Merge branch 'consistent_swapimprove_opt_conform' into 'master'

consistent criterion for conformance optimization in SwapImprove

See merge request jschoeberl/netgen!391
This commit is contained in:
Joachim Schöberl 2021-06-10 09:23:18 +00:00
commit 55175925e3
3 changed files with 157 additions and 143 deletions

View File

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

View File

@ -12,8 +12,10 @@ def readData(a, files):
ne1d=[] ne1d=[]
ne2d=[] ne2d=[]
ne3d=[] ne3d=[]
file=[]
for f in files: for f in files:
for t in a[f]: for t in a[f]:
file.append(f)
if t['ne1d']>0: if t['ne1d']>0:
ne1d.append(t['ne1d']) ne1d.append(t['ne1d'])
if t['ne2d']>0: if t['ne2d']>0:
@ -37,6 +39,7 @@ def readData(a, files):
"#edges" : ne1d, "#edges" : ne1d,
"#trigs" : ne2d, "#trigs" : ne2d,
"#tets" : ne3d, "#tets" : ne3d,
"file" : file,
} }
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -61,6 +64,15 @@ filenames = [f for f in s if f in s2]
data = readData(s, filenames) data = readData(s, filenames)
data2 = readData(s2, 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 n = len(data)+1
fig,ax = plt.subplots(figsize=(10,7)) fig,ax = plt.subplots(figsize=(10,7))
for i,d in enumerate(['min trig angle','min tet angle','max trig angle','max tet angle']): 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, "ne2d": 160,
"ne3d": 245, "ne3d": 244,
"quality_histogram": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 25, 37, 40, 42, 43, 30, 15, 3, 2]", "quality_histogram": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 25, 35, 41, 42, 42, 31, 15, 4, 2]",
"total_badness": 349.09002466 "total_badness": 346.48816749
}, },
{ {
"angles_tet": [ "angles_tet": [
@ -501,9 +501,9 @@
], ],
"ne1d": 190, "ne1d": 190,
"ne2d": 282, "ne2d": 282,
"ne3d": 589, "ne3d": 584,
"quality_histogram": "[0, 0, 0, 0, 2, 0, 0, 5, 2, 13, 40, 46, 62, 105, 90, 94, 60, 46, 20, 4]", "quality_histogram": "[0, 0, 0, 0, 2, 0, 1, 5, 2, 15, 44, 43, 59, 99, 94, 91, 62, 43, 21, 3]",
"total_badness": 862.89134979 "total_badness": 858.49088107
}, },
{ {
"angles_tet": [ "angles_tet": [
@ -1290,7 +1290,7 @@
}, },
{ {
"angles_tet": [ "angles_tet": [
16.097, 14.707,
160.17 160.17
], ],
"angles_trig": [ "angles_trig": [
@ -1300,8 +1300,8 @@
"ne1d": 104, "ne1d": 104,
"ne2d": 152, "ne2d": 152,
"ne3d": 124, "ne3d": 124,
"quality_histogram": "[0, 0, 0, 0, 10, 19, 39, 26, 9, 3, 1, 1, 5, 1, 3, 2, 3, 2, 0, 0]", "quality_histogram": "[0, 0, 0, 1, 8, 21, 38, 26, 9, 3, 1, 1, 5, 1, 3, 2, 3, 2, 0, 0]",
"total_badness": 353.53219387 "total_badness": 354.28790114
}, },
{ {
"angles_tet": [ "angles_tet": [
@ -1444,7 +1444,7 @@
"frame.step": [ "frame.step": [
{ {
"angles_tet": [ "angles_tet": [
2.8491, 2.9086,
171.1 171.1
], ],
"angles_trig": [ "angles_trig": [
@ -1453,39 +1453,39 @@
], ],
"ne1d": 10108, "ne1d": 10108,
"ne2d": 29958, "ne2d": 29958,
"ne3d": 152591, "ne3d": 152461,
"quality_histogram": "[0, 3, 1, 3, 8, 19, 67, 166, 526, 1268, 2848, 5750, 10287, 16353, 21624, 25759, 26679, 22815, 14557, 3858]", "quality_histogram": "[0, 3, 1, 3, 6, 18, 51, 153, 539, 1234, 2814, 5708, 10236, 16240, 21696, 25712, 26711, 22859, 14609, 3868]",
"total_badness": 201926.65352 "total_badness": 201603.34124
}, },
{ {
"angles_tet": [ "angles_tet": [
2.296, 2.296,
175.61 175.72
], ],
"angles_trig": [ "angles_trig": [
2.0087, 3.4731,
175.57 146.28
], ],
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"ne3d": 29065, "ne3d": 28875,
"quality_histogram": "[3, 4, 3, 11, 16, 45, 110, 221, 664, 978, 1581, 2526, 3121, 3891, 4274, 4114, 3456, 2388, 1312, 347]", "quality_histogram": "[3, 4, 4, 9, 16, 45, 103, 213, 662, 993, 1514, 2528, 3069, 3888, 4348, 4102, 3364, 2352, 1334, 324]",
"total_badness": 43056.520137 "total_badness": 42759.274327
}, },
{ {
"angles_tet": [ "angles_tet": [
2.171, 2.1678,
174.11 174.11
], ],
"angles_trig": [ "angles_trig": [
1.6035, 1.6035,
174.13 150.55
], ],
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"ne2d": 23596, "ne2d": 23596,
"ne3d": 80062, "ne3d": 79955,
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"total_badness": 110028.28336 "total_badness": 109848.90296
} }
], ],
"hinge.stl": [ "hinge.stl": [
@ -1500,24 +1500,24 @@
], ],
"ne1d": 456, "ne1d": 456,
"ne2d": 1212, "ne2d": 1212,
"ne3d": 1997, "ne3d": 1985,
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"total_badness": 2785.3969831 "total_badness": 2774.8392965
}, },
{ {
"angles_tet": [ "angles_tet": [
7.7862, 7.6058,
161.84 161.84
], ],
"angles_trig": [ "angles_trig": [
