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: 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
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
Commit 395bbad17802accecdc978462b0aed5a8828534b - mplot.maxes: parametrize tri plots
Author: I am
Author date (UTC): 2022-01-23 04:35
Committer name: I am
Committer date (UTC): 2022-01-23 04:35
Parent(s): 7e8b6eea3a27569542b5d15a2002ee1b77205672
Signer:
Signing key:
Signing status: N
Tree: 2b73e3b6eef3afbd05518167aa2de6044b9dc79c
File Lines added Lines deleted
mplot/maxes.py 8 8
mplot/mfigs.py 28 3
File mplot/maxes.py changed (mode: 100644) (index ab63a45..6daa9dd)
... ... def convergence_legend(ax):
175 175 #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit, #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit,
176 176 #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít
177 177 def tri_nodes_plot(ax, tri_space=None, tn_scheme=None, ms=2.5, mew=0.6, lw=0.7, def tri_nodes_plot(ax, tri_space=None, tn_scheme=None, ms=2.5, mew=0.6, lw=0.7,
178 linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
178 linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1], nrid=200):
179 179 from .. import simplex as six from .. import simplex as six
180 180 if tri_space is None: if tri_space is None:
181 181 tri_space = ax.space tri_space = ax.space
 
... ... def tri_nodes_plot(ax, tri_space=None, tn_scheme=None, ms=2.5, mew=0.6, lw=0.7,
224 224 on_delete_simplex=None) on_delete_simplex=None)
225 225
226 226 mart.setup(ax) mart.setup(ax)
227 mart.curly(ax, linewidths=linewidths)
227 mart.curly(ax, nrid=nrid, linewidths=linewidths)
228 228 Tri.integrate() Tri.integrate()
229 229 if tri_space == ax.space: if tri_space == ax.space:
230 230 mart.triplot(ax, color="#B2B2B2", lw=lw/1.4, zorder=100) mart.triplot(ax, color="#B2B2B2", lw=lw/1.4, zorder=100)
231 231 else: else:
232 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=lw/1.4, zorder=100)
232 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=lw/1.4, zorder=100, rasterized=True)
233 233
234 234 mart.plot_points(ax, ms=ms, mew=mew, zorder=100500) mart.plot_points(ax, ms=ms, mew=mew, zorder=100500)
235 235 try: try:
 
... ... def tri_GK_nodes_plot(ax, **kwargs):
249 249 #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit, #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit,
250 250 #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít
251 251 def tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1], def tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1],
252 ms=2.5, mew=0.6, lw=0.7, data_offset=0.5):
252 ms=2.5, mew=0.6, lw=0.7, data_offset=0.5, nrid=200):
253 253 from .. import simplex as six from .. import simplex as six
254 254 if tri_space is None: if tri_space is None:
255 255 tri_space = ax.space tri_space = ax.space
 
... ... def tri_plot(ax, tri_space=None, linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1],
262 262 on_delete_simplex=None) on_delete_simplex=None)
263 263
264 264 mart.setup(ax) mart.setup(ax)
265 mart.curly(ax, linewidths=linewidths)
265 mart.curly(ax, nrid=nrid, linewidths=linewidths)
266 266 if tri_space == ax.space: if tri_space == ax.space:
267 267 mart.triplot(ax, color="#B2B2B2", lw=lw/1.4, zorder=100) mart.triplot(ax, color="#B2B2B2", lw=lw/1.4, zorder=100)
268 268 else: else:
269 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=lw/1.4, zorder=100)
269 mart.tri_plot(ax, Tri=Tri, color="#B2B2B2", lw=lw/1.4, zorder=100, rasterized=True)
270 270
271 271 mart.plot_points(ax, ms=ms, mew=mew, zorder=100500) mart.plot_points(ax, ms=ms, mew=mew, zorder=100500)
272 272 try: try:
 
