| n_samples | n_windows | disjoint_training_windows | result |
|---|---|---|---|
| 109 | 48 | 0 | 0.211053 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| n_windows | range | 25 | 250 | int | |
| disjoint_training… | choice | False, True |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 499 | 13 | 512 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,n_windows,disjoint_training_windows 0,0_0,COMPLETED,Sobol,0.441360340085021229938888609468,313,70,True 1,1_0,COMPLETED,Sobol,0.247811952988247097273699637299,119,66,False 2,2_0,COMPLETED,Sobol,0.441360340085021229938888609468,597,132,True 3,3_0,COMPLETED,Sobol,0.441360340085021229938888609468,370,179,True 4,4_0,COMPLETED,Sobol,0.344336084021005239819146481750,332,143,False 5,5_0,COMPLETED,Sobol,0.441360340085021229938888609468,671,130,True 6,6_0,COMPLETED,Sobol,0.333333333333333370340767487505,498,193,False 7,7_0,COMPLETED,Sobol,0.332333083270817675192176920973,316,152,False 8,8_0,COMPLETED,Sobol,0.346086521630407650818028741924,540,208,False 9,9_0,COMPLETED,Sobol,0.317079269817454378888044175255,395,65,False 10,10_0,COMPLETED,Sobol,0.335583895973993517891642568429,433,25,False 11,11_0,COMPLETED,Sobol,0.441360340085021229938888609468,257,46,True 12,12_0,COMPLETED,Sobol,0.393598399599899950729309239250,789,27,False 13,13_0,COMPLETED,Sobol,0.322330582645661389840086030745,367,135,False 14,14_0,COMPLETED,Sobol,0.441360340085021229938888609468,746,143,True 15,15_0,COMPLETED,Sobol,0.441360340085021229938888609468,997,196,True 16,16_0,COMPLETED,Sobol,0.441360340085021229938888609468,429,134,True 17,17_0,COMPLETED,Sobol,0.320580145036259089863506233087,434,245,False 18,18_0,COMPLETED,Sobol,0.391347836959239803178434158326,935,225,False 19,19_0,COMPLETED,Sobol,0.374593648412103075173718025326,863,233,False 20,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False 21,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 22,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 23,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False 24,24_0,COMPLETED,BoTorch,0.441360340085021229938888609468,974,107,True 25,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 26,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 27,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 28,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 29,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 30,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 31,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 32,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False 33,33_0,COMPLETED,BoTorch,0.441360340085021229938888609468,956,246,True 34,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 35,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 36,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False 37,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False 38,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False 39,39_0,COMPLETED,BoTorch,0.441360340085021229938888609468,1000,250,True 40,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 41,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False 42,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 43,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 44,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 45,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 46,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 47,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 48,48_0,COMPLETED,BoTorch,0.377844461115278806850881210266,804,121,False 49,49_0,COMPLETED,BoTorch,0.326081520380094969091544498951,465,108,False 50,50_0,COMPLETED,BoTorch,0.302825706426606666710199533554,100,180,False 51,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 52,52_0,COMPLETED,BoTorch,0.272318079519879963079631579603,100,99,False 53,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 54,54_0,COMPLETED,BoTorch,0.373093273318329532450832175527,840,133,False 55,55_0,COMPLETED,BoTorch,0.389347336834208523903555487777,755,115,False 56,56_0,COMPLETED,BoTorch,0.301825456364090971561608967022,100,183,False 57,57_0,COMPLETED,BoTorch,0.382345586396599101952631372114,781,154,False 58,58_0,COMPLETED,BoTorch,0.280070017504376100880847388908,104,107,False 59,59_0,COMPLETED,BoTorch,0.341335333833458376417979707185,483,109,False 60,60_0,COMPLETED,BoTorch,0.384096024006001512951513632288,803,129,False 61,61_0,COMPLETED,BoTorch,0.355088772193048241021529065620,626,116,False 62,62_0,COMPLETED,BoTorch,0.441360340085021229938888609468,1000,25,True 63,63_0,COMPLETED,BoTorch,0.376844211052763222724593106250,890,140,False 64,64_0,COMPLETED,BoTorch,0.346086521630407650818028741924,402,138,False 65,65_0,COMPLETED,BoTorch,0.302825706426606666710199533554,163,112,False 66,66_0,COMPLETED,BoTorch,0.374093523380845227599422742060,735,135,False 67,67_0,COMPLETED,BoTorch,0.348087021755438819070604949957,548,112,False 68,68_0,COMPLETED,BoTorch,0.329832458114528659365305429674,285,211,False 69,69_0,COMPLETED,BoTorch,0.359839959989997515421578100359,200,194,False 70,70_0,COMPLETED,BoTorch,0.379594898724681217849763470440,1000,25,False 71,71_0,COMPLETED,BoTorch,0.335333833458364538593343695538,278,210,False 72,72_0,COMPLETED,BoTorch,0.383345836459114797101221938647,1000,77,False 73,73_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,25,True 74,74_0,COMPLETED,BoTorch,0.380095023755938954401756291190,1000,56,False 75,75_0,COMPLETED,BoTorch,0.347336834208552103220313256315,278,228,False 76,76_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,250,True 77,77_0,COMPLETED,BoTorch,0.390597649412353087328142464685,784,29,False 78,78_0,COMPLETED,BoTorch,0.441360340085021229938888609468,171,230,True 79,79_0,COMPLETED,BoTorch,0.441360340085021229938888609468,190,218,True 80,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False 81,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 82,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False 83,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False 84,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 85,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False 86,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False 87,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 88,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 89,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 90,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 91,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 92,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 93,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 94,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 95,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 96,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 97,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 98,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 99,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 100,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False 101,101_0,COMPLETED,BoTorch,0.312828207051762952062290423783,304,93,False 102,102_0,COMPLETED,BoTorch,0.232808202050512669245563301956,124,25,False 103,103_0,COMPLETED,BoTorch,0.312828207051762952062290423783,304,96,False 104,104_0,COMPLETED,BoTorch,0.441360340085021229938888609468,206,25,True 105,105_0,COMPLETED,BoTorch,0.311577894473618388637703446875,302,94,False 106,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 107,107_0,COMPLETED,BoTorch,0.313578394598649667912582117424,304,97,False 108,108_0,COMPLETED,BoTorch,0.441360340085021229938888609468,553,250,True 109,109_0,COMPLETED,BoTorch,0.441360340085021229938888609468,267,250,True 110,110_0,COMPLETED,BoTorch,0.313328332083020799636585707049,307,96,False 111,111_0,COMPLETED,BoTorch,0.314078519629907515486877400690,312,102,False 112,112_0,COMPLETED,BoTorch,0.441360340085021229938888609468,418,250,True 113,113_0,COMPLETED,BoTorch,0.312078019504876236211998730141,298,92,False 114,114_0,COMPLETED,BoTorch,0.441360340085021229938888609468,704,58,True 115,115_0,COMPLETED,BoTorch,0.441360340085021229938888609468,305,70,True 116,116_0,COMPLETED,BoTorch,0.441360340085021229938888609468,618,250,True 117,117_0,COMPLETED,BoTorch,0.441360340085021229938888609468,489,66,True 118,118_0,COMPLETED,BoTorch,0.441360340085021229938888609468,812,74,True 119,119_0,COMPLETED,BoTorch,0.441360340085021229938888609468,670,229,True 120,120_0,COMPLETED,BoTorch,0.441360340085021229938888609468,559,250,True 121,121_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,119,True 122,122_0,COMPLETED,BoTorch,0.441360340085021229938888609468,879,51,True 123,123_0,COMPLETED,BoTorch,0.441360340085021229938888609468,476,73,True 124,124_0,COMPLETED,BoTorch,0.441360340085021229938888609468,853,63,True 125,125_0,COMPLETED,BoTorch,0.441360340085021229938888609468,632,69,True 126,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 127,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 128,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 129,129_0,COMPLETED,BoTorch,0.348587146786696666644900233223,607,250,False 130,130_0,COMPLETED,BoTorch,0.340335083770942681269389140652,457,46,False 131,131_0,COMPLETED,BoTorch,0.380595148787196801976051574457,943,224,False 132,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 133,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 134,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 135,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 136,131_0,COMPLETED,BoTorch,0.380595148787196801976051574457,943,224,False 137,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 138,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 139,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 140,140_0,COMPLETED,BoTorch,0.441360340085021229938888609468,182,79,True 141,141_0,COMPLETED,BoTorch,0.334333583395848954467055591522,520,65,False 142,142_0,COMPLETED,BoTorch,0.383595898974743665377218349022,871,31,False 143,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 144,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 145,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 146,146_0,COMPLETED,BoTorch,0.325831457864466100815548088576,264,91,False 147,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 148,148_0,COMPLETED,BoTorch,0.314078519629907515486877400690,331,100,False 149,149_0,COMPLETED,BoTorch,0.354838709677419372745532655244,653,120,False 150,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 151,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False 152,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 153,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 154,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 155,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 156,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 157,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 158,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 159,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 160,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 161,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 162,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 163,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 164,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 165,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 166,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 167,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False 168,168_0,COMPLETED,BoTorch,0.220555138784696125320294868288,100,54,False 169,169_0,COMPLETED,BoTorch,0.399599899974993788553945250897,1000,202,False 170,170_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,55,False 171,171_0,COMPLETED,BoTorch,0.337584396099024797166521238978,416,176,False 172,172_0,COMPLETED,BoTorch,0.329832458114528659365305429674,515,89,False 173,173_0,COMPLETED,BoTorch,0.287571892973243259383764325321,100,127,False 174,174_0,COMPLETED,BoTorch,0.341335333833458376417979707185,590,90,False 175,175_0,COMPLETED,BoTorch,0.300575143785946519159324452630,100,162,False 176,176_0,COMPLETED,BoTorch,0.391347836959239803178434158326,1000,250,False 177,177_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,157,True 178,178_0,COMPLETED,BoTorch,0.327831957989497380090426759125,437,177,False 179,179_0,COMPLETED,BoTorch,0.400350087521880504404236944538,1000,204,False 180,180_0,COMPLETED,BoTorch,0.298074518629657392310150498815,100,157,False 181,181_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,53,False 182,182_0,COMPLETED,BoTorch,0.332333083270817675192176920973,511,89,False 183,181_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,53,False 184,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 185,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 186,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 187,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 188,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 189,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 190,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 191,191_0,COMPLETED,BoTorch,0.237809452363090811921608747070,126,47,False 192,192_0,COMPLETED,BoTorch,0.326081520380094969091544498951,402,67,False 193,193_0,COMPLETED,BoTorch,0.326081520380094969091544498951,387,96,False 194,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False 195,195_0,COMPLETED,BoTorch,0.299574893723430824010733886098,170,116,False 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418,418_0,COMPLETED,BoTorch,0.441360340085021229938888609468,901,166,True 419,419_0,COMPLETED,BoTorch,0.386596649162290528778385123587,957,204,False 420,420_0,COMPLETED,BoTorch,0.441360340085021229938888609468,873,167,True 421,73_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,25,True 422,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False 423,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False 424,424_0,COMPLETED,BoTorch,0.320080020005001242289210949821,230,230,False 425,425_0,COMPLETED,BoTorch,0.393098274568642214177316418500,987,137,False 426,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False 427,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False 428,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False 429,414_0,COMPLETED,BoTorch,0.217304326081520393643131683348,100,36,False 430,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False 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444,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False 445,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False 446,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False 447,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 448,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 449,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 450,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 451,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 452,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 453,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 454,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 455,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 456,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 457,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 458,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 459,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 460,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 461,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 462,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 463,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False 464,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 465,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 466,466_0,COMPLETED,BoTorch,0.335833958489622386167638978804,357,36,False 467,467_0,COMPLETED,BoTorch,0.265066266566641672852711053565,185,40,False 468,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 469,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 470,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 471,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 472,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 473,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 474,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 475,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 476,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 477,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 478,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 479,479_0,COMPLETED,BoTorch,0.441360340085021229938888609468,823,98,True 480,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 481,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 482,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 483,483_0,COMPLETED,BoTorch,0.441360340085021229938888609468,673,79,True 484,484_0,COMPLETED,BoTorch,0.310827706926731672787411753234,181,133,False 485,485_0,COMPLETED,BoTorch,0.372843210802700664174835765152,743,77,False 486,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 487,487_0,COMPLETED,BoTorch,0.271567891972993247229339885962,190,75,False 488,488_0,COMPLETED,BoTorch,0.328082020505126248366423169500,381,117,False 489,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 490,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 491,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 492,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 493,493_0,COMPLETED,BoTorch,0.441360340085021229938888609468,380,222,True 494,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False 495,495_0,COMPLETED,BoTorch,0.441360340085021229938888609468,311,27,True 496,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False 497,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False 498,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False 499,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False 500,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False 501,217_0,RUNNING,BoTorch,,100,45,False 502,217_0,RUNNING,BoTorch,,100,45,False 503,503_0,RUNNING,BoTorch,,358,31,False 504,200_0,RUNNING,BoTorch,,100,44,False 505,217_0,RUNNING,BoTorch,,100,45,False 506,200_0,RUNNING,BoTorch,,100,44,False 507,217_0,RUNNING,BoTorch,,100,45,False 508,217_0,RUNNING,BoTorch,,100,45,False 509,200_0,RUNNING,BoTorch,,100,44,False 510,217_0,RUNNING,BoTorch,,100,45,False 511,217_0,RUNNING,BoTorch,,100,45,False
