| n_samples | n_clusters | threshold | result |
|---|---|---|---|
| 116 | 3 | 0.01959 | 0.214304 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| n_clusters | range | 1 | 4 | int | |
| threshold | range | 0.002 | 0.07 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 499 | 11 | 510 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,n_clusters,threshold 0,0_0,COMPLETED,Sobol,0.401350337584396088530525048554,666,3,0.028584242939949038031510752944 1,1_0,COMPLETED,Sobol,0.375843960990247527576002539718,625,2,0.067526180174201727579585963213 2,2_0,COMPLETED,Sobol,0.267816954238559667977881417755,164,3,0.045199380021542313878857299869 3,3_0,COMPLETED,Sobol,0.292073018254563665507816949685,259,3,0.026936942666769027321382878881 4,4_0,COMPLETED,Sobol,0.394598649662415645877899805782,631,3,0.060791124667972332162868553951 5,5_0,COMPLETED,Sobol,0.329332333083270811791010146408,312,3,0.025539161641150713577541608856 6,6_0,COMPLETED,Sobol,0.393598399599899950729309239250,642,3,0.012773629657924176830641194158 7,7_0,COMPLETED,Sobol,0.397599399849962509279066580348,684,2,0.016543359749019145854553869412 8,8_0,COMPLETED,Sobol,0.406101525381345362930574083293,681,3,0.058793892875313766288591921239 9,9_0,COMPLETED,Sobol,0.228807201800450110695805960859,122,1,0.063470526002347468774722472062 10,10_0,COMPLETED,Sobol,0.403600900225056236081400129478,830,4,0.062141189690679318746724391076 11,11_0,COMPLETED,Sobol,0.372593148287071795898839354777,601,4,0.064661811921745540598926993425 12,12_0,COMPLETED,Sobol,0.292573143285821402059809770435,284,3,0.044170842789113526349886740263 13,13_0,COMPLETED,Sobol,0.388097024256064071501270973386,470,2,0.009218912739306688031160064156 14,14_0,COMPLETED,Sobol,0.422105526381595375084998522652,946,4,0.043293720655143266839992577388 15,15_0,COMPLETED,Sobol,0.222305576394098536319177128462,118,1,0.048615649089217193024037300120 16,16_0,COMPLETED,Sobol,0.358089522380595104422695840185,521,1,0.041026077955961234855486452489 17,17_0,COMPLETED,Sobol,0.314828707176794231337169094331,403,1,0.044509116344153887290246984776 18,18_0,COMPLETED,Sobol,0.230057514378594674120392937766,106,3,0.067531508628278974493142072788 19,19_0,COMPLETED,Sobol,0.398349587396849225129358273989,843,1,0.013562355425208807296888480209 20,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751 21,21_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.051099697696514990996607963325 22,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 23,23_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.056124776679056284645064067718 24,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751 25,25_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.042484812832510363000970698977 26,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751 27,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751 28,28_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.058456452430997188352002069678 29,29_0,COMPLETED,BoTorch,0.259814953738434661900669198076,192,1,0.070000000000000006661338147751 30,30_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.057680958242928154211526248218 31,31_0,COMPLETED,BoTorch,0.253063265816454108225741492788,153,2,0.070000000000000006661338147751 32,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 33,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 34,34_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.055258163496698695094089259783 35,35_0,COMPLETED,BoTorch,0.297074268567141808183862394799,300,4,0.021130204098317166561127322666 36,36_0,COMPLETED,BoTorch,0.317079269817454378888044175255,338,3,0.038013401226221023299078893842 37,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751 38,38_0,COMPLETED,BoTorch,0.314328582145536383762873811065,357,2,0.028104102327867877542111330058 39,39_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.044325461816871337961121213311 40,40_0,COMPLETED,BoTorch,0.269567391847961967954461215413,171,1,0.052103894560830560367392649823 41,41_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.005680264585283718165031885405 42,42_0,COMPLETED,BoTorch,0.247311827956989249699404354033,143,2,0.070000000000000006661338147751 43,43_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.002000000000000000041633363423 44,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423 45,45_0,COMPLETED,BoTorch,0.267816954238559667977881417755,164,3,0.070000000000000006661338147751 46,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 47,47_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,1,0.019960934068648454597916241937 48,48_0,COMPLETED,BoTorch,0.277069267316829237479680614342,170,1,0.053131528920576813479481614877 49,49_0,COMPLETED,BoTorch,0.297824456114028524034154088440,303,3,0.033598529337454823007202975305 50,50_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.015689822718647515598089370314 51,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 52,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423 53,53_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.070000000000000006661338147751 54,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 55,55_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.015051458887933561944794114140 56,56_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,1,0.027769610156313469240263458460 57,57_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.013919360150643631662825683293 58,58_0,COMPLETED,BoTorch,0.222055513878469668043180718087,131,3,0.002000000000000000041633363423 59,59_0,COMPLETED,BoTorch,0.230557639409852410672385758517,100,2,0.018314272236791183379178704627 60,60_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.038437308700684569284788949517 61,61_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.045409867824205835118434038122 62,62_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.060291340729379028218204439327 63,63_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.030902667569034356076507208400 64,64_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.048741910858706487263969364676 65,65_0,COMPLETED,BoTorch,0.215803950987746961942548296065,101,3,0.002000000000000000041633363423 66,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 67,67_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.044918444239609930934697956673 68,68_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.035067229552974664430475826293 69,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423 70,70_0,COMPLETED,BoTorch,0.269317329332333099678464805038,145,3,0.027695951796912879339096491549 71,71_0,COMPLETED,BoTorch,0.243560890222555670447945885826,157,3,0.011886353333828853992559793085 72,72_0,COMPLETED,BoTorch,0.279069767441860516754559284891,201,4,0.005317346998430295609838758253 73,73_0,COMPLETED,BoTorch,0.256064016004000971626908267353,180,1,0.002000000000000000041633363423 74,74_0,COMPLETED,BoTorch,0.291822955738934686209518076794,150,1,0.044697920349177445997757018858 75,75_0,COMPLETED,BoTorch,0.253813453363340824076033186429,138,3,0.015757285261072749571464868268 76,76_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.055858924605295151577522005937 77,77_0,COMPLETED,BoTorch,0.248562140535133813123991330940,176,2,0.008798780941663506688366069852 78,78_0,COMPLETED,BoTorch,0.271317829457364378953343475587,197,4,0.011191385950694611450817461673 79,79_0,COMPLETED,BoTorch,0.275318829707426826480798354169,232,4,0.002000000000000000041633363423 80,80_0,COMPLETED,BoTorch,0.278319579894973689881965128734,201,1,0.039925443738928437231727741619 81,81_0,COMPLETED,BoTorch,0.275318829707426826480798354169,224,3,0.006897893029174423720761843981 82,82_0,COMPLETED,BoTorch,0.249312328082020528974283024581,143,3,0.006638921130590880875788073467 83,83_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.008542072983910235783877595850 84,84_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.059676366036322092689658802556 85,85_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.008037252378822832415972143849 86,86_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.011267065622627766771635116072 87,87_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.007472335109961153985780324405 88,88_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.063086258853672028124037751695 89,89_0,COMPLETED,BoTorch,0.254063515878969692352029596805,110,4,0.068535930166707947908122378067 90,90_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006464219235339614330615454207 91,91_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.065105314133466790638138377290 92,92_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.050335439897640617268326224121 93,93_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.006885602604559948325402274349 94,94_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.055527757911963536441302125013 95,95_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.058723313529962767320924399428 96,96_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.058821888257426541146699605633 97,97_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.060998450069787157890033313379 98,98_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.005156000750960088377383705449 99,99_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.060342897893130034714381793037 100,100_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.063473149985319513022297144289 101,101_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.019066947830405189812097432878 102,102_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.007978961173036184031936990380 103,103_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.050465426179668537720601761976 104,104_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.065530611872739438772406117550 105,105_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.012093263107295004407659710921 106,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751 107,107_0,COMPLETED,BoTorch,0.281320330082520664305434365815,215,1,0.070000000000000006661338147751 108,108_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.065089570559170792374281688808 109,109_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.053316901476968367457054398528 110,110_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.010226484757627694183179656306 111,111_0,COMPLETED,BoTorch,0.216804201050262546068836400082,103,4,0.069958968840100985153718227139 112,112_0,COMPLETED,BoTorch,0.427356839209802497059342840657,1000,4,0.002000000000000000041633363423 113,113_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.050297074187200904726857686455 114,112_0,COMPLETED,BoTorch,0.427356839209802497059342840657,1000,4,0.002000000000000000041633363423 115,115_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.051713566429092430731806473432 116,116_0,COMPLETED,BoTorch,0.396099024756189077578483193065,733,4,0.014238896628742480840457140800 117,117_0,COMPLETED,BoTorch,0.418354588647161795833540054446,952,4,0.004272943333135866515737344429 118,118_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.064298526132668260002489546423 119,119_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.049831935713041793090116726717 120,120_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.047130645914487588610253254728 121,121_0,COMPLETED,BoTorch,0.430607651912978228736506025598,960,4,0.023697640344882531238113188010 122,122_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.017493329310289743722117705715 123,123_0,COMPLETED,BoTorch,0.424356089022255522635873603576,936,4,0.026729959002832343051281327462 124,124_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.046795836936598528277286845878 125,125_0,COMPLETED,BoTorch,0.428357089272318081185630944674,965,4,0.002000000000000000041633363423 126,126_0,COMPLETED,BoTorch,0.394598649662415645877899805782,838,2,0.025274909472885638550554432413 127,127_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.013730462798562641929533967300 128,128_0,COMPLETED,BoTorch,0.226556639159789963144930879935,102,1,0.047382847471888436818865386613 129,129_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.053263310328364565915393313844 130,130_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.044323912869006616699341094545 131,131_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.056963582551878721993432463933 132,132_0,COMPLETED,BoTorch,0.241810452613153259449063625652,142,4,0.036549992749848075890284349043 133,133_0,COMPLETED,BoTorch,0.415603900975243800708369690255,1000,1,0.070000000000000006661338147751 134,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 135,135_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.012899000969069665534227908665 136,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 137,137_0,COMPLETED,BoTorch,0.414103525881470369007786302973,899,2,0.059951513282835856843977495600 138,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 139,139_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.068180890191842563607949045945 140,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 141,141_0,COMPLETED,BoTorch,0.219304826206551672918010353897,123,2,0.019955650273226897828404702295 142,43_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.002000000000000000041633363423 143,143_0,COMPLETED,BoTorch,0.232808202050512669245563301956,118,2,0.002000000000000000041633363423 144,144_0,COMPLETED,BoTorch,0.223055763940985252169468822103,119,4,0.002000000000000000041633363423 145,145_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,3,0.048823123489359389337582939561 146,146_0,COMPLETED,BoTorch,0.291572893223305817933521666419,130,1,0.002000000000000000041633363423 147,147_0,COMPLETED,BoTorch,0.230557639409852410672385758517,117,3,0.002000000000000000041633363423 148,148_0,COMPLETED,BoTorch,0.234808702175543837498139509989,127,4,0.048850753544171919562355554945 149,149_0,COMPLETED,BoTorch,0.237559389847461832623309874180,125,1,0.002000000000000000041633363423 150,150_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,3,0.053510618080416690045542082999 151,151_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.049856461885348223039837733950 152,152_0,COMPLETED,BoTorch,0.231807951987996974096972735424,126,4,0.002000000000000000041633363423 153,153_0,COMPLETED,BoTorch,0.261565391347836961877248995734,208,4,0.046084836611876740797288931617 154,154_0,COMPLETED,BoTorch,0.280820205051262816731139082549,185,4,0.033664642784040257894595526977 155,155_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.055020807186573990332778549828 156,156_0,COMPLETED,BoTorch,0.285571392848212091131188117288,216,4,0.056910993225296843678329139493 157,157_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.047970761292783992146837590553 158,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 159,159_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.061469701447964195106798968027 160,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 161,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 162,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 163,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 164,164_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.053011136228890358423893047757 165,165_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006634891180175133892915617650 166,166_0,COMPLETED,BoTorch,0.237309327331832964347313463804,147,4,0.059671894255493337921869567708 167,167_0,COMPLETED,BoTorch,0.370092523130782669049665400962,506,1,0.018291861864607274201777187272 168,168_0,COMPLETED,BoTorch,0.287821955488872238682063198212,229,4,0.070000000000000006661338147751 169,169_0,COMPLETED,BoTorch,0.241810452613153259449063625652,136,4,0.051583151120916603815658163512 170,170_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.004676153686139434065283104758 171,171_0,COMPLETED,BoTorch,0.276319079769942521629388920701,236,4,0.070000000000000006661338147751 172,172_0,COMPLETED,BoTorch,0.266066516629157256978999157582,196,3,0.058862412021500859493627899610 173,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 174,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 175,175_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.053060558983427499879503841385 176,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423 177,177_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006111778264145709647914284091 178,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 179,179_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.055279179695991817466982354290 180,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 181,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 182,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 183,183_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.069996605374265566390512560702 184,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 185,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751 186,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 187,187_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.055038190191681109209032030094 188,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 189,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 190,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 191,32_0,RUNNING,BoTorch,,100,4,0.002000000000000000041633363423 192,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 193,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 194,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 195,195_0,COMPLETED,BoTorch,0.242560640160039975299355319294,147,3,0.049564243548287263696483506692 196,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 197,197_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.049922793700379775039266405656 198,198_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.009289231223537844708837418750 199,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 200,200_0,COMPLETED,BoTorch,0.219554888722180541194006764272,122,4,0.016239182449107623928963306525 201,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 202,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 203,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 204,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423 205,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423 206,206_0,COMPLETED,BoTorch,0.236309077269317380221025359788,106,3,0.003555340077775428174466920694 207,207_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,4,0.006489902563824730477581859134 208,208_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,3,0.014255349138286282223431200578 209,209_0,COMPLETED,BoTorch,0.236059014753688400922726486897,108,4,0.002000000000000000041633363423 210,210_0,RUNNING,BoTorch,,107,2,0.002000000000000000041633363423 211,211_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,2,0.025477107829473433220357492246 212,212_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,3,0.007295123353855708335513607921 213,213_0,COMPLETED,BoTorch,0.225306326581645399720343903027,109,4,0.012754302373462925862535044530 214,214_0,COMPLETED,BoTorch,0.229307326831707958270101244125,131,1,0.049894680862930147757783316820 215,215_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,2,0.007041236295683104932929818887 216,216_0,COMPLETED,BoTorch,0.253063265816454108225741492788,110,4,0.017965091678095415628213515902 217,217_0,COMPLETED,BoTorch,0.252813203300825239949745082413,115,1,0.032482441794649354049884237838 218,218_0,COMPLETED,BoTorch,0.220305076269067257044298457913,111,3,0.021107834581049228495075453793 219,219_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,3,0.010769448011299569828436162311 220,220_0,COMPLETED,BoTorch,0.225806451612903247294639186293,113,2,0.029677161617395253490059303658 221,221_0,COMPLETED,BoTorch,0.231057764441110258246681041783,129,1,0.037589872389515728567932484339 222,222_0,COMPLETED,BoTorch,0.217304326081520393643131683348,121,2,0.011552495938111416104443485153 223,223_0,COMPLETED,BoTorch,0.224056014003500836295756926120,134,4,0.004109763468849862363962976985 224,224_0,COMPLETED,BoTorch,0.232808202050512669245563301956,129,4,0.014512056479048717969049242527 225,225_0,COMPLETED,BoTorch,0.219804951237809409470003174647,107,4,0.050625304396742287771360224724 226,226_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,2,0.016093037408446975167208847779 227,227_0,COMPLETED,BoTorch,0.236309077269317380221025359788,130,2,0.063982925125766171303709484164 228,228_0,COMPLETED,BoTorch,0.262065516379094809451544279000,167,2,0.021790805834250602524004847282 229,229_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,1,0.050908429485285705551333990115 230,230_0,COMPLETED,BoTorch,0.237809452363090811921608747070,130,4,0.027136754766890938683765455153 231,231_0,COMPLETED,BoTorch,0.216304076019004698494541116816,123,4,0.067702432305510484855659569803 232,232_0,COMPLETED,BoTorch,0.268067016754188536253877828130,175,1,0.029365960110064322219347587861 233,233_0,COMPLETED,BoTorch,0.264566141535383825278415770299,165,1,0.004017306897918610994469013065 234,234_0,COMPLETED,BoTorch,0.277569392348086974031673435093,166,4,0.017352945022768420657577337352 235,235_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,1,0.047544817236011109595761325863 236,236_0,COMPLETED,BoTorch,0.214303575893973530241964908782,116,3,0.019590452305032361735026569249 237,237_0,COMPLETED,BoTorch,0.229557389347336826546097654500,109,2,0.045648298411843639399432248638 238,238_0,COMPLETED,BoTorch,0.280820205051262816731139082549,185,1,0.048354537952247415855122625317 239,239_0,COMPLETED,BoTorch,0.253313328332082976501737903163,184,1,0.023442729135017506547633558966 240,240_0,COMPLETED,BoTorch,0.274068517129282374078513839777,180,3,0.062106775200989450258504120939 241,241_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.029617585450613943820474815993 242,242_0,COMPLETED,BoTorch,0.233808452113028253371851405973,134,2,0.040794806100753699951155795134 243,243_0,COMPLETED,BoTorch,0.251812953238309544801154515881,183,4,0.002000000000000000041633363423 244,244_0,COMPLETED,BoTorch,0.244311077769442386298237579467,151,4,0.002000000000000000041633363423 245,245_0,COMPLETED,BoTorch,0.257064266066516666775498833886,188,1,0.015274139664493631071695567414 246,246_0,COMPLETED,BoTorch,0.276319079769942521629388920701,216,2,0.032566961459721231741948344052 247,247_0,COMPLETED,BoTorch,0.259564891222805682602370325185,203,1,0.066275465243463477227336966280 248,248_0,COMPLETED,BoTorch,0.255563890972743235074915446603,156,4,0.029784531157617269436777007741 249,249_0,COMPLETED,BoTorch,0.256314078519629950925207140244,182,1,0.031803077430592789631713657172 250,250_0,COMPLETED,BoTorch,0.218054513628407109493423376989,114,3,0.012063515692830435732663119097 251,251_0,COMPLETED,BoTorch,0.217304326081520393643131683348,122,3,0.062085411590224855171715745428 252,252_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,4,0.061405165922654422749893399214 253,253_0,COMPLETED,BoTorch,0.302575643910977798434203123179,140,2,0.055118672951406759430437176661 254,254_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,3,0.036907424255702460380579310595 255,255_0,COMPLETED,BoTorch,0.243310827706926691149647012935,115,4,0.007827371985366867596090045822 256,256_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,3,0.070000000000000006661338147751 257,257_0,COMPLETED,BoTorch,0.253313328332082976501737903163,125,3,0.009710557307893382533725556982 258,258_0,COMPLETED,BoTorch,0.221555388847211820468885434821,107,1,0.058011699526341185817468470987 259,259_0,COMPLETED,BoTorch,0.236309077269317380221025359788,130,4,0.070000000000000006661338147751 260,260_0,COMPLETED,BoTorch,0.249812453113278265526275845332,153,1,0.002000000000000000041633363423 261,261_0,COMPLETED,BoTorch,0.262065516379094809451544279000,207,4,0.024902041402254798674320568352 262,262_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.061356494900312384677132371280 263,263_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,4,0.070000000000000006661338147751 264,264_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,1,0.057950000533865876628514257618 265,265_0,COMPLETED,BoTorch,0.255313828457114255776616573712,156,4,0.058787988631421886354111450146 266,266_0,COMPLETED,BoTorch,0.241810452613153259449063625652,136,1,0.070000000000000006661338147751 267,267_0,COMPLETED,BoTorch,0.229057264316078978971802371234,118,2,0.038533881451254470285050501843 268,268_0,COMPLETED,BoTorch,0.294073518379594944782695620233,297,4,0.070000000000000006661338147751 269,269_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,3,0.056588914837283010861312959605 270,270_0,COMPLETED,BoTorch,0.324831207801950516689259984560,311,4,0.070000000000000006661338147751 271,271_0,COMPLETED,BoTorch,0.306326581645411377685661591386,331,1,0.070000000000000006661338147751 272,272_0,COMPLETED,BoTorch,0.292823205801450381358108643326,301,2,0.070000000000000006661338147751 273,273_0,COMPLETED,BoTorch,0.295323830957739397184980134625,298,1,0.062901301546378962648020660708 274,274_0,COMPLETED,BoTorch,0.285571392848212091131188117288,273,3,0.070000000000000006661338147751 275,275_0,COMPLETED,BoTorch,0.303075768942235534986195943929,317,1,0.049325586380696936905643212867 276,276_0,COMPLETED,BoTorch,0.317829457364341094738335868897,355,4,0.070000000000000006661338147751 277,277_0,COMPLETED,BoTorch,0.299574893723430824010733886098,288,1,0.045538350514646067090929193455 278,278_0,COMPLETED,BoTorch,0.315328832208051967889161915082,341,4,0.049646039392156518510468288241 279,279_0,COMPLETED,BoTorch,0.294073518379594944782695620233,297,1,0.066482328999565826199713569622 280,280_0,COMPLETED,BoTorch,0.298074518629657392310150498815,266,2,0.068325977107753704808956740635 281,281_0,COMPLETED,BoTorch,0.300575143785946519159324452630,288,3,0.062244618215255106963290643307 282,282_0,COMPLETED,BoTorch,0.299574893723430824010733886098,270,3,0.070000000000000006661338147751 283,283_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.044579762254865165638229029810 284,284_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,2,0.070000000000000006661338147751 285,285_0,COMPLETED,BoTorch,0.240810202550637675322775521636,115,2,0.018620524688221104514518344786 286,286_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.070000000000000006661338147751 