9.6143, 9.6143,
148.89 152.14
], ],
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"ne2d": 610, "ne2d": 610,
"ne3d": 789, "ne3d": 785,
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"total_badness": 1364.9012739 "total_badness": 1354.028297
}, },
{ {
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@ -1525,14 +1525,14 @@
164.51 164.51
], ],
"angles_trig": [ "angles_trig": [
12.656, 12.725,
152.72 144.55
], ],
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"total_badness": 1789.6525041 "total_badness": 1791.0009554
}, },
{ {
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@ -1540,29 +1540,29 @@
156.97 156.97
], ],
"angles_trig": [ "angles_trig": [
21.175, 21.769,
135.02 131.54
], ],
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"total_badness": 3558.447444 "total_badness": 3617.4946271
}, },
{ {
"angles_tet": [ "angles_tet": [
19.878, 19.878,
145.14 146.81
], ],
"angles_trig": [ "angles_trig": [
22.16, 21.108,
131.22 133.01
], ],
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"total_badness": 8654.5076132 "total_badness": 8659.2800224
}, },
{ {
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@ -1900,9 +1900,9 @@
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"total_badness": 21635.781365 "total_badness": 21630.784432
}, },
{ {
"angles_tet": [ "angles_tet": [
@ -2015,18 +2015,18 @@
"part1.stl": [ "part1.stl": [
{ {
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14.209, 13.063,
147.23 147.22
], ],
"angles_trig": [ "angles_trig": [
19.94, 19.94,
127.49 119.28
], ],
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"total_badness": 1752.4668505 "total_badness": 1707.0143419
}, },
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@ -2039,14 +2039,14 @@
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"total_badness": 824.2762646 "total_badness": 798.54900393
}, },
{ {
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19.623, 19.624,
148.63 148.62
], ],
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19.446, 19.446,
@ -2054,29 +2054,29 @@
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}, },
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21.731, 20.545,
138.73 150.39
], ],
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24.944, 23.604,
119.75 120.32
], ],
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146.09 146.42
], ],
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24.61, 24.61,
@ -2084,9 +2084,9 @@
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} }
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@ -2107,18 +2107,18 @@
}, },
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162.28 166.55
], ],
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140.96 144.13
], ],
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@ -2193,54 +2193,54 @@
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174.03 174.03
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170.0 152.74
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{ {
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1.1, 1.1,
172.16 172.17
], ],
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2.2863, 3.2068,
165.88 163.66
], ],
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{ {
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1.2152, 1.2152,
169.91 165.67
], ],
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1.1526, 1.1526,
158.98 152.31
], ],
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{ {
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@ -2253,9 +2253,9 @@
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}, },
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@ -2268,9 +2268,9 @@
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"total_badness": 344505.69105 "total_badness": 344465.16205
} }
], ],
"revolution.geo": [ "revolution.geo": [
@ -2377,9 +2377,9 @@
], ],
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{ {
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@ -2398,8 +2398,8 @@
}, },
{ {
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143.27 143.25
], ],
"angles_trig": [ "angles_trig": [
23.794, 23.794,
@ -2407,9 +2407,9 @@
], ],
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} }
], ],
"sculpture.geo": [ "sculpture.geo": [
@ -2425,8 +2425,8 @@
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}, },
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@ -2470,8 +2470,8 @@
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