... ... def tri_GK_plot(ax, **kwargs):
286 286 #č ten [plot] zásadně vytvaří svou obálku, nepouzívá onou ze skříňky, #č ten [plot] zásadně vytvaří svou obálku, nepouzívá onou ze skříňky,
287 287 #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit, #č protože já vím, že v těch obrázcích, ve kterých chcu ho použit,
288 288 #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít #č můde být třeba použit řez a skříňka tedy potřebné struktury může nemít
289 def convex_hull_plot(ax, tri_space=None, ms=2.5, mew=0.6, lw=0.7,
289 def convex_hull_plot(ax, tri_space=None, ms=2.5, mew=0.6, lw=0.7, nrid=200,
290 290 linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]): linewidths=[0.7, 0.5, 0.4, 0.3, 0.2, 0.1]):
291 291 from .. import convex_hull as khull from .. import convex_hull as khull
292 292 if tri_space is None: if tri_space is None:
293 293 tri_space = ax.space tri_space = ax.space
294 294
295 295 mart.setup(ax) mart.setup(ax)
296 mart.curly(ax, linewidths=linewidths)
296 mart.curly(ax, nrid=nrid, linewidths=linewidths)
297 297 qhull = khull.QHull(ax.sample_box, space=tri_space, incremental=False) qhull = khull.QHull(ax.sample_box, space=tri_space, incremental=False)
298 298 mart.qhull_plot(ax, qhull, color="#B2B2B2", lw=lw, zorder=100) mart.qhull_plot(ax, qhull, color="#B2B2B2", lw=lw, zorder=100)
299 299 mart.plot_points(ax, ms=ms, mew=mew, zorder=100500) mart.plot_points(ax, ms=ms, mew=mew, zorder=100500)
File mplot/mfigs.py changed (mode: 100644) (index 633a347..2de0a0e)
... ... from matplotlib import ticker
13 13 __all__ = [ __all__ = [
14 14 'convergence_diagram', 'convergence_legend', 'double_proxy_diagram', 'convergence_diagram', 'convergence_legend', 'double_proxy_diagram',
15 15 'four_branch_convergence', 'four_branch_convergence',
16 'double_tri_R_plot', 'double_tri_R_twins_plot', 'double_plot',
16 'double_tri_R_plot', 'double_tri_R_twins_plot', 'double_plot', 'double_pprod_R_plot',
17 17 'triple_plot', 'triple_wide', 'triple_wide_50_100', 'triple_plot_50_100', 'triple_plot', 'triple_wide', 'triple_wide_50_100', 'triple_plot_50_100',
18 18 'triple_nodes_plot', 'triple_wide_25_50', 'triple_nodes_plot', 'triple_wide_25_50',
19 19 'triple_plot_35_50', 'convex_plot_7', 'triple_plot_35_50', 'convex_plot_7',
 
... ... def double_tri_R_twins_plot(fig, sample_box, space):
104 104 ax1.sharey(ax) ax1.sharey(ax)
105 105 ax.set_ybound(y_bound) ax.set_ybound(y_bound)
106 106
107 def double_pprod_R_plot(fig, sample_box, space):
108 ax1 = ax = fig.add_subplot(121)
109 ax.sample_box = sample_box
110 ax.space = 'R'
111 try:
112 tn_scheme = sample_box.Tri.tn_scheme
113 except:
114 import quadpy
115 tn_scheme = quadpy.tn.grundmann_moeller(sample_box.nvar, 5)
116 linewidths = np.array([0.7, 0.5, 0.4, 0.3, 0.2, 0.1]) / 2
117 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme, nrid=2000,
118 ms=1.5, mew=0.4, lw=0.35, linewidths=linewidths)
119 ax.set_title(space_labels[space], pad=10)
120 ax.set_xlim(0, 35)
121 ax.set_ylim(0, 35)
122 ax = fig.add_subplot(122)
123 ax.sample_box = sample_box
124 ax.space = 'G'
125 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme, nrid=2000,
126 ms=1.5, mew=0.4, lw=0.35, linewidths=linewidths)
127 ax.set_title(space_labels['G'], pad=10)
128
107 129 def double_tri_R_plot(fig, sample_box, space): def double_tri_R_plot(fig, sample_box, space):
108 130 ax1 = ax = fig.add_subplot(121) ax1 = ax = fig.add_subplot(121)
109 131 ax.sample_box = sample_box ax.sample_box = sample_box
 
... ... def double_tri_R_plot(fig, sample_box, space):
114 136 import quadpy import quadpy
115 137 tn_scheme = quadpy.tn.grundmann_moeller(sample_box.nvar, 5) tn_scheme = quadpy.tn.grundmann_moeller(sample_box.nvar, 5)
116 138
117 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme)
139 linewidths = np.array([0.7, 0.5, 0.4, 0.3, 0.2, 0.1]) / 2
140 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme, nrid=2000,
141 ms=1.5, mew=0.4, lw=0.35, linewidths=linewidths)
118 142 ax.set_title(space_labels[space], pad=10) ax.set_title(space_labels[space], pad=10)
119 143 ax = fig.add_subplot(122) ax = fig.add_subplot(122)
120 144 ax.sample_box = sample_box ax.sample_box = sample_box
121 145 ax.space = 'G' ax.space = 'G'
122 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme)
146 maxes.tri_nodes_plot(ax, tri_space='R', tn_scheme=tn_scheme, nrid=2000,
147 ms=1.5, mew=0.4, lw=0.35, linewidths=linewidths)
123 148 ax.set_title(space_labels['G'], pad=10) ax.set_title(space_labels['G'], pad=10)
124 149
125 150 def double_plot(fig, sample_box, space, lim=100): def double_plot(fig, sample_box, space, lim=100):
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