start_time,end_time,run_time,program_string,n_samples,n_windows,disjoint_training_windows,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727565068,1727565098,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 119 n_windows 66 disjoint_training_windows False,119,66,False,0.2478119529882471,0,None,i7186,27,0.01791356930141626 1727565068,1727565106,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 332 n_windows 143 disjoint_training_windows False,332,143,False,0.34433608402100524,0,None,i7186,34,0.04251062765691423 1727565057,1727565108,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 313 n_windows 70 disjoint_training_windows True,313,70,True,0.44136034008502123,0,None,i7186,47,0 1727565068,1727565117,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 597 n_windows 132 disjoint_training_windows True,597,132,True,0.44136034008502123,0,None,i7186,46,0 1727565068,1727565118,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 370 n_windows 179 disjoint_training_windows True,370,179,True,0.44136034008502123,0,None,i7186,47,0 1727565068,1727565118,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 671 n_windows 130 disjoint_training_windows True,671,130,True,0.44136034008502123,0,None,i7186,47,0 1727565087,1727565123,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 395 n_windows 65 disjoint_training_windows False,395,65,False,0.3170792698174544,0,None,i7186,32,0.03609235642243894 1727565087,1727565123,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 316 n_windows 152 disjoint_training_windows False,316,152,False,0.3323330832708177,0,None,i7186,32,0.03439748826095413 1727565087,1727565124,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 498 n_windows 193 disjoint_training_windows False,498,193,False,0.33333333333333337,0,None,i7186,34,0.0440824491837245 1727565087,1727565125,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 540 n_windows 208 disjoint_training_windows False,540,208,False,0.34608652163040765,0,None,i7186,35,0.04226056514128531 1727565109,1727565156,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 997 n_windows 196 disjoint_training_windows True,997,196,True,0.44136034008502123,0,None,i7186,44,0 1727565105,1727565196,91,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 367 n_windows 135 disjoint_training_windows False,367,135,False,0.3223305826456614,0,None,i7181,29,0.035508877219304825 1727565107,1727565196,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 434 n_windows 245 disjoint_training_windows False,434,245,False,0.3205801450362591,0,None,i7181,29,0.04016629157289322 1727565105,1727565196,91,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 433 n_windows 25 disjoint_training_windows False,433,25,False,0.3355838959739935,0,None,i7181,29,0.038290822705676415 1727565105,1727565199,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 789 n_windows 27 disjoint_training_windows False,789,27,False,0.39359839959989995,0,None,i7181,32,0.06207801950487622 1727565107,1727565200,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 863 n_windows 233 disjoint_training_windows False,863,233,False,0.3745936484121031,0,None,i7181,33,0.06682920730182544 1727565107,1727565201,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 935 n_windows 225 disjoint_training_windows False,935,225,False,0.3913478369592398,0,None,i7181,34,0.06264066016504126 1727565107,1727565207,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 429 n_windows 134 disjoint_training_windows True,429,134,True,0.44136034008502123,0,None,i7181,40,0 1727565105,1727565207,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 257 n_windows 46 disjoint_training_windows True,257,46,True,0.44136034008502123,0,None,i7181,40,0 1727565105,1727565207,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 746 n_windows 143 disjoint_training_windows True,746,143,True,0.44136034008502123,0,None,i7181,40,0 1727565268,1727565296,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,24,0.011933538940290627 1727565268,1727565297,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,25,0.011857130949404016 1727565268,1727565297,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,26,0.011933538940290627 1727565288,1727565317,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,25,0.011933538940290627 1727565288,1727565317,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,26,0.011933538940290627 1727565288,1727565317,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,26,0.011933538940290627 1727565288,1727565317,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,25,0.011933538940290627 1727565297,1727565321,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7181,20,0.011933538940290627 1727565298,1727565323,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7181,21,0.011905754216331861 1727565298,1727565327,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,25,0.011933538940290627 1727565288,1727565330,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows False,100,250,False,0.37209302325581395,0,None,i7186,38,0.05396349087271818 1727565308,1727565339,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,27,0.011933538940290627 1727565288,1727565339,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 974 n_windows 107 disjoint_training_windows True,974,107,True,0.44136034008502123,0,None,i7186,47,0 1727565308,1727565350,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows False,100,250,False,0.37209302325581395,0,None,i7186,38,0.05396349087271818 1727565328,1727565356,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 51 disjoint_training_windows False,100,51,False,0.21230307576894225,0,None,i7186,25,0.011933538940290627 1727565328,1727565357,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,25,0.011905754216331861 1727565308,1727565357,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 956 n_windows 246 disjoint_training_windows True,956,246,True,0.44136034008502123,0,None,i7186,46,0 1727565328,1727565368,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows False,100,250,False,0.37209302325581395,0,None,i7186,36,0.05396349087271818 1727565329,1727565369,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows False,100,250,False,0.37209302325581395,0,None,i7186,36,0.05396349087271818 1727565327,1727565376,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 250 disjoint_training_windows True,1000,250,True,0.44136034008502123,0,None,i7181,45,0 1727565358,1727565386,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,25,0.01139474057703615 1727565358,1727565387,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,25,0.01139474057703615 1727565368,1727565396,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727565368,1727565396,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727565358,1727565399,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows False,100,250,False,0.37209302325581395,0,None,i7186,37,0.05396349087271818 1727565388,1727565415,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727565388,1727565416,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727565388,1727565416,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727565448,1727565490,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 804 n_windows 121 disjoint_training_windows False,804,121,False,0.3778444611152788,0,None,i7186,38,0.06601650412603151 1727565468,1727565497,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727565469,1727565498,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727565468,1727565500,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 99 disjoint_training_windows False,100,99,False,0.27231807951987996,0,None,i7186,28,0.01759963800473928 1727565468,1727565502,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 180 disjoint_training_windows False,100,180,False,0.30282570642660667,0,None,i7186,30,0.021192798199549886 1727565468,1727565504,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 465 n_windows 108 disjoint_training_windows False,465,108,False,0.32608152038009497,0,None,i7186,32,0.039478619654913734 1727565479,1727565514,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 183 disjoint_training_windows False,100,183,False,0.30182545636409097,0,None,i7186,31,0.028340418437942824 1727565479,1727565519,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 755 n_windows 115 disjoint_training_windows False,755,115,False,0.3893473368342085,0,None,i7186,36,0.05051262815703926 1727565488,1727565519,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 104 n_windows 107 disjoint_training_windows False,104,107,False,0.2800700175043761,0,None,i7181,27,0.018092023005751436 1727565479,1727565521,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 840 n_windows 133 disjoint_training_windows False,840,133,False,0.37309327331832953,0,None,i7186,38,0.06720430107526883 1727565488,1727565529,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 781 n_windows 154 disjoint_training_windows False,781,154,False,0.3823455863965991,0,None,i7186,37,0.06489122280570143 1727565508,1727565544,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 483 n_windows 109 disjoint_training_windows False,483,109,False,0.3413353338334584,0,None,i7186,32,0.04293930625513521 1727565568,1727565606,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 626 n_windows 116 disjoint_training_windows False,626,116,False,0.35508877219304824,0,None,i7186,34,0.047803617571059435 1727565568,1727565609,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 890 n_windows 140 disjoint_training_windows False,890,140,False,0.3768442110527632,0,None,i7186,37,0.0662665666416604 1727565568,1727565611,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 803 n_windows 129 disjoint_training_windows False,803,129,False,0.3840960240060015,0,None,i7186,39,0.06445361340335083 1727565568,1727565617,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 25 disjoint_training_windows True,1000,25,True,0.44136034008502123,0,None,i7186,45,0 1727565588,1727565621,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 163 n_windows 112 disjoint_training_windows False,163,112,False,0.30282570642660667,0,None,i7186,29,0.021192798199549886 1727565588,1727565626,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 402 n_windows 138 disjoint_training_windows False,402,138,False,0.34608652163040765,0,None,i7186,34,0.03697799449862465 1727565589,1727565626,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 548 n_windows 112 disjoint_training_windows False,548,112,False,0.3480870217554388,0,None,i7186,33,0.04897057597732767 1727565588,1727565628,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 735 n_windows 135 disjoint_training_windows False,735,135,False,0.3740935233808452,0,None,i7186,36,0.053563390847711924 1727565688,1727565725,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 278 n_windows 210 disjoint_training_windows False,278,210,False,0.33533383345836454,0,None,i7186,33,0.03832208052013004 1727565688,1727565725,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 285 n_windows 211 disjoint_training_windows False,285,211,False,0.32983245811452866,0,None,i7186,33,0.0346753355005418 1727565688,1727565728,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 200 n_windows 194 disjoint_training_windows False,200,194,False,0.3598399599899975,0,None,i7186,36,0.047011752938234556 1727565688,1727565731,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 25 disjoint_training_windows False,1000,25,False,0.3795948987246812,0,None,i7186,39,0.08743852629824121 1727565708,1727565745,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 278 n_windows 228 disjoint_training_windows False,278,228,False,0.3473368342085521,0,None,i7186,33,0.03682170542635659 1727565708,1727565751,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 77 disjoint_training_windows False,1000,77,False,0.3833458364591148,0,None,i7186,38,0.08618821372009668 1727565708,1727565751,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 56 disjoint_training_windows False,1000,56,False,0.38009502375593895,0,None,i7186,39,0.08727181795448863 1727565709,1727565759,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows True,100,25,True,0.44136034008502123,0,None,i7186,47,0 1727565721,1727565760,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 784 n_windows 29 disjoint_training_windows False,784,29,False,0.3905976494123531,0,None,i7186,36,0.05026256564141035 1727565721,1727565769,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 171 n_windows 230 disjoint_training_windows True,171,230,True,0.44136034008502123,0,None,i7181,45,0 1727565721,1727565770,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 250 disjoint_training_windows True,100,250,True,0.44136034008502123,0,None,i7186,45,0 1727565728,1727565775,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 190 n_windows 218 disjoint_training_windows True,190,218,True,0.44136034008502123,0,None,i7181,43,0 1727565768,1727565797,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,25,0.011718207329610179 1727565782,1727565810,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565788,1727565816,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,24,0.011718207329610179 1727565788,1727565816,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,25,0.011718207329610179 1727565808,1727565836,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565809,1727565836,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,24,0.011718207329610179 1727565809,1727565837,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565809,1727565838,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,24,0.011718207329610179 1727565868,1727565896,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565868,1727565897,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565872,1727565900,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565888,1727565917,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565888,1727565917,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565889,1727565919,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,27,0.011850184768414325 1727565903,1727565931,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565903,1727565931,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565903,1727565932,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565908,1727565937,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727565929,1727565956,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565929,1727565956,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565929,1727565958,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727565993,1727566027,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 304 n_windows 93 disjoint_training_windows False,304,93,False,0.31282820705176295,0,None,i7186,30,0.029916570051603808 1727566009,1727566037,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 124 n_windows 25 disjoint_training_windows False,124,25,False,0.23280820205051267,0,None,i7186,25,0.014610795556031864 1727566009,1727566042,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 304 n_windows 96 disjoint_training_windows False,304,96,False,0.31282820705176295,0,None,i7186,29,0.029916570051603808 1727566023,1727566056,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 302 n_windows 94 disjoint_training_windows False,302,94,False,0.3115778944736184,0,None,i7186,30,0.030030234831435132 1727566029,1727566057,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,25,0.01139474057703615 1727566023,1727566073,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 206 n_windows 25 disjoint_training_windows True,206,25,True,0.44136034008502123,0,None,i7186,46,0 1727566049,1727566083,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 304 n_windows 97 disjoint_training_windows False,304,97,False,0.31357839459864967,0,None,i7186,31,0.029848371183705015 1727566054,1727566087,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 307 n_windows 96 disjoint_training_windows False,307,96,False,0.3133283320830208,0,None,i7186,30,0.0328582145536384 1727566049,1727566098,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 553 n_windows 250 disjoint_training_windows True,553,250,True,0.44136034008502123,0,None,i7186,45,0 1727566049,1727566099,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 267 n_windows 250 disjoint_training_windows True,267,250,True,0.44136034008502123,0,None,i7186,46,0 1727566069,1727566103,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 312 n_windows 102 disjoint_training_windows False,312,102,False,0.3140785196299075,0,None,i7186,31,0.032783195798949734 1727566069,1727566117,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 418 n_windows 250 disjoint_training_windows True,418,250,True,0.44136034008502123,0,None,i7186,44,0 1727566084,1727566117,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 298 n_windows 92 disjoint_training_windows False,298,92,False,0.31207801950487624,0,None,i7186,29,0.027486038176210717 1727566170,1727566220,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 704 n_windows 58 disjoint_training_windows True,704,58,True,0.44136034008502123,0,None,i7186,46,0 1727566190,1727566239,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 305 n_windows 70 disjoint_training_windows True,305,70,True,0.44136034008502123,0,None,i7186,45,0 1727566190,1727566240,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 618 n_windows 250 disjoint_training_windows True,618,250,True,0.44136034008502123,0,None,i7186,47,0 1727566205,1727566254,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 489 n_windows 66 disjoint_training_windows True,489,66,True,0.44136034008502123,0,None,i7186,46,0 1727566205,1727566254,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 812 n_windows 74 disjoint_training_windows True,812,74,True,0.44136034008502123,0,None,i7186,47,0 1727566211,1727566261,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 670 n_windows 229 disjoint_training_windows True,670,229,True,0.44136034008502123,0,None,i7186,46,0 1727566230,1727566279,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 119 disjoint_training_windows True,100,119,True,0.44136034008502123,0,None,i7186,45,0 1727566230,1727566279,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 879 n_windows 51 disjoint_training_windows True,879,51,True,0.44136034008502123,0,None,i7186,45,0 1727566230,1727566280,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 559 n_windows 250 disjoint_training_windows True,559,250,True,0.44136034008502123,0,None,i7186,46,0 1727566235,1727566284,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 476 n_windows 73 disjoint_training_windows True,476,73,True,0.44136034008502123,0,None,i7186,45,0 1727566250,1727566298,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 853 n_windows 63 disjoint_training_windows True,853,63,True,0.44136034008502123,0,None,i7186,44,0 1727566250,1727566299,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 632 n_windows 69 disjoint_training_windows True,632,69,True,0.44136034008502123,0,None,i7186,45,0 1727566325,1727566354,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566350,1727566378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566350,1727566378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727566350,1727566388,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 607 n_windows 250 disjoint_training_windows False,607,250,False,0.34858714678669667,0,None,i7186,34,0.04888722180545136 1727566356,1727566391,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 457 n_windows 46 disjoint_training_windows False,457,46,False,0.3403350837709427,0,None,i7186,32,0.03769692423105777 1727566370,1727566399,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727566370,1727566413,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 