| n_samples | const | max_depth | threshold | result |
|---|---|---|---|---|
| 100 | 0.241459 | 4 | 0.799933 | 0.280321 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| const | range | 0.1 | 1 | float | |
| max_depth | range | 2 | 4 | int | |
| threshold | range | 0.2 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 496 | 5 | 501 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,const,max_depth,threshold 0,0_0,COMPLETED,Sobol,0.335324409068982198789399262751,770,0.860396921634674072265625000000,3,0.652106606960296764086137955019 1,1_0,COMPLETED,Sobol,0.332334780511336247421638745436,764,0.921641307976096890719475140941,4,0.618138791806995935296242805634 2,2_0,COMPLETED,Sobol,0.306536420646406182477505808492,288,0.727556094527244567871093750000,2,0.402172993123531385961655360006 3,3_0,COMPLETED,Sobol,0.314522431259044887852382998972,396,0.965359145682305075375495562184,4,0.791945690102875410332217143150 4,4_0,COMPLETED,Sobol,0.342163531114327068571867584978,991,0.409504476934671401977539062500,3,0.608869461901486053179155533144 5,5_0,COMPLETED,Sobol,0.300268933912204549763202976465,208,0.799999002739787079541144976247,2,0.250995732471346877368034711253 6,6_0,COMPLETED,Sobol,0.290699469368065566143854994152,142,0.263121476583182856145981531881,3,0.489005934819579157757374332505 7,7_0,COMPLETED,Sobol,0.329762421611191514081440345763,645,0.622587921842932656701918858744,3,0.447469019703567116863496266888 8,8_0,COMPLETED,Sobol,0.317889532079112413498478417750,488,0.258142252266407024041683371252,3,0.689469848945736929479721766256 9,9_0,COMPLETED,Sobol,0.317431259044862468243763942155,416,0.580732486862689412099314267834,4,0.569547017663717403124223892519 10,10_0,COMPLETED,Sobol,0.286869271587071872353646995180,123,0.538903581444174051284790039062,2,0.633077400177717297680146657513 11,11_0,COMPLETED,Sobol,0.308865171249397052477547731542,320,0.362202093191444918218735438131,2,0.685902833379805176861054860638 12,12_0,COMPLETED,Sobol,0.294678002894355972607343119307,189,0.884206730127334639135483485006,4,0.708167497813701807274355815025 13,13_0,COMPLETED,Sobol,0.337650747708634790811288439727,841,0.297480103000998474804816851247,2,0.594562431611120767449563118134 14,14_0,COMPLETED,Sobol,0.281863241678726450523129187786,101,0.709933402948081493377685546875,3,0.564544477686286017004135828756 15,15_0,COMPLETED,Sobol,0.336524360829715374343606981711,892,0.496680196840316057205200195312,3,0.289846991375088736120346766256 16,16_0,COMPLETED,Sobol,0.329694886637723061362237331196,608,0.536728464160114548953117719066,2,0.638166392780840530107866470644 17,17_0,COMPLETED,Sobol,0.326584659913169317313474948605,508,0.331185810174792982785163530934,2,0.470261613838374670226727403133 18,18_0,COMPLETED,Sobol,0.326427882296189131317021292489,540,0.958295811433345079421997070312,2,0.552558417618274733129624110006 19,19_0,COMPLETED,Sobol,0.325479980704293292426143580087,577,0.128723281808197503872648326251,4,0.522611335478723182390581314394 20,20_0,COMPLETED,BoTorch,0.281495417269657544068195420550,100,0.609321452868050839057900702755,3,0.681998001668484343795739732741 21,21_0,COMPLETED,BoTorch,0.282891944042450549368084011803,100,0.916864257433731899560314104747,3,0.602872682306803686635987560294 22,22_0,COMPLETED,BoTorch,0.281875301495417285302380605572,100,0.658374398297795204726412521268,3,0.518329805574232138631884936331 23,23_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.960510616683919238312228117138,2,0.612045294538625550551103060570 24,24_0,COMPLETED,BoTorch,0.281360347322720749652091853932,100,0.427829048344285589600133334898,3,0.800000000000000044408920985006 25,25_0,COMPLETED,BoTorch,0.282404727448142822687771058554,100,1.000000000000000000000000000000,3,0.512728388879016172552383068250 26,26_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.815513261488516105757184959657,3,0.756039753695846972192384782829 27,27_0,COMPLETED,BoTorch,0.281499035214664683479668383370,100,0.591968372613341564658639981644,3,0.601047660377892478678063525876 28,28_0,COMPLETED,BoTorch,0.280465508924264361745315454755,100,0.826218872883353050440291553969,4,0.396813813761074718478027989477 29,29_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.934889191570958844046401736705,2,0.800000000000000044408920985006 30,30_0,COMPLETED,BoTorch,0.280631934394597215565170245100,100,0.638608405299725290227286222944,4,0.500001793164955854464892581746 31,31_0,COMPLETED,BoTorch,0.282613362276893376190400886117,100,1.000000000000000000000000000000,3,0.669386791361630484331612933602 32,32_0,COMPLETED,BoTorch,0.281459237819585150752743629710,100,0.451297391615531506836589414888,4,0.800000000000000044408920985006 33,33_0,COMPLETED,BoTorch,0.281081765557163576474408728245,100,0.520289513378968782042477414507,3,0.755362847326083164389842750097 34,34_0,COMPLETED,BoTorch,0.281564158224794969243021114380,100,0.743092763907577502635604105308,3,0.759006378674263348926842809306 35,35_0,COMPLETED,BoTorch,0.282099614085865924018037276255,100,0.821277516312874156234613565175,3,0.498921533968188113394859328764 36,36_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.300650907567350744731271561250,3,0.737926794124446283618112829572 37,37_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241417225272304747374363387280,4,0.800000000000000044408920985006 38,38_0,COMPLETED,BoTorch,0.282976362759286059755936548754,100,0.181338553898054033464148915300,4,0.732373298136487660414672973275 39,39_0,COMPLETED,BoTorch,0.281845151953690309376554523624,100,0.736124375906407446557011553523,3,0.636220306295257609363602568919 40,40_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.936474834510386489050404179579,2,0.580857640703601130205413483054 41,41_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.745025148682936233512918988708 42,42_0,COMPLETED,BoTorch,0.282770139893873562186854542233,100,0.967592878961811897475797650259,3,0.721662735594759796597941203800 43,43_0,COMPLETED,BoTorch,0.283180173661360390369168271718,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 44,44_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.756360610252465703773339100735,2,0.800000000000000044408920985006 45,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 46,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 47,47_0,COMPLETED,BoTorch,0.280959961408586589293179258675,100,0.423004997172923613568684686470,4,0.613820130220930071374141334672 48,48_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.497438345472716703099536061927 49,49_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.712853337433981826265494419204 50,50_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.778809502403451592300598349539 51,51_0,COMPLETED,BoTorch,0.308610709117221437836064978910,291,1.000000000000000000000000000000,4,0.736225801117658562588985660113 52,52_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.672758683116077893515694086091 53,53_0,COMPLETED,BoTorch,0.293175349734684065339251901605,178,0.350532550453271474211192071380,4,0.672077814302944442914622413809 54,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 55,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 56,56_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.736860280736264083856212891988 57,57_0,COMPLETED,BoTorch,0.281918716835504068463080784568,100,0.424174431409560570571670723439,4,0.200000000000000011102230246252 58,58_0,COMPLETED,BoTorch,0.283689097925711508629831314465,100,1.000000000000000000000000000000,4,0.249768132809647047665180252807 59,59_0,COMPLETED,BoTorch,0.280510130246020228383940775529,100,0.685293236834761954057171351451,4,0.200000000000000011102230246252 60,60_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237190732898929723670988778395,3,0.800000000000000044408920985006 61,61_0,COMPLETED,BoTorch,0.282176796912686933538338962535,100,1.000000000000000000000000000000,4,0.396959911196361814322841610192 62,62_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.859361726238822343226786415471,2,0.732696036882744206053530433564 63,63_0,COMPLETED,BoTorch,0.281458031837916067274818487931,100,1.000000000000000000000000000000,3,0.244159263939566606449460550721 64,64_0,COMPLETED,BoTorch,0.292707428847081563283438754297,139,0.142601069644926164992071448978,4,0.305300559641144664269063468964 65,65_0,COMPLETED,BoTorch,0.280762180414857676069573244604,100,0.725144753088975746457833793102,4,0.314615323184019690661017421007 66,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 67,67_0,COMPLETED,BoTorch,0.294530873130728454434290597419,159,0.280586955431305740127356784797,4,0.247870311166896950538784949458 68,68_0,COMPLETED,BoTorch,0.280893632416787220051901385887,100,0.963790446769895337020273018425,4,0.477260939328049971219769531672 69,69_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.613084317228246300501837140473 70,70_0,COMPLETED,BoTorch,0.281099855282199717620983392408,100,0.751499880482005333526274171163,4,0.299180814121010718587001520063 71,43_0,COMPLETED,BoTorch,0.283180173661360390369168271718,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 72,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 73,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252 74,74_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.281167377959501085804561171244 75,75_0,COMPLETED,BoTorch,0.283259768451519566845320241555,100,0.921460015735613757748012631055,4,0.200000000000000011102230246252 76,76_0,COMPLETED,BoTorch,0.286892185238784347411922226456,125,0.122797158464674788236337121816,2,0.413839506380008159691641367317 77,77_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.589154859217490334266642548755,4,0.367861167533371835602906685381 78,78_0,COMPLETED,BoTorch,0.289292088760250809542640126892,151,0.404694710294062010014215502451,3,0.566854084895249688003104893141 79,79_0,COMPLETED,BoTorch,0.289357211770381095305992857902,147,0.600271114652549120371816115949,2,0.264892347300024755263336828648 80,80_0,COMPLETED,BoTorch,0.281144476603955584259608713182,100,0.613804671280130054178414411581,4,0.321506352084691693349327579199 81,81_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.283267553512524083991763745871 82,82_0,COMPLETED,BoTorch,0.281425470332850924393142122426,100,0.835649097619452163598907645792,4,0.800000000000000044408920985006 83,83_0,COMPLETED,BoTorch,0.281669078630004787733298599051,100,0.636749496464390607464167715079,4,0.327822098774013193089871265329 84,84_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200639529787448450681708322918 85,85_0,COMPLETED,BoTorch,0.281127592860588526590959190798,100,0.685344680141829476482939753623,4,0.259065354137725578631545886310 86,86_0,COMPLETED,BoTorch,0.290693439459720259776531747775,153,0.222805060878411675950871995155,4,0.590236678366413469731810437224 87,87_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.664428766986098984226316588320,2,0.200000000000000011102230246252 88,88_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.486769040665189500760590135542 89,89_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 90,90_0,COMPLETED,BoTorch,0.281234925229136512037086959026,100,0.386834572431698164862723388069,3,0.200000000000000011102230246252 91,91_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.336861597465170303777881599672,2,0.200000000000000011102230246252 92,92_0,COMPLETED,BoTorch,0.288061987457790658062606325984,128,0.627951237362264547137158388068,2,0.730747205408294187023443555518 93,93_0,COMPLETED,BoTorch,0.281484563434635792766869144543,100,0.597232767296283273417145665007,3,0.200000000000000011102230246252 94,94_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.542488690409693719018946467259 95,95_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.536290908246188302577195372578,2,0.200000000000000011102230246252 96,89_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 97,97_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.483373978732424824933389118087,2,0.200000000000000011102230246252 98,98_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.613342940689883264759885150852 99,99_0,COMPLETED,BoTorch,0.291619633381572596064756908163,152,0.224044626457482520809350035051,2,0.373370982527880257428876120684 100,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252 101,101_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.562034653035851317071092125843 102,102_0,COMPLETED,BoTorch,0.281213217559093120456736869528,100,0.970674418385605863868192955124,4,0.800000000000000044408920985006 103,103_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.425304332013971719828049344869 104,104_0,COMPLETED,BoTorch,0.283771104679208852061833567859,106,0.806552529066070666985410753114,3,0.623962608083840075678949688154 105,105_0,COMPLETED,BoTorch,0.281859623733719200089353762451,100,0.796904512163817413394895083911,3,0.332810063799171995668757517706 106,106_0,COMPLETED,BoTorch,0.307407139411480900825779372099,301,1.000000000000000000000000000000,4,0.800000000000000044408920985006 107,107_0,COMPLETED,BoTorch,0.297351664254703274004043578316,193,0.856877315870694267196938653797,4,0.620863983942546604488654793386 108,108_0,COMPLETED,BoTorch,0.281725759768451489151175337611,100,0.764708521252592032801942423248,4,0.601237841216216817308293229871 109,109_0,COMPLETED,BoTorch,0.281719729860106071761549628718,100,0.761062806205437869522256733035,4,0.589768805844644528058040577889 110,110_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.432520750019955324550124942107 111,111_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.626746313255761355875961271522 112,112_0,COMPLETED,BoTorch,0.281383260974433224710367085208,100,0.763807139264614187723623217607,4,0.585539488869372903856458378868 113,113_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.992109520789691834430357175734,2,0.686230971566745395762154657859 114,114_0,COMPLETED,BoTorch,0.281505065123010100869294092263,100,0.758347309919061074445778558584,4,0.583823773272484025653739081463 115,115_0,COMPLETED,BoTorch,0.285226724553786836757751643745,129,0.497986380238492731820088010863,2,0.800000000000000044408920985006 116,116_0,COMPLETED,BoTorch,0.281431500241196341782767831319,100,0.760786013273364702946821580554,4,0.583973937459685732420666681719 117,117_0,COMPLETED,BoTorch,0.281431500241196341782767831319,100,0.761765009526143677121012842690,4,0.585339075287317767148920211184 118,118_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.646128629360638639944625083444 119,119_0,COMPLETED,BoTorch,0.281530390738060742883419607097,100,0.761483785514172728525750244444,4,0.587471255609660736318744511664 120,120_0,COMPLETED,BoTorch,0.281383260974433224710367085208,100,0.761572241101902025484093883279,4,0.587140205914607249404468802823 121,121_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.956035714961293470182113196643,2,0.691248595321693581894351154915 122,122_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.979301169797443638032063972787,2,0.687755464536077165682570466743 123,123_0,COMPLETED,BoTorch,0.281099855282199717620983392408,100,0.763443554778661548354534716054,4,0.575816919533898352590028935083 124,124_0,COMPLETED,BoTorch,0.281547274481427911574371591996,100,0.764000505055555501066066881322,4,0.588869514431734630832693255797 125,125_0,COMPLETED,BoTorch,0.281511095031355518258919801156,100,0.758651080169266522901239113708,4,0.589415454505437197418871164700 126,126_0,COMPLETED,BoTorch,0.280510130246020228383940775529,100,0.707413678387941557978990658739,4,0.263397108697904391849675675985 127,127_0,COMPLETED,BoTorch,0.296694404245055443070100409386,191,0.732888648318413271631754923874,4,0.678138498042447634972518244467 128,128_0,COMPLETED,BoTorch,0.281517124939700935648545510048,100,0.759633947467053149971150105557,4,0.594096065027543973613433081482 129,129_0,COMPLETED,BoTorch,0.281708876025084431482525815227,100,0.772941140878985355477936991520,4,0.575762446500667657289795897668 130,130_0,COMPLETED,BoTorch,0.281357935359382582696241570375,100,0.999999999903765646180886506045,2,0.336828505003928913019706214982 131,131_0,COMPLETED,BoTorch,0.282821997105644040715333176195,100,0.850839362026055145982184058084,3,0.200000000000000011102230246252 132,132_0,COMPLETED,BoTorch,0.281285576459237796065337988693,100,0.864067689188233956087970000226,4,0.800000000000000044408920985006 133,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 134,134_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.663381660957313323834227958287 135,135_0,COMPLETED,BoTorch,0.289357211770381095305992857902,147,0.625447231970156947156169735536,2,0.200000000000000011102230246252 136,136_0,COMPLETED,BoTorch,0.308977327544621371835376066883,293,0.752633022318367705949526680342,3,0.200000000000000011102230246252 137,137_0,COMPLETED,BoTorch,0.287275687409551339079882836813,122,0.100000000000000005551115123126,2,0.541290418842662379717012299807 138,138_0,COMPLETED,BoTorch,0.282079112397491504893309866020,100,0.769227631307396908511009314680,4,0.482124334895646711185435151492 139,139_0,COMPLETED,BoTorch,0.298818137964302898978985467693,193,0.791687875623532932856107890984,4,0.721277801968501774965147887997 140,140_0,COMPLETED,BoTorch,0.297937771345875512807310769858,190,0.100000000000000005551115123126,2,0.800000000000000044408920985006 141,141_0,COMPLETED,BoTorch,0.300268933912204549763202976465,208,0.100000000000000005551115123126,2,0.800000000000000044408920985006 142,142_0,COMPLETED,BoTorch,0.295141099855282251773758162017,186,0.297017942879134799749607509511,3,0.800000000000000044408920985006 143,143_0,COMPLETED,BoTorch,0.308977327544621371835376066883,293,0.796229089700815850783044425043,2,0.351845961487870728667815001245 144,144_0,COMPLETED,BoTorch,0.288931500241196292932954747812,151,0.141069354642069239025303772905,2,0.590157209553597406070934994204 145,145_0,COMPLETED,BoTorch,0.282519295706705309001449677453,100,0.583591861538787282093210251332,4,0.585931117182778127272513302159 146,146_0,COMPLETED,BoTorch,0.289969850458273059601310706057,135,0.100000000000000005551115123126,4,0.800000000000000044408920985006 147,147_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.527646702591519467517855446204 148,148_0,COMPLETED,BoTorch,0.343461167390255672771104400454,1000,1.000000000000000000000000000000,4,0.800000000000000044408920985006 149,149_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.312585537543322633524667253369,2,0.800000000000000044408920985006 150,150_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.356912916209194741590238209028,2,0.403722327859890262047315445670 151,151_0,COMPLETED,BoTorch,0.298036661842740024930265008152,210,0.100000000000000005551115123126,4,0.800000000000000044408920985006 152,152_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.179399167858704799982660915703,3,0.719124564657684928192793449853 153,153_0,COMPLETED,BoTorch,0.295422093584177480884989108745,172,0.402403803677889637668840805418,4,0.565588693420973398495732453739 154,154_0,COMPLETED,BoTorch,0.296595513748191041969448633608,187,0.308712443362756494558141184825,2,0.614905852238070149695658983546 155,155_0,COMPLETED,BoTorch,0.288061987457790658062606325984,128,0.692484011662490406280312527088,2,0.733804207356910875859057341586 156,156_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.341440062150349166181229065842 157,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 158,158_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.413666576897602000784104347986,2,0.200000000000000011102230246252 159,159_0,COMPLETED,BoTorch,0.344202846116739014092900106334,992,0.362502074179377853369032891351,3,0.590927285417441550130490668380 160,160_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.216256696673561010779707203255,2,0.200000000000000011102230246252 161,161_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.257378545865637464729047678702,2,0.489471199348137375739753451853 162,162_0,COMPLETED,BoTorch,0.282635069946936767770750975615,100,0.621703206241048467184384662687,4,0.800000000000000044408920985006 163,163_0,COMPLETED,BoTorch,0.284050892426435108717441835324,104,0.348617984923893065030142679461,3,0.416746088186085161808591692534 164,164_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.924691595571182078927563452453,2,0.677674658077331182326474845468 165,165_0,COMPLETED,BoTorch,0.290050651230101319555387817672,144,1.000000000000000000000000000000,4,0.800000000000000044408920985006 166,166_0,COMPLETED,BoTorch,0.283656536420646365748154948960,119,1.000000000000000000000000000000,4,0.800000000000000044408920985006 167,167_0,COMPLETED,BoTorch,0.312005547515677772452136196080,303,0.240065488202822724383977970319,2,0.308074847560504239130096948429 168,168_0,COMPLETED,BoTorch,0.293486493005306270376308930281,196,0.805241348136261136936298044020,4,0.665151427294484398622387288924 169,169_0,COMPLETED,BoTorch,0.286649782923299567549690891610,124,0.880859543457893634510469382803,4,0.725871286748837052371641220816 170,170_0,COMPLETED,BoTorch,0.290756150506512267561731732712,134,0.686723816288070443292212985398,2,0.621855436311953391204099261813 171,171_0,COMPLETED,BoTorch,0.294649059334298080159442179138,162,1.000000000000000000000000000000,2,0.800000000000000044408920985006 172,172_0,COMPLETED,BoTorch,0.289512783405692197824521372240,141,0.755763226276860211605423955916,4,0.576149589739470835425549921638 173,173_0,COMPLETED,BoTorch,0.298479257115291884971952640626,174,0.835755180937133634877511667582,2,0.653303828079771831127686709806 174,174_0,RUNNING,BoTorch,,196,0.846472154190134995488392632979,4,0.671230032564440026732199839898 175,175_0,COMPLETED,BoTorch,0.295306319343946022115687810583,183,0.862411497078117705861188824201,2,0.266482041910973854736965904522 176,176_0,COMPLETED,BoTorch,0.290261698022190040013867928792,140,0.958830288424676435710125588230,3,0.646196299362590931281147277332 177,177_0,COMPLETED,BoTorch,0.288402074288470866569866757345,135,1.000000000000000000000000000000,4,0.200000000000000011102230246252 178,178_0,COMPLETED,BoTorch,0.291585865894838369705155400879,158,0.100000000000000005551115123126,4,0.215964072133795370600495289182 179,179_0,COMPLETED,BoTorch,0.286520742884708190523213033885,122,0.966225189864108768844630503736,4,0.754136680482391907887063098315 180,180_0,COMPLETED,BoTorch,0.285855041003376775243793872505,129,0.991745606669502177688002575451,4,0.200000000000000011102230246252 181,181_0,COMPLETED,BoTorch,0.288309213699951771836538227944,120,0.567853852172886974791765624104,2,0.766294260111517688827120764472 182,182_0,COMPLETED,BoTorch,0.291585865894838369705155400879,158,0.109133940135581863906111266260,4,0.213251008893593108073361008792 183,183_0,COMPLETED,BoTorch,0.298656536420646379070831244462,201,0.804966247471551543668510930729,3,0.694251561773776559860493762244 184,184_0,COMPLETED,BoTorch,0.289686444766039552511927013256,136,0.825278487845191421889978755644,4,0.755411197320168259494721496594 185,185_0,COMPLETED,BoTorch,0.287342016401350708321160709602,132,0.875138227399023471697603326902,2,0.483428504425656824228241248420 186,186_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.903546926302124409069449484377,2,0.674320702735722088405623253493 187,187_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.896050517688234071655983825622,2,0.673895512275354158227003154025 188,188_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.892678078944392994209522385063,2,0.676147998562335761363328856532 189,189_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.802858457916524992370455038326,2,0.800000000000000044408920985006 190,190_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.890338273437926264186614844220,2,0.800000000000000044408920985006 191,191_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.633807978920846393222632286779,2,0.200000000000000011102230246252 192,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 193,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 194,194_0,COMPLETED,BoTorch,0.283748191027496377003558336583,106,0.842385685228827618686864298070,3,0.746108818889442249044918753498 195,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 196,196_0,COMPLETED,BoTorch,0.282759286058851921907830728742,100,0.828384882570699865311780740740,3,0.232606379828846965285293890702 197,197_0,COMPLETED,BoTorch,0.289756391702846172186980311380,144,0.648820977258636411022507672897,3,0.359140510001706936282772630875 198,198_0,COMPLETED,BoTorch,0.293654124457308207674088862404,164,0.931122701007022102182020262262,2,0.619400872920816025768431245524 199,199_0,COMPLETED,BoTorch,0.288803666184273999384402031865,148,0.613921533114687889565175282769,4,0.295801448398209765322519615438 200,200_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.790346793803923364230001880060,2,0.329806698136218656713936070446 201,201_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.477832850297235045555765964309 202,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 203,203_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.478157063056679654255987088618 204,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 205,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 206,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 207,207_0,COMPLETED,BoTorch,0.297071876507477128370737773366,179,0.725234931272955130054924666183,4,0.790359520829561290611309232190 208,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 209,209_0,COMPLETED,BoTorch,0.281045586107091183158956937405,100,0.100000000000000005551115123126,4,0.425430027905785990149922781711 210,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 211,211_0,COMPLETED,BoTorch,0.281849975880366643288255090738,100,0.534422713022195128296232269349,3,0.281666505650945975247623209725 212,212_0,COMPLETED,BoTorch,0.295141099855282251773758162017,186,1.000000000000000000000000000000,2,0.243897113123416442448387897457 213,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 214,214_0,COMPLETED,BoTorch,0.283894114809454922720988179208,102,0.853847788821290087923898681765,3,0.669268090225853540253808660054 215,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 216,216_0,COMPLETED,BoTorch,0.284252291365171272374823274731,104,0.693454476070416436783716562786,2,0.565582385302276668959109429125 217,217_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.639511969747473152381189720472,2,0.322570601108127852363338661235 218,218_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.694032745577860099572831131809 219,219_0,COMPLETED,BoTorch,0.299260733236854759020673100167,196,1.000000000000000000000000000000,4,0.441932317499453386488994510728 220,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 221,221_0,COMPLETED,BoTorch,0.292029667149059313224768175132,161,1.000000000000000000000000000000,2,0.351487777975540871544524179626 222,222_0,COMPLETED,BoTorch,0.283317655571635351741122121894,100,0.368313527455379374764277145005,4,0.638261415881231419788832681661 223,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 224,224_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.961772034136499254231011946104,2,0.675451569484452951641628715151 225,225_0,COMPLETED,BoTorch,0.309821514712976364691598973877,313,0.290782173388597686702894407063,2,0.415311527434303273675908485529 226,226_0,COMPLETED,BoTorch,0.294552580800771846014640686917,167,0.765052246834325933022569188324,3,0.291337422741349494348384041587 227,227_0,COMPLETED,BoTorch,0.301274722624216062527580106689,264,0.714139358072574226277140496677,3,0.317561610644479130893813589864 228,228_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.946893960524655242672054100694,2,0.681764546498261125861972686835 229,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 230,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 231,231_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.945825682272717571663633862045,2,0.679525122740896492246065463405 232,232_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.939258035279958991559112746472,2,0.679920381057878442376818384218 233,233_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.336847463124218260421827153550,2,0.800000000000000044408920985006 234,234_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.746775983995577874452465039212,2,0.200000000000000011102230246252 235,235_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.706287288632600041005105140357,2,0.200000000000000011102230246252 236,236_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.294886917977688289838056334702,2,0.800000000000000044408920985006 237,237_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.347235553730743107259115731722,2,0.800000000000000044408920985006 238,238_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.691808413293654167475210670091,2,0.200000000000000011102230246252 239,239_0,RUNNING,BoTorch,,100,0.720201404691578783712202493916,2,0.200000000000000011102230246252 240,240_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.684760623974637550048782941303,2,0.200000000000000011102230246252 241,241_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.324187818068531530268927554062,2,0.800000000000000044408920985006 242,242_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.711099142693777608315031102393,2,0.200000000000000011102230246252 243,243_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.720356768618747977939165139105,2,0.200000000000000011102230246252 244,244_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.318190746164397519901712030332,2,0.800000000000000044408920985006 245,245_0,COMPLETED,BoTorch,0.292766521948866431657165776414,168,0.720223951753498958794352802215,4,0.200000000000000011102230246252 246,246_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.309451821798491122361696170628,2,0.800000000000000044408920985006 247,247_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.939416887099215225376269700064,2,0.800000000000000044408920985006 248,248_0,COMPLETED,BoTorch,0.290868306801736586919560068054,156,0.484304986466263409639054771105,3,0.264135348699061189847725472646 249,249_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.720324271547412187111092407577,2,0.200000000000000011102230246252 250,250_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.724377764276417757116632856196,2,0.200000000000000011102230246252 251,251_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.318522557795787752965566141938,2,0.800000000000000044408920985006 252,252_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.634339566338044447135757764045,2,0.363977600261018885419161961181 253,253_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.254328915742473404826284877345,2,0.800000000000000044408920985006 254,254_0,COMPLETED,BoTorch,0.282645923781958519072077251622,105,0.713946342586330162660601672542,3,0.467432476056995060975651767876 255,255_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.729984710096583611083076448267,2,0.200000000000000011102230246252 256,256_0,COMPLETED,BoTorch,0.310973227206946423173405946727,338,0.172470265068113803863525390625,2,0.401089510880410671234130859375 257,257_0,COMPLETED,BoTorch,0.286449589966232487370234593982,113,0.582232419247471488787937232701,2,0.438017121978734880194394918362 258,258_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.945234005818557432299087395222,2,0.778277176306792828341940548853 259,259_0,COMPLETED,BoTorch,0.341046792088760208905284798675,920,0.272794999930265458232270248118,3,0.358591073258661130473967659782 260,260_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.243928649865581076428000528722,2,0.800000000000000044408920985006 261,261_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237333160073314092253227158835,2,0.800000000000000044408920985006 262,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 263,263_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.369441418842299418301422520017 264,264_0,COMPLETED,BoTorch,0.295946695610226684358678994613,191,0.381868200409442581921837245318,3,0.236714764484911577291370576859 265,265_0,COMPLETED,BoTorch,0.293709599614085825614040459186,173,0.136156818069431528872570424937,3,0.200000000000000011102230246252 266,266_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.239537885141219458740025061161,2,0.800000000000000044408920985006 267,267_0,COMPLETED,BoTorch,0.290151953690303887611889877007,144,0.311371242452635144104533537757,3,0.360182827417835027539183556655 268,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 269,269_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237934382072108790984188431139,2,0.800000000000000044408920985006 270,270_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.731299265689706268211978112959,2,0.200000000000000011102230246252 271,271_0,COMPLETED,BoTorch,0.281279546550892378675712279801,100,0.240943779144106723366647315743,4,0.566295000121245561963689851837 272,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 273,273_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.236613375869878073132213103236,2,0.800000000000000044408920985006 274,274_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241752280893886728474484471008,4,0.799469125269815172885046195006 275,275_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.353826005526554465685507011585 276,276_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.418757746982561629423003068950,2,0.200000000000000011102230246252 277,277_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.759139117987122746811223805707,2,0.200000000000000011102230246252 278,278_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.753076537975211790509888487577,2,0.200000000000000011102230246252 279,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 280,280_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.396208080228131254152401652391,2,0.800000000000000044408920985006 281,281_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.942306761852117813482720976026,2,0.772063884130549160644818584842 282,282_0,COMPLETED,BoTorch,0.307311866859623750158903021656,271,0.601605985652138963715174213576,2,0.365474716273138933164688069155 283,283_0,COMPLETED,BoTorch,0.281291606367583213454963697586,101,0.942016719363089172212255562044,3,0.578064016110590772612454202317 284,284_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241459008415108811185234571894,4,0.799932660394794048386302165454 285,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 286,286_0,COMPLETED,BoTorch,0.281679932465026539034624875057,100,0.195967177747737447690568046710,4,0.538464591568163064572161147225 287,287_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.818810163660800394147543102008,2,0.255684039995552003787793182710 288,46_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 289,289_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.394239944425048838283487384615,2,0.800000000000000044408920985006 290,290_0,COMPLETED,BoTorch,0.301541244573082511948314277106,231,0.223921546235003099400984183376,4,0.607881632743191624435041831020 291,291_0,COMPLETED,BoTorch,0.294649059334298080159442179138,162,0.389297407551159935223950014915,2,0.551354561083939209709114948055 292,292_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.756909675226253408908405617694,2,0.200000000000000011102230246252 293,293_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.937995811541127211263813023834,2,0.800000000000000044408920985006 294,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 295,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 296,296_0,COMPLETED,BoTorch,0.309723830197781047068872339878,330,0.920502878243025124405107817438,2,0.296027440805140429347375174984 297,297_0,COMPLETED,BoTorch,0.290699469368065566143854994152,142,1.000000000000000000000000000000,2,0.200000000000000011102230246252 298,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 299,299_0,COMPLETED,BoTorch,0.294649059334298080159442179138,172,0.100000000000000005551115123126,4,0.618353330723201199958793949918 300,300_0,COMPLETED,BoTorch,0.281248191027496430294263518590,100,0.677609767744558610935712295031,4,0.200000000000000011102230246252 301,301_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.815861628814870121217950327264,2,0.542135642217584035051913815551 302,302_0,COMPLETED,BoTorch,0.281248191027496430294263518590,100,0.678003862189504813429152818571,4,0.200000000000000011102230246252 303,303_0,COMPLETED,BoTorch,0.289751567776169838275279744266,144,0.206628085521213644693006017405,4,0.200000000000000011102230246252 304,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 305,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 306,306_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.931197684081100152120313850901,2,0.800000000000000044408920985006 307,307_0,COMPLETED,BoTorch,0.293859141341051621765245727147,154,0.248735888944153771262790542096,4,0.419826463041161168643355949825 308,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 309,309_0,COMPLETED,BoTorch,0.284158224794983094163569603552,111,1.000000000000000000000000000000,4,0.431571366776208942273029833814 310,310_0,COMPLETED,BoTorch,0.284649059334298071277657982137,110,1.000000000000000000000000000000,4,0.200000000000000011102230246252 311,311_0,COMPLETED,BoTorch,0.285352146647370963350454076135,111,1.000000000000000000000000000000,4,0.800000000000000044408920985006 312,312_0,COMPLETED,BoTorch,0.285297877472262428888427621132,113,0.598450829195625422229909418093,4,0.800000000000000044408920985006 313,313_0,COMPLETED,BoTorch,0.292770139893873571068638739234,143,0.550643598842818438043877904420,2,0.495230220282225408467979832494 314,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 315,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 316,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 317,317_0,COMPLETED,BoTorch,0.287303424987940148049858635204,114,1.000000000000000000000000000000,4,0.200000000000000011102230246252 318,318_0,COMPLETED,BoTorch,0.285561987457790600331009045476,112,0.701978321328223797692658081360,4,0.200000000000000011102230246252 319,319_0,COMPLETED,BoTorch,0.285741678726483372408040395385,117,0.350691070304812169133867882920,4,0.333006167255020191220182823599 320,320_0,COMPLETED,BoTorch,0.285817655571635298450416939886,109,0.982790887140310598368841965566,4,0.200000000000000011102230246252 321,321_0,COMPLETED,BoTorch,0.282753256150506504518205019849,101,0.586912740140419386669634604914,4,0.244563848681499079740220281565 322,322_0,COMPLETED,BoTorch,0.286039556198745770210223327012,114,0.224506504423856390051383868922,4,0.800000000000000044408920985006 323,323_0,COMPLETED,BoTorch,0.284125663289917951281893238047,107,1.000000000000000000000000000000,2,0.200000000000000011102230246252 324,324_0,COMPLETED,BoTorch,0.283904968644476563000011992699,118,0.100000000000000005551115123126,4,0.800000000000000044408920985006 325,325_0,COMPLETED,BoTorch,0.298135552339604426030916783930,207,0.693248457573672816067755775293,4,0.403152721957673043640113519359 326,326_0,COMPLETED,BoTorch,0.286590689821514699175963869493,102,0.652787841229713783341992439091,4,0.288163083798190722273346864313 327,327_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.971892654795525445798887176352,2,0.650239992852036374770818838442 328,328_0,RUNNING,BoTorch,,100,0.735365273177978284735445413389,2,0.250279624768237729526276780234 329,329_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.738057814269197587364601531590,2,0.253357428818003493908861400996 330,330_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.307159218858608473734506105757,2,0.579336331161762863040110005386 331,331_0,COMPLETED,BoTorch,0.283765074770863434672207858966,114,0.694601496426105913251092260907,4,0.200000000000000011102230246252 332,332_0,COMPLETED,BoTorch,0.282089966232513256194636142027,100,0.262994676240536351397025782717,4,0.348791006323768582042532671039 333,333_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.733279903576728897540704110725,2,0.234658568632625796901436387998 334,334_0,COMPLETED,BoTorch,0.291859623733719208971137959452,155,0.611266164645309140190931884717,4,0.377892234085169254065306176926 335,335_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.716300194873249918003921266063,2,0.226653078973867855294699324986 336,336_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.466698001771278625504635328980 337,337_0,COMPLETED,BoTorch,0.287342016401350708321160709602,132,0.100000000000000005551115123126,2,0.427240721712862536740829000337 338,338_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.646647732194282509610161469027 339,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 340,340_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.648258159321058125001968619472 341,341_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.699812026107477058722849960759,2,0.227177933855437330645798965634 342,342_0,COMPLETED,BoTorch,0.285077182826821040606546375784,126,0.100000000000000005551115123126,3,0.800000000000000044408920985006 343,343_0,COMPLETED,BoTorch,0.288539556198745827941820607521,127,1.000000000000000000000000000000,2,0.200000000000000011102230246252 344,344_0,COMPLETED,BoTorch,0.300905692233478072594721197675,246,1.000000000000000000000000000000,2,0.200000000000000011102230246252 345,345_0,COMPLETED,BoTorch,0.286181862035697065493877744302,122,1.000000000000000000000000000000,2,0.200000000000000011102230246252 346,346_0,COMPLETED,BoTorch,0.286345875542691752357882251090,117,0.464186007431136582468411688751,4,0.605018579146897605980370826728 347,347_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.701920648346900044600715773413 348,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 349,349_0,COMPLETED,BoTorch,0.290048239266763152599537534115,137,0.123787445103548110481028743379,4,0.635628116312550117683599637530 