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".
List of commits:
Subject Hash Author Date (UTC)
convex_hull.QHull.get_chi_outside: count negative-valued facets c3f645977e19d66f7a402001485254ba30fdb2ce I am 2023-01-25 10:22:00
convex_hull.QHull.get_chi_outside: check and mask negative valued integrals 861f9bfdf5dab31c5c6b81566eb8db7d338307e9 I am 2023-01-24 22:27:43
dicebox.circumtri: estimation export hotfix 4f86660297b643446bacd3e254ded9faca148542 I am 2023-01-24 15:02:34
dicebox.circumtri: assemble QTri box 9e2114c0c6d5defcf3509a30fda3b6230eb9b9d1 I am 2023-01-24 14:48:48
dicebox._exploration: rework explore() af54a2693f6da4d874ade55f02990c87ed7ce9c7 I am 2023-01-24 09:54:51
convex_hull.QHull: implement erfc and gammaincc-based outside integrations for now ad0a665b53b6c8ca24a215c0e4ca73a5c1d145ad I am 2023-01-23 19:29:09
convex_hull.QHull: accept integrational crime 788f3ffe604a4cac9d19382250dd5ebe3cd5befc I am 2023-01-21 13:09:31
convex_hull.QHull: normal fix 083bbdc4cb41fdb3e357b1d0359705a2a65d6d27 I am 2023-01-20 19:07:17
convex_hull.QHull: implement chi cubature integration 7537b642967b7dd5d8475c20aff2a930a5bcd3ad I am 2023-01-20 15:54:05
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
Commit c3f645977e19d66f7a402001485254ba30fdb2ce - convex_hull.QHull.get_chi_outside: count negative-valued facets
Author: I am
Author date (UTC): 2023-01-25 10:22
Committer name: I am
Committer date (UTC): 2023-01-25 10:22
Parent(s): 861f9bfdf5dab31c5c6b81566eb8db7d338307e9
Signer:
Signing key:
Signing status: N
Tree: 386d173f5a32bcfbe3634cca1e0f803c01f2537d
File Lines added Lines deleted
wellmet/convex_hull.py 4 3
File wellmet/convex_hull.py changed (mode: 100644) (index e9b1594..149a356)
... ... class QHull:
1069 1069
1070 1070 vertices_model = hull.points[hull.simplices].transpose((1, 0, 2)) vertices_model = hull.points[hull.simplices].transpose((1, 0, 2))
1071 1071 result = hull.tn_scheme.integrate(hull._chi_outside_callback, vertices_model) result = hull.tn_scheme.integrate(hull._chi_outside_callback, vertices_model)
1072 mask = result > 0
1072 mask = result > 0 #č takto zapsana podmínka pokryvá i Nan
1073 1073 if not np.all(mask): if not np.all(mask):
1074 print("QHull: Negative values have appeared during integration")
1075 return np.sum(result[mask]) / hull.nsphere_surface_area
1074 number = len(mask) - np.count_nonzero(mask)
1075 print("QHull: Negative values in %s facets have appeared during integration" % number)
1076 return np.sum(result, where=mask) / hull.nsphere_surface_area
1076 1077 return np.sum(result) / hull.nsphere_surface_area return np.sum(result) / hull.nsphere_surface_area
1077 1078
1078 1079 # # quadpy # # quadpy
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