943 n_windows 224 disjoint_training_windows False,943,224,False,0.3805951487871968,0,None,i7186,39,0.08710510961073602 1727566386,1727566414,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566386,1727566414,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727566390,1727566418,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566410,1727566451,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 943 n_windows 224 disjoint_training_windows False,943,224,False,0.3805951487871968,0,None,i7186,37,0.08710510961073602 1727566491,1727566519,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566491,1727566519,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,25,0.011766830596538025 1727566506,1727566534,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566510,1727566547,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 520 n_windows 65 disjoint_training_windows False,520,65,False,0.33433358339584895,0,None,i7186,33,0.04393955631765084 1727566506,1727566555,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 182 n_windows 79 disjoint_training_windows True,182,79,True,0.44136034008502123,0,None,i7186,45,0 1727566531,1727566559,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727566536,1727566565,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,25,0.011919646578311243 1727566531,1727566572,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 871 n_windows 31 disjoint_training_windows False,871,31,False,0.38359589897474367,0,None,i7186,38,0.0645786446611653 1727566551,1727566579,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566551,1727566584,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 264 n_windows 91 disjoint_training_windows False,264,91,False,0.3258314578644661,0,None,i7186,30,0.02873445634135807 1727566566,1727566594,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566566,1727566600,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 331 n_windows 100 disjoint_training_windows False,331,100,False,0.3140785196299075,0,None,i7186,30,0.032783195798949734 1727566591,1727566618,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,24,0.011919646578311243 1727566591,1727566620,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 49 disjoint_training_windows False,100,49,False,0.2128032008002001,0,None,i7186,26,0.011919646578311243 1727566591,1727566630,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 653 n_windows 120 disjoint_training_windows False,653,120,False,0.3548387096774194,0,None,i7186,35,0.057414353588397096 1727566691,1727566719,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,25,0.011905754216331861 1727566711,1727566738,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566711,1727566739,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566717,1727566744,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566731,1727566759,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,25,0.011905754216331861 1727566747,1727566775,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566747,1727566775,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566747,1727566775,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566771,1727566799,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566771,1727566799,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566777,1727566805,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566791,1727566818,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566807,1727566836,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,25,0.011905754216331861 1727566807,1727566836,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,25,0.011905754216331861 1727566811,1727566838,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566831,1727566858,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows False,100,50,False,0.21330332583145784,0,None,i7186,24,0.011905754216331861 1727566946,1727566975,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 54 disjoint_training_windows False,100,54,False,0.22055513878469613,0,None,i7186,25,0.012392804083373786 1727566957,1727566986,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 55 disjoint_training_windows False,100,55,False,0.22405601400350084,0,None,i7186,25,0.012289837165173647 1727566946,1727566987,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 202 disjoint_training_windows False,1000,202,False,0.3995998999749938,0,None,i7186,37,0.08077019254813701 1727566966,1727567002,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 515 n_windows 89 disjoint_training_windows False,515,89,False,0.32983245811452866,0,None,i7186,32,0.04458257421498231 1727566966,1727567002,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 416 n_windows 176 disjoint_training_windows False,416,176,False,0.3375843960990248,0,None,i7186,32,0.04347515450291144 1727566986,1727567019,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 127 disjoint_training_windows False,100,127,False,0.28757189297324326,0,None,i7186,29,0.019685476924786754 1727566986,1727567023,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 590 n_windows 90 disjoint_training_windows False,590,90,False,0.3413353338334584,0,None,i7186,33,0.050095857297657746 1727567006,1727567036,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 162 disjoint_training_windows False,100,162,False,0.3005751437859465,0,None,i7181,27,0.02007854904902696 1727567006,1727567044,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 250 disjoint_training_windows False,1000,250,False,0.3913478369592398,0,None,i7181,35,0.08352088022005501 1727567026,1727567063,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 437 n_windows 177 disjoint_training_windows False,437,177,False,0.3278319579894974,0,None,i7186,33,0.044868359947129635 1727567018,1727567065,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 157 disjoint_training_windows True,100,157,True,0.44136034008502123,0,None,i7186,44,0 1727567046,1727567078,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 157 disjoint_training_windows False,100,157,False,0.2980745186296574,0,None,i7186,29,0.020225644646455734 1727567046,1727567088,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 204 disjoint_training_windows False,1000,204,False,0.4003500875218805,0,None,i7186,38,0.08052013003250812 1727567066,1727567094,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 53 disjoint_training_windows False,100,53,False,0.21930482620655167,0,None,i7186,25,0.012074447183224377 1727567066,1727567102,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 511 n_windows 89 disjoint_training_windows False,511,89,False,0.3323330832708177,0,None,i7186,32,0.044225342049798166 1727567079,1727567107,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 53 disjoint_training_windows False,100,53,False,0.21930482620655167,0,None,i7186,24,0.012074447183224377 1727567186,1727567214,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727567199,1727567227,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727567206,1727567234,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727567226,1727567254,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727567226,1727567254,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727567226,1727567256,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,26,0.011676530243672028 1727567246,1727567274,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727567246,1727567275,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 126 n_windows 47 disjoint_training_windows False,126,47,False,0.2378094523630908,0,None,i7186,25,0.014966704639122742 1727567260,1727567294,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 402 n_windows 67 disjoint_training_windows False,402,67,False,0.32608152038009497,0,None,i7186,31,0.03509210635992332 1727567266,1727567302,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 387 n_windows 96 disjoint_training_windows False,387,96,False,0.32608152038009497,0,None,i7186,32,0.03509210635992332 1727567286,1727567313,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,24,0.01139474057703615 1727567290,1727567322,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 170 n_windows 116 disjoint_training_windows False,170,116,False,0.2995748937234308,0,None,i7186,28,0.0228223722597316 1727567306,1727567335,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows False,100,25,False,0.22030507626906726,0,None,i7186,25,0.01139474057703615 1727567320,1727567350,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 123 n_windows 71 disjoint_training_windows False,123,71,False,0.26356589147286824,0,None,i7186,26,0.01801640886412079 1727567320,1727567354,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 402 n_windows 67 disjoint_training_windows False,402,67,False,0.32608152038009497,0,None,i7186,31,0.03509210635992332 1727567455,1727567483,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567471,1727567500,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567495,1727567523,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567495,1727567524,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,25,0.011857130949404016 1727567501,1727567529,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567531,1727567559,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567532,1727567560,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567515,1727567563,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 594 n_windows 31 disjoint_training_windows True,594,31,True,0.44136034008502123,0,None,i7186,45,0 1727567555,1727567583,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567562,1727567590,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567555,1727567591,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 533 n_windows 87 disjoint_training_windows False,533,87,False,0.3355838959739935,0,None,i7186,33,0.04376094023505876 1727567575,1727567612,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 358 n_windows 218 disjoint_training_windows False,358,218,False,0.3300825206301575,0,None,i7186,33,0.038978494623655914 1727567592,1727567619,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567595,1727567623,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567615,1727567643,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727567615,1727567655,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 798 n_windows 33 disjoint_training_windows False,798,33,False,0.38834708677169294,0,None,i7186,36,0.06339084771192798 1727567635,1727567663,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567786,1727567814,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567803,1727567831,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567803,1727567841,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 675 n_windows 43 disjoint_training_windows False,675,43,False,0.3563390847711928,0,None,i7186,35,0.05711427856964241 1727567826,1727567854,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567826,1727567855,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567833,1727567861,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567846,1727567873,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567863,1727567892,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567866,1727567899,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 196 n_windows 98 disjoint_training_windows False,196,98,False,0.3108277069267317,0,None,i7186,29,0.027590230891056097 1727567886,1727567914,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567894,1727567921,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567906,1727567935,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727567924,1727567951,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567926,1727567954,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567954,1727567981,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567966,1727567993,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727567946,1727567994,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 583 n_windows 187 disjoint_training_windows True,583,187,True,0.44136034008502123,0,None,i7186,44,0 1727568126,1727568154,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568146,1727568174,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568165,1727568205,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 915 n_windows 159 disjoint_training_windows False,915,159,False,0.37759439859964994,0,None,i7186,37,0.06607901975493873 1727568165,1727568214,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 805 n_windows 186 disjoint_training_windows True,805,186,True,0.44136034008502123,0,None,i7186,46,0 1727568187,1727568215,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568187,1727568215,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568195,1727568223,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568207,1727568238,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 184 n_windows 105 disjoint_training_windows False,184,105,False,0.2808202050512628,0,None,i7186,28,0.024072684837876134 1727568225,1727568253,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568246,1727568274,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568227,1727568275,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 50 disjoint_training_windows True,100,50,True,0.44136034008502123,0,None,i7186,45,0 1727568267,1727568295,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727568285,1727568312,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568285,1727568314,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727568307,1727568334,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568307,1727568335,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727568326,1727568362,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 529 n_windows 91 disjoint_training_windows False,529,91,False,0.3328332083020755,0,None,i7186,32,0.04415389561676133 1727568496,1727568524,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568527,1727568554,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568527,1727568554,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568547,1727568576,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727568547,1727568581,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 347 n_windows 25 disjoint_training_windows False,347,25,False,0.32183045761440365,0,None,i7186,31,0.03200800200050012 1727568557,1727568584,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568567,1727568602,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 476 n_windows 121 disjoint_training_windows False,476,121,False,0.3368342085521381,0,None,i7186,32,0.04358232415246668 1727568587,1727568615,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568607,1727568639,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 367 n_windows 28 disjoint_training_windows False,367,28,False,0.3170792698174544,0,None,i7181,29,0.032483120780195045 1727568617,1727568644,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568627,1727568665,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 339 n_windows 199 disjoint_training_windows False,339,199,False,0.3418354588647162,0,None,i7186,34,0.04286785982209838 1727568647,1727568675,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568647,1727568681,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 347 n_windows 25 disjoint_training_windows False,347,25,False,0.32183045761440365,0,None,i7186,30,0.03200800200050012 1727568667,1727568702,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 341 n_windows 102 disjoint_training_windows False,341,102,False,0.31807951987996996,0,None,i7186,31,0.035981217526603874 1727568678,1727568706,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727568687,1727568715,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727568707,1727568740,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 316 n_windows 81 disjoint_training_windows False,316,81,False,0.3115778944736184,0,None,i7186,29,0.033033258314578644 1727568917,1727568945,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 39 disjoint_training_windows False,100,39,False,0.22280570142535638,0,None,i7186,24,0.01132715611335266 1727568937,1727568964,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727568949,1727568984,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 525 n_windows 235 disjoint_training_windows False,525,235,False,0.3403350837709427,0,None,i7181,31,0.04308219912120888 1727568957,1727568985,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 28 disjoint_training_windows False,100,28,False,0.22130532633158284,0,None,i7186,24,0.011367706791562756 1727568977,1727569025,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 200 disjoint_training_windows True,100,200,True,0.44136034008502123,0,None,i7186,44,0 1727568997,1727569033,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 509 n_windows 234 disjoint_training_windows False,509,234,False,0.35583895973993496,0,None,i7186,32,0.040867359697067125 1727569017,1727569045,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 27 disjoint_training_windows False,100,27,False,0.22055513878469613,0,None,i7186,24,0.011387982130667803 1727568997,1727569046,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 186 n_windows 61 disjoint_training_windows True,186,61,True,0.44136034008502123,0,None,i7186,45,0 1727569037,1727569065,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 109 n_windows 48 disjoint_training_windows False,109,48,False,0.2110527631907977,0,None,i7186,24,0.013464303575893974 1727569037,1727569066,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,25,0.011718207329610179 1727569057,1727569093,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 387 n_windows 129 disjoint_training_windows False,387,129,False,0.32433108277069267,0,None,i7186,32,0.035286599427634686 1727569077,1727569109,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 173 n_windows 123 disjoint_training_windows False,173,123,False,0.2960740185046261,0,None,i7186,28,0.024702604222484194 1727569083,1727569116,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 173 n_windows 123 disjoint_training_windows False,173,123,False,0.2960740185046261,0,None,i7181,28,0.024702604222484194 1727569097,1727569138,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 183 disjoint_training_windows False,1000,183,False,0.39709927481870466,0,None,i7186,38,0.08160373426690005 1727569100,1727569140,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 236 disjoint_training_windows False,100,236,False,0.36034008502125536,0,None,i7186,36,0.0563140785196299 1727569117,1727569145,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 123 n_windows 47 disjoint_training_windows False,123,47,False,0.2515628907226807,0,None,i7186,25,0.013940985246311577 1727569130,1727569178,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 1000 n_windows 214 disjoint_training_windows True,1000,214,True,0.44136034008502123,0,None,i7186,44,0 1727569338,1727569366,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,25,0.012556169345366646 