| n_samples | n_clusters | threshold | result |
|---|---|---|---|
| 197 | 2 | 0.062247 | 0.265503 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| n_clusters | range | 1 | 4 | int | |
| threshold | range | 0.002 | 0.07 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 492 | 11 | 503 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,n_clusters,threshold 0,0_0,COMPLETED,Sobol,0.285714285714285698425385362498,348,2,0.060073542356491094196258018201 1,1_0,COMPLETED,Sobol,0.289734552263465117150076366670,467,1,0.060147356942296031911965314976 2,2_0,COMPLETED,Sobol,0.287696882916620766401649689215,256,3,0.062527635604143150249711879951 3,3_0,COMPLETED,Sobol,0.287917171494658008512601554685,244,1,0.027889940015971663445082384669 4,4_0,COMPLETED,Sobol,0.286265007159378748191613794916,358,4,0.068215497639030231447065943939 5,5_0,COMPLETED,Sobol,0.293754818812644535874767370842,668,2,0.033851351145654920427041645326 6,6_0,COMPLETED,Sobol,0.279656349818261928952267680870,709,4,0.034590727504342798359626698357 7,7_0,COMPLETED,Sobol,0.299647538275140457031398000254,225,3,0.065635051038116218324880435375 8,8_0,COMPLETED,Sobol,0.279656349818261928952267680870,579,4,0.015434440743178130805990377894 9,9_0,COMPLETED,Sobol,0.279656349818261928952267680870,832,1,0.005984044417738914635462865732 10,10_0,COMPLETED,Sobol,0.287476594338583524290697823744,781,3,0.068878439199179422591790000752 11,11_0,COMPLETED,Sobol,0.288578037228769734845457151096,457,1,0.042634507581591610247961909863 12,12_0,COMPLETED,Sobol,0.294801189558321352635061884939,219,1,0.017112214051187041308210723400 13,13_0,COMPLETED,Sobol,0.293589602379116687558280318626,858,3,0.065441622987389577725814149289 14,14_0,COMPLETED,Sobol,0.292322943055402628687033939059,788,2,0.043515014130622156618422025076 15,15_0,COMPLETED,Sobol,0.279656349818261928952267680870,864,2,0.020051056977361439437324008850 16,16_0,COMPLETED,Sobol,0.279656349818261928952267680870,869,2,0.023314345303922894003800792007 17,17_0,COMPLETED,Sobol,0.286485295737415990302565660386,228,4,0.045170483965426687367195057732 18,18_0,COMPLETED,Sobol,0.284282409957043680215349468199,169,3,0.003816889986395836063814979155 19,19_0,COMPLETED,Sobol,0.287586738627602200857324987737,606,1,0.046846737418323756985127914731 20,20_0,COMPLETED,BoTorch,0.279656349818261928952267680870,964,2,0.006440706098829535736216556074 21,21_0,COMPLETED,BoTorch,0.279656349818261928952267680870,870,4,0.015139047254553858121162157602 22,22_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.017930694452499305879822344423 23,23_0,COMPLETED,BoTorch,0.279656349818261928952267680870,759,3,0.003743313175126723735264278048 24,24_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.017818407251825763015773418374 25,25_0,COMPLETED,BoTorch,0.279656349818261928952267680870,826,2,0.002000000000000000041633363423 26,26_0,COMPLETED,BoTorch,0.279656349818261928952267680870,699,4,0.003961408212790632800415213666 27,27_0,COMPLETED,BoTorch,0.288082387928185967851391069416,535,4,0.028108361059398234704787000737 28,28_0,COMPLETED,BoTorch,0.279656349818261928952267680870,364,4,0.010995724716744630783393787965 29,29_0,COMPLETED,BoTorch,0.279656349818261928952267680870,726,4,0.021703111054831011883514690908 30,30_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.002000000000000000041633363423 31,31_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,2,0.018208644185763012135481631049 32,32_0,COMPLETED,BoTorch,0.279656349818261928952267680870,904,4,0.002000000000000000041633363423 33,33_0,COMPLETED,BoTorch,0.279656349818261928952267680870,826,3,0.014011524148584657625815630411 34,34_0,COMPLETED,BoTorch,0.287366450049564958746373122267,899,4,0.031426589315584348149901217084 35,35_0,COMPLETED,BoTorch,0.279656349818261928952267680870,608,3,0.002000000000000000041633363423 36,36_0,COMPLETED,BoTorch,0.279656349818261928952267680870,436,4,0.002000000000000000041633363423 37,37_0,COMPLETED,BoTorch,0.279656349818261928952267680870,884,3,0.002000000000000000041633363423 38,38_0,COMPLETED,BoTorch,0.279656349818261928952267680870,696,1,0.002000000000000000041633363423 39,39_0,COMPLETED,BoTorch,0.299922898997686981914512216463,100,4,0.014256247464616362413680761279 40,40_0,COMPLETED,BoTorch,0.279656349818261928952267680870,475,4,0.025392498925328021996961069817 41,41_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.009551141601888217189109120397 42,42_0,COMPLETED,BoTorch,0.279656349818261928952267680870,564,4,0.002000000000000000041633363423 43,43_0,COMPLETED,BoTorch,0.279656349818261928952267680870,705,2,0.008127114054511670737657169639 44,44_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.002000000000000000041633363423 45,45_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.016667694161320523171809782070 46,46_0,COMPLETED,BoTorch,0.279656349818261928952267680870,548,3,0.002000000000000000041633363423 47,47_0,COMPLETED,BoTorch,0.279656349818261928952267680870,742,4,0.012496500587452568156732191085 48,48_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.002000000000000000041633363423 49,49_0,COMPLETED,BoTorch,0.279656349818261928952267680870,897,3,0.009990621740445071452541014878 50,50_0,COMPLETED,BoTorch,0.287972243639167291284763905423,1000,4,0.027185138802143439529235280361 51,51_0,COMPLETED,BoTorch,0.279656349818261928952267680870,905,2,0.010372597541904102214083138733 52,52_0,COMPLETED,BoTorch,0.279656349818261928952267680870,785,3,0.009903397250269603199757106893 53,53_0,COMPLETED,BoTorch,0.279656349818261928952267680870,604,3,0.009669240543426120895742492678 54,54_0,COMPLETED,BoTorch,0.279656349818261928952267680870,525,2,0.002000000000000000041633363423 55,55_0,COMPLETED,BoTorch,0.279656349818261928952267680870,846,1,0.012643279132101425607515032823 56,56_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.010234041319064253461323943384 57,57_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.007747778179203849979739260334 58,58_0,COMPLETED,BoTorch,0.279656349818261928952267680870,420,3,0.002000000000000000041633363423 59,59_0,COMPLETED,BoTorch,0.279656349818261928952267680870,606,4,0.009813078067171162113813132066 60,60_0,COMPLETED,BoTorch,0.279656349818261928952267680870,510,4,0.009221661076831890596627872014 61,61_0,COMPLETED,BoTorch,0.279656349818261928952267680870,487,3,0.005293499506974084362065369191 62,62_0,COMPLETED,BoTorch,0.279656349818261928952267680870,546,1,0.002000000000000000041633363423 63,63_0,COMPLETED,BoTorch,0.279656349818261928952267680870,744,4,0.002000000000000000041633363423 64,64_0,COMPLETED,BoTorch,0.279656349818261928952267680870,627,2,0.002000000000000000041633363423 65,65_0,COMPLETED,BoTorch,0.279656349818261928952267680870,573,2,0.002000000000000000041633363423 66,66_0,COMPLETED,BoTorch,0.279656349818261928952267680870,866,1,0.002000000000000000041633363423 67,67_0,COMPLETED,BoTorch,0.279656349818261928952267680870,582,1,0.010675862727463281826034702249 68,68_0,COMPLETED,BoTorch,0.279656349818261928952267680870,484,2,0.008163946760472788957696543832 69,69_0,COMPLETED,BoTorch,0.290780923009141933910370880767,850,4,0.036027041959330409481854218257 70,70_0,COMPLETED,BoTorch,0.279656349818261928952267680870,439,2,0.023416705294092655942339575859 71,71_0,COMPLETED,BoTorch,0.287751955061130049173812039953,100,4,0.055261269664713341676076652220 72,72_0,COMPLETED,BoTorch,0.290175129419539601371980097611,1000,4,0.052930652833244995481010164440 73,73_0,COMPLETED,BoTorch,0.296949003194184379950115726388,100,4,0.024196377910024914614783853040 74,74_0,COMPLETED,BoTorch,0.288027315783676574056926256162,683,4,0.036636433437574361660082900016 75,75_0,COMPLETED,BoTorch,0.285879502147813657764174877229,993,4,0.035866962959401796351421864983 76,76_0,COMPLETED,BoTorch,0.278995484084150202619412084459,100,1,0.051536492364097824525615010316 77,77_0,COMPLETED,BoTorch,0.279656349818261928952267680870,421,4,0.021316259633873985146479412833 78,78_0,COMPLETED,BoTorch,0.279656349818261928952267680870,391,4,0.025150843702273807667157967671 79,79_0,COMPLETED,BoTorch,0.279656349818261928952267680870,766,4,0.030183878847912168352785045045 80,80_0,COMPLETED,BoTorch,0.286815728604471908980144689849,586,4,0.070000000000000006661338147751 81,81_0,COMPLETED,BoTorch,0.279656349818261928952267680870,440,4,0.019346768609091818946765783949 82,82_0,COMPLETED,BoTorch,0.287586738627602200857324987737,609,4,0.053039206429008035892369576914 83,83_0,COMPLETED,BoTorch,0.289514263685427875039124501200,307,4,0.055806514363990658567082903119 84,84_0,COMPLETED,BoTorch,0.279656349818261928952267680870,371,4,0.029166754372271772033364811705 85,85_0,COMPLETED,BoTorch,0.279656349818261928952267680870,290,4,0.002000000000000000041633363423 86,86_0,COMPLETED,BoTorch,0.279656349818261928952267680870,420,1,0.019583940469605806700137407006 87,87_0,COMPLETED,BoTorch,0.287917171494658008512601554685,602,4,0.066631494008342312396209194958 88,88_0,COMPLETED,BoTorch,0.279656349818261928952267680870,394,4,0.022564883309090816299047332905 89,89_0,COMPLETED,BoTorch,0.279656349818261928952267680870,743,4,0.031321141441351799117320808818 90,90_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,1,0.044668509970616304682877739651 91,91_0,COMPLETED,BoTorch,0.279656349818261928952267680870,777,1,0.025738610597478055186382306374 92,92_0,COMPLETED,BoTorch,0.279656349818261928952267680870,436,4,0.014786870176411103308677930102 93,93_0,COMPLETED,BoTorch,0.298876528252010165154217702366,124,1,0.045555649496423641919840008541 94,94_0,COMPLETED,BoTorch,0.279656349818261928952267680870,315,1,0.002000000000000000041633363423 95,95_0,COMPLETED,BoTorch,0.279656349818261928952267680870,469,4,0.017928754746189365376629609727 96,96_0,COMPLETED,BoTorch,0.279656349818261928952267680870,390,4,0.022014935013095503213431669565 97,97_0,COMPLETED,BoTorch,0.279656349818261928952267680870,732,4,0.032482221346659220784225396983 98,98_0,COMPLETED,BoTorch,0.279656349818261928952267680870,352,4,0.002000000000000000041633363423 99,99_0,COMPLETED,BoTorch,0.285989646436832223308499578707,935,1,0.018447054211126710787649329859 100,100_0,COMPLETED,BoTorch,0.279656349818261928952267680870,405,1,0.009624654229520763354388535049 101,101_0,COMPLETED,BoTorch,0.279656349818261928952267680870,508,1,0.017222582755166380985567542439 102,102_0,RUNNING,BoTorch,,770,1,0.018401945179709160160808067985 103,103_0,COMPLETED,BoTorch,0.279656349818261928952267680870,347,1,0.005531533048438877844810157569 104,104_0,COMPLETED,BoTorch,0.298325806806917115387989269948,136,1,0.048177471331925778019744655012 105,105_0,COMPLETED,BoTorch,0.279656349818261928952267680870,347,1,0.024839593095059217531250794764 106,106_0,COMPLETED,BoTorch,0.279656349818261928952267680870,339,4,0.020378378132624809515593966580 107,107_0,COMPLETED,BoTorch,0.279656349818261928952267680870,244,1,0.002000000000000000041633363423 108,108_0,COMPLETED,BoTorch,0.279656349818261928952267680870,442,1,0.002000000000000000041633363423 109,109_0,COMPLETED,BoTorch,0.279656349818261928952267680870,786,4,0.018249045682134278079100653258 110,110_0,COMPLETED,BoTorch,0.279656349818261928952267680870,455,1,0.010534204746145327952211445677 111,111_0,COMPLETED,BoTorch,0.279656349818261928952267680870,740,1,0.011592416774931056308584054193 112,112_0,COMPLETED,BoTorch,0.279656349818261928952267680870,395,1,0.002000000000000000041633363423 113,113_0,COMPLETED,BoTorch,0.279656349818261928952267680870,393,2,0.011260027405519743362583007240 114,114_0,COMPLETED,BoTorch,0.279656349818261928952267680870,787,1,0.015008084147379682771195064106 115,115_0,COMPLETED,BoTorch,0.279656349818261928952267680870,367,3,0.002000000000000000041633363423 116,116_0,COMPLETED,BoTorch,0.279656349818261928952267680870,921,1,0.013538210860071155094974670874 117,117_0,COMPLETED,BoTorch,0.279656349818261928952267680870,268,1,0.002000000000000000041633363423 118,118_0,COMPLETED,BoTorch,0.279656349818261928952267680870,301,1,0.016684095938235818379347108475 119,119_0,COMPLETED,BoTorch,0.279656349818261928952267680870,764,4,0.020259807580286386119450270371 120,120_0,COMPLETED,BoTorch,0.279656349818261928952267680870,259,4,0.002000000000000000041633363423 121,121_0,COMPLETED,BoTorch,0.279656349818261928952267680870,661,4,0.002000000000000000041633363423 122,122_0,COMPLETED,BoTorch,0.287311377905055675974210771528,719,4,0.052234761075212639280795912100 123,123_0,COMPLETED,BoTorch,0.279656349818261928952267680870,751,1,0.023026475226434632570526872541 124,124_0,COMPLETED,BoTorch,0.279656349818261928952267680870,726,1,0.002000000000000000041633363423 125,125_0,COMPLETED,BoTorch,0.286485295737415990302565660386,386,1,0.042620173539675475227195278194 126,126_0,COMPLETED,BoTorch,0.288027315783676574056926256162,710,4,0.070000000000000006661338147751 127,127_0,COMPLETED,BoTorch,0.279656349818261928952267680870,933,1,0.002000000000000000041633363423 128,128_0,COMPLETED,BoTorch,0.279656349818261928952267680870,940,4,0.002000000000000000041633363423 129,129_0,COMPLETED,BoTorch,0.279656349818261928952267680870,280,1,0.010334696655185332189730296193 130,130_0,COMPLETED,BoTorch,0.279656349818261928952267680870,848,4,0.002000000000000000041633363423 131,131_0,COMPLETED,BoTorch,0.287641810772111483629487338476,720,1,0.047840402845470336723465720752 132,132_0,COMPLETED,BoTorch,0.279656349818261928952267680870,298,4,0.020911091314045553157807688649 133,133_0,COMPLETED,BoTorch,0.286099790725850899875126742700,712,1,0.066605470857860796241034506693 134,134_0,COMPLETED,BoTorch,0.279656349818261928952267680870,931,4,0.002000000000000000041633363423 135,135_0,COMPLETED,BoTorch,0.279656349818261928952267680870,318,4,0.009697794364962979141164822749 136,136_0,COMPLETED,BoTorch,0.279656349818261928952267680870,976,4,0.002000000000000000041633363423 137,137_0,COMPLETED,BoTorch,0.279656349818261928952267680870,796,1,0.002000000000000000041633363423 138,138_0,COMPLETED,BoTorch,0.289349047251900026722637448984,310,1,0.029962486804000244833279253953 139,139_0,COMPLETED,BoTorch,0.279656349818261928952267680870,479,1,0.021370115018313921850801051505 140,140_0,COMPLETED,BoTorch,0.279656349818261928952267680870,883,1,0.017790692117950694661754340586 141,141_0,COMPLETED,BoTorch,0.279656349818261928952267680870,928,4,0.007927621356924657511355825079 142,142_0,COMPLETED,BoTorch,0.279656349818261928952267680870,926,1,0.005954968514654762970494950025 143,143_0,COMPLETED,BoTorch,0.279656349818261928952267680870,800,4,0.002000000000000000041633363423 144,144_0,COMPLETED,BoTorch,0.279656349818261928952267680870,297,4,0.029001730161232343319088755607 145,145_0,COMPLETED,BoTorch,0.279656349818261928952267680870,538,1,0.010785515252928886895111837418 146,146_0,COMPLETED,BoTorch,0.279656349818261928952267680870,368,1,0.019885498621174835243152756448 147,147_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,4,0.031790444402039597093789780047 148,148_0,COMPLETED,BoTorch,0.279656349818261928952267680870,975,1,0.002000000000000000041633363423 149,149_0,COMPLETED,BoTorch,0.279656349818261928952267680870,661,1,0.007718166204744521946556901071 150,150_0,COMPLETED,BoTorch,0.279656349818261928952267680870,291,4,0.012369767412565936537394151173 151,151_0,COMPLETED,BoTorch,0.279656349818261928952267680870,469,1,0.022141249336263588154416481757 152,152_0,COMPLETED,BoTorch,0.279656349818261928952267680870,558,4,0.010358608038920066873878056413 153,153_0,COMPLETED,BoTorch,0.288578037228769734845457151096,528,1,0.070000000000000006661338147751 154,154_0,COMPLETED,BoTorch,0.290505562286595409027256664558,291,1,0.034342157273375494008771369181 155,155_0,COMPLETED,BoTorch,0.279656349818261928952267680870,280,4,0.029324892674720388907605439499 156,156_0,COMPLETED,BoTorch,0.279656349818261928952267680870,276,4,0.034565433051895001759223902127 157,157_0,COMPLETED,BoTorch,0.279656349818261928952267680870,355,1,0.017826197918220841598380133064 158,158_0,COMPLETED,BoTorch,0.279656349818261928952267680870,281,4,0.027434853385193538888309205959 159,159_0,COMPLETED,BoTorch,0.279656349818261928952267680870,358,4,0.022510636274552207891641586457 160,160_0,COMPLETED,BoTorch,0.279656349818261928952267680870,501,1,0.002000000000000000041633363423 161,161_0,COMPLETED,BoTorch,0.279656349818261928952267680870,301,1,0.008449991812437012209113795791 162,162_0,COMPLETED,BoTorch,0.279656349818261928952267680870,370,1,0.023534979612626648337592172311 163,163_0,COMPLETED,BoTorch,0.279656349818261928952267680870,340,1,0.014963198545027395214290777403 164,164_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,2,0.013080975658625367680221174282 165,165_0,COMPLETED,BoTorch,0.279656349818261928952267680870,672,4,0.013583661514192298461201069415 166,166_0,COMPLETED,BoTorch,0.279656349818261928952267680870,822,1,0.020808366239038922351767268992 167,167_0,COMPLETED,BoTorch,0.279656349818261928952267680870,800,2,0.021992186894520412976383738624 168,168_0,COMPLETED,BoTorch,0.279656349818261928952267680870,303,2,0.006650509895926076144034855275 169,169_0,COMPLETED,BoTorch,0.279656349818261928952267680870,335,2,0.016071527829707239060086365612 170,170_0,COMPLETED,BoTorch,0.279656349818261928952267680870,829,4,0.009290259496907964792411505073 171,171_0,COMPLETED,BoTorch,0.279656349818261928952267680870,776,3,0.024777253092814401080890007734 172,172_0,COMPLETED,BoTorch,0.279656349818261928952267680870,677,1,0.015449072034617736953743261097 173,173_0,COMPLETED,BoTorch,0.279656349818261928952267680870,808,2,0.020377348860620120296616164524 174,174_0,COMPLETED,BoTorch,0.279656349818261928952267680870,341,4,0.028938127515462146177593893981 175,175_0,COMPLETED,BoTorch,0.279656349818261928952267680870,511,1,0.009517547642029748275471767727 176,176_0,COMPLETED,BoTorch,0.279656349818261928952267680870,659,1,0.002000000000000000041633363423 177,177_0,COMPLETED,BoTorch,0.279656349818261928952267680870,641,4,0.007993090707909093761696084357 178,178_0,COMPLETED,BoTorch,0.279656349818261928952267680870,584,4,0.008220707151701993689596470460 179,179_0,COMPLETED,BoTorch,0.279656349818261928952267680870,214,4,0.002000000000000000041633363423 180,180_0,COMPLETED,BoTorch,0.277012886881815134643147757743,100,1,0.070000000000000006661338147751 181,181_0,COMPLETED,BoTorch,0.279656349818261928952267680870,719,1,0.027008783000060904155859020648 182,182_0,COMPLETED,BoTorch,0.279656349818261928952267680870,393,4,0.002000000000000000041633363423 183,183_0,COMPLETED,BoTorch,0.278334618350038587308858950564,100,1,0.065880080134995563923006045570 184,184_0,COMPLETED,BoTorch,0.279656349818261928952267680870,421,4,0.007729875535686713504401890162 185,185_0,COMPLETED,BoTorch,0.277563608326908295431678652676,100,1,0.058196308311339255137095705095 186,186_0,COMPLETED,BoTorch,0.289349047251900026722637448984,100,4,0.040250449091722012573413280734 187,187_0,COMPLETED,BoTorch,0.279656349818261928952267680870,725,2,0.016970866054567039504785697090 188,188_0,COMPLETED,BoTorch,0.279215772662187444730363949930,100,2,0.070000000000000006661338147751 189,189_0,COMPLETED,BoTorch,0.287256305760546282179745958274,599,1,0.017356159258756553853686455113 190,190_0,COMPLETED,BoTorch,0.280923009141975987823514060437,100,1,0.037850096018771262063040694557 191,191_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,2,0.013841014359317874507504519954 192,192_0,COMPLETED,BoTorch,0.279656349818261928952267680870,318,4,0.039189737697284170170330952487 193,193_0,COMPLETED,BoTorch,0.279656349818261928952267680870,836,4,0.023291464365725739193990762033 194,194_0,COMPLETED,BoTorch,0.287256305760546282179745958274,946,1,0.070000000000000006661338147751 195,195_0,COMPLETED,BoTorch,0.289734552263465117150076366670,407,1,0.070000000000000006661338147751 196,196_0,COMPLETED,BoTorch,0.287807027205639442968276853207,302,1,0.070000000000000006661338147751 197,197_0,COMPLETED,BoTorch,0.286209935014869465419451444177,424,4,0.070000000000000006661338147751 198,198_0,COMPLETED,BoTorch,0.291772221610309467898503044125,942,4,0.070000000000000006661338147751 199,199_0,COMPLETED,BoTorch,0.279656349818261928952267680870,708,1,0.017516417260499950875329844280 200,200_0,COMPLETED,BoTorch,0.286705584315453232413517525856,401,1,0.063993137061508484353389292210 201,201_0,COMPLETED,BoTorch,0.289569335829937268833589314454,417,1,0.070000000000000006661338147751 202,202_0,COMPLETED,BoTorch,0.287586738627602200857324987737,972,1,0.070000000000000006661338147751 203,203_0,COMPLETED,BoTorch,0.287421522194074241518535473006,941,1,0.051628085262174827629877427171 204,204_0,COMPLETED,BoTorch,0.291662077321290902354178342648,1000,1,0.070000000000000006661338147751 205,205_0,COMPLETED,BoTorch,0.291607005176781619582015991909,965,3,0.064777525102117622379793715481 206,206_0,COMPLETED,BoTorch,0.290009912986011642033190582879,553,1,0.051797703559754625091127167025 207,207_0,COMPLETED,BoTorch,0.279656349818261928952267680870,345,4,0.039189627227657293506712932185 208,208_0,COMPLETED,BoTorch,0.291662077321290902354178342648,949,2,0.059281937100214045222035252891 209,209_0,COMPLETED,BoTorch,0.286485295737415990302565660386,535,4,0.052965435526424409218293476442 210,210_0,COMPLETED,BoTorch,0.287531666483092807062860174483,958,1,0.062468359480480323275841669783 211,211_0,COMPLETED,BoTorch,0.279656349818261928952267680870,698,4,0.021280322992214571742319861869 212,212_0,COMPLETED,BoTorch,0.289954840841502359261028232140,1000,4,0.052367305459067987893906348518 213,213_0,COMPLETED,BoTorch,0.279656349818261928952267680870,435,4,0.031661795191177191655107492352 214,214_0,COMPLETED,BoTorch,0.286760656459962515185679876595,385,4,0.053629996105913492476791049057 215,215_0,COMPLETED,BoTorch,0.291551933032272225787551178655,955,1,0.045211237296492380599577387557 216,216_0,COMPLETED,BoTorch,0.289679480118955834377914015931,548,4,0.052837943262066665306431190174 217,217_0,COMPLETED,BoTorch,0.286925872893490474524469391326,532,3,0.052601314963315437545077202230 218,218_0,COMPLETED,BoTorch,0.290560634431104691799419015297,654,1,0.070000000000000006661338147751 219,219_0,COMPLETED,BoTorch,0.291662077321290902354178342648,975,2,0.062563008611736886654952627396 220,220_0,COMPLETED,BoTorch,0.287972243639167291284763905423,898,4,0.031020764958533036947940786376 221,221_0,COMPLETED,BoTorch,0.279656349818261928952267680870,466,2,0.016322954893983540602953041798 222,222_0,COMPLETED,BoTorch,0.288192532217204533395715770894,1000,1,0.033455407544136492314912345591 223,223_0,COMPLETED,BoTorch,0.278004185482982668631279921101,100,1,0.061251386274439628687460412948 224,224_0,COMPLETED,BoTorch,0.279656349818261928952267680870,497,3,0.014459155919965683070871342863 225,225_0,COMPLETED,BoTorch,0.279656349818261928952267680870,930,4,0.015203880409340533169659792634 226,226_0,COMPLETED,BoTorch,0.279656349818261928952267680870,268,3,0.002000000000000000041633363423 227,227_0,COMPLETED,BoTorch,0.279656349818261928952267680870,725,4,0.027403669799318353117456581458 228,228_0,COMPLETED,BoTorch,0.279656349818261928952267680870,396,4,0.036118206688741952281596070407 229,229_0,COMPLETED,BoTorch,0.290780923009141933910370880767,1000,1,0.038560592764635741314105388255 230,230_0,COMPLETED,BoTorch,0.279656349818261928952267680870,418,2,0.026822622365337785155237071422 231,231_0,COMPLETED,BoTorch,0.306256195616257276270744114299,100,1,0.002000000000000000041633363423 232,232_0,COMPLETED,BoTorch,0.290230201564048884144142448349,839,1,0.028108647342234061028420910588 233,233_0,COMPLETED,BoTorch,0.279656349818261928952267680870,721,3,0.026174895459617035509136684368 234,234_0,COMPLETED,BoTorch,0.284722987113118164437253199139,152,1,0.002000000000000000041633363423 235,235_0,COMPLETED,BoTorch,0.279656349818261928952267680870,591,1,0.005487033887094552864205354581 236,236_0,COMPLETED,BoTorch,0.279656349818261928952267680870,410,4,0.041551491213006781777483666929 237,237_0,COMPLETED,BoTorch,0.279656349818261928952267680870,915,4,0.018599073912435294675393748776 238,238_0,COMPLETED,BoTorch,0.290175129419539601371980097611,380,4,0.043498777629223828178339772421 239,239_0,COMPLETED,BoTorch,0.279656349818261928952267680870,447,1,0.015765749668194037336199642141 240,240_0,COMPLETED,BoTorch,0.279656349818261928952267680870,970,4,0.012646866267227586944077444286 241,241_0,COMPLETED,BoTorch,0.298986672541028730698542403843,196,1,0.002000000000000000041633363423 242,242_0,COMPLETED,BoTorch,0.279656349818261928952267680870,321,4,0.002000000000000000041633363423 243,243_0,COMPLETED,BoTorch,0.279656349818261928952267680870,761,1,0.002000000000000000041633363423 244,244_0,COMPLETED,BoTorch,0.279656349818261928952267680870,905,4,0.015278432055362490410432840804 245,245_0,COMPLETED,BoTorch,0.279656349818261928952267680870,702,4,0.013424233069170999482766504229 246,246_0,COMPLETED,BoTorch,0.279656349818261928952267680870,411,4,0.030967336097893281843251855889 247,247_0,COMPLETED,BoTorch,0.279656349818261928952267680870,746,1,0.031893278353902460020563580656 248,248_0,COMPLETED,BoTorch,0.279656349818261928952267680870,468,4,0.002000000000000000041633363423 249,249_0,COMPLETED,BoTorch,0.285824430003304374992012526491,488,4,0.070000000000000006661338147751 250,250_0,COMPLETED,BoTorch,0.279656349818261928952267680870,899,1,0.002000000000000000041633363423 251,251_0,COMPLETED,BoTorch,0.279656349818261928952267680870,231,4,0.002000000000000000041633363423 252,252_0,COMPLETED,BoTorch,0.279656349818261928952267680870,681,2,0.007943685146221016873946396686 253,253_0,COMPLETED,BoTorch,0.279656349818261928952267680870,412,4,0.033681604174761757553024921208 254,254_0,COMPLETED,BoTorch,0.286485295737415990302565660386,114,1,0.070000000000000006661338147751 255,255_0,COMPLETED,BoTorch,0.279656349818261928952267680870,846,4,0.016862048559722120355530705638 256,256_0,COMPLETED,BoTorch,0.279656349818261928952267680870,977,4,0.014678437456792162163488058013 257,257_0,COMPLETED,BoTorch,0.279656349818261928952267680870,982,3,0.014924285841649183315116289350 258,258_0,COMPLETED,BoTorch,0.290835995153651327704835694021,153,4,0.002000000000000000041633363423 259,259_0,COMPLETED,BoTorch,0.279656349818261928952267680870,813,1,0.013043192489121741700630607852 260,260_0,COMPLETED,BoTorch,0.279656349818261928952267680870,561,1,0.010069824451222298897223872416 261,261_0,COMPLETED,BoTorch,0.279656349818261928952267680870,397,1,0.027855161772148750998212562990 262,262_0,COMPLETED,BoTorch,0.279656349818261928952267680870,471,1,0.002000000000000000041633363423 263,263_0,COMPLETED,BoTorch,0.279656349818261928952267680870,231,4,0.018532477914857375944635009546 264,264_0,COMPLETED,BoTorch,0.288412820795241775506667636364,428,4,0.051558582873067575202963297443 265,265_0,COMPLETED,BoTorch,0.286815728604471908980144689849,494,4,0.042473025077286764850104106017 266,266_0,COMPLETED,BoTorch,0.293314241656570051652863639902,165,4,0.002263560639133777117187884542 267,267_0,COMPLETED,BoTorch,0.286980945037999757296631742065,414,1,0.036275526718156272942827911265 268,268_0,COMPLETED,BoTorch,0.279656349818261928952267680870,777,4,0.002000000000000000041633363423 269,269_0,COMPLETED,BoTorch,0.289679480118955834377914015931,493,4,0.036553479894524239235398255232 270,270_0,COMPLETED,BoTorch,0.279656349818261928952267680870,258,4,0.017487905956549910230224398333 271,271_0,COMPLETED,BoTorch,0.279656349818261928952267680870,256,2,0.008337713539213500307800686073 272,272_0,COMPLETED,BoTorch,0.279656349818261928952267680870,720,4,0.002000000000000000041633363423 273,273_0,COMPLETED,BoTorch,0.279656349818261928952267680870,819,4,0.021115428025871157968751390399 274,274_0,COMPLETED,BoTorch,0.288357748650732492734505285625,257,4,0.035175846239309076224355266049 275,275_0,COMPLETED,BoTorch,0.279656349818261928952267680870,506,4,0.002000000000000000041633363423 276,276_0,COMPLETED,BoTorch,0.289128758673862784611685583513,260,4,0.041582170082006854328060541093 277,277_0,COMPLETED,BoTorch,0.290175129419539601371980097611,496,2,0.032019682938860247189971630632 278,278_0,COMPLETED,BoTorch,0.279656349818261928952267680870,630,4,0.002000000000000000041633363423 279,279_0,COMPLETED,BoTorch,0.279656349818261928952267680870,496,4,0.026502830116992739939441037222 280,280_0,COMPLETED,BoTorch,0.279656349818261928952267680870,456,4,0.029235090245046398138573096048 281,281_0,COMPLETED,BoTorch,0.287256305760546282179745958274,500,1,0.031965500401775662209225004062 282,282_0,COMPLETED,BoTorch,0.279656349818261928952267680870,394,2,0.023718795280982640782951875735 283,283_0,COMPLETED,BoTorch,0.279656349818261928952267680870,222,4,0.010333800755729595846621471367 284,284_0,COMPLETED,BoTorch,0.276296949003194236560432273109,163,2,0.014687421998613010842849035953 285,285_0,COMPLETED,BoTorch,0.279656349818261928952267680870,861,3,0.008080499292671143932165200852 286,286_0,COMPLETED,BoTorch,0.279656349818261928952267680870,427,2,0.012793358068905474542176214925 287,287_0,COMPLETED,BoTorch,0.279656349818261928952267680870,377,3,0.023634507327941652043978848496 288,288_0,COMPLETED,BoTorch,0.279656349818261928952267680870,850,2,0.002000000000000000041633363423 289,289_0,COMPLETED,BoTorch,0.279656349818261928952267680870,530,4,0.002000000000000000041633363423 290,290_0,COMPLETED,BoTorch,0.279656349818261928952267680870,453,3,0.002000000000000000041633363423 291,291_0,COMPLETED,BoTorch,0.279656349818261928952267680870,371,1,0.002000000000000000041633363423 292,292_0,COMPLETED,BoTorch,0.279215772662187444730363949930,161,2,0.012049453161752599061884438925 293,293_0,COMPLETED,BoTorch,0.276352021147703519332594623847,169,3,0.018729565588715628432492366073 294,294_0,COMPLETED,BoTorch,0.283180967066857580682892603363,194,4,0.027204501072598905042632111417 295,295_0,RUNNING,BoTorch,,157,2,0.014934132173623071784818172603 296,296_0,COMPLETED,BoTorch,0.279656349818261928952267680870,494,4,0.020111942237097439611925864256 297,297_0,RUNNING,BoTorch,,169,2,0.020425349644735919796278977856 298,298_0,COMPLETED,BoTorch,0.279105628373168879186039248452,162,1,0.016211551154121518758532971560 299,299_0,RUNNING,BoTorch,,166,2,0.012731138476481647023308418909 300,300_0,RUNNING,BoTorch,,190,4,0.034003512256229599663992502201 301,301_0,RUNNING,BoTorch,,172,3,0.025686632023076971798047907214 302,302_0,COMPLETED,BoTorch,0.277398391893380336092889137944,162,3,0.015042032898719734368420120063 303,303_0,COMPLETED,BoTorch,0.282520101332745854350037006952,160,2,0.019679515842868595953962795875 304,304_0,COMPLETED,BoTorch,0.278554906928075829419810816034,163,2,0.020271893784533930038538329654 305,305_0,COMPLETED,BoTorch,0.274534640378896299672817349347,172,3,0.016207992029838219627535522704 306,306_0,COMPLETED,BoTorch,0.283346183500385540021682118095,157,2,0.012194797287608040090023386881 307,307_0,COMPLETED,BoTorch,0.272496971032051948924390671891,175,3,0.019622216472985190560684998218 308,308_0,COMPLETED,BoTorch,0.286209935014869465419451444177,155,1,0.017049555308496236460946704483 309,309_0,COMPLETED,BoTorch,0.275250578257517308777835296496,167,3,0.014701459368054167059503001269 310,310_0,COMPLETED,BoTorch,0.287311377905055675974210771528,156,2,0.016630638911938444568505701682 311,311_0,COMPLETED,BoTorch,0.275140433968498743233510595019,172,2,0.015906752122015803407872880371 312,312_0,COMPLETED,BoTorch,0.276407093292212802104756974586,177,4,0.017237012279672562359289500478 313,313_0,COMPLETED,BoTorch,0.281583874876087714156369656848,184,4,0.016400623882014780247073915120 314,314_0,COMPLETED,BoTorch,0.273928846789293967134426566190,173,3,0.015837626389557143968822572333 315,315_0,COMPLETED,BoTorch,0.275030289679480066666883431026,172,4,0.019839744872604335446109047325 316,316_0,COMPLETED,BoTorch,0.274920145390461501122558729548,172,2,0.015476044820001089163952201488 317,317_0,COMPLETED,BoTorch,0.276572309725740761443546489318,173,4,0.017827523612431749044926476699 318,318_0,COMPLETED,BoTorch,0.269523075228549346959994181816,175,3,0.016564200466351375329310258167 319,319_0,COMPLETED,BoTorch,0.275250578257517308777835296496,172,2,0.015847902031847729831248727805 320,320_0,COMPLETED,BoTorch,0.273928846789293967134426566190,174,2,0.018209143754194426212400514942 321,321_0,COMPLETED,BoTorch,0.271450600286375132164096157794,177,1,0.028276298255084313104656956739 322,322_0,COMPLETED,BoTorch,0.272166538164996141269114104944,176,2,0.020677078609971234757214375577 323,323_0,COMPLETED,BoTorch,0.274589712523405693467282162601,175,1,0.021164917523141577038181537773 324,324_0,COMPLETED,BoTorch,0.270624518118735557514753509167,177,3,0.018251582723653389517259881814 325,325_0,COMPLETED,BoTorch,0.275470866835554550888787161966,174,3,0.018065821481402288828377322716 326,326_0,COMPLETED,BoTorch,0.272882476043617150374132052093,176,4,0.020259699135012319837212402263 327,327_0,RUNNING,BoTorch,,177,2,0.025295883900707812064201363000 328,328_0,COMPLETED,BoTorch,0.271891177442449616385999888735,180,1,0.033884766266700672154854601104 329,329_0,COMPLETED,BoTorch,0.270073796673642507748525076750,177,2,0.028367354288964641284565004753 330,330_0,COMPLETED,BoTorch,0.273102764621654392485083917563,176,2,0.023017978032247504716067965091 331,331_0,COMPLETED,BoTorch,0.271505672430884414936258508533,180,1,0.037825489079875380948880803089 332,332_0,COMPLETED,BoTorch,0.273598413922238159479149999243,179,1,0.026220326408885372138080072091 333,333_0,COMPLETED,BoTorch,0.272827403899107867601969701354,177,1,0.034636268648940392311796898639 334,334_0,COMPLETED,BoTorch,0.271505672430884414936258508533,176,2,0.031473258038310414352878296995 335,335_0,COMPLETED,BoTorch,0.272992620332635715918456753570,181,1,0.030914205550510144571152437720 336,336_0,COMPLETED,BoTorch,0.272772331754598473807504888100,179,2,0.026250558375380747544447501696 337,337_0,COMPLETED,BoTorch,0.267870910893270197661308884562,177,3,0.039162825869852875559917748660 338,338_0,COMPLETED,BoTorch,0.270128868818151790520687427488,176,2,0.030613623352180409364908797443 339,339_0,COMPLETED,BoTorch,0.266163674413481654568158774055,188,1,0.033074172182963122723631244071 340,340_0,COMPLETED,BoTorch,0.271450600286375132164096157794,179,3,0.043645293867668875920085014286 