350,350_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.683168209886624300253288311069 351,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 352,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 353,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 354,354_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.681790009815209208099417992344 355,355_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.293413752453075715731500849870,3,0.638941510254167788218637724640 356,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 357,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 358,358_0,COMPLETED,BoTorch,0.291091413410516142157291596959,135,0.654720299189325616318058109755,3,0.364988471237820133463714000754 359,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 360,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 361,361_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.683267204836155439551248491625 362,362_0,COMPLETED,BoTorch,0.282315484804630978388217954489,102,0.100031264823990079237958639169,4,0.400369881949983297264594739318 363,363_0,COMPLETED,BoTorch,0.330186927158707232976553314074,650,0.112108773733449684772978116598,2,0.482723747691611637655739741604 364,364_0,COMPLETED,BoTorch,0.292770139893873571068638739234,143,0.372512492257235816595084543223,2,0.516558816098636075508920839638 365,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 366,366_0,COMPLETED,BoTorch,0.305189339122045377727943105128,260,0.230342581504888760379756718066,2,0.654685843555364743728830489999 367,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 368,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 369,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 370,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 371,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 372,372_0,COMPLETED,BoTorch,0.313469609261939230471227801900,353,0.136421487521151851840528479443,3,0.448448955509056057522343508026 373,373_0,COMPLETED,BoTorch,0.281120356970574025723408340127,100,1.000000000000000000000000000000,4,0.605873338809452377518027788028 374,374_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.675962472824773286106392333750 375,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 376,376_0,COMPLETED,BoTorch,0.282536179450072366670099199837,104,0.513304304641691189203811518382,4,0.466753275142660406515915383352 377,377_0,COMPLETED,BoTorch,0.291435118186203601098327453656,169,0.669954463187605098184462804056,4,0.369497539475560188293457031250 378,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 379,379_0,COMPLETED,BoTorch,0.283346599131693244189023062063,101,0.435861302151740570565152665949,4,0.673637481354710576653133102809 380,380_0,COMPLETED,BoTorch,0.282023637240713886953358269238,101,0.292560564554253499380820358056,4,0.662058239053643360350065449893 381,381_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.654862444744664706774983642390 382,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 383,383_0,COMPLETED,BoTorch,0.281191509889049728876386780030,100,0.602335024523823037156944337767,4,0.200000000000000011102230246252 384,384_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.660544726383470393038521706330,2,0.200000000000000011102230246252 385,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 386,386_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.521611462796282787657276003301 387,387_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.672227173121904941766047159035 388,388_0,COMPLETED,BoTorch,0.288129522431258999759506878036,119,0.816194997124227827889342279377,2,0.207681618404539525268859279095 389,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 390,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 391,391_0,RUNNING,BoTorch,,100,0.668213667232075314572625757137,2,0.200000000000000011102230246252 392,392_0,COMPLETED,BoTorch,0.294165460684997603912904651224,159,1.000000000000000000000000000000,2,0.578088095756320474727374403301 393,393_0,COMPLETED,BoTorch,0.281702846116739014092900106334,101,0.216017986156130159525190492786,4,0.479109244307909853954896561845 394,394_0,COMPLETED,BoTorch,0.306905451037144283432667180023,261,0.565768510849323225109230861563,2,0.473259687775547832622180521867 395,395_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.660757790099959496643577949726 396,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 397,397_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.666030902813548597585224797513,2,0.200000000000000011102230246252 398,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 399,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 400,400_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.650307061379849393745189445326 401,401_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.658475572071168024912424243666,2,0.200000000000000011102230246252 402,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 403,403_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.663723509804278744006467150029,2,0.200000000000000011102230246252 404,404_0,COMPLETED,BoTorch,0.305120598166907841530814948783,283,0.466216888969391174413203771110,3,0.723988991230123835052268077561 405,405_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.661355100798511674753399347537,2,0.200000000000000011102230246252 406,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252 407,407_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.654484027375000998461018753005,2,0.200000000000000011102230246252 408,408_0,COMPLETED,BoTorch,0.295217076700434177816134706518,178,0.801776412202722665867327123124,3,0.200000000000000011102230246252 409,409_0,COMPLETED,BoTorch,0.281255426917510820139511906746,100,0.389589813666507134293226499722,3,0.202887558013209096108298012950 410,410_0,COMPLETED,BoTorch,0.284076218041485750731567350158,114,0.527916267410704631579676515685,4,0.774885322365777895470273506362 411,411_0,COMPLETED,BoTorch,0.281120356970574025723408340127,100,1.000000000000000000000000000000,4,0.597666242168518047073177967832 412,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252 413,413_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635264777201584696086911208113 414,414_0,COMPLETED,BoTorch,0.280724794983116310298498774500,100,0.775711710074054927588349528378,4,0.696086053631963519450209787465 415,415_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.535519833290465818720349489013 416,416_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.333999360419534263577645560872 417,417_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.691790460385156458222866149299,2,0.234740316630668188757624648133 418,418_0,COMPLETED,BoTorch,0.297621804148576973858553174068,200,0.811642138799668955151389582170,2,0.329620227622200834272092606625 419,419_0,COMPLETED,BoTorch,0.283082489146164961724139175203,104,0.229081666275175593883162150632,4,0.582411093260960743833720698603 420,420_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.637725406247186432651119503134 421,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 422,422_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.634907251342835543539422360482 423,423_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635935976241632450189911196503 424,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 425,425_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.603012648082953184314192185411 426,426_0,COMPLETED,BoTorch,0.343526290400385958534457131464,975,0.246512372405580493239796169291,4,0.518183293193174465685046925501 427,427_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635926558524608109834730385046 428,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252 429,429_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.363949951049462983032611873568 430,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 431,431_0,COMPLETED,BoTorch,0.284375301495417232011675423564,122,0.691888688531572038087347209512,4,0.291066321692046159341771272011 432,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 433,433_0,COMPLETED,BoTorch,0.283227206946454423963643876050,107,0.760077403719831345974000669230,3,0.535839154270179252037564765487 434,434_0,COMPLETED,BoTorch,0.284429570670525766473701878567,108,0.370783538924228661670667861472,4,0.699504754317558097298501706973 435,435_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.348512486251269737458358122240 436,436_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.735786325475950797603275077563,2,0.260364971085144392137777913376 437,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 438,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 439,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 440,440_0,COMPLETED,BoTorch,0.309382537385431755083686766739,309,0.648167157791927461829573076102,2,0.270232536679586576866540781339 441,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 442,442_0,COMPLETED,BoTorch,0.286872889532079122787422420515,109,0.348360262771811091120355285966,4,0.734122356570476863879548545810 443,443_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.249608341434244651591001229463,2,0.731122315698845115505832836789 444,444_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.648845223429570738815641561814 445,445_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.649023156330049033613249775954 446,446_0,COMPLETED,BoTorch,0.283628798842257556778179150569,103,1.000000000000000000000000000000,2,0.800000000000000044408920985006 447,447_0,COMPLETED,BoTorch,0.286115533043897696252599871514,116,1.000000000000000000000000000000,2,0.800000000000000044408920985006 448,448_0,COMPLETED,BoTorch,0.289173902556681183817488545174,127,0.290651420915175373593797303329,2,0.800000000000000044408920985006 449,449_0,COMPLETED,BoTorch,0.283702363724071426887007874029,110,0.477787157607551526972144984029,4,0.200000000000000011102230246252 450,450_0,COMPLETED,BoTorch,0.283749397009165460481483478361,107,0.100000000000000005551115123126,2,0.800000000000000044408920985006 451,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 452,452_0,COMPLETED,BoTorch,0.318865171249397061359331928543,435,0.116286427772718747863933685949,2,0.517277844168346767617094883462 453,453_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.279501291506880411574798017682 454,454_0,COMPLETED,BoTorch,0.284353593825373840431325334066,105,0.517743209601877030223704423406,4,0.467192514462177799217101892282 455,455_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.659110597564777522805457010691 456,456_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.688546091192570042238685346092,2,0.221963175324737305160738287668 457,457_0,COMPLETED,BoTorch,0.301494211287988367331536210258,236,0.470989179467911855958561773150,2,0.289822159380871635647736184183 458,458_0,COMPLETED,BoTorch,0.300836951278340536397593041329,244,0.274869911737801508877510059392,3,0.204143038196925613592114245876 459,459_0,COMPLETED,BoTorch,0.327773757838880852588658854074,588,0.568610160525820917953865318850,2,0.511512081224274828272768900206 460,460_0,COMPLETED,BoTorch,0.299688856729377728349561493815,215,0.582990275787747536107019641349,3,0.652690829324037768977007090143 461,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 462,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 463,463_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.604120249596454717000426626328 464,464_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.333176902125653928710136142399 465,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 466,466_0,COMPLETED,BoTorch,0.281181862035697061052985645802,100,1.000000000000000000000000000000,2,0.223920420696380684244530812066 467,467_0,COMPLETED,BoTorch,0.323429811866859595603784782725,466,0.546909577004780156350705055956,3,0.653826509973231884131905644608 468,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 469,469_0,COMPLETED,BoTorch,0.284761215629522390635486317478,105,0.549073798647054411858903222310,4,0.429844863709831270792705026906 470,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 471,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 472,472_0,COMPLETED,BoTorch,0.313024602026049203473689885868,363,0.459740689792995316231838387466,3,0.699272319106876927286009504314 473,473_0,COMPLETED,BoTorch,0.314517607332368553940682431858,346,0.800844034817981831331223929737,2,0.765979702456991651260409526003 474,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252 475,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006 476,476_0,COMPLETED,BoTorch,0.307492764109985494691557050828,253,0.477885722896779996382576882752,4,0.693706879518822372432396150543 477,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 478,478_0,COMPLETED,BoTorch,0.297221418234442813499640578812,207,0.824421917950646010453397138917,2,0.377361957639622436211368494696 479,479_0,COMPLETED,BoTorch,0.281356729377713499218316428596,101,0.594888748436878889158663241687,3,0.406293699629057092348460855646 480,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 481,481_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.677107815957357361824620056723,2,0.200000000000000011102230246252 482,482_0,COMPLETED,BoTorch,0.282357694163048678070992991707,100,0.403765947957050008731982870813,3,0.204178396174657189598988793477 483,483_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.611729181337196559198332579399 484,484_0,COMPLETED,BoTorch,0.316169802219006257359978917520,434,0.788818376550669331237486403552,4,0.569144276961538020387365577335 485,485_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.570604653360232139114316396444,2,0.452604792185083371069964641720 486,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 487,487_0,COMPLETED,BoTorch,0.319436806560540298427497418743,421,0.835108804060919807099594436295,2,0.776072829582627354838564315287 488,488_0,COMPLETED,BoTorch,0.297519295706705211301823510439,177,0.234867404295065029806721668137,3,0.299433263988684750245283794357 489,489_0,COMPLETED,BoTorch,0.298332127351664255776597656222,218,0.833839978461526243336265906692,2,0.355861118313146485014897280053 490,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 491,491_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.632054500997325741096233286953 492,492_0,COMPLETED,BoTorch,0.302403521466473756973414310778,226,0.300361972838011914710421024211,2,0.463006767265909668918055785980 493,493_0,COMPLETED,BoTorch,0.307311866859623750158903021656,271,0.800108825785393440455095515063,2,0.256185353857365127883127797759 494,494_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.633775103176349219324947625864 495,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 496,496_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.308214164665389311892340629129,3,0.200000000000000011102230246252 497,497_0,COMPLETED,BoTorch,0.283972503617945015719215007266,102,0.953943365159006240183714453451,4,0.655657573698599005496134850546 498,498_0,COMPLETED,BoTorch,0.285937047756874118675796125899,114,0.100000000000000005551115123126,4,0.200000000000000011102230246252 499,499_0,COMPLETED,BoTorch,0.286513506994693689655662183213,124,0.197355794351058072555460398689,4,0.301616928255286853577388228587 500,500_0,COMPLETED,BoTorch,0.286976603955619857799774763407,129,0.441036528192673249471056351467,4,0.800000000000000044408920985006
start_time,end_time,run_time,program_string,n_samples,const,max_depth,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727602479,1727603046,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 764 const 0.9216413079760969 max_depth 4 threshold 0.6181387918069959,764,0.9216413079760969,4,0.6181387918069959,0.33233478051133625,0,None,i7186,560,0.0005269828385693408 1727602500,1727603050,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 208 const 0.7999990027397871 max_depth 2 threshold 0.2509957324713469,208,0.7999990027397871,2,0.2509957324713469,0.30026893391220455,0,None,i7186,543,0.00015940318369478897 1727602499,1727603051,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 288 const 0.7275560945272446 max_depth 2 threshold 0.4021729931235314,288,0.7275560945272446,2,0.4021729931235314,0.3065364206464062,0,None,i7186,545,0.0002164163018551166 1727602477,1727603053,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 770 const 0.8603969216346741 max_depth 3 threshold 0.6521066069602968,770,0.8603969216346741,3,0.6521066069602968,0.3353244090689822,0,None,i7186,569,0.0005253439961838974 1727602499,1727603057,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 488 const 0.258142252266407 max_depth 3 threshold 0.6894698489457369,488,0.258142252266407,3,0.6894698489457369,0.3178895320791124,0,None,i7186,550,0.00035343927789965434 1727602499,1727603060,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 396 const 0.9653591456823051 max_depth 4 threshold 0.7919456901028754,396,0.9653591456823051,4,0.7919456901028754,0.3145224312590449,0,None,i7186,554,0.0002900927798822888 1727602499,1727603061,562,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 416 const 0.5807324868626894 max_depth 4 threshold 0.5695470176637174,416,0.5807324868626894,4,0.5695470176637174,0.31743125904486247,0,None,i7186,554,0.0003017351606135105 1727602499,1727603064,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.26312147658318286 max_depth 3 threshold 0.48900593481957916,142,0.26312147658318286,3,0.48900593481957916,0.29069946936806557,0,None,i7186,558,0.00011211374088655477 1727602499,1727603068,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 645 const 0.6225879218429327 max_depth 3 threshold 0.4474690197035671,645,0.6225879218429327,3,0.4474690197035671,0.3297624216111915,0,None,i7186,561,0.0004489050738391393 1727602499,1727603071,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 320 const 0.3622020931914449 max_depth 2 threshold 0.6859028333798052,320,0.3622020931914449,2,0.6859028333798052,0.30886517124939705,0,None,i7180,565,0.00023868286314017136 1727602499,1727603079,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 991 const 0.4095044769346714 max_depth 3 threshold 0.608869461901486,991,0.4095044769346714,3,0.608869461901486,0.34216353111432707,0,None,i7186,573,0.000659798918424861 1727602499,1727603100,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.538903581444174 max_depth 2 threshold 0.6330774001777173,123,0.538903581444174,2,0.6330774001777173,0.2868692715870719,0,None,i7181,587,9.824635235277362e-05 1727602535,1727603115,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 540 const 0.9582958114333451 max_depth 2 threshold 0.5525584176182747,540,0.9582958114333451,2,0.5525584176182747,0.32642788229618913,0,None,i7178,535,0.0003800933464403257 1727602535,1727603117,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 508 const 0.331185810174793 max_depth 2 threshold 0.47026161383837467,508,0.331185810174793,2,0.47026161383837467,0.3265846599131693,0,None,i7178,537,0.0003570769841945461 1727602535,1727603123,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 608 const 0.5367284641601145 max_depth 2 threshold 0.6381663927808405,608,0.5367284641601145,2,0.6381663927808405,0.32969488663772306,0,None,i7178,543,0.0004232960240502143 1727602535,1727603143,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 577 const 0.1287232818081975 max_depth 4 threshold 0.5226113354787232,577,0.1287232818081975,4,0.5226113354787232,0.3254799807042933,0,None,i7178,563,0.0004073530617153561 1727602535,1727603143,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 892 const 0.49668019684031606 max_depth 3 threshold 0.28984699137508874,892,0.49668019684031606,3,0.28984699137508874,0.3365243608297154,0,None,i7178,563,0.0006065412029875077 1727602507,1727603144,637,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 189 const 0.8842067301273346 max_depth 4 threshold 0.7081674978137018,189,0.8842067301273346,4,0.7081674978137018,0.294678002894356,0,None,i7180,631,0.00014754973807465222 1727602524,1727603201,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 841 const 0.2974801030009985 max_depth 2 threshold 0.5945624316111208,841,0.2974801030009985,2,0.5945624316111208,0.3376507477086348,0,None,i7179,621,0.0005697331937139922 1727602524,1727603212,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.7099334029480815 max_depth 3 threshold 0.564544477686286,101,0.7099334029480815,3,0.564544477686286,0.28186324167872645,0,None,i7179,632,8.19147544082848e-05 1727603402,1727603980,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9605106166839192 max_depth 2 threshold 0.6120452945386256,100,0.9605106166839192,2,0.6120452945386256,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727603422,1727603995,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9348891915709588 max_depth 2 threshold 0.8,100,0.9348891915709588,2,0.8,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727603402,1727604012,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4278290483442856 max_depth 3 threshold 0.8,100,0.4278290483442856,3,0.8,0.28136034732272075,0,None,i7186,602,8.120606313466784e-05 1727603395,1727604021,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6093214528680508 max_depth 3 threshold 0.6819980016684843,100,0.6093214528680508,3,0.6819980016684843,0.28149541726965754,0,None,i7186,618,8.11930650932098e-05 1727603402,1727604021,619,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6583743982977952 max_depth 3 threshold 0.5183298055742321,100,0.6583743982977952,3,0.5183298055742321,0.2818753014954173,0,None,i7186,612,8.112096971288961e-05 1727603407,1727604021,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5919683726133416 max_depth 3 threshold 0.6010476603778925,100,0.5919683726133416,3,0.6010476603778925,0.2814990352146647,0,None,i7186,607,8.119219203698221e-05 1727603403,1727604028,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.5127283888790162,100,1,3,0.5127283888790162,0.2824047274481428,0,None,i7186,618,8.099318164802707e-05 1727603402,1727604028,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8155132614885161 max_depth 3 threshold 0.756039753695847,100,0.8155132614885161,3,0.756039753695847,0.2817245537867824,0,None,i7186,619,8.115735583614206e-05 1727603402,1727604030,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9168642574337319 max_depth 3 threshold 0.6028726823068037,100,0.9168642574337319,3,0.6028726823068037,0.28289194404245055,0,None,i7186,620,8.087558169767522e-05 1727603456,1727604071,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.6693867913616305,100,1,3,0.6693867913616305,0.2826133622768934,0,None,i7127,602,8.094282325344572e-05 1727603469,1727604076,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.30065090756735074 max_depth 3 threshold 0.7379267941244463,100,0.30065090756735074,3,0.7379267941244463,0.28032440906898215,0,None,i7181,599,8.143634103881737e-05 1727603456,1727604088,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5202895133789688 max_depth 3 threshold 0.7553628473260832,100,0.5202895133789688,3,0.7553628473260832,0.2810817655571636,0,None,i7022,619,8.129288452190043e-05 1727603469,1727604098,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7430927639075775 max_depth 3 threshold 0.7590063786742633,100,0.7430927639075775,3,0.7590063786742633,0.28156415822479497,0,None,i7181,622,8.119607067128264e-05 1727603469,1727604109,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8212775163128742 max_depth 3 threshold 0.4989215339681881,100,0.8212775163128742,3,0.4989215339681881,0.2820996140858659,0,None,i7181,633,8.106682716148999e-05 1727603470,1727604117,647,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.18133855389805403 max_depth 4 threshold 0.7323732981364877,100,0.18133855389805403,4,0.7323732981364877,0.28297636275928606,0,None,i7181,640,8.081619210051458e-05 1727603469,1727604118,649,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.24141722527230475 max_depth 4 threshold 0.8,100,0.24141722527230475,4,0.8,0.2803207911239749,0,None,i7181,641,8.14765173484421e-05 1727603470,1727604121,651,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7361243759064074 max_depth 3 threshold 0.6362203062952576,100,0.7361243759064074,3,0.6362203062952576,0.2818451519536903,0,None,i7181,644,8.11282469375401e-05 1727603456,1727604191,735,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4512973916155315 max_depth 4 threshold 0.8,100,0.4512973916155315,4,0.8,0.28145923781958515,0,None,i7022,722,8.128025149669899e-05 1727603456,1727604261,805,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6386084052997253 max_depth 4 threshold 0.5000017931649559,100,0.6386084052997253,4,0.5000017931649559,0.2806319343945972,0,None,i7127,793,8.163783751726083e-05 1727603407,1727604337,930,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.826218872883353 max_depth 4 threshold 0.3968138137610747,100,0.826218872883353,4,0.3968138137610747,0.28046550892426436,0,None,i7186,923,8.187626639778132e-05 1727604008,1727604585,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9364748345103865 max_depth 2 threshold 0.5808576407036011,100,0.9364748345103865,2,0.5808576407036011,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727604029,1727604622,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7450251486829362,100,1,2,0.7450251486829362,0.28032440906898215,0,None,i7186,586,8.143634103881737e-05 1727604049,1727604672,623,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9675928789618119 max_depth 3 threshold 0.7216627355947598,100,0.9675928789618119,3,0.7216627355947598,0.28277013989387356,0,None,i7186,616,8.090498168526319e-05 1727604098,1727604673,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7563606102524657 max_depth 2 threshold 0.8,100,0.7563606102524657,2,0.8,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727604116,1727604690,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.4974383454727167,100,1,2,0.4974383454727167,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727604116,1727604700,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7186,576,8.143634103881737e-05 1727604116,1727604729,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,605,8.143634103881737e-05 1727604176,1727604770,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7128533374339818,100,1,2,0.7128533374339818,0.28032440906898215,0,None,i7181,587,8.143634103881737e-05 1727604176,1727604777,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 291 const 1 max_depth 4 threshold 0.7362258011176586,291,1,4,0.7362258011176586,0.30861070911722144,0,None,i7181,594,0.00021723930470414151 1727604188,1727604783,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 178 const 0.3505325504532715 max_depth 4 threshold 0.6720778143029444,178,0.3505325504532715,4,0.6720778143029444,0.29317534973468407,0,None,i7181,588,0.00013945218088503002 1727604176,1727604786,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.7788095024034516,100,0.1,3,0.7788095024034516,0.28032440906898215,0,None,i7181,603,8.143634103881737e-05 1727604116,1727604794,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4230049971729236 max_depth 4 threshold 0.6138201302209301,100,0.4230049971729236,4,0.6138201302209301,0.2809599614085866,0,None,i7186,671,8.138119237076367e-05 1727604188,1727604815,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.6727586831160779,100,0.1,4,0.6727586831160779,0.28062228654124455,0,None,i7181,619,8.138412363683339e-05 1727604236,1727604832,596,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7368602807362641,100,1,2,0.7368602807362641,0.28032440906898215,0,None,i7181,589,8.143634103881737e-05 1727604236,1727604842,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7181,599,8.143634103881737e-05 1727604236,1727604850,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7181,607,8.143634103881737e-05 1727604249,1727604967,718,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.42417443140956057 max_depth 4 threshold 0.2,100,0.42417443140956057,4,0.2,0.28191871683550407,0,None,i7181,711,8.120849738531051e-05 1727604096,1727605121,1025,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.2831801736613604,0,None,i7186,1018,8.137562113625633e-05 1727604313,1727605126,813,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.24976813280964705,100,1,4,0.24976813280964705,0.2836890979257115,0,None,i7174,801,8.1232170374291e-05 1727604609,1727605203,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.23719073289892972 max_depth 3 threshold 0.8,100,0.23719073289892972,3,0.8,0.28032440906898215,0,None,i7186,587,8.143634103881737e-05 1727604361,1727605240,879,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.685293236834762 max_depth 4 threshold 0.2,100,0.685293236834762,4,0.2,0.28051013024602023,0,None,i7186,872,8.178607580462887e-05 1727604699,1727605273,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8593617262388223 max_depth 2 threshold 0.7326960368827442,100,0.8593617262388223,2,0.7326960368827442,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727604729,1727605315,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 139 const 0.14260106964492616 max_depth 4 threshold 0.30530055964114466,139,0.14260106964492616,4,0.30530055964114466,0.29270742884708156,0,None,i7186,579,0.00010907178074072348 1727604756,1727605332,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727604716,1727605354,638,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.2441592639395666,100,1,3,0.2441592639395666,0.28145803183791607,0,None,i7186,631,8.128054279661906e-05 1727604789,1727605382,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 159 const 0.28058695543130574 max_depth 4 threshold 0.24787031116689695,159,0.28058695543130574,4,0.24787031116689695,0.29453087313072845,0,None,i7181,585,0.00012406160563298448 1727604838,1727605432,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6130843172282463,100,1,2,0.6130843172282463,0.28032440906898215,0,None,i7181,587,8.143634103881737e-05 1727604879,1727605472,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7181,586,8.143634103881737e-05 1727604896,1727605529,633,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.2,100,0.1,4,0.2,0.2818656536420646,0,None,i7181,625,8.108415569936173e-05 1727604917,1727605530,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.2811673779595011,100,0.1,3,0.2811673779595011,0.28032440906898215,0,None,i7181,606,8.143634103881737e-05 1727604992,1727605552,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.12279715846467479 max_depth 2 threshold 0.41383950638000816,125,0.12279715846467479,2,0.41383950638000816,0.28689218523878435,0,None,i7181,553,9.983935276753155e-05 1727604736,1727605614,878,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7251447530889757 max_depth 4 threshold 0.3146153231840197,100,0.7251447530889757,4,0.3146153231840197,0.2807621804148577,0,None,i7186,871,8.172498754267118e-05 1727605164,1727605618,454,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 151 const 0.404694710294062 max_depth 3 threshold 0.5668540848952497,151,0.404694710294062,3,0.5668540848952497,0.2892920887602508,0,None,i7174,448,0.0001197331112042507 1727604650,1727605641,991,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.3969599111963618,100,1,4,0.3969599111963618,0.28217679691268693,0,None,i7186,984,8.152043750988881e-05 1727605228,1727605780,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.6002711146525491 max_depth 2 threshold 0.26489234730002476,147,0.6002711146525491,2,0.26489234730002476,0.2893572117703811,0,None,i7186,545,0.00011652562668281485 1727604838,1727605804,966,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9637904467698953 max_depth 4 threshold 0.47726093932804997,100,0.9637904467698953,4,0.47726093932804997,0.2808936324167872,0,None,i7186,959,8.181209868910964e-05 1727604849,1727605808,959,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7514998804820053 max_depth 4 threshold 0.2991808141210107,100,0.7514998804820053,4,0.2991808141210107,0.2810998552821997,0,None,i7181,952,8.17223735404462e-05 1727605299,1727605875,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2832675535125241,100,0.1,2,0.2832675535125241,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727604877,1727605936,1059,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.2831801736613604,0,None,i7181,1052,8.137562113625633e-05 1727604916,1727605940,1024,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9214600157356138 max_depth 4 threshold 0.2,100,0.9214600157356138,4,0.2,0.28325976845151957,0,None,i7181,1016,8.12969906641728e-05 1727605145,1727605956,811,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5891548592174903 max_depth 4 threshold 0.36786116753337184,100,0.5891548592174903,4,0.36786116753337184,0.2817245537867824,0,None,i7186,804,8.139310705135235e-05 1727605388,1727605965,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.20063952978744845,100,1,2,0.20063952978744845,0.28118306801736614,0,None,i7186,570,8.124883256901154e-05 1727605468,1727606062,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 153 const 0.22280506087841168 max_depth 4 threshold 0.5902366783664135,153,0.22280506087841168,4,0.5902366783664135,0.29069343945972026,0,None,i7181,586,0.00012080695443196703 1727605268,1727606082,814,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.61380467128013 max_depth 4 threshold 0.3215063520846917,100,0.61380467128013,4,0.3215063520846917,0.2811444766039556,0,None,i7186,807,8.153352757491246e-05 1727605508,1727606101,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.664428766986099 max_depth 2 threshold 0.2,100,0.664428766986099,2,0.2,0.28032440906898215,0,None,i7181,585,8.143634103881737e-05 1727605568,1727606156,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.4867690406651895,100,0.1,2,0.4867690406651895,0.28032440906898215,0,None,i7181,581,8.143634103881737e-05 1727605679,1727606159,480,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5972327672962833 max_depth 3 threshold 0.2,100,0.5972327672962833,3,0.2,0.2814845634346358,0,None,i7174,474,8.121528254435993e-05 1727605568,1727606166,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28118306801736614,0,None,i7181,591,8.124883256901154e-05 1727605588,1727606188,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.38683457243169816 max_depth 3 threshold 0.2,100,0.38683457243169816,3,0.2,0.2812349252291365,0,None,i7181,593,8.123632178209468e-05 1727605368,1727606215,847,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6367494964643906 max_depth 4 threshold 0.3278220987740132,100,0.6367494964643906,4,0.3278220987740132,0.2816690786300048,0,None,i7186,840,8.144596764008578e-05 1727605348,1727606218,870,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8356490976194522 max_depth 4 threshold 0.8,100,0.8356490976194522,4,0.8,0.2814254703328509,0,None,i7186,863,8.15444653944919e-05 1727605659,1727606220,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.6279512373622645 max_depth 2 threshold 0.7307472054082942,128,0.6279512373622645,2,0.7307472054082942,0.28806198745779066,0,None,i7186,554,0.00010185164913804517 1727605659,1727606251,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3368615974651703 max_depth 2 threshold 0.2,100,0.3368615974651703,2,0.2,0.28032440906898215,0,None,i7186,585,8.143634103881737e-05 1727605420,1727606323,903,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6853446801418295 max_depth 4 threshold 0.2590653541377256,100,0.6853446801418295,4,0.2590653541377256,0.2811275928605885,0,None,i7181,895,8.163642417724926e-05 1727605804,1727606398,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.5424886904096937,100,0.1,3,0.5424886904096937,0.28032440906898215,0,None,i7186,587,8.143634103881737e-05 1727605840,1727606420,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5362909082461883 max_depth 2 threshold 0.2,100,0.5362909082461883,2,0.2,0.28032440906898215,0,None,i7186,573,8.143634103881737e-05 1727605844,1727606437,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28118306801736614,0,None,i7181,585,8.124883256901154e-05 1727605900,1727606475,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4833739787324248 max_depth 2 threshold 0.2,100,0.4833739787324248,2,0.2,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727605990,1727606544,554,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.22404462645748252 max_depth 2 threshold 0.37337098252788026,152,0.22404462645748252,2,0.37337098252788026,0.2916196333815726,0,None,i7186,546,0.00011966991039029937 1727605981,1727606574,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.6133429406898833,100,0.1,3,0.6133429406898833,0.28032440906898215,0,None,i7186,586,8.143634103881737e-05 1727606021,1727606612,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.5620346530358513,100,0.1,2,0.5620346530358513,0.28032440906898215,0,None,i7181,584,8.143634103881737e-05 1727606021,1727606629,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.2,100,0.1,3,0.2,0.28032440906898215,0,None,i7181,601,8.143634103881737e-05 1727606111,1727606706,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.4253043320139717,100,0.1,3,0.4253043320139717,0.28032440906898215,0,None,i7186,588,8.143634103881737e-05 1727606141,1727606776,635,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.8065525290660707 max_depth 3 threshold 0.6239626080838401,106,0.8065525290660707,3,0.6239626080838401,0.28377110467920885,0,None,i7181,628,8.551391359690637e-05 1727606201,1727606788,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 301 const 1 max_depth 4 threshold 0.8,301,1,4,0.8,0.3074071394114809,0,None,i7181,580,0.00022552820595819759 1727606201,1727606839,638,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7969045121638174 max_depth 3 threshold 0.332810063799172,100,0.7969045121638174,3,0.332810063799172,0.2818596237337192,0,None,i7181,631,8.112475386970789e-05 1727606222,1727606846,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 193 const 0.8568773158706943 max_depth 4 threshold 0.6208639839425466,193,0.8568773158706943,4,0.6208639839425466,0.2973516642547033,0,None,i7186,617,0.00014939786335564572 1727606282,1727606852,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.6267463132557614,100,0.1,2,0.6267463132557614,0.28032440906898215,0,None,i7181,563,8.143634103881737e-05 1727606282,1727606864,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.4325207500199553,100,0.1,2,0.4325207500199553,0.28032440906898215,0,None,i7186,574,8.143634103881737e-05 1727606347,1727606943,596,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9921095207896918 max_depth 2 threshold 0.6862309715667454,100,0.9921095207896918,2,0.6862309715667454,0.28032440906898215,0,None,i7181,586,8.143634103881737e-05 1727606290,1727606982,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7638071392646142 max_depth 4 threshold 0.5855394888693729,100,0.7638071392646142,4,0.5855394888693729,0.2813832609744332,0,None,i7174,686,8.157445905028738e-05 1727606082,1727607023,941,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9706744183856059 max_depth 4 threshold 0.8,100,0.9706744183856059,4,0.8,0.2812132175590931,0,None,i7181,934,8.167505735067326e-05 1727606463,1727607025,562,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.49798638023849273 max_depth 2 threshold 0.8,129,0.49798638023849273,2,0.8,0.28522672455378684,0,None,i7186,554,0.00010355828025587679 1727606231,1727607114,883,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.764708521252592 max_depth 4 threshold 0.6012378412162168,100,0.764708521252592,4,0.6012378412162168,0.2817257597684515,0,None,i7186,876,8.149144916322527e-05 1727606261,1727607158,897,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7610628062054379 max_depth 4 threshold 0.5897688058446445,100,0.7610628062054379,4,0.5897688058446445,0.28171972986010607,0,None,i7186,890,8.149291060489891e-05 1727606582,1727607166,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.6461286293606386,100,0.1,2,0.6461286293606386,0.28032440906898215,0,None,i7186,577,8.143634103881737e-05 1727606682,1727607268,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9560357149612935 max_depth 2 threshold 0.6912485953216936,100,0.9560357149612935,2,0.6912485953216936,0.28032440906898215,0,None,i7181,579,8.143634103881737e-05 1727606423,1727607298,875,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7583473099190611 max_depth 4 threshold 0.583823773272484,100,0.7583473099190611,4,0.583823773272484,0.2815050651230101,0,None,i7186,868,8.15449379284801e-05 1727606742,1727607319,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9793011697974436 max_depth 2 threshold 0.6877554645360772,100,0.9793011697974436,2,0.6877554645360772,0.28032440906898215,0,None,i7186,571,8.143634103881737e-05 1727606502,1727607382,880,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7617650095261437 max_depth 4 threshold 0.5853390752873178,100,0.7617650095261437,4,0.5853390752873178,0.28143150024119634,0,None,i7186,873,8.156276751689836e-05 1727606471,1727607385,914,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7607860132733647 max_depth 4 threshold 0.5839739374596857,100,0.7607860132733647,4,0.5839739374596857,0.28143150024119634,0,None,i7181,906,8.156276751689836e-05 1727607011,1727607471,460,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9999999999037656 max_depth 2 threshold 0.3368285050039289,100,0.9999999999037656,2,0.3368285050039289,0.2813579353593826,0,None,i7174,454,8.120664503173375e-05 1727606621,1727607497,876,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7614837855141727 max_depth 4 threshold 0.5874712556096607,100,0.7614837855141727,4,0.5874712556096607,0.28153039073806074,0,None,i7186,868,8.153879987345086e-05 1727606943,1727607551,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 191 const 0.7328886483184133 max_depth 4 threshold 0.6781384980424476,191,0.7328886483184133,4,0.6781384980424476,0.29669440424505544,0,None,i7181,601,0.00014811667808707402 