1727569341,1727569369,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 43 disjoint_training_windows False,105,43,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569357,1727569385,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569371,1727569401,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,26,0.012556169345366646 1727569377,1727569405,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569397,1727569425,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 43 disjoint_training_windows False,105,43,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569418,1727569446,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,25,0.012556169345366646 1727569431,1727569460,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 43 disjoint_training_windows False,105,43,False,0.22755688922230555,0,None,i7186,25,0.012556169345366646 1727569437,1727569465,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569458,1727569486,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,25,0.012556169345366646 1727569462,1727569493,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 233 n_windows 58 disjoint_training_windows False,233,58,False,0.29057264316079023,0,None,i7186,28,0.025095559604186756 1727569478,1727569505,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569492,1727569520,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 43 disjoint_training_windows False,105,43,False,0.22755688922230555,0,None,i7186,25,0.012556169345366646 1727569518,1727569545,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 44 disjoint_training_windows False,105,44,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569522,1727569549,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 105 n_windows 43 disjoint_training_windows False,105,43,False,0.22755688922230555,0,None,i7186,24,0.012556169345366646 1727569778,1727569808,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727569793,1727569820,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569798,1727569825,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569818,1727569846,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569838,1727569865,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569853,1727569884,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 156 n_windows 25 disjoint_training_windows False,156,25,False,0.25456364091022754,0,None,i7186,27,0.018445087462341775 1727569858,1727569886,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569878,1727569905,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569898,1727569926,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569918,1727569945,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569898,1727569948,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 844 n_windows 109 disjoint_training_windows True,844,109,True,0.44136034008502123,0,None,i7186,46,0 1727569938,1727569965,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727569958,1727569985,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,23,0.011815453863465865 1727569958,1727569987,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727569978,1727570009,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 286 n_windows 40 disjoint_training_windows False,286,40,False,0.30582645661415353,0,None,i7186,27,0.025852617000403946 1727569998,1727570026,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727570018,1727570064,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 692 n_windows 218 disjoint_training_windows True,692,218,True,0.44136034008502123,0,None,i7186,43,0 1727570275,1727570303,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570299,1727570326,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570299,1727570326,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570319,1727570346,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727570335,1727570363,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570339,1727570366,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570359,1727570407,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 391 n_windows 106 disjoint_training_windows True,391,106,True,0.44136034008502123,0,None,i7186,45,0 1727570378,1727570415,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 191 n_windows 223 disjoint_training_windows False,191,223,False,0.3403350837709427,0,None,i7186,33,0.04308219912120888 1727570396,1727570444,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 390 n_windows 134 disjoint_training_windows True,390,134,True,0.44136034008502123,0,None,i7186,45,0 1727570419,1727570446,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570419,1727570453,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 195 n_windows 142 disjoint_training_windows False,195,142,False,0.3113278319579895,0,None,i7186,31,0.030052967787401394 1727570439,1727570466,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570456,1727570483,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570459,1727570508,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 889 n_windows 190 disjoint_training_windows True,889,190,True,0.44136034008502123,0,None,i7186,46,0 1727570486,1727570513,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570499,1727570539,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 861 n_windows 198 disjoint_training_windows False,861,198,False,0.3735933983495874,0,None,i7186,36,0.06707926981745437 1727570516,1727570544,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727570808,1727570838,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727570828,1727570856,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727570848,1727570876,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727570848,1727570882,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 253 n_windows 75 disjoint_training_windows False,253,75,False,0.30107526881720426,0,None,i7186,31,0.030985018982018234 1727570868,1727570919,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 269 n_windows 156 disjoint_training_windows True,269,156,True,0.44136034008502123,0,None,i7186,48,0 1727570888,1727570921,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 253 n_windows 68 disjoint_training_windows False,253,68,False,0.28782195548887224,0,None,i7186,29,0.027237578625425585 1727570908,1727570936,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727570928,1727570956,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727570928,1727570956,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727570948,1727570976,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727570968,1727570996,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727570988,1727571016,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727571028,1727571056,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727571008,1727571058,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 537 n_windows 69 disjoint_training_windows True,537,69,True,0.44136034008502123,0,None,i7186,46,0 1727571050,1727571077,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727571049,1727571085,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 587 n_windows 133 disjoint_training_windows False,587,133,False,0.3493373343335834,0,None,i7186,33,0.04876219054763691 1727571069,1727571097,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727571335,1727571384,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 367 n_windows 30 disjoint_training_windows True,367,30,True,0.44136034008502123,0,None,i7186,45,0 1727571359,1727571387,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571375,1727571402,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571390,1727571425,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 171 n_windows 183 disjoint_training_windows False,171,183,False,0.32883220805201296,0,None,i7186,32,0.028461660869762897 1727571415,1727571452,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 643 n_windows 43 disjoint_training_windows False,643,43,False,0.35783945986496624,0,None,i7186,33,0.05681420355088772 1727571435,1727571463,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571435,1727571463,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571455,1727571483,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571475,1727571503,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571511,1727571541,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,25,0.011815453863465865 1727571495,1727571543,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 153 n_windows 190 disjoint_training_windows True,153,190,True,0.44136034008502123,0,None,i7186,44,0 1727571535,1727571564,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571541,1727571568,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727571555,1727571604,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 113 n_windows 48 disjoint_training_windows True,113,48,True,0.44136034008502123,0,None,i7186,46,0 1727571571,1727571618,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 940 n_windows 123 disjoint_training_windows True,940,123,True,0.44136034008502123,0,None,i7186,44,0 1727571870,1727571898,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727571890,1727571917,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,23,0.011857130949404016 1727571902,1727571930,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727571930,1727571977,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 992 n_windows 63 disjoint_training_windows True,992,63,True,0.44136034008502123,0,None,i7186,44,0 1727571932,1727571980,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 442 n_windows 126 disjoint_training_windows True,442,126,True,0.44136034008502123,0,None,i7186,44,0 1727571963,1727572002,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 719 n_windows 80 disjoint_training_windows False,719,80,False,0.3688422105526381,0,None,i7186,36,0.05461365341335335 1727571990,1727572017,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727571970,1727572018,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 408 n_windows 104 disjoint_training_windows True,408,104,True,0.44136034008502123,0,None,i7186,45,0 1727572010,1727572038,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,25,0.011857130949404016 1727572023,1727572050,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727572050,1727572082,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 146 n_windows 96 disjoint_training_windows False,146,96,False,0.2870717679419855,0,None,i7186,28,0.020872865275142313 1727572070,1727572106,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 282 n_windows 189 disjoint_training_windows False,282,189,False,0.3383345836459115,0,None,i7186,32,0.037946986746686666 1727572083,1727572111,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727572110,1727572137,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 43 disjoint_training_windows False,100,43,False,0.21505376344086025,0,None,i7186,24,0.011857130949404016 1727572090,1727572140,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 291 n_windows 215 disjoint_training_windows True,291,215,True,0.44136034008502123,0,None,i7186,46,0 1727572130,1727572166,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 566 n_windows 34 disjoint_training_windows False,566,34,False,0.3483370842710678,0,None,i7186,32,0.04892889889138951 1727572445,1727572472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572468,1727572495,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572488,1727572517,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,25,0.011381223684299452 1727572505,1727572532,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,23,0.011381223684299452 1727572528,1727572556,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,24,0.011718207329610179 1727572548,1727572575,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572565,1727572592,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572595,1727572622,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572588,1727572625,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 126 n_windows 213 disjoint_training_windows False,126,213,False,0.35233808452113025,0,None,i7186,33,0.036196549137284324 1727572625,1727572654,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,25,0.011381223684299452 1727572628,1727572655,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572656,1727572683,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,24,0.011381223684299452 1727572669,1727572706,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 162 n_windows 187 disjoint_training_windows False,162,187,False,0.32983245811452866,0,None,i7186,33,0.028370729045897835 1727572689,1727572716,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 41 disjoint_training_windows False,100,41,False,0.2200550137534384,0,None,i7186,23,0.011718207329610179 1727572708,1727572736,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 40 disjoint_training_windows False,100,40,False,0.2208052013003251,0,None,i7186,23,0.011381223684299452 1727573070,1727573098,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573090,1727573124,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 282 n_windows 100 disjoint_training_windows False,282,100,False,0.3293323330832708,0,None,i7186,30,0.031257814453613406 1727573108,1727573136,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727573110,1727573137,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573130,1727573158,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573150,1727573178,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,25,0.011850184768414325 1727573170,1727573197,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573190,1727573230,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 748 n_windows 85 disjoint_training_windows False,748,85,False,0.3868467116779195,0,None,i7186,36,0.051012753188297066 1727573210,1727573237,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573230,1727573258,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573250,1727573290,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 771 n_windows 47 disjoint_training_windows False,771,47,False,0.3888472118029508,0,None,i7186,37,0.05061265316329081 1727573270,1727573297,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573288,1727573315,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573310,1727573338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573330,1727573357,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573349,1727573378,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,26,0.011850184768414325 1727573370,1727573397,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 42 disjoint_training_windows False,100,42,False,0.21530382595648911,0,None,i7186,24,0.011850184768414325 1727573791,1727573818,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 36 disjoint_training_windows False,100,36,False,0.2173043260815204,0,None,i7186,24,0.011173846093102222 1727573770,1727573819,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 107 n_windows 28 disjoint_training_windows True,107,28,True,0.44136034008502123,0,None,i7186,45,0 1727573811,1727573838,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,24,0.011442049701614592 1727573831,1727573859,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,24,0.011442049701614592 1727573851,1727573878,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,24,0.011442049701614592 1727573871,1727573919,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 901 n_windows 166 disjoint_training_windows True,901,166,True,0.44136034008502123,0,None,i7186,44,0 1727573891,1727573931,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 957 n_windows 204 disjoint_training_windows False,957,204,False,0.38659664916229053,0,None,i7186,37,0.08510460948570477 1727573911,1727573958,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 873 n_windows 167 disjoint_training_windows True,873,167,True,0.44136034008502123,0,None,i7186,44,0 1727573931,1727573979,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 25 disjoint_training_windows True,100,25,True,0.44136034008502123,0,None,i7186,45,0 1727573951,1727573980,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,25,0.011442049701614592 1727573971,1727573998,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,24,0.011442049701614592 1727573991,1727574024,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 230 n_windows 230 disjoint_training_windows False,230,230,False,0.32008002000500124,0,None,i7186,30,0.029257314328582144 1727574011,1727574052,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 987 n_windows 137 disjoint_training_windows False,987,137,False,0.3930982745686422,0,None,i7186,37,0.08293740101692088 1727574031,1727574059,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,25,0.011442049701614592 1727574071,1727574099,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,24,0.011442049701614592 1727574071,1727574099,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 34 disjoint_training_windows False,100,34,False,0.21855463865966496,0,None,i7186,25,0.011442049701614592 1727574091,1727574118,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 36 disjoint_training_windows False,100,36,False,0.2173043260815204,0,None,i7186,24,0.011173846093102222 1727574524,1727574554,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727574551,1727574580,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727574554,1727574582,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727574571,1727574599,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727574591,1727574618,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574631,1727574659,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574644,1727574680,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 264 n_windows 197 disjoint_training_windows False,264,197,False,0.3463365841460365,0,None,i7186,32,0.03694673668417104 1727574674,1727574702,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574672,1727574705,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 132 n_windows 112 disjoint_training_windows False,132,112,False,0.30832708177044266,0,None,i7186,30,0.02223889305659748 1727574704,1727574739,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 138 n_windows 160 disjoint_training_windows False,138,160,False,0.31732933233308325,0,None,i7186,31,0.03245811452863216 1727574732,1727574759,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574752,1727574786,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 251 n_windows 149 disjoint_training_windows False,251,149,False,0.3205801450362591,0,None,i7186,31,0.032133033258314576 1727574765,1727574793,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,25,0.011711261148620488 1727574791,1727574819,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574812,1727574840,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574825,1727574852,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727574852,1727574879,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 46 disjoint_training_windows False,100,46,False,0.22030507626906726,0,None,i7186,24,0.011711261148620488 