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)
convex_hull: implement QHullCubature class 6f1b51ba6fe281f7cae4f44889db96787d916a99 I am 2023-01-20 10:46:54
convex_hull.QHull: add proof-of-concept get_inside() method 6013fbac742d6070771d7798c928538ebc54495e I am 2023-01-19 10:19:32
whitebox: add r exact for common nD boxes 7c4ab62f069f0efeb48a403dac5d27033003ded2 I am 2023-01-17 15:09:08
qt_gui: improve GRaph to show r-R bounds 59fa5bde7be2197b1f739bd1f829f927cef62603 I am 2023-01-17 14:26:16
dicebox.circumtri: calculate event's radia c6868cfbde7c8a86b73217da544f60e94a117bd8 I am 2023-01-16 06:12:34
simplex.Triangulation.get_averaged_mixed_normals: implement deep averaging 6dfb056e477479331c7a903ea52a98301fca4e71 I am 2023-01-15 23:26:43
replace flatten() by reshape(-1) everywhere. The former always allocates new array 97b35c989a5ad8e459bb60133222d50c7f78e1f0 I am 2023-01-15 12:04:30
qt_gui.qt_gui: explicitly export sample_box to console's namespace dd2e50245157082ddb645b693a8a8c573215d50c I am 2023-01-15 11:42:01
replace everywhere np.bool by just bool (it looks like newer numpy dropped it) ac208f89bbf0ad76d38790686041ea778a9b6376 I am 2023-01-15 11:31:56
simplex._Triangulation: implement averaged gradients() f6608597cf490dd6cf1a6af3b7d8e6d0a72732d9 I am 2023-01-15 09:37:14
add line and two lines problems for sensitivity analysis 0c620f7960399ed8b34e02d663772d0eb34e66f1 I am 2023-01-14 08:30:56
simplex.FullCubatureIntegration.get_sensitivities: do not normalize global gradient c648c484a5599a00236ab70c36cdf407c24f5306 I am 2023-01-14 04:51:16
simplex: implement sensitivities 65056319c51fd93deb76ce14e52c08869dfc04a2 I am 2023-01-13 11:27:46
mplot.maxes: prepare GRaph plot 68774535915af3deaf6104a0bbe9b3c4d24c0cec I am 2023-01-12 02:43:20
mplot.mgraph.tri_estimation_plot: use planar vertex estimator 1d300fa56248be9cdab5a4e5b0d4372f68fd5255 I am 2023-01-11 15:48:28
mplot.mart: implement lsf_boundary() contouring 8642d2f0c043f4e71af1cc45b9e25da326afd3ca I am 2023-01-11 05:20:00
mplot.mart.setup_labels: make labels more adaptive 60cd0b34a67b38df1c730dddc9e0771139818f33 I am 2023-01-11 04:35:47
simplex._SamplingTriangulation: self reference fix 79204ff15cba7c5ca24bb5b48e2073058acf00ae I am 2023-01-11 03:21:34
testcases.testcases_2D: add pf for rastrigin 4f3b35fe18496fcb9f59860174c876cb7a82749a I am 2023-01-10 23:39:45
dicebox.circumtri: fix initialization on ED loading. Use simple potential for space filling c02656e588a164685c4a750ce865c4b50f716563 I am 2023-01-10 23:37:30
Commit 6f1b51ba6fe281f7cae4f44889db96787d916a99 - convex_hull: implement QHullCubature class
Author: I am
Author date (UTC): 2023-01-20 10:46
Committer name: I am
Committer date (UTC): 2023-01-20 10:46
Parent(s): 6013fbac742d6070771d7798c928538ebc54495e
Signer:
Signing key:
Signing status: N
Tree: 2c6523384ff00720aa6305054f64c89f21355c72
File Lines added Lines deleted
wellmet/convex_hull.py 173 0
File wellmet/convex_hull.py changed (mode: 100644) (index 340a019..c5f0c29)
... ... from scipy import spatial # for QHull
8 8
9 9 import quadpy import quadpy
10 10
11 from collections import namedtuple
12
11 13 mpmath.mp.dps = 325 # to cover anything that double-presigion float can handle mpmath.mp.dps = 325 # to cover anything that double-presigion float can handle
12 14
13 15 # maximum radius, where norm.pdf() wasn't zero # maximum radius, where norm.pdf() wasn't zero
 
... ... class Grick:
1262 1264 return np.min((-hull.bm, hull.bp)) return np.min((-hull.bm, hull.bp))
1263 1265
1264 1266
1267
1268
1269
1270
1271 QHullCubatureEstimation = namedtuple('QHullCubatureEstimation', (
1272 'nvar',
1273 'nsim',
1274 'r', 'R',
1275 'nfacets',
1276 #'tn_scheme',
1277 'tn_scheme_points',
1278 'inside',
1279 'outside'
1280 ))
1281
1282
1283 class QHullCubature(QHull):
1284
1285 def __init__(hull, sample, tn_scheme, space='G', incremental=True, auto_update=True):
1286
1287 # for now
1288 #č jednotka dočasnosti - jeden furt
1289 assert space == 'G'
1290
1291 hull.tn_scheme = tn_scheme
1292 super().__init__(sample, space, incremental, auto_update)
1293
1294
1295 def regen(self):
1296 points = getattr(self.sample, self.space)
