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[ t ]
0 : [ 1 ]
1 : [ 2 ]
2 : [ 3 ]
3 : [ 4 ]
4 : [ 5 ]
5 : [ 6 ]
6 : [ 7 ]
7 : [ 8 ]
8 : [ 9 ]
9 : [ 10 ]
10 : [ 11 ]
11 : [ 12 ]
0 : [ 21.7 20.74 20.52 19.82 20.19 20.46 21.92 25.13 25.44 25.9 25.29 24.9 ]
1 : [ 24.56 24.04 22.43 21.52 22.2 22.9 23.01 24.47 26.42 26.51 24.54 23.69 ]
2 : [ 22.35 20.74 19.95 20.12 20.45 20.72 22.53 24.19 26.33 27.21 26.85 25.24 ]
3 : [ 23.74 22.45 21.68 21.49 21.08 22.07 22.47 23.06 25.22 25.83 23.05 21.79 ]
4 : [ 21.02 19.89 19.78 19.23 19.2 20.47 21.25 23.7 24.64 25.51 24.34 22.53 ]
5 : [ 21.12 20.56 19.75 19.47 19.35 20 21.1 23.67 24.78 25.76 25.06 23.3 ]
6 : [ 22.29 21.62 20.66 19.98 19.84 20.86 21.6 23.25 26.32 27.47 26.86 26.29 ]
7 : [ 24.92 23.82 22.33 21.88 21.76 22.4 23.75 24.96 26.74 27.07 26.45 25.08 ]
8 : [ 23.04 22.38 20.56 20.72 20.96 21.64 22.47 24.09 25.68 27.07 25.89 24.26 ]
9 : [ 22.63 21.45 20.34 20.41 20.91 21.99 22.69 24.57 25.72 26.15 24.72 23.76 ]
10 : [ 22.15 21.01 20.37 20.54 20.22 20.91 22.74 24.45 26.55 25.91 24.97 23.71 ]
11 : [ 22.39 20.55 19.96 19.75 20 21.07 22.06 24.11 25.55 24.63 23.57 23.48 ]
12 : [ 21.85 20.57 20.37 20.16 19.92 20.88 21.83 24.11 25.36 25.88 24.54 24.17 ]
13 : [ 22.6 22.01 21.45 21.15 21 21.92 22.83 24.17 24.98 25.04 24.53 21.96 ]
14 : [ 21.27 20.43 19.2 19.51 19.96 20.92 21.85 24.19 25.95 26.68 27.1 26.04 ]
15 : [ 24.57 23.34 22.02 21.38 21.67 22.3 23.45 25.27 25.92 25.41 24.18 23.07 ]
16 : [ 21.84 20.81 20.27 19.62 20.64 21.07 22.32 23.77 25.64 25.75 25.23 23.89 ]
17 : [ 22.18 20.99 19.73 19.18 19.41 20.21 21.3 23.17 24.79 25.17 24.09 22.54 ]
18 : [ 21.6 21.13 20.73 20.86 21.16 21.35 22.99 24.76 25.55 26.88 26.42 25.97 ]
19 : [ 24.3 22.24 21.01 20.68 21.8 22.33 23.45 24.92 25.63 25.57 24.48 23.13 ]
20 : [ 21.33 19.7 19.3 19.49 20.29 20.32 21.79 23.32 24.66 24.91 25.1 22.96 ]
21 : [ 21.86 21.26 19.86 19.79 20.07 21.13 21.96 24.62 26.81 27.31 26.35 25.51 ]
22 : [ 24.86 24.07 23.27 22.12 22.56 23.4 24.69 26.33 26.5 26.15 24.52 23.47 ]
23 : [ 21.51 20.75 19.47 19.24 19.68 20.71 21.61 23.32 24.92 26.04 25.29 24.42 ]
24 : [ 22.68 21.6 20.47 19.91 20.02 20.95 21.41 23.67 24.9 26.17 25.44 23.36 ]
25 : [ 21.71 20.98 20.05 18.8 19.13 19.61 20.94 23.73 25.63 26.21 25.78 25.33 ]
26 : [ 24.4 23.46 22.19 21.67 21.52 22 23.69 25.31 25.97 26.35 25.25 23.95 ]
27 : [ 22.82 21.75 20.28 19.85 20.98 21.65 22.49 24.33 25.99 25.54 25.03 23.47 ]
28 : [ 22.09 21.11 20.03 19.86 20.35 21.99 23.3 24.77 25.7 26.26 25.39 24.13 ]
29 : [ 23.29 22.18 21.2 21.3 21.61 22.2 23.25 24.77 25.77 26.61 25.8 24.54 ]
30 : [ 23.27 21.52 20.77 20.66 20.36 21.56 22.54 23.48 25.21 26.31 25.03 24.06 ]
31 : [ 22.75 21.38 20.52 19.93 20.55 21.41 23 24.29 25.49 25.21 24.5 23.97 ]
32 : [ 22.89 22.47 21.75 21.8 22.94 24.59 26.13 27.42 28.09 28.68 28.56 28.19 ]
33 : [ 27.44 25.95 23.78 22.24 21.86 21.9 23.01 24.18 25.18 26 25.16 23.23 ]
34 : [ 21.96 21.24 20.17 20.37 20.52 21.5 22.58 23.59 24.87 25.74 24.25 22.29 ]
35 : [ 21.75 20.44 19.29 19.44 19.9 20.69 22.4 24.61 26.06 25.91 24.58 23.38 ]
36 : [ 21.98 21.12 20.97 20.44 21.07 22.03 23 25.3 27.14 28.01 27.17 25.58 ]
37 : [ 24.06 22.78 21.73 21.45 22.39 22.63 23.47 24.64 25.74 25.78 24.54 23.6 ]
38 : [ 21.27 20.26 19.12 19.19 19.5 20.55 21.8 24.09 26.26 26.66 25.63 23.18 ]