287,287_0,COMPLETED,BoTorch,0.214803700925231266793957729533,116,3,0.043632649535592003819939321829 288,288_0,COMPLETED,BoTorch,0.214803700925231266793957729533,116,2,0.041266368934308655935794263314 289,289_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.054377379207671418248626338254 290,290_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.045946158658470091784575828342 291,291_0,COMPLETED,BoTorch,0.349087271817954514219195516489,445,4,0.070000000000000006661338147751 292,292_0,COMPLETED,BoTorch,0.349837459364841230069487210130,449,1,0.070000000000000006661338147751 293,293_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.057597145656594447848952711411 294,294_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.070000000000000006661338147751 295,295_0,COMPLETED,BoTorch,0.336084021005251365465937851695,429,4,0.070000000000000006661338147751 296,296_0,COMPLETED,BoTorch,0.228807201800450110695805960859,122,3,0.070000000000000006661338147751 297,297_0,COMPLETED,BoTorch,0.327831957989497380090426759125,391,1,0.038347356967922310855279022235 298,298_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.051700213467624829555280285831 299,299_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.060924718361499118068902447476 300,300_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.045454781832201529567782927188 301,301_0,COMPLETED,BoTorch,0.238809702425606396047896851087,120,1,0.070000000000000006661338147751 302,302_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.041203120012886736145407695631 303,303_0,COMPLETED,BoTorch,0.226556639159789963144930879935,115,3,0.016164745669418223439350867920 304,304_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.041718587268434033366037994028 305,305_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.070000000000000006661338147751 306,306_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.042790925968888143815505031853 307,307_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.059666580388239555399199076646 308,308_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.055420162754167674734606663378 309,309_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,3,0.070000000000000006661338147751 310,310_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.042545806243486826436761560899 311,311_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.063647927010197974384553276650 312,312_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.055987445168224216074381871522 313,313_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.064435875930838715230919433452 314,314_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.055532628574374784391487480661 315,315_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.048113969059482085410817120419 316,316_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.054387243157401204962653196162 317,317_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.052273391108551746364607737405 318,318_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.055421265292523787249212574579 319,319_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.060692607783736807203212038075 320,320_0,COMPLETED,BoTorch,0.217554388597149261919128093723,116,3,0.017508768298486975661942821603 321,321_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.062484351010241412360812773841 322,322_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.056894848173225522069973436601 323,323_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.046536929218029425558977862920 324,324_0,COMPLETED,BoTorch,0.288322080520130086256358481478,246,1,0.002000000000000000041633363423 325,325_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.058382281837509319988299694160 326,326_0,COMPLETED,BoTorch,0.293323330832708228932403926592,263,1,0.004160070166490126460090426264 327,327_0,COMPLETED,BoTorch,0.274068517129282374078513839777,220,4,0.010665684699611809385655369908 328,328_0,COMPLETED,BoTorch,0.282320580145036248431722469832,250,4,0.029873240072150135382411662022 329,329_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.045155273260620430730849506062 330,330_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.061794914468645954774839168522 331,331_0,COMPLETED,BoTorch,0.327081770442610664240135065484,379,4,0.002000000000000000041633363423 332,332_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.053684452154216973085443243008 333,333_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.063806892608406301503620738913 334,334_0,COMPLETED,BoTorch,0.295573893473368376483279007516,319,4,0.011158234088309669854166550351 335,335_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.054331540956466056746521076093 336,336_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.046071473302219119461131668913 337,337_0,COMPLETED,BoTorch,0.253313328332082976501737903163,125,4,0.009025850785447849314313550906 338,338_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,2,0.036144091662880316329076180182 339,339_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,2,0.032302490477854294004256274775 340,340_0,COMPLETED,BoTorch,0.304326081520380098410782920837,320,4,0.010920003002940933262143730076 341,341_0,COMPLETED,BoTorch,0.323580895223805953264673007652,374,4,0.024602903747595515626667150855 342,342_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.047200900229174412581212294526 343,343_0,COMPLETED,BoTorch,0.224806201550387552146048619761,113,1,0.061617515002674712321084626865 344,344_0,COMPLETED,BoTorch,0.319579894973743394714915666555,358,3,0.002000000000000000041633363423 345,345_0,COMPLETED,BoTorch,0.236559139784946248497021770163,118,3,0.019668020068970261393648257808 346,346_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.065531430098825046992594423045 347,347_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.052209735551805538933400896440 348,348_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.058146785428308055132529119646 349,349_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.057403621326084203202455569226 350,350_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.053575767135452964651243235039 351,351_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.062370783993535183764578277987 352,352_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.059035826622121388707054023826 353,353_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.063454976749003244584912408754 354,354_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.051017728207848431210003070646 355,355_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.064269720847156056042770444492 356,356_0,COMPLETED,BoTorch,0.217304326081520393643131683348,122,4,0.063088327344915512417955483215 357,357_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.069322621326920519368997020138 358,358_0,COMPLETED,BoTorch,0.226556639159789963144930879935,115,3,0.015959246688961769428116710401 359,359_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,2,0.052993808153652677273015569881 360,360_0,COMPLETED,BoTorch,0.239559889972493111898188544728,115,3,0.016701330526186639741093031830 361,361_0,COMPLETED,BoTorch,0.236559139784946248497021770163,119,3,0.020471670135727523809343608718 362,362_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.050514981235708848739118792537 363,363_0,COMPLETED,BoTorch,0.215803950987746961942548296065,111,2,0.002000000000000000041633363423 364,364_0,COMPLETED,BoTorch,0.229807451862965694822094064875,114,3,0.013207783738935175918416398133 365,365_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.054770089370395930172819021209 366,366_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.058635061479479680390802798229 367,367_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.055922420278304296736848755245 368,368_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.059383846063512768509440320486 369,369_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.047691051362535583080237699960 370,370_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,3,0.064066979446790447982884586509 371,371_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.044436392441585016721550260854 372,372_0,COMPLETED,BoTorch,0.222805701425356383893472411728,113,2,0.012434867175065394961919196248 373,373_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.058970502227004825690492850754 374,374_0,COMPLETED,BoTorch,0.226556639159789963144930879935,124,3,0.032873197917630388176224442986 375,375_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.047772963845249427627948080044 376,376_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.062436988506878905724750694617 377,377_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.016145392243376695262657705143 378,378_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.049100335637420514101059154655 379,379_0,COMPLETED,BoTorch,0.234808702175543837498139509989,120,2,0.025911205758834175705285218783 380,380_0,COMPLETED,BoTorch,0.216054013503375830218544706440,112,2,0.010154482690847572989856217873 381,381_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.051446866673149460602587623725 382,382_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.061566706047833742732056094837 383,383_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.041287429704039139977123085146 384,384_0,COMPLETED,BoTorch,0.223555888972243099743764105369,103,3,0.013584388695198049493151337686 385,385_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,2,0.064817451027813180464143272275 386,386_0,COMPLETED,BoTorch,0.222305576394098536319177128462,113,3,0.002002945708283992987119859208 387,387_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.050852411076752083995966557950 388,388_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.064514844864787090905622335413 389,389_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009891099708206103555130361826 390,390_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.055830834988912766969626488844 391,391_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,2,0.070000000000000006661338147751 392,392_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.054577580100996982148675584767 393,393_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.067264917892546141620080391021 394,394_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.056264265912313017603540998834 395,395_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.070000000000000006661338147751 396,396_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,4,0.054817799835189050250416897825 397,397_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.070000000000000006661338147751 398,398_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,3,0.035082057131355821877338740933 399,399_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.065502770071723295797205821600 400,400_0,COMPLETED,BoTorch,0.241560390097524391173067215277,128,4,0.066999960890648163625016309197 401,401_0,COMPLETED,BoTorch,0.219054763690922693619711481006,123,3,0.042386264329507115922179849576 402,402_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.070000000000000006661338147751 403,403_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,1,0.067672226461940043762410823547 404,404_0,COMPLETED,BoTorch,0.220305076269067257044298457913,112,2,0.010995707967038321345443208088 405,405_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,2,0.011579071128290921097181431776 406,406_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.010204838446544253760528420116 407,407_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.056497899010438813738321783831 408,408_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.030257986762832318750060522916 409,409_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.065787596295065975393612234257 410,410_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,2,0.036373576816930854038734111100 411,411_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.052007152822469926434223452816 412,412_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.062044047198609876547781283307 413,413_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.053261502567516529060842600529 414,414_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.056309305857676253403987232105 415,415_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,2,0.058472812350973464579073635150 416,416_0,COMPLETED,BoTorch,0.264316079019754957002419359924,164,3,0.002000000000000000041633363423 417,417_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,4,0.069339120485230013035682361533 418,418_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.053921429805717360772554513915 419,419_0,COMPLETED,BoTorch,0.231057764441110258246681041783,115,2,0.070000000000000006661338147751 420,420_0,COMPLETED,BoTorch,0.262065516379094809451544279000,167,3,0.009659435662515588466581206717 421,421_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.057724250730739376513689364856 422,422_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.053703047863779516946092229546 423,423_0,COMPLETED,BoTorch,0.244811202800700122850230400218,151,4,0.002097869490220044281364453198 424,424_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,2,0.032155365119678598617714015973 425,425_0,COMPLETED,BoTorch,0.218804701175293825343715070630,106,2,0.002000000000000000041633363423 426,426_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.056722967119800377089333665026 427,294_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.070000000000000006661338147751 428,428_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.063656470761059541496607039335 429,429_0,COMPLETED,BoTorch,0.237809452363090811921608747070,119,1,0.070000000000000006661338147751 430,430_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.062059483414467937756331394894 431,431_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009264479078473913942204376326 432,432_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.050902230507708819129408794879 433,433_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.058056755416066155306431539884 434,434_0,COMPLETED,BoTorch,0.221055263815953972894590151554,114,2,0.070000000000000006661338147751 435,435_0,COMPLETED,BoTorch,0.363840960240059962949032978941,554,4,0.002000000000000000041633363423 436,436_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,4,0.070000000000000006661338147751 437,437_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.060256433377437711162993849712 438,438_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.062520111964006905291846294404 439,439_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,4,0.070000000000000006661338147751 440,440_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.052670806376882428612162811987 441,441_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.052855686211518107531226462470 442,442_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.062335150751983252337407037658 443,436_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,4,0.070000000000000006661338147751 444,444_0,COMPLETED,BoTorch,0.234308577144286100946146689239,115,2,0.008660193920224115715633672608 445,445_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.055219573865808564694379612092 446,446_0,COMPLETED,BoTorch,0.228557139284821242419809550483,122,2,0.026599949826107659178742181894 447,447_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.052912122387360087383267881478 448,448_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,4,0.048228522354696212737223959266 449,449_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.043203893590213086894902261292 450,450_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.054734862287023448856881913116 451,451_0,COMPLETED,BoTorch,0.230557639409852410672385758517,118,2,0.044206954517093152712270409666 452,452_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,2,0.011057792693587642496311218565 453,453_0,COMPLETED,BoTorch,0.217554388597149261919128093723,112,2,0.010933196628926063198594675896 454,454_0,COMPLETED,BoTorch,0.228557139284821242419809550483,122,2,0.024941825066315120862370235955 455,455_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.055215563980349421924209707413 456,456_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.061372349243710287713948758892 457,457_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.002000000000000000041633363423 458,458_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.045591806662690095852674687649 459,459_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.054545646585192439359524030351 460,460_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.060033844769105344785220523818 461,461_0,COMPLETED,BoTorch,0.238559639909977527771900440712,125,3,0.002000000000000000041633363423 462,462_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.059517250651239854419838337662 463,463_0,COMPLETED,BoTorch,0.224306076519129815594055799011,124,1,0.003816817386661844162254464408 464,464_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.049619927577231201509810887273 465,465_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.052441081497670224975671260381 466,466_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.061436167427381276062714476893 467,467_0,COMPLETED,BoTorch,0.231557889472368105820976325049,129,2,0.002503337377019210566175821953 468,468_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.054053952087813120219728091342 469,469_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.060930292397190034814347114889 470,470_0,COMPLETED,BoTorch,0.223055763940985252169468822103,122,4,0.002000000000000000041633363423 471,471_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.059925608533900942553884760855 472,472_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.054256506724798307661483676156 473,473_0,COMPLETED,BoTorch,0.235808952238059532646730076522,127,4,0.002000000000000000041633363423 474,474_0,COMPLETED,BoTorch,0.228307076769192263121510677593,113,1,0.052454473630330483713279932090 475,475_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.061170593954924051827148900884 476,476_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.066073593338249150819230237630 477,477_0,COMPLETED,BoTorch,0.218804701175293825343715070630,112,2,0.009250981264741900411685548988 478,478_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.055374382627653137567413921261 479,479_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.044453740972655066132634971154 480,480_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,2,0.060346472203606213446924755317 481,481_0,COMPLETED,BoTorch,0.229807451862965694822094064875,114,2,0.011975823665444172011484980089 482,482_0,COMPLETED,BoTorch,0.228307076769192263121510677593,113,2,0.057304554447823466412081927501 483,483_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.061818776978195835725138351791 484,484_0,COMPLETED,BoTorch,0.223805951487871968019760515745,113,2,0.010794780314437028845286903334 485,485_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.065016179083497438462302397966 486,486_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.058732516630742206964477247766 487,487_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.061978569634611326011341958520 488,488_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.046989196248170952974199110486 489,489_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.058503019997978265853699753052 490,490_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,2,0.042947305646313875537867232879 491,491_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.041869325293697530476766388574 492,492_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.059182791935982165254515052766 493,493_0,COMPLETED,BoTorch,0.221055263815953972894590151554,114,2,0.066226854215023459038214070915 494,494_0,COMPLETED,BoTorch,0.269067266816704231402468394663,120,4,0.002000000000000000041633363423 495,495_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009609444742674264072768153255 496,496_0,COMPLETED,BoTorch,0.216304076019004698494541116816,123,1,0.070000000000000006661338147751 497,497_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.063216238051409176512684950922 498,498_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.063247223680475306295534210221 499,499_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.060441637068134705657040939286 500,500_0,COMPLETED,BoTorch,0.289072268067016802106650175119,130,3,0.008315962059984895182740416431 501,501_0,RUNNING,BoTorch,,100,3,0.007750668675751077978108849464 502,502_0,RUNNING,BoTorch,,109,2,0.002798961431528135257451594953 503,503_0,RUNNING,BoTorch,,116,1,0.053823193443523008328011059120 504,504_0,RUNNING,BoTorch,,117,4,0.062871028952552435176137635153 505,505_0,RUNNING,BoTorch,,118,2,0.053996874761292133759749134470 506,506_0,RUNNING,BoTorch,,125,2,0.036035574318616279965166171451 507,507_0,RUNNING,BoTorch,,113,2,0.059987925585050450028035129435 508,508_0,RUNNING,BoTorch,,113,2,0.059603341277663597630454006548 509,509_0,RUNNING,BoTorch,,112,2,0.008438877369189787258640933487
start_time,end_time,run_time,program_string,n_samples,n_clusters,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727546321,1727546352,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 164 n_clusters 3 threshold 0.045199380021542314,164,3,0.045199380021542314,0.26781695423855967,0,None,i7186,27,0.01626493579916718 1727546321,1727546355,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 259 n_clusters 3 threshold 0.026936942666769027,259,3,0.026936942666769027,0.29207301825456367,0,None,i7186,30,0.024988389954631512 1727546321,1727546356,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 312 n_clusters 3 threshold 0.025539161641150714,312,3,0.025539161641150714,0.3293323330832708,0,None,i7186,31,0.028416194957830366 1727546306,1727546363,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 666 n_clusters 3 threshold 0.028584242939949038,666,3,0.028584242939949038,0.4013503375843961,0,None,i7186,52,0.06014003500875219 1727546321,1727546371,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 625 n_clusters 2 threshold 0.06752618017420173,625,2,0.06752618017420173,0.3758439609902475,0,None,i7186,46,0.06651662915728933 1727546321,1727546374,53,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 631 n_clusters 3 threshold 0.06079112466797233,631,3,0.06079112466797233,0.39459864966241565,0,None,i7186,49,0.0618279569892473 1727546321,1727546377,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 684 n_clusters 2 threshold 0.016543359749019146,684,2,0.016543359749019146,0.3975993998499625,0,None,i7186,52,0.06107776944236058 1727546321,1727546380,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 642 n_clusters 3 threshold 0.012773629657924177,642,3,0.012773629657924177,0.39359839959989995,0,None,i7186,55,0.06207801950487622 1727546326,1727546381,55,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 681 n_clusters 3 threshold 0.058793892875313766,681,3,0.058793892875313766,0.40610152538134536,0,None,i7186,51,0.05895223805951487 1727546344,1727546416,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 1 threshold 0.06347052600234747,122,1,0.06347052600234747,0.2288072018004501,0,None,i7181,22,0.013325912123192087 1727546376,1727546418,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 106 n_clusters 3 threshold 0.06753150862827897,106,3,0.06753150862827897,0.23005751437859467,0,None,i7127,25,0.011440360090022505 1727546347,1727546420,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 1 threshold 0.04861564908921719,118,1,0.04861564908921719,0.22230557639409854,0,None,i7181,27,0.013112653163290822 1727546346,1727546426,80,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 284 n_clusters 3 threshold 0.044170842789113526,284,3,0.044170842789113526,0.2925731432858214,0,None,i7181,32,0.02911144452779862 1727546376,1727546427,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 403 n_clusters 1 threshold 0.04450911634415389,403,1,0.04450911634415389,0.31482870717679423,0,None,i7127,33,0.040885221305326326 1727546347,1727546430,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 521 n_clusters 1 threshold 0.041026077955961235,521,1,0.041026077955961235,0.3580895223805951,0,None,i7181,37,0.04730349253980162 1727546347,1727546430,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 470 n_clusters 2 threshold 0.009218912739306688,470,2,0.009218912739306688,0.38809702425606407,0,None,i7181,37,0.03625906476619154 1727546347,1727546444,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 946 n_clusters 4 threshold 0.04329372065514327,946,4,0.04329372065514327,0.4221055263815954,0,None,i7181,51,0.10990247561890473 1727546346,1727546447,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 601 n_clusters 4 threshold 0.06466181192174554,601,4,0.06466181192174554,0.3725931482870718,0,None,i7181,53,0.06732933233308326 1727546381,1727546475,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 843 n_clusters 1 threshold 0.013562355425208807,843,1,0.013562355425208807,0.3983495873968492,0,None,i7122,82,0.12178044511127781 1727546347,1727546481,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 830 n_clusters 4 threshold 0.06214118969067932,830,4,0.06214118969067932,0.40360090022505624,0,None,i7181,88,0.0794365257981162 1727546587,1727546615,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727546587,1727546616,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.056124776679056285,100,2,0.056124776679056285,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546587,1727546616,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.05109969769651499,100,1,0.05109969769651499,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546587,1727546617,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.07,100,2,0.07,0.2370592648162041,0,None,i7186,26,0.010653979284294757 