1727606642,1727607573,931,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.761572241101902 max_depth 4 threshold 0.5871402059146072,100,0.761572241101902,4,0.5871402059146072,0.2813832609744332,0,None,i7181,923,8.157445905028738e-05 1727606800,1727607690,890,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7634435547786615 max_depth 4 threshold 0.5758169195338984,100,0.7634435547786615,4,0.5758169195338984,0.2810998552821997,0,None,i7181,883,8.164314680894795e-05 1727607068,1727607691,623,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8508393620260551 max_depth 3 threshold 0.2,100,0.8508393620260551,3,0.2,0.28282199710564404,0,None,i7186,616,8.089246485886433e-05 1727606828,1727607725,897,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7640005050555555 max_depth 4 threshold 0.5888695144317346,100,0.7640005050555555,4,0.5888695144317346,0.2815472744814279,0,None,i7181,890,8.153470783676467e-05 1727606868,1727607745,877,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7586510801692665 max_depth 4 threshold 0.5894154545054372,100,0.7586510801692665,4,0.5894154545054372,0.2815110950313555,0,None,i7186,869,8.154347648680646e-05 1727607222,1727607774,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.625447231970157 max_depth 2 threshold 0.2,147,0.625447231970157,2,0.2,0.2893572117703811,0,None,i7186,545,0.00011652562668281485 1727607192,1727607779,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6633816609573133,100,1,2,0.6633816609573133,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727606922,1727607794,872,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7074136783879416 max_depth 4 threshold 0.2633971086979044,100,0.7074136783879416,4,0.2633971086979044,0.28051013024602023,0,None,i7186,865,8.178607580462887e-05 1727606943,1727607812,869,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7596339474670531 max_depth 4 threshold 0.594096065027544,100,0.7596339474670531,4,0.594096065027544,0.28151712493970094,0,None,i7181,862,8.154201504513282e-05 1727607302,1727607855,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 293 const 0.7526330223183677 max_depth 3 threshold 0.2,293,0.7526330223183677,3,0.2,0.30897732754462137,0,None,i7186,546,0.0002183619445026838 1727606982,1727607872,890,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7729411408789854 max_depth 4 threshold 0.5757624465006677,100,0.7729411408789854,4,0.5757624465006677,0.28170887602508443,0,None,i7181,883,8.149554119991143e-05 1727607161,1727607882,721,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7174,714,8.156770947957166e-05 1727607342,1727607907,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.1 max_depth 2 threshold 0.5412904188426624,122,0.1,2,0.5412904188426624,0.28727568740955134,0,None,i7186,558,9.731718411017294e-05 1727607089,1727608001,912,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.864067689188234 max_depth 4 threshold 0.8,100,0.864067689188234,4,0.8,0.2812855764592378,0,None,i7181,904,8.161791577131665e-05 1727607449,1727608017,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 190 const 0.1 max_depth 2 threshold 0.8,190,0.1,2,0.8,0.2979377713458755,0,None,i7181,560,0.0001466643939826047 1727607429,1727608032,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 193 const 0.7916878756235329 max_depth 4 threshold 0.7212778019685018,193,0.7916878756235329,4,0.7212778019685018,0.2988181379643029,0,None,i7186,595,0.0001487145112076496 1727607521,1727608078,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 208 const 0.1 max_depth 2 threshold 0.8,208,0.1,2,0.8,0.30026893391220455,0,None,i7186,550,0.00015940318369478897 1727607541,1727608100,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 186 const 0.2970179428791348 max_depth 3 threshold 0.8,186,0.2970179428791348,3,0.8,0.29514109985528225,0,None,i7181,552,0.00014482654964586663 1727607581,1727608129,548,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 293 const 0.7962290897008159 max_depth 2 threshold 0.35184596148787073,293,0.7962290897008159,2,0.35184596148787073,0.30897732754462137,0,None,i7181,541,0.0002183619445026838 1727607603,1727608159,556,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 151 const 0.14106935464206924 max_depth 2 threshold 0.5901572095535974,151,0.14106935464206924,2,0.5901572095535974,0.2889315002411963,0,None,i7181,549,0.00011986447314197547 1727607363,1727608297,934,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7692276313073969 max_depth 4 threshold 0.4821243348956467,100,0.7692276313073969,4,0.4821243348956467,0.2820791123974915,0,None,i7186,927,8.146504162464891e-05 1727607750,1727608336,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.1 max_depth 4 threshold 0.8,135,0.1,4,0.8,0.28996985045827306,0,None,i7186,579,0.00010680222356159098 1727607819,1727608403,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 1000 const 1 max_depth 4 threshold 0.8,1000,1,4,0.8,0.3434611673902557,0,None,i7186,576,0.0006630394000619887 1727607819,1727608452,633,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.5276467025915195,100,0.1,4,0.5276467025915195,0.28062228654124455,0,None,i7186,626,8.138412363683339e-05 1727608003,1727608465,462,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.34144006215034917,100,0.1,2,0.34144006215034917,0.28032440906898215,0,None,i7174,456,8.143634103881737e-05 1727607899,1727608476,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.35691291620919474 max_depth 2 threshold 0.40372232785989026,100,0.35691291620919474,2,0.40372232785989026,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727607914,1727608482,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 195 const 0.1793991678587048 max_depth 3 threshold 0.7191245646576849,195,0.1793991678587048,3,0.7191245646576849,0.2972575976845152,0,None,i7181,561,0.00015084754531110244 1727607899,1727608483,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.31258553754332263 max_depth 2 threshold 0.8,100,0.31258553754332263,2,0.8,0.28032440906898215,0,None,i7186,577,8.143634103881737e-05 1727607913,1727608483,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 210 const 0.1 max_depth 4 threshold 0.8,210,0.1,4,0.8,0.29803666184274,0,None,i7186,563,0.00016206829644031353 1727607987,1727608542,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.6924840116624904 max_depth 2 threshold 0.7338042073569109,128,0.6924840116624904,2,0.7338042073569109,0.28806198745779066,0,None,i7181,547,0.00010185164913804517 1727607987,1727608546,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 187 const 0.3087124433627565 max_depth 2 threshold 0.6149058522380701,187,0.3087124433627565,2,0.6149058522380701,0.29659551374819104,0,None,i7181,552,0.0001449544272745728 1727607750,1727608548,798,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5835918615387873 max_depth 4 threshold 0.5859311171827781,100,0.5835918615387873,4,0.5859311171827781,0.2825192957067053,0,None,i7186,790,8.118107050077775e-05 1727607967,1727608552,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 172 const 0.40240380367788964 max_depth 4 threshold 0.5655886934209734,172,0.40240380367788964,4,0.5655886934209734,0.2954220935841775,0,None,i7181,577,0.00013383294001317075 1727608079,1727608651,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.413666576897602 max_depth 2 threshold 0.2,100,0.413666576897602,2,0.2,0.28032440906898215,0,None,i7186,565,8.143634103881737e-05 1727608094,1727608663,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 992 const 0.36250207417937785 max_depth 3 threshold 0.5909272854174416,992,0.36250207417937785,3,0.5909272854174416,0.344202846116739,0,None,i7186,562,0.0006564907263769303 1727608124,1727608701,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.216256696673561 max_depth 2 threshold 0.2,100,0.216256696673561,2,0.2,0.28032440906898215,0,None,i7181,570,8.143634103881737e-05 1727608154,1727608737,583,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.25737854586563746 max_depth 2 threshold 0.4894711993481374,100,0.25737854586563746,2,0.4894711993481374,0.28032440906898215,0,None,i7181,576,8.143634103881737e-05 1727608201,1727608807,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 104 const 0.34861798492389307 max_depth 3 threshold 0.41674608818608516,104,0.34861798492389307,3,0.41674608818608516,0.2840508924264351,0,None,i7181,598,8.377034284733664e-05 1727608325,1727608895,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9246915955711821 max_depth 2 threshold 0.6776746580773312,100,0.9246915955711821,2,0.6776746580773312,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727608033,1727608956,923,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7181,915,8.156770947957166e-05 1727608179,1727608993,814,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6217032062410485 max_depth 4 threshold 0.8,100,0.6217032062410485,4,0.8,0.28263506994693677,0,None,i7181,807,8.115305156558137e-05 1727608482,1727609046,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 303 const 0.24006548820282272 max_depth 2 threshold 0.30807484756050424,303,0.24006548820282272,2,0.30807484756050424,0.3120055475156778,0,None,i7186,557,0.00022365053472239854 1727608364,1727609096,732,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 144 const 1 max_depth 4 threshold 0.8,144,1,4,0.8,0.2900506512301013,0,None,i7186,724,0.00011421415109223882 1727608604,1727609162,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 162 const 1 max_depth 2 threshold 0.8,162,1,2,0.8,0.2946490593342981,0,None,i7186,551,0.00012634248522142277 1727608604,1727609165,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 134 const 0.6867238162880704 max_depth 2 threshold 0.6218554363119534,134,0.6867238162880704,2,0.6218554363119534,0.29075615050651227,0,None,i7186,554,0.00010575414625384832 1727608574,1727609175,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 196 const 0.8052413481362611 max_depth 4 threshold 0.6651514272944844,196,0.8052413481362611,4,0.6651514272944844,0.29348649300530627,0,None,i7186,594,0.0001534876944231848 1727608686,1727609232,546,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 174 const 0.8357551809371336 max_depth 2 threshold 0.6533038280797718,174,0.8357551809371336,2,0.6533038280797718,0.2984792571152919,0,None,i7186,539,0.00013412267921940685 1727608706,1727609263,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 183 const 0.8624114970781177 max_depth 2 threshold 0.26648204191097385,183,0.8624114970781177,2,0.26648204191097385,0.295306319343946,0,None,i7181,550,0.00014245172612449135 1727608442,1727609266,824,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 4 threshold 0.8,119,1,4,0.8,0.28365653642064637,0,None,i7186,816,9.636867760025154e-05 1727608843,1727609284,441,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.567853852172887 max_depth 2 threshold 0.7662942601115177,120,0.567853852172887,2,0.7662942601115177,0.28830921369995177,0,None,i7174,434,9.54125225227112e-05 1727608706,1727609298,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.9588302884246764 max_depth 3 threshold 0.6461962993625909,140,0.9588302884246764,3,0.6461962993625909,0.29026169802219004,0,None,i7181,585,0.00011070870979517869 1727608694,1727609310,616,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 196 const 0.846472154190135 max_depth 4 threshold 0.67123003256444,196,0.846472154190135,4,0.67123003256444,0.29842378195851427,0,None,i7186,608,0.00015122370897179587 1727608626,1727609323,697,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.7557632262768602 max_depth 4 threshold 0.5761495897394708,141,0.7557632262768602,4,0.5761495897394708,0.2895127834056922,0,None,i7181,690,0.00011190688095748782 1727608587,1727609327,740,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.8808595434578936 max_depth 4 threshold 0.725871286748837,124,0.8808595434578936,4,0.725871286748837,0.28664978292329957,0,None,i7186,733,9.937303422094163e-05 1727608782,1727609372,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 158 const 0.1 max_depth 4 threshold 0.21596407213379537,158,0.1,4,0.21596407213379537,0.29158586589483837,0,None,i7181,581,0.00012438018792724107 1727608924,1727609503,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 158 const 0.10913394013558186 max_depth 4 threshold 0.2132510088935931,158,0.10913394013558186,4,0.2132510088935931,0.29158586589483837,0,None,i7186,572,0.00012438018792724107 1727608993,1727609565,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 201 const 0.8049662474715515 max_depth 3 threshold 0.6942515617737766,201,0.8049662474715515,3,0.6942515617737766,0.2986565364206464,0,None,i7181,565,0.00015485108758257643 1727608782,1727609617,835,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 135 const 1 max_depth 4 threshold 0.2,135,1,4,0.2,0.28840207428847087,0,None,i7181,826,0.00010776893548968218 1727608782,1727609621,839,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.9662251898641088 max_depth 4 threshold 0.7541366804823919,122,0.9662251898641088,4,0.7541366804823919,0.2865207428847082,0,None,i7181,830,9.797184041714773e-05 1727609085,1727609657,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.8751382273990235 max_depth 2 threshold 0.4834285044256568,132,0.8751382273990235,2,0.4834285044256568,0.2873420164013507,0,None,i7186,565,0.00010529218554604085 1727608815,1727609678,863,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.9917456066695022 max_depth 4 threshold 0.2,129,0.9917456066695022,4,0.2,0.2858550410033768,0,None,i7181,856,0.00010381507909113541 1727609133,1727609712,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9035469263021244 max_depth 2 threshold 0.6743207027357221,100,0.9035469263021244,2,0.6743207027357221,0.28032440906898215,0,None,i7186,572,8.143634103881737e-05 1727609033,1727609749,716,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.8252784878451914 max_depth 4 threshold 0.7554111973201683,136,0.8252784878451914,4,0.7554111973201683,0.28968644476603955,0,None,i7181,708,0.0001079135549802365 1727609205,1727609790,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8960505176882341 max_depth 2 threshold 0.6738955122753542,100,0.8960505176882341,2,0.6738955122753542,0.28032440906898215,0,None,i7186,578,8.143634103881737e-05 1727609227,1727609804,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.892678078944393 max_depth 2 threshold 0.6761479985623358,100,0.892678078944393,2,0.6761479985623358,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727609247,1727609820,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.802858457916525 max_depth 2 threshold 0.8,100,0.802858457916525,2,0.8,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727609287,1727609860,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8903382734379263 max_depth 2 threshold 0.8,100,0.8903382734379263,2,0.8,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727609326,1727609903,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727609326,1727609906,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6338079789208464 max_depth 2 threshold 0.2,100,0.6338079789208464,2,0.2,0.28032440906898215,0,None,i7186,573,8.143634103881737e-05 1727609386,1727609962,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727609407,1727610008,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7181,593,8.143634103881737e-05 1727609386,1727610018,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.8423856852288276 max_depth 3 threshold 0.7461088188894422,106,0.8423856852288276,3,0.7461088188894422,0.2837481910274964,0,None,i7181,625,8.551977686500067e-05 1727609446,1727610034,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.6488209772586364 max_depth 3 threshold 0.35914051000170694,144,0.6488209772586364,3,0.35914051000170694,0.28975639170284617,0,None,i7181,581,0.00011399898829908745 1727609446,1727610085,639,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8283848825706999 max_depth 3 threshold 0.23260637982884697,100,0.8283848825706999,3,0.23260637982884697,0.2827592860588519,0,None,i7181,632,8.090760148613735e-05 1727609535,1727610094,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 164 const 0.9311227010070221 max_depth 2 threshold 0.619400872920816,164,0.9311227010070221,2,0.619400872920816,0.2936541244573082,0,None,i7186,552,0.00012828534594881754 1727609598,1727610243,645,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 148 const 0.6139215331146879 max_depth 4 threshold 0.29580144839820977,148,0.6139215331146879,4,0.29580144839820977,0.288803666184274,0,None,i7181,637,0.00011759764659937298 1727609656,1727610247,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7903467938039234 max_depth 2 threshold 0.32980669813621866,100,0.7903467938039234,2,0.32980669813621866,0.28032440906898215,0,None,i7181,583,8.143634103881737e-05 1727609678,1727610305,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.47783285029723505,100,0.1,4,0.47783285029723505,0.28062228654124455,0,None,i7186,620,8.138412363683339e-05 1727609738,1727610324,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7186,578,8.143634103881737e-05 1727609777,1727610367,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7181,583,8.143634103881737e-05 1727609738,1727610371,633,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.47815706305667965,100,0.1,4,0.47815706305667965,0.28062228654124455,0,None,i7181,625,8.138412363683339e-05 1727609837,1727610407,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727609798,1727610409,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,604,8.143634103881737e-05 1727609878,1727610475,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 179 const 0.7252349312729551 max_depth 4 threshold 0.7903595208295613,179,0.7252349312729551,4,0.7903595208295613,0.29707187650747713,0,None,i7186,590,0.00013861235529522326 1727609897,1727610515,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,611,8.143634103881737e-05 1727609918,1727610529,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.425430027905786,100,0.1,4,0.425430027905786,0.2810455861070912,0,None,i7186,604,8.128200070176787e-05 1727610042,1727610599,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 186 const 1 max_depth 2 threshold 0.24389711312341644,186,1,2,0.24389711312341644,0.29514109985528225,0,None,i7181,550,0.00014482654964586663 1727610002,1727610617,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5344227130221951 max_depth 3 threshold 0.281666505650946,100,0.5344227130221951,3,0.281666505650946,0.28184997588036664,0,None,i7186,608,8.110750558290355e-05 1727610102,1727610677,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727610122,1727610706,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7181,577,8.143634103881737e-05 1727610108,1727610748,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.8538477888212901 max_depth 3 threshold 0.6692680902258535,102,0.8538477888212901,3,0.6692680902258535,0.2838941148094549,0,None,i7181,634,8.22618478715745e-05 1727610167,1727610753,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 104 const 0.6934544760704164 max_depth 2 threshold 0.5655823853022767,104,0.6934544760704164,2,0.5655823853022767,0.2842522913651713,0,None,i7181,579,8.371981626963063e-05 1727610187,1727610764,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6395119697474732 max_depth 2 threshold 0.32257060110812785,100,0.6395119697474732,2,0.32257060110812785,0.28032440906898215,0,None,i7181,570,8.143634103881737e-05 1727609982,1727610893,911,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7186,904,8.156770947957166e-05 1727610294,1727610901,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.6940327455778601,100,0.1,3,0.6940327455778601,0.28032440906898215,0,None,i7181,600,8.143634103881737e-05 1727610348,1727610923,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727610378,1727610939,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 161 const 1 max_depth 2 threshold 0.35148777797554087,161,1,2,0.35148777797554087,0.2920296671490593,0,None,i7181,553,0.00012657468422013968 1727610314,1727610942,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 196 const 1 max_depth 4 threshold 0.4419323174994534,196,1,4,0.4419323174994534,0.29926073323685476,0,None,i7186,621,0.00015082761276813262 1727610438,1727611011,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727610498,1727611045,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 313 const 0.2907821733885977 max_depth 2 threshold 0.4153115274343033,313,0.2907821733885977,2,0.4153115274343033,0.30982151471297636,0,None,i7186,540,0.00023273260143726783 1727610478,1727611054,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9617720341364993 max_depth 2 threshold 0.675451569484453,100,0.9617720341364993,2,0.675451569484453,0.28032440906898215,0,None,i7181,569,8.143634103881737e-05 1727610434,1727611087,653,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3683135274553794 max_depth 4 threshold 0.6382614158812314,100,0.3683135274553794,4,0.6382614158812314,0.28331765557163535,0,None,i7186,646,8.077282728561033e-05 1727610529,1727611093,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 167 const 0.7650522468343259 max_depth 3 threshold 0.2913374227413495,167,0.7650522468343259,3,0.2913374227413495,0.29455258080077185,0,None,i7186,557,0.00013030092595291986 1727610582,1727611140,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 264 const 0.7141393580725742 max_depth 3 threshold 0.31756161064447913,264,0.7141393580725742,3,0.31756161064447913,0.30127472262421606,0,None,i7186,551,0.0002017100359182821 1727610589,1727611166,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9468939605246552 max_depth 2 threshold 0.6817645464982611,100,0.9468939605246552,2,0.6817645464982611,0.28032440906898215,0,None,i7181,570,8.143634103881737e-05 1727610642,1727611215,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727610662,1727611230,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7181,561,8.143634103881737e-05 1727610722,1727611301,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9458256822727176 max_depth 2 threshold 0.6795251227408965,100,0.9458256822727176,2,0.6795251227408965,0.28032440906898215,0,None,i7186,572,8.143634103881737e-05 1727610763,1727611324,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.939258035279959 max_depth 2 threshold 0.6799203810578784,100,0.939258035279959,2,0.6799203810578784,0.28032440906898215,0,None,i7181,554,8.143634103881737e-05 1727610823,1727611383,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.33684746312421826 max_depth 2 threshold 0.8,100,0.33684746312421826,2,0.8,0.28032440906898215,0,None,i7181,553,8.143634103881737e-05 1727610823,1727611391,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7467759839955779 max_depth 2 threshold 0.2,100,0.7467759839955779,2,0.2,0.28032440906898215,0,None,i7181,561,8.143634103881737e-05 1727610849,1727611420,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7062872886326 max_depth 2 threshold 0.2,100,0.7062872886326,2,0.2,0.28032440906898215,0,None,i7181,564,8.143634103881737e-05 1727610937,1727611512,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2948869179776883 max_depth 2 threshold 0.8,100,0.2948869179776883,2,0.8,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727610977,1727611569,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3472355537307431 max_depth 2 threshold 0.8,100,0.3472355537307431,2,0.8,0.28032440906898215,0,None,i7186,585,8.143634103881737e-05 1727610997,1727611573,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6918084132936542 max_depth 2 threshold 0.2,100,0.6918084132936542,2,0.2,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727611057,1727611628,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6847606239746376 max_depth 2 threshold 0.2,100,0.6847606239746376,2,0.2,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727611037,1727611630,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7202014046915788 max_depth 2 threshold 0.2,100,0.7202014046915788,2,0.2,0.28032440906898215,0,None,i7186,586,8.143634103881737e-05 1727611117,1727611688,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.32418781806853153 max_depth 2 threshold 0.8,100,0.32418781806853153,2,0.8,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727611130,1727611706,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7110991426937776 max_depth 2 threshold 0.2,100,0.7110991426937776,2,0.2,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727611137,1727611710,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.720356768618748 max_depth 2 threshold 0.2,100,0.720356768618748,2,0.2,0.28032440906898215,0,None,i7181,566,8.143634103881737e-05 1727611220,1727611796,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3181907461643975 max_depth 2 threshold 0.8,100,0.3181907461643975,2,0.8,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727611250,1727611820,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3094518217984911 max_depth 2 threshold 0.8,100,0.3094518217984911,2,0.8,0.28032440906898215,0,None,i7181,563,8.143634103881737e-05 1727611239,1727611849,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 168 const 0.720223951753499 max_depth 4 threshold 0.2,168,0.720223951753499,4,0.2,0.29276652194886643,0,None,i7186,603,0.00013187062330375203 1727611328,1727611904,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9394168870992152 max_depth 2 threshold 0.8,100,0.9394168870992152,2,0.8,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727611348,1727611918,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 156 const 0.4843049864662634 max_depth 3 threshold 0.2641353486990612,156,0.4843049864662634,3,0.2641353486990612,0.2908683068017366,0,None,i7181,563,0.00012310544682505922 1727611368,1727611943,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7203242715474122 max_depth 2 threshold 0.2,100,0.7203242715474122,2,0.2,0.28032440906898215,0,None,i7181,568,8.143634103881737e-05 1727611428,1727612004,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7243777642764178 max_depth 2 threshold 0.2,100,0.7243777642764178,2,0.2,0.28032440906898215,0,None,i7181,569,8.143634103881737e-05 1727611431,1727612006,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.31852255779578775 max_depth 2 threshold 0.8,100,0.31852255779578775,2,0.8,0.28032440906898215,0,None,i7181,568,8.143634103881737e-05 1727611501,1727612045,544,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 195 const 0.6343395663380444 max_depth 2 threshold 0.3639776002610189,195,0.6343395663380444,2,0.3639776002610189,0.2972575976845152,0,None,i7181,537,0.00015084754531110244 1727611631,1727612074,443,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 338 const 0.1724702650681138 max_depth 2 threshold 0.40108951088041067,338,0.1724702650681138,2,0.40108951088041067,0.3109732272069464,0,None,i7174,426,0.0002503966164836644 1727611521,1727612093,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2543289157424734 max_depth 2 threshold 0.8,100,0.2543289157424734,2,0.8,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727611564,1727612124,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7299847100965836 max_depth 2 threshold 0.2,100,0.7299847100965836,2,0.2,0.28032440906898215,0,None,i7181,553,8.143634103881737e-05 1727611541,1727612145,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.7139463425863302 max_depth 3 threshold 0.46743247605699506,105,0.7139463425863302,3,0.46743247605699506,0.2826459237819585,0,None,i7181,596,8.495402970204218e-05 1727611652,1727612242,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.5822324192474715 max_depth 2 threshold 0.4380171219787349,113,0.5822324192474715,2,0.4380171219787349,0.2864495899662325,0,None,i7186,583,9.035428973826288e-05 1727611679,1727612254,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9452340058185574 max_depth 2 threshold 0.7782771763067928,100,0.9452340058185574,2,0.7782771763067928,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727611739,1727612315,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 920 const 0.27279499993026546 max_depth 3 threshold 0.35859107325866113,920,0.27279499993026546,3,0.35859107325866113,0.3410467920887602,0,None,i7186,569,0.0006153615265047973 1727611786,1727612360,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.24392864986558108 max_depth 2 threshold 0.8,100,0.24392864986558108,2,0.8,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727611806,1727612389,583,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2373331600733141 max_depth 2 threshold 0.8,100,0.2373331600733141,2,0.8,0.28032440906898215,0,None,i7186,575,8.143634103881737e-05 1727611846,1727612459,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,606,8.143634103881737e-05 1727611912,1727612467,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 191 const 0.3818682004094426 max_depth 3 threshold 0.23671476448491158,191,0.3818682004094426,3,0.23671476448491158,0.2959466956102267,0,None,i7186,548,0.00014839298774455866 1727611906,1727612495,589,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.3694414188422994,100,0.1,3,0.3694414188422994,0.28032440906898215,0,None,i7186,582,8.143634103881737e-05 1727611972,1727612530,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 173 const 0.13615681806943153 max_depth 3 threshold 0.2,173,0.13615681806943153,3,0.2,0.2937095996140858,0,None,i7186,551,0.00013532966586053135 1727611986,1727612549,563,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.23953788514121946 max_depth 2 threshold 0.8,100,0.23953788514121946,2,0.8,0.28032440906898215,0,None,i7181,557,8.143634103881737e-05 1727612026,1727612596,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.31137124245263514 max_depth 3 threshold 0.360182827417835,144,0.31137124245263514,3,0.360182827417835,0.2901519536903039,0,None,i7181,563,0.00011386159267996355 1727612106,1727612679,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2379343820721088 max_depth 2 threshold 0.8,100,0.2379343820721088,2,0.8,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727612086,1727612685,599,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,592,8.143634103881737e-05 1727612183,1727612750,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7312992656897063 max_depth 2 threshold 0.2,100,0.7312992656897063,2,0.2,0.28032440906898215,0,None,i7181,560,8.143634103881737e-05 1727612206,1727612797,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,584,8.143634103881737e-05 1727612206,1727612819,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.24094377914410672 max_depth 4 threshold 0.5662950001212456,100,0.24094377914410672,4,0.5662950001212456,0.2812795465508924,0,None,i7181,606,8.124515744812417e-05 1727612286,1727612874,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.23661337586987807 max_depth 2 threshold 0.8,100,0.23661337586987807,2,0.8,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727612304,1727612933,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.24175228089388673 max_depth 4 threshold 0.7994691252698152,100,0.24175228089388673,4,0.7994691252698152,0.2803207911239749,0,None,i7186,622,8.14765173484421e-05 1727612395,1727612968,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.41875774698256163 max_depth 2 threshold 0.2,100,0.41875774698256163,2,0.2,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727612386,1727612971,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.35382600552655447,100,0.1,3,0.35382600552655447,0.28032440906898215,0,None,i7186,578,8.143634103881737e-05 1727612506,1727613079,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7591391179871227 max_depth 2 threshold 0.2,100,0.7591391179871227,2,0.2,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727612515,1727613085,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7530765379752118 max_depth 2 threshold 0.2,100,0.7530765379752118,2,0.2,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727612526,1727613134,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,601,8.143634103881737e-05 1727612666,1727613240,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.39620808022813125 max_depth 2 threshold 0.8,100,0.39620808022813125,2,0.8,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727612697,1727613250,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 271 const 0.601605985652139 max_depth 2 threshold 0.36547471627313893,271,0.601605985652139,2,0.36547471627313893,0.30731186685962375,0,None,i7186,546,0.000203170446145386 1727612686,1727613267,581,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9423067618521178 max_depth 2 threshold 0.7720638841305492,100,0.9423067618521178,2,0.7720638841305492,0.28032440906898215,0,None,i7186,573,8.143634103881737e-05 1727612726,1727613348,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.9420167193630892 max_depth 3 threshold 0.5780640161105908,101,0.9420167193630892,3,0.5780640161105908,0.2812916063675832,0,None,i7181,616,8.211411528008189e-05 1727612906,1727613477,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8188101636608004 max_depth 2 threshold 0.255684039995552,100,0.8188101636608004,2,0.255684039995552,0.28032440906898215,0,None,i7181,564,8.143634103881737e-05 1727612866,1727613483,617,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2414590084151088 max_depth 4 threshold 0.799932660394794,100,0.2414590084151088,4,0.799932660394794,0.2803207911239749,0,None,i7181,610,8.14765173484421e-05 1727612886,1727613488,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,594,8.143634103881737e-05 1727612906,1727613519,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.19596717774773745 max_depth 4 threshold 0.5384645915681631,100,0.19596717774773745,4,0.5384645915681631,0.28167993246502654,0,None,i7181,606,8.112896177343605e-05 1727613066,1727613633,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 231 const 0.2239215462350031 max_depth 4 threshold 0.6078816327431916,231,0.2239215462350031,4,0.6078816327431916,0.3015412445730825,0,None,i7186,559,0.00017637582744862678 1727613060,1727613638,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.39423994442504884 max_depth 2 threshold 0.8,100,0.39423994442504884,2,0.8,0.28032440906898215,0,None,i7186,571,8.143634103881737e-05 1727613086,1727613639,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 162 const 0.38929740755115994 max_depth 2 threshold 0.5513545610839392,162,0.38929740755115994,2,0.5513545610839392,0.2946490593342981,0,None,i7186,546,0.00012634248522142277 1727613046,1727613658,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,605,8.143634103881737e-05 1727613210,1727613785,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7569096752262534 max_depth 2 threshold 0.2,100,0.7569096752262534,2,0.2,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727613226,1727613803,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9379958115411272 max_depth 2 threshold 0.8,100,0.9379958115411272,2,0.8,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727613240,1727613833,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,584,8.143634103881737e-05 1727613246,1727613865,619,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,612,8.143634103881737e-05 1727613386,1727613933,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 330 const 0.9205028782430251 max_depth 2 threshold 0.29602744080514043,330,0.9205028782430251,2,0.29602744080514043,0.30972383019778105,0,None,i7186,539,0.0002454105484220843 1727613406,1727613963,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 142 const 1 max_depth 2 threshold 0.2,142,1,2,0.2,0.29069946936806557,0,None,i7186,549,0.00011211374088655477 1727613422,1727613983,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 172 const 0.1 max_depth 4 threshold 0.6183533307232012,172,0.1,4,0.6183533307232012,0.2946490593342981,0,None,i7181,554,0.00013415370111270576 1727613406,1727614003,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,590,8.143634103881737e-05 1727613526,1727614090,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8158616288148701 max_depth 2 threshold 0.542135642217584,100,0.8158616288148701,2,0.542135642217584,0.28032440906898215,0,None,i7181,557,8.143634103881737e-05 1727613686,1727614268,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.20662808552121364 max_depth 4 threshold 0.2,144,0.20662808552121364,4,0.2,0.28975156777616984,0,None,i7186,575,0.00011400066385541825 1727613706,1727614281,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7186,568,8.143634103881737e-05 1727613693,1727614305,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,605,8.143634103881737e-05 1727613806,1727614385,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9311976840811002 max_depth 2 threshold 0.8,100,0.9311976840811002,2,0.8,0.28032440906898215,0,None,i7186,572,8.143634103881737e-05 1727613814,1727614396,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 154 const 0.24873588894415377 max_depth 4 threshold 0.41982646304116117,154,0.24873588894415377,4,0.41982646304116117,0.2938591413410516,0,None,i7186,575,0.00012039388472320481 1727613826,1727614404,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7181,571,8.143634103881737e-05 1727613506,1727614405,899,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6776097677445586 max_depth 4 threshold 0.2,100,0.6776097677445586,4,0.2,0.28124819102749643,0,None,i7186,891,8.160719534377667e-05 1727613663,1727614542,879,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6780038621895048 max_depth 4 threshold 0.2,100,0.6780038621895048,4,0.2,0.28124819102749643,0,None,i7186,872,8.160719534377667e-05 1727614176,1727614733,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.5506435988428184 max_depth 2 threshold 0.4952302202822254,143,0.5506435988428184,2,0.4952302202822254,0.29277013989387357,0,None,i7181,551,0.00011217293519215087 1727614046,1727614748,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.5984508291956254 max_depth 4 threshold 0.8,113,0.5984508291956254,4,0.8,0.28529787747226243,0,None,i7186,694,9.084150779455394e-05 1727614186,1727614760,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7181,567,8.143634103881737e-05 1727614206,1727614777,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7181,564,8.143634103881737e-05 1727614230,1727614801,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7181,564,8.143634103881737e-05 1727613904,1727614805,901,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 111 const 1 max_depth 4 threshold 0.43157136677620894,111,1,4,0.43157136677620894,0.2841582247949831,0,None,i7186,894,8.987648201963809e-05 1727613926,1727614856,930,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 4 threshold 0.2,110,1,4,0.2,0.28464905933429807,0,None,i7181,923,8.90014471780029e-05 1727614006,1727614860,854,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 111 const 1 max_depth 4 threshold 0.8,111,1,4,0.8,0.28535214664737096,0,None,i7186,847,8.948273671470218e-05 1727614387,1727615022,635,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.35069107030481217 max_depth 4 threshold 0.3330061672550202,117,0.35069107030481217,4,0.3330061672550202,0.2857416787264834,0,None,i7186,628,9.382033332093634e-05 1727614370,1727615171,801,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.7019783213282238 max_depth 4 threshold 0.2,112,0.7019783213282238,4,0.2,0.2855619874577906,0,None,i7186,793,9.01294716806205e-05 1727614290,1727615204,914,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.2,114,1,4,0.2,0.28730342498794015,0,None,i7186,907,9.146778811285721e-05 1727614651,1727615224,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 107 const 1 max_depth 2 threshold 0.2,107,1,2,0.2,0.28412566328991795,0,None,i7186,566,8.617290029065591e-05 1727614670,1727615258,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.1 max_depth 4 threshold 0.8,118,0.1,4,0.8,0.28390496864447656,0,None,i7181,580,9.509095086007469e-05 1727614651,1727615262,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.2245065044238564 max_depth 4 threshold 0.8,114,0.2245065044238564,4,0.8,0.28603955619874577,0,None,i7186,605,9.13122208575442e-05 1727614719,1727615309,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 207 const 0.6932484575736728 max_depth 4 threshold 0.40315272195767304,207,0.6932484575736728,4,0.40315272195767304,0.2981355523396044,0,None,i7181,583,0.00015975221112703023 1727614530,1727615317,787,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.5869127401404194 max_depth 4 threshold 0.24456384868149908,101,0.5869127401404194,4,0.24456384868149908,0.2827532561505065,0,None,i7186,779,8.193747808980994e-05 1727614508,1727615503,995,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 109 const 0.9827908871403106 max_depth 4 threshold 0.2,109,0.9827908871403106,4,0.2,0.2858176555716353,0,None,i7186,988,8.791472298684057e-05 1727615020,1727615560,540,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3071592188586085 max_depth 2 threshold 0.5793363311617629,100,0.3071592188586085,2,0.5793363311617629,0.28032440906898215,0,None,i7181,533,8.143634103881737e-05 1727614991,1727615563,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9718926547955254 max_depth 2 threshold 0.6502399928520364,100,0.9718926547955254,2,0.6502399928520364,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727615012,1727615573,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7380578142691976 max_depth 2 threshold 0.2533574288180035,100,0.7380578142691976,2,0.2533574288180035,0.28032440906898215,0,None,i7181,555,8.143634103881737e-05 1727615011,1727615584,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7353652731779783 max_depth 2 threshold 0.25027962476823773,100,0.7353652731779783,2,0.25027962476823773,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727614830,1727615669,839,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.6527878412297138 