341,341_0,COMPLETED,BoTorch,0.269963652384623831181897912757,177,3,0.035817874257732647269580894545 342,342_0,COMPLETED,BoTorch,0.271781033153430939819372724742,177,4,0.041512394191101815998923285633 343,343_0,COMPLETED,BoTorch,0.270128868818151790520687427488,179,3,0.038820016123572761990168089596 344,344_0,COMPLETED,BoTorch,0.273488269633219482912522835250,175,3,0.038244435168286169957063691527 345,345_0,COMPLETED,BoTorch,0.272772331754598473807504888100,176,2,0.042108038395846147938783587961 346,346_0,COMPLETED,BoTorch,0.271836105297940333613837537996,178,2,0.048519390783643936981839317468 347,347_0,COMPLETED,BoTorch,0.271891177442449616385999888735,177,1,0.036747981697240923726344163924 348,348_0,COMPLETED,BoTorch,0.271891177442449616385999888735,181,4,0.041037854567496014068783694029 349,349_0,COMPLETED,BoTorch,0.270844806696772799625705374638,176,4,0.037500582309701518957112398311 350,350_0,COMPLETED,BoTorch,0.270844806696772799625705374638,174,3,0.041222575107731286581103091748 351,351_0,COMPLETED,BoTorch,0.272056393875977575724789403466,186,2,0.038172004739016676677643147286 352,352_0,COMPLETED,BoTorch,0.266494107280537462223435341002,190,1,0.038807012985110425629109442980 353,353_0,RUNNING,BoTorch,,184,2,0.035599144497784888929725610751 354,354_0,COMPLETED,BoTorch,0.267044828725630623011966235936,190,1,0.042076218014404302703024285393 355,355_0,COMPLETED,BoTorch,0.266769468003083987106549557211,189,1,0.036871340923637870023998885927 356,356_0,COMPLETED,BoTorch,0.268366560193853964655374966242,188,2,0.041280186831874600417169318689 357,357_0,COMPLETED,BoTorch,0.269357858795021498643507129600,186,1,0.041327302688865752422486821160 358,358_0,COMPLETED,BoTorch,0.268696993060909772310651533189,184,2,0.038624303953074354267549495034 359,359_0,COMPLETED,BoTorch,0.271450600286375132164096157794,192,2,0.040771019581129858133650145646 360,360_0,COMPLETED,BoTorch,0.267981055182288763205633586040,188,1,0.037830028897096461271143397198 361,361_0,COMPLETED,BoTorch,0.267320189448177147895080452145,185,2,0.040762546566350137122736185802 362,362_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039316006248675899881206419195 363,363_0,COMPLETED,BoTorch,0.268421632338363247427537316980,191,1,0.039473029911967785765547489518 364,364_0,COMPLETED,BoTorch,0.279656349818261928952267680870,226,3,0.013517825051851398848734575608 365,365_0,COMPLETED,BoTorch,0.291386716598744377471064126439,150,3,0.052604891809975497241058661757 366,366_0,COMPLETED,BoTorch,0.274038991078312643701053730183,183,4,0.048333819158358604095582222726 367,367_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039253005381964370390512897302 368,368_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039209161085509577904240074986 369,369_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039228314530328739495601553244 370,370_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039332138420068385453731707457 371,371_0,COMPLETED,BoTorch,0.268421632338363247427537316980,191,1,0.039501823867151647506901213092 372,372_0,COMPLETED,BoTorch,0.268146271615816722544423100771,191,1,0.039596870645950917044775252407 373,373_0,COMPLETED,BoTorch,0.277563608326908295431678652676,165,3,0.031869653680882230328830218014 374,374_0,COMPLETED,BoTorch,0.265888313690935129685044557846,190,1,0.039643137892465346583392715729 375,375_0,RUNNING,BoTorch,,182,2,0.034089522559145379876355264059 376,376_0,COMPLETED,BoTorch,0.268146271615816722544423100771,188,1,0.039578162462622641648568588835 377,377_0,COMPLETED,BoTorch,0.265723097257407170346255043114,190,1,0.039303960179088770487965120992 378,378_0,COMPLETED,BoTorch,0.271395528141865849391933807055,178,2,0.026426640670929216203610678804 379,379_0,COMPLETED,BoTorch,0.270789734552263516853543023899,203,1,0.040621143173338869480915036547 380,380_0,COMPLETED,BoTorch,0.289404119396409309494799799722,317,3,0.055177293913886496512510859702 381,381_0,COMPLETED,BoTorch,0.276682454014759326987871190795,182,1,0.002000000000000000041633363423 382,382_0,COMPLETED,BoTorch,0.268201343760326005316585451510,192,1,0.040314579076765236220758481522 383,383_0,COMPLETED,BoTorch,0.268091199471307439772260750033,191,1,0.040182380832322776853526846708 384,384_0,COMPLETED,BoTorch,0.268311488049344681883212615503,191,1,0.040433881525531054079714010641 385,385_0,COMPLETED,BoTorch,0.273543341777728876706987648504,172,1,0.023187126721641970750198424867 386,386_0,COMPLETED,BoTorch,0.268036127326798157000098399294,191,1,0.040237728372775470597755287372 387,387_0,COMPLETED,BoTorch,0.267320189448177147895080452145,184,2,0.039598244445817748493610110927 388,388_0,COMPLETED,BoTorch,0.269633219517568023526621345809,176,3,0.029115120820980285032408829693 389,389_0,COMPLETED,BoTorch,0.267981055182288763205633586040,191,1,0.039954511609486299816840215726 390,390_0,COMPLETED,BoTorch,0.267981055182288763205633586040,191,1,0.040016054236621460826750507067 391,391_0,COMPLETED,BoTorch,0.297004075338693662722278077126,129,1,0.047237095065400726279225551707 392,392_0,COMPLETED,BoTorch,0.268091199471307439772260750033,191,1,0.040231524648860914061820892584 393,393_0,COMPLETED,BoTorch,0.272992620332635715918456753570,172,4,0.027347323193552340592216154391 394,394_0,COMPLETED,BoTorch,0.268036127326798157000098399294,191,1,0.039847850063241686824344611750 395,395_0,COMPLETED,BoTorch,0.271010023130300647942192426854,181,2,0.032820189318223273733376998962 396,396_0,COMPLETED,BoTorch,0.270844806696772799625705374638,181,3,0.035258429604458581263504157732 397,397_0,COMPLETED,BoTorch,0.272001321731468181930324590212,169,2,0.020425909651223969620836840022 398,398_0,COMPLETED,BoTorch,0.279656349818261928952267680870,285,4,0.031956100403417744826306545747 399,399_0,COMPLETED,BoTorch,0.266989756581121229217501422681,189,1,0.038429789046943288455260301362 400,400_0,COMPLETED,BoTorch,0.270073796673642507748525076750,172,4,0.045119261925883787089919252367 401,401_0,COMPLETED,BoTorch,0.295131622425377271312640914402,143,4,0.055176173235033487696199472339 402,402_0,COMPLETED,BoTorch,0.266824540147593380901014370465,182,1,0.064696512530347513592055008758 403,403_0,COMPLETED,BoTorch,0.270239013107170356065012128965,172,4,0.045090974188437424330366809500 404,404_0,COMPLETED,BoTorch,0.287862099350148725740439203946,161,1,0.052618324990871920276980233666 405,405_0,COMPLETED,BoTorch,0.271670888864412374275048023264,172,4,0.045254113619164197668265359198 406,406_0,COMPLETED,BoTorch,0.271065095274810041736657240108,172,4,0.048588218458016342593541736505 407,407_0,COMPLETED,BoTorch,0.269798435951095982865410860541,181,4,0.061938294572247233615858164057 408,408_0,COMPLETED,BoTorch,0.271781033153430939819372724742,175,3,0.056860824771946240352438195487 409,409_0,COMPLETED,BoTorch,0.269688291662077306298783696548,180,1,0.059420631317170014007444933668 410,410_0,COMPLETED,BoTorch,0.270459301685207598175963994436,189,1,0.060390205566937547876360525834 411,411_0,COMPLETED,BoTorch,0.269578147373058740754458995070,183,2,0.070000000000000006661338147751 412,412_0,COMPLETED,BoTorch,0.268531776627381923994164480973,186,1,0.068987470776403950201149939403 413,413_0,COMPLETED,BoTorch,0.269412930939530781415669480339,185,2,0.063411298505398727698434413469 414,414_0,COMPLETED,BoTorch,0.268366560193853964655374966242,191,1,0.052457616718961867785520070129 415,415_0,COMPLETED,BoTorch,0.267705694459742238322519369831,183,1,0.070000000000000006661338147751 416,416_0,COMPLETED,BoTorch,0.270459301685207598175963994436,179,2,0.070000000000000006661338147751 417,417_0,COMPLETED,BoTorch,0.267210045159158471328453288152,192,1,0.046474430434734120831663517492 418,418_0,COMPLETED,BoTorch,0.273157836766163675257246268302,188,1,0.066246124267845193922532587294 419,419_0,COMPLETED,BoTorch,0.269302786650512215871344778861,186,1,0.060229020366945267006553166311 420,420_0,COMPLETED,BoTorch,0.269082498072474973760392913391,186,2,0.064128385253653066855328290785 421,421_0,COMPLETED,BoTorch,0.268696993060909772310651533189,181,1,0.070000000000000006661338147751 422,422_0,COMPLETED,BoTorch,0.270844806696772799625705374638,175,4,0.070000000000000006661338147751 423,423_0,COMPLETED,BoTorch,0.268641920916400489538489182451,178,2,0.069770888753289989070438537055 424,424_0,COMPLETED,BoTorch,0.267154973014649188556290937413,191,1,0.043081364080792203252734395846 425,425_0,COMPLETED,BoTorch,0.270459301685207598175963994436,181,4,0.070000000000000006661338147751 426,426_0,COMPLETED,BoTorch,0.270679590263244840286915859906,180,4,0.060337352464674233465391495201 427,427_0,COMPLETED,BoTorch,0.286540367881925273074728011125,750,1,0.070000000000000006661338147751 428,428_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043853014050310640259411343322 429,429_0,COMPLETED,BoTorch,0.286705584315453232413517525856,736,1,0.056264391980682490479459545440 430,430_0,COMPLETED,BoTorch,0.269357858795021498643507129600,179,1,0.070000000000000006661338147751 431,431_0,COMPLETED,BoTorch,0.272441898887542666152228321153,181,2,0.066180236315984214412466712929 432,432_0,COMPLETED,BoTorch,0.268862209494437731649441047921,182,2,0.062872314691078762249887290636 433,433_0,COMPLETED,BoTorch,0.280592576274920180168237493490,106,1,0.065621580011945429555808573241 434,434_0,COMPLETED,BoTorch,0.270899878841282082397867725376,179,2,0.065395627863610930607940474601 435,435_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043796161093683720622049548865 436,436_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043826545058890914452476295082 437,437_0,COMPLETED,BoTorch,0.271560744575393808730723321787,180,3,0.066286756798987933603228839274 438,438_0,COMPLETED,BoTorch,0.266053530124463089023834072577,191,1,0.044227020807766685706052811611 439,439_0,COMPLETED,BoTorch,0.266108602268972371795996423316,191,1,0.044327921544894545446968692204 440,440_0,COMPLETED,BoTorch,0.266383962991518896679110639525,191,1,0.044207122194990761121502487185 441,441_0,COMPLETED,BoTorch,0.266108602268972371795996423316,191,1,0.044352469749228157214204770753 442,442_0,COMPLETED,BoTorch,0.272001321731468181930324590212,183,2,0.012726157885068554997953782504 443,443_0,COMPLETED,BoTorch,0.265943385835444412457206908584,191,1,0.044437814631024703804218489722 444,444_0,COMPLETED,BoTorch,0.266383962991518896679110639525,191,1,0.044204709537704486854714502897 445,445_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045315042490568620270341426703 446,446_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045325735258808133587571376211 447,447_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045298232897425495802057326955 448,448_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045322054976998080166872284735 449,449_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045347144044141994567720388432 450,450_0,COMPLETED,BoTorch,0.272937548188126433146294402832,210,1,0.066247462692500078129675955552 451,451_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045335046847855962892648307161 452,452_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045302927758385132250840143797 453,453_0,COMPLETED,BoTorch,0.269137570216984256532555264130,191,1,0.045606459780624587774244815819 454,454_0,COMPLETED,BoTorch,0.271065095274810041736657240108,186,2,0.052428746985923507795135378728 455,455_0,COMPLETED,BoTorch,0.268421632338363247427537316980,182,2,0.034049712631858443379062606482 456,456_0,COMPLETED,BoTorch,0.294140323824209737324508751044,155,1,0.070000000000000006661338147751 457,457_0,COMPLETED,BoTorch,0.266053530124463089023834072577,191,1,0.044587364811246553431001160561 458,458_0,COMPLETED,BoTorch,0.268972353783456297193765749398,182,2,0.049450938401479842265739961249 459,459_0,COMPLETED,BoTorch,0.268476704482872530199699667719,192,1,0.044400222471499092846691780778 460,460_0,COMPLETED,BoTorch,0.267705694459742238322519369831,193,1,0.044795347344806307754438989832 461,461_0,COMPLETED,BoTorch,0.266824540147593380901014370465,199,1,0.070000000000000006661338147751 462,456_0,COMPLETED,BoTorch,0.294140323824209737324508751044,155,1,0.070000000000000006661338147751 463,463_0,COMPLETED,BoTorch,0.268641920916400489538489182451,192,1,0.044078950725947974031981857479 464,464_0,COMPLETED,BoTorch,0.268807137349928448877278697182,218,1,0.060345959968902400383239381654 465,465_0,COMPLETED,BoTorch,0.275140433968498743233510595019,174,4,0.013537888485419233980389108751 466,466_0,COMPLETED,BoTorch,0.268752065205419055082813883928,218,1,0.060247723944732385625844273136 467,467_0,COMPLETED,BoTorch,0.295737416014979603851031697559,142,4,0.046643478410653048393808006722 468,468_0,COMPLETED,BoTorch,0.269302786650512215871344778861,195,3,0.070000000000000006661338147751 469,469_0,COMPLETED,BoTorch,0.284722987113118164437253199139,213,3,0.063488244955743775022050101597 470,470_0,COMPLETED,BoTorch,0.269192642361493539304717614868,200,1,0.057673942399209025821971152936 471,471_0,COMPLETED,BoTorch,0.268752065205419055082813883928,200,1,0.062831084226523004065967370479 472,472_0,COMPLETED,BoTorch,0.268146271615816722544423100771,199,1,0.053401405458704462314578620408 473,473_0,COMPLETED,BoTorch,0.280647648419429462940399844229,214,2,0.067631020846018463643645191041 474,474_0,COMPLETED,BoTorch,0.268201343760326005316585451510,199,1,0.063529220175546893822904337412 475,475_0,COMPLETED,BoTorch,0.269688291662077306298783696548,209,1,0.055128036151389732399952237074 476,476_0,COMPLETED,BoTorch,0.267815838748760914889146533824,212,1,0.055168137353005165801622666777 477,477_0,COMPLETED,BoTorch,0.267815838748760914889146533824,212,1,0.055160849876058591634286898397 478,478_0,COMPLETED,BoTorch,0.266879612292102663673176721204,222,1,0.067761168909431152007627474632 479,479_0,COMPLETED,BoTorch,0.267925983037779480433471235301,212,1,0.054653163399373531905478529325 480,480_0,COMPLETED,BoTorch,0.268752065205419055082813883928,213,1,0.054899910524051770410736139638 481,481_0,COMPLETED,BoTorch,0.268752065205419055082813883928,213,1,0.054874339079555030374013568917 482,482_0,COMPLETED,BoTorch,0.269963652384623831181897912757,193,2,0.070000000000000006661338147751 483,483_0,COMPLETED,BoTorch,0.268091199471307439772260750033,214,1,0.055694005477570585060931307453 484,484_0,COMPLETED,BoTorch,0.266659323714065421562224855734,198,1,0.070000000000000006661338147751 485,485_0,COMPLETED,BoTorch,0.286044718581341506080661929445,344,4,0.059530899242833869300373095257 486,486_0,COMPLETED,BoTorch,0.268091199471307439772260750033,214,1,0.055711880720697146140985012153 487,487_0,COMPLETED,BoTorch,0.269137570216984256532555264130,189,4,0.070000000000000006661338147751 488,488_0,COMPLETED,BoTorch,0.269137570216984256532555264130,220,1,0.063071873432338720899537065634 489,489_0,COMPLETED,BoTorch,0.269908580240114548409735562018,188,3,0.070000000000000006661338147751 490,490_0,COMPLETED,BoTorch,0.267485405881704996211567504361,189,3,0.065880375081519393365425685261 491,491_0,COMPLETED,BoTorch,0.268972353783456297193765749398,220,1,0.062976558202193402835611379942 492,492_0,COMPLETED,BoTorch,0.269578147373058740754458995070,200,1,0.070000000000000006661338147751 493,493_0,COMPLETED,BoTorch,0.269192642361493539304717614868,187,4,0.070000000000000006661338147751 494,494_0,COMPLETED,BoTorch,0.268862209494437731649441047921,220,1,0.062879306444814583865188239997 495,495_0,COMPLETED,BoTorch,0.265502808679369928235303177644,197,2,0.062247172405158542052294734503 496,496_0,COMPLETED,BoTorch,0.278114329772001345197907085094,208,3,0.068517613505915744842589276686 497,497_0,COMPLETED,BoTorch,0.268201343760326005316585451510,185,4,0.070000000000000006661338147751 498,498_0,COMPLETED,BoTorch,0.287641810772111483629487338476,902,1,0.070000000000000006661338147751 499,499_0,COMPLETED,BoTorch,0.274810001101442935578234028071,195,3,0.044594551928669907969737806752 500,500_0,COMPLETED,BoTorch,0.267981055182288763205633586040,188,1,0.042270254411103047598174953237 501,501_0,RUNNING,BoTorch,,193,1,0.044423205599560117162738492880 502,502_0,RUNNING,BoTorch,,885,1,0.064706386131606605238708596062
start_time,end_time,run_time,program_string,n_samples,n_clusters,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727382292,1727382370,78,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 244 n_clusters 1 threshold 0.027889940015971663,244,1,0.027889940015971663,0.287917171494658,0,None,i7037,75,0.02332305319969158 1727382275,1727382373,98,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 348 n_clusters 2 threshold 0.060073542356491094,348,2,0.060073542356491094,0.2857142857142857,0,None,i7037,94,0.024424496089877734 1727382292,1727382388,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 256 n_clusters 3 threshold 0.06252763560414315,256,3,0.06252763560414315,0.28769688291662077,0,None,i7037,92,0.0234331974887102 1727382292,1727382396,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 467 n_clusters 1 threshold 0.06014735694229603,467,1,0.06014735694229603,0.2897345522634651,0,None,i7037,101,0.022414362815288025 1727382412,1727382499,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 225 n_clusters 3 threshold 0.06563505103811622,225,3,0.06563505103811622,0.29964753827514046,0,None,i7037,84,0.004364467452362589 1727382392,1727382507,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 358 n_clusters 4 threshold 0.06821549763903023,358,4,0.06821549763903023,0.28626500715937875,0,None,i7037,111,0.02414913536733121 1727382392,1727382529,137,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 668 n_clusters 2 threshold 0.03385135114565492,668,2,0.03385135114565492,0.29375481881264454,0,None,i7037,133,0.020404229540698315 1727382396,1727382561,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 709 n_clusters 4 threshold 0.0345907275043428,709,4,0.0345907275043428,0.27965634981826193,0,None,i7037,161,0.05490692807577924 1727382590,1727382651,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 169 n_clusters 3 threshold 0.003816889986395836,169,3,0.003816889986395836,0.2842824099570437,0,None,i7035,51,0.0018540955318133423 1727382590,1727382653,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 219 n_clusters 1 threshold 0.01711221405118704,219,1,0.01711221405118704,0.29480118955832135,0,None,i7035,53,0.003976208833571982 1727382512,1727382658,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 832 n_clusters 1 threshold 0.005984044417738915,832,1,0.005984044417738915,0.27965634981826193,0,None,i7037,142,0.05490692807577924 1727382512,1727382662,150,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 579 n_clusters 4 threshold 0.01543444074317813,579,4,0.01543444074317813,0.27965634981826193,0,None,i7037,147,0.05490692807577924 1727382590,1727382675,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 228 n_clusters 4 threshold 0.04517048396542669,228,4,0.04517048396542669,0.286485295737416,0,None,i7035,75,0.016025994052208392 1727382572,1727382675,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 457 n_clusters 1 threshold 0.04263450758159161,457,1,0.04263450758159161,0.28857803722876973,0,None,i7037,99,0.022992620332635716 1727382590,1727382695,105,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 606 n_clusters 1 threshold 0.04684673741832376,606,1,0.04684673741832376,0.2875867386276022,0,None,i7035,95,0.023488269633219483 1727382548,1727382708,160,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 781 n_clusters 3 threshold 0.06887843919917942,781,3,0.06887843919917942,0.2874765943385835,0,None,i7037,156,0.02354334177772882 1727382590,1727382719,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 788 n_clusters 2 threshold 0.04351501413062216,788,2,0.04351501413062216,0.29232294305540263,0,None,i7035,119,0.02112016741931927 1727382590,1727382730,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 869 n_clusters 2 threshold 0.023314345303922894,869,2,0.023314345303922894,0.27965634981826193,0,None,i7035,130,0.05490692807577924 1727382590,1727382732,142,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 864 n_clusters 2 threshold 0.02005105697736144,864,2,0.02005105697736144,0.27965634981826193,0,None,i7035,131,0.05490692807577924 1727382590,1727382734,144,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 858 n_clusters 3 threshold 0.06544162298738958,858,3,0.06544162298738958,0.2935896023791167,0,None,i7035,134,0.02048683775746224 1727382880,1727382972,92,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 364 n_clusters 4 threshold 0.01099572471674463,364,4,0.01099572471674463,0.27965634981826193,0,None,i7035,89,0.05490692807577924 1727382880,1727382990,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 535 n_clusters 4 threshold 0.028108361059398235,535,4,0.028108361059398235,0.28808238792818597,0,None,i7035,107,0.0232404449829276 1727382880,1727383002,122,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 826 n_clusters 2 threshold 0.002,826,2,0.002,0.27965634981826193,0,None,i7035,119,0.05490692807577924 1727382880,1727383006,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.017930694452499306,1000,1,0.017930694452499306,0.27965634981826193,0,None,i7035,123,0.05490692807577924 1727382880,1727383009,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 699 n_clusters 4 threshold 0.003961408212790633,699,4,0.003961408212790633,0.27965634981826193,0,None,i7035,126,0.05490692807577924 1727382880,1727383009,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 759 n_clusters 3 threshold 0.0037433131751267237,759,3,0.0037433131751267237,0.27965634981826193,0,None,i7035,126,0.05490692807577924 1727382880,1727383012,132,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 964 n_clusters 2 threshold 0.006440706098829536,964,2,0.006440706098829536,0.27965634981826193,0,None,i7035,129,0.05490692807577924 1727382880,1727383022,142,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 870 n_clusters 4 threshold 0.015139047254553858,870,4,0.015139047254553858,0.27965634981826193,0,None,i7035,139,0.05490692807577924 1727382880,1727383027,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 3 threshold 0.017818407251825763,1000,3,0.017818407251825763,0.27965634981826193,0,None,i7035,144,0.05490692807577924 1727382932,1727383056,124,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.002,1000,1,0.002,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727382932,1727383060,128,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 726 n_clusters 4 threshold 0.021703111054831012,726,4,0.021703111054831012,0.27965634981826193,0,None,i7035,125,0.05490692807577924 1727383030,1727383066,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.014256247464616362,100,4,0.014256247464616362,0.299922898997687,0,None,i7035,32,0.0003981652746707378 1727382970,1727383102,132,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 2 threshold 0.018208644185763012,1000,2,0.018208644185763012,0.27965634981826193,0,None,i7035,128,0.05490692807577924 1727382970,1727383106,136,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 904 n_clusters 4 threshold 0.002,904,4,0.002,0.27965634981826193,0,None,i7035,133,0.05490692807577924 1727383012,1727383110,98,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 436 n_clusters 4 threshold 0.002,436,4,0.002,0.27965634981826193,0,None,i7035,94,0.05490692807577924 1727382992,1727383123,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 826 n_clusters 3 threshold 0.014011524148584658,826,3,0.014011524148584658,0.27965634981826193,0,None,i7035,128,0.05490692807577924 1727383012,1727383125,113,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 608 n_clusters 3 threshold 0.002,608,3,0.002,0.27965634981826193,0,None,i7035,110,0.05490692807577924 1727383031,1727383132,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 696 n_clusters 1 threshold 0.002,696,1,0.002,0.27965634981826193,0,None,i7035,98,0.05490692807577924 1727383000,1727383133,133,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 899 n_clusters 4 threshold 0.03142658931558435,899,4,0.03142658931558435,0.28736645004956496,0,None,i7035,130,0.023598413922238104 1727383031,1727383135,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 475 n_clusters 4 threshold 0.025392498925328022,475,4,0.025392498925328022,0.27965634981826193,0,None,i7035,101,0.05490692807577924 1727383032,1727383154,122,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.009551141601888217,1000,1,0.009551141601888217,0.27965634981826193,0,None,i7035,119,0.05490692807577924 1727383030,1727383164,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 884 n_clusters 3 threshold 0.002,884,3,0.002,0.27965634981826193,0,None,i7035,131,0.05490692807577924 1727383061,1727383172,111,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 564 n_clusters 4 threshold 0.002,564,4,0.002,0.27965634981826193,0,None,i7035,108,0.05490692807577924 1727383072,1727383181,109,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 705 n_clusters 2 threshold 0.00812711405451167,705,2,0.00812711405451167,0.27965634981826193,0,None,i7035,106,0.05490692807577924 1727383072,1727383212,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 3 threshold 0.002,1000,3,0.002,0.27965634981826193,0,None,i7035,137,0.05490692807577924 1727383112,1727383215,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 548 n_clusters 3 threshold 0.002,548,3,0.002,0.27965634981826193,0,None,i7035,100,0.05490692807577924 1727383121,1727383248,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 742 n_clusters 4 threshold 0.012496500587452568,742,4,0.012496500587452568,0.27965634981826193,0,None,i7035,124,0.05490692807577924 1727383112,1727383257,145,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.016667694161320523,1000,4,0.016667694161320523,0.27965634981826193,0,None,i7035,142,0.05490692807577924 1727383151,1727383260,109,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 604 n_clusters 3 threshold 0.009669240543426121,604,3,0.009669240543426121,0.27965634981826193,0,None,i7035,106,0.05490692807577924 1727383132,1727383266,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 897 n_clusters 3 threshold 0.009990621740445071,897,3,0.009990621740445071,0.27965634981826193,0,None,i7035,131,0.05490692807577924 1727383172,1727383268,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 525 n_clusters 2 threshold 0.002,525,2,0.002,0.27965634981826193,0,None,i7035,93,0.05490692807577924 1727383151,1727383275,124,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 905 n_clusters 2 threshold 0.010372597541904102,905,2,0.010372597541904102,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727383151,1727383278,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 785 n_clusters 3 threshold 0.009903397250269603,785,3,0.009903397250269603,0.27965634981826193,0,None,i7035,123,0.05490692807577924 1727383132,1727383279,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.002,1000,4,0.002,0.27965634981826193,0,None,i7035,143,0.05490692807577924 1727383192,1727383284,92,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 420 n_clusters 3 threshold 0.002,420,3,0.002,0.27965634981826193,0,None,i7035,89,0.05490692807577924 1727383151,1727383289,138,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.02718513880214344,1000,4,0.02718513880214344,0.2879722436391673,0,None,i7035,135,0.023295517127436938 1727383182,1727383294,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 846 n_clusters 1 threshold 0.012643279132101426,846,1,0.012643279132101426,0.27965634981826193,0,None,i7035,109,0.05490692807577924 1727383182,1727383322,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 3 threshold 0.00774777817920385,1000,3,0.00774777817920385,0.27965634981826193,0,None,i7035,137,0.05490692807577924 1727383182,1727383330,148,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.010234041319064253,1000,4,0.010234041319064253,0.27965634981826193,0,None,i7035,145,0.05490692807577924 1727383232,1727383337,105,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 510 n_clusters 4 threshold 0.00922166107683189,510,4,0.00922166107683189,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727383232,1727383350,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 606 n_clusters 4 threshold 0.009813078067171162,606,4,0.009813078067171162,0.27965634981826193,0,None,i7035,115,0.05490692807577924 1727383252,1727383351,99,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 487 n_clusters 3 threshold 0.005293499506974084,487,3,0.005293499506974084,0.27965634981826193,0,None,i7035,96,0.05490692807577924 1727383273,1727383363,90,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 546 n_clusters 1 threshold 0.002,546,1,0.002,0.27965634981826193,0,None,i7035,87,0.05490692807577924 1727383273,1727383373,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 573 n_clusters 2 threshold 0.002,573,2,0.002,0.27965634981826193,0,None,i7035,97,0.05490692807577924 1727383272,1727383378,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 627 n_clusters 2 threshold 0.002,627,2,0.002,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727383302,1727383396,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 484 n_clusters 2 threshold 0.008163946760472789,484,2,0.008163946760472789,0.27965634981826193,0,None,i7035,90,0.05490692807577924 1727383302,1727383397,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 582 n_clusters 1 threshold 0.010675862727463282,582,1,0.010675862727463282,0.27965634981826193,0,None,i7035,91,0.05490692807577924 1727383272,1727383403,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 744 n_clusters 4 threshold 0.002,744,4,0.002,0.27965634981826193,0,None,i7035,128,0.05490692807577924 1727383292,1727383411,119,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 866 n_clusters 1 threshold 0.002,866,1,0.002,0.27965634981826193,0,None,i7035,116,0.05490692807577924 1727383393,1727383422,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.05526126966471334,100,4,0.05526126966471334,0.28775195506113005,0,None,i7035,26,0.00041973904734120607 1727383423,1727383442,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.051536492364097825,100,1,0.051536492364097825,0.2789954840841502,0,None,i7035,15,0.0003843576752212064 1727383413,1727383446,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.024196377910024915,100,4,0.024196377910024915,0.2969490031941844,0,None,i7035,31,0.00038550501156514995 1727383393,1727383480,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 439 n_clusters 2 threshold 0.023416705294092656,439,2,0.023416705294092656,0.27965634981826193,0,None,i7035,84,0.05490692807577924 1727383393,1727383524,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 850 n_clusters 4 threshold 0.03602704195933041,850,4,0.03602704195933041,0.29078092300914193,0,None,i7035,128,0.021891177442449616 1727383413,1727383532,119,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 683 n_clusters 4 threshold 0.03663643343757436,683,4,0.03663643343757436,0.2880273157836766,0,None,i7035,116,0.023267981055182296 1727383413,1727383551,138,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 993 n_clusters 4 threshold 0.035866962959401796,993,4,0.035866962959401796,0.28587950214781366,0,None,i7035,135,0.024341887873113754 1727383413,1727383553,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.052930652833244995,1000,4,0.052930652833244995,0.2901751294195396,0,None,i7035,137,0.022194074237250783 1727383514,1727383613,99,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 421 n_clusters 4 threshold 0.021316259633873985,421,4,0.021316259633873985,0.27965634981826193,0,None,i7035,96,0.05490692807577924 1727383533,1727383629,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 391 n_clusters 4 threshold 0.025150843702273808,391,4,0.025150843702273808,0.27965634981826193,0,None,i7035,93,0.05490692807577924 1727383553,1727383636,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 307 n_clusters 4 threshold 0.05580651436399066,307,4,0.05580651436399066,0.2895142636854279,0,None,i7035,81,0.022524507104306646 1727383533,1727383643,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 586 n_clusters 4 threshold 0.07,586,4,0.07,0.2868157286044719,0,None,i7035,107,0.02387377464478463 1727383544,1727383644,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 440 n_clusters 4 threshold 0.01934676860909182,440,4,0.01934676860909182,0.27965634981826193,0,None,i7035,97,0.05490692807577924 1727383573,1727383654,81,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 290 n_clusters 4 threshold 0.002,290,4,0.002,0.27965634981826193,0,None,i7035,78,0.05490692807577924 1727383544,1727383658,114,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 609 n_clusters 4 threshold 0.053039206429008036,609,4,0.053039206429008036,0.2875867386276022,0,None,i7035,110,0.023488269633219483 1727383573,1727383662,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 371 n_clusters 4 threshold 0.029166754372271772,371,4,0.029166754372271772,0.27965634981826193,0,None,i7035,86,0.05490692807577924 1727383533,1727383664,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 766 n_clusters 4 threshold 0.03018387884791217,766,4,0.03018387884791217,0.27965634981826193,0,None,i7035,128,0.05490692807577924 1727383604,1727383679,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 420 n_clusters 1 threshold 0.019583940469605807,420,1,0.019583940469605807,0.27965634981826193,0,None,i7035,72,0.05490692807577924 1727383673,1727383692,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.044668509970616305,100,1,0.044668509970616305,0.27954620552924336,0,None,i7035,16,0.000399976523990953 1727383604,1727383714,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 602 n_clusters 4 threshold 0.06663149400834231,602,4,0.06663149400834231,0.287917171494658,0,None,i7035,107,0.02332305319969158 1727383693,1727383715,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 124 n_clusters 1 threshold 0.04555564949642364,124,1,0.04555564949642364,0.29887652825201017,0,None,i7035,18,0.0003498700945297157 1727383633,1727383726,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 394 n_clusters 4 threshold 