1727546592,1727546621,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.07,100,4,0.07,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546592,1727546621,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.04248481283251036,100,1,0.04248481283251036,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546607,1727546635,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.05845645243099719,100,1,0.05845645243099719,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546607,1727546635,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.07,100,4,0.07,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546607,1727546635,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.057680958242928154,100,2,0.057680958242928154,0.2378094523630908,0,None,i7181,25,0.010634237506745105 1727546607,1727546635,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.07,100,2,0.07,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727546607,1727546648,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 1 threshold 0.07,192,1,0.07,0.25981495373843466,0,None,i7181,35,0.02011029073057738 1727546623,1727546651,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727546623,1727546651,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546622,1727546652,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.055258163496698695,100,2,0.055258163496698695,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546623,1727546654,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 153 n_clusters 2 threshold 0.07,153,2,0.07,0.2530632658164541,0,None,i7186,28,0.01620196715845628 1727546622,1727546656,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 300 n_clusters 4 threshold 0.021130204098317167,300,4,0.021130204098317167,0.2970742685671418,0,None,i7181,30,0.028736350754355253 1727546627,1727546661,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 338 n_clusters 3 threshold 0.03801340122622102,338,3,0.03801340122622102,0.3170792698174544,0,None,i7186,31,0.032483120780195045 1727546647,1727546675,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.04432546181687134,100,2,0.04432546181687134,0.23355838959739939,0,None,i7186,24,0.01074610757952646 1727546647,1727546675,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.07,100,2,0.07,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727546647,1727546682,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 357 n_clusters 2 threshold 0.028104102327867878,357,2,0.028104102327867878,0.3143285821455364,0,None,i7186,32,0.03639798838598538 1727546727,1727546755,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.005680264585283718,100,1,0.005680264585283718,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727546727,1727546759,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 171 n_clusters 1 threshold 0.05210389456083056,171,1,0.05210389456083056,0.26956739184796197,0,None,i7186,29,0.01773062313197347 1727546743,1727546771,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.002,100,2,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546743,1727546772,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.002,100,1,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546743,1727546772,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546743,1727546773,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 143 n_clusters 2 threshold 0.07,143,2,0.07,0.24731182795698925,0,None,i7186,26,0.015176871140862138 1727546743,1727546774,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 164 n_clusters 3 threshold 0.07,164,3,0.07,0.26781695423855967,0,None,i7186,27,0.01626493579916718 1727546747,1727546776,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.019960934068648455,100,1,0.019960934068648455,0.2763190797699425,0,None,i7186,25,0.009620826259196377 1727546767,1727546798,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 170 n_clusters 1 threshold 0.053131528920576813,170,1,0.053131528920576813,0.27706926731682924,0,None,i7186,27,0.017373390966789314 1727546767,1727546800,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 303 n_clusters 3 threshold 0.03359852933745482,303,3,0.03359852933745482,0.2978244561140285,0,None,i7186,29,0.028673835125448025 1727546807,1727546835,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.015689822718647516,100,4,0.015689822718647516,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546827,1727546855,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.002,100,1,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546827,1727546855,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546827,1727546855,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546827,1727546856,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.07,100,3,0.07,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727546833,1727546862,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.015051458887933562,100,4,0.015051458887933562,0.2325581395348837,0,None,i7186,26,0.010772429949592661 1727546847,1727546875,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.018314272236791183,100,2,0.018314272236791183,0.2305576394098524,0,None,i7186,24,0.010825074689725064 1727546847,1727546875,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.013919360150643632,100,4,0.013919360150643632,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546847,1727546876,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.02776961015631347,100,1,0.02776961015631347,0.2763190797699425,0,None,i7186,25,0.009620826259196377 1727546847,1727546877,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 131 n_clusters 3 threshold 0.002,131,3,0.002,0.22205551387846967,0,None,i7186,26,0.014994820133604828 1727546923,1727546951,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.045409867824205835,100,4,0.045409867824205835,0.23355838959739939,0,None,i7186,24,0.01074610757952646 1727546923,1727546952,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.03843730870068457,100,4,0.03843730870068457,0.2763190797699425,0,None,i7186,25,0.009620826259196377 1727546927,1727546955,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.06029134072937903,100,1,0.06029134072937903,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727546947,1727546976,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.030902667569034356,100,4,0.030902667569034356,0.2763190797699425,0,None,i7186,25,0.009620826259196377 1727546947,1727546976,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546947,1727546976,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.04874191085870649,100,4,0.04874191085870649,0.23355838959739939,0,None,i7186,25,0.01074610757952646 1727546947,1727546976,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.04491844423960993,100,3,0.04491844423960993,0.23355838959739939,0,None,i7186,25,0.01074610757952646 1727546947,1727546977,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 101 n_clusters 3 threshold 0.002,101,3,0.002,0.21580395098774696,0,None,i7186,26,0.011213329648201523 1727546954,1727546982,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.035067229552974664,100,4,0.035067229552974664,0.2763190797699425,0,None,i7186,25,0.009620826259196377 1727546967,1727546996,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.002,100,1,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727546967,1727546996,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 145 n_clusters 3 threshold 0.02769595179691288,145,3,0.02769595179691288,0.2693173293323331,0,None,i7186,25,0.014330505703348912 1727547047,1727547078,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 201 n_clusters 4 threshold 0.005317346998430296,201,4,0.005317346998430296,0.2790697674418605,0,None,i7186,27,0.020157817232085797 1727547048,1727547079,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 157 n_clusters 3 threshold 0.011886353333828854,157,3,0.011886353333828854,0.24356089022255567,0,None,i7186,27,0.016597899474868717 1727547067,1727547096,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.05585892460529515,100,4,0.05585892460529515,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547067,1727547097,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 138 n_clusters 3 threshold 0.01575728526107275,138,3,0.01575728526107275,0.2538134533633408,0,None,i7186,27,0.014373963861335704 1727547067,1727547097,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 150 n_clusters 1 threshold 0.044697920349177446,150,1,0.044697920349177446,0.2918229557389347,0,None,i7186,27,0.014003500875218806 1727547067,1727547098,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 1 threshold 0.002,180,1,0.002,0.25606401600400097,0,None,i7186,28,0.018373641029304947 1727547075,1727547106,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.008798780941663507,176,2,0.008798780941663507,0.2485621405351338,0,None,i7186,28,0.018730873194489097 1727547076,1727547108,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 197 n_clusters 4 threshold 0.011191385950694611,197,4,0.011191385950694611,0.2713178294573644,0,None,i7186,28,0.020588480453446693 1727547087,1727547119,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 224 n_clusters 3 threshold 0.006897893029174424,224,3,0.006897893029174424,0.2753188297074268,0,None,i7186,28,0.022911977994498626 1727547087,1727547119,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 201 n_clusters 1 threshold 0.03992544373892844,201,1,0.03992544373892844,0.2783195798949737,0,None,i7186,28,0.020199494318023953 1727547087,1727547122,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 232 n_clusters 4 threshold 0.002,232,4,0.002,0.2753188297074268,0,None,i7186,31,0.02443944319413187 1727547105,1727547134,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 143 n_clusters 3 threshold 0.006638921130590881,143,3,0.006638921130590881,0.24931232808202053,0,None,i7186,25,0.015099928828360936 1727547195,1727547223,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.008542072983910236,100,3,0.008542072983910236,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727547249,1727547277,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.011267065622627767,100,3,0.011267065622627767,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547248,1727547277,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.05967636603632209,100,4,0.05967636603632209,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547248,1727547278,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.008037252378822832,100,2,0.008037252378822832,0.2325581395348837,0,None,i7186,26,0.010772429949592661 1727547256,1727547284,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.06308625885367203,100,3,0.06308625885367203,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547256,1727547284,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.007472335109961154,100,3,0.007472335109961154,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547269,1727547297,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.006464219235339614,100,3,0.006464219235339614,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727547268,1727547298,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 110 n_clusters 4 threshold 0.06853593016670795,110,4,0.06853593016670795,0.2540635158789697,0,None,i7186,26,0.011407263580601033 1727547286,1727547314,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.06510531413346679,100,3,0.06510531413346679,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547286,1727547315,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05033543989764062,100,3,0.05033543989764062,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727547368,1727547396,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.006885602604559948,100,4,0.006885602604559948,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547388,1727547416,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05882188825742654,100,3,0.05882188825742654,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727547388,1727547416,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05872331352996277,100,3,0.05872331352996277,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547388,1727547416,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.055527757911963536,100,3,0.055527757911963536,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547407,1727547435,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.005156000750960088,100,2,0.005156000750960088,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547407,1727547436,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.060342897893130035,100,3,0.060342897893130035,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547407,1727547437,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.06099845006978716,100,4,0.06099845006978716,0.2378094523630908,0,None,i7186,26,0.010634237506745105 1727547428,1727547456,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.01906694783040519,100,3,0.01906694783040519,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727547428,1727547456,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.007978961173036184,100,4,0.007978961173036184,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727547428,1727547458,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.06347314998531951,100,2,0.06347314998531951,0.2370592648162041,0,None,i7186,26,0.010653979284294757 1727547437,1727547465,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05046542617966854,100,3,0.05046542617966854,0.23355838959739939,0,None,i7186,24,0.01074610757952646 1727547528,1727547556,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.06553061187273944,100,1,0.06553061187273944,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727547548,1727547576,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.012093263107295004,100,2,0.012093263107295004,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727547549,1727547577,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.07,100,4,0.07,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727547558,1727547590,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 215 n_clusters 1 threshold 0.07,215,1,0.07,0.28132033008252066,0,None,i7186,28,0.02121118514922848 1727547568,1727547596,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.06508957055917079,100,1,0.06508957055917079,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727547608,1727547636,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.05331690147696837,100,4,0.05331690147696837,0.23355838959739939,0,None,i7186,24,0.01074610757952646 1727547747,1727547775,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.010226484757627694,100,4,0.010226484757627694,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547767,1727547795,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.050297074187200905,100,2,0.050297074187200905,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727547767,1727547796,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 103 n_clusters 4 threshold 0.06995896884010099,103,4,0.06995896884010099,0.21680420105026255,0,None,i7186,25,0.011489358826193036 1727547787,1727547816,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.05171356642909243,100,2,0.05171356642909243,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547770,1727547824,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.002,1000,4,0.002,0.4273568392098025,0,None,i7186,50,0.10727681920480117 1727547767,1727547828,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.002,1000,4,0.002,0.4273568392098025,0,None,i7186,57,0.10727681920480117 1727547787,1727547839,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 733 n_clusters 4 threshold 0.01423889662874248,733,4,0.01423889662874248,0.3960990247561891,0,None,i7186,48,0.08193715095440525 1727547922,1727547949,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.06429852613266826,100,3,0.06429852613266826,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727547932,1727547960,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.04983193571304179,100,2,0.04983193571304179,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547912,1727547969,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 952 n_clusters 4 threshold 0.0042729433331358665,952,4,0.0042729433331358665,0.4183545886471618,0,None,i7186,54,0.11177794448612152 1727547952,1727547980,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.04713064591448759,100,2,0.04713064591448759,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727547952,1727547981,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.017493329310289744,100,3,0.017493329310289744,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547972,1727548001,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.04679583693659853,100,2,0.04679583693659853,0.23355838959739939,0,None,i7186,25,0.01074610757952646 1727547952,1727548008,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 960 n_clusters 4 threshold 0.02369764034488253,960,4,0.02369764034488253,0.43060765191297823,0,None,i7186,52,0.1056514128532133 1727547952,1727548009,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 936 n_clusters 4 threshold 0.026729959002832343,936,4,0.026729959002832343,0.4243560890222555,0,None,i7186,53,0.10877719429857466 1727547982,1727548011,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.013730462798562642,100,3,0.013730462798562642,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727547992,1727548021,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 102 n_clusters 1 threshold 0.04738284747188844,102,1,0.04738284747188844,0.22655663915978996,0,None,i7186,25,0.011225779417827429 1727547972,1727548028,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 965 n_clusters 4 threshold 0.002,965,4,0.002,0.4283570892723181,0,None,i7186,52,0.10677669417354338 1727547972,1727548032,60,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 838 n_clusters 2 threshold 0.02527490947288564,838,2,0.02527490947288564,0.39459864966241565,0,None,i7186,56,0.1236559139784946 1727548012,1727548040,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.04432391286900662,100,2,0.04432391286900662,0.23355838959739939,0,None,i7186,24,0.01074610757952646 1727548012,1727548041,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.053263310328364566,100,1,0.053263310328364566,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727548102,1727548130,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.05696358255187872,100,1,0.05696358255187872,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727548133,1727548161,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548133,1727548163,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 142 n_clusters 4 threshold 0.036549992749848076,142,4,0.036549992749848076,0.24181045261315326,0,None,i7186,26,0.015388462500240445 1727548153,1727548181,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727548153,1727548182,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.012899000969069666,100,4,0.012899000969069666,0.2325581395348837,0,None,i7186,26,0.010772429949592661 1727548163,1727548191,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727548133,1727548196,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.07,1000,1,0.07,0.4156039009752438,0,None,i7186,59,0.11315328832208052 1727548173,1727548201,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548173,1727548201,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.06818089019184256,100,2,0.06818089019184256,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727548153,1727548210,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 899 n_clusters 2 threshold 0.05995151328283586,899,2,0.05995151328283586,0.41410352588147037,0,None,i7186,54,0.11390347586896724 1727548193,1727548221,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.002,100,2,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548193,1727548222,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 123 n_clusters 2 threshold 0.019955650273226898,123,2,0.019955650273226898,0.21930482620655167,0,None,i7186,25,0.013632440368156553 1727548353,1727548382,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.002,118,2,0.002,0.23280820205051267,0,None,i7186,26,0.013196847598996522 1727548373,1727548402,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 4 threshold 0.002,119,4,0.002,0.22305576394098525,0,None,i7186,25,0.013511442376723212 1727548373,1727548402,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 3 threshold 0.04882312348935939,121,3,0.04882312348935939,0.2235558889722431,0,None,i7186,25,0.013495309311198765 1727548374,1727548404,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 130 n_clusters 1 threshold 0.002,130,1,0.002,0.2915728932233058,0,None,i7186,26,0.012080606358486174 1727548393,1727548422,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 127 n_clusters 4 threshold 0.04885075354417192,127,4,0.04885075354417192,0.23480870217554384,0,None,i7186,26,0.014037992256684862 1727548393,1727548423,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.002,117,3,0.002,0.2305576394098524,0,None,i7186,27,0.012854776194048513 1727548404,1727548435,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 3 threshold 0.05351061808041669,124,3,0.05351061808041669,0.21930482620655167,0,None,i7186,28,0.013632440368156553 1727548404,1727548435,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 1 threshold 0.002,125,1,0.002,0.23755938984746183,0,None,i7186,28,0.0134783695923981 1727548413,1727548442,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 2 threshold 0.04985646188534822,117,2,0.04985646188534822,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727548433,1727548462,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 126 n_clusters 4 threshold 0.002,126,4,0.002,0.23180795198799697,0,None,i7186,25,0.013670084187713595 1727548433,1727548464,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 208 n_clusters 4 threshold 0.04608483661187674,208,4,0.04608483661187674,0.26156539134783696,0,None,i7186,28,0.02113028257064266 1727548453,1727548482,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 1 threshold 0.05502080718657399,118,1,0.05502080718657399,0.23030757689422354,0,None,i7186,26,0.012862590647661916 1727548453,1727548483,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 185 n_clusters 4 threshold 0.03366464278404026,185,4,0.03366464278404026,0.2808202050512628,0,None,i7186,26,0.018054513628407102 1727548464,1727548495,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 216 n_clusters 4 threshold 0.056910993225296844,216,4,0.056910993225296844,0.2855713928482121,0,None,i7186,28,0.020961122633599574 1727548473,1727548502,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.04797076129278399,120,4,0.04797076129278399,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727548638,1727548666,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.061469701447964195,100,4,0.061469701447964195,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727548638,1727548667,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727548645,1727548673,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548658,1727548686,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548658,1727548686,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548675,1727548704,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05301113622889036,100,3,0.05301113622889036,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727548675,1727548704,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727548698,1727548726,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.006634891180175134,100,3,0.006634891180175134,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548698,1727548727,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 147 n_clusters 4 threshold 0.05967189425549334,147,4,0.05967189425549334,0.23730932733183296,0,None,i7186,26,0.01556158270336815 1727548706,1727548744,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 506 n_clusters 1 threshold 0.018291861864607274,506,1,0.018291861864607274,0.37009252313078267,0,None,i7186,35,0.045302992414770364 1727548718,1727548750,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 229 n_clusters 4 threshold 0.07,229,4,0.07,0.28782195548887224,0,None,i7186,28,0.022130532633158288 1727548736,1727548764,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.004676153686139434,100,3,0.004676153686139434,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548736,1727548765,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 136 n_clusters 4 threshold 0.051583151120916604,136,4,0.051583151120916604,0.24181045261315326,0,None,i7186,25,0.014289286607366128 1727548736,1727548768,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 236 n_clusters 4 threshold 0.07,236,4,0.07,0.2763190797699425,0,None,i7186,28,0.024372759856630823 1727548758,1727548786,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548758,1727548790,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 196 n_clusters 3 threshold 0.05886241202150086,196,3,0.05886241202150086,0.26606651662915726,0,None,i7186,28,0.020880220055013755 1727548918,1727548945,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727548931,1727548958,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.0530605589834275,100,2,0.0530605589834275,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727548948,1727548975,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.002,100,1,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548948,1727548975,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.00611177826414571,100,3,0.00611177826414571,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727548971,1727548999,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.05527917969599182,100,3,0.05527917969599182,0.2378094523630908,0,None,i7186,24,0.010634237506745105 1727548971,1727549000,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727548971,1727549000,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727548991,1727549018,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727548991,1727549019,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549008,1727549036,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549008,1727549037,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.06999660537426557,100,2,0.06999660537426557,0.2370592648162041,0,None,i7186,25,0.010653979284294757 1727549031,1727549058,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.2370592648162041,0,None,i7186,24,0.010653979284294757 