39 : [ 22 21.12 20.32 19.87 20.33 21.31 22.19 24.02 25.88 26.16 25.22 24.05 ]
40 : [ 22.68 21 20.25 20.13 20.28 20.84 22.45 23.86 25.97 26.51 24.99 24.37 ]
41 : [ 23.05 22.05 21.08 20.75 21.13 22.18 23.43 24.83 26.68 27.76 27.68 26.31 ]
42 : [ 23.82 21.95 20.55 20.06 20.82 21.49 22.48 24.43 26.49 27.17 26.44 25.15 ]
43 : [ 23.76 22.06 21.05 20.83 20.99 21.64 22.75 24.32 25.79 25.43 24.32 23.22 ]
44 : [ 22.43 21.21 19.7 20.16 21.53 22.41 23.61 25.33 26.43 26.12 24.47 23.1 ]
45 : [ 22.45 21.23 20.01 20.17 20.15 21.2 22.02 23.84 25.71 26.09 23.85 22.89 ]
46 : [ 21.56 20.02 19.53 19.24 19.95 20.26 21.61 23.67 25.74 26.95 26.64 26.71 ]
47 : [ 26.27 25.59 24.8 24.4 24.58 25.63 26.92 28.22 28.98 29.15 28.61 27.69 ]
48 : [ 25.18 23.43 21.77 20.87 21.16 21.43 22.56 23.73 25.64 26.62 24.3 23.46 ]
49 : [ 21.83 20.44 19.75 19.23 20.05 20.51 21.72 23.86 25.71 26.19 25.84 24.1 ]
50 : [ 22.25 20.59 20.1 19.94 20.37 20.6 22.22 23.88 25.91 27.44 26.69 23.77 ]
51 : [ 21.74 20.88 19.9 19.39 19.52 20.49 21.96 23.64 26.06 27.53 26.53 24.8 ]
52 : [ 22.67 21.01 19.94 19.89 21.16 22.25 23.44 24.38 25.81 25.97 24.44 22.49 ]
53 : [ 21.58 20.75 20.14 20 20.99 21.92 22.99 24.6 25.81 25.94 25.32 23.05 ]
Karhunen-Loeve threshold=1e-05 quantile level=0.05
class=FieldMonteCarloResult name=Unnamed processSample_=class=ProcessSampleImplementation mesh=class=Mesh name=Unnamed dimension=1 vertices=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 description=[t] data=[[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12]] simplices=[[0,1],[1,2],[2,3],[3,4],[4,5],[5,6],[6,7],[7,8],[8,9],[9,10],[10,11]] values=[class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.7],[20.74],[20.52],[19.82],[20.19],[20.46],[21.92],[25.13],[25.44],[25.9],[25.29],[24.9]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.56],[24.04],[22.43],[21.52],[22.2],[22.9],[23.01],[24.47],[26.42],[26.51],[24.54],[23.69]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.35],[20.74],[19.95],[20.12],[20.45],[20.72],[22.53],[24.19],[26.33],[27.21],[26.85],[25.24]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.74],[22.45],[21.68],[21.49],[21.08],[22.07],[22.47],[23.06],[25.22],[25.83],[23.05],[21.79]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.02],[19.89],[19.78],[19.23],[19.2],[20.47],[21.25],[23.7],[24.64],[25.51],[24.34],[22.53]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.12],[20.56],[19.75],[19.47],[19.35],[20],[21.1],[23.67],[24.78],[25.76],[25.06],[23.3]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.29],[21.62],[20.66],[19.98],[19.84],[20.86],[21.6],[23.25],[26.32],[27.47],[26.86],[26.29]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.92],[23.82],[22.33],[21.88],[21.76],[22.4],[23.75],[24.96],[26.74],[27.07],[26.45],[25.08]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.04],[22.38],[20.56],[20.72],[20.96],[21.64],[22.47],[24.09],[25.68],[27.07],[25.89],[24.26]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.63],[21.45],[20.34],[20.41],[20.91],[21.99],[22.69],[24.57],[25.72],[26.15],[24.72],[23.76]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.15],[21.01],[20.37],[20.54],[20.22],[20.91],[22.74],[24.45],[26.55],[25.91],[24.97],[23.71]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.39],[20.55],[19.96],[19.75],[20],[21.07],[22.06],