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)
f_models: přidany chybějící pdf-ka 1f3c99bcd1eaeed9b8b84175be9189449a7dec5f Олёш 2020-06-25 20:28:33
qt_plot: connection to BlackBox feature added fcff3099dc61e568981e1f1236c29b5326547fd5 Олёш 2020-06-23 09:52:56
qt_plot: estimation graphs are implemented a31c5b3c344893f0f0b7022ee54544493e97c79a Олёш 2020-06-21 22:21:55
qt_plot: WIP 7cf97855991f717873d02ce65058790b324a319d Олёш 2020-06-21 10:17:04
qt_plot: minor changes 931ca9629256f37588e7ed4cb497f71c93954a7b Олёш 2020-06-15 06:01:10
candybox introduced, stores additional data 2d7e227d114a60ab47456bfc62308b16dba4eacf Олёш 2020-06-11 23:54:54
qt_plot: konečně něco interaktivního... 2bcc8842afe87d1e44e066fe168fcfedb8043947 Олёш 2020-06-07 07:35:57
WIP: matplotlib and QtGui ce3d38363636ba60fcddc0ebfe45c1cefadfc7a5 Олёш 2020-06-07 01:57:04
tri_estimations_graph added (by plot.ly) 1d307b1a8e26277c6259596eb4c912b3262d3ee7 Олёш 2020-06-04 15:03:57
directory structure changed d67e975a7f4c7b01388f469324f635c74cf17995 Олёш 2020-06-03 21:17:35
Commit 1f3c99bcd1eaeed9b8b84175be9189449a7dec5f - f_models: přidany chybějící pdf-ka
Author: Олёш
Author date (UTC): 2020-06-25 20:28
Committer name: Олёш
Committer date (UTC): 2020-06-25 20:28
Parent(s): fcff3099dc61e568981e1f1236c29b5326547fd5
Signer:
Signing key:
Signing status: N
Tree: 13513245a64bb46f2adf027d270f1863a415110c
File Lines added Lines deleted
f_models.py 6 18
File f_models.py changed (mode: 100644) (index 374874f..5a97b98)
... ... class Ingot:
20 20 Prazdná třida pro "nevypalené" vzorky, tj. bez přiřazeného rozdělení Prazdná třida pro "nevypalené" vzorky, tj. bez přiřazeného rozdělení
21 21 """ """
22 22 def __init__(self, data, attr='R'): def __init__(self, data, attr='R'):
23 # data? takový neslaný nazev...
24 # data suppose to be pandas compatible, i.e.
25 # nsim, nvar = data.shape
26 23 self.attr = attr self.attr = attr
27 24 try: try:
28 25 self.data = np.atleast_2d(getattr(data, attr)) self.data = np.atleast_2d(getattr(data, attr))
 
... ... class Ingot:
50 47 # Рекурсилы пезьдэт! # Рекурсилы пезьдэт!
51 48 if attr in ('attr', 'data'): if attr in ('attr', 'data'):
52 49 raise AttributeError(attr) raise AttributeError(attr)
53 #if attr in ('R', 'Rn', 'GK', 'G', 'P', 'U') and attr==self.space:
54 # return f._data
55 50 elif attr == 'nvar': elif attr == 'nvar':
56 51 nsim, nvar = self.data.shape nsim, nvar = self.data.shape
57 52 return nvar return nvar
 
... ... class SNorm:
135 130 def __getattr__(f, attr): def __getattr__(f, attr):
136 131 if attr in ('pdf_R', 'pdf_Rn', 'pdf_GK', 'pdf_G'): if attr in ('pdf_R', 'pdf_Rn', 'pdf_GK', 'pdf_G'):
137 132 return f._data[:,-1] return f._data[:,-1]
133 elif attr in ('pdf_P', 'pdf_U'):
134 return np.full(f.nsim, 1)
138 135 elif attr in ('R', 'Rn', 'GK', 'G'): elif attr in ('R', 'Rn', 'GK', 'G'):
139 136 return f._data[:,:f.__nvar] return f._data[:,:f.__nvar]
140 137 elif attr in ('P', 'U'): elif attr in ('P', 'U'):
 
... ... class SNorm:
207 204 f_copy.add_sample(sample, space) f_copy.add_sample(sample, space)
208 205 return f_copy return f_copy
209 206
210 # # drobná pomucka
211 # def new_random_sample(f, ns=1):
212 # f_copy = eval(f.__repr__())
213 # f_copy.add_sample(np.random.random((ns, f.nvar)), 'U')
214 # return f_copy
215
216 207
217 208
218 209
219
220
221 210 class UnCorD: # nic moc nazev, ale je přece lepší nez CommonJointDistribution class UnCorD: # nic moc nazev, ale je přece lepší nez CommonJointDistribution
222 211 """ """
223 212 Takes tuple of scipy stats distribution objects Takes tuple of scipy stats distribution objects
 
... ... class UnCorD: # nic moc nazev, ale je přece lepší nez CommonJointDistribution
264 253 def __getattr__(f, attr): def __getattr__(f, attr):
265 254 if attr == 'pdf_R': if attr == 'pdf_R':
266 255 return f._data[:,-2] return f._data[:,-2]
256 elif attr == 'pdf_Rn':
257 return f._data[:,-2] * np.prod(list(f.marginals[i].std() for i in range(f.nvar)))
267 258 elif attr in ('pdf_GK', 'pdf_G'): elif attr in ('pdf_GK', 'pdf_G'):
268 259 return f._data[:,-1] return f._data[:,-1]
260 elif attr in ('pdf_P', 'pdf_U'):
261 return np.full(f.nsim, 1)
269 262 elif attr == 'Rn': elif attr == 'Rn':
270 263 return f.__frame(0) return f.__frame(0)
271 264 elif attr == 'R': elif attr == 'R':
 
... ... class UnCorD: # nic moc nazev, ale je přece lepší nez CommonJointDistribution
340 333 f_copy.add_sample(sample, space) f_copy.add_sample(sample, space)
341 334 return f_copy return f_copy
342 335
343 # # drobná pomucka
344 # def new_random_sample(f, ns=1):
345 # f_copy = f()
346 # f_copy.add_sample(np.random.random((ns, f.nvar)), 'U')
347 # return f_copy
348 336
349 337 def _chain(f, sample_dict): def _chain(f, sample_dict):
350 338 # chain tam # chain tam
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