1727549091,1727549120,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727549099,1727549127,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.05503819019168111,118,4,0.05503819019168111,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727549111,1727549138,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549299,1727549327,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549319,1727549347,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549339,1727549367,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549339,1727549368,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727549359,1727549387,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549359,1727549387,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549370,1727549397,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549370,1727549399,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 147 n_clusters 3 threshold 0.049564243548287264,147,3,0.049564243548287264,0.24256064016003998,0,None,i7186,26,0.015359609133052494 1727549399,1727549428,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.009289231223537845,100,3,0.009289231223537845,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727549399,1727549428,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.049922793700379775,100,3,0.049922793700379775,0.23355838959739939,0,None,i7186,25,0.01074610757952646 1727549419,1727549447,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549419,1727549447,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 4 threshold 0.016239182449107624,122,4,0.016239182449107624,0.21955488872218054,0,None,i7186,25,0.013624373835394332 1727549431,1727549458,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549439,1727549468,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727549459,1727549487,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549459,1727549488,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 3 threshold 0.002,100,3,0.002,0.2325581395348837,0,None,i7186,25,0.010772429949592661 1727549479,1727549507,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.002,100,4,0.002,0.2325581395348837,0,None,i7186,24,0.010772429949592661 1727549720,1727549748,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 108 n_clusters 4 threshold 0.0064899025638247305,108,4,0.0064899025638247305,0.23755938984746183,0,None,i7186,24,0.01123197466033175 1727549720,1727549749,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 106 n_clusters 3 threshold 0.003555340077775428,106,3,0.003555340077775428,0.23630907726931738,0,None,i7186,25,0.011266705565280206 1727549732,1727549760,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 108 n_clusters 3 threshold 0.014255349138286282,108,3,0.014255349138286282,0.23755938984746183,0,None,i7186,24,0.01123197466033175 1727549739,1727549767,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 108 n_clusters 4 threshold 0.002,108,4,0.002,0.2360590147536884,0,None,i7186,24,0.011273651746269901 1727549760,1727549788,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.025477107829473433,112,2,0.025477107829473433,0.23105776444111026,0,None,i7186,24,0.01208390332877337 1727549760,1727549788,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 2 threshold 0.002,107,2,0.002,0.2218054513628407,0,None,i7186,25,0.011669584062682337 1727549779,1727549807,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 3 threshold 0.007295123353855708,107,3,0.007295123353855708,0.2253063265816454,0,None,i7186,24,0.011572337528826651 1727549792,1727549821,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 109 n_clusters 4 threshold 0.012754302373462926,109,4,0.012754302373462926,0.2253063265816454,0,None,i7186,25,0.012253063265816454 1727549792,1727549821,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 131 n_clusters 1 threshold 0.04989468086293015,131,1,0.04989468086293015,0.22930732683170796,0,None,i7186,25,0.014227694854748169 1727549820,1727549848,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 2 threshold 0.007041236295683105,107,2,0.007041236295683105,0.2253063265816454,0,None,i7186,25,0.011572337528826651 1727549840,1727549868,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 110 n_clusters 4 threshold 0.017965091678095416,110,4,0.017965091678095416,0.2530632658164541,0,None,i7186,24,0.011436682700086786 1727549852,1727549878,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.032482441794649354,115,1,0.032482441794649354,0.25281320330082524,0,None,i7181,22,0.011790826494502412 1727549880,1727549908,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 3 threshold 0.01076944801129957,107,3,0.01076944801129957,0.2253063265816454,0,None,i7186,24,0.011572337528826651 1727549880,1727549909,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 111 n_clusters 3 threshold 0.02110783458104923,111,3,0.02110783458104923,0.22030507626906726,0,None,i7186,25,0.012400158863245223 1727549900,1727549928,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.029677161617395253,113,2,0.029677161617395253,0.22580645161290325,0,None,i7186,25,0.012238353706073577 1727549900,1727549928,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 129 n_clusters 1 threshold 0.03758987238951573,129,1,0.03758987238951573,0.23105776444111026,0,None,i7186,25,0.014167334937182571 1727550216,1727550248,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 2 threshold 0.011552495938111416,121,2,0.011552495938111416,0.2173043260815204,0,None,i7186,25,0.013696972630254337 1727550220,1727550249,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 134 n_clusters 4 threshold 0.004109763468849862,134,4,0.004109763468849862,0.22405601400350084,0,None,i7186,25,0.014923373700568 1727550240,1727550267,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 4 threshold 0.05062530439674229,107,4,0.05062530439674229,0.2198049512378094,0,None,i7186,24,0.011725153510599873 1727550240,1727550269,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 129 n_clusters 4 threshold 0.014512056479048718,129,4,0.014512056479048718,0.23280820205051267,0,None,i7186,26,0.014106975019616972 1727550260,1727550289,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 130 n_clusters 2 threshold 0.06398292512576617,130,2,0.06398292512576617,0.23630907726931738,0,None,i7186,25,0.013986255184485775 1727550260,1727550290,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 2 threshold 0.016093037408446975,117,2,0.016093037408446975,0.2173043260815204,0,None,i7186,26,0.013268942235558889 1727550275,1727550305,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 167 n_clusters 2 threshold 0.021790805834250603,167,2,0.021790805834250603,0.2620655163790948,0,None,i7186,27,0.01808785529715762 1727550300,1727550329,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 130 n_clusters 4 threshold 0.02713675476689094,130,4,0.02713675476689094,0.2378094523630908,0,None,i7186,25,0.01393451811228669 1727550300,1727550329,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 1 threshold 0.050908429485285706,125,1,0.050908429485285706,0.23230807701925482,0,None,i7186,25,0.013653413353338334 1727550320,1727550349,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 123 n_clusters 4 threshold 0.06770243230551048,123,4,0.06770243230551048,0.2163040760190047,0,None,i7186,25,0.01372923876130323 1727550335,1727550365,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 165 n_clusters 1 threshold 0.004017306897918611,165,1,0.004017306897918611,0.2645661415353838,0,None,i7186,27,0.01715201527654641 1727550335,1727550366,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 1 threshold 0.029365960110064322,175,1,0.029365960110064322,0.26806701675418854,0,None,i7186,27,0.0178020695650103 1727550341,1727550372,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 166 n_clusters 4 threshold 0.01735294502276842,166,4,0.01735294502276842,0.277569392348087,0,None,i7186,27,0.01656095842142354 1727550360,1727550388,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 1 threshold 0.04754481723601111,125,1,0.04754481723601111,0.23230807701925482,0,None,i7186,25,0.013653413353338334 1727550680,1727550709,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.019590452305032362,116,3,0.019590452305032362,0.21430357589397353,0,None,i7186,25,0.012957784900770646 1727550697,1727550726,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 109 n_clusters 2 threshold 0.04564829841184364,109,2,0.04564829841184364,0.22955738934733683,0,None,i7186,25,0.012128032008002 1727550697,1727550727,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 185 n_clusters 1 threshold 0.048354537952247416,185,1,0.048354537952247416,0.2808202050512628,0,None,i7186,26,0.018054513628407102 1727550720,1727550750,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 3 threshold 0.06210677520098945,180,3,0.06210677520098945,0.2740685171292824,0,None,i7186,26,0.017516283832862974 1727550720,1727550751,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 184 n_clusters 1 threshold 0.023442729135017507,184,1,0.023442729135017507,0.253313328332083,0,None,i7186,27,0.019429857464366092 1727550740,1727550768,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.029617585450613944,119,3,0.029617585450613944,0.23530882720680169,0,None,i7186,25,0.013116182271374295 1727550758,1727550787,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 134 n_clusters 2 threshold 0.0407948061007537,134,2,0.0407948061007537,0.23380845211302825,0,None,i7186,25,0.01457507233951345 1727550758,1727550788,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 183 n_clusters 4 threshold 0.002,183,4,0.002,0.25181295323830954,0,None,i7186,26,0.019504876219054765 1727550780,1727550810,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 151 n_clusters 4 threshold 0.002,151,4,0.002,0.2443110777694424,0,None,i7186,26,0.0165666416604151 1727550780,1727550811,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.015274139664493631,188,1,0.015274139664493631,0.25706426606651667,0,None,i7186,27,0.020255063765941484 1727550800,1727550832,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 216 n_clusters 2 threshold 0.03256696145972123,216,2,0.03256696145972123,0.2763190797699425,0,None,i7186,28,0.02150537634408602 1727550818,1727550848,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 203 n_clusters 1 threshold 0.06627546524346348,203,1,0.06627546524346348,0.2595648912228057,0,None,i7186,27,0.02124142146647773 1727550860,1727550890,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 156 n_clusters 4 threshold 0.02978453115761727,156,4,0.02978453115761727,0.25556389097274324,0,None,i7186,26,0.0160977744436109 1727550878,1727550908,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 1 threshold 0.03180307743059279,182,1,0.03180307743059279,0.25631407851962995,0,None,i7186,26,0.019279819954988744 1727551210,1727551239,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 3 threshold 0.012063515692830436,114,3,0.012063515692830436,0.2180545136284071,0,None,i7186,25,0.012844120120939325 1727551260,1727551289,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 3 threshold 0.062085411590224855,122,3,0.062085411590224855,0.2173043260815204,0,None,i7186,25,0.013696972630254337 1727551270,1727551299,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 4 threshold 0.06140516592265442,125,4,0.06140516592265442,0.23230807701925482,0,None,i7186,25,0.013653413353338334 1727551279,1727551308,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 140 n_clusters 2 threshold 0.05511867295140676,140,2,0.05511867295140676,0.3025756439109778,0,None,i7186,25,0.012567956804015816 1727551300,1727551328,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.007827371985366868,115,4,0.007827371985366868,0.2433108277069267,0,None,i7186,25,0.012078777270075095 1727551300,1727551329,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 3 threshold 0.03690742425570246,112,3,0.03690742425570246,0.23105776444111026,0,None,i7186,25,0.01208390332877337 1727551320,1727551348,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 3 threshold 0.07,121,3,0.07,0.22155538884721182,0,None,i7186,25,0.013559841573296549 1727551330,1727551359,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 3 threshold 0.009710557307893383,125,3,0.009710557307893383,0.253313328332083,0,None,i7186,25,0.012953238309577396 1727551340,1727551368,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 107 n_clusters 1 threshold 0.058011699526341186,107,1,0.058011699526341186,0.22155538884721182,0,None,i7186,24,0.011676530243672028 1727551360,1727551388,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 130 n_clusters 4 threshold 0.07,130,4,0.07,0.23630907726931738,0,None,i7186,25,0.013986255184485775 1727551380,1727551410,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 153 n_clusters 1 threshold 0.002,153,1,0.002,0.24981245311327827,0,None,i7186,26,0.016337417687755273 1727551380,1727551411,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 207 n_clusters 4 threshold 0.0249020414022548,207,4,0.0249020414022548,0.2620655163790948,0,None,i7186,27,0.02110249784668389 1727551400,1727551428,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 4 threshold 0.061356494900312385,121,4,0.061356494900312385,0.22155538884721182,0,None,i7186,24,0.013559841573296549 1727551420,1727551448,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 138 n_clusters 1 threshold 0.05795000053386588,138,1,0.05795000053386588,0.23480870217554384,0,None,i7186,25,0.015077843534957815 1727551420,1727551449,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 137 n_clusters 4 threshold 0.07,137,4,0.07,0.23355838959739939,0,None,i7186,25,0.014584003143643052 1727551452,1727551481,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 156 n_clusters 4 threshold 0.058787988631421886,156,4,0.058787988631421886,0.25531382845711426,0,None,i7186,25,0.01610819371509544 1727551787,1727551816,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 136 n_clusters 1 threshold 0.07,136,1,0.07,0.24181045261315326,0,None,i7186,25,0.014289286607366128 1727551806,1727551835,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.03853388145125447,118,2,0.03853388145125447,0.22905726431607898,0,None,i7186,26,0.012901662915728933 1727551813,1727551848,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 297 n_clusters 4 threshold 0.07,297,4,0.07,0.29407351837959494,0,None,i7186,32,0.02898641326998416 1727551826,1727551855,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.05658891483728301,119,3,0.05658891483728301,0.23730932733183296,0,None,i7186,25,0.013051650009276512 1727551843,1727551876,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 311 n_clusters 4 threshold 0.07,311,4,0.07,0.3248312078019505,0,None,i7186,29,0.02882538816522312 1727551866,1727551901,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 331 n_clusters 1 threshold 0.07,331,1,0.07,0.3063265816454114,0,None,i7186,31,0.03050762690672668 1727551874,1727551906,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 301 n_clusters 2 threshold 0.07,301,2,0.07,0.2928232058014504,0,None,i7186,29,0.02909060598482954 1727551886,1727551919,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 298 n_clusters 1 threshold 0.06290130154637896,298,1,0.06290130154637896,0.2953238309577394,0,None,i7186,29,0.028882220555138786 1727551904,1727551936,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 273 n_clusters 3 threshold 0.07,273,3,0.07,0.2855713928482121,0,None,i7186,29,0.02741069882855329 1727551926,1727551961,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 317 n_clusters 1 threshold 0.04932558638069694,317,1,0.04932558638069694,0.30307576894223553,0,None,i7186,31,0.03080315533428812 1727551926,1727551962,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 355 n_clusters 4 threshold 0.07,355,4,0.07,0.3178294573643411,0,None,i7186,32,0.03600900225056264 1727551946,1727551979,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 288 n_clusters 1 threshold 0.04553835051464607,288,1,0.04553835051464607,0.2995748937234308,0,None,i7186,29,0.028527965324664502 1727551964,1727551997,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 297 n_clusters 1 threshold 0.06648232899956583,297,1,0.06648232899956583,0.29407351837959494,0,None,i7186,29,0.02898641326998416 1727551964,1727551998,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 341 n_clusters 4 threshold 0.04964603939215652,341,4,0.04964603939215652,0.31532883220805197,0,None,i7186,30,0.032658164541135286 1727551986,1727552020,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 266 n_clusters 2 threshold 0.0683259771077537,266,2,0.0683259771077537,0.2980745186296574,0,None,i7186,30,0.026448919922288264 1727551995,1727552028,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 288 n_clusters 3 threshold 0.06224461821525511,288,3,0.06224461821525511,0.3005751437859465,0,None,i7186,30,0.028444611152788193 1727552006,1727552039,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 270 n_clusters 3 threshold 0.07,270,3,0.07,0.2995748937234308,0,None,i7186,29,0.02633350645353646 1727552323,1727552353,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.044579762254865166,115,2,0.044579762254865166,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727552343,1727552371,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.07,118,2,0.07,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727552357,1727552385,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.018620524688221105,115,2,0.018620524688221105,0.24081020255063768,0,None,i7186,25,0.01215455378996264 1727552383,1727552411,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.043632649535592004,116,3,0.043632649535592004,0.21480370092523127,0,None,i7186,24,0.012942629596793138 1727552383,1727552412,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 1 threshold 0.07,117,1,0.07,0.21830457614403598,0,None,i7186,25,0.013237684421105277 1727552403,1727552431,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 2 threshold 0.041266368934308656,116,2,0.041266368934308656,0.21480370092523127,0,None,i7186,24,0.012942629596793138 1727552418,1727552447,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.05437737920767142,117,3,0.05437737920767142,0.2218054513628407,0,None,i7186,26,0.01312828207051763 1727552423,1727552451,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.04594615865847009,114,1,0.04594615865847009,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727552443,1727552480,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 445 n_clusters 4 threshold 0.07,445,4,0.07,0.3490872718179545,0,None,i7186,34,0.041831886543064335 1727552463,1727552500,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 449 n_clusters 1 threshold 0.07,449,1,0.07,0.34983745936484123,0,None,i7186,33,0.04172471689350909 1727552478,1727552506,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 1 threshold 0.05759714565659445,118,1,0.05759714565659445,0.23030757689422354,0,None,i7186,24,0.012862590647661916 1727552483,1727552511,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.07,117,4,0.07,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727552509,1727552538,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 3 threshold 0.07,122,3,0.07,0.2288072018004501,0,None,i7186,25,0.013325912123192087 1727552503,1727552539,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 429 n_clusters 4 threshold 0.07,429,4,0.07,0.33608402100525137,0,None,i7186,32,0.038228307076769184 1727552523,1727552561,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 391 n_clusters 1 threshold 0.03834735696792231,391,1,0.03834735696792231,0.3278319579894974,0,None,i7186,34,0.03925981495373843 1727552840,1727552869,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.05170021346762483,116,3,0.05170021346762483,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727552864,1727552892,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06092471836149912,117,4,0.06092471836149912,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727552870,1727552898,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.04545478183220153,116,3,0.04545478183220153,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727552884,1727552913,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 1 threshold 0.07,120,1,0.07,0.2388097024256064,0,None,i7186,25,0.013003250812703175 1727552900,1727552929,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.041203120012886736,119,3,0.041203120012886736,0.23530882720680169,0,None,i7186,25,0.013116182271374295 1727552904,1727552932,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.016164745669418223,115,3,0.016164745669418223,0.22655663915978996,0,None,i7186,25,0.012586479953321662 1727552924,1727552952,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 2 threshold 0.04171858726843403,117,2,0.04171858726843403,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727552944,1727552974,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 4 threshold 0.07,121,4,0.07,0.22155538884721182,0,None,i7186,27,0.013559841573296549 1727552961,1727552990,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.042790925968888144,117,3,0.042790925968888144,0.2218054513628407,0,None,i7186,26,0.01312828207051763 1727552984,1727553012,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.055420162754167675,116,4,0.055420162754167675,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727552984,1727553012,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.059666580388239555,117,3,0.059666580388239555,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727553004,1727553033,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.07,119,3,0.07,0.23455863965991497,0,None,i7186,26,0.013140381869660964 1727553021,1727553050,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.042545806243486826,117,3,0.042545806243486826,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727553024,1727553053,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 1 threshold 0.06364792701019797,118,1,0.06364792701019797,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727553044,1727553072,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.055987445168224216,116,4,0.055987445168224216,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727553430,1727553459,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 4 threshold 0.06443587593083872,121,4,0.06443587593083872,0.22155538884721182,0,None,i7186,25,0.013559841573296549 1727553444,1727553472,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 1 threshold 0.055532628574374784,116,1,0.055532628574374784,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727553450,1727553478,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.048113969059482085,114,2,0.048113969059482085,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727553470,1727553498,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.054387243157401205,116,3,0.054387243157401205,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727553474,1727553501,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.052273391108551746,114,1,0.052273391108551746,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727553504,1727553532,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05542126529252379,114,2,0.05542126529252379,0.22405601400350084,0,None,i7186,25,0.012662256473209212 1727553510,1727553538,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 1 threshold 0.06069260778373681,117,1,0.06069260778373681,0.21830457614403598,0,None,i7186,25,0.013237684421105277 1727553530,1727553558,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.017508768298486976,116,3,0.017508768298486976,0.21755438859714926,0,None,i7186,24,0.012859275424916836 1727553550,1727553579,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.06248435101024141,120,4,0.06248435101024141,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727553565,1727553593,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.05689484817322552,116,4,0.05689484817322552,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727553570,1727553598,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 3 threshold 0.046536929218029426,116,3,0.046536929218029426,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727553590,1727553623,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 246 n_clusters 1 threshold 0.002,246,1,0.002,0.2883220805201301,0,None,i7186,30,0.025256314078519627 1727553610,1727553638,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 1 threshold 0.05838228183750932,116,1,0.05838228183750932,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727553625,1727553658,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 263 n_clusters 1 threshold 0.0041600701664901265,263,1,0.0041600701664901265,0.29332333083270823,0,None,i7186,29,0.02681439590666897 1727553650,1727553682,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 250 n_clusters 4 threshold 0.029873240072150135,250,4,0.029873240072150135,0.28232058014503625,0,None,i7186,28,0.025684992676740615 1727553650,1727553683,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 220 n_clusters 4 threshold 0.01066568469961181,220,4,0.01066568469961181,0.2740685171292824,0,None,i7186,28,0.022990122530632654 1727554139,1727554168,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 2 threshold 0.04515527326062043,116,2,0.04515527326062043,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727554159,1727554187,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 2 threshold 0.061794914468645955,116,2,0.061794914468645955,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727554159,1727554195,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 379 n_clusters 4 threshold 0.002,379,4,0.002,0.32708177044261066,0,None,i7186,33,0.03935358839709927 1727554168,1727554197,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.05368445215421697,115,3,0.05368445215421697,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727554199,1727554228,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.0638068926084063,114,2,0.0638068926084063,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727554199,1727554232,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 319 n_clusters 