0.022564883309090816,394,4,0.022564883309090816,0.27965634981826193,0,None,i7035,90,0.05490692807577924 1727383693,1727383760,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 315 n_clusters 1 threshold 0.002,315,1,0.002,0.27965634981826193,0,None,i7035,64,0.05490692807577924 1727383693,1727383792,99,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 436 n_clusters 4 threshold 0.014786870176411103,436,4,0.014786870176411103,0.27965634981826193,0,None,i7035,95,0.05490692807577924 1727383693,1727383801,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 777 n_clusters 1 threshold 0.025738610597478055,777,1,0.025738610597478055,0.27965634981826193,0,None,i7035,104,0.05490692807577924 1727383713,1727383806,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 390 n_clusters 4 threshold 0.022014935013095503,390,4,0.022014935013095503,0.27965634981826193,0,None,i7035,90,0.05490692807577924 1727383665,1727383808,143,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 743 n_clusters 4 threshold 0.0313211414413518,743,4,0.0313211414413518,0.27965634981826193,0,None,i7035,139,0.05490692807577924 1727383713,1727383815,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 469 n_clusters 4 threshold 0.017928754746189365,469,4,0.017928754746189365,0.27965634981826193,0,None,i7035,98,0.05490692807577924 1727383833,1727383853,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 136 n_clusters 1 threshold 0.04817747133192578,136,1,0.04817747133192578,0.2983258068069171,0,None,i7035,17,0.00036603506148610154 1727383793,1727383882,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 352 n_clusters 4 threshold 0.002,352,4,0.002,0.27965634981826193,0,None,i7035,86,0.05490692807577924 1727383813,1727383890,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 405 n_clusters 1 threshold 0.009624654229520763,405,1,0.009624654229520763,0.27965634981826193,0,None,i7035,73,0.05490692807577924 1727383813,1727383900,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 508 n_clusters 1 threshold 0.01722258275516638,508,1,0.01722258275516638,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727383833,1727383904,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 347 n_clusters 1 threshold 0.005531533048438878,347,1,0.005531533048438878,0.27965634981826193,0,None,i7035,68,0.05490692807577924 1727383786,1727383915,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 732 n_clusters 4 threshold 0.03248222134665922,732,4,0.03248222134665922,0.27965634981826193,0,None,i7035,126,0.05490692807577924 1727383846,1727383917,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 347 n_clusters 1 threshold 0.024839593095059218,347,1,0.024839593095059218,0.27965634981826193,0,None,i7035,68,0.05490692807577924 1727383833,1727383941,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 770 n_clusters 1 threshold 0.01840194517970916,770,1,0.01840194517970916,0.27965634981826193,0,None,i7035,105,0.05490692807577924 1727383813,1727383944,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 935 n_clusters 1 threshold 0.01844705421112671,935,1,0.01844705421112671,0.2859896464368322,0,None,i7035,127,0.02428681572860447 1727383893,1727383952,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 244 n_clusters 1 threshold 0.002,244,1,0.002,0.27965634981826193,0,None,i7035,56,0.05490692807577924 1727383873,1727383961,88,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 339 n_clusters 4 threshold 0.02037837813262481,339,4,0.02037837813262481,0.27965634981826193,0,None,i7035,84,0.05490692807577924 1727383953,1727384035,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 442 n_clusters 1 threshold 0.002,442,1,0.002,0.27965634981826193,0,None,i7035,79,0.05490692807577924 1727383973,1727384045,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 395 n_clusters 1 threshold 0.002,395,1,0.002,0.27965634981826193,0,None,i7035,69,0.05490692807577924 1727383967,1727384046,79,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 455 n_clusters 1 threshold 0.010534204746145328,455,1,0.010534204746145328,0.27965634981826193,0,None,i7035,76,0.05490692807577924 1727383967,1727384071,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 740 n_clusters 1 threshold 0.011592416774931056,740,1,0.011592416774931056,0.27965634981826193,0,None,i7035,101,0.05490692807577924 1727383993,1727384074,81,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 393 n_clusters 2 threshold 0.011260027405519743,393,2,0.011260027405519743,0.27965634981826193,0,None,i7035,78,0.05490692807577924 1727383993,1727384079,86,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 367 n_clusters 3 threshold 0.002,367,3,0.002,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727383953,1727384082,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 786 n_clusters 4 threshold 0.018249045682134278,786,4,0.018249045682134278,0.27965634981826193,0,None,i7035,126,0.05490692807577924 1727383993,1727384100,107,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 787 n_clusters 1 threshold 0.015008084147379683,787,1,0.015008084147379683,0.27965634981826193,0,None,i7035,105,0.05490692807577924 1727383997,1727384113,116,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 921 n_clusters 1 threshold 0.013538210860071155,921,1,0.013538210860071155,0.27965634981826193,0,None,i7035,113,0.05490692807577924 1727384113,1727384180,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 301 n_clusters 1 threshold 0.01668409593823582,301,1,0.01668409593823582,0.27965634981826193,0,None,i7035,64,0.05490692807577924 1727384113,1727384186,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 268 n_clusters 1 threshold 0.002,268,1,0.002,0.27965634981826193,0,None,i7035,69,0.05490692807577924 1727384133,1727384214,81,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 259 n_clusters 4 threshold 0.002,259,4,0.002,0.27965634981826193,0,None,i7035,78,0.05490692807577924 1727384153,1727384228,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 386 n_clusters 1 threshold 0.042620173539675475,386,1,0.042620173539675475,0.286485295737416,0,None,i7035,71,0.024038991078312588 1727384118,1727384253,135,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 764 n_clusters 4 threshold 0.020259807580286386,764,4,0.020259807580286386,0.27965634981826193,0,None,i7035,132,0.05490692807577924 1727384148,1727384254,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 726 n_clusters 1 threshold 0.002,726,1,0.002,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727384148,1727384256,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 751 n_clusters 1 threshold 0.023026475226434633,751,1,0.023026475226434633,0.27965634981826193,0,None,i7035,104,0.05490692807577924 1727384133,1727384258,125,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 661 n_clusters 4 threshold 0.002,661,4,0.002,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727384133,1727384258,125,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 719 n_clusters 4 threshold 0.05223476107521264,719,4,0.05223476107521264,0.2873113779050557,0,None,i7035,121,0.023625949994492745 1727384194,1727384334,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 710 n_clusters 4 threshold 0.07,710,4,0.07,0.2880273157836766,0,None,i7035,137,0.023267981055182296 1727384294,1727384356,62,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 280 n_clusters 1 threshold 0.010334696655185332,280,1,0.010334696655185332,0.27965634981826193,0,None,i7035,59,0.05490692807577924 1727384299,1727384379,80,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 298 n_clusters 4 threshold 0.020911091314045553,298,4,0.020911091314045553,0.27965634981826193,0,None,i7035,77,0.05490692807577924 1727384269,1727384385,116,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 933 n_clusters 1 threshold 0.002,933,1,0.002,0.27965634981826193,0,None,i7035,112,0.05490692807577924 1727384294,1727384389,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 720 n_clusters 1 threshold 0.04784040284547034,720,1,0.04784040284547034,0.2876418107721115,0,None,i7035,92,0.02346073356096484 1727384313,1727384409,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 712 n_clusters 1 threshold 0.0666054708578608,712,1,0.0666054708578608,0.2860997907258509,0,None,i7035,92,0.024231743584095133 1727384273,1727384413,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 940 n_clusters 4 threshold 0.002,940,4,0.002,0.27965634981826193,0,None,i7035,136,0.05490692807577924 1727384294,1727384427,133,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 848 n_clusters 4 threshold 0.002,848,4,0.002,0.27965634981826193,0,None,i7035,130,0.05490692807577924 1727384353,1727384440,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 318 n_clusters 4 threshold 0.00969779436496298,318,4,0.00969779436496298,0.27965634981826193,0,None,i7035,84,0.05490692807577924 1727384313,1727384453,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 931 n_clusters 4 threshold 0.002,931,4,0.002,0.27965634981826193,0,None,i7035,137,0.05490692807577924 1727384451,1727384526,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 310 n_clusters 1 threshold 0.029962486804000245,310,1,0.029962486804000245,0.2893490472519,0,None,i7035,71,0.02260711532107057 1727384450,1727384535,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 479 n_clusters 1 threshold 0.021370115018313922,479,1,0.021370115018313922,0.27965634981826193,0,None,i7035,81,0.05490692807577924 1727384433,1727384545,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 796 n_clusters 1 threshold 0.002,796,1,0.002,0.27965634981826193,0,None,i7035,108,0.05490692807577924 1727384481,1727384566,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 297 n_clusters 4 threshold 0.029001730161232343,297,4,0.029001730161232343,0.27965634981826193,0,None,i7035,82,0.05490692807577924 1727384420,1727384568,148,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 976 n_clusters 4 threshold 0.002,976,4,0.002,0.27965634981826193,0,None,i7035,145,0.05490692807577924 1727384454,1727384574,120,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 883 n_clusters 1 threshold 0.017790692117950695,883,1,0.017790692117950695,0.27965634981826193,0,None,i7035,118,0.05490692807577924 1727384474,1727384598,124,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 926 n_clusters 1 threshold 0.005954968514654763,926,1,0.005954968514654763,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727384481,1727384619,138,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 800 n_clusters 4 threshold 0.002,800,4,0.002,0.27965634981826193,0,None,i7035,135,0.05490692807577924 1727384474,1727384621,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 928 n_clusters 4 threshold 0.007927621356924658,928,4,0.007927621356924658,0.27965634981826193,0,None,i7035,144,0.05490692807577924 1727384615,1727384688,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 368 n_clusters 1 threshold 0.019885498621174835,368,1,0.019885498621174835,0.27965634981826193,0,None,i7035,70,0.05490692807577924 1727384595,1727384696,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 538 n_clusters 1 threshold 0.010785515252928887,538,1,0.010785515252928887,0.27965634981826193,0,None,i7035,98,0.05490692807577924 1727384615,1727384700,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 328 n_clusters 4 threshold 0.0317904444020396,328,4,0.0317904444020396,0.27965634981826193,0,None,i7035,82,0.05490692807577924 1727384634,1727384717,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 291 n_clusters 4 threshold 0.012369767412565937,291,4,0.012369767412565937,0.27965634981826193,0,None,i7035,80,0.05490692807577924 1727384632,1727384730,98,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 661 n_clusters 1 threshold 0.007718166204744522,661,1,0.007718166204744522,0.27965634981826193,0,None,i7035,95,0.05490692807577924 1727384654,1727384734,80,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 469 n_clusters 1 threshold 0.022141249336263588,469,1,0.022141249336263588,0.27965634981826193,0,None,i7035,77,0.05490692807577924 1727384662,1727384744,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 528 n_clusters 1 threshold 0.07,528,1,0.07,0.28857803722876973,0,None,i7035,79,0.022992620332635716 1727384632,1727384750,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 975 n_clusters 1 threshold 0.002,975,1,0.002,0.27965634981826193,0,None,i7035,116,0.05490692807577924 1727384692,1727384754,62,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 291 n_clusters 1 threshold 0.034342157273375494,291,1,0.034342157273375494,0.2905055622865954,0,None,i7035,59,0.02202885780372288 1727384654,1727384767,113,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 558 n_clusters 4 threshold 0.010358608038920067,558,4,0.010358608038920067,0.27965634981826193,0,None,i7035,110,0.05490692807577924 1727384783,1727384863,80,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 280 n_clusters 4 threshold 0.02932489267472039,280,4,0.02932489267472039,0.27965634981826193,0,None,i7035,77,0.05490692807577924 1727384813,1727384885,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 355 n_clusters 1 threshold 0.01782619791822084,355,1,0.01782619791822084,0.27965634981826193,0,None,i7035,69,0.05490692807577924 1727384813,1727384896,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 276 n_clusters 4 threshold 0.034565433051895,276,4,0.034565433051895,0.27965634981826193,0,None,i7035,79,0.05490692807577924 1727384843,1727384911,68,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 301 n_clusters 1 threshold 0.008449991812437012,301,1,0.008449991812437012,0.27965634981826193,0,None,i7035,65,0.05490692807577924 1727384834,1727384916,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 281 n_clusters 4 threshold 0.02743485338519354,281,4,0.02743485338519354,0.27965634981826193,0,None,i7035,79,0.05490692807577924 1727384844,1727384919,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 370 n_clusters 1 threshold 0.02353497961262665,370,1,0.02353497961262665,0.27965634981826193,0,None,i7035,72,0.05490692807577924 1727384834,1727384923,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 501 n_clusters 1 threshold 0.002,501,1,0.002,0.27965634981826193,0,None,i7035,85,0.05490692807577924 1727384854,1727384924,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 340 n_clusters 1 threshold 0.014963198545027395,340,1,0.014963198545027395,0.27965634981826193,0,None,i7035,67,0.05490692807577924 1727384834,1727384927,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 358 n_clusters 4 threshold 0.022510636274552208,358,4,0.022510636274552208,0.27965634981826193,0,None,i7035,90,0.05490692807577924 1727385075,1727385148,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 328 n_clusters 2 threshold 0.013080975658625368,328,2,0.013080975658625368,0.27965634981826193,0,None,i7035,70,0.05490692807577924 1727385075,1727385150,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 303 n_clusters 2 threshold 0.006650509895926076,303,2,0.006650509895926076,0.27965634981826193,0,None,i7035,72,0.05490692807577924 1727385075,1727385153,78,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 335 n_clusters 2 threshold 0.01607152782970724,335,2,0.01607152782970724,0.27965634981826193,0,None,i7035,76,0.05490692807577924 1727385075,1727385191,116,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 822 n_clusters 1 threshold 0.020808366239038922,822,1,0.020808366239038922,0.27965634981826193,0,None,i7035,113,0.05490692807577924 1727385075,1727385196,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 800 n_clusters 2 threshold 0.021992186894520413,800,2,0.021992186894520413,0.27965634981826193,0,None,i7035,118,0.05490692807577924 1727385094,1727385198,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 677 n_clusters 1 threshold 0.015449072034617737,677,1,0.015449072034617737,0.27965634981826193,0,None,i7035,100,0.05490692807577924 1727385075,1727385202,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 672 n_clusters 4 threshold 0.013583661514192298,672,4,0.013583661514192298,0.27965634981826193,0,None,i7035,124,0.05490692807577924 1727385094,1727385224,130,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 776 n_clusters 3 threshold 0.0247772530928144,776,3,0.0247772530928144,0.27965634981826193,0,None,i7035,126,0.05490692807577924 1727385085,1727385224,139,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 829 n_clusters 4 threshold 0.009290259496907965,829,4,0.009290259496907965,0.27965634981826193,0,None,i7035,136,0.05490692807577924 1727385114,1727385235,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 808 n_clusters 2 threshold 0.02037734886062012,808,2,0.02037734886062012,0.27965634981826193,0,None,i7035,118,0.05490692807577924 1727385274,1727385292,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.07,100,1,0.07,0.27701288688181513,0,None,i7035,14,0.00039000069683121626 1727385214,1727385303,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 341 n_clusters 4 threshold 0.028938127515462146,341,4,0.028938127515462146,0.27965634981826193,0,None,i7035,85,0.05490692807577924 1727385234,1727385319,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 511 n_clusters 1 threshold 0.009517547642029748,511,1,0.009517547642029748,0.27965634981826193,0,None,i7035,82,0.05490692807577924 1727385274,1727385347,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 214 n_clusters 4 threshold 0.002,214,4,0.002,0.27965634981826193,0,None,i7035,70,0.05490692807577924 1727385254,1727385355,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 659 n_clusters 1 threshold 0.002,659,1,0.002,0.27965634981826193,0,None,i7035,97,0.05490692807577924 1727385254,1727385374,120,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 641 n_clusters 4 threshold 0.007993090707909094,641,4,0.007993090707909094,0.27965634981826193,0,None,i7035,117,0.05490692807577924 1727385266,1727385382,116,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 584 n_clusters 4 threshold 0.008220707151701994,584,4,0.008220707151701994,0.27965634981826193,0,None,i7035,113,0.05490692807577924 1727385295,1727385390,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 393 n_clusters 4 threshold 0.002,393,4,0.002,0.27965634981826193,0,None,i7035,92,0.05490692807577924 1727385295,1727385398,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 719 n_clusters 1 threshold 0.027008783000060904,719,1,0.027008783000060904,0.27965634981826193,0,None,i7035,100,0.05490692807577924 1727385415,1727385431,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.06588008013499556,100,1,0.06588008013499556,0.2783346183500386,0,None,i7035,13,0.0003810093262990839 1727385447,1727385464,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.058196308311339255,100,1,0.058196308311339255,0.2775636083269083,0,None,i7035,14,0.0003915819378558319 1727385455,1727385485,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 4 threshold 0.04025044909172201,100,4,0.04025044909172201,0.2893490472519,0,None,i7035,27,0.00042447115154815 1727385475,1727385497,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 2 threshold 0.07,100,2,0.07,0.27921577266218744,0,None,i7035,19,0.0004240555127216652 1727385495,1727385515,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.03785009601877126,100,1,0.03785009601877126,0.280923009141976,0,None,i7035,17,0.00040469681950021163 1727385435,1727385532,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 421 n_clusters 4 threshold 0.0077298755356867135,421,4,0.0077298755356867135,0.27965634981826193,0,None,i7035,94,0.05490692807577924 1727385475,1727385565,90,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 599 n_clusters 1 threshold 0.017356159258756554,599,1,0.017356159258756554,0.2872563057605463,0,None,i7035,86,0.023653486066747442 1727385475,1727385587,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 725 n_clusters 2 threshold 0.01697086605456704,725,2,0.01697086605456704,0.27965634981826193,0,None,i7035,109,0.05490692807577924 1727385508,1727385592,84,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 318 n_clusters 4 threshold 0.03918973769728417,318,4,0.03918973769728417,0.27965634981826193,0,None,i7035,80,0.05490692807577924 1727385495,1727385622,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 2 threshold 0.013841014359317875,1000,2,0.013841014359317875,0.27965634981826193,0,None,i7035,124,0.05490692807577924 1727385515,1727385646,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 836 n_clusters 4 threshold 0.02329146436572574,836,4,0.02329146436572574,0.27965634981826193,0,None,i7035,127,0.05490692807577924 1727385856,1727385920,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 302 n_clusters 1 threshold 0.07,302,1,0.07,0.28780702720563944,0,None,i7035,61,0.023378125344200862 1727385856,1727385929,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 407 n_clusters 1 threshold 0.07,407,1,0.07,0.2897345522634651,0,None,i7035,70,0.022414362815288025 1727385856,1727385929,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 401 n_clusters 1 threshold 0.06399313706150848,401,1,0.06399313706150848,0.28670558431545323,0,None,i7035,70,0.023928846789293967 1727385856,1727385929,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 417 n_clusters 1 threshold 0.07,417,1,0.07,0.28956933582993727,0,None,i7035,70,0.02249697103205195 1727385856,1727385952,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 424 n_clusters 4 threshold 0.07,424,4,0.07,0.28620993501486947,0,None,i7035,92,0.02417667143958585 1727385856,1727385958,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 708 n_clusters 1 threshold 0.01751641726049995,708,1,0.01751641726049995,0.27965634981826193,0,None,i7035,98,0.05490692807577924 1727385856,1727385968,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 972 n_clusters 1 threshold 0.07,972,1,0.07,0.2875867386276022,0,None,i7035,108,0.023488269633219483 1727385856,1727385968,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 946 n_clusters 1 threshold 0.07,946,1,0.07,0.2872563057605463,0,None,i7035,109,0.023653486066747442 1727385856,1727385990,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 942 n_clusters 4 threshold 0.07,942,4,0.07,0.29177222161030947,0,None,i7035,131,0.02139552814186585 1727385901,1727386013,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.07,1000,1,0.07,0.2916620773212909,0,None,i7035,109,0.021450600286375132 1727385901,1727386014,113,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 941 n_clusters 1 threshold 0.05162808526217483,941,1,0.05162808526217483,0.28742152219407424,0,None,i7035,110,0.023570877849983463 1727385935,1727386022,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 345 n_clusters 4 threshold 0.039189627227657294,345,4,0.039189627227657294,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727385935,1727386023,88,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 553 n_clusters 1 threshold 0.051797703559754625,553,1,0.051797703559754625,0.29000991298601164,0,None,i7035,84,0.022276682454014762 1727385935,1727386056,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 949 n_clusters 2 threshold 0.059281937100214045,949,2,0.059281937100214045,0.2916620773212909,0,None,i7035,117,0.021450600286375132 1727385955,1727386061,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 535 n_clusters 4 threshold 0.05296543552642441,535,4,0.05296543552642441,0.286485295737416,0,None,i7035,103,0.024038991078312588 1727385931,1727386066,135,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 965 n_clusters 3 threshold 0.06477752510211762,965,3,0.06477752510211762,0.2916070051767816,0,None,i7035,131,0.021478136358629774 1727385955,1727386069,114,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 958 n_clusters 1 threshold 0.06246835948048032,958,1,0.06246835948048032,0.2875316664830928,0,None,i7035,111,0.02351580570547418 1727385955,1727386080,125,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 698 n_clusters 4 threshold 0.021280322992214572,698,4,0.021280322992214572,0.27965634981826193,0,None,i7035,122,0.05490692807577924 1727385975,1727386123,148,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 4 threshold 0.05236730545906799,1000,4,0.05236730545906799,0.28995484084150236,0,None,i7035,145,0.022304218526269404 1727386113,1727386225,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 435 n_clusters 4 threshold 0.03166179519117719,435,4,0.03166179519117719,0.27965634981826193,0,None,i7035,109,0.05490692807577924 1727386143,1727386245,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 385 n_clusters 4 threshold 0.05362999610591349,385,4,0.05362999610591349,0.2867606564599625,0,None,i7035,99,0.023901310717039326 1727386173,1727386270,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 654 n_clusters 1 threshold 0.07,654,1,0.07,0.2905606344311047,0,None,i7035,94,0.022001321731468237 1727386156,1727386276,120,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 548 n_clusters 4 threshold 0.052837943262066665,548,4,0.052837943262066665,0.28967948011895583,0,None,i7035,117,0.022441898887542666 1727386156,1727386282,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 955 n_clusters 1 threshold 0.04521123729649238,955,1,0.04521123729649238,0.2915519330322722,0,None,i7035,123,0.02150567243088447 1727386173,1727386285,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 532 n_clusters 3 threshold 0.05260131496331544,532,3,0.05260131496331544,0.2869258728934905,0,None,i7035,109,0.023818702500275346 1727386196,1727386319,123,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 975 n_clusters 2 threshold 0.06256300861173689,975,2,0.06256300861173689,0.2916620773212909,0,None,i7035,121,0.021450600286375132 1727386196,1727386329,133,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 898 n_clusters 4 threshold 0.031020764958533037,898,4,0.031020764958533037,0.2879722436391673,0,None,i7035,130,0.023295517127436938 1727386692,1727386709,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.06125138627443963,100,1,0.06125138627443963,0.27800418548298267,0,None,i7035,14,0.00038854347471049144 1727386716,1727386746,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 100 n_clusters 1 threshold 0.002,100,1,0.002,0.3062561956162573,0,None,i7035,26,0.00035831749718713785 1727386692,1727386769,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 268 n_clusters 3 threshold 0.002,268,3,0.002,0.27965634981826193,0,None,i7035,73,0.05490692807577924 1727386692,1727386780,88,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 466 n_clusters 2 threshold 0.01632295489398354,466,2,0.01632295489398354,0.27965634981826193,0,None,i7035,84,0.05490692807577924 1727386692,1727386787,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 396 n_clusters 4 threshold 0.03611820668874195,396,4,0.03611820668874195,0.27965634981826193,0,None,i7035,91,0.05490692807577924 1727386692,1727386791,99,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 497 n_clusters 3 threshold 0.014459155919965683,497,3,0.014459155919965683,0.27965634981826193,0,None,i7035,95,0.05490692807577924 1727386692,1727386805,113,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.03856059276463574,1000,1,0.03856059276463574,0.29078092300914193,0,None,i7035,109,0.021891177442449616 1727386692,1727386806,114,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 1000 n_clusters 1 threshold 0.03345540754413649,1000,1,0.03345540754413649,0.28819253221720453,0,None,i7035,111,0.023185372838418317 1727386777,1727386806,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 152 n_clusters 1 threshold 0.002,152,1,0.002,0.28472298711311816,0,None,i7035,26,0.0008170539472282459 1727386692,1727386823,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 725 n_clusters 4 threshold 0.027403669799318353,725,4,0.027403669799318353,0.27965634981826193,0,None,i7035,127,0.05490692807577924 1727386692,1727386836,144,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 930 n_clusters 4 threshold 0.015203880409340533,930,4,0.015203880409340533,0.27965634981826193,0,None,i7035,140,0.05490692807577924 1727386756,1727386868,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 839 n_clusters 1 threshold 0.02810864734223406,839,1,0.02810864734223406,0.2902302015640489,0,None,i7035,109,0.02216653816499614 1727386735,1727386882,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 418 n_clusters 2 threshold 0.026822622365337785,418,2,0.026822622365337785,0.27965634981826193,0,None,i7186,108,0.05490692807577924 1727386797,1727386900,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 591 n_clusters 1 threshold 0.005487033887094553,591,1,0.005487033887094553,0.27965634981826193,0,None,i7035,100,0.05490692807577924 1727386777,1727386902,125,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 721 n_clusters 3 threshold 0.026174895459617036,721,3,0.026174895459617036,0.27965634981826193,0,None,i7035,122,0.05490692807577924 1727386797,1727386903,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 410 n_clusters 4 threshold 0.04155149121300678,410,4,0.04155149121300678,0.27965634981826193,0,None,i7035,104,0.05490692807577924 1727386837,1727386930,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 380 n_clusters 4 threshold 0.04349877762922383,380,4,0.04349877762922383,0.2901751294195396,0,None,i7035,90,0.022194074237250783 1727386857,1727386937,80,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 447 n_clusters 1 threshold 0.015765749668194037,447,1,0.015765749668194037,0.27965634981826193,0,None,i7035,77,0.05490692807577924 1727386817,1727386960,143,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 915 n_clusters 4 threshold 0.018599073912435295,915,4,0.018599073912435295,0.27965634981826193,0,None,i7035,140,0.05490692807577924 1727387077,1727387130,53,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 196 n_clusters 1 threshold 0.002,196,1,0.002,0.29898667254102873,0,None,i7035,50,0.0044470756691265545 1727387079,1727387166,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 321 n_clusters 4 threshold 0.002,321,4,0.002,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727387097,1727387203,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 761 n_clusters 1 threshold 0.002,761,1,0.002,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727387077,1727387226,149,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 970 n_clusters 4 threshold 0.012646866267227587,970,4,0.012646866267227587,0.27965634981826193,0,None,i7035,146,0.05490692807577924 1727387137,1727387235,98,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 411 n_clusters 4 threshold 0.030967336097893282,411,4,0.030967336097893282,0.27965634981826193,0,None,i7035,95,0.05490692807577924 1727387110,1727387236,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 702 n_clusters 4 threshold 0.013424233069171,702,4,0.013424233069171,0.27965634981826193,0,None,i7035,123,0.05490692807577924 1727387140,1727387242,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 468 n_clusters 4 threshold 0.002,468,4,0.002,0.27965634981826193,0,None,i7035,98,0.05490692807577924 1727387137,1727387243,106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 746 n_clusters 1 threshold 0.03189327835390246,746,1,0.03189327835390246,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727387110,1727387251,141,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 905 n_clusters 4 threshold 0.01527843205536249,905,4,0.01527843205536249,0.27965634981826193,0,None,i7035,138,0.05490692807577924 1727387157,1727387257,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 488 n_clusters 4 threshold 0.07,488,4,0.07,0.2858244300033044,0,None,i7035,97,0.024369423945368396 1727387170,1727387294,124,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 899 n_clusters 1 threshold 0.002,899,1,0.002,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727387578,1727387600,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 114 n_clusters 1 threshold 0.07,114,1,0.07,0.286485295737416,0,None,i7035,18,0.00039551812874866063 1727387578,1727387627,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 153 n_clusters 4 threshold 0.002,153,4,0.002,0.2908359951536513,0,None,i7035,46,0.001243056976067217 1727387578,1727387663,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 231 n_clusters 4 threshold 0.002,231,4,0.002,0.27965634981826193,0,None,i7035,81,0.05490692807577924 1727387578,1727387688,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 813 n_clusters 1 threshold 0.013043192489121742,813,1,0.013043192489121742,0.27965634981826193,0,None,i7035,106,0.05490692807577924 1727387578,1727387688,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 412 n_clusters 4 threshold 0.03368160417476176,412,4,0.03368160417476176,0.27965634981826193,0,None,i7035,106,0.05490692807577924 1727387578,1727387703,125,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 681 n_clusters 2 threshold 0.007943685146221017,681,2,0.007943685146221017,0.27965634981826193,0,None,i7035,121,0.05490692807577924 1727387578,1727387735,157,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 846 n_clusters 4 threshold 0.01686204855972212,846,4,0.01686204855972212,0.27965634981826193,0,None,i7035,153,0.05490692807577924 1727387578,1727387740,162,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 982 n_clusters 3 threshold 0.014924285841649183,982,3,0.014924285841649183,0.27965634981826193,0,None,i7035,158,0.05490692807577924 1727387578,1727387748,170,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 977 n_clusters 4 threshold 0.014678437456792162,977,4,0.014678437456792162,0.27965634981826193,0,None,i7035,166,0.05490692807577924 1727387684,1727387771,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 397 n_clusters 1 threshold 0.02785516177214875,397,1,0.02785516177214875,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727387678,1727387780,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 561 n_clusters 1 threshold 0.010069824451222299,561,1,0.010069824451222299,0.27965634981826193,0,None,i7035,99,0.05490692807577924 