max_depth 4 threshold 0.2881630837981907,102,0.6527878412297138,4,0.2881630837981907,0.2865906898215147,0,None,i7186,831,8.185987875355837e-05 1727615170,1727615714,544,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7332799035767289 max_depth 2 threshold 0.2346585686326258,100,0.7332799035767289,2,0.2346585686326258,0.28032440906898215,0,None,i7181,537,8.143634103881737e-05 1727615150,1727615763,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.26299467624053635 max_depth 4 threshold 0.3487910063237686,100,0.26299467624053635,4,0.3487910063237686,0.28208996623251326,0,None,i7181,604,8.106915587337816e-05 1727615231,1727615800,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7163001948732499 max_depth 2 threshold 0.22665307897386786,100,0.7163001948732499,2,0.22665307897386786,0.28032440906898215,0,None,i7186,562,8.143634103881737e-05 1727615191,1727615809,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 155 const 0.6112661646453091 max_depth 4 threshold 0.37789223408516925,155,0.6112661646453091,4,0.37789223408516925,0.2918596237337192,0,None,i7186,612,0.00012199770119049747 1727615051,1727615817,766,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.6946014964261059 max_depth 4 threshold 0.2,114,0.6946014964261059,4,0.2,0.28376507477086343,0,None,i7186,760,9.216613465114604e-05 1727615352,1727615909,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.1 max_depth 2 threshold 0.42724072171286254,132,0.1,2,0.42724072171286254,0.2873420164013507,0,None,i7186,551,0.00010529218554604085 1727615331,1727615925,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.4666980017712786,100,0.1,3,0.4666980017712786,0.28032440906898215,0,None,i7186,587,8.143634103881737e-05 1727615491,1727616067,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6466477321942825,100,1,2,0.6466477321942825,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727615531,1727616076,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6998120261074771 max_depth 2 threshold 0.22717793385543733,100,0.6998120261074771,2,0.22717793385543733,0.28032440906898215,0,None,i7181,538,8.143634103881737e-05 1727615504,1727616086,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,574,8.143634103881737e-05 1727615511,1727616094,583,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6482581593210581,100,1,2,0.6482581593210581,0.28032440906898215,0,None,i7186,577,8.143634103881737e-05 1727615591,1727616162,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1 max_depth 3 threshold 0.8,126,0.1,3,0.8,0.28507718282682104,0,None,i7186,564,0.00010118002923680788 1727615746,1727616291,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 246 const 1 max_depth 2 threshold 0.2,246,1,2,0.2,0.3009056922334781,0,None,i7186,538,0.00018816248473735366 1727615731,1727616292,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 127 const 1 max_depth 2 threshold 0.2,127,1,2,0.2,0.28853955619874583,0,None,i7186,554,0.00010092522145133981 1727615770,1727616314,544,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 2 threshold 0.2,122,1,2,0.2,0.28618186203569707,0,None,i7181,537,9.763908680995348e-05 1727615891,1727616463,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7019206483469,100,1,2,0.7019206483469,0.28032440906898215,0,None,i7186,565,8.143634103881737e-05 1727615911,1727616522,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,604,8.143634103881737e-05 1727615871,1727616530,659,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.4641860074311366 max_depth 4 threshold 0.6050185791468976,117,0.4641860074311366,4,0.6050185791468976,0.28634587554269175,0,None,i7186,652,9.370262884239254e-05 1727616079,1727616657,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.12378744510354811 max_depth 4 threshold 0.6356281163125501,137,0.12378744510354811,4,0.6356281163125501,0.29004823926676315,0,None,i7186,571,0.00010836458683052076 1727616109,1727616703,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6831682098866243,100,1,2,0.6831682098866243,0.28032440906898215,0,None,i7186,587,8.143634103881737e-05 1727616131,1727616721,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,583,8.143634103881737e-05 1727616131,1727616744,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,607,8.143634103881737e-05 1727616320,1727616898,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6817900098152092,100,1,2,0.6817900098152092,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727616311,1727616909,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,591,8.143634103881737e-05 1727616350,1727616909,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 195 const 0.2934137524530757 max_depth 3 threshold 0.6389415102541678,195,0.2934137524530757,3,0.6389415102541678,0.2972575976845152,0,None,i7186,552,0.00015084754531110244 1727616371,1727616960,589,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,582,8.143634103881737e-05 1727616511,1727617086,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.6547202991893256 max_depth 3 threshold 0.36498847123782013,135,0.6547202991893256,3,0.36498847123782013,0.29109141341051614,0,None,i7186,568,0.00010647168260762307 1727616531,1727617105,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727616571,1727617120,549,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6832672048361554,100,1,2,0.6832672048361554,0.28032440906898215,0,None,i7181,543,8.143634103881737e-05 1727616551,1727617160,609,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,602,8.143634103881737e-05 1727616371,1727617229,858,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7181,852,8.156770947957166e-05 1727616713,1727617281,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 650 const 0.11210877373344968 max_depth 2 threshold 0.48272374769161164,650,0.11210877373344968,2,0.48272374769161164,0.33018692715870723,0,None,i7186,561,0.0004517622478135192 1727616731,1727617284,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.3725124922572358 max_depth 2 threshold 0.5165588160986361,143,0.3725124922572358,2,0.5165588160986361,0.29277013989387357,0,None,i7186,546,0.00011217293519215087 1727616711,1727617336,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.10003126482399008 max_depth 4 threshold 0.4003698819499833,102,0.10003126482399008,4,0.4003698819499833,0.282315484804631,0,None,i7186,617,8.258956550474606e-05 1727616871,1727617377,506,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 260 const 0.23034258150488876 max_depth 2 threshold 0.6546858435553647,260,0.23034258150488876,2,0.6546858435553647,0.3051893391220454,0,None,i7181,500,0.00019621715175274378 1727616891,1727617438,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7181,541,8.143634103881737e-05 1727616864,1727617456,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,585,8.143634103881737e-05 1727616951,1727617540,589,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,581,8.143634103881737e-05 1727617091,1727617699,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,601,8.143634103881737e-05 1727617106,1727617712,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,600,8.143634103881737e-05 1727617131,1727617739,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,601,8.143634103881737e-05 1727617271,1727617821,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 353 const 0.13642148752115185 max_depth 3 threshold 0.44844895550905606,353,0.13642148752115185,3,0.44844895550905606,0.31346960926193923,0,None,i7186,543,0.00025936104749061306 1727617311,1727617888,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6759624728247733,100,1,2,0.6759624728247733,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727617331,1727617952,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,613,8.143634103881737e-05 1727617528,1727618147,619,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 169 const 0.6699544631876051 max_depth 4 threshold 0.3694975394755602,169,0.6699544631876051,4,0.3694975394755602,0.2914351181862036,0,None,i7186,611,0.00013322087345153625 1727617551,1727618162,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,603,8.143634103881737e-05 1727617558,1727618187,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.43586130215174057 max_depth 4 threshold 0.6736374813547106,101,0.43586130215174057,4,0.6736374813547106,0.28334659913169324,0,None,i7181,621,8.163280797370718e-05 1727617286,1727618228,942,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.6058733388094524,100,1,4,0.6058733388094524,0.281120356970574,0,None,i7186,935,8.173722932427686e-05 1727617498,1727618243,745,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 104 const 0.5133043046416912 max_depth 4 threshold 0.4667532751426604,104,0.5133043046416912,4,0.4667532751426604,0.28253617945007237,0,None,i7186,738,8.431958259573822e-05 1727617831,1727618364,533,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6605447263834704 max_depth 2 threshold 0.2,100,0.6605447263834704,2,0.2,0.28032440906898215,0,None,i7181,526,8.143634103881737e-05 1727617792,1727618366,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6548624447446647,100,1,2,0.6548624447446647,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727617770,1727618404,634,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.2925605645542535 max_depth 4 threshold 0.6620582390536434,101,0.2925605645542535,4,0.6620582390536434,0.2820236372407139,0,None,i7186,626,8.187566214360348e-05 1727617812,1727618425,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,607,8.143634103881737e-05 1727618072,1727618603,531,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.8161949971242278 max_depth 2 threshold 0.20768161840453953,119,0.8161949971242278,2,0.20768161840453953,0.288129522431259,0,None,i7181,525,9.469708773604937e-05 1727618071,1727618616,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.6722271731219049,100,0.1,3,0.6722271731219049,0.28032440906898215,0,None,i7181,539,8.143634103881737e-05 1727618052,1727618626,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.5216114627962828,100,1,2,0.5216114627962828,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727617830,1727618668,838,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.602335024523823 max_depth 4 threshold 0.2,100,0.602335024523823,4,0.2,0.28119150988904973,0,None,i7186,831,8.156162972314586e-05 1727618192,1727618797,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,598,8.143634103881737e-05 1727618373,1727618919,546,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 159 const 1 max_depth 2 threshold 0.5780880957563205,159,1,2,0.5780880957563205,0.2941654606849976,0,None,i7186,539,0.0001242017715116691 1727618343,1727618930,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6682136672320753 max_depth 2 threshold 0.2,100,0.6682136672320753,2,0.2,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727618032,1727618955,923,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7186,916,8.156770947957166e-05 1727618392,1727619018,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.21601798615613016 max_depth 4 threshold 0.47910924430790985,101,0.21601798615613016,4,0.47910924430790985,0.281702846116739,0,None,i7186,619,8.19538466729661e-05 1727618161,1727619069,908,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.281655812831645,0,None,i7186,900,8.156770947957166e-05 1727618552,1727619098,546,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 261 const 0.5657685108493232 max_depth 2 threshold 0.47325968777554783,261,0.5657685108493232,2,0.47325968777554783,0.3069054510371443,0,None,i7186,539,0.00019587785137202435 1727618592,1727619114,522,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6660309028135486 max_depth 2 threshold 0.2,100,0.6660309028135486,2,0.2,0.28032440906898215,0,None,i7181,516,8.143634103881737e-05 1727618572,1727619125,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,546,8.143634103881737e-05 1727618572,1727619143,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6607577900999595,100,1,2,0.6607577900999595,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727618704,1727619310,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,599,8.143634103881737e-05 1727618732,1727619340,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,601,8.143634103881737e-05 1727618912,1727619486,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6503070613798494,100,1,2,0.6503070613798494,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727618932,1727619502,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.658475572071168 max_depth 2 threshold 0.2,100,0.658475572071168,2,0.2,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727618972,1727619545,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6637235098042787 max_depth 2 threshold 0.2,100,0.6637235098042787,2,0.2,0.28032440906898215,0,None,i7186,565,8.143634103881737e-05 1727618952,1727619577,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,618,8.143634103881737e-05 1727619126,1727619673,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 283 const 0.4662168889693912 max_depth 3 threshold 0.7239889912301238,283,0.4662168889693912,3,0.7239889912301238,0.30512059816690784,0,None,i7186,540,0.00021354189819045128 1727619172,1727619745,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6613551007985117 max_depth 2 threshold 0.2,100,0.6613551007985117,2,0.2,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727619186,1727619773,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.2,100,0.1,3,0.2,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727619453,1727620030,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.654484027375001 max_depth 2 threshold 0.2,100,0.654484027375001,2,0.2,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727619488,1727620047,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 178 const 0.8017764122027227 max_depth 3 threshold 0.2,178,0.8017764122027227,3,0.2,0.2952170767004342,0,None,i7186,552,0.00013857552697101107 1727619493,1727620090,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.38958981366650713 max_depth 3 threshold 0.2028875580132091,100,0.38958981366650713,3,0.2028875580132091,0.2812554269175108,0,None,i7186,590,8.123137565703454e-05 1727619561,1727620098,537,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.2,100,0.1,3,0.2,0.28032440906898215,0,None,i7181,530,8.143634103881737e-05 1727619512,1727620196,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.5279162674107046 max_depth 4 threshold 0.7748853223657779,114,0.5279162674107046,4,0.7748853223657779,0.28407621804148575,0,None,i7186,676,9.195352822209672e-05 1727619793,1727620388,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6352647772015847,100,1,2,0.6352647772015847,0.28032440906898215,0,None,i7186,588,8.143634103881737e-05 1727619851,1727620423,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.5355198332904658,100,0.1,2,0.5355198332904658,0.28032440906898215,0,None,i7186,565,8.143634103881737e-05 1727619853,1727620429,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.33399936041953426,100,0.1,2,0.33399936041953426,0.28032440906898215,0,None,i7186,569,8.143634103881737e-05 1727619873,1727620452,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6917904603851565 max_depth 2 threshold 0.2347403166306682,100,0.6917904603851565,2,0.2347403166306682,0.28032440906898215,0,None,i7186,572,8.143634103881737e-05 1727619533,1727620472,939,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.597666242168518,100,1,4,0.597666242168518,0.281120356970574,0,None,i7186,932,8.173722932427686e-05 1727620053,1727620605,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 200 const 0.811642138799669 max_depth 2 threshold 0.32962022762220083,200,0.811642138799669,2,0.32962022762220083,0.297621804148577,0,None,i7186,545,0.00015452854552211383 1727620092,1727620617,525,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6377254062471864,100,1,2,0.6377254062471864,0.28032440906898215,0,None,i7181,518,8.143634103881737e-05 1727619820,1727620669,849,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7757117100740549 max_depth 4 threshold 0.6960860536319635,100,0.7757117100740549,4,0.6960860536319635,0.2807247949831163,0,None,i7186,842,8.169444865135723e-05 1727620063,1727620681,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 104 const 0.2290816662751756 max_depth 4 threshold 0.5824110932609607,104,0.2290816662751756,4,0.5824110932609607,0.28308248914616496,0,None,i7186,612,8.403437637885922e-05 1727620113,1727620719,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,599,8.143634103881737e-05 1727620333,1727620896,563,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6349072513428355,100,1,2,0.6349072513428355,0.28032440906898215,0,None,i7186,555,8.143634103881737e-05 1727620353,1727620898,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6359359762416325,100,1,2,0.6359359762416325,0.28032440906898215,0,None,i7181,536,8.143634103881737e-05 1727620374,1727620913,539,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6030126480829532,100,1,2,0.6030126480829532,0.28032440906898215,0,None,i7181,533,8.143634103881737e-05 1727620353,1727620933,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,570,8.143634103881737e-05 1727620453,1727621032,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 975 const 0.2465123724055805 max_depth 4 threshold 0.5181832931931745,975,0.2465123724055805,4,0.5181832931931745,0.34352629040038596,0,None,i7186,572,0.0006457251496836071 1727620653,1727621214,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6359265585246081,100,1,2,0.6359265585246081,0.28032440906898215,0,None,i7186,554,8.143634103881737e-05 1727620693,1727621263,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.363949951049463,100,0.1,2,0.363949951049463,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727620673,1727621284,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.2,100,0.1,4,0.2,0.2818656536420646,0,None,i7186,604,8.108415569936173e-05 1727620713,1727621312,599,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,591,8.143634103881737e-05 1727620999,1727621570,571,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.28032440906898215,0,None,i7186,564,8.143634103881737e-05 1727621015,1727621612,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.7600774037198313 max_depth 3 threshold 0.5358391542701793,107,0.7600774037198313,3,0.5358391542701793,0.2832272069464544,0,None,i7186,590,8.642712937502312e-05 1727621055,1727621632,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.34851248625126974,100,0.1,2,0.34851248625126974,0.28032440906898215,0,None,i7186,570,8.143634103881737e-05 1727621029,1727621669,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.37078353892422866 max_depth 4 threshold 0.6995047543175581,108,0.37078353892422866,4,0.6995047543175581,0.28442957067052577,0,None,i7186,632,8.694731708691168e-05 1727620969,1727621716,747,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.691888688531572 max_depth 4 threshold 0.29106632169204616,122,0.691888688531572,4,0.29106632169204616,0.28437530149541723,0,None,i7186,737,9.846050103910913e-05 1727621415,1727621988,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7357863254759508 max_depth 2 threshold 0.2603649710851444,100,0.7357863254759508,2,0.2603649710851444,0.28032440906898215,0,None,i7186,566,8.143634103881737e-05 1727621496,1727622042,546,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 309 const 0.6481671577919275 max_depth 2 threshold 0.2702325366795866,309,0.6481671577919275,2,0.2702325366795866,0.30938253738543176,0,None,i7186,539,0.00023010957615994572 1727621453,1727622056,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,596,8.143634103881737e-05 1727621476,1727622060,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,577,8.143634103881737e-05 1727621455,1727622060,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,598,8.143634103881737e-05 1727621513,1727622080,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7181,560,8.143634103881737e-05 1727621816,1727622357,541,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 195 const 0.24960834143424465 max_depth 2 threshold 0.7311223156988451,195,0.24960834143424465,2,0.7311223156988451,0.2972575976845152,0,None,i7186,535,0.00015084754531110244 1727621816,1727622385,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6488452234295707,100,1,2,0.6488452234295707,0.28032440906898215,0,None,i7186,562,8.143634103881737e-05 1727621836,1727622406,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.649023156330049,100,1,2,0.649023156330049,0.28032440906898215,0,None,i7186,563,8.143634103881737e-05 1727621776,1727622425,649,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 109 const 0.3483602627718111 max_depth 4 threshold 0.7341223565704769,109,0.3483602627718111,4,0.7341223565704769,0.2868728895320791,0,None,i7186,641,8.710512746220464e-05 1727622156,1727622720,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 103 const 1 max_depth 2 threshold 0.8,103,1,2,0.8,0.28362879884225756,0,None,i7186,557,8.306352302451216e-05 1727622196,1727622756,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 116 const 1 max_depth 2 threshold 0.8,116,1,2,0.8,0.2861155330438977,0,None,i7186,553,9.284945326581458e-05 1727622210,1727622763,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.2906514209151754 max_depth 2 threshold 0.8,127,0.2906514209151754,2,0.8,0.2891739025566812,0,None,i7186,547,0.00010073087514069784 1727622256,1727622815,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.1 max_depth 2 threshold 0.8,107,0.1,2,0.8,0.28374939700916546,0,None,i7186,552,8.627002633111861e-05 1727622237,1727622930,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 110 const 0.4777871576075515 max_depth 4 threshold 0.2,110,0.4777871576075515,4,0.2,0.2837023637240714,0,None,i7186,686,8.885090783940657e-05 1727622616,1727623163,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 435 const 0.11628642777271875 max_depth 2 threshold 0.5172778441683468,435,0.11628642777271875,2,0.5172778441683468,0.31886517124939706,0,None,i7186,540,0.0003138450238893199 1727622596,1727623194,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,590,8.143634103881737e-05 1727622633,1727623200,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2795012915068804,100,0.1,2,0.2795012915068804,0.28032440906898215,0,None,i7186,560,8.143634103881737e-05 1727622694,1727623221,527,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.68854609119257 max_depth 2 threshold 0.2219631753247373,100,0.68854609119257,2,0.2219631753247373,0.28032440906898215,0,None,i7181,517,8.143634103881737e-05 1727622664,1727623232,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6591105975647775,100,1,2,0.6591105975647775,0.28032440906898215,0,None,i7186,561,8.143634103881737e-05 1727622656,1727623402,746,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.517743209601877 max_depth 4 threshold 0.4671925144621778,105,0.517743209601877,4,0.4671925144621778,0.28435359382537384,0,None,i7186,739,8.473606431052709e-05 1727622956,1727623501,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 236 const 0.47098917946791186 max_depth 2 threshold 0.28982215938087164,236,0.47098917946791186,2,0.28982215938087164,0.30149421128798837,0,None,i7186,538,0.00018021940075622472 1727622997,1727623546,549,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 244 const 0.2748699117378015 max_depth 3 threshold 0.2041430381969256,244,0.2748699117378015,3,0.2041430381969256,0.30083695127834054,0,None,i7186,542,0.00018665245893912798 1727623027,1727623574,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 215 const 0.5829902757877475 max_depth 3 threshold 0.6526908293240378,215,0.5829902757877475,3,0.6526908293240378,0.29968885672937773,0,None,i7186,539,0.00016507812359260437 1727623017,1727623578,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 588 const 0.5686101605258209 max_depth 2 threshold 0.5115120812242748,588,0.5686101605258209,2,0.5115120812242748,0.32777375783888085,0,None,i7186,554,0.00041161095202416753 1727623337,1727623916,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6041202495964547,100,1,2,0.6041202495964547,0.28032440906898215,0,None,i7186,572,8.143634103881737e-05 1727623297,1727623918,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,614,8.143634103881737e-05 1727623331,1727623918,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727623357,1727623923,566,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.33317690212565393,100,0.1,2,0.33317690212565393,0.28032440906898215,0,None,i7186,559,8.143634103881737e-05 1727623377,1727623967,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,583,8.143634103881737e-05 1727623657,1727624229,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.22392042069638068,100,1,2,0.22392042069638068,0.28118186203569706,0,None,i7186,566,8.12491235175445e-05 1727623695,1727624246,551,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 466 const 0.5469095770047802 max_depth 3 threshold 0.6538265099732319,466,0.5469095770047802,3,0.6538265099732319,0.3234298118668596,0,None,i7186,544,0.00033130446248488116 1727623717,1727624320,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,596,8.143634103881737e-05 1727623755,1727624359,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,597,8.143634103881737e-05 1727623737,1727624487,750,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.5490737986470544 max_depth 4 threshold 0.42984486370983127,105,0.5490737986470544,4,0.42984486370983127,0.2847612156295224,0,None,i7186,743,8.463252816520107e-05 1727624058,1727624640,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,575,8.143634103881737e-05 1727624089,1727624647,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 363 const 0.4597406897929953 max_depth 3 threshold 0.6992723191068769,363,0.4597406897929953,3,0.6992723191068769,0.3130246020260492,0,None,i7186,551,0.0002670182810769437 1727624118,1727624665,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 346 const 0.8008440348179818 max_depth 2 threshold 0.7659797024569917,346,0.8008440348179818,2,0.7659797024569917,0.31451760733236855,0,None,i7186,541,0.0002532903770313442 1727624150,1727624737,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.8,100,0.1,3,0.8,0.28032440906898215,0,None,i7186,580,8.143634103881737e-05 1727624139,1727624749,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.2,100,0.1,4,0.2,0.2818656536420646,0,None,i7186,603,8.108415569936173e-05 1727624438,1727624995,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 253 const 0.47788572289678 max_depth 4 threshold 0.6937068795188224,253,0.47788572289678,4,0.6937068795188224,0.3074927641099855,0,None,i7186,550,0.00018954011899019138 1727624479,1727625028,549,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 207 const 0.824421917950646 max_depth 2 threshold 0.37736195763962244,207,0.824421917950646,2,0.37736195763962244,0.2972214182344428,0,None,i7186,542,0.0001602088215691689 1727624478,1727625041,563,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28032440906898215,0,None,i7186,556,8.143634103881737e-05 1727624498,1727625096,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.5948887484368789 max_depth 3 threshold 0.4062936996290571,101,0.5948887484368789,3,0.4062936996290571,0.2813567293777135,0,None,i7186,592,8.203820366517316e-05 1727624518,1727625119,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,594,8.143634103881737e-05 1727624877,1727625453,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6771078159573574 max_depth 2 threshold 0.2,100,0.6771078159573574,2,0.2,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727624937,1727625502,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6117291813371966,100,1,2,0.6117291813371966,0.28032440906898215,0,None,i7186,557,8.143634103881737e-05 1727624918,1727625508,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.40376594795705 max_depth 3 threshold 0.2041783961746572,100,0.40376594795705,3,0.2041783961746572,0.2823576941630487,0,None,i7186,583,8.096544869791805e-05 1727624958,1727625512,554,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 434 const 0.7888183765506693 max_depth 4 threshold 0.569144276961538,434,0.7888183765506693,4,0.569144276961538,0.31616980221900626,0,None,i7186,547,0.00031601013277163633 1727624968,1727625532,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5706046533602321 max_depth 2 threshold 0.45260479218508337,100,0.5706046533602321,2,0.45260479218508337,0.28032440906898215,0,None,i7186,557,8.143634103881737e-05 1727625299,1727625857,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 177 const 0.23486740429506503 max_depth 3 threshold 0.29943326398868475,177,0.23486740429506503,3,0.29943326398868475,0.2975192957067052,0,None,i7186,551,0.00013682330628062074 1727625318,1727625858,540,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 218 const 0.8338399784615262 max_depth 2 threshold 0.3558611183131465,218,0.8338399784615262,2,0.3558611183131465,0.29833212735166426,0,None,i7186,532,0.00016813642907114923 1727625258,1727625862,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,597,8.143634103881737e-05 1727625298,1727625879,581,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 421 const 0.8351088040609198 max_depth 2 threshold 0.7760728295826274,421,0.8351088040609198,2,0.7760728295826274,0.3194368065605403,0,None,i7186,573,0.0003033766996497751 1727625339,1727625943,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,597,8.143634103881737e-05 1727625679,1727626253,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6320545009973257,100,1,2,0.6320545009973257,0.28032440906898215,0,None,i7186,567,8.143634103881737e-05 1727625719,1727626255,536,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 226 const 0.3003619728380119 max_depth 2 threshold 0.46300676726590967,226,0.3003619728380119,2,0.46300676726590967,0.30240352146647376,0,None,i7186,529,0.0001720588645307771 1727625739,1727626272,533,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 271 const 0.8001088257853934 max_depth 2 threshold 0.25618535385736513,271,0.8001088257853934,2,0.25618535385736513,0.30731186685962375,0,None,i7186,527,0.000203170446145386 1727625754,1727626316,562,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6337751031763492,100,1,2,0.6337751031763492,0.28032440906898215,0,None,i7186,555,8.143634103881737e-05 1727625779,1727626372,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.28032440906898215,0,None,i7186,586,8.143634103881737e-05 1727626269,1727626870,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3082141646653893 max_depth 3 threshold 0.2,100,0.3082141646653893,3,0.2,0.2817245537867824,0,None,i7186,594,8.11181966777169e-05 1727626339,1727626914,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.1 max_depth 4 threshold 0.2,114,0.1,4,0.2,0.2859370477568741,0,None,i7186,568,9.131530012624996e-05 1727626380,1727626960,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.19735579435105807 max_depth 4 threshold 0.30161692825528685,124,0.19735579435105807,4,0.30161692825528685,0.2865135069946937,0,None,i7186,573,9.914626743081822e-05 1727626379,1727626984,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.44103652819267325 max_depth 4 threshold 0.8,129,0.44103652819267325,4,0.8,0.28697660395561986,0,None,i7186,597,0.00010304572698016784 1727626319,1727627190,871,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.9539433651590062 max_depth 4 threshold 0.655657573698599,102,0.9539433651590062,4,0.655657573698599,0.283972503617945,0,None,i7186,864,8.262913790152745e-05
timestamp,ram_usage_mb,cpu_usage_percent 1727602469,479.9140625,49.7 1727602469,479.92578125,52.1 1727602469,479.92578125,49.8 1727602469,479.92578125,39.4 1727602469,479.92578125,53.2 1727602469,479.92578125,50.2 1727602469,479.92578125,40.6 1727602515,484.234375,49.8 1727602515,484.234375,54.3 1727602515,484.234375,49.7 1727602515,484.234375,56.5 1727602516,484.234375,49.8 1727602516,484.234375,40.0 1727602516,484.234375,50.9 1727602516,484.234375,51.2 1727602518,484.234375,49.8 1727602518,484.234375,53.5 1727602518,484.234375,49.1 1727602518,484.234375,52.5 1727602519,484.234375,49.7 1727602519,484.234375,54.3 1727602519,484.234375,48.5 1727602519,484.234375,58.7 1727602521,484.234375,49.7 1727602521,484.234375,54.3 1727602521,484.234375,46.8 1727602521,484.234375,56.8 1727602522,484.234375,49.7 1727602522,484.234375,54.3 1727602522,484.234375,48.1 1727602522,484.234375,53.3 1727602523,484.234375,49.8 1727602523,484.234375,44.7 1727602523,484.234375,52.6 1727602523,484.234375,42.4 1727602525,484.234375,49.8 1727602525,484.234375,56.2 1727602525,484.234375,47.7 1727602525,484.234375,56.8 1727602526,484.234375,49.7 1727602526,484.234375,47.5 1727602526,484.234375,51.4 1727602526,484.234375,41.2 1727602528,484.234375,49.8 1727602528,484.234375,47.2 1727602528,484.234375,50.9 1727602528,484.234375,51.2 1727602529,484.234375,49.8 1727602529,484.234375,39.4 1727602529,484.234375,53.4 1727602529,484.234375,42.4 1727602531,484.234375,49.7 1727602531,484.234375,54.3 1727602531,484.234375,49.1 1727602531,484.234375,56.5 1727602532,484.234375,49.7 1727602532,484.234375,53.2 1727602532,484.234375,50.0 1727602532,484.234375,58.7 1727602533,484.234375,49.8 1727602533,484.234375,55.3 1727602533,484.234375,47.6 1727602533,484.234375,42.4 1727602535,484.234375,49.8 1727602535,484.234375,38.2 1727602535,484.234375,50.5 1727602535,484.234375,54.8 1727602536,484.234375,49.8 1727602536,484.234375,37.1 1727602536,484.234375,54.2 1727602536,484.234375,39.4 1727602538,484.234375,49.8 1727602538,484.234375,55.6 1727602538,484.234375,47.6 1727602538,484.234375,51.3 1727602539,484.234375,49.8 1727602539,484.234375,38.2 1727602539,484.234375,52.5 1727602539,484.234375,40.6 1727602540,484.234375,49.8 1727602540,484.234375,38.2 1727602540,484.234375,54.3 1727602540,484.234375,40.6 1727602542,484.234375,49.7 1727602542,484.234375,54.3 1727602542,484.234375,49.1 1727602542,484.234375,54.5 1727602547,484.234375,49.7 1727602547,484.234375,38.2 1727602547,484.234375,48.6 1727602547,484.234375,55.8 1727602548,484.234375,49.7 1727602548,484.234375,36.4 1727602548,484.234375,52.9 1727602548,484.234375,40.6 1727602550,484.234375,49.7 1727602550,484.234375,55.3 1727602550,484.234375,48.1 1727602550,484.234375,55.6 1727602551,484.234375,49.7 1727602551,484.234375,55.3 1727602551,484.234375,47.7 1727602551,484.234375,56.8 1727602553,484.234375,49.7 1727602553,484.234375,40.0 1727602553,484.234375,52.4 1727602553,484.234375,37.5 1727602554,484.234375,49.8 1727602554,484.234375,55.3 1727602554,484.234375,49.5 1727602554,484.234375,42.4 1727602555,484.234375,49.8 1727602555,484.234375,54.2 1727602555,484.234375,48.1 1727602555,484.234375,57.4 1727602557,484.234375,49.7 1727602557,484.234375,39.4 1727602557,484.234375,49.0 1727602557,484.234375,58.7 1727602558,484.234375,49.7 1727602558,484.234375,37.1 1727602558,484.234375,53.3 1727602558,484.234375,38.7 1727602560,484.234375,49.8 1727602560,484.234375,51.2 1727602560,484.234375,50.9 1727602560,484.234375,40.6 1727602561,484.234375,49.8 1727602561,484.234375,54.2 1727602561,484.234375,50.0 1727602561,484.234375,40.6 1727602562,484.234375,49.8 1727602562,484.234375,37.5 1727602562,484.234375,53.7 1727602562,484.234375,40.6 1727602564,484.234375,49.8 1727602564,484.234375,56.2 1727602564,484.234375,49.0 1727602564,484.234375,52.2 1727602565,484.234375,49.7 1727602565,484.234375,55.3 1727602565,484.234375,49.1 1727602565,484.234375,47.2 1727602567,484.234375,49.7 1727602567,484.234375,55.3 1727602567,484.234375,48.1 1727602567,484.234375,56.8 1727602568,484.234375,49.7 1727602568,484.234375,54.3 1727602568,484.234375,49.0 1727602568,484.234375,55.3 1727602569,484.234375,49.7 1727602569,484.234375,41.2 1727602569,484.234375,54.2 1727602569,484.234375,38.7 1727602571,484.234375,49.8 1727602571,484.234375,38.2 1727602571,484.234375,53.8 1727602571,484.234375,38.7 1727602572,484.234375,49.7 1727602572,484.234375,56.5 1727602572,484.234375,48.6 1727602572,484.234375,56.8 1727602574,484.234375,49.8 1727602574,484.234375,55.6 1727602574,484.234375,48.1 1727602574,484.234375,42.4 1727602575,484.234375,49.7 1727602575,484.234375,56.2 1727602575,484.234375,48.1 1727602575,484.234375,57.8 1727602576,484.234375,49.7 1727602576,484.234375,51.1 1727602576,484.234375,49.5 1727602576,484.234375,41.9 1727602578,484.234375,49.7 1727602578,484.234375,55.6 1727602578,484.234375,50.0 1727602578,484.234375,53.2 1727602579,484.234375,49.8 1727602579,484.234375,38.2 1727602579,484.234375,53.7 1727602579,484.234375,38.7 1727602581,484.234375,49.8 1727602581,484.234375,40.0 1727602581,484.234375,53.4 1727602581,484.234375,40.6 1727602582,484.234375,49.8 1727602582,484.234375,50.0 1727602582,484.234375,48.6 1727602582,484.234375,54.3 1727602584,484.234375,49.8 1727602584,484.234375,51.2 1727602584,484.234375,49.1 1727602584,484.234375,53.2 1727602585,484.234375,49.7 1727602585,484.234375,56.5 1727602585,484.234375,49.1 1727602585,484.234375,38.7 1727602586,484.234375,49.7 1727602586,484.234375,54.3 1727602586,484.234375,48.1 1727602586,484.234375,55.6 1727602588,484.234375,49.7 1727602588,484.234375,41.7 1727602588,484.234375,52.1 1727602588,484.234375,38.7 1727602589,484.234375,49.8 1727602589,484.234375,54.3 1727602589,484.234375,48.1 1727602589,484.234375,55.6 1727602591,484.234375,49.7 1727602591,484.234375,35.3 1727602591,484.234375,53.1 1727602591,484.234375,41.5 1727602592,484.234375,49.8 1727602592,484.234375,54.3 1727602592,484.234375,49.1 1727602592,484.234375,38.7 1727602593,484.234375,49.8 1727602593,484.234375,40.0 1727602593,484.234375,49.5 1727602593,484.234375,52.5 1727602595,484.234375,49.8 1727602595,484.234375,54.3 1727602595,484.234375,48.6 1727602595,484.234375,56.5 1727602596,484.234375,49.7 1727602596,484.234375,51.2 1727602596,484.234375,50.5 1727602596,484.234375,37.5 1727602598,484.234375,49.8 1727602598,484.234375,54.3 1727602598,484.234375,48.1 1727602598,484.234375,58.7 1727602599,484.234375,49.7 1727602599,484.234375,48.7 1727602599,484.234375,51.4 1727602599,484.234375,40.6 1727602600,484.234375,49.8 1727602600,484.234375,41.2 1727602600,484.234375,49.5 1727602600,484.234375,56.8 1727602602,484.234375,49.7 1727602602,484.234375,51.2 1727602602,484.234375,49.5 1727602602,484.234375,38.7 1727602603,484.2421875,49.8 1727602603,484.2421875,40.0 1727602603,484.2421875,54.3 1727602603,484.2421875,38.7 1727602604,484.2421875,49.8 1727602604,484.2421875,55.6 1727602604,484.2421875,49.1 1727602604,484.2421875,56.5 1727602606,484.2421875,49.7 1727602606,484.2421875,43.2 1727602606,484.2421875,51.7 1727602606,484.2421875,43.2 1727602607,484.2421875,49.8 1727602607,484.2421875,55.3 1727602607,484.2421875,47.2 1727602607,484.2421875,42.9 1727602609,484.2421875,49.8 1727602609,484.2421875,40.0 1727602609,484.2421875,53.8 1727602609,484.2421875,40.0 1727602610,484.2421875,49.8 1727602610,484.2421875,36.4 1727602610,484.2421875,53.3 1727602610,484.2421875,41.2 1727602612,484.2421875,49.8 1727602612,484.2421875,56.2 1727602612,484.2421875,48.1 1727602612,484.2421875,44.1 1727602613,484.2421875,49.8 1727602613,484.2421875,56.5 1727602613,484.2421875,48.6 1727602613,484.2421875,56.8 1727602614,484.2421875,49.8 1727602614,484.2421875,41.7 1727602614,484.2421875,53.7 1727602614,484.2421875,40.6 1727602616,484.2421875,49.7 1727602616,484.2421875,55.3 1727602616,484.2421875,49.0 1727602616,484.2421875,39.4 1727602617,484.2421875,49.7 1727602617,484.2421875,55.3 1727602617,484.2421875,49.0 1727602617,484.2421875,55.6 1727602619,484.2421875,49.7 1727602619,484.2421875,54.3 1727602619,484.2421875,48.6 1727602619,484.2421875,56.8 1727602620,484.2421875,49.8 1727602620,484.2421875,54.3 1727602620,484.2421875,48.6 1727602620,484.2421875,53.2 1727602621,484.2421875,49.7 1727602621,484.2421875,54.3 1727602621,484.2421875,47.2 1727602621,484.2421875,58.7 1727602623,484.2421875,49.8 1727602623,484.2421875,53.2 1727602623,484.2421875,49.6 1727602623,484.2421875,40.6 1727602624,484.2421875,49.8 1727602624,484.2421875,41.7 1727602624,484.2421875,50.5 1727602624,484.2421875,51.2 1727602626,484.2421875,49.8 1727602626,484.2421875,36.4 1727602626,484.2421875,53.7 1727602626,484.2421875,36.7 1727602627,484.2421875,49.8 1727602627,484.2421875,55.1 1727602627,484.2421875,47.2 1727602627,484.2421875,48.7 1727602628,484.2421875,49.7 1727602628,484.2421875,55.6 1727602628,484.2421875,51.6 1727602628,484.2421875,39.4 1727602630,484.2421875,49.7 1727602630,484.2421875,57.4 1727602630,484.2421875,48.1 1727602630,484.2421875,55.8 1727602631,484.2421875,49.7 1727602631,484.2421875,55.3 1727602631,484.2421875,48.6 1727602631,484.2421875,55.6 1727602633,484.2421875,49.7 1727602633,484.2421875,51.0 1727602633,484.2421875,51.7 1727602633,484.2421875,39.4 1727602634,484.2421875,49.8 1727602634,484.2421875,53.2 1727602634,484.2421875,50.0 1727602634,484.2421875,54.5 1727602635,484.2421875,49.7 1727602635,484.2421875,54.3 1727602635,484.2421875,48.6 1727602635,484.2421875,54.5 1727602637,484.2421875,49.8 1727602637,484.2421875,41.2 1727602637,484.2421875,53.4 1727602637,484.2421875,39.4 1727602638,484.2421875,49.8 1727602638,484.2421875,56.5 1727602638,484.2421875,48.1 1727602638,484.2421875,53.7 1727602640,484.2421875,49.7 1727602640,484.2421875,54.3 1727602640,484.2421875,48.1 1727602640,484.2421875,57.8 1727602641,484.2421875,49.7 1727602641,484.2421875,54.3 1727602641,484.2421875,49.1 1727602641,484.2421875,56.5 1727602642,484.2421875,49.7 1727602642,484.2421875,54.2 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This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