4 threshold 0.01115823408830967,319,4,0.01115823408830967,0.2955738934733684,0,None,i7186,30,0.03148514401327604 1727554219,1727554246,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.05433154095646606,115,3,0.05433154095646606,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727554239,1727554267,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.04607147330221912,115,2,0.04607147330221912,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727554259,1727554287,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 4 threshold 0.00902585078544785,125,4,0.00902585078544785,0.253313328332083,0,None,i7186,24,0.012953238309577396 1727554279,1727554308,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 2 threshold 0.036144091662880316,119,2,0.036144091662880316,0.23530882720680169,0,None,i7186,25,0.013116182271374295 1727554289,1727554317,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 2 threshold 0.032302490477854294,121,2,0.032302490477854294,0.2235558889722431,0,None,i7186,25,0.013495309311198765 1727554299,1727554332,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 320 n_clusters 4 threshold 0.010920003002940933,320,4,0.010920003002940933,0.3043260815203801,0,None,i7186,30,0.030689490554456796 1727554320,1727554357,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 374 n_clusters 4 threshold 0.024602903747595516,374,4,0.024602903747595516,0.32358089522380595,0,None,i7186,33,0.035369953599510985 1727554339,1727554367,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.04720090022917441,115,3,0.04720090022917441,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727554349,1727554377,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.06161751500267471,113,1,0.06161751500267471,0.22480620155038755,0,None,i7186,24,0.012267772825559332 1727554379,1727554417,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 358 n_clusters 3 threshold 0.002,358,3,0.002,0.3195798949737434,0,None,i7186,34,0.03581450918285127 1727554862,1727554891,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 3 threshold 0.01966802006897026,118,3,0.01966802006897026,0.23655913978494625,0,None,i7186,25,0.01307584960756318 1727554886,1727554914,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.06553143009882505,115,3,0.06553143009882505,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727554886,1727554914,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05220973555180554,114,2,0.05220973555180554,0.22405601400350084,0,None,i7186,25,0.012662256473209212 1727554906,1727554935,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.058146785428308055,114,2,0.058146785428308055,0.21655413853463368,0,None,i7186,26,0.012889586032871853 1727554922,1727554950,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.0574036213260842,115,1,0.0574036213260842,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727554946,1727554975,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.053575767135452965,116,4,0.053575767135452965,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727554952,1727554981,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.062370783993535184,117,3,0.062370783993535184,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727554966,1727554995,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.05903582662212139,118,4,0.05903582662212139,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727554983,1727555012,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.06345497674900324,120,4,0.06345497674900324,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727555006,1727555034,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05101772820784843,114,2,0.05101772820784843,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727555026,1727555055,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06426972084715606,117,4,0.06426972084715606,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727555043,1727555071,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 4 threshold 0.06308832734491551,122,4,0.06308832734491551,0.2173043260815204,0,None,i7186,24,0.013696972630254337 1727555067,1727555094,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.01595924668896177,115,3,0.01595924668896177,0.22655663915978996,0,None,i7186,24,0.012586479953321662 1727555067,1727555096,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 4 threshold 0.06932262132692052,121,4,0.06932262132692052,0.22155538884721182,0,None,i7186,26,0.013559841573296549 1727555086,1727555114,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.05299380815365268,118,2,0.05299380815365268,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727555104,1727555132,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.01670133052618664,115,3,0.01670133052618664,0.2395598899724931,0,None,i7186,24,0.012192442049906415 1727555126,1727555155,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.020471670135727524,119,3,0.020471670135727524,0.23655913978494625,0,None,i7186,25,0.01307584960756318 1727555665,1727555695,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.05051498123570885,114,1,0.05051498123570885,0.22405601400350084,0,None,i7186,25,0.012662256473209212 1727555705,1727555733,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 3 threshold 0.013207783738935176,114,3,0.013207783738935176,0.2298074518629657,0,None,i7186,24,0.012487970477467853 1727555705,1727555734,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 111 n_clusters 2 threshold 0.002,111,2,0.002,0.21580395098774696,0,None,i7186,25,0.012912318988838118 1727555725,1727555753,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.05477008937039593,113,1,0.05477008937039593,0.2235558889722431,0,None,i7186,24,0.012304546724916522 1727555738,1727555765,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.05863506147947968,115,3,0.05863506147947968,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727555765,1727555794,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.0559224202783043,115,3,0.0559224202783043,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727555768,1727555795,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.05938384606351277,115,1,0.05938384606351277,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727555798,1727555827,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.04769105136253558,115,2,0.04769105136253558,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727555805,1727555833,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.06406697944679045,119,3,0.06406697944679045,0.23455863965991497,0,None,i7186,25,0.013140381869660964 1727555825,1727555855,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 2 threshold 0.04443639244158502,117,2,0.04443639244158502,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727555845,1727555874,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.012434867175065395,113,2,0.012434867175065395,0.22280570142535638,0,None,i7186,25,0.012700144733152983 1727555858,1727555886,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.058970502227004826,116,4,0.058970502227004826,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727555885,1727555914,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 3 threshold 0.03287319791763039,124,3,0.03287319791763039,0.22655663915978996,0,None,i7186,25,0.013398510918052093 1727555905,1727555933,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.04777296384524943,115,3,0.04777296384524943,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727555919,1727555947,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 1 threshold 0.062436988506878906,117,1,0.062436988506878906,0.21830457614403598,0,None,i7186,25,0.013237684421105277 1727555945,1727555974,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.016145392243376695,117,3,0.016145392243376695,0.2173043260815204,0,None,i7186,24,0.013268942235558889 1727556492,1727556520,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.049100335637420514,115,3,0.049100335637420514,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727556521,1727556549,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.010154482690847573,112,2,0.010154482690847573,0.21605401350337583,0,None,i7186,25,0.012525190121059676 1727556521,1727556549,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 2 threshold 0.025911205758834176,120,2,0.025911205758834176,0.23480870217554384,0,None,i7186,25,0.013132315336898742 1727556541,1727556569,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.05144686667314946,114,1,0.05144686667314946,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727556561,1727556589,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06156670604783374,117,4,0.06156670604783374,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727556581,1727556610,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 2 threshold 0.04128742970403914,119,2,0.04128742970403914,0.23730932733183296,0,None,i7186,25,0.013051650009276512 1727556601,1727556628,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 103 n_clusters 3 threshold 0.01358438869519805,103,3,0.01358438869519805,0.2235558889722431,0,None,i7186,24,0.011306880774247614 1727556621,1727556650,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.06481745102781318,118,2,0.06481745102781318,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727556641,1727556670,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 3 threshold 0.002002945708283993,113,3,0.002002945708283993,0.22230557639409854,0,None,i7186,25,0.012715300037130494 1727556661,1727556689,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.050852411076752084,115,2,0.050852411076752084,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727556673,1727556701,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.06451484486478709,120,4,0.06451484486478709,0.2378094523630908,0,None,i7186,24,0.013035516943752065 1727556701,1727556730,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.009891099708206104,113,2,0.009891099708206104,0.2225556389097274,0,None,i7186,25,0.01270772238514174 1727556721,1727556750,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.05583083498891277,120,4,0.05583083498891277,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727556733,1727556762,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 2 threshold 0.07,117,2,0.07,0.21830457614403598,0,None,i7186,25,0.013237684421105277 1727556741,1727556770,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.05457758010099698,118,4,0.05457758010099698,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727556761,1727556789,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06726491789254614,117,4,0.06726491789254614,0.2173043260815204,0,None,i7186,24,0.013268942235558889 1727557427,1727557455,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.05626426591231302,115,4,0.05626426591231302,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727557456,1727557485,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.07,120,4,0.07,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727557477,1727557505,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 4 threshold 0.05481779983518905,114,4,0.05481779983518905,0.22405601400350084,0,None,i7186,25,0.012662256473209212 1727557487,1727557515,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 1 threshold 0.07,116,1,0.07,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727557497,1727557525,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 3 threshold 0.03508205713135582,120,3,0.03508205713135582,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727557517,1727557545,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 1 threshold 0.0655027700717233,118,1,0.0655027700717233,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727557536,1727557565,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 128 n_clusters 4 threshold 0.06699996089064816,128,4,0.06699996089064816,0.2415603900975244,0,None,i7186,25,0.01380517543178898 1727557557,1727557585,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 123 n_clusters 3 threshold 0.042386264329507116,123,3,0.042386264329507116,0.2190547636909227,0,None,i7186,25,0.013640506900918779 1727557577,1727557604,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.07,115,3,0.07,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727557597,1727557626,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 1 threshold 0.06767222646194004,119,1,0.06767222646194004,0.23455863965991497,0,None,i7186,25,0.013140381869660964 1727557617,1727557645,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.010995707967038321,112,2,0.010995707967038321,0.22030507626906726,0,None,i7186,25,0.012400158863245223 1727557637,1727557665,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.011579071128290921,113,2,0.011579071128290921,0.22605651412853212,0,None,i7186,25,0.012601635257299173 1727557657,1727557687,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.010204838446544254,113,2,0.010204838446544254,0.2225556389097274,0,None,i7186,27,0.01270772238514174 1727557677,1727557705,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.056497899010438814,113,1,0.056497899010438814,0.2235558889722431,0,None,i7186,25,0.012304546724916522 1727557697,1727557725,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 3 threshold 0.03025798676283232,119,3,0.03025798676283232,0.23530882720680169,0,None,i7186,25,0.013116182271374295 1727557717,1727557745,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 2 threshold 0.06578759629506598,116,2,0.06578759629506598,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727557737,1727557765,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.036373576816930854,112,2,0.036373576816930854,0.23105776444111026,0,None,i7186,24,0.01208390332877337 1727558434,1727558464,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.052007152822469926,117,4,0.052007152822469926,0.2218054513628407,0,None,i7186,26,0.01312828207051763 1727558457,1727558484,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.06204404719860988,116,4,0.06204404719860988,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727558477,1727558505,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.05326150256751653,114,1,0.05326150256751653,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727558497,1727558525,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.05630930585767625,115,1,0.05630930585767625,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727558514,1727558542,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.058472812350973465,113,2,0.058472812350973465,0.2235558889722431,0,None,i7186,24,0.012304546724916522 1727558537,1727558567,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 164 n_clusters 3 threshold 0.002,164,3,0.002,0.26431607901975496,0,None,i7186,27,0.01716338175452954 1727558557,1727558586,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 4 threshold 0.06933912048523001,114,4,0.06933912048523001,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727558574,1727558602,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.05392142980571736,114,1,0.05392142980571736,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727558597,1727558625,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.07,115,2,0.07,0.23105776444111026,0,None,i7186,24,0.012450082217524078 1727558617,1727558647,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 167 n_clusters 3 threshold 0.009659435662515588,167,3,0.009659435662515588,0.2620655163790948,0,None,i7186,26,0.01808785529715762 1727558637,1727558666,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.057724250730739377,115,4,0.057724250730739377,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727558657,1727558685,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05370304786377952,114,2,0.05370304786377952,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727558677,1727558707,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 151 n_clusters 4 threshold 0.0020978694902200443,151,4,0.0020978694902200443,0.24481120280070012,0,None,i7186,26,0.01654580311744603 1727558697,1727558725,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 2 threshold 0.0321553651196786,124,2,0.0321553651196786,0.21930482620655167,0,None,i7186,24,0.013632440368156553 1727558717,1727558746,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 106 n_clusters 2 threshold 0.002,106,2,0.002,0.21880470117529383,0,None,i7186,25,0.011752938234558639 1727558737,1727558765,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 1 threshold 0.05672296711980038,116,1,0.05672296711980038,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727559505,1727559536,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.07,117,4,0.07,0.2173043260815204,0,None,i7186,26,0.013268942235558889 1727559526,1727559553,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.06365647076105954,116,4,0.06365647076105954,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727559546,1727559575,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 1 threshold 0.07,119,1,0.07,0.2378094523630908,0,None,i7186,25,0.013035516943752065 1727559566,1727559594,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 3 threshold 0.06205948341446794,115,3,0.06205948341446794,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727559586,1727559616,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.009264479078473914,113,2,0.009264479078473914,0.2225556389097274,0,None,i7186,26,0.01270772238514174 1727559606,1727559634,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.05090223050770882,117,4,0.05090223050770882,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727559630,1727559658,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.058056755416066155,116,4,0.058056755416066155,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727559660,1727559687,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.07,114,2,0.07,0.22105526381595397,0,None,i7186,24,0.012753188297074268 1727559690,1727559718,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.07,118,4,0.07,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727559706,1727559734,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.06025643337743771,114,1,0.06025643337743771,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727559686,1727559741,55,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 554 n_clusters 4 threshold 0.002,554,4,0.002,0.36384096024005996,0,None,i7186,51,0.06951737934483622 1727559727,1727559755,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.0625201119640069,117,4,0.0625201119640069,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727559746,1727559775,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 4 threshold 0.07,119,4,0.07,0.23455863965991497,0,None,i7186,25,0.013140381869660964 1727559780,1727559809,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.05267080637688243,114,1,0.05267080637688243,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727559806,1727559834,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.05285568621151811,113,1,0.05285568621151811,0.2235558889722431,0,None,i7186,24,0.012304546724916522 1727559826,1727559855,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.06233515075198325,114,2,0.06233515075198325,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727559841,1727559869,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.07,118,4,0.07,0.23030757689422354,0,None,i7186,25,0.012862590647661916 1727560714,1727560743,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.008660193920224116,115,2,0.008660193920224116,0.2343085771442861,0,None,i7186,25,0.012351572741670265 1727560744,1727560772,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.055219573865808565,115,1,0.055219573865808565,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727560774,1727560803,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 2 threshold 0.02659994982610766,122,2,0.02659994982610766,0.22855713928482124,0,None,i7186,25,0.01333397865595431 1727560794,1727560822,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.05291212238736009,113,1,0.05291212238736009,0.2235558889722431,0,None,i7186,25,0.012304546724916522 1727560805,1727560833,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 4 threshold 0.04822852235469621,124,4,0.04822852235469621,0.21930482620655167,0,None,i7186,24,0.013632440368156553 1727560815,1727560843,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.04320389359021309,114,2,0.04320389359021309,0.22405601400350084,0,None,i7186,25,0.012662256473209212 1727560854,1727560882,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.05473486228702345,115,1,0.05473486228702345,0.2325581395348837,0,None,i7186,25,0.01240461630559155 1727560865,1727560894,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 2 threshold 0.04420695451709315,118,2,0.04420695451709315,0.2305576394098524,0,None,i7186,25,0.012854776194048513 1727560895,1727560923,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.011057792693587642,113,2,0.011057792693587642,0.22605651412853212,0,None,i7186,25,0.012601635257299173 1727560914,1727560942,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.010933196628926063,112,2,0.010933196628926063,0.21755438859714926,0,None,i7186,24,0.012481061441831046 1727560926,1727560953,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 2 threshold 0.02494182506631512,122,2,0.02494182506631512,0.22855713928482124,0,None,i7186,24,0.01333397865595431 1727560954,1727560982,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.05521556398034942,113,1,0.05521556398034942,0.2235558889722431,0,None,i7186,25,0.012304546724916522 1727560974,1727561002,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.06137234924371029,116,4,0.06137234924371029,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727560994,1727561023,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.002,113,2,0.002,0.2225556389097274,0,None,i7186,25,0.01270772238514174 1727561014,1727561042,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.045591806662690096,114,2,0.045591806662690096,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727561034,1727561063,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.05454564658519244,115,4,0.05454564658519244,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727562013,1727562047,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.060033844769105345,118,4,0.060033844769105345,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727562042,1727562071,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 125 n_clusters 3 threshold 0.002,125,3,0.002,0.23855963990997753,0,None,i7186,24,0.013445027923647577 1727562065,1727562094,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.059517250651239854,113,1,0.059517250651239854,0.2235558889722431,0,None,i7186,25,0.012304546724916522 1727562072,1727562101,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 1 threshold 0.003816817386661844,124,1,0.003816817386661844,0.22430607651912982,0,None,i7186,25,0.0139201467033425 1727562103,1727562132,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 3 threshold 0.0496199275772312,117,3,0.0496199275772312,0.2218054513628407,0,None,i7186,25,0.01312828207051763 1727562125,1727562153,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.052441081497670225,115,4,0.052441081497670225,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727562145,1727562173,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.061436167427381276,114,2,0.061436167427381276,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727562163,1727562192,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 129 n_clusters 2 threshold 0.0025033373770192106,129,2,0.0025033373770192106,0.2315578894723681,0,None,i7186,25,0.014150089246449542 1727562185,1727562213,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.05405395208781312,115,4,0.05405395208781312,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727562205,1727562233,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.060930292397190035,116,4,0.060930292397190035,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727562225,1727562254,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 122 n_clusters 4 threshold 0.002,122,4,0.002,0.22305576394098525,0,None,i7186,25,0.013511442376723212 1727562253,1727562281,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05992560853390094,114,2,0.05992560853390094,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727562283,1727562312,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 1 threshold 0.05425650672479831,116,1,0.05425650672479831,0.22155538884721182,0,None,i7186,25,0.012738032993096757 1727562305,1727562335,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 127 n_clusters 4 threshold 0.002,127,4,0.002,0.23580895223805953,0,None,i7186,26,0.014003500875218804 1727562313,1727562341,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 1 threshold 0.052454473630330484,113,1,0.052454473630330484,0.22830707676919226,0,None,i7186,24,0.012164805907359194 1727562345,1727562374,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06117059395492405,117,4,0.06117059395492405,0.2173043260815204,0,None,i7186,25,0.013268942235558889 1727562365,1727562394,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06607359333824915,117,4,0.06607359333824915,0.2173043260815204,0,None,i7186,24,0.013268942235558889 1727563354,1727563383,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 112 n_clusters 2 threshold 0.0092509812647419,112,2,0.0092509812647419,0.21880470117529383,0,None,i7186,25,0.012444287542473854 1727563374,1727563402,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.05537438262765314,114,2,0.05537438262765314,0.22405601400350084,0,None,i7186,24,0.012662256473209212 1727563394,1727563423,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 2 threshold 0.044453740972655066,115,2,0.044453740972655066,0.23230807701925482,0,None,i7186,25,0.012412193957580303 1727563414,1727563442,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.06034647220360621,113,2,0.06034647220360621,0.2235558889722431,0,None,i7186,24,0.012304546724916522 1727563454,1727563483,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.011975823665444172,114,2,0.011975823665444172,0.2298074518629657,0,None,i7186,25,0.012487970477467853 1727563474,1727563502,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.057304554447823466,113,2,0.057304554447823466,0.22830707676919226,0,None,i7186,24,0.012164805907359194 1727563490,1727563518,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 116 n_clusters 4 threshold 0.061818776978195836,116,4,0.061818776978195836,0.22155538884721182,0,None,i7186,24,0.012738032993096757 1727563514,1727563542,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.010794780314437029,113,2,0.010794780314437029,0.22380595148787197,0,None,i7186,24,0.012669834125197966 1727563534,1727563564,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.06501617908349744,114,2,0.06501617908349744,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727563550,1727563577,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 1 threshold 0.05873251663074221,115,1,0.05873251663074221,0.2325581395348837,0,None,i7186,24,0.01240461630559155 1727563574,1727563603,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 118 n_clusters 4 threshold 0.061978569634611326,118,4,0.061978569634611326,0.22230557639409854,0,None,i7186,25,0.013112653163290822 1727563610,1727563639,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 2 threshold 0.04698919624817095,119,2,0.04698919624817095,0.23730932733183296,0,None,i7186,25,0.013051650009276512 1727563634,1727563662,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.058503019997978266,114,2,0.058503019997978266,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727563654,1727563683,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 121 n_clusters 2 threshold 0.042947305646313876,121,2,0.042947305646313876,0.2235558889722431,0,None,i7186,25,0.013495309311198765 1727563670,1727563698,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 119 n_clusters 2 threshold 0.04186932529369753,119,2,0.04186932529369753,0.23730932733183296,0,None,i7186,25,0.013051650009276512 1727563694,1727563723,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.059182791935982165,114,2,0.059182791935982165,0.21655413853463368,0,None,i7186,25,0.012889586032871853 1727564787,1727564821,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.06622685421502346,114,2,0.06622685421502346,0.22105526381595397,0,None,i7186,25,0.012753188297074268 1727564816,1727564844,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 120 n_clusters 4 threshold 0.002,120,4,0.002,0.26906726681670423,0,None,i7186,24,0.012027200348474213 1727564846,1727564875,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 113 n_clusters 2 threshold 0.009609444742674264,113,2,0.009609444742674264,0.2225556389097274,0,None,i7186,25,0.01270772238514174 1727564867,1727564896,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 123 n_clusters 1 threshold 0.07,123,1,0.07,0.2163040760190047,0,None,i7186,25,0.01372923876130323 1727564887,1727564917,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 117 n_clusters 4 threshold 0.06321623805140918,117,4,0.06321623805140918,0.2173043260815204,0,None,i7186,26,0.013268942235558889 1727564906,1727564936,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 115 n_clusters 4 threshold 0.0632472236804753,115,4,0.0632472236804753,0.23230807701925482,0,None,i7186,24,0.012412193957580303 1727564927,1727564955,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 2 threshold 0.060441637068134706,114,2,0.060441637068134706,0.21655413853463368,0,None,i7186,24,0.012889586032871853 1727564967,1727564996,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 SemiParametricLogLikelihood n_samples 130 n_clusters 3 threshold 0.008315962059984895,130,3,0.008315962059984895,0.2890722680670168,0,None,i7186,25,0.012166834812151311