1727387698,1727387791,93,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 471 n_clusters 1 threshold 0.002,471,1,0.002,0.27965634981826193,0,None,i7035,89,0.05490692807577924 1727387714,1727387800,86,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 231 n_clusters 4 threshold 0.018532477914857376,231,4,0.018532477914857376,0.27965634981826193,0,None,i7035,82,0.05490692807577924 1727387745,1727387809,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 165 n_clusters 4 threshold 0.002263560639133777,165,4,0.002263560639133777,0.29331424165657005,0,None,i7035,61,0.0020624518118735557 1727387738,1727387832,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 428 n_clusters 4 threshold 0.051558582873067575,428,4,0.051558582873067575,0.2884128207952418,0,None,i7035,91,0.023075228549399696 1727387738,1727387838,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 494 n_clusters 4 threshold 0.042473025077286765,494,4,0.042473025077286765,0.2868157286044719,0,None,i7035,97,0.02387377464478463 1727387775,1727387860,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 414 n_clusters 1 threshold 0.03627552671815627,414,1,0.03627552671815627,0.28698094503799976,0,None,i7035,82,0.023791166428020705 1727387798,1727387910,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 493 n_clusters 4 threshold 0.03655347989452424,493,4,0.03655347989452424,0.28967948011895583,0,None,i7035,108,0.022441898887542666 1727387775,1727387921,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 777 n_clusters 4 threshold 0.002,777,4,0.002,0.27965634981826193,0,None,i7035,143,0.05490692807577924 1727388108,1727388180,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 256 n_clusters 2 threshold 0.0083377135392135,256,2,0.0083377135392135,0.27965634981826193,0,None,i7035,70,0.05490692807577924 1727388107,1727388183,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 257 n_clusters 4 threshold 0.035175846239309076,257,4,0.035175846239309076,0.2883577486507325,0,None,i7035,73,0.023102764621654337 1727388108,1727388193,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 258 n_clusters 4 threshold 0.01748790595654991,258,4,0.01748790595654991,0.27965634981826193,0,None,i7035,82,0.05490692807577924 1727388134,1727388211,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 260 n_clusters 4 threshold 0.041582170082006854,260,4,0.041582170082006854,0.2891287586738628,0,None,i7035,74,0.02271725961008919 1727388137,1727388224,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 496 n_clusters 2 threshold 0.03201968293886025,496,2,0.03201968293886025,0.2901751294195396,0,None,i7035,83,0.022194074237250783 1727388107,1727388238,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 819 n_clusters 4 threshold 0.021115428025871158,819,4,0.021115428025871158,0.27965634981826193,0,None,i7035,127,0.05490692807577924 1727388134,1727388238,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 506 n_clusters 4 threshold 0.002,506,4,0.002,0.27965634981826193,0,None,i7035,102,0.05490692807577924 1727388108,1727388250,142,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 720 n_clusters 4 threshold 0.002,720,4,0.002,0.27965634981826193,0,None,i7035,139,0.05490692807577924 1727388154,1727388269,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 630 n_clusters 4 threshold 0.002,630,4,0.002,0.27965634981826193,0,None,i7035,112,0.05490692807577924 1727388198,1727388292,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 500 n_clusters 1 threshold 0.03196550040177566,500,1,0.03196550040177566,0.2872563057605463,0,None,i7035,91,0.023653486066747442 1727388194,1727388293,99,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 456 n_clusters 4 threshold 0.029235090245046398,456,4,0.029235090245046398,0.27965634981826193,0,None,i7035,95,0.05490692807577924 1727388194,1727388314,120,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 496 n_clusters 4 threshold 0.02650283011699274,496,4,0.02650283011699274,0.27965634981826193,0,None,i7035,116,0.05490692807577924 1727388454,1727388489,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 163 n_clusters 2 threshold 0.01468742199861301,163,2,0.01468742199861301,0.27629694900319424,0,None,i7035,32,0.0009213657192509475 1727388434,1727388526,92,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 394 n_clusters 2 threshold 0.02371879528098264,394,2,0.02371879528098264,0.27965634981826193,0,None,i7035,89,0.05490692807577924 1727388454,1727388537,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 222 n_clusters 4 threshold 0.010333800755729596,222,4,0.010333800755729596,0.27965634981826193,0,None,i7035,79,0.05490692807577924 1727388494,1727388580,86,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 377 n_clusters 3 threshold 0.023634507327941652,377,3,0.023634507327941652,0.27965634981826193,0,None,i7035,83,0.05490692807577924 1727388494,1727388591,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 427 n_clusters 2 threshold 0.012793358068905475,427,2,0.012793358068905475,0.27965634981826193,0,None,i7035,93,0.05490692807577924 1727388470,1727388617,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 861 n_clusters 3 threshold 0.008080499292671144,861,3,0.008080499292671144,0.27965634981826193,0,None,i7035,144,0.05490692807577924 1727388500,1727388618,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 850 n_clusters 2 threshold 0.002,850,2,0.002,0.27965634981826193,0,None,i7035,115,0.05490692807577924 1727388514,1727388622,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 530 n_clusters 4 threshold 0.002,530,4,0.002,0.27965634981826193,0,None,i7035,104,0.05490692807577924 1727388554,1727388635,81,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 371 n_clusters 1 threshold 0.002,371,1,0.002,0.27965634981826193,0,None,i7035,77,0.05490692807577924 1727388530,1727388637,107,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 453 n_clusters 3 threshold 0.002,453,3,0.002,0.27965634981826193,0,None,i7035,103,0.05490692807577924 1727388975,1727389017,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 161 n_clusters 2 threshold 0.012049453161752599,161,2,0.012049453161752599,0.27921577266218744,0,None,i7186,38,0.000875035184981214 1727389035,1727389083,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 169 n_clusters 3 threshold 0.01872956558871563,169,3,0.01872956558871563,0.2763520211477035,0,None,i7186,44,0.0010543812394236437 1727389096,1727389171,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 194 n_clusters 4 threshold 0.027204501072598905,194,4,0.027204501072598905,0.2831809670668576,0,None,i7186,71,0.0023255464658702885 1727389166,1727389195,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 160 n_clusters 2 threshold 0.019679515842868596,160,2,0.019679515842868596,0.28252010133274585,0,None,i7035,25,0.0007767638292730644 1727389167,1727389195,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 162 n_clusters 1 threshold 0.01621155115412152,162,1,0.01621155115412152,0.2791056283731689,0,None,i7035,25,0.0007810936552235533 1727389166,1727389199,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 162 n_clusters 3 threshold 0.015042032898719734,162,3,0.015042032898719734,0.27739839189338034,0,None,i7035,29,0.0009335179905348129 1727389167,1727389201,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 169 n_clusters 2 threshold 0.02042534964473592,169,2,0.02042534964473592,0.2720563938759776,0,None,i7035,31,0.0009886761180956563 1727389167,1727389201,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 157 n_clusters 2 threshold 0.014934132173623072,157,2,0.014934132173623072,0.2820244520321621,0,None,i7035,31,0.0007960428160890769 1727389167,1727389204,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 166 n_clusters 2 threshold 0.012731138476481647,166,2,0.012731138476481647,0.2727723317545985,0,None,i7035,33,0.0010435704671424653 1727389167,1727389206,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 3 threshold 0.025686632023076972,172,3,0.025686632023076972,0.27216653816499614,0,None,i7035,36,0.0010908149324738207 1727389176,1727389215,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 163 n_clusters 2 threshold 0.02027189378453393,163,2,0.02027189378453393,0.27855490692807583,0,None,i7186,35,0.0008452739755746682 1727389167,1727389221,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 190 n_clusters 4 threshold 0.0340035122562296,190,4,0.0340035122562296,0.27178103315343094,0,None,i7035,51,0.0017874844617877996 1727389195,1727389232,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 3 threshold 0.01620799202983822,172,3,0.01620799202983822,0.2745346403788963,0,None,i7035,34,0.0012205785497368903 1727389215,1727389249,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 157 n_clusters 2 threshold 0.01219479728760804,157,2,0.01219479728760804,0.28334618350038554,0,None,i7035,30,0.0007879552983639327 1727389227,1727389279,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 3 threshold 0.01962221647298519,175,3,0.01962221647298519,0.27249697103205195,0,None,i7186,48,0.0012621636384479995 1727389255,1727389281,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 155 n_clusters 1 threshold 0.017049555308496236,155,1,0.017049555308496236,0.28620993501486947,0,None,i7035,22,0.0006199146522970731 1727389167,1727389282,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 494 n_clusters 4 threshold 0.02011194223709744,494,4,0.02011194223709744,0.27965634981826193,0,None,i7035,112,0.05490692807577924 1727389255,1727389294,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 167 n_clusters 3 threshold 0.014701459368054167,167,3,0.014701459368054167,0.2752505782575173,0,None,i7035,35,0.0010552216260444039 1727389275,1727389307,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 156 n_clusters 2 threshold 0.016630638911938445,156,2,0.016630638911938445,0.2873113779050557,0,None,i7035,28,0.0007052522386415745 1727389802,1727389835,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 2 threshold 0.01584790203184773,172,2,0.01584790203184773,0.2752505782575173,0,None,i7035,30,0.0010744074737906657 1727389802,1727389836,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 2 threshold 0.01547604482000109,172,2,0.01547604482000109,0.2749201453904615,0,None,i7035,31,0.0010804153441007712 1727389803,1727389841,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 2 threshold 0.015906752122015803,172,2,0.015906752122015803,0.27514043396849874,0,None,i7035,35,0.0010764100972273669 1727389802,1727389842,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.019839744872604335,172,4,0.019839744872604335,0.27503028967948007,0,None,i7035,37,0.001210463257888242 1727389802,1727389843,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 3 threshold 0.016564200466351375,175,3,0.016564200466351375,0.26952307522854935,0,None,i7035,38,0.0014091285671185777 1727389802,1727389844,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 173 n_clusters 4 threshold 0.01782752361243175,173,4,0.01782752361243175,0.27657230972574076,0,None,i7035,39,0.0012558843389187644 1727389802,1727389847,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 173 n_clusters 3 threshold 0.015837626389557144,173,3,0.015837626389557144,0.27392884678929397,0,None,i7035,41,0.0012586279693064574 1727389802,1727389854,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 4 threshold 0.017237012279672562,177,4,0.017237012279672562,0.2764070932922128,0,None,i7035,49,0.001448397400594778 1727389802,1727389864,62,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 184 n_clusters 4 threshold 0.01640062388201478,184,4,0.01640062388201478,0.2815838748760877,0,None,i7035,58,0.0021103645775966485 1727389856,1727389880,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 1 threshold 0.021164917523141577,175,1,0.021164917523141577,0.2745897125234057,0,None,i7035,21,0.0008918399521283318 1727389856,1727389884,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 1 threshold 0.028276298255084313,177,1,0.028276298255084313,0.27145060028637513,0,None,i7035,24,0.0009248880739651293 1727389857,1727389890,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.020677078609971235,176,2,0.020677078609971235,0.27216653816499614,0,None,i7035,30,0.001173140587754864 1727389856,1727389894,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 3 threshold 0.01825158272365339,177,3,0.01825158272365339,0.27062451811873556,0,None,i7035,35,0.0013557121531333695 1727389857,1727389895,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 174 n_clusters 2 threshold 0.018209143754194426,174,2,0.018209143754194426,0.27392884678929397,0,None,i7035,34,0.0010984389550310902 1727389916,1727389960,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 174 n_clusters 3 threshold 0.01806582148140229,174,3,0.01806582148140229,0.27547086683555455,0,None,i7035,41,0.0012265025516760286 1727389957,1727390005,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 4 threshold 0.02025969913501232,176,4,0.02025969913501232,0.27288247604361715,0,None,i7035,44,0.0013657891838308172 1727390363,1727390387,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 1 threshold 0.03388476626670067,180,1,0.03388476626670067,0.2718911774424496,0,None,i7035,21,0.0009321165951276763 1727390363,1727390395,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 2 threshold 0.02836735428896464,177,2,0.02836735428896464,0.2700737966736425,0,None,i7035,28,0.001108089528316576 1727390363,1727390396,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 2 threshold 0.025295883900707812,177,2,0.025295883900707812,0.2708448066967728,0,None,i7035,29,0.0011545124112587477 1727390383,1727390407,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 1 threshold 0.03782548907987538,180,1,0.03782548907987538,0.2715056724308844,0,None,i7035,20,0.0009240781894870516 1727390376,1727390409,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.023017978032247505,176,2,0.023017978032247505,0.2731027646216544,0,None,i7035,30,0.001134078235080547 1727390403,1727390429,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 1 threshold 0.026220326408885372,179,1,0.026220326408885372,0.27359841392223816,0,None,i7035,22,0.0009641996094248535 1727390436,1727390459,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 1 threshold 0.03463626864894039,177,1,0.03463626864894039,0.27282740389910787,0,None,i7035,20,0.0008664166960126205 1727390436,1727390467,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.031473258038310414,176,2,0.031473258038310414,0.2715056724308844,0,None,i7035,28,0.0010301199489363853 1727390464,1727390491,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 1 threshold 0.030914205550510145,181,1,0.030914205550510145,0.2729926203326357,0,None,i7035,24,0.0009895220803769066 1727390464,1727390498,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 2 threshold 0.026250558375380748,179,2,0.026250558375380748,0.2727723317545985,0,None,i7035,31,0.0012072677953216754 1727390484,1727390517,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 3 threshold 0.039162825869852876,177,3,0.039162825869852876,0.2678709108932702,0,None,i7035,30,0.001126645396995487 1727390497,1727390528,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.03061362335218041,176,2,0.03061362335218041,0.2701288688181518,0,None,i7035,28,0.001052690499964789 1727390524,1727390554,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.03307417218296312,188,1,0.03307417218296312,0.26616367441348165,0,None,i7035,27,0.0011755054293538323 1727391151,1727391177,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 1 threshold 0.036747981697240924,177,1,0.036747981697240924,0.2718911774424496,0,None,i7035,20,0.0008796029841345677 1727391151,1727391183,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 178 n_clusters 2 threshold 0.04851939078364394,178,2,0.04851939078364394,0.27183610529794033,0,None,i7035,26,0.0009766700627822436 1727391151,1727391183,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 2 threshold 0.04210803839584615,176,2,0.04210803839584615,0.2727723317545985,0,None,i7035,26,0.0009620415243969602 1727391151,1727391187,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 3 threshold 0.03824443516828617,175,3,0.03824443516828617,0.2734882696332195,0,None,i7035,30,0.0010314359268268549 1727391152,1727391192,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 3 threshold 0.043645293867668876,179,3,0.043645293867668876,0.27145060028637513,0,None,i7035,35,0.0011033752461338385 1727391152,1727391192,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 3 threshold 0.03882001612357276,179,3,0.03882001612357276,0.2701288688181518,0,None,i7035,35,0.001146680723175931 1727391152,1727391192,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 3 threshold 0.03581787425773265,177,3,0.03581787425773265,0.26996365238462383,0,None,i7035,36,0.0011496310166317874 1727391151,1727391193,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 4 threshold 0.041037854567496014,181,4,0.041037854567496014,0.2718911774424496,0,None,i7035,36,0.0012245453308540061 1727391152,1727391195,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 177 n_clusters 4 threshold 0.041512394191101816,177,4,0.041512394191101816,0.27178103315343094,0,None,i7035,38,0.0011585547437513515 1727391191,1727391217,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 190 n_clusters 1 threshold 0.038807012985110426,190,1,0.038807012985110426,0.26649410728053746,0,None,i7035,24,0.0011499810514485841 1727391191,1727391223,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 174 n_clusters 3 threshold 0.04122257510773129,174,3,0.04122257510773129,0.2708448066967728,0,None,i7035,29,0.0010241642357940505 1727391191,1727391225,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 2 threshold 0.03817200473901668,186,2,0.03817200473901668,0.2720563938759776,0,None,i7035,31,0.0011978191430774298 1727391191,1727391227,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 4 threshold 0.03750058230970152,176,4,0.03750058230970152,0.2708448066967728,0,None,i7035,33,0.0011545124112587477 1727391575,1727391600,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 1 threshold 0.04132730268886575,186,1,0.04132730268886575,0.2693578587950215,0,None,i7035,22,0.0010481472664674585 1727391575,1727391601,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 189 n_clusters 1 threshold 0.03687134092363787,189,1,0.03687134092363787,0.266769468003084,0,None,i7035,23,0.001145313920557965 1727391575,1727391603,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 190 n_clusters 1 threshold 0.0420762180144043,190,1,0.0420762180144043,0.2670448287256306,0,None,i7035,25,0.001140646789667344 1727391575,1727391607,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 2 threshold 0.0412801868318746,188,2,0.0412801868318746,0.26836656019385396,0,None,i7035,29,0.001244838285323584 1727391575,1727391607,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 184 n_clusters 2 threshold 0.038624303953074354,184,2,0.038624303953074354,0.2686969930609098,0,None,i7035,29,0.0012156665973165584 1727391575,1727391611,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 184 n_clusters 2 threshold 0.03559914449778489,184,2,0.03559914449778489,0.269082498072475,0,None,i7035,33,0.0012550094469909413 1727391586,1727391622,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 2 threshold 0.04077101958112986,192,2,0.04077101958112986,0.27145060028637513,0,None,i7035,33,0.0014293724779461088 1727391614,1727391641,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.03783002889709646,188,1,0.03783002889709646,0.26798105518228876,0,None,i7035,23,0.0011060322355619194 1727391614,1727391646,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 185 n_clusters 2 threshold 0.04076254656635014,185,2,0.04076254656635014,0.26732018944817715,0,None,i7035,29,0.0012411629605153106 1727392445,1727392472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.039332138420068385,191,1,0.039332138420068385,0.2685868487718912,0,None,i7035,24,0.0011145108566798765 1727392445,1727392472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03922831453032874,191,1,0.03922831453032874,0.2685868487718912,0,None,i7035,24,0.0011145108566798765 1727392445,1727392472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03925300538196437,191,1,0.03925300538196437,0.2685868487718912,0,None,i7035,24,0.0011145108566798765 1727392445,1727392472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03920916108550958,191,1,0.03920916108550958,0.2685868487718912,0,None,i7035,24,0.0011145108566798765 1727392445,1727392473,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 150 n_clusters 3 threshold 0.0526048918099755,150,3,0.0526048918099755,0.2913867165987444,0,None,i7035,25,0.0005079595446505505 1727392445,1727392476,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.039473029911967786,191,1,0.039473029911967786,0.26842163233836325,0,None,i7035,27,0.0011173111352142488 1727392445,1727392476,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.0393160062486759,191,1,0.0393160062486759,0.2685868487718912,0,None,i7035,27,0.0011145108566798765 1727392445,1727392482,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 183 n_clusters 4 threshold 0.048333819158358604,183,4,0.048333819158358604,0.27403899107831264,0,None,i7035,34,0.0011596922738017555 1727392485,1727392515,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03959687064595092,191,1,0.03959687064595092,0.2681462716158167,0,None,i7035,26,0.0011219782661048678 1727392485,1727392515,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03950182386715165,191,1,0.03950182386715165,0.26842163233836325,0,None,i7035,27,0.0011173111352142488 1727392485,1727392521,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 165 n_clusters 3 threshold 0.03186965368088223,165,3,0.03186965368088223,0.2775636083269083,0,None,i7035,32,0.0008871778279546192 1727392445,1727392524,79,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 226 n_clusters 3 threshold 0.013517825051851399,226,3,0.013517825051851399,0.27965634981826193,0,None,i7035,75,0.05490692807577924 1727392844,1727392870,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 190 n_clusters 1 threshold 0.03964313789246535,190,1,0.03964313789246535,0.26588831369093513,0,None,i7035,23,0.0011602487394079457 1727392844,1727392870,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.03957816246262264,188,1,0.03957816246262264,0.2681462716158167,0,None,i7035,23,0.0011032786283364535 1727392844,1727392871,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 190 n_clusters 1 threshold 0.03930396017908877,190,1,0.03930396017908877,0.26572309725740717,0,None,i7035,23,0.001163049017942318 1727392844,1727392876,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 203 n_clusters 1 threshold 0.04062114317333887,203,1,0.04062114317333887,0.2707897345522635,0,None,i7035,29,0.0013521969098668173 1727392844,1727392877,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 2 threshold 0.03408952255914538,182,2,0.03408952255914538,0.26803612732679816,0,None,i7035,29,0.0012279048516519587 1727392844,1727392877,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 178 n_clusters 2 threshold 0.026426640670929216,178,2,0.026426640670929216,0.27139552814186585,0,None,i7035,29,0.001124061806681037 1727392884,1727392929,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 1 threshold 0.002,182,1,0.002,0.2766824540147593,0,None,i7035,42,0.0025069797957062868 1727392858,1727392939,81,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 317 n_clusters 3 threshold 0.055177293913886497,317,3,0.055177293913886497,0.2894041193964093,0,None,i7035,78,0.02257957924881593 1727393705,1727393772,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04018238083232278,191,1,0.04018238083232278,0.26809119947130744,0,None,i7037,31,0.0011229116922829912 1727393730,1727393772,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.040433881525531054,191,1,0.040433881525531054,0.2683114880493447,0,None,i7037,31,0.0011191779875704957 1727393687,1727393772,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 1 threshold 0.040314579076765236,192,1,0.040314579076765236,0.268201343760326,0,None,i7037,31,0.0011210448399267443 1727393766,1727393789,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 129 n_clusters 1 threshold 0.047237095065400726,129,1,0.047237095065400726,0.29700407533869366,0,None,i7035,20,0.00037559202555347503 1727393766,1727393795,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04001605423662146,191,1,0.04001605423662146,0.26798105518228876,0,None,i7035,26,0.00112477854463924 1727393766,1727393796,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.040231524648860914,191,1,0.040231524648860914,0.26809119947130744,0,None,i7035,26,0.0011229116922829912 1727393766,1727393797,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.0399545116094863,191,1,0.0399545116094863,0.26798105518228876,0,None,i7035,28,0.00112477854463924 1727393766,1727393801,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 1 threshold 0.02318712672164197,172,1,0.02318712672164197,0.2735433417777289,0,None,i7035,31,0.0008811543121489137 1727393766,1727393804,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04023772837277547,191,1,0.04023772837277547,0.26803612732679816,0,None,i7035,33,0.0011238451184611147 1727393766,1727393813,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 184 n_clusters 2 threshold 0.03959824444581775,184,2,0.03959824444581775,0.26732018944817715,0,None,i7035,42,0.0011968357119254782 1727393766,1727393817,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 176 n_clusters 3 threshold 0.029115120820980285,176,3,0.029115120820980285,0.269633219517568,0,None,i7035,46,0.0011555316035434982 1727394037,1727394072,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.03984785006324169,191,1,0.03984785006324169,0.26803612732679816,0,None,i7122,31,0.0011238451184611147 1727394017,1727394073,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.02734732319355234,172,4,0.02734732319355234,0.2729926203326357,0,None,i7122,45,0.0011154612542430583 1727394059,1727394102,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 2 threshold 0.032820189318223274,181,2,0.032820189318223274,0.27101002313030065,0,None,i7122,39,0.0011515084761036958 1727394079,1727394118,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 169 n_clusters 2 threshold 0.02042590965122397,169,2,0.02042590965122397,0.2720013217314682,0,None,i7122,35,0.001005510767492512 1727394086,1727394138,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 3 threshold 0.03525842960445858,181,3,0.03525842960445858,0.2708448066967728,0,None,i7106,44,0.0012699636523846225 1727394708,1727394756,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 189 n_clusters 1 threshold 0.03842978904694329,189,1,0.03842978904694329,0.26698975658112123,0,None,i7033,32,0.0011415802158454694 1727394730,1727394774,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.04511926192588379,172,4,0.04511926192588379,0.2700737966736425,0,None,i7033,40,0.0010201459149581178 1727394764,1727394791,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 1 threshold 0.06469651253034751,182,1,0.06469651253034751,0.2668245401475934,0,None,i7033,24,0.0009002459889121406 1727394764,1727394800,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 143 n_clusters 4 threshold 0.05517617323503349,143,4,0.05517617323503349,0.29513162242537727,0,None,i7033,32,0.00046390182904310464 1727394790,1727394813,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 161 n_clusters 1 threshold 0.05261832499087192,161,1,0.05261832499087192,0.2878620993501487,0,None,i7033,20,0.0005107790106137934 1727394708,1727394829,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 285 n_clusters 4 threshold 0.031956100403417745,285,4,0.031956100403417745,0.27965634981826193,0,None,i7033,105,0.05490692807577924 1727394790,1727394834,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.045090974188437424,172,4,0.045090974188437424,0.27023901310717036,0,None,i7033,40,0.001017523431886247 1727394810,1727394853,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.0452541136191642,172,4,0.0452541136191642,0.2716708888644124,0,None,i7033,39,0.000994795245263358 1727394810,1727394854,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 172 n_clusters 4 threshold 0.04858821845801634,172,4,0.04858821845801634,0.27106509527481004,0,None,i7033,40,0.001004411016526887 1727394810,1727394857,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 4 threshold 0.061938294572247234,181,4,0.061938294572247234,0.269798435951096,0,None,i7033,43,0.0011128371269811714 1727394824,1727394865,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 3 threshold 0.05686082477194624,175,3,0.05686082477194624,0.27178103315343094,0,None,i7033,37,0.0009775305650402029 1727394850,1727394877,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 1 threshold 0.059420631317170014,180,1,0.059420631317170014,0.2696882916620773,0,None,i7033,23,0.0008620626353856882 1727395752,1727395784,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 189 n_clusters 1 threshold 0.06039020556693755,189,1,0.06039020556693755,0.2704593016852076,0,None,i7033,28,0.0009828259635507126 1727395793,1727395830,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 183 n_clusters 2 threshold 0.07,183,2,0.07,0.26957814737305874,0,None,i7033,33,0.001028013364173733 1727395834,1727395861,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 1 threshold 0.06898747077640395,186,1,0.06898747077640395,0.2685317766273819,0,None,i7033,23,0.0008893407120084054 1727395874,1727395913,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 185 n_clusters 2 threshold 0.06341129850539873,185,2,0.06341129850539873,0.2694129309395308,0,None,i7033,35,0.0010644271864995597 1727395926,1727395960,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.05245761671896187,191,1,0.05245761671896187,0.26836656019385396,0,None,i7033,30,0.0010815808052811468 1727395965,1727395991,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 183 n_clusters 1 threshold 0.07,183,1,0.07,0.26770569445974224,0,None,i7033,22,0.0008768065112666012 1727396005,1727396042,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 2 threshold 0.07,179,2,0.07,0.2704593016852076,0,None,i7033,33,0.0009828259635507126 1727396047,1727396084,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 1 threshold 0.04647443043473412,192,1,0.04647443043473412,0.26721004515915847,0,None,i7033,31,0.0011188824026140908 1727396096,1727396127,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.0662461242678452,188,1,0.0662461242678452,0.2731578367661637,0,None,i7033,26,0.0008997816551447095 1727396132,1727396162,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 1 threshold 0.06022902036694527,186,1,0.06022902036694527,0.2693027866505122,0,None,i7033,26,0.0009426116328332131 1727396172,1727396212,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 2 threshold 0.06412838525365307,186,2,0.06412838525365307,0.269082498072475,0,None,i7033,36,0.0011061100210767619 1727396221,1727396247,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 1 threshold 0.07,181,1,0.07,0.2686969930609098,0,None,i7033,22,0.0008416153366037712 1727396252,1727396294,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 175 n_clusters 4 threshold 0.07,175,4,0.07,0.2708448066967728,0,None,i7033,37,0.0009768951172189405 1727396298,1727396336,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 178 n_clusters 2 threshold 0.06977088875328999,178,2,0.06977088875328999,0.2686419209164005,0,None,i7033,32,0.0009954707333273247 1727396346,1727396383,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.0430813640807922,191,1,0.0430813640807922,0.2671549730146492,0,None,i7033,32,0.0011387799373110971 1727396400,1727396444,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 4 threshold 0.07,181,4,0.07,0.2704593016852076,0,None,i7033,40,0.0010647281271799387 1727396460,1727396507,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 4 threshold 0.06033735246467423,180,4,0.06033735246467423,0.27067959026324484,0,None,i7033,41,0.001079040661911171 1727396521,1727396655,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 750 n_clusters 1 threshold 0.07,750,1,0.07,0.2865403678819253,0,None,i7033,130,0.024011455006057947 1727396660,1727396696,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04385301405031064,191,1,0.04385301405031064,0.26654917942504686,0,None,i7033,32,0.0011490476252704587 1727396703,1727396838,135,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 736 n_clusters 1 threshold 0.05626439198068249,736,1,0.05626439198068249,0.28670558431545323,0,None,i7033,129,0.023928846789293967 1727396848,1727396883,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 1 threshold 0.07,179,1,0.07,0.2693578587950215,0,None,i7033,22,0.0008225965888731953 1727396889,1727396930,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 181 n_clusters 2 threshold 0.06618023631598421,181,2,0.06618023631598421,0.27244189888754267,0,None,i7033,34,0.0009672045379447072 1727396946,1727396984,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 2 threshold 0.06287231469107876,182,2,0.06287231469107876,0.26886220949443773,0,None,i7033,35,0.0010561416100252612 1727397001,1727397024,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 106 n_clusters 1 threshold 0.06562158001194543,106,1,0.06562158001194543,0.2805925762749202,0,None,i7033,19,0.00039233878132177914 1727397037,1727397074,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 179 n_clusters 2 threshold 0.06539562786361093,179,2,0.06539562786361093,0.2708998788412821,0,None,i7033,34,0.0009912986011675288 1727397081,1727397117,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04379616109368372,191,1,0.04379616109368372,0.26654917942504686,0,None,i7033,32,0.0011490476252704587 1727397126,1727397161,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.043826545058890914,191,1,0.043826545058890914,0.26654917942504686,0,None,i7033,31,0.0011490476252704587 