submitit INFO (2024-09-29 11:34:34,810) - Starting with JobEnvironment(job_id=2290632, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-29 11:34:34,812) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/BayesianNonparametricDetectionMethod_PokerHand/0/single_runs/2290632/2290632_submitted.pkl
Parameters: {"n_samples": 770, "const": 0.8603969216346741, "max_depth": 3, "threshold": 0.6521066069602968}
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 PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 770 const 0.8603969216346741 max_depth 3 threshold 0.6521066069602968
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: 1727601632.0
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: 1727601632.0
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 PokerHand 1000 BayesianNonparametricDetectionMethod n_samples 770 const 0.8603969216346741 max_depth 3 threshold 0.6521066069602968
stdout:
RESULT: 0.3353244090689822
OO-Info: runtime: 569
OO-Info: lpd: 0.0005253439961838974
Result: 0.3353244090689822
EXIT_CODE: 0
submitit INFO (2024-09-29 11:44:13,888) - Job completed successfully
submitit INFO (2024-09-29 11:44:13,891) - Exiting after successful completion
Job-Infos
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}
menu[role="tablist"] button {
white-space: nowrap;
min-width: 100px;
}
.container {
max-width: 100% !important;
}
.gridjs-sort {
min-width: 1px !important;
}
td.gridjs-td {
overflow: clip;
}
.title-bar-text {
font-size: 22px;
display: block ruby;
}
.title-bar {
height: fit-content;
}
.window {
width: fit-content;
min-width: 100%;
}
.top_link {
display: inline-block;
padding: 5px 5px;
background-color: #007bff; /* Blau, kannst du anpassen */
color: white;
text-decoration: none;
font-size: 16px;
font-weight: bold;
border-radius: 6px;
border: 2px solid #0056b3;
text-align: center;
transition: all 0.3s ease-in-out;
}
.top_link:hover {
background-color: #0056b3;
border-color: #004494;
}
.top_link:active {
background-color: #003366;
border-color: #002244;
}
button {
color: black;
}
.share_folder_buttons {
width: fit-content;
}
button {
background: #fcfcfe;
border-color: #919b9c;
border-top-color: rgb(145, 155, 156);
border-bottom-color: rgb(145, 155, 156);
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c;
}
button {
padding-bottom: 2px;
margin-top: -2px;
background-color: #ece9d8;
position: relative;
z-index: 8;
margin-left: -3px;
margin-bottom: 1px;
}
.window {
min-width: 1100px;
}
[role="tab"] {
padding: 10px !important;
}
[role="tabpanel"] {
min-width: fit-content;
}
select {
border: 1px solid #7f9db9;
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-variant: oldstyle-nums;
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
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: 0 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;
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 65px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
margin-top: 3px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
width: inherit;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.hide_on_mobile {
display: none;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
white-space: pre;
font-size: 2em;
}
}
.header_button {
white-space: pre;
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
input::placeholder {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
}
.gridjs-th-content {
overflow: visible !important;
}
.error_text {
color: red;
}
h1, h2, h3, h4, h5, h6 {
margin-top: 1em;
font-weight: bold;
color: #333;
border-left: 5px solid #ccc;
padding-left: 0.5em;
}
.no_cursive {
font-style: normal;
}
.caveat {
background-color: #fff8b3;
border: 1px solid #f2d600;
padding: 1em 1em 1em 70px;
position: relative;
font-family: sans-serif;
color: #665500;
margin: 1em 0;
border-radius: 4px;
}
.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
margin-top: 0;
margin-bottom: 0.5em;
font-weight: bold;
}
.caveat::before {
content: "⚠️";
font-size: 50px;
line-height: 1;
position: absolute;
left: 10px;
top: 50%;
transform: translateY(-50%);
pointer-events: none;
user-select: none;
}
.caveat.warning::before { content: "⚠️"; }
.caveat.stop::before { content: "🛑"; }
.caveat.exclamation::before { content: "❗"; }
.caveat.alarm::before { content: "🚨"; }
.caveat.tip::before { content: "💡"; }
.tutorial_icon {
display: inline-block;
font-size: 1.3em;
line-height: 1;
vertical-align: middle;
transform: translateY(-10%);
padding: 0.2em 0;
}
.highlight {
background-color: yellow;
font-weight: bold;
}
#searchResults li {
opacity: 0;
transform: translateY(8px);
animation: fadeInUp 0.3s ease-out forwards;
animation-delay: 0.05s;
list-style: none;
margin-bottom: 5px;
}
@keyframes fadeInUp {
to {
opacity: 1;
transform: translateY(0);
}
}
.search_headline {
font-weight: bold;
margin-top: 1em;
margin-bottom: 0.3em;
color: #444;
}
.search_share_path {
color: black;
display: block ruby;
margin-top: 20px;
}
@media print {
#scads_bar {
display: none !important;
}
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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 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}
: :-webkit-scrollbar-button: vertical: end{
height: 17px;
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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
menu[role=tablist] button{
background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
margin-left: -1px;
margin-right: 2px;
border-radius: 0;
border-color: #91a7b4;
border-top-right-radius: 3px;
border-top-left-radius: 3px;
padding: 0 12px 3px
}
menu[role=tablist] button: hover{
box-shadow: unset;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]{
border-color: #919b9c;
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]: first-of-type: before{
content: "";
display: block;
position: absolute;
z-index: -1;
top: 100%;
left: -1px;
height: 2px;
width: 0;
border-left: 1px solid #919b9c
}
[role=tabpanel]{
box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
}
ul.tree-view{
-webkit-font-smoothing: auto;
border: 1px solid #7f9db9;
padding: 2px 5px
}
@keyframes sliding{
0%{
transform: translateX(-30px)
}
to{
transform: translateX(100%)
}
}
progress{
box-sizing: border-box;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
height: 14px;
border: 1px solid #686868;
border-radius: 4px;
padding: 1px 2px 1px 0;
overflow: hidden;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress,progress: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px
}
progress[value]: :-webkit-progress-bar{
background-color: transparent
}
progress[value]: :-webkit-progress-value{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress[value]: :-moz-progress-bar{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-webkit-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-webkit-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]){
position: relative
}
progress: not([value]): before{
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before,progress: not([value]): before: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): after{
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): after,progress: not([value]): after: not([value]){
animation: sliding 2s linear 0s infinite
}
progress: not([value]): after: not([value]){
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-moz-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-moz-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress:not([value])::-moz-progress-bar {
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite;
}
progress:not([value])::after {
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
}
progress:not([value])::before {
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868;
}
Element {
}
progress:not([value]) {
position: relative;
}
progress:not([value]) {
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px;
}
</style>
</head>
<body>
<script>
var log = console.log;
var theme = 'light';
var special_col_names = ["trial_index","arm_name","trial_status","generation_method","generation_node","hostname","run_time","start_time","exit_code","signal","end_time","program_string"]
var result_names = [];
var result_min_max = [];
var tab_results_headers_json = [
"trial_index",
"arm_name",
"trial_status",
"generation_method",
"result",
"n_samples",
"const",
"max_depth",
"threshold"
];
var tab_results_csv_json = [
[
0,
"0_0",
"COMPLETED",
"Sobol",
0.3353244090689822,
770,
0.8603969216346741,
3,
0.6521066069602968
],
[
1,
"1_0",
"COMPLETED",
"Sobol",
0.33233478051133625,
764,
0.9216413079760969,
4,
0.6181387918069959
],
[
2,
"2_0",
"COMPLETED",
"Sobol",
0.3065364206464062,
288,
0.7275560945272446,
2,
0.4021729931235314
],
[
3,
"3_0",
"COMPLETED",
"Sobol",
0.3145224312590449,
396,
0.9653591456823051,
4,
0.7919456901028754
],
[
4,
"4_0",
"COMPLETED",
"Sobol",
0.34216353111432707,
991,
0.4095044769346714,
3,
0.608869461901486
],
[
5,
"5_0",
"COMPLETED",
"Sobol",
0.30026893391220455,
208,
0.7999990027397871,
2,
0.2509957324713469
],
[
6,
"6_0",
"COMPLETED",
"Sobol",
0.29069946936806557,
142,
0.26312147658318286,
3,
0.48900593481957916
],
[
7,
"7_0",
"COMPLETED",
"Sobol",
0.3297624216111915,
645,
0.6225879218429327,
3,
0.4474690197035671
],
[
8,
"8_0",
"COMPLETED",
"Sobol",
0.3178895320791124,
488,
0.258142252266407,
3,
0.6894698489457369
],
[
9,
"9_0",
"COMPLETED",
"Sobol",
0.31743125904486247,
416,
0.5807324868626894,
4,
0.5695470176637174
],
[
10,
"10_0",
"COMPLETED",
"Sobol",
0.2868692715870719,
123,
0.538903581444174,
2,
0.6330774001777173
],
[
11,
"11_0",
"COMPLETED",
"Sobol",
0.30886517124939705,
320,
0.3622020931914449,
2,
0.6859028333798052
],
[
12,
"12_0",
"COMPLETED",
"Sobol",
0.294678002894356,
189,
0.8842067301273346,
4,
0.7081674978137018
],
[
13,
"13_0",
"COMPLETED",
"Sobol",
0.3376507477086348,
841,
0.2974801030009985,
2,
0.5945624316111208
],
[
14,
"14_0",
"COMPLETED",
"Sobol",
0.28186324167872645,
101,
0.7099334029480815,
3,
0.564544477686286
],
[
15,
"15_0",
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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, no_height = 0) {
layout["width"] = get_graph_width();
if (!no_height) {
layout["height"] = get_graph_height();
}
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true,
ellipsis: false
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
!col.startsWith("OO_Info") &&
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) &&
!col.startsWith("OO_Info") &&
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));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#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) &&
!col.startsWith("OO_Info") &&
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;
}
if (!col.startsWith("OO_Info")) {
return;
}
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) &&
!col.startsWith("OO_Info") &&
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) &&
!col.startsWith("OO_Info") &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#tab_gpu_usage").data("loaded") === "true") {
return;
}
Object.keys(gpu_usage).forEach(node => {
const nodeData = gpu_usage[node];
var timestamps = [];
var gpuUtilizations = [];
var temperatures = [];
nodeData.forEach(entry => {
try {
var timestamp = new Date(entry[0]* 1000);
var utilization = parseFloat(entry[1]);
var temperature = parseFloat(entry[2]);
if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
timestamps.push(timestamp);
gpuUtilizations.push(utilization);
temperatures.push(temperature);
} else {
console.warn("Invalid data point:", entry);
}
} catch (error) {
console.error("Error processing GPU data entry:", error, entry);
}
});
var trace1 = {
x: timestamps,
y: gpuUtilizations,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Utilization (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: temperatures,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Temperature (°C)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'GPU Usage Over Time - ' + node,
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("GPU Utilization (%)"),
overlaying: 'y',
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("GPU Temperature (°C)"),
overlaying: 'y',
side: 'right',
position: 0.85,
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var divId = 'gpu_usage_plot_' + node;
if (!document.getElementById(divId)) {
var div = document.createElement('div');
div.id = divId;
div.className = 'gpu-usage-plot';
document.getElementById('tab_gpu_usage').appendChild(div);
}
var plotData = [trace1, trace2];
Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
});
$("#tab_gpu_usage").data("loaded", "true");
}
function plotResultsDistributionByGenerationMethod() {
if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
return;
}
var res_col = result_names[0];
var gen_method_col = "generation_node";
var data = {};
tab_results_csv_json.forEach(row => {
var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
var result = row[tab_results_headers_json.indexOf(res_col)];
if (!data[gen_method]) {
data[gen_method] = [];
}
data[gen_method].push(result);
});
var traces = Object.keys(data).map(method => {
return {
y: data[method],
type: 'box',
name: method,
boxpoints: 'outliers',
jitter: 0.5,
pointpos: 0
};
});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: get_axis_title_data("Generation Method")
},
boxmode: 'group'
};
Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
}
function plotJobStatusDistribution() {
if ($("#plotJobStatusDistribution").data("loaded") === "true") {
return;
}
var status_col = "trial_status";
var status_counts = {};
tab_results_csv_json.forEach(row => {
var status = row[tab_results_headers_json.indexOf(status_col)];
if (status) {
status_counts[status] = (status_counts[status] || 0) + 1;
}
});
var statuses = Object.keys(status_counts);
var counts = Object.values(status_counts);
var colors = statuses.map((status, i) =>
status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
);
var trace = {
x: statuses,
y: counts,
type: 'bar',
marker: { color: colors }
};
var layout = {
title: 'Distribution of Job Status',
xaxis: { title: 'Trial Status' },
yaxis: { title: 'Nr. of jobs' }
};
Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_node"]').forEach(el => {
let text = el.textContent.toLowerCase();
let color = text.includes("manual") ? "green" :
text.includes("sobol") ? "orange" :
text.includes("saasbo") ? "pink" :
text.includes("uniform") ? "lightblue" :
text.includes("legacy_gpei") ? "sienna" :
text.includes("bo_mixed") ? "aqua" :
text.includes("randomforest") ? "darkseagreen" :
text.includes("external_generator") ? "purple" :
text.includes("botorch") ? "yellow" : "";
if (color !== "") {
el.style.backgroundColor = color;
}
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" :
el.textContent.includes("ABANDONED") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
if (values.length === 0) return;
let min = Math.min(...values);
let max = Math.max(...values);
let range = max - min || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value)) return;
let ratio = (value - min) / range;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${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';
}
}
function demo_mode(nr_sec = 3) {
let i = 0;
let tabs = $('menu[role="tablist"] > button');
setInterval(() => {
tabs.attr('aria-selected', 'false').removeClass('active');
let tab = tabs.eq(i % tabs.length);
tab.attr('aria-selected', 'true').addClass('active');
tab.trigger('click');
i++;
}, nr_sec * 1000);
}
function resizePlotlyCharts() {
const plotlyElements = document.querySelectorAll('.js-plotly-plot');
if (plotlyElements.length) {
const windowWidth = window.innerWidth;
const windowHeight = window.innerHeight;
const newWidth = windowWidth * 0.9;
const newHeight = windowHeight * 0.9;
plotlyElements.forEach(function(element, index) {
const layout = {
width: newWidth,
height: newHeight,
plot_bgcolor: 'rgba(0, 0, 0, 0)',
paper_bgcolor: 'rgba(0, 0, 0, 0)',
};
Plotly.relayout(element, layout)
});
}
make_text_in_parallel_plot_nicer();
apply_theme_based_on_system_preferences();
}
function plotTimelineFromGlobals() {
if (
typeof tab_results_headers_json === "undefined" ||
typeof tab_results_csv_json === "undefined" ||
!Array.isArray(tab_results_headers_json) ||
!Array.isArray(tab_results_csv_json)
) {
console.warn("Global variables 'tab_results_headers_json' or 'tab_results_csv_json' missing or invalid.");
return null;
}
const headers = tab_results_headers_json;
const data = tab_results_csv_json;
const col = name => headers.indexOf(name);
const ix_trial_index = col("trial_index");
const ix_start_time = col("start_time");
const ix_end_time = col("end_time");
const ix_status = col("trial_status");
if ([ix_trial_index, ix_start_time, ix_end_time, ix_status].some(ix => ix === -1)) {
console.warn("One or more needed columns missing");
return null;
}
const traces = [];
// Add dummy traces for legend
traces.push({
type: "scatter",
mode: "lines",
x: [null, null],
y: [null, null],
line: { color: "green", width: 4 },
name: "COMPLETED",
showlegend: true,
hoverinfo: "none"
});
traces.push({
type: "scatter",
mode: "lines",
x: [null, null],
y: [null, null],
line: { color: "yellow", width: 4 },
name: "RUNNING",
showlegend: true,
hoverinfo: "none"
});
traces.push({
type: "scatter",
mode: "lines",
x: [null, null],
y: [null, null],
line: { color: "red", width: 4 },
name: "FAILED/OTHER",
showlegend: true,
hoverinfo: "none"
});
for (const row of data) {
const trial_index = row[ix_trial_index];
const start = row[ix_start_time];
const end = row[ix_end_time];
const status = row[ix_status];
if (
trial_index === "" || start === "" || end === "" ||
isNaN(start) || isNaN(end)
) continue;
let color = "red"; // default
if (status === "COMPLETED") color = "green";
else if (status === "RUNNING") color = "yellow";
traces.push({
type: "scatter",
mode: "lines",
x: [new Date(start * 1000), new Date(end * 1000)],
y: [trial_index, trial_index],
line: { color: color, width: 4 },
name: `Trial ${trial_index} (${status})`,
showlegend: false,
hoverinfo: "x+y+name"
});
}
if (traces.length <= 3) { // only dummy traces added
console.warn("No valid data for plotting found.");
return null;
}
const layout = {
title: "Trial Timeline",
xaxis: {
title: "Time",
type: "date"
},
yaxis: {
title: "Trial Index",
autorange: "reversed"
},
margin: { t: 50 }
};
Plotly.newPlot('plot_timeline', traces, add_default_layout_data(layout));
return true;
}
window.addEventListener('load', updatePreWidths);
window.addEventListener('resize', updatePreWidths);
$(document).ready(function() {
colorize_table_entries();
add_up_down_arrows_for_scrolling();
add_colorize_to_gridjs_table();
});
window.addEventListener('resize', function() {
resizePlotlyCharts();
});
"use strict";
function get_row_by_index(idx) {
if (!Object.keys(window).includes("tab_results_csv_json")) {
error("tab_results_csv_json is not defined");
return;
}
if (!Object.keys(window).includes("tab_results_headers_json")) {
error("tab_results_headers_json is not defined");
return;
}
var trial_index_col_idx = tab_results_headers_json.indexOf("trial_index");
if(trial_index_col_idx == -1) {
error(`"trial_index" could not be found in tab_results_headers_json. Cannot continue`);
return null;
}
for (var i = 0; i < tab_results_csv_json.length; i++) {
var row = tab_results_csv_json[i];
var trial_index = row[trial_index_col_idx];
if (trial_index == idx) {
return row;
}
}
return null;
}
function load_pareto_graph_from_idxs () {
if (!Object.keys(window).includes("pareto_idxs")) {
error("pareto_idxs is not defined");
return;
}
if (!Object.keys(window).includes("tab_results_csv_json")) {
error("tab_results_csv_json is not defined");
return;
}
if (!Object.keys(window).includes("tab_results_headers_json")) {
error("tab_results_headers_json is not defined");
return;
}
if(pareto_idxs === null) {
var err_msg = "pareto_idxs is null. Cannot plot or create tables from empty data. This can be caused by a defective <tt>pareto_idxs.json</tt> file. Please try reloading, or re-calculating the pareto-front and re-submitting if this problem persists.";
$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
return;
}
var table = get_pareto_table_data_from_idx();
var html_tables = createParetoTablesFromData(table);
$("#pareto_from_idxs_table").html(html_tables);
renderParetoFrontPlots(table);
apply_theme_based_on_system_preferences();
}
function renderParetoFrontPlots(data) {
try {
let container = document.getElementById("pareto_front_idxs_plot_container");
if (!container) {
console.error("DIV with id 'pareto_front_idxs_plot_container' not found.");
return;
}
container.innerHTML = "";
if(data === undefined || data === null) {
var err_msg = "There was an error getting the data for Pareto-Fronts. See the developer's console to see further details.";
$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
return;
}
Object.keys(data).forEach((key, idx) => {
if (!key.startsWith("Pareto front for ")) return;
let label = key.replace("Pareto front for ", "");
let [xKey, yKey] = label.split("/");
if (!xKey || !yKey) {
console.warn("Could not extract two objectives from key:", key);
return;
}
let entries = data[key];
let x = [];
let y = [];
let hoverTexts = [];
entries.forEach((entry) => {
let results = entry.results || {};
let values = entry.values || {};
let xVal = (results[xKey] || [])[0];
let yVal = (results[yKey] || [])[0];
if (xVal === undefined || yVal === undefined) {
console.warn("Missing values for", xKey, yKey, "in", entry);
return;
}
x.push(xVal);
y.push(yVal);
let hoverInfo = [];
if ("trial_index" in values) {
hoverInfo.push(`<b>Trial Index:</b> ${values.trial_index[0]}`);
}
Object.keys(values)
.filter(k => k !== "trial_index")
.sort()
.forEach(k => {
hoverInfo.push(`<b>${k}:</b> ${values[k][0]}`);
});
Object.keys(results)
.sort()
.forEach(k => {
hoverInfo.push(`<b>${k}:</b> ${results[k][0]}`);
});
hoverTexts.push(hoverInfo.join("<br>"));
});
let wrapper = document.createElement("div");
wrapper.style.marginBottom = "30px";
let titleEl = document.createElement("h3");
titleEl.textContent = `Pareto Front: ${xKey} (${getMinMaxByResultName(xKey)}) vs ${yKey} (${getMinMaxByResultName(yKey)})`;
wrapper.appendChild(titleEl);
let divId = `pareto_plot_${idx}`;
let plotDiv = document.createElement("div");
plotDiv.id = divId;
plotDiv.style.width = "100%";
plotDiv.style.height = "400px";
wrapper.appendChild(plotDiv);
container.appendChild(wrapper);
let trace = {
x: x,
y: y,
text: hoverTexts,
hoverinfo: "text",
mode: "markers",
type: "scatter",
marker: {
size: 8,
color: 'rgb(31, 119, 180)',
line: {
width: 1,
color: 'black'
}
},
name: label
};
let layout = {
xaxis: { title: { text: xKey } },
yaxis: { title: { text: yKey } },
margin: { t: 10, l: 60, r: 20, b: 50 },
hovermode: "closest",
showlegend: false
};
Plotly.newPlot(divId, [trace], add_default_layout_data(layout, 1));
});
} catch (e) {
console.error("Error while rendering Pareto front plots:", e);
}
}
function createParetoTablesFromData(data) {
try {
var container = document.createElement("div");
var parsedData;
try {
parsedData = typeof data === "string" ? JSON.parse(data) : data;
} catch (e) {
console.error("JSON parsing failed:", e);
return container;
}
for (var sectionTitle in parsedData) {
if (!parsedData.hasOwnProperty(sectionTitle)) {
continue;
}
var sectionData = parsedData[sectionTitle];
var heading = document.createElement("h2");
heading.textContent = sectionTitle;
container.appendChild(heading);
var table = document.createElement("table");
table.style.borderCollapse = "collapse";
table.style.marginBottom = "2em";
table.style.width = "100%";
var thead = document.createElement("thead");
var headerRow = document.createElement("tr");
var allValueKeys = new Set();
var allResultKeys = new Set();
sectionData.forEach(entry => {
var values = entry.values || {};
var results = entry.results || {};
Object.keys(values).forEach(key => {
allValueKeys.add(key);
});
Object.keys(results).forEach(key => {
allResultKeys.add(key);
});
});
var sortedValueKeys = Array.from(allValueKeys).sort();
var sortedResultKeys = Array.from(allResultKeys).sort();
if (sortedValueKeys.includes("trial_index")) {
sortedValueKeys = sortedValueKeys.filter(k => k !== "trial_index");
sortedValueKeys.unshift("trial_index");
}
var allColumns = [...sortedValueKeys, ...sortedResultKeys];
allColumns.forEach(col => {
var th = document.createElement("th");
th.textContent = col;
th.style.border = "1px solid black";
th.style.padding = "4px";
headerRow.appendChild(th);
});
thead.appendChild(headerRow);
table.appendChild(thead);
var tbody = document.createElement("tbody");
sectionData.forEach(entry => {
var tr = document.createElement("tr");
allColumns.forEach(col => {
var td = document.createElement("td");
td.style.border = "1px solid black";
td.style.padding = "4px";
var value = null;
if (col in entry.values) {
value = entry.values[col];
} else if (col in entry.results) {
value = entry.results[col];
}
if (Array.isArray(value)) {
td.textContent = value.join(", ");
} else {
td.textContent = value !== null && value !== undefined ? value : "";
}
tr.appendChild(td);
});
tbody.appendChild(tr);
});
table.appendChild(tbody);
container.appendChild(table);
}
return container;
} catch (err) {
console.error("Unexpected error:", err);
var errorDiv = document.createElement("div");
errorDiv.textContent = "Error generating tables.";
return errorDiv;
}
}
function get_pareto_table_data_from_idx () {
if (!Object.keys(window).includes("pareto_idxs")) {
error("pareto_idxs is not defined");
return;
}
if (!Object.keys(window).includes("tab_results_csv_json")) {
error("tab_results_csv_json is not defined");
return;
}
if (!Object.keys(window).includes("tab_results_headers_json")) {
error("tab_results_headers_json is not defined");
return;
}
var x_keys = Object.keys(pareto_idxs);
var tables = {};
for (var i = 0; i < x_keys.length; i++) {
var x_key = x_keys[i];
var y_keys = Object.keys(pareto_idxs[x_key]);
for (var j = 0; j < y_keys.length; j++) {
var y_key = y_keys[j];
var indices = pareto_idxs[x_key][y_key];
for (var k = 0; k < indices.length; k++) {
var idx = indices[k];
var row = get_row_by_index(idx);
if(row === null) {
error(`Error getting the row for index ${idx}`);
return;
}
var row_dict = {
"results": {},
"values": {},
};
for (var l = 0; l < tab_results_headers_json.length; l++) {
var header = tab_results_headers_json[l];
if (!special_col_names.includes(header) || header == "trial_index") {
var val = row[l];
if (result_names.includes(header)) {
if (!Object.keys(row_dict["results"]).includes(header)) {
row_dict["results"][header] = [];
}
row_dict["results"][header].push(val);
} else {
if (!Object.keys(row_dict["values"]).includes(header)) {
row_dict["values"][header] = [];
}
row_dict["values"][header].push(val);
}
}
}
var table_key = `Pareto front for ${x_key}/${y_key}`;
if(!Object.keys(tables).includes(table_key)) {
tables[table_key] = [];
}
tables[table_key].push(row_dict);
}
}
}
return tables;
}
function getMinMaxByResultName(resultName) {
try {
if (typeof resultName !== "string") {
error("Parameter resultName must be a string");
return;
}
if (!Array.isArray(result_names)) {
error("Global variable result_names is not an array or undefined");
return;
}
if (!Array.isArray(result_min_max)) {
error("Global variable result_min_max is not an array or undefined");
return;
}
if (result_names.length !== result_min_max.length) {
error("Global arrays result_names and result_min_max must have the same length");
return;
}
var index = result_names.indexOf(resultName);
if (index === -1) {
error("Result name '" + resultName + "' not found in result_names");
return;
}
var minMaxValue = result_min_max[index];
if (minMaxValue !== "min" && minMaxValue !== "max") {
error("Value for result name '" + resultName + "' is invalid: expected 'min' or 'max'");
return;
}
return minMaxValue;
} catch (e) {
error("Unexpected error: " + e.message);
}
}
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>const</th><th>max_depth</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 100</td><td>0.241459</td><td>4</td><td>0.799933</td><td>0.280321 </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> const</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> max_depth</td><td>range</td><td>2</td><td>4</td><td></td><td>int </td></tr><tr><td> threshold</td><td>range</td><td>0.2</td><td>0.8</td><td></td><td>float </td></tr></tbody></table><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>496</td>
<td>5</td>
<td>501</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,const,max_depth,threshold
0,0_0,COMPLETED,Sobol,0.335324409068982198789399262751,770,0.860396921634674072265625000000,3,0.652106606960296764086137955019
1,1_0,COMPLETED,Sobol,0.332334780511336247421638745436,764,0.921641307976096890719475140941,4,0.618138791806995935296242805634
2,2_0,COMPLETED,Sobol,0.306536420646406182477505808492,288,0.727556094527244567871093750000,2,0.402172993123531385961655360006
3,3_0,COMPLETED,Sobol,0.314522431259044887852382998972,396,0.965359145682305075375495562184,4,0.791945690102875410332217143150
4,4_0,COMPLETED,Sobol,0.342163531114327068571867584978,991,0.409504476934671401977539062500,3,0.608869461901486053179155533144
5,5_0,COMPLETED,Sobol,0.300268933912204549763202976465,208,0.799999002739787079541144976247,2,0.250995732471346877368034711253
6,6_0,COMPLETED,Sobol,0.290699469368065566143854994152,142,0.263121476583182856145981531881,3,0.489005934819579157757374332505
7,7_0,COMPLETED,Sobol,0.329762421611191514081440345763,645,0.622587921842932656701918858744,3,0.447469019703567116863496266888
8,8_0,COMPLETED,Sobol,0.317889532079112413498478417750,488,0.258142252266407024041683371252,3,0.689469848945736929479721766256
9,9_0,COMPLETED,Sobol,0.317431259044862468243763942155,416,0.580732486862689412099314267834,4,0.569547017663717403124223892519
10,10_0,COMPLETED,Sobol,0.286869271587071872353646995180,123,0.538903581444174051284790039062,2,0.633077400177717297680146657513
11,11_0,COMPLETED,Sobol,0.308865171249397052477547731542,320,0.362202093191444918218735438131,2,0.685902833379805176861054860638
12,12_0,COMPLETED,Sobol,0.294678002894355972607343119307,189,0.884206730127334639135483485006,4,0.708167497813701807274355815025
13,13_0,COMPLETED,Sobol,0.337650747708634790811288439727,841,0.297480103000998474804816851247,2,0.594562431611120767449563118134
14,14_0,COMPLETED,Sobol,0.281863241678726450523129187786,101,0.709933402948081493377685546875,3,0.564544477686286017004135828756
15,15_0,COMPLETED,Sobol,0.336524360829715374343606981711,892,0.496680196840316057205200195312,3,0.289846991375088736120346766256
16,16_0,COMPLETED,Sobol,0.329694886637723061362237331196,608,0.536728464160114548953117719066,2,0.638166392780840530107866470644
17,17_0,COMPLETED,Sobol,0.326584659913169317313474948605,508,0.331185810174792982785163530934,2,0.470261613838374670226727403133
18,18_0,COMPLETED,Sobol,0.326427882296189131317021292489,540,0.958295811433345079421997070312,2,0.552558417618274733129624110006
19,19_0,COMPLETED,Sobol,0.325479980704293292426143580087,577,0.128723281808197503872648326251,4,0.522611335478723182390581314394
20,20_0,COMPLETED,BoTorch,0.281495417269657544068195420550,100,0.609321452868050839057900702755,3,0.681998001668484343795739732741
21,21_0,COMPLETED,BoTorch,0.282891944042450549368084011803,100,0.916864257433731899560314104747,3,0.602872682306803686635987560294
22,22_0,COMPLETED,BoTorch,0.281875301495417285302380605572,100,0.658374398297795204726412521268,3,0.518329805574232138631884936331
23,23_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.960510616683919238312228117138,2,0.612045294538625550551103060570
24,24_0,COMPLETED,BoTorch,0.281360347322720749652091853932,100,0.427829048344285589600133334898,3,0.800000000000000044408920985006
25,25_0,COMPLETED,BoTorch,0.282404727448142822687771058554,100,1.000000000000000000000000000000,3,0.512728388879016172552383068250
26,26_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.815513261488516105757184959657,3,0.756039753695846972192384782829
27,27_0,COMPLETED,BoTorch,0.281499035214664683479668383370,100,0.591968372613341564658639981644,3,0.601047660377892478678063525876
28,28_0,COMPLETED,BoTorch,0.280465508924264361745315454755,100,0.826218872883353050440291553969,4,0.396813813761074718478027989477
29,29_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.934889191570958844046401736705,2,0.800000000000000044408920985006
30,30_0,COMPLETED,BoTorch,0.280631934394597215565170245100,100,0.638608405299725290227286222944,4,0.500001793164955854464892581746
31,31_0,COMPLETED,BoTorch,0.282613362276893376190400886117,100,1.000000000000000000000000000000,3,0.669386791361630484331612933602
32,32_0,COMPLETED,BoTorch,0.281459237819585150752743629710,100,0.451297391615531506836589414888,4,0.800000000000000044408920985006
33,33_0,COMPLETED,BoTorch,0.281081765557163576474408728245,100,0.520289513378968782042477414507,3,0.755362847326083164389842750097
34,34_0,COMPLETED,BoTorch,0.281564158224794969243021114380,100,0.743092763907577502635604105308,3,0.759006378674263348926842809306
35,35_0,COMPLETED,BoTorch,0.282099614085865924018037276255,100,0.821277516312874156234613565175,3,0.498921533968188113394859328764
36,36_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.300650907567350744731271561250,3,0.737926794124446283618112829572
37,37_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241417225272304747374363387280,4,0.800000000000000044408920985006
38,38_0,COMPLETED,BoTorch,0.282976362759286059755936548754,100,0.181338553898054033464148915300,4,0.732373298136487660414672973275
39,39_0,COMPLETED,BoTorch,0.281845151953690309376554523624,100,0.736124375906407446557011553523,3,0.636220306295257609363602568919
40,40_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.936474834510386489050404179579,2,0.580857640703601130205413483054
41,41_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.745025148682936233512918988708
42,42_0,COMPLETED,BoTorch,0.282770139893873562186854542233,100,0.967592878961811897475797650259,3,0.721662735594759796597941203800
43,43_0,COMPLETED,BoTorch,0.283180173661360390369168271718,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
44,44_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.756360610252465703773339100735,2,0.800000000000000044408920985006
45,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
46,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
47,47_0,COMPLETED,BoTorch,0.280959961408586589293179258675,100,0.423004997172923613568684686470,4,0.613820130220930071374141334672
48,48_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.497438345472716703099536061927
49,49_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.712853337433981826265494419204
50,50_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.778809502403451592300598349539
51,51_0,COMPLETED,BoTorch,0.308610709117221437836064978910,291,1.000000000000000000000000000000,4,0.736225801117658562588985660113
52,52_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.672758683116077893515694086091
53,53_0,COMPLETED,BoTorch,0.293175349734684065339251901605,178,0.350532550453271474211192071380,4,0.672077814302944442914622413809
54,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
55,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
56,56_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.736860280736264083856212891988
57,57_0,COMPLETED,BoTorch,0.281918716835504068463080784568,100,0.424174431409560570571670723439,4,0.200000000000000011102230246252
58,58_0,COMPLETED,BoTorch,0.283689097925711508629831314465,100,1.000000000000000000000000000000,4,0.249768132809647047665180252807
59,59_0,COMPLETED,BoTorch,0.280510130246020228383940775529,100,0.685293236834761954057171351451,4,0.200000000000000011102230246252
60,60_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237190732898929723670988778395,3,0.800000000000000044408920985006
61,61_0,COMPLETED,BoTorch,0.282176796912686933538338962535,100,1.000000000000000000000000000000,4,0.396959911196361814322841610192
62,62_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.859361726238822343226786415471,2,0.732696036882744206053530433564
63,63_0,COMPLETED,BoTorch,0.281458031837916067274818487931,100,1.000000000000000000000000000000,3,0.244159263939566606449460550721
64,64_0,COMPLETED,BoTorch,0.292707428847081563283438754297,139,0.142601069644926164992071448978,4,0.305300559641144664269063468964
65,65_0,COMPLETED,BoTorch,0.280762180414857676069573244604,100,0.725144753088975746457833793102,4,0.314615323184019690661017421007
66,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
67,67_0,COMPLETED,BoTorch,0.294530873130728454434290597419,159,0.280586955431305740127356784797,4,0.247870311166896950538784949458
68,68_0,COMPLETED,BoTorch,0.280893632416787220051901385887,100,0.963790446769895337020273018425,4,0.477260939328049971219769531672
69,69_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.613084317228246300501837140473
70,70_0,COMPLETED,BoTorch,0.281099855282199717620983392408,100,0.751499880482005333526274171163,4,0.299180814121010718587001520063
71,43_0,COMPLETED,BoTorch,0.283180173661360390369168271718,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
72,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
73,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252
74,74_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.281167377959501085804561171244
75,75_0,COMPLETED,BoTorch,0.283259768451519566845320241555,100,0.921460015735613757748012631055,4,0.200000000000000011102230246252
76,76_0,COMPLETED,BoTorch,0.286892185238784347411922226456,125,0.122797158464674788236337121816,2,0.413839506380008159691641367317
77,77_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.589154859217490334266642548755,4,0.367861167533371835602906685381
78,78_0,COMPLETED,BoTorch,0.289292088760250809542640126892,151,0.404694710294062010014215502451,3,0.566854084895249688003104893141
79,79_0,COMPLETED,BoTorch,0.289357211770381095305992857902,147,0.600271114652549120371816115949,2,0.264892347300024755263336828648
80,80_0,COMPLETED,BoTorch,0.281144476603955584259608713182,100,0.613804671280130054178414411581,4,0.321506352084691693349327579199
81,81_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.283267553512524083991763745871
82,82_0,COMPLETED,BoTorch,0.281425470332850924393142122426,100,0.835649097619452163598907645792,4,0.800000000000000044408920985006
83,83_0,COMPLETED,BoTorch,0.281669078630004787733298599051,100,0.636749496464390607464167715079,4,0.327822098774013193089871265329
84,84_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200639529787448450681708322918
85,85_0,COMPLETED,BoTorch,0.281127592860588526590959190798,100,0.685344680141829476482939753623,4,0.259065354137725578631545886310
86,86_0,COMPLETED,BoTorch,0.290693439459720259776531747775,153,0.222805060878411675950871995155,4,0.590236678366413469731810437224
87,87_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.664428766986098984226316588320,2,0.200000000000000011102230246252
88,88_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.486769040665189500760590135542
89,89_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
90,90_0,COMPLETED,BoTorch,0.281234925229136512037086959026,100,0.386834572431698164862723388069,3,0.200000000000000011102230246252
91,91_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.336861597465170303777881599672,2,0.200000000000000011102230246252
92,92_0,COMPLETED,BoTorch,0.288061987457790658062606325984,128,0.627951237362264547137158388068,2,0.730747205408294187023443555518
93,93_0,COMPLETED,BoTorch,0.281484563434635792766869144543,100,0.597232767296283273417145665007,3,0.200000000000000011102230246252
94,94_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.542488690409693719018946467259
95,95_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.536290908246188302577195372578,2,0.200000000000000011102230246252
96,89_0,COMPLETED,BoTorch,0.281183068017366144530910787580,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
97,97_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.483373978732424824933389118087,2,0.200000000000000011102230246252
98,98_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.613342940689883264759885150852
99,99_0,COMPLETED,BoTorch,0.291619633381572596064756908163,152,0.224044626457482520809350035051,2,0.373370982527880257428876120684
100,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252
101,101_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.562034653035851317071092125843
102,102_0,COMPLETED,BoTorch,0.281213217559093120456736869528,100,0.970674418385605863868192955124,4,0.800000000000000044408920985006
103,103_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.425304332013971719828049344869
104,104_0,COMPLETED,BoTorch,0.283771104679208852061833567859,106,0.806552529066070666985410753114,3,0.623962608083840075678949688154
105,105_0,COMPLETED,BoTorch,0.281859623733719200089353762451,100,0.796904512163817413394895083911,3,0.332810063799171995668757517706
106,106_0,COMPLETED,BoTorch,0.307407139411480900825779372099,301,1.000000000000000000000000000000,4,0.800000000000000044408920985006
107,107_0,COMPLETED,BoTorch,0.297351664254703274004043578316,193,0.856877315870694267196938653797,4,0.620863983942546604488654793386
108,108_0,COMPLETED,BoTorch,0.281725759768451489151175337611,100,0.764708521252592032801942423248,4,0.601237841216216817308293229871
109,109_0,COMPLETED,BoTorch,0.281719729860106071761549628718,100,0.761062806205437869522256733035,4,0.589768805844644528058040577889
110,110_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.432520750019955324550124942107
111,111_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.626746313255761355875961271522
112,112_0,COMPLETED,BoTorch,0.281383260974433224710367085208,100,0.763807139264614187723623217607,4,0.585539488869372903856458378868
