consistent criterion for conformance optimization in SwapImprove

Only small effect on output Meshes
This commit is contained in:
Matthias Hochsteger 2021-06-10 10:55:28 +02:00
parent 85f26ac5fa
commit 2a17451c96
3 changed files with 157 additions and 143 deletions

View File

@ -2245,7 +2245,7 @@ double MeshOptimize3d :: SwapImproveEdge (Mesh & mesh, OPTIMIZEGOAL goal,
bad2 += 1e4;
if (goal == OPT_CONFORM && bad2 < 1e4)
if (goal == OPT_CONFORM && NotTooBad(bad1, bad2))
{
INDEX_3 face(pi3, pi4, pi5);
face.Sort();
@ -2253,8 +2253,7 @@ double MeshOptimize3d :: SwapImproveEdge (Mesh & mesh, OPTIMIZEGOAL goal,
{
// (*testout) << "3->2 swap, could improve conformity, bad1 = " << bad1
// << ", bad2 = " << bad2 << endl;
if (bad2 < 1e4)
bad1 = 2 * bad2;
bad2 = bad1 + IMPROVEMENT_CONFORMING_EDGE;
}
}
@ -2650,6 +2649,9 @@ double MeshOptimize3d :: SwapImproveEdge (Mesh & mesh, OPTIMIZEGOAL goal,
if (confface != -1)
bestl = confface;
if(confface != -1 || confedge != -1)
badopt = bad1 + IMPROVEMENT_CONFORMING_EDGE;
if (bestl != -1)
{
// (*mycout) << nsuround << "->" << 2 * (nsuround-2) << " " << flush;

View File

@ -12,8 +12,10 @@ def readData(a, files):
ne1d=[]
ne2d=[]
ne3d=[]
file=[]
for f in files:
for t in a[f]:
file.append(f)
if t['ne1d']>0:
ne1d.append(t['ne1d'])
if t['ne2d']>0:
@ -37,6 +39,7 @@ def readData(a, files):
"#edges" : ne1d,
"#trigs" : ne2d,
"#tets" : ne3d,
"file" : file,
}
import matplotlib.pyplot as plt
@ -61,6 +64,15 @@ filenames = [f for f in s if f in s2]
data = readData(s, filenames)
data2 = readData(s2, filenames)
assert(len(data) == len(data2))
for bad1,bad2, f1, f2 in zip(data['badness'], data2['badness'], data['file'], data2['file']):
assert f1==f2
if bad2>0 and bad2>1.1*bad1:
print(f"file {f1} got worse: {bad1} -> {bad2}")
if bad2>0 and bad2<0.9*bad1:
print(f"file {f1} got better: {bad1} -> {bad2}")
n = len(data)+1
fig,ax = plt.subplots(figsize=(10,7))
for i,d in enumerate(['min trig angle','min tet angle','max trig angle','max tet angle']):

View File

@ -486,9 +486,9 @@
],
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"ne2d": 160,
"ne3d": 245,
"quality_histogram": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 25, 37, 40, 42, 43, 30, 15, 3, 2]",
"total_badness": 349.09002466
"ne3d": 244,
"quality_histogram": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 25, 35, 41, 42, 42, 31, 15, 4, 2]",
"total_badness": 346.48816749
},
{
"angles_tet": [
@ -501,9 +501,9 @@
],
"ne1d": 190,
"ne2d": 282,
"ne3d": 589,
"quality_histogram": "[0, 0, 0, 0, 2, 0, 0, 5, 2, 13, 40, 46, 62, 105, 90, 94, 60, 46, 20, 4]",
"total_badness": 862.89134979
"ne3d": 584,
"quality_histogram": "[0, 0, 0, 0, 2, 0, 1, 5, 2, 15, 44, 43, 59, 99, 94, 91, 62, 43, 21, 3]",
"total_badness": 858.49088107
},
{
"angles_tet": [
@ -1290,7 +1290,7 @@
},
{
"angles_tet": [
16.097,
14.707,
160.17
],
"angles_trig": [
@ -1300,8 +1300,8 @@
"ne1d": 104,
"ne2d": 152,
"ne3d": 124,
"quality_histogram": "[0, 0, 0, 0, 10, 19, 39, 26, 9, 3, 1, 1, 5, 1, 3, 2, 3, 2, 0, 0]",
"total_badness": 353.53219387
"quality_histogram": "[0, 0, 0, 1, 8, 21, 38, 26, 9, 3, 1, 1, 5, 1, 3, 2, 3, 2, 0, 0]",
"total_badness": 354.28790114
},
{
"angles_tet": [
@ -1444,7 +1444,7 @@
"frame.step": [
{
"angles_tet": [
2.8491,
2.9086,
171.1
],
"angles_trig": [
@ -1453,39 +1453,39 @@
],
"ne1d": 10108,
"ne2d": 29958,
"ne3d": 152591,
"quality_histogram": "[0, 3, 1, 3, 8, 19, 67, 166, 526, 1268, 2848, 5750, 10287, 16353, 21624, 25759, 26679, 22815, 14557, 3858]",
"total_badness": 201926.65352
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"quality_histogram": "[0, 3, 1, 3, 6, 18, 51, 153, 539, 1234, 2814, 5708, 10236, 16240, 21696, 25712, 26711, 22859, 14609, 3868]",
"total_badness": 201603.34124
},
{
"angles_tet": [
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],
"angles_trig": [
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3.4731,
146.28
],
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"ne3d": 29065,
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"total_badness": 42759.274327
},
{
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],
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],
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"total_badness": 110028.28336
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"total_badness": 109848.90296
}
],
"hinge.stl": [
@ -1500,24 +1500,24 @@
],
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"ne2d": 1212,
"ne3d": 1997,
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"total_badness": 2774.8392965
},
{
"angles_tet": [
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],
"angles_trig": [
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],
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"total_badness": 1364.9012739
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"total_badness": 1354.028297
},
{
"angles_tet": [
@ -1525,14 +1525,14 @@
164.51
],
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144.55
],
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"total_badness": 1789.6525041
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"total_badness": 1791.0009554
},
{
"angles_tet": [
@ -1540,29 +1540,29 @@
156.97
],
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21.769,
131.54
],
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},
{
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],
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],
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"total_badness": 8659.2800224
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{
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@ -1900,9 +1900,9 @@
],
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"total_badness": 21630.784432
},
{
"angles_tet": [
@ -2015,18 +2015,18 @@
"part1.stl": [
{
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13.063,
147.22
],
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19.94,
127.49
119.28
],
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"total_badness": 1707.0143419
},
{
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@ -2039,14 +2039,14 @@
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},
{
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],
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@ -2054,29 +2054,29 @@
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],
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120.32
],
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],
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@ -2084,9 +2084,9 @@
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}
],
"period.geo": [
@ -2107,18 +2107,18 @@
},
{
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],
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],
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@ -2193,54 +2193,54 @@
],
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@ -2253,9 +2253,9 @@
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@ -2268,9 +2268,9 @@
],
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"total_badness": 344465.16205
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@ -2377,9 +2377,9 @@
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@ -2398,8 +2398,8 @@
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@ -2407,9 +2407,9 @@
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}
],
"sculpture.geo": [
@ -2425,8 +2425,8 @@
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@ -2470,8 +2470,8 @@
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@ -3161,13 +3161,13 @@
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"total_badness": 7464.5609796
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{
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],
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@ -3175,14 +3175,14 @@
],
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@ -3190,9 +3190,9 @@
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@ -3205,9 +3205,9 @@
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@ -3220,9 +3220,9 @@
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@ -3516,4 +3516,4 @@
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