[24.11],[25.55],[24.63],[23.57],[23.48]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.85],[20.57],[20.37],[20.16],[19.92],[20.88],[21.83],[24.11],[25.36],[25.88],[24.54],[24.17]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.6],[22.01],[21.45],[21.15],[21],[21.92],[22.83],[24.17],[24.98],[25.04],[24.53],[21.96]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.27],[20.43],[19.2],[19.51],[19.96],[20.92],[21.85],[24.19],[25.95],[26.68],[27.1],[26.04]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.57],[23.34],[22.02],[21.38],[21.67],[22.3],[23.45],[25.27],[25.92],[25.41],[24.18],[23.07]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.84],[20.81],[20.27],[19.62],[20.64],[21.07],[22.32],[23.77],[25.64],[25.75],[25.23],[23.89]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.18],[20.99],[19.73],[19.18],[19.41],[20.21],[21.3],[23.17],[24.79],[25.17],[24.09],[22.54]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.6],[21.13],[20.73],[20.86],[21.16],[21.35],[22.99],[24.76],[25.55],[26.88],[26.42],[25.97]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.3],[22.24],[21.01],[20.68],[21.8],[22.33],[23.45],[24.92],[25.63],[25.57],[24.48],[23.13]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.33],[19.7],[19.3],[19.49],[20.29],[20.32],[21.79],[23.32],[24.66],[24.91],[25.1],[22.96]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.86],[21.26],[19.86],[19.79],[20.07],[21.13],[21.96],[24.62],[26.81],[27.31],[26.35],[25.51]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.86],[24.07],[23.27],[22.12],[22.56],[23.4],[24.69],[26.33],[26.5],[26.15],[24.52],[23.47]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.51],[20.75],[19.47],[19.24],[19.68],[20.71],[21.61],[23.32],[24.92],[26.04],[25.29],[24.42]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.68],[21.6],[20.47],[19.91],[20.02],[20.95],[21.41],[23.67],[24.9],[26.17],[25.44],[23.36]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.71],[20.98],[20.05],[18.8],[19.13],[19.61],[20.94],[23.73],[25.63],[26.21],[25.78],[25.33]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.4],[23.46],[22.19],[21.67],[21.52],[22],[23.69],[25.31],[25.97],[26.35],[25.25],[23.95]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.82],[21.75],[20.28],[19.85],[20.98],[21.65],[22.49],[24.33],[25.99],[25.54],[25.03],[23.47]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.09],[21.11],[20.03],[19.86],[20.35],[21.99],[23.3],[24.77],[25.7],[26.26],[25.39],[24.13]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.29],[22.18],[21.2],[21.3],[21.61],[22.2],[23.25],[24.77],[25.77],[26.61],[25.8],[24.54]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.27],[21.52],[20.77],[20.66],[20.36],[21.56],[22.54],[23.48],[25.21],[26.31],[25.03],[24.06]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.75],[21.38],[20.52],[19.93],[20.55],[21.41],[23],[24.29],[25.49],[25.21],[24.5],[23.97]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.89],[22.47],[21.75],[21.8],[22.94],[24.59],[26.13],[27.42],[28.09],[28.68],[28.56],[28.19]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[27.44],[25.95],[23.78],[22.24],[21.86],[21.9],[23.01],[24.18],[25.18],[26],[25.16],[23.23]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.96],[21.24],[20.17],[20.37],[20.52],