1727575277,1727575305,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575307,1727575334,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575327,1727575355,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,25,0.011766830596538025 1727575338,1727575367,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,25,0.011766830596538025 1727575367,1727575395,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575387,1727575415,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575407,1727575436,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,25,0.011766830596538025 1727575427,1727575455,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575448,1727575475,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575487,1727575516,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,25,0.011766830596538025 1727575507,1727575535,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575518,1727575545,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575548,1727575576,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575567,1727575595,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575578,1727575605,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575607,1727575636,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727575627,1727575655,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 48 disjoint_training_windows False,100,48,False,0.21830457614403598,0,None,i7186,24,0.011766830596538025 1727576029,1727576059,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576060,1727576088,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576071,1727576104,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 357 n_windows 36 disjoint_training_windows False,357,36,False,0.3358339584896224,0,None,i7186,30,0.030607651912978245 1727576091,1727576121,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 185 n_windows 40 disjoint_training_windows False,185,40,False,0.2650662665666417,0,None,i7186,26,0.020935789502931287 1727576121,1727576149,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576151,1727576178,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576171,1727576199,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576181,1727576208,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576211,1727576239,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727576231,1727576258,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576252,1727576281,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727576271,1727576298,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576302,1727576330,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727576331,1727576359,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576351,1727576379,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576362,1727576409,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 823 n_windows 98 disjoint_training_windows True,823,98,True,0.44136034008502123,0,None,i7186,44,0 1727576844,1727576873,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576871,1727576899,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576891,1727576919,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727576911,1727576959,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 673 n_windows 79 disjoint_training_windows True,673,79,True,0.44136034008502123,0,None,i7186,44,0 1727576934,1727576967,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 181 n_windows 133 disjoint_training_windows False,181,133,False,0.3108277069267317,0,None,i7186,30,0.025467905437897936 1727576964,1727577002,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 743 n_windows 77 disjoint_training_windows False,743,77,False,0.37284321080270066,0,None,i7186,35,0.053813453363340834 1727576991,1727577020,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727577011,1727577042,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 190 n_windows 75 disjoint_training_windows False,190,75,False,0.27156789197299325,0,None,i7186,27,0.02468950570976077 1727577024,1727577059,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 381 n_windows 117 disjoint_training_windows False,381,117,False,0.32808202050512625,0,None,i7186,31,0.03486982856825318 1727577052,1727577079,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727577072,1727577100,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727577091,1727577119,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727577115,1727577142,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727577145,1727577192,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 380 n_windows 222 disjoint_training_windows True,380,222,True,0.44136034008502123,0,None,i7186,44,0 1727577172,1727577200,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 47 disjoint_training_windows False,100,47,False,0.22155538884721182,0,None,i7186,25,0.011676530243672028 1727577191,1727577239,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 311 n_windows 27 disjoint_training_windows True,311,27,True,0.44136034008502123,0,None,i7186,44,0 1727577687,1727577715,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727577711,1727577738,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727577731,1727577759,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727577747,1727577775,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 45 disjoint_training_windows False,100,45,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865 1727577771,1727577799,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 100 n_windows 44 disjoint_training_windows False,100,44,False,0.21655413853463368,0,None,i7186,24,0.011815453863465865
timestamp,ram_usage_mb,cpu_usage_percent 1727565050,475.1640625,49.7 1727565050,475.1640625,51.6 1727565050,475.1640625,49.8 1727565050,475.1640625,37.5 1727565050,475.1640625,57.4 1727565051,475.1640625,49.6 1727565051,475.1640625,55.3 1727565095,483.65625,49.8 1727565095,483.65625,54.2 1727565095,483.65625,49.4 1727565095,483.65625,55.3 1727565097,483.65625,49.7 1727565097,483.65625,48.8 1727565097,483.65625,50.0 1727565097,483.65625,54.5 1727565099,483.65625,49.7 1727565099,483.65625,38.2 1727565099,483.65625,50.9 1727565099,483.65625,47.5 1727565101,484.5625,49.9 1727565101,484.5625,38.2 1727565101,484.5625,49.5 1727565101,484.5625,56.5 1727565103,484.5625,49.8 1727565103,484.5625,51.1 1727565103,484.5625,48.1 1727565103,484.5625,56.5 1727565106,484.5625,49.9 1727565106,484.5625,36.4 1727565106,484.5625,53.4 1727565106,484.5625,39.4 1727565108,484.5625,49.9 1727565108,484.5625,36.4 1727565108,484.5625,53.4 1727565108,484.5625,41.2 1727565111,484.671875,49.9 1727565111,484.671875,37.5 1727565111,484.671875,48.6 1727565111,484.671875,56.8 1727565113,484.671875,49.8 1727565113,484.671875,55.3 1727565113,484.671875,49.4 1727565113,484.671875,55.6 1727565115,484.671875,49.9 1727565115,484.671875,38.2 1727565115,484.671875,48.6 1727565115,484.671875,56.8 1727565117,484.671875,49.8 1727565117,484.671875,54.3 1727565117,484.671875,48.6 1727565117,484.671875,56.5 1727565120,484.6953125,49.9 1727565120,484.703125,38.2 1727565120,484.703125,51.6 1727565120,484.703125,37.5 1727565123,484.703125,49.9 1727565123,484.703125,37.5 1727565123,484.703125,52.0 1727565123,484.703125,40.6 1727565125,484.703125,49.9 1727565125,484.703125,53.2 1727565125,484.703125,47.2 1727565125,484.703125,57.8 1727565128,484.74609375,49.9 1727565128,484.74609375,38.2 1727565128,484.74609375,52.5 1727565128,484.74609375,42.4 1727565130,484.75,49.8 1727565130,484.75,38.2 1727565130,484.75,53.3 1727565130,484.75,40.6 1727565132,484.75,49.9 1727565132,484.75,54.3 1727565132,484.75,47.6 1727565132,484.75,55.6 1727565134,484.75,49.8 1727565134,484.75,40.0 1727565134,484.75,53.3 1727565134,484.75,38.7 1727565137,484.75,49.9 1727565137,484.75,40.0 1727565137,484.75,51.2 1727565137,484.75,54.3 1727565139,484.75,49.8 1727565139,484.75,36.4 1727565139,484.75,52.9 1727565139,484.75,38.7 1727565141,484.75,49.9 1727565141,484.75,39.4 1727565141,484.75,52.5 1727565141,484.75,52.4 1727565143,484.75,49.9 1727565143,484.75,38.2 1727565143,484.75,52.4 1727565143,484.75,39.4 1727565145,484.75,49.9 1727565145,484.75,36.4 1727565145,484.75,53.3 1727565145,484.75,39.4 1727565147,484.75,49.8 1727565147,484.75,53.3 1727565147,484.75,51.2 1727565147,484.75,54.5 1727565150,484.75,49.8 1727565150,484.75,39.4 1727565150,484.75,53.3 1727565150,484.75,41.2 1727565152,484.75,49.9 1727565152,484.75,54.3 1727565152,484.75,49.6 1727565152,484.75,45.9 1727565154,484.75,49.9 1727565154,484.75,39.4 1727565154,484.75,52.0 1727565154,484.75,55.6 1727565156,484.75,49.8 1727565156,484.75,54.3 1727565156,484.75,46.8 1727565156,484.75,57.8 1727565158,484.78515625,49.9 1727565158,484.78515625,48.8 1727565158,484.78515625,52.9 1727565158,484.78515625,37.5 1727565160,484.78515625,49.8 1727565160,484.78515625,55.6 1727565160,484.78515625,46.3 1727565160,484.78515625,57.4 1727565163,484.78515625,49.8 1727565163,484.78515625,37.5 1727565163,484.78515625,52.9 1727565163,484.78515625,42.4 1727565165,484.78515625,49.9 1727565165,484.78515625,36.4 1727565165,484.78515625,52.8 1727565165,484.78515625,51.2 1727565168,484.78515625,49.8 1727565168,484.78515625,38.2 1727565168,484.78515625,51.2 1727565168,484.78515625,52.5 1727565170,484.78515625,49.8 1727565170,484.78515625,39.4 1727565170,484.78515625,52.5 1727565170,484.78515625,42.4 1727565172,484.78515625,49.8 1727565172,484.78515625,54.3 1727565172,484.78515625,53.2 1727565172,484.78515625,37.5 1727565174,484.78515625,49.9 1727565174,484.78515625,56.2 1727565174,484.78515625,51.2 1727565174,484.78515625,37.5 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This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
submitit INFO (2024-09-29 01:10:55,069) - Starting with JobEnvironment(job_id=2289469, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-29 01:10:55,071) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/UDetect_OutdoorObjects/0/single_runs/2289469/2289469_submitted.pkl
Parameters: {"n_samples": 313, "n_windows": 70, "disjoint_training_windows": "True"}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 313 n_windows 70 disjoint_training_windows True
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate
UID: 2063769
GID: 200270
Status-Change-Time: 1724762786.0
Size: 2165 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727564968.0
Last modification: 1724759185.0
Hostname: i7186
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727564968.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 UDetect n_samples 313 n_windows 70 disjoint_training_windows True
stdout:
RESULT: 0.44136034008502123
OO-Info: runtime: 47
OO-Info: lpd: 0
Result: 0.44136034008502123
EXIT_CODE: 0
submitit INFO (2024-09-29 01:11:48,015) - Job completed successfully
submitit INFO (2024-09-29 01:11:48,017) - Exiting after successful completion
Job-Infos
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background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 65px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
margin-top: 3px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
width: inherit;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.hide_on_mobile {
display: none;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
input::placeholder {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
}
.gridjs-th-content {
overflow: visible !important;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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: :-webkit-scrollbar-button: vertical: end{
height: 17px;
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}
: :-webkit-scrollbar-button: horizontal: start{
width: 17px;
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}
: :-webkit-scrollbar-button: horizontal: end{
width: 17px;
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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
menu[role=tablist] button{
background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
margin-left: -1px;
margin-right: 2px;
border-radius: 0;
border-color: #91a7b4;
border-top-right-radius: 3px;
border-top-left-radius: 3px;
padding: 0 12px 3px
}
menu[role=tablist] button: hover{
box-shadow: unset;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]{
border-color: #919b9c;
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]: first-of-type: before{
content: "";
display: block;
position: absolute;
z-index: -1;
top: 100%;
left: -1px;
height: 2px;
width: 0;
border-left: 1px solid #919b9c
}
[role=tabpanel]{
box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
}
ul.tree-view{
-webkit-font-smoothing: auto;
border: 1px solid #7f9db9;
padding: 2px 5px
}
@keyframes sliding{
0%{
transform: translateX(-30px)
}
to{
transform: translateX(100%)
}
}
progress{
box-sizing: border-box;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
height: 14px;
border: 1px solid #686868;
border-radius: 4px;
padding: 1px 2px 1px 0;
overflow: hidden;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress,progress: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px
}
progress[value]: :-webkit-progress-bar{
background-color: transparent
}
progress[value]: :-webkit-progress-value{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress[value]: :-moz-progress-bar{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-webkit-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-webkit-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]){
position: relative
}
progress: not([value]): before{
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before,progress: not([value]): before: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): after{
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): after,progress: not([value]): after: not([value]){
animation: sliding 2s linear 0s infinite
}
progress: not([value]): after: not([value]){
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-moz-progress-bar{
width: 100%;
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441.19921875,
50.9
],
[
1727570007,
441.19921875,
39.4
],
[
1727570519,
439.44921875,
50.1
],
[
1727570519,
439.44921875,
54.3
],
[
1727570519,
439.44921875,
50.4
],
[
1727570519,
439.44921875,
38.7
],
[
1727571063,
439.55078125,
50.2
],
[
1727571063,
439.55078125,
55.3
],
[
1727571063,
439.55078125,
50.2
],
[
1727571063,
439.55078125,
38.7
],
[
1727571578,
454.7734375,
50.2
],
[
1727571578,
454.7734375,
48.8
],
[
1727571578,
454.7734375,
49.3
],
[
1727571578,
454.7734375,
58.7
],
[
1727572128,
465.62109375,
50.1
],
[
1727572128,
465.62109375,
53.2
],
[
1727572128,
465.62109375,
49.9
],
[
1727572128,
465.62109375,
39.4
],
[
1727572706,
466.26171875,
50.2
],
[
1727572706,
466.26171875,
43.6
],
[
1727572707,
466.26171875,
49.7
],
[
1727572707,
466.26171875,
56.8
],
[
1727573371,
458.609375,
50.1
],
[
1727573371,
458.609375,
53.1
],
[
1727573371,
458.609375,
48.6
],
[
1727573371,
458.609375,
56.8
],
[
1727574092,
470.6484375,
50.2
],
[
1727574092,
470.6484375,
54.2
],
[
1727574092,
470.6484375,
49.5
],
[
1727574092,
470.6484375,
40.6
],
[
1727574840,
475.68359375,
50.2
],
[
1727574840,
475.68359375,
42.1
],
[
1727574840,
475.68359375,
51
],
[
1727574840,
475.68359375,
39.4
],
[
1727575623,
480.14453125,
50.2
],
[
1727575623,
480.14453125,
40
],
[
1727575623,
480.14453125,
50.3
],
[
1727575623,
480.14453125,
55.8
],
[
1727576365,
487.921875,
50.1
],
[
1727576365,
487.921875,
39.4
],
[
1727576365,
487.921875,
50.7
],
[
1727576365,
487.921875,
37.5
],
[
1727577186,
496.4296875,
50.2
],
[
1727577186,
496.4296875,
56
],
[
1727577186,
496.4296875,
50.5
],
[
1727577186,
496.4296875,
41.2
],
[
1727577787,
497.55078125,
50.2
],
[
1727577787,
497.55078125,
53.1
],
[
1727577805,
497.51953125,
49.8
],
[
1727577805,
497.51953125,
55.6
]
];
var tab_main_worker_cpu_ram_headers_json = [
"timestamp",
"ram_usage_mb",
"cpu_usage_percent"
];
"use strict";
function add_default_layout_data (layout) {
layout["width"] = get_graph_width();
layout["height"] = get_graph_height();
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true,
ellipsis: false
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 2) {
console.error("Not enough columns for Scatter-Plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
showlegend: false
};
let subDiv = document.createElement("div");
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
symbol: data.map(d => d.result === null ? 'x' : 'circle'),
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter',
showlegend: false
};
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
$("#plotScatter2d").data("loaded", "true");
}
function plotScatter3d() {
if ($("#plotScatter3d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter3d");
if (!plotDiv) {
console.error("Div element with id 'plotScatter3d' not found");
return;
}
plotDiv.innerHTML = "";
var minInput3d = document.getElementById("minValue3d");
var maxInput3d = document.getElementById("maxValue3d");
if (!minInput3d || !maxInput3d) {
minInput3d = document.createElement("input");
minInput3d.id = "minValue3d";
minInput3d.type = "number";
minInput3d.placeholder = "Min Value";
minInput3d.step = "any";
maxInput3d = document.createElement("input");
maxInput3d.id = "maxValue3d";
maxInput3d.type = "number";
maxInput3d.placeholder = "Max Value";
maxInput3d.step = "any";
var inputContainer3d = document.createElement("div");
inputContainer3d.style.marginBottom = "10px";
inputContainer3d.appendChild(minInput3d);
inputContainer3d.appendChild(maxInput3d);
plotDiv.appendChild(inputContainer3d);
}
var select3d = document.getElementById("select3dScatter");
if (result_names.length > 1 && !select3d) {
if (!select3d) {
select3d = document.createElement("select");
select3d.id = "select3dScatter";
select3d.style.marginBottom = "10px";
select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
select3d.addEventListener("change", updatePlots3d);
plotDiv.appendChild(select3d);
}
}
minInput3d.addEventListener("input", updatePlots3d);
maxInput3d.addEventListener("input", updatePlots3d);
updatePlots3d();
async function updatePlots3d() {
var selectedResult = select3d ? select3d.value : result_names[0];
var minValue3d = parseFloat(minInput3d.value);
var maxValue3d = parseFloat(maxInput3d.value);
if (isNaN(minValue3d)) minValue3d = -Infinity;
if (isNaN(maxValue3d)) maxValue3d = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 3) {
console.error("Not enough columns for 3D scatter plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
for (let k = j + 1; k < numericColumns.length; k++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let zCol = numericColumns[k];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let zIndex = tab_results_headers_json.indexOf(zCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
z: parseFloat(row[zIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
scene: {
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
zaxis: {
title: get_axis_title_data(zCol)
}
},
showlegend: false
};
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
z: data.map(d => d.z),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter3d',
