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.tri_estimation_plot: draw p_mixed line navíc b59ec8cbaeb671a130edee5df206d57099ecb9bf I am 2022-01-27 16:41:53
testcases.gaussian_2D: fix four branch pf_exact f9d0f71ad6a4e6af6843c6b36688b8a002495bdc I am 2022-01-27 06:14:40
mplot: add mprod plot, polish pprod 045c5401dad6abca019da21fd42da5bfb671b909 I am 2022-01-26 04:16:00
mplot.mfigs: add quadruple plot 8ef6b85bedc31f1908ce25634bda84bdc07edef9 I am 2022-01-26 01:30:01
testcases.gaussian_2D: add final pf value to metaball case 5be29e59b1cd789da32d62caff1dcfb9c231a40e I am 2022-01-26 01:28:52
g_models: add boundary finding for metaball function 5ea3011d11572d2fa98d77819cc1ba333e6b4016 I am 2022-01-26 00:38:21
mplot: polish ad5819ced9f1be64c8d731813c575c1ec854421d I am 2022-01-25 03:39:22
testcases.gaussian_2D: add pf_exact to the black swan 38e19e42884234afc467a0ee7af9699af1f84e96 I am 2022-01-25 03:37:59
mplot.maxes: pass nrod to plot_boundaries() b87c6c58900b2b7f9a11f238eb9b7f2b054e9abf I am 2022-01-24 21:06:35
mplot: fix global settings overwrite 28a6304a895b0f2f53482ae086f0f2900adfd65e I am 2022-01-24 21:02:56
qt_gui.qt_dicebox: add dumb DiceBox widget d609cbbd928859a7c2ebf05ecdec2878d5356f9c I am 2022-01-24 18:43:53
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
Commit b59ec8cbaeb671a130edee5df206d57099ecb9bf - mplot.mgraph.tri_estimation_plot: draw p_mixed line navíc
Author: I am
Author date (UTC): 2022-01-27 16:41
Committer name: I am
Committer date (UTC): 2022-01-27 16:41
Parent(s): f9d0f71ad6a4e6af6843c6b36688b8a002495bdc
Signer:
Signing key:
Signing status: N
Tree: 74da462411f2f6d91af5119d762507a6955d6b78
File Lines added Lines deleted
mplot/mgraph.py 9 4
File mplot/mgraph.py changed (mode: 100644) (index 0357958..8dd8466)
... ... def trii_estimation_fill(ax, df):
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 75 def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$", def tri_estimation_plot(ax, df, pf_exact=None, pf_exact_method="$p_f$",
76 plot_outside=True, **kwargs):
76 plot_outside=True, plot_mix=True, **kwargs):
77 77 # some default values # some default values
78 78 # if not ax.get_xlabel(): # if not ax.get_xlabel():
79 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="$p_f$",
88 88
89 89 v = df['vertex_estimation'].to_numpy() v = df['vertex_estimation'].to_numpy()
90 90 wv = df['weighted_vertex_estimation'].to_numpy() wv = df['weighted_vertex_estimation'].to_numpy()
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)
91 ax.plot(df.index, wv, '-r', label="weighted $p_f$ estimation", zorder=15, **kwargs)
92 ax.plot(df.index, v, '-m', label="simple $p_f$ estimation", zorder=150, **kwargs)
93 93
94 94
95 95 #č teď čáry #č teď čáry
96 96 if plot_outside: if plot_outside:
97 97 o = df['outside'].to_numpy() o = df['outside'].to_numpy()
98 98 ax.plot(df.index, o, '-', color="#AAAAAA", \ ax.plot(df.index, o, '-', color="#AAAAAA", \
99 label="outside domain estimation", zorder=15, **kwargs)
99 label="outside domain estimation", zorder=100500, **kwargs)
100
101 if plot_mix:
102 m = df['mix'].to_numpy()
103 ax.plot(df.index, m, '-', color="#FF8000", \
104 label="mixed domain estimation", zorder=1050, **kwargs)
100 105
101 106 if pf_exact is not None: if pf_exact is not None:
102 107 ax.axhline(pf_exact, c='b', label=pf_exact_method, **kwargs) ax.axhline(pf_exact, c='b', label=pf_exact_method, **kwargs)
Hints:
Before first commit, do not forget to setup your git environment:
git config --global user.name "your_name_here"
git config --global user.email "your@email_here"

Clone this repository using HTTP(S):
git clone https://rocketgit.com/user/iam-git/WellMet

Clone this repository using ssh (do not forget to upload a key first):
git clone ssh://rocketgit@ssh.rocketgit.com/user/iam-git/WellMet

Clone this repository using git:
git clone git://git.rocketgit.com/user/iam-git/WellMet

You are allowed to anonymously push to this repository.
This means that your pushed commits will automatically be transformed into a merge request:
... clone the repository ...
... make some changes and some commits ...
git push origin main