1727397167,1727397210,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 180 n_clusters 3 threshold 0.06628675679898793,180,3,0.06628675679898793,0.2715607445753938,0,None,i7033,39,0.0010463707456768352 1727397214,1727397249,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.044227020807766686,191,1,0.044227020807766686,0.2660535301244631,0,None,i7033,32,0.0011574484608735734 1727397254,1727397290,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.044327921544894545,191,1,0.044327921544894545,0.26610860226897237,0,None,i7033,32,0.00115651503469545 1727397307,1727397343,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04420712219499076,191,1,0.04420712219499076,0.2663839629915189,0,None,i7033,32,0.001151847903804831 1727397354,1727397390,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04435246974922816,191,1,0.04435246974922816,0.26610860226897237,0,None,i7033,32,0.00115651503469545 1727397394,1727397453,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 183 n_clusters 2 threshold 0.012726157885068555,183,2,0.012726157885068555,0.2720013217314682,0,None,i7033,55,0.0018891414419556286 1727397457,1727397493,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.044437814631024704,191,1,0.044437814631024704,0.2659433858354444,0,None,i7033,32,0.0011593153132298222 1727397497,1727397533,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04420470953770449,191,1,0.04420470953770449,0.2663839629915189,0,None,i7033,32,0.001151847903804831 1727400141,1727400226,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04531504249056862,191,1,0.04531504249056862,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400244,1727400279,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.045325735258808134,191,1,0.045325735258808134,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400284,1727400319,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.045298232897425496,191,1,0.045298232897425496,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400324,1727400359,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04532205497699808,191,1,0.04532205497699808,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400366,1727400401,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.045347144044141995,191,1,0.045347144044141995,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400406,1727400447,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 210 n_clusters 1 threshold 0.06624746269250008,210,1,0.06624746269250008,0.27293754818812643,0,None,i7186,37,0.0012792800234974477 1727400451,1727400486,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04533504684785596,191,1,0.04533504684785596,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400491,1727400526,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04530292775838513,191,1,0.04530292775838513,0.26875206520541906,0,None,i7186,31,0.0010931820685097478 1727400548,1727400582,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04560645978062459,191,1,0.04560645978062459,0.26913757021698426,0,None,i7186,31,0.0010867569849836612 1727400587,1727400627,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 186 n_clusters 2 threshold 0.05242874698592351,186,2,0.05242874698592351,0.27106509527481004,0,None,i7186,36,0.0010725066786643031 1727400632,1727400676,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 2 threshold 0.03404971263185844,182,2,0.03404971263185844,0.26842163233836325,0,None,i7186,40,0.0012207658699563088 1727400681,1727400703,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 155 n_clusters 1 threshold 0.07,155,1,0.07,0.29414032382420974,0,None,i7186,18,0.0004167314852559941 1727400708,1727400744,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 191 n_clusters 1 threshold 0.04458736481124655,191,1,0.04458736481124655,0.2660535301244631,0,None,i7186,31,0.0011574484608735734 1727400760,1727400800,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 182 n_clusters 2 threshold 0.04945093840147984,182,2,0.04945093840147984,0.2689723537834563,0,None,i7186,36,0.0010895105922091271 1727400808,1727400843,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 1 threshold 0.04440022247149909,192,1,0.04440022247149909,0.26847670448287253,0,None,i7186,31,0.0011163777090361254 1727400850,1727400885,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 193 n_clusters 1 threshold 0.04479534734480631,193,1,0.04479534734480631,0.26770569445974224,0,None,i7186,31,0.001129445675529859 1727400909,1727400943,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 199 n_clusters 1 threshold 0.07,199,1,0.07,0.2668245401475934,0,None,i7186,29,0.0011253074861401757 1727400948,1727400970,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 155 n_clusters 1 threshold 0.07,155,1,0.07,0.29414032382420974,0,None,i7186,18,0.0004167314852559941 1727403500,1727403536,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 192 n_clusters 1 threshold 0.044078950725947974,192,1,0.044078950725947974,0.2686419209164005,0,None,i7186,31,0.001113577430501753 1727403543,1727403588,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 218 n_clusters 1 threshold 0.0603459599689024,218,1,0.0603459599689024,0.26880713734992845,0,None,i7186,41,0.0015603774277637018 1727403604,1727403663,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 174 n_clusters 4 threshold 0.013537888485419234,174,4,0.013537888485419234,0.27514043396849874,0,None,i7186,55,0.001443964764573297 1727403681,1727403726,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 218 n_clusters 1 threshold 0.060247723944732386,218,1,0.060247723944732386,0.26875206520541906,0,None,i7186,41,0.0015616886692996397 1727403750,1727403788,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 142 n_clusters 4 threshold 0.04664347841065305,142,4,0.04664347841065305,0.2957374160149796,0,None,i7186,34,0.0004734861204763605 1727403793,1727403844,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 195 n_clusters 3 threshold 0.07,195,3,0.07,0.2693027866505122,0,None,i7186,47,0.0014139174492498198 1727403850,1727403921,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 213 n_clusters 3 threshold 0.06348824495574378,213,3,0.06348824495574378,0.28472298711311816,0,None,i7186,67,0.0022554546455857164 1727403930,1727403968,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 200 n_clusters 1 threshold 0.057673942399209026,200,1,0.057673942399209026,0.26919264236149354,0,None,i7186,34,0.001206487906565007 1727403973,1727404009,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 200 n_clusters 1 threshold 0.062831084226523,200,1,0.062831084226523,0.26875206520541906,0,None,i7186,32,0.0011712665019747298 1727404029,1727404066,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 199 n_clusters 1 threshold 0.05340140545870446,199,1,0.05340140545870446,0.2681462716158167,0,None,i7186,33,0.0012035766854579491 1727404071,1727404132,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 214 n_clusters 2 threshold 0.06763102084601846,214,2,0.06763102084601846,0.28064764841942946,0,None,i7186,57,0.0019176907463062307 1727404150,1727404190,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 199 n_clusters 1 threshold 0.0635292201755469,199,1,0.0635292201755469,0.268201343760326,0,None,i7186,32,0.0011603797465908406 1727404211,1727404255,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 209 n_clusters 1 threshold 0.05512803615138973,209,1,0.05512803615138973,0.2696882916620773,0,None,i7186,39,0.001436771058976147 1727404271,1727404319,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 212 n_clusters 1 threshold 0.055168137353005166,212,1,0.055168137353005166,0.2678158387487609,0,None,i7186,40,0.0015471430364475118 1727404331,1727404375,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 212 n_clusters 1 threshold 0.05516084987605859,212,1,0.05516084987605859,0.2678158387487609,0,None,i7186,40,0.0015471430364475118 1727404392,1727404436,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 222 n_clusters 1 threshold 0.06776116890943115,222,1,0.06776116890943115,0.26687961229210266,0,None,i7186,40,0.0016062708815214586 1727404451,1727404495,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 212 n_clusters 1 threshold 0.05465316339937353,212,1,0.05465316339937353,0.2679259830377795,0,None,i7186,40,0.0015445815413540568 1727404511,1727404556,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 213 n_clusters 1 threshold 0.05489991052405177,213,1,0.05489991052405177,0.26875206520541906,0,None,i7186,41,0.0015253703281531365 1727404573,1727404618,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 213 n_clusters 1 threshold 0.05487433907955503,213,1,0.05487433907955503,0.26875206520541906,0,None,i7186,40,0.0015253703281531365 1727404631,1727404674,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 193 n_clusters 2 threshold 0.07,193,2,0.07,0.26996365238462383,0,None,i7186,39,0.001214704470403398 1727404691,1727404735,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 214 n_clusters 1 threshold 0.055694005477570585,214,1,0.055694005477570585,0.26809119947130744,0,None,i7186,40,0.0015407392987138717 1727404751,1727404785,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 198 n_clusters 1 threshold 0.07,198,1,0.07,0.2666593237140654,0,None,i7186,30,0.0011280610933656417 1727404789,1727404904,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 344 n_clusters 4 threshold 0.05953089924283387,344,4,0.05953089924283387,0.2860447185813415,0,None,i7186,111,0.02425927965634983 1727404912,1727404960,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 189 n_clusters 4 threshold 0.07,189,4,0.07,0.26913757021698426,0,None,i7186,44,0.0012302909263965974 1727404969,1727405014,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 189 n_clusters 3 threshold 0.0658803750815194,189,3,0.0658803750815194,0.267485405881705,0,None,i7186,41,0.0012381033969314616 1727404934,1727405034,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 214 n_clusters 1 threshold 0.055711880720697146,214,1,0.055711880720697146,0.26809119947130744,0,None,i7037,39,0.0015407392987138717 1727404934,1727405034,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 3 threshold 0.07,188,3,0.07,0.26990858024011455,0,None,i7037,39,0.0011506144477837388 1727404934,1727405034,100,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 220 n_clusters 1 threshold 0.06307187343233872,220,1,0.06307187343233872,0.26913757021698426,0,None,i7037,39,0.0015525099785480873 1727405000,1727405043,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 220 n_clusters 1 threshold 0.0629765582021934,220,1,0.0629765582021934,0.2689723537834563,0,None,i7037,39,0.001556443703155896 1727405370,1727405409,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 200 n_clusters 1 threshold 0.07,200,1,0.07,0.26957814737305874,0,None,i7127,31,0.0011362252972446523 1727405415,1727405463,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 187 n_clusters 4 threshold 0.07,187,4,0.07,0.26919264236149354,0,None,i7127,44,0.001206487906565007 1727405435,1727405479,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 220 n_clusters 1 threshold 0.06287930644481458,220,1,0.06287930644481458,0.26886220949443773,0,None,i7127,40,0.0015590661862277665 1727405475,1727405522,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 197 n_clusters 2 threshold 0.06224717240515854,197,2,0.06224717240515854,0.26550280867936993,0,None,i7127,43,0.001464684694396468 1727405495,1727405559,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 208 n_clusters 3 threshold 0.06851761350591574,208,3,0.06851761350591574,0.27811432977200135,0,None,i7127,59,0.0018742886514667527 1727407941,1727407989,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 185 n_clusters 4 threshold 0.07,185,4,0.07,0.268201343760326,0,None,i7186,42,0.0011811008134942486 1727407993,1727408141,148,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 902 n_clusters 1 threshold 0.07,902,1,0.07,0.2876418107721115,0,None,i7186,143,0.02346073356096484 1727408146,1727408208,62,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 195 n_clusters 3 threshold 0.04459455192866991,195,3,0.04459455192866991,0.27481000110144294,0,None,i7186,54,0.0016089996814746214 1727408231,1727408268,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 188 n_clusters 1 threshold 0.04227025441110305,188,1,0.04227025441110305,0.26798105518228876,0,None,i7186,31,0.001087900559569101
timestamp,ram_usage_mb,cpu_usage_percent 1727382268,477.28125,49.7 1727382268,477.328125,48.6 1727382268,477.328125,49.6 1727382268,477.328125,55.3 1727382268,477.328125,47.2 1727382268,477.328125,50.4 1727382268,477.328125,39.4 1727382313,482.03125,49.8 1727382313,482.03125,55.6 1727382313,482.03125,49.9 1727382313,482.03125,40.6 1727382315,482.0390625,49.8 1727382315,482.0390625,54.3 1727382315,482.0390625,48.6 1727382315,482.0390625,39.4 1727382317,482.0390625,49.7 1727382317,482.0390625,39.4 1727382317,482.0390625,49.1 1727382317,482.0390625,58.1 1727382318,482.0390625,49.7 1727382318,482.0390625,53.2 1727382318,482.0390625,48.6 1727382318,482.0390625,55.6 1727382319,482.0390625,49.7 1727382319,482.0390625,55.3 1727382319,482.0390625,51.3 1727382319,482.0390625,43.2 1727382321,482.0390625,49.8 1727382321,482.0390625,55.1 1727382321,482.0390625,47.2 1727382321,482.0390625,55.6 1727382322,482.0390625,49.7 1727382322,482.0390625,36.4 1727382322,482.0390625,53.4 1727382322,482.0390625,40.6 1727382324,482.0390625,49.8 1727382324,482.0390625,49.1 1727382324,482.0390625,48.2 1727382324,482.0390625,52.5 1727382325,482.0390625,49.7 1727382325,482.0390625,54.3 1727382325,482.0390625,47.2 1727382325,482.0390625,59.6 1727382326,482.0390625,49.7 1727382326,482.0390625,54.3 1727382326,482.0390625,47.2 1727382326,482.0390625,58.1 1727382328,482.0390625,49.7 1727382328,482.0390625,51.0 1727382328,482.0390625,52.0 1727382328,482.0390625,50.0 1727382329,482.0390625,49.7 1727382329,482.0390625,39.4 1727382329,482.0390625,52.8 1727382329,482.0390625,53.2 1727382330,482.0390625,49.7 1727382330,482.0390625,46.2 1727382330,482.0390625,51.7 1727382330,482.0390625,37.5 1727382332,482.0390625,49.8 1727382332,482.0390625,54.3 1727382332,482.0390625,48.1 1727382332,482.0390625,42.4 1727382333,482.0390625,49.8 1727382333,482.0390625,54.2 1727382333,482.0390625,48.1 1727382333,482.0390625,42.4 1727382335,482.04296875,49.8 1727382335,482.04296875,52.2 1727382335,482.04296875,52.9 1727382335,482.04296875,40.6 1727382336,482.04296875,49.7 1727382336,482.04296875,54.3 1727382336,482.04296875,50.4 1727382336,482.04296875,48.7 1727382337,482.04296875,49.7 1727382337,482.04296875,44.2 1727382337,482.04296875,51.8 1727382337,482.04296875,51.3 1727382339,482.04296875,49.7 1727382339,482.04296875,56.5 1727382339,482.04296875,47.7 1727382339,482.04296875,55.6 1727382340,482.04296875,49.8 1727382340,482.04296875,52.1 1727382340,482.04296875,49.5 1727382340,482.04296875,56.5 1727382342,482.04296875,49.7 1727382342,482.04296875,54.3 1727382342,482.04296875,49.0 1727382342,482.04296875,51.3 1727382343,482.04296875,49.7 1727382343,482.04296875,56.3 1727382343,482.04296875,49.6 1727382343,482.04296875,44.1 1727382344,482.04296875,49.8 1727382344,482.04296875,45.9 1727382344,482.04296875,51.3 1727382344,482.04296875,40.6 1727382346,482.04296875,49.8 1727382346,482.04296875,36.4 1727382346,482.04296875,54.7 1727382346,482.04296875,42.4 1727382347,482.04296875,49.7 1727382347,482.04296875,56.5 1727382347,482.04296875,49.0 1727382347,482.04296875,50.0 1727382349,482.04296875,49.7 1727382349,482.04296875,55.3 1727382349,482.04296875,48.1 1727382349,482.04296875,57.4 1727382350,482.04296875,49.7 1727382350,482.04296875,37.5 1727382350,482.04296875,52.5 1727382350,482.04296875,40.6 1727382351,482.04296875,49.7 1727382351,482.04296875,39.4 1727382351,482.04296875,53.4 1727382351,482.04296875,40.6 1727382353,482.04296875,49.7 1727382353,482.04296875,54.3 1727382353,482.04296875,48.6 1727382353,482.04296875,56.8 1727382354,482.05078125,49.7 1727382354,482.05078125,55.6 1727382354,482.05078125,48.5 1727382354,482.05078125,44.4 1727382355,482.05078125,49.7 1727382355,482.05078125,53.2 1727382355,482.05078125,48.1 1727382356,482.05078125,56.5 1727382357,482.05078125,49.7 1727382357,482.05078125,54.3 1727382357,482.05078125,50.0 1727382357,482.05078125,40.6 1727382358,482.05078125,49.7 1727382358,482.05078125,54.3 1727382358,482.05078125,50.8 1727382358,482.05078125,40.6 1727382360,482.05078125,49.8 1727382360,482.05078125,53.2 1727382360,482.05078125,50.0 1727382360,482.05078125,40.6 1727382361,482.05078125,49.8 1727382361,482.05078125,54.3 1727382361,482.05078125,47.7 1727382361,482.05078125,55.6 1727382362,482.05078125,49.8 1727382362,482.05078125,54.3 1727382362,482.05078125,52.5 1727382362,482.05078125,40.6 1727382364,482.05078125,49.8 1727382364,482.05078125,39.4 1727382364,482.05078125,54.2 1727382364,482.05078125,38.7 1727382365,482.05078125,49.7 1727382365,482.05078125,53.2 1727382365,482.05078125,48.6 1727382365,482.05078125,55.6 1727382367,482.05078125,49.7 1727382367,482.05078125,52.3 1727382367,482.05078125,52.0 1727382367,482.05078125,39.4 1727382368,482.05078125,49.8 1727382368,482.05078125,37.5 1727382368,482.05078125,52.9 1727382368,482.05078125,41.2 1727382369,482.05078125,49.8 1727382369,482.05078125,38.2 1727382369,482.05078125,54.6 1727382369,482.05078125,37.5 1727382371,482.05078125,49.7 1727382371,482.05078125,53.2 1727382371,482.05078125,53.3 1727382371,482.05078125,41.9 1727382373,484.875,49.9 1727382373,484.875,55.6 1727382373,484.875,52.0 1727382373,484.875,40.6 1727382376,485.15234375,49.9 1727382376,485.15234375,53.2 1727382376,485.15234375,48.2 1727382376,485.15234375,53.5 1727382378,485.15234375,49.8 1727382378,485.15234375,54.2 1727382378,485.15234375,48.1 1727382378,485.15234375,56.8 1727382380,485.15234375,49.8 1727382380,485.15234375,55.3 1727382380,485.15234375,46.5 1727382380,485.15234375,52.4 1727382383,485.15234375,49.9 1727382383,485.15234375,54.3 1727382383,485.15234375,48.6 1727382383,485.15234375,54.2 1727382385,485.15234375,49.9 1727382385,485.15234375,54.3 1727382385,485.15234375,46.7 1727382385,485.15234375,58.7 1727382387,485.15234375,49.8 1727382387,485.15234375,53.3 1727382387,485.15234375,48.5 1727382387,485.15234375,57.8 1727382390,485.17578125,49.9 1727382390,485.17578125,56.5 1727382390,485.17578125,51.2 1727382390,485.17578125,40.6 1727382392,485.17578125,49.9 1727382392,485.17578125,35.3 1727382392,485.17578125,52.0 1727382392,485.17578125,41.9 1727382394,485.17578125,49.9 1727382394,485.17578125,55.3 1727382394,485.17578125,50.8 1727382394,485.17578125,38.7 1727382396,485.17578125,49.9 1727382396,485.17578125,42.1 1727382396,485.17578125,50.8 1727382396,485.17578125,55.3 1727382399,485.17578125,49.9 1727382399,485.17578125,55.6 1727382399,485.17578125,50.4 1727382399,485.17578125,41.9 1727382401,485.17578125,49.9 1727382401,485.17578125,37.1 1727382401,485.17578125,51.2 1727382401,485.17578125,57.4 1727382403,485.21875,49.8 1727382403,485.21875,34.3 1727382403,485.21875,52.1 1727382403,485.21875,40.6 1727382406,485.21875,49.9 1727382406,485.21875,57.8 1727382406,485.21875,47.2 1727382406,485.21875,55.6 1727382408,485.21875,49.8 1727382408,485.21875,53.1 1727382408,485.21875,47.3 1727382408,485.21875,56.8 1727382410,485.21875,49.8 1727382410,485.21875,55.6 1727382410,485.21875,47.6 1727382410,485.21875,58.7 1727382412,485.21875,49.8 1727382412,485.21875,55.3 1727382412,485.21875,53.2 1727382412,485.21875,37.5 1727382414,485.21875,49.9 1727382414,485.21875,54.2 1727382414,485.21875,48.7 1727382414,485.21875,48.7 1727382416,485.21875,49.9 1727382416,485.21875,38.2 1727382416,485.21875,50.8 1727382416,485.21875,54.3 1727382419,485.21875,49.9 1727382419,485.21875,54.3 1727382419,485.21875,48.8 1727382419,485.21875,44.1 1727382421,485.21875,49.9 1727382421,485.21875,54.2 1727382421,485.21875,51.7 1727382421,485.21875,35.3 1727382423,485.21875,49.9 1727382423,485.21875,48.8 1727382423,485.21875,52.0 1727382423,485.21875,40.0 1727382425,485.21875,49.9 1727382425,485.21875,40.0 1727382425,485.21875,50.0 1727382425,485.21875,57.4 1727382427,485.21875,49.8 1727382427,485.21875,53.2 1727382427,485.21875,50.4 1727382427,485.21875,56.5 1727382430,485.21875,49.8 1727382430,485.21875,52.1 1727382430,485.21875,50.4 1727382430,485.21875,41.2 1727382432,485.21875,49.9 1727382432,485.21875,55.3 1727382432,485.21875,50.8 1727382432,485.21875,39.4 1727382434,485.21875,49.9 1727382434,485.21875,55.3 1727382434,485.21875,51.6 1727382434,485.21875,40.6 1727382436,485.21875,49.9 1727382436,485.21875,54.3 1727382436,485.21875,48.8 1727382436,485.21875,42.4 1727382439,485.21875,49.9 1727382439,485.21875,38.2 1727382439,485.21875,50.4 1727382439,485.21875,56.8 1727382441,485.22265625,49.9 1727382441,485.22265625,41.0 1727382441,485.22265625,50.4 1727382441,485.22265625,57.4 1727382443,485.22265625,49.8 1727382443,485.22265625,56.5 1727382443,485.22265625,48.1 1727382443,485.22265625,57.8 1727382445,485.2265625,49.9 1727382445,485.2265625,35.3 1727382445,485.2265625,51.6 1727382445,485.2265625,58.7 1727382448,485.2265625,49.8 1727382448,485.2265625,57.4 1727382448,485.2265625,50.0 1727382448,485.2265625,38.7 1727382450,485.2265625,49.9 1727382450,485.2265625,53.1 1727382450,485.2265625,49.6 1727382450,485.2265625,55.6 1727382452,485.2265625,49.9 1727382452,485.2265625,48.0 1727382452,485.2265625,48.4 1727382452,485.2265625,55.6 1727382454,485.2265625,49.9 1727382454,485.2265625,38.2 1727382454,485.2265625,51.2 1727382454,485.2265625,54.5 1727382456,485.2265625,49.9 1727382456,485.2265625,55.6 1727382456,485.2265625,46.4 1727382456,485.2265625,55.6 1727382459,485.2265625,49.8 1727382459,485.2265625,54.3 1727382459,485.2265625,51.2 1727382459,485.2265625,37.5 1727382461,485.23046875,49.9 1727382461,485.23046875,38.2 1727382461,485.23046875,52.0 1727382461,485.23046875,56.5 1727382463,485.23046875,49.8 1727382463,485.23046875,55.1 1727382463,485.23046875,50.8 1727382463,485.23046875,39.4 1727382465,485.23046875,49.9 1727382465,485.23046875,36.4 1727382465,485.23046875,50.4 1727382465,485.23046875,56.5 1727382467,485.23046875,49.8 1727382467,485.23046875,55.3 1727382467,485.23046875,51.2 1727382467,485.23046875,43.2 1727382469,485.23046875,49.9 1727382469,485.23046875,53.2 1727382469,485.23046875,48.5 1727382469,485.23046875,54.3 1727382472,485.23046875,49.9 1727382472,485.23046875,39.4 1727382472,485.23046875,50.4 1727382472,485.23046875,48.6 1727382474,485.23046875,49.9 1727382474,485.23046875,52.1 1727382474,485.23046875,51.2 1727382474,485.23046875,55.8 1727382476,485.23046875,49.8 1727382476,485.23046875,54.3 1727382476,485.23046875,50.8 1727382476,485.23046875,53.2 1727382478,485.23046875,49.8 1727382478,485.23046875,55.3 1727382478,485.23046875,49.6 1727382478,485.23046875,37.5 1727382481,485.23046875,49.9 1727382481,485.23046875,54.2 1727382481,485.23046875,48.1 1727382481,485.23046875,56.5 1727382483,485.23046875,49.9 1727382483,485.23046875,37.5 1727382483,485.23046875,51.2 1727382483,485.23046875,57.8 1727382485,485.23046875,49.9 1727382485,485.23046875,35.3 1727382485,485.23046875,50.8 1727382485,485.23046875,56.5 1727382487,485.23046875,49.8 1727382487,485.23046875,46.2 1727382487,485.23046875,48.8 1727382487,485.23046875,54.2 1727382490,485.2421875,49.9 1727382490,485.2421875,40.0 1727382490,485.2421875,52.8 1727382490,485.2421875,44.1 1727382492,485.2421875,49.9 1727382492,485.2421875,38.2 1727382492,485.2421875,50.4 1727382492,485.2421875,56.8 1727382494,485.2421875,49.9 1727382494,485.2421875,38.2 1727382494,485.2421875,50.0 1727382494,485.2421875,55.6 1727382496,485.2421875,49.9 1727382496,485.2421875,55.3 1727382496,485.2421875,46.3 1727382496,485.2421875,54.2 1727382498,485.2421875,49.9 1727382498,485.2421875,53.2 1727382498,485.2421875,49.2 1727382498,485.2421875,40.6 1727382501,485.24609375,49.9 1727382501,485.24609375,38.9 1727382501,485.24609375,53.2 1727382501,485.24609375,36.8 1727382503,485.24609375,49.9 1727382503,485.24609375,55.6 1727382503,485.24609375,49.6 1727382503,485.24609375,47.6 1727382505,485.24609375,49.9 1727382505,485.24609375,55.6 1727382505,485.24609375,49.2 1727382505,485.24609375,39.4 1727382508,485.24609375,49.9 1727382508,485.24609375,39.4 1727382508,485.24609375,50.0 1727382508,485.24609375,55.6 1727382510,485.24609375,49.9 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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 (2024-09-26 22:24:33,325) - Starting with JobEnvironment(job_id=2241446, hostname=i7037, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-26 22:24:33,342) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/SemiParametricLogLikelihood_NOAAWeather/0/single_runs/2241446/2241446_submitted.pkl
Parameters: {"n_samples": 348, "n_clusters": 2, "threshold": 0.060073542356491094}
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 NOAAWeather 1000 SemiParametricLogLikelihood n_samples 348 n_clusters 2 threshold 0.060073542356491094
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: 1727381930.0
Last modification: 1724759185.0
Hostname: i7037
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: 1727381989.0
Last modification: 1727277242.0
Hostname: i7037
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 SemiParametricLogLikelihood n_samples 348 n_clusters 2 threshold 0.060073542356491094
stdout:
RESULT: 0.2857142857142857
OO-Info: runtime: 94
OO-Info: lpd: 0.024424496089877734
Result: 0.2857142857142857
EXIT_CODE: 0
submitit INFO (2024-09-26 22:26:13,534) - Job completed successfully
submitit INFO (2024-09-26 22:26:13,536) - Exiting after successful completion
Job-Infos
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background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
background-size: 15px;
font-size: 11px;
border: none;
background-color: #fff;
box-sizing: border-box;
height: 21px;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
position: relative;
padding: 5px 32px 32px 5px;
background-position: top 50% right 2px;
background-repeat: no-repeat;
border-radius: 0;
border: 1px solid black;
}
body {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
font-variant: oldstyle-nums;
text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 7h1'/%3E%3Cpath stroke='%23c5d5fc' d='M9 3h1M3 9h1m3 0h1'/%3E%3Cpath stroke='%23c5d3fc' d='M10 3h1'/%3E%3Cpath stroke='%23bed0fc' d='M12 3h1M9 4h1m-7 7h1m0 1h1'/%3E%3Cpath stroke='%23bccdfa' d='M13 3h1'/%3E%3Cpath stroke='%23baccf4' d='M14 3h1'/%3E%3Cpath stroke='%23bdcbda' d='M16 3h1'/%3E%3Cpath stroke='%23c4d4f7' d='M2 4h1'/%3E%3Cpath stroke='%23cddbfc' d='M5 4h1M3 6h1'/%3E%3Cpath stroke='%23c8d5fb' d='M8 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M10 4h3M9 5h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 4h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c2d5' d='M16 4h1'/%3E%3Cpath stroke='%23bed0f8' d='M2 5h1'/%3E%3Cpath stroke='%23ceddfd' d='M4 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M6 5h2M3 8h2'/%3E%3Cpath stroke='%234d6185' d='M8 5h1M7 6h3M6 7h5M5 8h3m1 0h3M4 9h3m3 0h3m-8 1h1m5 0h1'/%3E%3Cpath stroke='%23bacdfc' d='M10 5h1m1 0h2M3 12h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1m-2 1h1m1 0h2m-1 1h1'/%3E%3Cpath stroke='%23a8bbd4' d='M16 5h1'/%3E%3Cpath stroke='%23cddafc' d='M4 6h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1'/%3E%3Cpath stroke='%23a4b8d3' d='M16 6h1'/%3E%3Cpath stroke='%23cad8fd' d='M4 7h2'/%3E%3Cpath stroke='%23b6cefb' d='M11 7h1m0 1h1'/%3E%3Cpath stroke='%23bacbf4' d='M14 7h1'/%3E%3Cpath stroke='%23a0b5d3' d='M16 7h1m-1 1h1m-1 5h1'/%3E%3Cpath stroke='%23c1d3fb' d='M8 8h1'/%3E%3Cpath stroke='%23b6cdfb' d='M13 8h1m-5 5h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 8h1'/%3E%3Cpath stroke='%23b4c8f6' d='M2 9h1'/%3E%3Cpath stroke='%23c2d5fc' d='M8 9h1m-1 1h1m-3 1h2'/%3E%3Cpath stroke='%23bdd3fb' d='M9 9h1m-2 3h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 9h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 9h1'/%3E%3Cpath stroke='%239fb5d2' d='M16 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c7f6' d='M2 10h1'/%3E%3Cpath stroke='%23c3d5fd' d='M7 10h1'/%3E%3Cpath stroke='%23bad4fc' d='M9 10h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M10 10h1m1 0h1m-2 2h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 10h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 10h1m-6 4h2'/%3E%3Cpath stroke='%23adc3f6' d='M2 11h1'/%3E%3Cpath stroke='%23c3d3fd' d='M5 11h1'/%3E%3Cpath stroke='%23c1d5fb' d='M8 11h1'/%3E%3Cpath stroke='%23b7d3fc' d='M10 11h1m-2 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 11h1'/%3E%3Cpath stroke='%23afcefb' d='M12 11h1'/%3E%3Cpath stroke='%23aecafa' d='M13 11h1'/%3E%3Cpath stroke='%23b1c8f3' d='M14 11h1'/%3E%3Cpath stroke='%23acc2f5' d='M2 12h1'/%3E%3Cpath stroke='%23c1d2fb' d='M5 12h1'/%3E%3Cpath stroke='%23bed1fc' d='M6 12h2'/%3E%3Cpath stroke='%23b6d1fb' d='M10 12h1'/%3E%3Cpath stroke='%23afccfb' d='M12 12h1'/%3E%3Cpath stroke='%23adc9f9' d='M13 12h1m-2 1h1'/%3E%3Cpath stroke='%23b1c5f3' d='M14 12h1'/%3E%3Cpath stroke='%23aac0f3' d='M2 13h1'/%3E%3Cpath stroke='%23b7cbf9' d='M3 13h1'/%3E%3Cpath stroke='%23b9cefb' d='M4 13h1'/%3E%3Cpath stroke='%23bbcef9' d='M7 13h1'/%3E%3Cpath stroke='%23b9cffb' d='M8 13h1'/%3E%3Cpath stroke='%23b2cdfb' d='M10 13h1'/%3E%3Cpath stroke='%23b0cbf9' d='M11 13h1'/%3E%3Cpath stroke='%23aec8f7' d='M13 13h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath stroke='%23b1c6f3' d='M12 14h1'/%3E%3Cpath stroke='%23b0c4f2' d='M13 14h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 14h1'/%3E%3Cpath stroke='%23aec0d6' d='M16 14h1'/%3E%3Cpath stroke='%23c3d4e7' d='M1 15h1'/%3E%3Cpath stroke='%23aec4e5' d='M15 15h1'/%3E%3Cpath stroke='%23edf1f3' d='M1 16h1'/%3E%3Cpath stroke='%23aac0e1' d='M2 16h1'/%3E%3Cpath stroke='%2394b1d9' d='M3 16h1'/%3E%3Cpath stroke='%2388a7d8' d='M4 16h1'/%3E%3Cpath stroke='%2383a4d3' d='M5 16h1'/%3E%3Cpath stroke='%237da0d4' d='M6 16h1m3 0h3'/%3E%3Cpath stroke='%237e9fd2' d='M7 16h1'/%3E%3Cpath stroke='%237c9fd3' d='M8 16h2'/%3E%3Cpath stroke='%2382a4d6' d='M13 16h1'/%3E%3Cpath stroke='%2394b0dd' d='M14 16h1'/%3E%3Cpath stroke='%23ecf2f7' d='M15 16h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h3'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M6 6h2M3 7h1'/%3E%3Cpath 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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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}
.title-bar-controls button[aria-label=Maximize]: not(: disabled): active{
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}
.title-bar-controls button[aria-label=Restore]{
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}
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}
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stroke='%23b5381a' d='M9 4h1M4 9h1'/%3E%3Cpath stroke='%23b8391a' d='M10 4h1m-7 6h1'/%3E%3Cpath stroke='%23ba3a1b' d='M11 4h1m-8 7h2'/%3E%3Cpath stroke='%23bc3b1c' d='M12 4h1m-9 8h1'/%3E%3Cpath stroke='%23bd3c1c' d='M13 4h1m-1 1h1m-2 1h1m-7 6h1m-3 1h2'/%3E%3Cpath stroke='%23be3d1c' d='M14 4h3m-1 1h1m-1 1h1M4 14h1m-1 1h1m-1 1h2'/%3E%3Cpath stroke='%23bf3d1c' d='M17 4h3m-3 1h3m-2 1h2m-1 1h1M4 17h2m-2 1h4m-4 1h4'/%3E%3Cpath stroke='%235b1d0d' d='M1 5h1'/%3E%3Cpath stroke='%239c3016' d='M3 5h1'/%3E%3Cpath stroke='%23a43217' d='M4 5h1'/%3E%3Cpath stroke='%23b8553e' d='M5 5h1'/%3E%3Cpath stroke='%23d59485' d='M6 5h1M5 6h1'/%3E%3Cpath stroke='%23b33619' d='M7 5h1'/%3E%3Cpath stroke='%23b53719' d='M8 5h1'/%3E%3Cpath stroke='%23b8381a' d='M9 5h1M6 8h1'/%3E%3Cpath stroke='%23b9391b' d='M10 5h1'/%3E%3Cpath stroke='%23ba391b' d='M11 5h1M6 9h1m-2 1h1'/%3E%3Cpath stroke='%23bc3b1b' d='M12 5h1m-2 1h1m-6 5h1m-2 1h1'/%3E%3Cpath stroke='%23dc9887' d='M14 5h1'/%3E%3Cpath stroke='%23c85d42' d='M15 5h1M5 15h1'/%3E%3Cpath stroke='%23611f0e' d='M1 6h1'/%3E%3Cpath stroke='%23a23217' d='M3 6h1'/%3E%3Cpath stroke='%23d79585' d='M6 6h1'/%3E%3Cpath stroke='%23d89585' d='M7 6h1'/%3E%3Cpath stroke='%23b8371a' d='M8 6h1'/%3E%3Cpath stroke='%23ba391a' d='M9 6h1'/%3E%3Cpath stroke='%23bb3a1b' d='M10 6h1m-5 4h1'/%3E%3Cpath stroke='%23dd9887' d='M13 6h3m-4 1h1m-2 1h1M9 9h1m-2 2h1m-2 1h1m-2 1h1m-2 1h2'/%3E%3Cpath stroke='%23c03e1d' d='M17 6h1m-2 1h3m0 1h1m-1 1h1M7 16h1m-2 1h2m0 1h1'/%3E%3Cpath stroke='%2365200e' d='M1 7h1'/%3E%3Cpath stroke='%23902d15' d='M2 7h1'/%3E%3Cpath stroke='%23a73418' d='M3 7h1'/%3E%3Cpath stroke='%23af3518' d='M4 7h1'/%3E%3Cpath stroke='%23b43619' d='M5 7h1'/%3E%3Cpath stroke='%23d99585' d='M6 7h1'/%3E%3Cpath stroke='%23da9686' d='M7 7h1'/%3E%3Cpath stroke='%23db9686' d='M8 7h1M7 8h1'/%3E%3Cpath stroke='%23bc3a1b' d='M9 7h1M7 9h1'/%3E%3Cpath stroke='%23bd3b1b' d='M10 7h1m-4 3h1'/%3E%3Cpath stroke='%23be3c1c' d='M11 7h1m-2 1h1m-3 2h1m-2 1h1'/%3E%3Cpath stroke='%23de9987' d='M13 7h2m-3 1h2m-4 1h2m-3 1h1m-2 2h1m-2 2h1'/%3E%3Cpath stroke='%23c03f1d' d='M15 7h1m-9 8h1'/%3E%3Cpath stroke='%236a220f' d='M1 8h1'/%3E%3Cpath stroke='%23952f15' d='M2 8h1'/%3E%3Cpath stroke='%23ac3518' d='M3 8h1'/%3E%3Cpath stroke='%23b63719' d='M5 8h1'/%3E%3Cpath stroke='%23dc9786' d='M8 8h2M8 9h1'/%3E%3Cpath stroke='%23c2401d' d='M14 8h1m2 0h1m1 3h1M8 14h1m-1 2h1m-1 1h1m0 1h1m1 1h1'/%3E%3Cpath stroke='%23c2401e' d='M15 8h2m1 1h1M8 15h1'/%3E%3Cpath stroke='%23c13f1d' d='M18 8h1m0 2h1M9 19h2'/%3E%3Cpath stroke='%23702410' d='M1 9h1'/%3E%3Cpath stroke='%239b3016' d='M2 9h1'/%3E%3Cpath stroke='%23b03619' d='M3 9h1'/%3E%3Cpath stroke='%23b9381a' d='M5 9h1'/%3E%3Cpath stroke='%23df9a88' d='M12 9h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23c4421e' d='M13 9h1m2 0h2m0 1h1M9 13h1m9 1h1m-1 1h1M9 16h1m9 0h1M9 17h1m0 1h1m3 1h3'/%3E%3Cpath stroke='%23c5431e' d='M14 9h1'/%3E%3Cpath stroke='%23c5431f' d='M15 9h1m-4 1h1m5 1h1m-9 1h1m-2 2h1m-1 1h1m0 2h1m0 1h1m6 0h1'/%3E%3Cpath stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
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1727404044,
507.72265625,
50.7
],
[
1727404044,
507.72265625,
45.9
],
[
1727404270,
501.28515625,
50.2
],
[
1727404270,
501.28515625,
53.2
],
[
1727404270,
501.28515625,