showlegend: false
};
let subDiv = document.createElement("div");
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function load_pareto_graph() {
if($("#tab_pareto_fronts").data("loaded") == "true") {
return;
}
var data = pareto_front_data;
if (!data || typeof data !== "object") {
console.error("Invalid data format for pareto_front_data");
return;
}
if (!Object.keys(data).length) {
console.warn("No data found in pareto_front_data");
return;
}
let categories = Object.keys(data);
let allMetrics = new Set();
function extractMetrics(obj, prefix = "") {
let keys = Object.keys(obj);
for (let key of keys) {
let newPrefix = prefix ? `${prefix} -> ${key}` : key;
if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
extractMetrics(obj[key], newPrefix);
} else {
if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
allMetrics.add(newPrefix);
}
}
}
}
for (let cat of categories) {
extractMetrics(data[cat]);
}
allMetrics = Array.from(allMetrics);
function extractValues(obj, metricPath, values) {
let parts = metricPath.split(" -> ");
let data = obj;
for (let part of parts) {
if (data && typeof data === "object") {
data = data[part];
} else {
return;
}
}
if (Array.isArray(data)) {
values.push(...data);
}
}
let graphContainer = document.getElementById("pareto_front_graphs_container");
graphContainer.classList.add("invert_in_dark_mode");
graphContainer.innerHTML = "";
var already_plotted = [];
for (let i = 0; i < allMetrics.length; i++) {
for (let j = i + 1; j < allMetrics.length; j++) {
let xMetric = allMetrics[i];
let yMetric = allMetrics[j];
let xValues = [];
let yValues = [];
for (let cat of categories) {
let metricData = data[cat];
extractValues(metricData, xMetric, xValues);
extractValues(metricData, yMetric, yValues);
}
xValues = xValues.filter(v => v !== undefined && v !== null);
yValues = yValues.filter(v => v !== undefined && v !== null);
let cleanXMetric = xMetric.replace(/.* -> /g, "");
let cleanYMetric = yMetric.replace(/.* -> /g, "");
let plot_key = `${cleanXMetric}-${cleanYMetric}`;
if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
let div = document.createElement("div");
div.id = `pareto_front_graph_${i}_${j}`;
div.style.marginBottom = "20px";
graphContainer.appendChild(div);
let layout = {
title: `${cleanXMetric} vs ${cleanYMetric}`,
xaxis: {
title: get_axis_title_data(cleanXMetric)
},
yaxis: {
title: get_axis_title_data(cleanYMetric)
},
hovermode: "closest"
};
let trace = {
x: xValues,
y: yValues,
mode: "markers",
marker: {
size: get_marker_size(),
},
type: "scatter",
name: `${cleanXMetric} vs ${cleanYMetric}`
};
Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
already_plotted.push(plot_key);
}
}
}
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
$("#tab_pareto_fronts").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#worker_cpu_ram_pre").data("loaded") == "true") {
return;
}
const logData = $("#worker_cpu_ram_pre").text();
const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
const hostData = {};
logData.split("\n").forEach(line => {
line = line.trim();
const match = line.match(regex);
if (match) {
const timestamp = new Date(parseInt(match[1]) * 1000);
const hostname = match[2];
const cpu = parseFloat(match[3]);
const ram = parseFloat(match[4]);
if (!hostData[hostname]) {
hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
}
hostData[hostname].timestamps.push(timestamp);
hostData[hostname].cpuUsage.push(cpu);
hostData[hostname].ramUsage.push(ram);
}
});
if (!Object.keys(hostData).length) {
console.log("No valid data found");
return;
}
const container = document.getElementById("cpuRamWorkerChartContainer");
container.innerHTML = "";
var i = 1;
Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
const chartId = `workerChart_${index}`;
const chartDiv = document.createElement("div");
chartDiv.id = chartId;
chartDiv.style.marginBottom = "40px";
container.appendChild(chartDiv);
const cpuTrace = {
x: timestamps,
y: cpuUsage,
mode: "lines+markers",
name: "CPU Usage (%)",
yaxis: "y1",
line: {
color: "red"
}
};
const ramTrace = {
x: timestamps,
y: ramUsage,
mode: "lines+markers",
name: "RAM Usage (MB)",
yaxis: "y2",
line: {
color: "blue"
}
};
const layout = {
title: `Worker CPU and RAM Usage - ${hostname}`,
xaxis: {
title: get_axis_title_data("Timestamp", "date")
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
side: "left",
color: "red"
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
side: "right",
overlaying: "y",
color: "blue"
},
showlegend: true
};
Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
i++;
});
$("#plot_worker_cpu_ram_button").remove();
$("#worker_cpu_ram_pre").data("loaded", "true");
}
function load_log_file(log_nr, filename) {
var pre_id = `single_run_${log_nr}_pre`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
fetch(url)
.then(response => response.json())
.then(data => {
if (data.data) {
$("#" + pre_id).html(data.data);
$("#" + pre_id).data("loaded", true);
} else {
log(`No 'data' key found in response.`);
}
$("#spinner_log_" + log_nr).remove();
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#spinner_log_" + log_nr).remove();
});
}
}
function load_debug_log () {
var pre_id = `here_debuglogs_go`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
fetch(url)
.then(response => response.json())
.then(data => {
$("#debug_log_spinner").remove();
if (data.data) {
try {
$("#" + pre_id).html(data.data);
} catch (err) {
$("#" + pre_id).text(`Error loading data: ${err}`);
}
$("#" + pre_id).data("loaded", true);
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
} else {
log(`No 'data' key found in response.`);
}
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#debug_log_spinner").remove();
});
}
}
function plotBoxplot() {
if ($("#plotBoxplot").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough numeric columns for Boxplot");
return;
}
var resultIndex = tab_results_headers_json.findIndex(function(header) {
return result_names.includes(header.toLowerCase());
});
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
var plotDiv = document.getElementById("plotBoxplot");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'box',
name: col,
boxmean: 'sd',
marker: {
color: 'rgb(0, 255, 0)'
},
};
});
let layout = {
title: 'Boxplot of Numerical Columns',
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Value")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotBoxplot").data("loaded", "true");
}
function plotHeatmap() {
if ($("#plotHeatmap").data("loaded") === "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col => {
if (special_col_names.includes(col) || result_names.includes(col)) {
return false;
}
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.every(row => {
let value = parseFloat(row[index]);
return !isNaN(value) && isFinite(value);
});
});
if (numericColumns.length < 2) {
console.error("Not enough valid numeric columns for Heatmap");
return;
}
var columnData = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.map(row => parseFloat(row[index]));
});
var dataMatrix = numericColumns.map((_, i) =>
numericColumns.map((_, j) => {
let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
return values.reduce((a, b) => a + b, 0) / values.length;
})
);
var trace = {
z: dataMatrix,
x: numericColumns,
y: numericColumns,
colorscale: 'Viridis',
type: 'heatmap'
};
var layout = {
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
var plotDiv = document.getElementById("plotHeatmap");
plotDiv.innerHTML = "";
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotHeatmap").data("loaded", "true");
}
function plotHistogram() {
if ($("#plotHistogram").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Histogram");
return;
}
var plotDiv = document.getElementById("plotHistogram");
plotDiv.innerHTML = "";
const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
let traces = numericColumns.map((col, index) => {
let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
return {
x: data,
type: 'histogram',
name: col,
opacity: 0.7,
marker: {
color: colorPalette[index % colorPalette.length]
},
autobinx: true
};
});
let layout = {
title: 'Histogram of Numerical Columns',
xaxis: {
title: get_axis_title_data("Value")
},
yaxis: {
title: get_axis_title_data("Frequency")
},
showlegend: true,
barmode: 'overlay'
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotHistogram").data("loaded", "true");
}
function plotViolin() {
if ($("#plotViolin").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#tab_gpu_usage").data("loaded") === "true") {
return;
}
Object.keys(gpu_usage).forEach(node => {
const nodeData = gpu_usage[node];
var timestamps = [];
var gpuUtilizations = [];
var temperatures = [];
nodeData.forEach(entry => {
try {
var timestamp = new Date(entry[0]* 1000);
var utilization = parseFloat(entry[1]);
var temperature = parseFloat(entry[2]);
if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
timestamps.push(timestamp);
gpuUtilizations.push(utilization);
temperatures.push(temperature);
} else {
console.warn("Invalid data point:", entry);
}
} catch (error) {
console.error("Error processing GPU data entry:", error, entry);
}
});
var trace1 = {
x: timestamps,
y: gpuUtilizations,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Utilization (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: temperatures,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Temperature (°C)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'GPU Usage Over Time - ' + node,
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("GPU Utilization (%)"),
overlaying: 'y',
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("GPU Temperature (°C)"),
overlaying: 'y',
side: 'right',
position: 0.85,
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var divId = 'gpu_usage_plot_' + node;
if (!document.getElementById(divId)) {
var div = document.createElement('div');
div.id = divId;
div.className = 'gpu-usage-plot';
document.getElementById('tab_gpu_usage').appendChild(div);
}
var plotData = [trace1, trace2];
Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
});
$("#tab_gpu_usage").data("loaded", "true");
}
function plotResultsDistributionByGenerationMethod() {
if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
return;
}
var res_col = result_names[0];
var gen_method_col = "generation_node";
var data = {};
tab_results_csv_json.forEach(row => {
var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
var result = row[tab_results_headers_json.indexOf(res_col)];
if (!data[gen_method]) {
data[gen_method] = [];
}
data[gen_method].push(result);
});
var traces = Object.keys(data).map(method => {
return {
y: data[method],
type: 'box',
name: method,
boxpoints: 'outliers',
jitter: 0.5,
pointpos: 0
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});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: get_axis_title_data("Generation Method")
},
boxmode: 'group'
};
Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
}
function plotJobStatusDistribution() {
if ($("#plotJobStatusDistribution").data("loaded") === "true") {
return;
}
var status_col = "trial_status";
var status_counts = {};
tab_results_csv_json.forEach(row => {
var status = row[tab_results_headers_json.indexOf(status_col)];
if (status) {
status_counts[status] = (status_counts[status] || 0) + 1;
}
});
var statuses = Object.keys(status_counts);
var counts = Object.values(status_counts);
var colors = statuses.map((status, i) =>
status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
);
var trace = {
x: statuses,
y: counts,
type: 'bar',
marker: { color: colors }
};
var layout = {
title: 'Distribution of Job Status',
xaxis: { title: 'Trial Status' },
yaxis: { title: 'Nr. of jobs' }
};
Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
$("#plotJobStatusDistribution").data("loaded", "true");
}
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document.querySelectorAll('[data-column-id="generation_node"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
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result_names.forEach((name, index) => {
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el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
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});
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function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
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el.style.backgroundColor = colorMap[value];
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setTimeout(() => {
if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
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_colorize_table_entries_by_generation_node_or_hostname();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
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}, 300);
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function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
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$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>n_windows</th><th>disjoint_training_windows</th><th>result </th></tr></thead><tbody><tr><td> 109</td><td>48</td><td>0</td><td>0.211053 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int </td></tr><tr><td> n_windows</td><td>range</td><td>25</td><td>250</td><td></td><td>int </td></tr><tr><td> disjoint_training…</td><td>choice</td><td></td><td></td><td>False, True</td><td></td></tr></tbody></table><br><h2>Number of evaluations:</h2>
<table>
<tbody>
<tr>
<th>Failed</th>
<th>Succeeded</th>
<th>Running</th>
<th>Total</th>
</tr>
<tr>
<td>0</td>
<td>499</td>
<td>13</td>
<td>512</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,n_windows,disjoint_training_windows
0,0_0,COMPLETED,Sobol,0.441360340085021229938888609468,313,70,True
1,1_0,COMPLETED,Sobol,0.247811952988247097273699637299,119,66,False
2,2_0,COMPLETED,Sobol,0.441360340085021229938888609468,597,132,True
3,3_0,COMPLETED,Sobol,0.441360340085021229938888609468,370,179,True
4,4_0,COMPLETED,Sobol,0.344336084021005239819146481750,332,143,False
5,5_0,COMPLETED,Sobol,0.441360340085021229938888609468,671,130,True
6,6_0,COMPLETED,Sobol,0.333333333333333370340767487505,498,193,False
7,7_0,COMPLETED,Sobol,0.332333083270817675192176920973,316,152,False
8,8_0,COMPLETED,Sobol,0.346086521630407650818028741924,540,208,False
9,9_0,COMPLETED,Sobol,0.317079269817454378888044175255,395,65,False
10,10_0,COMPLETED,Sobol,0.335583895973993517891642568429,433,25,False
11,11_0,COMPLETED,Sobol,0.441360340085021229938888609468,257,46,True
12,12_0,COMPLETED,Sobol,0.393598399599899950729309239250,789,27,False
13,13_0,COMPLETED,Sobol,0.322330582645661389840086030745,367,135,False
14,14_0,COMPLETED,Sobol,0.441360340085021229938888609468,746,143,True
15,15_0,COMPLETED,Sobol,0.441360340085021229938888609468,997,196,True
16,16_0,COMPLETED,Sobol,0.441360340085021229938888609468,429,134,True
17,17_0,COMPLETED,Sobol,0.320580145036259089863506233087,434,245,False
18,18_0,COMPLETED,Sobol,0.391347836959239803178434158326,935,225,False
19,19_0,COMPLETED,Sobol,0.374593648412103075173718025326,863,233,False
20,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
21,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
22,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
23,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False
24,24_0,COMPLETED,BoTorch,0.441360340085021229938888609468,974,107,True
25,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
26,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
27,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
28,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
29,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
30,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
31,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
32,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False
33,33_0,COMPLETED,BoTorch,0.441360340085021229938888609468,956,246,True
34,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
35,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
36,21_0,COMPLETED,BoTorch,0.212303075768942250967086238234,100,51,False
37,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False
38,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False
39,39_0,COMPLETED,BoTorch,0.441360340085021229938888609468,1000,250,True
40,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
41,23_0,COMPLETED,BoTorch,0.372093023255813948324544071511,100,250,False
42,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
43,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
44,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
45,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
46,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
47,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
48,48_0,COMPLETED,BoTorch,0.377844461115278806850881210266,804,121,False
49,49_0,COMPLETED,BoTorch,0.326081520380094969091544498951,465,108,False
50,50_0,COMPLETED,BoTorch,0.302825706426606666710199533554,100,180,False
51,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
52,52_0,COMPLETED,BoTorch,0.272318079519879963079631579603,100,99,False
53,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
54,54_0,COMPLETED,BoTorch,0.373093273318329532450832175527,840,133,False
55,55_0,COMPLETED,BoTorch,0.389347336834208523903555487777,755,115,False
56,56_0,COMPLETED,BoTorch,0.301825456364090971561608967022,100,183,False
57,57_0,COMPLETED,BoTorch,0.382345586396599101952631372114,781,154,False
58,58_0,COMPLETED,BoTorch,0.280070017504376100880847388908,104,107,False
59,59_0,COMPLETED,BoTorch,0.341335333833458376417979707185,483,109,False
60,60_0,COMPLETED,BoTorch,0.384096024006001512951513632288,803,129,False
61,61_0,COMPLETED,BoTorch,0.355088772193048241021529065620,626,116,False
62,62_0,COMPLETED,BoTorch,0.441360340085021229938888609468,1000,25,True
63,63_0,COMPLETED,BoTorch,0.376844211052763222724593106250,890,140,False
64,64_0,COMPLETED,BoTorch,0.346086521630407650818028741924,402,138,False
65,65_0,COMPLETED,BoTorch,0.302825706426606666710199533554,163,112,False
66,66_0,COMPLETED,BoTorch,0.374093523380845227599422742060,735,135,False
67,67_0,COMPLETED,BoTorch,0.348087021755438819070604949957,548,112,False
68,68_0,COMPLETED,BoTorch,0.329832458114528659365305429674,285,211,False
69,69_0,COMPLETED,BoTorch,0.359839959989997515421578100359,200,194,False
70,70_0,COMPLETED,BoTorch,0.379594898724681217849763470440,1000,25,False
71,71_0,COMPLETED,BoTorch,0.335333833458364538593343695538,278,210,False
72,72_0,COMPLETED,BoTorch,0.383345836459114797101221938647,1000,77,False
73,73_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,25,True
74,74_0,COMPLETED,BoTorch,0.380095023755938954401756291190,1000,56,False
75,75_0,COMPLETED,BoTorch,0.347336834208552103220313256315,278,228,False
76,76_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,250,True
77,77_0,COMPLETED,BoTorch,0.390597649412353087328142464685,784,29,False
78,78_0,COMPLETED,BoTorch,0.441360340085021229938888609468,171,230,True
79,79_0,COMPLETED,BoTorch,0.441360340085021229938888609468,190,218,True