[21.5],[22.58],[23.59],[24.87],[25.74],[24.25],[22.29]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.75],[20.44],[19.29],[19.44],[19.9],[20.69],[22.4],[24.61],[26.06],[25.91],[24.58],[23.38]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.98],[21.12],[20.97],[20.44],[21.07],[22.03],[23],[25.3],[27.14],[28.01],[27.17],[25.58]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[24.06],[22.78],[21.73],[21.45],[22.39],[22.63],[23.47],[24.64],[25.74],[25.78],[24.54],[23.6]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.27],[20.26],[19.12],[19.19],[19.5],[20.55],[21.8],[24.09],[26.26],[26.66],[25.63],[23.18]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22],[21.12],[20.32],[19.87],[20.33],[21.31],[22.19],[24.02],[25.88],[26.16],[25.22],[24.05]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.68],[21],[20.25],[20.13],[20.28],[20.84],[22.45],[23.86],[25.97],[26.51],[24.99],[24.37]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.05],[22.05],[21.08],[20.75],[21.13],[22.18],[23.43],[24.83],[26.68],[27.76],[27.68],[26.31]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.82],[21.95],[20.55],[20.06],[20.82],[21.49],[22.48],[24.43],[26.49],[27.17],[26.44],[25.15]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[23.76],[22.06],[21.05],[20.83],[20.99],[21.64],[22.75],[24.32],[25.79],[25.43],[24.32],[23.22]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.43],[21.21],[19.7],[20.16],[21.53],[22.41],[23.61],[25.33],[26.43],[26.12],[24.47],[23.1]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.45],[21.23],[20.01],[20.17],[20.15],[21.2],[22.02],[23.84],[25.71],[26.09],[23.85],[22.89]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.56],[20.02],[19.53],[19.24],[19.95],[20.26],[21.61],[23.67],[25.74],[26.95],[26.64],[26.71]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[26.27],[25.59],[24.8],[24.4],[24.58],[25.63],[26.92],[28.22],[28.98],[29.15],[28.61],[27.69]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[25.18],[23.43],[21.77],[20.87],[21.16],[21.43],[22.56],[23.73],[25.64],[26.62],[24.3],[23.46]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.83],[20.44],[19.75],[19.23],[20.05],[20.51],[21.72],[23.86],[25.71],[26.19],[25.84],[24.1]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.25],[20.59],[20.1],[19.94],[20.37],[20.6],[22.22],[23.88],[25.91],[27.44],[26.69],[23.77]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.74],[20.88],[19.9],[19.39],[19.52],[20.49],[21.96],[23.64],[26.06],[27.53],[26.53],[24.8]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.67],[21.01],[19.94],[19.89],[21.16],[22.25],[23.44],[24.38],[25.81],[25.97],[24.44],[22.49]],class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.58],[20.75],[20.14],[20],[20.99],[21.92],[22.99],[24.6],[25.81],[25.94],[25.32],[23.05]]] meanSample_=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[22.765],[21.6319],[20.6707],[20.3694],[20.7413],[21.5083],[22.6485],[24.3776],[25.865],[26.3385],[25.3865],[24.1219]] lowerQuantileSample_=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[21.27],[20.068],[19.292],[19.198],[19.362],[20.22],[21.26],[23.264],[24.782],[25.066],[23.898],[22.33]] upperQuantileSample_=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=12 dimension=1 data=[[25.128],[24.064],[23.102],[22.072],[22.526],[23.3],[24.502],[26.13],[27.074],[27.96],[27.578],[26.63]] xiSamples_=[ [ Xi_0 Xi_1 Xi_2 Xi_3 Xi_4 Xi_5 Xi_6 Xi_7 Xi_8 Xi_9 Xi_10 Xi_11 ]