50.1
],
[
1727404270,
501.28515625,
39.4
],
[
1727404575,
515.2109375,
50.2
],
[
1727404575,
515.2109375,
56.2
],
[
1727404575,
515.2109375,
49.5
],
[
1727404575,
515.2109375,
55.6
],
[
1727404945,
503.21875,
50.2
],
[
1727404945,
503.21875,
54.2
],
[
1727404945,
503.21875,
50.5
],
[
1727404945,
503.21875,
40.6
],
[
1727405330,
529.71875,
50.2
],
[
1727405330,
529.71875,
56.3
],
[
1727405330,
529.71875,
49.6
],
[
1727405330,
529.71875,
60
],
[
1727405780,
503.29296875,
50.2
],
[
1727405780,
503.29296875,
38.2
],
[
1727408275,
501.5078125,
49.6
],
[
1727408275,
501.5078125,
54.3
]
];
var tab_main_worker_cpu_ram_headers_json = [
"timestamp",
"ram_usage_mb",
"cpu_usage_percent"
];
"use strict";
function add_default_layout_data (layout) {
layout["width"] = get_graph_width();
layout["height"] = get_graph_height();
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 2) {
console.error("Not enough columns for Scatter-Plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
showlegend: false
};
let subDiv = document.createElement("div");
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
symbol: data.map(d => d.result === null ? 'x' : 'circle'),
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter',
showlegend: false
};
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
$("#plotScatter2d").data("loaded", "true");
}
function plotScatter3d() {
if ($("#plotScatter3d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter3d");
if (!plotDiv) {
console.error("Div element with id 'plotScatter3d' not found");
return;
}
plotDiv.innerHTML = "";
var minInput3d = document.getElementById("minValue3d");
var maxInput3d = document.getElementById("maxValue3d");
if (!minInput3d || !maxInput3d) {
minInput3d = document.createElement("input");
minInput3d.id = "minValue3d";
minInput3d.type = "number";
minInput3d.placeholder = "Min Value";
minInput3d.step = "any";
maxInput3d = document.createElement("input");
maxInput3d.id = "maxValue3d";
maxInput3d.type = "number";
maxInput3d.placeholder = "Max Value";
maxInput3d.step = "any";
var inputContainer3d = document.createElement("div");
inputContainer3d.style.marginBottom = "10px";
inputContainer3d.appendChild(minInput3d);
inputContainer3d.appendChild(maxInput3d);
plotDiv.appendChild(inputContainer3d);
}
var select3d = document.getElementById("select3dScatter");
if (result_names.length > 1 && !select3d) {
if (!select3d) {
select3d = document.createElement("select");
select3d.id = "select3dScatter";
select3d.style.marginBottom = "10px";
select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
select3d.addEventListener("change", updatePlots3d);
plotDiv.appendChild(select3d);
}
}
minInput3d.addEventListener("input", updatePlots3d);
maxInput3d.addEventListener("input", updatePlots3d);
updatePlots3d();
async function updatePlots3d() {
var selectedResult = select3d ? select3d.value : result_names[0];
var minValue3d = parseFloat(minInput3d.value);
var maxValue3d = parseFloat(maxInput3d.value);
if (isNaN(minValue3d)) minValue3d = -Infinity;
if (isNaN(maxValue3d)) maxValue3d = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 3) {
console.error("Not enough columns for 3D scatter plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
for (let k = j + 1; k < numericColumns.length; k++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let zCol = numericColumns[k];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let zIndex = tab_results_headers_json.indexOf(zCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
z: parseFloat(row[zIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
scene: {
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
zaxis: {
title: get_axis_title_data(zCol)
}
},
showlegend: false
};
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
z: data.map(d => d.z),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter3d',
showlegend: false
};
let subDiv = document.createElement("div");
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function load_pareto_graph() {
if($("#tab_pareto_fronts").data("loaded") == "true") {
return;
}
var data = pareto_front_data;
if (!data || typeof data !== "object") {
console.error("Invalid data format for pareto_front_data");
return;
}
if (!Object.keys(data).length) {
console.warn("No data found in pareto_front_data");
return;
}
let categories = Object.keys(data);
let allMetrics = new Set();
function extractMetrics(obj, prefix = "") {
let keys = Object.keys(obj);
for (let key of keys) {
let newPrefix = prefix ? `${prefix} -> ${key}` : key;
if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
extractMetrics(obj[key], newPrefix);
} else {
if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
allMetrics.add(newPrefix);
}
}
}
}
for (let cat of categories) {
extractMetrics(data[cat]);
}
allMetrics = Array.from(allMetrics);
function extractValues(obj, metricPath, values) {
let parts = metricPath.split(" -> ");
let data = obj;
for (let part of parts) {
if (data && typeof data === "object") {
data = data[part];
} else {
return;
}
}
if (Array.isArray(data)) {
values.push(...data);
}
}
let graphContainer = document.getElementById("pareto_front_graphs_container");
graphContainer.classList.add("invert_in_dark_mode");
graphContainer.innerHTML = "";
var already_plotted = [];
for (let i = 0; i < allMetrics.length; i++) {
for (let j = i + 1; j < allMetrics.length; j++) {
let xMetric = allMetrics[i];
let yMetric = allMetrics[j];
let xValues = [];
let yValues = [];
for (let cat of categories) {
let metricData = data[cat];
extractValues(metricData, xMetric, xValues);
extractValues(metricData, yMetric, yValues);
}
xValues = xValues.filter(v => v !== undefined && v !== null);
yValues = yValues.filter(v => v !== undefined && v !== null);
let cleanXMetric = xMetric.replace(/.* -> /g, "");
let cleanYMetric = yMetric.replace(/.* -> /g, "");
let plot_key = `${cleanXMetric}-${cleanYMetric}`;
if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
let div = document.createElement("div");
div.id = `pareto_front_graph_${i}_${j}`;
div.style.marginBottom = "20px";
graphContainer.appendChild(div);
let layout = {
title: `${cleanXMetric} vs ${cleanYMetric}`,
xaxis: {
title: get_axis_title_data(cleanXMetric)
},
yaxis: {
title: get_axis_title_data(cleanYMetric)
},
hovermode: "closest"
};
let trace = {
x: xValues,
y: yValues,
mode: "markers",
marker: {
size: get_marker_size(),
},
type: "scatter",
name: `${cleanXMetric} vs ${cleanYMetric}`
};
Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
already_plotted.push(plot_key);
}
}
}
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
$("#tab_pareto_fronts").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#worker_cpu_ram_pre").data("loaded") == "true") {
return;
}
const logData = $("#worker_cpu_ram_pre").text();
const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
const hostData = {};
logData.split("\n").forEach(line => {
line = line.trim();
const match = line.match(regex);
if (match) {
const timestamp = new Date(parseInt(match[1]) * 1000);
const hostname = match[2];
const cpu = parseFloat(match[3]);
const ram = parseFloat(match[4]);
if (!hostData[hostname]) {
hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
}
hostData[hostname].timestamps.push(timestamp);
hostData[hostname].cpuUsage.push(cpu);
hostData[hostname].ramUsage.push(ram);
}
});
if (!Object.keys(hostData).length) {
console.log("No valid data found");
return;
}
const container = document.getElementById("cpuRamWorkerChartContainer");
container.innerHTML = "";
var i = 1;
Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
const chartId = `workerChart_${index}`;
const chartDiv = document.createElement("div");
chartDiv.id = chartId;
chartDiv.style.marginBottom = "40px";
container.appendChild(chartDiv);
const cpuTrace = {
x: timestamps,
y: cpuUsage,
mode: "lines+markers",
name: "CPU Usage (%)",
yaxis: "y1",
line: {
color: "red"
}
};
const ramTrace = {
x: timestamps,
y: ramUsage,
mode: "lines+markers",
name: "RAM Usage (MB)",
yaxis: "y2",
line: {
color: "blue"
}
};
const layout = {
title: `Worker CPU and RAM Usage - ${hostname}`,
xaxis: {
title: get_axis_title_data("Timestamp", "date")
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
side: "left",
color: "red"
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
side: "right",
overlaying: "y",
color: "blue"
},
showlegend: true
};
Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
i++;
});
$("#plot_worker_cpu_ram_button").remove();
$("#worker_cpu_ram_pre").data("loaded", "true");
}
function load_log_file(log_nr, filename) {
var pre_id = `single_run_${log_nr}_pre`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
fetch(url)
.then(response => response.json())
.then(data => {
if (data.data) {
$("#" + pre_id).html(data.data);
$("#" + pre_id).data("loaded", true);
} else {
log(`No 'data' key found in response.`);
}
$("#spinner_log_" + log_nr).remove();
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#spinner_log_" + log_nr).remove();
});
}
}
function load_debug_log () {
var pre_id = `here_debuglogs_go`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
fetch(url)
.then(response => response.json())
.then(data => {
$("#debug_log_spinner").remove();
if (data.data) {
try {
$("#" + pre_id).html(data.data);
} catch (err) {
$("#" + pre_id).text(`Error loading data: ${err}`);
}
$("#" + pre_id).data("loaded", true);
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
} else {
log(`No 'data' key found in response.`);
}
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#debug_log_spinner").remove();
});
}
}
function plotBoxplot() {
if ($("#plotBoxplot").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough numeric columns for Boxplot");
return;
}
var resultIndex = tab_results_headers_json.findIndex(function(header) {
return result_names.includes(header.toLowerCase());
});
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
var plotDiv = document.getElementById("plotBoxplot");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'box',
name: col,
boxmean: 'sd',
marker: {
color: 'rgb(0, 255, 0)'
},
};
});
let layout = {
title: 'Boxplot of Numerical Columns',
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Value")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotBoxplot").data("loaded", "true");
}
function plotHeatmap() {
if ($("#plotHeatmap").data("loaded") === "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col => {
if (special_col_names.includes(col) || result_names.includes(col)) {
return false;
}
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.every(row => {
let value = parseFloat(row[index]);
return !isNaN(value) && isFinite(value);
});
});
if (numericColumns.length < 2) {
console.error("Not enough valid numeric columns for Heatmap");
return;
}
var columnData = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.map(row => parseFloat(row[index]));
});
var dataMatrix = numericColumns.map((_, i) =>
numericColumns.map((_, j) => {
let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
return values.reduce((a, b) => a + b, 0) / values.length;
})
);
var trace = {
z: dataMatrix,
x: numericColumns,
y: numericColumns,
colorscale: 'Viridis',
type: 'heatmap'
};
var layout = {
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
var plotDiv = document.getElementById("plotHeatmap");
plotDiv.innerHTML = "";
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotHeatmap").data("loaded", "true");
}
function plotHistogram() {
if ($("#plotHistogram").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Histogram");
return;
}
var plotDiv = document.getElementById("plotHistogram");
plotDiv.innerHTML = "";
const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
let traces = numericColumns.map((col, index) => {
let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
return {
x: data,
type: 'histogram',
name: col,
opacity: 0.7,
marker: {
color: colorPalette[index % colorPalette.length]
},
autobinx: true
};
});
let layout = {
title: 'Histogram of Numerical Columns',
xaxis: {
title: get_axis_title_data("Value")
},
yaxis: {
title: get_axis_title_data("Frequency")
},
showlegend: true,
barmode: 'overlay'
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotHistogram").data("loaded", "true");
}
function plotViolin() {
if ($("#plotViolin").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#tab_gpu_usage").data("loaded") === "true") {
return;
}
Object.keys(gpu_usage).forEach(node => {
const nodeData = gpu_usage[node];
var timestamps = [];
var gpuUtilizations = [];
var temperatures = [];
nodeData.forEach(entry => {
try {
var timestamp = new Date(entry[0]* 1000);
var utilization = parseFloat(entry[1]);
var temperature = parseFloat(entry[2]);
if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
timestamps.push(timestamp);
gpuUtilizations.push(utilization);
temperatures.push(temperature);
} else {
console.warn("Invalid data point:", entry);
}
} catch (error) {
console.error("Error processing GPU data entry:", error, entry);
}
});
var trace1 = {
x: timestamps,
y: gpuUtilizations,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Utilization (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: temperatures,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Temperature (°C)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'GPU Usage Over Time - ' + node,
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("GPU Utilization (%)"),
overlaying: 'y',
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("GPU Temperature (°C)"),
overlaying: 'y',
side: 'right',
position: 0.85,
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var divId = 'gpu_usage_plot_' + node;
if (!document.getElementById(divId)) {
var div = document.createElement('div');
div.id = divId;
div.className = 'gpu-usage-plot';
document.getElementById('tab_gpu_usage').appendChild(div);
}
var plotData = [trace1, trace2];
Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
});
$("#tab_gpu_usage").data("loaded", "true");
}
function plotResultsDistributionByGenerationMethod() {
if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
return;
}
var res_col = result_names[0];
var gen_method_col = "generation_method";
var data = {};
tab_results_csv_json.forEach(row => {
var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
var result = row[tab_results_headers_json.indexOf(res_col)];
if (!data[gen_method]) {
data[gen_method] = [];
}
data[gen_method].push(result);
});
var traces = Object.keys(data).map(method => {
return {
y: data[method],
type: 'box',
name: method,
boxpoints: 'outliers', // Zeigt nur Ausreißer außerhalb der Whiskers
jitter: 0.5, // Erhöht die Streuung der Punkte für bessere Sichtbarkeit
pointpos: 0 // Position der Punkte innerhalb der Box
};
});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: "Generation Method"
},
boxmode: 'group' // Gruppiert die Boxplots nach Generation Method
};
Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
}
function plotJobStatusDistribution() {
if ($("#plotJobStatusDistribution").data("loaded") === "true") {
return;
}
var status_col = "trial_status";
var status_counts = {};
tab_results_csv_json.forEach(row => {
var status = row[tab_results_headers_json.indexOf(status_col)];
if (status) {
status_counts[status] = (status_counts[status] || 0) + 1;
}
});
var statuses = Object.keys(status_counts);
var counts = Object.values(status_counts);
var colors = statuses.map((status, i) =>
status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
);
var trace = {
x: statuses,
y: counts,
type: 'bar',
marker: { color: colors }
};
var layout = {
title: 'Distribution of Job Status',
xaxis: { title: 'Trial Status' },
yaxis: { title: 'Nr. of jobs' }
};
Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
if (values.length === 0) return;
let min = Math.min(...values);
let max = Math.max(...values);
let range = max - min || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value)) return;
let ratio = (value - min) / range;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
if (values.length === 0) return;
let logValues = values.map(v => Math.log(v));
let logMin = Math.min(...logValues);
let logMax = Math.max(...logValues);
let logRange = logMax - logMin || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value) || value <= 0) return;
let logValue = Math.log(value);
let ratio = (logValue - logMin) / logRange;
if (minMax === "max") ratio = 1 - ratio;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
});
}
function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
let colorMap = {};
uniqueValues.forEach((value, index) => {
let hue = Math.round((360 / uniqueValues.length) * index);
colorMap[value] = `hsl(${hue}, 70%, 60%)`;
});
cells.forEach(el => {
let value = el.textContent.trim();
if (colorMap[value]) {
el.style.backgroundColor = colorMap[value];
el.classList.add("invert_in_dark_mode");
}
});
});
}
function colorize_table_entries () {
setTimeout(() => {
if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
_colorize_table_entries_by_trial_status();
_colorize_table_entries_by_results();
_colorize_table_entries_by_run_time();
_colorize_table_entries_by_generation_method();
_colorize_table_entries_by_generation_node_or_hostname();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
searchInput.addEventListener("input", colorize_table_entries);
}
}
function updatePreWidths() {
var width = window.innerWidth * 0.95;
var pres = document.getElementsByTagName('pre');
for (var i = 0; i < pres.length; i++) {
pres[i].style.width = width + 'px';
}
}
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();
});
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>n_clusters</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 197</td><td>2</td><td>0.062247</td><td>0.265503 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int </td></tr><tr><td> n_clusters</td><td>range</td><td>1</td><td>4</td><td></td><td>int </td></tr><tr><td> threshold</td><td>range</td><td>0.002</td><td>0.07</td><td></td><td>float </td></tr></tbody></table><br><h2>Number of evaluations:</h2>
<table>
<tbody>
<tr>
<th>Failed</th>
<th>Succeeded</th>
<th>Running</th>
<th>Total</th>
</tr>
<tr>
<td>0</td>
<td>492</td>
<td>11</td>
<td>503</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,n_clusters,threshold
0,0_0,COMPLETED,Sobol,0.285714285714285698425385362498,348,2,0.060073542356491094196258018201
1,1_0,COMPLETED,Sobol,0.289734552263465117150076366670,467,1,0.060147356942296031911965314976
2,2_0,COMPLETED,Sobol,0.287696882916620766401649689215,256,3,0.062527635604143150249711879951
3,3_0,COMPLETED,Sobol,0.287917171494658008512601554685,244,1,0.027889940015971663445082384669
4,4_0,COMPLETED,Sobol,0.286265007159378748191613794916,358,4,0.068215497639030231447065943939
5,5_0,COMPLETED,Sobol,0.293754818812644535874767370842,668,2,0.033851351145654920427041645326
6,6_0,COMPLETED,Sobol,0.279656349818261928952267680870,709,4,0.034590727504342798359626698357
7,7_0,COMPLETED,Sobol,0.299647538275140457031398000254,225,3,0.065635051038116218324880435375
8,8_0,COMPLETED,Sobol,0.279656349818261928952267680870,579,4,0.015434440743178130805990377894
9,9_0,COMPLETED,Sobol,0.279656349818261928952267680870,832,1,0.005984044417738914635462865732
10,10_0,COMPLETED,Sobol,0.287476594338583524290697823744,781,3,0.068878439199179422591790000752
11,11_0,COMPLETED,Sobol,0.288578037228769734845457151096,457,1,0.042634507581591610247961909863
12,12_0,COMPLETED,Sobol,0.294801189558321352635061884939,219,1,0.017112214051187041308210723400
13,13_0,COMPLETED,Sobol,0.293589602379116687558280318626,858,3,0.065441622987389577725814149289
14,14_0,COMPLETED,Sobol,0.292322943055402628687033939059,788,2,0.043515014130622156618422025076
15,15_0,COMPLETED,Sobol,0.279656349818261928952267680870,864,2,0.020051056977361439437324008850
16,16_0,COMPLETED,Sobol,0.279656349818261928952267680870,869,2,0.023314345303922894003800792007
17,17_0,COMPLETED,Sobol,0.286485295737415990302565660386,228,4,0.045170483965426687367195057732
18,18_0,COMPLETED,Sobol,0.284282409957043680215349468199,169,3,0.003816889986395836063814979155
19,19_0,COMPLETED,Sobol,0.287586738627602200857324987737,606,1,0.046846737418323756985127914731
20,20_0,COMPLETED,BoTorch,0.279656349818261928952267680870,964,2,0.006440706098829535736216556074
21,21_0,COMPLETED,BoTorch,0.279656349818261928952267680870,870,4,0.015139047254553858121162157602
22,22_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.017930694452499305879822344423
23,23_0,COMPLETED,BoTorch,0.279656349818261928952267680870,759,3,0.003743313175126723735264278048
24,24_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.017818407251825763015773418374
25,25_0,COMPLETED,BoTorch,0.279656349818261928952267680870,826,2,0.002000000000000000041633363423
26,26_0,COMPLETED,BoTorch,0.279656349818261928952267680870,699,4,0.003961408212790632800415213666
27,27_0,COMPLETED,BoTorch,0.288082387928185967851391069416,535,4,0.028108361059398234704787000737
28,28_0,COMPLETED,BoTorch,0.279656349818261928952267680870,364,4,0.010995724716744630783393787965
29,29_0,COMPLETED,BoTorch,0.279656349818261928952267680870,726,4,0.021703111054831011883514690908
30,30_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.002000000000000000041633363423
31,31_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,2,0.018208644185763012135481631049
32,32_0,COMPLETED,BoTorch,0.279656349818261928952267680870,904,4,0.002000000000000000041633363423
33,33_0,COMPLETED,BoTorch,0.279656349818261928952267680870,826,3,0.014011524148584657625815630411
34,34_0,COMPLETED,BoTorch,0.287366450049564958746373122267,899,4,0.031426589315584348149901217084
35,35_0,COMPLETED,BoTorch,0.279656349818261928952267680870,608,3,0.002000000000000000041633363423
36,36_0,COMPLETED,BoTorch,0.279656349818261928952267680870,436,4,0.002000000000000000041633363423
37,37_0,COMPLETED,BoTorch,0.279656349818261928952267680870,884,3,0.002000000000000000041633363423
38,38_0,COMPLETED,BoTorch,0.279656349818261928952267680870,696,1,0.002000000000000000041633363423
39,39_0,COMPLETED,BoTorch,0.299922898997686981914512216463,100,4,0.014256247464616362413680761279
40,40_0,COMPLETED,BoTorch,0.279656349818261928952267680870,475,4,0.025392498925328021996961069817
41,41_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,1,0.009551141601888217189109120397
42,42_0,COMPLETED,BoTorch,0.279656349818261928952267680870,564,4,0.002000000000000000041633363423
43,43_0,COMPLETED,BoTorch,0.279656349818261928952267680870,705,2,0.008127114054511670737657169639
44,44_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.002000000000000000041633363423
45,45_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.016667694161320523171809782070
46,46_0,COMPLETED,BoTorch,0.279656349818261928952267680870,548,3,0.002000000000000000041633363423
47,47_0,COMPLETED,BoTorch,0.279656349818261928952267680870,742,4,0.012496500587452568156732191085
48,48_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.002000000000000000041633363423
49,49_0,COMPLETED,BoTorch,0.279656349818261928952267680870,897,3,0.009990621740445071452541014878
50,50_0,COMPLETED,BoTorch,0.287972243639167291284763905423,1000,4,0.027185138802143439529235280361
51,51_0,COMPLETED,BoTorch,0.279656349818261928952267680870,905,2,0.010372597541904102214083138733
52,52_0,COMPLETED,BoTorch,0.279656349818261928952267680870,785,3,0.009903397250269603199757106893
53,53_0,COMPLETED,BoTorch,0.279656349818261928952267680870,604,3,0.009669240543426120895742492678
54,54_0,COMPLETED,BoTorch,0.279656349818261928952267680870,525,2,0.002000000000000000041633363423
55,55_0,COMPLETED,BoTorch,0.279656349818261928952267680870,846,1,0.012643279132101425607515032823
56,56_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,4,0.010234041319064253461323943384
57,57_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,3,0.007747778179203849979739260334
58,58_0,COMPLETED,BoTorch,0.279656349818261928952267680870,420,3,0.002000000000000000041633363423
59,59_0,COMPLETED,BoTorch,0.279656349818261928952267680870,606,4,0.009813078067171162113813132066
60,60_0,COMPLETED,BoTorch,0.279656349818261928952267680870,510,4,0.009221661076831890596627872014
61,61_0,COMPLETED,BoTorch,0.279656349818261928952267680870,487,3,0.005293499506974084362065369191
62,62_0,COMPLETED,BoTorch,0.279656349818261928952267680870,546,1,0.002000000000000000041633363423
63,63_0,COMPLETED,BoTorch,0.279656349818261928952267680870,744,4,0.002000000000000000041633363423
64,64_0,COMPLETED,BoTorch,0.279656349818261928952267680870,627,2,0.002000000000000000041633363423
65,65_0,COMPLETED,BoTorch,0.279656349818261928952267680870,573,2,0.002000000000000000041633363423
66,66_0,COMPLETED,BoTorch,0.279656349818261928952267680870,866,1,0.002000000000000000041633363423
67,67_0,COMPLETED,BoTorch,0.279656349818261928952267680870,582,1,0.010675862727463281826034702249
68,68_0,COMPLETED,BoTorch,0.279656349818261928952267680870,484,2,0.008163946760472788957696543832
69,69_0,COMPLETED,BoTorch,0.290780923009141933910370880767,850,4,0.036027041959330409481854218257
70,70_0,COMPLETED,BoTorch,0.279656349818261928952267680870,439,2,0.023416705294092655942339575859
71,71_0,COMPLETED,BoTorch,0.287751955061130049173812039953,100,4,0.055261269664713341676076652220
72,72_0,COMPLETED,BoTorch,0.290175129419539601371980097611,1000,4,0.052930652833244995481010164440
73,73_0,COMPLETED,BoTorch,0.296949003194184379950115726388,100,4,0.024196377910024914614783853040
74,74_0,COMPLETED,BoTorch,0.288027315783676574056926256162,683,4,0.036636433437574361660082900016
75,75_0,COMPLETED,BoTorch,0.285879502147813657764174877229,993,4,0.035866962959401796351421864983
76,76_0,COMPLETED,BoTorch,0.278995484084150202619412084459,100,1,0.051536492364097824525615010316
77,77_0,COMPLETED,BoTorch,0.279656349818261928952267680870,421,4,0.021316259633873985146479412833
78,78_0,COMPLETED,BoTorch,0.279656349818261928952267680870,391,4,0.025150843702273807667157967671
79,79_0,COMPLETED,BoTorch,0.279656349818261928952267680870,766,4,0.030183878847912168352785045045
80,80_0,COMPLETED,BoTorch,0.286815728604471908980144689849,586,4,0.070000000000000006661338147751
81,81_0,COMPLETED,BoTorch,0.279656349818261928952267680870,440,4,0.019346768609091818946765783949
82,82_0,COMPLETED,BoTorch,0.287586738627602200857324987737,609,4,0.053039206429008035892369576914
83,83_0,COMPLETED,BoTorch,0.289514263685427875039124501200,307,4,0.055806514363990658567082903119
84,84_0,COMPLETED,BoTorch,0.279656349818261928952267680870,371,4,0.029166754372271772033364811705
85,85_0,COMPLETED,BoTorch,0.279656349818261928952267680870,290,4,0.002000000000000000041633363423
86,86_0,COMPLETED,BoTorch,0.279656349818261928952267680870,420,1,0.019583940469605806700137407006
87,87_0,COMPLETED,BoTorch,0.287917171494658008512601554685,602,4,0.066631494008342312396209194958
88,88_0,COMPLETED,BoTorch,0.279656349818261928952267680870,394,4,0.022564883309090816299047332905
89,89_0,COMPLETED,BoTorch,0.279656349818261928952267680870,743,4,0.031321141441351799117320808818
90,90_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,1,0.044668509970616304682877739651
91,91_0,COMPLETED,BoTorch,0.279656349818261928952267680870,777,1,0.025738610597478055186382306374
92,92_0,COMPLETED,BoTorch,0.279656349818261928952267680870,436,4,0.014786870176411103308677930102
93,93_0,COMPLETED,BoTorch,0.298876528252010165154217702366,124,1,0.045555649496423641919840008541
94,94_0,COMPLETED,BoTorch,0.279656349818261928952267680870,315,1,0.002000000000000000041633363423
95,95_0,COMPLETED,BoTorch,0.279656349818261928952267680870,469,4,0.017928754746189365376629609727
96,96_0,COMPLETED,BoTorch,0.279656349818261928952267680870,390,4,0.022014935013095503213431669565
97,97_0,COMPLETED,BoTorch,0.279656349818261928952267680870,732,4,0.032482221346659220784225396983
98,98_0,COMPLETED,BoTorch,0.279656349818261928952267680870,352,4,0.002000000000000000041633363423
99,99_0,COMPLETED,BoTorch,0.285989646436832223308499578707,935,1,0.018447054211126710787649329859
100,100_0,COMPLETED,BoTorch,0.279656349818261928952267680870,405,1,0.009624654229520763354388535049
101,101_0,COMPLETED,BoTorch,0.279656349818261928952267680870,508,1,0.017222582755166380985567542439
102,102_0,RUNNING,BoTorch,,770,1,0.018401945179709160160808067985
103,103_0,COMPLETED,BoTorch,0.279656349818261928952267680870,347,1,0.005531533048438877844810157569
104,104_0,COMPLETED,BoTorch,0.298325806806917115387989269948,136,1,0.048177471331925778019744655012
105,105_0,COMPLETED,BoTorch,0.279656349818261928952267680870,347,1,0.024839593095059217531250794764
106,106_0,COMPLETED,BoTorch,0.279656349818261928952267680870,339,4,0.020378378132624809515593966580
107,107_0,COMPLETED,BoTorch,0.279656349818261928952267680870,244,1,0.002000000000000000041633363423
108,108_0,COMPLETED,BoTorch,0.279656349818261928952267680870,442,1,0.002000000000000000041633363423
109,109_0,COMPLETED,BoTorch,0.279656349818261928952267680870,786,4,0.018249045682134278079100653258
110,110_0,COMPLETED,BoTorch,0.279656349818261928952267680870,455,1,0.010534204746145327952211445677
111,111_0,COMPLETED,BoTorch,0.279656349818261928952267680870,740,1,0.011592416774931056308584054193
112,112_0,COMPLETED,BoTorch,0.279656349818261928952267680870,395,1,0.002000000000000000041633363423
113,113_0,COMPLETED,BoTorch,0.279656349818261928952267680870,393,2,0.011260027405519743362583007240
114,114_0,COMPLETED,BoTorch,0.279656349818261928952267680870,787,1,0.015008084147379682771195064106
115,115_0,COMPLETED,BoTorch,0.279656349818261928952267680870,367,3,0.002000000000000000041633363423
116,116_0,COMPLETED,BoTorch,0.279656349818261928952267680870,921,1,0.013538210860071155094974670874
117,117_0,COMPLETED,BoTorch,0.279656349818261928952267680870,268,1,0.002000000000000000041633363423
118,118_0,COMPLETED,BoTorch,0.279656349818261928952267680870,301,1,0.016684095938235818379347108475
119,119_0,COMPLETED,BoTorch,0.279656349818261928952267680870,764,4,0.020259807580286386119450270371
120,120_0,COMPLETED,BoTorch,0.279656349818261928952267680870,259,4,0.002000000000000000041633363423
121,121_0,COMPLETED,BoTorch,0.279656349818261928952267680870,661,4,0.002000000000000000041633363423
122,122_0,COMPLETED,BoTorch,0.287311377905055675974210771528,719,4,0.052234761075212639280795912100
123,123_0,COMPLETED,BoTorch,0.279656349818261928952267680870,751,1,0.023026475226434632570526872541
124,124_0,COMPLETED,BoTorch,0.279656349818261928952267680870,726,1,0.002000000000000000041633363423
125,125_0,COMPLETED,BoTorch,0.286485295737415990302565660386,386,1,0.042620173539675475227195278194
126,126_0,COMPLETED,BoTorch,0.288027315783676574056926256162,710,4,0.070000000000000006661338147751
127,127_0,COMPLETED,BoTorch,0.279656349818261928952267680870,933,1,0.002000000000000000041633363423
128,128_0,COMPLETED,BoTorch,0.279656349818261928952267680870,940,4,0.002000000000000000041633363423
129,129_0,COMPLETED,BoTorch,0.279656349818261928952267680870,280,1,0.010334696655185332189730296193
130,130_0,COMPLETED,BoTorch,0.279656349818261928952267680870,848,4,0.002000000000000000041633363423
131,131_0,COMPLETED,BoTorch,0.287641810772111483629487338476,720,1,0.047840402845470336723465720752
132,132_0,COMPLETED,BoTorch,0.279656349818261928952267680870,298,4,0.020911091314045553157807688649
133,133_0,COMPLETED,BoTorch,0.286099790725850899875126742700,712,1,0.066605470857860796241034506693
134,134_0,COMPLETED,BoTorch,0.279656349818261928952267680870,931,4,0.002000000000000000041633363423
135,135_0,COMPLETED,BoTorch,0.279656349818261928952267680870,318,4,0.009697794364962979141164822749
136,136_0,COMPLETED,BoTorch,0.279656349818261928952267680870,976,4,0.002000000000000000041633363423
137,137_0,COMPLETED,BoTorch,0.279656349818261928952267680870,796,1,0.002000000000000000041633363423
138,138_0,COMPLETED,BoTorch,0.289349047251900026722637448984,310,1,0.029962486804000244833279253953
139,139_0,COMPLETED,BoTorch,0.279656349818261928952267680870,479,1,0.021370115018313921850801051505
140,140_0,COMPLETED,BoTorch,0.279656349818261928952267680870,883,1,0.017790692117950694661754340586
141,141_0,COMPLETED,BoTorch,0.279656349818261928952267680870,928,4,0.007927621356924657511355825079
142,142_0,COMPLETED,BoTorch,0.279656349818261928952267680870,926,1,0.005954968514654762970494950025
143,143_0,COMPLETED,BoTorch,0.279656349818261928952267680870,800,4,0.002000000000000000041633363423
144,144_0,COMPLETED,BoTorch,0.279656349818261928952267680870,297,4,0.029001730161232343319088755607
145,145_0,COMPLETED,BoTorch,0.279656349818261928952267680870,538,1,0.010785515252928886895111837418
146,146_0,COMPLETED,BoTorch,0.279656349818261928952267680870,368,1,0.019885498621174835243152756448
147,147_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,4,0.031790444402039597093789780047
148,148_0,COMPLETED,BoTorch,0.279656349818261928952267680870,975,1,0.002000000000000000041633363423
149,149_0,COMPLETED,BoTorch,0.279656349818261928952267680870,661,1,0.007718166204744521946556901071
150,150_0,COMPLETED,BoTorch,0.279656349818261928952267680870,291,4,0.012369767412565936537394151173
151,151_0,COMPLETED,BoTorch,0.279656349818261928952267680870,469,1,0.022141249336263588154416481757
152,152_0,COMPLETED,BoTorch,0.279656349818261928952267680870,558,4,0.010358608038920066873878056413
153,153_0,COMPLETED,BoTorch,0.288578037228769734845457151096,528,1,0.070000000000000006661338147751
154,154_0,COMPLETED,BoTorch,0.290505562286595409027256664558,291,1,0.034342157273375494008771369181
155,155_0,COMPLETED,BoTorch,0.279656349818261928952267680870,280,4,0.029324892674720388907605439499
156,156_0,COMPLETED,BoTorch,0.279656349818261928952267680870,276,4,0.034565433051895001759223902127
157,157_0,COMPLETED,BoTorch,0.279656349818261928952267680870,355,1,0.017826197918220841598380133064
158,158_0,COMPLETED,BoTorch,0.279656349818261928952267680870,281,4,0.027434853385193538888309205959
159,159_0,COMPLETED,BoTorch,0.279656349818261928952267680870,358,4,0.022510636274552207891641586457
160,160_0,COMPLETED,BoTorch,0.279656349818261928952267680870,501,1,0.002000000000000000041633363423