113,113_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.992109520789691834430357175734,2,0.686230971566745395762154657859
114,114_0,COMPLETED,BoTorch,0.281505065123010100869294092263,100,0.758347309919061074445778558584,4,0.583823773272484025653739081463
115,115_0,COMPLETED,BoTorch,0.285226724553786836757751643745,129,0.497986380238492731820088010863,2,0.800000000000000044408920985006
116,116_0,COMPLETED,BoTorch,0.281431500241196341782767831319,100,0.760786013273364702946821580554,4,0.583973937459685732420666681719
117,117_0,COMPLETED,BoTorch,0.281431500241196341782767831319,100,0.761765009526143677121012842690,4,0.585339075287317767148920211184
118,118_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.646128629360638639944625083444
119,119_0,COMPLETED,BoTorch,0.281530390738060742883419607097,100,0.761483785514172728525750244444,4,0.587471255609660736318744511664
120,120_0,COMPLETED,BoTorch,0.281383260974433224710367085208,100,0.761572241101902025484093883279,4,0.587140205914607249404468802823
121,121_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.956035714961293470182113196643,2,0.691248595321693581894351154915
122,122_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.979301169797443638032063972787,2,0.687755464536077165682570466743
123,123_0,COMPLETED,BoTorch,0.281099855282199717620983392408,100,0.763443554778661548354534716054,4,0.575816919533898352590028935083
124,124_0,COMPLETED,BoTorch,0.281547274481427911574371591996,100,0.764000505055555501066066881322,4,0.588869514431734630832693255797
125,125_0,COMPLETED,BoTorch,0.281511095031355518258919801156,100,0.758651080169266522901239113708,4,0.589415454505437197418871164700
126,126_0,COMPLETED,BoTorch,0.280510130246020228383940775529,100,0.707413678387941557978990658739,4,0.263397108697904391849675675985
127,127_0,COMPLETED,BoTorch,0.296694404245055443070100409386,191,0.732888648318413271631754923874,4,0.678138498042447634972518244467
128,128_0,COMPLETED,BoTorch,0.281517124939700935648545510048,100,0.759633947467053149971150105557,4,0.594096065027543973613433081482
129,129_0,COMPLETED,BoTorch,0.281708876025084431482525815227,100,0.772941140878985355477936991520,4,0.575762446500667657289795897668
130,130_0,COMPLETED,BoTorch,0.281357935359382582696241570375,100,0.999999999903765646180886506045,2,0.336828505003928913019706214982
131,131_0,COMPLETED,BoTorch,0.282821997105644040715333176195,100,0.850839362026055145982184058084,3,0.200000000000000011102230246252
132,132_0,COMPLETED,BoTorch,0.281285576459237796065337988693,100,0.864067689188233956087970000226,4,0.800000000000000044408920985006
133,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
134,134_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.663381660957313323834227958287
135,135_0,COMPLETED,BoTorch,0.289357211770381095305992857902,147,0.625447231970156947156169735536,2,0.200000000000000011102230246252
136,136_0,COMPLETED,BoTorch,0.308977327544621371835376066883,293,0.752633022318367705949526680342,3,0.200000000000000011102230246252
137,137_0,COMPLETED,BoTorch,0.287275687409551339079882836813,122,0.100000000000000005551115123126,2,0.541290418842662379717012299807
138,138_0,COMPLETED,BoTorch,0.282079112397491504893309866020,100,0.769227631307396908511009314680,4,0.482124334895646711185435151492
139,139_0,COMPLETED,BoTorch,0.298818137964302898978985467693,193,0.791687875623532932856107890984,4,0.721277801968501774965147887997
140,140_0,COMPLETED,BoTorch,0.297937771345875512807310769858,190,0.100000000000000005551115123126,2,0.800000000000000044408920985006
141,141_0,COMPLETED,BoTorch,0.300268933912204549763202976465,208,0.100000000000000005551115123126,2,0.800000000000000044408920985006
142,142_0,COMPLETED,BoTorch,0.295141099855282251773758162017,186,0.297017942879134799749607509511,3,0.800000000000000044408920985006
143,143_0,COMPLETED,BoTorch,0.308977327544621371835376066883,293,0.796229089700815850783044425043,2,0.351845961487870728667815001245
144,144_0,COMPLETED,BoTorch,0.288931500241196292932954747812,151,0.141069354642069239025303772905,2,0.590157209553597406070934994204
145,145_0,COMPLETED,BoTorch,0.282519295706705309001449677453,100,0.583591861538787282093210251332,4,0.585931117182778127272513302159
146,146_0,COMPLETED,BoTorch,0.289969850458273059601310706057,135,0.100000000000000005551115123126,4,0.800000000000000044408920985006
147,147_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.527646702591519467517855446204
148,148_0,COMPLETED,BoTorch,0.343461167390255672771104400454,1000,1.000000000000000000000000000000,4,0.800000000000000044408920985006
149,149_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.312585537543322633524667253369,2,0.800000000000000044408920985006
150,150_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.356912916209194741590238209028,2,0.403722327859890262047315445670
151,151_0,COMPLETED,BoTorch,0.298036661842740024930265008152,210,0.100000000000000005551115123126,4,0.800000000000000044408920985006
152,152_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.179399167858704799982660915703,3,0.719124564657684928192793449853
153,153_0,COMPLETED,BoTorch,0.295422093584177480884989108745,172,0.402403803677889637668840805418,4,0.565588693420973398495732453739
154,154_0,COMPLETED,BoTorch,0.296595513748191041969448633608,187,0.308712443362756494558141184825,2,0.614905852238070149695658983546
155,155_0,COMPLETED,BoTorch,0.288061987457790658062606325984,128,0.692484011662490406280312527088,2,0.733804207356910875859057341586
156,156_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.341440062150349166181229065842
157,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
158,158_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.413666576897602000784104347986,2,0.200000000000000011102230246252
159,159_0,COMPLETED,BoTorch,0.344202846116739014092900106334,992,0.362502074179377853369032891351,3,0.590927285417441550130490668380
160,160_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.216256696673561010779707203255,2,0.200000000000000011102230246252
161,161_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.257378545865637464729047678702,2,0.489471199348137375739753451853
162,162_0,COMPLETED,BoTorch,0.282635069946936767770750975615,100,0.621703206241048467184384662687,4,0.800000000000000044408920985006
163,163_0,COMPLETED,BoTorch,0.284050892426435108717441835324,104,0.348617984923893065030142679461,3,0.416746088186085161808591692534
164,164_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.924691595571182078927563452453,2,0.677674658077331182326474845468
165,165_0,COMPLETED,BoTorch,0.290050651230101319555387817672,144,1.000000000000000000000000000000,4,0.800000000000000044408920985006
166,166_0,COMPLETED,BoTorch,0.283656536420646365748154948960,119,1.000000000000000000000000000000,4,0.800000000000000044408920985006
167,167_0,COMPLETED,BoTorch,0.312005547515677772452136196080,303,0.240065488202822724383977970319,2,0.308074847560504239130096948429
168,168_0,COMPLETED,BoTorch,0.293486493005306270376308930281,196,0.805241348136261136936298044020,4,0.665151427294484398622387288924
169,169_0,COMPLETED,BoTorch,0.286649782923299567549690891610,124,0.880859543457893634510469382803,4,0.725871286748837052371641220816
170,170_0,COMPLETED,BoTorch,0.290756150506512267561731732712,134,0.686723816288070443292212985398,2,0.621855436311953391204099261813
171,171_0,COMPLETED,BoTorch,0.294649059334298080159442179138,162,1.000000000000000000000000000000,2,0.800000000000000044408920985006
172,172_0,COMPLETED,BoTorch,0.289512783405692197824521372240,141,0.755763226276860211605423955916,4,0.576149589739470835425549921638
173,173_0,COMPLETED,BoTorch,0.298479257115291884971952640626,174,0.835755180937133634877511667582,2,0.653303828079771831127686709806
174,174_0,RUNNING,BoTorch,,196,0.846472154190134995488392632979,4,0.671230032564440026732199839898
175,175_0,COMPLETED,BoTorch,0.295306319343946022115687810583,183,0.862411497078117705861188824201,2,0.266482041910973854736965904522
176,176_0,COMPLETED,BoTorch,0.290261698022190040013867928792,140,0.958830288424676435710125588230,3,0.646196299362590931281147277332
177,177_0,COMPLETED,BoTorch,0.288402074288470866569866757345,135,1.000000000000000000000000000000,4,0.200000000000000011102230246252
178,178_0,COMPLETED,BoTorch,0.291585865894838369705155400879,158,0.100000000000000005551115123126,4,0.215964072133795370600495289182
179,179_0,COMPLETED,BoTorch,0.286520742884708190523213033885,122,0.966225189864108768844630503736,4,0.754136680482391907887063098315
180,180_0,COMPLETED,BoTorch,0.285855041003376775243793872505,129,0.991745606669502177688002575451,4,0.200000000000000011102230246252
181,181_0,COMPLETED,BoTorch,0.288309213699951771836538227944,120,0.567853852172886974791765624104,2,0.766294260111517688827120764472
182,182_0,COMPLETED,BoTorch,0.291585865894838369705155400879,158,0.109133940135581863906111266260,4,0.213251008893593108073361008792
183,183_0,COMPLETED,BoTorch,0.298656536420646379070831244462,201,0.804966247471551543668510930729,3,0.694251561773776559860493762244
184,184_0,COMPLETED,BoTorch,0.289686444766039552511927013256,136,0.825278487845191421889978755644,4,0.755411197320168259494721496594
185,185_0,COMPLETED,BoTorch,0.287342016401350708321160709602,132,0.875138227399023471697603326902,2,0.483428504425656824228241248420
186,186_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.903546926302124409069449484377,2,0.674320702735722088405623253493
187,187_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.896050517688234071655983825622,2,0.673895512275354158227003154025
188,188_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.892678078944392994209522385063,2,0.676147998562335761363328856532
189,189_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.802858457916524992370455038326,2,0.800000000000000044408920985006
190,190_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.890338273437926264186614844220,2,0.800000000000000044408920985006
191,191_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.633807978920846393222632286779,2,0.200000000000000011102230246252
192,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
193,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
194,194_0,COMPLETED,BoTorch,0.283748191027496377003558336583,106,0.842385685228827618686864298070,3,0.746108818889442249044918753498
195,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
196,196_0,COMPLETED,BoTorch,0.282759286058851921907830728742,100,0.828384882570699865311780740740,3,0.232606379828846965285293890702
197,197_0,COMPLETED,BoTorch,0.289756391702846172186980311380,144,0.648820977258636411022507672897,3,0.359140510001706936282772630875
198,198_0,COMPLETED,BoTorch,0.293654124457308207674088862404,164,0.931122701007022102182020262262,2,0.619400872920816025768431245524
199,199_0,COMPLETED,BoTorch,0.288803666184273999384402031865,148,0.613921533114687889565175282769,4,0.295801448398209765322519615438
200,200_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.790346793803923364230001880060,2,0.329806698136218656713936070446
201,201_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.477832850297235045555765964309
202,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
203,203_0,COMPLETED,BoTorch,0.280622286541244547741769110871,100,0.100000000000000005551115123126,4,0.478157063056679654255987088618
204,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
205,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
206,45_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
207,207_0,COMPLETED,BoTorch,0.297071876507477128370737773366,179,0.725234931272955130054924666183,4,0.790359520829561290611309232190
208,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
209,209_0,COMPLETED,BoTorch,0.281045586107091183158956937405,100,0.100000000000000005551115123126,4,0.425430027905785990149922781711
210,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
211,211_0,COMPLETED,BoTorch,0.281849975880366643288255090738,100,0.534422713022195128296232269349,3,0.281666505650945975247623209725
212,212_0,COMPLETED,BoTorch,0.295141099855282251773758162017,186,1.000000000000000000000000000000,2,0.243897113123416442448387897457
213,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
214,214_0,COMPLETED,BoTorch,0.283894114809454922720988179208,102,0.853847788821290087923898681765,3,0.669268090225853540253808660054
215,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
216,216_0,COMPLETED,BoTorch,0.284252291365171272374823274731,104,0.693454476070416436783716562786,2,0.565582385302276668959109429125
217,217_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.639511969747473152381189720472,2,0.322570601108127852363338661235
218,218_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.694032745577860099572831131809
219,219_0,COMPLETED,BoTorch,0.299260733236854759020673100167,196,1.000000000000000000000000000000,4,0.441932317499453386488994510728
220,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
221,221_0,COMPLETED,BoTorch,0.292029667149059313224768175132,161,1.000000000000000000000000000000,2,0.351487777975540871544524179626
222,222_0,COMPLETED,BoTorch,0.283317655571635351741122121894,100,0.368313527455379374764277145005,4,0.638261415881231419788832681661
223,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
224,224_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.961772034136499254231011946104,2,0.675451569484452951641628715151
225,225_0,COMPLETED,BoTorch,0.309821514712976364691598973877,313,0.290782173388597686702894407063,2,0.415311527434303273675908485529
226,226_0,COMPLETED,BoTorch,0.294552580800771846014640686917,167,0.765052246834325933022569188324,3,0.291337422741349494348384041587
227,227_0,COMPLETED,BoTorch,0.301274722624216062527580106689,264,0.714139358072574226277140496677,3,0.317561610644479130893813589864
228,228_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.946893960524655242672054100694,2,0.681764546498261125861972686835
229,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
230,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
231,231_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.945825682272717571663633862045,2,0.679525122740896492246065463405
232,232_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.939258035279958991559112746472,2,0.679920381057878442376818384218
233,233_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.336847463124218260421827153550,2,0.800000000000000044408920985006
234,234_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.746775983995577874452465039212,2,0.200000000000000011102230246252
235,235_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.706287288632600041005105140357,2,0.200000000000000011102230246252
236,236_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.294886917977688289838056334702,2,0.800000000000000044408920985006
237,237_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.347235553730743107259115731722,2,0.800000000000000044408920985006
238,238_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.691808413293654167475210670091,2,0.200000000000000011102230246252
239,239_0,RUNNING,BoTorch,,100,0.720201404691578783712202493916,2,0.200000000000000011102230246252
240,240_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.684760623974637550048782941303,2,0.200000000000000011102230246252
241,241_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.324187818068531530268927554062,2,0.800000000000000044408920985006
242,242_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.711099142693777608315031102393,2,0.200000000000000011102230246252
243,243_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.720356768618747977939165139105,2,0.200000000000000011102230246252
244,244_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.318190746164397519901712030332,2,0.800000000000000044408920985006
245,245_0,COMPLETED,BoTorch,0.292766521948866431657165776414,168,0.720223951753498958794352802215,4,0.200000000000000011102230246252
246,246_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.309451821798491122361696170628,2,0.800000000000000044408920985006
247,247_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.939416887099215225376269700064,2,0.800000000000000044408920985006
248,248_0,COMPLETED,BoTorch,0.290868306801736586919560068054,156,0.484304986466263409639054771105,3,0.264135348699061189847725472646
249,249_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.720324271547412187111092407577,2,0.200000000000000011102230246252
250,250_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.724377764276417757116632856196,2,0.200000000000000011102230246252
251,251_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.318522557795787752965566141938,2,0.800000000000000044408920985006
252,252_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.634339566338044447135757764045,2,0.363977600261018885419161961181
253,253_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.254328915742473404826284877345,2,0.800000000000000044408920985006
254,254_0,COMPLETED,BoTorch,0.282645923781958519072077251622,105,0.713946342586330162660601672542,3,0.467432476056995060975651767876
255,255_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.729984710096583611083076448267,2,0.200000000000000011102230246252
256,256_0,COMPLETED,BoTorch,0.310973227206946423173405946727,338,0.172470265068113803863525390625,2,0.401089510880410671234130859375
257,257_0,COMPLETED,BoTorch,0.286449589966232487370234593982,113,0.582232419247471488787937232701,2,0.438017121978734880194394918362
258,258_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.945234005818557432299087395222,2,0.778277176306792828341940548853
259,259_0,COMPLETED,BoTorch,0.341046792088760208905284798675,920,0.272794999930265458232270248118,3,0.358591073258661130473967659782
260,260_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.243928649865581076428000528722,2,0.800000000000000044408920985006
261,261_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237333160073314092253227158835,2,0.800000000000000044408920985006
262,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
263,263_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.369441418842299418301422520017
264,264_0,COMPLETED,BoTorch,0.295946695610226684358678994613,191,0.381868200409442581921837245318,3,0.236714764484911577291370576859
265,265_0,COMPLETED,BoTorch,0.293709599614085825614040459186,173,0.136156818069431528872570424937,3,0.200000000000000011102230246252
266,266_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.239537885141219458740025061161,2,0.800000000000000044408920985006
267,267_0,COMPLETED,BoTorch,0.290151953690303887611889877007,144,0.311371242452635144104533537757,3,0.360182827417835027539183556655
268,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
269,269_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.237934382072108790984188431139,2,0.800000000000000044408920985006
270,270_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.731299265689706268211978112959,2,0.200000000000000011102230246252
271,271_0,COMPLETED,BoTorch,0.281279546550892378675712279801,100,0.240943779144106723366647315743,4,0.566295000121245561963689851837
272,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
273,273_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.236613375869878073132213103236,2,0.800000000000000044408920985006
274,274_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241752280893886728474484471008,4,0.799469125269815172885046195006
275,275_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.353826005526554465685507011585
276,276_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.418757746982561629423003068950,2,0.200000000000000011102230246252
277,277_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.759139117987122746811223805707,2,0.200000000000000011102230246252
278,278_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.753076537975211790509888487577,2,0.200000000000000011102230246252
279,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
280,280_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.396208080228131254152401652391,2,0.800000000000000044408920985006
281,281_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.942306761852117813482720976026,2,0.772063884130549160644818584842
282,282_0,COMPLETED,BoTorch,0.307311866859623750158903021656,271,0.601605985652138963715174213576,2,0.365474716273138933164688069155
283,283_0,COMPLETED,BoTorch,0.281291606367583213454963697586,101,0.942016719363089172212255562044,3,0.578064016110590772612454202317
284,284_0,COMPLETED,BoTorch,0.280320791123974899505810753908,100,0.241459008415108811185234571894,4,0.799932660394794048386302165454
285,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
286,286_0,COMPLETED,BoTorch,0.281679932465026539034624875057,100,0.195967177747737447690568046710,4,0.538464591568163064572161147225
287,287_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.818810163660800394147543102008,2,0.255684039995552003787793182710
288,46_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
289,289_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.394239944425048838283487384615,2,0.800000000000000044408920985006
290,290_0,COMPLETED,BoTorch,0.301541244573082511948314277106,231,0.223921546235003099400984183376,4,0.607881632743191624435041831020
291,291_0,COMPLETED,BoTorch,0.294649059334298080159442179138,162,0.389297407551159935223950014915,2,0.551354561083939209709114948055
292,292_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.756909675226253408908405617694,2,0.200000000000000011102230246252
293,293_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.937995811541127211263813023834,2,0.800000000000000044408920985006
294,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
295,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
296,296_0,COMPLETED,BoTorch,0.309723830197781047068872339878,330,0.920502878243025124405107817438,2,0.296027440805140429347375174984
297,297_0,COMPLETED,BoTorch,0.290699469368065566143854994152,142,1.000000000000000000000000000000,2,0.200000000000000011102230246252
298,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
299,299_0,COMPLETED,BoTorch,0.294649059334298080159442179138,172,0.100000000000000005551115123126,4,0.618353330723201199958793949918
300,300_0,COMPLETED,BoTorch,0.281248191027496430294263518590,100,0.677609767744558610935712295031,4,0.200000000000000011102230246252
301,301_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.815861628814870121217950327264,2,0.542135642217584035051913815551
302,302_0,COMPLETED,BoTorch,0.281248191027496430294263518590,100,0.678003862189504813429152818571,4,0.200000000000000011102230246252
303,303_0,COMPLETED,BoTorch,0.289751567776169838275279744266,144,0.206628085521213644693006017405,4,0.200000000000000011102230246252
304,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
305,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
306,306_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.931197684081100152120313850901,2,0.800000000000000044408920985006
307,307_0,COMPLETED,BoTorch,0.293859141341051621765245727147,154,0.248735888944153771262790542096,4,0.419826463041161168643355949825
308,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
309,309_0,COMPLETED,BoTorch,0.284158224794983094163569603552,111,1.000000000000000000000000000000,4,0.431571366776208942273029833814
310,310_0,COMPLETED,BoTorch,0.284649059334298071277657982137,110,1.000000000000000000000000000000,4,0.200000000000000011102230246252
311,311_0,COMPLETED,BoTorch,0.285352146647370963350454076135,111,1.000000000000000000000000000000,4,0.800000000000000044408920985006
312,312_0,COMPLETED,BoTorch,0.285297877472262428888427621132,113,0.598450829195625422229909418093,4,0.800000000000000044408920985006
313,313_0,COMPLETED,BoTorch,0.292770139893873571068638739234,143,0.550643598842818438043877904420,2,0.495230220282225408467979832494
314,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
315,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
316,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
317,317_0,COMPLETED,BoTorch,0.287303424987940148049858635204,114,1.000000000000000000000000000000,4,0.200000000000000011102230246252
318,318_0,COMPLETED,BoTorch,0.285561987457790600331009045476,112,0.701978321328223797692658081360,4,0.200000000000000011102230246252
319,319_0,COMPLETED,BoTorch,0.285741678726483372408040395385,117,0.350691070304812169133867882920,4,0.333006167255020191220182823599
320,320_0,COMPLETED,BoTorch,0.285817655571635298450416939886,109,0.982790887140310598368841965566,4,0.200000000000000011102230246252
321,321_0,COMPLETED,BoTorch,0.282753256150506504518205019849,101,0.586912740140419386669634604914,4,0.244563848681499079740220281565
322,322_0,COMPLETED,BoTorch,0.286039556198745770210223327012,114,0.224506504423856390051383868922,4,0.800000000000000044408920985006
323,323_0,COMPLETED,BoTorch,0.284125663289917951281893238047,107,1.000000000000000000000000000000,2,0.200000000000000011102230246252
324,324_0,COMPLETED,BoTorch,0.283904968644476563000011992699,118,0.100000000000000005551115123126,4,0.800000000000000044408920985006
325,325_0,COMPLETED,BoTorch,0.298135552339604426030916783930,207,0.693248457573672816067755775293,4,0.403152721957673043640113519359
326,326_0,COMPLETED,BoTorch,0.286590689821514699175963869493,102,0.652787841229713783341992439091,4,0.288163083798190722273346864313
327,327_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.971892654795525445798887176352,2,0.650239992852036374770818838442
328,328_0,RUNNING,BoTorch,,100,0.735365273177978284735445413389,2,0.250279624768237729526276780234
329,329_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.738057814269197587364601531590,2,0.253357428818003493908861400996
330,330_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.307159218858608473734506105757,2,0.579336331161762863040110005386
331,331_0,COMPLETED,BoTorch,0.283765074770863434672207858966,114,0.694601496426105913251092260907,4,0.200000000000000011102230246252
332,332_0,COMPLETED,BoTorch,0.282089966232513256194636142027,100,0.262994676240536351397025782717,4,0.348791006323768582042532671039
333,333_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.733279903576728897540704110725,2,0.234658568632625796901436387998
334,334_0,COMPLETED,BoTorch,0.291859623733719208971137959452,155,0.611266164645309140190931884717,4,0.377892234085169254065306176926
335,335_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.716300194873249918003921266063,2,0.226653078973867855294699324986
336,336_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.466698001771278625504635328980
337,337_0,COMPLETED,BoTorch,0.287342016401350708321160709602,132,0.100000000000000005551115123126,2,0.427240721712862536740829000337
338,338_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.646647732194282509610161469027
339,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
340,340_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.648258159321058125001968619472
341,341_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.699812026107477058722849960759,2,0.227177933855437330645798965634
342,342_0,COMPLETED,BoTorch,0.285077182826821040606546375784,126,0.100000000000000005551115123126,3,0.800000000000000044408920985006
343,343_0,COMPLETED,BoTorch,0.288539556198745827941820607521,127,1.000000000000000000000000000000,2,0.200000000000000011102230246252
344,344_0,COMPLETED,BoTorch,0.300905692233478072594721197675,246,1.000000000000000000000000000000,2,0.200000000000000011102230246252
345,345_0,COMPLETED,BoTorch,0.286181862035697065493877744302,122,1.000000000000000000000000000000,2,0.200000000000000011102230246252
346,346_0,COMPLETED,BoTorch,0.286345875542691752357882251090,117,0.464186007431136582468411688751,4,0.605018579146897605980370826728
347,347_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.701920648346900044600715773413
348,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
349,349_0,COMPLETED,BoTorch,0.290048239266763152599537534115,137,0.123787445103548110481028743379,4,0.635628116312550117683599637530
350,350_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.683168209886624300253288311069
351,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
352,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
353,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
354,354_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.681790009815209208099417992344
355,355_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.293413752453075715731500849870,3,0.638941510254167788218637724640
356,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
357,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
358,358_0,COMPLETED,BoTorch,0.291091413410516142157291596959,135,0.654720299189325616318058109755,3,0.364988471237820133463714000754
359,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
360,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
361,361_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.683267204836155439551248491625
362,362_0,COMPLETED,BoTorch,0.282315484804630978388217954489,102,0.100031264823990079237958639169,4,0.400369881949983297264594739318
363,363_0,COMPLETED,BoTorch,0.330186927158707232976553314074,650,0.112108773733449684772978116598,2,0.482723747691611637655739741604
364,364_0,COMPLETED,BoTorch,0.292770139893873571068638739234,143,0.372512492257235816595084543223,2,0.516558816098636075508920839638
365,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
366,366_0,COMPLETED,BoTorch,0.305189339122045377727943105128,260,0.230342581504888760379756718066,2,0.654685843555364743728830489999
367,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
368,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
369,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
370,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
371,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
372,372_0,COMPLETED,BoTorch,0.313469609261939230471227801900,353,0.136421487521151851840528479443,3,0.448448955509056057522343508026
373,373_0,COMPLETED,BoTorch,0.281120356970574025723408340127,100,1.000000000000000000000000000000,4,0.605873338809452377518027788028
374,374_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.675962472824773286106392333750
375,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
376,376_0,COMPLETED,BoTorch,0.282536179450072366670099199837,104,0.513304304641691189203811518382,4,0.466753275142660406515915383352
377,377_0,COMPLETED,BoTorch,0.291435118186203601098327453656,169,0.669954463187605098184462804056,4,0.369497539475560188293457031250
378,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
379,379_0,COMPLETED,BoTorch,0.283346599131693244189023062063,101,0.435861302151740570565152665949,4,0.673637481354710576653133102809
380,380_0,COMPLETED,BoTorch,0.282023637240713886953358269238,101,0.292560564554253499380820358056,4,0.662058239053643360350065449893
381,381_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.654862444744664706774983642390
382,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
383,383_0,COMPLETED,BoTorch,0.281191509889049728876386780030,100,0.602335024523823037156944337767,4,0.200000000000000011102230246252
384,384_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.660544726383470393038521706330,2,0.200000000000000011102230246252
385,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
386,386_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.521611462796282787657276003301
387,387_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.672227173121904941766047159035
388,388_0,COMPLETED,BoTorch,0.288129522431258999759506878036,119,0.816194997124227827889342279377,2,0.207681618404539525268859279095
389,133_0,COMPLETED,BoTorch,0.281655812831644980498424502002,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
390,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
391,391_0,RUNNING,BoTorch,,100,0.668213667232075314572625757137,2,0.200000000000000011102230246252
392,392_0,COMPLETED,BoTorch,0.294165460684997603912904651224,159,1.000000000000000000000000000000,2,0.578088095756320474727374403301
393,393_0,COMPLETED,BoTorch,0.281702846116739014092900106334,101,0.216017986156130159525190492786,4,0.479109244307909853954896561845
394,394_0,COMPLETED,BoTorch,0.306905451037144283432667180023,261,0.565768510849323225109230861563,2,0.473259687775547832622180521867
395,395_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.660757790099959496643577949726
396,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
397,397_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.666030902813548597585224797513,2,0.200000000000000011102230246252
398,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
399,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
400,400_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.650307061379849393745189445326
401,401_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.658475572071168024912424243666,2,0.200000000000000011102230246252
402,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
403,403_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.663723509804278744006467150029,2,0.200000000000000011102230246252
404,404_0,COMPLETED,BoTorch,0.305120598166907841530814948783,283,0.466216888969391174413203771110,3,0.723988991230123835052268077561
405,405_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.661355100798511674753399347537,2,0.200000000000000011102230246252
406,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252
407,407_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.654484027375000998461018753005,2,0.200000000000000011102230246252
408,408_0,COMPLETED,BoTorch,0.295217076700434177816134706518,178,0.801776412202722665867327123124,3,0.200000000000000011102230246252
409,409_0,COMPLETED,BoTorch,0.281255426917510820139511906746,100,0.389589813666507134293226499722,3,0.202887558013209096108298012950
410,410_0,COMPLETED,BoTorch,0.284076218041485750731567350158,114,0.527916267410704631579676515685,4,0.774885322365777895470273506362
411,411_0,COMPLETED,BoTorch,0.281120356970574025723408340127,100,1.000000000000000000000000000000,4,0.597666242168518047073177967832
412,100_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252
413,413_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635264777201584696086911208113
414,414_0,COMPLETED,BoTorch,0.280724794983116310298498774500,100,0.775711710074054927588349528378,4,0.696086053631963519450209787465
415,415_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.535519833290465818720349489013
416,416_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.333999360419534263577645560872
417,417_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.691790460385156458222866149299,2,0.234740316630668188757624648133
418,418_0,COMPLETED,BoTorch,0.297621804148576973858553174068,200,0.811642138799668955151389582170,2,0.329620227622200834272092606625
419,419_0,COMPLETED,BoTorch,0.283082489146164961724139175203,104,0.229081666275175593883162150632,4,0.582411093260960743833720698603
420,420_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.637725406247186432651119503134
421,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
422,422_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.634907251342835543539422360482
423,423_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635935976241632450189911196503
424,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
425,425_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.603012648082953184314192185411
426,426_0,COMPLETED,BoTorch,0.343526290400385958534457131464,975,0.246512372405580493239796169291,4,0.518183293193174465685046925501
427,427_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.635926558524608109834730385046
428,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252
429,429_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.363949951049462983032611873568
430,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
431,431_0,COMPLETED,BoTorch,0.284375301495417232011675423564,122,0.691888688531572038087347209512,4,0.291066321692046159341771272011
432,66_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
433,433_0,COMPLETED,BoTorch,0.283227206946454423963643876050,107,0.760077403719831345974000669230,3,0.535839154270179252037564765487
434,434_0,COMPLETED,BoTorch,0.284429570670525766473701878567,108,0.370783538924228661670667861472,4,0.699504754317558097298501706973
435,435_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.348512486251269737458358122240
436,436_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.735786325475950797603275077563,2,0.260364971085144392137777913376
437,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
438,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
439,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
440,440_0,COMPLETED,BoTorch,0.309382537385431755083686766739,309,0.648167157791927461829573076102,2,0.270232536679586576866540781339
441,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
442,442_0,COMPLETED,BoTorch,0.286872889532079122787422420515,109,0.348360262771811091120355285966,4,0.734122356570476863879548545810
443,443_0,COMPLETED,BoTorch,0.297257597684515206815092369652,195,0.249608341434244651591001229463,2,0.731122315698845115505832836789
444,444_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.648845223429570738815641561814
445,445_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.649023156330049033613249775954
446,446_0,COMPLETED,BoTorch,0.283628798842257556778179150569,103,1.000000000000000000000000000000,2,0.800000000000000044408920985006
447,447_0,COMPLETED,BoTorch,0.286115533043897696252599871514,116,1.000000000000000000000000000000,2,0.800000000000000044408920985006
448,448_0,COMPLETED,BoTorch,0.289173902556681183817488545174,127,0.290651420915175373593797303329,2,0.800000000000000044408920985006
449,449_0,COMPLETED,BoTorch,0.283702363724071426887007874029,110,0.477787157607551526972144984029,4,0.200000000000000011102230246252
450,450_0,COMPLETED,BoTorch,0.283749397009165460481483478361,107,0.100000000000000005551115123126,2,0.800000000000000044408920985006
451,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
452,452_0,COMPLETED,BoTorch,0.318865171249397061359331928543,435,0.116286427772718747863933685949,2,0.517277844168346767617094883462
453,453_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.279501291506880411574798017682
454,454_0,COMPLETED,BoTorch,0.284353593825373840431325334066,105,0.517743209601877030223704423406,4,0.467192514462177799217101892282
455,455_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.659110597564777522805457010691
456,456_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.688546091192570042238685346092,2,0.221963175324737305160738287668
457,457_0,COMPLETED,BoTorch,0.301494211287988367331536210258,236,0.470989179467911855958561773150,2,0.289822159380871635647736184183
458,458_0,COMPLETED,BoTorch,0.300836951278340536397593041329,244,0.274869911737801508877510059392,3,0.204143038196925613592114245876
459,459_0,COMPLETED,BoTorch,0.327773757838880852588658854074,588,0.568610160525820917953865318850,2,0.511512081224274828272768900206
460,460_0,COMPLETED,BoTorch,0.299688856729377728349561493815,215,0.582990275787747536107019641349,3,0.652690829324037768977007090143
461,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
462,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
463,463_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.604120249596454717000426626328
464,464_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.333176902125653928710136142399
465,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
466,466_0,COMPLETED,BoTorch,0.281181862035697061052985645802,100,1.000000000000000000000000000000,2,0.223920420696380684244530812066
467,467_0,COMPLETED,BoTorch,0.323429811866859595603784782725,466,0.546909577004780156350705055956,3,0.653826509973231884131905644608
468,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
469,469_0,COMPLETED,BoTorch,0.284761215629522390635486317478,105,0.549073798647054411858903222310,4,0.429844863709831270792705026906
470,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
471,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
472,472_0,COMPLETED,BoTorch,0.313024602026049203473689885868,363,0.459740689792995316231838387466,3,0.699272319106876927286009504314
473,473_0,COMPLETED,BoTorch,0.314517607332368553940682431858,346,0.800844034817981831331223929737,2,0.765979702456991651260409526003
474,73_0,COMPLETED,BoTorch,0.281865653642064617478979471343,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252
475,54_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,3,0.800000000000000044408920985006
476,476_0,COMPLETED,BoTorch,0.307492764109985494691557050828,253,0.477885722896779996382576882752,4,0.693706879518822372432396150543
477,72_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
478,478_0,COMPLETED,BoTorch,0.297221418234442813499640578812,207,0.824421917950646010453397138917,2,0.377361957639622436211368494696
479,479_0,COMPLETED,BoTorch,0.281356729377713499218316428596,101,0.594888748436878889158663241687,3,0.406293699629057092348460855646
480,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
481,481_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.677107815957357361824620056723,2,0.200000000000000011102230246252
482,482_0,COMPLETED,BoTorch,0.282357694163048678070992991707,100,0.403765947957050008731982870813,3,0.204178396174657189598988793477
483,483_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.611729181337196559198332579399
484,484_0,COMPLETED,BoTorch,0.316169802219006257359978917520,434,0.788818376550669331237486403552,4,0.569144276961538020387365577335
