R_nodes_plot', 'tri_GK_nodes_plot',
 
... ... __all__ = [
37 39
38 40
39 41
42 def rejection_sampling_plot(ax, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
43 from .. import simplex as six
44
45 #č možná zrovna zde nebudem robiť vědu a použijme tringulaciju ze skříňky?
46 nodes = ax.sample_box.f_model(1000)
47 # -1 = 'out', 0=success, 1=failure, 2=mix
48 event_ids = six.filter(ax.sample_box.Tri, nodes)
49 mart.plot_sample(ax, nodes[event_ids==-1], ls='', marker='.',\
50 mec="#00007E", mfc="#00007E", ms=1.5, alpha=0.5,\
51 rasterized=True)
52 mart.plot_sample(ax, nodes[event_ids==2], ls='', marker='.',\
53 mec="#00007E", mfc="#00007E", ms=1.5, alpha=0.5,\
54 rasterized=True)
55
56 mart.setup(ax)
57 mart.curly(ax, linewidths=linewidths)
58 mart.triplot(ax, color="#B2B2B2", lw=0.5, zorder=1000)
59 mart.plot_points(ax, ms=5, zorder=100500)
60 try:
61 mart.plot_boundaries(ax, lw=0.7, zorder=10500)
62 except:
63 pass
64 ax.xaxis.set_major_formatter(ticker.NullFormatter())
65 ax.yaxis.set_major_formatter(ticker.NullFormatter())
66 ax.xaxis.set_major_locator(ticker.NullLocator())
67 ax.yaxis.set_major_locator(ticker.NullLocator())
40 68
41 69
42 70 def candidates_sampling_plot(ax, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]): def candidates_sampling_plot(ax, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
 
... ... def candidates_sampling_plot(ax, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
75 103 mart.plot_boundaries(ax, lw=0.7, zorder=10500) mart.plot_boundaries(ax, lw=0.7, zorder=10500)
76 104 except: except:
77 105 pass pass
106 ax.xaxis.set_major_formatter(ticker.NullFormatter())
107 ax.yaxis.set_major_formatter(ticker.NullFormatter())
108 ax.xaxis.set_major_locator(ticker.NullLocator())
109 ax.yaxis.set_major_locator(ticker.NullLocator())
78 110
79 111 def candidates_plot(ax): def candidates_plot(ax):
80 112 tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3], data_offset=0.4) tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3], data_offset=0.4)
 
... ... def tri_nodes_plot(ax, tri_space=None, tn_scheme=None,\
129 161 tn_scheme = ax.sample_box.Tri.tn_scheme tn_scheme = ax.sample_box.Tri.tn_scheme
130 162 except: except:
131 163 import quadpy import quadpy
132 tn_scheme = quadpy.tn.grundmann_moeller(sample_box.nvar, 5)
164 tn_scheme = quadpy.tn.grundmann_moeller(ax.sample_box.nvar, 5)
133 165
134 166
135 167 def _draw_nodes(*args, **kwargs): def _draw_nodes(*args, **kwargs):
 
... ... def tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1], data
215 247 mart.plot_boundaries(ax, lw=0.7, zorder=1050) mart.plot_boundaries(ax, lw=0.7, zorder=1050)
216 248 except: except:
217 249 pass pass
218 mart.setup_labels(ax, data_offset)
250 mart.setup_labels(ax, data_offset=data_offset)
219 251
220 252
221 253
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