161,161_0,COMPLETED,BoTorch,0.279656349818261928952267680870,301,1,0.008449991812437012209113795791
162,162_0,COMPLETED,BoTorch,0.279656349818261928952267680870,370,1,0.023534979612626648337592172311
163,163_0,COMPLETED,BoTorch,0.279656349818261928952267680870,340,1,0.014963198545027395214290777403
164,164_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,2,0.013080975658625367680221174282
165,165_0,COMPLETED,BoTorch,0.279656349818261928952267680870,672,4,0.013583661514192298461201069415
166,166_0,COMPLETED,BoTorch,0.279656349818261928952267680870,822,1,0.020808366239038922351767268992
167,167_0,COMPLETED,BoTorch,0.279656349818261928952267680870,800,2,0.021992186894520412976383738624
168,168_0,COMPLETED,BoTorch,0.279656349818261928952267680870,303,2,0.006650509895926076144034855275
169,169_0,COMPLETED,BoTorch,0.279656349818261928952267680870,335,2,0.016071527829707239060086365612
170,170_0,COMPLETED,BoTorch,0.279656349818261928952267680870,829,4,0.009290259496907964792411505073
171,171_0,COMPLETED,BoTorch,0.279656349818261928952267680870,776,3,0.024777253092814401080890007734
172,172_0,COMPLETED,BoTorch,0.279656349818261928952267680870,677,1,0.015449072034617736953743261097
173,173_0,COMPLETED,BoTorch,0.279656349818261928952267680870,808,2,0.020377348860620120296616164524
174,174_0,COMPLETED,BoTorch,0.279656349818261928952267680870,341,4,0.028938127515462146177593893981
175,175_0,COMPLETED,BoTorch,0.279656349818261928952267680870,511,1,0.009517547642029748275471767727
176,176_0,COMPLETED,BoTorch,0.279656349818261928952267680870,659,1,0.002000000000000000041633363423
177,177_0,COMPLETED,BoTorch,0.279656349818261928952267680870,641,4,0.007993090707909093761696084357
178,178_0,COMPLETED,BoTorch,0.279656349818261928952267680870,584,4,0.008220707151701993689596470460
179,179_0,COMPLETED,BoTorch,0.279656349818261928952267680870,214,4,0.002000000000000000041633363423
180,180_0,COMPLETED,BoTorch,0.277012886881815134643147757743,100,1,0.070000000000000006661338147751
181,181_0,COMPLETED,BoTorch,0.279656349818261928952267680870,719,1,0.027008783000060904155859020648
182,182_0,COMPLETED,BoTorch,0.279656349818261928952267680870,393,4,0.002000000000000000041633363423
183,183_0,COMPLETED,BoTorch,0.278334618350038587308858950564,100,1,0.065880080134995563923006045570
184,184_0,COMPLETED,BoTorch,0.279656349818261928952267680870,421,4,0.007729875535686713504401890162
185,185_0,COMPLETED,BoTorch,0.277563608326908295431678652676,100,1,0.058196308311339255137095705095
186,186_0,COMPLETED,BoTorch,0.289349047251900026722637448984,100,4,0.040250449091722012573413280734
187,187_0,COMPLETED,BoTorch,0.279656349818261928952267680870,725,2,0.016970866054567039504785697090
188,188_0,COMPLETED,BoTorch,0.279215772662187444730363949930,100,2,0.070000000000000006661338147751
189,189_0,COMPLETED,BoTorch,0.287256305760546282179745958274,599,1,0.017356159258756553853686455113
190,190_0,COMPLETED,BoTorch,0.280923009141975987823514060437,100,1,0.037850096018771262063040694557
191,191_0,COMPLETED,BoTorch,0.279656349818261928952267680870,1000,2,0.013841014359317874507504519954
192,192_0,COMPLETED,BoTorch,0.279656349818261928952267680870,318,4,0.039189737697284170170330952487
193,193_0,COMPLETED,BoTorch,0.279656349818261928952267680870,836,4,0.023291464365725739193990762033
194,194_0,COMPLETED,BoTorch,0.287256305760546282179745958274,946,1,0.070000000000000006661338147751
195,195_0,COMPLETED,BoTorch,0.289734552263465117150076366670,407,1,0.070000000000000006661338147751
196,196_0,COMPLETED,BoTorch,0.287807027205639442968276853207,302,1,0.070000000000000006661338147751
197,197_0,COMPLETED,BoTorch,0.286209935014869465419451444177,424,4,0.070000000000000006661338147751
198,198_0,COMPLETED,BoTorch,0.291772221610309467898503044125,942,4,0.070000000000000006661338147751
199,199_0,COMPLETED,BoTorch,0.279656349818261928952267680870,708,1,0.017516417260499950875329844280
200,200_0,COMPLETED,BoTorch,0.286705584315453232413517525856,401,1,0.063993137061508484353389292210
201,201_0,COMPLETED,BoTorch,0.289569335829937268833589314454,417,1,0.070000000000000006661338147751
202,202_0,COMPLETED,BoTorch,0.287586738627602200857324987737,972,1,0.070000000000000006661338147751
203,203_0,COMPLETED,BoTorch,0.287421522194074241518535473006,941,1,0.051628085262174827629877427171
204,204_0,COMPLETED,BoTorch,0.291662077321290902354178342648,1000,1,0.070000000000000006661338147751
205,205_0,COMPLETED,BoTorch,0.291607005176781619582015991909,965,3,0.064777525102117622379793715481
206,206_0,COMPLETED,BoTorch,0.290009912986011642033190582879,553,1,0.051797703559754625091127167025
207,207_0,COMPLETED,BoTorch,0.279656349818261928952267680870,345,4,0.039189627227657293506712932185
208,208_0,COMPLETED,BoTorch,0.291662077321290902354178342648,949,2,0.059281937100214045222035252891
209,209_0,COMPLETED,BoTorch,0.286485295737415990302565660386,535,4,0.052965435526424409218293476442
210,210_0,COMPLETED,BoTorch,0.287531666483092807062860174483,958,1,0.062468359480480323275841669783
211,211_0,COMPLETED,BoTorch,0.279656349818261928952267680870,698,4,0.021280322992214571742319861869
212,212_0,COMPLETED,BoTorch,0.289954840841502359261028232140,1000,4,0.052367305459067987893906348518
213,213_0,COMPLETED,BoTorch,0.279656349818261928952267680870,435,4,0.031661795191177191655107492352
214,214_0,COMPLETED,BoTorch,0.286760656459962515185679876595,385,4,0.053629996105913492476791049057
215,215_0,COMPLETED,BoTorch,0.291551933032272225787551178655,955,1,0.045211237296492380599577387557
216,216_0,COMPLETED,BoTorch,0.289679480118955834377914015931,548,4,0.052837943262066665306431190174
217,217_0,COMPLETED,BoTorch,0.286925872893490474524469391326,532,3,0.052601314963315437545077202230
218,218_0,COMPLETED,BoTorch,0.290560634431104691799419015297,654,1,0.070000000000000006661338147751
219,219_0,COMPLETED,BoTorch,0.291662077321290902354178342648,975,2,0.062563008611736886654952627396
220,220_0,COMPLETED,BoTorch,0.287972243639167291284763905423,898,4,0.031020764958533036947940786376
221,221_0,COMPLETED,BoTorch,0.279656349818261928952267680870,466,2,0.016322954893983540602953041798
222,222_0,COMPLETED,BoTorch,0.288192532217204533395715770894,1000,1,0.033455407544136492314912345591
223,223_0,COMPLETED,BoTorch,0.278004185482982668631279921101,100,1,0.061251386274439628687460412948
224,224_0,COMPLETED,BoTorch,0.279656349818261928952267680870,497,3,0.014459155919965683070871342863
225,225_0,COMPLETED,BoTorch,0.279656349818261928952267680870,930,4,0.015203880409340533169659792634
226,226_0,COMPLETED,BoTorch,0.279656349818261928952267680870,268,3,0.002000000000000000041633363423
227,227_0,COMPLETED,BoTorch,0.279656349818261928952267680870,725,4,0.027403669799318353117456581458
228,228_0,COMPLETED,BoTorch,0.279656349818261928952267680870,396,4,0.036118206688741952281596070407
229,229_0,COMPLETED,BoTorch,0.290780923009141933910370880767,1000,1,0.038560592764635741314105388255
230,230_0,COMPLETED,BoTorch,0.279656349818261928952267680870,418,2,0.026822622365337785155237071422
231,231_0,COMPLETED,BoTorch,0.306256195616257276270744114299,100,1,0.002000000000000000041633363423
232,232_0,COMPLETED,BoTorch,0.290230201564048884144142448349,839,1,0.028108647342234061028420910588
233,233_0,COMPLETED,BoTorch,0.279656349818261928952267680870,721,3,0.026174895459617035509136684368
234,234_0,COMPLETED,BoTorch,0.284722987113118164437253199139,152,1,0.002000000000000000041633363423
235,235_0,COMPLETED,BoTorch,0.279656349818261928952267680870,591,1,0.005487033887094552864205354581
236,236_0,COMPLETED,BoTorch,0.279656349818261928952267680870,410,4,0.041551491213006781777483666929
237,237_0,COMPLETED,BoTorch,0.279656349818261928952267680870,915,4,0.018599073912435294675393748776
238,238_0,COMPLETED,BoTorch,0.290175129419539601371980097611,380,4,0.043498777629223828178339772421
239,239_0,COMPLETED,BoTorch,0.279656349818261928952267680870,447,1,0.015765749668194037336199642141
240,240_0,COMPLETED,BoTorch,0.279656349818261928952267680870,970,4,0.012646866267227586944077444286
241,241_0,COMPLETED,BoTorch,0.298986672541028730698542403843,196,1,0.002000000000000000041633363423
242,242_0,COMPLETED,BoTorch,0.279656349818261928952267680870,321,4,0.002000000000000000041633363423
243,243_0,COMPLETED,BoTorch,0.279656349818261928952267680870,761,1,0.002000000000000000041633363423
244,244_0,COMPLETED,BoTorch,0.279656349818261928952267680870,905,4,0.015278432055362490410432840804
245,245_0,COMPLETED,BoTorch,0.279656349818261928952267680870,702,4,0.013424233069170999482766504229
246,246_0,COMPLETED,BoTorch,0.279656349818261928952267680870,411,4,0.030967336097893281843251855889
247,247_0,COMPLETED,BoTorch,0.279656349818261928952267680870,746,1,0.031893278353902460020563580656
248,248_0,COMPLETED,BoTorch,0.279656349818261928952267680870,468,4,0.002000000000000000041633363423
249,249_0,COMPLETED,BoTorch,0.285824430003304374992012526491,488,4,0.070000000000000006661338147751
250,250_0,COMPLETED,BoTorch,0.279656349818261928952267680870,899,1,0.002000000000000000041633363423
251,251_0,COMPLETED,BoTorch,0.279656349818261928952267680870,231,4,0.002000000000000000041633363423
252,252_0,COMPLETED,BoTorch,0.279656349818261928952267680870,681,2,0.007943685146221016873946396686
253,253_0,COMPLETED,BoTorch,0.279656349818261928952267680870,412,4,0.033681604174761757553024921208
254,254_0,COMPLETED,BoTorch,0.286485295737415990302565660386,114,1,0.070000000000000006661338147751
255,255_0,COMPLETED,BoTorch,0.279656349818261928952267680870,846,4,0.016862048559722120355530705638
256,256_0,COMPLETED,BoTorch,0.279656349818261928952267680870,977,4,0.014678437456792162163488058013
257,257_0,COMPLETED,BoTorch,0.279656349818261928952267680870,982,3,0.014924285841649183315116289350
258,258_0,COMPLETED,BoTorch,0.290835995153651327704835694021,153,4,0.002000000000000000041633363423
259,259_0,COMPLETED,BoTorch,0.279656349818261928952267680870,813,1,0.013043192489121741700630607852
260,260_0,COMPLETED,BoTorch,0.279656349818261928952267680870,561,1,0.010069824451222298897223872416
261,261_0,COMPLETED,BoTorch,0.279656349818261928952267680870,397,1,0.027855161772148750998212562990
262,262_0,COMPLETED,BoTorch,0.279656349818261928952267680870,471,1,0.002000000000000000041633363423
263,263_0,COMPLETED,BoTorch,0.279656349818261928952267680870,231,4,0.018532477914857375944635009546
264,264_0,COMPLETED,BoTorch,0.288412820795241775506667636364,428,4,0.051558582873067575202963297443
265,265_0,COMPLETED,BoTorch,0.286815728604471908980144689849,494,4,0.042473025077286764850104106017
266,266_0,COMPLETED,BoTorch,0.293314241656570051652863639902,165,4,0.002263560639133777117187884542
267,267_0,COMPLETED,BoTorch,0.286980945037999757296631742065,414,1,0.036275526718156272942827911265
268,268_0,COMPLETED,BoTorch,0.279656349818261928952267680870,777,4,0.002000000000000000041633363423
269,269_0,COMPLETED,BoTorch,0.289679480118955834377914015931,493,4,0.036553479894524239235398255232
270,270_0,COMPLETED,BoTorch,0.279656349818261928952267680870,258,4,0.017487905956549910230224398333
271,271_0,COMPLETED,BoTorch,0.279656349818261928952267680870,256,2,0.008337713539213500307800686073
272,272_0,COMPLETED,BoTorch,0.279656349818261928952267680870,720,4,0.002000000000000000041633363423
273,273_0,COMPLETED,BoTorch,0.279656349818261928952267680870,819,4,0.021115428025871157968751390399
274,274_0,COMPLETED,BoTorch,0.288357748650732492734505285625,257,4,0.035175846239309076224355266049
275,275_0,COMPLETED,BoTorch,0.279656349818261928952267680870,506,4,0.002000000000000000041633363423
276,276_0,COMPLETED,BoTorch,0.289128758673862784611685583513,260,4,0.041582170082006854328060541093
277,277_0,COMPLETED,BoTorch,0.290175129419539601371980097611,496,2,0.032019682938860247189971630632
278,278_0,COMPLETED,BoTorch,0.279656349818261928952267680870,630,4,0.002000000000000000041633363423
279,279_0,COMPLETED,BoTorch,0.279656349818261928952267680870,496,4,0.026502830116992739939441037222
280,280_0,COMPLETED,BoTorch,0.279656349818261928952267680870,456,4,0.029235090245046398138573096048
281,281_0,COMPLETED,BoTorch,0.287256305760546282179745958274,500,1,0.031965500401775662209225004062
282,282_0,COMPLETED,BoTorch,0.279656349818261928952267680870,394,2,0.023718795280982640782951875735
283,283_0,COMPLETED,BoTorch,0.279656349818261928952267680870,222,4,0.010333800755729595846621471367
284,284_0,COMPLETED,BoTorch,0.276296949003194236560432273109,163,2,0.014687421998613010842849035953
285,285_0,COMPLETED,BoTorch,0.279656349818261928952267680870,861,3,0.008080499292671143932165200852
286,286_0,COMPLETED,BoTorch,0.279656349818261928952267680870,427,2,0.012793358068905474542176214925
287,287_0,COMPLETED,BoTorch,0.279656349818261928952267680870,377,3,0.023634507327941652043978848496
288,288_0,COMPLETED,BoTorch,0.279656349818261928952267680870,850,2,0.002000000000000000041633363423
289,289_0,COMPLETED,BoTorch,0.279656349818261928952267680870,530,4,0.002000000000000000041633363423
290,290_0,COMPLETED,BoTorch,0.279656349818261928952267680870,453,3,0.002000000000000000041633363423
291,291_0,COMPLETED,BoTorch,0.279656349818261928952267680870,371,1,0.002000000000000000041633363423
292,292_0,COMPLETED,BoTorch,0.279215772662187444730363949930,161,2,0.012049453161752599061884438925
293,293_0,COMPLETED,BoTorch,0.276352021147703519332594623847,169,3,0.018729565588715628432492366073
294,294_0,COMPLETED,BoTorch,0.283180967066857580682892603363,194,4,0.027204501072598905042632111417
295,295_0,RUNNING,BoTorch,,157,2,0.014934132173623071784818172603
296,296_0,COMPLETED,BoTorch,0.279656349818261928952267680870,494,4,0.020111942237097439611925864256
297,297_0,RUNNING,BoTorch,,169,2,0.020425349644735919796278977856
298,298_0,COMPLETED,BoTorch,0.279105628373168879186039248452,162,1,0.016211551154121518758532971560
299,299_0,RUNNING,BoTorch,,166,2,0.012731138476481647023308418909
300,300_0,RUNNING,BoTorch,,190,4,0.034003512256229599663992502201
301,301_0,RUNNING,BoTorch,,172,3,0.025686632023076971798047907214
302,302_0,COMPLETED,BoTorch,0.277398391893380336092889137944,162,3,0.015042032898719734368420120063
303,303_0,COMPLETED,BoTorch,0.282520101332745854350037006952,160,2,0.019679515842868595953962795875
304,304_0,COMPLETED,BoTorch,0.278554906928075829419810816034,163,2,0.020271893784533930038538329654
305,305_0,COMPLETED,BoTorch,0.274534640378896299672817349347,172,3,0.016207992029838219627535522704
306,306_0,COMPLETED,BoTorch,0.283346183500385540021682118095,157,2,0.012194797287608040090023386881
307,307_0,COMPLETED,BoTorch,0.272496971032051948924390671891,175,3,0.019622216472985190560684998218
308,308_0,COMPLETED,BoTorch,0.286209935014869465419451444177,155,1,0.017049555308496236460946704483
309,309_0,COMPLETED,BoTorch,0.275250578257517308777835296496,167,3,0.014701459368054167059503001269
310,310_0,COMPLETED,BoTorch,0.287311377905055675974210771528,156,2,0.016630638911938444568505701682
311,311_0,COMPLETED,BoTorch,0.275140433968498743233510595019,172,2,0.015906752122015803407872880371
312,312_0,COMPLETED,BoTorch,0.276407093292212802104756974586,177,4,0.017237012279672562359289500478
313,313_0,COMPLETED,BoTorch,0.281583874876087714156369656848,184,4,0.016400623882014780247073915120
314,314_0,COMPLETED,BoTorch,0.273928846789293967134426566190,173,3,0.015837626389557143968822572333
315,315_0,COMPLETED,BoTorch,0.275030289679480066666883431026,172,4,0.019839744872604335446109047325
316,316_0,COMPLETED,BoTorch,0.274920145390461501122558729548,172,2,0.015476044820001089163952201488
317,317_0,COMPLETED,BoTorch,0.276572309725740761443546489318,173,4,0.017827523612431749044926476699
318,318_0,COMPLETED,BoTorch,0.269523075228549346959994181816,175,3,0.016564200466351375329310258167
319,319_0,COMPLETED,BoTorch,0.275250578257517308777835296496,172,2,0.015847902031847729831248727805
320,320_0,COMPLETED,BoTorch,0.273928846789293967134426566190,174,2,0.018209143754194426212400514942
321,321_0,COMPLETED,BoTorch,0.271450600286375132164096157794,177,1,0.028276298255084313104656956739
322,322_0,COMPLETED,BoTorch,0.272166538164996141269114104944,176,2,0.020677078609971234757214375577
323,323_0,COMPLETED,BoTorch,0.274589712523405693467282162601,175,1,0.021164917523141577038181537773
324,324_0,COMPLETED,BoTorch,0.270624518118735557514753509167,177,3,0.018251582723653389517259881814
325,325_0,COMPLETED,BoTorch,0.275470866835554550888787161966,174,3,0.018065821481402288828377322716
326,326_0,COMPLETED,BoTorch,0.272882476043617150374132052093,176,4,0.020259699135012319837212402263
327,327_0,RUNNING,BoTorch,,177,2,0.025295883900707812064201363000
328,328_0,COMPLETED,BoTorch,0.271891177442449616385999888735,180,1,0.033884766266700672154854601104
329,329_0,COMPLETED,BoTorch,0.270073796673642507748525076750,177,2,0.028367354288964641284565004753
330,330_0,COMPLETED,BoTorch,0.273102764621654392485083917563,176,2,0.023017978032247504716067965091
331,331_0,COMPLETED,BoTorch,0.271505672430884414936258508533,180,1,0.037825489079875380948880803089
332,332_0,COMPLETED,BoTorch,0.273598413922238159479149999243,179,1,0.026220326408885372138080072091
333,333_0,COMPLETED,BoTorch,0.272827403899107867601969701354,177,1,0.034636268648940392311796898639
334,334_0,COMPLETED,BoTorch,0.271505672430884414936258508533,176,2,0.031473258038310414352878296995
335,335_0,COMPLETED,BoTorch,0.272992620332635715918456753570,181,1,0.030914205550510144571152437720
336,336_0,COMPLETED,BoTorch,0.272772331754598473807504888100,179,2,0.026250558375380747544447501696
337,337_0,COMPLETED,BoTorch,0.267870910893270197661308884562,177,3,0.039162825869852875559917748660
338,338_0,COMPLETED,BoTorch,0.270128868818151790520687427488,176,2,0.030613623352180409364908797443
339,339_0,COMPLETED,BoTorch,0.266163674413481654568158774055,188,1,0.033074172182963122723631244071
340,340_0,COMPLETED,BoTorch,0.271450600286375132164096157794,179,3,0.043645293867668875920085014286
341,341_0,COMPLETED,BoTorch,0.269963652384623831181897912757,177,3,0.035817874257732647269580894545
342,342_0,COMPLETED,BoTorch,0.271781033153430939819372724742,177,4,0.041512394191101815998923285633
343,343_0,COMPLETED,BoTorch,0.270128868818151790520687427488,179,3,0.038820016123572761990168089596
344,344_0,COMPLETED,BoTorch,0.273488269633219482912522835250,175,3,0.038244435168286169957063691527
345,345_0,COMPLETED,BoTorch,0.272772331754598473807504888100,176,2,0.042108038395846147938783587961
346,346_0,COMPLETED,BoTorch,0.271836105297940333613837537996,178,2,0.048519390783643936981839317468
347,347_0,COMPLETED,BoTorch,0.271891177442449616385999888735,177,1,0.036747981697240923726344163924
348,348_0,COMPLETED,BoTorch,0.271891177442449616385999888735,181,4,0.041037854567496014068783694029
349,349_0,COMPLETED,BoTorch,0.270844806696772799625705374638,176,4,0.037500582309701518957112398311
350,350_0,COMPLETED,BoTorch,0.270844806696772799625705374638,174,3,0.041222575107731286581103091748
351,351_0,COMPLETED,BoTorch,0.272056393875977575724789403466,186,2,0.038172004739016676677643147286
352,352_0,COMPLETED,BoTorch,0.266494107280537462223435341002,190,1,0.038807012985110425629109442980
353,353_0,RUNNING,BoTorch,,184,2,0.035599144497784888929725610751
354,354_0,COMPLETED,BoTorch,0.267044828725630623011966235936,190,1,0.042076218014404302703024285393
355,355_0,COMPLETED,BoTorch,0.266769468003083987106549557211,189,1,0.036871340923637870023998885927
356,356_0,COMPLETED,BoTorch,0.268366560193853964655374966242,188,2,0.041280186831874600417169318689
357,357_0,COMPLETED,BoTorch,0.269357858795021498643507129600,186,1,0.041327302688865752422486821160
358,358_0,COMPLETED,BoTorch,0.268696993060909772310651533189,184,2,0.038624303953074354267549495034
359,359_0,COMPLETED,BoTorch,0.271450600286375132164096157794,192,2,0.040771019581129858133650145646
360,360_0,COMPLETED,BoTorch,0.267981055182288763205633586040,188,1,0.037830028897096461271143397198
361,361_0,COMPLETED,BoTorch,0.267320189448177147895080452145,185,2,0.040762546566350137122736185802
362,362_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039316006248675899881206419195
363,363_0,COMPLETED,BoTorch,0.268421632338363247427537316980,191,1,0.039473029911967785765547489518
364,364_0,COMPLETED,BoTorch,0.279656349818261928952267680870,226,3,0.013517825051851398848734575608
365,365_0,COMPLETED,BoTorch,0.291386716598744377471064126439,150,3,0.052604891809975497241058661757
366,366_0,COMPLETED,BoTorch,0.274038991078312643701053730183,183,4,0.048333819158358604095582222726
367,367_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039253005381964370390512897302
368,368_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039209161085509577904240074986
369,369_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039228314530328739495601553244
370,370_0,COMPLETED,BoTorch,0.268586848771891206766326831712,191,1,0.039332138420068385453731707457
371,371_0,COMPLETED,BoTorch,0.268421632338363247427537316980,191,1,0.039501823867151647506901213092
372,372_0,COMPLETED,BoTorch,0.268146271615816722544423100771,191,1,0.039596870645950917044775252407
373,373_0,COMPLETED,BoTorch,0.277563608326908295431678652676,165,3,0.031869653680882230328830218014
374,374_0,COMPLETED,BoTorch,0.265888313690935129685044557846,190,1,0.039643137892465346583392715729
375,375_0,RUNNING,BoTorch,,182,2,0.034089522559145379876355264059
376,376_0,COMPLETED,BoTorch,0.268146271615816722544423100771,188,1,0.039578162462622641648568588835
377,377_0,COMPLETED,BoTorch,0.265723097257407170346255043114,190,1,0.039303960179088770487965120992
378,378_0,COMPLETED,BoTorch,0.271395528141865849391933807055,178,2,0.026426640670929216203610678804
379,379_0,COMPLETED,BoTorch,0.270789734552263516853543023899,203,1,0.040621143173338869480915036547
380,380_0,COMPLETED,BoTorch,0.289404119396409309494799799722,317,3,0.055177293913886496512510859702
381,381_0,COMPLETED,BoTorch,0.276682454014759326987871190795,182,1,0.002000000000000000041633363423
382,382_0,COMPLETED,BoTorch,0.268201343760326005316585451510,192,1,0.040314579076765236220758481522
383,383_0,COMPLETED,BoTorch,0.268091199471307439772260750033,191,1,0.040182380832322776853526846708
384,384_0,COMPLETED,BoTorch,0.268311488049344681883212615503,191,1,0.040433881525531054079714010641
385,385_0,COMPLETED,BoTorch,0.273543341777728876706987648504,172,1,0.023187126721641970750198424867
386,386_0,COMPLETED,BoTorch,0.268036127326798157000098399294,191,1,0.040237728372775470597755287372
387,387_0,COMPLETED,BoTorch,0.267320189448177147895080452145,184,2,0.039598244445817748493610110927
388,388_0,COMPLETED,BoTorch,0.269633219517568023526621345809,176,3,0.029115120820980285032408829693
389,389_0,COMPLETED,BoTorch,0.267981055182288763205633586040,191,1,0.039954511609486299816840215726
390,390_0,COMPLETED,BoTorch,0.267981055182288763205633586040,191,1,0.040016054236621460826750507067
391,391_0,COMPLETED,BoTorch,0.297004075338693662722278077126,129,1,0.047237095065400726279225551707
392,392_0,COMPLETED,BoTorch,0.268091199471307439772260750033,191,1,0.040231524648860914061820892584
393,393_0,COMPLETED,BoTorch,0.272992620332635715918456753570,172,4,0.027347323193552340592216154391
394,394_0,COMPLETED,BoTorch,0.268036127326798157000098399294,191,1,0.039847850063241686824344611750
395,395_0,COMPLETED,BoTorch,0.271010023130300647942192426854,181,2,0.032820189318223273733376998962
396,396_0,COMPLETED,BoTorch,0.270844806696772799625705374638,181,3,0.035258429604458581263504157732
397,397_0,COMPLETED,BoTorch,0.272001321731468181930324590212,169,2,0.020425909651223969620836840022
398,398_0,COMPLETED,BoTorch,0.279656349818261928952267680870,285,4,0.031956100403417744826306545747
399,399_0,COMPLETED,BoTorch,0.266989756581121229217501422681,189,1,0.038429789046943288455260301362
400,400_0,COMPLETED,BoTorch,0.270073796673642507748525076750,172,4,0.045119261925883787089919252367
401,401_0,COMPLETED,BoTorch,0.295131622425377271312640914402,143,4,0.055176173235033487696199472339
402,402_0,COMPLETED,BoTorch,0.266824540147593380901014370465,182,1,0.064696512530347513592055008758
403,403_0,COMPLETED,BoTorch,0.270239013107170356065012128965,172,4,0.045090974188437424330366809500
404,404_0,COMPLETED,BoTorch,0.287862099350148725740439203946,161,1,0.052618324990871920276980233666
405,405_0,COMPLETED,BoTorch,0.271670888864412374275048023264,172,4,0.045254113619164197668265359198
406,406_0,COMPLETED,BoTorch,0.271065095274810041736657240108,172,4,0.048588218458016342593541736505
407,407_0,COMPLETED,BoTorch,0.269798435951095982865410860541,181,4,0.061938294572247233615858164057
408,408_0,COMPLETED,BoTorch,0.271781033153430939819372724742,175,3,0.056860824771946240352438195487
409,409_0,COMPLETED,BoTorch,0.269688291662077306298783696548,180,1,0.059420631317170014007444933668
410,410_0,COMPLETED,BoTorch,0.270459301685207598175963994436,189,1,0.060390205566937547876360525834
411,411_0,COMPLETED,BoTorch,0.269578147373058740754458995070,183,2,0.070000000000000006661338147751
412,412_0,COMPLETED,BoTorch,0.268531776627381923994164480973,186,1,0.068987470776403950201149939403
413,413_0,COMPLETED,BoTorch,0.269412930939530781415669480339,185,2,0.063411298505398727698434413469
414,414_0,COMPLETED,BoTorch,0.268366560193853964655374966242,191,1,0.052457616718961867785520070129
415,415_0,COMPLETED,BoTorch,0.267705694459742238322519369831,183,1,0.070000000000000006661338147751
416,416_0,COMPLETED,BoTorch,0.270459301685207598175963994436,179,2,0.070000000000000006661338147751
417,417_0,COMPLETED,BoTorch,0.267210045159158471328453288152,192,1,0.046474430434734120831663517492
418,418_0,COMPLETED,BoTorch,0.273157836766163675257246268302,188,1,0.066246124267845193922532587294
419,419_0,COMPLETED,BoTorch,0.269302786650512215871344778861,186,1,0.060229020366945267006553166311
420,420_0,COMPLETED,BoTorch,0.269082498072474973760392913391,186,2,0.064128385253653066855328290785
421,421_0,COMPLETED,BoTorch,0.268696993060909772310651533189,181,1,0.070000000000000006661338147751
422,422_0,COMPLETED,BoTorch,0.270844806696772799625705374638,175,4,0.070000000000000006661338147751
423,423_0,COMPLETED,BoTorch,0.268641920916400489538489182451,178,2,0.069770888753289989070438537055
424,424_0,COMPLETED,BoTorch,0.267154973014649188556290937413,191,1,0.043081364080792203252734395846
425,425_0,COMPLETED,BoTorch,0.270459301685207598175963994436,181,4,0.070000000000000006661338147751
426,426_0,COMPLETED,BoTorch,0.270679590263244840286915859906,180,4,0.060337352464674233465391495201
427,427_0,COMPLETED,BoTorch,0.286540367881925273074728011125,750,1,0.070000000000000006661338147751
428,428_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043853014050310640259411343322
429,429_0,COMPLETED,BoTorch,0.286705584315453232413517525856,736,1,0.056264391980682490479459545440
430,430_0,COMPLETED,BoTorch,0.269357858795021498643507129600,179,1,0.070000000000000006661338147751
431,431_0,COMPLETED,BoTorch,0.272441898887542666152228321153,181,2,0.066180236315984214412466712929
432,432_0,COMPLETED,BoTorch,0.268862209494437731649441047921,182,2,0.062872314691078762249887290636
433,433_0,COMPLETED,BoTorch,0.280592576274920180168237493490,106,1,0.065621580011945429555808573241
434,434_0,COMPLETED,BoTorch,0.270899878841282082397867725376,179,2,0.065395627863610930607940474601
435,435_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043796161093683720622049548865
436,436_0,COMPLETED,BoTorch,0.266549179425046856017900154256,191,1,0.043826545058890914452476295082
437,437_0,COMPLETED,BoTorch,0.271560744575393808730723321787,180,3,0.066286756798987933603228839274
438,438_0,COMPLETED,BoTorch,0.266053530124463089023834072577,191,1,0.044227020807766685706052811611
439,439_0,COMPLETED,BoTorch,0.266108602268972371795996423316,191,1,0.044327921544894545446968692204
440,440_0,COMPLETED,BoTorch,0.266383962991518896679110639525,191,1,0.044207122194990761121502487185
441,441_0,COMPLETED,BoTorch,0.266108602268972371795996423316,191,1,0.044352469749228157214204770753
442,442_0,COMPLETED,BoTorch,0.272001321731468181930324590212,183,2,0.012726157885068554997953782504
443,443_0,COMPLETED,BoTorch,0.265943385835444412457206908584,191,1,0.044437814631024703804218489722
444,444_0,COMPLETED,BoTorch,0.266383962991518896679110639525,191,1,0.044204709537704486854714502897
445,445_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045315042490568620270341426703
446,446_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045325735258808133587571376211
447,447_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045298232897425495802057326955
448,448_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045322054976998080166872284735
449,449_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045347144044141994567720388432
450,450_0,COMPLETED,BoTorch,0.272937548188126433146294402832,210,1,0.066247462692500078129675955552
451,451_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045335046847855962892648307161
452,452_0,COMPLETED,BoTorch,0.268752065205419055082813883928,191,1,0.045302927758385132250840143797
453,453_0,COMPLETED,BoTorch,0.269137570216984256532555264130,191,1,0.045606459780624587774244815819
454,454_0,COMPLETED,BoTorch,0.271065095274810041736657240108,186,2,0.052428746985923507795135378728
455,455_0,COMPLETED,BoTorch,0.268421632338363247427537316980,182,2,0.034049712631858443379062606482
456,456_0,COMPLETED,BoTorch,0.294140323824209737324508751044,155,1,0.070000000000000006661338147751
457,457_0,COMPLETED,BoTorch,0.266053530124463089023834072577,191,1,0.044587364811246553431001160561
458,458_0,COMPLETED,BoTorch,0.268972353783456297193765749398,182,2,0.049450938401479842265739961249
459,459_0,COMPLETED,BoTorch,0.268476704482872530199699667719,192,1,0.044400222471499092846691780778
460,460_0,COMPLETED,BoTorch,0.267705694459742238322519369831,193,1,0.044795347344806307754438989832
461,461_0,COMPLETED,BoTorch,0.266824540147593380901014370465,199,1,0.070000000000000006661338147751
462,456_0,COMPLETED,BoTorch,0.294140323824209737324508751044,155,1,0.070000000000000006661338147751
463,463_0,COMPLETED,BoTorch,0.268641920916400489538489182451,192,1,0.044078950725947974031981857479
464,464_0,COMPLETED,BoTorch,0.268807137349928448877278697182,218,1,0.060345959968902400383239381654
465,465_0,COMPLETED,BoTorch,0.275140433968498743233510595019,174,4,0.013537888485419233980389108751
466,466_0,COMPLETED,BoTorch,0.268752065205419055082813883928,218,1,0.060247723944732385625844273136
467,467_0,COMPLETED,BoTorch,0.295737416014979603851031697559,142,4,0.046643478410653048393808006722
468,468_0,COMPLETED,BoTorch,0.269302786650512215871344778861,195,3,0.070000000000000006661338147751
469,469_0,COMPLETED,BoTorch,0.284722987113118164437253199139,213,3,0.063488244955743775022050101597
470,470_0,COMPLETED,BoTorch,0.269192642361493539304717614868,200,1,0.057673942399209025821971152936
471,471_0,COMPLETED,BoTorch,0.268752065205419055082813883928,200,1,0.062831084226523004065967370479
472,472_0,COMPLETED,BoTorch,0.268146271615816722544423100771,199,1,0.053401405458704462314578620408
473,473_0,COMPLETED,BoTorch,0.280647648419429462940399844229,214,2,0.067631020846018463643645191041
474,474_0,COMPLETED,BoTorch,0.268201343760326005316585451510,199,1,0.063529220175546893822904337412
475,475_0,COMPLETED,BoTorch,0.269688291662077306298783696548,209,1,0.055128036151389732399952237074
476,476_0,COMPLETED,BoTorch,0.267815838748760914889146533824,212,1,0.055168137353005165801622666777
477,477_0,COMPLETED,BoTorch,0.267815838748760914889146533824,212,1,0.055160849876058591634286898397
478,478_0,COMPLETED,BoTorch,0.266879612292102663673176721204,222,1,0.067761168909431152007627474632
479,479_0,COMPLETED,BoTorch,0.267925983037779480433471235301,212,1,0.054653163399373531905478529325
480,480_0,COMPLETED,BoTorch,0.268752065205419055082813883928,213,1,0.054899910524051770410736139638
481,481_0,COMPLETED,BoTorch,0.268752065205419055082813883928,213,1,0.054874339079555030374013568917
482,482_0,COMPLETED,BoTorch,0.269963652384623831181897912757,193,2,0.070000000000000006661338147751
483,483_0,COMPLETED,BoTorch,0.268091199471307439772260750033,214,1,0.055694005477570585060931307453
484,484_0,COMPLETED,BoTorch,0.266659323714065421562224855734,198,1,0.070000000000000006661338147751
485,485_0,COMPLETED,BoTorch,0.286044718581341506080661929445,344,4,0.059530899242833869300373095257
486,486_0,COMPLETED,BoTorch,0.268091199471307439772260750033,214,1,0.055711880720697146140985012153
487,487_0,COMPLETED,BoTorch,0.269137570216984256532555264130,189,4,0.070000000000000006661338147751
488,488_0,COMPLETED,BoTorch,0.269137570216984256532555264130,220,1,0.063071873432338720899537065634
489,489_0,COMPLETED,BoTorch,0.269908580240114548409735562018,188,3,0.070000000000000006661338147751
490,490_0,COMPLETED,BoTorch,0.267485405881704996211567504361,189,3,0.065880375081519393365425685261
491,491_0,COMPLETED,BoTorch,0.268972353783456297193765749398,220,1,0.062976558202193402835611379942
492,492_0,COMPLETED,BoTorch,0.269578147373058740754458995070,200,1,0.070000000000000006661338147751
493,493_0,COMPLETED,BoTorch,0.269192642361493539304717614868,187,4,0.070000000000000006661338147751
494,494_0,COMPLETED,BoTorch,0.268862209494437731649441047921,220,1,0.062879306444814583865188239997
495,495_0,COMPLETED,BoTorch,0.265502808679369928235303177644,197,2,0.062247172405158542052294734503
496,496_0,COMPLETED,BoTorch,0.278114329772001345197907085094,208,3,0.068517613505915744842589276686
497,497_0,COMPLETED,BoTorch,0.268201343760326005316585451510,185,4,0.070000000000000006661338147751
498,498_0,COMPLETED,BoTorch,0.287641810772111483629487338476,902,1,0.070000000000000006661338147751
499,499_0,COMPLETED,BoTorch,0.274810001101442935578234028071,195,3,0.044594551928669907969737806752
500,500_0,COMPLETED,BoTorch,0.267981055182288763205633586040,188,1,0.042270254411103047598174953237
501,501_0,RUNNING,BoTorch,,193,1,0.044423205599560117162738492880
502,502_0,RUNNING,BoTorch,,885,1,0.064706386131606605238708596062
</pre>
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<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> CPU/RAM-Usage (main)</h1>