0 : [ -0.518638 -0.334126 -0.166017 -2.89898 0.840206 -1.27267 0.259533 -0.110612 -0.0351358 2.38882 0.505122 0.0597695 ]
1 : [ 1.18866 1.13632 0.719227 1.05707 0.601325 0.0407882 -1.62385 -1.56993 -2.70582 0.467759 0.302878 -1.17029 ]
2 : [ -0.242253 -1.26576 0.449698 0.479879 -0.216262 0.221158 0.667182 2.38302 0.278821 0.246556 -0.548077 -0.0950099 ]
3 : [ 0.134332 2.01273 0.306993 2.02446 -1.23086 -1.71909 -2.404 0.480497 -0.185203 -0.301569 0.349301 0.214025 ]
4 : [ -1.36928 0.432047 -0.403875 -0.443322 -0.480392 -1.85652 0.615503 -1.32931 0.512184 0.197549 1.58523 1.9093 ]
5 : [ -1.22041 0.0861756 0.489614 -0.873805 -0.387963 -1.41082 1.05185 -0.983126 -0.0586318 0.578201 -0.971345 -0.35141 ]
6 : [ -0.257097 -1.25532 2.13204 0.442403 0.00356089 -0.169768 -1.94007 -0.479487 -0.729151 -1.82138 0.272511 -0.288187 ]
7 : [ 1.42767 -0.0353161 1.23318 0.0609397 0.280832 0.244165 0.367062 0.85244 0.143397 -1.38287 -1.09504 -0.106242 ]
8 : [ 0.200976 -0.14888 0.839051 1.15044 -0.792971 0.322893 0.217522 -1.08226 0.154442 1.30386 -2.27295 -0.654172 ]
9 : [ -0.0369149 0.289031 -0.783918 0.311234 -0.0417466 0.173461 -0.715793 -0.869242 0.0495736 0.99809 -0.763771 1.12869 ]
10 : [ -0.272814 0.00997919 -0.603961 -0.140189 1.27903 -1.59996 -0.0490788 2.4766 -0.208609 -2.12747 -1.50296 -0.832378 ]
11 : [ -0.802217 0.864851 -1.05747 -1.42151 1.30728 0.492797 -1.68753 0.837288 -0.154533 -1.19838 -0.128719 2.06668 ]
12 : [ -0.667025 0.162792 -0.170696 -1.07084 0.0133072 -1.57603 -1.55838 0.239244 0.450127 0.595191 0.0494289 1.5804 ]
13 : [ 0.0705411 1.34436 -0.491098 -0.634582 -2.04808 -1.4292 1.35281 -0.454256 -1.15708 -2.04992 -0.337072 0.0438419 ]
14 : [ -0.654507 -1.60921 0.255317 -0.732754 -0.497998 1.30046 0.00960052 -0.711076 -0.794827 -0.564798 -2.33793 1.17987 ]
15 : [ 0.884485 1.43385 -0.282189 -1.04907 1.1232 0.0770187 0.528282 -0.0366557 -0.120944 0.00124874 -0.486565 0.211568 ]
16 : [ -0.53603 -0.000512983 -0.324112 -0.306247 -0.584044 0.636192 -0.201601 0.410228 -1.5523 -0.848803 1.81657 -1.83945 ]
17 : [ -1.22282 0.889728 0.430943 -0.105278 0.490324 0.398377 0.503826 -0.668666 0.490191 -0.661123 0.0385773 -0.384787 ]
18 : [ 0.143921 -0.965429 -0.0976956 -1.68158 -2.08319 -0.782339 -0.905852 0.491709 0.482189 1.79467 -0.999106 -2.1948 ]
19 : [ 0.497726 1.00506 -0.902186 -0.148335 0.0767542 1.98845 0.615607 0.488152 0.195385 1.25998 0.686198 1.32444 ]
20 : [ -1.24625 0.243895 -0.674304 -0.878696 -2.21485 1.05034 1.12125 1.97182 -1.41529 -0.179287 -0.139065 -0.118129 ]
21 : [ -0.255285 -1.26685 0.385896 0.556115 1.82333 -0.429926 -0.464363 -1.01165 -0.914229 0.255901 -1.69211 0.242658 ]
22 : [ 1.84262 1.25248 -0.840215 -1.04928 1.02321 -1.27397 0.523767 -0.770384 0.321962 0.219816 1.71573 -0.903765 ]
23 : [ -1.00647 -0.230339 0.411364 -0.220953 -0.796899 1.06634 -0.750613 -1.72582 0.840106 -0.181422 -0.484492 -0.706246 ]
24 : [ -0.549225 0.285695 1.10687 0.0610291 -0.765274 -0.116208 1.11688 -1.72451 -0.334128 0.648959 -0.00457662 2.031 ]
25 : [ -1.06713 -0.687824 1.56664 -1.70169 1.83405 -0.00579789 -0.134505 -0.82454 -0.23371 -0.177606 1.03886 -1.41584 ]