485,485_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.570604653360232139114316396444,2,0.452604792185083371069964641720
486,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
487,487_0,COMPLETED,BoTorch,0.319436806560540298427497418743,421,0.835108804060919807099594436295,2,0.776072829582627354838564315287
488,488_0,COMPLETED,BoTorch,0.297519295706705211301823510439,177,0.234867404295065029806721668137,3,0.299433263988684750245283794357
489,489_0,COMPLETED,BoTorch,0.298332127351664255776597656222,218,0.833839978461526243336265906692,2,0.355861118313146485014897280053
490,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
491,491_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.632054500997325741096233286953
492,492_0,COMPLETED,BoTorch,0.302403521466473756973414310778,226,0.300361972838011914710421024211,2,0.463006767265909668918055785980
493,493_0,COMPLETED,BoTorch,0.307311866859623750158903021656,271,0.800108825785393440455095515063,2,0.256185353857365127883127797759
494,494_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,1.000000000000000000000000000000,2,0.633775103176349219324947625864
495,46_0,COMPLETED,BoTorch,0.280324409068982149939586179244,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
496,496_0,COMPLETED,BoTorch,0.281724553786782405673250195832,100,0.308214164665389311892340629129,3,0.200000000000000011102230246252
497,497_0,COMPLETED,BoTorch,0.283972503617945015719215007266,102,0.953943365159006240183714453451,4,0.655657573698599005496134850546
498,498_0,COMPLETED,BoTorch,0.285937047756874118675796125899,114,0.100000000000000005551115123126,4,0.200000000000000011102230246252
499,499_0,COMPLETED,BoTorch,0.286513506994693689655662183213,124,0.197355794351058072555460398689,4,0.301616928255286853577388228587
500,500_0,COMPLETED,BoTorch,0.286976603955619857799774763407,129,0.441036528192673249471056351467,4,0.800000000000000044408920985006
</pre>
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<script>
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<h1> CPU/RAM-Usage (main)</h1>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727602469,479.9140625,49.7
1727602469,479.92578125,52.1
1727602469,479.92578125,49.8
1727602469,479.92578125,39.4
1727602469,479.92578125,53.2
1727602469,479.92578125,50.2
1727602469,479.92578125,40.6
1727602515,484.234375,49.8
1727602515,484.234375,54.3
1727602515,484.234375,49.7
1727602515,484.234375,56.5
1727602516,484.234375,49.8
1727602516,484.234375,40.0
1727602516,484.234375,50.9
1727602516,484.234375,51.2
1727602518,484.234375,49.8
1727602518,484.234375,53.5
1727602518,484.234375,49.1
1727602518,484.234375,52.5
1727602519,484.234375,49.7
1727602519,484.234375,54.3
1727602519,484.234375,48.5
1727602519,484.234375,58.7
1727602521,484.234375,49.7
1727602521,484.234375,54.3
1727602521,484.234375,46.8
1727602521,484.234375,56.8
1727602522,484.234375,49.7
1727602522,484.234375,54.3
1727602522,484.234375,48.1
1727602522,484.234375,53.3
1727602523,484.234375,49.8
1727602523,484.234375,44.7
1727602523,484.234375,52.6
1727602523,484.234375,42.4
1727602525,484.234375,49.8
1727602525,484.234375,56.2
1727602525,484.234375,47.7
1727602525,484.234375,56.8
1727602526,484.234375,49.7
1727602526,484.234375,47.5
1727602526,484.234375,51.4
1727602526,484.234375,41.2
1727602528,484.234375,49.8
1727602528,484.234375,47.2
1727602528,484.234375,50.9
1727602528,484.234375,51.2
1727602529,484.234375,49.8
1727602529,484.234375,39.4
1727602529,484.234375,53.4
1727602529,484.234375,42.4
1727602531,484.234375,49.7
1727602531,484.234375,54.3
1727602531,484.234375,49.1
1727602531,484.234375,56.5
1727602532,484.234375,49.7
1727602532,484.234375,53.2
1727602532,484.234375,50.0
1727602532,484.234375,58.7
1727602533,484.234375,49.8
1727602533,484.234375,55.3
1727602533,484.234375,47.6
1727602533,484.234375,42.4
1727602535,484.234375,49.8
1727602535,484.234375,38.2
1727602535,484.234375,50.5
1727602535,484.234375,54.8
1727602536,484.234375,49.8
1727602536,484.234375,37.1
1727602536,484.234375,54.2
1727602536,484.234375,39.4
1727602538,484.234375,49.8
1727602538,484.234375,55.6
1727602538,484.234375,47.6
1727602538,484.234375,51.3
1727602539,484.234375,49.8
1727602539,484.234375,38.2
1727602539,484.234375,52.5
1727602539,484.234375,40.6
1727602540,484.234375,49.8
1727602540,484.234375,38.2
1727602540,484.234375,54.3
1727602540,484.234375,40.6
1727602542,484.234375,49.7
1727602542,484.234375,54.3
1727602542,484.234375,49.1
1727602542,484.234375,54.5
1727602547,484.234375,49.7
1727602547,484.234375,38.2
1727602547,484.234375,48.6
1727602547,484.234375,55.8
1727602548,484.234375,49.7
1727602548,484.234375,36.4
1727602548,484.234375,52.9
1727602548,484.234375,40.6
1727602550,484.234375,49.7
1727602550,484.234375,55.3
1727602550,484.234375,48.1
1727602550,484.234375,55.6
1727602551,484.234375,49.7
1727602551,484.234375,55.3
1727602551,484.234375,47.7
1727602551,484.234375,56.8
1727602553,484.234375,49.7
1727602553,484.234375,40.0
1727602553,484.234375,52.4
1727602553,484.234375,37.5
1727602554,484.234375,49.8
1727602554,484.234375,55.3
1727602554,484.234375,49.5
1727602554,484.234375,42.4
1727602555,484.234375,49.8
1727602555,484.234375,54.2
1727602555,484.234375,48.1
1727602555,484.234375,57.4
1727602557,484.234375,49.7
1727602557,484.234375,39.4
1727602557,484.234375,49.0
1727602557,484.234375,58.7
1727602558,484.234375,49.7
1727602558,484.234375,37.1
1727602558,484.234375,53.3
1727602558,484.234375,38.7
1727602560,484.234375,49.8
1727602560,484.234375,51.2
1727602560,484.234375,50.9
1727602560,484.234375,40.6
1727602561,484.234375,49.8
1727602561,484.234375,54.2
1727602561,484.234375,50.0
1727602561,484.234375,40.6
1727602562,484.234375,49.8
1727602562,484.234375,37.5
1727602562,484.234375,53.7
1727602562,484.234375,40.6
1727602564,484.234375,49.8
1727602564,484.234375,56.2
1727602564,484.234375,49.0
1727602564,484.234375,52.2
1727602565,484.234375,49.7
1727602565,484.234375,55.3
1727602565,484.234375,49.1
1727602565,484.234375,47.2
1727602567,484.234375,49.7
1727602567,484.234375,55.3
1727602567,484.234375,48.1
1727602567,484.234375,56.8
1727602568,484.234375,49.7
1727602568,484.234375,54.3
1727602568,484.234375,49.0
1727602568,484.234375,55.3
1727602569,484.234375,49.7
1727602569,484.234375,41.2
1727602569,484.234375,54.2
1727602569,484.234375,38.7
1727602571,484.234375,49.8
1727602571,484.234375,38.2
1727602571,484.234375,53.8
1727602571,484.234375,38.7
1727602572,484.234375,49.7
1727602572,484.234375,56.5
1727602572,484.234375,48.6
1727602572,484.234375,56.8
1727602574,484.234375,49.8
1727602574,484.234375,55.6
1727602574,484.234375,48.1
1727602574,484.234375,42.4
1727602575,484.234375,49.7
1727602575,484.234375,56.2
1727602575,484.234375,48.1
1727602575,484.234375,57.8
1727602576,484.234375,49.7
1727602576,484.234375,51.1
1727602576,484.234375,49.5
1727602576,484.234375,41.9
1727602578,484.234375,49.7
1727602578,484.234375,55.6
1727602578,484.234375,50.0
1727602578,484.234375,53.2
1727602579,484.234375,49.8
1727602579,484.234375,38.2
1727602579,484.234375,53.7
1727602579,484.234375,38.7
1727602581,484.234375,49.8
1727602581,484.234375,40.0
1727602581,484.234375,53.4
1727602581,484.234375,40.6
1727602582,484.234375,49.8
1727602582,484.234375,50.0
1727602582,484.234375,48.6
1727602582,484.234375,54.3
1727602584,484.234375,49.8
1727602584,484.234375,51.2
1727602584,484.234375,49.1
1727602584,484.234375,53.2
1727602585,484.234375,49.7
1727602585,484.234375,56.5
1727602585,484.234375,49.1
1727602585,484.234375,38.7
1727602586,484.234375,49.7
1727602586,484.234375,54.3
1727602586,484.234375,48.1
1727602586,484.234375,55.6
1727602588,484.234375,49.7
1727602588,484.234375,41.7
1727602588,484.234375,52.1
1727602588,484.234375,38.7
1727602589,484.234375,49.8
1727602589,484.234375,54.3
1727602589,484.234375,48.1
1727602589,484.234375,55.6
1727602591,484.234375,49.7
1727602591,484.234375,35.3
1727602591,484.234375,53.1
1727602591,484.234375,41.5
1727602592,484.234375,49.8
1727602592,484.234375,54.3
1727602592,484.234375,49.1
1727602592,484.234375,38.7
1727602593,484.234375,49.8
1727602593,484.234375,40.0
1727602593,484.234375,49.5
1727602593,484.234375,52.5
1727602595,484.234375,49.8
1727602595,484.234375,54.3
1727602595,484.234375,48.6
1727602595,484.234375,56.5
1727602596,484.234375,49.7
1727602596,484.234375,51.2
1727602596,484.234375,50.5
1727602596,484.234375,37.5
1727602598,484.234375,49.8
1727602598,484.234375,54.3
1727602598,484.234375,48.1
1727602598,484.234375,58.7
1727602599,484.234375,49.7
1727602599,484.234375,48.7
1727602599,484.234375,51.4
1727602599,484.234375,40.6
1727602600,484.234375,49.8
1727602600,484.234375,41.2
1727602600,484.234375,49.5
1727602600,484.234375,56.8
1727602602,484.234375,49.7
1727602602,484.234375,51.2
1727602602,484.234375,49.5
1727602602,484.234375,38.7
1727602603,484.2421875,49.8
1727602603,484.2421875,40.0
1727602603,484.2421875,54.3
1727602603,484.2421875,38.7
1727602604,484.2421875,49.8
1727602604,484.2421875,55.6
1727602604,484.2421875,49.1
1727602604,484.2421875,56.5
1727602606,484.2421875,49.7
1727602606,484.2421875,43.2
1727602606,484.2421875,51.7
1727602606,484.2421875,43.2
1727602607,484.2421875,49.8
1727602607,484.2421875,55.3
1727602607,484.2421875,47.2
1727602607,484.2421875,42.9
1727602609,484.2421875,49.8
1727602609,484.2421875,40.0
1727602609,484.2421875,53.8
1727602609,484.2421875,40.0
1727602610,484.2421875,49.8
1727602610,484.2421875,36.4
1727602610,484.2421875,53.3
1727602610,484.2421875,41.2
1727602612,484.2421875,49.8
1727602612,484.2421875,56.2
1727602612,484.2421875,48.1
1727602612,484.2421875,44.1
1727602613,484.2421875,49.8
1727602613,484.2421875,56.5
1727602613,484.2421875,48.6
1727602613,484.2421875,56.8
1727602614,484.2421875,49.8
1727602614,484.2421875,41.7
1727602614,484.2421875,53.7
1727602614,484.2421875,40.6
1727602616,484.2421875,49.7
1727602616,484.2421875,55.3
1727602616,484.2421875,49.0
1727602616,484.2421875,39.4
1727602617,484.2421875,49.7
1727602617,484.2421875,55.3
1727602617,484.2421875,49.0
1727602617,484.2421875,55.6
1727602619,484.2421875,49.7
1727602619,484.2421875,54.3
1727602619,484.2421875,48.6
1727602619,484.2421875,56.8
1727602620,484.2421875,49.8
1727602620,484.2421875,54.3
1727602620,484.2421875,48.6
1727602620,484.2421875,53.2
1727602621,484.2421875,49.7
1727602621,484.2421875,54.3
1727602621,484.2421875,47.2
1727602621,484.2421875,58.7
1727602623,484.2421875,49.8
1727602623,484.2421875,53.2
1727602623,484.2421875,49.6
1727602623,484.2421875,40.6
1727602624,484.2421875,49.8
1727602624,484.2421875,41.7
1727602624,484.2421875,50.5
1727602624,484.2421875,51.2
1727602626,484.2421875,49.8
1727602626,484.2421875,36.4
1727602626,484.2421875,53.7
1727602626,484.2421875,36.7
1727602627,484.2421875,49.8
1727602627,484.2421875,55.1
1727602627,484.2421875,47.2
1727602627,484.2421875,48.7
1727602628,484.2421875,49.7
1727602628,484.2421875,55.6
1727602628,484.2421875,51.6
1727602628,484.2421875,39.4
1727602630,484.2421875,49.7
1727602630,484.2421875,57.4
1727602630,484.2421875,48.1
1727602630,484.2421875,55.8
1727602631,484.2421875,49.7
1727602631,484.2421875,55.3
1727602631,484.2421875,48.6
1727602631,484.2421875,55.6
1727602633,484.2421875,49.7
1727602633,484.2421875,51.0
1727602633,484.2421875,51.7
1727602633,484.2421875,39.4
1727602634,484.2421875,49.8
1727602634,484.2421875,53.2
1727602634,484.2421875,50.0
1727602634,484.2421875,54.5
1727602635,484.2421875,49.7
1727602635,484.2421875,54.3
1727602635,484.2421875,48.6
1727602635,484.2421875,54.5
1727602637,484.2421875,49.8
1727602637,484.2421875,41.2
1727602637,484.2421875,53.4
1727602637,484.2421875,39.4
1727602638,484.2421875,49.8
1727602638,484.2421875,56.5
1727602638,484.2421875,48.1
1727602638,484.2421875,53.7
1727602640,484.2421875,49.7
1727602640,484.2421875,54.3
1727602640,484.2421875,48.1
1727602640,484.2421875,57.8
1727602641,484.2421875,49.7
1727602641,484.2421875,54.3
1727602641,484.2421875,49.1
1727602641,484.2421875,56.5
1727602642,484.2421875,49.7
1727602642,484.2421875,54.2
1727602642,484.2421875,46.4
1727602642,484.2421875,57.8
1727602644,484.2421875,49.8
1727602644,484.2421875,43.2
1727602644,484.2421875,52.9
1727602644,484.2421875,40.6
1727602645,484.2421875,49.7
1727602645,484.2421875,54.3
1727602645,484.2421875,49.1
1727602645,484.2421875,56.8
1727602647,484.2421875,49.7
1727602647,484.2421875,56.7
1727602647,484.2421875,49.4
1727602647,484.2421875,40.0
1727602648,484.2421875,49.8
1727602648,484.2421875,39.4
1727602648,484.2421875,53.8
1727602648,484.2421875,38.7
1727602650,484.2421875,49.8
1727602650,484.2421875,41.2
1727602650,484.2421875,54.2
1727602650,484.2421875,38.7
1727602651,484.2421875,49.8
1727602651,484.2421875,54.3
1727602651,484.2421875,47.7
1727602651,484.2421875,56.5
1727602652,484.2421875,49.7
1727602652,484.2421875,55.3
1727602652,484.2421875,48.5
1727602652,484.2421875,57.4
1727602654,484.2421875,49.8
1727602654,484.2421875,38.2
1727602654,484.2421875,54.3
1727602654,484.2421875,42.9
1727602655,484.2421875,49.8
1727602655,484.2421875,54.2
1727602655,484.2421875,50.0
1727602655,484.2421875,37.5
1727602657,484.2421875,49.8
1727602657,484.2421875,36.4
1727602657,484.2421875,53.8
1727602657,484.2421875,38.7
1727602658,484.2421875,49.8
1727602658,484.2421875,38.2
1727602658,484.2421875,49.5
1727602658,484.2421875,56.8
1727602659,484.2421875,49.7
1727602659,484.2421875,54.3
1727602659,484.2421875,49.1
1727602659,484.2421875,56.8
1727602661,484.2421875,49.7
1727602661,484.2421875,55.6
1727602661,484.2421875,48.1
1727602661,484.2421875,56.8
1727602662,484.2421875,49.7
1727602662,484.2421875,55.3
1727602662,484.2421875,49.0
1727602662,484.2421875,56.8
1727602664,484.2421875,49.8
1727602664,484.2421875,44.7
1727602664,484.2421875,48.1
1727602664,484.2421875,56.5
1727602665,484.2421875,49.7
1727602665,484.2421875,55.3
1727602665,484.2421875,49.1
1727602665,484.2421875,55.6
1727602666,484.2421875,49.7
1727602666,484.2421875,54.3
1727602666,484.2421875,49.0
1727602666,484.2421875,56.5
1727602668,484.2421875,49.7
1727602668,484.2421875,54.3
1727602668,484.2421875,48.6
1727602668,484.2421875,58.7
1727602669,484.2421875,49.7
1727602669,484.2421875,38.2
1727602669,484.2421875,54.2
1727602669,484.2421875,37.5
1727602671,484.2421875,49.7
1727602671,484.2421875,57.4
1727602671,484.2421875,49.7
1727602671,484.2421875,40.6
1727602672,484.2421875,49.8
1727602672,484.2421875,54.3
1727602672,484.2421875,48.1
1727602672,484.2421875,57.8
1727602673,484.2421875,49.7
1727602673,484.2421875,42.1
1727602673,484.2421875,53.4
1727602673,484.2421875,42.4
1727602675,484.2421875,49.8
1727602675,484.2421875,44.7
1727602675,484.2421875,53.3
1727602675,484.2421875,40.6
1727602676,484.2421875,49.7
1727602676,484.2421875,54.2
1727602676,484.2421875,48.1
1727602676,484.2421875,52.4
1727602678,484.2421875,49.7
1727602678,484.2421875,39.4
1727602678,484.2421875,53.7
1727602678,484.2421875,45.5
1727602679,484.2421875,49.7
1727602679,484.2421875,51.2
1727602679,484.2421875,48.1
1727602679,484.2421875,57.8
1727602680,484.2421875,49.8
1727602680,484.2421875,36.4
1727602680,484.2421875,49.5
1727602680,484.2421875,56.8
1727602682,484.2421875,49.8
1727602682,484.2421875,54.3
1727602682,484.2421875,49.5
1727602682,484.2421875,57.4
1727602683,484.2421875,49.7
1727602683,484.2421875,54.3
1727602683,484.2421875,47.7
1727602683,484.2421875,56.8
1727602685,484.2421875,49.8
1727602685,484.2421875,54.3
1727602685,484.2421875,49.5
1727602685,484.2421875,53.2
1727602686,484.2421875,49.7
1727602686,484.2421875,55.6
1727602686,484.2421875,48.6
1727602686,484.2421875,55.6
1727602687,484.2421875,49.7
1727602687,484.2421875,38.2
1727602687,484.2421875,49.0
1727602687,484.2421875,57.8
1727602689,484.2421875,49.7
1727602689,484.2421875,54.2
1727602689,484.2421875,47.2
1727602689,484.2421875,56.8
1727602690,484.2421875,49.7
1727602690,484.2421875,41.2
1727602690,484.2421875,49.0
1727602690,484.2421875,58.7
1727602692,484.2421875,49.7
1727602692,484.2421875,55.6
1727602692,484.2421875,49.5
1727602692,484.2421875,56.8
1727602693,484.2421875,49.7
1727602693,484.2421875,52.4
1727602693,484.2421875,49.5
1727602693,484.2421875,52.5
1727602694,484.2421875,49.8
1727602694,484.2421875,55.6
1727602694,484.2421875,48.1
1727602694,484.2421875,42.4
1727602696,484.2421875,49.8
1727602696,484.2421875,42.1
1727602696,484.2421875,52.1
1727602696,484.2421875,40.6
1727602697,484.2421875,49.8
1727602697,484.2421875,56.3
1727602697,484.2421875,47.6
1727602697,484.2421875,45.9
1727602699,484.2421875,49.8
1727602699,484.2421875,54.3
1727602699,484.2421875,48.1
1727602699,484.2421875,53.5
1727602700,484.2421875,49.7
1727602700,484.2421875,54.3
1727602700,484.2421875,48.1
1727602700,484.2421875,52.5
1727602701,484.2421875,49.7
1727602701,484.2421875,50.0
1727602701,484.2421875,48.6
1727602701,484.2421875,57.8
1727602703,484.2421875,49.8
1727602703,484.2421875,36.4
1727602703,484.2421875,49.7
1727602703,484.2421875,56.8
1727602704,484.2421875,49.8
1727602704,484.2421875,56.5
1727602704,484.2421875,46.8
1727602704,484.2421875,56.8
1727602706,484.2421875,49.7
1727602706,484.2421875,56.5
1727602706,484.2421875,46.8
1727602706,484.2421875,57.8
1727602707,484.2421875,49.8
1727602707,484.2421875,41.7
1727602707,484.2421875,52.6
1727602707,484.2421875,40.6
1727602708,484.2421875,49.7
1727602708,484.2421875,49.0
1727602708,484.2421875,51.2
1727602708,484.2421875,44.1
1727602710,484.2421875,49.7
1727602710,484.2421875,55.6
1727602710,484.2421875,49.5
1727602710,484.2421875,55.8
1727602711,484.2421875,49.8
1727602711,484.2421875,55.3
1727602711,484.2421875,47.7
1727602711,484.2421875,56.8
1727602713,484.2421875,49.7
1727602713,484.2421875,39.4
1727602713,484.2421875,53.3
1727602713,484.2421875,40.6
1727602714,484.2421875,49.8
1727602714,484.2421875,53.2
1727602714,484.2421875,49.5
1727602714,484.2421875,37.5
1727602715,484.2421875,49.8
1727602715,484.2421875,40.0
1727602715,484.2421875,53.9
1727602715,484.2421875,37.5
1727602717,484.2421875,49.8
1727602717,484.2421875,38.9
1727602717,484.2421875,53.4
1727602717,484.2421875,39.4
1727602718,484.2421875,49.8
1727602718,484.2421875,38.2
1727602718,484.2421875,52.5
1727602718,484.2421875,40.6
1727602720,484.2421875,49.8
1727602720,484.2421875,53.2
1727602720,484.2421875,48.2
1727602720,484.2421875,55.6
1727602721,484.2421875,49.7
1727602721,484.2421875,53.2
1727602721,484.2421875,50.0
1727602721,484.2421875,57.8
1727602722,484.2421875,49.7
1727602722,484.2421875,47.4
1727602722,484.2421875,51.8
1727602722,484.2421875,41.2
1727602724,484.2421875,49.8
1727602724,484.2421875,55.3
1727602724,484.2421875,47.6
1727602724,484.2421875,54.3
1727602725,484.2421875,49.7
1727602725,484.2421875,44.7
1727602725,484.2421875,48.6
1727602725,484.2421875,57.8
1727602727,484.2421875,49.7
1727602727,484.2421875,54.3
1727602727,484.2421875,48.1
1727602727,484.2421875,48.7
1727602728,484.2421875,49.7
1727602728,484.2421875,55.6
1727602728,484.2421875,48.6
1727602728,484.2421875,56.5
1727602729,484.2421875,49.7
1727602729,484.2421875,54.3
1727602729,484.2421875,48.6
1727602729,484.2421875,47.4
1727602731,484.2421875,49.8
1727602731,484.2421875,39.4
1727602731,484.2421875,53.4
1727602731,484.2421875,44.1
1727602732,484.2421875,49.7
1727602732,484.2421875,54.3
1727602732,484.2421875,49.0
1727602732,484.2421875,57.4
1727602734,484.2421875,49.7
1727602734,484.2421875,54.3
1727602734,484.2421875,49.5
1727602734,484.2421875,55.6
1727602735,484.2421875,49.7
1727602735,484.2421875,54.3
1727602735,484.2421875,49.5
1727602735,484.2421875,39.4
1727602736,484.2421875,49.8
1727602736,484.2421875,55.6
1727602736,484.2421875,49.1
1727602736,484.2421875,55.6
1727602738,484.2421875,49.7
1727602738,484.2421875,56.3
1727602738,484.2421875,46.4
1727602738,484.2421875,56.8
1727602739,484.2421875,49.8
1727602739,484.2421875,55.3
1727602739,484.2421875,47.2
1727602739,484.2421875,58.7
1727602741,484.2421875,49.8
1727602741,484.2421875,56.2
1727602741,484.2421875,49.5
1727602741,484.2421875,56.5
1727602742,484.2421875,49.8
1727602742,484.2421875,54.3
1727602742,484.2421875,47.7
1727602742,484.2421875,56.5
1727602743,484.2421875,49.8
1727602743,484.2421875,38.2
1727602743,484.2421875,51.8
1727602743,484.2421875,48.8
1727602745,484.2421875,49.7
1727602745,484.2421875,54.3
1727602745,484.2421875,50.8
1727602745,484.2421875,45.7
1727602746,484.2421875,49.7
1727602746,484.2421875,54.3
1727602746,484.2421875,48.1
1727602746,484.2421875,55.3
1727602748,484.2421875,49.7
1727602748,484.2421875,48.8
1727602748,484.2421875,50.9
1727602748,484.2421875,41.2
1727602749,484.2421875,49.7
1727602749,484.2421875,51.2
1727602749,484.2421875,50.0
1727602749,484.2421875,52.4
1727602750,484.2421875,49.7
1727602750,484.2421875,55.3
1727602750,484.2421875,47.2
1727602750,484.2421875,56.5
1727602752,484.2421875,49.7
1727602752,484.2421875,54.3
1727602752,484.2421875,49.0
1727602752,484.2421875,42.4
1727602753,484.2421875,49.8
1727602753,484.2421875,55.6
1727602753,484.2421875,48.1
1727602753,484.2421875,56.8
1727602755,484.2421875,49.7
1727602755,484.2421875,48.7
1727602755,484.2421875,52.5
1727602755,484.2421875,40.6
1727602756,484.2421875,49.7
1727602756,484.2421875,55.3
1727602756,484.2421875,48.6
1727602756,484.2421875,38.7
1727602757,484.2421875,49.8
1727602757,484.2421875,51.0
1727602757,484.2421875,48.1
1727602757,484.2421875,55.6
1727602759,484.2421875,49.7
1727602759,484.2421875,51.0
1727602759,484.2421875,48.1
1727602759,484.2421875,51.0
1727602760,484.2421875,49.8
1727602760,484.2421875,54.2
1727602760,484.2421875,46.3
1727602760,484.2421875,56.5
1727602762,484.2421875,49.7
1727602762,484.2421875,54.3
1727602762,484.2421875,47.2
1727602762,484.2421875,56.5
1727602763,484.2421875,49.7
1727602763,484.2421875,54.2
1727602763,484.2421875,49.1
1727602763,484.2421875,55.6
1727602764,484.2421875,49.7
1727602764,484.2421875,54.3
1727602764,484.2421875,53.2
1727602764,484.2421875,38.7
1727602766,484.2421875,49.7
1727602766,484.2421875,53.2
1727602766,484.2421875,50.8
1727602766,484.2421875,40.6
1727602767,484.2421875,49.7
1727602767,484.2421875,55.3
1727602767,484.2421875,47.2
1727602767,484.2421875,56.5
1727602769,484.2421875,49.7
1727602769,484.2421875,52.3
1727602769,484.2421875,48.6
1727602769,484.2421875,48.7
1727602770,484.2421875,49.8
1727602770,484.2421875,50.0
1727602770,484.2421875,49.5
1727602770,484.2421875,37.5
1727602771,484.2421875,49.8
1727602771,484.2421875,53.2
1727602771,484.2421875,49.0
1727602771,484.2421875,51.2
1727602773,484.2421875,49.7
1727602773,484.2421875,56.5
1727602773,484.2421875,49.3
1727602773,484.2421875,57.8
1727602774,484.2421875,49.8
1727602774,484.2421875,53.1
1727602774,484.2421875,48.6
1727602774,484.2421875,50.0
1727602776,484.2421875,49.8
1727602776,484.2421875,39.4
1727602776,484.2421875,49.0
1727602776,484.2421875,55.6
1727602777,484.2421875,49.8
1727602777,484.2421875,38.2
1727602777,484.2421875,53.8
1727602777,484.2421875,40.6
1727602778,484.2421875,49.8
1727602778,484.2421875,41.7
1727602778,484.2421875,49.0
1727602778,484.2421875,56.8
1727602780,484.2421875,49.7
1727602780,484.2421875,38.2
1727602780,484.2421875,54.2
1727602780,484.2421875,37.5
1727602781,484.2421875,49.8
1727602781,484.2421875,45.9
1727602781,484.2421875,50.4
1727602781,484.2421875,45.9
1727602783,484.2421875,49.7
1727602783,484.2421875,54.3
1727602783,484.2421875,49.5
1727602783,484.2421875,56.8
1727602784,484.2421875,49.8
1727602784,484.2421875,38.9
1727602784,484.2421875,53.9
1727602784,484.2421875,40.6
1727602785,484.2421875,49.7
1727602785,484.2421875,51.2
1727602785,484.2421875,51.4
1727602785,484.2421875,36.4
1727602787,484.2421875,49.8
1727602787,484.2421875,43.2
1727602787,484.2421875,51.3
1727602787,484.2421875,38.7
1727602788,484.2421875,49.7
1727602788,484.2421875,54.3
1727602788,484.2421875,49.1
1727602788,484.2421875,56.5
1727602790,484.2421875,49.7
1727602790,484.2421875,55.3
1727602790,484.2421875,49.5
1727602790,484.2421875,56.8
1727602791,484.2421875,49.7
1727602791,484.2421875,55.6
1727602791,484.2421875,48.5
1727602791,484.2421875,39.4
1727602792,484.2421875,49.8
1727602792,484.2421875,45.9
1727602792,484.2421875,50.4
1727602792,484.2421875,48.7
1727602794,484.2421875,49.8
1727602794,484.2421875,54.3
1727602794,484.2421875,49.0
1727602794,484.2421875,48.7
1727602795,484.25,49.8
1727602795,484.25,50.0
1727602795,484.25,50.5
1727602795,484.25,41.2
1727602797,484.25,49.8
1727602797,484.25,54.3
1727602797,484.25,48.6
1727602797,484.25,48.6
1727602798,484.25,49.7
1727602798,484.25,55.3
1727602798,484.25,48.1
1727602798,484.25,54.5
1727602799,484.25,49.7
1727602799,484.25,39.4
1727602799,484.25,52.5
1727602799,484.25,40.6
1727602801,484.25390625,49.8
1727602801,484.25390625,41.7
1727602801,484.25390625,52.6
1727602801,484.25390625,38.7
1727602802,484.25390625,49.8
1727602802,484.25390625,55.6
1727602802,484.25390625,48.6
1727602802,484.25390625,38.7
1727602804,484.25390625,49.8
1727602804,484.25390625,37.1
1727602804,484.25390625,52.9
1727602804,484.25390625,40.6
1727602805,484.25390625,49.8
1727602805,484.25390625,40.0
1727602805,484.25390625,51.6
1727602805,484.25390625,53.2
1727602807,484.25390625,49.7
1727602807,484.25390625,39.4
1727602807,484.25390625,53.3
1727602807,484.25390625,39.4
1727602808,484.25390625,49.7
1727602808,484.25390625,54.3
1727602808,484.25390625,49.5
1727602808,484.25390625,45.9
1727602809,484.25390625,49.7
1727602809,484.25390625,54.3
1727602809,484.25390625,49.0
1727602809,484.25390625,57.4
1727602811,484.25390625,49.7
1727602811,484.25390625,54.3
1727602811,484.25390625,48.5
1727602811,484.25390625,57.8
1727602812,484.25390625,49.7
1727602812,484.25390625,54.3
1727602812,484.25390625,50.0
1727602812,484.25390625,38.7
1727602814,484.25390625,49.8
1727602814,484.25390625,50.0
1727602814,484.25390625,49.1
1727602814,484.25390625,56.8
1727602815,484.25390625,49.7
1727602815,484.25390625,54.3
1727602815,484.25390625,49.5
1727602815,484.25390625,37.5
1727602816,484.25390625,49.8
1727602816,484.25390625,48.8
1727602816,484.25390625,51.9
1727602816,484.25390625,40.6
1727602818,484.25390625,49.7
1727602818,484.25390625,54.3
1727602818,484.25390625,48.1
1727602818,484.25390625,57.8
1727602819,484.25390625,49.7
1727602819,484.25390625,38.2
1727602819,484.25390625,54.2
1727602819,484.25390625,44.1
1727602820,484.25390625,49.8
1727602820,484.25390625,36.4
1727602820,484.25390625,49.4
1727602820,484.25390625,56.8
1727602822,484.25390625,49.8
1727602822,484.25390625,54.3
1727602822,484.25390625,49.5
1727602822,484.25390625,37.5
1727602823,484.25390625,49.8
1727602823,484.25390625,45.9
1727602823,484.25390625,51.2
1727602823,484.25390625,40.6
1727602825,484.25390625,49.8
1727602825,484.25390625,54.3
1727602825,484.25390625,47.2
1727602825,484.25390625,55.6
1727602826,484.25390625,49.7
1727602826,484.25390625,54.3
1727602826,484.25390625,48.6
1727602826,484.25390625,39.4
1727602827,484.25390625,49.8
1727602827,484.25390625,35.3
1727602827,484.25390625,52.9
1727602827,484.25390625,39.4
1727602829,484.25390625,49.8
1727602829,484.25390625,46.2
1727602829,484.25390625,52.6
1727602829,484.25390625,40.6
1727602830,484.25390625,49.7
1727602830,484.25390625,54.3
1727602830,484.25390625,50.5
1727602830,484.25390625,38.7
1727602832,484.25390625,49.8
1727602832,484.25390625,38.9
1727602832,484.25390625,53.0
1727602832,484.25390625,44.1
1727602833,484.25390625,49.7
1727602833,484.25390625,54.3
1727602833,484.25390625,49.4
1727602833,484.25390625,39.4
1727602835,484.25390625,49.8
1727602835,484.25390625,55.3
1727602835,484.25390625,48.6
1727602835,484.25390625,40.6
1727602836,484.25390625,49.8
1727602836,484.25390625,43.2
1727602836,484.25390625,52.9
1727602836,484.25390625,40.6
1727602837,484.25390625,49.7
1727602837,484.25390625,54.3
1727602837,484.25390625,50.0
1727602837,484.25390625,39.4
1727602839,484.25390625,49.8
1727602839,484.25390625,50.0
1727602839,484.25390625,50.0
1727602839,484.25390625,38.7
1727602840,484.25390625,49.8
1727602840,484.25390625,48.8
1727602840,484.25390625,49.1
1727602840,484.25390625,55.6
1727602841,484.25390625,49.7
1727602841,484.25390625,54.2
1727602841,484.25390625,47.2
1727602841,484.25390625,57.8
1727602843,484.25390625,49.7
1727602843,484.25390625,39.4
1727602843,484.25390625,48.1
1727602843,484.25390625,57.8
1727602844,484.25390625,49.8
1727602844,484.25390625,54.3
1727602844,484.25390625,49.1
1727602844,484.25390625,55.6
1727602846,484.25390625,49.7
1727602846,484.25390625,54.3
1727602846,484.25390625,47.6
1727602846,484.25390625,56.8
1727602847,484.25390625,49.7
1727602847,484.25390625,56.5
1727602847,484.25390625,49.1
1727602847,484.25390625,54.3
1727602848,484.25390625,49.7
1727602848,484.25390625,55.3
1727602848,484.25390625,47.6
1727602848,484.25390625,56.8
1727602850,484.25390625,49.7
1727602850,484.25390625,37.5
1727602850,484.25390625,54.6
1727602850,484.25390625,39.4
1727602851,484.25390625,49.8
1727602851,484.25390625,38.2
1727602851,484.25390625,54.2
1727602851,484.25390625,40.6
1727602853,484.25390625,49.7
1727602853,484.25390625,56.5
1727602853,484.25390625,48.1
1727602853,484.25390625,40.6
1727602854,484.25390625,49.8
1727602854,484.25390625,55.3
1727602854,484.25390625,49.0
1727602854,484.25390625,55.3
1727602855,484.2578125,49.7
1727602855,484.2578125,54.3
1727602855,484.2578125,49.0
1727602855,484.2578125,40.6
1727602857,484.2578125,49.8
1727602857,484.2578125,53.1
1727602857,484.2578125,46.4
1727602857,484.2578125,56.8
1727602858,484.2578125,49.7
1727602858,484.2578125,56.5
1727602858,484.2578125,48.5
1727602858,484.2578125,42.4
1727602860,484.2578125,49.7
1727602860,484.2578125,55.6
1727602860,484.2578125,49.1
1727602860,484.2578125,56.8
1727602861,484.2578125,49.8
1727602861,484.2578125,55.3
1727602861,484.2578125,48.6
1727602861,484.2578125,54.8
1727602862,484.2578125,49.8
1727602862,484.2578125,48.8
1727602862,484.2578125,49.1
1727602862,484.2578125,56.5
1727602864,484.2578125,49.7
1727602864,484.2578125,54.3
1727602864,484.2578125,48.1
1727602864,484.2578125,56.0
1727602865,484.2578125,49.7
1727602865,484.2578125,54.2
1727602865,484.2578125,48.5
1727602865,484.2578125,39.4
1727602867,484.2578125,49.8
1727602867,484.2578125,55.3
1727602867,484.2578125,48.1
1727602867,484.2578125,56.8
1727602868,484.2578125,49.7
1727602868,484.2578125,55.6
1727602868,484.2578125,47.7
1727602868,484.2578125,56.8
1727602869,484.2578125,49.7
1727602869,484.2578125,54.3
1727602869,484.2578125,47.6
1727602869,484.2578125,57.8
1727602871,484.2578125,49.7
1727602871,484.2578125,53.2
1727602871,484.2578125,46.8
1727602871,484.2578125,56.5
1727602872,484.2578125,49.7
1727602872,484.2578125,56.2
1727602872,484.2578125,48.5
1727602872,484.2578125,56.8
1727602874,484.2578125,49.7
1727602874,484.2578125,54.2
1727602874,484.2578125,48.5
1727602874,484.2578125,41.2
1727602875,484.2578125,49.8
1727602875,484.2578125,54.3
1727602875,484.2578125,48.1
1727602875,484.2578125,55.1
1727602876,484.2578125,49.7
1727602876,484.2578125,55.3
1727602876,484.2578125,49.1
1727602876,484.2578125,39.4
1727602878,484.2578125,49.8
1727602878,484.2578125,52.3
1727602878,484.2578125,50.5
1727602878,484.2578125,40.6
1727602879,484.2578125,49.8
1727602879,484.2578125,54.3
1727602879,484.2578125,48.1
1727602879,484.2578125,58.7
1727602881,484.2578125,49.7
1727602881,484.2578125,38.2
1727602881,484.2578125,50.9
1727602881,484.2578125,52.4
1727602882,484.2578125,49.8
1727602882,484.2578125,37.1
1727602882,484.2578125,53.8
1727602882,484.2578125,38.7
1727602883,484.2578125,49.8
1727602883,484.2578125,48.8
1727602883,484.2578125,49.5
1727602883,484.2578125,59.1
1727602885,484.2578125,49.8
1727602885,484.2578125,37.5
1727602885,484.2578125,48.1
1727602885,484.2578125,57.8
1727602886,484.2578125,49.7
1727602886,484.2578125,56.5
1727602886,484.2578125,50.0
1727602886,484.2578125,39.4
1727602888,484.2578125,49.7
1727602888,484.2578125,56.5
1727602888,484.2578125,49.5
1727602888,484.2578125,53.1
1727602889,484.2578125,49.7
1727602889,484.2578125,39.4
1727602889,484.2578125,50.0
1727602889,484.2578125,54.5
1727602890,484.2578125,49.7
1727602890,484.2578125,54.3
1727602890,484.2578125,47.2
1727602890,484.2578125,57.8
1727602892,484.2578125,49.8
1727602892,484.2578125,56.5
1727602892,484.2578125,47.2
1727602892,484.2578125,54.0
1727602893,484.2578125,49.7
1727602893,484.2578125,39.4
1727602893,484.2578125,53.8
1727602893,484.2578125,39.4
1727602895,484.2578125,49.7
1727602895,484.2578125,54.3
1727602895,484.2578125,49.1
1727602895,484.2578125,56.8
1727602896,484.2578125,49.7
1727602896,484.2578125,54.3
1727602896,484.2578125,48.1
1727602896,484.2578125,57.8
1727602897,484.2578125,49.7
1727602897,484.2578125,54.2
1727602897,484.2578125,49.0
1727602897,484.2578125,56.5
1727602899,484.2578125,49.8
1727602899,484.2578125,54.3
1727602899,484.2578125,48.6
1727602899,484.2578125,42.4
1727602900,484.2578125,49.7
1727602900,484.2578125,54.3
1727602900,484.2578125,49.5
1727602900,484.2578125,44.1
1727602902,484.2578125,49.8
1727602902,484.2578125,38.2
1727602902,484.2578125,50.9
1727602902,484.2578125,52.6
1727602903,484.2578125,49.8
1727602903,484.2578125,39.4
1727602903,484.2578125,51.8
1727602903,484.2578125,50.0
1727602904,484.2578125,49.8
1727602904,484.2578125,55.6
1727602904,484.2578125,48.6
1727602904,484.2578125,45.7
1727602906,484.2578125,49.8
1727602906,484.2578125,39.4
1727602906,484.2578125,49.0
1727602906,484.2578125,57.8
1727602907,484.2578125,49.7
1727602907,484.2578125,56.5
1727602907,484.2578125,49.0
1727602907,484.2578125,39.4
1727602909,484.2578125,49.8
1727602909,484.2578125,37.1
1727602909,484.2578125,54.2
1727602909,484.2578125,38.7
1727602910,484.2578125,49.7
1727602910,484.2578125,55.3
1727602910,484.2578125,49.0
1727602910,484.2578125,40.6
1727602911,484.2578125,49.8
1727602911,484.2578125,40.0
1727602911,484.2578125,53.8
1727602911,484.2578125,38.7
1727602913,484.2578125,49.8
1727602913,484.2578125,54.3
1727602913,484.2578125,46.7
1727602913,484.2578125,58.7
1727602914,484.2578125,49.8
1727602914,484.2578125,38.2
1727602914,484.2578125,50.0
1727602914,484.2578125,55.8
1727602916,484.2578125,49.8
1727602916,484.2578125,38.2
1727602916,484.2578125,53.7
1727602916,484.2578125,38.7
1727602917,484.2578125,49.8
1727602917,484.2578125,42.1
1727602917,484.2578125,51.3
1727602917,484.2578125,40.6
1727602918,484.2578125,49.7
1727602918,484.2578125,54.3
1727602918,484.2578125,48.6
1727602918,484.2578125,56.8
1727602920,484.2578125,49.8
1727602920,484.2578125,47.4
1727602920,484.2578125,49.5
1727602920,484.2578125,56.8
1727602921,484.2578125,49.7
1727602921,484.2578125,52.2
1727602921,484.2578125,50.5
1727602921,484.2578125,37.5
1727602923,484.2578125,49.8
1727602923,484.2578125,54.3
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1727602923,484.2578125,40.6
1727602924,484.2578125,49.8
1727602924,484.2578125,55.1
1727602924,484.2578125,49.7
1727602924,484.2578125,45.9
1727602925,484.2734375,49.7
1727602925,484.2734375,39.4
1727602925,484.2734375,48.5
1727602925,484.2734375,57.8
1727602927,484.2890625,49.7
1727602927,484.2890625,53.2
1727602927,484.2890625,49.1
1727602927,484.2890625,55.3
1727602928,484.2890625,49.7
1727602928,484.2890625,55.3
1727602928,484.2890625,48.4
1727602928,484.2890625,48.6
1727602930,484.296875,49.8
1727602930,484.296875,47.4
1727602930,484.296875,49.5
1727602930,484.296875,56.8
1727602931,484.296875,49.7
1727602931,484.296875,54.2
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1727602931,484.296875,47.4
1727602932,484.296875,49.7
1727602932,484.296875,53.1
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1727602932,484.296875,40.0
1727602934,484.296875,49.8
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1727602934,484.296875,39.4
1727602935,484.296875,49.8
1727602935,484.296875,39.4
1727602935,484.296875,52.0
1727602935,484.296875,38.7
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1727602937,484.296875,53.4
1727602937,484.296875,40.6
1727602938,484.296875,49.8
1727602938,484.296875,36.4
1727602938,484.296875,54.5
1727602938,484.296875,38.7
1727602939,484.296875,49.8
1727602939,484.296875,37.1
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1727602939,484.296875,42.4
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1727602941,484.296875,57.8
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1727602942,484.296875,59.6
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1727602944,484.296875,56.3
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1727602944,484.296875,40.6
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1727602945,484.296875,55.3
1727602945,484.296875,48.6
1727602945,484.296875,41.9
1727602946,484.296875,49.8
1727602946,484.296875,39.4
1727602946,484.296875,54.7
1727602946,484.296875,41.2
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1727602948,484.296875,55.3
1727602948,484.296875,47.6
1727602948,484.296875,59.1
1727602949,484.296875,49.7
1727602949,484.296875,35.3
1727602949,484.296875,52.3
1727602949,484.296875,54.5
1727602951,484.296875,49.7
1727602951,484.296875,55.6
1727602951,484.296875,48.6
1727602951,484.296875,40.6
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1727602952,484.296875,53.2
1727602952,484.296875,49.0
1727602952,484.296875,55.6
1727602953,484.296875,49.7
1727602953,484.296875,41.7
1727602953,484.296875,53.7
1727602953,484.296875,40.6
1727602955,484.296875,49.8
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1727602955,484.296875,40.6
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1727602956,484.296875,54.3
1727602956,484.296875,49.1
1727602956,484.296875,55.6
1727602958,484.296875,49.7
1727602958,484.296875,55.3
1727602958,484.296875,47.7
1727602958,484.296875,51.3
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1727602959,484.296875,38.2
1727602959,484.296875,49.0
1727602959,484.296875,58.3
1727602960,484.296875,49.7
1727602960,484.296875,51.0
1727602960,484.296875,47.2
1727602960,484.296875,57.8
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1727602962,484.296875,53.1
1727602962,484.296875,48.1
1727602962,484.296875,55.6
1727602963,484.296875,49.7
1727602963,484.296875,40.0
1727602963,484.296875,53.8
1727602963,484.296875,38.2
1727602965,484.296875,49.8
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1727602965,484.296875,38.7
1727602966,484.296875,49.8
1727602966,484.296875,55.3
1727602966,484.296875,47.6
1727602966,484.296875,44.4
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1727602967,484.296875,49.0
1727602967,484.296875,53.7
1727602969,484.296875,49.7
1727602969,484.296875,43.2
1727602969,484.296875,51.8
1727602969,484.296875,40.6
1727602970,484.296875,49.8
1727602970,484.296875,55.6
1727602970,484.296875,50.0
1727602970,484.296875,38.7
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1727602972,484.296875,56.5
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1727602972,484.296875,51.2
1727602973,484.296875,49.7
1727602973,484.296875,54.3
1727602973,484.296875,47.7
1727602973,484.296875,57.8
1727602974,484.296875,49.7
1727602974,484.296875,55.3
1727602974,484.296875,47.2
1727602974,484.296875,58.1
1727602976,484.296875,49.8
1727602976,484.296875,38.9
1727602976,484.296875,52.5
1727602976,484.296875,56.5
1727602977,484.296875,49.7
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1727602977,484.296875,50.0
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1727602979,484.296875,49.8
1727602979,484.296875,55.3
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1727602979,484.296875,55.6
1727602980,484.296875,49.8
1727602980,484.296875,36.4
1727602980,484.296875,53.3
1727602980,484.296875,40.6
1727602981,484.296875,49.7
1727602981,484.296875,54.3
1727602981,484.296875,50.0
1727602981,484.296875,40.6
1727602983,484.296875,49.8
1727602983,484.296875,54.3
1727602983,484.296875,48.6
1727602983,484.296875,44.4
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1727602984,484.296875,50.0
1727602984,484.296875,49.1
1727602984,484.296875,41.9
1727602986,484.296875,49.8
1727602986,484.296875,38.9
1727602986,484.296875,55.1
1727602986,484.296875,40.0
1727602987,484.296875,49.7
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1727602987,484.296875,44.7
1727602988,484.296875,49.8
1727602988,484.296875,55.6
1727602988,484.296875,47.6
1727602988,484.296875,47.2
1727602990,484.296875,49.7
1727602990,484.296875,53.2
1727602990,484.296875,50.8
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1727602991,484.296875,49.8
1727602991,484.296875,40.0
1727602991,484.296875,52.9
1727602991,484.296875,38.7
1727602993,484.296875,49.8
1727602993,484.296875,38.7
1727602993,484.296875,49.5
1727602993,484.296875,54.3
1727602994,484.296875,49.8
1727602994,484.296875,55.3
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1727602996,484.296875,56.8
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1727602997,484.296875,51.2
1727602997,484.296875,50.9
1727602997,484.296875,41.2
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1727602998,484.296875,56.5
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1727603000,484.296875,40.6
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1727603005,484.296875,49.7
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1727603005,484.296875,48.2
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1727603008,484.296875,49.8
1727603008,484.296875,37.5
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1727603009,484.296875,49.8
1727603009,484.296875,38.9
1727603009,484.296875,53.8
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1727603015,484.296875,37.1
1727603015,484.296875,50.5
1727603015,484.296875,51.2
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1727603017,484.296875,39.4
1727603017,484.296875,52.1
1727603017,484.296875,44.4
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1727603019,484.296875,51.2
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1727603021,484.296875,57.4
1727603021,484.296875,48.1
1727603021,484.296875,45.9
1727603022,484.296875,49.8
1727603022,484.296875,38.2
1727603022,484.296875,53.3