<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727382268,477.28125,49.7
1727382268,477.328125,48.6
1727382268,477.328125,49.6
1727382268,477.328125,55.3
1727382268,477.328125,47.2
1727382268,477.328125,50.4
1727382268,477.328125,39.4
1727382313,482.03125,49.8
1727382313,482.03125,55.6
1727382313,482.03125,49.9
1727382313,482.03125,40.6
1727382315,482.0390625,49.8
1727382315,482.0390625,54.3
1727382315,482.0390625,48.6
1727382315,482.0390625,39.4
1727382317,482.0390625,49.7
1727382317,482.0390625,39.4
1727382317,482.0390625,49.1
1727382317,482.0390625,58.1
1727382318,482.0390625,49.7
1727382318,482.0390625,53.2
1727382318,482.0390625,48.6
1727382318,482.0390625,55.6
1727382319,482.0390625,49.7
1727382319,482.0390625,55.3
1727382319,482.0390625,51.3
1727382319,482.0390625,43.2
1727382321,482.0390625,49.8
1727382321,482.0390625,55.1
1727382321,482.0390625,47.2
1727382321,482.0390625,55.6
1727382322,482.0390625,49.7
1727382322,482.0390625,36.4
1727382322,482.0390625,53.4
1727382322,482.0390625,40.6
1727382324,482.0390625,49.8
1727382324,482.0390625,49.1
1727382324,482.0390625,48.2
1727382324,482.0390625,52.5
1727382325,482.0390625,49.7
1727382325,482.0390625,54.3
1727382325,482.0390625,47.2
1727382325,482.0390625,59.6
1727382326,482.0390625,49.7
1727382326,482.0390625,54.3
1727382326,482.0390625,47.2
1727382326,482.0390625,58.1
1727382328,482.0390625,49.7
1727382328,482.0390625,51.0
1727382328,482.0390625,52.0
1727382328,482.0390625,50.0
1727382329,482.0390625,49.7
1727382329,482.0390625,39.4
1727382329,482.0390625,52.8
1727382329,482.0390625,53.2
1727382330,482.0390625,49.7
1727382330,482.0390625,46.2
1727382330,482.0390625,51.7
1727382330,482.0390625,37.5
1727382332,482.0390625,49.8
1727382332,482.0390625,54.3
1727382332,482.0390625,48.1
1727382332,482.0390625,42.4
1727382333,482.0390625,49.8
1727382333,482.0390625,54.2
1727382333,482.0390625,48.1
1727382333,482.0390625,42.4
1727382335,482.04296875,49.8
1727382335,482.04296875,52.2
1727382335,482.04296875,52.9
1727382335,482.04296875,40.6
1727382336,482.04296875,49.7
1727382336,482.04296875,54.3
1727382336,482.04296875,50.4
1727382336,482.04296875,48.7
1727382337,482.04296875,49.7
1727382337,482.04296875,44.2
1727382337,482.04296875,51.8
1727382337,482.04296875,51.3
1727382339,482.04296875,49.7
1727382339,482.04296875,56.5
1727382339,482.04296875,47.7
1727382339,482.04296875,55.6
1727382340,482.04296875,49.8
1727382340,482.04296875,52.1
1727382340,482.04296875,49.5
1727382340,482.04296875,56.5
1727382342,482.04296875,49.7
1727382342,482.04296875,54.3
1727382342,482.04296875,49.0
1727382342,482.04296875,51.3
1727382343,482.04296875,49.7
1727382343,482.04296875,56.3
1727382343,482.04296875,49.6
1727382343,482.04296875,44.1
1727382344,482.04296875,49.8
1727382344,482.04296875,45.9
1727382344,482.04296875,51.3
1727382344,482.04296875,40.6
1727382346,482.04296875,49.8
1727382346,482.04296875,36.4
1727382346,482.04296875,54.7
1727382346,482.04296875,42.4
1727382347,482.04296875,49.7
1727382347,482.04296875,56.5
1727382347,482.04296875,49.0
1727382347,482.04296875,50.0
1727382349,482.04296875,49.7
1727382349,482.04296875,55.3
1727382349,482.04296875,48.1
1727382349,482.04296875,57.4
1727382350,482.04296875,49.7
1727382350,482.04296875,37.5
1727382350,482.04296875,52.5
1727382350,482.04296875,40.6
1727382351,482.04296875,49.7
1727382351,482.04296875,39.4
1727382351,482.04296875,53.4
1727382351,482.04296875,40.6
1727382353,482.04296875,49.7
1727382353,482.04296875,54.3
1727382353,482.04296875,48.6
1727382353,482.04296875,56.8
1727382354,482.05078125,49.7
1727382354,482.05078125,55.6
1727382354,482.05078125,48.5
1727382354,482.05078125,44.4
1727382355,482.05078125,49.7
1727382355,482.05078125,53.2
1727382355,482.05078125,48.1
1727382356,482.05078125,56.5
1727382357,482.05078125,49.7
1727382357,482.05078125,54.3
1727382357,482.05078125,50.0
1727382357,482.05078125,40.6
1727382358,482.05078125,49.7
1727382358,482.05078125,54.3
1727382358,482.05078125,50.8
1727382358,482.05078125,40.6
1727382360,482.05078125,49.8
1727382360,482.05078125,53.2
1727382360,482.05078125,50.0
1727382360,482.05078125,40.6
1727382361,482.05078125,49.8
1727382361,482.05078125,54.3
1727382361,482.05078125,47.7
1727382361,482.05078125,55.6
1727382362,482.05078125,49.8
1727382362,482.05078125,54.3
1727382362,482.05078125,52.5
1727382362,482.05078125,40.6
1727382364,482.05078125,49.8
1727382364,482.05078125,39.4
1727382364,482.05078125,54.2
1727382364,482.05078125,38.7
1727382365,482.05078125,49.7
1727382365,482.05078125,53.2
1727382365,482.05078125,48.6
1727382365,482.05078125,55.6
1727382367,482.05078125,49.7
1727382367,482.05078125,52.3
1727382367,482.05078125,52.0
1727382367,482.05078125,39.4
1727382368,482.05078125,49.8
1727382368,482.05078125,37.5
1727382368,482.05078125,52.9
1727382368,482.05078125,41.2
1727382369,482.05078125,49.8
1727382369,482.05078125,38.2
1727382369,482.05078125,54.6
1727382369,482.05078125,37.5
1727382371,482.05078125,49.7
1727382371,482.05078125,53.2
1727382371,482.05078125,53.3
1727382371,482.05078125,41.9
1727382373,484.875,49.9
1727382373,484.875,55.6
1727382373,484.875,52.0
1727382373,484.875,40.6
1727382376,485.15234375,49.9
1727382376,485.15234375,53.2
1727382376,485.15234375,48.2
1727382376,485.15234375,53.5
1727382378,485.15234375,49.8
1727382378,485.15234375,54.2
1727382378,485.15234375,48.1
1727382378,485.15234375,56.8
1727382380,485.15234375,49.8
1727382380,485.15234375,55.3
1727382380,485.15234375,46.5
1727382380,485.15234375,52.4
1727382383,485.15234375,49.9
1727382383,485.15234375,54.3
1727382383,485.15234375,48.6
1727382383,485.15234375,54.2
1727382385,485.15234375,49.9
1727382385,485.15234375,54.3
1727382385,485.15234375,46.7
1727382385,485.15234375,58.7
1727382387,485.15234375,49.8
1727382387,485.15234375,53.3
1727382387,485.15234375,48.5
1727382387,485.15234375,57.8
1727382390,485.17578125,49.9
1727382390,485.17578125,56.5
1727382390,485.17578125,51.2
1727382390,485.17578125,40.6
1727382392,485.17578125,49.9
1727382392,485.17578125,35.3
1727382392,485.17578125,52.0
1727382392,485.17578125,41.9
1727382394,485.17578125,49.9
1727382394,485.17578125,55.3
1727382394,485.17578125,50.8
1727382394,485.17578125,38.7
1727382396,485.17578125,49.9
1727382396,485.17578125,42.1
1727382396,485.17578125,50.8
1727382396,485.17578125,55.3
1727382399,485.17578125,49.9
1727382399,485.17578125,55.6
1727382399,485.17578125,50.4
1727382399,485.17578125,41.9
1727382401,485.17578125,49.9
1727382401,485.17578125,37.1
1727382401,485.17578125,51.2
1727382401,485.17578125,57.4
1727382403,485.21875,49.8
1727382403,485.21875,34.3
1727382403,485.21875,52.1
1727382403,485.21875,40.6
1727382406,485.21875,49.9
1727382406,485.21875,57.8
1727382406,485.21875,47.2
1727382406,485.21875,55.6
1727382408,485.21875,49.8
1727382408,485.21875,53.1
1727382408,485.21875,47.3
1727382408,485.21875,56.8
1727382410,485.21875,49.8
1727382410,485.21875,55.6
1727382410,485.21875,47.6
1727382410,485.21875,58.7
1727382412,485.21875,49.8
1727382412,485.21875,55.3
1727382412,485.21875,53.2
1727382412,485.21875,37.5
1727382414,485.21875,49.9
1727382414,485.21875,54.2
1727382414,485.21875,48.7
1727382414,485.21875,48.7
1727382416,485.21875,49.9
1727382416,485.21875,38.2
1727382416,485.21875,50.8
1727382416,485.21875,54.3
1727382419,485.21875,49.9
1727382419,485.21875,54.3
1727382419,485.21875,48.8
1727382419,485.21875,44.1
1727382421,485.21875,49.9
1727382421,485.21875,54.2
1727382421,485.21875,51.7
1727382421,485.21875,35.3
1727382423,485.21875,49.9
1727382423,485.21875,48.8
1727382423,485.21875,52.0
1727382423,485.21875,40.0
1727382425,485.21875,49.9
1727382425,485.21875,40.0
1727382425,485.21875,50.0
1727382425,485.21875,57.4
1727382427,485.21875,49.8
1727382427,485.21875,53.2
1727382427,485.21875,50.4
1727382427,485.21875,56.5
1727382430,485.21875,49.8
1727382430,485.21875,52.1
1727382430,485.21875,50.4
1727382430,485.21875,41.2
1727382432,485.21875,49.9
1727382432,485.21875,55.3
1727382432,485.21875,50.8
1727382432,485.21875,39.4
1727382434,485.21875,49.9
1727382434,485.21875,55.3
1727382434,485.21875,51.6
1727382434,485.21875,40.6
1727382436,485.21875,49.9
1727382436,485.21875,54.3
1727382436,485.21875,48.8
1727382436,485.21875,42.4
1727382439,485.21875,49.9
1727382439,485.21875,38.2
1727382439,485.21875,50.4
1727382439,485.21875,56.8
1727382441,485.22265625,49.9
1727382441,485.22265625,41.0
1727382441,485.22265625,50.4
1727382441,485.22265625,57.4
1727382443,485.22265625,49.8
1727382443,485.22265625,56.5
1727382443,485.22265625,48.1
1727382443,485.22265625,57.8
1727382445,485.2265625,49.9
1727382445,485.2265625,35.3
1727382445,485.2265625,51.6
1727382445,485.2265625,58.7
1727382448,485.2265625,49.8
1727382448,485.2265625,57.4
1727382448,485.2265625,50.0
1727382448,485.2265625,38.7
1727382450,485.2265625,49.9
1727382450,485.2265625,53.1
1727382450,485.2265625,49.6
1727382450,485.2265625,55.6
1727382452,485.2265625,49.9
1727382452,485.2265625,48.0
1727382452,485.2265625,48.4
1727382452,485.2265625,55.6
1727382454,485.2265625,49.9
1727382454,485.2265625,38.2
1727382454,485.2265625,51.2
1727382454,485.2265625,54.5
1727382456,485.2265625,49.9
1727382456,485.2265625,55.6
1727382456,485.2265625,46.4
1727382456,485.2265625,55.6
1727382459,485.2265625,49.8
1727382459,485.2265625,54.3
1727382459,485.2265625,51.2
1727382459,485.2265625,37.5
1727382461,485.23046875,49.9
1727382461,485.23046875,38.2
1727382461,485.23046875,52.0
1727382461,485.23046875,56.5
1727382463,485.23046875,49.8
1727382463,485.23046875,55.1
1727382463,485.23046875,50.8
1727382463,485.23046875,39.4
1727382465,485.23046875,49.9
1727382465,485.23046875,36.4
1727382465,485.23046875,50.4
1727382465,485.23046875,56.5
1727382467,485.23046875,49.8
1727382467,485.23046875,55.3
1727382467,485.23046875,51.2
1727382467,485.23046875,43.2
1727382469,485.23046875,49.9
1727382469,485.23046875,53.2
1727382469,485.23046875,48.5
1727382469,485.23046875,54.3
1727382472,485.23046875,49.9
1727382472,485.23046875,39.4
1727382472,485.23046875,50.4
1727382472,485.23046875,48.6
1727382474,485.23046875,49.9
1727382474,485.23046875,52.1
1727382474,485.23046875,51.2
1727382474,485.23046875,55.8
1727382476,485.23046875,49.8
1727382476,485.23046875,54.3
1727382476,485.23046875,50.8
1727382476,485.23046875,53.2
1727382478,485.23046875,49.8
1727382478,485.23046875,55.3
1727382478,485.23046875,49.6
1727382478,485.23046875,37.5
1727382481,485.23046875,49.9
1727382481,485.23046875,54.2
1727382481,485.23046875,48.1
1727382481,485.23046875,56.5
1727382483,485.23046875,49.9
1727382483,485.23046875,37.5
1727382483,485.23046875,51.2
1727382483,485.23046875,57.8
1727382485,485.23046875,49.9
1727382485,485.23046875,35.3
1727382485,485.23046875,50.8
1727382485,485.23046875,56.5
1727382487,485.23046875,49.8
1727382487,485.23046875,46.2
1727382487,485.23046875,48.8
1727382487,485.23046875,54.2
1727382490,485.2421875,49.9
1727382490,485.2421875,40.0
1727382490,485.2421875,52.8
1727382490,485.2421875,44.1
1727382492,485.2421875,49.9
1727382492,485.2421875,38.2
1727382492,485.2421875,50.4
1727382492,485.2421875,56.8
1727382494,485.2421875,49.9
1727382494,485.2421875,38.2
1727382494,485.2421875,50.0
1727382494,485.2421875,55.6
1727382496,485.2421875,49.9
1727382496,485.2421875,55.3
1727382496,485.2421875,46.3
1727382496,485.2421875,54.2
1727382498,485.2421875,49.9
1727382498,485.2421875,53.2
1727382498,485.2421875,49.2
1727382498,485.2421875,40.6
1727382501,485.24609375,49.9
1727382501,485.24609375,38.9
1727382501,485.24609375,53.2
1727382501,485.24609375,36.8
1727382503,485.24609375,49.9
1727382503,485.24609375,55.6
1727382503,485.24609375,49.6
1727382503,485.24609375,47.6
1727382505,485.24609375,49.9
1727382505,485.24609375,55.6
1727382505,485.24609375,49.2
1727382505,485.24609375,39.4
1727382508,485.24609375,49.9
1727382508,485.24609375,39.4
1727382508,485.24609375,50.0
1727382508,485.24609375,55.6
1727382510,485.24609375,49.9
1727382510,485.24609375,52.1
1727382510,485.24609375,48.8
1727382510,485.24609375,58.3
1727382512,485.24609375,49.9
1727382512,485.24609375,54.3
1727382512,485.24609375,50.0
1727382512,485.24609375,39.4
1727382514,485.3671875,49.9
1727382514,485.3671875,37.5
1727382514,485.3671875,53.8
1727382514,485.3671875,40.6
1727382517,485.3671875,49.8
1727382517,485.3671875,54.3
1727382517,485.3671875,48.1
1727382517,485.3671875,57.8
1727382519,485.3671875,49.8
1727382519,485.3671875,55.6
1727382519,485.3671875,48.8
1727382519,485.3671875,48.6
1727382521,485.3671875,49.8
1727382521,485.3671875,39.4
1727382521,485.3671875,50.0
1727382521,485.3671875,55.6
1727382523,485.3671875,49.9
1727382523,485.3671875,46.2
1727382523,485.3671875,50.0
1727382523,485.3671875,57.8
1727382525,485.3671875,49.9
1727382525,485.3671875,38.2
1727382525,485.3671875,52.4
1727382525,485.3671875,42.9
1727382527,485.3671875,49.9
1727382527,485.3671875,38.2
1727382527,485.3671875,52.3
1727382527,485.3671875,55.6
1727382530,485.3671875,49.9
1727382530,485.3671875,38.2
1727382530,485.3671875,48.8
1727382530,485.3671875,57.8
1727382532,485.3671875,49.9
1727382532,485.3671875,44.7
1727382532,485.3671875,52.3
1727382532,485.3671875,40.6
1727382534,485.3671875,49.9
1727382534,485.3671875,41.2
1727382534,485.3671875,50.0
1727382534,485.3671875,57.8
1727382536,485.3671875,49.8
1727382536,485.3671875,54.3
1727382536,485.3671875,50.4
1727382536,485.3671875,39.4
1727382539,485.3671875,49.9
1727382539,485.3671875,55.3
1727382539,485.3671875,48.5
1727382539,485.3671875,54.3
1727382541,485.3671875,49.9
1727382541,485.3671875,54.3
1727382541,485.3671875,48.9
1727382541,485.3671875,57.8
1727382543,485.3671875,49.9
1727382543,485.3671875,51.0
1727382543,485.3671875,50.0
1727382543,485.3671875,52.4
1727382545,485.3671875,49.9
1727382545,485.3671875,37.1
1727382545,485.3671875,50.6
1727382545,485.3671875,56.8
1727382547,485.3671875,49.8
1727382547,485.3671875,50.0
1727382547,485.3671875,49.7
1727382547,485.3671875,52.0
1727382549,485.3671875,49.9
1727382549,485.3671875,37.5
1727382549,485.3671875,49.6
1727382549,485.3671875,54.3
1727382552,485.37890625,49.8
1727382552,485.37890625,55.3
1727382552,485.37890625,48.9
1727382552,485.37890625,40.6
1727382554,485.37890625,49.9
1727382554,485.37890625,53.2
1727382554,485.37890625,48.9
1727382554,485.37890625,55.6
1727382556,485.3828125,49.9
1727382556,485.3828125,46.5
1727382556,485.3828125,52.1
1727382556,485.3828125,42.4
1727382558,485.3828125,49.8
1727382558,485.3828125,56.5
1727382558,485.3828125,48.9
1727382558,485.3828125,55.6
1727382560,485.3828125,49.8
1727382560,485.3828125,55.3
1727382560,485.3828125,47.7
1727382560,485.3828125,57.8
1727382563,485.3828125,49.9
1727382563,485.3828125,54.3
1727382563,485.3828125,47.4
1727382563,485.3828125,55.6
1727382565,485.3828125,49.9
1727382565,485.3828125,40.0
1727382565,485.3828125,52.4
1727382565,485.3828125,39.4
1727382567,485.3828125,49.9
1727382567,485.3828125,55.6
1727382567,485.3828125,47.7
1727382567,485.3828125,44.1
1727382569,485.3828125,49.9
1727382569,485.3828125,53.2
1727382569,485.3828125,48.5
1727382569,485.3828125,52.4
1727382572,485.3828125,49.9
1727382572,485.3828125,54.3
1727382572,485.3828125,51.3
1727382572,485.3828125,40.6
1727382574,485.3828125,49.9
1727382574,485.3828125,54.3
1727382574,485.3828125,48.9
1727382574,485.3828125,40.6
1727382576,485.3828125,49.8
1727382576,485.3828125,54.3
1727382576,485.3828125,48.9
1727382576,485.3828125,56.8
1727382578,485.3828125,49.8
1727382578,485.3828125,54.3
1727382578,485.3828125,48.5
1727382578,485.3828125,56.5
1727382580,485.3828125,49.8
1727382580,485.3828125,55.3
1727382580,485.3828125,50.7
1727382580,485.3828125,42.1
1727382582,485.3828125,49.8
1727382582,485.3828125,46.2
1727382582,485.3828125,52.7
1727382582,485.3828125,44.1
1727382585,485.3828125,49.9
1727382585,485.3828125,44.7
1727382585,485.3828125,50.7
1727382585,485.3828125,54.3
1727382587,485.4453125,49.8
1727382587,485.4453125,56.2
1727382587,485.4453125,50.7
1727382587,485.4453125,40.6
1727382589,485.4453125,49.9
1727382589,485.4453125,36.1
1727382589,485.4453125,52.1
1727382589,485.4453125,40.6
1727382591,485.4453125,49.9
1727382591,485.4453125,55.6
1727382591,485.4453125,49.3
1727382591,485.4453125,52.5
1727382593,485.4453125,49.9
1727382593,485.4453125,53.2
1727382593,485.4453125,46.7
1727382593,485.4453125,58.7
1727382595,485.4453125,49.9
1727382595,485.4453125,40.0
1727382595,485.4453125,50.7
1727382595,485.4453125,52.5
1727382598,485.4453125,49.9
1727382598,485.4453125,37.5
1727382598,485.4453125,51.4
1727382598,485.4453125,52.3
1727382600,485.4453125,49.9
1727382600,485.4453125,55.3
1727382600,485.4453125,48.5
1727382600,485.4453125,57.8
1727382602,485.4453125,49.9
1727382602,485.4453125,54.2
1727382602,485.4453125,51.0
1727382602,485.4453125,39.4
1727382604,485.4453125,49.9
1727382604,485.4453125,44.7
1727382604,485.4453125,51.0
1727382604,485.4453125,54.5
1727382606,485.4453125,49.9
1727382606,485.4453125,52.0
1727382606,485.4453125,50.0
1727382606,485.4453125,40.6
1727382608,485.4453125,49.8
1727382608,485.4453125,53.2
1727382608,485.4453125,50.0
1727382608,485.4453125,45.9
1727382611,485.4453125,49.8
1727382611,485.4453125,55.3
1727382611,485.4453125,52.3
1727382611,485.4453125,40.6
1727382613,485.4453125,49.8
1727382613,485.4453125,55.1
1727382613,485.4453125,51.1
1727382613,485.4453125,40.6
1727382615,485.4453125,49.9
1727382615,485.4453125,43.6
1727382615,485.4453125,51.3
1727382615,485.4453125,48.7
1727382617,485.4453125,49.8
1727382617,485.4453125,54.3
1727382617,485.4453125,47.8
1727382617,485.4453125,57.8
1727382619,485.4453125,49.9
1727382619,485.4453125,38.2
1727382619,485.4453125,51.3
1727382619,485.4453125,54.5
1727382621,485.4453125,49.9
1727382621,485.4453125,50.0
1727382621,485.4453125,47.6
1727382621,485.4453125,55.6
1727382624,485.4453125,49.9
1727382624,485.4453125,50.0
1727382624,485.4453125,49.4
1727382624,485.4453125,54.8
1727382626,485.4453125,49.8
1727382626,485.4453125,54.3
1727382626,485.4453125,51.0
1727382626,485.4453125,38.7
1727382628,485.4453125,49.9
1727382628,485.4453125,38.2
1727382628,485.4453125,52.0
1727382628,485.4453125,39.4
1727382630,485.4453125,49.9
1727382630,485.4453125,42.9
1727382630,485.4453125,50.7
1727382630,485.4453125,39.4
1727382632,485.4453125,49.9
1727382632,485.4453125,39.4
1727382632,485.4453125,51.4
1727382632,485.4453125,47.2
1727382634,485.4453125,49.9
1727382634,485.4453125,56.5
1727382634,485.4453125,51.3
1727382634,485.4453125,40.6
1727382636,485.4453125,49.8
1727382636,485.4453125,54.3
1727382636,485.4453125,49.4
1727382636,485.4453125,55.6
1727382639,485.4453125,49.9
1727382639,485.4453125,53.1
1727382639,485.4453125,47.8
1727382639,485.4453125,56.8
1727382641,485.4453125,49.8
1727382641,485.4453125,53.6
1727382641,485.4453125,49.0
1727382641,485.4453125,44.4
1727382643,485.4453125,49.8
1727382643,485.4453125,52.1
1727382643,485.4453125,50.0
1727382643,485.4453125,41.2
1727382645,485.4453125,49.9
1727382645,485.4453125,56.3
1727382645,485.4453125,48.4
1727382645,485.4453125,57.1
1727382647,485.4453125,49.8
1727382647,485.4453125,36.4
1727382647,485.4453125,53.3
1727382647,485.4453125,38.7
1727382650,485.4453125,49.9
1727382650,485.4453125,56.3
1727382650,485.4453125,48.4
1727382650,485.4453125,51.3
1727382652,485.4453125,49.8
1727382652,485.4453125,51.0
1727382652,485.4453125,51.3
1727382652,485.4453125,51.3
1727382655,485.51953125,49.9
1727382655,485.51953125,36.4
1727382655,485.51953125,49.4
1727382655,485.51953125,57.8
1727382657,485.51953125,49.8
1727382657,485.51953125,39.4
1727382657,485.51953125,50.3
1727382657,485.51953125,56.5
1727382659,485.51953125,49.8
1727382659,485.51953125,55.3
1727382659,485.51953125,47.6
1727382659,485.51953125,51.2
1727382661,485.51953125,49.9
1727382661,485.51953125,42.9
1727382661,485.51953125,52.7
1727382661,485.51953125,37.5
1727382664,485.51953125,49.9
1727382664,485.51953125,56.5
1727382664,485.51953125,49.7
1727382664,485.51953125,56.5
1727382666,485.51953125,49.8
1727382666,485.51953125,56.5
1727382666,485.51953125,49.7
1727382666,485.51953125,40.0
1727382668,485.51953125,49.9
1727382668,485.51953125,54.3
1727382668,485.51953125,49.7
1727382668,485.51953125,40.6
1727382670,485.51953125,49.9
1727382670,485.51953125,38.2
1727382670,485.51953125,50.3
1727382670,485.51953125,57.8
1727382672,485.51953125,49.8
1727382672,485.51953125,54.3
1727382672,485.51953125,52.0
1727382672,485.51953125,39.4
1727382674,485.51953125,49.8
1727382674,485.51953125,57.4
1727382674,485.51953125,49.4
1727382674,485.51953125,55.3
1727382677,485.59375,49.9
1727382677,485.59375,54.3
1727382677,485.59375,49.7
1727382677,485.59375,42.9
1727382679,485.59375,49.8
1727382679,485.59375,53.1
1727382679,485.59375,49.7
1727382679,485.59375,57.8
1727382681,485.59375,49.8
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1727382681,485.59375,39.4
1727382683,485.59375,49.9
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1727382699,485.59375,56.5
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1727382701,485.59375,52.3
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1727382703,485.59375,55.3
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1727382707,485.59375,49.8
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1727382710,485.62890625,43.6
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1727382714,485.62890625,49.9
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1727382718,485.62890625,49.8
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1727382718,485.62890625,48.7
1727382718,485.62890625,56.8
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1727382721,485.62890625,40.0
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1727382723,485.62890625,42.4
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1727382725,485.62890625,45.9
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1727382727,485.62890625,55.6
1727382729,485.62890625,49.8
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1727382734,485.62890625,49.9
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1727382734,485.62890625,42.4
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1727382857,526.25,52.1
1727382857,526.25,50.6
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1727382858,526.25,57.4
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1727382860,526.25,51.2
1727382860,526.25,51.1
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1727382877,526.25,49.8
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1727401845,490.609375,56.5
1727401863,490.609375,50.0
1727401863,490.609375,53.2
1727401863,490.609375,50.8
1727401863,490.609375,38.7
1727401881,490.609375,50.0
1727401881,490.609375,56.5
1727401881,490.609375,49.2
1727401881,490.609375,56.5
1727401900,490.3984375,50.0
1727401900,490.3984375,53.2
1727401900,490.3984375,49.5
1727401900,490.3984375,54.3
1727401918,490.52734375,50.0
1727401918,490.52734375,38.2
1727401918,490.52734375,51.0
1727401918,490.52734375,37.5
1727401936,490.52734375,50.0
1727401936,490.52734375,39.4
1727401936,490.52734375,51.0
1727401936,490.52734375,38.2
1727401954,490.52734375,50.0
1727401954,490.52734375,54.3
1727401955,490.52734375,49.7
1727401955,490.52734375,56.5
1727401973,490.52734375,50.0
1727401973,490.52734375,53.2
1727401973,490.52734375,50.5
1727401973,490.52734375,39.4
1727401991,490.52734375,50.0
1727401991,490.52734375,36.4
1727401991,490.52734375,49.9
1727401991,490.52734375,55.6
1727402009,490.52734375,50.0
1727402009,490.52734375,46.3
1727402009,490.52734375,49.4
1727402009,490.52734375,58.7
1727402028,490.52734375,50.0
1727402028,490.52734375,53.1
1727402028,490.52734375,50.0
1727402028,490.52734375,42.4
1727402046,490.52734375,50.0
1727402046,490.52734375,47.5
1727402046,490.52734375,49.9
1727402046,490.52734375,52.1
1727402064,490.52734375,50.0
1727402064,490.52734375,55.3
1727402064,490.52734375,50.3
1727402064,490.52734375,36.4
1727402083,490.52734375,50.0
1727402083,490.52734375,36.4
1727402083,490.52734375,50.8
1727402083,490.52734375,41.7
1727402101,490.48828125,50.0
1727402101,490.48828125,54.3
1727402101,490.48828125,49.8
1727402101,490.48828125,53.2
1727402119,490.5,50.0
1727402119,490.5,54.2
1727402119,490.5,49.7
1727402119,490.5,53.2
1727402137,490.5,50.0
1727402137,490.5,37.1
1727402137,490.5,51.2
1727402137,490.5,37.5
1727402155,490.5,50.0
1727402155,490.5,44.7
1727402155,490.5,50.7
1727402155,490.5,41.9
1727402173,490.5,50.0
1727402173,490.5,44.7
1727402173,490.5,50.9
1727402173,490.5,54.3
1727402192,490.5,50.0
1727402192,490.5,39.4
1727402192,490.5,50.9
1727402192,490.5,39.4
1727402210,490.5,50.0
1727402210,490.5,53.5
1727402210,490.5,49.4
1727402210,490.5,55.6
1727402228,490.5,50.0
1727402228,490.5,37.1
1727402228,490.5,51.0
1727402228,490.5,37.5
1727402246,490.5,50.0
1727402246,490.5,36.4
1727402246,490.5,51.0
1727402246,490.5,39.4
1727402264,490.52734375,50.0
1727402264,490.52734375,52.9
1727402264,490.52734375,48.9
1727402264,490.52734375,55.6
1727402282,490.45703125,50.0
1727402282,490.45703125,54.3
1727402282,490.45703125,49.9
1727402282,490.45703125,54.5
1727402301,490.45703125,50.0
1727402301,490.45703125,40.5
1727402301,490.45703125,50.7
1727402301,490.45703125,47.5
1727402319,491.23046875,50.0
1727402319,491.23046875,53.1
1727402319,491.23046875,49.1
1727402319,491.23046875,54.3
1727402338,490.45703125,50.0
1727402338,490.45703125,38.9
1727402338,490.45703125,50.2
1727402338,490.45703125,55.6
1727402356,490.45703125,50.0
1727402356,490.45703125,54.3
1727402356,490.45703125,49.8
1727402356,490.45703125,39.4
1727402375,490.45703125,50.0
1727402375,490.45703125,48.1
1727402375,490.45703125,50.8
1727402375,490.45703125,40.6
1727402393,490.45703125,50.0
1727402393,490.45703125,40.0
1727402393,490.45703125,50.1
1727402393,490.45703125,56.5
1727402411,490.45703125,50.0
1727402411,490.45703125,55.3
1727402411,490.45703125,48.9
1727402411,490.45703125,57.8
1727402429,490.45703125,50.0
1727402429,490.45703125,55.1
1727402429,490.45703125,49.6
1727402429,490.45703125,43.2
1727402448,490.45703125,50.0
1727402448,490.45703125,47.4
1727402448,490.45703125,50.5
1727402448,490.45703125,40.6
1727402466,490.45703125,50.0
1727402466,490.45703125,38.2
1727402466,490.45703125,50.1
1727402466,490.45703125,56.8
1727402484,490.45703125,50.0
1727402484,490.45703125,52.2
1727402484,490.45703125,49.1
1727402484,490.45703125,55.3
1727402502,490.45703125,50.0
1727402502,490.45703125,53.1
1727402502,490.45703125,49.1
1727402502,490.45703125,57.4
1727402521,490.45703125,50.0
1727402521,490.45703125,38.2
1727402521,490.45703125,50.3
1727402521,490.45703125,54.3
1727402539,490.45703125,50.0
1727402539,490.45703125,40.0
1727402539,490.45703125,51.2
1727402539,490.45703125,36.4
1727402558,490.45703125,50.0
1727402558,490.45703125,56.3
1727402558,490.45703125,49.6
1727402558,490.45703125,40.6
1727402576,490.45703125,50.0
1727402576,490.45703125,37.5
1727402576,490.45703125,50.8
1727402576,490.45703125,51.2
1727402595,490.45703125,50.0
1727402595,490.45703125,55.3
1727402595,490.45703125,49.3
1727402595,490.45703125,55.6
1727402613,490.45703125,50.0
1727402613,490.45703125,38.2
1727402613,490.45703125,50.3
1727402613,490.45703125,53.7
1727402631,490.45703125,50.0
1727402631,490.45703125,53.2
1727402631,490.45703125,49.4
1727402631,490.45703125,58.3
1727402649,490.45703125,50.0
1727402649,490.45703125,53.2
1727402649,490.45703125,49.4
1727402649,490.45703125,57.8
1727402667,490.45703125,50.0
1727402667,490.45703125,37.1
1727402667,490.45703125,50.3
1727402667,490.45703125,57.4
1727402685,490.45703125,50.0
1727402685,490.45703125,50.0
1727402685,490.45703125,49.5
1727402685,490.45703125,53.7
1727402703,490.45703125,50.0
1727402703,490.45703125,53.2
1727402703,490.45703125,50.2
1727402703,490.45703125,51.3
1727402722,490.45703125,50.0
1727402722,490.45703125,55.3
1727402722,490.45703125,49.4
1727402722,490.45703125,55.6
1727402740,490.45703125,50.0
1727402740,490.45703125,53.1
1727402740,490.45703125,49.5
1727402740,490.45703125,56.8
1727402758,490.45703125,50.0
1727402758,490.45703125,53.2
1727402758,490.45703125,50.2
1727402758,490.45703125,41.2
1727402776,490.45703125,50.0
1727402776,490.45703125,55.6
1727402776,490.45703125,50.4
1727402776,490.45703125,39.4
1727402795,490.45703125,50.0
1727402795,490.45703125,53.2
1727402795,490.45703125,49.7
1727402795,490.45703125,54.5
1727402814,490.45703125,50.0
1727402814,490.45703125,56.3
1727402814,490.45703125,49.6
1727402814,490.45703125,55.6
1727402832,490.45703125,50.0
1727402832,490.45703125,53.2
1727402832,490.45703125,50.0
1727402832,490.45703125,55.6
1727402850,491.2265625,50.0
1727402850,491.2265625,40.0
1727402850,491.2265625,50.4
1727402850,491.2265625,52.1
1727402868,490.45703125,50.0
1727402868,490.45703125,55.3
1727402868,490.45703125,49.1
1727402868,490.45703125,55.6
1727402887,490.45703125,50.0
1727402887,490.45703125,54.3
1727402887,490.45703125,49.4
1727402887,490.45703125,56.5
1727402906,490.45703125,50.0
1727402906,490.45703125,53.1
1727402906,490.45703125,50.3
1727402906,490.45703125,36.4
1727402924,490.45703125,50.0
1727402924,490.45703125,54.2
1727402924,490.45703125,49.9
1727402924,490.45703125,37.5
1727402942,490.45703125,50.0
1727402942,490.45703125,36.4
1727402942,490.45703125,50.8
1727402942,490.45703125,40.6
1727402960,490.45703125,50.0
1727402960,490.45703125,51.0
1727402960,490.45703125,50.4
1727402960,490.45703125,41.9
1727402978,490.45703125,50.0
1727402978,490.45703125,55.3
1727402978,490.45703125,50.0
1727402978,490.45703125,40.6
1727402996,491.2109375,50.0
1727402996,491.2109375,53.2
1727402996,491.2109375,50.3
1727402997,491.2109375,42.4
1727403014,490.45703125,50.0
1727403014,490.45703125,39.4
1727403014,490.45703125,50.3
1727403014,490.45703125,52.5
1727403033,490.45703125,50.0
1727403033,490.45703125,43.6
1727403033,490.45703125,50.5
1727403033,490.45703125,47.4
1727403051,490.45703125,50.0
1727403051,490.45703125,45.0
1727403051,490.45703125,50.5
1727403051,490.45703125,39.4
1727403070,490.45703125,50.0
1727403070,490.45703125,38.2
1727403070,490.45703125,49.9
1727403070,490.45703125,55.3
1727403088,490.4453125,50.0
1727403088,490.4453125,55.3
1727403088,490.4453125,49.9
1727403088,490.4453125,57.4
1727403106,491.26171875,50.0
1727403106,491.26171875,54.2
1727403106,491.26171875,49.5
1727403106,491.26171875,59.6
1727403124,490.45703125,50.0
1727403124,490.45703125,38.9
1727403124,490.45703125,50.1
1727403124,490.45703125,54.3
1727403143,490.45703125,50.0
1727403143,490.45703125,53.2
1727403143,490.45703125,49.9
1727403143,490.45703125,52.1
1727403161,490.45703125,50.0
1727403161,490.45703125,53.1
1727403161,490.45703125,49.7
1727403161,490.45703125,56.5
1727403179,490.45703125,50.0
1727403179,490.45703125,55.3
1727403179,490.45703125,50.0
1727403179,490.45703125,56.5
1727403197,491.2265625,50.0
1727403197,491.2265625,38.2
1727403197,491.2265625,50.6
1727403197,491.2265625,52.3
1727403216,490.45703125,50.0
1727403216,490.45703125,37.1
1727403216,490.45703125,50.6
1727403216,490.45703125,38.7
1727403234,490.45703125,50.0
1727403234,490.45703125,44.4
1727403234,490.45703125,49.9
1727403234,490.45703125,56.8
1727403252,490.45703125,50.0
1727403252,490.45703125,41.2
1727403252,490.45703125,49.6
1727403252,490.45703125,57.8
1727403270,491.2265625,50.0
1727403270,491.2265625,38.2
1727403270,491.2265625,50.2
1727403270,491.2265625,56.8
1727403288,490.45703125,50.0
1727403288,490.45703125,52.1
1727403288,490.45703125,49.8
1727403288,490.45703125,56.3
1727403307,490.45703125,50.0
1727403307,490.45703125,37.1
1727403307,490.45703125,50.2
1727403307,490.45703125,56.8
1727403325,490.45703125,50.0
1727403325,490.45703125,54.3
1727403325,490.45703125,50.5
1727403325,490.45703125,44.4
1727403343,491.2265625,50.0
1727403343,491.2265625,53.2
1727403343,491.2265625,49.7
1727403343,491.2265625,55.6
1727403361,491.2265625,50.0
1727403361,491.2265625,54.3
1727403361,491.2265625,49.8
1727403361,491.2265625,56.8
1727403380,491.44140625,50.0
1727403380,491.44140625,54.2
1727403380,491.44140625,49.6
1727403380,491.44140625,54.8
1727403398,491.2265625,50.0
1727403398,491.2265625,53.2
1727403399,491.2265625,50.0
1727403399,491.2265625,42.9
1727403417,490.45703125,50.0
1727403417,490.45703125,54.3
1727403417,490.45703125,50.2
1727403417,490.45703125,42.9
1727403435,490.45703125,50.0
1727403435,490.45703125,51.2
1727403435,490.45703125,49.7
1727403435,490.45703125,53.1
1727403454,490.45703125,50.0
1727403454,490.45703125,38.2
1727403454,490.45703125,50.6
1727403454,490.45703125,38.7
1727403472,490.45703125,50.0
1727403472,490.45703125,46.2
1727403472,490.45703125,50.4
1727403472,490.45703125,47.2
1727403490,490.45703125,50.0
1727403490,490.45703125,36.4
1727403490,490.45703125,50.6
1727403490,490.45703125,42.4
1727403508,490.45703125,50.0
1727403508,490.45703125,51.2
1727403508,490.45703125,49.8
1727403508,490.45703125,45.9
1727403527,491.2265625,50.0
1727403527,491.2265625,54.3
1727403527,491.2265625,50.4
1727403527,491.2265625,39.4
1727403550,490.51953125,50.0
1727403550,490.51953125,52.0
1727403550,490.51953125,51.0
1727403550,490.51953125,36.4
1727403647,499.33203125,50.2
1727403647,499.33203125,55.6
1727403647,499.33203125,49.6
1727403647,499.33203125,56.5
1727403734,516.4375,50.1
1727403734,516.4375,42.1
1727403734,516.4375,50.3
1727403734,516.4375,54.8
1727403893,511.84375,50.2
1727403893,511.84375,48.8
1727403893,511.84375,49.5
1727403893,511.84375,54.3
1727404044,507.72265625,50.1
1727404044,507.72265625,40.0
1727404044,507.72265625,50.7
1727404044,507.72265625,45.9
1727404270,501.28515625,50.2
1727404270,501.28515625,53.2
1727404270,501.28515625,50.1
1727404270,501.28515625,39.4
1727404575,515.2109375,50.2
1727404575,515.2109375,56.2
1727404575,515.2109375,49.5
1727404575,515.2109375,55.6
1727404945,503.21875,50.2
1727404945,503.21875,54.2
1727404945,503.21875,50.5
1727404945,503.21875,40.6
1727405330,529.71875,50.2
1727405330,529.71875,56.3
1727405330,529.71875,49.6
1727405330,529.71875,60.0
1727405780,503.29296875,50.2
1727405780,503.29296875,38.2
1727408275,501.5078125,49.6
1727408275,501.5078125,54.3
</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>