26 : [ 1.06241 0.693862 0.438143 -0.9441 0.442821 -0.751945 1.03054 0.369088 1.4479 0.236381 -0.889659 -1.04898 ]
27 : [ -0.142515 0.374775 -0.467345 0.0107532 0.693777 1.6902 0.589235 -0.591448 -2.10948 -0.767919 -0.198547 -0.388457 ]
28 : [ -0.145302 -0.266809 -1.14846 0.0111017 -0.0979643 0.627183 0.390285 -1.70315 2.41059 -1.2009 0.347431 0.193128 ]
29 : [ 0.621837 -0.0152639 -0.0625053 -0.224295 -1.40081 0.0913386 -0.135249 0.221628 -0.0260873 1.03272 -1.10879 0.474017 ]
30 : [ -0.137131 0.362481 0.568369 0.461514 -1.36065 0.141938 -1.35538 0.467958 2.03416 -0.834048 0.268918 1.90771 ]
31 : [ -0.244409 0.549376 -0.694139 -1.33274 0.311706 1.28024 -0.571335 0.244233 1.19875 -1.57485 0.992892 -1.0034 ]
32 : [ 2.32343 -2.57077 -2.00387 -0.403456 -0.607439 0.687901 -0.530987 -1.35326 1.34276 -0.616987 0.489123 -0.416396 ]
33 : [ 1.61837 2.06212 3.41711 -0.829653 0.0740782 1.16236 1.88256 -0.0259141 0.897949 -0.206922 -0.202926 0.252172 ]
34 : [ -0.493933 0.891234 -0.655895 0.994526 -1.75513 -0.577939 0.113284 -0.706544 0.869687 -0.296745 -0.817945 -1.38588 ]
35 : [ -0.795111 -0.0629135 -1.17852 0.196179 1.94235 -0.203953 0.630737 0.673861 1.0143 0.340343 -1.06457 -0.667774 ]
36 : [ 0.467266 -1.64063 -0.235847 0.79134 0.264926 -1.71815 0.25103 -0.166536 -1.47424 0.646081 2.00771 0.750556 ]
37 : [ 0.855485 1.07327 -0.478537 -0.204155 -1.05184 0.970919 -0.945664 0.528097 -1.57959 1.18703 0.0670708 -0.640032 ]
38 : [ -0.983049 -0.659864 -0.397906 1.67945 1.36565 -0.920729 1.72343 -0.0820537 -0.337232 -0.542172 -0.689713 0.136684 ]
39 : [ -0.403837 -0.094712 -0.115424 0.117842 0.162104 -0.206599 -0.537184 -0.593282 -0.882055 -0.801846 0.558494 -0.232251 ]
40 : [ -0.376977 -0.11152 0.153333 0.462918 0.567815 -0.176729 -1.22275 1.86671 1.26185 0.227769 0.283656 -0.53323 ]
41 : [ 0.754284 -1.52351 0.662888 0.250206 -0.739559 0.648318 0.219797 -0.198861 0.441571 -1.16581 0.320667 0.456908 ]
42 : [ 0.228312 -0.723905 0.907987 0.793778 1.09597 1.6502 0.360468 0.380713 -0.206799 0.68282 0.505348 1.81799 ]
43 : [ 0.139383 1.01034 -0.180669 -0.350497 0.501178 0.29702 -0.324153 1.25771 -0.344304 -0.593498 -0.577281 1.14486 ]
44 : [ 0.123575 0.211941 -2.42071 1.29023 1.05194 1.0417 0.340227 -0.272286 -0.208786 1.45861 -1.0835 -0.771181 ]
45 : [ -0.55666 0.70363 -0.344857 1.31318 0.95689 -0.930275 -1.21052 -0.0955883 0.479507 0.725539 -1.25156 0.383979 ]
46 : [ -0.841652 -1.58826 0.841269 -1.025 -0.405858 1.22656 -1.80676 1.04935 0.139614 1.28164 0.756368 0.0240327 ]
47 : [ 4.22569 -1.41152 -0.283548 -0.269684 0.0630054 -1.29865 0.0737603 0.58371 -0.528891 -0.365615 -0.20649 1.16943 ]
48 : [ 0.499494 1.20736 1.66915 1.19284 0.981478 0.685755 -0.825231 0.552494 1.34334 1.74239 0.972172 -0.854521 ]
49 : [ -0.83206 -0.530109 0.182881 -0.0800032 0.15333 0.525753 0.900487 0.458092 -1.16633 0.0928792 1.17659 0.161481 ]
50 : [ -0.430122 -0.921137 0.59674 1.71142 -0.930864 -0.845058 2.18662 1.88765 0.014029 1.15424 1.35346 0.17869 ]
51 : [ -0.639736 -1.18011 1.05854 1.34152 0.35239 -0.65673 0.535333 -0.355978 1.31802 -0.654088 0.984371 -1.27606 ]
52 : [ -0.130833 0.527143 -1.81979 1.80145 -0.242189 1.31624 0.533496 -0.111041 0.647772 -0.0580238 1.53101 -0.202946 ]
53 : [ -0.232772 -0.0159144 -1.53751 0.456859 -0.944997 -0.127001 1.1889 -0.564832 -1.31679 -0.59099 0.879078 -0.562085 ]] correlationFunction_=[EvaluationImplementation]