1727603022,484.296875,38.7
1727603023,484.296875,49.8
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1727603024,484.296875,47.7
1727603024,484.296875,57.8
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1727603025,484.296875,55.6
1727603025,484.296875,48.6
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1727603029,484.296875,37.5
1727603029,484.296875,50.6
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1727603031,484.296875,49.7
1727603031,484.296875,42.9
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1727603031,484.296875,40.6
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1727603032,484.296875,50.0
1727603032,484.296875,39.4
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1727603039,484.296875,48.1
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1727603060,485.36328125,49.9
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1727603082,485.4375,56.5
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1727603089,485.4375,49.9
1727603089,485.4375,54.3
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1727603091,485.4375,49.9
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1727603091,485.4375,40.6
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1727603093,485.4375,50.0
1727603093,485.4375,50.0
1727603093,485.4375,52.5
1727603096,485.4375,49.9
1727603096,485.4375,55.3
1727603096,485.4375,51.2
1727603096,485.4375,41.0
1727603098,485.4375,49.9
1727603098,485.4375,39.4
1727603098,485.4375,53.5
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1727603100,485.4375,55.3
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1727603100,485.4375,40.6
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1727603103,485.4375,38.2
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1727603103,485.4375,56.5
1727603105,485.4375,49.9
1727603105,485.4375,40.0
1727603105,485.4375,51.6
1727603105,485.4375,42.9
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1727603107,485.4375,55.6
1727603107,485.4375,51.2
1727603107,485.4375,40.6
1727603109,485.4921875,49.9
1727603109,485.4921875,55.3
1727603109,485.4921875,50.4
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1727603111,485.4921875,49.9
1727603111,485.4921875,38.2
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1727603111,485.4921875,56.5
1727603113,485.4921875,49.9
1727603113,485.4921875,44.7
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1727603113,485.4921875,40.6
1727603116,485.4921875,49.9
1727603116,485.4921875,54.3
1727603116,485.4921875,50.4
1727603116,485.4921875,45.7
1727603119,485.5234375,49.9
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1727603119,485.5234375,40.0
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1727603121,485.5234375,40.6
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1727603123,485.5234375,38.7
1727603125,485.5234375,49.9
1727603125,485.5234375,36.4
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1727603125,485.5234375,56.5
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1727603543,524.3359375,36.4
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1727603545,524.3359375,52.4
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1727603550,524.3359375,52.7
1727603550,524.3359375,39.4
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1727603552,524.3359375,55.6
1727603552,524.3359375,47.0
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1727603554,524.3359375,49.8
1727603554,524.3359375,39.4
1727603554,524.3359375,51.7
1727603554,524.3359375,42.4
1727603555,524.3359375,49.8
1727603555,524.3359375,39.4
1727603555,524.3359375,51.0
1727603555,524.3359375,40.6
1727603557,524.3359375,49.8
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1727603557,524.3359375,47.1
1727603557,524.3359375,57.4
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1727603559,524.3359375,36.4
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1727603560,524.3359375,40.6
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1727603562,524.3359375,47.8
1727603562,524.3359375,57.8
1727603564,524.3359375,49.8
1727603564,524.3359375,38.2
1727603564,524.3359375,52.1
1727603564,524.3359375,40.6
1727603565,524.3359375,49.8
1727603565,524.3359375,54.3
1727603565,524.3359375,48.1
1727603565,524.3359375,56.5
1727603567,524.3359375,49.8
1727603567,524.3359375,56.5
1727603567,524.3359375,47.4
1727603567,524.3359375,56.8
1727603569,524.3359375,49.8
1727603569,524.3359375,53.2
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1727603569,524.3359375,55.6
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1727603570,524.3359375,54.2
1727603570,524.3359375,47.4
1727603570,524.3359375,57.4
1727603572,524.3359375,49.8
1727603572,524.3359375,54.3
1727603572,524.3359375,49.3
1727603572,524.3359375,51.2
1727603574,524.3359375,49.8
1727603574,524.3359375,39.4
1727603574,524.3359375,52.1
1727603574,524.3359375,40.6
1727603575,524.3359375,49.8
1727603575,524.3359375,56.3
1727603575,524.3359375,50.0
1727603575,524.3359375,40.6
1727603577,524.3359375,49.8
1727603577,524.3359375,44.4
1727603577,524.3359375,51.0
1727603577,524.3359375,39.4
1727603579,524.3359375,49.8
1727603579,524.3359375,38.2
1727603579,524.3359375,51.7
1727603579,524.3359375,44.7
1727603581,524.3359375,49.8
1727603581,524.3359375,56.5
1727603581,524.3359375,47.4
1727603581,524.3359375,57.4
1727603582,524.3359375,49.8
1727603582,524.3359375,54.3
1727603582,524.3359375,49.3
1727603582,524.3359375,53.7
1727603584,524.3359375,49.8
1727603584,524.3359375,54.3
1727603584,524.3359375,51.0
1727603584,524.3359375,40.6
1727603586,524.3359375,49.8
1727603586,524.3359375,56.5
1727603586,524.3359375,51.3
1727603586,524.3359375,38.7
1727603587,524.3359375,49.8
1727603587,524.3359375,53.2
1727603587,524.3359375,51.0
1727603587,524.3359375,38.9
1727603589,524.3359375,49.8
1727603589,524.3359375,53.2
1727603589,524.3359375,47.4
1727603589,524.3359375,57.4
1727603591,524.3359375,49.8
1727603591,524.3359375,50.0
1727603591,524.3359375,48.9
1727603591,524.3359375,55.6
1727603592,524.3359375,49.8
1727603592,524.3359375,54.3
1727603592,524.3359375,47.1
1727603592,524.3359375,55.6
1727603594,524.3359375,49.8
1727603594,524.3359375,53.1
1727603594,524.3359375,50.3
1727603594,524.3359375,40.6
1727603596,524.3359375,49.6
1727603596,524.3359375,40.0
1727603596,524.3359375,52.4
1727603596,524.3359375,42.4
1727603598,524.3359375,49.8
1727603598,524.3359375,39.4
1727603598,524.3359375,51.0
1727603598,524.3359375,58.7
1727603599,524.3359375,49.8
1727603599,524.3359375,37.5
1727603599,524.3359375,51.3
1727603599,524.3359375,53.7
1727603601,524.3359375,49.8
1727603601,524.3359375,54.3
1727603601,524.3359375,48.5
1727603601,524.3359375,54.3
1727603603,524.3359375,49.8
1727603603,524.3359375,54.3
1727603603,524.3359375,48.5
1727603603,524.3359375,54.5
1727603604,524.3359375,49.8
1727603604,524.3359375,55.3
1727603604,524.3359375,48.1
1727603604,524.3359375,55.6
1727603606,524.3359375,49.8
1727603606,524.3359375,38.2
1727603606,524.3359375,52.0
1727603606,524.3359375,39.4
1727603608,524.3359375,49.8
1727603608,524.3359375,55.6
1727603608,524.3359375,49.3
1727603608,524.3359375,48.6
1727603609,524.3359375,49.8
1727603609,524.3359375,36.4
1727603609,524.3359375,52.0
1727603609,524.3359375,40.6
1727603611,524.3359375,49.8
1727603611,524.3359375,50.0
1727603611,524.3359375,49.3
1727603611,524.3359375,51.2
1727603613,524.3359375,49.8
1727603613,524.3359375,41.2
1727603613,524.3359375,51.8
1727603613,524.3359375,47.2
1727603614,524.3359375,49.8
1727603614,524.3359375,37.5
1727603614,524.3359375,52.4
1727603614,524.3359375,43.2
1727603616,524.3359375,49.8
1727603616,524.3359375,54.2
1727603616,524.3359375,48.1
1727603616,524.3359375,56.5
1727603618,524.3359375,49.8
1727603618,524.3359375,55.3
1727603618,524.3359375,47.1
1727603618,524.3359375,56.8
1727603619,524.3359375,49.8
1727603619,524.3359375,55.3
1727603619,524.3359375,47.8
1727603619,524.3359375,56.8
1727603621,524.3359375,49.8
1727603621,524.3359375,39.4
1727603621,524.3359375,52.4
1727603621,524.3359375,39.4
1727603623,524.3359375,49.8
1727603623,524.3359375,54.2
1727603623,524.3359375,47.4
1727603623,524.3359375,56.8
1727603625,524.3359375,49.8
1727603625,524.3359375,55.6
1727603625,524.3359375,51.0
1727603625,524.3359375,37.5
1727603626,524.3359375,49.8
1727603626,524.3359375,51.1
1727603626,524.3359375,49.3
1727603626,524.3359375,50.0
1727603628,524.3359375,49.8
1727603628,524.3359375,37.1
1727603628,524.3359375,51.0
1727603628,524.3359375,56.3
1727603630,524.3359375,49.8
1727603630,524.3359375,37.5
1727603630,524.3359375,51.3
1727603630,524.3359375,55.6
1727603631,524.3359375,49.8
1727603631,524.3359375,55.3
1727603631,524.3359375,48.5
1727603631,524.3359375,38.7
1727603633,524.3359375,49.8
1727603633,524.3359375,36.4
1727603633,524.3359375,53.4
1727603633,524.3359375,42.4
1727603635,524.3359375,49.8
1727603635,524.3359375,54.3
1727603635,524.3359375,47.7
1727603635,524.3359375,59.6
1727603636,524.3359375,49.8
1727603636,524.3359375,56.5
1727603636,524.3359375,50.3
1727603636,524.3359375,37.1
1727603638,524.3359375,49.8
1727603638,524.3359375,44.7
1727603638,524.3359375,50.7
1727603638,524.3359375,48.6
1727603640,524.3359375,49.8
1727603640,524.3359375,43.2
1727603640,524.3359375,49.7
1727603640,524.3359375,55.6
1727603641,524.3359375,49.8
1727603641,524.3359375,39.4
1727603641,524.3359375,52.7
1727603641,524.3359375,40.6
1727603643,524.3359375,49.8
1727603643,524.3359375,55.6
1727603643,524.3359375,48.1
1727603643,524.3359375,57.8
1727603645,524.3359375,49.8
1727603645,524.3359375,37.5
1727603645,524.3359375,51.7
1727603645,524.3359375,40.6
1727603646,524.3359375,49.8
1727603646,524.3359375,54.3
1727603646,524.3359375,51.3
1727603646,524.3359375,38.7
1727603648,524.3359375,49.8
1727603648,524.3359375,52.2
1727603648,524.3359375,46.8
1727603648,524.3359375,56.5
1727603650,524.3359375,49.8
1727603650,524.3359375,56.5
1727603650,524.3359375,47.8
1727603650,524.3359375,56.5
1727603651,524.3359375,49.8
1727603651,524.3359375,55.6
1727603651,524.3359375,51.0
1727603651,524.3359375,40.6
1727603653,524.3359375,49.8
1727603653,524.3359375,55.3
1727603653,524.3359375,51.0
1727603653,524.3359375,42.4
1727603655,524.3359375,49.8
1727603655,524.3359375,51.0
1727603655,524.3359375,50.0
1727603655,524.3359375,54.3
1727603657,524.3359375,49.8
1727603657,524.3359375,47.5
1727603657,524.3359375,49.3
1727603657,524.3359375,55.6
1727603658,524.3359375,49.8
1727603658,524.3359375,40.0
1727603658,524.3359375,52.3
1727603658,524.3359375,37.1
1727603660,524.3359375,49.8
1727603660,524.3359375,54.3
1727603660,524.3359375,50.0
1727603660,524.3359375,56.5
1727603662,524.3359375,49.8
1727603662,524.3359375,36.4
1727603662,524.3359375,50.6
1727603662,524.3359375,56.8
1727603663,524.3359375,49.8
1727603663,524.3359375,54.0
1727603663,524.3359375,47.1
1727603663,524.3359375,56.5
1727603665,524.3359375,49.8
1727603665,524.3359375,43.2
1727603665,524.3359375,51.7
1727603665,524.3359375,40.6
1727603667,524.3359375,49.8
1727603667,524.3359375,56.5
1727603667,524.3359375,50.7
1727603667,524.3359375,41.9
1727603669,524.3359375,49.8
1727603669,524.3359375,53.2
1727603669,524.3359375,46.7
1727603669,524.3359375,56.5
1727603670,524.3359375,49.8
1727603670,524.3359375,54.3
1727603670,524.3359375,52.0
1727603670,524.3359375,39.4
1727603672,524.3359375,49.8
1727603672,524.3359375,55.3
1727603672,524.3359375,50.3
1727603672,524.3359375,40.6
1727603674,524.3359375,49.8
1727603674,524.3359375,50.0
1727603674,524.3359375,49.0
1727603674,524.3359375,55.6
1727603675,524.3359375,49.8
1727603675,524.3359375,54.3
1727603675,524.3359375,47.4
1727603675,524.3359375,56.5
1727603677,524.3359375,49.8
1727603677,524.3359375,56.5
1727603677,524.3359375,47.8
1727603677,524.3359375,57.8
1727603679,524.3359375,49.8
1727603679,524.3359375,54.0
1727603679,524.3359375,51.0
1727603679,524.3359375,45.7
1727603680,524.3359375,49.8
1727603680,524.3359375,39.4
1727603680,524.3359375,52.1
1727603680,524.3359375,38.7
1727603682,524.3359375,49.8
1727603682,524.3359375,38.2
1727603682,524.3359375,52.7
1727603682,524.3359375,38.7
1727603684,524.3359375,49.8
1727603684,524.3359375,55.6
1727603684,524.3359375,48.5
1727603684,524.3359375,56.8
1727603685,524.3359375,49.8
1727603685,524.3359375,55.3
1727603685,524.3359375,48.1
1727603685,524.3359375,52.5
1727603687,524.3359375,49.8
1727603687,524.3359375,55.3
1727603687,524.3359375,49.7
1727603687,524.3359375,47.2
1727603689,524.3359375,49.8
1727603689,524.3359375,47.5
1727603689,524.3359375,52.3
1727603689,524.3359375,40.6
1727603691,524.3359375,49.8
1727603691,524.3359375,35.3
1727603691,524.3359375,50.7
1727603691,524.3359375,57.8
1727603692,524.3359375,49.8
1727603692,524.3359375,39.4
1727603692,524.3359375,51.7
1727603692,524.3359375,56.3
1727603694,524.3359375,49.8
1727603694,524.3359375,54.3
1727603694,524.3359375,51.7
1727603694,524.3359375,40.6
1727603696,524.3359375,49.8
1727603696,524.3359375,40.6
1727603696,524.3359375,52.4
1727603696,524.3359375,40.6
1727603697,524.3359375,49.8
1727603697,524.3359375,38.2
1727603697,524.3359375,51.3
1727603697,524.3359375,50.0
1727603699,524.3359375,49.8
1727603699,524.3359375,55.6
1727603699,524.3359375,46.8
1727603699,524.3359375,57.8
1727603701,524.3359375,49.8
1727603701,524.3359375,40.0
1727603701,524.3359375,51.4
1727603701,524.3359375,52.9
1727603702,524.3359375,49.8
1727603702,524.3359375,39.4
1727603702,524.3359375,52.4
1727603702,524.3359375,42.4
1727603704,524.3359375,49.8
1727603704,524.3359375,37.5
1727603704,524.3359375,52.0
1727603704,524.3359375,40.6
1727603706,524.3359375,49.8
1727603706,524.3359375,54.3
1727603706,524.3359375,51.0
1727603706,524.3359375,40.6
1727603707,524.3359375,49.8
1727603707,524.3359375,54.3
1727603707,524.3359375,49.7
1727603707,524.3359375,39.4
1727603709,524.3359375,49.8
1727603709,524.3359375,55.6
1727603709,524.3359375,47.8
1727603709,524.3359375,56.8
1727603711,524.3359375,49.8
1727603711,524.3359375,50.0
1727603711,524.3359375,52.0
1727603711,524.3359375,38.7
1727603712,524.3359375,49.8
1727603712,524.3359375,54.3
1727603712,524.3359375,52.3
1727603713,524.3359375,39.4
1727603714,524.3359375,49.8
1727603714,524.3359375,55.3
1727603714,524.3359375,48.5
1727603714,524.3359375,51.2
1727603716,524.3359375,49.8
1727603716,524.3359375,43.2
1727603716,524.3359375,52.7
1727603716,524.3359375,37.5
1727603718,524.3359375,49.8
1727603718,524.3359375,54.3
1727603718,524.3359375,46.7
1727603718,524.3359375,56.8
1727603719,529.3359375,49.8
1727603719,529.3359375,56.5
1727603719,529.3359375,47.4
1727603719,529.3359375,57.8
1727603721,529.3359375,49.8
1727603721,529.3359375,38.2
1727603721,529.3359375,51.7
1727603721,529.3359375,38.7
1727603723,529.3359375,49.8
1727603723,529.3359375,37.1
1727603723,529.3359375,51.3
1727603723,529.3359375,56.5
1727603724,529.3359375,49.8
1727603724,529.3359375,55.6
1727603724,529.3359375,48.1
1727603724,529.3359375,57.8
1727603726,529.3359375,49.8
1727603726,529.3359375,39.4
1727603726,529.3359375,53.1
1727603726,529.3359375,38.7
1727603728,529.3359375,49.8
1727603728,529.3359375,41.2
1727603728,529.3359375,52.1
1727603728,529.3359375,41.9
1727603729,529.3359375,49.8
1727603729,529.3359375,53.2
1727603729,529.3359375,51.7
1727603729,529.3359375,38.7
1727603731,529.3359375,49.8
1727603731,529.3359375,38.2
1727603731,529.3359375,51.3
1727603731,529.3359375,53.3
1727603733,529.3359375,49.8
1727603733,529.3359375,55.6
1727603733,529.3359375,48.1
1727603733,529.3359375,57.8
1727603734,529.3359375,49.8
1727603734,529.3359375,39.4
1727603734,529.3359375,52.7
1727603734,529.3359375,40.6
1727603736,529.3359375,49.8
1727603736,529.3359375,52.3
1727603736,529.3359375,46.8
1727603736,529.3359375,55.6
1727603738,529.3359375,49.8
1727603738,529.3359375,39.4
1727603738,529.3359375,52.7
1727603738,529.3359375,53.2
1727603739,529.3359375,49.8
1727603739,529.3359375,55.6
1727603739,529.3359375,48.1
1727603739,529.3359375,54.5
1727603741,529.3359375,49.8
1727603741,529.3359375,52.0
1727603741,529.3359375,48.1
1727603741,529.3359375,55.8
1727603743,529.3359375,49.8
1727603743,529.3359375,39.4
1727603743,529.3359375,51.7
1727603743,529.3359375,40.6
1727603745,529.3359375,49.8
1727603745,529.3359375,39.4
1727603745,529.3359375,51.7
1727603745,529.3359375,38.7
1727603746,529.3359375,49.8
1727603746,529.3359375,51.2
1727603746,529.3359375,52.4
1727603746,529.3359375,40.6
1727603748,529.3359375,49.8
1727603748,529.3359375,54.3
1727603748,529.3359375,48.9
1727603748,529.3359375,50.0
1727603750,529.3359375,49.8
1727603750,529.3359375,57.4
1727603750,529.3359375,51.4
1727603750,529.3359375,37.5
1727603751,529.3359375,49.8
1727603751,529.3359375,54.3
1727603751,529.3359375,48.1
1727603751,529.3359375,57.8
1727603753,529.3359375,49.8
1727603753,529.3359375,56.5
1727603753,529.3359375,48.5
1727603753,529.3359375,55.8
1727603755,529.3359375,49.8
1727603755,529.3359375,36.4
1727603755,529.3359375,52.0
1727603755,529.3359375,56.3
1727603756,529.3359375,49.8
1727603756,529.3359375,56.3
1727603756,529.3359375,48.1
1727603756,529.3359375,57.8
1727603758,529.3359375,49.8
1727603758,529.3359375,53.2
1727603758,529.3359375,47.4
1727603758,529.3359375,56.8
1727603760,529.3359375,49.8
1727603760,529.3359375,54.3
1727603760,529.3359375,48.9
1727603760,529.3359375,57.8
1727603761,529.3359375,49.8
1727603761,529.3359375,54.3
1727603761,529.3359375,47.8
1727603761,529.3359375,57.4
1727603763,529.3359375,49.8
1727603763,529.3359375,38.2
1727603763,529.3359375,51.0
1727603763,529.3359375,56.5
1727603765,529.3359375,49.8
1727603765,529.3359375,37.1
1727603765,529.3359375,52.4
1727603765,529.3359375,37.5
1727603766,529.3359375,49.8
1727603766,529.3359375,38.2
1727603766,529.3359375,52.7
1727603766,529.3359375,38.7
1727603768,529.3359375,49.8
1727603768,529.3359375,55.3
1727603768,529.3359375,47.0
1727603768,529.3359375,56.5
1727603770,529.3359375,49.8
1727603770,529.3359375,38.7
1727603770,529.3359375,49.3
1727603770,529.3359375,56.8
1727603772,529.3359375,49.8
1727603772,529.3359375,40.0
1727603772,529.3359375,49.6
1727603772,529.3359375,52.2
1727603773,529.3359375,49.8
1727603773,529.3359375,54.3
1727603773,529.3359375,51.7
1727603773,529.3359375,48.6
1727603775,529.3359375,49.8
1727603775,529.3359375,37.5
1727603775,529.3359375,52.7
1727603775,529.3359375,40.6
1727603777,529.3359375,49.8
1727603777,529.3359375,55.3
1727603777,529.3359375,46.7
1727603777,529.3359375,57.8
1727603778,529.3359375,49.8
1727603778,529.3359375,36.4
1727603778,529.3359375,52.0
1727603778,529.3359375,41.9
1727603780,529.3359375,49.8
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1727604467,541.71875,51.7
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1727604475,541.71875,40.6
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1727604481,541.71875,38.2
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1727604481,541.71875,48.6
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1727604483,541.71875,54.3
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1727604485,541.71875,37.5
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1727604487,541.71875,53.2
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1727604487,541.71875,51.2
1727604489,541.71875,49.8
1727604489,541.71875,54.3
1727604489,541.71875,49.4
1727604489,541.71875,56.5
1727604491,541.71875,49.8
1727604491,541.71875,42.9
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1727604491,541.71875,57.8
1727604493,541.71875,49.8
1727604493,541.71875,52.2
1727604493,541.71875,49.1
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1727604545,541.71875,51.1
1727604545,541.71875,50.6
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1727604547,541.71875,42.4
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1727604549,541.71875,57.8
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1727604555,541.71875,47.8
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1727604557,541.71875,56.3
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1727604557,541.71875,36.4
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1727604561,541.71875,49.8
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1727604567,541.71875,56.8
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1727604616,530.73828125,49.8
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1727604618,530.73828125,49.8
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1727604620,530.73828125,34.3
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1727604622,530.73828125,51.1
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1727604932,546.76953125,49.9
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1727604936,546.76953125,49.8
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1727604938,546.76953125,49.9
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1727604943,546.76953125,49.8
1727604943,546.76953125,55.6
1727604943,546.76953125,51.2
1727604943,546.76953125,42.4
1727604945,546.76953125,49.9
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1727604945,546.76953125,56.8
1727604947,546.76953125,49.8
1727604947,546.76953125,38.2
1727604947,546.76953125,51.7
1727604947,546.76953125,38.7
1727604949,546.76953125,49.8
1727604949,546.76953125,55.3
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1727604951,546.76953125,49.9
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1727604953,546.76953125,51.1
1727604953,546.76953125,39.4
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1727604956,546.76953125,53.3
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1727604958,546.76953125,36.4
1727604958,546.76953125,51.7
1727604958,546.76953125,42.4
1727604960,546.76953125,49.9
1727604960,546.76953125,55.3
1727604960,546.76953125,47.5
1727604960,546.76953125,55.6
1727604962,546.76953125,49.9
1727604962,546.76953125,38.2
1727604962,546.76953125,51.4
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1727605427,547.74609375,55.3
1727605427,547.74609375,49.7
1727605427,547.74609375,51.3
1727605430,548.51953125,49.9
1727605430,548.51953125,37.5
1727605430,548.51953125,50.3
1727605430,548.51953125,58.7
1727605432,547.74609375,49.8
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1727605432,547.74609375,48.5
1727605432,547.74609375,54.8
1727605457,547.50390625,50.2
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1727605461,547.56640625,55.3
1727605464,547.5625,49.9
1727605464,547.5625,55.6
1727605464,547.5625,49.7
1727605464,547.5625,38.7
1727605466,547.5625,49.9
1727605466,547.5625,38.2
1727605466,547.5625,52.4
1727605466,547.5625,42.9
1727605468,547.56640625,49.9
1727605468,547.56640625,43.2
1727605468,547.56640625,49.7
1727605468,547.56640625,56.5
1727605470,547.56640625,49.9
1727605470,547.56640625,37.5
1727605470,547.56640625,51.4
1727605470,547.56640625,40.6
1727605473,547.5703125,49.9
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1727605502,550.46484375,48.7
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1727605507,550.46484375,50.9
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1727605509,550.46484375,50.6
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1727605511,550.46484375,50.9
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1727605514,550.46484375,49.9
1727605514,550.46484375,52.3
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1727605514,550.46484375,55.6
1727605516,550.46484375,49.9
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1727605516,550.46484375,50.0
1727605516,550.46484375,55.6
1727605518,550.46484375,49.9
1727605518,550.46484375,54.3
1727605518,550.46484375,50.8
1727605518,550.46484375,40.6
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1727605521,550.46484375,55.8
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1727605523,550.46484375,48.8
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1727605525,550.46484375,49.9
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1727605525,550.46484375,49.4
1727605525,550.46484375,47.4
1727605527,550.46484375,49.9
1727605527,550.46484375,54.3
1727605527,550.46484375,47.0
1727605527,550.46484375,56.8
1727605531,550.46484375,49.9
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1727605531,550.46484375,49.4
1727605531,550.46484375,58.7
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1727605576,548.03125,54.8
1727605578,548.03125,49.9
1727605578,548.03125,54.3
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1727605578,548.03125,40.6
1727605581,548.03125,49.9
1727605581,548.03125,41.2
1727605581,548.03125,50.8
1727605581,548.03125,55.6
1727605583,548.03125,49.8
1727605583,548.03125,54.2
1727605583,548.03125,48.8
1727605583,548.03125,48.6
1727605586,548.03125,49.9
1727605586,548.03125,47.5
1727605586,548.03125,51.5
1727605586,548.03125,40.6
1727605588,548.03125,49.9
1727605588,548.03125,47.5
1727605588,548.03125,51.1
1727605588,548.03125,38.7
1727605590,548.03125,49.9
1727605590,548.03125,47.6
1727605590,548.03125,49.7
1727605590,548.03125,53.1
1727605593,548.03125,49.9
1727605593,548.03125,54.3
1727605593,548.03125,50.6
1727605593,548.03125,39.4
1727605595,548.03125,49.9
1727605595,548.03125,55.6
1727605595,548.03125,50.8
1727605595,548.03125,39.4
1727605597,548.03125,49.9
1727605597,548.03125,54.3
1727605597,548.03125,50.6
1727605597,548.03125,39.4
1727605600,548.03125,49.9
1727605600,548.03125,44.7
1727605600,548.03125,50.0
1727605600,548.03125,55.6
1727605602,548.03125,49.9
1727605602,548.03125,55.1
1727605602,548.03125,50.6
1727605602,548.03125,45.9
1727605604,548.03125,49.9
1727605604,548.03125,43.2
1727605604,548.03125,49.4
1727605604,548.03125,57.8
1727605607,548.03125,49.9
1727605607,548.03125,47.5
1727605607,548.03125,50.5
1727605607,548.03125,47.2
1727605609,548.03125,49.9
1727605609,548.03125,55.6
1727605609,548.03125,50.6
1727605609,548.03125,38.7
1727605611,548.03125,49.9
1727605611,548.03125,55.3
1727605611,548.03125,51.5
1727605611,548.03125,39.4
1727605613,548.03125,49.9
1727605613,548.03125,54.3
1727605613,548.03125,49.2
1727605613,548.03125,57.8
1727605617,548.03125,49.9
1727605617,548.03125,54.3
1727605617,548.03125,51.1
1727605617,548.03125,38.7
1727605649,549.51171875,50.2
1727605649,549.51171875,54.3
1727605649,549.51171875,49.2
1727605649,549.51171875,50.0
1727605669,549.7265625,50.2
1727605669,549.7265625,55.6
1727605669,549.7265625,48.2
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1727605671,549.7265625,51.0
1727605671,549.7265625,50.3
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1727605674,549.7265625,49.9
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1727605674,549.7265625,52.3
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1727605690,549.734375,52.3
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1727605714,549.734375,49.7
1727605714,549.734375,57.8
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1727605716,549.734375,54.8
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1727605719,549.734375,50.5
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1727605721,549.734375,49.9
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1727605721,549.734375,49.9
1727605721,549.734375,42.4
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1727605728,549.734375,49.9
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1727605731,549.734375,40.6
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1727605733,549.734375,38.7
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1727605802,551.2578125,49.7
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1727605847,548.9765625,49.9
1727605847,548.9765625,37.5
1727605847,548.9765625,51.1
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1727605849,548.9765625,49.8
1727605849,548.9765625,51.2
1727605849,548.9765625,52.0
1727605849,548.9765625,38.7
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1727605854,548.9765625,49.9
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1727605854,548.9765625,55.6
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1727605859,548.9765625,36.4
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1727605862,548.9765625,51.0
1727605862,548.9765625,51.8
1727605862,548.9765625,39.4
1727605864,549.0703125,49.9
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1727605864,549.0703125,49.2
1727605864,549.0703125,57.8
1727605866,549.0703125,49.9
1727605866,549.0703125,38.2
1727605866,549.0703125,49.2
1727605866,549.0703125,56.8
1727605869,549.0703125,49.9
1727605869,549.0703125,38.2
1727605869,549.0703125,50.3
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1727605873,549.0703125,55.6
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1727605873,549.0703125,40.6
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1727605876,549.0703125,50.0
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1727605895,551.38671875,50.1
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1727605895,551.38671875,52.0
1727605895,551.38671875,39.4
1727605897,551.38671875,49.9
1727605897,551.38671875,47.8
1727605897,551.38671875,51.5
1727605897,551.38671875,53.3
1727605900,551.38671875,49.9
1727605900,551.38671875,40.0
1727605900,551.38671875,52.6
1727605900,551.38671875,38.7
1727605902,551.38671875,49.9
1727605902,551.38671875,56.2
1727605902,551.38671875,49.2
1727605902,551.38671875,39.4
1727605905,551.38671875,49.9
1727605905,551.38671875,38.2
1727605905,551.38671875,50.3
1727605905,551.38671875,53.2
1727605907,551.38671875,49.9
1727605907,551.38671875,53.2
1727605907,551.38671875,48.6
1727605907,551.38671875,56.5
1727605910,551.38671875,49.9
1727605910,551.38671875,55.6
1727605910,551.38671875,50.0
1727605910,551.38671875,54.2
1727605912,551.38671875,49.8
1727605912,551.38671875,54.3
1727605912,551.38671875,49.5
1727605912,551.38671875,51.2
1727605915,551.38671875,49.9
1727605915,551.38671875,54.3
1727605915,551.38671875,49.7
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1727606012,550.609375,50.2
1727606012,550.609375,55.3
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1727606012,550.609375,41.2
1727606015,550.60546875,49.9
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1727606030,550.578125,44.1
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1727606035,550.578125,49.9
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1727606049,550.58984375,49.9
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1727606081,550.921875,56.5
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1727606083,550.8984375,55.3
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1727606130,551.921875,51.8
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1727606313,550.6796875,50.7
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1727606323,550.625,40.6
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1727606497,442.421875,43.6
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1727606513,442.45703125,49.9
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1727606518,442.453125,49.9
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1727606518,442.453125,38.7
1727606521,442.45703125,49.9
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1727606523,442.453125,49.9
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1727606523,442.453125,42.9
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1727606526,442.4609375,55.3
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1727606526,442.4609375,37.5
1727606529,442.4609375,49.9
1727606529,442.4609375,44.7
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1727606531,442.4609375,49.9
1727606531,442.4609375,40.5
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1727606531,442.4609375,53.1
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1727606537,442.46875,49.9
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1727606540,442.45703125,49.9
1727606540,442.45703125,53.3
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1727606540,442.45703125,39.4
1727606542,442.4609375,49.9
1727606542,442.4609375,38.2
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1727606546,442.43359375,49.9
1727606546,442.43359375,55.3
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1727606546,442.43359375,39.4
1727606564,445.0703125,50.1
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1727606564,445.0703125,50.7
1727606564,445.0703125,57.4
1727606567,445.07421875,49.9
1727606567,445.07421875,54.2
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1727606567,445.07421875,40.6
1727606569,445.046875,49.9
1727606569,445.046875,57.1
1727606569,445.046875,49.3
1727606569,445.046875,37.5
1727606572,445.04296875,49.9
1727606572,445.04296875,53.2
1727606572,445.04296875,50.5
1727606572,445.04296875,38.7
1727606575,445.046875,49.9
1727606575,445.046875,53.2
1727606575,445.046875,51.7
1727606575,445.046875,38.7
1727606607,445.1640625,50.2
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1727611563,446.796875,37.5
1727611563,446.796875,50.0
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1727611579,446.62109375,50.0
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1727611579,446.62109375,51.3
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1727611644,447.98828125,50.2
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1727611644,447.98828125,50.2
1727611644,447.98828125,38.7
1727611679,437.3203125,50.1
1727611679,437.3203125,56.5
1727611679,437.3203125,48.4
1727611679,437.3203125,57.8
1727611693,442.8125,50.0
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1727611739,440.18359375,50.2
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1727611739,440.18359375,48.8
1727611739,440.18359375,57.4
1727611796,448.765625,50.1
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1727611846,444.14453125,50.1
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1727612097,458.5234375,50.1
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1727612097,458.5234375,50.6
1727612097,458.5234375,48.6
1727612198,444.23046875,50.2
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1727612198,444.23046875,39.4
1727612291,446.24609375,50.2
1727612291,446.24609375,54.3
1727612291,446.24609375,48.9
1727612291,446.24609375,57.8
1727612388,451.40625,50.2
1727612388,451.40625,53.2
1727612388,451.40625,50.9
1727612388,451.40625,40.6
1727612526,461.5390625,50.2
1727612526,461.5390625,54.3
1727612526,461.5390625,49.8
1727612526,461.5390625,56.8
1727612711,456.22265625,50.2
1727612711,456.22265625,44.2
1727612711,456.22265625,50.6
1727612711,456.22265625,38.7
1727612904,445.08203125,50.2
1727612904,445.08203125,54.3
1727612904,445.08203125,48.6
1727612904,445.08203125,59.6
1727613077,455.40625,50.2
1727613077,455.40625,36.4
1727613077,455.40625,50.6
1727613077,455.40625,55.6
1727613250,451.1328125,50.2
1727613250,451.1328125,36.4
1727613250,451.1328125,50.6
1727613250,451.1328125,57.8
1727613418,449.79296875,50.2
1727613418,449.79296875,54.3
1727613418,449.79296875,48.7
1727613418,449.79296875,54.3
1727613514,457.35546875,50.2
1727613514,457.35546875,54.2
1727613514,457.35546875,50.2
1727613514,457.35546875,47.2
1727613704,448.65234375,50.2
1727613704,448.65234375,54.3
1727613704,448.65234375,50.3
1727613704,448.65234375,38.7
1727613824,459.7109375,50.2
1727613824,459.7109375,54.2
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1727613921,464.59375,38.7
1727614029,458.21484375,50.2
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1727614029,458.21484375,56.5
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1727614208,453.83984375,49.1
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1727614279,450.30078125,50.2
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1727614385,451.97265625,50.2
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1727614524,455.4140625,50.2
1727614524,455.4140625,38.2
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1727614524,455.4140625,51.3
1727614660,453.8984375,50.2
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1727614660,453.8984375,49.9
1727614660,453.8984375,57.4
1727614719,446.38671875,50.2
1727614719,446.38671875,37.5
1727614719,446.38671875,50.3
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1727614732,447.2734375,50.0
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1727614732,447.2734375,50.6
1727614732,447.2734375,45.9
1727614825,462.96875,50.2
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1727614825,462.96875,38.7
1727615036,456.16015625,50.2
1727615036,456.16015625,55.3
1727615036,456.16015625,49.5
1727615036,456.16015625,46.2
1727615180,467.32421875,50.2
1727615180,467.32421875,55.1
1727615180,467.32421875,50.1
1727615180,467.32421875,39.4
1727615237,454.3515625,50.1
1727615237,454.3515625,55.6
1727615237,454.3515625,49.3
1727615237,454.3515625,57.8
1727615348,458.859375,50.2
1727615348,458.859375,52.3
1727615348,458.859375,49.0
1727615348,458.859375,56.8
1727615525,467.65625,50.2
1727615525,467.65625,57.4
1727615525,467.65625,49.4
1727615525,467.65625,54.5
1727615593,456.640625,50.2
1727615593,456.640625,54.2
1727615593,456.640625,50.8
1727615593,456.640625,40.6
1727615757,460.43359375,50.2
1727615757,460.43359375,55.3
1727615757,460.43359375,50.1
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1727615904,460.921875,50.2
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1727615904,460.921875,55.6
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1727616130,461.33984375,50.7
1727616130,461.33984375,48.7
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1727616735,460.94140625,50.2
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1727616735,460.94140625,42.4
1727616884,459.59765625,50.2
1727616884,459.59765625,54.3
1727616884,459.59765625,49.4
1727616884,459.59765625,56.8
1727616952,463.08984375,50.1
1727616952,463.08984375,40.0
1727616952,463.08984375,49.6
1727616952,463.08984375,58.7
1727617126,474.69921875,50.2
1727617126,474.69921875,38.2
1727617126,474.69921875,50.7
1727617126,474.69921875,56.8
1727617320,461.8203125,50.2
1727617320,461.8203125,55.6
1727617320,461.8203125,49.9
1727617320,461.8203125,54.8
1727617558,477.3046875,50.2
1727617558,477.3046875,50.6
1727617558,477.3046875,49.9
1727617558,477.3046875,56.8
1727617837,472.7421875,50.2
1727617837,472.7421875,51.0
1727617837,472.7421875,50.3
1727617837,472.7421875,38.7
1727618073,488.1328125,50.2
1727618073,488.1328125,56.5
1727618073,488.1328125,49.0
1727618073,488.1328125,57.8
1727618183,494.07421875,50.1
1727618183,494.07421875,56.5
1727618183,494.07421875,50.1
1727618183,494.07421875,42.4
1727618382,489.84765625,50.2
1727618382,489.84765625,56.3
1727618382,489.84765625,50.0
1727618382,489.84765625,50.0
1727618588,481.859375,50.2
1727618588,481.859375,54.3
1727618588,481.859375,50.5
1727618588,481.859375,39.4
1727618721,492.82421875,50.2
1727618721,492.82421875,56.5
1727618721,492.82421875,50.0
1727618721,492.82421875,40.6
1727618971,468.99609375,50.2
1727618971,468.99609375,55.3
1727618971,468.99609375,49.4
1727618971,468.99609375,40.6
1727619177,473.89453125,50.2
1727619177,473.89453125,55.3
1727619177,473.89453125,49.7
1727619177,473.89453125,40.6
1727619545,469.515625,50.2
1727619545,469.515625,53.2
1727619545,469.515625,49.0
1727619545,469.515625,57.8
1727619869,497.9140625,50.2
1727619869,497.9140625,54.3
1727619869,497.9140625,50.5
1727619869,497.9140625,39.4
1727620099,470.32421875,50.2
1727620099,470.32421875,54.3
1727620099,470.32421875,49.5
1727620099,470.32421875,55.6
1727620369,495.64453125,50.2
1727620369,495.64453125,53.3
1727620369,495.64453125,49.0
1727620369,495.64453125,57.8
1727620459,473.32421875,50.2
1727620459,473.32421875,55.3
1727620459,473.32421875,50.0
1727620459,473.32421875,48.6
1727620708,478.80078125,50.2
1727620708,478.80078125,55.3
1727620708,478.80078125,49.2
1727620708,478.80078125,56.5
1727621049,506.33984375,50.2
1727621049,506.33984375,56.2
1727621049,506.33984375,50.2
1727621049,506.33984375,37.5
1727621504,502.43359375,50.2
1727621504,502.43359375,53.1
1727621504,502.43359375,49.8
1727621504,502.43359375,56.8
1727621832,506.60546875,50.2
1727621832,506.60546875,37.5
1727621832,506.60546875,50.1
1727621832,506.60546875,56.5
1727622244,496.25,50.2
1727622244,496.25,54.3
1727622244,496.25,49.6
1727622244,496.25,54.3
1727622679,521.36328125,50.2
1727622679,521.36328125,55.3
1727622679,521.36328125,49.5
1727622679,521.36328125,45.7
1727623020,497.44921875,50.2
1727623020,497.44921875,43.9
1727623020,497.44921875,50.7
1727623020,497.44921875,37.5
1727623372,515.5390625,50.2
1727623372,515.5390625,54.3
1727623372,515.5390625,49.3
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1727625766,491.60546875,50.2
1727625766,491.60546875,54.2
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1727625766,491.60546875,57.8
1727626386,517.03125,50.3
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1727627197,517.04296875,37.5
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<h1> Parallel Plot</h1>
<div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1> Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1> Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1> Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1> Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1> Heatmap</h1>
<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
<h1>Correlation Heatmap Explanation</h1>
<p>
This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
</p>
<h2>How It Works</h2>
<p>
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
</p>
<ul>
<li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
<li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
<li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
</ul>
<h2>Color Scale: Yellow to Purple (Viridis)</h2>
<p>
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
</p>
<ul>
<li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
<li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
<li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
<li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
</ul>
<h2>What the Heatmap Shows</h2>
<p>
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
</p>
</body>
</html>