timestamp,ram_usage_mb,cpu_usage_percent 1727546300,475.28125,49.7 1727546300,475.3125,47.2 1727546300,475.359375,49.8 1727546300,475.359375,50.0 1727546300,475.359375,55.6 1727546300,475.359375,50.0 1727546300,475.359375,39.4 1727546347,483.87890625,49.8 1727546347,483.87890625,54.3 1727546347,483.87890625,49.5 1727546347,483.87890625,56.8 1727546348,483.87890625,49.7 1727546348,483.87890625,55.3 1727546348,483.87890625,47.7 1727546348,483.87890625,52.4 1727546350,483.87890625,49.7 1727546350,483.87890625,55.3 1727546350,483.87890625,47.7 1727546350,483.87890625,56.5 1727546351,483.87890625,49.7 1727546351,483.87890625,56.3 1727546351,483.87890625,52.5 1727546351,483.87890625,39.4 1727546352,483.87890625,49.7 1727546352,483.87890625,54.3 1727546352,483.87890625,48.1 1727546352,483.87890625,55.6 1727546354,484.69921875,49.8 1727546354,484.69921875,55.3 1727546354,484.69921875,48.1 1727546354,484.69921875,41.2 1727546357,484.8203125,49.9 1727546357,484.8203125,55.6 1727546357,484.8203125,48.6 1727546357,484.8203125,56.5 1727546359,484.8203125,49.9 1727546359,484.8203125,53.2 1727546359,484.8203125,52.4 1727546359,484.8203125,40.6 1727546361,484.89453125,49.9 1727546361,484.89453125,43.2 1727546361,484.89453125,52.6 1727546361,484.89453125,38.2 1727546364,484.89453125,49.9 1727546364,484.89453125,53.2 1727546364,484.89453125,49.6 1727546364,484.89453125,52.4 1727546366,484.90234375,49.9 1727546366,484.90234375,38.2 1727546366,484.90234375,53.8 1727546366,484.90234375,41.2 1727546368,484.90234375,49.9 1727546368,484.90234375,52.1 1727546368,484.90234375,52.0 1727546368,484.90234375,40.6 1727546371,484.90625,49.9 1727546371,484.90625,39.4 1727546371,484.90625,53.7 1727546371,484.90625,37.5 1727546373,484.90625,49.9 1727546373,484.90625,39.4 1727546373,484.90625,51.6 1727546373,484.90625,56.8 1727546376,484.90625,49.9 1727546376,484.90625,41.2 1727546376,484.90625,53.4 1727546376,484.90625,39.4 1727546378,484.90625,49.8 1727546378,484.90625,55.3 1727546378,484.90625,48.1 1727546378,484.90625,56.8 1727546380,484.96875,49.9 1727546380,484.96875,46.3 1727546380,484.96875,50.4 1727546380,484.96875,38.7 1727546383,484.98046875,49.9 1727546383,484.98046875,54.3 1727546383,484.98046875,47.7 1727546383,484.98046875,55.6 1727546385,484.98046875,49.9 1727546385,484.98046875,52.1 1727546385,484.98046875,47.7 1727546385,484.98046875,57.4 1727546387,484.98046875,49.8 1727546387,484.98046875,54.3 1727546387,484.98046875,46.4 1727546387,484.98046875,56.8 1727546390,484.98046875,49.8 1727546390,484.98046875,54.3 1727546390,484.98046875,52.8 1727546390,484.98046875,39.4 1727546392,484.98046875,49.8 1727546392,484.98046875,55.3 1727546392,484.98046875,51.6 1727546392,484.98046875,38.7 1727546394,484.98046875,49.9 1727546394,484.98046875,55.6 1727546394,484.98046875,46.9 1727546394,484.98046875,56.8 1727546396,484.98046875,49.9 1727546396,484.98046875,48.8 1727546396,484.98046875,48.7 1727546396,484.98046875,55.6 1727546398,484.98046875,49.8 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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 (2024-09-28 19:58:24,192) - Starting with JobEnvironment(job_id=2287276, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-28 19:58:24,194) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/SemiParametricLogLikelihood_OutdoorObjects/0/single_runs/2287276/2287276_submitted.pkl
Parameters: {"n_samples": 666, "n_clusters": 3, "threshold": 0.028584242939949038}
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 SemiParametricLogLikelihood n_samples 666 n_clusters 3 threshold 0.028584242939949038
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: 1727546057.0
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: 1727546059.0
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 SemiParametricLogLikelihood n_samples 666 n_clusters 3 threshold 0.028584242939949038
stdout:
RESULT: 0.4013503375843961
OO-Info: runtime: 52
OO-Info: lpd: 0.06014003500875219
Result: 0.4013503375843961
EXIT_CODE: 0
submitit INFO (2024-09-28 19:59:23,774) - Job completed successfully
submitit INFO (2024-09-28 19:59:23,777) - Exiting after successful completion
Job-Infos
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background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
background-size: 15px;
font-size: 11px;
border: none;
background-color: #fff;
box-sizing: border-box;
height: 21px;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
position: relative;
padding: 5px 32px 32px 5px;
background-position: top 50% right 2px;
background-repeat: no-repeat;
border-radius: 0;
border: 1px solid black;
}
body {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
font-variant: oldstyle-nums;
text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
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;
}
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%;
min-height: 80px;
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: 60px;
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;
}
.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;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.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;
}
/*! 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 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 7h1'/%3E%3Cpath stroke='%23c5d5fc' d='M9 3h1M3 9h1m3 0h1'/%3E%3Cpath stroke='%23c5d3fc' d='M10 3h1'/%3E%3Cpath stroke='%23bed0fc' d='M12 3h1M9 4h1m-7 7h1m0 1h1'/%3E%3Cpath stroke='%23bccdfa' d='M13 3h1'/%3E%3Cpath stroke='%23baccf4' d='M14 3h1'/%3E%3Cpath stroke='%23bdcbda' d='M16 3h1'/%3E%3Cpath stroke='%23c4d4f7' d='M2 4h1'/%3E%3Cpath stroke='%23cddbfc' d='M5 4h1M3 6h1'/%3E%3Cpath stroke='%23c8d5fb' d='M8 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M10 4h3M9 5h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 4h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c2d5' d='M16 4h1'/%3E%3Cpath stroke='%23bed0f8' d='M2 5h1'/%3E%3Cpath stroke='%23ceddfd' d='M4 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M6 5h2M3 8h2'/%3E%3Cpath stroke='%234d6185' d='M8 5h1M7 6h3M6 7h5M5 8h3m1 0h3M4 9h3m3 0h3m-8 1h1m5 0h1'/%3E%3Cpath stroke='%23bacdfc' d='M10 5h1m1 0h2M3 12h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1m-2 1h1m1 0h2m-1 1h1'/%3E%3Cpath stroke='%23a8bbd4' d='M16 5h1'/%3E%3Cpath stroke='%23cddafc' d='M4 6h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1'/%3E%3Cpath stroke='%23a4b8d3' d='M16 6h1'/%3E%3Cpath stroke='%23cad8fd' d='M4 7h2'/%3E%3Cpath stroke='%23b6cefb' d='M11 7h1m0 1h1'/%3E%3Cpath stroke='%23bacbf4' d='M14 7h1'/%3E%3Cpath stroke='%23a0b5d3' d='M16 7h1m-1 1h1m-1 5h1'/%3E%3Cpath stroke='%23c1d3fb' d='M8 8h1'/%3E%3Cpath stroke='%23b6cdfb' d='M13 8h1m-5 5h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 8h1'/%3E%3Cpath stroke='%23b4c8f6' d='M2 9h1'/%3E%3Cpath stroke='%23c2d5fc' d='M8 9h1m-1 1h1m-3 1h2'/%3E%3Cpath stroke='%23bdd3fb' d='M9 9h1m-2 3h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 9h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 9h1'/%3E%3Cpath stroke='%239fb5d2' d='M16 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c7f6' d='M2 10h1'/%3E%3Cpath stroke='%23c3d5fd' d='M7 10h1'/%3E%3Cpath stroke='%23bad4fc' d='M9 10h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M10 10h1m1 0h1m-2 2h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 10h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 10h1m-6 4h2'/%3E%3Cpath stroke='%23adc3f6' d='M2 11h1'/%3E%3Cpath stroke='%23c3d3fd' d='M5 11h1'/%3E%3Cpath stroke='%23c1d5fb' d='M8 11h1'/%3E%3Cpath stroke='%23b7d3fc' d='M10 11h1m-2 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 11h1'/%3E%3Cpath stroke='%23afcefb' d='M12 11h1'/%3E%3Cpath stroke='%23aecafa' d='M13 11h1'/%3E%3Cpath stroke='%23b1c8f3' d='M14 11h1'/%3E%3Cpath stroke='%23acc2f5' d='M2 12h1'/%3E%3Cpath stroke='%23c1d2fb' d='M5 12h1'/%3E%3Cpath stroke='%23bed1fc' d='M6 12h2'/%3E%3Cpath stroke='%23b6d1fb' d='M10 12h1'/%3E%3Cpath stroke='%23afccfb' d='M12 12h1'/%3E%3Cpath stroke='%23adc9f9' d='M13 12h1m-2 1h1'/%3E%3Cpath stroke='%23b1c5f3' d='M14 12h1'/%3E%3Cpath stroke='%23aac0f3' d='M2 13h1'/%3E%3Cpath stroke='%23b7cbf9' d='M3 13h1'/%3E%3Cpath stroke='%23b9cefb' d='M4 13h1'/%3E%3Cpath stroke='%23bbcef9' d='M7 13h1'/%3E%3Cpath stroke='%23b9cffb' d='M8 13h1'/%3E%3Cpath stroke='%23b2cdfb' d='M10 13h1'/%3E%3Cpath stroke='%23b0cbf9' d='M11 13h1'/%3E%3Cpath stroke='%23aec8f7' d='M13 13h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath stroke='%23b1c6f3' d='M12 14h1'/%3E%3Cpath stroke='%23b0c4f2' d='M13 14h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 14h1'/%3E%3Cpath stroke='%23aec0d6' d='M16 14h1'/%3E%3Cpath stroke='%23c3d4e7' d='M1 15h1'/%3E%3Cpath stroke='%23aec4e5' d='M15 15h1'/%3E%3Cpath stroke='%23edf1f3' d='M1 16h1'/%3E%3Cpath stroke='%23aac0e1' d='M2 16h1'/%3E%3Cpath stroke='%2394b1d9' d='M3 16h1'/%3E%3Cpath stroke='%2388a7d8' d='M4 16h1'/%3E%3Cpath stroke='%2383a4d3' d='M5 16h1'/%3E%3Cpath stroke='%237da0d4' d='M6 16h1m3 0h3'/%3E%3Cpath stroke='%237e9fd2' d='M7 16h1'/%3E%3Cpath stroke='%237c9fd3' d='M8 16h2'/%3E%3Cpath stroke='%2382a4d6' d='M13 16h1'/%3E%3Cpath stroke='%2394b0dd' d='M14 16h1'/%3E%3Cpath stroke='%23ecf2f7' d='M15 16h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
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 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h3'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M6 6h2M3 7h1'/%3E%3Cpath 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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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}
.title-bar-controls button[aria-label=Maximize]: not(: disabled): active{
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}
.title-bar-controls button[aria-label=Restore]{
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}
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}
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stroke='%23b5381a' d='M9 4h1M4 9h1'/%3E%3Cpath stroke='%23b8391a' d='M10 4h1m-7 6h1'/%3E%3Cpath stroke='%23ba3a1b' d='M11 4h1m-8 7h2'/%3E%3Cpath stroke='%23bc3b1c' d='M12 4h1m-9 8h1'/%3E%3Cpath stroke='%23bd3c1c' d='M13 4h1m-1 1h1m-2 1h1m-7 6h1m-3 1h2'/%3E%3Cpath stroke='%23be3d1c' d='M14 4h3m-1 1h1m-1 1h1M4 14h1m-1 1h1m-1 1h2'/%3E%3Cpath stroke='%23bf3d1c' d='M17 4h3m-3 1h3m-2 1h2m-1 1h1M4 17h2m-2 1h4m-4 1h4'/%3E%3Cpath stroke='%235b1d0d' d='M1 5h1'/%3E%3Cpath stroke='%239c3016' d='M3 5h1'/%3E%3Cpath stroke='%23a43217' d='M4 5h1'/%3E%3Cpath stroke='%23b8553e' d='M5 5h1'/%3E%3Cpath stroke='%23d59485' d='M6 5h1M5 6h1'/%3E%3Cpath stroke='%23b33619' d='M7 5h1'/%3E%3Cpath stroke='%23b53719' d='M8 5h1'/%3E%3Cpath stroke='%23b8381a' d='M9 5h1M6 8h1'/%3E%3Cpath stroke='%23b9391b' d='M10 5h1'/%3E%3Cpath stroke='%23ba391b' d='M11 5h1M6 9h1m-2 1h1'/%3E%3Cpath stroke='%23bc3b1b' d='M12 5h1m-2 1h1m-6 5h1m-2 1h1'/%3E%3Cpath stroke='%23dc9887' d='M14 5h1'/%3E%3Cpath stroke='%23c85d42' d='M15 5h1M5 15h1'/%3E%3Cpath stroke='%23611f0e' d='M1 6h1'/%3E%3Cpath stroke='%23a23217' d='M3 6h1'/%3E%3Cpath stroke='%23d79585' d='M6 6h1'/%3E%3Cpath stroke='%23d89585' d='M7 6h1'/%3E%3Cpath stroke='%23b8371a' d='M8 6h1'/%3E%3Cpath stroke='%23ba391a' d='M9 6h1'/%3E%3Cpath stroke='%23bb3a1b' d='M10 6h1m-5 4h1'/%3E%3Cpath stroke='%23dd9887' d='M13 6h3m-4 1h1m-2 1h1M9 9h1m-2 2h1m-2 1h1m-2 1h1m-2 1h2'/%3E%3Cpath stroke='%23c03e1d' d='M17 6h1m-2 1h3m0 1h1m-1 1h1M7 16h1m-2 1h2m0 1h1'/%3E%3Cpath stroke='%2365200e' d='M1 7h1'/%3E%3Cpath stroke='%23902d15' d='M2 7h1'/%3E%3Cpath stroke='%23a73418' d='M3 7h1'/%3E%3Cpath stroke='%23af3518' d='M4 7h1'/%3E%3Cpath stroke='%23b43619' d='M5 7h1'/%3E%3Cpath stroke='%23d99585' d='M6 7h1'/%3E%3Cpath stroke='%23da9686' d='M7 7h1'/%3E%3Cpath stroke='%23db9686' d='M8 7h1M7 8h1'/%3E%3Cpath stroke='%23bc3a1b' d='M9 7h1M7 9h1'/%3E%3Cpath stroke='%23bd3b1b' d='M10 7h1m-4 3h1'/%3E%3Cpath stroke='%23be3c1c' d='M11 7h1m-2 1h1m-3 2h1m-2 1h1'/%3E%3Cpath stroke='%23de9987' d='M13 7h2m-3 1h2m-4 1h2m-3 1h1m-2 2h1m-2 2h1'/%3E%3Cpath stroke='%23c03f1d' d='M15 7h1m-9 8h1'/%3E%3Cpath stroke='%236a220f' d='M1 8h1'/%3E%3Cpath stroke='%23952f15' d='M2 8h1'/%3E%3Cpath stroke='%23ac3518' d='M3 8h1'/%3E%3Cpath stroke='%23b63719' d='M5 8h1'/%3E%3Cpath stroke='%23dc9786' d='M8 8h2M8 9h1'/%3E%3Cpath stroke='%23c2401d' d='M14 8h1m2 0h1m1 3h1M8 14h1m-1 2h1m-1 1h1m0 1h1m1 1h1'/%3E%3Cpath stroke='%23c2401e' d='M15 8h2m1 1h1M8 15h1'/%3E%3Cpath stroke='%23c13f1d' d='M18 8h1m0 2h1M9 19h2'/%3E%3Cpath stroke='%23702410' d='M1 9h1'/%3E%3Cpath stroke='%239b3016' d='M2 9h1'/%3E%3Cpath stroke='%23b03619' d='M3 9h1'/%3E%3Cpath stroke='%23b9381a' d='M5 9h1'/%3E%3Cpath stroke='%23df9a88' d='M12 9h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23c4421e' d='M13 9h1m2 0h2m0 1h1M9 13h1m9 1h1m-1 1h1M9 16h1m9 0h1M9 17h1m0 1h1m3 1h3'/%3E%3Cpath stroke='%23c5431e' d='M14 9h1'/%3E%3Cpath stroke='%23c5431f' d='M15 9h1m-4 1h1m5 1h1m-9 1h1m-2 2h1m-1 1h1m0 2h1m0 1h1m6 0h1'/%3E%3Cpath 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
}
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];
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
}).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_method";
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', // Zeigt nur Ausreißer außerhalb der Whiskers
jitter: 0.5, // Erhöht die Streuung der Punkte für bessere Sichtbarkeit
pointpos: 0 // Position der Punkte innerhalb der Box
};
});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: "Generation Method"
},
boxmode: 'group' // Gruppiert die Boxplots nach Generation Method
};
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));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
if (values.length === 0) return;
let min = Math.min(...values);
let max = Math.max(...values);
let range = max - min || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value)) return;
let ratio = (value - min) / range;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
if (values.length === 0) return;
let logValues = values.map(v => Math.log(v));
let logMin = Math.min(...logValues);
let logMax = Math.max(...logValues);
let logRange = logMax - logMin || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value) || value <= 0) return;
let logValue = Math.log(value);
let ratio = (logValue - logMin) / logRange;
if (minMax === "max") ratio = 1 - ratio;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
});
}
function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
let colorMap = {};
uniqueValues.forEach((value, index) => {
let hue = Math.round((360 / uniqueValues.length) * index);
colorMap[value] = `hsl(${hue}, 70%, 60%)`;
});
cells.forEach(el => {
let value = el.textContent.trim();
if (colorMap[value]) {
el.style.backgroundColor = colorMap[value];
el.classList.add("invert_in_dark_mode");
}
});
});
}
function colorize_table_entries () {
setTimeout(() => {
if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
_colorize_table_entries_by_trial_status();
_colorize_table_entries_by_results();
_colorize_table_entries_by_run_time();
_colorize_table_entries_by_generation_method();
_colorize_table_entries_by_generation_node_or_hostname();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
searchInput.addEventListener("input", colorize_table_entries);
}
}
function updatePreWidths() {
var width = window.innerWidth * 0.95;
var pres = document.getElementsByTagName('pre');
for (var i = 0; i < pres.length; i++) {
pres[i].style.width = width + 'px';
}
}
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<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>n_clusters</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 116</td><td>3</td><td>0.01959</td><td>0.214304 </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_clusters</td><td>range</td><td>1</td><td>4</td><td></td><td>int </td></tr><tr><td> threshold</td><td>range</td><td>0.002</td><td>0.07</td><td></td><td>float </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>11</td>
<td>510</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_clusters,threshold
0,0_0,COMPLETED,Sobol,0.401350337584396088530525048554,666,3,0.028584242939949038031510752944
1,1_0,COMPLETED,Sobol,0.375843960990247527576002539718,625,2,0.067526180174201727579585963213
2,2_0,COMPLETED,Sobol,0.267816954238559667977881417755,164,3,0.045199380021542313878857299869
3,3_0,COMPLETED,Sobol,0.292073018254563665507816949685,259,3,0.026936942666769027321382878881
4,4_0,COMPLETED,Sobol,0.394598649662415645877899805782,631,3,0.060791124667972332162868553951
5,5_0,COMPLETED,Sobol,0.329332333083270811791010146408,312,3,0.025539161641150713577541608856
6,6_0,COMPLETED,Sobol,0.393598399599899950729309239250,642,3,0.012773629657924176830641194158
7,7_0,COMPLETED,Sobol,0.397599399849962509279066580348,684,2,0.016543359749019145854553869412
8,8_0,COMPLETED,Sobol,0.406101525381345362930574083293,681,3,0.058793892875313766288591921239
9,9_0,COMPLETED,Sobol,0.228807201800450110695805960859,122,1,0.063470526002347468774722472062
10,10_0,COMPLETED,Sobol,0.403600900225056236081400129478,830,4,0.062141189690679318746724391076
11,11_0,COMPLETED,Sobol,0.372593148287071795898839354777,601,4,0.064661811921745540598926993425
12,12_0,COMPLETED,Sobol,0.292573143285821402059809770435,284,3,0.044170842789113526349886740263
13,13_0,COMPLETED,Sobol,0.388097024256064071501270973386,470,2,0.009218912739306688031160064156
14,14_0,COMPLETED,Sobol,0.422105526381595375084998522652,946,4,0.043293720655143266839992577388
15,15_0,COMPLETED,Sobol,0.222305576394098536319177128462,118,1,0.048615649089217193024037300120
16,16_0,COMPLETED,Sobol,0.358089522380595104422695840185,521,1,0.041026077955961234855486452489
17,17_0,COMPLETED,Sobol,0.314828707176794231337169094331,403,1,0.044509116344153887290246984776
18,18_0,COMPLETED,Sobol,0.230057514378594674120392937766,106,3,0.067531508628278974493142072788
19,19_0,COMPLETED,Sobol,0.398349587396849225129358273989,843,1,0.013562355425208807296888480209
20,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751
21,21_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.051099697696514990996607963325
22,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
23,23_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.056124776679056284645064067718
24,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751
25,25_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.042484812832510363000970698977
26,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751
27,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751
28,28_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.058456452430997188352002069678
29,29_0,COMPLETED,BoTorch,0.259814953738434661900669198076,192,1,0.070000000000000006661338147751
30,30_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.057680958242928154211526248218
31,31_0,COMPLETED,BoTorch,0.253063265816454108225741492788,153,2,0.070000000000000006661338147751
32,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
33,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
34,34_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.055258163496698695094089259783
35,35_0,COMPLETED,BoTorch,0.297074268567141808183862394799,300,4,0.021130204098317166561127322666
36,36_0,COMPLETED,BoTorch,0.317079269817454378888044175255,338,3,0.038013401226221023299078893842
37,20_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.070000000000000006661338147751
38,38_0,COMPLETED,BoTorch,0.314328582145536383762873811065,357,2,0.028104102327867877542111330058
39,39_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.044325461816871337961121213311
40,40_0,COMPLETED,BoTorch,0.269567391847961967954461215413,171,1,0.052103894560830560367392649823
41,41_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.005680264585283718165031885405
42,42_0,COMPLETED,BoTorch,0.247311827956989249699404354033,143,2,0.070000000000000006661338147751
43,43_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.002000000000000000041633363423
44,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423
45,45_0,COMPLETED,BoTorch,0.267816954238559667977881417755,164,3,0.070000000000000006661338147751
46,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
47,47_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,1,0.019960934068648454597916241937
48,48_0,COMPLETED,BoTorch,0.277069267316829237479680614342,170,1,0.053131528920576813479481614877
49,49_0,COMPLETED,BoTorch,0.297824456114028524034154088440,303,3,0.033598529337454823007202975305
50,50_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.015689822718647515598089370314
51,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
52,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423
53,53_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.070000000000000006661338147751
54,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
55,55_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.015051458887933561944794114140
56,56_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,1,0.027769610156313469240263458460
57,57_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.013919360150643631662825683293
58,58_0,COMPLETED,BoTorch,0.222055513878469668043180718087,131,3,0.002000000000000000041633363423
59,59_0,COMPLETED,BoTorch,0.230557639409852410672385758517,100,2,0.018314272236791183379178704627
60,60_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.038437308700684569284788949517
61,61_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.045409867824205835118434038122
62,62_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.060291340729379028218204439327
63,63_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.030902667569034356076507208400
64,64_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.048741910858706487263969364676
65,65_0,COMPLETED,BoTorch,0.215803950987746961942548296065,101,3,0.002000000000000000041633363423
66,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
67,67_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.044918444239609930934697956673
68,68_0,COMPLETED,BoTorch,0.276319079769942521629388920701,100,4,0.035067229552974664430475826293
69,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423
70,70_0,COMPLETED,BoTorch,0.269317329332333099678464805038,145,3,0.027695951796912879339096491549
71,71_0,COMPLETED,BoTorch,0.243560890222555670447945885826,157,3,0.011886353333828853992559793085