#!/usr/bin/env python
import openturns as ot
import persalys
myStudy = persalys.Study('myStudy')
persalys.Study.Add(myStudy)
t = persalys.Variable('t', 0, 'date')
meshModel = persalys.GridMeshModel([t], ot.Interval([1], [12]), [12])
collection = [[[21.7], [20.74], [20.52], [19.82], [20.19], [20.46], [21.92], [25.13], [25.44], [25.9], [25.29], [24.9]],
[[24.56], [24.04], [22.43], [21.52], [22.2], [22.9], [23.01], [24.47], [26.42], [26.51], [24.54], [23.69]],
[[22.35], [20.74], [19.95], [20.12], [20.45], [20.72], [22.53], [24.19], [26.33], [27.21], [26.85], [25.24]],
[[23.74], [22.45], [21.68], [21.49], [21.08], [22.07], [22.47], [23.06], [25.22], [25.83], [23.05], [21.79]],
[[21.02], [19.89], [19.78], [19.23], [19.2], [20.47], [21.25], [23.7], [24.64], [25.51], [24.34], [22.53]],
[[21.12], [20.56], [19.75], [19.47], [19.35], [20], [21.1], [23.67], [24.78], [25.76], [25.06], [23.3]],
[[22.29], [21.62], [20.66], [19.98], [19.84], [20.86], [21.6], [23.25], [26.32], [27.47], [26.86], [26.29]],
[[24.92], [23.82], [22.33], [21.88], [21.76], [22.4], [23.75], [24.96], [26.74], [27.07], [26.45], [25.08]],
[[23.04], [22.38], [20.56], [20.72], [20.96], [21.64], [22.47], [24.09], [25.68], [27.07], [25.89], [24.26]],
[[22.63], [21.45], [20.34], [20.41], [20.91], [21.99], [22.69], [24.57], [25.72], [26.15], [24.72], [23.76]],
[[22.15], [21.01], [20.37], [20.54], [20.22], [20.91], [22.74], [24.45], [26.55], [25.91], [24.97], [23.71]],
[[22.39], [20.55], [19.96], [19.75], [20], [21.07], [22.06], [24.11], [25.55], [24.63], [23.57], [23.48]],
[[21.85], [20.57], [20.37], [20.16], [19.92], [20.88], [21.83], [24.11], [25.36], [25.88], [24.54], [24.17]],
[[22.6], [22.01], [21.45], [21.15], [21], [21.92], [22.83], [24.17], [24.98], [25.04], [24.53], [21.96]],
[[21.27], [20.43], [19.2], [19.51], [19.96], [20.92], [21.85], [24.19], [25.95], [26.68], [27.1], [26.04]],
[[24.57], [23.34], [22.02], [21.38], [21.67], [22.3], [23.45], [25.27], [25.92], [25.41], [24.18], [23.07]],
[[21.84], [20.81], [20.27], [19.62], [20.64], [21.07], [22.32], [23.77], [25.64], [25.75], [25.23], [23.89]],
[[22.18], [20.99], [19.73], [19.18], [19.41], [20.21], [21.3], [23.17], [24.79], [25.17], [24.09], [22.54]],
[[21.6], [21.13], [20.73], [20.86], [21.16], [21.35], [22.99], [24.76], [25.55], [26.88], [26.42], [25.97]],
[[24.3], [22.24], [21.01], [20.68], [21.8], [22.33], [23.45], [24.92], [25.63], [25.57], [24.48], [23.13]],
[[21.33], [19.7], [19.3], [19.49], [20.29], [20.32], [21.79], [23.32], [24.66], [24.91], [25.1], [22.96]],
[[21.86], [21.26], [19.86], [19.79], [20.07], [21.13], [21.96], [24.62], [26.81], [27.31], [26.35], [25.51]],
[[24.86], [24.07], [23.27], [22.12], [22.56], [23.4], [24.69], [26.33], [26.5], [26.15], [24.52], [23.47]],
[[21.51], [20.75], [19.47], [19.24], [19.68], [20.71], [21.61], [23.32], [24.92], [26.04], [25.29], [24.42]],
[[22.68], [21.6], [20.47], [19.91], [20.02], [20.95], [21.41], [23.67], [24.9], [26.17], [25.44], [23.36]],
[[21.71], [20.98], [20.05], [18.8], [19.13], [19.61], [20.94], [23.73], [25.63], [26.21], [25.78], [25.33]],
[[24.4], [23.46], [22.19], [21.67], [21.52], [22], [23.69], [25.31], [25.97], [26.35], [25.25], [23.95]],