80,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
81,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
82,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
83,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
84,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
85,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
86,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
87,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
88,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
89,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
90,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
91,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
92,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
93,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
94,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
95,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
96,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
97,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
98,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
99,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
100,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
101,101_0,COMPLETED,BoTorch,0.312828207051762952062290423783,304,93,False
102,102_0,COMPLETED,BoTorch,0.232808202050512669245563301956,124,25,False
103,103_0,COMPLETED,BoTorch,0.312828207051762952062290423783,304,96,False
104,104_0,COMPLETED,BoTorch,0.441360340085021229938888609468,206,25,True
105,105_0,COMPLETED,BoTorch,0.311577894473618388637703446875,302,94,False
106,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
107,107_0,COMPLETED,BoTorch,0.313578394598649667912582117424,304,97,False
108,108_0,COMPLETED,BoTorch,0.441360340085021229938888609468,553,250,True
109,109_0,COMPLETED,BoTorch,0.441360340085021229938888609468,267,250,True
110,110_0,COMPLETED,BoTorch,0.313328332083020799636585707049,307,96,False
111,111_0,COMPLETED,BoTorch,0.314078519629907515486877400690,312,102,False
112,112_0,COMPLETED,BoTorch,0.441360340085021229938888609468,418,250,True
113,113_0,COMPLETED,BoTorch,0.312078019504876236211998730141,298,92,False
114,114_0,COMPLETED,BoTorch,0.441360340085021229938888609468,704,58,True
115,115_0,COMPLETED,BoTorch,0.441360340085021229938888609468,305,70,True
116,116_0,COMPLETED,BoTorch,0.441360340085021229938888609468,618,250,True
117,117_0,COMPLETED,BoTorch,0.441360340085021229938888609468,489,66,True
118,118_0,COMPLETED,BoTorch,0.441360340085021229938888609468,812,74,True
119,119_0,COMPLETED,BoTorch,0.441360340085021229938888609468,670,229,True
120,120_0,COMPLETED,BoTorch,0.441360340085021229938888609468,559,250,True
121,121_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,119,True
122,122_0,COMPLETED,BoTorch,0.441360340085021229938888609468,879,51,True
123,123_0,COMPLETED,BoTorch,0.441360340085021229938888609468,476,73,True
124,124_0,COMPLETED,BoTorch,0.441360340085021229938888609468,853,63,True
125,125_0,COMPLETED,BoTorch,0.441360340085021229938888609468,632,69,True
126,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
127,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
128,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
129,129_0,COMPLETED,BoTorch,0.348587146786696666644900233223,607,250,False
130,130_0,COMPLETED,BoTorch,0.340335083770942681269389140652,457,46,False
131,131_0,COMPLETED,BoTorch,0.380595148787196801976051574457,943,224,False
132,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
133,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
134,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
135,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
136,131_0,COMPLETED,BoTorch,0.380595148787196801976051574457,943,224,False
137,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
138,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
139,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
140,140_0,COMPLETED,BoTorch,0.441360340085021229938888609468,182,79,True
141,141_0,COMPLETED,BoTorch,0.334333583395848954467055591522,520,65,False
142,142_0,COMPLETED,BoTorch,0.383595898974743665377218349022,871,31,False
143,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
144,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
145,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
146,146_0,COMPLETED,BoTorch,0.325831457864466100815548088576,264,91,False
147,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
148,148_0,COMPLETED,BoTorch,0.314078519629907515486877400690,331,100,False
149,149_0,COMPLETED,BoTorch,0.354838709677419372745532655244,653,120,False
150,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
151,51_0,COMPLETED,BoTorch,0.212803200800200098541381521500,100,49,False
152,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
153,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
154,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
155,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
156,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
157,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
158,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
159,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
160,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
161,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
162,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
163,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
164,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
165,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
166,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
167,30_0,COMPLETED,BoTorch,0.213303325831457835093374342250,100,50,False
168,168_0,COMPLETED,BoTorch,0.220555138784696125320294868288,100,54,False
169,169_0,COMPLETED,BoTorch,0.399599899974993788553945250897,1000,202,False
170,170_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,55,False
171,171_0,COMPLETED,BoTorch,0.337584396099024797166521238978,416,176,False
172,172_0,COMPLETED,BoTorch,0.329832458114528659365305429674,515,89,False
173,173_0,COMPLETED,BoTorch,0.287571892973243259383764325321,100,127,False
174,174_0,COMPLETED,BoTorch,0.341335333833458376417979707185,590,90,False
175,175_0,COMPLETED,BoTorch,0.300575143785946519159324452630,100,162,False
176,176_0,COMPLETED,BoTorch,0.391347836959239803178434158326,1000,250,False
177,177_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,157,True
178,178_0,COMPLETED,BoTorch,0.327831957989497380090426759125,437,177,False
179,179_0,COMPLETED,BoTorch,0.400350087521880504404236944538,1000,204,False
180,180_0,COMPLETED,BoTorch,0.298074518629657392310150498815,100,157,False
181,181_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,53,False
182,182_0,COMPLETED,BoTorch,0.332333083270817675192176920973,511,89,False
183,181_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,53,False
184,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
185,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
186,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
187,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
188,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
189,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
190,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
191,191_0,COMPLETED,BoTorch,0.237809452363090811921608747070,126,47,False
192,192_0,COMPLETED,BoTorch,0.326081520380094969091544498951,402,67,False
193,193_0,COMPLETED,BoTorch,0.326081520380094969091544498951,387,96,False
194,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
195,195_0,COMPLETED,BoTorch,0.299574893723430824010733886098,170,116,False
196,40_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,25,False
197,192_0,COMPLETED,BoTorch,0.326081520380094969091544498951,402,67,False
198,198_0,COMPLETED,BoTorch,0.263565891472868241152127666282,123,71,False
199,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
200,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
201,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
202,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
203,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
204,204_0,COMPLETED,BoTorch,0.441360340085021229938888609468,594,31,True
205,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
206,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
207,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
208,208_0,COMPLETED,BoTorch,0.335583895973993517891642568429,533,87,False
209,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
210,210_0,COMPLETED,BoTorch,0.330082520630157527641301840049,358,218,False
211,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
212,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
213,213_0,COMPLETED,BoTorch,0.388347086771692939777267383761,798,33,False
214,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
215,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
216,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
217,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
218,218_0,COMPLETED,BoTorch,0.356339084771192804446116042527,675,43,False
219,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
220,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
221,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
222,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
223,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
224,224_0,COMPLETED,BoTorch,0.310827706926731672787411753234,196,98,False
225,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
226,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
227,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
228,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
229,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
230,230_0,COMPLETED,BoTorch,0.441360340085021229938888609468,583,187,True
231,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
232,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
233,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
234,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
235,235_0,COMPLETED,BoTorch,0.441360340085021229938888609468,805,186,True
236,236_0,COMPLETED,BoTorch,0.377594398599649938574884799891,915,159,False
237,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
238,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
239,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
240,240_0,COMPLETED,BoTorch,0.280820205051262816731139082549,184,105,False
241,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
242,242_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,50,True
243,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
244,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
245,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
246,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
247,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
248,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
249,249_0,COMPLETED,BoTorch,0.332833208302075522766472204239,529,91,False
250,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
251,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
252,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
253,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
254,254_0,COMPLETED,BoTorch,0.321830457614403653288093209994,347,25,False
255,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
256,256_0,COMPLETED,BoTorch,0.336834208552138081316229545337,476,121,False
257,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
258,258_0,COMPLETED,BoTorch,0.317079269817454378888044175255,367,28,False
259,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
260,260_0,COMPLETED,BoTorch,0.341835458864716223992274990451,339,199,False
261,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
262,254_0,COMPLETED,BoTorch,0.321830457614403653288093209994,347,25,False
263,263_0,COMPLETED,BoTorch,0.318079519879969963014332279272,341,102,False
264,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
265,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
266,266_0,COMPLETED,BoTorch,0.311577894473618388637703446875,316,81,False
267,267_0,COMPLETED,BoTorch,0.222805701425356383893472411728,100,39,False
268,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
269,269_0,COMPLETED,BoTorch,0.340335083770942681269389140652,525,235,False
270,270_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,28,False
271,271_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,200,True
272,272_0,RUNNING,BoTorch,,186,61,True
273,273_0,COMPLETED,BoTorch,0.355838959739934956871820759261,509,234,False
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275,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
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278,278_0,COMPLETED,BoTorch,0.296074018504626113035271828267,173,123,False
279,278_0,COMPLETED,BoTorch,0.296074018504626113035271828267,173,123,False
280,280_0,COMPLETED,BoTorch,0.397099274818704661704771297082,1000,183,False
281,281_0,COMPLETED,BoTorch,0.360340085021255362995873383625,100,236,False
282,282_0,COMPLETED,BoTorch,0.251562890722680676525158105505,123,47,False
283,283_0,COMPLETED,BoTorch,0.441360340085021229938888609468,1000,214,True
284,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
285,285_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,43,False
286,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
287,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
288,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
289,285_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,43,False
290,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
291,285_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,43,False
292,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
293,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
294,294_0,COMPLETED,BoTorch,0.290572643160790233807233562402,233,58,False
295,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
296,285_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,43,False
297,284_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,44,False
298,285_0,COMPLETED,BoTorch,0.227556889222305547271218983951,105,43,False
299,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
300,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
301,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
302,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
303,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
304,304_0,COMPLETED,BoTorch,0.254563640910227539926324880071,156,25,False
305,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
306,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
307,307_0,COMPLETED,BoTorch,0.441360340085021229938888609468,844,109,True
308,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
309,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
310,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
311,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
312,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
313,313_0,COMPLETED,BoTorch,0.305826456614153530111366308120,286,40,False
314,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
315,315_0,COMPLETED,BoTorch,0.441360340085021229938888609468,692,218,True
316,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
317,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
318,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
319,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
320,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
321,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
322,322_0,COMPLETED,BoTorch,0.441360340085021229938888609468,391,106,True
323,323_0,COMPLETED,BoTorch,0.340335083770942681269389140652,191,223,False
324,324_0,COMPLETED,BoTorch,0.441360340085021229938888609468,390,134,True
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326,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
327,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
328,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
329,329_0,COMPLETED,BoTorch,0.441360340085021229938888609468,889,190,True
330,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
331,331_0,COMPLETED,BoTorch,0.373593398349587380025127458794,861,198,False
332,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
333,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
334,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
335,335_0,COMPLETED,BoTorch,0.301075268817204255711317273381,253,75,False
336,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
337,337_0,COMPLETED,BoTorch,0.441360340085021229938888609468,269,156,True
338,338_0,COMPLETED,BoTorch,0.287821955488872238682063198212,253,68,False
339,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
340,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
341,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
342,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
343,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
344,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
345,345_0,RUNNING,BoTorch,,537,69,True
346,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
347,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
348,348_0,COMPLETED,BoTorch,0.349337334333583382495191926864,587,133,False
349,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
350,350_0,COMPLETED,BoTorch,0.441360340085021229938888609468,367,30,True
351,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
352,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
353,353_0,COMPLETED,BoTorch,0.328832208052012964216714863142,171,183,False
354,354_0,COMPLETED,BoTorch,0.357839459864966236146699429810,643,43,False
355,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
356,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
357,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
358,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
359,359_0,COMPLETED,BoTorch,0.441360340085021229938888609468,153,190,True
360,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
361,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
362,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
363,363_0,COMPLETED,BoTorch,0.441360340085021229938888609468,113,48,True
364,364_0,COMPLETED,BoTorch,0.441360340085021229938888609468,940,123,True
365,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
366,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
367,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
368,368_0,COMPLETED,BoTorch,0.441360340085021229938888609468,992,63,True
369,369_0,COMPLETED,BoTorch,0.441360340085021229938888609468,442,126,True
370,370_0,COMPLETED,BoTorch,0.368842210552638105625078424055,719,80,False
371,371_0,COMPLETED,BoTorch,0.441360340085021229938888609468,408,104,True
372,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
373,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
374,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
375,375_0,COMPLETED,BoTorch,0.287071767941985522831771504570,146,96,False
376,376_0,COMPLETED,BoTorch,0.338334583645911513016812932619,282,189,False
377,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