72,72_0,COMPLETED,BoTorch,0.279069767441860516754559284891,201,4,0.005317346998430295609838758253
73,73_0,COMPLETED,BoTorch,0.256064016004000971626908267353,180,1,0.002000000000000000041633363423
74,74_0,COMPLETED,BoTorch,0.291822955738934686209518076794,150,1,0.044697920349177445997757018858
75,75_0,COMPLETED,BoTorch,0.253813453363340824076033186429,138,3,0.015757285261072749571464868268
76,76_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.055858924605295151577522005937
77,77_0,COMPLETED,BoTorch,0.248562140535133813123991330940,176,2,0.008798780941663506688366069852
78,78_0,COMPLETED,BoTorch,0.271317829457364378953343475587,197,4,0.011191385950694611450817461673
79,79_0,COMPLETED,BoTorch,0.275318829707426826480798354169,232,4,0.002000000000000000041633363423
80,80_0,COMPLETED,BoTorch,0.278319579894973689881965128734,201,1,0.039925443738928437231727741619
81,81_0,COMPLETED,BoTorch,0.275318829707426826480798354169,224,3,0.006897893029174423720761843981
82,82_0,COMPLETED,BoTorch,0.249312328082020528974283024581,143,3,0.006638921130590880875788073467
83,83_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.008542072983910235783877595850
84,84_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.059676366036322092689658802556
85,85_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.008037252378822832415972143849
86,86_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.011267065622627766771635116072
87,87_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.007472335109961153985780324405
88,88_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.063086258853672028124037751695
89,89_0,COMPLETED,BoTorch,0.254063515878969692352029596805,110,4,0.068535930166707947908122378067
90,90_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006464219235339614330615454207
91,91_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.065105314133466790638138377290
92,92_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.050335439897640617268326224121
93,93_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.006885602604559948325402274349
94,94_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.055527757911963536441302125013
95,95_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.058723313529962767320924399428
96,96_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.058821888257426541146699605633
97,97_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.060998450069787157890033313379
98,98_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.005156000750960088377383705449
99,99_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.060342897893130034714381793037
100,100_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.063473149985319513022297144289
101,101_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.019066947830405189812097432878
102,102_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.007978961173036184031936990380
103,103_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.050465426179668537720601761976
104,104_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.065530611872739438772406117550
105,105_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.012093263107295004407659710921
106,24_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.070000000000000006661338147751
107,107_0,COMPLETED,BoTorch,0.281320330082520664305434365815,215,1,0.070000000000000006661338147751
108,108_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.065089570559170792374281688808
109,109_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,4,0.053316901476968367457054398528
110,110_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.010226484757627694183179656306
111,111_0,COMPLETED,BoTorch,0.216804201050262546068836400082,103,4,0.069958968840100985153718227139
112,112_0,COMPLETED,BoTorch,0.427356839209802497059342840657,1000,4,0.002000000000000000041633363423
113,113_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.050297074187200904726857686455
114,112_0,COMPLETED,BoTorch,0.427356839209802497059342840657,1000,4,0.002000000000000000041633363423
115,115_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.051713566429092430731806473432
116,116_0,COMPLETED,BoTorch,0.396099024756189077578483193065,733,4,0.014238896628742480840457140800
117,117_0,COMPLETED,BoTorch,0.418354588647161795833540054446,952,4,0.004272943333135866515737344429
118,118_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.064298526132668260002489546423
119,119_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.049831935713041793090116726717
120,120_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.047130645914487588610253254728
121,121_0,COMPLETED,BoTorch,0.430607651912978228736506025598,960,4,0.023697640344882531238113188010
122,122_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.017493329310289743722117705715
123,123_0,COMPLETED,BoTorch,0.424356089022255522635873603576,936,4,0.026729959002832343051281327462
124,124_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.046795836936598528277286845878
125,125_0,COMPLETED,BoTorch,0.428357089272318081185630944674,965,4,0.002000000000000000041633363423
126,126_0,COMPLETED,BoTorch,0.394598649662415645877899805782,838,2,0.025274909472885638550554432413
127,127_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.013730462798562641929533967300
128,128_0,COMPLETED,BoTorch,0.226556639159789963144930879935,102,1,0.047382847471888436818865386613
129,129_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.053263310328364565915393313844
130,130_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,2,0.044323912869006616699341094545
131,131_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,1,0.056963582551878721993432463933
132,132_0,COMPLETED,BoTorch,0.241810452613153259449063625652,142,4,0.036549992749848075890284349043
133,133_0,COMPLETED,BoTorch,0.415603900975243800708369690255,1000,1,0.070000000000000006661338147751
134,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
135,135_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.012899000969069665534227908665
136,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
137,137_0,COMPLETED,BoTorch,0.414103525881470369007786302973,899,2,0.059951513282835856843977495600
138,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
139,139_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.068180890191842563607949045945
140,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
141,141_0,COMPLETED,BoTorch,0.219304826206551672918010353897,123,2,0.019955650273226897828404702295
142,43_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,2,0.002000000000000000041633363423
143,143_0,COMPLETED,BoTorch,0.232808202050512669245563301956,118,2,0.002000000000000000041633363423
144,144_0,COMPLETED,BoTorch,0.223055763940985252169468822103,119,4,0.002000000000000000041633363423
145,145_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,3,0.048823123489359389337582939561
146,146_0,COMPLETED,BoTorch,0.291572893223305817933521666419,130,1,0.002000000000000000041633363423
147,147_0,COMPLETED,BoTorch,0.230557639409852410672385758517,117,3,0.002000000000000000041633363423
148,148_0,COMPLETED,BoTorch,0.234808702175543837498139509989,127,4,0.048850753544171919562355554945
149,149_0,COMPLETED,BoTorch,0.237559389847461832623309874180,125,1,0.002000000000000000041633363423
150,150_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,3,0.053510618080416690045542082999
151,151_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.049856461885348223039837733950
152,152_0,COMPLETED,BoTorch,0.231807951987996974096972735424,126,4,0.002000000000000000041633363423
153,153_0,COMPLETED,BoTorch,0.261565391347836961877248995734,208,4,0.046084836611876740797288931617
154,154_0,COMPLETED,BoTorch,0.280820205051262816731139082549,185,4,0.033664642784040257894595526977
155,155_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.055020807186573990332778549828
156,156_0,COMPLETED,BoTorch,0.285571392848212091131188117288,216,4,0.056910993225296843678329139493
157,157_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.047970761292783992146837590553
158,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
159,159_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,4,0.061469701447964195106798968027
160,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
161,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
162,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
163,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
164,164_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.053011136228890358423893047757
165,165_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006634891180175133892915617650
166,166_0,COMPLETED,BoTorch,0.237309327331832964347313463804,147,4,0.059671894255493337921869567708
167,167_0,COMPLETED,BoTorch,0.370092523130782669049665400962,506,1,0.018291861864607274201777187272
168,168_0,COMPLETED,BoTorch,0.287821955488872238682063198212,229,4,0.070000000000000006661338147751
169,169_0,COMPLETED,BoTorch,0.241810452613153259449063625652,136,4,0.051583151120916603815658163512
170,170_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.004676153686139434065283104758
171,171_0,COMPLETED,BoTorch,0.276319079769942521629388920701,236,4,0.070000000000000006661338147751
172,172_0,COMPLETED,BoTorch,0.266066516629157256978999157582,196,3,0.058862412021500859493627899610
173,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
174,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
175,175_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,2,0.053060558983427499879503841385
176,44_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,1,0.002000000000000000041633363423
177,177_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.006111778264145709647914284091
178,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
179,179_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,3,0.055279179695991817466982354290
180,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
181,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
182,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
183,183_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,2,0.069996605374265566390512560702
184,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
185,22_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,1,0.070000000000000006661338147751
186,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
187,187_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.055038190191681109209032030094
188,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
189,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
190,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
191,32_0,RUNNING,BoTorch,,100,4,0.002000000000000000041633363423
192,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
193,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
194,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
195,195_0,COMPLETED,BoTorch,0.242560640160039975299355319294,147,3,0.049564243548287263696483506692
196,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
197,197_0,COMPLETED,BoTorch,0.233558389597399385095854995598,100,3,0.049922793700379775039266405656
198,198_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.009289231223537844708837418750
199,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
200,200_0,COMPLETED,BoTorch,0.219554888722180541194006764272,122,4,0.016239182449107623928963306525
201,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
202,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
203,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
204,51_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,3,0.002000000000000000041633363423
205,32_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,4,0.002000000000000000041633363423
206,206_0,COMPLETED,BoTorch,0.236309077269317380221025359788,106,3,0.003555340077775428174466920694
207,207_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,4,0.006489902563824730477581859134
208,208_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,3,0.014255349138286282223431200578
209,209_0,COMPLETED,BoTorch,0.236059014753688400922726486897,108,4,0.002000000000000000041633363423
210,210_0,RUNNING,BoTorch,,107,2,0.002000000000000000041633363423
211,211_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,2,0.025477107829473433220357492246
212,212_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,3,0.007295123353855708335513607921
213,213_0,COMPLETED,BoTorch,0.225306326581645399720343903027,109,4,0.012754302373462925862535044530
214,214_0,COMPLETED,BoTorch,0.229307326831707958270101244125,131,1,0.049894680862930147757783316820
215,215_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,2,0.007041236295683104932929818887
216,216_0,COMPLETED,BoTorch,0.253063265816454108225741492788,110,4,0.017965091678095415628213515902
217,217_0,COMPLETED,BoTorch,0.252813203300825239949745082413,115,1,0.032482441794649354049884237838
218,218_0,COMPLETED,BoTorch,0.220305076269067257044298457913,111,3,0.021107834581049228495075453793
219,219_0,COMPLETED,BoTorch,0.225306326581645399720343903027,107,3,0.010769448011299569828436162311
220,220_0,COMPLETED,BoTorch,0.225806451612903247294639186293,113,2,0.029677161617395253490059303658
221,221_0,COMPLETED,BoTorch,0.231057764441110258246681041783,129,1,0.037589872389515728567932484339
222,222_0,COMPLETED,BoTorch,0.217304326081520393643131683348,121,2,0.011552495938111416104443485153
223,223_0,COMPLETED,BoTorch,0.224056014003500836295756926120,134,4,0.004109763468849862363962976985
224,224_0,COMPLETED,BoTorch,0.232808202050512669245563301956,129,4,0.014512056479048717969049242527
225,225_0,COMPLETED,BoTorch,0.219804951237809409470003174647,107,4,0.050625304396742287771360224724
226,226_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,2,0.016093037408446975167208847779
227,227_0,COMPLETED,BoTorch,0.236309077269317380221025359788,130,2,0.063982925125766171303709484164
228,228_0,COMPLETED,BoTorch,0.262065516379094809451544279000,167,2,0.021790805834250602524004847282
229,229_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,1,0.050908429485285705551333990115
230,230_0,COMPLETED,BoTorch,0.237809452363090811921608747070,130,4,0.027136754766890938683765455153
231,231_0,COMPLETED,BoTorch,0.216304076019004698494541116816,123,4,0.067702432305510484855659569803
232,232_0,COMPLETED,BoTorch,0.268067016754188536253877828130,175,1,0.029365960110064322219347587861
233,233_0,COMPLETED,BoTorch,0.264566141535383825278415770299,165,1,0.004017306897918610994469013065
234,234_0,COMPLETED,BoTorch,0.277569392348086974031673435093,166,4,0.017352945022768420657577337352
235,235_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,1,0.047544817236011109595761325863
236,236_0,COMPLETED,BoTorch,0.214303575893973530241964908782,116,3,0.019590452305032361735026569249
237,237_0,COMPLETED,BoTorch,0.229557389347336826546097654500,109,2,0.045648298411843639399432248638
238,238_0,COMPLETED,BoTorch,0.280820205051262816731139082549,185,1,0.048354537952247415855122625317
239,239_0,COMPLETED,BoTorch,0.253313328332082976501737903163,184,1,0.023442729135017506547633558966
240,240_0,COMPLETED,BoTorch,0.274068517129282374078513839777,180,3,0.062106775200989450258504120939
241,241_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.029617585450613943820474815993
242,242_0,COMPLETED,BoTorch,0.233808452113028253371851405973,134,2,0.040794806100753699951155795134
243,243_0,COMPLETED,BoTorch,0.251812953238309544801154515881,183,4,0.002000000000000000041633363423
244,244_0,COMPLETED,BoTorch,0.244311077769442386298237579467,151,4,0.002000000000000000041633363423
245,245_0,COMPLETED,BoTorch,0.257064266066516666775498833886,188,1,0.015274139664493631071695567414
246,246_0,COMPLETED,BoTorch,0.276319079769942521629388920701,216,2,0.032566961459721231741948344052
247,247_0,COMPLETED,BoTorch,0.259564891222805682602370325185,203,1,0.066275465243463477227336966280
248,248_0,COMPLETED,BoTorch,0.255563890972743235074915446603,156,4,0.029784531157617269436777007741
249,249_0,COMPLETED,BoTorch,0.256314078519629950925207140244,182,1,0.031803077430592789631713657172
250,250_0,COMPLETED,BoTorch,0.218054513628407109493423376989,114,3,0.012063515692830435732663119097
251,251_0,COMPLETED,BoTorch,0.217304326081520393643131683348,122,3,0.062085411590224855171715745428
252,252_0,COMPLETED,BoTorch,0.232308077019254821671268018690,125,4,0.061405165922654422749893399214
253,253_0,COMPLETED,BoTorch,0.302575643910977798434203123179,140,2,0.055118672951406759430437176661
254,254_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,3,0.036907424255702460380579310595
255,255_0,COMPLETED,BoTorch,0.243310827706926691149647012935,115,4,0.007827371985366867596090045822
256,256_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,3,0.070000000000000006661338147751
257,257_0,COMPLETED,BoTorch,0.253313328332082976501737903163,125,3,0.009710557307893382533725556982
258,258_0,COMPLETED,BoTorch,0.221555388847211820468885434821,107,1,0.058011699526341185817468470987
259,259_0,COMPLETED,BoTorch,0.236309077269317380221025359788,130,4,0.070000000000000006661338147751
260,260_0,COMPLETED,BoTorch,0.249812453113278265526275845332,153,1,0.002000000000000000041633363423
261,261_0,COMPLETED,BoTorch,0.262065516379094809451544279000,207,4,0.024902041402254798674320568352
262,262_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.061356494900312384677132371280
263,263_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,4,0.070000000000000006661338147751
264,264_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,1,0.057950000533865876628514257618
265,265_0,COMPLETED,BoTorch,0.255313828457114255776616573712,156,4,0.058787988631421886354111450146
266,266_0,COMPLETED,BoTorch,0.241810452613153259449063625652,136,1,0.070000000000000006661338147751
267,267_0,COMPLETED,BoTorch,0.229057264316078978971802371234,118,2,0.038533881451254470285050501843
268,268_0,COMPLETED,BoTorch,0.294073518379594944782695620233,297,4,0.070000000000000006661338147751
269,269_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,3,0.056588914837283010861312959605
270,270_0,COMPLETED,BoTorch,0.324831207801950516689259984560,311,4,0.070000000000000006661338147751
271,271_0,COMPLETED,BoTorch,0.306326581645411377685661591386,331,1,0.070000000000000006661338147751
272,272_0,COMPLETED,BoTorch,0.292823205801450381358108643326,301,2,0.070000000000000006661338147751
273,273_0,COMPLETED,BoTorch,0.295323830957739397184980134625,298,1,0.062901301546378962648020660708
274,274_0,COMPLETED,BoTorch,0.285571392848212091131188117288,273,3,0.070000000000000006661338147751
275,275_0,COMPLETED,BoTorch,0.303075768942235534986195943929,317,1,0.049325586380696936905643212867
276,276_0,COMPLETED,BoTorch,0.317829457364341094738335868897,355,4,0.070000000000000006661338147751
277,277_0,COMPLETED,BoTorch,0.299574893723430824010733886098,288,1,0.045538350514646067090929193455
278,278_0,COMPLETED,BoTorch,0.315328832208051967889161915082,341,4,0.049646039392156518510468288241
279,279_0,COMPLETED,BoTorch,0.294073518379594944782695620233,297,1,0.066482328999565826199713569622
280,280_0,COMPLETED,BoTorch,0.298074518629657392310150498815,266,2,0.068325977107753704808956740635
281,281_0,COMPLETED,BoTorch,0.300575143785946519159324452630,288,3,0.062244618215255106963290643307
282,282_0,COMPLETED,BoTorch,0.299574893723430824010733886098,270,3,0.070000000000000006661338147751
283,283_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.044579762254865165638229029810
284,284_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,2,0.070000000000000006661338147751
285,285_0,COMPLETED,BoTorch,0.240810202550637675322775521636,115,2,0.018620524688221104514518344786
286,286_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.070000000000000006661338147751
287,287_0,COMPLETED,BoTorch,0.214803700925231266793957729533,116,3,0.043632649535592003819939321829
288,288_0,COMPLETED,BoTorch,0.214803700925231266793957729533,116,2,0.041266368934308655935794263314
289,289_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.054377379207671418248626338254
290,290_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.045946158658470091784575828342
291,291_0,COMPLETED,BoTorch,0.349087271817954514219195516489,445,4,0.070000000000000006661338147751
292,292_0,COMPLETED,BoTorch,0.349837459364841230069487210130,449,1,0.070000000000000006661338147751
293,293_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.057597145656594447848952711411
294,294_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.070000000000000006661338147751
295,295_0,COMPLETED,BoTorch,0.336084021005251365465937851695,429,4,0.070000000000000006661338147751
296,296_0,COMPLETED,BoTorch,0.228807201800450110695805960859,122,3,0.070000000000000006661338147751
297,297_0,COMPLETED,BoTorch,0.327831957989497380090426759125,391,1,0.038347356967922310855279022235
298,298_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.051700213467624829555280285831
299,299_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.060924718361499118068902447476
300,300_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.045454781832201529567782927188
301,301_0,COMPLETED,BoTorch,0.238809702425606396047896851087,120,1,0.070000000000000006661338147751
302,302_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.041203120012886736145407695631
303,303_0,COMPLETED,BoTorch,0.226556639159789963144930879935,115,3,0.016164745669418223439350867920
304,304_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.041718587268434033366037994028
305,305_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.070000000000000006661338147751
306,306_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.042790925968888143815505031853
307,307_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.059666580388239555399199076646
308,308_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.055420162754167674734606663378
309,309_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,3,0.070000000000000006661338147751
310,310_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.042545806243486826436761560899
311,311_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.063647927010197974384553276650
312,312_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.055987445168224216074381871522
313,313_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.064435875930838715230919433452
314,314_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.055532628574374784391487480661
315,315_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.048113969059482085410817120419
316,316_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.054387243157401204962653196162
317,317_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.052273391108551746364607737405
318,318_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.055421265292523787249212574579
319,319_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.060692607783736807203212038075
320,320_0,COMPLETED,BoTorch,0.217554388597149261919128093723,116,3,0.017508768298486975661942821603
321,321_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.062484351010241412360812773841
322,322_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.056894848173225522069973436601
323,323_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,3,0.046536929218029425558977862920
324,324_0,COMPLETED,BoTorch,0.288322080520130086256358481478,246,1,0.002000000000000000041633363423
325,325_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.058382281837509319988299694160
326,326_0,COMPLETED,BoTorch,0.293323330832708228932403926592,263,1,0.004160070166490126460090426264
327,327_0,COMPLETED,BoTorch,0.274068517129282374078513839777,220,4,0.010665684699611809385655369908
328,328_0,COMPLETED,BoTorch,0.282320580145036248431722469832,250,4,0.029873240072150135382411662022
329,329_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.045155273260620430730849506062
330,330_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.061794914468645954774839168522
331,331_0,COMPLETED,BoTorch,0.327081770442610664240135065484,379,4,0.002000000000000000041633363423
332,332_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.053684452154216973085443243008
333,333_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.063806892608406301503620738913
334,334_0,COMPLETED,BoTorch,0.295573893473368376483279007516,319,4,0.011158234088309669854166550351
335,335_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.054331540956466056746521076093
336,336_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.046071473302219119461131668913
337,337_0,COMPLETED,BoTorch,0.253313328332082976501737903163,125,4,0.009025850785447849314313550906
338,338_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,2,0.036144091662880316329076180182
339,339_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,2,0.032302490477854294004256274775
340,340_0,COMPLETED,BoTorch,0.304326081520380098410782920837,320,4,0.010920003002940933262143730076
341,341_0,COMPLETED,BoTorch,0.323580895223805953264673007652,374,4,0.024602903747595515626667150855
342,342_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.047200900229174412581212294526
343,343_0,COMPLETED,BoTorch,0.224806201550387552146048619761,113,1,0.061617515002674712321084626865
344,344_0,COMPLETED,BoTorch,0.319579894973743394714915666555,358,3,0.002000000000000000041633363423
345,345_0,COMPLETED,BoTorch,0.236559139784946248497021770163,118,3,0.019668020068970261393648257808
346,346_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.065531430098825046992594423045
347,347_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.052209735551805538933400896440
348,348_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.058146785428308055132529119646
349,349_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.057403621326084203202455569226
350,350_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.053575767135452964651243235039
351,351_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.062370783993535183764578277987
352,352_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.059035826622121388707054023826
353,353_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.063454976749003244584912408754