[[22.82], [21.75], [20.28], [19.85], [20.98], [21.65], [22.49], [24.33], [25.99], [25.54], [25.03], [23.47]],
[[22.09], [21.11], [20.03], [19.86], [20.35], [21.99], [23.3], [24.77], [25.7], [26.26], [25.39], [24.13]],
[[23.29], [22.18], [21.2], [21.3], [21.61], [22.2], [23.25], [24.77], [25.77], [26.61], [25.8], [24.54]],
[[23.27], [21.52], [20.77], [20.66], [20.36], [21.56], [22.54], [23.48], [25.21], [26.31], [25.03], [24.06]],
[[22.75], [21.38], [20.52], [19.93], [20.55], [21.41], [23], [24.29], [25.49], [25.21], [24.5], [23.97]],
[[22.89], [22.47], [21.75], [21.8], [22.94], [24.59], [26.13], [27.42], [28.09], [28.68], [28.56], [28.19]],
[[27.44], [25.95], [23.78], [22.24], [21.86], [21.9], [23.01], [24.18], [25.18], [26], [25.16], [23.23]],
[[21.96], [21.24], [20.17], [20.37], [20.52], [21.5], [22.58], [23.59], [24.87], [25.74], [24.25], [22.29]],
[[21.75], [20.44], [19.29], [19.44], [19.9], [20.69], [22.4], [24.61], [26.06], [25.91], [24.58], [23.38]],
[[21.98], [21.12], [20.97], [20.44], [21.07], [22.03], [23], [25.3], [27.14], [28.01], [27.17], [25.58]],
[[24.06], [22.78], [21.73], [21.45], [22.39], [22.63], [23.47], [24.64], [25.74], [25.78], [24.54], [23.6]],
[[21.27], [20.26], [19.12], [19.19], [19.5], [20.55], [21.8], [24.09], [26.26], [26.66], [25.63], [23.18]],
[[22], [21.12], [20.32], [19.87], [20.33], [21.31], [22.19], [24.02], [25.88], [26.16], [25.22], [24.05]],
[[22.68], [21], [20.25], [20.13], [20.28], [20.84], [22.45], [23.86], [25.97], [26.51], [24.99], [24.37]],
[[23.05], [22.05], [21.08], [20.75], [21.13], [22.18], [23.43], [24.83], [26.68], [27.76], [27.68], [26.31]],
[[23.82], [21.95], [20.55], [20.06], [20.82], [21.49], [22.48], [24.43], [26.49], [27.17], [26.44], [25.15]],
[[23.76], [22.06], [21.05], [20.83], [20.99], [21.64], [22.75], [24.32], [25.79], [25.43], [24.32], [23.22]],
[[22.43], [21.21], [19.7], [20.16], [21.53], [22.41], [23.61], [25.33], [26.43], [26.12], [24.47], [23.1]],
[[22.45], [21.23], [20.01], [20.17], [20.15], [21.2], [22.02], [23.84], [25.71], [26.09], [23.85], [22.89]],
[[21.56], [20.02], [19.53], [19.24], [19.95], [20.26], [21.61], [23.67], [25.74], [26.95], [26.64], [26.71]],
[[26.27], [25.59], [24.8], [24.4], [24.58], [25.63], [26.92], [28.22], [28.98], [29.15], [28.61], [27.69]],
[[25.18], [23.43], [21.77], [20.87], [21.16], [21.43], [22.56], [23.73], [25.64], [26.62], [24.3], [23.46]],
[[21.83], [20.44], [19.75], [19.23], [20.05], [20.51], [21.72], [23.86], [25.71], [26.19], [25.84], [24.1]],
[[22.25], [20.59], [20.1], [19.94], [20.37], [20.6], [22.22], [23.88], [25.91], [27.44], [26.69], [23.77]],
[[21.74], [20.88], [19.9], [19.39], [19.52], [20.49], [21.96], [23.64], [26.06], [27.53], [26.53], [24.8]],
[[22.67], [21.01], [19.94], [19.89], [21.16], [22.25], [23.44], [24.38], [25.81], [25.97], [24.44], [22.49]],
[[21.58], [20.75], [20.14], [20], [20.99], [21.92], [22.99], [24.6], [25.81], [25.94], [25.32], [23.05]]]
mesh = meshModel.getMesh()
pSample = ot.ProcessSample(mesh, collection)
myModel = persalys.DataFieldModel('myModel', meshModel, pSample)
myStudy.add(myModel)
myAnalysis = persalys.FieldKarhunenLoeveAnalysis('myAnalysis', myModel)
myAnalysis.setKarhunenLoeveThreshold(1e-05)
myAnalysis.setQuantileLevel(0.05)
myStudy.add(myAnalysis)
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