378,378_0,COMPLETED,BoTorch,0.441360340085021229938888609468,291,215,True
379,20_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,43,False
380,380_0,COMPLETED,BoTorch,0.348337084271067798368903822848,566,34,False
381,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
382,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
383,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
384,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
385,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
386,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
387,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
388,388_0,COMPLETED,BoTorch,0.352338084521130245896358701430,126,213,False
389,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
390,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
391,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
392,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
393,393_0,COMPLETED,BoTorch,0.329832458114528659365305429674,162,187,False
394,80_0,COMPLETED,BoTorch,0.220055013753438388768302047538,100,41,False
395,381_0,COMPLETED,BoTorch,0.220805201300325104618593741179,100,40,False
396,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
397,397_0,COMPLETED,BoTorch,0.329332333083270811791010146408,282,100,False
398,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
399,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
400,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
401,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
402,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
403,403_0,COMPLETED,BoTorch,0.386846711677919508076683996478,748,85,False
404,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
405,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
406,406_0,COMPLETED,BoTorch,0.388847211802950787351562667027,771,47,False
407,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
408,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
409,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
410,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
411,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
412,81_0,COMPLETED,BoTorch,0.215303825956489114368253012799,100,42,False
413,413_0,COMPLETED,BoTorch,0.441360340085021229938888609468,107,28,True
414,414_0,COMPLETED,BoTorch,0.217304326081520393643131683348,100,36,False
415,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
416,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
417,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
418,418_0,COMPLETED,BoTorch,0.441360340085021229938888609468,901,166,True
419,419_0,COMPLETED,BoTorch,0.386596649162290528778385123587,957,204,False
420,420_0,COMPLETED,BoTorch,0.441360340085021229938888609468,873,167,True
421,73_0,COMPLETED,BoTorch,0.441360340085021229938888609468,100,25,True
422,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
423,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
424,424_0,COMPLETED,BoTorch,0.320080020005001242289210949821,230,230,False
425,425_0,COMPLETED,BoTorch,0.393098274568642214177316418500,987,137,False
426,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
427,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
428,415_0,COMPLETED,BoTorch,0.218554638659664957067718660255,100,34,False
429,414_0,COMPLETED,BoTorch,0.217304326081520393643131683348,100,36,False
430,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
431,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
432,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
433,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
434,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
435,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
436,436_0,COMPLETED,BoTorch,0.346336584146036519094025152299,264,197,False
437,437_0,COMPLETED,BoTorch,0.308327081770442656960540261935,132,112,False
438,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
439,439_0,COMPLETED,BoTorch,0.317329332333083247164040585631,138,160,False
440,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
441,441_0,COMPLETED,BoTorch,0.320580145036259089863506233087,251,149,False
442,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
443,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
444,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
445,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
446,250_0,COMPLETED,BoTorch,0.220305076269067257044298457913,100,46,False
447,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
448,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
449,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
450,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
451,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
452,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
453,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
454,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
455,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
456,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
457,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
458,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
459,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
460,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
461,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
462,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
463,137_0,COMPLETED,BoTorch,0.218304576144035977769419787364,100,48,False
464,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
465,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
466,466_0,COMPLETED,BoTorch,0.335833958489622386167638978804,357,36,False
467,467_0,COMPLETED,BoTorch,0.265066266566641672852711053565,185,40,False
468,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
469,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
470,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
471,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
472,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
473,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
474,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
475,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
476,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
477,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
478,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
479,479_0,COMPLETED,BoTorch,0.441360340085021229938888609468,823,98,True
480,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
481,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
482,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
483,483_0,COMPLETED,BoTorch,0.441360340085021229938888609468,673,79,True
484,484_0,COMPLETED,BoTorch,0.310827706926731672787411753234,181,133,False
485,485_0,COMPLETED,BoTorch,0.372843210802700664174835765152,743,77,False
486,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
487,487_0,COMPLETED,BoTorch,0.271567891972993247229339885962,190,75,False
488,488_0,COMPLETED,BoTorch,0.328082020505126248366423169500,381,117,False
489,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
490,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
491,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
492,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
493,493_0,COMPLETED,BoTorch,0.441360340085021229938888609468,380,222,True
494,184_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,47,False
495,495_0,COMPLETED,BoTorch,0.441360340085021229938888609468,311,27,True
496,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
497,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
498,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
499,217_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,45,False
500,200_0,COMPLETED,BoTorch,0.216554138534633677792839989706,100,44,False
501,217_0,RUNNING,BoTorch,,100,45,False
502,217_0,RUNNING,BoTorch,,100,45,False
503,503_0,RUNNING,BoTorch,,358,31,False
504,200_0,RUNNING,BoTorch,,100,44,False
505,217_0,RUNNING,BoTorch,,100,45,False
506,200_0,RUNNING,BoTorch,,100,44,False
507,217_0,RUNNING,BoTorch,,100,45,False
508,217_0,RUNNING,BoTorch,,100,45,False
509,200_0,RUNNING,BoTorch,,100,44,False
510,217_0,RUNNING,BoTorch,,100,45,False
511,217_0,RUNNING,BoTorch,,100,45,False
</pre>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> CPU/RAM-Usage (main)</h1>
<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727565050,475.1640625,49.7
1727565050,475.1640625,51.6
1727565050,475.1640625,49.8
1727565050,475.1640625,37.5
1727565050,475.1640625,57.4
1727565051,475.1640625,49.6
1727565051,475.1640625,55.3
1727565095,483.65625,49.8
1727565095,483.65625,54.2
1727565095,483.65625,49.4
1727565095,483.65625,55.3
1727565097,483.65625,49.7
1727565097,483.65625,48.8
1727565097,483.65625,50.0
1727565097,483.65625,54.5
1727565099,483.65625,49.7
1727565099,483.65625,38.2
1727565099,483.65625,50.9
1727565099,483.65625,47.5
1727565101,484.5625,49.9
1727565101,484.5625,38.2
1727565101,484.5625,49.5
1727565101,484.5625,56.5
1727565103,484.5625,49.8
1727565103,484.5625,51.1
1727565103,484.5625,48.1
1727565103,484.5625,56.5
1727565106,484.5625,49.9
1727565106,484.5625,36.4
1727565106,484.5625,53.4
1727565106,484.5625,39.4
1727565108,484.5625,49.9
1727565108,484.5625,36.4
1727565108,484.5625,53.4
1727565108,484.5625,41.2
1727565111,484.671875,49.9
1727565111,484.671875,37.5
1727565111,484.671875,48.6
1727565111,484.671875,56.8
1727565113,484.671875,49.8
1727565113,484.671875,55.3
1727565113,484.671875,49.4
1727565113,484.671875,55.6
1727565115,484.671875,49.9
1727565115,484.671875,38.2
1727565115,484.671875,48.6
1727565115,484.671875,56.8
1727565117,484.671875,49.8
1727565117,484.671875,54.3
1727565117,484.671875,48.6
1727565117,484.671875,56.5
1727565120,484.6953125,49.9
1727565120,484.703125,38.2
1727565120,484.703125,51.6
1727565120,484.703125,37.5
1727565123,484.703125,49.9
1727565123,484.703125,37.5
1727565123,484.703125,52.0
1727565123,484.703125,40.6
1727565125,484.703125,49.9
1727565125,484.703125,53.2
1727565125,484.703125,47.2
1727565125,484.703125,57.8
1727565128,484.74609375,49.9
1727565128,484.74609375,38.2
1727565128,484.74609375,52.5
1727565128,484.74609375,42.4
1727565130,484.75,49.8
1727565130,484.75,38.2
1727565130,484.75,53.3
1727565130,484.75,40.6
1727565132,484.75,49.9
1727565132,484.75,54.3
1727565132,484.75,47.6
1727565132,484.75,55.6
1727565134,484.75,49.8
1727565134,484.75,40.0
1727565134,484.75,53.3
1727565134,484.75,38.7
1727565137,484.75,49.9
1727565137,484.75,40.0
1727565137,484.75,51.2
1727565137,484.75,54.3
1727565139,484.75,49.8
1727565139,484.75,36.4
1727565139,484.75,52.9
1727565139,484.75,38.7
1727565141,484.75,49.9
1727565141,484.75,39.4
1727565141,484.75,52.5
1727565141,484.75,52.4
1727565143,484.75,49.9
1727565143,484.75,38.2
1727565143,484.75,52.4
1727565143,484.75,39.4
1727565145,484.75,49.9
1727565145,484.75,36.4
1727565145,484.75,53.3
1727565145,484.75,39.4
1727565147,484.75,49.8
1727565147,484.75,53.3
1727565147,484.75,51.2
1727565147,484.75,54.5
1727565150,484.75,49.8
1727565150,484.75,39.4
1727565150,484.75,53.3
1727565150,484.75,41.2
1727565152,484.75,49.9
1727565152,484.75,54.3
1727565152,484.75,49.6
1727565152,484.75,45.9
1727565154,484.75,49.9
1727565154,484.75,39.4
1727565154,484.75,52.0
1727565154,484.75,55.6
1727565156,484.75,49.8
1727565156,484.75,54.3
1727565156,484.75,46.8
1727565156,484.75,57.8
1727565158,484.78515625,49.9
1727565158,484.78515625,48.8
1727565158,484.78515625,52.9
1727565158,484.78515625,37.5
1727565160,484.78515625,49.8
1727565160,484.78515625,55.6
1727565160,484.78515625,46.3
1727565160,484.78515625,57.4
1727565163,484.78515625,49.8
1727565163,484.78515625,37.5
1727565163,484.78515625,52.9
1727565163,484.78515625,42.4
1727565165,484.78515625,49.9
1727565165,484.78515625,36.4
1727565165,484.78515625,52.8
1727565165,484.78515625,51.2
1727565168,484.78515625,49.8
1727565168,484.78515625,38.2
1727565168,484.78515625,51.2
1727565168,484.78515625,52.5
1727565170,484.78515625,49.8
1727565170,484.78515625,39.4
1727565170,484.78515625,52.5
1727565170,484.78515625,42.4
1727565172,484.78515625,49.8
1727565172,484.78515625,54.3
1727565172,484.78515625,53.2
1727565172,484.78515625,37.5
1727565174,484.78515625,49.9
1727565174,484.78515625,56.2
1727565174,484.78515625,51.2
1727565174,484.78515625,37.5
1727565176,484.79296875,49.9
1727565176,484.79296875,56.0
1727565176,484.79296875,49.6
1727565176,484.79296875,40.6
1727565179,484.79296875,49.9
1727565179,484.79296875,35.3
1727565179,484.79296875,52.0
1727565179,484.79296875,55.6
1727565181,484.79296875,49.9
1727565181,484.79296875,52.3
1727565181,484.79296875,48.2
1727565181,484.79296875,55.6
1727565183,484.79296875,49.8
1727565183,484.79296875,55.3
1727565183,484.79296875,50.4
1727565183,484.79296875,42.4
1727565185,484.79296875,49.9
1727565185,484.79296875,55.6
1727565185,484.79296875,47.7
1727565185,484.79296875,54.8
1727565187,484.79296875,49.8
1727565187,484.79296875,38.9
1727565187,484.79296875,51.2
1727565187,484.79296875,56.8
1727565189,484.79296875,49.8
1727565189,484.79296875,39.4
1727565189,484.79296875,51.6
1727565189,484.79296875,56.8
1727565191,484.79296875,49.8
1727565191,484.79296875,55.3
1727565191,484.79296875,51.6
1727565191,484.79296875,36.4
1727565193,484.79296875,49.9
1727565193,484.79296875,38.2
1727565193,484.79296875,51.2
1727565193,484.79296875,56.5
1727565196,484.79296875,49.8
1727565196,484.79296875,58.7
1727565196,484.79296875,46.0
1727565196,484.79296875,55.8
1727565198,484.8203125,49.9
1727565198,484.8203125,47.2
1727565198,484.8203125,51.6
1727565198,484.8203125,37.5
1727565201,484.8203125,49.9
1727565201,484.8203125,56.5
1727565201,484.8203125,50.0
1727565201,484.8203125,40.6
1727565203,484.8203125,49.9
1727565203,484.8203125,56.2
1727565203,484.8203125,51.6
1727565203,484.8203125,38.7
1727565206,484.8203125,49.8
1727565206,484.8203125,40.0
1727565206,484.8203125,49.6
1727565206,484.8203125,55.6
1727565208,484.8671875,49.9
1727565208,484.8671875,38.2
1727565208,484.8671875,50.8
1727565208,484.8671875,56.8
1727565321,523.19140625,50.1
1727565321,523.19140625,56.5
1727565321,523.19140625,48.5
1727565321,523.19140625,56.5
1727565375,524.984375,50.1
1727565375,524.984375,37.1
1727565375,524.984375,50.4
1727565375,524.984375,56.5
1727565488,534.0,50.2
1727565488,534.0,38.2
1727565488,534.0,50.3
1727565488,534.0,56.5
1727565582,539.0390625,50.2
1727565582,539.0390625,48.8
1727565582,539.0390625,52.0
1727565582,539.0390625,40.6
1727565722,535.72265625,50.2
1727565722,535.72265625,56.5
1727565722,535.72265625,50.0
1727565722,535.72265625,41.2
1727565806,537.48046875,50.1
1727565806,537.48046875,47.5
1727565806,537.48046875,49.0
1727565806,537.48046875,56.5
1727565924,540.33203125,50.1
1727565924,540.33203125,38.9
1727565924,540.33203125,51.8
1727565924,540.33203125,47.5
1727566069,540.34765625,50.1
1727566069,540.34765625,50.0
1727566069,540.34765625,48.5
1727566069,540.34765625,56.5
1727566245,541.43359375,50.2
1727566245,541.43359375,39.4
1727566245,541.43359375,51.1
1727566245,541.43359375,55.6
1727566397,544.296875,50.1
1727566397,544.296875,48.8
1727566397,544.296875,50.2
1727566397,544.296875,55.6
1727566591,542.4453125,50.1
1727566591,542.4453125,38.2
1727566591,542.4453125,52.2
1727566591,542.4453125,38.7
1727566819,548.125,50.1
1727566819,548.125,53.2
1727566819,548.125,50.0
1727566819,548.125,56.8
1727567072,551.79296875,50.1
1727567072,551.79296875,54.3
1727567072,551.79296875,49.3
1727567072,551.79296875,50.0
1727567319,548.54296875,50.1
1727567319,548.54296875,56.5
1727567319,548.54296875,50.0
1727567319,548.54296875,44.1
1727567629,552.83203125,50.1
1727567629,552.83203125,39.4
1727567629,552.83203125,50.9
1727567629,552.83203125,53.7
1727567964,561.74609375,50.1
1727567964,561.74609375,36.4
1727567964,561.74609375,50.2
1727567964,561.74609375,56.8
1727568321,557.95703125,50.1
1727568321,557.95703125,44.4
1727568321,557.95703125,49.4
1727568321,557.95703125,56.5
1727568698,562.7265625,50.1
1727568698,562.7265625,39.4
1727568698,562.7265625,51.1
1727568698,562.7265625,54.2
1727569131,433.05078125,50.1
1727569131,433.05078125,51.8
1727569131,433.05078125,49.4
1727569131,433.05078125,56.5
1727569525,450.078125,50.1
1727569525,450.078125,55.6
1727569525,450.078125,50.0
1727569525,450.078125,53.5
1727570007,441.19921875,50.1
1727570007,441.19921875,56.5
1727570007,441.19921875,50.9
1727570007,441.19921875,39.4
1727570519,439.44921875,50.1
1727570519,439.44921875,54.3
1727570519,439.44921875,50.4
1727570519,439.44921875,38.7
1727571063,439.55078125,50.2
1727571063,439.55078125,55.3
1727571063,439.55078125,50.2
1727571063,439.55078125,38.7
1727571578,454.7734375,50.2
1727571578,454.7734375,48.8
1727571578,454.7734375,49.3
1727571578,454.7734375,58.7
1727572128,465.62109375,50.1
1727572128,465.62109375,53.2
1727572128,465.62109375,49.9
1727572128,465.62109375,39.4
1727572706,466.26171875,50.2
1727572706,466.26171875,43.6
1727572707,466.26171875,49.7
1727572707,466.26171875,56.8
1727573371,458.609375,50.1
1727573371,458.609375,53.1
1727573371,458.609375,48.6
1727573371,458.609375,56.8
1727574092,470.6484375,50.2
1727574092,470.6484375,54.2
1727574092,470.6484375,49.5
1727574092,470.6484375,40.6
1727574840,475.68359375,50.2
1727574840,475.68359375,42.1
1727574840,475.68359375,51.0
1727574840,475.68359375,39.4
1727575623,480.14453125,50.2
1727575623,480.14453125,40.0
1727575623,480.14453125,50.3
1727575623,480.14453125,55.8
1727576365,487.921875,50.1
1727576365,487.921875,39.4
1727576365,487.921875,50.7
1727576365,487.921875,37.5
1727577186,496.4296875,50.2
1727577186,496.4296875,56.0
1727577186,496.4296875,50.5
1727577186,496.4296875,41.2
1727577787,497.55078125,50.2
1727577787,497.55078125,53.1
1727577805,497.51953125,49.8
1727577805,497.51953125,55.6
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<h1> Parallel Plot</h1>
<div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1> Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1> Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1> Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1> Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1> Heatmap</h1>
<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
<h1>Correlation Heatmap Explanation</h1>
<p>
This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
</p>
<h2>How It Works</h2>
<p>
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
</p>
<ul>
<li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
<li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
<li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
</ul>
<h2>Color Scale: Yellow to Purple (Viridis)</h2>
<p>
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
</p>
<ul>
<li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
<li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
<li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
<li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
</ul>
<h2>What the Heatmap Shows</h2>
<p>
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
</p>
</body>
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