354,354_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.051017728207848431210003070646
355,355_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.064269720847156056042770444492
356,356_0,COMPLETED,BoTorch,0.217304326081520393643131683348,122,4,0.063088327344915512417955483215
357,357_0,COMPLETED,BoTorch,0.221555388847211820468885434821,121,4,0.069322621326920519368997020138
358,358_0,COMPLETED,BoTorch,0.226556639159789963144930879935,115,3,0.015959246688961769428116710401
359,359_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,2,0.052993808153652677273015569881
360,360_0,COMPLETED,BoTorch,0.239559889972493111898188544728,115,3,0.016701330526186639741093031830
361,361_0,COMPLETED,BoTorch,0.236559139784946248497021770163,119,3,0.020471670135727523809343608718
362,362_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.050514981235708848739118792537
363,363_0,COMPLETED,BoTorch,0.215803950987746961942548296065,111,2,0.002000000000000000041633363423
364,364_0,COMPLETED,BoTorch,0.229807451862965694822094064875,114,3,0.013207783738935175918416398133
365,365_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.054770089370395930172819021209
366,366_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.058635061479479680390802798229
367,367_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.055922420278304296736848755245
368,368_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.059383846063512768509440320486
369,369_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.047691051362535583080237699960
370,370_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,3,0.064066979446790447982884586509
371,371_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,2,0.044436392441585016721550260854
372,372_0,COMPLETED,BoTorch,0.222805701425356383893472411728,113,2,0.012434867175065394961919196248
373,373_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.058970502227004825690492850754
374,374_0,COMPLETED,BoTorch,0.226556639159789963144930879935,124,3,0.032873197917630388176224442986
375,375_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.047772963845249427627948080044
376,376_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,1,0.062436988506878905724750694617
377,377_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,3,0.016145392243376695262657705143
378,378_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,3,0.049100335637420514101059154655
379,379_0,COMPLETED,BoTorch,0.234808702175543837498139509989,120,2,0.025911205758834175705285218783
380,380_0,COMPLETED,BoTorch,0.216054013503375830218544706440,112,2,0.010154482690847572989856217873
381,381_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,1,0.051446866673149460602587623725
382,382_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.061566706047833742732056094837
383,383_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.041287429704039139977123085146
384,384_0,COMPLETED,BoTorch,0.223555888972243099743764105369,103,3,0.013584388695198049493151337686
385,385_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,2,0.064817451027813180464143272275
386,386_0,COMPLETED,BoTorch,0.222305576394098536319177128462,113,3,0.002002945708283992987119859208
387,387_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.050852411076752083995966557950
388,388_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.064514844864787090905622335413
389,389_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009891099708206103555130361826
390,390_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.055830834988912766969626488844
391,391_0,COMPLETED,BoTorch,0.218304576144035977769419787364,117,2,0.070000000000000006661338147751
392,392_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.054577580100996982148675584767
393,393_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.067264917892546141620080391021
394,394_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.056264265912313017603540998834
395,395_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,4,0.070000000000000006661338147751
396,396_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,4,0.054817799835189050250416897825
397,397_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.070000000000000006661338147751
398,398_0,COMPLETED,BoTorch,0.237809452363090811921608747070,120,3,0.035082057131355821877338740933
399,399_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,1,0.065502770071723295797205821600
400,400_0,COMPLETED,BoTorch,0.241560390097524391173067215277,128,4,0.066999960890648163625016309197
401,401_0,COMPLETED,BoTorch,0.219054763690922693619711481006,123,3,0.042386264329507115922179849576
402,402_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.070000000000000006661338147751
403,403_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,1,0.067672226461940043762410823547
404,404_0,COMPLETED,BoTorch,0.220305076269067257044298457913,112,2,0.010995707967038321345443208088
405,405_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,2,0.011579071128290921097181431776
406,406_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.010204838446544253760528420116
407,407_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.056497899010438813738321783831
408,408_0,COMPLETED,BoTorch,0.235308827206801685072434793256,119,3,0.030257986762832318750060522916
409,409_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,2,0.065787596295065975393612234257
410,410_0,COMPLETED,BoTorch,0.231057764441110258246681041783,112,2,0.036373576816930854038734111100
411,411_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.052007152822469926434223452816
412,412_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.062044047198609876547781283307
413,413_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.053261502567516529060842600529
414,414_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.056309305857676253403987232105
415,415_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,2,0.058472812350973464579073635150
416,416_0,COMPLETED,BoTorch,0.264316079019754957002419359924,164,3,0.002000000000000000041633363423
417,417_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,4,0.069339120485230013035682361533
418,418_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.053921429805717360772554513915
419,419_0,COMPLETED,BoTorch,0.231057764441110258246681041783,115,2,0.070000000000000006661338147751
420,420_0,COMPLETED,BoTorch,0.262065516379094809451544279000,167,3,0.009659435662515588466581206717
421,421_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.057724250730739376513689364856
422,422_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.053703047863779516946092229546
423,423_0,COMPLETED,BoTorch,0.244811202800700122850230400218,151,4,0.002097869490220044281364453198
424,424_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,2,0.032155365119678598617714015973
425,425_0,COMPLETED,BoTorch,0.218804701175293825343715070630,106,2,0.002000000000000000041633363423
426,426_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.056722967119800377089333665026
427,294_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.070000000000000006661338147751
428,428_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.063656470761059541496607039335
429,429_0,COMPLETED,BoTorch,0.237809452363090811921608747070,119,1,0.070000000000000006661338147751
430,430_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,3,0.062059483414467937756331394894
431,431_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009264479078473913942204376326
432,432_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,4,0.050902230507708819129408794879
433,433_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.058056755416066155306431539884
434,434_0,COMPLETED,BoTorch,0.221055263815953972894590151554,114,2,0.070000000000000006661338147751
435,435_0,COMPLETED,BoTorch,0.363840960240059962949032978941,554,4,0.002000000000000000041633363423
436,436_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,4,0.070000000000000006661338147751
437,437_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.060256433377437711162993849712
438,438_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.062520111964006905291846294404
439,439_0,COMPLETED,BoTorch,0.234558639659914969222143099614,119,4,0.070000000000000006661338147751
440,440_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,1,0.052670806376882428612162811987
441,441_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.052855686211518107531226462470
442,442_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.062335150751983252337407037658
443,436_0,COMPLETED,BoTorch,0.230307576894223542396389348141,118,4,0.070000000000000006661338147751
444,444_0,COMPLETED,BoTorch,0.234308577144286100946146689239,115,2,0.008660193920224115715633672608
445,445_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.055219573865808564694379612092
446,446_0,COMPLETED,BoTorch,0.228557139284821242419809550483,122,2,0.026599949826107659178742181894
447,447_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.052912122387360087383267881478
448,448_0,COMPLETED,BoTorch,0.219304826206551672918010353897,124,4,0.048228522354696212737223959266
449,449_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.043203893590213086894902261292
450,450_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.054734862287023448856881913116
451,451_0,COMPLETED,BoTorch,0.230557639409852410672385758517,118,2,0.044206954517093152712270409666
452,452_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,2,0.011057792693587642496311218565
453,453_0,COMPLETED,BoTorch,0.217554388597149261919128093723,112,2,0.010933196628926063198594675896
454,454_0,COMPLETED,BoTorch,0.228557139284821242419809550483,122,2,0.024941825066315120862370235955
455,455_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.055215563980349421924209707413
456,456_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.061372349243710287713948758892
457,457_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.002000000000000000041633363423
458,458_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.045591806662690095852674687649
459,459_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.054545646585192439359524030351
460,460_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.060033844769105344785220523818
461,461_0,COMPLETED,BoTorch,0.238559639909977527771900440712,125,3,0.002000000000000000041633363423
462,462_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,1,0.059517250651239854419838337662
463,463_0,COMPLETED,BoTorch,0.224306076519129815594055799011,124,1,0.003816817386661844162254464408
464,464_0,COMPLETED,BoTorch,0.221805451362840688744881845196,117,3,0.049619927577231201509810887273
465,465_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.052441081497670224975671260381
466,466_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.061436167427381276062714476893
467,467_0,COMPLETED,BoTorch,0.231557889472368105820976325049,129,2,0.002503337377019210566175821953
468,468_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.054053952087813120219728091342
469,469_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.060930292397190034814347114889
470,470_0,COMPLETED,BoTorch,0.223055763940985252169468822103,122,4,0.002000000000000000041633363423
471,471_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.059925608533900942553884760855
472,472_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,1,0.054256506724798307661483676156
473,473_0,COMPLETED,BoTorch,0.235808952238059532646730076522,127,4,0.002000000000000000041633363423
474,474_0,COMPLETED,BoTorch,0.228307076769192263121510677593,113,1,0.052454473630330483713279932090
475,475_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.061170593954924051827148900884
476,476_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.066073593338249150819230237630
477,477_0,COMPLETED,BoTorch,0.218804701175293825343715070630,112,2,0.009250981264741900411685548988
478,478_0,COMPLETED,BoTorch,0.224056014003500836295756926120,114,2,0.055374382627653137567413921261
479,479_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,2,0.044453740972655066132634971154
480,480_0,COMPLETED,BoTorch,0.223555888972243099743764105369,113,2,0.060346472203606213446924755317
481,481_0,COMPLETED,BoTorch,0.229807451862965694822094064875,114,2,0.011975823665444172011484980089
482,482_0,COMPLETED,BoTorch,0.228307076769192263121510677593,113,2,0.057304554447823466412081927501
483,483_0,COMPLETED,BoTorch,0.221555388847211820468885434821,116,4,0.061818776978195835725138351791
484,484_0,COMPLETED,BoTorch,0.223805951487871968019760515745,113,2,0.010794780314437028845286903334
485,485_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.065016179083497438462302397966
486,486_0,COMPLETED,BoTorch,0.232558139534883689947264429065,115,1,0.058732516630742206964477247766
487,487_0,COMPLETED,BoTorch,0.222305576394098536319177128462,118,4,0.061978569634611326011341958520
488,488_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.046989196248170952974199110486
489,489_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.058503019997978265853699753052
490,490_0,COMPLETED,BoTorch,0.223555888972243099743764105369,121,2,0.042947305646313875537867232879
491,491_0,COMPLETED,BoTorch,0.237309327331832964347313463804,119,2,0.041869325293697530476766388574
492,492_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.059182791935982165254515052766
493,493_0,COMPLETED,BoTorch,0.221055263815953972894590151554,114,2,0.066226854215023459038214070915
494,494_0,COMPLETED,BoTorch,0.269067266816704231402468394663,120,4,0.002000000000000000041633363423
495,495_0,COMPLETED,BoTorch,0.222555638909727404595173538837,113,2,0.009609444742674264072768153255
496,496_0,COMPLETED,BoTorch,0.216304076019004698494541116816,123,1,0.070000000000000006661338147751
497,497_0,COMPLETED,BoTorch,0.217304326081520393643131683348,117,4,0.063216238051409176512684950922
498,498_0,COMPLETED,BoTorch,0.232308077019254821671268018690,115,4,0.063247223680475306295534210221
499,499_0,COMPLETED,BoTorch,0.216554138534633677792839989706,114,2,0.060441637068134705657040939286
500,500_0,COMPLETED,BoTorch,0.289072268067016802106650175119,130,3,0.008315962059984895182740416431
501,501_0,RUNNING,BoTorch,,100,3,0.007750668675751077978108849464
502,502_0,RUNNING,BoTorch,,109,2,0.002798961431528135257451594953
503,503_0,RUNNING,BoTorch,,116,1,0.053823193443523008328011059120
504,504_0,RUNNING,BoTorch,,117,4,0.062871028952552435176137635153
505,505_0,RUNNING,BoTorch,,118,2,0.053996874761292133759749134470
506,506_0,RUNNING,BoTorch,,125,2,0.036035574318616279965166171451
507,507_0,RUNNING,BoTorch,,113,2,0.059987925585050450028035129435
508,508_0,RUNNING,BoTorch,,113,2,0.059603341277663597630454006548
509,509_0,RUNNING,BoTorch,,112,2,0.008438877369189787258640933487
</pre>
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<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>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727546300,475.28125,49.7
1727546300,475.3125,47.2
1727546300,475.359375,49.8
1727546300,475.359375,50.0
1727546300,475.359375,55.6
1727546300,475.359375,50.0
1727546300,475.359375,39.4
1727546347,483.87890625,49.8
1727546347,483.87890625,54.3
1727546347,483.87890625,49.5
1727546347,483.87890625,56.8
1727546348,483.87890625,49.7
1727546348,483.87890625,55.3
1727546348,483.87890625,47.7
1727546348,483.87890625,52.4
1727546350,483.87890625,49.7
1727546350,483.87890625,55.3
1727546350,483.87890625,47.7
1727546350,483.87890625,56.5
1727546351,483.87890625,49.7
1727546351,483.87890625,56.3
1727546351,483.87890625,52.5
1727546351,483.87890625,39.4
1727546352,483.87890625,49.7
1727546352,483.87890625,54.3
1727546352,483.87890625,48.1
1727546352,483.87890625,55.6
1727546354,484.69921875,49.8
1727546354,484.69921875,55.3
1727546354,484.69921875,48.1
1727546354,484.69921875,41.2
1727546357,484.8203125,49.9
1727546357,484.8203125,55.6
1727546357,484.8203125,48.6
1727546357,484.8203125,56.5
1727546359,484.8203125,49.9
1727546359,484.8203125,53.2
1727546359,484.8203125,52.4
1727546359,484.8203125,40.6
1727546361,484.89453125,49.9
1727546361,484.89453125,43.2
1727546361,484.89453125,52.6
1727546361,484.89453125,38.2
1727546364,484.89453125,49.9
1727546364,484.89453125,53.2
1727546364,484.89453125,49.6
1727546364,484.89453125,52.4
1727546366,484.90234375,49.9
1727546366,484.90234375,38.2
1727546366,484.90234375,53.8
1727546366,484.90234375,41.2
1727546368,484.90234375,49.9
1727546368,484.90234375,52.1
1727546368,484.90234375,52.0
1727546368,484.90234375,40.6
1727546371,484.90625,49.9
1727546371,484.90625,39.4
1727546371,484.90625,53.7
1727546371,484.90625,37.5
1727546373,484.90625,49.9
1727546373,484.90625,39.4
1727546373,484.90625,51.6
1727546373,484.90625,56.8
1727546376,484.90625,49.9
1727546376,484.90625,41.2
1727546376,484.90625,53.4
1727546376,484.90625,39.4
1727546378,484.90625,49.8
1727546378,484.90625,55.3
1727546378,484.90625,48.1
1727546378,484.90625,56.8
1727546380,484.96875,49.9
1727546380,484.96875,46.3
1727546380,484.96875,50.4
1727546380,484.96875,38.7
1727546383,484.98046875,49.9
1727546383,484.98046875,54.3
1727546383,484.98046875,47.7
1727546383,484.98046875,55.6
1727546385,484.98046875,49.9
1727546385,484.98046875,52.1
1727546385,484.98046875,47.7
1727546385,484.98046875,57.4
1727546387,484.98046875,49.8
1727546387,484.98046875,54.3
1727546387,484.98046875,46.4
1727546387,484.98046875,56.8
1727546390,484.98046875,49.8
1727546390,484.98046875,54.3
1727546390,484.98046875,52.8
1727546390,484.98046875,39.4
1727546392,484.98046875,49.8
1727546392,484.98046875,55.3
1727546392,484.98046875,51.6
1727546392,484.98046875,38.7
1727546394,484.98046875,49.9
1727546394,484.98046875,55.6
1727546394,484.98046875,46.9
1727546394,484.98046875,56.8
1727546396,484.98046875,49.9
1727546396,484.98046875,48.8
1727546396,484.98046875,48.7
1727546396,484.98046875,55.6
1727546398,484.98046875,49.8
1727546398,484.98046875,55.6
1727546398,484.98046875,47.2
1727546398,484.98046875,56.5
1727546400,484.98046875,49.9
1727546400,484.98046875,57.4
1727546400,484.98046875,49.6
1727546400,484.98046875,47.4
1727546402,484.98046875,49.9
1727546402,484.98046875,55.3
1727546402,484.98046875,52.4
1727546402,484.98046875,40.6
1727546405,484.98046875,49.9
1727546405,484.98046875,54.3
1727546405,484.98046875,51.3
1727546405,484.98046875,45.7
1727546407,484.98046875,49.9
1727546407,484.98046875,39.4
1727546407,484.98046875,51.2
1727546407,484.98046875,40.6
1727546409,484.98046875,49.9
1727546409,484.98046875,54.3
1727546409,484.98046875,50.4
1727546409,484.98046875,45.9
1727546411,484.98046875,49.9
1727546411,484.98046875,55.1
1727546411,484.98046875,45.0
1727546411,484.98046875,56.8
1727546413,484.98046875,49.8
1727546413,484.98046875,38.2
1727546413,484.98046875,50.8
1727546413,484.98046875,56.3
1727546415,484.98046875,49.9
1727546415,484.98046875,44.7
1727546415,484.98046875,51.2
1727546415,484.98046875,53.7
1727546418,484.984375,49.9
1727546418,484.984375,53.2
1727546418,484.984375,52.8
1727546418,484.984375,37.5
1727546421,485.0234375,49.8
1727546421,485.0234375,53.2
1727546421,485.0234375,50.8
1727546421,485.0234375,40.6
1727546423,485.0234375,49.9
1727546423,485.0234375,55.3
1727546423,485.0234375,50.0
1727546423,485.0234375,40.6
1727546425,485.0234375,49.9
1727546425,485.0234375,55.3
1727546425,485.0234375,45.9
1727546425,485.0234375,58.1
1727546428,485.05078125,49.9
1727546428,485.05078125,55.6
1727546428,485.05078125,50.8
1727546428,485.05078125,38.7
1727546430,485.05078125,49.9
1727546430,485.05078125,55.3
1727546430,485.05078125,46.4
1727546430,485.05078125,56.8
1727546433,485.05078125,49.9
1727546433,485.05078125,50.0
1727546433,485.05078125,47.4
1727546433,485.05078125,55.6
1727546435,485.05078125,49.9
1727546435,485.05078125,41.7
1727546435,485.05078125,50.0
1727546435,485.05078125,56.3
1727546437,485.05078125,49.8
1727546437,485.05078125,38.2
1727546437,485.05078125,50.4
1727546437,485.05078125,55.6
1727546439,485.05078125,49.8
1727546439,485.05078125,55.3
1727546439,485.05078125,50.8
1727546439,485.05078125,39.4
1727546441,485.05078125,49.8
1727546441,485.05078125,54.3
1727546441,485.05078125,50.4
1727546441,485.05078125,48.7
1727546443,485.05078125,49.9
1727546443,485.05078125,47.5
1727546443,485.05078125,50.0
1727546443,485.05078125,56.5
1727546445,485.05078125,49.8
1727546445,485.05078125,55.6
1727546445,485.05078125,50.0
1727546445,485.05078125,47.5
1727546448,485.09765625,49.9
1727546448,485.09765625,54.3
1727546448,485.09765625,45.5
1727546448,485.09765625,57.8
1727546450,485.10546875,49.9
1727546450,485.10546875,55.6
1727546450,485.10546875,51.2
1727546450,485.10546875,39.4
1727546452,485.10546875,49.8
1727546452,485.10546875,38.2
1727546452,485.10546875,50.4
1727546452,485.10546875,56.8
1727546454,485.10546875,49.8
1727546454,485.10546875,39.4
1727546454,485.10546875,50.8
1727546454,485.10546875,56.5
1727546456,485.10546875,49.8
1727546456,485.10546875,41.2
1727546456,485.10546875,50.4
1727546456,485.10546875,55.6
1727546459,485.11328125,49.8
1727546459,485.11328125,55.3
1727546459,485.11328125,50.0
1727546459,485.11328125,39.4
1727546461,485.11328125,49.9
1727546461,485.11328125,54.3
1727546461,485.11328125,50.4
1727546461,485.11328125,53.2
1727546463,485.11328125,50.0
1727546463,485.11328125,39.4
1727546463,485.11328125,52.8
1727546463,485.11328125,41.2
1727546465,485.11328125,49.9
1727546465,485.11328125,50.0
1727546465,485.11328125,47.5
1727546465,485.11328125,55.6
1727546467,485.11328125,49.8
1727546467,485.11328125,52.3
1727546467,485.11328125,47.0
1727546467,485.11328125,56.8
1727546469,485.11328125,49.8
1727546469,485.11328125,55.3
1727546469,485.11328125,50.4
1727546469,485.11328125,39.4
1727546471,485.11328125,49.8
1727546471,485.11328125,53.2
1727546471,485.11328125,49.2
1727546471,485.11328125,40.6
1727546473,485.11328125,49.9
1727546473,485.11328125,38.2
1727546473,485.11328125,49.2
1727546473,485.11328125,58.7
1727546475,485.11328125,49.9
1727546475,485.11328125,38.2
1727546475,485.11328125,48.9
1727546475,485.11328125,56.8
1727546478,485.11328125,49.9
1727546478,485.11328125,35.3
1727546478,485.11328125,52.1
1727546478,485.11328125,45.9
1727546480,485.11328125,49.8
1727546480,485.11328125,54.3
1727546480,485.11328125,48.5
1727546480,485.11328125,58.7
1727546482,485.11328125,49.8
1727546482,485.11328125,53.2
1727546482,485.11328125,49.2
1727546482,485.11328125,47.4
1727546634,522.92578125,50.2
1727546634,522.92578125,54.2
1727546634,522.92578125,47.7
1727546634,522.92578125,56.5
1727546750,526.0390625,50.2
1727546750,526.0390625,55.1
1727546750,526.0390625,47.4
1727546750,526.0390625,56.5
1727546844,526.51953125,50.2
1727546844,526.51953125,54.3
1727546844,526.51953125,49.4
1727546844,526.51953125,56.8
1727546960,528.4765625,50.2
1727546960,528.4765625,54.3
1727546960,528.4765625,49.4
1727546960,528.4765625,55.6
1727547090,534.2734375,50.2
1727547090,534.2734375,47.5
1727547091,534.2734375,51.5
1727547091,534.2734375,38.9
1727547274,537.5703125,50.1
1727547274,537.5703125,40.6
1727547274,537.5703125,49.7
1727547274,537.5703125,59.5
1727547428,535.5625,50.2
1727547428,535.5625,54.2
1727547428,535.5625,50.3
1727547428,535.5625,39.4
1727547784,541.5625,49.9
1727547784,541.5625,53.2
1727547784,541.5625,47.2
1727547784,541.5625,57.8
1727548000,546.8984375,50.2
1727548000,546.8984375,38.2
1727548000,546.8984375,51.8
1727548000,546.8984375,39.4
1727548194,552.0390625,50.2
1727548194,552.0390625,47.0
1727548194,552.0390625,49.7
1727548194,552.0390625,57.8
1727548468,553.515625,50.2
1727548468,553.515625,36.4
1727548468,553.515625,51.0
1727548468,553.515625,57.8
1727548755,559.359375,50.2
1727548755,559.359375,54.3
1727548755,559.359375,50.2
1727548755,559.359375,51.2
1727549103,563.2578125,50.1
1727549103,563.2578125,54.3
1727549103,563.2578125,49.5
1727549103,563.2578125,56.1
1727549466,562.48828125,50.1
1727549466,562.48828125,43.6
1727549466,562.48828125,51.1
1727549466,562.48828125,39.4
1727549897,562.48828125,50.2
1727549897,562.48828125,43.2
1727549897,562.48828125,50.2
1727549897,562.48828125,57.8
1727550356,456.9609375,50.2
1727550356,456.9609375,53.3
1727550356,456.9609375,50.2
1727550356,456.9609375,42.4
1727550874,460.60546875,50.2
1727550874,460.60546875,55.3
1727550874,460.60546875,50.0
1727550874,460.60546875,38.7
1727551448,427.421875,50.2
1727551448,427.421875,55.3
1727551448,427.421875,48.7
1727551448,427.421875,55.6
1727552009,431.65625,50.2
1727552009,431.65625,53.2
1727552009,431.65625,48.5
1727552009,431.65625,56.8
1727552525,442.95703125,50.2
1727552525,442.95703125,56.5
1727552525,442.95703125,50.4
1727552525,442.95703125,40.6
1727553044,444.3359375,50.2
1727553044,444.3359375,57.4
1727553044,444.3359375,49.0
1727553044,444.3359375,57.8
1727553652,445.609375,50.2
1727553652,445.609375,55.3
1727553652,445.609375,49.8
1727553652,445.609375,38.7
1727554373,457.0703125,50.2
1727554373,457.0703125,53.1
1727554373,457.0703125,49.6
1727554373,457.0703125,54.3
1727555124,468.37890625,50.2
1727555124,468.37890625,38.2
1727555125,468.37890625,51.0
1727555125,468.37890625,37.5
1727555931,458.953125,50.2
1727555931,458.953125,35.3
1727555931,458.953125,50.6
1727555931,458.953125,55.6
1727556763,491.8125,50.2
1727556763,491.8125,38.2
1727556763,491.8125,51.2
1727556763,491.8125,41.2
1727557736,511.03125,50.2
1727557736,511.03125,38.2
1727557736,511.03125,51.2
1727557736,511.03125,40.6
1727558740,518.13671875,50.2
1727558740,518.13671875,54.2
1727558740,518.13671875,50.3
1727558740,518.13671875,39.4
1727559833,480.15234375,50.2
1727559833,480.15234375,40.0
1727559833,480.15234375,50.6
1727559833,480.15234375,56.5
1727561036,498.796875,50.2
1727561036,498.796875,55.6
1727561036,498.796875,49.4
1727561036,498.796875,56.8
1727562363,548.37109375,50.2
1727562363,548.37109375,54.3
1727562363,548.37109375,49.9
1727562363,548.37109375,37.5
1727563693,537.8671875,50.2
1727563693,537.8671875,53.1
1727563693,537.8671875,50.4
1727563693,537.8671875,39.4
1727564967,622.46875,50.2
1727564967,622.46875,38.2
1727565003,621.7578125,49.7
1727565003,621.7578125,47.6
</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>
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