1297 self.convex_hull = spatial.ConvexHull(points, incremental=self.incremental)
1298 self.facets = dict()
1299
1300 self.newly_estimated = 0
1301 self.newly_invalidated = 0
1302
1303 simplices = self.convex_hull.simplices
1304 x_normals = self.convex_hull.equations[:,0]
1305
1306 for indices, normal_x in zip(simplices, x_normals):
1307 facet = tuple(indices)
1308 self._integrate_facet(facet, indices, normal_x)
1309
1310 def update(self):
1311 if self.enough_points and (self.convex_hull.npoints < self.sample.nsim):
1312 if self.incremental:
1313 self.__update()
1314 else:
1315 self.regen()
1316
1317 def get_R(self):
1318 sum_squared = np.sum(np.square(self.sample.G), axis=1)
1319 #index = np.argmax(sum_squared)
1320 return np.sqrt(np.nanmax(sum_squared))
1321
1322
1323 def get_inside(hull):
1324 hull._update()
1325 if hull.enough_points:
1326 p_inside = mpmath.mpf(0)
1327 for p_facet in hull.facets.values():
1328 p_inside += p_facet
1329 return p_inside
1330 else:
1331 return 0
1332
1333
1334
1335 def __update(hull):
1336
1337 hull._hull_update() #č jako vždy, chyby nechytáme
1338
1339 #č podstata problému byla a je v tom,
1340 #č že numpy neumí pracovat s věktory, případně s submaticemi
1341 #č jako s celky. Má routiny pouze pro 1D množiny.
1342 #č Navíc, numpy vektory nejsou hashable, nejde je přímo hodit
1343 #č ani do setů, ani do slovníků
1344 #č Nezbyvá nám nic jineho, než prévest ndarray na množinu n-tic.
1345
1346 #č na rozdil od triangulace, zde potřebujeme i normály, aspoň první složku
1347 #č jenomže výtahnout ji z numpy equations později na zakladě indexů by bylo velmi dráho.
1348 new_facets = dict(zip((tuple(simplex) for simplex in hull.simplices), hull.equations[:,0]))
1349
1350 # possible naive implementation
1351 #to_invalidate = sx.simplices_set - new_simplices_set
1352 #to_estimate = new_simplices_set - sx.simplices_set
1353
1354 facets = hull.facets # dict
1355 #č vymejšlím, jak by se mohlo ušetřit,
1356 #č aby se neprobíhala smyčka přes obrovský set na dvakrat
1357 # Update the set, keeping only elements found in either set, but not in both
1358 either_facets = new_facets.keys() ^ facets.keys() # set
1359 to_estimate = either_facets - facets.keys() # set
1360 to_invalidate = either_facets - to_estimate # set
1361
1362 hull.newly_estimated = len(to_estimate)
1363 hull.newly_invalidated = len(to_invalidate)
1364
1365 #č invalidace
1366 for key in to_invalidate:
1367 del facets[key]
1368
1369
1370 for facet in to_estimate:
1371 indices = np.array(facet)
1372 normal_x = new_facets[facet]
1373 hull._integrate_facet(facet, indices, normal_x)
1374
1375
1376
1377 def _hull_update(self):
1378 points = getattr(self.sample, self.space)
1379 self.convex_hull.add_points(points[self.convex_hull.npoints:])
1380
1381
1382
1383 def get_convex_hull_estimation(hull):
1384 inside = hull.get_inside()
1385 return QHullCubatureEstimation(
1386 hull.sample.nvar,
1387 hull.npoints,
1388 hull.get_r(), hull.get_R(),
1389 hull.nsimplex,
1390 #'tn_scheme',
1391 hull.tn_scheme.points.shape[1],
1392 float(inside),
1393 float(1 - inside)
1394 )
1265 1395
1396
1397 def _integrate_facet(hull, facet, indices, normal_x):
1398
1399
1400 vertices_model = hull.points[indices]
1266 1401
1402 # nvar x n_integration_nodes
1403 x = quadpy.tn.transform(hull.tn_scheme.points, vertices_model.T)
1404 vol = quadpy.tn.get_vol(vertices_model)
1405
1406 if not np.isfinite(vol):
1407 print("Incorrect volume has occured in simplex %s" % indices)
1408 hull.facets[facet] = 0
1409 return
1410
1411 nvar = hull.sample.nvar
1412 store = mpmath.mpf(0)
1413
1414 for node, weight in zip(x.T, hull.tn_scheme.weights):
1415 node_iterator = iter(node)
1416 node_store = mpmath.ncdf(next(node_iterator))
1417 for __ in range(nvar - 1):
1418 node_store *= mpmath.npdf(next(node_iterator))
1419
1420 store += node_store * weight
1421
1422
1423 if store < 0:
1424 print("Negative measure has occured in facet %s" % indices)
1425 hull.facets[facet] = 0
1426 return
1427
1428 hull.facets[facet] = store * normal_x * vol
1429
1430
1431
1432
1433
1434
1435
1436
1437
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1439
1267 1440
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